Avian metapneumovirus subgroup C induces autophagy through the ATF6 UPR pathway.

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Hou, Lei; Wei, Li; Zhu, Shanshan; Wang, Jing; Quan, Rong; Li, Zixuan; Liu, Jue

2017-10-03

An increasing number of studies have demonstrated that macroautophagy/autophagy plays an important role in the infectious processes of diverse pathogens. However, it remains unknown whether autophagy is induced in avian metapneumovirus (aMPV)-infected host cells, and, if so, how this occurs. Here, we report that aMPV subgroup C (aMPV/C) induces autophagy in cultured cells. We demonstrated this relationship by detecting classical autophagic features, including the formation of autophagsomes, the presence of GFP-LC3 puncta and the conversation of LC3-I into LC3-II. Also, we used pharmacological regulators and siRNAs targeting ATG7 or LC3 to examine the role of autophagy in aMPV/C replication. The results showed that autophagy is required for efficient replication of aMPV/C. Moreover, infection with aMPV/C promotes autophagosome maturation and induces a complete autophagic process. Finally, the ATF6 pathway, of which one component is the unfolded protein response (UPR), becomes activated in aMPV/C-infected cells. Knockdown of ATF6 inhibited aMPV/C-induced autophagy and viral replication. Collectively, these results not only show that autophagy promotes aMPV/C replication in the cultured cells, but also reveal that the molecular mechanisms underlying aMPV/C-induced autophagy depends on regulation of the ER stress-related UPR pathway.

Deduced amino acid sequence of the small hydrophobic protein of US avian pneumovirus has greater identity with that of human metapneumovirus than those of non-US avian pneumoviruses.

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Yunus, Abdul S; Govindarajan, Dhanasekaran; Huang, Zhuhui; Samal, Siba K

2003-05-01

We report here the nucleotide and deduced amino acid (aa) sequences of the small hydrophobic (SH) gene of the avian pneumovirus strain Colorado (APV/CO). The SH gene of APV/CO is 628 nucleotides in length from gene-start to gene-end. The longest ORF of the SH gene encoded a protein of 177 aas in length. Comparison of the deduced aa sequence of the SH protein of APV/CO with the corresponding published sequences of other members of genera metapneumovirus showed 28% identity with the newly discovered human metapneumovirus (hMPV), but no discernable identity with the APV subgroup A or B. Collectively, this data supports the hypothesis that: (i) APV/CO is distinct from European APV subgroups and belongs to the novel subgroup APV/C (APV/US); (ii) APV/CO is more closely related to hMPV, a mammalian metapneumovirus, than to either APV subgroup A or B. The SH gene of APV/CO was cloned using a genomic walk strategy which initiated cDNA synthesis from genomic RNA that traversed the genes in the order 3'-M-F-M2-SH-G-5', thus confirming that gene-order of APV/CO conforms in the genus Metapneumovirus. We also provide the sequences of transcription-signals and the M-F, F-M2, M2-SH and SH-G intergenic regions of APV/CO.

Avian metapneumovirus (AMPV) attachment protein involvement in probable virus evolution concurrent with mass live vaccine introduction.

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Cecchinato, Mattia; Catelli, Elena; Lupini, Caterina; Ricchizzi, Enrico; Clubbe, Jayne; Battilani, Mara; Naylor, Clive J

2010-11-20

Avian metapneumoviruses detected in Northern Italy between 1987 and 2007 were sequenced in their fusion (F) and attachment (G) genes together with the same genes from isolates collected throughout western European prior to 1994. Fusion protein genes sequences were highly conserved while G protein sequences showed much greater heterogeneity. Phylogenetic studies based on both genes clearly showed that later Italian viruses were significantly different to all earlier virus detections, including early detections from Italy. Furthermore a serine residue in the G proteins and lysine residue in the fusion protein were exclusive to Italian viruses, indicating that later viruses probably arose within the country and the notion that these later viruses evolved from earlier Italian progenitors cannot be discounted. Biocomputing analysis applied to F and G proteins of later Italian viruses predicted that only G contained altered T cell epitopes. It appears likely that Italian field viruses evolved in response to selection pressure from vaccine induced immunity. Copyright © 2010 Elsevier B.V. All rights reserved.

Avian metapneumovirus subtype A in China and subtypes A and B in Nigeria.

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Owoade, A A; Ducatez, M F; Hübschen, J M; Sausy, A; Chen, H; Guan, Y; Muller, C P

2008-09-01

In order to detect and characterize avian metapneumovirus, organs or swabs were collected from 697 chicken and 110 turkeys from commercial farms in Southwestern Nigeria and from 107 chickens from live bird markets in Southeastern China. In Nigeria, 15% and 6% of the chicken and turkey samples, respectively, and 39% of the chicken samples from China, were positive for aMPV genome by PCR. The sequence of a 400 nt fragment of the attachment protein gene (G gene) revealed the presence of aMPV subtype A in both Nigeria and Southeastern China. Essentially identical subtype A viruses were found in both countries and were also previously reported from Brazil and the United Kingdom, suggesting a link between these countries or a common source of this subtype. In Nigeria, subtype B was also found, which may be a reflection of chicken importations from most major poultry-producing countries in Europe and Asia. In order to justify countermeasures, further studies are warranted to better understand the metapneumoviruses and their impact on poultry production.

Parallel logic gates in synthetic gene networks induced by non-Gaussian noise.

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Xu, Yong; Jin, Xiaoqin; Zhang, Huiqing

2013-11-01

The recent idea of logical stochastic resonance is verified in synthetic gene networks induced by non-Gaussian noise. We realize the switching between two kinds of logic gates under optimal moderate noise intensity by varying two different tunable parameters in a single gene network. Furthermore, in order to obtain more logic operations, thus providing additional information processing capacity, we obtain in a two-dimensional toggle switch model two complementary logic gates and realize the transformation between two logic gates via the methods of changing different parameters. These simulated results contribute to improve the computational power and functionality of the networks.

Generation and biological assessment of recombinant avian metapneumovirus subgroup C (aMPV-C) viruses containing different length of the G gene.

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Yu, Qingzhong; Estevez, Carlos; Song, Minxun; Kapczynski, Darrell; Zsak, Laszlo

2010-02-01

Genetic variation in length of the G gene among different avian metapneumovirus subgroup C (aMPV-C) isolates has been reported. However, its biological significance in virus replication, pathogenicity and immunity is unknown. In this study, we developed a reverse genetics system for aMPV-C and generated two Colorado (CO) strain-based recombinant viruses containing either the full-length G gene derived from a Canadian goose isolate or a C-terminally truncated G gene of the CO strain. The truncated short G (sG) gene encoded 252 amino acids (aa), which is 333 aa shorter than the full-length G (585 aa). The biological properties of these two recombinant G variants were assessed in Vero cells and in specific-pathogen-free (SPF) turkeys. In Vero cells, the short G variant displayed a similar level of growth dynamics and virus titers as the parental aMPV-CO strain, whereas the full-length G variant replicated less efficiently than the sG variant during the first 72 h post-infection. Both of the G variants induced typical cytopathic effects (CPE) that were indistinguishable from those seen with the parental aMPV-CO infection. In SPF turkeys, both of the G variants were attenuated and caused little or no disease signs, but the full-length G variant appeared to grow more readily in tracheal tissue than the sG variant during the first 5 days post-infection. Both G variants were immunogenic and induced a slightly different level of antibody response. These results demonstrated that the large portion (333 aa) of the extracellular domain of the viral attachment protein is not essential for virus viability in vitro and in vivo, but may play a role in enhancing virus attachment specificity and immunity in a natural host. (c) 2009 Elsevier B.V. All rights reserved.

Genetic diversity and evolution of human metapneumovirus fusion protein over twenty years

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Liem Alexis

2009-09-01

Full Text Available Abstract Background Human metapneumovirus (HMPV is an important cause of acute respiratory illness in children. We examined the diversity and molecular evolution of HMPV using 85 full-length F (fusion gene sequences collected over a 20-year period. Results The F gene sequences fell into two major groups, each with two subgroups, which exhibited a mean of 96% identity by predicted amino acid sequences. Amino acid identity within and between subgroups was higher than nucleotide identity, suggesting structural or functional constraints on F protein diversity. There was minimal progressive drift over time, and the genetic lineages were stable over the 20-year period. Several canonical amino acid differences discriminated between major subgroups, and polymorphic variations tended to cluster in discrete regions. The estimated rate of mutation was 7.12 × 10-4 substitutions/site/year and the estimated time to most recent common HMPV ancestor was 97 years (95% likelihood range 66-194 years. Analysis suggested that HMPV diverged from avian metapneumovirus type C (AMPV-C 269 years ago (95% likelihood range 106-382 years. Conclusion HMPV F protein remains conserved over decades. HMPV appears to have diverged from AMPV-C fairly recently.

Genetic diversity and evolution of human metapneumovirus fusion protein over twenty years

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Yang, Chin-Fen; Wang, Chiaoyin K; Tollefson, Sharon J; Piyaratna, Rohith; Lintao, Linda D; Chu, Marla; Liem, Alexis; Mark, Mary; Spaete, Richard R; Crowe, James E; Williams, John V

2009-01-01

Background Human metapneumovirus (HMPV) is an important cause of acute respiratory illness in children. We examined the diversity and molecular evolution of HMPV using 85 full-length F (fusion) gene sequences collected over a 20-year period. Results The F gene sequences fell into two major groups, each with two subgroups, which exhibited a mean of 96% identity by predicted amino acid sequences. Amino acid identity within and between subgroups was higher than nucleotide identity, suggesting structural or functional constraints on F protein diversity. There was minimal progressive drift over time, and the genetic lineages were stable over the 20-year period. Several canonical amino acid differences discriminated between major subgroups, and polymorphic variations tended to cluster in discrete regions. The estimated rate of mutation was 7.12 × 10-4 substitutions/site/year and the estimated time to most recent common HMPV ancestor was 97 years (95% likelihood range 66-194 years). Analysis suggested that HMPV diverged from avian metapneumovirus type C (AMPV-C) 269 years ago (95% likelihood range 106-382 years). Conclusion HMPV F protein remains conserved over decades. HMPV appears to have diverged from AMPV-C fairly recently. PMID:19740442

Genetic variability of attachment (G and Fusion (F protein genes of human metapneumovirus strains circulating during 2006-2009 in Kolkata, Eastern India

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Chawla-Sarkar Mamta

2011-02-01

Full Text Available Abstract Background Human metapneumovirus (hMPV is associated with the acute respiratory tract infection (ARTI in all the age groups. However, there is limited information on prevalence and genetic diversity of human metapneumovirus (hMPV strains circulating in India. Objective To study prevalence and genomic diversity of hMPV strains among ARTI patients reporting in outpatient departments of hospitals in Kolkata, Eastern India. Methods Nasal and/or throat swabs from 2309 patients during January 2006 to December 2009, were screened for the presence of hMPV by RT-PCR of nucleocapsid (N gene. The G and F genes of representative hMPV positive samples were sequenced. Results 118 of 2309 (5.11% clinical samples were positive for hMPV. The majority (≈80% of the positive cases were detected during July−November all through the study period. Genetic analysis revealed that 77% strains belong to A2 subgroup whereas rest clustered in B1 subgroup. G sequences showed higher diversity at the nucleotide and amino acid level. In contrast, less than 10% variation was observed in F gene of representative strains of all four years. Sequence analysis also revealed changes in the position of stop codon in G protein, which resulted in variable length (217-231 aa polypeptides. Conclusion The study suggests that approximately 5% of ARTI in the region were caused by hMPV. This is the first report on the genetic variability of G and F gene of hMPV strains from India which clearly shows that the G protein of hMPV is continuously evolving. Though the study partially fulfills lacunae of information, further studies from other regions are necessary for better understanding of prevalence, epidemiology and virus evolution in Indian subcontinent.

Avian Metapneumovirus circulation in Italian broiler farms.

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Tucciarone, Claudia Maria; Franzo, Giovanni; Lupini, Caterina; Alejo, Carolina Torres; Listorti, Valeria; Mescolini, Giulia; Brandão, Paulo Eduardo; Martini, Marco; Catelli, Elena; Cecchinato, Mattia

2018-02-01

With increasing frequency, avian Metapneumovirus (aMPV) is reported to induce respiratory signs in chickens. An adequate knowledge of current aMPV prevalence among Italian broilers is lacking, with little information available on its economical and health impact on the poultry industry. In order to collect preliminary data on the epidemiological context of aMPV in broiler flocks, a survey was performed in areas of Northern Italy with high poultry density from 2014 to 2016. Upper respiratory tract swabs were collected and processed by A and B subtype-specific multiplex real-time reverse transcription PCR (RT-PCR). Samples were also screened for infectious bronchitis virus (IBV) by generic RT-PCR and sequencing. Productive data and respiratory signs were detailed where possible. The high prevalence of aMPV was confirmed in broilers older than 26 d and also attested in IBV-negative farms. All aMPV detections belonged to subtype B. Italian strain genetic variability was evaluated by the partial attachment (G) gene sequencing of selected strains and compared with contemporary turkey strains and previously published aMPV references, revealing no host specificity and the progressive evolution of this virus in Italy. © 2017 Poultry Science Association Inc.

Avian Metapneumovirus Molecular Biology and Development of Genetically Engineered Vaccines

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Avian metapneumovirus (aMPV) is an economically important pathogen of turkeys with a worldwide distribution. aMPV is a member of the genus Metapneumovirus within the subfamily Pneumovirinae of the family Paramyxoviridae. The genome of aMPV is a non-segmented, single-stranded, negative-sense RNA of 1...

Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.

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Yinyin Yuan

Full Text Available Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/.

Topology and cellular localization of the small hydrophobic protein of avian metapneumovirus.

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Deng, Qiji; Weng, Yuejin; Lu, Wuxun; Demers, Andrew; Song, Minxun; Wang, Dan; Yu, Qingzhong; Li, Feng

2011-09-01

The small hydrophobic protein (SH) is a type II integral membrane protein that is packaged into virions and is only present in certain paramyxoviruses including metapneumovirus. In addition to a highly divergent primary sequence, SH proteins vary significantly in size amongst the different viruses. Human respiratory syncytial virus (HRSV) encodes the smallest SH protein consisting of only 64 amino acids, while metapneumoviruses have the longest SH protein ranging from 174 to 179 amino acids in length. Little is currently known about the cellular localization and topology of the metapneumovirus SH protein. Here we characterize for the first time metapneumovirus SH protein with respect to topology, subcellular localization, and transport using avian metapneumovirus subgroup C (AMPV-C) as a model system. We show that AMPV-C SH is an integral membrane protein with N(in)C(out) orientation located in both the plasma membrane as well as within intracellular compartments, which is similar to what has been described previously for SH proteins of other paramyxoviruses. Furthermore, we demonstrate that AMPV-C SH protein localizes in the endoplasmic reticulum (ER), Golgi, and cell surface, and is transported through ER-Golgi secretory pathway. Copyright © 2011 Elsevier B.V. All rights reserved.

Chimeric Recombinant Human Metapneumoviruses with the Nucleoprotein or Phosphoprotein Open Reading Frame Replaced by That of Avian Metapneumovirus Exhibit Improved Growth In Vitro and Attenuation In Vivo

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Pham, Quynh N.; Biacchesi, Stéphane; Skiadopoulos, Mario H.; Murphy, Brian R.; Collins, Peter L.; Buchholz, Ursula J.

2005-01-01

Chimeric versions of recombinant human metapneumovirus (HMPV) were generated by replacing the nucleoprotein (N) or phosphoprotein (P) open reading frame with its counterpart from the closely related avian metapneumovirus (AMPV) subgroup C. In Vero cells, AMPV replicated to an approximately 100-fold-higher titer than HMPV. Surprisingly, the N and P chimeric viruses replicated to a peak titer that was 11- and 25-fold higher, respectively, than that of parental HMPV. The basis for this effect is not known but was not due to obvious changes in the efficiency of gene expression. AMPV and the N and P chimeras were evaluated for replication, immunogenicity, and protective efficacy in hamsters. AMPV was attenuated compared to HMPV in this mammalian host on day 5 postinfection, but not on day 3, and only in the nasal turbinates. In contrast, the N and P chimeras were reduced approximately 100-fold in both the upper and lower respiratory tract on day 3 postinfection, although there was little difference by day 5. The N and P chimeras induced a high level of neutralizing serum antibodies and protective efficacy against HMPV; AMPV was only weakly immunogenic and protective against HMPV challenge, reflecting antigenic differences. In African green monkeys immunized intranasally and intratracheally, the mean peak titer of the P chimera was reduced 100- and 1,000-fold in the upper and lower respiratory tracts, whereas the N chimera was reduced only 10-fold in the lower respiratory tract. Both chimeras were comparable to wild-type HMPV in immunogenicity and protective efficacy. Thus, the P chimera is a promising live HMPV vaccine candidate that paradoxically combines improved growth in vitro with attenuation in vivo. PMID:16306583

Avian metapneumovirus in the USA

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In the United States of America (USA), avian metapneumovirus (aMPV) causes an upper respiratory tract infection in turkeys; no outbreaks have been reported in commercial chicken flocks. Typical clinical signs of the disease in turkey poults include coughing, sneezing, nasal discharge, tracheal rale...

Human Metapneumovirus in Turkey Poults

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Velayudhan, Binu T.; Nagaraja, Kakambi V.; Thachil, Anil J.; Shaw, Daniel P.; Gray, Gregory C.

2006-01-01

This study was conducted to reexamine the hypothesis that human metapneumovirus (hMPV) will not infect turkeys. Six groups of 2-week-old turkeys (20 per group) were inoculated oculonasally with 1 of the following: noninfected cell suspension; hMPV genotype A1, A2, B1, or B2; or avian metapneumovirus (aMPV) subtype C. Poults inoculated with hMPV showed nasal discharge days 4–9 postexposure. Specific viral RNA and antigen were detected by reverse-transcription PCR and immunohistochemical evaluation, respectively, in nasal turbinates of birds exposed to hMPV. Nasal turbinates of hMPV-infected turkeys showed inflammatory changes and mucus accumulation. Each of the 4 hMPV genotypes caused a transient infection in turkeys as evidenced by clinical signs, detection of hMPV in turbinates, and histopathologic examination. Detailed investigation of cross-species pathogenicity of hMPV and aMPV and its importance for human and animal health is needed. PMID:17235379

Identification of a truncated nucleoprotein in avian metapneumovirus-infected cells encoded by a second AUG, in-frame to the full-length gene

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Alvarez, Rene; Seal, Bruce S

2005-01-01

Background Avian metapneumoviruses (aMPV) cause an upper respiratory disease with low mortality, but high morbidity primarily in commercial turkeys. There are three types of aMPV (A, B, C) of which the C type is found only in the United States. Viruses related to aMPV include human, bovine, ovine, and caprine respiratory syncytial viruses and pneumonia virus of mice, as well as the recently identified human metapneumovirus (hMPV). The aMPV and hMPV have become the type viruses of a new genus within the Metapneumovirus. The aMPV nucleoprotein (N) amino acid sequences of serotypes A, B, and C were aligned for comparative analysis. Based on predicted antigenicity of consensus protein sequences, five aMPV-specific N peptides were synthesized for development of peptide-antigens and antisera. Results The presence of two aMPV nucleoprotein (N) gene encoded polypeptides was detected in aMPV/C/US/Co and aMPV/A/UK/3b infected Vero cells. Nucleoprotein 1 (N1) encoded from the first open reading frame (ORF) was predicted to be 394 amino acids in length for aMPV/C/US/Co and 391 amino acids in length for aMPV/A/UK/3b with approximate molecular weights of 43.3 kilodaltons and 42.7 kilodaltons, respectively. Nucleoprotein 2 (N2) was hypothesized to be encoded by a second downstream ORF in-frame with ORF1 and encoded a protein predicted to contain 328 amino acids for aMPV/C/US/Co or 259 amino acids for aMPV/A/UK/3b with approximate molecular weights of 36 kilodaltons and 28.3 kilodaltons, respectively. Peptide antibodies to the N-terminal and C-terminal portions of the aMPV N protein confirmed presence of these products in both aMPV/C/US/Co- and aMPV/A/UK/3b-infected Vero cells. N1 and N2 for aMPV/C/US/Co ORFs were molecularly cloned and expressed in Vero cells utilizing eukaryotic expression vectors to confirm identity of the aMPV encoded proteins. Conclusion This is the first reported identification of potential, accessory in-frame N2 ORF gene products among members of the

Identification of a truncated nucleoprotein in avian metapneumovirus-infected cells encoded by a second AUG, in-frame to the full-length gene

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Alvarez Rene

2005-04-01

Full Text Available Abstract Background Avian metapneumoviruses (aMPV cause an upper respiratory disease with low mortality, but high morbidity primarily in commercial turkeys. There are three types of aMPV (A, B, C of which the C type is found only in the United States. Viruses related to aMPV include human, bovine, ovine, and caprine respiratory syncytial viruses and pneumonia virus of mice, as well as the recently identified human metapneumovirus (hMPV. The aMPV and hMPV have become the type viruses of a new genus within the Metapneumovirus. The aMPV nucleoprotein (N amino acid sequences of serotypes A, B, and C were aligned for comparative analysis. Based on predicted antigenicity of consensus protein sequences, five aMPV-specific N peptides were synthesized for development of peptide-antigens and antisera. Results The presence of two aMPV nucleoprotein (N gene encoded polypeptides was detected in aMPV/C/US/Co and aMPV/A/UK/3b infected Vero cells. Nucleoprotein 1 (N1 encoded from the first open reading frame (ORF was predicted to be 394 amino acids in length for aMPV/C/US/Co and 391 amino acids in length for aMPV/A/UK/3b with approximate molecular weights of 43.3 kilodaltons and 42.7 kilodaltons, respectively. Nucleoprotein 2 (N2 was hypothesized to be encoded by a second downstream ORF in-frame with ORF1 and encoded a protein predicted to contain 328 amino acids for aMPV/C/US/Co or 259 amino acids for aMPV/A/UK/3b with approximate molecular weights of 36 kilodaltons and 28.3 kilodaltons, respectively. Peptide antibodies to the N-terminal and C-terminal portions of the aMPV N protein confirmed presence of these products in both aMPV/C/US/Co- and aMPV/A/UK/3b-infected Vero cells. N1 and N2 for aMPV/C/US/Co ORFs were molecularly cloned and expressed in Vero cells utilizing eukaryotic expression vectors to confirm identity of the aMPV encoded proteins. Conclusion This is the first reported identification of potential, accessory in-frame N2 ORF gene products among

Trypsin- and low pH-mediated fusogenicity of avian metapneumovirus fusion proteins is determined by residues at positions 100, 101 and 294.

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Yun, Bingling; Guan, Xiaolu; Liu, Yongzhen; Gao, Yanni; Wang, Yongqiang; Qi, Xiaole; Cui, Hongyu; Liu, Changjun; Zhang, Yanping; Gao, Li; Li, Kai; Gao, Honglei; Gao, Yulong; Wang, Xiaomei

2015-10-26

Avian metapneumovirus (aMPV) and human metapneumovirus (hMPV) are members of the genus Metapneumovirus in the subfamily Pneumovirinae. Metapneumovirus fusion (F) protein mediates the fusion of host cells with the virus membrane for infection. Trypsin- and/or low pH-induced membrane fusion is a strain-dependent phenomenon for hMPV. Here, we demonstrated that three subtypes of aMPV (aMPV/A, aMPV/B, and aMPV/C) F proteins promoted cell-cell fusion in the absence of trypsin. Indeed, in the presence of trypsin, only aMPV/C F protein fusogenicity was enhanced. Mutagenesis of the amino acids at position 100 and/or 101, located at a putative cleavage region in aMPV F proteins, revealed that the trypsin-mediated fusogenicity of aMPV F proteins is regulated by the residues at positions 100 and 101. Moreover, we demonstrated that aMPV/A and aMPV/B F proteins mediated cell-cell fusion independent of low pH, whereas the aMPV/C F protein did not. Mutagenesis of the residue at position 294 in the aMPV/A, aMPV/B, and aMPV/C F proteins showed that 294G played a critical role in F protein-mediated fusion under low pH conditions. These findings on aMPV F protein-induced cell-cell fusion provide new insights into the molecular mechanisms underlying membrane fusion and pathogenesis of aMPV.

Alcohol-induced histone acetylation reveals a gene network involved in alcohol tolerance.

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Alfredo Ghezzi

Full Text Available Sustained or repeated exposure to sedating drugs, such as alcohol, triggers homeostatic adaptations in the brain that lead to the development of drug tolerance and dependence. These adaptations involve long-term changes in the transcription of drug-responsive genes as well as an epigenetic restructuring of chromosomal regions that is thought to signal and maintain the altered transcriptional state. Alcohol-induced epigenetic changes have been shown to be important in the long-term adaptation that leads to alcohol tolerance and dependence endophenotypes. A major constraint impeding progress is that alcohol produces a surfeit of changes in gene expression, most of which may not make any meaningful contribution to the ethanol response under study. Here we used a novel genomic epigenetic approach to find genes relevant for functional alcohol tolerance by exploiting the commonalities of two chemically distinct alcohols. In Drosophila melanogaster, ethanol and benzyl alcohol induce mutual cross-tolerance, indicating that they share a common mechanism for producing tolerance. We surveyed the genome-wide changes in histone acetylation that occur in response to these drugs. Each drug induces modifications in a large number of genes. The genes that respond similarly to either treatment, however, represent a subgroup enriched for genes important for the common tolerance response. Genes were functionally tested for behavioral tolerance to the sedative effects of ethanol and benzyl alcohol using mutant and inducible RNAi stocks. We identified a network of genes that are essential for the development of tolerance to sedation by alcohol.

Specificity and functional interaction of the polymerase complex proteins of human and avian metapneumoviruses

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M.T. de Graaf (Marieke); S. Herfst (Sander); E.J.A. Schrauwen (Eefje); Y. Choi (Ying); B.G. van den Hoogen (Bernadette); A.D.M.E. Osterhaus (Albert); R.A.M. Fouchier (Ron)

2008-01-01

textabstractHuman metapneumovirus (HMPV) and avian metapneumovirus (AMPV) have a similar genome organization and protein composition, but a different host range. AMPV subgroup C (AMPV-C) is more closely relaled to HMPV than other AMPVs. To investigate the specificity and functional interaction of

Recovery of avian metapneumovirus subgroup C from cDNA: cross-recognition of avian and human metapneumovirus support proteins.

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Govindarajan, Dhanasekaran; Buchholz, Ursula J; Samal, Siba K

2006-06-01

Avian metapneumovirus (AMPV) causes an acute respiratory disease in turkeys and is associated with "swollen head syndrome" in chickens, contributing to significant economic losses for the U.S. poultry industry. With a long-term goal of developing a better vaccine for controlling AMPV in the United States, we established a reverse genetics system to produce infectious AMPV of subgroup C entirely from cDNA. A cDNA clone encoding the entire 14,150-nucleotide genome of AMPV subgroup C strain Colorado (AMPV/CO) was generated by assembling five cDNA fragments between the T7 RNA polymerase promoter and the autocatalytic hepatitis delta virus ribozyme of a transcription plasmid, pBR 322. Transfection of this plasmid, along with the expression plasmids encoding the N, P, M2-1, and L proteins of AMPV/CO, into cells stably expressing T7 RNA polymerase resulted in the recovery of infectious AMPV/CO. Characterization of the recombinant AMPV/CO showed that its growth properties in tissue culture were similar to those of the parental virus. The potential of AMPV/CO to serve as a viral vector was also assessed by generating another recombinant virus, rAMPV/CO-GFP, that expressed the enhanced green fluorescent protein (GFP) as a foreign protein. Interestingly, GFP-expressing AMPV and GFP-expressing human metapneumovirus (HMPV) could be recovered using the support plasmids of either virus, denoting that the genome promoters are conserved between the two metapneumoviruses and can be cross-recognized by the polymerase complex proteins of either virus. These results indicate a close functional relationship between AMPV/CO and HMPV.

Contribution of the attachment G glycoprotein to pathogenicity and immunogenicity of avian metapneumovirus subgroup C.

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Govindarajan, Dhanasekaran; Kim, Shin-Hee; Samal, Siba K

2010-03-01

Avian metapneumovirus (AMPV) causes an upper respiratory tract infection in turkeys leading to serious economic losses to the turkey industry. The G glycoprotein of AMPV is known to be associated with viral attachment and pathogenesis. In this study, we determined the role of the G glycoprotein in the pathogenicity and immunogenicity of AMPV strain Colorado (AMPV/CO). Recombinant AMPV/CO lacking the G protein (rAMPV/CO-deltaG) was generated using a reverse-genetics system. The recovered rAMPV/CO-deltaG replicated slightly better than did wild-type AMPV in Vero cells. However, deletion of the G gene in AMPV resulted in attenuation of the virus in turkeys. The mutant virus induced less-severe clinical signs and a weaker immune response in turkeys than did the wild-type AMPV. Our results suggest that the G glycoprotein is an important determinant for the pathogenicity and immunogenicity of AMPV.

Human Metapneumovirus Induces Formation of Inclusion Bodies for Efficient Genome Replication and Transcription.

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Cifuentes-Muñoz, Nicolás; Branttie, Jean; Slaughter, Kerri Beth; Dutch, Rebecca Ellis

2017-12-15

Human metapneumovirus (HMPV) causes significant upper and lower respiratory disease in all age groups worldwide. The virus possesses a negative-sense single-stranded RNA genome of approximately 13.3 kb encapsidated by multiple copies of the nucleoprotein (N), giving rise to helical nucleocapsids. In addition, copies of the phosphoprotein (P) and the large RNA polymerase (L) decorate the viral nucleocapsids. After viral attachment, endocytosis, and fusion mediated by the viral glycoproteins, HMPV nucleocapsids are released into the cell cytoplasm. To visualize the subsequent steps of genome transcription and replication, a fluorescence in situ hybridization (FISH) protocol was established to detect different viral RNA subpopulations in infected cells. The FISH probes were specific for detection of HMPV positive-sense RNA (+RNA) and viral genomic RNA (vRNA). Time course analysis of human bronchial epithelial BEAS-2B cells infected with HMPV revealed the formation of inclusion bodies (IBs) from early times postinfection. HMPV IBs were shown to be cytoplasmic sites of active transcription and replication, with the translation of viral proteins being closely associated. Inclusion body formation was consistent with an actin-dependent coalescence of multiple early replicative sites. Time course quantitative reverse transcription-PCR analysis suggested that the coalescence of inclusion bodies is a strategy to efficiently replicate and transcribe the viral genome. These results provide a better understanding of the steps following HMPV entry and have important clinical implications. IMPORTANCE Human metapneumovirus (HMPV) is a recently discovered pathogen that affects human populations of all ages worldwide. Reinfections are common throughout life, but no vaccines or antiviral treatments are currently available. In this work, a spatiotemporal analysis of HMPV replication and transcription in bronchial epithelial cell-derived immortal cells was performed. HMPV was shown to

Antigenic and genetic variability of human metapneumoviruses

NARCIS (Netherlands)

S. Herfst (Sander); L. Sprong; P.A. Cane; E. Forleo-Neto; A.D.M.E. Osterhaus (Albert); R.A.M. Fouchier (Ron); R.L. de Swart (Rik); B.G. van den Hoogen (Bernadette)

2004-01-01

textabstractHuman metapneumovirus (HMPV) is a member of the subfamily Pneumovirinae within the family Paramyxo- viridae. Other members of this subfamily, respiratory syncytial virus and avian pneumovirus, can be divided into subgroups on the basis of genetic or antigenic differences or both. For

Rational design of avian metapneumovirus live attenuated vaccines by inhibiting viral messenger RNA cap methyltransferase

Science.gov (United States)

Avian metapneumovirus (aMPV), also known as avian pneumovirus or turkey rhinotracheitis, is a non-segmented negative-sense RNA virus belonging to the family of Paramyxoviridae, the subfamily Pneumovirinae, and the genus Metapneumovirus. aMPV is the causative agent of respiratory tract infection and ...

Development and optimization of a direct plaque assay for human and avian metapneumoviruses

Science.gov (United States)

Zhang, Yu; Wei, Yongwei; Li, Junan; Li, Jianrong

2012-01-01

The genus Metapneumovirus within the subfamily Pneumovirinae and family Paramyxoviridae includes only two viruses, human metapneumovirus (hMPV) and avian metapneumovirus (aMPV), which cause respiratory disease in humans and birds, respectively. These two viruses grow poorly in cell culture and other quantitation methods, such as indirect immuno-staining and immuno-fluorescent assays, are expensive, time consuming, and do not allow for plaque purification of the virus. In order to enhance research efforts for studying these two viruses, a direct plaque assay for both hMPV and aMPV has been developed. By optimizing the chemical components of the agarose overlay, it was found that both hMPV with a trypsin-independent F cleavage site and aMPV formed clear and countable plaques in a number of mammalian cell lines (such as Vero-E6 and LLC-MK2 cells) after 5 days of incubation. The plaque forming assay has similar sensitivity and reliability as the currently used immunological methods for viral quantitation. The plaque assay is also a more simple, rapid, and economical method compared to immunological assays, and in addition allows for plaque purification of the viruses. The direct plaque assay will be a valuable method for the quantitation and evaluation of the biological properties of some metapneumoviruses. PMID:22684013

Localization of a Region in the Fusion Protein of Avian Metapneumovirus That Modulates Cell-Cell Fusion

Science.gov (United States)

Wei, Yongwei; Feng, Kurtis; Yao, Xiangjie; Cai, Hui; Li, Junan; Mirza, Anne M.; Iorio, Ronald M.

