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

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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.

A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

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Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

2013-01-01

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

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Ching Lee Koo

2013-01-01

Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature

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Ning Ye

2015-01-01

Full Text Available The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

I just ran a thousand analyses: benefits of multiple testing in understanding equivocal evidence on gene-environment interactions.

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Vera E Heininga

Full Text Available In psychiatric genetics research, the volume of ambivalent findings on gene-environment interactions (G x E is growing at an accelerating pace. In response to the surging suspicions of systematic distortion, we challenge the notion of chance capitalization as a possible contributor. Beyond qualifying multiple testing as a mere methodological issue that, if uncorrected, leads to chance capitalization, we advance towards illustrating the potential benefits of multiple tests in understanding equivocal evidence in genetics literature.We focused on the interaction between the serotonin-transporter-linked promotor region (5-HTTLPR and childhood adversities with regard to depression. After testing 2160 interactions with all relevant measures available within the Dutch population study of adolescents TRAILS, we calculated percentages of significant (p < .05 effects for several subsets of regressions. Using chance capitalization (i.e. overall significance rate of 5% alpha and randomly distributed findings as a competing hypothesis, we expected more significant effects in the subsets of regressions involving: 1 interview-based instead of questionnaire-based measures; 2 abuse instead of milder childhood adversities; and 3 early instead of later adversities. Furthermore, we expected equal significance percentages across 4 male and female subsamples, and 5 various genotypic models of 5-HTTLPR.We found differences in the percentages of significant interactions among the subsets of analyses, including those regarding sex-specific subsamples and genetic modeling, but often in unexpected directions. Overall, the percentage of significant interactions was 7.9% which is only slightly above the 5% that might be expected based on chance.Taken together, multiple testing provides a novel approach to better understand equivocal evidence on G x E, showing that methodological differences across studies are a likely reason for heterogeneity in findings - but chance

Multiple analytical approaches reveal distinct gene-environment interactions in smokers and non smokers in lung cancer.

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Rakhshan Ihsan

Full Text Available Complex disease such as cancer results from interactions of multiple genetic and environmental factors. Studying these factors singularly cannot explain the underlying pathogenetic mechanism of the disease. Multi-analytical approach, including logistic regression (LR, classification and regression tree (CART and multifactor dimensionality reduction (MDR, was applied in 188 lung cancer cases and 290 controls to explore high order interactions among xenobiotic metabolizing genes and environmental risk factors. Smoking was identified as the predominant risk factor by all three analytical approaches. Individually, CYP1A1*2A polymorphism was significantly associated with increased lung cancer risk (OR = 1.69;95%CI = 1.11-2.59,p = 0.01, whereas EPHX1 Tyr113His and SULT1A1 Arg213His conferred reduced risk (OR = 0.40;95%CI = 0.25-0.65,p<0.001 and OR = 0.51;95%CI = 0.33-0.78,p = 0.002 respectively. In smokers, EPHX1 Tyr113His and SULT1A1 Arg213His polymorphisms reduced the risk of lung cancer, whereas CYP1A1*2A, CYP1A1*2C and GSTP1 Ile105Val imparted increased risk in non-smokers only. While exploring non-linear interactions through CART analysis, smokers carrying the combination of EPHX1 113TC (Tyr/His, SULT1A1 213GG (Arg/Arg or AA (His/His and GSTM1 null genotypes showed the highest risk for lung cancer (OR = 3.73;95%CI = 1.33-10.55,p = 0.006, whereas combined effect of CYP1A1*2A 6235CC or TC, SULT1A1 213GG (Arg/Arg and betel quid chewing showed maximum risk in non-smokers (OR = 2.93;95%CI = 1.15-7.51,p = 0.01. MDR analysis identified two distinct predictor models for the risk of lung cancer in smokers (tobacco chewing, EPHX1 Tyr113His, and SULT1A1 Arg213His and non-smokers (CYP1A1*2A, GSTP1 Ile105Val and SULT1A1 Arg213His with testing balance accuracy (TBA of 0.6436 and 0.6677 respectively. Interaction entropy interpretations of MDR results showed non-additive interactions of tobacco chewing with

A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility.

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Bush, W S; McCauley, J L; DeJager, P L; Dudek, S M; Hafler, D A; Gibson, R A; Matthews, P M; Kappos, L; Naegelin, Y; Polman, C H; Hauser, S L; Oksenberg, J; Haines, J L; Ritchie, M D

2011-07-01

Gene-gene interactions are proposed as an important component of the genetic architecture of complex diseases, and are just beginning to be evaluated in the context of genome-wide association studies (GWAS). In addition to detecting epistasis, a benefit to interaction analysis is that it also increases power to detect weak main effects. We conducted a knowledge-driven interaction analysis of a GWAS of 931 multiple sclerosis (MS) trios to discover gene-gene interactions within established biological contexts. We identify heterogeneous signals, including a gene-gene interaction between CHRM3 (muscarinic cholinergic receptor 3) and MYLK (myosin light-chain kinase) (joint P=0.0002), an interaction between two phospholipase C-β isoforms, PLCβ1 and PLCβ4 (joint P=0.0098), and a modest interaction between ACTN1 (actinin alpha 1) and MYH9 (myosin heavy chain 9) (joint P=0.0326), all localized to calcium-signaled cytoskeletal regulation. Furthermore, we discover a main effect (joint P=5.2E-5) previously unidentified by single-locus analysis within another related gene, SCIN (scinderin), a calcium-binding cytoskeleton regulatory protein. This work illustrates that knowledge-driven interaction analysis of GWAS data is a feasible approach to identify new genetic effects. The results of this study are among the first gene-gene interactions and non-immune susceptibility loci for MS. Further, the implicated genes cluster within inter-related biological mechanisms that suggest a neurodegenerative component to MS.

A Multiple Interaction Analysis Reveals ADRB3 as a Potential Candidate for Gallbladder Cancer Predisposition via a Complex Interaction with Other Candidate Gene Variations

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Rajani Rai

2015-11-01

Full Text Available Gallbladder cancer is the most common and a highly aggressive biliary tract malignancy with a dismal outcome. The pathogenesis of the disease is multifactorial, comprising the combined effect of multiple genetic variations of mild consequence along with numerous dietary and environmental risk factors. Previously, we demonstrated the association of several candidate gene variations with GBC risk. In this study, we aimed to identify the combination of gene variants and their possible interactions contributing towards genetic susceptibility of GBC. Here, we performed Multifactor-Dimensionality Reduction (MDR and Classification and Regression Tree Analysis (CRT to investigate the gene–gene interactions and the combined effect of 14 SNPs in nine genes (DR4 (rs20576, rs6557634; FAS (rs2234767; FASL (rs763110; DCC (rs2229080, rs4078288, rs7504990, rs714; PSCA (rs2294008, rs2978974; ADRA2A (rs1801253; ADRB1 (rs1800544; ADRB3 (rs4994; CYP17 (rs2486758 involved in various signaling pathways. Genotyping was accomplished by PCR-RFLP or Taqman allelic discrimination assays. SPSS software version 16.0 and MDR software version 2.0 were used for all the statistical analysis. Single locus investigation demonstrated significant association of DR4 (rs20576, rs6557634, DCC (rs714, rs2229080, rs4078288 and ADRB3 (rs4994 polymorphisms with GBC risk. MDR analysis revealed ADRB3 (rs4994 to be crucial candidate in GBC susceptibility that may act either alone (p < 0.0001, CVC = 10/10 or in combination with DCC (rs714 and rs2229080, p < 0.0001, CVC = 9/10. Our CRT results are in agreement with the above findings. Further, in-silico results of studied SNPs advocated their role in splicing, transcriptional and/or protein coding regulation. Overall, our result suggested complex interactions amongst the studied SNPs and ADRB3 rs4994 as candidate influencing GBC susceptibility.

Synergistic interactions of biotic and abiotic environmental stressors on gene expression.

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Altshuler, Ianina; McLeod, Anne M; Colbourne, John K; Yan, Norman D; Cristescu, Melania E

2015-03-01

Understanding the response of organisms to multiple stressors is critical for predicting if populations can adapt to rapid environmental change. Natural and anthropogenic stressors often interact, complicating general predictions. In this study, we examined the interactive and cumulative effects of two common environmental stressors, lowered calcium concentration, an anthropogenic stressor, and predator presence, a natural stressor, on the water flea Daphnia pulex. We analyzed expression changes of five genes involved in calcium homeostasis - cuticle proteins (Cutie, Icp2), calbindin (Calb), and calcium pump and channel (Serca and Ip3R) - using real-time quantitative PCR (RT-qPCR) in a full factorial experiment. We observed strong synergistic interactions between low calcium concentration and predator presence. While the Ip3R gene was not affected by the stressors, the other four genes were affected in their transcriptional levels by the combination of the stressors. Transcriptional patterns of genes that code for cuticle proteins (Cutie and Icp2) and a sarcoplasmic calcium pump (Serca) only responded to the combination of stressors, changing their relative expression levels in a synergistic response, while a calcium-binding protein (Calb) responded to low calcium stress and the combination of both stressors. The expression pattern of these genes (Cutie, Icp2, and Serca) were nonlinear, yet they were dose dependent across the calcium gradient. Multiple stressors can have complex, often unexpected effects on ecosystems. This study demonstrates that the dominant interaction for the set of tested genes appears to be synergism. We argue that gene expression patterns can be used to understand and predict the type of interaction expected when organisms are exposed simultaneously to natural and anthropogenic stressors.

Gene-based testing of interactions in association studies of quantitative traits.

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Li Ma

Full Text Available Various methods have been developed for identifying gene-gene interactions in genome-wide association studies (GWAS. However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene-gene interaction (GGG tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein-protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.

Class II HLA interactions modulate genetic risk for multiple sclerosis

DEFF Research Database (Denmark)

Moutsianas, Loukas; Jostins, Luke; Beecham, Ashley H

2015-01-01

Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17...

Genome-wide diet-gene interaction analyses for risk of colorectal cancer.

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Jane C Figueiredo

2014-04-01

Full Text Available Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3 and processed meat consumption (OR = 1.17; p = 8.7E-09, which was consistently observed across studies (p heterogeneity = 0.78. The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively and null among those with the GG genotype (OR = 1.03. Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention.

Inverse gene-for-gene interactions contribute additively to tan spot susceptibility in wheat.

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Liu, Zhaohui; Zurn, Jason D; Kariyawasam, Gayan; Faris, Justin D; Shi, Gongjun; Hansen, Jana; Rasmussen, Jack B; Acevedo, Maricelis

2017-06-01

Tan spot susceptibility is conferred by multiple interactions of necrotrophic effector and host sensitivity genes. Tan spot of wheat, caused by Pyrenophora tritici-repentis, is an important disease in almost all wheat-growing areas of the world. The disease system is known to involve at least three fungal-produced necrotrophic effectors (NEs) that interact with the corresponding host sensitivity (S) genes in an inverse gene-for-gene manner to induce disease. However, it is unknown if the effects of these NE-S gene interactions contribute additively to the development of tan spot. In this work, we conducted disease evaluations using different races and quantitative trait loci (QTL) analysis in a wheat recombinant inbred line (RIL) population derived from a cross between two susceptible genotypes, LMPG-6 and PI 626573. The two parental lines each harbored a single known NE sensitivity gene with LMPG-6 having the Ptr ToxC sensitivity gene Tsc1 and PI 626573 having the Ptr ToxA sensitivity gene Tsn1. Transgressive segregation was observed in the population for all races. QTL mapping revealed that both loci (Tsn1 and Tsc1) were significantly associated with susceptibility to race 1 isolates, which produce both Ptr ToxA and Ptr ToxC, and the two genes contributed additively to tan spot susceptibility. For isolates of races 2 and 3, which produce only Ptr ToxA and Ptr ToxC, only Tsn1 and Tsc1 were associated with tan spot susceptibility, respectively. This work clearly demonstrates that tan spot susceptibility in this population is due primarily to two NE-S interactions. Breeders should remove both sensitivity genes from wheat lines to obtain high levels of tan spot resistance.

Duplicability of self-interacting human genes.

LENUS (Irish Health Repository)

Pérez-Bercoff, Asa

2010-01-01

BACKGROUND: There is increasing interest in the evolution of protein-protein interactions because this should ultimately be informative of the patterns of evolution of new protein functions within the cell. One model proposes that the evolution of new protein-protein interactions and protein complexes proceeds through the duplication of self-interacting genes. This model is supported by data from yeast. We examined the relationship between gene duplication and self-interaction in the human genome. RESULTS: We investigated the patterns of self-interaction and duplication among 34808 interactions encoded by 8881 human genes, and show that self-interacting proteins are encoded by genes with higher duplicability than genes whose proteins lack this type of interaction. We show that this result is robust against the system used to define duplicate genes. Finally we compared the presence of self-interactions amongst proteins whose genes have duplicated either through whole-genome duplication (WGD) or small-scale duplication (SSD), and show that the former tend to have more interactions in general. After controlling for age differences between the two sets of duplicates this result can be explained by the time since the gene duplication. CONCLUSIONS: Genes encoding self-interacting proteins tend to have higher duplicability than proteins lacking self-interactions. Moreover these duplicate genes have more often arisen through whole-genome rather than small-scale duplication. Finally, self-interacting WGD genes tend to have more interaction partners in general in the PIN, which can be explained by their overall greater age. This work adds to our growing knowledge of the importance of contextual factors in gene duplicability.

Gene-Environment Interactions in the Development of Complex Disease Phenotypes

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Kenneth Olden

2008-03-01

Full Text Available The lack of knowledge about the earliest events in disease development is due to the multi-factorial nature of disease risk. This information gap is the consequence of the lack of appreciation for the fact that most diseases arise from the complex interactions between genes and the environment as a function of the age or stage of development of the individual. Whether an environmental exposure causes illness or not is dependent on the efficiency of the so-called “environmental response machinery” (i.e., the complex of metabolic pathways that can modulate response to environmental perturbations that one has inherited. Thus, elucidating the causes of most chronic diseases will require an understanding of both the genetic and environmental contribution to their etiology. Unfortunately, the exploration of the relationship between genes and the environment has been hampered in the past by the limited knowledge of the human genome, and by the inclination of scientists to study disease development using experimental models that consider exposure to a single environmental agent. Rarely in the past were interactions between multiple genes or between genes and environmental agents considered in studies of human disease etiology. The most critical issue is how to relate exposure-disease association studies to pathways and mechanisms. To understand how genes and environmental factors interact to perturb biological pathways to cause injury or disease, scientists will need tools with the capacity to monitor the global expression of thousands of genes, proteins and metabolites simultaneously. The generation of such data in multiple species can be used to identify conserved and functionally significant genes and pathways involved in geneenvironment interactions. Ultimately, it is this knowledge that will be used to guide agencies such as the U.S. Department of Health and Human Services in decisions regarding biomedical research funding

Association of peroxisome proliferator-activated receptor single-nucleotide polymorphisms and gene-gene interactions with the lipoprotein(a)

Institute of Scientific and Technical Information of China (English)

解惠坚

2014-01-01

Objective To examine the associations of 10 singlenucleotide polymorphisms(SNPs)in peroxisome proliferator-activated receptor(PPARs)gene with lipoprotein(a)level,and to investigate if there is gene-gene interaction among the SNPs on lipoprotein(a)level.Methods Totally 644 subjects(234 men and 410 women)were enrolled from Prevention of Multiple Metabolic Disorders and Metabolic Syndrome Study Cohort,which was an urban community survey study conducted in Jiangsu province.Ten SNPs in PPARα(rs135539,rs4253778,

Multiple interactions between maternally-activated signalling pathways control Xenopus nodal-related genes.

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Rex, Maria; Hilton, Emma; Old, Robert

2002-03-01

We have investigated the induction of the six Xenopus nodal-related genes, Xnr1-Xnr6, by maternal determinants. The beta-catenin pathway was modelled by stimulation using Xwnt8, activin-like signalling was modelled by activin, and VegT action was studied by overexpression in animal cap explants. Combinations of factors were examined, and previously unrecognised interactions were revealed in animal caps and whole embryos. For the induction of Xnr5 and Xnr6 in whole embryos, using a beta-catenin antisense morpholino oligonucleotide or a dominant negative XTcf3, we have demonstrated an absolute permissive requirement for the beta-catenin/Tcf pathway, in addition to the requirement for VegT action. In animal caps Xnr5 and Xnr6 are induced in response to VegT overexpression, and this induction is dependent upon the concomitant activation of the beta-catenin pathway that VegT initiates in animal caps. For the induction of Xnr3, VegT interacts negatively so as to inhibit the induction otherwise observed with wnt-signalling alone. The negative effect of VegT is not the result of a general inhibition of wnt-signalling, and does not result from an inhibition of wnt-induced siamois expression. A 294 bp proximal promoter fragment of the Xnr3 gene is sufficient to mediate the negative effect of VegT. Further experiments, employing cycloheximide to examine the dependence of Xnr gene expression upon proteins translated after the mid-blastula stage, demonstrated that Xnrs 4, 5 and 6 are 'primary' Xnr genes whose expression in the late blastula is solely dependent upon factors present before the mid-blastula stage.

Multiple Taf subunits of TFIID interact with Ino2 activation domains and contribute to expression of genes required for yeast phospholipid biosynthesis.

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Hintze, Stefan; Engelhardt, Maike; van Diepen, Laura; Witt, Eric; Schüller, Hans-Joachim

2017-12-01

Expression of phospholipid biosynthetic genes in yeast requires activator protein Ino2 which can bind to the UAS element inositol/choline-responsive element (ICRE) and trigger activation of target genes, using two separate transcriptional activation domains, TAD1 and TAD2. However, it is still unknown which cofactors mediate activation by TADs of Ino2. Here, we show that multiple subunits of basal transcription factor TFIID (TBP-associated factors Taf1, Taf4, Taf6, Taf10 and Taf12) are able to interact in vitro with activation domains of Ino2. Interaction was no longer observed with activation-defective variants of TAD1. We were able to identify two nonoverlapping regions in the N-terminus of Taf1 (aa 1-100 and aa 182-250) each of which could interact with TAD1 of Ino2 as well as with TAD4 of activator Adr1. Specific missense mutations within Taf1 domain aa 182-250 affecting basic and hydrophobic residues prevented interaction with wild-type TAD1 and caused reduced expression of INO1. Using chromatin immunoprecipitation we demonstrated Ino2-dependent recruitment of Taf1 and Taf6 to ICRE-containing promoters INO1 and CHO2. Transcriptional derepression of INO1 was no longer possible with temperature-sensitive taf1 and taf6 mutants cultivated under nonpermissive conditions. This result supports the hypothesis of Taf-dependent expression of structural genes activated by Ino2. © 2017 John Wiley & Sons Ltd.

A novel test for gene-ancestry interactions in genome-wide association data.

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Joanna L Davies

Full Text Available Genome-wide association study (GWAS data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into "ancestry groups" and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from gene-collection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions.

Research progress in machine learning methods for gene-gene interaction detection.

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Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

2018-03-20

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario

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Onay Venus

2007-08-01

Full Text Available Abstract Background There is growing evidence that gene-gene interactions are ubiquitous in determining the susceptibility to common human diseases. The investigation of such gene-gene interactions presents new statistical challenges for studies with relatively small sample sizes as the number of potential interactions in the genome can be large. Breast cancer provides a useful paradigm to study genetically complex diseases because commonly occurring single nucleotide polymorphisms (SNPs may additively or synergistically disturb the system-wide communication of the cellular processes leading to cancer development. Methods In this study, we systematically studied SNP-SNP interactions among 19 SNPs from 18 key genes involved in major cancer pathways in a sample of 398 breast cancer cases and 372 controls from Ontario. We discuss the methodological issues associated with the detection of SNP-SNP interactions in this dataset by applying and comparing three commonly used methods: the logistic regression model, classification and regression trees (CART, and the multifactor dimensionality reduction (MDR method. Results Our analyses show evidence for several simple (two-way and complex (multi-way SNP-SNP interactions associated with breast cancer. For example, all three methods identified XPD-[Lys751Gln]*IL10-[G(-1082A] as the most significant two-way interaction. CART and MDR identified the same critical SNPs participating in complex interactions. Our results suggest that the use of multiple statistical approaches (or an integrated approach rather than a single methodology could be the best strategy to elucidate complex gene interactions that have generally very different patterns. Conclusion The strategy used here has the potential to identify complex biological relationships among breast cancer genes and processes. This will lead to the discovery of novel biological information, which will improve breast cancer risk management.

Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

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Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

A combination test for detection of gene-environment interaction in cohort studies.

Science.gov (United States)

Coombes, Brandon; Basu, Saonli; McGue, Matt

2017-07-01

Identifying gene-environment (G-E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G-E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome-wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene. We extend recently developed score statistic based genetic association testing approaches to the G-E interaction testing problem. We also propose tests for interaction using gene-based summary measures that pool variants together. Although it has recently been shown that these summary measures can be biased and may lead to inflated type I error, we show that under several realistic scenarios, we can still provide valid tests of interaction. These tests use significantly less degrees of freedom and thus can have much higher power to detect interaction. Additionally, we demonstrate that the iSeq-aSum-min test, which combines a gene-based summary measure test, iSeq-aSum-G, and an interaction-based summary measure test, iSeq-aSum-I, provides a powerful alternative to test G-E interaction. We demonstrate the performance of these approaches using simulation studies and illustrate their performance to study interaction between the SNPs in several candidate genes and family climate environment on alcohol consumption using the Minnesota Center for Twin and Family Research dataset. © 2017 WILEY PERIODICALS, INC.

The ALMT Gene Family Performs Multiple Functions in Plants

Directory of Open Access Journals (Sweden)

Jie Liu

2018-02-01

Full Text Available The aluminium activated malate transporter (ALMT gene family is named after the first member of the family identified in wheat (Triticum aestivum L.. The product of this gene controls resistance to aluminium (Al toxicity. ALMT genes encode transmembrane proteins that function as anion channels and perform multiple functions involving the transport of organic anions (e.g., carboxylates and inorganic anions in cells. They share a PF11744 domain and are classified in the Fusaric acid resistance protein-like superfamily, CL0307. The proteins typically have five to seven transmembrane regions in the N-terminal half and a long hydrophillic C-terminal tail but predictions of secondary structure vary. Although widely spread in plants, relatively little information is available on the roles performed by other members of this family. In this review, we summarized functions of ALMT gene families, including Al resistance, stomatal function, mineral nutrition, microbe interactions, fruit acidity, light response and seed development.

A Nonlinear Model for Gene-Based Gene-Environment Interaction

Directory of Open Access Journals (Sweden)

Jian Sa

2016-06-01

Full Text Available A vast amount of literature has confirmed the role of gene-environment (G×E interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.

Assessing interactions between HLA-DRB1*15 and infectious mononucleosis on the risk of multiple sclerosis.

Science.gov (United States)

Disanto, Giulio; Hall, Carolina; Lucas, Robyn; Ponsonby, Anne-Louise; Berlanga-Taylor, Antonio J; Giovannoni, Gavin; Ramagopalan, Sreeram V

2013-09-01

Gene-environment interactions may shed light on the mechanisms underlying multiple sclerosis (MS). We pooled data from two case-control studies on incident demyelination and used different methods to assess interaction between HLA-DRB1*15 (DRB1-15) and history of infectious mononucleosis (IM). Individuals exposed to both factors were at substantially increased risk of disease (OR=7.32, 95% CI=4.92-10.90). In logistic regression models, DRB1-15 and IM status were independent predictors of disease while their interaction term was not (DRB1-15*IM: OR=1.35, 95% CI=0.79-2.23). However, interaction on an additive scale was evident (Synergy index=2.09, 95% CI=1.59-2.59; excess risk due to interaction=3.30, 95%CI=0.47-6.12; attributable proportion due to interaction=45%, 95% CI=22-68%). This suggests, if the additive model is appropriate, the DRB1-15 and IM may be involved in the same causal process leading to MS and highlights the benefit of reporting gene-environment interactions on both a multiplicative and additive scale.

Multiple genes encode the major surface glycoprotein of Pneumocystis carinii

DEFF Research Database (Denmark)

Kovacs, J A; Powell, F; Edman, J C

1993-01-01

hydrophobic region at the carboxyl terminus. The presence of multiple related msg genes encoding the major surface glycoprotein of P. carinii suggests that antigenic variation is a possible mechanism for evading host defenses. Further characterization of this family of genes should allow the development......The major surface antigen of Pneumocystis carinii, a life-threatening opportunistic pathogen in human immunodeficiency virus-infected patients, is an abundant glycoprotein that functions in host-organism interactions. A monoclonal antibody to this antigen is protective in animals, and thus...... blot studies using chromosomal or restricted DNA, the major surface glycoproteins are the products of a multicopy family of genes. The predicted protein has an M(r) of approximately 123,000, is relatively rich in cysteine residues (5.5%) that are very strongly conserved, and contains a well conserved...

An evolvable oestrogen receptor activity sensor: development of a modular system for integrating multiple genes into the yeast genome

NARCIS (Netherlands)

Fox, J.E.; Bridgham, J.T.; Bovee, T.F.H.; Thornton, J.W.

2007-01-01

To study a gene interaction network, we developed a gene-targeting strategy that allows efficient and stable genomic integration of multiple genetic constructs at distinct target loci in the yeast genome. This gene-targeting strategy uses a modular plasmid with a recyclable selectable marker and a

Fractional populations in multiple gene inheritance.