2012-01-01

The genus Metapneumovirus within the subfamily Pneumovirinae of the family Paramyxoviridae includes two members, human metapneumovirus (hMPV) and avian metapneumovirus (aMPV), causing respiratory tract infections in humans and birds, respectively. Paramyxoviruses enter host cells by fusing the viral envelope with a host cell membrane. Membrane fusion of hMPV appears to be unique, in that fusion of some hMPV strains requires low pH. Here, we show that the fusion (F) proteins of aMPV promote fusion in the absence of the attachment protein and low pH is not required. Furthermore, there are notable differences in cell-cell fusion among aMPV subtypes. Trypsin was required for cell-cell fusion induced by subtype B but not subtypes A and C. The F protein of aMPV subtype A was highly fusogenic, whereas those from subtypes B and C were not. By construction and evaluation of chimeric F proteins composed of domains from the F proteins of subtypes A and B, we localized a region composed of amino acid residues 170 to 338 in the F protein that is responsible for the hyperfusogenic phenotype of the F from subtype A. Further mutagenesis analysis revealed that residues R295, G297, and K323 in this region collectively contributed to the hyperfusogenicity. Taken together, we have identified a region in the aMPV F protein that modulates the extent of membrane fusion. A model for fusion consistent with these data is presented. PMID:22915815

Localization of a region in the fusion protein of avian metapneumovirus that modulates cell-cell fusion.

Science.gov (United States)

Wei, Yongwei; Feng, Kurtis; Yao, Xiangjie; Cai, Hui; Li, Junan; Mirza, Anne M; Iorio, Ronald M; Li, Jianrong

2012-11-01

The genus Metapneumovirus within the subfamily Pneumovirinae of the family Paramyxoviridae includes two members, human metapneumovirus (hMPV) and avian metapneumovirus (aMPV), causing respiratory tract infections in humans and birds, respectively. Paramyxoviruses enter host cells by fusing the viral envelope with a host cell membrane. Membrane fusion of hMPV appears to be unique, in that fusion of some hMPV strains requires low pH. Here, we show that the fusion (F) proteins of aMPV promote fusion in the absence of the attachment protein and low pH is not required. Furthermore, there are notable differences in cell-cell fusion among aMPV subtypes. Trypsin was required for cell-cell fusion induced by subtype B but not subtypes A and C. The F protein of aMPV subtype A was highly fusogenic, whereas those from subtypes B and C were not. By construction and evaluation of chimeric F proteins composed of domains from the F proteins of subtypes A and B, we localized a region composed of amino acid residues 170 to 338 in the F protein that is responsible for the hyperfusogenic phenotype of the F from subtype A. Further mutagenesis analysis revealed that residues R295, G297, and K323 in this region collectively contributed to the hyperfusogenicity. Taken together, we have identified a region in the aMPV F protein that modulates the extent of membrane fusion. A model for fusion consistent with these data is presented.

Effects of Cyclosporin A induced T-lymphocyte depletion on the course of avian Metapneumovirus (aMPV) infection in turkeys

DEFF Research Database (Denmark)

Rubbenstroth, Dennis; Dalgaard, Tina S; Kothlow, Sonja

2010-01-01

The avian Metapneumovirus (aMPV) causes an economically important acute respiratory disease in turkeys (turkey rhinotracheitis, TRT).While antibodies were shownto be insufficient for protection against a MPV-infection, the role of T-lymphocytes in the control of aMPV-infection is not clear...... to untreated controls (P infection...

[Respiratory infections caused by metapneumovirus in elderly patients].

Science.gov (United States)

Fica C, Alberto; Hernández C, Loreto; Porte T, Lorena; Castro S, Marcelo; Weitzel, Thomas

2011-04-01

Human metapneumovirus infections are increasingly recognized among adult patients and the aim of this report is to present a series of 4 cases admitted during the winter of 2010. All were detected by direct fluorescence anti-bodies assay of respiratory samples and all were female patients with an age range of 79 to 95 years, including two bedridden cases, one with dementia and three with chronic obstructive pulmonary disease. One patient presented with parainfluenza 3 virus coinfection. Patients presented with pneumonía in 3 cases (interstitial pattern in 2 and lobar consolidation in the other) or acute exacerbation of chronic bronchitis in the remaining case. Symptoms were present for 3 to 7 days before admission and 3 have wheezing. All had hypoxemic or global respiratory failure and lymphopenia (ventilation. Human metapneumovirus infections can decompensate elderly patients with chronic respiratory diseases generating hospital admission and a prolonged morbidity marked by obstructive manifestations and sometimes can become into death.

The pathogenicity of avian metapneumovirus subtype C wild bird isolates in domestic turkeys

Directory of Open Access Journals (Sweden)

Cha Ra Mi

2013-01-01

Full Text Available Abstract Background Avian metapneumovirus subtype C (aMPV/C causes severe upper respiratory disease in turkeys. Previous report revealed the presence of aMPV/C in wild birds in the southeast regions of the U.S. Methods In this study, aMPV/C positive oral swabs from American coots (AC and Canada geese (CG were passaged three times in the respiratory tract of specific pathogen free (SPF turkeys and used as aMPV/C P3 virus isolates in subsequent studies. Results Wild bird P3 isolates showed similar growth characteristics when compared to virulent aMPV/C in chicken embryo fibroblast ( CEF cell cultures and their glycoprotein G gene sequence was closely related to the G gene of aMPV/C Colorado reference virus. Three-day-old commercial or SPF turkeys were inoculated oculonasally with wild bird aMPV/C P3 isolates. At 5 and 7 days post-inoculation (DPI, severe clinical signs were observed in both of the AC and CG virus-exposed groups. Viral RNA was detected in tracheal swabs by reverse transcriptase polymerase chain reaction (RT-PCR. In addition, immunohistochemistry showed virus replication in the nasal turbinate and trachea. All virus-exposed turkeys developed positive antibody response by 14 DPI. Conclusions Our data demonstrate that aMPV/C wild bird isolates induced typical aMPV/C disease in the domestic turkeys.

The pathogenicity of avian metapneumovirus subtype C wild bird isolates in domestic turkeys.

Science.gov (United States)

Cha, Ra Mi; Yu, Qingzhong; Zsak, Laszlo

2013-01-30

Avian metapneumovirus subtype C (aMPV/C) causes severe upper respiratory disease in turkeys. Previous report revealed the presence of aMPV/C in wild birds in the southeast regions of the U.S. In this study, aMPV/C positive oral swabs from American coots (AC) and Canada geese (CG) were passaged three times in the respiratory tract of specific pathogen free (SPF) turkeys and used as aMPV/C P3 virus isolates in subsequent studies. Wild bird P3 isolates showed similar growth characteristics when compared to virulent aMPV/C in chicken embryo fibroblast ( CEF) cell cultures and their glycoprotein G gene sequence was closely related to the G gene of aMPV/C Colorado reference virus. Three-day-old commercial or SPF turkeys were inoculated oculonasally with wild bird aMPV/C P3 isolates. At 5 and 7 days post-inoculation (DPI), severe clinical signs were observed in both of the AC and CG virus-exposed groups. Viral RNA was detected in tracheal swabs by reverse transcriptase polymerase chain reaction (RT-PCR). In addition, immunohistochemistry showed virus replication in the nasal turbinate and trachea. All virus-exposed turkeys developed positive antibody response by 14 DPI. Our data demonstrate that aMPV/C wild bird isolates induced typical aMPV/C disease in the domestic turkeys.

The cellular endosomal sorting complex required for transport pathway is not involved in avian metapneumovirus budding in a virus-like-particle expression system.

Science.gov (United States)

Weng, Yuejin; Lu, Wuxun; Harmon, Aaron; Xiang, Xiaoxiao; Deng, Qiji; Song, Minxun; Wang, Dan; Yu, Qingzhong; Li, Feng

2011-05-01

Avian metapneumovirus (AMPV) is a paramyxovirus that principally causes respiratory disease and egg production drops in turkeys and chickens. Together with its closely related human metapneumovirus (HMPV), they comprise the genus Metapneumovirus in the family Paramyxoviridae. Little is currently known about the mechanisms involved in the budding of metapneumovirus. By using AMPV as a model system, we showed that the matrix (M) protein by itself was insufficient to form virus-like-particles (VLPs). The incorporation of M into VLPs was shown to occur only when both the viral nucleoprotein (N) and the fusion (F) proteins were co-expressed. Furthermore, we provided evidence indicating that two YSKL and YAGL segments encoded within the M protein were not a functional late domain, and the endosomal sorting complex required for transport (ESCRT) machinery was not involved in metapneumovirus budding, consistent with a recent observation that human respiratory syncytial virus, closely related to HMPV, uses an ESCRT-independent budding mechanism. Taken together, these results suggest that metapneumovirus budding is independent of the ESCRT pathway and the minimal budding machinery described here will aid our future understanding of metapneumovirus assembly and egress.

Species-specific deletion of the viral attachment glycoprotein of avian metapneumovirus.

Science.gov (United States)

Kong, Byung-Whi; Foster, Linda K; Foster, Douglas N

2008-03-01

The avian metapneumovirus (AMPV) genome encodes the fusion (F), small hydrophobic (SH), and attachment glycoprotein (G) as envelope glycoproteins. The F and G proteins mainly function to allow viral entry into host cells during the early steps of the virus life cycle. The highly variable AMPV G protein is a major determinant for distinguishing virus subtypes. Sequence analysis was used to determine if any differences between avian or mammalian cell propagated subtype C AMPV could be detected for the 1.8kb G gene. As a result, the complete 1.8kb G gene was found to be present when AMPV was propagated in our immortal turkey turbinate (TT-1) cell line regardless of passage number. Surprisingly, AMPV propagated for 15 or more passages in mammalian Vero cells revealed an essentially deleted G gene in the viral genome, resulting in no G gene mRNA expression. Although the Vero cell propagated AMPV genome contained a small 122 nucleotide fragment of the G gene, no other mRNA variants were detected from either mammalian or avian propagated AMPV. The G gene truncation might be caused by cellular molecular mechanisms that are species-specific. The lack of viral gene deletions suggests that avian cell propagated AMPV will provide a better alternative host for live recombinant vaccine development based on a reverse genetics system.

Population Dynamics and Rates of Molecular Evolution of a Recently Emerged Paramyxovirus, Avian Metapneumovirus Subtype C▿ †

OpenAIRE

Padhi, Abinash; Poss, Mary

2008-01-01

We report the existence of two distinct sublineages of avian metapneumovirus (MPV) subtype C, a virus which has caused serious economic loss in commercial turkey farms in the United States. This subtype is closely related to human MPV, infects multiple avian species, and is globally distributed. The evolutionary rates of this virus are estimated to be 1.3 × 10−3 to 7 × 10−3 substitutions per site per year, and coalescent estimates place its emergence between 1991 and 1996. The four genes exam...

Comparative Evaluation Of Conventional Rt-pcr And Real-time Rt-pcr (rrt-pcr) For Detection Of Avian Metapneumovirus Subtype A [comparação Entre As Técnicas De Rt-pcr Convencional E Rt-pcr Em Tempo Real Para A Detecção Do Metapneumovírus Aviários Subtipo A

OpenAIRE

Ferreira H.L.; Spilki F.R.; dos Santos M.M.A.B.; de Almeida R.S.; Arns C.W.

2009-01-01

Avian metapneumovirus (AMPV) belongs to Metapneumovirus genus of Paramyxoviridae family. Virus isolation, serology, and detection of genomic RNA are used as diagnostic methods for AMPV. The aim of the present study was to compare the detection of six subgroup A AMPV isolates (AMPV/A) viral RNA by using different conventional and real time RT-PCR methods. Two new RT-PCR tests and two real time RT-PCR tests, both detecting fusion (F) gene and nucleocapsid (N) gene were compared with an establis...

Complete Genome Sequence of an Avian Metapneumovirus Subtype A Strain Isolated from Chicken (Gallus gallus) in Brazil

OpenAIRE

Rizotto, La?s S.; Scagion, Guilherme P.; Cardoso, Tereza C.; Sim?o, Raphael M.; Caserta, Leonardo C.; Benassi, Julia C.; Keid, Lara B.; Oliveira, Tr?cia M. F. de S.; Soares, Rodrigo M.; Arns, Clarice W.; Van Borm, Steven; Ferreira, Helena L.

2017-01-01

ABSTRACT We report here the complete genome sequence of an avian metapneumovirus (aMPV) isolated from a tracheal tissue sample of a commercial layer flock. The complete genome sequence of aMPV-A/chicken/Brazil-SP/669/2003 was obtained using MiSeq (Illumina, Inc.) sequencing. Phylogenetic analysis of the complete genome classified the isolate as avian metapneumovirus subtype A.

A Small-Molecule Inducible Synthetic Circuit for Control of the SOS Gene Network without DNA Damage.

Science.gov (United States)

Kubiak, Jeffrey M; Culyba, Matthew J; Liu, Monica Yun; Mo, Charlie Y; Goulian, Mark; Kohli, Rahul M

2017-11-17

The bacterial SOS stress-response pathway is a pro-mutagenic DNA repair system that mediates bacterial survival and adaptation to genotoxic stressors, including antibiotics and UV light. The SOS pathway is composed of a network of genes under the control of the transcriptional repressor, LexA. Activation of the pathway involves linked but distinct events: an initial DNA damage event leads to activation of RecA, which promotes autoproteolysis of LexA, abrogating its repressor function and leading to induction of the SOS gene network. These linked events can each independently contribute to DNA repair and mutagenesis, making it difficult to separate the contributions of the different events to observed phenotypes. We therefore devised a novel synthetic circuit to unlink these events and permit induction of the SOS gene network in the absence of DNA damage or RecA activation via orthogonal cleavage of LexA. Strains engineered with the synthetic SOS circuit demonstrate small-molecule inducible expression of SOS genes as well as the associated resistance to UV light. Exploiting our ability to activate SOS genes independently of upstream events, we further demonstrate that the majority of SOS-mediated mutagenesis on the chromosome does not readily occur with orthogonal pathway induction alone, but instead requires DNA damage. More generally, our approach provides an exemplar for using synthetic circuit design to separate an environmental stressor from its associated stress-response pathway.

Deletion of the M2-2 gene from avian metapneumovirus subgroup C impairs virus replication and immunogenicity in Turkeys.

Science.gov (United States)

Yu, Qingzhong; Estevez, Carlos N; Roth, Jason P; Hu, Haixia; Zsak, Laszlo

2011-06-01

The second matrix (M2) gene of avian metapneumovirus subgroup C (aMPV-C) contains two overlapping open reading frames (ORFs), encoding two putative proteins, M2-1 and M2-2. Both proteins are believed to be involved in viral RNA transcription or replication. To further characterize the function of the M2-2 protein in virus replication, the non-overlapping region of the M2-2 ORF was deleted from an infectious cDNA clone of the aMPV-C strain, and a viable virus was rescued by using reverse genetics technology. The recombinant virus, raMPV-C ΔM2-2, was characterized in vitro and in vivo. In Vero cells, raMPV-C ΔM2-2 replicated slightly less efficiently than the parental virus, 10-fold reduction at 48-h post-infection. The raMPV-C ΔM2-2 virus induced typical cytopathic effects (CPE) that were indistinguishable from those seen with the parental virus infection. In specific-pathogen-free (SPF) turkeys, raMPV-C ΔM2-2 was attenuated and caused no clinical signs of disease. Less than 20% of the inoculated birds shed detectable virus in tracheal tissue during the first 5 days post-infection, and no virus shedding was detected afterward. Forty percent of infected birds produced a weak antibody response at 14 days post-infection. Upon challenge with a virulent aMPV-C strain, more than 80% of the raMPV-C ΔM2-2-inoculated birds showed typical disease signs and virus shedding in tracheal tissue. These results suggest that the M2-2 protein of aMPV-C virus is not essential for virus replication in vitro, but is required for sufficient virus replication to maintain pathogenicity and immunogenicity in the natural host.

Deconstructing the pluripotency gene regulatory network

KAUST Repository

Li, Mo

2018-04-04

Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

Deconstructing the pluripotency gene regulatory network

KAUST Repository

Li, Mo; Belmonte, Juan Carlos Izpisua

2018-01-01

Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

Human metapneumovirus and respiratory syncytial virus in hospitalized danish children with acute respiratory tract infection

DEFF Research Database (Denmark)

von Linstow, Marie-Louise; Larsen, Hans Henrik; Eugen-Olsen, Jesper

2004-01-01

The newly discovered human metapneumovirus (hMPV) has been shown to be associated with respiratory illness. We determined the frequencies and clinical features of hMPV and respiratory syncytial virus (RSV) infections in 374 Danish children with 383 episodes of acute respiratory tract infection...... children 1-6 months of age. Asthmatic bronchitis was diagnosed in 66.7% of hMPV and 10.6% of RSV-infected children (p infected children required respiratory support. hMPV is present in young.......6%) ARTI episodes by real-time reverse transcription-polymerase chain reaction using primers targeting the hMPV N gene and the RSV L gene. Two children were co-infected with hMPV and RSV. They were excluded from statistical analysis. Hospitalization for ARTI caused by hMPV was restricted to very young...

Complete Genome Sequence of an Avian Metapneumovirus Subtype A Strain Isolated from Chicken (Gallus gallus) in Brazil.

Science.gov (United States)

Rizotto, Laís S; Scagion, Guilherme P; Cardoso, Tereza C; Simão, Raphael M; Caserta, Leonardo C; Benassi, Julia C; Keid, Lara B; Oliveira, Trícia M F de S; Soares, Rodrigo M; Arns, Clarice W; Van Borm, Steven; Ferreira, Helena L

2017-07-20

We report here the complete genome sequence of an avian metapneumovirus (aMPV) isolated from a tracheal tissue sample of a commercial layer flock. The complete genome sequence of aMPV-A/chicken/Brazil-SP/669/2003 was obtained using MiSeq (Illumina, Inc.) sequencing. Phylogenetic analysis of the complete genome classified the isolate as avian metapneumovirus subtype A. Copyright © 2017 Rizotto et al.

Comparison of initial high resolution computed tomography features in viral pneumonia between metapneumovirus infection and severe acute respiratory syndrome

International Nuclear Information System (INIS)

Wong, Cheuk Kei Kathy; Lai, Vincent; Wong, Yiu Chung

2012-01-01

Objective: To review and compare initial high resolution computed tomography (HRCT) findings in patients with metapneumovirus pneumonia and severe acute respiratory syndrome (SARS-Coronovirus). Materials and methods: 4 cases of metapneumovirus pneumonia (mean age of 52.3 years) in an institutional outbreak (Castle Peak Hospital) in 2008 and 38 cases of SARS-coronovirus (mean age of 39.6 years) admitted to Tuen Mun hospital during an epidemic outbreak in 2003 were included. HRCT findings of the lungs for all patients were retrospectively reviewed by two independent radiologists. Results: In the metapneumovirus group, common HRCT features were ground glass opacities (100%), consolidation (100%), parenchymal band (100%), bronchiectasis (75%). Crazy paving pattern was absent. They were predominantly subpleural and basal in location and bilateral involvement was observed in 50% of patients. In the SARS group, common HRCT features were ground glass opacities (92.1%), interlobular septal thickening (86.8%), crazy paving pattern (73.7%) and consolidation (68%). Bronchiectasis was not seen. Majority of patient demonstrated segmental or lobar in distribution and bilateral involvement was observed in 44.7% of patients. Pleural effusion and lymphadenopathy were of consistent rare features in both groups. Conclusion: Ground glass opacities, interlobular septal thickening and consolidations were consistent HRCT manifestations in both metapneumovirus infection and SARS. The presence of bronchiectasis (0% in SARS) may point towards metapneumovirus while crazy paving pattern is more suggestive of SARS.

Brazilian avian metapneumovirus subtypes A and B: experimental infection of broilers and evaluation of vaccine efficacy

OpenAIRE

Márcia B. dos Santos; Matheus C. Martini; Helena L. Ferreira; Luciana H.A. da Silva; Paulo A. Fellipe; Fernando R. Spilki; Clarice W. Arns

2012-01-01

Santos M.B., Martini M.C., Ferreira H.L., Silva L.H.A., Fellipe P.A., Spilki F.R. & Arns C.W. 2012. Brazilian avian metapneumovirus subtypes A and B: experimental infection of broilers and evaluation of vaccine efficacy. Pesquisa Veterinaria Brasileira 32(12):1257-1262. Laboratorio de Virologia, Instituto de Biologia, Universidade Estadual de Campinas, Rua Monteiro Lobato s/n, Cx. Postal 6109, Campinas, SP 13083-970, Brazil. E-mail: Avian metapneumovirus (aMPV) is a respirator...

First Report of Avian Metapneumovirus Subtype B Field Strain in a Romanian Broiler Flock During an Outbreak of Respiratory Disease.

Science.gov (United States)

Franzo, Giovanni; Tucciarone, Claudia Maria; Enache, Mirel; Bejan, Violeta; Ramon, Gema; Koutoulis, Konstantinos C; Cecchinato, Mattia

2017-06-01

Avian metapneumovirus (aMPV) represents one of the most prevalent diseases of turkey, especially in combination with other pathogens, and its frequency is also increasing among chickens. Despite this evidence, epidemiologic data are poor and scattered, severely preventing control of the disease even in highly developed areas such as Europe. In the present study, the detection and characterization of an aMPV subtype B strain circulating in a vaccinated but symptomatic Romanian broiler flock is reported for the first time. The phylogenetic analysis based on the partial G gene sequence demonstrates the close relationship of the Romanian virus with a group of recently emerged Italian field strains for which vaccine-induced protection was experimentally proven to be partial. These preliminary results allow us to hypothesize the spreading of vaccine-escaping aMPV subtype B strains through Europe and, consequently, dictate the carrying out of a more systematic survey to confirm this theory and enforce adequate countermeasures.

Recovery of human metapneumovirus from cDNA: optimization of growth in vitro and expression of additional genes

International Nuclear Information System (INIS)

Biacchesi, Stephane; Skiadopoulos, Mario H.; Tran, Kim C.; Murphy, Brian R.; Collins, Peter L.; Buchholz, Ursula J.

2004-01-01

Human metapneumovirus (HMPV) is a recently recognized causative agent of respiratory tract disease in individuals of all ages and especially young infants. HMPV remains poorly characterized and has been reported to replicate inefficiently in vitro. Complete consensus sequences were recently determined for two isolates representing the two proposed HMPV genetic subgroups (Biacchesi et al., Virology 315 (1) (2003) 1). We have developed a reverse genetic system to produce one of these isolates, CAN97-83, entirely from cDNA. We also recovered a version, rHMPV-GFP, in which the enhanced green fluorescent protein (GFP) was expressed from a transcription cassette inserted as the first gene, leaving the 41-nt leader region and first 16 nt of the N gene undisturbed. The ability to monitor GFP expression in living cells greatly facilitated the initial recovery of this slow-growing virus. In addition, the ability to express a foreign gene from an engineered transcription cassette confirmed the identification of the HMPV transcription signals and identified the F gene-end signal as being highly efficient for transcription termination. The ability to recover virus containing a foreign insert in this position indicated that the viral promoter is contained within the 3'-terminal 57 nt of the genome. Recombinant HMPV replicated in vitro as efficiently as biologically derived HMPV, whereas the kinetics and final yield of rHMPV-GFP were reduced several-fold. Conditions for trypsin treatment were investigated, providing for improved virus yields. Another version of HMPV, rHMPV+G1F23, was recovered that contained a second copy of the G gene and two extra copies of F in promoter-proximal positions in the order G1-F2-F3. Thus, this recombinant genome would encode 11 mRNAs rather than eight and would be 17.3 kb long, 30% longer than that of the natural virus. Nonetheless, the rHMPV+G1F23 virus replicated in vitro with an efficiency that was only modestly reduced compared to rHMPV and was

Prevalence and clinical symptoms of human metapneumovirus infection in hospitalized patients

NARCIS (Netherlands)

B.G. van den Hoogen (Bernadette); G.J.J. van Doornum (Gerard); J.C. Fockens (John); J.J. Cornelissen (Jan); W.E.Ph. Beyer (Walter); R. de Groot (Ronald); A.D.M.E. Osterhaus (Albert); R.A.M. Fouchier (Ron)

2003-01-01

textabstractDuring a 17-month period, we performed retrospective analyses of the prevalence of and clinical symptoms associated with human metapneumovirus (hMPV) infection, among patients in a university hospital in The Netherlands. All available nasal-aspirate, throat-swab, sputum, and

Population dynamics and rates of molecular evolution of a recently emerged paramyxovirus, avian metapneumovirus subtype C.

Science.gov (United States)

Padhi, Abinash; Poss, Mary

2009-02-01

We report the existence of two distinct sublineages of avian metapneumovirus (MPV) subtype C, a virus which has caused serious economic loss in commercial turkey farms in the United States. This subtype is closely related to human MPV, infects multiple avian species, and is globally distributed. The evolutionary rates of this virus are estimated to be 1.3 x 10(-3) to 7 x 10(-3) substitutions per site per year, and coalescent estimates place its emergence between 1991 and 1996. The four genes examined show a concordant demographic pattern which is characterized by a rapid increase in population size followed by stable population grown until the present.

Epidemiology and genetic variability of human metapneumovirus during a 4-year-long study in Southeastern Brazil.

Science.gov (United States)

Oliveira, Danielle B L; Durigon, Edison L; Carvalho, Ariane C L; Leal, Andréa L; Souza, Thereza S; Thomazelli, Luciano M; Moraes, Claudia T P; Vieira, Sandra E; Gilio, Alfredo E; Stewien, Klaus E

2009-05-01

Epidemiological and molecular characteristics of human metapneumovirus (hMPV) were compared with human respiratory syncytial virus (hRSV) in infants and young children admitted for acute lower respiratory tract infections in a prospective study during four consecutive years in subtropical Brazil. GeneScan polymerase chain assays (GeneScan RT-PCR) were used to detect hMPV and hRSV in nasopharyngeal aspirates of 1,670 children during January 2003 to December 2006. hMPV and hRSV were detected, respectively, in 191 (11.4%) and in 702 (42%) of the children admitted with acute lower respiratory tract infections at the Sao Paulo University Hospital. Sequencing data of the hMPV F gene revealed that two groups of the virus, each divided into two subgroups, co-circulated during three consecutive years. It was also shown that a clear dominance of genotype B1 occurred during the years 2004 and 2005, followed by genotype A2 during 2006. Copyright 2009 Wiley-Liss, Inc.

Transcriptomic network analysis of micronuclei-related genes: a case study

DEFF Research Database (Denmark)

van Leeuwen, D. M.; Pedersen, Marie; Knudsen, Lisbeth E.

2011-01-01

checkpoint and aneuploidy. The MN-related gene network was tested against a transcriptomics case study associated with MN measurements. In this case study, transcriptomic data from children and adults differentially exposed to ambient air pollution in the Czech Republic were analysed and visualised......Mechanistically relevant information on responses of humans to xenobiotic exposure in relation to chemically induced biological effects, such as micronuclei (MN) formation can be obtained through large-scale transcriptomics studies. Network analysis may enhance the analysis and visualisation...... of such data. Therefore, this study aimed to develop a 'MN formation' network based on a priori knowledge, by using the pathway tool MetaCore. The gene network contained 27 genes and three gene complexes that are related to processes involved in MN formation, e.g. spindle assembly checkpoint, cell cycle...

[Cellular adhesion signal transduction network of tumor necrosis factor-alpha induced hepatocellular carcinoma cells].

Science.gov (United States)

Zheng, Yongchang; Du, Shunda; Xu, Haifeng; Xu, Yiyao; Zhao, Haitao; Chi, Tianyi; Lu, Xin; Sang, Xinting; Mao, Yilei

2014-11-18

To systemically explore the cellular adhesion signal transduction network of tumor necrosis factor-alpha (TNF-α)-induced hepatocellular carcinoma cells with bioinformatics tools. Published microarray dataset of TNF-α-induced HepG2, human transcription factor database HTRI and human protein-protein interaction database HPRD were used to construct and analyze the signal transduction network. In the signal transduction network, MYC and SP1 were the key nodes of signaling transduction. Several genes from the network were closely related with cellular adhesion.Epidermal growth factor receptor (EGFR) is a possible key gene of effectively regulating cellular adhesion during the induction of TNF-α. EGFR is a possible key gene for TNF-α-induced metastasis of hepatocellular carcinoma.

Synthetic tetracycline-inducible regulatory networks: computer-aided design of dynamic phenotypes

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Kaznessis Yiannis N

2007-01-01

Full Text Available Abstract Background Tightly regulated gene networks, precisely controlling the expression of protein molecules, have received considerable interest by the biomedical community due to their promising applications. Among the most well studied inducible transcription systems are the tetracycline regulatory expression systems based on the tetracycline resistance operon of Escherichia coli, Tet-Off (tTA and Tet-On (rtTA. Despite their initial success and improved designs, limitations still persist, such as low inducer sensitivity. Instead of looking at these networks statically, and simply changing or mutating the promoter and operator regions with trial and error, a systematic investigation of the dynamic behavior of the network can result in rational design of regulatory gene expression systems. Sophisticated algorithms can accurately capture the dynamical behavior of gene networks. With computer aided design, we aim to improve the synthesis of regulatory networks and propose new designs that enable tighter control of expression. Results In this paper we engineer novel networks by recombining existing genes or part of genes. We synthesize four novel regulatory networks based on the Tet-Off and Tet-On systems. We model all the known individual biomolecular interactions involved in transcription, translation, regulation and induction. With multiple time-scale stochastic-discrete and stochastic-continuous models we accurately capture the transient and steady state dynamics of these networks. Important biomolecular interactions are identified and the strength of the interactions engineered to satisfy design criteria. A set of clear design rules is developed and appropriate mutants of regulatory proteins and operator sites are proposed. Conclusion The complexity of biomolecular interactions is accurately captured through computer simulations. Computer simulations allow us to look into the molecular level, portray the dynamic behavior of gene regulatory

Topology and cellular localization of the small hydrophobic protein of avian metapneumovirus

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The small hydrophobic protein (SH) is a type II integral membrane protein that is packaged into virions and is only present in certain paramyxoviruses including metapneumovirus. In addition to a highly divergent primary sequence, SH proteins vary significantly in size among the different viruses. Hu...

Discovering implicit entity relation with the gene-citation-gene network.

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Min Song

Full Text Available In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner.

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

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Raza, Khalid; Alam, Mansaf

2016-10-01

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genes and Gene Networks Involved in Sodium Fluoride-Elicited Cell Death Accompanying Endoplasmic Reticulum Stress in Oral Epithelial Cells

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Yoshiaki Tabuchi

2014-05-01

Full Text Available Here, to understand the molecular mechanisms underlying cell death induced by sodium fluoride (NaF, we analyzed gene expression patterns in rat oral epithelial ROE2 cells exposed to NaF using global-scale microarrays and bioinformatics tools. A relatively high concentration of NaF (2 mM induced cell death concomitant with decreases in mitochondrial membrane potential, chromatin condensation and caspase-3 activation. Using 980 probe sets, we identified 432 up-regulated and 548 down-regulated genes, that were differentially expressed by >2.5-fold in the cells treated with 2 mM of NaF and categorized them into 4 groups by K-means clustering. Ingenuity® pathway analysis revealed several gene networks from gene clusters. The gene networks Up-I and Up-II included many up-regulated genes that were mainly associated with the biological function of induction or prevention of cell death, respectively, such as Atf3, Ddit3 and Fos (for Up-I and Atf4 and Hspa5 (for Up-II. Interestingly, knockdown of Ddit3 and Hspa5 significantly increased and decreased the number of viable cells, respectively. Moreover, several endoplasmic reticulum (ER stress-related genes including, Ddit3, Atf4 and Hapa5, were observed in these gene networks. These findings will provide further insight into the molecular mechanisms of NaF-induced cell death accompanying ER stress in oral epithelial cells.

Human metapneumovirus - what we know now [version 1; referees: 2 approved

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Nazly Shafagati

2018-02-01

Full Text Available Human metapneumovirus (HMPV is a leading cause of acute respiratory infection, particularly in children, immunocompromised patients, and the elderly. HMPV, which is closely related to avian metapneumovirus subtype C, has circulated for at least 65 years, and nearly every child will be infected with HMPV by the age of 5. However, immunity is incomplete, and re-infections occur throughout adult life. Symptoms are similar to those of other respiratory viral infections, ranging from mild (cough, rhinorrhea, and fever to more severe (bronchiolitis and pneumonia. The preferred method for diagnosis is reverse transcription-polymerase chain reaction as HMPV is difficult to culture. Although there have been many advances made in the past 16 years since its discovery, there are still no US Food and Drug Administration-approved antivirals or vaccines available to treat HMPV. Both small animal and non-human primate models have been established for the study of HMPV. This review will focus on the epidemiology, transmission, and clinical manifestations in humans as well as the animal models of HMPV pathogenesis and host immune response.