Science.gov (United States)

Chung, Myung-Hoon; Kim, Chul Koo; Nahm, Kyun

2003-01-22

With complete knowledge of the human genome sequence, one of the most interesting tasks remaining is to understand the functions of individual genes and how they communicate. Using the information about genes (locus, allele, mutation rate, fitness, etc.), we attempt to explain population demographic data. This population evolution study could complement and enhance biologists' understanding about genes. We present a general approach to study population genetics in complex situations. In the present approach, multiple allele inheritance, multiple loci inheritance, natural selection and mutations are allowed simultaneously in order to consider a more realistic situation. A simulation program is presented so that readers can readily carry out studies with their own parameters. It is shown that the multiplicity of the loci greatly affects the demographic results of fractional population ratios. Furthermore, the study indicates that some high infant mortality rates due to congenital anomalies can be attributed to multiple loci inheritance. The simulation program can be downloaded from http://won.hongik.ac.kr/~mhchung/index_files/yapop.htm. In order to run this program, one needs Visual Studio.NET platform, which can be downloaded from http://msdn.microsoft.com/netframework/downloads/default.asp.

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

Science.gov (United States)

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.

Improving functional modules discovery by enriching interaction networks with gene profiles

KAUST Repository

Salem, Saeed

2013-05-01

Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.

A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility

DEFF Research Database (Denmark)

Damotte, V; Guillot-Noel, L; Patsopoulos, N A

2014-01-01

adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes...... in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell...... belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted...

Pharmacogenomics of Hypertension and Preeclampsia: Focus on Gene–Gene Interactions

Directory of Open Access Journals (Sweden)

Marcelo R. Luizon

2018-02-01

Full Text Available Hypertension is a leading cause of cardiovascular mortality, but only about half of patients on antihypertensive therapy achieve blood pressure control. Preeclampsia is defined as pregnancy-induced hypertension and proteinuria, and is associated with increased maternal and perinatal mortality and morbidity. Similarly, a large number of patients with preeclampsia are non-responsive to antihypertensive therapy. Pharmacogenomics may help to guide the personalized treatment for non-responsive hypertensive patients. There is evidence for the association of genetic variants with variable response to the most commonly used antihypertensive drugs. However, further replication is needed to confirm these associations in different populations. The failure to replicate findings from single-locus association studies has prompted the search for novel statistical methods for data analysis, which are required to detect the complex effects from multiple genes to drug response phenotypes. Notably, gene–gene interaction analyses have been applied to pharmacogenetic studies, including antihypertensive drug response. In this perspective article, we present advances of considering the interactions among genetic polymorphisms of different candidate genes within pathways relevant to antihypertensive drug response, and we highlight recent findings related to gene–gene interactions on pharmacogenetics of hypertension and preeclampsia. Finally, we discuss the future directions that are needed to unravel additional genes and variants involved in the responsiveness to antihypertensive drugs.

Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction : A systematic review on CYP2C9, CYP2C19 and CYP2D6

NARCIS (Netherlands)

Bahar, Muh Akbar; Setiawan, Didik; Hak, Eelko; Wilffert, Bob

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple

Spin-orbit interaction in multiple quantum wells

International Nuclear Information System (INIS)

Hao, Ya-Fei

2015-01-01

In this paper, we investigate how the structure of multiple quantum wells affects spin-orbit interactions. To increase the interface-related Rashba spin splitting and the strength of the interface-related Rashba spin-orbit interaction, we designed three kinds of multiple quantum wells. We demonstrate that the structure of the multiple quantum wells strongly affected the interface-related Rashba spin-orbit interaction, increasing the interface-related Rashba spin splitting to up to 26% larger in multiple quantum wells than in a stepped quantum well. We also show that the cubic Dresselhaus spin-orbit interaction similarly influenced the spin relaxation time of multiple quantum wells and that of a stepped quantum well. The increase in the interface-related Rashba spin splitting originates from the relationship between interface-related Rashba spin splitting and electron probability density. Our results suggest that multiple quantum wells can be good candidates for spintronic devices

Spin-orbit interaction in multiple quantum wells

Energy Technology Data Exchange (ETDEWEB)

Hao, Ya-Fei, E-mail: haoyafei@zjnu.cn [Physics Department, Zhejiang Normal University, Zhejiang 321004 (China)

2015-01-07

In this paper, we investigate how the structure of multiple quantum wells affects spin-orbit interactions. To increase the interface-related Rashba spin splitting and the strength of the interface-related Rashba spin-orbit interaction, we designed three kinds of multiple quantum wells. We demonstrate that the structure of the multiple quantum wells strongly affected the interface-related Rashba spin-orbit interaction, increasing the interface-related Rashba spin splitting to up to 26% larger in multiple quantum wells than in a stepped quantum well. We also show that the cubic Dresselhaus spin-orbit interaction similarly influenced the spin relaxation time of multiple quantum wells and that of a stepped quantum well. The increase in the interface-related Rashba spin splitting originates from the relationship between interface-related Rashba spin splitting and electron probability density. Our results suggest that multiple quantum wells can be good candidates for spintronic devices.

C-State: an interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search.

Science.gov (United States)

Sowpati, Divya Tej; Srivastava, Surabhi; Dhawan, Jyotsna; Mishra, Rakesh K

2017-09-13

Comparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge. We developed a fast and interactive web app, C-State (Chromatin-State), to query and plot chromatin landscapes across multiple loci and cell types. C-State has an interactive, JavaScript-based graphical user interface and runs locally in modern web browsers that are pre-installed on all computers, thus eliminating the need for cumbersome data transfer, pre-processing and prior programming knowledge. C-State is unique in its ability to extract and analyze multi-gene epigenetic information. It allows for powerful GUI-based pattern searching and visualization. We include a case study to demonstrate its potential for identifying user-defined epigenetic trends in context of gene expression profiles.

Environmental confounding in gene-environment interaction studies.

Science.gov (United States)

Vanderweele, Tyler J; Ko, Yi-An; Mukherjee, Bhramar

2013-07-01

We show that, in the presence of uncontrolled environmental confounding, joint tests for the presence of a main genetic effect and gene-environment interaction will be biased if the genetic and environmental factors are correlated, even if there is no effect of either the genetic factor or the environmental factor on the disease. When environmental confounding is ignored, such tests will in fact reject the joint null of no genetic effect with a probability that tends to 1 as the sample size increases. This problem with the joint test vanishes under gene-environment independence, but it still persists if estimating the gene-environment interaction parameter itself is of interest. Uncontrolled environmental confounding will bias estimates of gene-environment interaction parameters even under gene-environment independence, but it will not do so if the unmeasured confounding variable itself does not interact with the genetic factor. Under gene-environment independence, if the interaction parameter without controlling for the environmental confounder is nonzero, then there is gene-environment interaction either between the genetic factor and the environmental factor of interest or between the genetic factor and the unmeasured environmental confounder. We evaluate several recently proposed joint tests in a simulation study and discuss the implications of these results for the conduct of gene-environment interaction studies.

Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6.

Science.gov (United States)

Bahar, Muh Akbar; Setiawan, Didik; Hak, Eelko; Wilffert, Bob

2017-05-01

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple biotransformation pathways, which is referred to as drug-drug-gene interaction (DDGI). In this systematic review, we report the impact of pharmacogenetics on DDI and DDGI in which three major drug-metabolizing enzymes - CYP2C9, CYP2C19 and CYP2D6 - are central. We observed that several DDI and DDGI are highly gene-dependent, leading to a different magnitude of interaction. Precision drug therapy should take pharmacogenetics into account when drug interactions in clinical practice are expected.

Gene-gene interactions and gene polymorphisms of VEGFA and EG-VEGF gene systems in recurrent pregnancy loss.

Science.gov (United States)

Su, Mei-Tsz; Lin, Sheng-Hsiang; Chen, Yi-Chi; Kuo, Pao-Lin

2014-06-01

Both vascular endothelial growth factor A (VEGFA) and endocrine gland-derived vascular endothelial growth factor (EG-VEGF) systems play major roles in angiogenesis. A body of evidence suggests VEGFs regulate critical processes during pregnancy and have been associated with recurrent pregnancy loss (RPL). However, little information is available regarding the interaction of these two major major angiogenesis-related systems in early human pregnancy. This study was conducted to investigate the association of gene polymorphisms and gene-gene interaction among genes in VEGFA and EG-VEGF systems and idiopathic RPL. A total of 98 women with history of idiopathic RPL and 142 controls were included, and 5 functional SNPs selected from VEGFA, KDR, EG-VEGF (PROK1), PROKR1 and PROKR2 were genotyped. We used multifactor dimensionality reduction (MDR) analysis to choose a best model and evaluate gene-gene interactions. Ingenuity pathways analysis (IPA) was introduced to explore possible complex interactions. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL (P<0.01). The MDR test revealed that the KDR (Q472H) polymorphism was the best loci to be associated with RPL (P=0.02). IPA revealed EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3 signaling pathways. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL. EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3.

ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.

Directory of Open Access Journals (Sweden)

Brett A McKinney

Full Text Available Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k for each gene to optimize the Relief-F test statistics (importance scores for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to

Gene-Gene and Gene-Environment Interactions in the Etiology of Breast Cancer

National Research Council Canada - National Science Library

Adegoke, Olufemi

2003-01-01

The objective of this CDA is to evaluate the gene-gene and gene-environment interactions in the etiology of breast cancer in two ongoing case-control studies, the Shanghai Breast Cancer Study (SBCS...

Overview of diet-gene interactions and the example of xanthophylls.

Science.gov (United States)

Demmig-Adams, Barbara; Adams, William W

2010-01-01

This chapter provides an overview of diet-gene interaction and the role of dietary factors in human health and disease. Human master control genes that regulate processes of fundamental importance, such as cell proliferation and the immune response, are introduced and their modulation by nutraceuticals, produced by plants and photosynthetic microbes, is reviewed. Emphasis is placed on antioxidants and polyunsaturated fatty acids as regulators of master control genes. Furthermore, a case study is presented on xanthophylls, a group of carotenoids with multiple health benefits in the protection against eye disease and other chronic diseases, as well as the synergism between xanthophylls and other dietary factors. Lastly, dietary sources of the xanthophylls zeaxanthin and lutein are reviewed and their enhancement via genetic engineering is discussed.

The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study.

Science.gov (United States)

Lekman, Magnus; Hössjer, Ola; Andrews, Peter; Källberg, Henrik; Uvehag, Daniel; Charney, Dennis; Manji, Husseini; Rush, John A; McMahon, Francis J; Moore, Jason H; Kockum, Ingrid

2014-01-01

Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear. This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach. Although none of the interaction survived correction for multiple comparisons, the results provide important information for future genetic interaction studies in complex disorders. Among the 0.5% most significant observations, none had been reported previously for risk to MDD. Within this group of interactions, less than 0.03% would have been detectable based on main effect approach or an a priori algorithm. We evaluated correlations among the three different models and conclude that all three algorithms detected the same interactions to a low degree. Although the top interactions had a surprisingly large effect size for MDD (e.g. additive dominant model Puncorrected = 9.10E-9 with attributable proportion (AP) value = 0.58 and multiplicative recessive model with Puncorrected = 6.95E-5 with odds ratio (OR estimated from β3) value = 4.99) the area under the curve (AUC) estimates were low (< 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (< 0.15). We conclude that the top interactions on their own did not explain much of the genetic variance of MDD. The different statistical interaction methods we used in the present study did not identify the same pairs of interacting markers. Genetic interaction studies may uncover previously

Intervene: a tool for intersection and visualization of multiple gene or genomic region sets.

Science.gov (United States)

Khan, Aziz; Mathelier, Anthony

2017-05-31

A common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited. To address this gap, we have developed the Intervene tool, which provides an easy and automated interface for the effective intersection and visualization of genomic region or list sets, thus facilitating their analysis and interpretation. Intervene contains three modules: venn to generate Venn diagrams of up to six sets, upset to generate UpSet plots of multiple sets, and pairwise to compute and visualize intersections of multiple sets as clustered heat maps. Intervene, and its interactive web ShinyApp companion, generate publication-quality figures for the interpretation of genomic region and list sets. Intervene and its web application companion provide an easy command line and an interactive web interface to compute intersections of multiple genomic and list sets. They have the capacity to plot intersections using easy-to-interpret visual approaches. Intervene is developed and designed to meet the needs of both computer scientists and biologists. The source code is freely available at https://bitbucket.org/CBGR/intervene , with the web application available at https://asntech.shinyapps.io/intervene .

Three gene expression vector sets for concurrently expressing multiple genes in Saccharomyces cerevisiae.

Science.gov (United States)

Ishii, Jun; Kondo, Takashi; Makino, Harumi; Ogura, Akira; Matsuda, Fumio; Kondo, Akihiko

2014-05-01

Yeast has the potential to be used in bulk-scale fermentative production of fuels and chemicals due to its tolerance for low pH and robustness for autolysis. However, expression of multiple external genes in one host yeast strain is considerably labor-intensive due to the lack of polycistronic transcription. To promote the metabolic engineering of yeast, we generated systematic and convenient genetic engineering tools to express multiple genes in Saccharomyces cerevisiae. We constructed a series of multi-copy and integration vector sets for concurrently expressing two or three genes in S. cerevisiae by embedding three classical promoters. The comparative expression capabilities of the constructed vectors were monitored with green fluorescent protein, and the concurrent expression of genes was monitored with three different fluorescent proteins. Our multiple gene expression tool will be helpful to the advanced construction of genetically engineered yeast strains in a variety of research fields other than metabolic engineering. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Univariate and multiple linear regression analyses for 23 single nucleotide polymorphisms in 14 genes predisposing to chronic glomerular diseases and IgA nephropathy in Han Chinese.

Science.gov (United States)

Wang, Hui; Sui, Weiguo; Xue, Wen; Wu, Junyong; Chen, Jiejing; Dai, Yong

2014-09-01

Immunoglobulin A nephropathy (IgAN) is a complex trait regulated by the interaction among multiple physiologic regulatory systems and probably involving numerous genes, which leads to inconsistent findings in genetic studies. One possibility of failure to replicate some single-locus results is that the underlying genetics of IgAN nephropathy is based on multiple genes with minor effects. To learn the association between 23 single nucleotide polymorphisms (SNPs) in 14 genes predisposing to chronic glomerular diseases and IgAN in Han males, the 23 SNPs genotypes of 21 Han males were detected and analyzed with a BaiO gene chip, and their associations were analyzed with univariate analysis and multiple linear regression analysis. Analysis showed that CTLA4 rs231726 and CR2 rs1048971 revealed a significant association with IgAN. These findings support the multi-gene nature of the etiology of IgAN and propose a potential gene-gene interactive model for future studies.

The role of gene-gene interaction in the prediction of criminal behavior.

Science.gov (United States)

Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

2014-04-01

A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies.

Science.gov (United States)

Kurbasic, Azra; Poveda, Alaitz; Chen, Yan; Agren, Asa; Engberg, Elisabeth; Hu, Frank B; Johansson, Ingegerd; Barroso, Ines; Brändström, Anders; Hallmans, Göran; Renström, Frida; Franks, Paul W

2014-12-01

Most complex diseases have well-established genetic and non-genetic risk factors. In some instances, these risk factors are likely to interact, whereby their joint effects convey a level of risk that is either significantly more or less than the sum of these risks. Characterizing these gene-environment interactions may help elucidate the biology of complex diseases, as well as to guide strategies for their targeted prevention. In most cases, the detection of gene-environment interactions will require sample sizes in excess of those needed to detect the marginal effects of the genetic and environmental risk factors. Although many consortia have been formed, comprising multiple diverse cohorts to detect gene-environment interactions, few robust examples of such interactions have been discovered. This may be because combining data across studies, usually through meta-analysis of summary data from the contributing cohorts, is often a statistically inefficient approach for the detection of gene-environment interactions. Ideally, single, very large and well-genotyped prospective cohorts, with validated measures of environmental risk factor and disease outcomes should be used to study interactions. The presence of strong founder effects within those cohorts might further strengthen the capacity to detect novel genetic effects and gene-environment interactions. Access to accurate genealogical data would also aid in studying the diploid nature of the human genome, such as genomic imprinting (parent-of-origin effects). Here we describe two studies from northern Sweden (the GLACIER and VIKING studies) that fulfill these characteristics.

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

Science.gov (United States)

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

Evidence for gene-environment interaction in a genome wide study of isolated, non-syndromic cleft palate

Science.gov (United States)

Beaty, Terri H.; Ruczinski, Ingo; Murray, Jeffrey C.; Marazita, Mary L.; Munger, Ronald G.; Hetmanski, Jacqueline B.; Murray, Tanda; Redett, Richard J.; Fallin, M. Daniele; Liang, Kung Yee; Wu, Tao; Patel, Poorav J.; Jin, Sheng C.; Zhang, Tian Xiao; Schwender, Holger; Wu-Chou, Yah Huei; Chen, Philip K; Chong, Samuel S; Cheah, Felicia; Yeow, Vincent; Ye, Xiaoqian; Wang, Hong; Huang, Shangzhi; Jabs, Ethylin W.; Shi, Bing; Wilcox, Allen J.; Lie, Rolv T.; Jee, Sun Ha; Christensen, Kaare; Doheny, Kimberley F.; Pugh, Elizabeth W.; Ling, Hua; Scott, Alan F.

2011-01-01

Non-syndromic cleft palate (CP) is a common birth defect with a complex and heterogeneous etiology involving both genetic and environmental risk factors. We conducted a genome wide association study (GWAS) using 550 case-parent trios, ascertained through a CP case collected in an international consortium. Family based association tests of single nucleotide polymorphisms (SNP) and three common maternal exposures (maternal smoking, alcohol consumption and multivitamin supplementation) were used in a combined 2 df test for gene (G) and gene-environment (G×E) interaction simultaneously, plus a separate 1 df test for G×E interaction alone. Conditional logistic regression models were used to estimate effects on risk to exposed and unexposed children. While no SNP achieved genome wide significance when considered alone, markers in several genes attained or approached genome wide significance when G×E interaction was included. Among these, MLLT3 and SMC2 on chromosome 9 showed multiple SNPs resulting in increased risk if the mother consumed alcohol during the peri-conceptual period (3 months prior to conception through the first trimester). TBK1 on chr. 12 and ZNF236 on chr. 18 showed multiple SNPs associated with higher risk of CP in the presence of maternal smoking. Additional evidence of reduced risk due to G×E interaction in the presence of multivitamin supplementation was observed for SNPs in BAALC on chr. 8. These results emphasize the need to consider G×E interaction when searching for genes influencing risk to complex and heterogeneous disorders, such as non-syndromic CP. PMID:21618603

Inference of gene-phenotype associations via protein-protein interaction and orthology.

Directory of Open Access Journals (Sweden)

Panwen Wang

Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.

Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

Science.gov (United States)

Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

2017-10-03

With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

Gene-based interaction analysis shows GABAergic genes interacting with parenting in adolescent depressive symptoms

NARCIS (Netherlands)

Van Assche, Evelien; Moons, Tim; Cinar, Ozan; Viechtbauer, Wolfgang; Oldehinkel, Albertine J.; Van Leeuwen, Karla; Verschueren, Karine; Colpin, Hilde; Lambrechts, Diether; Van den Noortgate, Wim; Goossens, Luc; Claes, Stephan; van Winkel, Ruud

2017-01-01

BACKGROUND: Most gene-environment interaction studies (G × E) have focused on single candidate genes. This approach is criticized for its expectations of large effect sizes and occurrence of spurious results. We describe an approach that accounts for the polygenic nature of most psychiatric

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.

Science.gov (United States)

Kwon, Minseok; Leem, Sangseob; Yoon, Joon; Park, Taesung

2018-03-19

With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants. Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes. The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

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Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (Plogistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk

Directory of Open Access Journals (Sweden)

Lewei Duan

2013-01-01

Full Text Available A variety of methods have been proposed for studying the association of multiple genes thought to be involved in a common pathway for a particular disease. Here, we present an extension of a Bayesian hierarchical modeling strategy that allows for multiple SNPs within each gene, with external prior information at either the SNP or gene level. The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. The method is applied to data on 504 SNPs in 38 candidate genes involved in DNA damage response in the WECARE study of second breast cancers in relation to radiotherapy exposure.

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

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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.

Screening for interaction effects in gene expression data.

Directory of Open Access Journals (Sweden)

Peter J Castaldi

Full Text Available Expression quantitative trait (eQTL studies are a powerful tool for identifying genetic variants that affect levels of messenger RNA. Since gene expression is controlled by a complex network of gene-regulating factors, one way to identify these factors is to search for interaction effects between genetic variants and mRNA levels of transcription factors (TFs and their respective target genes. However, identification of interaction effects in gene expression data pose a variety of methodological challenges, and it has become clear that such analyses should be conducted and interpreted with caution. Investigating the validity and interpretability of several interaction tests when screening for eQTL SNPs whose effect on the target gene expression is modified by the expression level of a transcription factor, we characterized two important methodological issues. First, we stress the scale-dependency of interaction effects and highlight that commonly applied transformation of gene expression data can induce or remove interactions, making interpretation of results more challenging. We then demonstrate that, in the setting of moderate to strong interaction effects on the order of what may be reasonably expected for eQTL studies, standard interaction screening can be biased due to heteroscedasticity induced by true interactions. Using simulation and real data analysis, we outline a set of reasonable minimum conditions and sample size requirements for reliable detection of variant-by-environment and variant-by-TF interactions using the heteroscedasticity consistent covariance-based approach.

Interactive effects of antioxidant genes and air pollution on respiratory function and airway disease: a HuGE review.

Science.gov (United States)

Minelli, Cosetta; Wei, Igor; Sagoo, Gurdeep; Jarvis, Debbie; Shaheen, Seif; Burney, Peter

2011-03-15

Susceptibility to the respiratory effects of air pollution varies between individuals. Although some evidence suggests higher susceptibility for subjects carrying variants of antioxidant genes, findings from gene-pollution interaction studies conflict in terms of the presence and direction of interactions. The authors conducted a systematic review on antioxidant gene-pollution interactions which included 15 studies, with 12 supporting the presence of interactions. For the glutathione S-transferase M1 gene (GSTM1) (n=10 studies), only 1 study found interaction with the null genotype alone, although 5 observed interactions when GSTM1 was evaluated jointly with other genes (mainly NAD(P)H dehydrogenase [quinone] 1 (NQO1)). All studies on the glutathione S-transferase P1 (GSTP1) Ile105Val polymorphism (n=11) provided some evidence of interaction, but findings conflicted in terms of risk allele. Results were negative for glutathione S-transferase T1 (GSTT1) (n=3) and positive for heme oxygenase 1 (HMOX-1) (n=2). Meta-analysis could not be performed because there were insufficient data available for any specific gene-pollutant-outcome combination. Overall the evidence supports the presence of gene-pollution interactions, although which pollutant interacts with which gene is unclear. However, issues regarding multiple testing, selective reporting, and publication bias raise the possibility of false-positive findings. Larger studies with greater accuracy of pollution assessment and improved quality of conduct and reporting are required. © The Author 2011. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.

Gene-gene, gene-environment, gene-nutrient interactions and single nucleotide polymorphisms of inflammatory cytokines.

Science.gov (United States)

Nadeem, Amina; Mumtaz, Sadaf; Naveed, Abdul Khaliq; Aslam, Muhammad; Siddiqui, Arif; Lodhi, Ghulam Mustafa; Ahmad, Tausif

2015-05-15

Inflammation plays a significant role in the etiology of type 2 diabetes mellitus (T2DM). The rise in the pro-inflammatory cytokines is the essential step in glucotoxicity and lipotoxicity induced mitochondrial injury, oxidative stress and beta cell apoptosis in T2DM. Among the recognized markers are interleukin (IL)-6, IL-1, IL-10, IL-18, tissue necrosis factor-alpha (TNF-α), C-reactive protein, resistin, adiponectin, tissue plasminogen activator, fibrinogen and heptoglobins. Diabetes mellitus has firm genetic and very strong environmental influence; exhibiting a polygenic mode of inheritance. Many single nucleotide polymorphisms (SNPs) in various genes including those of pro and anti-inflammatory cytokines have been reported as a risk for T2DM. Not all the SNPs have been confirmed by unifying results in different studies and wide variations have been reported in various ethnic groups. The inter-ethnic variations can be explained by the fact that gene expression may be regulated by gene-gene, gene-environment and gene-nutrient interactions. This review highlights the impact of these interactions on determining the role of single nucleotide polymorphism of IL-6, TNF-α, resistin and adiponectin in pathogenesis of T2DM.

Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules.

Science.gov (United States)

Curtis, Ross E; Kim, Seyoung; Woolford, John L; Xu, Wenjie; Xing, Eric P

2013-03-21

Association analysis using genome-wide expression quantitative trait locus (eQTL) data investigates the effect that genetic variation has on cellular pathways and leads to the discovery of candidate regulators. Traditional analysis of eQTL data via pairwise statistical significance tests or linear regression does not leverage the availability of the structural information of the transcriptome, such as presence of gene networks that reveal correlation and potentially regulatory relationships among the study genes. We employ a new eQTL mapping algorithm, GFlasso, which we have previously developed for sparse structured regression, to reanalyze a genome-wide yeast dataset. GFlasso fully takes into account the dependencies among expression traits to suppress false positives and to enhance the signal/noise ratio. Thus, GFlasso leverages the gene-interaction network to discover the pleiotropic effects of genetic loci that perturb the expression level of multiple (rather than individual) genes, which enables us to gain more power in detecting previously neglected signals that are marginally weak but pleiotropically significant. While eQTL hotspots in yeast have been reported previously as genomic regions controlling multiple genes, our analysis reveals additional novel eQTL hotspots and, more interestingly, uncovers groups of multiple contributing eQTL hotspots that affect the expression level of functional gene modules. To our knowledge, our study is the first to report this type of gene regulation stemming from multiple eQTL hotspots. Additionally, we report the results from in-depth bioinformatics analysis for three groups of these eQTL hotspots: ribosome biogenesis, telomere silencing, and retrotransposon biology. We suggest candidate regulators for the functional gene modules that map to each group of hotspots. Not only do we find that many of these candidate regulators contain mutations in the promoter and coding regions of the genes, in the case of the Ribi group

A Study of Multiplicities in Hadronic Interactions

Energy Technology Data Exchange (ETDEWEB)

Estrada Tristan, Nora Patricia; /San Luis Potosi U.

2006-02-01

Using data from the SELEX (Fermilab E781) experiment obtained with a minimum-bias trigger, we study multiplicity and angular distributions of secondary particles produced in interactions in the experimental targets. We observe interactions of {Sigma}{sup -}, proton, {pi}{sup -}, and {pi}{sup +}, at beam momenta between 250 GeV/c and 650 GeV/c, in copper, polyethylene, graphite, and beryllium targets. We show that the multiplicity and angular distributions for meson and baryon beams at the same momentum are identical. We also show that the mean multiplicity increases with beam momentum, and presents only small variations with the target material.

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems.

Science.gov (United States)

Salleh, Faridah Hani Mohamed; Zainudin, Suhaila; Arif, Shereena M

2017-01-01

Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.

Evidence for gene-environment interaction in a genome wide study of nonsyndromic cleft palate

DEFF Research Database (Denmark)

Beaty, Terri H; Ruczinski, Ingo; Murray, Jeffrey C

2011-01-01

Nonsyndromic cleft palate (CP) is a common birth defect with a complex and heterogeneous etiology involving both genetic and environmental risk factors. We conducted a genome-wide association study (GWAS) using 550 case-parent trios, ascertained through a CP case collected in an international...... consortium. Family-based association tests of single nucleotide polymorphisms (SNP) and three common maternal exposures (maternal smoking, alcohol consumption, and multivitamin supplementation) were used in a combined 2 df test for gene (G) and gene-environment (G × E) interaction simultaneously, plus...... multiple SNPs associated with higher risk of CP in the presence of maternal smoking. Additional evidence of reduced risk due to G × E interaction in the presence of multivitamin supplementation was observed for SNPs in BAALC on chr. 8. These results emphasize the need to consider G × E interaction when...

Systematic Search for Gene-Gene Interaction Effect on Prostate Cancer Risk

Science.gov (United States)

2013-07-01

Systematic Search for Gene-Gene Interaction 5a. CONTRACT NUMBER Effect on Prostate Cancer Risk 5b. GRANT NUMBER W81XWH-09-1-0488 5c. PROGRAM...Supported by this grant ) 1. Tao S, Wang Z, Feng J, Hsu FC, Jin G, Kin ST, Zhang Z, Gronberg H, Zheng, SL, Isaacs WB, XU J, Sun J. A Genome-Wide Search for...order interactions among estrogen- metabolism genes in sporadic breast cancer. Am J Hum Genet, 69, 138-47. 48. Marchini, J., Donnelly, P. and Cardon

Gene-environment interaction involving recently identified colorectal cancer susceptibility loci

Science.gov (United States)

Kantor, Elizabeth D.; Hutter, Carolyn M.; Minnier, Jessica; Berndt, Sonja I.; Brenner, Hermann; Caan, Bette J.; Campbell, Peter T.; Carlson, Christopher S.; Casey, Graham; Chan, Andrew T.; Chang-Claude, Jenny; Chanock, Stephen J.; Cotterchio, Michelle; Du, Mengmeng; Duggan, David; Fuchs, Charles S.; Giovannucci, Edward L.; Gong, Jian; Harrison, Tabitha A.; Hayes, Richard B.; Henderson, Brian E.; Hoffmeister, Michael; Hopper, John L.; Jenkins, Mark A.; Jiao, Shuo; Kolonel, Laurence N.; Le Marchand, Loic; Lemire, Mathieu; Ma, Jing; Newcomb, Polly A.; Ochs-Balcom, Heather M.; Pflugeisen, Bethann M.; Potter, John D.; Rudolph, Anja; Schoen, Robert E.; Seminara, Daniela; Slattery, Martha L.; Stelling, Deanna L.; Thomas, Fridtjof; Thornquist, Mark; Ulrich, Cornelia M.; Warnick, Greg S.; Zanke, Brent W.; Peters, Ulrike; Hsu, Li; White, Emily

2014-01-01

BACKGROUND Genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) that are associated with risk of colorectal cancer (CRC). Prior research has evaluated the presence of gene-environment interaction involving the first 10 identified susceptibility loci, but little work has been conducted on interaction involving SNPs at recently identified susceptibility loci, including: rs10911251, rs6691170, rs6687758, rs11903757, rs10936599, rs647161, rs1321311, rs719725, rs1665650, rs3824999, rs7136702, rs11169552, rs59336, rs3217810, rs4925386, and rs2423279. METHODS Data on 9160 cases and 9280 controls from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and Colon Cancer Family Registry (CCFR) were used to evaluate the presence of interaction involving the above-listed SNPs and sex, body mass index (BMI), alcohol consumption, smoking, aspirin use, post-menopausal hormone (PMH) use, as well as intake of dietary calcium, dietary fiber, dietary folate, red meat, processed meat, fruit, and vegetables. Interaction was evaluated using a fixed-effects meta-analysis of an efficient Empirical Bayes estimator, and permutation was used to account for multiple comparisons. RESULTS None of the permutation-adjusted p-values reached statistical significance. CONCLUSIONS The associations between recently identified genetic susceptibility loci and CRC are not strongly modified by sex, BMI, alcohol, smoking, aspirin, PMH use, and various dietary factors. IMPACT Results suggest no evidence of strong gene-environment interactions involving the recently identified 16 susceptibility loci for CRC taken one at a time. PMID:24994789

A kernel regression approach to gene-gene interaction detection for case-control studies.

Science.gov (United States)

Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

Prostate cancer risk locus at 8q24 as a regulatory hub by physical interactions with multiple genomic loci across the genome.

Science.gov (United States)

Du, Meijun; Yuan, Tiezheng; Schilter, Kala F; Dittmar, Rachel L; Mackinnon, Alexander; Huang, Xiaoyi; Tschannen, Michael; Worthey, Elizabeth; Jacob, Howard; Xia, Shu; Gao, Jianzhong; Tillmans, Lori; Lu, Yan; Liu, Pengyuan; Thibodeau, Stephen N; Wang, Liang

2015-01-01

Chromosome 8q24 locus contains regulatory variants that modulate genetic risk to various cancers including prostate cancer (PC). However, the biological mechanism underlying this regulation is not well understood. Here, we developed a chromosome conformation capture (3C)-based multi-target sequencing technology and systematically examined three PC risk regions at the 8q24 locus and their potential regulatory targets across human genome in six cell lines. We observed frequent physical contacts of this risk locus with multiple genomic regions, in particular, inter-chromosomal interaction with CD96 at 3q13 and intra-chromosomal interaction with MYC at 8q24. We identified at least five interaction hot spots within the predicted functional regulatory elements at the 8q24 risk locus. We also found intra-chromosomal interaction genes PVT1, FAM84B and GSDMC and inter-chromosomal interaction gene CXorf36 in most of the six cell lines. Other gene regions appeared to be cell line-specific, such as RRP12 in LNCaP, USP14 in DU-145 and SMIN3 in lymphoblastoid cell line. We further found that the 8q24 functional domains more likely interacted with genomic regions containing genes enriched in critical pathways such as Wnt signaling and promoter motifs such as E2F1 and TCF3. This result suggests that the risk locus may function as a regulatory hub by physical interactions with multiple genes important for prostate carcinogenesis. Further understanding genetic effect and biological mechanism of these chromatin interactions will shed light on the newly discovered regulatory role of the risk locus in PC etiology and progression. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Aberrant gene promoter methylation associated with sporadic multiple colorectal cancer.

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Victoria Gonzalo

Full Text Available BACKGROUND: Colorectal cancer (CRC multiplicity has been mainly related to polyposis and non-polyposis hereditary syndromes. In sporadic CRC, aberrant gene promoter methylation has been shown to play a key role in carcinogenesis, although little is known about its involvement in multiplicity. To assess the effect of methylation in tumor multiplicity in sporadic CRC, hypermethylation of key tumor suppressor genes was evaluated in patients with both multiple and solitary tumors, as a proof-of-concept of an underlying epigenetic defect. METHODOLOGY/PRINCIPAL FINDINGS: We examined a total of 47 synchronous/metachronous primary CRC from 41 patients, and 41 gender, age (5-year intervals and tumor location-paired patients with solitary tumors. Exclusion criteria were polyposis syndromes, Lynch syndrome and inflammatory bowel disease. DNA methylation at the promoter region of the MGMT, CDKN2A, SFRP1, TMEFF2, HS3ST2 (3OST2, RASSF1A and GATA4 genes was evaluated by quantitative methylation specific PCR in both tumor and corresponding normal appearing colorectal mucosa samples. Overall, patients with multiple lesions exhibited a higher degree of methylation in tumor samples than those with solitary tumors regarding all evaluated genes. After adjusting for age and gender, binomial logistic regression analysis identified methylation of MGMT2 (OR, 1.48; 95% CI, 1.10 to 1.97; p = 0.008 and RASSF1A (OR, 2.04; 95% CI, 1.01 to 4.13; p = 0.047 as variables independently associated with tumor multiplicity, being the risk related to methylation of any of these two genes 4.57 (95% CI, 1.53 to 13.61; p = 0.006. Moreover, in six patients in whom both tumors were available, we found a correlation in the methylation levels of MGMT2 (r = 0.64, p = 0.17, SFRP1 (r = 0.83, 0.06, HPP1 (r = 0.64, p = 0.17, 3OST2 (r = 0.83, p = 0.06 and GATA4 (r = 0.6, p = 0.24. Methylation in normal appearing colorectal mucosa from patients with multiple and solitary CRC showed no relevant

Parallel Beam-Beam Simulation Incorporating Multiple Bunches and Multiple Interaction Regions

CERN Document Server

Jones, F W; Pieloni, T

2007-01-01

The simulation code COMBI has been developed to enable the study of coherent beam-beam effects in the full collision scenario of the LHC, with multiple bunches interacting at multiple crossing points over many turns. The program structure and input are conceived in a general way which allows arbitrary numbers and placements of bunches and interaction points (IP's), together with procedural options for head-on and parasitic collisions (in the strong-strong sense), beam transport, statistics gathering, harmonic analysis, and periodic output of simulation data. The scale of this problem, once we go beyond the simplest case of a pair of bunches interacting once per turn, quickly escalates into the parallel computing arena, and herein we will describe the construction of an MPI-based version of COMBI able to utilize arbitrary numbers of processors to support efficient calculation of multi-bunch multi-IP interactions and transport. Implementing the parallel version did not require extensive disruption of the basic ...

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

Directory of Open Access Journals (Sweden)

Faridah Hani Mohamed Salleh

2017-01-01

Full Text Available Gene regulatory network (GRN reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C as a direct interaction (A → C. Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.

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

Science.gov (United States)

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

Association and Interaction Effect of AGTR1 and AGTR2 Gene Polymorphisms with Dietary Pattern on Metabolic Risk Factors of Cardiovascular Disease in Malaysian Adults

OpenAIRE

Yap, Roseline Wai Kuan; Shidoji, Yoshihiro; Yap, Wai Sum; Masaki, Motofumi

2017-01-01

Gene-diet interaction using a multifactorial approach is preferred to study the multiple risk factors of cardiovascular disease (CVD). This study examined the association and gene-diet interaction effects of the angiotensin II type 1 receptor (AGTR1) gene (rs5186), and type 2 receptor (AGTR2) gene (rs1403543) polymorphisms on metabolic risk factors of CVD in Malaysian adults. CVD parameters (BMI, blood pressure, glycated hemoglobin, total cholesterol (TC), triglycerides, low-density lipoprote...

Detection of Gene Interactions Based on Syntactic Relations

Directory of Open Access Journals (Sweden)

Mi-Young Kim

2008-01-01

Full Text Available Interactions between proteins and genes are considered essential in the description of biomolecular phenomena, and networks of interactions are applied in a system's biology approach. Recently, many studies have sought to extract information from biomolecular text using natural language processing technology. Previous studies have asserted that linguistic information is useful for improving the detection of gene interactions. In particular, syntactic relations among linguistic information are good for detecting gene interactions. However, previous systems give a reasonably good precision but poor recall. To improve recall without sacrificing precision, this paper proposes a three-phase method for detecting gene interactions based on syntactic relations. In the first phase, we retrieve syntactic encapsulation categories for each candidate agent and target. In the second phase, we construct a verb list that indicates the nature of the interaction between pairs of genes. In the last phase, we determine direction rules to detect which of two genes is the agent or target. Even without biomolecular knowledge, our method performs reasonably well using a small training dataset. While the first phase contributes to improve recall, the second and third phases contribute to improve precision. In the experimental results using ICML 05 Workshop on Learning Language in Logic (LLL05 data, our proposed method gave an F-measure of 67.2% for the test data, significantly outperforming previous methods. We also describe the contribution of each phase to the performance.

The Cumulative Effect of Gene-Gene and Gene-Environment Interactions on the Risk of Prostate Cancer in Chinese Men

Directory of Open Access Journals (Sweden)

Ming Liu

2016-01-01

Full Text Available Prostate cancer (PCa is a multifactorial disease involving complex genetic and environmental factors interactions. Gene-gene and gene-environment interactions associated with PCa in Chinese men are less studied. We explored the association between 36 SNPs and PCa in 574 subjects from northern China. Body mass index (BMI, smoking, and alcohol consumption were determined through self-administered questionnaires in 134 PCa patients. Then gene-gene and gene-environment interactions among the PCa-associated SNPs were analyzed using the generalized multifactor dimensionality reduction (GMDR and logistic regression methods. Allelic and genotypic association analyses showed that six variants were associated with PCa and the cumulative effect suggested men who carried any combination of 1, 2, or ≥3 risk genotypes had a gradually increased PCa risk (odds ratios (ORs = 1.79–4.41. GMDR analysis identified the best gene-gene interaction model with scores of 10 for both the cross-validation consistency and sign tests. For gene-environment interactions, rs6983561 CC and rs16901966 GG in individuals with a BMI ≥ 28 had ORs of 7.66 (p = 0.032 and 5.33 (p = 0.046, respectively. rs7679673 CC + CA and rs12653946 TT in individuals that smoked had ORs of 2.77 (p = 0.007 and 3.11 (p = 0.024, respectively. rs7679673 CC in individuals that consumed alcohol had an OR of 4.37 (p = 0.041. These results suggest that polymorphisms, either individually or by interacting with other genes or environmental factors, contribute to an increased risk of PCa.

Childhood temperament: passive gene-environment correlation, gene-environment interaction, and the hidden importance of the family environment.

Science.gov (United States)

Lemery-Chalfant, Kathryn; Kao, Karen; Swann, Gregory; Goldsmith, H Hill

2013-02-01

Biological parents pass on genotypes to their children, as well as provide home environments that correlate with their genotypes; thus, the association between the home environment and children's temperament can be genetically (i.e., passive gene-environment correlation) or environmentally mediated. Furthermore, family environments may suppress or facilitate the heritability of children's temperament (i.e., gene-environment interaction). The sample comprised 807 twin pairs (mean age = 7.93 years) from the longitudinal Wisconsin Twin Project. Important passive gene-environment correlations emerged, such that home environments were less chaotic for children with high effortful control, and this association was genetically mediated. Children with high extraversion/surgency experienced more chaotic home environments, and this correlation was also genetically mediated. In addition, heritability of children's temperament was moderated by home environments, such that effortful control and extraversion/surgency were more heritable in chaotic homes, and negative affectivity was more heritable under crowded or unsafe home conditions. Modeling multiple types of gene-environment interplay uncovered the complex role of genetic factors and the hidden importance of the family environment for children's temperament and development more generally.

Novel interactions between vertebrate Hox genes

NARCIS (Netherlands)

Hooiveld, MHW; Morgan, R; Rieden, PID; Houtzager, E; Pannese, M; Damen, K; Boncinelli, E; Durston, AJ

1999-01-01

Understanding why metazoan Hox/HOM-C genes are expressed in spatiotemporal sequences showing colinearity with their genomic sequence is a central challenge in developmental biology. Here, we studied the consequences of ectopically expressing Hox genes to investigate whether Hox-Hox interactions

Multiple Parton Interactions in ALICE

CERN Multimedia

CERN. Geneva

2013-01-01

We will present in detail the measurement of the charged particle multiplicity dependence of per-trigger pair yields in azimuthal direction induced by low-energetic di-jets produced in proton-proton collisions. Using two-particle angular correlations with low transverse momentum thresholds, jet properties are measured on a statistical basis down to the lowest possible jet energies. The analysis can give information about the contribution from multiple parton interactions to particle production. Moreover, the results allow to optimize the parametrization of the jet fragmentation in phenomenological mode...

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

Science.gov (United States)

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.

Horizontal acquisition of multiple mitochondrial genes from a parasitic plant followed by gene conversion with host mitochondrial genes

Science.gov (United States)

2010-01-01

Background Horizontal gene transfer (HGT) is relatively common in plant mitochondrial genomes but the mechanisms, extent and consequences of transfer remain largely unknown. Previous results indicate that parasitic plants are often involved as either transfer donors or recipients, suggesting that direct contact between parasite and host facilitates genetic transfer among plants. Results In order to uncover the mechanistic details of plant-to-plant HGT, the extent and evolutionary fate of transfer was investigated between two groups: the parasitic genus Cuscuta and a small clade of Plantago species. A broad polymerase chain reaction (PCR) survey of mitochondrial genes revealed that at least three genes (atp1, atp6 and matR) were recently transferred from Cuscuta to Plantago. Quantitative PCR assays show that these three genes have a mitochondrial location in the one species line of Plantago examined. Patterns of sequence evolution suggest that these foreign genes degraded into pseudogenes shortly after transfer and reverse transcription (RT)-PCR analyses demonstrate that none are detectably transcribed. Three cases of gene conversion were detected between native and foreign copies of the atp1 gene. The identical phylogenetic distribution of the three foreign genes within Plantago and the retention of cytidines at ancestral positions of RNA editing indicate that these genes were probably acquired via a single, DNA-mediated transfer event. However, samplings of multiple individuals from two of the three species in the recipient Plantago clade revealed complex and perplexing phylogenetic discrepancies and patterns of sequence divergence for all three of the foreign genes. Conclusions This study reports the best evidence to date that multiple mitochondrial genes can be transferred via a single HGT event and that transfer occurred via a strictly DNA-level intermediate. The discovery of gene conversion between co-resident foreign and native mitochondrial copies suggests

Horizontal acquisition of multiple mitochondrial genes from a parasitic plant followed by gene conversion with host mitochondrial genes

Directory of Open Access Journals (Sweden)

Hao Weilong

2010-12-01

Full Text Available Abstract Background Horizontal gene transfer (HGT is relatively common in plant mitochondrial genomes but the mechanisms, extent and consequences of transfer remain largely unknown. Previous results indicate that parasitic plants are often involved as either transfer donors or recipients, suggesting that direct contact between parasite and host facilitates genetic transfer among plants. Results In order to uncover the mechanistic details of plant-to-plant HGT, the extent and evolutionary fate of transfer was investigated between two groups: the parasitic genus Cuscuta and a small clade of Plantago species. A broad polymerase chain reaction (PCR survey of mitochondrial genes revealed that at least three genes (atp1, atp6 and matR were recently transferred from Cuscuta to Plantago. Quantitative PCR assays show that these three genes have a mitochondrial location in the one species line of Plantago examined. Patterns of sequence evolution suggest that these foreign genes degraded into pseudogenes shortly after transfer and reverse transcription (RT-PCR analyses demonstrate that none are detectably transcribed. Three cases of gene conversion were detected between native and foreign copies of the atp1 gene. The identical phylogenetic distribution of the three foreign genes within Plantago and the retention of cytidines at ancestral positions of RNA editing indicate that these genes were probably acquired via a single, DNA-mediated transfer event. However, samplings of multiple individuals from two of the three species in the recipient Plantago clade revealed complex and perplexing phylogenetic discrepancies and patterns of sequence divergence for all three of the foreign genes. Conclusions This study reports the best evidence to date that multiple mitochondrial genes can be transferred via a single HGT event and that transfer occurred via a strictly DNA-level intermediate. The discovery of gene conversion between co-resident foreign and native

Class II HLA interactions modulate genetic risk for multiple sclerosis

Science.gov (United States)

Dilthey, Alexander T; Xifara, Dionysia K; Ban, Maria; Shah, Tejas S; Patsopoulos, Nikolaos A; Alfredsson, Lars; Anderson, Carl A; Attfield, Katherine E; Baranzini, Sergio E; Barrett, Jeffrey; Binder, Thomas M C; Booth, David; Buck, Dorothea; Celius, Elisabeth G; Cotsapas, Chris; D’Alfonso, Sandra; Dendrou, Calliope A; Donnelly, Peter; Dubois, Bénédicte; Fontaine, Bertrand; Fugger, Lars; Goris, An; Gourraud, Pierre-Antoine; Graetz, Christiane; Hemmer, Bernhard; Hillert, Jan; Kockum, Ingrid; Leslie, Stephen; Lill, Christina M; Martinelli-Boneschi, Filippo; Oksenberg, Jorge R; Olsson, Tomas; Oturai, Annette; Saarela, Janna; Søndergaard, Helle Bach; Spurkland, Anne; Taylor, Bruce; Winkelmann, Juliane; Zipp, Frauke; Haines, Jonathan L; Pericak-Vance, Margaret A; Spencer, Chris C A; Stewart, Graeme; Hafler, David A; Ivinson, Adrian J; Harbo, Hanne F; Hauser, Stephen L; De Jager, Philip L; Compston, Alastair; McCauley, Jacob L; Sawcer, Stephen; McVean, Gil

2016-01-01

Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01–HLA-DRB1*15:01 and HLA-DQB1*03:01–HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles. PMID:26343388

Leveraging gene-environment interactions and endotypes for asthma gene discovery

DEFF Research Database (Denmark)

Bønnelykke, Klaus; Ober, Carole

2016-01-01

, such as childhood asthma with severe exacerbations, and on relevant exposures that are involved in gene-environment interactions (GEIs), such as rhinovirus infections, will improve detection of asthma genes and our understanding of the underlying mechanisms. We will discuss the challenges of considering GEIs......Asthma is a heterogeneous clinical syndrome that includes subtypes of disease with different underlying causes and disease mechanisms. Asthma is caused by a complex interaction between genes and environmental exposures; early-life exposures in particular play an important role. Asthma is also...... heritable, and a number of susceptibility variants have been discovered in genome-wide association studies, although the known risk alleles explain only a small proportion of the heritability. In this review, we present evidence supporting the hypothesis that focusing on more specific asthma phenotypes...

Interaction between the RGS6 gene and psychosocial stress on obesity-related traits.

Science.gov (United States)

Kim, Hyun-Jin; Min, Jin-Young; Min, Kyoung-Bok

2017-03-31

Obesity is a major risk factor for chronic diseases and arises from the interactions between environmental factors and multiple genes. Psychosocial stress may affect the risk for obesity, modifying food intake and choice. A recent study suggested regulator of G-protein signaling 6 (RGS6) as a novel candidate gene for obesity in terms of reward-related feeding under stress. In this study, we tried to verify the unidentified connection between RGS6 and human obesity with psychosocial stress in a Korean population. A total of 1,462 adult subjects, who participated in the Korean Association Resource cohort project, were included for this analysis. Obesity-related traits including waist circumference, body mass index, and visceral adipose tissue were recorded. A total of 4 intronic SNPs for the RGS6 gene were used for this study. We found that interactions between SNP rs2239219 and psychosocial stress are significantly associated with abdominal obesity (p = 0.007). As risk allele of this SNP increased, prevalence of abdominal obesity under high-stress conditions gradually increased (p = 0.013). However, we found no SNPs-by-stress interaction effect on other adiposity phenotypes. This study suggests that RGS6 is closely linked to stress-induced abdominal obesity in Korean adults.

Association testing to detect gene-gene interactions on sex chromosomes in trio data

Directory of Open Access Journals (Sweden)

Yeonok eLee

2013-11-01

Full Text Available Autism Spectrum Disorder (ASD occurs more often among males than females in a 4:1 ratio. Among theories used to explain the causes of ASD, the X chromosome and the Y chromosome theories attribute ASD to X-linked mutation and the male-limited gene expressions on the Y chromosome, respectively. Despite the rationale of the theory, studies have failed to attribute the sex-biased ratio to the significant linkage or association on the regions of interest on X chromosome. We further study the gender biased ratio by examining the possible interaction effects between two genes in the sex chromosomes. We propose a logistic regression model with mixed effects to detect gene-gene interactions on sex chromosomes. We investigated the power and type I error rates of the approach for a range of minor allele frequencies and varying linkage disequilibrium between markers and QTLs. We also evaluated the robustness of the model to population stratification. We applied the model to a trio-family data set with an ASD affected male child to study gene-gene interactions on sex chromosomes.

Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

Science.gov (United States)

Yang, Cheng-Hong; Chang, Hsueh-Wei

2014-01-01

Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer. PMID:24895547

Environmental and gene-environment interactions and risk of rheumatoid arthritis

Science.gov (United States)

Karlson, Elizabeth W.; Deane, Kevin

2012-01-01

Multiple environmental factors including hormones, dietary factors, infections and exposure to tobacco smoke as well as gene-environment interactions have been associated with increased risk for rheumatoid arthritis (RA). Importantly, the growing understanding of the prolonged period prior to the first onset of symptoms of RA suggests that these environmental and genetic factors are likely acting to drive the development of RA-related autoimmunity long before the appearance of the first joint symptoms and clinical findings that are characteristic of RA. Herein we will review these factors and interactions, especially those that have been investigated in a prospective fashion prior to the symptomatic onset of RA. We will also discuss how these factors may be explored in future study to further the understanding of the pathogenesis of RA, and ultimately perhaps develop preventive measures for this disease. PMID:22819092

A model of gene-gene and gene-environment interactions and its implications for targeting environmental interventions by genotype

Directory of Open Access Journals (Sweden)

Wallace Helen M

2006-10-01

Full Text Available Abstract Background The potential public health benefits of targeting environmental interventions by genotype depend on the environmental and genetic contributions to the variance of common diseases, and the magnitude of any gene-environment interaction. In the absence of prior knowledge of all risk factors, twin, family and environmental data may help to define the potential limits of these benefits in a given population. However, a general methodology to analyze twin data is required because of the potential importance of gene-gene interactions (epistasis, gene-environment interactions, and conditions that break the 'equal environments' assumption for monozygotic and dizygotic twins. Method A new model for gene-gene and gene-environment interactions is developed that abandons the assumptions of the classical twin study, including Fisher's (1918 assumption that genes act as risk factors for common traits in a manner necessarily dominated by an additive polygenic term. Provided there are no confounders, the model can be used to implement a top-down approach to quantifying the potential utility of genetic prediction and prevention, using twin, family and environmental data. The results describe a solution space for each disease or trait, which may or may not include the classical twin study result. Each point in the solution space corresponds to a different model of genotypic risk and gene-environment interaction. Conclusion The results show that the potential for reducing the incidence of common diseases using environmental interventions targeted by genotype may be limited, except in special cases. The model also confirms that the importance of an individual's genotype in determining their risk of complex diseases tends to be exaggerated by the classical twin studies method, owing to the 'equal environments' assumption and the assumption of no gene-environment interaction. In addition, if phenotypes are genetically robust, because of epistasis

Gene-environment interaction in Major Depression: focus on experience-dependent biological systems

Directory of Open Access Journals (Sweden)

Nicola eLopizzo

2015-05-01

Full Text Available Major Depressive Disorder (MDD is a multifactorial and polygenic disorder, where multiple and partially overlapping sets of susceptibility genes interact each other and with the environment, predisposing individuals to the development of the illness. Thus, MDD results from a complex interplay of vulnerability genes and environmental factors that act cumulatively throughout individual's lifetime. Among these environmental factors, stressful life experiences, especially those occurring early in life, have been suggested to exert a crucial impact on brain development, leading to permanent functional changes that may contribute to life long risk for mental health outcomes. In this review we will discuss how genetic variants (polymorphisms, SNPs within genes operating in neurobiological systems that mediate stress response and synaptic plasticity, can impact, by themselves, the vulnerability risk for MDD; we will also consider how this MDD risk can be further modulated when gene X environment interaction is taken into account. Finally, we will discuss the role of epigenetic mechanisms, and in particular of DNA methylation and miRNAs expression changes, in mediating the effect of the stress on the vulnerability risk to develop MDD. Taken together, in this review we aim to underlie the role of genetic and epigenetic processes involved in stress and neuroplasticity related biological systems on development of MDD after exposure to early life stress, thereby building the basis for future research and clinical interventions.

Reference gene selection for quantitative gene expression studies during biological invasions: A test on multiple genes and tissues in a model ascidian Ciona savignyi.

Science.gov (United States)

Huang, Xuena; Gao, Yangchun; Jiang, Bei; Zhou, Zunchun; Zhan, Aibin

2016-01-15

As invasive species have successfully colonized a wide range of dramatically different local environments, they offer a good opportunity to study interactions between species and rapidly changing environments. Gene expression represents one of the primary and crucial mechanisms for rapid adaptation to local environments. Here, we aim to select reference genes for quantitative gene expression analysis based on quantitative Real-Time PCR (qRT-PCR) for a model invasive ascidian, Ciona savignyi. We analyzed the stability of ten candidate reference genes in three tissues (siphon, pharynx and intestine) under two key environmental stresses (temperature and salinity) in the marine realm based on three programs (geNorm, NormFinder and delta Ct method). Our results demonstrated only minor difference for stability rankings among the three methods. The use of different single reference gene might influence the data interpretation, while multiple reference genes could minimize possible errors. Therefore, reference gene combinations were recommended for different tissues - the optimal reference gene combination for siphon was RPS15 and RPL17 under temperature stress, and RPL17, UBQ and TubA under salinity treatment; for pharynx, TubB, TubA and RPL17 were the most stable genes under temperature stress, while TubB, TubA and UBQ were the best under salinity stress; for intestine, UBQ, RPS15 and RPL17 were the most reliable reference genes under both treatments. Our results suggest that the necessity of selection and test of reference genes for different tissues under varying environmental stresses. The results obtained here are expected to reveal mechanisms of gene expression-mediated invasion success using C. savignyi as a model species. Copyright © 2015 Elsevier B.V. All rights reserved.

Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives

International Nuclear Information System (INIS)

Warmflash, Aryeh; Siggia, Eric D; Francois, Paul

2012-01-01

The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input–output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria. (paper)

Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.

Science.gov (United States)

Warmflash, Aryeh; Francois, Paul; Siggia, Eric D

2012-10-01

The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

Science.gov (United States)

Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

2014-12-01

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Gene-physical activity interactions and their impact on diabetes

DEFF Research Database (Denmark)

Oskari Kilpeläinen, Tuomas; Franks, Paul W

2014-01-01

to an equal bout of physical activity. Individuals with specific genetic profiles are also expected to be more responsive to the beneficial effects of physical activity in the prevention of type 2 diabetes. Identification of such gene-physical activity interactions could give new insights into the biological...... the reader to the recent advances in the genetics of type 2 diabetes, summarize the current evidence on gene-physical activity interactions in relation to type 2 diabetes, and outline how information on gene-physical activity interactions might help improve the prevention and treatment of type 2 diabetes....... Finally, we will discuss the existing and emerging strategies that might enhance our ability to identify and exploit gene-physical activity interactions in the etiology of type 2 diabetes. © 2014 S. Karger AG, Basel....

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

Directory of Open Access Journals (Sweden)

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

Science.gov (United States)

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

Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

Science.gov (United States)

Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

2004-03-05

Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

Genetic susceptibility loci, environmental exposures, and Parkinson's disease: a case-control study of gene-environment interactions.

Science.gov (United States)

Chung, Sun Ju; Armasu, Sebastian M; Anderson, Kari J; Biernacka, Joanna M; Lesnick, Timothy G; Rider, David N; Cunningham, Julie M; Ahlskog, J Eric; Frigerio, Roberta; Maraganore, Demetrius M

2013-06-01

Prior studies causally linked mutations in SNCA, MAPT, and LRRK2 genes with familial Parkinsonism. Genome-wide association studies have demonstrated association of single nucleotide polymorphisms (SNPs) in those three genes with sporadic Parkinson's disease (PD) susceptibility worldwide. Here we investigated the interactions between SNPs in those three susceptibility genes and environmental exposures (pesticides application, tobacco smoking, coffee drinking, and alcohol drinking) also associated with PD susceptibility. Pairwise interactions between environmental exposures and 18 variants (16 SNPs and two variable number tandem repeats, or "VNTRs") in SNCA, MAPT and LRRK2, were investigated using data from 1098 PD cases from the upper Midwest, USA and 1098 matched controls. Environmental exposures were assessed using a validated telephone interview script. Five pairwise interactions had uncorrected P-values coffee drinking × MAPT H1/H2 haplotype or MAPT rs16940806, and alcohol drinking × MAPT rs2435211. None of these interactions remained significant after Bonferroni correction. Secondary analyses in strata defined by type of control (sibling or unrelated), sex, or age at onset of the case also did not identify significant interactions after Bonferroni correction. This study documented limited pairwise interactions between established genetic and environmental risk factors for PD; however, the associations were not significant after correction for multiple testing. Copyright © 2013 Elsevier Ltd. All rights reserved.

Pediatric Multiple Sclerosis: Genes, Environment, and a Comprehensive Therapeutic Approach.

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Cappa, Ryan; Theroux, Liana; Brenton, J Nicholas

2017-10-01

Pediatric multiple sclerosis is an increasingly recognized and studied disorder that accounts for 3% to 10% of all patients with multiple sclerosis. The risk for pediatric multiple sclerosis is thought to reflect a complex interplay between environmental and genetic risk factors. Environmental exposures, including sunlight (ultraviolet radiation, vitamin D levels), infections (Epstein-Barr virus), passive smoking, and obesity, have been identified as potential risk factors in youth. Genetic predisposition contributes to the risk of multiple sclerosis, and the major histocompatibility complex on chromosome 6 makes the single largest contribution to susceptibility to multiple sclerosis. With the use of large-scale genome-wide association studies, other non-major histocompatibility complex alleles have been identified as independent risk factors for the disease. The bridge between environment and genes likely lies in the study of epigenetic processes, which are environmentally-influenced mechanisms through which gene expression may be modified. This article will review these topics to provide a framework for discussion of a comprehensive approach to counseling and ultimately treating the pediatric patient with multiple sclerosis. Copyright © 2017 Elsevier Inc. All rights reserved.

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

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Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

2015-01-01

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Phase I metabolic genes and risk of lung cancer: multiple polymorphisms and mRNA expression.

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Melissa Rotunno

2009-05-01

Full Text Available Polymorphisms in genes coding for enzymes that activate tobacco lung carcinogens may generate inter-individual differences in lung cancer risk. Previous studies had limited sample sizes, poor exposure characterization, and a few single nucleotide polymorphisms (SNPs tested in candidate genes. We analyzed 25 SNPs (some previously untested in 2101 primary lung cancer cases and 2120 population controls from the Environment And Genetics in Lung cancer Etiology (EAGLE study from six phase I metabolic genes, including cytochrome P450s, microsomal epoxide hydrolase, and myeloperoxidase. We evaluated the main genotype effects and genotype-smoking interactions in lung cancer risk overall and in the major histology subtypes. We tested the combined effect of multiple SNPs on lung cancer risk and on gene expression. Findings were prioritized based on significance thresholds and consistency across different analyses, and accounted for multiple testing and prior knowledge. Two haplotypes in EPHX1 were significantly associated with lung cancer risk in the overall population. In addition, CYP1B1 and CYP2A6 polymorphisms were inversely associated with adenocarcinoma and squamous cell carcinoma risk, respectively. Moreover, the association between CYP1A1 rs2606345 genotype and lung cancer was significantly modified by intensity of cigarette smoking, suggesting an underlying dose-response mechanism. Finally, increasing number of variants at CYP1A1/A2 genes revealed significant protection in never smokers and risk in ever smokers. Results were supported by differential gene expression in non-tumor lung tissue samples with down-regulation of CYP1A1 in never smokers and up-regulation in smokers from CYP1A1/A2 SNPs. The significant haplotype associations emphasize that the effect of multiple SNPs may be important despite null single SNP-associations, and warrants consideration in genome-wide association studies (GWAS. Our findings emphasize the necessity of post

Large-scale extraction of gene interactions from full-text literature using DeepDive.

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Mallory, Emily K; Zhang, Ce; Ré, Christopher; Altman, Russ B

2016-01-01

A complete repository of gene-gene interactions is key for understanding cellular processes, human disease and drug response. These gene-gene interactions include both protein-protein interactions and transcription factor interactions. The majority of known interactions are found in the biomedical literature. Interaction databases, such as BioGRID and ChEA, annotate these gene-gene interactions; however, curation becomes difficult as the literature grows exponentially. DeepDive is a trained system for extracting information from a variety of sources, including text. In this work, we used DeepDive to extract both protein-protein and transcription factor interactions from over 100,000 full-text PLOS articles. We built an extractor for gene-gene interactions that identified candidate gene-gene relations within an input sentence. For each candidate relation, DeepDive computed a probability that the relation was a correct interaction. We evaluated this system against the Database of Interacting Proteins and against randomly curated extractions. Our system achieved 76% precision and 49% recall in extracting direct and indirect interactions involving gene symbols co-occurring in a sentence. For randomly curated extractions, the system achieved between 62% and 83% precision based on direct or indirect interactions, as well as sentence-level and document-level precision. Overall, our system extracted 3356 unique gene pairs using 724 features from over 100,000 full-text articles. Application source code is publicly available at https://github.com/edoughty/deepdive_genegene_app russ.altman@stanford.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

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.

Interactions Between Variation in Candidate Genes and Environmental Factors in the Etiology of Schizophrenia and Bipolar Disorder: a Systematic Review.

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Misiak, Błażej; Stramecki, Filip; Gawęda, Łukasz; Prochwicz, Katarzyna; Sąsiadek, Maria M; Moustafa, Ahmed A; Frydecka, Dorota

2017-08-18

Schizophrenia and bipolar disorder (BD) are complex and multidimensional disorders with high heritability rates. The contribution of genetic factors to the etiology of these disorders is increasingly being recognized as the action of multiple risk variants with small effect sizes, which might explain only a minor part of susceptibility. On the other site, numerous environmental factors have been found to play an important role in their causality. Therefore, in recent years, several studies focused on gene × environment interactions that are believed to bridge the gap between genetic underpinnings and environmental insults. In this article, we performed a systematic review of studies investigating gene × environment interactions in BD and schizophrenia spectrum phenotypes. In the majority of studies from this field, interacting effects of variation in genes encoding catechol-O-methyltransferase (COMT), brain-derived neurotrophic factor (BDNF), and FK506-binding protein 5 (FKBP5) have been explored. Almost consistently, these studies revealed that polymorphisms in COMT, BDNF, and FKBP5 genes might interact with early life stress and cannabis abuse or dependence, influencing various outcomes of schizophrenia spectrum disorders and BD. Other interactions still require further replication in larger clinical and non-clinical samples. In addition, future studies should address the direction of causality and potential mechanisms of the relationship between gene × environment interactions and various categories of outcomes in schizophrenia and BD.

Understanding Epistatic Interactions between Genes Targeted by Non-coding Regulatory Elements in Complex Diseases

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Min Kyung Sung

2014-12-01

Full Text Available Genome-wide association studies have proven the highly polygenic architecture of complex diseases or traits; therefore, single-locus-based methods are usually unable to detect all involved loci, especially when individual loci exert small effects. Moreover, the majority of associated single-nucleotide polymorphisms resides in non-coding regions, making it difficult to understand their phenotypic contribution. In this work, we studied epistatic interactions associated with three common diseases using Korea Association Resource (KARE data: type 2 diabetes mellitus (DM, hypertension (HT, and coronary artery disease (CAD. We showed that epistatic single-nucleotide polymorphisms (SNPs were enriched in enhancers, as well as in DNase I footprints (the Encyclopedia of DNA Elements [ENCODE] Project Consortium 2012, which suggested that the disruption of the regulatory regions where transcription factors bind may be involved in the disease mechanism. Accordingly, to identify the genes affected by the SNPs, we employed whole-genome multiple-cell-type enhancer data which discovered using DNase I profiles and Cap Analysis Gene Expression (CAGE. Assigned genes were significantly enriched in known disease associated gene sets, which were explored based on the literature, suggesting that this approach is useful for detecting relevant affected genes. In our knowledge-based epistatic network, the three diseases share many associated genes and are also closely related with each other through many epistatic interactions. These findings elucidate the genetic basis of the close relationship between DM, HT, and CAD.

Gene-Lifestyle Interactions in Obesity.

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van Vliet-Ostaptchouk, Jana V; Snieder, Harold; Lagou, Vasiliki

2012-01-01

Obesity is a complex multifaceted disease resulting from interactions between genetics and lifestyle. The proportion of phenotypic variance ascribed to genetic variance is 0.4 to 0.7 for obesity and recent years have seen considerable success in identifying disease-susceptibility variants. Although with the advent of genome-wide association studies the list of genetic variants predisposing to obesity has significantly increased the identified variants only explain a fraction of disease heritability. Studies of gene-environment interactions can provide more insight into the biological mechanisms involved in obesity despite the challenges associated with such designs. Epigenetic changes that affect gene function without DNA sequence modifications may be a key factor explaining interindividual differences in obesity, with both genetic and environmental factors influencing the epigenome. Disentangling the relative contributions of genetic, environmental and epigenetic marks to the establishment of obesity is a major challenge given the complex interplay between these determinants.

[Gene-gene interaction on central obesity in school-aged children in China].

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Fu, L W; Zhang, M X; Wu, L J; Gao, L W; Mi, J

2017-07-10

Objective: To investigate possible effect of 6 obesity-associated SNPs in contribution to central obesity and examine whether there is an interaction in the 6 SNPs in the cause of central obesity in school-aged children in China. Methods: A total of 3 502 school-aged children who were included in Beijing Child and Adolescent Metabolic Syndrome (BCAMS) Study were selected, and based on the age and sex specific waist circumference (WC) standards in the BCAMS study, 1 196 central obese cases and 2 306 controls were identified. Genomic DNA was extracted from peripheral blood white cells using the salt fractionation method. A total of 6 single nucleotide polymorphisms ( FTO rs9939609, MC4R rs17782313, BDNF rs6265, PCSK1 rs6235, SH2B1 rs4788102, and CSK rs1378942) were genotyped by TaqMan allelic discrimination assays with the GeneAmp 7900 sequence detection system (Applied Biosystems, Foster City, CA, USA). Logistic regression model was used to investigate the association between 6 SNPs and central obesity. Gene-gene interactions among 6 polymorphic loci were analyzed by using the Generalized Multifactor Dimensionality Reduction (GMDR) method, and then logistic regression model was constructed to confirm the best combination of loci identified in the GMDR. Results: After adjusting gender, age, Tanner stage, physical activity and family history of obesity, the FTO rs9939609-A, MC4R rs17782313-C and BDNF rs6265-G alleles were associated with central obesity under additive genetic model ( OR =1.24, 95 %CI : 1.06-1.45, P =0.008; OR =1.26, 95 %CI : 1.11-1.43, P =2.98×10(-4); OR =1.18, 95 % CI : 1.06-1.32, P =0.003). GMDR analysis showed a significant gene-gene interaction between MC4R rs17782313 and BDNF rs6265 ( P =0.001). The best two-locus combination showed the cross-validation consistency of 10/10 and testing accuracy of 0.539. This interaction showed the maximum consistency and minimum prediction error among all gene-gene interaction models evaluated. Moreover, the

Multiple sclerosis and herpesvirus interaction

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Guilherme Sciascia do Olival

2013-09-01

Full Text Available Multiple sclerosis is the most common autoimmune inflammatory demyelinating disease of the central nervous system, and its etiology is believed to have both genetic and environmental components. Several viruses have already been implicated as triggers and there are several studies that implicate members of the Herpesviridae family in the pathogenesis of MS. The most important characteristic of these viruses is that they have periods of latency and exacerbations within their biological sanctuary, the central nervous system. The Epstein-Barr, cytomegalovirus, human herpesvirus 6 and human herpesvirus 7 viruses are the members that are most studied as being possible triggers of multiple sclerosis. According to evidence in the literature, the herpesvirus family is strongly involved in the pathogenesis of this disease, but it is unlikely that they are the only component responsible for its development. There are probably multiple triggers and more studies are necessary to investigate and define these interactions.

Blood lead levels, iron metabolism gene polymorphisms and homocysteine: a gene-environment interaction study.

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Kim, Kyoung-Nam; Lee, Mee-Ri; Lim, Youn-Hee; Hong, Yun-Chul

2017-12-01

Homocysteine has been causally associated with various adverse health outcomes. Evidence supporting the relationship between lead and homocysteine levels has been accumulating, but most prior studies have not focused on the interaction with genetic polymorphisms. From a community-based prospective cohort, we analysed 386 participants (aged 41-71 years) with information regarding blood lead and plasma homocysteine levels. Blood lead levels were measured between 2001 and 2003, and plasma homocysteine levels were measured in 2007. Interactions of lead levels with 42 genotyped single-nucleotide polymorphisms (SNPs) in five genes ( TF , HFE , CBS , BHMT and MTR ) were assessed via a 2-degree of freedom (df) joint test and a 1-df interaction test. In secondary analyses using imputation, we further assessed 58 imputed SNPs in the TF and MTHFR genes. Blood lead concentrations were positively associated with plasma homocysteine levels (p=0.0276). Six SNPs in the TF and MTR genes were screened using the 2-df joint test, and among them, three SNPs in the TF gene showed interactions with lead with respect to homocysteine levels through the 1-df interaction test (plead levels. Blood lead levels were positively associated with plasma homocysteine levels measured 4-6 years later, and three SNPs in the TF gene modified the association. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Spatially Uniform ReliefF (SURF for computationally-efficient filtering of gene-gene interactions

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Greene Casey S

2009-09-01

Full Text Available Abstract Background Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF, which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF. Results SURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm. Conclusion Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be

M-GCAT: interactively and efficiently constructing large-scale multiple genome comparison frameworks in closely related species

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Messeguer Xavier

2006-10-01

Full Text Available Abstract Background Due to recent advances in whole genome shotgun sequencing and assembly technologies, the financial cost of decoding an organism's DNA has been drastically reduced, resulting in a recent explosion of genomic sequencing projects. This increase in related genomic data will allow for in depth studies of evolution in closely related species through multiple whole genome comparisons. Results To facilitate such comparisons, we present an interactive multiple genome comparison and alignment tool, M-GCAT, that can efficiently construct multiple genome comparison frameworks in closely related species. M-GCAT is able to compare and identify highly conserved regions in up to 20 closely related bacterial species in minutes on a standard computer, and as many as 90 (containing 75 cloned genomes from a set of 15 published enterobacterial genomes in an hour. M-GCAT also incorporates a novel comparative genomics data visualization interface allowing the user to globally and locally examine and inspect the conserved regions and gene annotations. Conclusion M-GCAT is an interactive comparative genomics tool well suited for quickly generating multiple genome comparisons frameworks and alignments among closely related species. M-GCAT is freely available for download for academic and non-commercial use at: http://alggen.lsi.upc.es/recerca/align/mgcat/intro-mgcat.html.

You've gotta be lucky: Coverage and the elusive gene-gene interaction.

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Reimherr, Matthew; Nicolae, Dan L

2011-01-01

Genome-wide association studies (GWAS) have led to a large number of single-SNP association findings, but there has been, so far, no investigation resulting in the discovery of a replicable gene-gene interaction. In this paper, we examine some of the possible explanations for the lack of findings, and argue that coverage of causal variation not only has a large effect on the loss in power, but that the effect is larger than in the single-SNP analyses. We show that the product of linkage disequilibrium measures, r², between causal and tested SNPs offers a good approximation to the loss in efficiency as defined by the ratio of sample sizes that lead to similar power. We also demonstrate that, in addition to the huge search space, the loss in power due to coverage when using commercially available platforms makes the search for gene-gene interactions daunting. © 2010 The Authors Annals of Human Genetics © 2010 Blackwell Publishing Ltd/University College London.

Simple and Efficient Targeting of Multiple Genes Through CRISPR-Cas9 in Physcomitrella patens

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Mauricio Lopez-Obando

2016-11-01

Full Text Available Powerful genome editing technologies are needed for efficient gene function analysis. The CRISPR-Cas9 system has been adapted as an efficient gene-knock-out technology in a variety of species. However, in a number of situations, knocking out or modifying a single gene is not sufficient; this is particularly true for genes belonging to a common family, or for genes showing redundant functions. Like many plants, the model organism Physcomitrella patens has experienced multiple events of polyploidization during evolution that has resulted in a number of families of duplicated genes. Here, we report a robust CRISPR-Cas9 system, based on the codelivery of a CAS9 expressing cassette, multiple sgRNA vectors, and a cassette for transient transformation selection, for gene knock-out in multiple gene families. We demonstrate that CRISPR-Cas9-mediated targeting of five different genes allows the selection of a quintuple mutant, and all possible subcombinations of mutants, in one experiment, with no mutations detected in potential off-target sequences. Furthermore, we confirmed the observation that the presence of repeats in the vicinity of the cutting region favors deletion due to the alternative end joining pathway, for which induced frameshift mutations can be potentially predicted. Because the number of multiple gene families in Physcomitrella is substantial, this tool opens new perspectives to study the role of expanded gene families in the colonization of land by plants.