Molecular comparisons of full length metapneumovirus (MPV genomes, including newly determined French AMPV-C and -D isolates, further supports possible subclassification within the MPV Genus.

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Paul A Brown

Full Text Available Four avian metapneumovirus (AMPV subgroups (A-D have been reported previously based on genetic and antigenic differences. However, until now full length sequences of the only known isolates of European subgroup C and subgroup D viruses (duck and turkey origin, respectively have been unavailable. These full length sequences were determined and compared with other full length AMPV and human metapneumoviruses (HMPV sequences reported previously, using phylogenetics, comparisons of nucleic and amino acid sequences and study of codon usage bias. Results confirmed that subgroup C viruses were more closely related to HMPV than they were to the other AMPV subgroups in the study. This was consistent with previous findings using partial genome sequences. Closer relationships between AMPV-A, B and D were also evident throughout the majority of results. Three metapneumovirus "clusters" HMPV, AMPV-C and AMPV-A, B and D were further supported by codon bias and phylogenetics. The data presented here together with those of previous studies describing antigenic relationships also between AMPV-A, B and D and between AMPV-C and HMPV may call for a subclassification of metapneumoviruses similar to that used for avian paramyxoviruses, grouping AMPV-A, B and D as type I metapneumoviruses and AMPV-C and HMPV as type II.

Population Dynamics and Rates of Molecular Evolution of a Recently Emerged Paramyxovirus, Avian Metapneumovirus Subtype C▿ †

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Padhi, Abinash; Poss, Mary

2009-01-01

We report the existence of two distinct sublineages of avian metapneumovirus (MPV) subtype C, a virus which has caused serious economic loss in commercial turkey farms in the United States. This subtype is closely related to human MPV, infects multiple avian species, and is globally distributed. The evolutionary rates of this virus are estimated to be 1.3 × 10−3 to 7 × 10−3 substitutions per site per year, and coalescent estimates place its emergence between 1991 and 1996. The four genes examined show a concordant demographic pattern which is characterized by a rapid increase in population size followed by stable population grown until the present. PMID:19052092

Molecular detection of infectious bronchitis and avian metapneumoviruses in Oman backyard poultry.

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Al-Shekaili, Thunai; Baylis, Matthew; Ganapathy, Kannan

2015-04-01

Infectious bronchitis virus (IBV) and avian metapneumovirus (aMPV) are economically important viral pathogens infecting chickens globally. Identification of endemic IBV and aMPV strains promotes better control of both diseases and prevents production losses. Orophrayngeal swab samples were taken from 2317 birds within 243 different backyard flocks in Oman. Swabs from each flock were examined by RT-PCR using part-S1 and G gene primers for IBV and aMPV respectively. Thirty-nine chicken flocks were positive for IBV. Thirty two of these were genotyped and they were closely related to 793/B, M41, D274, IS/1494/06 and IS/885/00. 793/B-like IBV was also found in one turkey and one duck flock. Five flocks were positive for aMPV subtype B. Though no disease was witnessed at the time of sampling, identified viruses including variant IBV strains, may still pose a threat for both backyard and commercial poultry in Oman. Copyright © 2014 Elsevier Ltd. All rights reserved.

Molecular Comparisons of Full Length Metapneumovirus (MPV) Genomes, Including Newly Determined French AMPV-C and –D Isolates, Further Supports Possible Subclassification within the MPV Genus

Science.gov (United States)

Brown, Paul A.; Lemaitre, Evelyne; Briand, François-Xavier; Courtillon, Céline; Guionie, Olivier; Allée, Chantal; Toquin, Didier; Bayon-Auboyer, Marie-Hélène; Jestin, Véronique; Eterradossi, Nicolas

2014-01-01

Four avian metapneumovirus (AMPV) subgroups (A–D) have been reported previously based on genetic and antigenic differences. However, until now full length sequences of the only known isolates of European subgroup C and subgroup D viruses (duck and turkey origin, respectively) have been unavailable. These full length sequences were determined and compared with other full length AMPV and human metapneumoviruses (HMPV) sequences reported previously, using phylogenetics, comparisons of nucleic and amino acid sequences and study of codon usage bias. Results confirmed that subgroup C viruses were more closely related to HMPV than they were to the other AMPV subgroups in the study. This was consistent with previous findings using partial genome sequences. Closer relationships between AMPV-A, B and D were also evident throughout the majority of results. Three metapneumovirus “clusters” HMPV, AMPV-C and AMPV-A, B and D were further supported by codon bias and phylogenetics. The data presented here together with those of previous studies describing antigenic relationships also between AMPV-A, B and D and between AMPV-C and HMPV may call for a subclassification of metapneumoviruses similar to that used for avian paramyxoviruses, grouping AMPV-A, B and D as type I metapneumoviruses and AMPV-C and HMPV as type II. PMID:25036224

Serologic evidence of avian metapneumovirus infection among adults occupationally exposed to Turkeys.

Science.gov (United States)

Kayali, Ghazi; Ortiz, Ernesto J; Chorazy, Margaret L; Nagaraja, Kakambi V; DeBeauchamp, Jennifer; Webby, Richard J; Gray, Gregory C

2011-11-01

Genetically similar, the avian metapneumovirus (aMPV) and the human MPV (hMPV) are the only viruses in the Metapneumovirus genus. Previous research demonstrated the ability of hMPV to cause clinical disease in turkeys. In this controlled, cross-sectional, seroepidemiological study, we examined the hypothesis that aMPV might infect humans. We enrolled 95 adults occupationally exposed to turkeys and 82 nonexposed controls. Sera from study participants were examined for antibodies against aMPV and hMPV. Both in bivariate (OR=3.2; 95% CI: 1.1-9.2) and in multivariate modelling adjusting for antibody to hMPV (OR=4.1; 95% CI: 1.3-13.1), meat-processing workers were found to have an increased odds of previous infection with aMPV compared to controls. While hMPV antibody cross-reactivity is evident, these data suggest that occupational exposure to turkeys is a risk factor for human infection with aMPV. More studies are needed to validate these findings, to identify modes of aMPV transmission, and to determine risk factors associated with infection.

Estudo do efeito da interferencia por RNA (RNAi) na replicação do metapneumovirus aviario (AMPV) subtipo A in vitro

OpenAIRE

Helena Lage Ferreira

2007-01-01

Resumo: O metapneumovírus aviário (AMPV) é o agente primário da rinotraqueíte dos perus (TRT). O AMPV pertence à família Paramyxoviridae, subfamília Pneumovirinae, gênero Metapneumovirus. Também está associado à síndrome da cabeça inchada (SHS) em galinhas e é responsável por significativas perdas econômicas em sua produção. O presente estudo foi dividido em três partes. A primeira parte do trabalho consistiu em avaliar a beta-actina, gene utilizado como controle interno das técnicas molecula...

[Human Metapneumovirus (hMPV) associated to severe bronchial asthmatic crisis].

Science.gov (United States)

López, M A; Kusznierz, G F; Imaz, M S; Cociglio, R; Tedeschi, F A; Zalazar, F E

2006-01-01

Human Metapneumovirus (hMPV) is a recently reported agent of acute infection in the respiratory tract. It has been found in children as well as in young adults and elders. The clinical manifestations produced by hMPV are indistinguishable from those by common respiratory virus, and can evolve from asymptomatic infection into severe pneumonia. On the other hand, some authors have described cases of bronchial asthma exacerbation associated with hMPV infection. In this work we report a case of a child who presented a severe bronchial asthmatic crisis with a suspected viral associated infection. Immunofluorescence tests yielded negative results for sincitial respiratory virus, adenovirus, a-b influenza virus and parainfluenza 1, 2, 3, virus. In an attempt to detect the presence of hMPV, a RT-PCR was carried out to amplify sequences from both N and F genes. Using this approach, a positive result for hMPV was obtained. To our knowledge, this is the first description of a case of asthma exacerbation associated to hMPV in our region. In addition, these results are similar to previous reports where it was hypothesized that, like RSV, hMPV can trigger a respiratory chronic disease as asthma.

Metapneumovirus humano (hMPV asociado con exacerbación de asma aguda bronquial severa Human Metapneumovirus (hMPV associated to severe bronchial asthmatic crisis

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M. A. López

2006-09-01

Full Text Available El metapneumovirus humano (hMPV es un nuevo agente causal de infección aguda del tracto respiratorio, recientemente reportado tras su hallazgo en niños, jóvenes, adultos y ancianos. Las manifestaciones clínicas producidas por el hMPV son indistinguibles de aquellas provocadas por los virus respiratorios clásicamente conocidos, y varían desde infección asintomática hasta neumonía complicada. Por otro lado, se han descrito casos de exacerbación de asma bronquial asociados a la infección con hMPV. En este trabajo se describe el caso de un niño hospitalizado que presentó una crisis asmática bronquial severa con sospecha de una infección viral asociada. Por el test de inmunofluorescencia indirecta no se detectaron virus sincicial respiratorio (VSR, adenovirus, virus influenza a - b ni virus parainfluenza 1, 2 y 3. En un intento por detectar la presencia de hMPV, se realizó una RT-PCR para la amplificación de los genes N y F con resultado positivo. Conforme a nuestro conocimiento, esta sería la primera descripción de un caso de exacerbación de asma asociado a hMPV en nuestra región. Los resultados de este estudio serían similares a los reportados por otros autores, quienes postulan que, a semejanza de lo que ocurre con el VSR, una infección por hMPV puede gatillar una enfermedad respiratoria crónica, como el asma.Human Metapneumovirus (hMPV is a recently reported agent of acute infection in the respiratory tract. It has been found in children as well as in young adults and elders. The clinical manifestations produced by hMPV are indistinguishable from those by common respiratory virus, and can evolve from asymptomatic infection into severe pneumonia. On the other hand, some authors have described cases of bronchial asthma exacerbation associated with hMPV infection. In this work we report a case of a child who presented a severe bronchial asthmatic crisis with a suspected viral associated infection. Immunofluorescence tests

Estudos experimentais com isolados do metapneumovirus aviário (aMPV) subtipos A e B em frangos de corte

OpenAIRE

Márcia Bianchi dos Santos

2010-01-01

Resumo: O Metapneumovirus aviário (aMPV) pertence à família Paramyxoviridae, subfamília Pneumovirinae, gênero Metapneumovirus. O vírus, relatado pela primeira vez no Brasil em 1995, é o agente etiológico da Rinotraqueíte em perus (TRT) e está associado também à Síndrome da Cabeça Inchada (SHS) em frangos e poedeiras comerciais. O presente estudo foi dividido em três partes. Na primeira foi avaliada a suscetibilidades de oito sistemas celulares para a propagação de amostras virais do aMPV subt...

Genetic diversity of human metapneumovirus in hospitalized children with acute respiratory infections in Croatia.

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Jagušić, Maja; Slović, Anamarija; Ljubin-Sternak, Sunčanica; Mlinarić-Galinović, Gordana; Forčić, Dubravko

2017-11-01

Human metapneumovirus (HMPV) is recognized as a global and frequent cause of acute respiratory tract infections among people of all ages. The objectives of this study were molecular epidemiology and evolutionary analysis of HMPV strains which produced moderate and severe acute respiratory tract infections in children in Croatia during four consecutive seasons (2011-2014). A total of 117 HMPV-positive samples collected from hospitalized pediatric patients presenting with acute respiratory tract infections and tested by direct immunofluorescence assay were first analyzed by amplifying a part of the F gene. Sixteen samples were further analyzed based on complete F, G, and SH gene sequences. HMPV genome was identified in 92 of 117 samples (78%) and the circulation of multiple lineages of HMPV was confirmed. In 2011, 2012, and 2014, subgroups A2 and B2 co-circulated, while B1 gained prevalence in 2013 and 2014. The study established the presence of a novel subcluster A2c in Croatia. This subcluster has only recently been detected in East and Southeast Asia. This study provides new insights into epidemiology and genetic diversity of HMPV in this part of Europe. © 2017 Wiley Periodicals, Inc.

Effects of cyclosporin A induced T-lymphocyte depletion on the course of avian Metapneumovirus (aMPV) infection in turkeys.

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Rubbenstroth, Dennis; Dalgaard, Tina S; Kothlow, Sonja; Juul-Madsen, Helle R; Rautenschlein, Silke

2010-05-01

The avian Metapneumovirus (aMPV) causes an economically important acute respiratory disease in turkeys (turkey rhinotracheitis, TRT). While antibodies were shown to be insufficient for protection against aMPV-infection, the role of T-lymphocytes in the control of aMPV-infection is not clear. In this study we investigated the role of T-lymphocytes in aMPV-pathogenesis in a T-cell-suppression model in turkeys. T-cell-intact turkeys and turkeys partly depleted of functional CD4(+) and CD8(+) T-lymphocytes by Cyclosporin A (CsA) treatment were inoculated with the virulent aMPV subtype A strain BUT 8544. CsA-treatment resulted in a significant reduction of absolute numbers of circulating CD4(+) and CD8alpha(+) T-lymphocytes by up to 82 and 65%, respectively (P<0.05). Proportions of proliferating T-cells within mitogen-stimulated peripheral blood mononuclear cells were reduced by similar levels in CsA-treated birds compared to untreated controls (P<0.05). CsA-treated turkeys showed delayed recovery from aMPV-induced clinical signs and histopathological lesions and a prolonged detection of aMPV in choanal swabs. The results of this study show that T-lymphocytes play an important role in the control of primary aMPV-infection in turkeys. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Protection against avian metapneumovirus subtype C in turkeys immunized via the respiratory tract with inactivated virus.

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Cha, Ra Mi; Khatri, Mahesh; Sharma, Jagdev M

2011-01-10

Avian metapneumovirus subtype C (aMPV/C) causes a severe upper respiratory tract (URT) infection in turkeys. Turkeys were inoculated oculonasally with inactivated aMPV/C adjuvanted with synthetic double-stranded RNA polyriboinosinic polyribocytidylic acid (Poly IC). Immunized turkeys had elevated numbers of mucosal IgA+ cells in the URT and increased levels of virus-specific IgG and IgA in the lachrymal fluid and IgG in the serum. After 7 or 21 days post immunization, turkeys were challenged oculonasally with pathogenic aMPV/C. Immunized groups were protected against respiratory lesions induced by the challenge virus. Further, the viral copy number of the challenge virus in the URT were significantly lower in the immunized turkeys than in the unimmunized turkeys (P<0.05). These results showed that inactivated aMPV/C administered by the respiratory route induced protective immunity against pathogenic virus challenge. Copyright © 2010 Elsevier Ltd. All rights reserved.

Ground rules of the pluripotency gene regulatory network.

KAUST Repository

Li, Mo

2017-01-03

Pluripotency is a state that exists transiently in the early embryo and, remarkably, can be recapitulated in vitro by deriving embryonic stem cells or by reprogramming somatic cells to become induced pluripotent stem cells. The state of pluripotency, which is stabilized by an interconnected network of pluripotency-associated genes, integrates external signals and exerts control over the decision between self-renewal and differentiation at the transcriptional, post-transcriptional and epigenetic levels. Recent evidence of alternative pluripotency states indicates the regulatory flexibility of this network. Insights into the underlying principles of the pluripotency network may provide unprecedented opportunities for studying development and for regenerative medicine.

Ground rules of the pluripotency gene regulatory network.

KAUST Repository

Li, Mo; Belmonte, Juan Carlos Izpisua

2017-01-01

Pluripotency is a state that exists transiently in the early embryo and, remarkably, can be recapitulated in vitro by deriving embryonic stem cells or by reprogramming somatic cells to become induced pluripotent stem cells. The state of pluripotency, which is stabilized by an interconnected network of pluripotency-associated genes, integrates external signals and exerts control over the decision between self-renewal and differentiation at the transcriptional, post-transcriptional and epigenetic levels. Recent evidence of alternative pluripotency states indicates the regulatory flexibility of this network. Insights into the underlying principles of the pluripotency network may provide unprecedented opportunities for studying development and for regenerative medicine.

Detection by reverse transcriptase-polymerase chain reaction and molecular characterization of subtype B avian metapneumovirus isolated in Brazil.

Science.gov (United States)

Chacón, Jorge Luis; Brandão, Paulo E; Buim, Marcos; Villarreal, Laura; Ferreira, Antonio J Piantino

2007-10-01

Subtype B avian metapneumovirus (aMPV) was isolated and detected by reverse transcriptase-polymerase chain reaction (RT-PCR) in Brazilian commercial laying chicken flocks with no history of vaccination against aMPV and presenting respiratory signs and decreased egg production. RT-PCR results from samples from three affected flocks revealed that the three isolates were subtype B. Partial sequence analysis of the G glycoprotein gene confirmed that the samples belonged to subtype B and were not of the vaccine type. Comparison of nucleotide and amino acid sequences of the G gene of the three Brazilian aMPV samples with subtype B isolates from other countries revealed 95.1% to 96.1% identity. Nucleotide sequences showed 100% identity among the Brazilian subtype B samples and 95.6% identity with the subtype B vaccine strain used in Brazil. This work describes the circulation of subtype B aMPV in Brazil and discusses its importance in terms of disease epidemiology.

Biophysical Constraints Arising from Compositional Context in Synthetic Gene Networks.

Science.gov (United States)

Yeung, Enoch; Dy, Aaron J; Martin, Kyle B; Ng, Andrew H; Del Vecchio, Domitilla; Beck, James L; Collins, James J; Murray, Richard M

2017-07-26

Synthetic gene expression is highly sensitive to intragenic compositional context (promoter structure, spacing regions between promoter and coding sequences, and ribosome binding sites). However, much less is known about the effects of intergenic compositional context (spatial arrangement and orientation of entire genes on DNA) on expression levels in synthetic gene networks. We compare expression of induced genes arranged in convergent, divergent, or tandem orientations. Induction of convergent genes yielded up to 400% higher expression, greater ultrasensitivity, and dynamic range than divergent- or tandem-oriented genes. Orientation affects gene expression whether one or both genes are induced. We postulate that transcriptional interference in divergent and tandem genes, mediated by supercoiling, can explain differences in expression and validate this hypothesis through modeling and in vitro supercoiling relaxation experiments. Treatment with gyrase abrogated intergenic context effects, bringing expression levels within 30% of each other. We rebuilt the toggle switch with convergent genes, taking advantage of supercoiling effects to improve threshold detection and switch stability. Copyright © 2017 Elsevier Inc. All rights reserved.

Microarray-based screening of differentially expressed genes in glucocorticoid-induced avascular necrosis

Science.gov (United States)

Huang, Gangyong; Wei, Yibing; Zhao, Guanglei; Xia, Jun; Wang, Siqun; Wu, Jianguo; Chen, Feiyan; Chen, Jie; Shi, Jingshen

2017-01-01

The underlying mechanisms of glucocorticoid (GC)-induced avascular necrosis of the femoral head (ANFH) have yet to be fully understood, in particular the mechanisms associated with the change of gene expression pattern. The present study aimed to identify key genes with a differential expression pattern in GC-induced ANFH. E-MEXP-2751 microarray data were downloaded from the ArrayExpress database. Differentially expressed genes (DEGs) were identified in 5 femoral head samples of steroid-induced ANFH rats compared with 5 placebo-treated rat samples. Gene Ontology (GO) and pathway enrichment analyses were performed upon these DEGs. A total 93 DEGs (46 upregulated and 47 downregulated genes) were identified in GC-induced ANFH samples. These DEGs were enriched in different GO terms and pathways, including chondrocyte differentiation and detection of chemical stimuli. The enrichment map revealed that skeletal system development was interconnected with several other GO terms by gene overlap. The literature mined network analysis revealed that 5 upregulated genes were associated with femoral necrosis, including parathyroid hormone receptor 1 (PTHR1), vitamin D (1,25-Dihydroxyvitamin D3) receptor (VDR), collagen, type II, α1, proprotein convertase subtilisin/kexin type 6 and zinc finger protein 354C (ZFP354C). In addition, ZFP354C and VDR were identified to transcription factors. Furthermore, PTHR1 was revealed to interact with VDR, and α-2-macroglobulin (A2M) interacted with fibronectin 1 (FN1) in the PPI network. PTHR1 may be involved in GC-induced ANFH via interacting with VDR. A2M may also be involved in the development of GC-induced ANFH through interacting with FN1. An improved understanding of the molecular mechanisms underlying GC-induced ANFH may provide novel targets for diagnostics and therapeutic treatment. PMID:28393228

Microarray‑based screening of differentially expressed genes in glucocorticoid‑induced avascular necrosis.

Science.gov (United States)

Huang, Gangyong; Wei, Yibing; Zhao, Guanglei; Xia, Jun; Wang, Siqun; Wu, Jianguo; Chen, Feiyan; Chen, Jie; Shi, Jingshen

2017-06-01

The underlying mechanisms of glucocorticoid (GC)‑induced avascular necrosis of the femoral head (ANFH) have yet to be fully understood, in particular the mechanisms associated with the change of gene expression pattern. The present study aimed to identify key genes with a differential expression pattern in GC‑induced ANFH. E‑MEXP‑2751 microarray data were downloaded from the ArrayExpress database. Differentially expressed genes (DEGs) were identified in 5 femoral head samples of steroid‑induced ANFH rats compared with 5 placebo‑treated rat samples. Gene Ontology (GO) and pathway enrichment analyses were performed upon these DEGs. A total 93 DEGs (46 upregulated and 47 downregulated genes) were identified in GC‑induced ANFH samples. These DEGs were enriched in different GO terms and pathways, including chondrocyte differentiation and detection of chemical stimuli. The enrichment map revealed that skeletal system development was interconnected with several other GO terms by gene overlap. The literature mined network analysis revealed that 5 upregulated genes were associated with femoral necrosis, including parathyroid hormone receptor 1 (PTHR1), vitamin D (1,25‑Dihydroxyvitamin D3) receptor (VDR), collagen, type II, α1, proprotein convertase subtilisin/kexin type 6 and zinc finger protein 354C (ZFP354C). In addition, ZFP354C and VDR were identified to transcription factors. Furthermore, PTHR1 was revealed to interact with VDR, and α‑2‑macroglobulin (A2M) interacted with fibronectin 1 (FN1) in the PPI network. PTHR1 may be involved in GC‑induced ANFH via interacting with VDR. A2M may also be involved in the development of GC‑induced ANFH through interacting with FN1. An improved understanding of the molecular mechanisms underlying GC‑induced ANFH may provide novel targets for diagnostics and therapeutic treatment.

Constructing an integrated gene similarity network for the identification of disease genes.

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Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

Human metapneumovirus M2-2 protein inhibits innate immune response in monocyte-derived dendritic cells.

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Junping Ren

Full Text Available Human metapneumovirus (hMPV is a leading cause of lower respiratory infection in young children, the elderly and immunocompromised patients. Repeated hMPV infections occur throughout life. However, immune evasion mechanisms of hMPV infection are largely unknown. Recently, our group has demonstrated that hMPV M2-2 protein, an important virulence factor, contributes to immune evasion in airway epithelial cells by targeting the mitochondrial antiviral-signaling protein (MAVS. Whether M2-2 regulates the innate immunity in human dendritic cells (DC, an important family of immune cells controlling antigen presenting, is currently unknown. We found that human DC infected with a virus lacking M2-2 protein expression (rhMPV-ΔM2-2 produced higher levels of cytokines, chemokines and IFNs, compared to cells infected with wild-type virus (rhMPV-WT, suggesting that M2-2 protein inhibits innate immunity in human DC. In parallel, we found that myeloid differentiation primary response gene 88 (MyD88, an essential adaptor for Toll-like receptors (TLRs, plays a critical role in inducing immune response of human DC, as downregulation of MyD88 by siRNA blocked the induction of immune regulatory molecules by hMPV. Since M2-2 is a cytoplasmic protein, we investigated whether M2-2 interferes with MyD88-mediated antiviral signaling. We found that indeed M2-2 protein associated with MyD88 and inhibited MyD88-dependent gene transcription. In this study, we also identified the domains of M2-2 responsible for its immune inhibitory function in human DC. In summary, our results demonstrate that M2-2 contributes to hMPV immune evasion by inhibiting MyD88-dependent cellular responses in human DC.

Detection of avian metapneumovirus subtypes in turkeys using RT-PCR.

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Ongor, H; Karahan, M; Kalin, R; Bulut, H; Cetinkaya, B

2010-03-20

This study investigated the prevalence of avian metapneumovirus (aMPV) and the detection of molecular subtypes of field strains of the virus using RT-PCR in clinically healthy turkeys and those showing signs of respiratory disease. In the RT-PCR examination of 624 tracheal tissue samples collected from a local turkey abattoir, 2.9 per cent (18/624) of samples tested positive. In the examination of tracheal swab samples collected from flocks with respiratory problems, 18 of 20 samples tested positive. When the results were assessed at flock level, aMPV infection was detected in only one of the 23 clinically healthy turkey flocks, whereas all four flocks with respiratory problems were infected. Molecular typing using primers specific to the attachment glycoprotein (G) gene showed that all 36 positive samples belonged to subtype B. Partial sequence analysis of DNA samples showed 95 per cent homology between the field types and the reference strain aMPV subtype B. Whereas clinically healthy turkeys had been vaccinated with a subtype A virus vaccine, the flocks with respiratory problems had been vaccinated with a subtype B virus vaccine. Despite four blind passages of RT-PCR-positive samples on Vero and chicken embryo fibroblast cells, no cytopathic effect was detected by microscopic examination.

Isolation and characterization of avian metapneumovirus from chickens in Korea.

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Kwon, Ji-Sun; Lee, Hyun-Jeong; Jeong, Seung-Hwan; Park, Jeong-Yong; Hong, Young-Ho; Lee, Youn-Jeong; Youn, Ho-Sik; Lee, Dong-Woo; Do, Sun-Hee; Park, Seung-Yong; Choi, In-Soo; Lee, Joong-Bok; Song, Chang-Seon

2010-03-01

Avian metapneumovirus (aMPV) causes upper respiratory tract infections in chickens and turkeys. Although the swollen head syndrome (SHS) associated with aMPV in chickens has been reported in Korea since 1992, this is the study isolating aMPV from chickens in this country. We examined 780 oropharyngeal swab or nasal turbinate samples collected from 130 chicken flocks to investigate the prevalence of aMPV and to isolate aMPV from chickens from 2004-2008. Twelve aMPV subtype A and 13 subtype B strains were detected from clinical samples by the aMPV subtype A and B multiplex real-time reverse transcription polymerase chain reaction (RRT-PCR). Partial sequence analysis of the G glycoprotein gene confirmed that the detected aMPVs belonged to subtypes A and B. Two aMPVs subtype A out of the 25 detected aMPVs were isolated by Vero cell passage. In animal experiments with an aMPV isolate, viral RNA was detected in nasal discharge, although no clinical signs of SHS were observed in chickens. In contrast to chickens, turkeys showed severe nasal discharge and a relatively higher titer of viral excretion than chickens. Here, we reveal the co-circulation of aMPV subtypes A and B, and isolate aMPVs from chicken flocks in Korea.

RNAi-Based Identification of Gene-Specific Nuclear Cofactor Networks Regulating Interleukin-1 Target Genes

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Johanna Meier-Soelch

2018-04-01

Full Text Available The potent proinflammatory cytokine interleukin (IL-1 triggers gene expression through the NF-κB signaling pathway. Here, we investigated the cofactor requirements of strongly regulated IL-1 target genes whose expression is impaired in p65 NF-κB-deficient murine embryonic fibroblasts. By two independent small-hairpin (shRNA screens, we examined 170 genes annotated to encode nuclear cofactors for their role in Cxcl2 mRNA expression and identified 22 factors that modulated basal or IL-1-inducible Cxcl2 levels. The functions of 16 of these factors were validated for Cxcl2 and further analyzed for their role in regulation of 10 additional IL-1 target genes by RT-qPCR. These data reveal that each inducible gene has its own (quantitative requirement of cofactors to maintain basal levels and to respond to IL-1. Twelve factors (Epc1, H2afz, Kdm2b, Kdm6a, Mbd3, Mta2, Phf21a, Ruvbl1, Sin3b, Suv420h1, Taf1, and Ube3a have not been previously implicated in inflammatory cytokine functions. Bioinformatics analysis indicates that they are components of complex nuclear protein networks that regulate chromatin functions and gene transcription. Collectively, these data suggest that downstream from the essential NF-κB signal each cytokine-inducible target gene has further subtle requirements for individual sets of nuclear cofactors that shape its transcriptional activation profile.

Gene coexpression network analysis as a source of functional annotation for rice genes.

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Kevin L Childs

Full Text Available With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional

Gene regulatory networks elucidating huanglongbing disease mechanisms.

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Federico Martinelli

Full Text Available Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas, especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation, sucrose metabolism (upregulation, and starch biosynthesis (upregulation. In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70 was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur.

Production of monoclonal antibodies for Avian Metapneumovirus (SHS-BR-121 isolated in Brazil

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LT Coswig

2007-12-01

Full Text Available Avian Metapneumovirus (aMPV, also called Turkey Rhinotracheitis Virus (TRTV, is an upper respiratory tract infection of turkeys, chickens and other avian species. Five monoclonal antibodies (MAbs were created against the Brazilian isolate (SHS-BR-121 of aMPV, MAbs 1A5B8; 1C1C4; 2C2E9 and 2A4C3 of IgG1 and MAb 1C1F8 of IgG2a. Four Mabs (1A5B8; 1C1C4; 2C2E9 and 2A4C3 showed neutralizing activity and three (1A5B8; 1C1C4 and 2A4C3 inhibited cellular fusion in vitro. These MAbs were used to investigate antigenic relationship among three strains (SHS-BR-121, STG 854/88 and TRT 1439/91 of aMPV subtypes A and B using cross-neutralization test. The results confirm that the monoclonal antibodies described can be used as a valuable tool in the epizootiological and serological studies, and also for the specific diagnosis of the subtypes in the infection for Avian Metapneumovirus.

Effect of amino acid sequence variations at position 149 on the fusogenic activity of the subtype B avian metapneumovirus fusion protein.

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Yun, Bingling; Gao, Yanni; Liu, Yongzhen; Guan, Xiaolu; Wang, Yongqiang; Qi, Xiaole; Gao, Honglei; Liu, Changjun; Cui, Hongyu; Zhang, Yanping; Gao, Yulong; Wang, Xiaomei

2015-10-01

The entry of enveloped viruses into host cells requires the fusion of viral and cell membranes. These membrane fusion reactions are mediated by virus-encoded glycoproteins. In the case of avian metapneumovirus (aMPV), the fusion (F) protein alone can mediate virus entry and induce syncytium formation in vitro. To investigate the fusogenic activity of the aMPV F protein, we compared the fusogenic activities of three subtypes of aMPV F proteins using a TCSD50 assay developed in this study. Interestingly, we found that the F protein of aMPV subtype B (aMPV/B) strain VCO3/60616 (aMPV/vB) was hyperfusogenic when compared with F proteins of aMPV/B strain aMPV/f (aMPV/fB), aMPV subtype A (aMPV/A), and aMPV subtype C (aMPV/C). We then further demonstrated that the amino acid (aa) residue 149F contributed to the hyperfusogenic activity of the aMPV/vB F protein. Moreover, we revealed that residue 149F had no effect on the fusogenic activities of aMPV/A, aMPV/C, and human metapneumovirus (hMPV) F proteins. Collectively, we provide the first evidence that the amino acid at position 149 affects the fusogenic activity of the aMPV/B F protein, and our findings will provide new insights into the fusogenic mechanism of this protein.

Characterization of the Fusion and Attachment Glycoproteins of Human Metapneumovirus and Human Serosurvey to Determine Reinfection Rates

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2007-06-27

Metapneumovirus genus. The Paramyxoviridae are in the taxonomical order Mononegavirales which includes Bornaviridae, Rhabdoviridae and Filoviridae which... Rhabdoviridae plant virus, replicate in the cytoplasm (66). The Paramyxoviridae are enveloped viruses and have been defined by the fusion glycoprotein

Interactive visualization of gene regulatory networks with associated gene expression time series data

NARCIS (Netherlands)

Westenberg, M.A.; Hijum, van S.A.F.T.; Lulko, A.T.; Kuipers, O.P.; Roerdink, J.B.T.M.; Linsen, L.; Hagen, H.; Hamann, B.

2008-01-01

We present GENeVis, an application to visualize gene expression time series data in a gene regulatory network context. This is a network of regulator proteins that regulate the expression of their respective target genes. The networks are represented as graphs, in which the nodes represent genes,

Development of Recombinant Newcastle Disease Viruses Expressing the Glycoprotein (G) of Avian Metapneumovirus as Bivalent Vaccines

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Using reverse genetics technology, Newcastle disease virus (NDV) LaSota strain-based recombinant viruses were engineered to express the glycoprotein (G) of avian metapneumovirus (aMPV), subtype A, B or C, as bivalent vaccines. These recombinant viruses were slightly attenuated in vivo, yet maintaine...

Pathogenic and immunogenic responses in turkeys following in ovo exposure to avian metapneumovirus subtype C.

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Cha, Ra Mi; Khatri, Mahesh; Mutnal, Manohar; Sharma, Jagdev M

2011-03-15

Commercial turkey eggs, free of antibodies to avian metapneumovirus subtype C (aMPV/C), were inoculated with aMPV/C at embryonation day (ED) 24. There was no detectable effect of virus inoculation on the hatchability of eggs. At 4 days post inoculation (DPI) (the day of hatch (ED 28)) and 9 DPI (5 days after hatch), virus replication was detected by quantitative RT-PCR in the turbinate, trachea and lung but not in the thymus or spleen. Mild histological lesions characterized by lymphoid cell infiltration were evident in the turbinate mucosa. Virus exposure inhibited the mitogenic response of splenocytes and thymocytes and upregulated gene expression of IFN-γ and IL-10 in the turbinate tissue. Turkeys hatching from virus-exposed eggs had aMPV/C-specific IgG in the serum and the lachrymal fluid. At 3 week of age, in ovo immunized turkeys were protected against a challenge with pathogenic aMPV/C. Published by Elsevier B.V.

Inferring gene networks from discrete expression data

KAUST Repository

Zhang, L.

2013-07-18

The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.

Current approaches to gene regulatory network modelling

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Brazma Alvis

2007-09-01

Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

Genes2FANs: connecting genes through functional association networks

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2012-01-01

Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in

Protection by recombinant viral proteins against a respiratory challenge with virulent avian metapneumovirus.

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Chary, Parag; Njenga, M Kariuki; Sharma, Jagdev M

2005-12-15

Protection by recombinant avian metapneumovirus (aMPV) N or M proteins against a respiratory challenge with virulent aMPV was examined. N, M or N+M proteins were administered intramuscularly (IM) with incomplete Freund's adjuvant (IFA) or by the oculonasal (ON) route with cholera toxin-B (CTB). Each turkey received 40 or 80 microg of each recombinant protein. Birds were considered protected against challenge if the challenge virus was not detectable in the choanal swabs by RT-PCR. At a dose of 40 microg/bird, N protein given with IFA by the IM route protected eight out of nine birds. M protein at the same dose protected three out of seven birds, while a combination of N+M proteins (40 microg each) protected three out of four birds. At a dose of 80 microg of each of N and M proteins per bird given with IFA by the IM route, 100% protection was achieved. ON immunization with a mixture of N and M proteins induced partial protection when the proteins were given with CTB; no detectable protection was noted without CTB. N and M proteins induced anti-aMPV antibodies, although protection against virulent virus challenge did not appear to be associated with the level or presence of antibodies.

Evolutionary conservation and network structure characterize genes of phenotypic relevance for mitosis in human.

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Marek Ostaszewski

Full Text Available The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research.

Gene networks and toxicity pathways induced by acute cadmium exposure in adult largemouth bass (Micropterus salmoides).

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Mehinto, Alvine C; Prucha, Melinda S; Colli-Dula, Reyna C; Kroll, Kevin J; Lavelle, Candice M; Barber, David S; Vulpe, Christopher D; Denslow, Nancy D

2014-07-01

Cadmium is a heavy metal that can accumulate to toxic levels in the environment leading to detrimental effects in animals and humans including kidney, liver and lung injuries. Using a transcriptomics approach, genes and cellular pathways affected by a low dose of cadmium were investigated. Adult largemouth bass were intraperitoneally injected with 20μg/kg of cadmium chloride (mean exposure level - 2.6μg of cadmium per fish) and microarray analyses were conducted in the liver and testis 48h after injection. Transcriptomic profiles identified in response to cadmium exposure were tissue-specific with the most differential expression changes found in the liver tissues, which also contained much higher levels of cadmium than the testis. Acute exposure to a low dose of cadmium induced oxidative stress response and oxidative damage pathways in the liver. The mRNA levels of antioxidants such as catalase increased and numerous transcripts related to DNA damage and DNA repair were significantly altered. Hepatic mRNA levels of metallothionein, a molecular marker of metal exposure, did not increase significantly after 48h exposure. Carbohydrate metabolic pathways were also disrupted with hepatic transcripts such as UDP-glucose, pyrophosphorylase 2, and sorbitol dehydrogenase highly induced. Both tissues exhibited a disruption of steroid signaling pathways. In the testis, estrogen receptor beta and transcripts linked to cholesterol metabolism were suppressed. On the contrary, genes involved in cholesterol metabolism were highly increased in the liver including genes encoding for the rate limiting steroidogenic acute regulatory protein and the catalytic enzyme 7-dehydrocholesterol reductase. Integration of the transcriptomic data using functional enrichment analyses revealed a number of enriched gene networks associated with previously reported adverse outcomes of cadmium exposure such as liver toxicity and impaired reproduction. Copyright © 2014 Elsevier B.V. All rights

Biochemical characterization of the small hydrophobic protein of avian metapneumovirus.

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Deng, Qiji; Song, Minxun; Demers, Andrew; Weng, Yuejin; Lu, Wuxun; Wang, Dan; Kaushik, Radhey S; Yu, Qingzhong; Li, Feng

2012-08-01

Avian metapneumovirus (AMPV) is a paramyxovirus that has three membrane proteins (G, F, and SH). Among them, the SH protein is a small type II integral membrane protein that is incorporated into virions and is only present in certain paramyxoviruses. In the present study, we show that the AMPV SH protein is modified by N-linked glycans and can be released into the extracellular environment. Furthermore, we demonstrate that glycosylated AMPV SH proteins form homodimers through cysteine-mediated disulfide bonds, which has not been reported previously for SH proteins of paramyxoviruses. Copyright © 2012 Elsevier B.V. All rights reserved.

Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins.

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Stefano Varrella

Full Text Available Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure.

Network-based association of hypoxia-responsive genes with cardiovascular diseases

International Nuclear Information System (INIS)

Wang, Rui-Sheng; Oldham, William M; Loscalzo, Joseph

2014-01-01

Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology. (paper)

Acute Viral Respiratory Infection Rapidly Induces a CD8+ T Cell Exhaustion-like Phenotype.

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Erickson, John J; Lu, Pengcheng; Wen, Sherry; Hastings, Andrew K; Gilchuk, Pavlo; Joyce, Sebastian; Shyr, Yu; Williams, John V

2015-11-01

Acute viral infections typically generate functional effector CD8(+) T cells (TCD8) that aid in pathogen clearance. However, during acute viral lower respiratory infection, lung TCD8 are functionally impaired and do not optimally control viral replication. T cells also become unresponsive to Ag during chronic infections and cancer via signaling by inhibitory receptors such as programmed cell death-1 (PD-1). PD-1 also contributes to TCD8 impairment during viral lower respiratory infection, but how it regulates TCD8 impairment and the connection between this state and T cell exhaustion during chronic infections are unknown. In this study, we show that PD-1 operates in a cell-intrinsic manner to impair lung TCD8. In light of this, we compared global gene expression profiles of impaired epitope-specific lung TCD8 to functional spleen TCD8 in the same human metapneumovirus-infected mice. These two populations differentially regulate hundreds of genes, including the upregulation of numerous inhibitory receptors by lung TCD8. We then compared the gene expression of TCD8 during human metapneumovirus infection to those in acute or chronic lymphocytic choriomeningitis virus infection. We find that the immunophenotype of lung TCD8 more closely resembles T cell exhaustion late into chronic infection than do functional effector T cells arising early in acute infection. Finally, we demonstrate that trafficking to the infected lung alone is insufficient for TCD8 impairment or inhibitory receptor upregulation, but that viral Ag-induced TCR signaling is also required. Our results indicate that viral Ag in infected lungs rapidly induces an exhaustion-like state in lung TCD8 characterized by progressive functional impairment and upregulation of numerous inhibitory receptors. Copyright © 2015 by The American Association of Immunologists, Inc.

Structure and self-assembly of the calcium binding matrix protein of human metapneumovirus.

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Leyrat, Cedric; Renner, Max; Harlos, Karl; Huiskonen, Juha T; Grimes, Jonathan M

2014-01-07

The matrix protein (M) of paramyxoviruses plays a key role in determining virion morphology by directing viral assembly and budding. Here, we report the crystal structure of the human metapneumovirus M at 2.8 Å resolution in its native dimeric state. The structure reveals the presence of a high-affinity Ca²⁺ binding site. Molecular dynamics simulations (MDS) predict a secondary lower-affinity site that correlates well with data from fluorescence-based thermal shift assays. By combining small-angle X-ray scattering with MDS and ensemble analysis, we captured the structure and dynamics of M in solution. Our analysis reveals a large positively charged patch on the protein surface that is involved in membrane interaction. Structural analysis of DOPC-induced polymerization of M into helical filaments using electron microscopy leads to a model of M self-assembly. The conservation of the Ca²⁺ binding sites suggests a role for calcium in the replication and morphogenesis of pneumoviruses. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

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Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

2010-03-01

The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice

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Shuchi eSmita

2015-12-01

Full Text Available MYB transcription factor (TF is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by top down and guide gene approaches. More than 50% of OsMYBs were strongly correlated under fifty experimental conditions with 51 hub genes via top down approach. Further, clusters were identified using Markov Clustering (MCL. To maximize the clustering performance, parameter evaluation of the MCL inflation score (I was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by guide gene approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought

The human metapneumovirus matrix protein stimulates the inflammatory immune response in vitro.

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Audrey Bagnaud-Baule

Full Text Available Each year, during winter months, human Metapneumovirus (hMPV is associated with epidemics of bronchiolitis resulting in the hospitalization of many infants. Bronchiolitis is an acute illness of the lower respiratory tract with a consequent inflammation of the bronchioles. The rapid onset of inflammation suggests the innate immune response may have a role to play in the pathogenesis of this hMPV infection. Since, the matrix protein is one of the most abundant proteins in the Paramyxoviridae family virion, we hypothesized that the inflammatory modulation observed in hMPV infected patients may be partly associated with the matrix protein (M-hMPV response. By western blot analysis, we detected a soluble form of M-hMPV released from hMPV infected cell as well as from M-hMPV transfected HEK 293T cells suggesting that M-hMPV may be directly in contact with antigen presenting cells (APCs during the course of infection. Moreover, flow cytometry and confocal microscopy allowed determining that M-hMPV was taken up by dendritic cells (moDCs and macrophages inducing their activation. Furthermore, these moDCs enter into a maturation process inducing the secretion of a broad range of inflammatory cytokines when exposed to M-hMPV. Additionally, M-hMPV activated DCs were shown to stimulate IL-2 and IFN-γ production by allogeneic T lymphocytes. This M-hMPV-mediated activation and antigen presentation of APCs may in part explain the marked inflammatory immune response observed in pathology induced by hMPV in patients.

Identification of potential crucial genes associated with steroid-induced necrosis of femoral head based on gene expression profile.

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Lin, Zhe; Lin, Yongsheng

2017-09-05

The aim of this study was to explore potential crucial genes associated with the steroid-induced necrosis of femoral head (SINFH) and to provide valid biological information for further investigation of SINFH. Gene expression profile of GSE26316, generated from 3 SINFH rat samples and 3 normal rat samples were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using LIMMA package. After functional enrichment analyses of DEGs, protein-protein interaction (PPI) network and sub-PPI network analyses were conducted based on the STRING database and cytoscape. In total, 59 up-regulated DEGs and 156 downregulated DEGs were identified. The up-regulated DEGs were mainly involved in functions about immunity (e.g. Fcer1A and Il7R), and the downregulated DEGs were mainly enriched in muscle system process (e.g. Tnni2, Mylpf and Myl1). The PPI network of DEGs consisted of 123 nodes and 300 interactions. Tnni2, Mylpf, and Myl1 were the top 3 outstanding genes based on both subgraph centrality and degree centrality evaluation. These three genes interacted with each other in the network. Furthermore, the significant network module was composed of 22 downregulated genes (e.g. Tnni2, Mylpf and Myl1). These genes were mainly enriched in functions like muscle system process. The DEGs related to the regulation of immune system process (e.g. Fcer1A and Il7R), and DEGs correlated with muscle system process (e.g. Tnni2, Mylpf and Myl1) may be closely associated with the progress of SINFH, which is still needed to be confirmed by experiments. Copyright © 2017 Elsevier B.V. All rights reserved.

Mucosal vaccination with formalin-inactivated avian metapneumovirus subtype C does not protect turkeys following intranasal challenge.

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Kapczynski, Darrell R; Perkins, Laura L; Sellers, Holly S

2008-03-01

Studies were performed to determine if mucosal vaccination with inactivated avian metapneumovirus (aMPV) subtype C protected turkey poults from clinical disease and virus replication following mucosal challenge. Decreases in clinical disease were not observed in vaccinated groups, and the vaccine failed to inhibit virus replication in the tracheas of 96% of vaccinated birds. Histopathologically, enhancement of pulmonary lesions following virus challenge was associated with birds receiving the inactivated aMPV vaccine compared to unvaccinated birds. As determined by an enzyme-linked immunosorbent assay (ELISA), all virus-challenged groups increased serum immunoglobulin (Ig) G and IgA antibody production against the virus following challenge; however, the unvaccinated aMPV-challenged group displayed the highest increases in virus-neutralizing antibody. On the basis of these results it is concluded that intranasal vaccination with inactivated aMPV does not induce protective immunity, reduce virus shedding, or result in decreased histopathologic lesions.

Finding gene regulatory network candidates using the gene expression knowledge base.

Science.gov (United States)

Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Sparsity in Model Gene Regulatory Networks

International Nuclear Information System (INIS)

Zagorski, M.

2011-01-01

We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

Directory of Open Access Journals (Sweden)

Liang Jinghang

2012-08-01

Full Text Available Abstract Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs. As a logical model, probabilistic Boolean networks (PBNs consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n or O(nN2n for a sparse matrix. Results This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN. An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n, where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a

Chest radiographic features of human metapneumovirus infection in pediatric patients

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Hilmes, Melissa A.; Daniel Dunnavant, F.; Singh, Sudha P.; Ellis, Wendy D. [Vanderbilt University School of Medicine, Department of Radiology, Nashville, TN (United States); Payne, Daniel C. [Centers for Disease Control and Prevention, Atlanta, GA (United States); Zhu, Yuwei [Vanderbilt University School of Medicine, Department of Biostatistics, Nashville, TN (United States); Griffin, Marie R. [Vanderbilt University School of Medicine, Department of Health Policy, Nashville, TN (United States); Edwards, Kathryn M. [Vanderbilt University School of Medicine, Department of Pediatrics, Nashville, TN (United States); Williams, John V. [University of Pittsburgh School of Medicine, Department of Pediatrics, Pittsburgh, PA (United States); University of Pittsburgh of UPMC, Children' s Hospital of Pittsburgh, Pittsburgh, PA (United States)

2017-12-15

Human metapneumovirus (HMPV) was identified in 2001 and is a common cause of acute respiratory illness in young children. The radiologic characteristics of laboratory-confirmed HMPV acute respiratory illness in young children have not been systematically assessed. We systematically evaluated the radiographic characteristics of acute respiratory illness associated with HMPV in a prospective cohort of pediatric patients. We included chest radiographs from children <5 years old with acute respiratory illness who were enrolled in the prospective New Vaccine Surveillance Network (NVSN) study from 2003 to 2009 and were diagnosed with HMPV by reverse transcription-polymerase chain reaction (RT-PCR). Of 215 HMPV-positive subjects enrolled at our tertiary care children's hospital, 68 had chest radiographs obtained by the treating clinician that were available for review. Two fellowship-trained pediatric radiologists, independently and then in consensus, retrospectively evaluated these chest radiographs for their radiographic features. Parahilar opacities were the most commonly observed abnormality, occurring in 87% of children with HMPV. Hyperinflation also occurred frequently (69%). Atelectasis (40%) and consolidation (18%) appeared less frequently. Pleural effusion and pneumothorax were not seen on any radiographs. The clinical presentations of HMPV include bronchiolitis, croup and pneumonia. Dominant chest radiographic abnormalities include parahilar opacities and hyperinflation, with occasional consolidation. Recognition of the imaging patterns seen with common viral illnesses like respiratory syncytial virus (RSV) and HMPV might facilitate diagnosis and limit unnecessary antibiotic treatment. (orig.)

Chest radiographic features of human metapneumovirus infection in pediatric patients

International Nuclear Information System (INIS)

Hilmes, Melissa A.; Daniel Dunnavant, F.; Singh, Sudha P.; Ellis, Wendy D.; Payne, Daniel C.; Zhu, Yuwei; Griffin, Marie R.; Edwards, Kathryn M.; Williams, John V.

2017-01-01

Human metapneumovirus (HMPV) was identified in 2001 and is a common cause of acute respiratory illness in young children. The radiologic characteristics of laboratory-confirmed HMPV acute respiratory illness in young children have not been systematically assessed. We systematically evaluated the radiographic characteristics of acute respiratory illness associated with HMPV in a prospective cohort of pediatric patients. We included chest radiographs from children <5 years old with acute respiratory illness who were enrolled in the prospective New Vaccine Surveillance Network (NVSN) study from 2003 to 2009 and were diagnosed with HMPV by reverse transcription-polymerase chain reaction (RT-PCR). Of 215 HMPV-positive subjects enrolled at our tertiary care children's hospital, 68 had chest radiographs obtained by the treating clinician that were available for review. Two fellowship-trained pediatric radiologists, independently and then in consensus, retrospectively evaluated these chest radiographs for their radiographic features. Parahilar opacities were the most commonly observed abnormality, occurring in 87% of children with HMPV. Hyperinflation also occurred frequently (69%). Atelectasis (40%) and consolidation (18%) appeared less frequently. Pleural effusion and pneumothorax were not seen on any radiographs. The clinical presentations of HMPV include bronchiolitis, croup and pneumonia. Dominant chest radiographic abnormalities include parahilar opacities and hyperinflation, with occasional consolidation. Recognition of the imaging patterns seen with common viral illnesses like respiratory syncytial virus (RSV) and HMPV might facilitate diagnosis and limit unnecessary antibiotic treatment. (orig.)

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.

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Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-03-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

Directory of Open Access Journals (Sweden)

Ma'ayan Avi

2007-10-01

Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

KAUST Repository

Fujii, Chisato

2015-04-16

Gene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and non-linear which makes learning the networks a challenging problem and existing gene network inference methods do not give consistent results. Current state-of-the-art study uses the average-ranking-based consensus method to combine and average the ranked predictions from individual methods. However each individual method has an equal contribution to the consen- sus prediction. We have developed a linear programming-based consensus approach which uses learned weights from linear programming among individual methods such that the methods have di↵erent weights depending on their performance. Our result reveals that assigning di↵erent weights to individual methods rather than giving them equal weights improves the performance of the consensus. The linear programming- based consensus method is evaluated and it had the best performance on in silico and Saccharomyces cerevisiae networks, and the second best on the Escherichia coli network outperformed by Inferelator Pipeline method which gives inconsistent results across a wide range of microarray data sets.

Combinatorial explosion in model gene networks

Science.gov (United States)

Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

A Reverse Genetics Approach for the Design of Methyltransferase-Defective Live Attenuated Avian Metapneumovirus Vaccines.

Science.gov (United States)

Zhang, Yu; Sun, Jing; Wei, Yongwei; Li, Jianrong

2016-01-01

Avian metapneumovirus (aMPV), also known as avian pneumovirus or turkey rhinotracheitis virus, is the causative agent of turkey rhinotracheitis and is associated with swollen head syndrome in chickens. aMPV belongs to the family Paramyxoviridae which includes many important human pathogens such as human respiratory syncytial virus (RSV), human metapneumovirus (hMPV), and human parainfluenza virus type 3 (PIV3). The family also includes highly lethal emerging pathogens such as Nipah virus and Hendra virus, as well as agriculturally important viruses such as Newcastle disease virus (NDV). For many of these viruses, there is no effective vaccine. Here, we describe a reverse genetics approach to develop live attenuated aMPV vaccines by inhibiting the viral mRNA cap methyltransferase. The viral mRNA cap methyltransferase is an excellent target for the attenuation of paramyxoviruses because it plays essential roles in mRNA stability, efficient viral protein translation and innate immunity. We have described in detail the materials and methods used to generate recombinant aMPVs that lack viral mRNA cap methyltransferase activity. We have also provided methods to evaluate the genetic stability, pathogenesis, and immunogenicity of live aMPV vaccine candidates in turkeys.

Networks in biological systems: An investigation of the Gene Ontology as an evolving network

International Nuclear Information System (INIS)

Coronnello, C; Tumminello, M; Micciche, S; Mantegna, R.N.

2009-01-01

Many biological systems can be described as networks where different elements interact, in order to perform biological processes. We introduce a network associated with the Gene Ontology. Specifically, we construct a correlation-based network where the vertices are the terms of the Gene Ontology and the link between each two terms is weighted on the basis of the number of genes that they have in common. We analyze a filtered network obtained from the correlation-based network and we characterize its evolution over different releases of the Gene Ontology.

Learning gene regulatory networks from gene expression data using weighted consensus

KAUST Repository

Fujii, Chisato; Kuwahara, Hiroyuki; Yu, Ge; Guo, Lili; Gao, Xin

2016-01-01

An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

Learning gene regulatory networks from gene expression data using weighted consensus

KAUST Repository

Fujii, Chisato

2016-08-25

An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

Directory of Open Access Journals (Sweden)

Cielito C Reyes-Gibby

Full Text Available Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA, a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive and thymine degradation pathways (p = 1.06-08 were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis. The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67. In conclusion, gene network analysis identified novel molecules and

Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

Science.gov (United States)

Reyes-Gibby, Cielito C; Melkonian, Stephanie C; Wang, Jian; Yu, Robert K; Shelburne, Samuel A; Lu, Charles; Gunn, Gary Brandon; Chambers, Mark S; Hanna, Ehab Y; Yeung, Sai-Ching J; Shete, Sanjay

2017-01-01

Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA), a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive) and thymine degradation pathways (p = 1.06-08) were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis). The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67). In conclusion, gene network analysis identified novel molecules and biological

Learning a Markov Logic network for supervised gene regulatory network inference.

Science.gov (United States)

Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

2013-09-12

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

Gene networks and toxicity pathways induced by acute cadmium exposure in adult largemouth bass (Micropterus salmoides)

International Nuclear Information System (INIS)

Mehinto, Alvine C.; Prucha, Melinda S.; Colli-Dula, Reyna C.; Kroll, Kevin J.; Lavelle, Candice M.; Barber, David S.; Vulpe, Christopher D.; Denslow, Nancy D.

2014-01-01

Highlights: • Low-level acute cadmium exposure elicited tissue-specific gene expression changes. • Molecular initiating events included oxidative stress and disruption of DNA repair. • Metallothionein, a marker of metal exposure, was not significantly affected. • We report effects of cadmium on cholesterol metabolism and steroid synthesis. • Diabetic complications and impaired reproduction are potential adverse outcomes. - Abstract: Cadmium is a heavy metal that can accumulate to toxic levels in the environment leading to detrimental effects in animals and humans including kidney, liver and lung injuries. Using a transcriptomics approach, genes and cellular pathways affected by a low dose of cadmium were investigated. Adult largemouth bass were intraperitoneally injected with 20 μg/kg of cadmium chloride (mean exposure level – 2.6 μg of cadmium per fish) and microarray analyses were conducted in the liver and testis 48 h after injection. Transcriptomic profiles identified in response to cadmium exposure were tissue-specific with the most differential expression changes found in the liver tissues, which also contained much higher levels of cadmium than the testis. Acute exposure to a low dose of cadmium induced oxidative stress response and oxidative damage pathways in the liver. The mRNA levels of antioxidants such as catalase increased and numerous transcripts related to DNA damage and DNA repair were significantly altered. Hepatic mRNA levels of metallothionein, a molecular marker of metal exposure, did not increase significantly after 48 h exposure. Carbohydrate metabolic pathways were also disrupted with hepatic transcripts such as UDP-glucose, pyrophosphorylase 2, and sorbitol dehydrogenase highly induced. Both tissues exhibited a disruption of steroid signaling pathways. In the testis, estrogen receptor beta and transcripts linked to cholesterol metabolism were suppressed. On the contrary, genes involved in cholesterol metabolism were highly

Gene networks and toxicity pathways induced by acute cadmium exposure in adult largemouth bass (Micropterus salmoides)

Energy Technology Data Exchange (ETDEWEB)

Mehinto, Alvine C., E-mail: alvinam@sccwrp.org [Southern California Coastal Water Research Project, Costa Mesa, CA 92626 (United States); Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611 (United States); Prucha, Melinda S. [Department of Human Genetics, Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322 (United States); Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611 (United States); Colli-Dula, Reyna C.; Kroll, Kevin J.; Lavelle, Candice M.; Barber, David S. [Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611 (United States); Vulpe, Christopher D. [Department of Nutritional Sciences and Toxicology, University of California, Berkeley, CA 94720 (United States); Denslow, Nancy D. [Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611 (United States)

2014-07-01

Highlights: • Low-level acute cadmium exposure elicited tissue-specific gene expression changes. • Molecular initiating events included oxidative stress and disruption of DNA repair. • Metallothionein, a marker of metal exposure, was not significantly affected. • We report effects of cadmium on cholesterol metabolism and steroid synthesis. • Diabetic complications and impaired reproduction are potential adverse outcomes. - Abstract: Cadmium is a heavy metal that can accumulate to toxic levels in the environment leading to detrimental effects in animals and humans including kidney, liver and lung injuries. Using a transcriptomics approach, genes and cellular pathways affected by a low dose of cadmium were investigated. Adult largemouth bass were intraperitoneally injected with 20 μg/kg of cadmium chloride (mean exposure level – 2.6 μg of cadmium per fish) and microarray analyses were conducted in the liver and testis 48 h after injection. Transcriptomic profiles identified in response to cadmium exposure were tissue-specific with the most differential expression changes found in the liver tissues, which also contained much higher levels of cadmium than the testis. Acute exposure to a low dose of cadmium induced oxidative stress response and oxidative damage pathways in the liver. The mRNA levels of antioxidants such as catalase increased and numerous transcripts related to DNA damage and DNA repair were significantly altered. Hepatic mRNA levels of metallothionein, a molecular marker of metal exposure, did not increase significantly after 48 h exposure. Carbohydrate metabolic pathways were also disrupted with hepatic transcripts such as UDP-glucose, pyrophosphorylase 2, and sorbitol dehydrogenase highly induced. Both tissues exhibited a disruption of steroid signaling pathways. In the testis, estrogen receptor beta and transcripts linked to cholesterol metabolism were suppressed. On the contrary, genes involved in cholesterol metabolism were highly

Mutated Genes in Schizophrenia Map to Brain Networks

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... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks ... have a high number of spontaneous mutations in genes that form a network in the front region ...

Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes.

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Fei Xiao

Full Text Available Combining path consistency (PC algorithms with conditional mutual information (CMI are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference, to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.

Humane metapneumovirus (HMPV) associated pulmonary infections in immunocompromised adults—Initial CT findings, disease course and comparison to respiratory-syncytial-virus (RSV) induced pulmonary infections

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Syha, R.; Beck, R.; Hetzel, J.; Ketelsen, D.; Grosse, U.; Springer, F.; Horger, M.

2012-01-01

Aim: To describe computed tomography (CT)-imaging findings in human metapneumovirus (HMPV)-related pulmonary infection as well as their temporal course and to analyze resemblances/differences to pulmonary infection induced by the closely related respiratory-syncytial-virus (RSV) in immunocompromised patients. Materials and methods: Chest-CT-scans of 10 HMPV PCR-positive patients experiencing pulmonary symptoms were evaluated retrospectively with respect to imaging findings and their distribution and results were then compared with data acquired in 13 patients with RSV pulmonary infection. Subsequently, we analyzed the course of chest-findings in HMPV patients. Results: In HMPV, 8/10 patients showed asymmetric pulmonary findings, whereas 13/13 patients with RSV-pneumonia presented more symmetrical bilateral pulmonary infiltrates. Image analysis yielded in HMPV patients following results: ground-glass-opacity (GGO) (n = 6), parenchymal airspace consolidations (n = 5), ill-defined nodular-like centrilobular opacities (n = 9), bronchial wall thickening (n = 8). In comparison, results in RSV patients were: GGO (n = 10), parenchymal airspace consolidations (n = 9), ill-defined nodular-like centrilobular opacities (n = 10), bronchial wall thickening (n = 4). In the course of the disease, signs of acute HMPV interstitial pneumonia regressed transforming temporarily in part into findings compatible with bronchitis/bronchiolitis. Conclusions: Early chest-CT findings in patients with HMPV-related pulmonary symptoms are compatible with asymmetric acute interstitial pneumonia accompanied by signs of bronchitis; the former transforming with time into bronchitis and bronchiolitis before they resolve. On the contrary, RSV-induced pulmonary infection exhibits mainly symmetric acute interstitial pneumonia.

Humane metapneumovirus (HMPV) associated pulmonary infections in immunocompromised adults—Initial CT findings, disease course and comparison to respiratory-syncytial-virus (RSV) induced pulmonary infections

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Syha, R., E-mail: roland.syha@med.uni-tuebingen.de [Department of Diagnostic Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen (Germany); Beck, R. [Institute of Medical Virology, Eberhard-Karls-University, Elfriede-Authorn-Str. 6, 72076 Tübingen (Germany); Hetzel, J. [Department of Medical Oncology and Hematology, Eberhard-Karls-University, Otfried-Müller-Str. 10, 72070 Tübingen (Germany); Ketelsen, D.; Grosse, U.; Springer, F.; Horger, M. [Department of Diagnostic Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen (Germany)

2012-12-15

Aim: To describe computed tomography (CT)-imaging findings in human metapneumovirus (HMPV)-related pulmonary infection as well as their temporal course and to analyze resemblances/differences to pulmonary infection induced by the closely related respiratory-syncytial-virus (RSV) in immunocompromised patients. Materials and methods: Chest-CT-scans of 10 HMPV PCR-positive patients experiencing pulmonary symptoms were evaluated retrospectively with respect to imaging findings and their distribution and results were then compared with data acquired in 13 patients with RSV pulmonary infection. Subsequently, we analyzed the course of chest-findings in HMPV patients. Results: In HMPV, 8/10 patients showed asymmetric pulmonary findings, whereas 13/13 patients with RSV-pneumonia presented more symmetrical bilateral pulmonary infiltrates. Image analysis yielded in HMPV patients following results: ground-glass-opacity (GGO) (n = 6), parenchymal airspace consolidations (n = 5), ill-defined nodular-like centrilobular opacities (n = 9), bronchial wall thickening (n = 8). In comparison, results in RSV patients were: GGO (n = 10), parenchymal airspace consolidations (n = 9), ill-defined nodular-like centrilobular opacities (n = 10), bronchial wall thickening (n = 4). In the course of the disease, signs of acute HMPV interstitial pneumonia regressed transforming temporarily in part into findings compatible with bronchitis/bronchiolitis. Conclusions: Early chest-CT findings in patients with HMPV-related pulmonary symptoms are compatible with asymmetric acute interstitial pneumonia accompanied by signs of bronchitis; the former transforming with time into bronchitis and bronchiolitis before they resolve. On the contrary, RSV-induced pulmonary infection exhibits mainly symmetric acute interstitial pneumonia.

Resistance Genes in Global Crop Breeding Networks.

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Garrett, K A; Andersen, K F; Asche, F; Bowden, R L; Forbes, G A; Kulakow, P A; Zhou, B

2017-10-01

Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Deregulation of an imprinted gene network in prostate cancer.

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Ribarska, Teodora; Goering, Wolfgang; Droop, Johanna; Bastian, Klaus-Marius; Ingenwerth, Marc; Schulz, Wolfgang A

2014-05-01

Multiple epigenetic alterations contribute to prostate cancer progression by deregulating gene expression. Epigenetic mechanisms, especially differential DNA methylation at imprinting control regions (termed DMRs), normally ensure the exclusive expression of imprinted genes from one specific parental allele. We therefore wondered to which extent imprinted genes become deregulated in prostate cancer and, if so, whether deregulation is due to altered DNA methylation at DMRs. Therefore, we selected presumptive deregulated imprinted genes from a previously conducted in silico analysis and from the literature and analyzed their expression in prostate cancer tissues by qRT-PCR. We found significantly diminished expression of PLAGL1/ZAC1, MEG3, NDN, CDKN1C, IGF2, and H19, while LIT1 was significantly overexpressed. The PPP1R9A gene, which is imprinted in selected tissues only, was strongly overexpressed, but was expressed biallelically in benign and cancerous prostatic tissues. Expression of many of these genes was strongly correlated, suggesting co-regulation, as in an imprinted gene network (IGN) reported in mice. Deregulation of the network genes also correlated with EZH2 and HOXC6 overexpression. Pyrosequencing analysis of all relevant DMRs revealed generally stable DNA methylation between benign and cancerous prostatic tissues, but frequent hypo- and hyper-methylation was observed at the H19 DMR in both benign and cancerous tissues. Re-expression of the ZAC1 transcription factor induced H19, CDKN1C and IGF2, supporting its function as a nodal regulator of the IGN. Our results indicate that a group of imprinted genes are coordinately deregulated in prostate cancers, independently of DNA methylation changes.

BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

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Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

2015-06-12

During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

Multiscale Embedded Gene Co-expression Network Analysis.

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Won-Min Song

2015-11-01

Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Multiscale Embedded Gene Co-expression Network Analysis.

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Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Identifying Candidate Reprogramming Genes in Mouse Induced Pluripotent Stem Cells.

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Gao, Fang; Li, Jingyu; Zhang, Heng; Yang, Xu; An, Tiezhu

2017-08-01

Factor-based induced reprogramming approaches have tremendous potential for human regenerative medicine, but the efficiencies of these approaches are still low. In this study, we analyzed the global transcriptional profiles of mouse induced pluripotent stem cells (miPSCs) and mouse embryonic stem cells (mESCs) from seven different labs and present here the first successful clustering according to cell type, not by lab of origin. We identified 2131 different expression genes (DEs) as candidate pluripotency-associated genes by comparing mESCs/miPSCs with somatic cells and 720 DEs between miPSCs and mESCs. Interestingly, there was a significant overlap between the two DE sets. Therefore, we defined the overlap DEs as "consensus DEs" including 313 miPSC-specific genes expressed at a higher level in miPSCs versus mESCs and 184 mESC-specific genes in total and reasoned that these may contribute to the differences in pluripotency between mESCs and miPSCs. A classification of "consensus DEs" according to their different expression levels between somatic cells and mESCs/miPSCs shows that 86% of the miPSC-specific genes are more highly expressed in somatic cells, while 73% of mESC-specific genes are highly expressed in mESCs/miPSCs, indicating that the miPSCs have not efficiently silenced the expression pattern of the somatic cells from which they are derived and failed to completely induce the genes with high expression levels in mESCs. We further revealed a strong correlation between oocyte-enriched factors and insufficiently induced mESC-specific genes and identified 11 hub genes via network analysis. In light of these findings, we postulated that these key hub genes might not only drive somatic cell nuclear transfer (SCNT) reprogramming but also augment the efficiency and quality of miPSC reprogramming.

Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

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Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

2016-05-01

Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Characterization of Genes for Beef Marbling Based on Applying Gene Coexpression Network

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Dajeong Lim

2014-01-01

Full Text Available Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in M. longissimus with divergent marbling phenotype (marbling scores 2 to 7 using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60 and dihydropyrimidine dehydrogenase (DPYD are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness.

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

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Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

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Vipin Narang

Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

Clinical Features of Human Metapneumovirus Infection in Ambulatory Children Aged 5-13 Years.

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Howard, Leigh M; Edwards, Kathryn M; Zhu, Yuwei; Griffin, Marie R; Weinberg, Geoffrey A; Szilagyi, Peter G; Staat, Mary A; Payne, Daniel C; Williams, John V

2018-05-15

We detected human metapneumovirus (HMPV) in 54 (5%) of 1055 children aged 5 to 13 years with acute respiratory illness (ARI) identified by outpatient and emergency department surveillance between November and May 2003-2009. Its clinical features were similar to those of HMPV-negative ARI, except a diagnosis of pneumonia was more likely (13% vs 4%, respectively; P = .005) and a diagnosis of pharyngitis (7% vs 24%, respectively; P = .005) was less likely in patients with HMPV- positive ARI than those with HMPV-negative ARI.

The Toll-like receptor gene family is integrated into human DNA damage and p53 networks.

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Daniel Menendez

2011-03-01

Full Text Available In recent years the functions that the p53 tumor suppressor plays in human biology have been greatly extended beyond "guardian of the genome." Our studies of promoter response element sequences targeted by the p53 master regulatory transcription factor suggest a general role for this DNA damage and stress-responsive regulator in the control of human Toll-like receptor (TLR gene expression. The TLR gene family mediates innate immunity to a wide variety of pathogenic threats through recognition of conserved pathogen-associated molecular motifs. Using primary human immune cells, we have examined expression of the entire TLR gene family following exposure to anti-cancer agents that induce the p53 network. Expression of all TLR genes, TLR1 to TLR10, in blood lymphocytes and alveolar macrophages from healthy volunteers can be induced by DNA metabolic stressors. However, there is considerable inter-individual variability. Most of the TLR genes respond to p53 via canonical as well as noncanonical promoter binding sites. Importantly, the integration of the TLR gene family into the p53 network is unique to primates, a recurrent theme raised for other gene families in our previous studies. Furthermore, a polymorphism in a TLR8 response element provides the first human example of a p53 target sequence specifically responsible for endogenous gene induction. These findings-demonstrating that the human innate immune system, including downstream induction of cytokines, can be modulated by DNA metabolic stress-have many implications for health and disease, as well as for understanding the evolution of damage and p53 responsive networks.

Analysis of antigenic cross-reactivity between subgroup C avian pneumovirus and human metapneumovirus by using recombinant fusion proteins.

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Luo, L; Sabara, M I; Li, Y

2009-10-01

Avian pneumovirus subgroup C (APV/C) has recently been reported to be more closely related to human metapneumovirus (hMPV) as determined by sequence analysis. To examine the antigenic relationship between APV/C and hMPV, the APV/C fusion (F) gene was cloned and expressed as an uncleaved glycoprotein in a baculovirus system. The reactivity of the APV/C F protein with antibodies against APV subgroups A, B, C, and hMPV was examined by Western blot analysis. The results showed that the expressed APV/C F protein was not only recognized by APV/C-specific antibodies but also by antibodies raised against hMPV. Previously expressed recombinant hMPV F protein also reacted with APV/C-specific antibodies, suggesting that there was significant antigenic cross-reactivity and a potential evolutionary relationship between hMPV and APV/C. Interestingly, the recombinant F proteins from APV/C and hMPV were not recognized by polyclonal antibodies specific to APV subgroups A and B.

Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

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Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

2012-01-01

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

Crowdsourcing the nodulation gene network discovery environment.

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Li, Yupeng; Jackson, Scott A

2016-05-26

The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created. The platform implements the jQuery and vis.js JavaScript libraries, so that users are able to interactively visualize and edit the gene network, and easily access the information about the network, e.g. gene lists, gene interactions and gene functional annotations. In addition, all the gene information is written on MediaWiki pages, enabling users to edit and contribute to the network curation. Utilizing the continuously updated, collaboratively written, and community-reviewed Wikipedia model, the platform could, in a short time, become a comprehensive knowledge base of nodulation-related pathways. The platform could also be used for other biological processes, and thus has great potential for integrating and advancing our understanding of the functional genomics and systems biology of any process for any species. The platform is available at http://crowd.bioops.info/ , and the source code can be openly accessed at https://github.com/bioops/crowdnodnet under MIT License.

Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

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Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

2017-01-01

The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

Induced pluripotency with endogenous and inducible genes

International Nuclear Information System (INIS)

Duinsbergen, Dirk; Eriksson, Malin; Hoen, Peter A.C. 't; Frisen, Jonas; Mikkers, Harald

2008-01-01

The recent discovery that two partly overlapping sets of four genes induce nuclear reprogramming of mouse and even human cells has opened up new possibilities for cell replacement therapies. Although the combination of genes that induce pluripotency differs to some extent, Oct4 and Sox2 appear to be a prerequisite. The introduction of four genes, several of which been linked with cancer, using retroviral approaches is however unlikely to be suitable for future clinical applications. Towards developing a safer reprogramming protocol, we investigated whether cell types that express one of the most critical reprogramming genes endogenously are predisposed to reprogramming. We show here that three of the original four pluripotency transcription factors (Oct4, Klf4 and c-Myc or MYCER TAM ) induced reprogramming of mouse neural stem (NS) cells exploiting endogenous SoxB1 protein levels in these cells. The reprogrammed neural stem cells differentiated into cells of each germ layer in vitro and in vivo, and contributed to mouse development in vivo. Thus a combinatorial approach taking advantage of endogenously expressed genes and inducible transgenes may contribute to the development of improved reprogramming protocols

First evidence of avian metapneumovirus subtype A infection in turkeys in Egypt.

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Abdel-Azeem, Abdel-Azeem Sayed; Franzo, Giovanni; Dalle Zotte, Antonella; Drigo, Michele; Catelli, Elena; Lupini, Caterina; Martini, Marco; Cecchinato, Mattia

2014-08-01

Although avian metapneumovirus (aMPV) infection has been reported in most regions of the world, to date, only subtype B has been detected in Egypt. At the end of November 2013, dry oropharyngeal swabs were collected during an outbreak of respiratory diseases in a free-range, multi-age turkey dealer farm in Northern Upper Egypt. The clinical signs that appeared when turkeys were 3 weeks-old were characterized by ocular and nasal discharge and swelling of sinuses. aMPV of subtype A was detected by real-time reverse transcription-polymerase chain reaction. In order to confirm the results and obtain more information on the molecular characteristics of the virus, F and G protein genes were partially sequenced and compared with previously published sequences deposited in GenBank by using BLAST. Subtype of the strain was confirmed by sequencing of partial F and G protein genes. The highest percentages of identity were observed when G sequence of the Egyptian strain was compared with the sequence of an aMPV-A isolated in Nigeria (96.4 %) and when the F sequence was compared with strains isolated respectively in Italy and in UK (97.1 %). Moreover, the alignment of the sequences with commercial subtype A vaccine or vaccine-derived strains showed differences in the Egyptian strain that indicate its probable field origin. The detection of aMPV in the investigated turkey flock highlights some relevant epidemiological issues regarding the role that multi-age farms and dealers may play in perpetuating aMPV infection within and among farms. To our knowledge, this is the first report of aMPV subtype A in Egypt.

Mutational robustness of gene regulatory networks.

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Aalt D J van Dijk

Full Text Available Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive. In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

Learning gene networks under SNP perturbations using eQTL datasets.

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Lingxue Zhang

2014-02-01

Full Text Available The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network

A network of genes, genetic disorders, and brain areas.

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Satoru Hayasaka

Full Text Available The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

Fitness Effects of Network Non-Linearity Induced by Gene Expression Noise

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Ray, Christian; Cooper, Tim; Balazsi, Gabor

2012-02-01

In the non-equilibrium dynamics of growing microbial cells, metabolic enzymes can create non-linearities in metabolite concentration because of non-linear degradation (utilization): an enzyme can saturate in the process of metabolite utilization. Increasing metabolite production past the saturation point then results in an ultrasensitive metabolite response. If the production rate of a metabolite depends on a second enzyme or other protein-mediated process, uncorrelated gene expression noise can thus cause transient metabolite concentration bursts. Such bursts are physiologically unnecessary and may represent a source of selection against the ultrasensitive switch, especially if the fluctuating metabolic intermediate is toxic. Selection may therefore favor correlated gene expression fluctuations for enzymes in the same pathway, such as by same-operon membership in bacteria. Using a modified experimental lac operon system, we are undertaking a combined theoretical-experimental approach to demonstrate that (i) the lac operon has an implicit ultrasensitive switch that we predict is avoided by gene expression correlations induced by same-operon membership; (ii) bacterial growth rates are sensitive to crossing the ultrasensitive threshold. Our results suggest that correlations in intrinsic gene expression noise are exploited by evolution to ameliorate the detrimental effects of nonlinearities in metabolite concentrations.

Methyltransferase-defective avian metapneumovirus vaccines provide complete protection against challenge with the homologous Colorado strain and the heterologous Minnesota strain.

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Sun, Jing; Wei, Yongwei; Rauf, Abdul; Zhang, Yu; Ma, Yuanmei; Zhang, Xiaodong; Shilo, Konstantin; Yu, Qingzhong; Saif, Y M; Lu, Xingmeng; Yu, Lian; Li, Jianrong

2014-11-01

Avian metapneumovirus (aMPV), also known as avian pneumovirus or turkey rhinotracheitis virus, is the causative agent of turkey rhinotracheitis and is associated with swollen head syndrome in chickens. Since its discovery in the 1970s, aMPV has been recognized as an economically important pathogen in the poultry industry worldwide. The conserved region VI (CR VI) of the large (L) polymerase proteins of paramyxoviruses catalyzes methyltransferase (MTase) activities that typically methylate viral mRNAs at guanine N-7 (G-N-7) and ribose 2'-O positions. In this study, we generated a panel of recombinant aMPV (raMPV) Colorado strains carrying mutations in the S-adenosyl methionine (SAM) binding site in the CR VI of L protein. These recombinant viruses were specifically defective in ribose 2'-O, but not G-N-7 methylation and were genetically stable and highly attenuated in cell culture and viral replication in the upper and lower respiratory tracts of specific-pathogen-free (SPF) young turkeys. Importantly, turkeys vaccinated with these MTase-defective raMPVs triggered a high level of neutralizing antibody and were completely protected from challenge with homologous aMPV Colorado strain and heterologous aMPV Minnesota strain. Collectively, our results indicate (i) that aMPV lacking 2'-O methylation is highly attenuated in vitro and in vivo and (ii) that inhibition of mRNA cap MTase can serve as a novel target to rationally design live attenuated vaccines for aMPV and perhaps other paramyxoviruses. Paramyxoviruses include many economically and agriculturally important viruses such as avian metapneumovirus (aMPV), and Newcastle disease virus (NDV), human pathogens such as human respiratory syncytial virus, human metapneumovirus, human parainfluenza virus type 3, and measles virus, and highly lethal emerging pathogens such as Nipah virus and Hendra virus. For many of them, there is no effective vaccine or antiviral drug. These viruses share common strategies for viral gene

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes

OpenAIRE

Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-01-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix–loop–helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks e...

Discovering disease-associated genes in weighted protein-protein interaction networks

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Cui, Ying; Cai, Meng; Stanley, H. Eugene

2018-04-01

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

A platform for rapid prototyping of synthetic gene networks in mammalian cells

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Duportet, Xavier; Wroblewska, Liliana; Guye, Patrick; Li, Yinqing; Eyquem, Justin; Rieders, Julianne; Rimchala, Tharathorn; Batt, Gregory; Weiss, Ron

2014-01-01

Mammalian synthetic biology may provide novel therapeutic strategies, help decipher new paths for drug discovery and facilitate synthesis of valuable molecules. Yet, our capacity to genetically program cells is currently hampered by the lack of efficient approaches to streamline the design, construction and screening of synthetic gene networks. To address this problem, here we present a framework for modular and combinatorial assembly of functional (multi)gene expression vectors and their efficient and specific targeted integration into a well-defined chromosomal context in mammalian cells. We demonstrate the potential of this framework by assembling and integrating different functional mammalian regulatory networks including the largest gene circuit built and chromosomally integrated to date (6 transcription units, 27kb) encoding an inducible memory device. Using a library of 18 different circuits as a proof of concept, we also demonstrate that our method enables one-pot/single-flask chromosomal integration and screening of circuit libraries. This rapid and powerful prototyping platform is well suited for comparative studies of genetic regulatory elements, genes and multi-gene circuits as well as facile development of libraries of isogenic engineered cell lines. PMID:25378321

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

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Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

The Reconstruction and Analysis of Gene Regulatory Networks.

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Zheng, Guangyong; Huang, Tao

2018-01-01

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

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Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

2017-06-16

The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis

Human metapneumovirus and respiratory syncytial virus in hospitalized danish children with acute respiratory tract infection

DEFF Research Database (Denmark)

von Linstow, Marie-Louise; Henrik Larsen, Hans; Koch, Anders

2004-01-01

The newly discovered human metapneumovirus (hMPV) has been shown to be associated with respiratory illness. We determined the frequencies and clinical features of hMPV and respiratory syncytial virus (RSV) infections in 374 Danish children with 383 episodes of acute respiratory tract infection...... children 1-6 months of age. Asthmatic bronchitis was diagnosed in 66.7% of hMPV and 10.6% of RSV-infected children (p respiratory support. hMPV is present in young...

Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

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Ettore Mosca

2017-09-01

Full Text Available Autism spectrum disorder (ASD is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

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Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

Regulation of endogenous human gene expression by ligand-inducible TALE transcription factors.

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Mercer, Andrew C; Gaj, Thomas; Sirk, Shannon J; Lamb, Brian M; Barbas, Carlos F

2014-10-17

The construction of increasingly sophisticated synthetic biological circuits is dependent on the development of extensible tools capable of providing specific control of gene expression in eukaryotic cells. Here, we describe a new class of synthetic transcription factors that activate gene expression in response to extracellular chemical stimuli. These inducible activators consist of customizable transcription activator-like effector (TALE) proteins combined with steroid hormone receptor ligand-binding domains. We demonstrate that these ligand-responsive TALE transcription factors allow for tunable and conditional control of gene activation and can be used to regulate the expression of endogenous genes in human cells. Since TALEs can be designed to recognize any contiguous DNA sequence, the conditional gene regulatory system described herein will enable the design of advanced synthetic gene networks.

Fused Regression for Multi-source Gene Regulatory Network Inference.

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Kari Y Lam

2016-12-01

Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods

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Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E.; Re, Matteo

2014-01-01

Objective In the context of “network medicine”, gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. Materials and methods We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. Results The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different “informativeness” embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Conclusions Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further

Avaliação in vitro da atividade antiviral de extratos de plantas frente ao metapneumovirus aviário (AMPV) e vírus respiratório sincicial bovino (BRSV)

OpenAIRE

Matheus Cavalheiro Martini

2010-01-01

Resumo: Para avaliar a atividade antiviral dos extratos de plantas brasileiras foram eleitos o Metapneumovirus aviário (aMPV) e o vírus Respiratório sincicial bovino (BRSV) pertences à família Paramyxoviridae, subfamília Pneumovirinae, gêneros Metapneumovirus e Pneumovirus respectivamente. Tanto o aMPV quanto o BRSV são vírus semelhantes aos que causam doenças em humanos como o vírus respiratório sincicial humano (HRSV) e metapneumovírus humano (hMPV). O objetivo do presente trabalho foi aval...

Inferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancer.

Science.gov (United States)

Zhou, Xionghui; Liu, Juan

2014-01-01

Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer). In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B) means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis). Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene signature for

Individual contributions of the human metapneumovirus F, G, and SH surface glycoproteins to the induction of neutralizing antibodies and protective immunity

International Nuclear Information System (INIS)

Skiadopoulos, Mario H.; Biacchesi, Stephane; Buchholz, Ursula J.; Amaro-Carambot, Emerito; Surman, Sonja R.; Collins, Peter L.; Murphy, Brian R.

2006-01-01

We evaluated the individual contributions of the three surface glycoproteins of human metapneumovirus (HMPV), namely the fusion F, attachment G, and small hydrophobic SH proteins, to the induction of serum HMPV-binding antibodies, serum HMPV-neutralizing antibodies, and protective immunity. Using reverse genetics, each HMPV protein was expressed individually from an added gene in recombinant human parainfluenza virus type 1 (rHPIV1) and used to infect hamsters once or twice by the intranasal route. The F protein was highly immunogenic and protective, whereas G and SH were only weakly or negligibly immunogenic and protective, respectively. Thus, in contrast to other paramyxoviruses, the HMPV attachment G protein is not a major neutralization or protective antigen. Also, although the SH protein of HMPV is a virion protein that is much larger than its counterparts in previously studied paramyxoviruses, it does not appear to be a significant neutralization or protective antigen

Identification of PEG-induced water stress responsive transcripts using co-expression network in Eucalyptus grandis.

Science.gov (United States)

Ghosh Dasgupta, Modhumita; Dharanishanthi, Veeramuthu

2017-09-05

Ecophysiological studies in Eucalyptus have shown that water is the principal factor limiting stem growth. Effect of water deficit conditions on physiological and biochemical parameters has been extensively reported in Eucalyptus. The present study was conducted to identify major polyethylene glycol induced water stress responsive transcripts in Eucalyptus grandis using gene co-expression network. A customized array representing 3359 water stress responsive genes was designed to document their expression in leaves of E. grandis cuttings subjected to -0.225MPa of PEG treatment. The differentially expressed transcripts were documented and significantly co-expressed transcripts were used for construction of network. The co-expression network was constructed with 915 nodes and 3454 edges with degree ranging from 2 to 45. Ninety four GO categories and 117 functional pathways were identified in the network. MCODE analysis generated 27 modules and module 6 with 479 nodes and 1005 edges was identified as the biologically relevant network. The major water responsive transcripts represented in the module included dehydrin, osmotin, LEA protein, expansin, arabinogalactans, heat shock proteins, major facilitator proteins, ARM repeat proteins, raffinose synthase, tonoplast intrinsic protein and transcription factors like DREB2A, ARF9, AGL24, UNE12, WLIM1 and MYB66, MYB70, MYB 55, MYB 16 and MYB 103. The coordinated analysis of gene expression patterns and coexpression networks developed in this study identified an array of transcripts that may regulate PEG induced water stress responses in E. grandis. Copyright © 2017 Elsevier B.V. All rights reserved.

Reveal genes functionally associated with ACADS by a network study.

Science.gov (United States)

Chen, Yulong; Su, Zhiguang

2015-09-15

Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.

Integration of biological networks and gene expression data using Cytoscape

DEFF Research Database (Denmark)

Cline, M.S.; Smoot, M.; Cerami, E.

2007-01-01

of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules......Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context...... and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape....

Transcriptional delay stabilizes bistable gene networks.

Science.gov (United States)

Gupta, Chinmaya; López, José Manuel; Ott, William; Josić, Krešimir; Bennett, Matthew R

2013-08-02

Transcriptional delay can significantly impact the dynamics of gene networks. Here we examine how such delay affects bistable systems. We investigate several stochastic models of bistable gene networks and find that increasing delay dramatically increases the mean residence times near stable states. To explain this, we introduce a non-Markovian, analytically tractable reduced model. The model shows that stabilization is the consequence of an increased number of failed transitions between stable states. Each of the bistable systems that we simulate behaves in this manner.

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

Science.gov (United States)

Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gene regulation is governed by a core network in hepatocellular carcinoma.

Science.gov (United States)

Gu, Zuguang; Zhang, Chenyu; Wang, Jin

2012-05-01

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to HCC and we believe further

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks.

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Zhang, Yihua; Li, Wan; Feng, Yuyan; Guo, Shanshan; Zhao, Xilei; Wang, Yahui; He, Yuehan; He, Weiming; Chen, Lina

2017-11-28

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.

Avian metapneumovirus subtypes circulating in Brazilian vaccinated and nonvaccinated chicken and turkey farms.

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Chacón, Jorge Luis; Mizuma, Matheus; Vejarano, Maria P; Toquín, Didier; Eterradossi, Nicolas; Patnayak, Devi P; Goyal, Sagar M; Ferreira, Antonio J Piantino

2011-03-01

Avian metapneumovirus (AMPV) causes turkey rhinotracheitis and is associated with swollen head syndrome in chickens, which is usually accompanied by secondary infections that increase mortality. AMPVs circulating in Brazilian vaccinated and nonvaccinated commercial chicken and turkey farms were detected using a universal reverse transcriptase (RT)-PCR assay that can detect the four recognized subtypes of AMPV. The AMPV status of 228 farms with respiratory and reproductive disturbances was investigated. AMPV was detected in broiler, hen, breeder, and turkey farms from six different geographic regions of Brazil. The detected viruses were subtyped using a nested RT-PCR assay and sequence analysis of the G gene. Only subtypes A and B were detected in both vaccinated and nonvaccinated farms. AMPV-A and AMPV-B were detected in 15 and 23 farms, respectively, while both subtypes were simultaneously found in one hen farm. Both vaccine and field viruses were detected in nonvaccinated farms. In five cases, the detected subtype was different than the vaccine subtype. Field subtype B virus was detected mainly during the final years of the survey period. These viruses showed high molecular similarity (more than 96% nucleotide similarity) among themselves and formed a unique phylogenetic group, suggesting that they may have originated from a common strain. These results demonstrate the cocirculation of subtypes A and B in Brazilian commercial farms.

Nucleotide and Predicted Amino Acid Sequence-Based Analysis of the Avian Metapneumovirus Type C Cell Attachment Glycoprotein Gene: Phylogenetic Analysis and Molecular Epidemiology of U.S. Pneumoviruses

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Alvarez, Rene; Lwamba, Humphrey M.; Kapczynski, Darrell R.; Njenga, M. Kariuki; Seal, Bruce S.

2003-01-01

A serologically distinct avian metapneumovirus (aMPV) was isolated in the United States after an outbreak of turkey rhinotracheitis (TRT) in February 1997. The newly recognized U.S. virus was subsequently demonstrated to be genetically distinct from European subtypes and was designated aMPV serotype C (aMPV/C). We have determined the nucleotide sequence of the gene encoding the cell attachment glycoprotein (G) of aMPV/C (Colorado strain and three Minnesota isolates) and predicted amino acid sequence by sequencing cloned cDNAs synthesized from intracellular RNA of aMPV/C-infected cells. The nucleotide sequence comprised 1,321 nucleotides with only one predicted open reading frame encoding a protein of 435 amino acids, with a predicted Mr of 48,840. The structural characteristics of the predicted G protein of aMPV/C were similar to those of the human respiratory syncytial virus (hRSV) attachment G protein, including two mucin-like regions (heparin-binding domains) flanking both sides of a CX3C chemokine motif present in a conserved hydrophobic pocket. Comparison of the deduced G-protein amino acid sequence of aMPV/C with those of aMPV serotypes A, B, and D, as well as hRSV revealed overall predicted amino acid sequence identities ranging from 4 to 16.5%, suggesting a distant relationship. However, G-protein sequence identities ranged from 72 to 97% when aMPV/C was compared to other members within the aMPV/C subtype or 21% for the recently identified human MPV (hMPV) G protein. Ratios of nonsynonymous to synonymous nucleotide changes were greater than one in the G gene when comparing the more recent Minnesota isolates to the original Colorado isolate. Epidemiologically, this indicates positive selection among U.S. isolates since the first outbreak of TRT in the United States. PMID:12682171

Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

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Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

2012-01-01

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

Combining many interaction networks to predict gene function and analyze gene lists.

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Mostafavi, Sara; Morris, Quaid

2012-05-01

In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nitrate-induced genes in tomato roots. Array analysis reveals novel genes that may play a role in nitrogen nutrition.

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Wang, Y H; Garvin, D F; Kochian, L V

2001-09-01

A subtractive tomato (Lycopersicon esculentum) root cDNA library enriched in genes up-regulated by changes in plant mineral status was screened with labeled mRNA from roots of both nitrate-induced and mineral nutrient-deficient (-nitrogen [N], -phosphorus, -potassium [K], -sulfur, -magnesium, -calcium, -iron, -zinc, and -copper) tomato plants. A subset of cDNAs was selected from this library based on mineral nutrient-related changes in expression. Additional cDNAs were selected from a second mineral-deficient tomato root library based on sequence homology to known genes. These selection processes yielded a set of 1,280 mineral nutrition-related cDNAs that were arrayed on nylon membranes for further analysis. These high-density arrays were hybridized with mRNA from tomato plants exposed to nitrate at different time points after N was withheld for 48 h, for plants that were grown on nitrate/ammonium for 5 weeks prior to the withholding of N. One hundred-fifteen genes were found to be up-regulated by nitrate resupply. Among these genes were several previously identified as nitrate responsive, including nitrate transporters, nitrate and nitrite reductase, and metabolic enzymes such as transaldolase, transketolase, malate dehydrogenase, asparagine synthetase, and histidine decarboxylase. We also identified 14 novel nitrate-inducible genes, including: (a) water channels, (b) root phosphate and K(+) transporters, (c) genes potentially involved in transcriptional regulation, (d) stress response genes, and (e) ribosomal protein genes. In addition, both families of nitrate transporters were also found to be inducible by phosphate, K, and iron deficiencies. The identification of these novel nitrate-inducible genes is providing avenues of research that will yield new insights into the molecular basis of plant N nutrition, as well as possible networking between the regulation of N, phosphorus, and K nutrition.

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

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Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E; Re, Matteo

2014-06-01

In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both

The integration of weighted human gene association networks based on link prediction.

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Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

2017-01-31

Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

Paper-based synthetic gene networks.

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Pardee, Keith; Green, Alexander A; Ferrante, Tom; Cameron, D Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J

2014-11-06

Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

Paper-based Synthetic Gene Networks

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Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

2014-01-01

Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks.

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Saik, Olga V; Demenkov, Pavel S; Ivanisenko, Timofey V; Bragina, Elena Yu; Freidin, Maxim B; Goncharova, Irina A; Dosenko, Victor E; Zolotareva, Olga I; Hofestaedt, Ralf; Lavrik, Inna N; Rogaev, Evgeny I; Ivanisenko, Vladimir A

2018-02-13

Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in

Inferring time-varying network topologies from gene expression data.

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Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

Comprehensive Analysis of Gene Expression Profiles of Sepsis-Induced Multiorgan Failure Identified Its Valuable Biomarkers.

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Wang, Yumei; Yin, Xiaoling; Yang, Fang

2018-02-01

Sepsis is an inflammatory-related disease, and severe sepsis would induce multiorgan dysfunction, which is the most common cause of death of patients in noncoronary intensive care units. Progression of novel therapeutic strategies has proven to be of little impact on the mortality of severe sepsis, and unfortunately, its mechanisms still remain poorly understood. In this study, we analyzed gene expression profiles of severe sepsis with failure of lung, kidney, and liver for the identification of potential biomarkers. We first downloaded the gene expression profiles from the Gene Expression Omnibus and performed preprocessing of raw microarray data sets and identification of differential expression genes (DEGs) through the R programming software; then, significantly enriched functions of DEGs in lung, kidney, and liver failure sepsis samples were obtained from the Database for Annotation, Visualization, and Integrated Discovery; finally, protein-protein interaction network was constructed for DEGs based on the STRING database, and network modules were also obtained through the MCODE cluster method. As a result, lung failure sepsis has the highest number of DEGs of 859, whereas the number of DEGs in kidney and liver failure sepsis samples is 178 and 175, respectively. In addition, 17 overlaps were obtained among the three lists of DEGs. Biological processes related to immune and inflammatory response were found to be significantly enriched in DEGs. Network and module analysis identified four gene clusters in which all or most of genes were upregulated. The expression changes of Icam1 and Socs3 were further validated through quantitative PCR analysis. This study should shed light on the development of sepsis and provide potential therapeutic targets for sepsis-induced multiorgan failure.

Inference of cancer-specific gene regulatory networks using soft computing rules.

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Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Development of a vaccine-challenge model for avian metapneumovirus subtype C in turkeys.

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Velayudhan, Binu T; Noll, Sally L; Thachil, Anil J; Shaw, Daniel P; Goyal, Sagar M; Halvorson, David A; Nagaraja, Kakambi V

2007-02-26

The objective of this study was to evaluate different preparations of avian metapneumovirus (aMPV) subtype C as vaccine challenge in turkeys. Two aMPV isolates and their respective nasal turbinate homogenates after propagation in turkeys were used in the study. Significantly higher clinical sign scores were recorded in birds inoculated with 20 or 2% turbinate homogenate of recent isolate. Birds in the above groups showed more pronounced histopathological lesions, and a higher percentage of birds showed viral RNA and antigen in tissues. The data demonstrated that nasal turbinate homogenate of recent isolate produced severe clinical signs and lesions in turkeys and could be an ideal candidate for vaccine-challenge studies.

Identification of human disease genes from interactome network using graphlet interaction.

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Xiao-Dong Wang

Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

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Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

2014-01-01

Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

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Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.

2018-03-01

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

A flood-based information flow analysis and network minimization method for gene regulatory networks.

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Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

2013-04-24

Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

Investigations on the protective role of passively transferred antibodies against avian metapneumovirus infection in turkeys.

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Rubbenstroth, Dennis; Rautenschlein, Silke

2009-12-01

The avian metapneumovirus (aMPV) is the causative agent of an acute respiratory disease in turkeys, which causes considerable economic losses to the poultry industry. Currently attenuated live and inactivated vaccines are widely used to control the disease, but vaccine breaks are frequently observed. For improvement of current vaccination strategies it is necessary to gain enhanced knowledge of the immune mechanisms against aMPV infection. Field observations suggest that vaccine-induced aMPV-specific antibodies are not indicative for protection. In the present study we investigated the role of antibodies in protection of turkeys against aMPV. In two experiments, commercial turkey poults received aMPV-specific antibodies by intravenous injection. The antibody transfer resulted in increased antibody levels in the sera. Virus-specific antibodies were also detected on mucosal surfaces such as the trachea, conjunctivae and gall bladder. Turkeys were subsequently challenged with a virulent aMPV subtype A strain. Development of clinical signs, virus detection by polymerase chain reaction and histopathological changes of tracheal mucosa in challenged turkeys with and without passively transferred antibodies were comparable with each other. Our results suggest that humoral immunity does not provide sufficient protection against aMPV infection. Thus, the measurement of vaccine-induced aMPV antibody response may not be considered as an adequate indicator of vaccine efficacy. Further research on the protective role of cell-mediated immune mechanisms is necessary to improve current vaccine strategies.