Gene-Gene Interactions in the Folate Metabolic Pathway and the Risk of Conotruncal Heart Defects

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Philip J. Lupo

2010-01-01

Full Text Available Conotruncal and related heart defects (CTRD are common, complex malformations. Although there are few established risk factors, there is evidence that genetic variation in the folate metabolic pathway influences CTRD risk. This study was undertaken to assess the association between inherited (i.e., case and maternal gene-gene interactions in this pathway and the risk of CTRD. Case-parent triads (n=727, ascertained from the Children's Hospital of Philadelphia, were genotyped for ten functional variants of nine folate metabolic genes. Analyses of inherited genotypes were consistent with the previously reported association between MTHFR A1298C and CTRD (adjusted P=.02, but provided no evidence that CTRD was associated with inherited gene-gene interactions. Analyses of the maternal genotypes provided evidence of a MTHFR C677T/CBS 844ins68 interaction and CTRD risk (unadjusted P=.02. This association is consistent with the effects of this genotype combination on folate-homocysteine biochemistry but remains to be confirmed in independent study populations.

Local tax interaction with multiple tax instruments: evidence from Flemish municipalities

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S. VAN PARYS; B. MERLEVEDE; T. VERBEKE

2010-01-01

We investigate the long run result of strategic interaction among local jurisdictions using multiple tax instruments. Most studies about local policy interaction only consider a single policy instrument. With multiple tax instruments, however, tax interaction is more complex. We construct a simple theoretical framework based on a basic spillover model, with two tax rates and immobile resources. We show that the signs of within and cross tax interaction crucially depend on the extent to which ...

Evolutionary Dynamics of Tumor-Stroma Interactions in Multiple Myeloma.

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Javad Salimi Sartakhti

Full Text Available Cancer cells and stromal cells cooperate by exchanging diffusible factors that sustain tumor growth, a form of frequency-dependent selection that can be studied in the framework of evolutionary game theory. In the case of multiple myeloma, three types of cells (malignant plasma cells, osteoblasts and osteoclasts exchange growth factors with different effects, and tumor-stroma interactions have been analysed using a model of cooperation with pairwise interactions. Here we show that a model in which growth factors have autocrine and paracrine effects on multiple cells, a more realistic assumption for tumor-stroma interactions, leads to different results, with implications for disease progression and treatment. In particular, the model reveals that reducing the number of malignant plasma cells below a critical threshold can lead to their extinction and thus to restore a healthy balance between osteoclast and osteoblast, a result in line with current therapies against multiple myeloma.

Interactions Between SNP Alleles at Multiple Loci and Variation in Skin Pigmentation in 122 Caucasians

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Sumiko Anno

2007-01-01

Full Text Available This study was undertaken to clarify the molecular basis for human skin color variation and the environmental adaptability to ultraviolet irradiation, with the ultimate goal of predicting the impact of changes in future environments on human health risk. One hundred twenty-two Caucasians living in Toledo, Ohio participated. Back and cheek skin were assayed for melanin as a quantitative trait marker. Buccal cell samples were collected and used for DNA extraction. DNA was used for SNP genotyping using the Masscode™ system, which entails two-step PCR amplification and a platform chemistry which allows cleavable mass spectrometry tags. The results show gene-gene interaction between SNP alleles at multiple loci (not necessarily on the same chromosome contributes to inter-individual skin color variation while suggesting a high probability of linkage disequilibrium. Confirmation of these findings requires further study with other ethic groups to analyze the associations between SNP alleles at multiple loci and human skin color variation. Our overarching goal is to use remote sensing data to clarify the interaction between atmospheric environments and SNP allelic frequency and investigate human adaptability to ultraviolet irradiation. Such information should greatly assist in the prediction of the health effects of future environmental changes such as ozone depletion and increased ultraviolet exposure. If such health effects are to some extent predictable, it might be possible to prepare for such changes in advance and thus reduce the extent of their impact.

Microsatellite polymorphisms associated with human behavioural and psychological phenotypes including a gene-environment interaction.

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Bagshaw, Andrew T M; Horwood, L John; Fergusson, David M; Gemmell, Neil J; Kennedy, Martin A

2017-02-03

The genetic and environmental influences on human personality and behaviour are a complex matter of ongoing debate. Accumulating evidence indicates that short tandem repeats (STRs) in regulatory regions are good candidates to explain heritability not accessed by genome-wide association studies. We tested for associations between the genotypes of four selected repeats and 18 traits relating to personality, behaviour, cognitive ability and mental health in a well-studied longitudinal birth cohort (n = 458-589) using one way analysis of variance. The repeats were a highly conserved poly-AC microsatellite in the upstream promoter region of the T-box brain 1 (TBR1) gene and three previously studied STRs in the activating enhancer-binding protein 2-beta (AP2-β) and androgen receptor (AR) genes. Where significance was found we used multiple regression to assess the influence of confounding factors. Carriers of the shorter, most common, allele of the AR gene's GGN microsatellite polymorphism had fewer anxiety-related symptoms, which was consistent with previous studies, but in our study this was not significant following Bonferroni correction. No associations with two repeats in the AP2-β gene withstood this correction. A novel finding was that carriers of the minor allele of the TBR1 AC microsatellite were at higher risk of conduct problems in childhood at age 7-9 (p = 0.0007, which did pass Bonferroni correction). Including maternal smoking during pregnancy (MSDP) in models controlling for potentially confounding influences showed that an interaction between TBR1 genotype and MSDP was a significant predictor of conduct problems in childhood and adolescence (p behaviour up to age 25 years (p ≤ 0.02). This interaction remained significant after controlling for possible confounders including maternal age at birth, socio-economic status and education, and offspring birth weight. The potential functional importance of the TBR1 gene's promoter microsatellite

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.

Pollen Killer Gene S35 Function Requires Interaction with an Activator That Maps Close to S24, Another Pollen Killer Gene in Rice

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Takahiko Kubo

2016-05-01

Full Text Available Pollen killer genes disable noncarrier pollens, and are responsible for male sterility and segregation distortion in hybrid populations of distantly related plant species. The genetic networks and the molecular mechanisms underlying the pollen killer system remain largely unknown. Two pollen killer genes, S24 and S35, have been found in an intersubspecific cross of Oryza sativa ssp. indica and japonica. The effect of S24 is counteracted by an unlinked locus EFS. Additionally, S35 has been proposed to interact with S24 to induce pollen sterility. These genetic interactions are suggestive of a single S24-centric genetic pathway (EFS–S24–S35 for the pollen killer system. To examine this hypothetical genetic pathway, the S35 and the S24 regions were further characterized and genetically dissected in this study. Our results indicated that S35 causes pollen sterility independently of both the EFS and S24 genes, but is dependent on a novel gene close to the S24 locus, named incentive for killing pollen (INK. We confirmed the phenotypic effect of the INK gene separately from the S24 gene, and identified the INK locus within an interval of less than 0.6 Mb on rice chromosome 5. This study characterized the genetic effect of the two independent genetic pathways of INK–S35 and EFS–S24 in indica–japonica hybrid progeny. Our results provide clear evidence that hybrid male sterility in rice is caused by several pollen killer networks with multiple factors positively and negatively regulating pollen killer genes.

Pollen Killer Gene S35 Function Requires Interaction with an Activator That Maps Close to S24, Another Pollen Killer Gene in Rice.

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Kubo, Takahiko; Yoshimura, Atsushi; Kurata, Nori

2016-05-03

Pollen killer genes disable noncarrier pollens, and are responsible for male sterility and segregation distortion in hybrid populations of distantly related plant species. The genetic networks and the molecular mechanisms underlying the pollen killer system remain largely unknown. Two pollen killer genes, S24 and S35, have been found in an intersubspecific cross of Oryza sativa ssp. indica and japonica The effect of S24 is counteracted by an unlinked locus EFS Additionally, S35 has been proposed to interact with S24 to induce pollen sterility. These genetic interactions are suggestive of a single S24-centric genetic pathway (EFS-S24-S35) for the pollen killer system. To examine this hypothetical genetic pathway, the S35 and the S24 regions were further characterized and genetically dissected in this study. Our results indicated that S35 causes pollen sterility independently of both the EFS and S24 genes, but is dependent on a novel gene close to the S24 locus, named incentive for killing pollen (INK). We confirmed the phenotypic effect of the INK gene separately from the S24 gene, and identified the INK locus within an interval of less than 0.6 Mb on rice chromosome 5. This study characterized the genetic effect of the two independent genetic pathways of INK-S35 and EFS-S24 in indica-japonica hybrid progeny. Our results provide clear evidence that hybrid male sterility in rice is caused by several pollen killer networks with multiple factors positively and negatively regulating pollen killer genes. Copyright © 2016 Kubo et al.

Multiplicity distributions in high-energy neutrino interactions

International Nuclear Information System (INIS)

Chapman, J.W.; Coffin, C.T.; Diamond, R.N.; French, H.; Louis, W.; Roe, B.P.; Seidl, A.A.; Vander Velde, J.C.; Berge, J.P.; Bogert, D.V.; DiBianca, F.A.; Cundy, D.C.; Dunaitsev, A.; Efremenko, V.; Ermolov, P.; Fowler, W.; Hanft, R.; Harigel, G.; Huson, F.R.; Kolganov, V.; Mukhin, A.; Nezrick, F.A.; Rjabov, Y.; Scott, W.G.; Smart, W.

1976-01-01

Results from the Fermilab 15-ft bubble chamber on the charged-particle multiplicity distributions produced in high-energy charged-current neutrino-proton interactions are presented. Comparisons are made to γp, ep, μp, and inclusive pp scattering. The mean hadronic multiplicity appears to depend only on the mass of the excited hadronic state, independent of the mode of excitation. A fit to the neutrino data gives = (1.09+-0.38) +(1.09+-0.03)lnW 2

The evolution of multiple isotypic IgM heavy chain genes in the shark.

Science.gov (United States)

Lee, Victor; Huang, Jing Li; Lui, Ming Fai; Malecek, Karolina; Ohta, Yuko; Mooers, Arne; Hsu, Ellen

2008-06-01

The IgM H chain gene organization of cartilaginous fishes consists of 15-200 miniloci, each with a few gene segments (V(H)-D1-D2-J(H)) and one C gene. This is a gene arrangement ancestral to the complex IgH locus that exists in all other vertebrate classes. To understand the molecular evolution of this system, we studied the nurse shark, which has relatively fewer loci, and characterized the IgH isotypes for organization, functionality, and the somatic diversification mechanisms that act upon them. Gene numbers differ slightly between individuals ( approximately 15), but five active IgM subclasses are always present. Each gene undergoes rearrangement that is strictly confined within the minilocus; in B cells there is no interaction between adjacent loci located > or =120 kb apart. Without combinatorial events, the shark IgM H chain repertoire is based on junctional diversity and, subsequently, somatic hypermutation. We suggest that the significant contribution by junctional diversification reflects the selected novelty introduced by RAG in the early vertebrate ancestor, whereas combinatorial diversity coevolved with the complex translocon organization. Moreover, unlike other cartilaginous fishes, there are no germline-joined VDJ at any nurse shark mu locus, and we suggest that such genes, when functional, are species-specific and may have specialized roles. With an entire complement of IgM genes available for the first time, phylogenetic analyses were performed to examine how the multiple Ig loci evolved. We found that all domains changed at comparable rates, but V(H) appears to be under strong positive selection for increased amino acid sequence diversity, and surprisingly, so does Cmicro2.

Upregulation of Immunoglobulin-related Genes in Cortical Sections from Multiple Sclerosis Patients

NARCIS (Netherlands)

Torkildsen, O.; Stansberg, C.; Angelskar, S.M.; Kooi, E.J.; Geurts, J.J.G.; van der Valk, P.; Myhr, K.M.; Steen, V.M.; Bo, L.

2010-01-01

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS). Microarray-based global gene expression profiling is a promising method, used to study potential genes involved in the pathogenesis of the disease. In the present study, we have examined global gene expression in

DGIdb 3.0: a redesign and expansion of the drug-gene interaction database.

Science.gov (United States)

Cotto, Kelsy C; Wagner, Alex H; Feng, Yang-Yang; Kiwala, Susanna; Coffman, Adam C; Spies, Gregory; Wollam, Alex; Spies, Nicholas C; Griffith, Obi L; Griffith, Malachi

2018-01-04

The drug-gene interaction database (DGIdb, www.dgidb.org) consolidates, organizes and presents drug-gene interactions and gene druggability information from papers, databases and web resources. DGIdb normalizes content from 30 disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API) and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included 24 sources were updated. Six new resources were added, bringing the total number of sources to 30. These updates and additions of sources have cumulatively resulted in 56 309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and anti-neoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes and drug-gene interactions, including listings of PubMed IDs, interaction type and other interaction metadata.

Gene-environment interactions linking air pollution and inflammation in Parkinson's disease.

Science.gov (United States)

Lee, Pei-Chen; Raaschou-Nielsen, Ole; Lill, Christina M; Bertram, Lars; Sinsheimer, Janet S; Hansen, Johnni; Ritz, Beate

2016-11-01

Both air pollution exposure and systemic inflammation have been linked to Parkinson's disease (PD). In the PASIDA study, 408 incident cases of PD diagnosed in 2006-2009 and their 495 population controls were interviewed and provided DNA samples. Markers of long term traffic related air pollution measures were derived from geographic information systems (GIS)-based modeling. Furthermore, we genotyped functional polymorphisms in genes encoding proinflammatory cytokines, namely rs1800629 in TNFα (tumor necrosis factor alpha) and rs16944 in IL1B (interleukin-1β). In logistic regression models, long-term exposure to NO 2 increased PD risk overall (odds ratio (OR)=1.06 per 2.94μg/m 3 increase, 95% CI=1.00-1.13). The OR for PD in individuals with high NO 2 exposure (≧75th percentile) and the AA genotype of IL1B rs16944 was 3.10 (95% CI=1.14-8.38) compared with individuals with lower NO 2 exposure (<75th percentile) and the GG genotype. The interaction term was nominally significant on the multiplicative scale (p=0.01). We did not find significant gene-environment interactions with TNF rs1800629. Our finds may provide suggestive evidence that a combination of traffic-related air pollution and genetic variation in the proinflammatory cytokine gene IL1B contribute to risk of developing PD. However, as statistical evidence was only modest in this large sample we cannot rule out that these results represent a chance finding, and additional replication efforts are warranted. Copyright © 2016 Elsevier Inc. All rights reserved.

Exploring Plant Co-Expression and Gene-Gene Interactions with CORNET 3.0.

Science.gov (United States)

Van Bel, Michiel; Coppens, Frederik

2017-01-01

Selecting and filtering a reference expression and interaction dataset when studying specific pathways and regulatory interactions can be a very time-consuming and error-prone task. In order to reduce the duplicated efforts required to amass such datasets, we have created the CORNET (CORrelation NETworks) platform which allows for easy access to a wide variety of data types: coexpression data, protein-protein interactions, regulatory interactions, and functional annotations. The CORNET platform outputs its results in either text format or through the Cytoscape framework, which is automatically launched by the CORNET website.CORNET 3.0 is the third iteration of the web platform designed for the user exploration of the coexpression space of plant genomes, with a focus on the model species Arabidopsis thaliana. Here we describe the platform: the tools, data, and best practices when using the platform. We indicate how the platform can be used to infer networks from a set of input genes, such as upregulated genes from an expression experiment. By exploring the network, new target and regulator genes can be discovered, allowing for follow-up experiments and more in-depth study. We also indicate how to avoid common pitfalls when evaluating the networks and how to avoid over interpretation of the results.All CORNET versions are available at http://bioinformatics.psb.ugent.be/cornet/ .

Gene expression analysis of interferon-beta treatment in multiple sclerosis

DEFF Research Database (Denmark)

Sellebjerg, F.; Datta, P.; Larsen, J.

2008-01-01

by treatment with IFN-beta. We use DNA microarrays to study gene expression in 10 multiple sclerosis (MS) patients who began de novo treatment with IFN-beta. After the first injection of IFN-beta, the expression of 74 out of 3428 genes changed at least two-fold and statistically significantly (after Bonferroni......Treatment with interferon-beta (IFN-beta) induces the expression of hundreds of genes in blood mononuclear cells, and the expression of several genes has been proposed as a marker of the effect of treatment with IFN-beta. However, to date no molecules have been identified that are stably induced...

Evidence for plasticity genotypes in a gene-gene-environment interaction : the TRAILS study

NARCIS (Netherlands)

Nederhof, E; Bouma, Esther; Riese, Harriette; Laceulle, Odilia; Ormel, J.; Oldehinkel, A.J.

2010-01-01

The purpose was to study how functional polymorphisms in the brain derived neurotrophic factor gene (BDNF val66met) and the serotonin transporter gene linked promotor region (5-HTTLPR) interact with childhood adversities in predicting Effortful Control. Effortful Control refers to the ability to

The Integration of Epistasis Network and Functional Interactions in a GWAS Implicates RXR Pathway Genes in the Immune Response to Smallpox Vaccine.

Directory of Open Access Journals (Sweden)

Brett A McKinney

Full Text Available Although many diseases and traits show large heritability, few genetic variants have been found to strongly separate phenotype groups by genotype. Complex regulatory networks of variants and expression of multiple genes lead to small individual-variant effects and difficulty replicating the effect of any single variant in an affected pathway. Interaction network modeling of GWAS identifies effects ignored by univariate models, but population differences may still cause specific genes to not replicate. Integrative network models may help detect indirect effects of variants in the underlying biological pathway. In this study, we used gene-level functional interaction information from the Integrative Multi-species Prediction (IMP tool to reveal important genes associated with a complex phenotype through evidence from epistasis networks and pathway enrichment. We test this method for augmenting variant-based network analyses with functional interactions by applying it to a smallpox vaccine immune response GWAS. The integrative analysis spotlights the role of genes related to retinoid X receptor alpha (RXRA, which has been implicated in a previous epistasis network analysis of smallpox vaccine.

GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies.

Science.gov (United States)

Yung, Ling Sing; Yang, Can; Wan, Xiang; Yu, Weichuan

2011-05-01

Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in genome-wide association studies (GWAS). Boolean operation-based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared with central processing units (CPUs), graphic processing units (GPUs) are highly parallel hardware and provide massive computing resources. We are, therefore, motivated to use GPUs to further speed up the analysis of gene-gene interactions. We implement the BOOST method based on a GPU framework and name it GBOOST. GBOOST achieves a 40-fold speedup compared with BOOST. It completes the analysis of Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data within 1.34 h on a desktop computer equipped with Nvidia GeForce GTX 285 display card. GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.

Genome-wide analysis of E. coli cell-gene interactions.

Science.gov (United States)

Cardinale, S; Cambray, G

2017-11-23

The pursuit of standardization and reliability in synthetic biology has achieved, in recent years, a number of advances in the design of more predictable genetic parts for biological circuits. However, even with the development of high-throughput screening methods and whole-cell models, it is still not possible to predict reliably how a synthetic genetic construct interacts with all cellular endogenous systems. This study presents a genome-wide analysis of how the expression of synthetic genes is affected by systematic perturbations of cellular functions. We found that most perturbations modulate expression indirectly through an effect on cell size, putting forward the existence of a generic Size-Expression interaction in the model prokaryote Escherichia coli. The Size-Expression interaction was quantified by inserting a dual fluorescent reporter gene construct into each of the 3822 single-gene deletion strains comprised in the KEIO collection. Cellular size was measured for single cells via flow cytometry. Regression analyses were used to discriminate between expression-specific and gene-specific effects. Functions of the deleted genes broadly mapped onto three systems with distinct primary influence on the Size-Expression map. Perturbations in the Division and Biosynthesis (DB) system led to a large-cell and high-expression phenotype. In contrast, disruptions of the Membrane and Motility (MM) system caused small-cell and low-expression phenotypes. The Energy, Protein synthesis and Ribosome (EPR) system was predominantly associated with smaller cells and positive feedback on ribosome function. Feedback between cell growth and gene expression is widespread across cell systems. Even though most gene disruptions proximally affect one component of the Size-Expression interaction, the effect therefore ultimately propagates to both. More specifically, we describe the dual impact of growth on cell size and gene expression through cell division and ribosomal content

Genome-wide search for gene-gene interactions in colorectal cancer.

Directory of Open Access Journals (Sweden)

Shuo Jiao

Full Text Available Genome-wide association studies (GWAS have successfully identified a number of single-nucleotide polymorphisms (SNPs associated with colorectal cancer (CRC risk. However, these susceptibility loci known today explain only a small fraction of the genetic risk. Gene-gene interaction (GxG is considered to be one source of the missing heritability. To address this, we performed a genome-wide search for pair-wise GxG associated with CRC risk using 8,380 cases and 10,558 controls in the discovery phase and 2,527 cases and 2,658 controls in the replication phase. We developed a simple, but powerful method for testing interaction, which we term the Average Risk Due to Interaction (ARDI. With this method, we conducted a genome-wide search to identify SNPs showing evidence for GxG with previously identified CRC susceptibility loci from 14 independent regions. We also conducted a genome-wide search for GxG using the marginal association screening and examining interaction among SNPs that pass the screening threshold (p<10(-4. For the known locus rs10795668 (10p14, we found an interacting SNP rs367615 (5q21 with replication p = 0.01 and combined p = 4.19×10(-8. Among the top marginal SNPs after LD pruning (n = 163, we identified an interaction between rs1571218 (20p12.3 and rs10879357 (12q21.1 (nominal combined p = 2.51×10(-6; Bonferroni adjusted p = 0.03. Our study represents the first comprehensive search for GxG in CRC, and our results may provide new insight into the genetic etiology of CRC.

Circadian Enhancers Coordinate Multiple Phases of Rhythmic Gene Transcription In Vivo

Science.gov (United States)

Fang, Bin; Everett, Logan J.; Jager, Jennifer; Briggs, Erika; Armour, Sean M.; Feng, Dan; Roy, Ankur; Gerhart-Hines, Zachary; Sun, Zheng; Lazar, Mitchell A.

2014-01-01

SUMMARY Mammalian transcriptomes display complex circadian rhythms with multiple phases of gene expression that cannot be accounted for by current models of the molecular clock. We have determined the underlying mechanisms by measuring nascent RNA transcription around the clock in mouse liver. Unbiased examination of eRNAs that cluster in specific circadian phases identified functional enhancers driven by distinct transcription factors (TFs). We further identify on a global scale the components of the TF cistromes that function to orchestrate circadian gene expression. Integrated genomic analyses also revealed novel mechanisms by which a single circadian factor controls opposing transcriptional phases. These findings shed new light on the diversity and specificity of TF function in the generation of multiple phases of circadian gene transcription in a mammalian organ. PMID:25416951

Behavioral science and the study of gene-nutrition and gene-physical activity interactions in obesity research.

Science.gov (United States)

Faith, Myles S

2008-12-01

This report summarizes emerging opportunities for behavioral science to help advance the field of gene-environment and gene-behavior interactions, based on presentations at The National Cancer Institute (NCI) Workshop, "Gene-Nutrition and Gene-Physical Activity Interactions in the Etiology of Obesity." Three opportunities are highlighted: (i) designing potent behavioral "challenges" in experiments, (ii) determining viable behavioral phenotypes for genetics studies, and (iii) identifying specific measures of the environment or environmental exposures. Additional points are underscored, including the need to incorporate novel findings from neuroimaging studies regarding motivation and drive for eating and physical activity. Advances in behavioral science theory and methods can play an important role in advancing understanding of gene-brain-behavior relationships in obesity onset.

Gene interactions and genetics for yield and its attributes in grass ...

Indian Academy of Sciences (India)

A. K. PARIHAR

explaining the manifestation of complex traits such as yield. ... interactions (i, j, l) contributed towards the inheritance of traits in the given crosses. ... Keywords. grass pea; scaling test; gene interactions; gene effects; heritability; Lathyrus sativus.

A non-inheritable maternal Cas9-based multiple-gene editing system in mice

OpenAIRE

Takayuki Sakurai; Akiko Kamiyoshi; Hisaka Kawate; Chie Mori; Satoshi Watanabe; Megumu Tanaka; Ryuichi Uetake; Masahiro Sato; Takayuki Shindo

2016-01-01

The CRISPR/Cas9 system is capable of editing multiple genes through one-step zygote injection. The preexisting method is largely based on the co-injection of Cas9 DNA (or mRNA) and guide RNAs (gRNAs); however, it is unclear how many genes can be simultaneously edited by this method, and a reliable means to generate transgenic (Tg) animals with multiple gene editing has yet to be developed. Here, we employed non-inheritable maternal Cas9 (maCas9) protein derived from Tg mice with systemic Cas9...

Effect of Gene and Physical Activity Interaction on Trunk Fat Percentage Among the Newfoundland Population

Directory of Open Access Journals (Sweden)

Anthony Payne

2014-01-01

Full Text Available Objective To explore the effect of FTO gene and physical activity interaction on trunk fat percentage. Design and Methods Subjects are 3,004 individuals from Newfoundland and Labrador whose trunk fat percentage and physical activity were recorded, and who were genotyped for 11 single-nucleotide polymorphisms (SNPs in the FTO gene. Subjects were stratified by gender. Multiple tests and multiple regressions were used to analyze the effects of physical activity, variants of FTO , age, and their interactions on trunk fat percentage. Dietary information and other environmental factors were not considered. Results Higher levels of physical activity tend to reduce trunk fat percentage in all individuals. Furthermore, in males, rs9939609 and rs1421085 were significant (α = 0.05 in explaining central body fat, but no SNPs were significant in females. For highly active males, trunk fat percentage varied significantly between variants of rs9939609 and rs1421085, but there is no significant effect among individuals with low activity. The other SNPs examined were not significant in explaining trunk fat percentage. Conclusions Homozygous male carriers of non-obesity risk alleles at rs9939609 and rs1421085 will have significant reduction in central body fat from physical activity in contrast to homozygous males of the obesity-risk alleles. The additive effect of these SNPs is found in males with high physical activity only.

A study for association and interaction analysis to metabolic syndrome and the ESR1 gene on cardiovascular autonomic neuropathy in a Chinese Han population.

Science.gov (United States)

Zeng, Fangfang; Zhou, Linuo; Tang, Zihui

2016-01-01

The aim of this study was to investigate the association and interaction of metabolic syndrome (MetS) and estrogen receptor alpha 1 (ESR1) gene polymorphisms on cardiovascular autonomic neuropathy (CAN). A large-scale, population-based study was conducted to analyze the interaction of MetS and ESR1 gene polymorphisms to CAN, including a total of 1977 Chinese subjects. The most common studied single nucleotide polymorphism of ESR1 gene-rs9340799, was genotyped. Multiple logistic regression (MLR) was performed to evaluate the interaction effect of environmental variables and gene polymorphisms. Interaction on an additive scale can be calculated by using the relative excess risk due to interaction (RERI), the proportion attributable to interaction (AP), and the synergy index (S). After controlling potential confounders, MLR showed that significant association between MetS and CAN (p interaction was estimated by using RETI = 0.396 (95 % CI 0.262 to 0.598), AP = 0.216 (95 % CI -0.784 to 1.216) and S = 1.906 (95 % CI 0.905 to 4.015). The present findings suggest that MetS is significantly associated with CAN and provide evidence for the hypothesis that MetS and ESR1 gene polymorphism (rs9340799) have interactive effects on CAN. ClinicalTrials gov Identifier NCT02461342.

Influence of SNPs in nutrient-sensitive candidate genes and gene-diet interactions on blood lipids

DEFF Research Database (Denmark)

Brahe, Lena Kirchner; Angquist, Lars; Larsen, Lesli Hingstrup

2013-01-01

Blood lipid response to a given dietary intervention could be determined by the effect of diet, gene variants or gene-diet interactions. The objective of the present study was to investigate whether variants in presumed nutrient-sensitive genes involved in lipid metabolism modified lipid profile ...

Gene-environment interactions involving functional variants

DEFF Research Database (Denmark)

Barrdahl, Myrto; Rudolph, Anja; Hopper, John L

2017-01-01

.36, 95% CI: 1.16-1.59, pint  = 1.9 × 10(-5) ) in relation to ER- disease risk. The remaining two gene-environment interactions were also identified in relation to ER- breast cancer risk and were found between 3p21-rs6796502 and age at menarche (ORint  = 1.26, 95% CI: 1.12-1.43, pint =1.8 × 10...... epidemiological breast cancer risk factors in relation to breast cancer. Analyses were conducted on up to 58,573 subjects (26,968 cases and 31,605 controls) from the Breast Cancer Association Consortium, in one of the largest studies of its kind. Analyses were carried out separately for estrogen receptor (ER......) positive (ER+) and ER negative (ER-) disease. The Bayesian False Discovery Probability (BFDP) was computed to assess the noteworthiness of the results. Four potential gene-environment interactions were identified as noteworthy (BFDP 

Thermo-Regulation of Genes Mediating Motility and Plant Interactions in Pseudomonas syringae

Science.gov (United States)

Hockett, Kevin L.; Burch, Adrien Y.; Lindow, Steven E.

2013-01-01

Pseudomonas syringae is an important phyllosphere colonist that utilizes flagellum-mediated motility both as a means to explore leaf surfaces, as well as to invade into leaf interiors, where it survives as a pathogen. We found that multiple forms of flagellum-mediated motility are thermo-suppressed, including swarming and swimming motility. Suppression of swarming motility occurs between 28° and 30°C, which coincides with the optimal growth temperature of P. syringae. Both fliC (encoding flagellin) and syfA (encoding a non-ribosomal peptide synthetase involved in syringafactin biosynthesis) were suppressed with increasing temperature. RNA-seq revealed 1440 genes of the P. syringae genome are temperature sensitive in expression. Genes involved in polysaccharide synthesis and regulation, phage and IS elements, type VI secretion, chemosensing and chemotaxis, translation, flagellar synthesis and motility, and phytotoxin synthesis and transport were generally repressed at 30°C, while genes involved in transcriptional regulation, quaternary ammonium compound metabolism and transport, chaperone/heat shock proteins, and hypothetical genes were generally induced at 30°C. Deletion of flgM, a key regulator in the transition from class III to class IV gene expression, led to elevated and constitutive expression of fliC regardless of temperature, but did not affect thermo-regulation of syfA. This work highlights the importance of temperature in the biology of P. syringae, as many genes encoding traits important for plant-microbe interactions were thermo-regulated. PMID:23527276

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach.

Directory of Open Access Journals (Sweden)

Jing Zhao

Full Text Available Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.

The interaction of combined effects of the BDNF and PRKCG genes and negative life events in major depressive disorder.

Science.gov (United States)

Yang, Chunxia; Sun, Ning; Liu, Zhifen; Li, Xinrong; Xu, Yong; Zhang, Kerang

2016-03-30

Major depressive disorder (MDD) is a mental disorder that results from complex interplay between multiple and partially overlapping sets of susceptibility genes and environmental factors. The brain derived neurotrophic factor (BDNF) and Protein kinase C gamma type (PRKCG) are logical candidate genes in MDD. Among diverse environmental factors, negative life events have been suggested to exert a crucial impact on brain development. In the present study, we hypothesized that interactions between genetic variants in BDNF and PRKCG and negative life events may play an important role in the development of MDD. We recruited a total of 406 patients with MDD and 391 age- and gender-matched control subjects. Gene-environment interactions were analyzed using generalized multifactor dimensionality reduction (GMDR). Under a dominant model, we observed a significant three-way interaction among BDNF rs6265, PRKCG rs3745406, and negative life events. The gene-environment combination of PRKCG rs3745406 C allele, BDNF rs6265 G allele and high level of negative life events (C-G-HN) was significantly associated with MDD (OR, 5.97; 95% CI, 2.71-13.15). To our knowledge, this is the first report of evidence that the BDNF-PRKCG interaction may modify the relationship between negative life events and MDD in the Chinese population. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Gene × Smoking Interactions on Human Brain Gene Expression: Finding Common Mechanisms in Adolescents and Adults

Science.gov (United States)

Wolock, Samuel L.; Yates, Andrew; Petrill, Stephen A.; Bohland, Jason W.; Blair, Clancy; Li, Ning; Machiraju, Raghu; Huang, Kun; Bartlett, Christopher W.

2013-01-01

Background: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets…

Diet-gene interactions between dietary fat intake and common polymorphisms in determining lipid metabolism

Energy Technology Data Exchange (ETDEWEB)

Corella, D.

2009-07-01

Current dietary guidelines for fat intake have not taken into consideration the possible genetic differences underlying the individual variability in responsiveness to dietary components. Genetic variability has been identified in humans for all the known lipid metabolism-related genes resulting in a plethora of candidate genes and genetic variants to examine in diet-gene interaction studies focused on fat consumption. Some examples of fat-gene interaction are reviewed. These include: the interaction between total intake and the 14C/T in the hepatic lipase gene promoter in determining high-density lipoprotein cholesterol (HDL-C) metabolism; the interaction between polyunsaturated fatty acids (PUFA) and the 5G/A polymorphism in the APOA1 gene plasma HDL-C concentrations; the interaction between PUFA and the L162V polymorphism in the PPARA gene in determining triglycerides and APOC3 concentrations; and the interaction between PUFA intake and the -1131T>C in the APOA5 gene in determining triglyceride metabolism. Although hundreds of diet-gene interaction studies in lipid metabolism have been published, the level of evidence to make specific nutritional recommendations to the population is still low and more research in nutrigenetics has to be undertaken. (Author) 31 refs.

Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS.

Science.gov (United States)

Pan, David Z; Garske, Kristina M; Alvarez, Marcus; Bhagat, Yash V; Boocock, James; Nikkola, Elina; Miao, Zong; Raulerson, Chelsea K; Cantor, Rita M; Civelek, Mete; Glastonbury, Craig A; Small, Kerrin S; Boehnke, Michael; Lusis, Aldons J; Sinsheimer, Janet S; Mohlke, Karen L; Laakso, Markku; Pajukanta, Päivi; Ko, Arthur

2018-04-17

Increased adiposity is a hallmark of obesity and overweight, which affect 2.2 billion people world-wide. Understanding the genetic and molecular mechanisms that underlie obesity-related phenotypes can help to improve treatment options and drug development. Here we perform promoter Capture Hi-C in human adipocytes to investigate interactions between gene promoters and distal elements as a transcription-regulating mechanism contributing to these phenotypes. We find that promoter-interacting elements in human adipocytes are enriched for adipose-related transcription factor motifs, such as PPARG and CEBPB, and contribute to heritability of cis-regulated gene expression. We further intersect these data with published genome-wide association studies for BMI and BMI-related metabolic traits to identify the genes that are under genetic cis regulation in human adipocytes via chromosomal interactions. This integrative genomics approach identifies four cis-eQTL-eGene relationships associated with BMI or obesity-related traits, including rs4776984 and MAP2K5, which we further confirm by EMSA, and highlights 38 additional candidate genes.

A Convenient Cas9-based Conditional Knockout Strategy for Simultaneously Targeting Multiple Genes in Mouse.

Science.gov (United States)

Chen, Jiang; Du, Yinan; He, Xueyan; Huang, Xingxu; Shi, Yun S

2017-03-31

The most powerful way to probe protein function is to characterize the consequence of its deletion. Compared to conventional gene knockout (KO), conditional knockout (cKO) provides an advanced gene targeting strategy with which gene deletion can be performed in a spatially and temporally restricted manner. However, for most species that are amphiploid, the widely used Cre-flox conditional KO (cKO) system would need targeting loci in both alleles to be loxP flanked, which in practice, requires time and labor consuming breeding. This is considerably significant when one is dealing with multiple genes. CRISPR/Cas9 genome modulation system is advantaged in its capability in targeting multiple sites simultaneously. Here we propose a strategy that could achieve conditional KO of multiple genes in mouse with Cre recombinase dependent Cas9 expression. By transgenic construction of loxP-stop-loxP (LSL) controlled Cas9 (LSL-Cas9) together with sgRNAs targeting EGFP, we showed that the fluorescence molecule could be eliminated in a Cre-dependent manner. We further verified the efficacy of this novel strategy to target multiple sites by deleting c-Maf and MafB simultaneously in macrophages specifically. Compared to the traditional Cre-flox cKO strategy, this sgRNAs-LSL-Cas9 cKO system is simpler and faster, and would make conditional manipulation of multiple genes feasible.

Multiple parton interactions in photoproduction at HERA/H1

Energy Technology Data Exchange (ETDEWEB)

Magro, Lluis Marti

2009-02-15

Photoproduction data of HERA-I are analysed by requiring dijets with transverse momenta of at least 5 GeV. The two jets define in azimuth a towards region (leading jet), an away region (usually the 2nd jet) and transverse regions between them. The charged particle and jet with low transverse momentum multiplicity, so called minijets, are measured in these regions as a function of the variables x{sup obs}{sub {gamma}} and P{sup Jet{sub 1T}} (leading jet). The measurement is compared to predictions including parton showers and matrix elements at leading order in {alpha}{sub s}. Some predictions include contributions from multiple parton interactions and use different parton evolution equations. It was found that existing MC programs do not fully describe the measurements but the description can be improved by including multiple parton interactions. (orig.)

PSP: rapid identification of orthologous coding genes under positive selection across multiple closely related prokaryotic genomes.

Science.gov (United States)

Su, Fei; Ou, Hong-Yu; Tao, Fei; Tang, Hongzhi; Xu, Ping

2013-12-27

With genomic sequences of many closely related bacterial strains made available by deep sequencing, it is now possible to investigate trends in prokaryotic microevolution. Positive selection is a sub-process of microevolution, in which a particular mutation is favored, causing the allele frequency to continuously shift in one direction. Wide scanning of prokaryotic genomes has shown that positive selection at the molecular level is much more frequent than expected. Genes with significant positive selection may play key roles in bacterial adaption to different environmental pressures. However, selection pressure analyses are computationally intensive and awkward to configure. Here we describe an open access web server, which is designated as PSP (Positive Selection analysis for Prokaryotic genomes) for performing evolutionary analysis on orthologous coding genes, specially designed for rapid comparison of dozens of closely related prokaryotic genomes. Remarkably, PSP facilitates functional exploration at the multiple levels by assignments and enrichments of KO, GO or COG terms. To illustrate this user-friendly tool, we analyzed Escherichia coli and Bacillus cereus genomes and found that several genes, which play key roles in human infection and antibiotic resistance, show significant evidence of positive selection. PSP is freely available to all users without any login requirement at: http://db-mml.sjtu.edu.cn/PSP/. PSP ultimately allows researchers to do genome-scale analysis for evolutionary selection across multiple prokaryotic genomes rapidly and easily, and identify the genes undergoing positive selection, which may play key roles in the interactions of host-pathogen and/or environmental adaptation.

Association of circadian rhythm genes ARNTL/BMAL1 and CLOCK with multiple sclerosis.

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Polona Lavtar

Full Text Available Prevalence of multiple sclerosis varies with geographic latitude. We hypothesized that this fact might be partially associated with the influence of latitude on circadian rhythm and consequently that genetic variability of key circadian rhythm regulators, ARNTL and CLOCK genes, might contribute to the risk for multiple sclerosis. Our aim was to analyse selected polymorphisms of ARNTL and CLOCK, and their association with multiple sclerosis. A total of 900 Caucasian patients and 1024 healthy controls were compared for genetic signature at 8 SNPs, 4 for each of both genes. We found a statistically significant difference in genotype (ARNTL rs3789327, P = 7.5·10-5; CLOCK rs6811520 P = 0.02 distributions in patients and controls. The ARNTL rs3789327 CC genotype was associated with higher risk for multiple sclerosis at an OR of 1.67 (95% CI 1.35-2.07, P = 0.0001 and the CLOCK rs6811520 genotype CC at an OR of 1.40 (95% CI 1.13-1.73, P = 0.002. The results of this study suggest that genetic variability in the ARNTL and CLOCK genes might be associated with risk for multiple sclerosis.

Assessment of gene-by-sex interaction effect on bone mineral density

DEFF Research Database (Denmark)

Liu, Ching-Ti; Estrada, Karol; Yerges-Armstrong, Laura M

2012-01-01

Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however, the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and ......Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however, the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome...

Mean multiplicity of secondary particles in hadron-nuclear interactions

International Nuclear Information System (INIS)

Alaverdyan, G.B.; Pak, A.S.

1980-01-01

The mean multiplicity of secondary particles in hA interactions is examined in the framework of the multiplex scattering theory. The dependence of the secondary particle multiplicity coefficient Rsub(6)=anti nsub(hA)/anti nsub(hN) (where anti nsub(hA) and anti nsub(hN) are mean multiplicities of secondary relativistic particles in hA and hN interactions, respectively) on the energy and type of incident particles and atomic number of a target nucleus is analysed. It is shown that predictions of the leading particle cascade model are in satisfactory agreement with the experimental data if the uncertainties of the inelasticity in hN interactions are taken into account. The value Rsub(A) weakly depends both on the incident particle energy and the form of parametrization anti nsub(hN)(E). Allowance of energy losses fluctuation of leading particle results in the Rsub(A) value decrease. From the model of leading particles it does not follow that Rsub(a) strictly depends on the type of incident particles at the fixed value of mean number of collisions. But quantitative values of Rsub(A) for different types of particles and at one value of anti ν, (i.e. at properly chosen value) coincide. The value of Rsub(A) is profoundly dependent on the values of inelasticity factor in hN interactions

Genetic evaluation with major genes and polygenic inheritance when some animals are not genotyped using gene content multiple-trait BLUP.

Science.gov (United States)

Legarra, Andrés; Vitezica, Zulma G

2015-11-17

In pedigreed populations with a major gene segregating for a quantitative trait, it is not clear how to use pedigree, genotype and phenotype information when some individuals are not genotyped. We propose to consider gene content at the major gene as a second trait correlated to the quantitative trait, in a gene content multiple-trait best linear unbiased prediction (GCMTBLUP) method. The genetic covariance between the trait and gene content at the major gene is a function of the substitution effect of the gene. This genetic covariance can be written in a multiple-trait form that accommodates any pattern of missing values for either genotype or phenotype data. Effects of major gene alleles and the genetic covariance between genotype at the major gene and the phenotype can be estimated using standard EM-REML or Gibbs sampling. Prediction of breeding values with genotypes at the major gene can use multiple-trait BLUP software. Major genes with more than two alleles can be considered by including negative covariances between gene contents at each different allele. We simulated two scenarios: a selected and an unselected trait with heritabilities of 0.05 and 0.5, respectively. In both cases, the major gene explained half the genetic variation. Competing methods used imputed gene contents derived by the method of Gengler et al. or by iterative peeling. Imputed gene contents, in contrast to GCMTBLUP, do not consider information on the quantitative trait for genotype prediction. GCMTBLUP gave unbiased estimates of the gene effect, in contrast to the other methods, with less bias and better or equal accuracy of prediction. GCMTBLUP improved estimation of genotypes in non-genotyped individuals, in particular if these individuals had own phenotype records and the trait had a high heritability. Ignoring the major gene in genetic evaluation led to serious biases and decreased prediction accuracy. CGMTBLUP is the best linear predictor of additive genetic merit including

Novel method to load multiple genes onto a mammalian artificial chromosome.

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Anna Tóth

Full Text Available Mammalian artificial chromosomes are natural chromosome-based vectors that may carry a vast amount of genetic material in terms of both size and number. They are reasonably stable and segregate well in both mitosis and meiosis. A platform artificial chromosome expression system (ACEs was earlier described with multiple loading sites for a modified lambda-integrase enzyme. It has been shown that this ACEs is suitable for high-level industrial protein production and the treatment of a mouse model for a devastating human disorder, Krabbe's disease. ACEs-treated mutant mice carrying a therapeutic gene lived more than four times longer than untreated counterparts. This novel gene therapy method is called combined mammalian artificial chromosome-stem cell therapy. At present, this method suffers from the limitation that a new selection marker gene should be present for each therapeutic gene loaded onto the ACEs. Complex diseases require the cooperative action of several genes for treatment, but only a limited number of selection marker genes are available and there is also a risk of serious side-effects caused by the unwanted expression of these marker genes in mammalian cells, organs and organisms. We describe here a novel method to load multiple genes onto the ACEs by using only two selectable marker genes. These markers may be removed from the ACEs before therapeutic application. This novel technology could revolutionize gene therapeutic applications targeting the treatment of complex disorders and cancers. It could also speed up cell therapy by allowing researchers to engineer a chromosome with a predetermined set of genetic factors to differentiate adult stem cells, embryonic stem cells and induced pluripotent stem (iPS cells into cell types of therapeutic value. It is also a suitable tool for the investigation of complex biochemical pathways in basic science by producing an ACEs with several genes from a signal transduction pathway of interest.

Novel method to load multiple genes onto a mammalian artificial chromosome.

Science.gov (United States)

Tóth, Anna; Fodor, Katalin; Praznovszky, Tünde; Tubak, Vilmos; Udvardy, Andor; Hadlaczky, Gyula; Katona, Robert L

2014-01-01

Mammalian artificial chromosomes are natural chromosome-based vectors that may carry a vast amount of genetic material in terms of both size and number. They are reasonably stable and segregate well in both mitosis and meiosis. A platform artificial chromosome expression system (ACEs) was earlier described with multiple loading sites for a modified lambda-integrase enzyme. It has been shown that this ACEs is suitable for high-level industrial protein production and the treatment of a mouse model for a devastating human disorder, Krabbe's disease. ACEs-treated mutant mice carrying a therapeutic gene lived more than four times longer than untreated counterparts. This novel gene therapy method is called combined mammalian artificial chromosome-stem cell therapy. At present, this method suffers from the limitation that a new selection marker gene should be present for each therapeutic gene loaded onto the ACEs. Complex diseases require the cooperative action of several genes for treatment, but only a limited number of selection marker genes are available and there is also a risk of serious side-effects caused by the unwanted expression of these marker genes in mammalian cells, organs and organisms. We describe here a novel method to load multiple genes onto the ACEs by using only two selectable marker genes. These markers may be removed from the ACEs before therapeutic application. This novel technology could revolutionize gene therapeutic applications targeting the treatment of complex disorders and cancers. It could also speed up cell therapy by allowing researchers to engineer a chromosome with a predetermined set of genetic factors to differentiate adult stem cells, embryonic stem cells and induced pluripotent stem (iPS) cells into cell types of therapeutic value. It is also a suitable tool for the investigation of complex biochemical pathways in basic science by producing an ACEs with several genes from a signal transduction pathway of interest.

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk

DEFF Research Database (Denmark)

Usset, Joseph L; Raghavan, Rama; Tyrer, Jonathan P

2016-01-01

and non-obese women. METHODS: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy...... Future work is needed to develop powerful statistical methods able to detect these complex interactions. IMPACT: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC...

Does the FTO Gene Interact with the Socio‐Economic Status on the Obesity Development Among Young European Children?

DEFF Research Database (Denmark)

Foraita, Ronja; Günther, Frauke; Gwozdz, Wencke

Various twin studies revealed that the influence of genetic factors on psychological diseases or behavior is more expressed in socio‐economically advantaged environments. Other studies predominantly show an inverse relation between socio‐economic status (SES) and childhood obesity in western...... developed countries. The aim of this study is to investigate whether the FTO gene interacts with the socio‐economic status (SES) on childhood obesity in a subsample of the IDEFICS cohort (N=4406). A structural equation model (SEM) is applied with the latent constructs obesity, dietary habits, physical...... activity and fitness habits, and parental SES to estimate the main effects of the latter three variables and a FTO polymorphism on obesity. Further, a multiple group SEM is used to explore whether an interaction effect between the single nucleotide polymorphism rs9939609 within the FTO gene and SES exists...

FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets.

Science.gov (United States)

Tiys, Evgeny S; Ivanisenko, Timofey V; Demenkov, Pavel S; Ivanisenko, Vladimir A

2018-02-09

Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members. We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones. FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of

IGEMS: The Consortium on Interplay of Genes and Environment Across Multiple Studies

DEFF Research Database (Denmark)

Pedersen, Nancy L; Christensen, Kaare; Dahl, Anna K

2013-01-01

The Interplay of Genes and Environment across Multiple Studies (IGEMS) group is a consortium of eight longitudinal twin studies established to explore the nature of social context effects and gene-environment interplay in late-life functioning. The resulting analysis of the combined data from ove...

Action of multiple intra-QTL genes concerted around a co-localized transcription factor underpins a large effect QTL

Science.gov (United States)

Dixit, Shalabh; Kumar Biswal, Akshaya; Min, Aye; Henry, Amelia; Oane, Rowena H.; Raorane, Manish L.; Longkumer, Toshisangba; Pabuayon, Isaiah M.; Mutte, Sumanth K.; Vardarajan, Adithi R.; Miro, Berta; Govindan, Ganesan; Albano-Enriquez, Blesilda; Pueffeld, Mandy; Sreenivasulu, Nese; Slamet-Loedin, Inez; Sundarvelpandian, Kalaipandian; Tsai, Yuan-Ching; Raghuvanshi, Saurabh; Hsing, Yue-Ie C.; Kumar, Arvind; Kohli, Ajay

2015-01-01

Sub-QTLs and multiple intra-QTL genes are hypothesized to underpin large-effect QTLs. Known QTLs over gene families, biosynthetic pathways or certain traits represent functional gene-clusters of genes of the same gene ontology (GO). Gene-clusters containing genes of different GO have not been elaborated, except in silico as coexpressed genes within QTLs. Here we demonstrate the requirement of multiple intra-QTL genes for the full impact of QTL qDTY12.1 on rice yield under drought. Multiple evidences are presented for the need of the transcription factor ‘no apical meristem’ (OsNAM12.1) and its co-localized target genes of separate GO categories for qDTY12.1 function, raising a regulon-like model of genetic architecture. The molecular underpinnings of qDTY12.1 support its effectiveness in further improving a drought tolerant genotype and for its validity in multiple genotypes/ecosystems/environments. Resolving the combinatorial value of OsNAM12.1 with individual intra-QTL genes notwithstanding, identification and analyses of qDTY12.1has fast-tracked rice improvement towards food security. PMID:26507552

Multiple mutualist effects on genomewide expression in the tripartite association between Medicago truncatula, nitrogen-fixing bacteria and mycorrhizal fungi.