System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

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Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

2018-01-01

Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

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Wang, Yi Kan; Hurley, Daniel G; Schnell, Santiago; Print, Cristin G; Crampin, Edmund J

2013-01-01

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

A hybrid network-based method for the detection of disease-related genes

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Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

2018-02-01

Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon

Directory of Open Access Journals (Sweden)

Satoru Koda

2017-11-01

Full Text Available We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX model with a group smoothly clipped absolute deviation (SCAD method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon. To reveal the diurnal changes in the transcriptome in B. distachyon, we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon. On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon, aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

The integration of weighted gene association networks based on information entropy.

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Yang, Fan; Wu, Duzhi; Lin, Limei; Yang, Jian; Yang, Tinghong; Zhao, Jing

2017-01-01

Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.

Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster

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Kim Man-Sun

2012-05-01

Full Text Available Abstract Background Network motifs provided a “conceptual tool” for understanding the functional principles of biological networks, but such motifs have primarily been used to consider static network structures. Static networks, however, cannot be used to reveal time- and region-specific traits of biological systems. To overcome this limitation, we proposed the concept of a “spatiotemporal network motif,” a spatiotemporal sequence of network motifs of sub-networks which are active only at specific time points and body parts. Results On the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns. Conclusions Taken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks.

Respiratory syncytial virus infection induces higher Toll-like receptor-3 expression and TNF-α production than human metapneumovirus infection.

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Ying Dou

Full Text Available Respiratory syncytial virus (RSV and human metapneumovirus (hMPV are common causes of respiratory infections in children. Diseases caused by hMPV are generally considered to be less severe than those caused by RSV; the underlying mechanisms, however, remain unknown. In the present study, the expressions of TLRs in airway epithelial cells and lungs of BALB/c mice infected by hMPV or RSV were measured in an attempt to explore the differences in the airway inflammation caused by the two viruses. Our results demonstrate that both hMPV and RSV infection upregulated the expressions of TLRs and inflammatory cytokines. Specifically, the TLR3 expression was revealed to be elevated in vitro and in mouse lungs. IFN-α produced by A549 cells after RSV or hMPV infection remained undistinguishable, whereas production of TNF-α was significantly higher after RSV infection than hMPV infection either in the presence or absence of Poly I:C. This study provides a clue that more severe clinical syndrome of RSV infection may be due to the greater magnitude of induction of airway inflammation by RSV involving TLR3 activation and production of TNF-α.

Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

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Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

2017-08-01

This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Inferring Phylogenetic Networks from Gene Order Data

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Alexey Anatolievich Morozov

2013-01-01

Full Text Available Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary, sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm. Binary encoding can also be useful, but only when the methods mentioned above cannot be used.

Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

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Xiaosheng Wang

2010-03-01

Full Text Available Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Avian metapneumovirus excretion in vaccinated and non-vaccinated specified pathogen free laying chickens.

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Hess, M; Huggins, M B; Mudzamiri, R; Heincz, U

2004-02-01

Vaccinated and non-vaccinated specified pathogen-free White Leghorn laying chickens were challenged at peak of lay by the intravenous or oculonasal route with a virulent avian metapneumovirus (aMPV) subtype B chicken strain. Severe clinical signs and a drop in egg production were induced in the non-vaccinated intravenously challenged birds whereas the vaccinates were not affected. Live virus excretion was demonstrated in the faeces and respiratory tract of non-vaccinated hens for up to 7 days post intravenous challenge. After oculonasal challenge, virus excretion could only be demonstrated in the respiratory tract for up to 5 days. No live virus excretion was found in either the faeces or the respiratory tract of vaccinated birds. Concurrent with live virus isolation, the presence of viral RNA was demonstrated by single reverse transcription-polymerase chain reaction (RT-PCR). Nested RT-PCR was more sensitive and viral RNA could be detected in non-vaccinated birds up to 28 days post either intravenous or oculonasal challenge, at which time the experiment was terminated. Viral RNA was detected for up to 12 days in vaccinated birds. This is the first study investigating excretion of aMPV and viral RNA in vaccinated and non-vaccinated laying hens challenged under experimental conditions. The results are of importance with regard to the persistence of aMPV and the appropriate diagnostic detection method in laying birds.

The HOX genes are expressed, in vivo, in human tooth germs: in vitro cAMP exposure of dental pulp cells results in parallel HOX network activation and neuronal differentiation.

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D'Antò, Vincenzo; Cantile, Monica; D'Armiento, Maria; Schiavo, Giulia; Spagnuolo, Gianrico; Terracciano, Luigi; Vecchione, Raffaela; Cillo, Clemente

2006-03-01

Homeobox-containing genes play a crucial role in odontogenesis. After the detection of Dlx and Msx genes in overlapping domains along maxillary and mandibular processes, a homeobox odontogenic code has been proposed to explain the interaction between different homeobox genes during dental lamina patterning. No role has so far been assigned to the Hox gene network in the homeobox odontogenic code due to studies on specific Hox genes and evolutionary considerations. Despite its involvement in early patterning during embryonal development, the HOX gene network, the most repeat-poor regions of the human genome, controls the phenotype identity of adult eukaryotic cells. Here, according to our results, the HOX gene network appears to be active in human tooth germs between 18 and 24 weeks of development. The immunohistochemical localization of specific HOX proteins mostly concerns the epithelial tooth germ compartment. Furthermore, only a few genes of the network are active in embryonal retromolar tissues, as well as in ectomesenchymal dental pulp cells (DPC) grown in vitro from adult human molar. Exposure of DPCs to cAMP induces the expression of from three to nine total HOX genes of the network in parallel with phenotype modifications with traits of neuronal differentiation. Our observations suggest that: (i) by combining its component genes, the HOX gene network determines the phenotype identity of epithelial and ectomesenchymal cells interacting in the generation of human tooth germ; (ii) cAMP treatment activates the HOX network and induces, in parallel, a neuronal-like phenotype in human primary ectomesenchymal dental pulp cells. 2005 Wiley-Liss, Inc.

Comparative evaluation of conventional RT-PCR and real-time RT-PCR (RRT-PCR for detection of avian metapneumovirus subtype A Comparação entre as técnicas de RT-PCR convencional e RT-PCR em tempo real para a detecção do metapneumovírus aviários subtipo A

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Helena Lage Ferreira

2009-08-01

Full Text Available Avian metapneumovirus (AMPV belongs to Metapneumovirus genus of Paramyxoviridae family. Virus isolation, serology, and detection of genomic RNA are used as diagnostic methods for AMPV. The aim of the present study was to compare the detection of six subgroup A AMPV isolates (AMPV/A viral RNA by using different conventional and real time RT-PCR methods. Two new RT-PCR tests and two real time RT-PCR tests, both detecting fusion (F gene and nucleocapsid (N gene were compared with an established test for the attachment (G gene. All the RT-PCR tested assays were able to detect the AMPV/A. The lower detection limits were observed using the N-, F- based RRT-PCR and F-based conventional RT-PCR (10(0.3 to 10¹ TCID50 mL-1. The present study suggests that the conventional F-based RT-PCR presented similar detection limit when compared to N- and F-based RRT-PCR and they can be successfully used for AMPV/A detection.O metapneumovírus aviário (AMPV pertence ao gênero Metapneumovirus, família Paramyxoviridae. Isolamento viral, sorologia e detecção do RNA genômico são atualmente as técnicas utilizadas para o diagnóstico desse agente. O objetivo do presente estudo foi comparar a detecção de RNA viral de seis isolados de AMPV, subtipo A (AMPV/A, utilizando diferentes métodos de RT-PCR convencional e real time RT-PCR (RRT-PCR. Duas novas técnicas de RT-PCR convencional e duas técnicas de RRT-PCR, ambas para a detecção dos genes da nucleoproteína (N e da proteína de fusão (F, foram comparadas com um RT-PCR previamente estabelecido para a detecção do AMPV (gene da glicoproteína -G. Todos esses métodos foram capazes de detectar os isolados AMPV/A. As técnicas RRT-PCR (genes F e N mostraram os menores limites de detecção (10(0.3 to 10¹ TCID50 mL-1. Os resultados sugerem que as técnicas RT-PCR convencional (gene F e as técnicas de RRT-PCR (gene F e N desenvolvidas no presente estudo podem ser utilizadas com sucesso para a detecção do

Tissue-specific functional networks for prioritizing phenotype and disease genes.

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Yuanfang Guan

Full Text Available Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as "functionality" and "functional relationships" are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome.

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Quevedo-Tumailli, Viviana F; Ortega-Tenezaca, Bernabé; González-Díaz, Humbert

2018-03-02

The spatial distribution of genes in chromosomes seems not to be random. For instance, only 10% of genes are transcribed from bidirectional promoters in humans, and many more are organized into larger clusters. This raises intriguing questions previously asked by different authors. We would like to add a few more questions in this context, related to gene orientation inversions. Does gene orientation (inversion) follow a random pattern? Is it relevant to biological activity somehow? We define a new kind of network coined as the gene orientation inversion network (GOIN). GOIN's complex network encodes short- and long-range patterns of inversion of the orientation of pairs of gene in the chromosome. We selected Plasmodium falciparum as a case of study due to the high relevance of this parasite to public health (causal agent of malaria). We constructed here for the first time all of the GOINs for the genome of this parasite. These networks have an average of 383 nodes (genes in one chromosome) and 1314 links (pairs of gene with inverse orientation). We calculated node centralities and other parameters of these networks. These numerical parameters were used to study different properties of gene inversion patterns, for example, distribution, local communities, similarity to Erdös-Rényi random networks, randomness, and so on. We find clues that seem to indicate that gene orientation inversion does not follow a random pattern. We noted that some gene communities in the GOINs tend to group genes encoding for RIFIN-related proteins in the proteome of the parasite. RIFIN-like proteins are a second family of clonally variant proteins expressed on the surface of red cells infected with Plasmodium falciparum. Consequently, we used these centralities as input of machine learning (ML) models to predict the RIFIN-like activity of 5365 proteins in the proteome of Plasmodium sp. The best linear ML model found discriminates RIFIN-like from other proteins with sensitivity and

A pathway-based network analysis of hypertension-related genes

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Wang, Huan; Hu, Jing-Bo; Xu, Chuan-Yun; Zhang, De-Hai; Yan, Qian; Xu, Ming; Cao, Ke-Fei; Zhang, Xu-Sheng

2016-02-01

Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.

Uncovering co-expression gene network modules regulating fruit acidity in diverse apples.

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Bai, Yang; Dougherty, Laura; Cheng, Lailiang; Zhong, Gan-Yuan; Xu, Kenong

2015-08-16

Acidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates. Guided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P acidity. Overall, this study provides important insight into the Ma1-mediated gene network controlling acidity in mature apple fruit of diverse genetic background.

Analysis of expression and glycosylation of avian metapneumovirus attachment glycoprotein from recombinant baculoviruses.

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Luo, Lizhong; Nishi, Krista; MacLeod, Erin; Sabara, Marta I; Li, Yan

2010-11-01

Recently, we reported the expression and glycosylation of avian metapneumovirus attachment glycoprotein (AMPV/C G protein) in eukaryotic cell lines by a transient-expression method. In the present study, we investigated the biosynthesis and O-linked glycosylation of the AMPV/C G protein in a baculovirus expression system. The results showed that the insect cell-produced G protein migrated more rapidly in SDS-PAGE as compared to LLC-MK2 cell-derived G proteins owing to glycosylation differences. The fully processed, mature form of G protein migrated between 78 and 86 kDa, which is smaller than the 110 kDa mature form of G expressed in LLC-MK2 cells. In addition, several immature G gene products migrating at 40-48 and 60-70 kDa were also detected by SDS-PAGE and represented glycosylated intermediates. The addition of the antibiotic tunicamycin, which blocks early steps of glycosylation, to insect cell culture resulted in the disappearance of two glycosylated forms of the G protein and identified a 38 kDa unglycosylated precursor. The maturation of the G protein was completely blocked by monensin, suggesting that the O-linked glycosylation of G initiated in the trans-Golgi compartment. The presence of O-linked sugars on the mature protein was further confirmed by lectin Arachis hypogaea binding assay. Furthermore, antigenic features of the G protein expressed in insect cells were evaluated by ELISA. Crown Copyright © 2010. Published by Elsevier B.V. All rights reserved.

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

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Marco Grimaldi

Full Text Available RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.

Efficacy of gamithromycin against Ornithobacterium rhinotracheale in turkey poults pre-infected with avian metapneumovirus.

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Watteyn, Anneleen; Devreese, Mathias; Plessers, Elke; Wyns, Heidi; Garmyn, An; Reddy, Vishwanatha R A P; Pasmans, Frank; Martel, An; Haesebrouck, Freddy; De Backer, Patrick; Croubels, Siska

2016-10-01

Ornithobacterium rhinotracheale is an avian respiratory pathogen that affects turkeys. The objective of this study was to evaluate the clinical efficacy of gamithromycin (GAM) against O. rhinotracheale in turkeys. The birds were inoculated oculonasally with 10(8) colony-forming units (cfu) of O. rhinotracheale, preceded by infection with avian metapneumovirus. In addition to a negative (CONTR-) and a positive control group (CONTR+) there were two treated groups administered GAM (6 mg/kg) either subcutaneously (GAM SC) or orally (GAM PO) by administration as a single bolus at one-day post-bacterial infection (p.b.i.). From the start of the avian metapneumovirus infection until the end of the experiment, the turkeys were examined clinically and scored daily. In addition, tracheal swabs were collected at several days p.b.i. Necropsy was performed at 4, 8 and 12 days p.b.i. to evaluate the presence of gross lesions, and to collect trachea and lung tissue samples and air sac swabs for O. rhinotracheale quantification. The clinical score of the GAM SC group showed slightly lower values and birds recovered earlier than those in the GAM PO and CONTR+ groups. O. rhinotracheale cfus were significantly reduced in tracheal swabs of the SC group between 2 and 4 days p.b.i. At necropsy, CONTR+ showed higher O. rhinotracheale cfu in lung tissues compared to the treated groups. Moreover, at 8 days p.b.i. only the lung samples of CONTR+ were positive. In conclusion, the efficacy of GAM against O. rhinotracheale was demonstrated, especially in the lung tissue. However, the PO bolus administration of the commercially available product was not as efficacious as the SC bolus.

Hybrid stochastic simplifications for multiscale gene networks

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Debussche Arnaud

2009-09-01

Full Text Available Abstract Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion 123 which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

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Reddy, Anupama; Huang, C Chris; Liu, Huiqing; Delisi, Charles; Nevalainen, Marja T; Szalma, Sandor; Bhanot, Gyan

2010-01-01

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily

Generation of recombinant newcastle disease viruses, expressing the glycoprotein (G) of avian metapneumovirus, subtype A, or B, for use as bivalent vaccines

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Using reverse genetics technology, Newcastle disease virus (NDV) LaSota strain-based recombinant viruses were engineered to express the glycoprotein (G) of avian metapneumovirus (aMPV), subtype A, or B, as bivalent vaccines. These recombinant viruses, rLS/aMPV-A G and rLS/aMPV-B G, were slightly att...

Inducement of radionuclides targeting therapy by gene transfection

International Nuclear Information System (INIS)

Luo Quanyong

2001-01-01

The author presents an overview of gene transfection methods to genetically induce tumor cells to express enhanced levels of cell surface antigens and receptors to intake radiolabeled antibody and peptide targeting and thus increase their therapeutic effect in radiotherapy. The current research include inducement of radioimmunotherapy through CEA gene transfection, inducement of iodine-131 therapy by sodium iodide symporter gene transfection and inducement of MIBG therapy by noradrenaline transporter gene transfection. These studies raise the prospect that gene-therapy techniques could be used to enable the treatment of a wide range of tumors with radiopharmaceuticals of established clinical acceptability

Synchronous versus asynchronous modeling of gene regulatory networks.

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Garg, Abhishek; Di Cara, Alessandro; Xenarios, Ioannis; Mendoza, Luis; De Micheli, Giovanni

2008-09-01

In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Detection and subtyping avian metapneumovirus from turkeys in Iran.

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Mayahi, Mansour; Momtaz, Hassan; Jafari, Ramezan Ali; Zamani, Pejman

2017-01-01

Avian metapneumovirus (aMPV) causes diseases like rhinotracheitis in turkeys, swollen head syndrome in chickens and avian rhinotracheitis in other birds. Causing respiratory problems, aMPV adversely affects production and inflicts immense economic losses and mortalities, especially in turkey flocks. In recent years, several serological and molecular studies have been conducted on this virus, especially in poultry in Asia and Iran. The purpose of the present study was detecting and subtyping aMPV by reverse transcriptase polymerase chain reaction (RT-PCR) from non-vaccinated, commercial turkey flocks in Iran for the first time. Sixty three meat-type unvaccinated turkey flocks from several provinces of Iran were sampled in major turkey abattoirs. Samples were tested by RT-PCR for detecting and subtyping aMPV. The results showed that 26 samples from three flocks (4.10%) were positive for viral RNA and all of the viruses were found to be subtype B of aMPV. As a result, vaccination especially against subtype B of aMPV should be considered in turkey flocks in Iran to control aMPV infections.

Molecular detection and isolation of avian metapneumovirus in Mexico.

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Rivera-Benitez, José Francisco; Martínez-Bautista, Rebeca; Ríos-Cambre, Francisco; Ramírez-Mendoza, Humberto

2014-01-01

We conducted a longitudinal study to detect and isolate avian metapneumovirus (aMPV) in two highly productive poultry areas in Mexico. A total of 968 breeder hens and pullets from 2 to 73 weeks of age were analysed. Serology was performed to detect aMPV antibodies and 105 samples of tracheal tissue were collected, pooled by age, and used for attempted virus isolation and aMPV nested reverse transcriptase-polymerase chain reaction (nRT-PCR). The serological analysis indicated that 100% of the sampled chickens showed aMPV antibodies by 12 weeks of age. Five pools of pullet samples collected at 3 to 8 weeks of age were positive by nRT-PCR and the sequences obtained indicated 98 to 99% similarity with the reported sequences for aMPV subtype A. Virus isolation of nRT-PCR-positive samples was successfully attempted using chicken embryo lung and trachea mixed cultures with subsequent adaptation to Vero cells. This is the first report of detection and isolation of aMPV in Mexico.

SELANSI: a toolbox for simulation of stochastic gene regulatory networks.

Science.gov (United States)

Pájaro, Manuel; Otero-Muras, Irene; Vázquez, Carlos; Alonso, Antonio A

2018-03-01

Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi. antonio@iim.csic.es. © The Author(s) 2017. Published by Oxford University Press.

Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

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Sungyoung Lee

2012-12-01

Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

Network graph analysis of gene-gene interactions in genome-wide association study data.

Science.gov (United States)

Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

2012-12-01

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

Acute Vhl gene inactivation induces cardiac HIF-dependent erythropoietin gene expression.

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Marta Miró-Murillo

Full Text Available Von Hippel Lindau (Vhl gene inactivation results in embryonic lethality. The consequences of its inactivation in adult mice, and of the ensuing activation of the hypoxia-inducible factors (HIFs, have been explored mainly in a tissue-specific manner. This mid-gestation lethality can be also circumvented by using a floxed Vhl allele in combination with an ubiquitous tamoxifen-inducible recombinase Cre-ER(T2. Here, we characterize a widespread reduction in Vhl gene expression in Vhl(floxed-UBC-Cre-ER(T2 adult mice after dietary tamoxifen administration, a convenient route of administration that has yet to be fully characterized for global gene inactivation. Vhl gene inactivation rapidly resulted in a marked splenomegaly and skin erythema, accompanied by renal and hepatic induction of the erythropoietin (Epo gene, indicative of the in vivo activation of the oxygen sensing HIF pathway. We show that acute Vhl gene inactivation also induced Epo gene expression in the heart, revealing cardiac tissue to be an extra-renal source of EPO. Indeed, primary cardiomyocytes and HL-1 cardiac cells both induce Epo gene expression when exposed to low O(2 tension in a HIF-dependent manner. Thus, as well as demonstrating the potential of dietary tamoxifen administration for gene inactivation studies in UBC-Cre-ER(T2 mouse lines, this data provides evidence of a cardiac oxygen-sensing VHL/HIF/EPO pathway in adult mice.

Down-Regulation of Gene Expression by RNA-Induced Gene Silencing

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Travella, Silvia; Keller, Beat

Down-regulation of endogenous genes via post-transcriptional gene silencing (PTGS) is a key to the characterization of gene function in plants. Many RNA-based silencing mechanisms such as post-transcriptional gene silencing, co-suppression, quelling, and RNA interference (RNAi) have been discovered among species of different kingdoms (plants, fungi, and animals). One of the most interesting discoveries was RNAi, a sequence-specific gene-silencing mechanism initiated by the introduction of double-stranded RNA (dsRNA), homologous in sequence to the silenced gene, which triggers degradation of mRNA. Infection of plants with modified viruses can also induce RNA silencing and is referred to as virus-induced gene silencing (VIGS). In contrast to insertional mutagenesis, these emerging new reverse genetic approaches represent a powerful tool for exploring gene function and for manipulating gene expression experimentally in cereal species such as barley and wheat. We examined how RNAi and VIGS have been used to assess gene function in barley and wheat, including molecular mechanisms involved in the process and available methodological elements, such as vectors, inoculation procedures, and analysis of silenced phenotypes.

A Regulatory Network Analysis of Orphan Genes in Arabidopsis Thaliana

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Singh, Pramesh; Chen, Tianlong; Arendsee, Zebulun; Wurtele, Eve S.; Bassler, Kevin E.

Orphan genes, which are genes unique to each particular species, have recently drawn significant attention for their potential usefulness for organismal robustness. Their origin and regulatory interaction patterns remain largely undiscovered. Recently, methods that use the context likelihood of relatedness to infer a network followed by modularity maximizing community detection algorithms on the inferred network to find the functional structure of regulatory networks were shown to be effective. We apply improved versions of these methods to gene expression data from Arabidopsis thaliana, identify groups (clusters) of interacting genes with related patterns of expression and analyze the structure within those groups. Focusing on clusters that contain orphan genes, we compare the identified clusters to gene ontology (GO) terms, regulons, and pathway designations and analyze their hierarchical structure. We predict new regulatory interactions and unravel the structure of the regulatory interaction patterns of orphan genes. Work supported by the NSF through Grants DMR-1507371 and IOS-1546858.

Network statistics of genetically-driven gene co-expression modules in mouse crosses

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Marie-Pier eScott-Boyer

2013-12-01

Full Text Available In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS. For six out of the 7 networks, we found that linkage to module QTLs (mQTLs could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as genetically-driven had network statistic properties (density, centralization and heterogeneity that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

Avian metapneumovirus infection of chicken and turkey tracheal organ cultures: comparison of virus-host interactions.

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Hartmann, Sandra; Sid, Hicham; Rautenschlein, Silke

2015-01-01

Avian metapneumovirus (aMPV) is a pathogen with worldwide distribution, which can cause high economic losses in infected poultry. aMPV mainly causes infection of the upper respiratory tract in both chickens and turkeys, although turkeys seem to be more susceptible. Little is known about virus-host interactions at epithelial surfaces after aMPV infection. Tracheal organ cultures (TOC) are a suitable model to investigate virus-host interaction in the respiratory epithelium. Therefore, we investigated virus replication rates and lesion development in chicken and turkey TOC after infection with a virulent aMPV subtype A strain. Aspects of the innate immune response, such as interferon-α and inducible nitric oxide synthase mRNA expression, as well as virus-induced apoptosis were determined. The aMPV-replication rate was higher in turkey (TTOC) compared to chicken TOC (CTOC) (P < 0.05), providing circumstantial evidence that indeed turkeys may be more susceptible. The interferon-α response was down-regulated from 2 to 144 hours post infection in both species compared to virus-free controls (P < 0.05); this was more significant for CTOC than TTOC. Inducible nitric oxide synthase expression was significantly up-regulated in aMPV-A-infected TTOC and CTOC compared to virus-free controls (P < 0.05). However, the results suggest that NO may play a different role in aMPV pathogenesis between turkeys and chickens as indicated by differences in apoptosis rate and lesion development between species. Overall, our study reveals differences in innate immune response regulation and therefore may explain differences in aMPV - A replication rates between infected TTOC and CTOC, which subsequently lead to more severe clinical signs and a higher rate of secondary infections in turkeys.

Learning gene regulatory networks from only positive and unlabeled data

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Elkan Charles

2010-05-01

Full Text Available Abstract Background Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact. Results A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement. Conclusions Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

Inferring the gene network underlying the branching of tomato inflorescence.

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Laura Astola

Full Text Available The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior.

Chronic occupational exposure to arsenic induces carcinogenic gene signaling networks and neoplastic transformation in human lung epithelial cells

International Nuclear Information System (INIS)

Stueckle, Todd A.; Lu, Yongju; Davis, Mary E.; Wang, Liying; Jiang, Bing-Hua; Holaskova, Ida; Schafer, Rosana; Barnett, John B.; Rojanasakul, Yon

2012-01-01

Chronic arsenic exposure remains a human health risk; however a clear mode of action to understand gene signaling-driven arsenic carcinogenesis is currently lacking. This study chronically exposed human lung epithelial BEAS-2B cells to low-dose arsenic trioxide to elucidate cancer promoting gene signaling networks associated with arsenic-transformed (B-As) cells. Following a 6 month exposure, exposed cells were assessed for enhanced cell proliferation, colony formation, invasion ability and in vivo tumor formation compared to control cell lines. Collected mRNA was subjected to whole genome expression microarray profiling followed by in silico Ingenuity Pathway Analysis (IPA) to identify lung carcinogenesis modes of action. B-As cells displayed significant increases in proliferation, colony formation and invasion ability compared to BEAS-2B cells. B-As injections into nude mice resulted in development of primary and secondary metastatic tumors. Arsenic exposure resulted in widespread up-regulation of genes associated with mitochondrial metabolism and increased reactive oxygen species protection suggesting mitochondrial dysfunction. Carcinogenic initiation via reactive oxygen species and epigenetic mechanisms was further supported by altered DNA repair, histone, and ROS-sensitive signaling. NF-κB, MAPK and NCOR1 signaling disrupted PPARα/δ-mediated lipid homeostasis. A ‘pro-cancer’ gene signaling network identified increased survival, proliferation, inflammation, metabolism, anti-apoptosis and mobility signaling. IPA-ranked signaling networks identified altered p21, EF1α, Akt, MAPK, and NF-κB signaling networks promoting genetic disorder, altered cell cycle, cancer and changes in nucleic acid and energy metabolism. In conclusion, transformed B-As cells with their whole genome expression profile provide an in vitro arsenic model for future lung cancer signaling research and data for chronic arsenic exposure risk assessment. Highlights: ► Chronic As 2 O 3

Chronic occupational exposure to arsenic induces carcinogenic gene signaling networks and neoplastic transformation in human lung epithelial cells

Energy Technology Data Exchange (ETDEWEB)

Stueckle, Todd A., E-mail: tstueckle@hsc.wvu.edu [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States); Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Lu, Yongju, E-mail: yongju6@hotmail.com [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States); Davis, Mary E., E-mail: mdavis@wvu.edu [Department of Physiology, West Virginia University, Morgantown, WV 26506 (United States); Wang, Liying, E-mail: lmw6@cdc.gov [Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Jiang, Bing-Hua, E-mail: bhjiang@jefferson.edu [Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA 19107 (United States); Holaskova, Ida, E-mail: iholaskova@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Schafer, Rosana, E-mail: rschafer@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Barnett, John B., E-mail: jbarnett@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Rojanasakul, Yon, E-mail: yrojan@hsc.wvu.edu [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States)

2012-06-01

Chronic arsenic exposure remains a human health risk; however a clear mode of action to understand gene signaling-driven arsenic carcinogenesis is currently lacking. This study chronically exposed human lung epithelial BEAS-2B cells to low-dose arsenic trioxide to elucidate cancer promoting gene signaling networks associated with arsenic-transformed (B-As) cells. Following a 6 month exposure, exposed cells were assessed for enhanced cell proliferation, colony formation, invasion ability and in vivo tumor formation compared to control cell lines. Collected mRNA was subjected to whole genome expression microarray profiling followed by in silico Ingenuity Pathway Analysis (IPA) to identify lung carcinogenesis modes of action. B-As cells displayed significant increases in proliferation, colony formation and invasion ability compared to BEAS-2B cells. B-As injections into nude mice resulted in development of primary and secondary metastatic tumors. Arsenic exposure resulted in widespread up-regulation of genes associated with mitochondrial metabolism and increased reactive oxygen species protection suggesting mitochondrial dysfunction. Carcinogenic initiation via reactive oxygen species and epigenetic mechanisms was further supported by altered DNA repair, histone, and ROS-sensitive signaling. NF-κB, MAPK and NCOR1 signaling disrupted PPARα/δ-mediated lipid homeostasis. A ‘pro-cancer’ gene signaling network identified increased survival, proliferation, inflammation, metabolism, anti-apoptosis and mobility signaling. IPA-ranked signaling networks identified altered p21, EF1α, Akt, MAPK, and NF-κB signaling networks promoting genetic disorder, altered cell cycle, cancer and changes in nucleic acid and energy metabolism. In conclusion, transformed B-As cells with their whole genome expression profile provide an in vitro arsenic model for future lung cancer signaling research and data for chronic arsenic exposure risk assessment. Highlights: ► Chronic As{sub 2}O

Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

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Hur, Junguk; Özgür, Arzucan; He, Yongqun

2017-03-14

Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

On the dynamics of a gene regulatory network

International Nuclear Information System (INIS)

Grammaticos, B; Carstea, A S; Ramani, A

2006-01-01

We examine the dynamics of a network of genes focusing on a periodic chain of genes, of arbitrary length. We show that within a given class of sigmoids representing the equilibrium probability of the binding of the RNA polymerase to the core promoter, the system possesses a single stable fixed point. By slightly modifying the sigmoid, introducing 'stiffer' forms, we show that it is possible to find network configurations exhibiting bistable behaviour. Our results do not depend crucially on the length of the chain considered: calculations with finite chains lead to similar results. However, a realistic study of regulatory genetic networks would require the consideration of more complex topologies and interactions

Functional modules by relating protein interaction networks and gene expression.

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Tornow, Sabine; Mewes, H W

2003-11-01

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

Characterization of differentially expressed genes using high-dimensional co-expression networks

DEFF Research Database (Denmark)

Coelho Goncalves de Abreu, Gabriel; Labouriau, Rodrigo S.

2010-01-01

We present a technique to characterize differentially expressed genes in terms of their position in a high-dimensional co-expression network. The set-up of Gaussian graphical models is used to construct representations of the co-expression network in such a way that redundancy and the propagation...... that allow to make effective inference in problems with high degree of complexity (e.g. several thousands of genes) and small number of observations (e.g. 10-100) as typically occurs in high throughput gene expression studies. Taking advantage of the internal structure of decomposable graphical models, we...... construct a compact representation of the co-expression network that allows to identify the regions with high concentration of differentially expressed genes. It is argued that differentially expressed genes located in highly interconnected regions of the co-expression network are less informative than...

Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

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Benjamin A Logsdon

Full Text Available Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL, which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1, and genes involved in endocytosis (RCY1, the spindle checkpoint (BUB2, sulfonate catabolism (JLP1, and cell-cell communication (PRM7. Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.

Local and global responses in complex gene regulation networks

Science.gov (United States)

Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

2009-04-01

An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.

The impact of gene expression variation on the robustness and evolvability of a developmental gene regulatory network.

Directory of Open Access Journals (Sweden)

David A Garfield

2013-10-01

Full Text Available Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear, allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.

Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk.