Science.gov (United States)

Afkhami, Michelle E; Stinchcombe, John R

2016-10-01

While all species interact with multiple mutualists, the fitness consequences and molecular mechanisms underlying these interactions remain largely unknown. We combined factorial ecological experiments with genomewide expression analyses to examine the phenotypic and transcriptomic responses of model legume Medicago truncatula to rhizobia and mycorrhizal fungi. We found synergistic effects of these mutualists on plant performance and examined unique features of plant gene expression responses to multiple mutualists. There were genomewide signatures of mutualists and multiple mutualists on expression, with partners often affecting unique sets of genes. Mycorrhizal fungi had stronger effects on plant expression than rhizobia, with 70% of differentially expressed genes affected by fungi. Fungal and bacterial mutualists had joint effects on 10% of differentially expressed genes, including unexpected, nonadditive effects on some genes with important functions such as nutrient metabolism. For a subset of genes, interacting with multiple mutualists even led to reversals in the direction of expression (shifts from up to downregulation) compared to interacting with single mutualists. Rhizobia also affected the expression of several mycorrhizal genes, including those involved in nutrient transfer to host plants, indicating that partner species can also impact each other's molecular phenotypes. Collectively, these data illustrate the diverse molecular mechanisms and transcriptional responses associated with the synergistic benefits of multiple mutualists. © 2016 John Wiley & Sons Ltd.

Genetic interactions of MAF1 identify a role for Med20 in transcriptional repression of ribosomal protein genes.

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Ian M Willis

2008-07-01

Full Text Available Transcriptional repression of ribosomal components and tRNAs is coordinately regulated in response to a wide variety of environmental stresses. Part of this response involves the convergence of different nutritional and stress signaling pathways on Maf1, a protein that is essential for repressing transcription by RNA polymerase (pol III in Saccharomyces cerevisiae. Here we identify the functions buffering yeast cells that are unable to down-regulate transcription by RNA pol III. MAF1 genetic interactions identified in screens of non-essential gene-deletions and conditionally expressed essential genes reveal a highly interconnected network of 64 genes involved in ribosome biogenesis, RNA pol II transcription, tRNA modification, ubiquitin-dependent proteolysis and other processes. A survey of non-essential MAF1 synthetic sick/lethal (SSL genes identified six gene-deletions that are defective in transcriptional repression of ribosomal protein (RP genes following rapamycin treatment. This subset of MAF1 SSL genes included MED20 which encodes a head module subunit of the RNA pol II Mediator complex. Genetic interactions between MAF1 and subunits in each structural module of Mediator were investigated to examine the functional relationship between these transcriptional regulators. Gene expression profiling identified a prominent and highly selective role for Med20 in the repression of RP gene transcription under multiple conditions. In addition, attenuated repression of RP genes by rapamycin was observed in a strain deleted for the Mediator tail module subunit Med16. The data suggest that Mediator and Maf1 function in parallel pathways to negatively regulate RP mRNA and tRNA synthesis.

Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis

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Sandra Hellberg

2016-09-01

Full Text Available Multiple sclerosis (MS is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.

Prioritization of gene regulatory interactions from large-scale modules in yeast

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Bringas Ricardo

2008-01-01

Full Text Available Abstract Background The identification of groups of co-regulated genes and their transcription factors, called transcriptional modules, has been a focus of many studies about biological systems. While methods have been developed to derive numerous modules from genome-wide data, individual links between regulatory proteins and target genes still need experimental verification. In this work, we aim to prioritize regulator-target links within transcriptional modules based on three types of large-scale data sources. Results Starting with putative transcriptional modules from ChIP-chip data, we first derive modules in which target genes show both expression and function coherence. The most reliable regulatory links between transcription factors and target genes are established by identifying intersection of target genes in coherent modules for each enriched functional category. Using a combination of genome-wide yeast data in normal growth conditions and two different reference datasets, we show that our method predicts regulatory interactions with significantly higher predictive power than ChIP-chip binding data alone. A comparison with results from other studies highlights that our approach provides a reliable and complementary set of regulatory interactions. Based on our results, we can also identify functionally interacting target genes, for instance, a group of co-regulated proteins related to cell wall synthesis. Furthermore, we report novel conserved binding sites of a glycoprotein-encoding gene, CIS3, regulated by Swi6-Swi4 and Ndd1-Fkh2-Mcm1 complexes. Conclusion We provide a simple method to prioritize individual TF-gene interactions from large-scale transcriptional modules. In comparison with other published works, we predict a complementary set of regulatory interactions which yields a similar or higher prediction accuracy at the expense of sensitivity. Therefore, our method can serve as an alternative approach to prioritization for

A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility

NARCIS (Netherlands)

Bush, W.S.; McCauley, J.L.; DeJager, P.L.; Dudek, S.M.; Hafler, D.A.; Gibson, R.A.; Matthews, P.M.; Kappos, L.; Naegelin, Y.; Polman, C.H.; Hauser, S.L.; Oksenberg, J.; Haines, J.L.; Ritchie, M.D.

2011-01-01

Gene-gene interactions are proposed as an important component of the genetic architecture of complex diseases, and are just beginning to be evaluated in the context of genome-wide association studies (GWAS). In addition to detecting epistasis, a benefit to interaction analysis is that it also

Interactions among Candidate Genes Selected by Meta-Analyses Resulting in Higher Risk of Ischemic Stroke in a Chinese Population.

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Man Luo

Full Text Available Ischemic stroke (IS is a multifactorial disorder caused by both genetic and environmental factors. The combined effects of multiple susceptibility genes might result in a higher risk for IS than a single gene. Therefore, we investigated whether interactions among multiple susceptibility genes were associated with an increased risk of IS by evaluating gene polymorphisms identified in previous meta-analyses, including methylenetetrahydrofolate reductase (MTHFR C677T, beta fibrinogen (FGB, β-FG A455G and T148C, apolipoprotein E (APOE ε2-4, angiotensin-converting enzyme (ACE insertion/deletion (I/D, and endothelial nitric oxide synthase (eNOS G894T. In order to examine these interactions, 712 patients with IS and 774 controls in a Chinese Han population were genotyped using the SNaPshot method, and multifactor dimensionality reduction analysis was used to detect potential interactions among the candidate genes. The results of this study found that ACE I/D and β-FG T148C were significant synergistic contributors to IS. In particular, the ACE DD + β-FG 148CC, ACE DD + β-FG 148CT, and ACE ID + β-FG 148CC genotype combinations resulted in higher risk of IS. After adjusting for potential confounding IS risk factors (age, gender, family history of IS, hypertension history and history of diabetes mellitus using a logistic analysis, a significant correlation between the genotype combinations and IS patients persisted (overall stroke: adjusted odds ratio [OR] = 1.57, 95% confidence interval [CI]: 1.22-2.02, P < 0.001, large artery atherosclerosis subtype: adjusted OR = 1.50, 95% CI: 1.08-2.07, P = 0.016, small-artery occlusion subtype: adjusted OR = 2.04, 95% CI: 1.43-2.91, P < 0.001. The results of this study indicate that the ACE I/D and β-FG T148C combination may result in significantly higher risk of IS in this Chinese population.

Hybrid male sterility in rice controlled by interaction between divergent alleles of two adjacent genes.

Science.gov (United States)

Long, Yunming; Zhao, Lifeng; Niu, Baixiao; Su, Jing; Wu, Hao; Chen, Yuanling; Zhang, Qunyu; Guo, Jingxin; Zhuang, Chuxiong; Mei, Mantong; Xia, Jixing; Wang, Lan; Wu, Haibin; Liu, Yao-Guang

2008-12-02

Sterility is common in hybrids between divergent populations, such as the indica and japonica subspecies of Asian cultivated rice (Oryza sativa). Although multiple loci for plant hybrid sterility have been identified, it remains unknown how alleles of the loci interact at the molecular level. Here we show that a locus for indica-japonica hybrid male sterility, Sa, comprises two adjacent genes, SaM and SaF, encoding a small ubiquitin-like modifier E3 ligase-like protein and an F-box protein, respectively. Most indica cultivars contain a haplotype SaM(+)SaF(+), whereas all japonica cultivars have SaM(-)SaF(-) that diverged by nucleotide variations in wild rice. Male semi-sterility in this heterozygous complex locus is caused by abortion of pollen carrying SaM(-). This allele-specific gamete elimination results from a selective interaction of SaF(+) with SaM(-), a truncated protein, but not with SaM(+) because of the presence of an inhibitory domain, although SaM(+) is required for this male sterility. Lack of any one of the three alleles in recombinant plants does not produce male sterility. We propose a two-gene/three-component interaction model for this hybrid male sterility system. The findings have implications for overcoming male sterility in inter-subspecific hybrid rice breeding.

Oscillations in the interactions among multiple solitons in an optical fibre

Energy Technology Data Exchange (ETDEWEB)

Hu, Wen-Qiang; Gao, Yi-Tian; Zhao, Chen; Feng, Yu-Jie; Su, Chuan-Qi [Beijing University of Aeronautics and Astronautics (China). Ministry of Education Key Laboratory of Fluid Mechanics; Beijing University of Aeronautics and Astronautics (China). National Laboratory for Computational Fluid Dynamics

2016-07-01

In this article, under the investigation on the interactions among multiple solitons for an eighth-order nonlinear Schroedinger equation in an optical fibre, oscillations in the interaction zones are observed theoretically. With different coefficients of the operators in this equation, we find that (1) the oscillations in the solitonic interaction zones have different forms with different spectral parameters of this equation; (2) the oscillations in the interactions among the multiple solitons are affected by the choice of spectral parameters, the dispersive effects and nonlinearity of the eighth-order operator; (3) the second-, fifth-, sixth-, and seventh-order operators restrain oscillations in the solitonic interaction zones and the higher-order operators have stronger attenuated effects than the lower ones, while the third- and fourth-order operators stimulate and extend the scope of oscillations.

A pipeline to determine RT-QPCR control genes for evolutionary studies: application to primate gene expression across multiple tissues.

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Olivier Fedrigo

Full Text Available Because many species-specific phenotypic differences are assumed to be caused by differential regulation of gene expression, many recent investigations have focused on measuring transcript abundance. Despite the availability of high-throughput platforms, quantitative real-time polymerase chain reaction (RT-QPCR is often the method of choice because of its low cost and wider dynamic range. However, the accuracy of this technique heavily relies on the use of multiple valid control genes for normalization. We created a pipeline for choosing genes potentially useful as RT-QPCR control genes for measuring expression between human and chimpanzee samples across multiple tissues, using published microarrays and a measure of tissue-specificity. We identified 13 genes from the pipeline and from commonly used control genes: ACTB, USP49, ARGHGEF2, GSK3A, TBP, SDHA, EIF2B2, GPDH, YWHAZ, HPTR1, RPL13A, HMBS, and EEF2. We then tested these candidate genes and validated their expression stability across species. We established the rank order of the most preferable set of genes for single and combined tissues. Our results suggest that for at least three tissues (cerebral cortex, liver, and skeletal muscle, EIF2B2, EEF2, HMBS, and SDHA are useful genes for normalizing human and chimpanzee expression using RT-QPCR. Interestingly, other commonly used control genes, including TBP, GAPDH, and, especially ACTB do not perform as well. This pipeline could be easily adapted to other species for which expression data exist, providing taxonomically appropriate control genes for comparisons of gene expression among species.

A search engine to identify pathway genes from expression data on multiple organisms

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Zambon Alexander C

2007-05-01

Full Text Available Abstract Background The completion of several genome projects showed that most genes have not yet been characterized, especially in multicellular organisms. Although most genes have unknown functions, a large collection of data is available describing their transcriptional activities under many different experimental conditions. In many cases, the coregulatation of a set of genes across a set of conditions can be used to infer roles for genes of unknown function. Results We developed a search engine, the Multiple-Species Gene Recommender (MSGR, which scans gene expression datasets from multiple organisms to identify genes that participate in a genetic pathway. The MSGR takes a query consisting of a list of genes that function together in a genetic pathway from one of six organisms: Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Arabidopsis thaliana, and Helicobacter pylori. Using a probabilistic method to merge searches, the MSGR identifies genes that are significantly coregulated with the query genes in one or more of those organisms. The MSGR achieves its highest accuracy for many human pathways when searches are combined across species. We describe specific examples in which new genes were identified to be involved in a neuromuscular signaling pathway and a cell-adhesion pathway. Conclusion The search engine can scan large collections of gene expression data for new genes that are significantly coregulated with a pathway of interest. By integrating searches across organisms, the MSGR can identify pathway members whose coregulation is either ancient or newly evolved.

Development of the Multiple Gene Knockout System with One-Step PCR in Thermoacidophilic Crenarchaeon Sulfolobus acidocaldarius

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Shoji Suzuki

2017-01-01

Full Text Available Multiple gene knockout systems developed in the thermoacidophilic crenarchaeon Sulfolobus acidocaldarius are powerful genetic tools. However, plasmid construction typically requires several steps. Alternatively, PCR tailing for high-throughput gene disruption was also developed in S. acidocaldarius, but repeated gene knockout based on PCR tailing has been limited due to lack of a genetic marker system. In this study, we demonstrated efficient homologous recombination frequency (2.8 × 104 ± 6.9 × 103 colonies/μg DNA by optimizing the transformation conditions. This optimized protocol allowed to develop reliable gene knockout via double crossover using short homologous arms and to establish the multiple gene knockout system with one-step PCR (MONSTER. In the MONSTER, a multiple gene knockout cassette was simply and rapidly constructed by one-step PCR without plasmid construction, and the PCR product can be immediately used for target gene deletion. As an example of the applications of this strategy, we successfully made a DNA photolyase- (phr- and arginine decarboxylase- (argD- deficient strain of S. acidocaldarius. In addition, an agmatine selection system consisting of an agmatine-auxotrophic strain and argD marker was also established. The MONSTER provides an alternative strategy that enables the very simple construction of multiple gene knockout cassettes for genetic studies in S. acidocaldarius.

A novel approach to simulate gene-environment interactions in complex diseases

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Nicodemi Mario

2010-01-01

Full Text Available Abstract Background Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.. Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS, a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte

Methylobacterium-plant interaction genes regulated by plant exudate and quorum sensing molecules

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Manuella Nóbrega Dourado

2013-12-01

Full Text Available Bacteria from the genus Methylobacterium interact symbiotically (endophytically and epiphytically with different plant species. These interactions can promote plant growth or induce systemic resistance, increasing plant fitness. The plant colonization is guided by molecular communication between bacteria-bacteria and bacteria-plants, where the bacteria recognize specific exuded compounds by other bacteria (e.g. homoserine molecules and/or by the plant roots (e.g. flavonoids, ethanol and methanol, respectively. In this context, the aim of this study was to evaluate the effect of quorum sensing molecules (N-acyl-homoserine lactones and plant exudates (including ethanol in the expression of a series of bacterial genes involved in Methylobacterium-plant interaction. The selected genes are related to bacterial metabolism (mxaF, adaptation to stressful environment (crtI, phoU and sss, to interactions with plant metabolism compounds (acdS and pathogenicity (patatin and phoU. Under in vitro conditions, our results showed the differential expression of some important genes related to metabolism, stress and pathogenesis, thereby AHL molecules up-regulate all tested genes, except phoU, while plant exudates induce only mxaF gene expression. In the presence of plant exudates there is a lower bacterial density (due the endophytic and epiphytic colonization, which produce less AHL, leading to down regulation of genes when compared to the control. Therefore, bacterial density, more than plant exudate, influences the expression of genes related to plant-bacteria interaction.

Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration

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Kelemen Arpad

2008-08-01

Full Text Available Abstract Background This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. Results and conclusion Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.

Bayesian inference based modelling for gene transcriptional dynamics by integrating multiple source of knowledge

Directory of Open Access Journals (Sweden)

Wang Shu-Qiang

2012-07-01

Full Text Available Abstract Background A key challenge in the post genome era is to identify genome-wide transcriptional regulatory networks, which specify the interactions between transcription factors and their target genes. Numerous methods have been developed for reconstructing gene regulatory networks from expression data. However, most of them are based on coarse grained qualitative models, and cannot provide a quantitative view of regulatory systems. Results A binding affinity based regulatory model is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity and the activity level of transcription factor (TF are incorporated into a general learning model. The sequence features of the promoter and the possible occupancy of nucleosomes are exploited to estimate the binding probability of regulators. Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate. Conclusions We testify the proposed approach on two real-world microarray datasets. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than previous models can do.

Synchronization in networks with multiple interaction layers

Science.gov (United States)

del Genio, Charo I.; Gómez-Gardeñes, Jesús; Bonamassa, Ivan; Boccaletti, Stefano

2016-01-01

The structure of many real-world systems is best captured by networks consisting of several interaction layers. Understanding how a multilayered structure of connections affects the synchronization properties of dynamical systems evolving on top of it is a highly relevant endeavor in mathematics and physics and has potential applications in several socially relevant topics, such as power grid engineering and neural dynamics. We propose a general framework to assess the stability of the synchronized state in networks with multiple interaction layers, deriving a necessary condition that generalizes the master stability function approach. We validate our method by applying it to a network of Rössler oscillators with a double layer of interactions and show that highly rich phenomenology emerges from this. This includes cases where the stability of synchronization can be induced even if both layers would have individually induced unstable synchrony, an effect genuinely arising from the true multilayer structure of the interactions among the units in the network. PMID:28138540

Interaction between IL-6 and TNF-α genotypes associated with bacteremia in multiple myeloma patients submitted to autologous stem cell transplantation (ASCT).

Science.gov (United States)

Trigo, Fernanda M B; Luizon, Marcelo R; Dutra, Hélio S; Maiolino, Angelo; Nucci, Márcio; Simões, Belinda P

2014-01-01

Stem cell transplantation affects patient׳s vulnerability to infections due to immunological changes related to chemotherapy. Multiple myeloma is characterized by susceptibility to infections, and IL-6 and TNF-α increased levels affect immune response (IR). Polymorphisms in promoter region of cytokine genes may alter expression levels and affect IR. We performed interaction analysis of IL-6 (-174G/C) and TNF-α (-308G/A) polymorphisms with infection susceptibility in 148 patients classified accordingly to infection status and found an interaction when compared groups with and without bacteremia (p=0.0380). The interaction may be more important than single effects for the IR associated with the infection susceptibility in ASCT.

Multiple parton interactions at the LHC

CERN Document Server

Gaunt, Jonathan Richard

2018-01-01

Many high-energy collider experiments (including the current Large Hadron Collider at CERN) involve the collision of hadrons. Hadrons are composite particles consisting of partons (quarks and gluons), and this means that in any hadron–hadron collision there will typically be multiple collisions of the constituents — i.e. multiple parton interactions (MPI). Understanding the nature of the MPI is important in terms of searching for new physics in the products of the scatters, and also in its own right to gain a greater understanding of hadron structure. This book aims at providing a pedagogical introduction and a comprehensive review of different research lines linked by an involvement of MPI phenomena. It is written by pioneers as well as young leading scientists, and reviews both experimental findings and theoretical developments, discussing also the remaining open issues.

Multiplicity distribution and multiplicity moment of black and grey particles in high energy nucleus–nucleus interactions

International Nuclear Information System (INIS)

Ghosh, Dipak; Deb, Argha; Datta, Utpal; Bhattacharyya, S.

2011-01-01

In this paper we have studied the multiplicity distribution of black and grey particles emitted from 16 O–AgBr interactions at 2.1 AGeV and 60 AGeV. We have also calculated the multiplicity moment up to the fifth order for both the interactions and for both kinds of emitted particles. The variation of multiplicity moment with the order number has been investigated. It is seen that in the case of black particles multiplicity moment up to fourth order remains almost constant as energy increases from 2.1 AGeV to 60 AGeV. Fifth order multiplicity moment increases insignificantly with energy. However in the case of grey particles no such constancy of multiplicity moment with energy of the projectile beam is obtained. Later we have extended our study on the basis of Regge–Mueller approach to find the existence of second order correlation during the emission of black as well as the grey particles. The second Mueller moment is found to be positive and it increases as energy increases in the case of black particles. On the contrary in the case of grey particles the second Mueller moment decreases with energy. It can be concluded that as energy increases correlation among the black particles increases. On the other hand with the increase of energy correlation among the grey particles is found to diminish. (author)

Insulators form gene loops by interacting with promoters in Drosophila.

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Erokhin, Maksim; Davydova, Anna; Kyrchanova, Olga; Parshikov, Alexander; Georgiev, Pavel; Chetverina, Darya

2011-09-01

Chromatin insulators are regulatory elements involved in the modulation of enhancer-promoter communication. The 1A2 and Wari insulators are located immediately downstream of the Drosophila yellow and white genes, respectively. Using an assay based on the yeast GAL4 activator, we have found that both insulators are able to interact with their target promoters in transgenic lines, forming gene loops. The existence of an insulator-promoter loop is confirmed by the fact that insulator proteins could be detected on the promoter only in the presence of an insulator in the transgene. The upstream promoter regions, which are required for long-distance stimulation by enhancers, are not essential for promoter-insulator interactions. Both insulators support basal activity of the yellow and white promoters in eyes. Thus, the ability of insulators to interact with promoters might play an important role in the regulation of basal gene transcription.

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

A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions.

Science.gov (United States)

Sameith, Katrin; Amini, Saman; Groot Koerkamp, Marian J A; van Leenen, Dik; Brok, Mariel; Brabers, Nathalie; Lijnzaad, Philip; van Hooff, Sander R; Benschop, Joris J; Lenstra, Tineke L; Apweiler, Eva; van Wageningen, Sake; Snel, Berend; Holstege, Frank C P; Kemmeren, Patrick

2015-12-23

Genetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering pathway organization and understanding the relationship between genotype, phenotype and disease. To investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, "buffering by induced dependency" and "alleviation by derepression". These mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships.

Plant interactions with multiple insect herbivores: from community to genes

NARCIS (Netherlands)

Stam, J.M.; Kroes, A.; Li, Y.; Gols, R.; Loon, van J.J.A.; Poelman, E.H.; Dicke, M.

2014-01-01

Every plant is a member of a complex insect community that consists of tens to hundreds of species that belong to different trophic levels. The dynamics of this community are critically influenced by the plant, which mediates interactions between community members that can occur on the plant

Simultaneous gene finding in multiple genomes.

Science.gov (United States)

König, Stefanie; Romoth, Lars W; Gerischer, Lizzy; Stanke, Mario

2016-11-15

As the tree of life is populated with sequenced genomes ever more densely, the new challenge is the accurate and consistent annotation of entire clades of genomes. We address this problem with a new approach to comparative gene finding that takes a multiple genome alignment of closely related species and simultaneously predicts the location and structure of protein-coding genes in all input genomes, thereby exploiting negative selection and sequence conservation. The model prefers potential gene structures in the different genomes that are in agreement with each other, or-if not-where the exon gains and losses are plausible given the species tree. We formulate the multi-species gene finding problem as a binary labeling problem on a graph. The resulting optimization problem is NP hard, but can be efficiently approximated using a subgradient-based dual decomposition approach. The proposed method was tested on whole-genome alignments of 12 vertebrate and 12 Drosophila species. The accuracy was evaluated for human, mouse and Drosophila melanogaster and compared to competing methods. Results suggest that our method is well-suited for annotation of (a large number of) genomes of closely related species within a clade, in particular, when RNA-Seq data are available for many of the genomes. The transfer of existing annotations from one genome to another via the genome alignment is more accurate than previous approaches that are based on protein-spliced alignments, when the genomes are at close to medium distances. The method is implemented in C ++ as part of Augustus and available open source at http://bioinf.uni-greifswald.de/augustus/ CONTACT: stefaniekoenig@ymail.com or mario.stanke@uni-greifswald.deSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models.

Science.gov (United States)

Moran, Paula; Stokes, Jennifer; Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John; O'Tuathaigh, Colm

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

Science.gov (United States)

Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia. PMID:27725886

THE MULTIPLE CHOICE PROBLEM WITH INTERACTIONS BETWEEN CRITERIA

Directory of Open Access Journals (Sweden)

Luiz Flavio Autran Monteiro Gomes

2015-12-01

Full Text Available ABSTRACT An important problem in Multi-Criteria Decision Analysis arises when one must select at least two alternatives at the same time. This can be denoted as a multiple choice problem. In other words, instead of evaluating each of the alternatives separately, they must be combined into groups of n alternatives, where n = 2. When the multiple choice problem must be solved under multiple criteria, the result is a multi-criteria, multiple choice problem. In this paper, it is shown through examples how this problemcan be tackled on a bipolar scale. The Choquet integral is used in this paper to take care of interactions between criteria. A numerical application example is conducted using data from SEBRAE-RJ, a non-profit private organization that has the mission of promoting competitiveness, sustainable developmentand entrepreneurship in the state of Rio de Janeiro, Brazil. The paper closes with suggestions for future research.

Msx homeobox gene family and craniofacial development.

Science.gov (United States)

Alappat, Sylvia; Zhang, Zun Yi; Chen, Yi Ping

2003-12-01

Vertebrate Msx genes are unlinked, homeobox-containing genes that bear homology to the Drosophila muscle segment homeobox gene. These genes are expressed at multiple sites of tissue-tissue interactions during vertebrate embryonic development. Inductive interactions mediated by the Msx genes are essential for normal craniofacial, limb and ectodermal organ morphogenesis, and are also essential to survival in mice, as manifested by the phenotypic abnormalities shown in knockout mice and in humans. This review summarizes studies on the expression, regulation, and functional analysis of Msx genes that bear relevance to craniofacial development in humans and mice. Key words: Msx genes, craniofacial, tooth, cleft palate, suture, development, transcription factor, signaling molecule.