Science.gov (United States)

Kar, Siddhartha P; Tyrer, Jonathan P; Li, Qiyuan; Lawrenson, Kate; Aben, Katja K H; Anton-Culver, Hoda; Antonenkova, Natalia; Chenevix-Trench, Georgia; Baker, Helen; Bandera, Elisa V; Bean, Yukie T; Beckmann, Matthias W; Berchuck, Andrew; Bisogna, Maria; Bjørge, Line; Bogdanova, Natalia; Brinton, Louise; Brooks-Wilson, Angela; Butzow, Ralf; Campbell, Ian; Carty, Karen; Chang-Claude, Jenny; Chen, Yian Ann; Chen, Zhihua; Cook, Linda S; Cramer, Daniel; Cunningham, Julie M; Cybulski, Cezary; Dansonka-Mieszkowska, Agnieszka; Dennis, Joe; Dicks, Ed; Doherty, Jennifer A; Dörk, Thilo; du Bois, Andreas; Dürst, Matthias; Eccles, Diana; Easton, Douglas F; Edwards, Robert P; Ekici, Arif B; Fasching, Peter A; Fridley, Brooke L; Gao, Yu-Tang; Gentry-Maharaj, Aleksandra; Giles, Graham G; Glasspool, Rosalind; Goode, Ellen L; Goodman, Marc T; Grownwald, Jacek; Harrington, Patricia; Harter, Philipp; Hein, Alexander; Heitz, Florian; Hildebrandt, Michelle A T; Hillemanns, Peter; Hogdall, Estrid; Hogdall, Claus K; Hosono, Satoyo; Iversen, Edwin S; Jakubowska, Anna; Paul, James; Jensen, Allan; Ji, Bu-Tian; Karlan, Beth Y; Kjaer, Susanne K; Kelemen, Linda E; Kellar, Melissa; Kelley, Joseph; Kiemeney, Lambertus A; Krakstad, Camilla; Kupryjanczyk, Jolanta; Lambrechts, Diether; Lambrechts, Sandrina; Le, Nhu D; Lee, Alice W; Lele, Shashi; Leminen, Arto; Lester, Jenny; Levine, Douglas A; Liang, Dong; Lissowska, Jolanta; Lu, Karen; Lubinski, Jan; Lundvall, Lene; Massuger, Leon; Matsuo, Keitaro; McGuire, Valerie; McLaughlin, John R; McNeish, Iain A; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B; Narod, Steven A; Nedergaard, Lotte; Ness, Roberta B; Nevanlinna, Heli; Odunsi, Kunle; Olson, Sara H; Orlow, Irene; Orsulic, Sandra; Weber, Rachel Palmieri; Pearce, Celeste Leigh; Pejovic, Tanja; Pelttari, Liisa M; Permuth-Wey, Jennifer; Phelan, Catherine M; Pike, Malcolm C; Poole, Elizabeth M; Ramus, Susan J; Risch, Harvey A; Rosen, Barry; Rossing, Mary Anne; Rothstein, Joseph H; Rudolph, Anja; Runnebaum, Ingo B; Rzepecka, Iwona K; Salvesen, Helga B; Schildkraut, Joellen M; Schwaab, Ira; Shu, Xiao-Ou; Shvetsov, Yurii B; Siddiqui, Nadeem; Sieh, Weiva; Song, Honglin; Southey, Melissa C; Sucheston-Campbell, Lara E; Tangen, Ingvild L; Teo, Soo-Hwang; Terry, Kathryn L; Thompson, Pamela J; Timorek, Agnieszka; Tsai, Ya-Yu; Tworoger, Shelley S; van Altena, Anne M; Van Nieuwenhuysen, Els; Vergote, Ignace; Vierkant, Robert A; Wang-Gohrke, Shan; Walsh, Christine; Wentzensen, Nicolas; Whittemore, Alice S; Wicklund, Kristine G; Wilkens, Lynne R; Woo, Yin-Ling; Wu, Xifeng; Wu, Anna; Yang, Hannah; Zheng, Wei; Ziogas, Argyrios; Sellers, Thomas A; Monteiro, Alvaro N A; Freedman, Matthew L; Gayther, Simon A; Pharoah, Paul D P

2015-10-01

Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations. We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. ©2015 American Association for Cancer Research.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

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Joeri Ruyssinck

Full Text Available One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made

Singular Perturbation Analysis and Gene Regulatory Networks with Delay

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Shlykova, Irina; Ponosov, Arcady

2009-09-01

There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

Study on radiation-inducible genes

Energy Technology Data Exchange (ETDEWEB)

Lim, Sang Yong; Kim, Dong Ho; Joe, Min Ho; Park, Hae Jun; Song, Hyu Npa

2012-01-15

Radiation-inducible genes of E. coli, which is a model strain for bacterial study, and Salmonella, which is a typical strain for pathogenic bacteria were compared through omic analysis. Heat shock response genes and prophage genes were induced by radiation in Salmonella, not in E. coli. Among prophage genes tested, STM2628 showed the highest activation by radiation, and approximately 1 kb promoter region was turned out to be necessary for radiation response. To screen an artificial promoter showing activation by 2 Gy, the high-throughput screening method using fluorescent MUG substrate was established. The use of bacteria as anticancer agents has attracted interest. In this study, we tried to develop tumor targeting bacteria in which the radiation-inducible promoter activate a transgene encoding a cytotoxic protein. To do this, a tumor-targeting hfq Salmonella mutant strain was constructed, and we found that its virulence decreased. For outward secretion of anticancer protein produced inside bacteria, the signal peptide of SspH1 was determined and the signal peptide was proven to be able to secrete an anticancer protein. Tumor xenograft mouse model was secured, which can be used for efficiency evaluation of bacterial tumor therapy.

Study on radiation-inducible genes

International Nuclear Information System (INIS)

Lim, Sang Yong; Kim, Dong Ho; Joe, Min Ho; Park, Hae Jun; Song, Hyu Npa

2012-01-01

Radiation-inducible genes of E. coli, which is a model strain for bacterial study, and Salmonella, which is a typical strain for pathogenic bacteria were compared through omic analysis. Heat shock response genes and prophage genes were induced by radiation in Salmonella, not in E. coli. Among prophage genes tested, STM2628 showed the highest activation by radiation, and approximately 1 kb promoter region was turned out to be necessary for radiation response. To screen an artificial promoter showing activation by 2 Gy, the high-throughput screening method using fluorescent MUG substrate was established. The use of bacteria as anticancer agents has attracted interest. In this study, we tried to develop tumor targeting bacteria in which the radiation-inducible promoter activate a transgene encoding a cytotoxic protein. To do this, a tumor-targeting hfq Salmonella mutant strain was constructed, and we found that its virulence decreased. For outward secretion of anticancer protein produced inside bacteria, the signal peptide of SspH1 was determined and the signal peptide was proven to be able to secrete an anticancer protein. Tumor xenograft mouse model was secured, which can be used for efficiency evaluation of bacterial tumor therapy

fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study.

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Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand

2015-01-07

Network study on genes and proteins offers functional basics of the complexity of gene and protein, and its interacting partners. The gene fatty acid-binding protein 4 (fabp4) is found to be highly expressed in adipose tissue, and is one of the most abundant proteins in mature adipocytes. Our investigations on functional modules of fabp4 provide useful information on the functional genes interacting with fabp4, their biochemical properties and their regulatory functions. The present study shows that there are eight set of candidate genes: acp1, ext2, insr, lipe, ostf1, sncg, usp15, and vim that are strongly and functionally linked up with fabp4. Gene ontological analysis of network modules of fabp4 provides an explicit idea on the functional aspect of fabp4 and its interacting nodes. The hierarchal mapping on gene ontology indicates gene specific processes and functions as well as their compartmentalization in tissues. The fabp4 along with its interacting genes are involved in lipid metabolic activity and are integrated in multi-cellular processes of tissues and organs. They also have important protein/enzyme binding activity. Our study elucidated disease-associated nsSNP prediction for fabp4 and it is interesting to note that there are four rsID׳s (rs1051231, rs3204631, rs140925685 and rs141169989) with disease allelic variation (T104P, T126P, G27D and G90V respectively). On the whole, our gene network analysis presents a clear insight about the interactions and functions associated with fabp4 gene network. Copyright © 2014 Elsevier Ltd. All rights reserved.

MINER: exploratory analysis of gene interaction networks by machine learning from expression data

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Sivieng Jane

2009-12-01

Full Text Available Abstract Background The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. Results We have developed MINER (Microarray Interactive Network Exploration and Representation, an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Conclusion Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

Immune responses and interactions following simultaneous application of live Newcastle disease, infectious bronchitis and avian metapneumovirus vaccines in specific-pathogen-free chicks.

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Awad, Faez; Forrester, Anne; Baylis, Matthew; Lemiere, Stephane; Jones, Richard; Ganapathy, Kannan

2015-02-01

Interactions between live Newcastle disease virus (NDV), avian metapneumovirus (aMPV) and infectious bronchitis virus (IBV) vaccines following simultaneous vaccination of day old specific pathogen free (SPF) chicks were evaluated. The chicks were divided into eight groups: seven vaccinated against NDV, aMPV and IBV (single, dual or triple) and one unvaccinated as control. Haemagglutination inhibition (HI) NDV antibody titres were similar across all groups but were above protective titres. aMPV vaccine when given with other live vaccines suppressed levels of aMPV enzyme-linked immunosorbent assay (ELISA) antibodies. Cellular and local immunity induced by administration of NDV, aMPV or IBV vaccines (individually or together) showed significant increase in CD4+, CD8+ and IgA bearing B-cells in the trachea compared to the unvaccinated group. Differences between the vaccinated groups were insignificant. Simultaneous vaccination with live NDV, aMPV and IBV did not affect the protection conferred against aMPV or IBV. Copyright © 2014 Elsevier Ltd. All rights reserved.

Coordinations between gene modules control the operation of plant amino acid metabolic networks

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Galili Gad

2009-01-01

Full Text Available Abstract Background Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results. Results Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes. Conclusion Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.

A single polymerase (L) mutation in avian metapneumovirus increased virulence and partially maintained virus viability at an elevated temperature.

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Brown, Paul A; Lupini, Caterina; Catelli, Elena; Clubbe, Jayne; Ricchizzi, Enrico; Naylor, Clive J

2011-02-01

Previously, a virulent avian metapneumovirus, farm isolate Italy 309/04, was shown to have been derived from a live vaccine. Virulence due to the five nucleotide mutations associated with the reversion to virulence was investigated by their addition to the genome of the vaccine strain using reverse genetics. Virulence of these recombinant viruses was determined by infection of 1-day-old turkeys. Disease levels resulting from the combined two matrix mutations was indistinguishable from that produced by the recombinant vaccine, whereas the combined three L gene mutations increased disease to a level (P<0.0001) that was indistinguishable from that caused by the revertant Italy 309/04 virus. Testing of the L mutations individually showed that two mutations did not increase virulence, while the third mutation, corresponding to an asparagine to aspartic acid substitution, produced virulence indistinguishable from that caused by Italy 309/04. In contrast to the vaccine, the virulent mutant also showed increased viability at temperatures typical of turkey core tissues. The notion that increased viral virulence resulted from enhanced ability to replicate in tissues away from the cool respiratory tract, cannot be discounted.

Introduction: Cancer Gene Networks.

Science.gov (United States)

Clarke, Robert

2017-01-01

Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

Pregnancy-induced gene expression changes in vivo among women with rheumatoid arthritis: a pilot study.

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Goin, Dana E; Smed, Mette Kiel; Pachter, Lior; Purdom, Elizabeth; Nelson, J Lee; Kjærgaard, Hanne; Olsen, Jørn; Hetland, Merete Lund; Zoffmann, Vibeke; Ottesen, Bent; Jawaheer, Damini

2017-05-25

Little is known about gene expression changes induced by pregnancy in women with rheumatoid arthritis (RA) and healthy women because the few studies previously conducted did not have pre-pregnancy samples available as baseline. We have established a cohort of women with RA and healthy women followed prospectively from a pre-pregnancy baseline. In this study, we tested the hypothesis that pregnancy-induced changes in gene expression among women with RA who improve during pregnancy (pregDAS improved ) overlap substantially with changes observed among healthy women and differ from changes observed among women with RA who worsen during pregnancy (pregDAS worse ). Global gene expression profiles were generated by RNA sequencing (RNA-seq) from 11 women with RA and 5 healthy women before pregnancy (T0) and at the third trimester (T3). Among the women with RA, eight showed an improvement in disease activity by T3, whereas three worsened. Differential expression analysis was used to identify genes demonstrating significant changes in expression within each of the RA and healthy groups (T3 vs T0), as well as between the groups at each time point. Gene set enrichment was assessed in terms of Gene Ontology processes and protein networks. A total of 1296 genes were differentially expressed between T3 and T0 among the 8 pregDAS improved women, with 161 genes showing at least two-fold change (FC) in expression by T3. The majority (108 of 161 genes) were also differentially expressed among healthy women (qexpression between the pregDAS improved and pregDAS worse groups, all of which were inducible by type I interferon (IFN). These IFN-inducible genes were over-expressed at T3 compared to the T0 baseline among the pregDAS improved women. In our pilot RNA-seq dataset, increased pregnancy-induced expression of type I IFN-inducible genes was observed among women with RA who improved during pregnancy, but not among women who worsened. These findings warrant further investigation into

Transcriptional control in the segmentation gene network of Drosophila.

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Mark D Schroeder

2004-09-01

Full Text Available The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross- regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

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Guo Zheng

2006-01-01

Full Text Available Abstract Background It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. Results In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network to address the underlying regulations of genes that can span any unit(s of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. Conclusion We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex

Excretion patterns of human metapneumovirus and respiratory syncytial virus among young children

DEFF Research Database (Denmark)

von Linstow, Marie-Louise; Eugen-Olsen, Jesper; Koch, A

2006-01-01

of the infected children showed to have an upper respiratory tract infection when following up. CONCLUSION: Viral RNA was present in nasal secretions, saliva, sweat, and faeces, but whether or not the virions were infectious and constitute a potential mode of transmission remains to be shown in future studies.......BACKGROUND: As respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) cause serious respiratory tract infections, the routes of transmission of these viruses are important to elucidate. We examined the modes of virus shedding and shedding duration of RSV and hMPV in young children....... METHODS: From each child in a group of 44 children (37 RSV-positive, 6 hMPV-positive, and 1 co-infected child), aged between 0.5-38 months, hospitalised at Hvidovre Hospital, Copenhagen, Denmark, one nasopharyngeal aspirate (NPA), saliva, urine, and faeces sample were collected at inclusion and weekly...

Building gene co-expression networks using transcriptomics data for systems biology investigations

DEFF Research Database (Denmark)

Kadarmideen, Haja; Watson-Haigh, Nathan S.

2012-01-01

Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four......) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT...... (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended....

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

Science.gov (United States)

Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

2016-01-01

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

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Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Global similarity and local divergence in human and mouse gene co-expression networks

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Koonin Eugene V

2006-09-01

Full Text Available Abstract Background A genome-wide comparative analysis of human and mouse gene expression patterns was performed in order to evaluate the evolutionary divergence of mammalian gene expression. Tissue-specific expression profiles were analyzed for 9,105 human-mouse orthologous gene pairs across 28 tissues. Expression profiles were resolved into species-specific coexpression networks, and the topological properties of the networks were compared between species. Results At the global level, the topological properties of the human and mouse gene coexpression networks are, essentially, identical. For instance, both networks have topologies with small-world and scale-free properties as well as closely similar average node degrees, clustering coefficients, and path lengths. However, the human and mouse coexpression networks are highly divergent at the local level: only a small fraction ( Conclusion The dissonance between global versus local network divergence suggests that the interspecies similarity of the global network properties is of limited biological significance, at best, and that the biologically relevant aspects of the architectures of gene coexpression are specific and particular, rather than universal. Nevertheless, there is substantial evolutionary conservation of the local network structure which is compatible with the notion that gene coexpression networks are subject to purifying selection.

Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections

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Lipner, Ettie M.; Garcia, Benjamin J.; Strong, Michael

2016-01-01

Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects. PMID:26751573

Construction of functional linkage gene networks by data integration.

Science.gov (United States)

Linghu, Bolan; Franzosa, Eric A; Xia, Yu

2013-01-01

Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.

ICan: an integrated co-alteration network to identify ovarian cancer-related genes.

Science.gov (United States)

Zhou, Yuanshuai; Liu, Yongjing; Li, Kening; Zhang, Rui; Qiu, Fujun; Zhao, Ning; Xu, Yan

2015-01-01

Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183). In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.

Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development.

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Palumbo, Maria Concetta; Zenoni, Sara; Fasoli, Marianna; Massonnet, Mélanie; Farina, Lorenzo; Castiglione, Filippo; Pezzotti, Mario; Paci, Paola

2014-12-01

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named "fight-club hubs" characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named "switch genes" was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops. © 2014 American Society of Plant Biologists. All rights reserved.

Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors

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Frank Emmert-Streib

2013-02-01

Full Text Available The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I observational gene expression data: normal environmental condition, (II interventional gene expression data: growth in rich media, (III interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

Artificial neural network inference (ANNI: a study on gene-gene interaction for biomarkers in childhood sarcomas.

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Dong Ling Tong

Full Text Available OBJECTIVE: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI. METHOD: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. RESULTS: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS; FCGRT and OLFM1 in Ewing's sarcoma (EWS suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. CONCLUSIONS: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.

Production of monoclonal antibodies for Avian Metapneumovirus (SHS-BR-121) isolated in Brazil

OpenAIRE

Coswig,LT; Stach-Machado,DR; Arns,CW

2007-01-01

Avian Metapneumovirus (aMPV), also called Turkey Rhinotracheitis Virus (TRTV), is an upper respiratory tract infection of turkeys, chickens and other avian species. Five monoclonal antibodies (MAbs) were created against the Brazilian isolate (SHS-BR-121) of aMPV, MAbs 1A5B8; 1C1C4; 2C2E9 and 2A4C3 of IgG1 and MAb 1C1F8 of IgG2a. Four Mabs (1A5B8; 1C1C4; 2C2E9 and 2A4C3) showed neutralizing activity and three (1A5B8; 1C1C4 and 2A4C3) inhibited cellular fusion in vitro. These MAbs were used to ...

Sequence-based model of gap gene regulatory network.

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Kozlov, Konstantin; Gursky, Vitaly; Kulakovskiy, Ivan; Samsonova, Maria

2014-01-01

The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded. The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3

Protection by recombinant Newcastle disease viruses (NDV) expressing the glycoprotein (G) of avian metapneumovirus (aMPV) subtype A or B against challenge with virulent NDV and aMPV

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Avian metapneumovirus (aMPV) and Newcastle disease virus (NDV) are threatening avian pathogens that cause sporadic but serious respiratory diseases in poultry worldwide. Although, vaccination, combined with strict biosecurity practices, has been the recommendation for controlling these diseases in t...

The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks

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Frank eEmmert-Streib

2014-02-01

Full Text Available In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of $351$ patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome $21$ is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

Annotating gene sets by mining large literature collections with protein networks.

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Wang, Sheng; Ma, Jianzhu; Yu, Michael Ku; Zheng, Fan; Huang, Edward W; Han, Jiawei; Peng, Jian; Ideker, Trey

2018-01-01

Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

Inferring the conservative causal core of gene regulatory networks

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Emmert-Streib Frank

2010-09-01

Full Text Available Abstract Background Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. Results In this paper, we introduce a novel gene regulatory network inference (GRNI algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. Conclusions For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Inferring the conservative causal core of gene regulatory networks.

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Altay, Gökmen; Emmert-Streib, Frank

2010-09-28

Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

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Ghanat Bari, Mehrab; Ung, Choong Yong; Zhang, Cheng; Zhu, Shizhen; Li, Hu

2017-08-01

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 10 8 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

In-Vitro Antiviral Activities of Extracts of Plants of The Brazilian Cerrado against the Avian Metapneumovirus (aMPV

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LK Kohn

2015-09-01

Full Text Available ABSTRACTAvian metapneumovirus (aMPV is a negative-sense single-stranded RNA enveloped virus of the Metapneumovirus genus belonging to theParamyxoviridae family. This virus may cause significant economic losses to the poultry industry, despite vaccination, which is the main tool for controlling and preventing aMPV. The aim of this study was to evaluate the antiviral activity of extracts of four different native plants of the Brazilian Cerrado against aMPV. The antiviral activity against aMPV was determined by titration. This technique measures the ability of plant extract dilutions (25 to 2.5 µg mL-1 to inhibit the cytopathic effect (CPE of the virus, expressed as inhibition percentage (IP. The maximum nontoxic concentration (MNTC of the extracts used in antiviral assay was 25 µg mL-1for Aspidosperma tomentosumand Gaylussacia brasiliensis, and 2.5 µg mL-1for Arrabidaea chicaand Virola sebifera. Twelve different extracts derived from four plant species collected from the Brazilian Cerrado were screened for antiviral activity against aMPV. G. brasiliensis, A. chica,and V. sebifera extracts presented inhibition rates of 99% in the early viral replication stages, suggesting that these extracts act during the adsorption phase. On the other hand, A. tomentosum inhibited 99% virus replication after the virus entered the cell. The biomonitored fractioning of extracts active against aMPV may be a tool to identify the active compounds of plant extracts and to determine their precise mode of action.

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

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Xiaodong Cai

Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

Modeling stochasticity and robustness in gene regulatory networks.

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Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; De Micheli, Giovanni; Xenarios, Ioannis

2009-06-15

Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

Disease candidate gene identification and prioritization using protein interaction networks

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Aronow Bruce J

2009-02-01

Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

Semi-supervised prediction of gene regulatory networks using ...

Indian Academy of Sciences (India)

2015-09-28

Sep 28, 2015 ... Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging ... two types of methods differ primarily based on whether ..... negligible, allowing us to draw the qualitative conclusions .... research will be conducted to develop additional biologically.

Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

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Ferreira, Gustavo Rodrigues; Nakaya, Helder Imoto; Costa, Luciano da Fontoura

2018-04-01

The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

PPARalpha siRNA-treated expression profiles uncover the causal sufficiency network for compound-induced liver hypertrophy.

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Xudong Dai

2007-03-01

Full Text Available Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs against the gene for peroxisome proliferator-activated receptor alpha (Ppara, our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARalpha-induced liver hypertrophy is supported by their ability to predict non-PPARalpha-induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005. Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced

Network Completion for Static Gene Expression Data

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Natsu Nakajima

2014-01-01

Full Text Available We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network completion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent with the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we present a new method for network completion using dynamic programming and least-squares fitting. This method can find an optimal solution in polynomial time if the maximum indegree of the network is bounded by a constant. We evaluate the effectiveness of our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method can distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal gene expression data.

Evolution of Cis-Regulatory Elements and Regulatory Networks in Duplicated Genes of Arabidopsis.

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Arsovski, Andrej A; Pradinuk, Julian; Guo, Xu Qiu; Wang, Sishuo; Adams, Keith L

2015-12-01

Plant genomes contain large numbers of duplicated genes that contribute to the evolution of new functions. Following duplication, genes can exhibit divergence in their coding sequence and their expression patterns. Changes in the cis-regulatory element landscape can result in changes in gene expression patterns. High-throughput methods developed recently can identify potential cis-regulatory elements on a genome-wide scale. Here, we use a recent comprehensive data set of DNase I sequencing-identified cis-regulatory binding sites (footprints) at single-base-pair resolution to compare binding sites and network connectivity in duplicated gene pairs in Arabidopsis (Arabidopsis thaliana). We found that duplicated gene pairs vary greatly in their cis-regulatory element architecture, resulting in changes in regulatory network connectivity. Whole-genome duplicates (WGDs) have approximately twice as many footprints in their promoters left by potential regulatory proteins than do tandem duplicates (TDs). The WGDs have a greater average number of footprint differences between paralogs than TDs. The footprints, in turn, result in more regulatory network connections between WGDs and other genes, forming denser, more complex regulatory networks than shown by TDs. When comparing regulatory connections between duplicates, WGDs had more pairs in which the two genes are either partially or fully diverged in their network connections, but fewer genes with no network connections than the TDs. There is evidence of younger TDs and WGDs having fewer unique connections compared with older duplicates. This study provides insights into cis-regulatory element evolution and network divergence in duplicated genes. © 2015 American Society of Plant Biologists. All Rights Reserved.

A gene regulatory network armature for T-lymphocyte specification

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Fung, Elizabeth-sharon [Los Alamos National Laboratory

2008-01-01

Choice of a T-lymphoid fate by hematopoietic progenitor cells depends on sustained Notch-Delta signaling combined with tightly-regulated activities of multiple transcription factors. To dissect the regulatory network connections that mediate this process, we have used high-resolution analysis of regulatory gene expression trajectories from the beginning to the end of specification; tests of the short-term Notchdependence of these gene expression changes; and perturbation analyses of the effects of overexpression of two essential transcription factors, namely PU.l and GATA-3. Quantitative expression measurements of >50 transcription factor and marker genes have been used to derive the principal components of regulatory change through which T-cell precursors progress from primitive multipotency to T-lineage commitment. Distinct parts of the path reveal separate contributions of Notch signaling, GATA-3 activity, and downregulation of PU.l. Using BioTapestry, the results have been assembled into a draft gene regulatory network for the specification of T-cell precursors and the choice of T as opposed to myeloid dendritic or mast-cell fates. This network also accommodates effects of E proteins and mutual repression circuits of Gfil against Egr-2 and of TCF-l against PU.l as proposed elsewhere, but requires additional functions that remain unidentified. Distinctive features of this network structure include the intense dose-dependence of GATA-3 effects; the gene-specific modulation of PU.l activity based on Notch activity; the lack of direct opposition between PU.l and GATA-3; and the need for a distinct, late-acting repressive function or functions to extinguish stem and progenitor-derived regulatory gene expression.

Clinical Significance of Human Metapneumovirus in Refractory Status Epilepticus and Encephalitis: Case Report and Review of the Literature

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Aysel Vehapoglu

2015-01-01

Full Text Available Encephalitis is a complex neurological disease that is associated with significant morbidity and mortality, and the etiology of the disease is often not identified. Human metapneumovirus (hMPV is a common cause of upper and lower respiratory tract infections in children. Few reports are available showing possible involvement of hMPV in development of neurologic complications. Here, we describe an infant, the youngest case in literature, with refractory status epilepticus and severe encephalitis in whom hMPV was detected in respiratory samples and review diagnostic workup of patient with encephalitis.

Gene Expression Profile in the Early Stage of Angiotensin II-induced Cardiac Remodeling: a Time Series Microarray Study in a Mouse Model

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Meng-Qiu Dang

2015-01-01

Full Text Available Background/Aims: Angiotensin II (Ang II plays a critical role in the cardiac remodeling contributing to heart failure. However, the gene expression profiles induced by Ang II in the early stage of cardiac remodeling remain unknown. Methods: Wild-type male mice (C57BL/6 background, 10-weeek-old were infused with Ang II (1500 ng/kg/min for 7 days. Blood pressure was measured. Cardiac function and remodeling were examined by echocardiography, H&E and Masson staining. The time series microarrays were then conducted to detected gene expression profiles. Results: Microarray results identified that 1,489 genes were differentially expressed in the hearts at day 1, 3 and 7 of Ang II injection. These genes were further classified into 26 profiles by hierarchical cluster analysis. Of them, 4 profiles were significant (No. 19, 8, 21 and 22 and contained 904 genes. Gene Ontology showed that these genes mainly participate in metabolic process, oxidation-reduction process, extracellular matrix organization, apoptotic process, immune response, and others. Significant pathways included focal adhesion, ECM-receptor interaction, cytokine-cytokine receptor interaction, MAPK and insulin signaling pathways, which were known to play important roles in Ang II-induced cardiac remodeling. Moreover, gene co-expression networks analysis suggested that serine/cysteine peptidase inhibitor, member 1 (Serpine1, also known as PAI-1 localized in the core of the network. Conclusions: Our results indicate that many genes are mainly involved in metabolism, inflammation, cardiac fibrosis and hypertrophy. Serpine1 may play a central role in the development of Ang II-induced cardiac remodeling at the early stage.

Structural dissection of human metapneumovirus phosphoprotein using small angle x-ray scattering.

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Renner, Max; Paesen, Guido C; Grison, Claire M; Granier, Sébastien; Grimes, Jonathan M; Leyrat, Cédric

2017-11-01

The phosphoprotein (P) is the main and essential cofactor of the RNA polymerase (L) of non-segmented, negative-strand RNA viruses. P positions the viral polymerase onto its nucleoprotein-RNA template and acts as a chaperone of the nucleoprotein (N), thereby preventing nonspecific encapsidation of cellular RNAs. The phosphoprotein of human metapneumovirus (HMPV) forms homotetramers composed of a stable oligomerization domain (P core ) flanked by large intrinsically disordered regions (IDRs). Here we combined x-ray crystallography of P core with small angle x-ray scattering (SAXS)-based ensemble modeling of the full-length P protein and several of its fragments to provide a structural description of P that captures its dynamic character, and highlights the presence of varyingly stable structural elements within the IDRs. We discuss the implications of the structural properties of HMPV P for the assembly and functioning of the viral transcription/replication machinery.

Past climate change on Sky Islands drives novelty in a core developmental gene network and its phenotype.

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Favé, Marie-Julie; Johnson, Robert A; Cover, Stefan; Handschuh, Stephan; Metscher, Brian D; Müller, Gerd B; Gopalan, Shyamalika; Abouheif, Ehab

2015-09-04

A fundamental and enduring problem in evolutionary biology is to understand how populations differentiate in the wild, yet little is known about what role organismal development plays in this process. Organismal development integrates environmental inputs with the action of gene regulatory networks to generate the phenotype. Core developmental gene networks have been highly conserved for millions of years across all animals, and therefore, organismal development may bias variation available for selection to work on. Biased variation may facilitate repeatable phenotypic responses when exposed to similar environmental inputs and ecological changes. To gain a more complete understanding of population differentiation in the wild, we integrated evolutionary developmental biology with population genetics, morphology, paleoecology and ecology. This integration was made possible by studying how populations of the ant species Monomorium emersoni respond to climatic and ecological changes across five 'Sky Islands' in Arizona, which are mountain ranges separated by vast 'seas' of desert. Sky Islands represent a replicated natural experiment allowing us to determine how repeatable is the response of M. emersoni populations to climate and ecological changes at the phenotypic, developmental, and gene network levels. We show that a core developmental gene network and its phenotype has kept pace with ecological and climate change on each Sky Island over the last ~90,000 years before present (BP). This response has produced two types of evolutionary change within an ant species: one type is unpredictable and contingent on the pattern of isolation of Sky lsland populations by climate warming, resulting in slight changes in gene expression, organ growth, and morphology. The other type is predictable and deterministic, resulting in the repeated evolution of a novel wingless queen phenotype and its underlying gene network in response to habitat changes induced by climate warming. Our

On the Interplay between Entropy and Robustness of Gene Regulatory Networks

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Bor-Sen Chen

2010-05-01

Full Text Available The interplay between entropy and robustness of gene network is a core mechanism of systems biology. The entropy is a measure of randomness or disorder of a physical system due to random parameter fluctuation and environmental noises in gene regulatory networks. The robustness of a gene regulatory network, which can be measured as the ability to tolerate the random parameter fluctuation and to attenuate the effect of environmental noise, will be discussed from the robust H∞ stabilization and filtering perspective. In this review, we will also discuss their balancing roles in evolution and potential applications in systems and synthetic biology.

A novel mutual information-based Boolean network inference method from time-series gene expression data.

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Shohag Barman

Full Text Available Inferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately.In this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods.Taken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network.

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining.

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Kreula, Sanna M; Kaewphan, Suwisa; Ginter, Filip; Jones, Patrik R

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to ( i ) discover novel candidate associations between different genes or proteins in the network, and ( ii ) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.

An integer optimization algorithm for robust identification of non-linear gene regulatory networks

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Chemmangattuvalappil Nishanth

2012-09-01

Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

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Barone, Antonio; Toti, Paolo; Giuca, Maria Rita; Derchi, Giacomo; Covani, Ugo

2015-07-01

In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

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Ernst, Mathias; Du, Yang; Warsow, Gregor; Hamed, Mohamed; Endlich, Nicole; Endlich, Karlhans; Murua Escobar, Hugo; Sklarz, Lisa-Madeleine; Sender, Sina; Junghanß, Christian; Möller, Steffen; Fuellen, Georg; Struckmann, Stephan

2017-02-16

To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.

Noise transmission and delay-induced stochasticoscillations in biochemical network motifs

Institute of Scientific and Technical Information of China (English)

Liu Sheng-Jun; Wang Qi; Liu Bo; Yan Shi-Wei; Fumihiko Sakata

2011-01-01

With the aid of stochastic delayed-feedback differential equations,we derive an analytic expression for the power spectra of reacting molecules included in a generic biological network motif that is incorporated with a feedback mechanism and time delays in gene regulation.We systematically analyse the effects of time delays,the feedback mechanism,and biological stochasticity on the power spectra.It has been clarified that the time delays together with the feedback mechanism can induce stochastic oscillations at the molecular level and invalidate the noise addition rule for a modular description of the noise propagator.Delay-induced stochastic resonance can be expected,which is related to the stability loss of the reaction systems and Hopf bifurcation occurring for solutions of the corresponding deterministic reaction equations.Through the analysis of the power spectrum,a new approach is proposed to estimate the oscillation period.

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

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Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

Identification and network-enabled characterization of auxin response factor genes in Medicago truncatula

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David J. Burks

2016-12-01

Full Text Available The Auxin Response Factor (ARF family of transcription factors is an important regulator of environmental response and symbiotic nodulation in the legume Medicago truncatula. While previous studies have identified members of this family, a recent spurt in gene expression data coupled with genome update and reannotation calls for a reassessment of the prevalence of ARF genes and their interaction networks in M. truncatula. We performed a comprehensive analysis of the M. truncatula genome and transcriptome that entailed search for novel ARF genes and the co-expression networks. Our investigation revealed 8 novel M. truncatula ARF (MtARF genes, of the total 22 identified, and uncovered novel gene co-expression networks as well. Furthermore, the topological clustering and single enrichment analysis of several network models revealed the roles of individual members of the MtARF family in nitrogen regulation, nodule initiation, and post-embryonic development through a specialized protein packaging and secretory pathway. In summary, this study not just shines new light on an important gene family, but also provides a guideline for identification of new members of gene families and their functional characterization through network analyses.

Unveiling network-based functional features through integration of gene expression into protein networks.

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Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali

2018-06-01

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.

Digital Signal Processing and Control for the Study of Gene Networks

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Shin, Yong-Jun

2016-04-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Screening of hypoxia-inducible genes in sporadic ALS.

LENUS (Irish Health Repository)

Cronin, Simon

2008-10-01

Genetic variations in two hypoxia-inducible angiogenic genes, VEGF and ANG, have been linked with sporadic amyotrophic lateral sclerosis (SALS). Common variations in these genes may reduce the levels or functioning of their products. VEGF and ANG belong to a larger group of angiogenic genes that are up-regulated under hypoxic conditions. We hypothesized that common genetic variation across other members of this group may also predispose to sporadic ALS. To screen other hypoxia-inducible angiogenic genes for association with SALS, we selected 112 tagging single nucleotide polymorphisms (tgSNPs) that captured the common genetic variation across 16 VEGF-like and eight ANG-like hypoxia-inducible genes. Screening for association was performed in 270 Irish individuals with typical SALS and 272 ethnically matched unrelated controls. SNPs showing association in the Irish phase were genotyped in a replication sample of 281 Swedish sporadic ALS patients and 286 Swedish controls. Seven markers showed association in the Irish. The one modest replication signal observed in the Swedish replication sample, at rs3801158 in the gene inhibin beta A, was for the opposite allele vs. the Irish cohort. We failed to detect association of common variation across 24 candidate hypoxia-inducible angiogenic genes with SALS.

A fast and efficient gene-network reconstruction method from multiple over-expression experiments

Directory of Open Access Journals (Sweden)

Thurner Stefan

2009-08-01

Full Text Available Abstract Background Reverse engineering of gene regulatory networks presents one of the big challenges in systems biology. Gene regulatory networks are usually inferred from a set of single-gene over-expressions and/or knockout experiments. Functional relationships between genes are retrieved either from the steady state gene expressions or from respective time series. Results We present a novel algorithm for gene network reconstruction on the basis of steady-state gene-chip data from over-expression experiments. The algorithm is based on a straight forward solution of a linear gene-dynamics equation, where experimental data is fed in as a first predictor for the solution. We compare the algorithm's performance with the NIR algorithm, both on the well known E. coli experimental data and on in-silico experiments. Conclusion We show superiority of the proposed algorithm in the number of correctly reconstructed links and discuss computational time and robustness. The proposed algorithm is not limited by combinatorial explosion problems and can be used in principle for large networks.

Gene co-expression networks shed light into diseases of brain iron accumulation.

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Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M; Botía, Juan A; Collingwood, Joanna F; Hardy, John; Milward, Elizabeth A; Ryten, Mina; Houlden, Henry

2016-03-01

Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis

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Scott, RE; Ghule, PN; Stein, JL; Stein, GS

2015-01-01

The early stages of carcinogenesis are linked to defects in the cell cycle. A series of cell cycle checkpoints are involved in this process. The G1/S checkpoint that serves to integrate the control of cell proliferation and differentiation is linked to carcinogenesis and the mitotic spindle checkpoint with the development of chromosomal instability. This paper presents the outcome of systems biology studies designed to evaluate if networks of covariate cell cycle gene transcripts exist in proliferative mammalian tissues including mice, rats and humans. The GeneNetwork website that contains numerous gene expression datasets from different species, sexes and tissues represents the foundational resource for these studies (www.genenetwork.org). In addition, WebGestalt, a gene ontology tool, facilitated the identification of expression networks of genes that co-vary with key cell cycle targets, especially Cdc20 and Plk1 (www.bioinfo.vanderbilt.edu/webgestalt). Cell cycle expression networks of such covariate mRNAs exist in multiple proliferative tissues including liver, lung, pituitary, adipose and lymphoid tissues among others but not in brain or retina that have low proliferative potential. Sixty-three covariate cell cycle gene transcripts (mRNAs) compose the average cell cycle network with p = e−13 to e−36. Cell cycle expression networks show species, sex and tissue variability and they are enriched in mRNA transcripts associated with mitosis many of which are associated with chromosomal instability. PMID:25808367

Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses.

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Luo, Jie; Xu, Pei; Cao, Peijian; Wan, Hongjian; Lv, Xiaonan; Xu, Shengchun; Wang, Gangjun; Cook, Melloni N; Jones, Byron C; Lu, Lu; Wang, Xusheng

2018-01-01

Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE) but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1), down-regulation in NOE but rescue in RSE (pattern 2), up-regulation in both restraint stress followed by a saline injection (RSS) and NOE, and further amplification in RSE (pattern 3), and up-regulation in RSS but reduction in both NOE and RSE (pattern 4). We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs) to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA) signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses.

Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses

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Jie Luo

2018-04-01

Full Text Available Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1, down-regulation in NOE but rescue in RSE (pattern 2, up-regulation in both restraint stress followed by a saline injection (RSS and NOE, and further amplification in RSE (pattern 3, and up-regulation in RSS but reduction in both NOE and RSE (pattern 4. We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses.

Controlling gene networks and cell fate with precision-targeted DNA-binding proteins and small-molecule-based genome readers.

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Eguchi, Asuka; Lee, Garrett O; Wan, Fang; Erwin, Graham S; Ansari, Aseem Z

2014-09-15

Transcription factors control the fate of a cell by regulating the expression of genes and regulatory networks. Recent successes in inducing pluripotency in terminally differentiated cells as well as directing differentiation with natural transcription factors has lent credence to the efforts that aim to direct cell fate with rationally designed transcription factors. Because DNA-binding factors are modular in design, they can be engineered to target specific genomic sequences and perform pre-programmed regulatory functions upon binding. Such precision-tailored factors can serve as molecular tools to reprogramme or differentiate cells in a targeted manner. Using different types of engineered DNA binders, both regulatory transcriptional controls of gene networks, as well as permanent alteration of genomic content, can be implemented to study cell fate decisions. In the present review, we describe the current state of the art in artificial transcription factor design and the exciting prospect of employing artificial DNA-binding factors to manipulate the transcriptional networks as well as epigenetic landscapes that govern cell fate.

Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.

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Jia, Bin; Wang, Xiaodong

2013-12-17

: The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF3), and the fifth-degree cubature Kalman filter (CKF5) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.

An algebra-based method for inferring gene regulatory networks.

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Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

2014-03-26

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease.

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Johnson, Michael R; Shkura, Kirill; Langley, Sarah R; Delahaye-Duriez, Andree; Srivastava, Prashant; Hill, W David; Rackham, Owen J L; Davies, Gail; Harris, Sarah E; Moreno-Moral, Aida; Rotival, Maxime; Speed, Doug; Petrovski, Slavé; Katz, Anaïs; Hayward, Caroline; Porteous, David J; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Starr, John M; Liewald, David C; Visconti, Alessia; Falchi, Mario; Bottolo, Leonardo; Rossetti, Tiziana; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Grote, Alexander; Helmstaedter, Christoph; Becker, Albert J; Kaminski, Rafal M; Deary, Ian J; Petretto, Enrico

2016-02-01

Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease-associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.

Controlling noise-induced behavior of excitable networks

International Nuclear Information System (INIS)

Patidar, S; Pototsky, A; Janson, N B

2009-01-01

The paper demonstrates the possibility to control the collective behavior of a large network of excitable stochastic units, in which oscillations are induced merely by external random input. Each network element is represented by the FitzHugh-Nagumo system under the influence of noise, and the elements are coupled through the mean field. As known previously, the collective behavior of units in such a network can range from synchronous to non-synchronous spiking with a variety of states in between. We apply the Pyragas delayed feedback to the mean field of the network and demonstrate that this technique is capable of suppressing or weakening the collective synchrony, or of inducing the synchrony where it was absent. On the plane of control parameters we indicate the areas where suppression of synchrony is achieved. To explain the numerical observations on a qualitative level, we use the semi-analytic approach based on the cumulant expansion of the distribution density within Gaussian approximation. We perform bifurcation analysis of the obtained cumulant equations with delay and demonstrate that the regions of stability of its steady state have qualitatively the same structure as the regions of synchrony suppression of the original stochastic equations. We also demonstrate the delay-induced multistability in the stochastic network. These results are relevant to the control of unwanted behavior in neural networks.

The capacity for multistability in small gene regulatory networks

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Grotewold Erich

2009-09-01

Full Text Available Abstract Background Recent years have seen a dramatic increase in the use of mathematical modeling to gain insight into gene regulatory network behavior across many different organisms. In particular, there has been considerable interest in using mathematical tools to understand how multistable regulatory networks may contribute to developmental processes such as cell fate determination. Indeed, such a network may subserve the formation of unicellular leaf hairs (trichomes in the model plant Arabidopsis thaliana. Results In order to investigate the capacity of small gene regulatory networks to generate multiple equilibria, we present a chemical reaction network (CRN-based modeling formalism and describe a number of methods for CRN analysis in a parameter-free context. These methods are compared and applied to a full set of one-component subnetworks, as well as a large random sample from 40,680 similarly constructed two-component subnetworks. We find that positive feedback and cooperativity mediated by transcription factor (TF dimerization is a requirement for one-component subnetwork bistability. For subnetworks with two components, the presence of these processes increases the probability that a randomly sampled subnetwork will exhibit multiple equilibria, although we find several examples of bistable two-component subnetworks that do not involve cooperative TF-promoter binding. In the specific case of epidermal differentiation in Arabidopsis, dimerization of the GL3-GL1 complex and cooperative sequential binding of GL3-GL1 to the CPC promoter are each independently sufficient for bistability. Conclusion Computational methods utilizing CRN-specific theorems to rule out bistability in small gene regulatory networks are far superior to techniques generally applicable to deterministic ODE systems. Using these methods to conduct an unbiased survey of parameter-free deterministic models of small networks, and the Arabidopsis epidermal cell

Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

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Chen Xin

2012-10-01

Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable

Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

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Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

2013-03-28

Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. Copyright © 2013 Elsevier Inc. All rights reserved.

FastGCN: a GPU accelerated tool for fast gene co-expression networks.

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Meimei Liang

Full Text Available Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

Epigenetic Modulation of Brain Gene Networks for Cocaine and Alcohol Abuse

Directory of Open Access Journals (Sweden)

Sean P Farris

2015-05-01

Full Text Available Cocaine and alcohol are two substances of abuse that prominently affect the central nervous system (CNS. Repeated exposure to cocaine and alcohol leads to longstanding changes in gene expression, and subsequent functional CNS plasticity, throughout multiple brain regions. Epigenetic modifications of histones are one proposed mechanism guiding these enduring changes to the transcriptome. Characterizing the large number of available biological relationships as network models can reveal unexpected biochemical relationships. Clustering analysis of variation from whole-genome sequencing of gene expression (RNA-Seq and histone H3 lysine 4 trimethylation (H3K4me3 events (ChIP-Seq revealed the underlying structure of the transcriptional and epigenomic landscape within hippocampal postmortem brain tissue of drug abusers and control cases. Distinct sets of interrelated networks for cocaine and alcohol abuse were determined for each abusive substance. The network approach identified subsets of functionally related genes that are regulated in agreement with H3K4me3 changes, suggesting cause and effect relationships between this epigenetic mark and gene expression. Gene expression networks consisted of recognized substrates for addiction, such as the dopamine- and cAMP-regulated neuronal phosphoprotein PPP1R1B / DARPP-32 and the vesicular glutamate transporter SLC17A7 / VGLUT1 as well as potentially novel molecular targets for substance abuse. Through a systems biology based approach our results illustrate the utility of integrating epigenetic and transcript expression to establish relevant biological networks in the human brain for addiction. Future work with laboratory models may clarify the functional relevance of these gene networks for cocaine and alcohol, and provide a framework for the development of medications for the treatment of addiction.

Estudio clínico-epidemiológico y molecular de Metapneumovirus Humano en pacientes con Infecciones Respiratorias Agudas (IRA) en Venezuela

OpenAIRE

Tovar H, Cerraf E; Moncho S, Alessandra; Fernandez S, David; Aguilar M, Marwan S; Morón, Dulce

2014-01-01

El Metapneumovirus Humano (MPVh) ha sido asociado con Infecciones Respiratorias Agudas (IRA) en pacientes de todas las edades. Estudios epidemiológicos indican la prevalencia del MPVh alrededor del mundo, sin embargo, en Venezuela poco se conoce sobre su comportamiento en la población. Este estudio pretende describir el comportamiento epidemiológico de la infección por MPVh en pacientes venezolanos. Se evaluaron por RT-PCR multiplex 1812 hisopados nasales (HN) provenientes de pacientes con di...

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

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Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

Evidence of avian metapneumovirus subtype C infection of wild birds in Georgia, South Carolina, Arkansas and Ohio, USA.

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Turpin, E A; Stallknecht, D E; Slemons, R D; Zsak, L; Swayne, D E

2008-06-01

Metapneumoviruses (MPVs) were first reported in avian species (aMPVs) in the late 1970s and in humans in 2001. Although aMPVs have been reported in Europe and Asia for over 20 years, the virus first appeared in the United States in 1996, leaving many to question the origin of the virus and why it proved to be a different subtype from those found elsewhere. To examine the potential role of migratory waterfowl and other wild birds in aMPV spread, our study focused on determining whether populations of wild birds have evidence of aMPV infection. Serum samples from multiple species were initially screened using a blocking enzyme-linked immunosorbent assay. Antibodies to aMPVs were identified in five of the 15 species tested: American coots, American crows, Canada geese, cattle egrets, and rock pigeons. The presence of aMPV-specific antibodies was confirmed with virus neutralization and western blot assays. Oral swabs were collected from wild bird species with the highest percentage of aMPV-seropositive serum samples: the American coots and Canada geese. From these swabs, 17 aMPV-positive samples were identified, 11 from coots and six from geese. Sequence analysis of the matrix, attachment gene and short hydrophobic genes revealed that these viruses belong to subtype C aMPV. The detection of aMPV antibodies and the presence of virus in wild birds in Georgia, South Carolina, Arkansas and Ohio demonstrates that wild birds can serve as a reservoir of subtype C aMPV, and may provide a potential mechanism to spread aMPVs to poultry in other regions of the United States and possibly to other countries in Central and South America.

Estimating immunoregulatory gene networks in human herpesvirus type 6-infected T cells

International Nuclear Information System (INIS)

Takaku, Tomoiku; Ohyashiki, Junko H.; Zhang, Yu; Ohyashiki, Kazuma

2005-01-01

The immune response to viral infection involves complex network of dynamic gene and protein interactions. We present here the dynamic gene network of the host immune response during human herpesvirus type 6 (HHV-6) infection in an adult T-cell leukemia cell line. Using a pathway-focused oligonucleotide DNA microarray, we found a possible association between chemokine genes regulating Th1/Th2 balance and genes regulating T-cell proliferation during HHV-6B infection. Gene network analysis using an integrated comprehensive workbench, VoyaGene, revealed that a gene encoding a TEC-family kinase, ITK, might be a putative modulator in the host immune response against HHV-6B infection. We conclude that Th2-dominated inflammatory reaction in host cells may play an important role in HHV-6B-infected T cells, thereby suggesting the possibility that ITK might be a therapeutic target in diseases related to dysregulation of Th1/Th2 balance. This study describes a novel approach to find genes related with the complex host-virus interaction using microarray data employing the Bayesian statistical framework

A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

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Kentzoglanakis, Kyriakos; Poole, Matthew

2012-01-01

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

Inferring gene networks from discrete expression data

KAUST Repository

Zhang, L.; Mallick, B. K.

2013-01-01

graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which

An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

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Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

2018-05-14

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives

Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.

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Wei, Jiangyong; Hu, Xiaohua; Zou, Xiufen; Tian, Tianhai

2017-12-28

Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate

Investigating Gene Function in Cereal Rust Fungi by Plant-Mediated Virus-Induced Gene Silencing.

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Panwar, Vinay; Bakkeren, Guus

2017-01-01

Cereal rust fungi are destructive pathogens, threatening grain production worldwide. Targeted breeding for resistance utilizing host resistance genes has been effective. However, breakdown of resistance occurs frequently and continued efforts are needed to understand how these fungi overcome resistance and to expand the range of available resistance genes. Whole genome sequencing, transcriptomic and proteomic studies followed by genome-wide computational and comparative analyses have identified large repertoire of genes in rust fungi among which are candidates predicted to code for pathogenicity and virulence factors. Some of these genes represent defence triggering avirulence effectors. However, functions of most genes still needs to be assessed to understand the biology of these obligate biotrophic pathogens. Since genetic manipulations such as gene deletion and genetic transformation are not yet feasible in rust fungi, performing functional gene studies is challenging. Recently, Host-induced gene silencing (HIGS) has emerged as a useful tool to characterize gene function in rust fungi while infecting and growing in host plants. We utilized Barley stripe mosaic virus-mediated virus induced gene silencing (BSMV-VIGS) to induce HIGS of candidate rust fungal genes in the wheat host to determine their role in plant-fungal interactions. Here, we describe the methods for using BSMV-VIGS in wheat for functional genomics study in cereal rust fungi.

Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

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Sapna Kumari

Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

Integrated Network Analysis Identifies Fight-Club Nodes as a Class of Hubs Encompassing Key Putative Switch Genes That Induce Major Transcriptome Reprogramming during Grapevine Development[W][OPEN

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Palumbo, Maria Concetta; Zenoni, Sara; Fasoli, Marianna; Massonnet, Mélanie; Farina, Lorenzo; Castiglione, Filippo; Pezzotti, Mario; Paci, Paola

2014-01-01

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named “fight-club hubs” characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named “switch genes” was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops. PMID:25490918

Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks.

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Zhou, Xuezhong; Liu, Baoyan; Wu, Zhaohui; Feng, Yi

2007-10-01

The amount of biomedical data in different disciplines is growing at an exponential rate. Integrating these significant knowledge sources to generate novel hypotheses for systems biology research is difficult. Traditional Chinese medicine (TCM) is a completely different discipline, and is a complementary knowledge system to modern biomedical science. This paper uses a significant TCM bibliographic literature database in China, together with MEDLINE, to help discover novel gene functional knowledge. We present an integrative mining approach to uncover the functional gene relationships from MEDLINE and TCM bibliographic literature. This paper introduces TCM literature (about 50,000 records) as one knowledge source for constructing literature-based gene networks. We use the TCM diagnosis, TCM syndrome, to automatically congregate the related genes. The syndrome-gene relationships are discovered based on the syndrome-disease relationships extracted from TCM literature and the disease-gene relationships in MEDLINE. Based on the bubble-bootstrapping and relation weight computing methods, we have developed a prototype system called MeDisco/3S, which has name entity and relation extraction, and online analytical processing (OLAP) capabilities, to perform the integrative mining process. We have got about 200,000 syndrome-gene relations, which could help generate syndrome-based gene networks, and help analyze the functional knowledge of genes from syndrome perspective. We take the gene network of Kidney-Yang Deficiency syndrome (KYD syndrome) and the functional analysis of some genes, such as CRH (corticotropin releasing hormone), PTH (parathyroid hormone), PRL (prolactin), BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 2, early onset), to demonstrate the preliminary results. The underlying hypothesis is that the related genes of the same syndrome will have some biological functional relationships, and will constitute a functional network. This paper presents

Iron homeostasis in Arabidopsis thaliana: transcriptomic analyses reveal novel FIT-regulated genes, iron deficiency marker genes and functional gene networks.

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Mai, Hans-Jörg; Pateyron, Stéphanie; Bauer, Petra

2016-10-03

FIT (FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR) is the central regulator of iron uptake in Arabidopsis thaliana roots. We performed transcriptome analyses of six day-old seedlings and roots of six week-old plants using wild type, a fit knock-out mutant and a FIT over-expression line grown under iron-sufficient or iron-deficient conditions. We compared genes regulated in a FIT-dependent manner depending on the developmental stage of the plants. We assembled a high likelihood dataset which we used to perform co-expression and functional analysis of the most stably iron deficiency-induced genes. 448 genes were found FIT-regulated. Out of these, 34 genes were robustly FIT-regulated in root and seedling samples and included 13 novel FIT-dependent genes. Three hundred thirty-one genes showed differential regulation in response to the presence and absence of FIT only in the root samples, while this was the case for 83 genes in the seedling samples. We assembled a virtual dataset of iron-regulated genes based on a total of 14 transcriptomic analyses of iron-deficient and iron-sufficient wild-type plants to pinpoint the best marker genes for iron deficiency and analyzed this dataset in depth. Co-expression analysis of this dataset revealed 13 distinct regulons part of which predominantly contained functionally related genes. We could enlarge the list of FIT-dependent genes and discriminate between genes that are robustly FIT-regulated in roots and seedlings or only in one of those. FIT-regulated genes were mostly induced, few of them were repressed by FIT. With the analysis of a virtual dataset we could filter out and pinpoint new candidates among the most reliable marker genes for iron deficiency. Moreover, co-expression and functional analysis of this virtual dataset revealed iron deficiency-induced and functionally distinct regulons.

Photo-induced Mass Transport through Polymer Networks

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Meng, Yuan; Anthamatten, Mitchell

2014-03-01

Among adaptable materials, photo-responsive polymers are especially attractive as they allow for spatiotemporal stimuli and response. We have recently developed a macromolecular network capable of photo-induced mass transport of covalently bound species. The system comprises of crosslinked chains that form an elastic network and photosensitive fluorescent arms that become mobile upon irradiation. We form loosely crosslinked polymer networks by Michael-Addition between multifunctional thiols and small molecule containing acrylate end-groups. The arms are connected to the network by allyl sulfide, that undergoes addition-fragmentation chain transfer (AFCT) in the presence of free radicals, releasing diffusible fluorophore. The networks are loaded with photoinitiator to allow for spatial modulation of the AFCT reactions. FRAP experiments within bulk elastomers are conducted to establish correlations between the fluorophore's diffusion coefficient and experimental variables such as network architecture, temperature and UV intensity. Photo-induced mass transport between two contacted films is demonstrated, and release of fluorophore into a solvent is investigated. Spatial and temporal control of mass transport could benefit drug release, printing, and sensing applications.

Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

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Xiaobo Guo

Full Text Available Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs. It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC curve and the precision-recall (PR curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

A network approach to predict pathogenic genes for Fusarium graminearum.

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Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

2010-10-04

Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which

A network approach to predict pathogenic genes for Fusarium graminearum.

Directory of Open Access Journals (Sweden)

Xiaoping Liu

Full Text Available Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB, which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other

An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series.

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Wang, Zidong; Liu, Xiaohui; Liu, Yurong; Liang, Jinling; Vinciotti, Veronica

2009-01-01

In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.

Comparative analysis of TCDD-induced AhR-mediated gene expression in human, mouse and rat primary B cells

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Kovalova, Natalia, E-mail: kovalova@msu.edu [Department of Pharmacology and Toxicology, Michigan State University, Lansing, MI 48824 (United States); Institute for Integrative Toxicology, Michigan State University, Lansing, MI 48824 (United States); Nault, Rance, E-mail: naultran@msu.edu [Institute for Integrative Toxicology, Michigan State University, Lansing, MI 48824 (United States); Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI 48824 (United States); Crawford, Robert, E-mail: crawfo28@msu.edu [Institute for Integrative Toxicology, Michigan State University, Lansing, MI 48824 (United States); Zacharewski, Timothy R., E-mail: tzachare@msu.edu [Institute for Integrative Toxicology, Michigan State University, Lansing, MI 48824 (United States); Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI 48824 (United States); Kaminski, Norbert E., E-mail: kamins11@msu.edu [Department of Pharmacology and Toxicology, Michigan State University, Lansing, MI 48824 (United States); Institute for Integrative Toxicology, Michigan State University, Lansing, MI 48824 (United States)

2017-02-01

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental pollutant that activates the aryl hydrocarbon receptor (AhR) resulting in altered gene expression. In vivo, in vitro, and ex vivo studies have demonstrated that B cells are directly impaired by TCDD, and are a sensitive target as evidenced by suppression of antibody responses. The window of sensitivity to TCDD-induced suppression of IgM secretion among mouse, rat and human B cells is similar. Specifically, TCDD must be present within the initial 12 h post B cell stimulation, indicating that TCDD disrupts early signaling network(s) necessary for B lymphocyte activation and differentiation. Therefore, we hypothesized that TCDD treatment across three different species (mouse, rat and human) triggers a conserved, B cell-specific mechanism that is involved in TCDD-induced immunosuppression. RNA sequencing (RNA-Seq) was used to identify B cell-specific orthologous genes that are differentially expressed in response to TCDD in primary mouse, rat and human B cells. Time course studies identified TCDD-elicited differential expression of 515 human, 2371 mouse and 712 rat orthologous genes over the 24-h period. 28 orthologs were differentially expressed in response to TCDD in all three species. Overrepresented pathways enriched in all three species included cytokine-cytokine receptor interaction, ECM-receptor interaction, focal adhesion, regulation of actin cytoskeleton and pathways in cancer. Differentially expressed genes functionally associated with cell-cell signaling in humans, immune response in mice, and oxidation reduction in rats. Overall, these results suggest that despite the conservation of the AhR and its signaling mechanism, TCDD elicits species-specific gene expression changes. - Highlights: • Kovalova TAAP Highlights Nov. 2016 • RNA-Seq identified TCDD-induced gene expression in PWM-activated primary B cells. • TCDD elicited differential expression of 515 human, 2371 mouse and 712

Developing integrated crop knowledge networks to advance candidate gene discovery.

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Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

2016-12-01

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes

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Larremore, Daniel B.; Clauset, Aaron; Buckee, Caroline O.

2013-01-01

The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. PMID:24130474

A network approach to analyzing highly recombinant malaria parasite genes.

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Larremore, Daniel B; Clauset, Aaron; Buckee, Caroline O

2013-01-01

The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.

A network approach to analyzing highly recombinant malaria parasite genes.

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Daniel B Larremore

Full Text Available The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs, and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.

Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

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Nolan Priedigkeit

2015-02-01

Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

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Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Antagonistic Coevolution Drives Whack-a-Mole Sensitivity in Gene Regulatory Networks.

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Jeewoen Shin

2015-10-01

Full Text Available Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems. However, robustness often coexists with its counterpart, evolvability--the ability of perturbations to generate new phenotypes. Previous models of gene regulatory network evolution have shown that robustness evolves under stabilizing selection, but it is unclear how robustness and evolvability will emerge in common coevolutionary scenarios. We consider a two-species model of coevolution involving one host and one parasite population. By using two interacting species, key model parameters that determine the fitness landscapes become emergent properties of the model, avoiding the need to impose these parameters externally. In our study, parasites are modeled on species such as cuckoos where mimicry of the host phenotype confers high fitness to the parasite but lower fitness to the host. Here, frequent phenotype changes are favored as each population continually adapts to the other population. Sensitivity evolves at the network level such that point mutations can induce large phenotype changes. Crucially, the sensitive points of the network are broadly distributed throughout the network and continually relocate. Each time sensitive points in the network are mutated, new ones appear to take their place. We have therefore named this phenomenon "whack-a-mole" sensitivity, after a popular fun park game. We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.

[Weighted gene co-expression network analysis in biomedicine research].

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Liu, Wei; Li, Li; Ye, Hua; Tu, Wei

2017-11-25

High-throughput biological technologies are now widely applied in biology and medicine, allowing scientists to monitor thousands of parameters simultaneously in a specific sample. However, it is still an enormous challenge to mine useful information from high-throughput data. The emergence of network biology provides deeper insights into complex bio-system and reveals the modularity in tissue/cellular networks. Correlation networks are increasingly used in bioinformatics applications. Weighted gene co-expression network analysis (WGCNA) tool can detect clusters of highly correlated genes. Therefore, we systematically reviewed the application of WGCNA in the study of disease diagnosis, pathogenesis and other related fields. First, we introduced principle, workflow, advantages and disadvantages of WGCNA. Second, we presented the application of WGCNA in disease, physiology, drug, evolution and genome annotation. Then, we indicated the application of WGCNA in newly developed high-throughput methods. We hope this review will help to promote the application of WGCNA in biomedicine research.

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

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Bhavnani Suresh K

2010-11-01

Full Text Available Abstract Background In a recent study, two-dimensional (2D network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method revealed that genes implicated in many diseases (non-specific genes tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.

Co-expression network analysis of duplicate genes in maize (Zea mays L.) reveals no subgenome bias.

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Li, Lin; Briskine, Roman; Schaefer, Robert; Schnable, Patrick S; Myers, Chad L; Flagel, Lex E; Springer, Nathan M; Muehlbauer, Gary J

2016-11-04

Gene duplication is prevalent in many species and can result in coding and regulatory divergence. Gene duplications can be classified as whole genome duplication (WGD), tandem and inserted (non-syntenic). In maize, WGD resulted in the subgenomes maize1 and maize2, of which maize1 is considered the dominant subgenome. However, the landscape of co-expression network divergence of duplicate genes in maize is still largely uncharacterized. To address the consequence of gene duplication on co-expression network divergence, we developed a gene co-expression network from RNA-seq data derived from 64 different tissues/stages of the maize reference inbred-B73. WGD, tandem and inserted gene duplications exhibited distinct regulatory divergence. Inserted duplicate genes were more likely to be singletons in the co-expression networks, while WGD duplicate genes were likely to be co-expressed with other genes. Tandem duplicate genes were enriched in the co-expression pattern where co-expressed genes were nearly identical for the duplicates in the network. Older gene duplications exhibit more extensive co-expression variation than younger duplications. Overall, non-syntenic genes primarily from inserted duplications show more co-expression divergence. Also, such enlarged co-expression divergence is significantly related to duplication age. Moreover, subgenome dominance was not observed in the co-expression networks - maize1 and maize2 exhibit similar levels of intra subgenome correlations. Intriguingly, the level of inter subgenome co-expression was similar to the level of intra subgenome correlations, and genes from specific subgenomes were not likely to be the enriched in co-expression network modules and the hub genes were not predominantly from any specific subgenomes in maize. Our work provides a comprehensive analysis of maize co-expression network divergence for three different types of gene duplications and identifies potential relationships between duplication types

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

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Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

2018-02-16

Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

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Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

2005-04-15

The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

Integration of gene expression and methylation to unravel biological networks in glioblastoma patients.

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Gadaleta, Francesco; Bessonov, Kyrylo; Van Steen, Kristel

2017-02-01

The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work, we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylation data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genomic or Expression-Methylation (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach, Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified, respectively, 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Although the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response, and several cancer types. Importantly, we observed significant overrepresentation of cancer-related pathways including glioma, especially in the XORnet network, suggesting a nonignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors. © 2016 WILEY PERIODICALS, INC.

A reconstruction problem for a class of phylogenetic networks with lateral gene transfers.

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Cardona, Gabriel; Pons, Joan Carles; Rosselló, Francesc

2015-01-01

Lateral, or Horizontal, Gene Transfers are a type of asymmetric evolutionary events where genetic material is transferred from one species to another. In this paper we consider LGT networks, a general model of phylogenetic networks with lateral gene transfers which consist, roughly, of a principal rooted tree with its leaves labelled on a set of taxa, and a set of extra secondary arcs between nodes in this tree representing lateral gene transfers. An LGT network gives rise in a natural way to a principal phylogenetic subtree and a set of secondary phylogenetic subtrees, which, roughly, represent, respectively, the main line of evolution of most genes and the secondary lines of evolution through lateral gene transfers. We introduce a set of simple conditions on an LGT network that guarantee that its principal and secondary phylogenetic subtrees are pairwise different and that these subtrees determine, up to isomorphism, the LGT network. We then give an algorithm that, given a set of pairwise different phylogenetic trees [Formula: see text] on the same set of taxa, outputs, when it exists, the LGT network that satisfies these conditions and such that its principal phylogenetic tree is [Formula: see text] and its secondary phylogenetic trees are [Formula: see text].

Co-regulation of metabolic genes is better explained by flux coupling than by network distance.

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Richard A Notebaart

2008-01-01

Full Text Available To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

Identification of novel light-induced genes in the suprachiasmatic nucleus

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Piontkivska Helen

2007-11-01

Full Text Available Abstract Background The transmission of information about the photic environment to the circadian clock involves a complex array of neurotransmitters, receptors, and second messenger systems. Exposure of an animal to light during the subjective night initiates rapid transcription of a number of immediate-early genes in the suprachiasmatic nucleus of the hypothalamus. Some of these genes have known roles in entraining the circadian clock, while others have unknown functions. Using laser capture microscopy, microarray analysis, and quantitative real-time PCR, we performed a comprehensive screen for changes in gene expression immediately following a 30 minute light pulse in suprachiasmatic nucleus of mice. Results The results of the microarray screen successfully identified previously known light-induced genes as well as several novel genes that may be important in the circadian clock. Newly identified light-induced genes include early growth response 2, proviral integration site 3, growth-arrest and DNA-damage-inducible 45 beta, and TCDD-inducible poly(ADP-ribose polymerase. Comparative analysis of promoter sequences revealed the presence of evolutionarily conserved CRE and associated TATA box elements in most of the light-induced genes, while other core clock genes generally lack this combination of promoter elements. Conclusion The photic signalling cascade in the suprachiasmatic nucleus activates an array of immediate-early genes, most of which have unknown functions in the circadian clock. Detected evolutionary conservation of CRE and TATA box elements in promoters of light-induced genes suggest that the functional role of these elements has likely remained the same over evolutionary time across mammalian orders.