The empirical content of models with multiple equilibria in economies with social interactions

OpenAIRE

Alberto Bisin; Andrea Moro; Giorgio Topa

2011-01-01

We study a general class of models with social interactions that might display multiple equilibria. We propose an estimation procedure for these models and evaluate its efficiency and computational feasibility relative to different approaches taken to the curse of dimensionality implied by the multiplicity. Using data on smoking among teenagers, we implement the proposed estimation procedure to understand how group interactions affect health-related choices. We find that interaction effects a...

Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies

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Jennifer A. Smith

2017-12-01

Full Text Available Inter-individual variability in blood pressure (BP is influenced by both genetic and non-genetic factors including socioeconomic and psychosocial stressors. A deeper understanding of the gene-by-socioeconomic/psychosocial factor interactions on BP may help to identify individuals that are genetically susceptible to high BP in specific social contexts. In this study, we used a genomic region-based method for longitudinal analysis, Longitudinal Gene-Environment-Wide Interaction Studies (LGEWIS, to evaluate the effects of interactions between known socioeconomic/psychosocial and genetic risk factors on systolic and diastolic BP in four large epidemiologic cohorts of European and/or African ancestry. After correction for multiple testing, two interactions were significantly associated with diastolic BP. In European ancestry participants, outward/trait anger score had a significant interaction with the C10orf107 genomic region (p = 0.0019. In African ancestry participants, depressive symptom score had a significant interaction with the HFE genomic region (p = 0.0048. This study provides a foundation for using genomic region-based longitudinal analysis to identify subgroups of the population that may be at greater risk of elevated BP due to the combined influence of genetic and socioeconomic/psychosocial risk factors.

Decomposition of the Total Effect in the Presence of Multiple Mediators and Interactions.

Science.gov (United States)

Bellavia, Andrea; Valeri, Linda

2018-06-01

Mediation analysis allows decomposing a total effect into a direct effect of the exposure on the outcome and an indirect effect operating through a number of possible hypothesized pathways. Recent studies have provided formal definitions of direct and indirect effects when multiple mediators are of interest and have described parametric and semiparametric methods for their estimation. Investigating direct and indirect effects with multiple mediators, however, can be challenging in the presence of multiple exposure-mediator and mediator-mediator interactions. In this paper we derive a decomposition of the total effect that unifies mediation and interaction when multiple mediators are present. We illustrate the properties of the proposed framework in a secondary analysis of a pragmatic trial for the treatment of schizophrenia. The decomposition is employed to investigate the interplay of side effects and psychiatric symptoms in explaining the effect of antipsychotic medication on quality of life in schizophrenia patients. Our result offers a valuable tool to identify the proportions of total effect due to mediation and interaction when more than one mediator is present, providing the finest decomposition of the total effect that unifies multiple mediators and interactions.

Gene-gene interactions of IRF5, STAT4, IKZF1 and ETS1 in systemic lupus erythematosus.

Science.gov (United States)

Dang, J; Shan, S; Li, J; Zhao, H; Xin, Q; Liu, Y; Bian, X; Liu, Q

2014-06-01

Interferon (IFN) activation signaling and T helper 17 (Th17)-cell/B-cell regulation play a critical role in the pathogenesis of systemic lupus erythematosus (SLE). Several studies have provided convincing evidence that polymorphisms in IRF5, STAT4, IKZF1 and ETS1 from these pathways may be involved in SLE by affecting gene expression or epistasis. We analyzed the genetic interaction in known SLE susceptibility loci from the four genes in northern Han Chinese. A total of 946 northern Han Chinese participated in this study (370 unrelated SLE patients and 576 healthy controls). Subjects underwent genotyping for the single-nucleotide polymorphisms (SNPs) rs2004640 in IRF5, rs7574865 in STAT4, rs4917014 in IKZF1 and rs1128334 in ETS1 by use of a TaqMan SNP genotyping assay and direct sequencing. Gene-gene interaction analysis involved direct counting, multifactor dimensionality reduction (MDR) and linear regression analysis. SLE patients and controls differed in allele frequencies of rs7574865, rs1128334 (P < 0.001) and rs4917014 (P < 0.01). Direct counting revealed that the frequency of risk homozygote combinations was higher for SLE patients than controls (P < 0.01). Furthermore, 2-, 3- and 4-way gene-gene epistasis in SLE was confirmed by parametric methods and MDR analysis. Gene expression analysis partially supported the findings. Our study confirmed the association of the IFN pathway or Th17/B-cells and the pathogenesis of SLE, and gene-gene interaction in this pathway may increase the risk of SLE. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Measured Gene-by-Environment Interaction in Relation to Attention-Deficit/Hyperactivity Disorder

Science.gov (United States)

Nigg, Joel; Nikolas, Molly; Burt, S. Alexandra

2010-01-01

Objective: To summarize and evaluate the state of knowledge regarding the role of measured gene-by-environment interactions in relation to attention-deficit/hyperactivity disorder. Method: A selective review of methodologic issues was followed by a systematic search for relevant articles on measured gene-by-environment interactions; the search…

An association study of 13 SNPs from seven candidate genes with pediatric asthma and a preliminary study for genetic testing by multiple variants in Taiwanese population.

Science.gov (United States)

Wang, Jiu-Yao; Liou, Ya-Huei; Wu, Ying-Jye; Hsiao, Ya-Hsin; Wu, Lawrence Shih-Hsin

2009-03-01

Asthma is one of the most common chronic diseases in children. It is caused by complex interactions between various genetic factors and exposures to environmental allergens and irritants. Because of the heterogeneity of the disease and the genetic and cultural differences among different populations, a proper association study and genetic testing for asthma and susceptibility genes is difficult to perform. We assessed 13 single-nucleotide polymorphisms (SNPs) in seven well-known asthma susceptibility genes and looked for association with pediatric asthma using 449 asthmatic subjects and 512 non-asthma subjects in Taiwanese population. CD14-159 C/T and MS4A2 Glu237Gly were identified to have difference in genotype/allele frequencies between the control group and asthma patients. Moreover, the genotype synergistic analysis showed that the co-contribution of two functional SNPs was riskier or more protective from asthma attack. Our study provided a genotype synergistic method for studying gene-gene interaction on polymorphism basis and genetic testing using multiple polymorphisms.

Stable carbon isotope fractionation of chlorinated ethenes by a microbial consortium containing multiple dechlorinating genes.

Science.gov (United States)

Liu, Na; Ding, Longzhen; Li, Haijun; Zhang, Pengpeng; Zheng, Jixing; Weng, Chih-Huang

2018-08-01

The study aimed to determine the possible contribution of specific growth conditions and community structures to variable carbon enrichment factors (Ɛ- carbon ) values for the degradation of chlorinated ethenes (CEs) by a bacterial consortium with multiple dechlorinating genes. Ɛ- carbon values for trichloroethylene, cis-1,2-dichloroethylene, and vinyl chloride were -7.24% ± 0.59%, -14.6% ± 1.71%, and -21.1% ± 1.14%, respectively, during their degradation by a microbial consortium containing multiple dechlorinating genes including tceA and vcrA. The Ɛ- carbon values of all CEs were not greatly affected by changes in growth conditions and community structures, which directly or indirectly affected reductive dechlorination of CEs by this consortium. Stability analysis provided evidence that the presence of multiple dechlorinating genes within a microbial consortium had little effect on carbon isotope fractionation, as long as the genes have definite, non-overlapping functions. Copyright © 2018 Elsevier Ltd. All rights reserved.

ToxCast Data Expands Universe of Chemical-Gene Interactions (SOT)

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Characterizing the effects of chemicals in biological systems is often summarized by chemical-gene interactions, which have sparse coverage in literature. The ToxCast chemical screening program has produced bioactivity data for nearly 2000 chemicals and over 450 gene targets. Thi...

A multicolor panel of TALE-KRAB based transcriptional repressor vectors enabling knockdown of multiple gene targets.

Science.gov (United States)

Zhang, Zhonghui; Wu, Elise; Qian, Zhijian; Wu, Wen-Shu

2014-12-05

Stable and efficient knockdown of multiple gene targets is highly desirable for dissection of molecular pathways. Because it allows sequence-specific DNA binding, transcription activator-like effector (TALE) offers a new genetic perturbation technique that allows for gene-specific repression. Here, we constructed a multicolor lentiviral TALE-Kruppel-associated box (KRAB) expression vector platform that enables knockdown of multiple gene targets. This platform is fully compatible with the Golden Gate TALEN and TAL Effector Kit 2.0, a widely used and efficient method for TALE assembly. We showed that this multicolor TALE-KRAB vector system when combined together with bone marrow transplantation could quickly knock down c-kit and PU.1 genes in hematopoietic stem and progenitor cells of recipient mice. Furthermore, our data demonstrated that this platform simultaneously knocked down both c-Kit and PU.1 genes in the same primary cell populations. Together, our results suggest that this multicolor TALE-KRAB vector platform is a promising and versatile tool for knockdown of multiple gene targets and could greatly facilitate dissection of molecular pathways.

Gene-gene interaction between MSX1 and TP63 in Asian case-parent trios with nonsyndromic cleft lip with or without cleft palate.

Science.gov (United States)

Liu, Dongjing; Schwender, Holger; Wang, Mengying; Wang, Hong; Wang, Ping; Zhu, Hongping; Zhou, Zhibo; Li, Jing; Wu, Tao; Beaty, Terri H

2018-03-01

Small ubiquitin-like modification, also known as sumoylation, is a crucial post-translational regulatory mechanisms involved in development of the lip and palate. Recent studies reported two sumoylation target genes, MSX1 and TP63, to have achieved genome-wide level significance in tests of association with nonsyndromic clefts. Here, we performed a candidate gene analysis considering gene-gene and gene-environment interaction for SUMO1, MSX1, and TP63 to further explore the etiology of nonsyndromic cleft lip with or without cleft palate (NSCL/P). A total of 130 single-nucleotide polymorphisms (SNPs) in or near SUMO1, MSX1, and TP63 was analyzed among 1,038 Asian NSCL/P trios ascertained through an international consortium. Conditional logistic regression models were used to explore gene-gene (G × G) and gene-environment (G × E) interaction involving maternal environmental tobacco smoke and multivitamin supplementation. Bonferroni correction was used for G × E analysis and permutation tests were used for G × G analysis. While transmission disequilibrium tests and gene-environment interaction analysis showed no significant results, we did find signals of gene-gene interaction between SNPs near MSX1 and TP63. Three pairwise interactions yielded significant p values in permutation tests (rs884690 and rs9290890 with p = 9.34 × 10 -5 and empirical p = 1.00 × 10 -4 , rs1022136 and rs4687098 with p = 2.41 × 10 -4 and empirical p = 2.95 × 10 -4 , rs6819546 and rs9681004 with p = 5.15 × 10 -4 and empirical p = 3.02 × 10 -4 ). Gene-gene interaction between MSX1 and TP63 may influence the risk of NSCL/P in Asian populations. Our study provided additional understanding of the genetic etiology of NSCL/P and underlined the importance of considering gene-gene interaction in the etiology of this common craniofacial malformation. © 2018 Wiley Periodicals, Inc.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

Directory of Open Access Journals (Sweden)

Paula Moran

2016-01-01

Full Text Available The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia.

A database and tool, IM Browser, for exploring and integrating emerging gene and protein interaction data for Drosophila

Directory of Open Access Journals (Sweden)

Parrish Jodi R

2006-04-01

Full Text Available Abstract Background Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets. Description Here we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes

Supporting a child with multiple disabilities to participate in social interaction

DEFF Research Database (Denmark)

Norén, Niklas; Pilesjö, Maja Sigurd

2016-01-01

Asking a question can be a highly challenging task for a person with multiple disabilities, but questions have not received much attention in research on augmentative and alternative communication (AAC). Conversation analysis is employed to examine an instance of multiparty interaction where...... a speech and language therapist supports a child with multiple disabilities to ask a question with a communication board. The question is accomplished through a practice where the action is built as a trajectory of interactional steps. Each step is built using ways of involvement that establish different...

Calculation of contribution of multiple interactions and efficiency of neutron detectors

International Nuclear Information System (INIS)

Androsenko, A.A.; Androsenko, P.A.; Kazakov, L.E.; Kononov, V.N.; Poletaev, E.D.

1986-01-01

Results of calculation of multiple neutron interactions contribution to efficiency of detectors with 6 Li glass and 10 B plate in the energy range of 0.01-1 MeV are given. The calculation was performed by the Monte-Carlo method using BRAND program complex. It is shown that a correction value for multiple neutron interaction in 6 Li glass of 1 mm thickness constitutes 4.5 % at energy of up to 100 keV and at higher energies has a complex energy dependence reaching 25 % at 440 keV

PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

Science.gov (United States)

Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

2011-06-01

networks have been identified, including scale free distribution of the vertex degree, network motifs, and modularity, to name a few. These studies of network organization require the network to be as complete as possible, which given the limitations of experimental techniques is not currently the case. Therefore, experimental procedures for detecting biomolecular interactions should be complemented by computational approaches. The paper by Lees et al provides a review of computational methods, integrating multiple independent sources of data to infer physical and functional protein-protein interaction networks. One of the important aspects of protein interactions that should be accounted for in the prediction of protein interaction networks is that many proteins are composed of distinct domains. Protein domains may mediate protein interactions while proteins and their interaction networks may gain complexity through gene duplication and expansion of existing domain architectures via domain rearrangements. The latter mechanisms have been explored in detail in the paper by Cohen-Gihon et al. Protein-protein interactions are not the only component of the cell's interactome. Regulation of cell activity can be achieved at the level of transcription and involve a transcription factor—DNA binding which typically requires recognition of a specific DNA sequence motif. Chip-Chip and the more recent Chip-Seq technologies allow in vivo identification of DNA binding sites and, together with novel in vitro approaches, provide data necessary for deciphering the corresponding binding motifs. Such information, complemented by structures of protein-DNA complexes and knowledge of the differences in binding sites among homologs, opens the door to constructing predictive binding models. The paper by Persikov and Singh provides an example of such a model in the Cys2His2 zinc finger family. Recent studies have indicated that the presence of such binding motifs is, however, neither necessary

An Interactive Database of Cocaine-Responsive Gene Expression

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Willard M. Freeman

2002-01-01

Full Text Available The postgenomic era of large-scale gene expression studies is inundating drug abuse researchers and many other scientists with findings related to gene expression. This information is distributed across many different journals, and requires laborious literature searches. Here, we present an interactive database that combines existing information related to cocaine-mediated changes in gene expression in an easy-to-use format. The database is limited to statistically significant changes in mRNA or protein expression after cocaine administration. The Flash-based program is integrated into a Web page, and organizes changes in gene expression based on neuroanatomical region, general function, and gene name. Accompanying each gene is a description of the gene, links to the original publications, and a link to the appropriate OMIM (Online Mendelian Inheritance in Man entry. The nature of this review allows for timely modifications and rapid inclusion of new publications, and should help researchers build second-generation hypotheses on the role of gene expression changes in the physiology and behavior of cocaine abuse. Furthermore, this method of organizing large volumes of scientific information can easily be adapted to assist researchers in fields outside of drug abuse.

Nitrogen Cycle Evaluation (NiCE) Chip for the Simultaneous Analysis of Multiple N-Cycle Associated Genes.

Science.gov (United States)

Oshiki, Mamoru; Segawa, Takahiro; Ishii, Satoshi

2018-02-02

Various microorganisms play key roles in the Nitrogen (N) cycle. Quantitative PCR (qPCR) and PCR-amplicon sequencing of the N cycle functional genes allow us to analyze the abundance and diversity of microbes responsible in the N transforming reactions in various environmental samples. However, analysis of multiple target genes can be cumbersome and expensive. PCR-independent analysis, such as metagenomics and metatranscriptomics, is useful but expensive especially when we analyze multiple samples and try to detect N cycle functional genes present at relatively low abundance. Here, we present the application of microfluidic qPCR chip technology to simultaneously quantify and prepare amplicon sequence libraries for multiple N cycle functional genes as well as taxon-specific 16S rRNA gene markers for many samples. This approach, named as N cycle evaluation (NiCE) chip, was evaluated by using DNA from pure and artificially mixed bacterial cultures and by comparing the results with those obtained by conventional qPCR and amplicon sequencing methods. Quantitative results obtained by the NiCE chip were comparable to those obtained by conventional qPCR. In addition, the NiCE chip was successfully applied to examine abundance and diversity of N cycle functional genes in wastewater samples. Although non-specific amplification was detected on the NiCE chip, this could be overcome by optimizing the primer sequences in the future. As the NiCE chip can provide high-throughput format to quantify and prepare sequence libraries for multiple N cycle functional genes, this tool should advance our ability to explore N cycling in various samples. Importance. We report a novel approach, namely Nitrogen Cycle Evaluation (NiCE) chip by using microfluidic qPCR chip technology. By sequencing the amplicons recovered from the NiCE chip, we can assess diversities of the N cycle functional genes. The NiCE chip technology is applicable to analyze the temporal dynamics of the N cycle gene

The interaction between multiple bubbles and the free surface

International Nuclear Information System (INIS)

Zhang Aman; Yao Xiongliang

2008-01-01

The flow is assumed to be potential, and a boundary integral method is used to solve the Laplace equation for the velocity potential to investigate the shape and the position of the bubble. A 3D code to study the bubble dynamics is developed, and the calculation results agree well with the experimental data. Numerical analyses are carried out for the interaction between multiple bubbles near the free surface including in-phase and out-of-phase bubbles. The calculation result shows that the bubble period increases with the decrease of the distance between bubble centres because of the depression effect between multiple bubbles. The depression has no relationship with the free surface and it is more apparent for out-of-phase bubbles. There are great differences in dynamic behaviour between the in-phase bubbles and the out-of-phase bubbles due to the depression effect. Furthermore, the interaction among eight bubbles is simulated with a three-dimensional model, and the evolving process and the relevant physical phenomena are presented. These phenomena can give a reference to the future work on the power of bubbles induced by multiple charges exploding simultaneously or continuously

A gene-gene interaction between polymorphisms in the OCT2 and MATE1 genes influences the renal clearance of metformin

DEFF Research Database (Denmark)

Hougaard Christensen, Mette Marie; Pedersen, Rasmus Steen; Stage, Tore Bjerregaard

2013-01-01

The aim of this study was to determine the association between the renal clearance (CL(renal)) of metformin in healthy Caucasian volunteers and the single-nucleotide polymorphism (SNP) c.808G>T (rs316019) in OCT2 as well as the relevance of the gene-gene interactions between this SNP and (a) the ...

A viral microRNA down-regulates multiple cell cycle genes through mRNA 5'UTRs.

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Finn Grey

2010-06-01

Full Text Available Global gene expression data combined with bioinformatic analysis provides strong evidence that mammalian miRNAs mediate repression of gene expression primarily through binding sites within the 3' untranslated region (UTR. Using RNA induced silencing complex immunoprecipitation (RISC-IP techniques we have identified multiple cellular targets for a human cytomegalovirus (HCMV miRNA, miR-US25-1. Strikingly, this miRNA binds target sites primarily within 5'UTRs, mediating significant reduction in gene expression. Intriguingly, many of the genes targeted by miR-US25-1 are associated with cell cycle control, including cyclin E2, BRCC3, EID1, MAPRE2, and CD147, suggesting that miR-US25-1 is targeting genes within a related pathway. Deletion of miR-US25-1 from HCMV results in over expression of cyclin E2 in the context of viral infection. Our studies demonstrate that a viral miRNA mediates translational repression of multiple cellular genes by targeting mRNA 5'UTRs.

A BAC-bacterial recombination method to generate physically linked multiple gene reporter DNA constructs

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Gong Shiaochin

2009-03-01

Full Text Available Abstract Background Reporter gene mice are valuable animal models for biological research providing a gene expression readout that can contribute to cellular characterization within the context of a developmental process. With the advancement of bacterial recombination techniques to engineer reporter gene constructs from BAC genomic clones and the generation of optically distinguishable fluorescent protein reporter genes, there is an unprecedented capability to engineer more informative transgenic reporter mouse models relative to what has been traditionally available. Results We demonstrate here our first effort on the development of a three stage bacterial recombination strategy to physically link multiple genes together with their respective fluorescent protein (FP reporters in one DNA fragment. This strategy uses bacterial recombination techniques to: (1 subclone genes of interest into BAC linking vectors, (2 insert desired reporter genes into respective genes and (3 link different gene-reporters together. As proof of concept, we have generated a single DNA fragment containing the genes Trap, Dmp1, and Ibsp driving the expression of ECFP, mCherry, and Topaz FP reporter genes, respectively. Using this DNA construct, we have successfully generated transgenic reporter mice that retain two to three gene readouts. Conclusion The three stage methodology to link multiple genes with their respective fluorescent protein reporter works with reasonable efficiency. Moreover, gene linkage allows for their common chromosomal integration into a single locus. However, the testing of this multi-reporter DNA construct by transgenesis does suggest that the linkage of two different genes together, despite their large size, can still create a positional effect. We believe that gene choice, genomic DNA fragment size and the presence of endogenous insulator elements are critical variables.

A BAC-bacterial recombination method to generate physically linked multiple gene reporter DNA constructs.

Science.gov (United States)

Maye, Peter; Stover, Mary Louise; Liu, Yaling; Rowe, David W; Gong, Shiaochin; Lichtler, Alexander C

2009-03-13

Reporter gene mice are valuable animal models for biological research providing a gene expression readout that can contribute to cellular characterization within the context of a developmental process. With the advancement of bacterial recombination techniques to engineer reporter gene constructs from BAC genomic clones and the generation of optically distinguishable fluorescent protein reporter genes, there is an unprecedented capability to engineer more informative transgenic reporter mouse models relative to what has been traditionally available. We demonstrate here our first effort on the development of a three stage bacterial recombination strategy to physically link multiple genes together with their respective fluorescent protein (FP) reporters in one DNA fragment. This strategy uses bacterial recombination techniques to: (1) subclone genes of interest into BAC linking vectors, (2) insert desired reporter genes into respective genes and (3) link different gene-reporters together. As proof of concept, we have generated a single DNA fragment containing the genes Trap, Dmp1, and Ibsp driving the expression of ECFP, mCherry, and Topaz FP reporter genes, respectively. Using this DNA construct, we have successfully generated transgenic reporter mice that retain two to three gene readouts. The three stage methodology to link multiple genes with their respective fluorescent protein reporter works with reasonable efficiency. Moreover, gene linkage allows for their common chromosomal integration into a single locus. However, the testing of this multi-reporter DNA construct by transgenesis does suggest that the linkage of two different genes together, despite their large size, can still create a positional effect. We believe that gene choice, genomic DNA fragment size and the presence of endogenous insulator elements are critical variables.

Entropy and Multifractality for the Myeloma Multiple TET 2 Gene

Directory of Open Access Journals (Sweden)

Carlo Cattani

2012-01-01

Full Text Available The nucleotide and amino-acid distributions are studied for two variants of mRNA of gene that codes for a protein which is involved in multiple myeloid. Some patches and symmetries are singled out, thus, showing some distinctions between the two variants. Fractal dimensions and entropy are discussed as well.

Stress Effects on Multiple Memory System Interactions

Science.gov (United States)

Ness, Deborah; Calabrese, Pasquale

2016-01-01

Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour. PMID:27034845

Stress Effects on Multiple Memory System Interactions.

Science.gov (United States)

Ness, Deborah; Calabrese, Pasquale

2016-01-01

Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour.

Stress Effects on Multiple Memory System Interactions

Directory of Open Access Journals (Sweden)

Deborah Ness

2016-01-01

Full Text Available Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour.

Adaptive Horizontal Gene Transfers between Multiple Cheese-Associated Fungi.

Science.gov (United States)

Ropars, Jeanne; Rodríguez de la Vega, Ricardo C; López-Villavicencio, Manuela; Gouzy, Jérôme; Sallet, Erika; Dumas, Émilie; Lacoste, Sandrine; Debuchy, Robert; Dupont, Joëlle; Branca, Antoine; Giraud, Tatiana

2015-10-05

Domestication is an excellent model for studies of adaptation because it involves recent and strong selection on a few, identified traits [1-5]. Few studies have focused on the domestication of fungi, with notable exceptions [6-11], despite their importance to bioindustry [12] and to a general understanding of adaptation in eukaryotes [5]. Penicillium fungi are ubiquitous molds among which two distantly related species have been independently selected for cheese making-P. roqueforti for blue cheeses like Roquefort and P. camemberti for soft cheeses like Camembert. The selected traits include morphology, aromatic profile, lipolytic and proteolytic activities, and ability to grow at low temperatures, in a matrix containing bacterial and fungal competitors [13-15]. By comparing the genomes of ten Penicillium species, we show that adaptation to cheese was associated with multiple recent horizontal transfers of large genomic regions carrying crucial metabolic genes. We identified seven horizontally transferred regions (HTRs) spanning more than 10 kb each, flanked by specific transposable elements, and displaying nearly 100% identity between distant Penicillium species. Two HTRs carried genes with functions involved in the utilization of cheese nutrients or competition and were found nearly identical in multiple strains and species of cheese-associated Penicillium fungi, indicating recent selective sweeps; they were experimentally associated with faster growth and greater competitiveness on cheese and contained genes highly expressed in the early stage of cheese maturation. These findings have industrial and food safety implications and improve our understanding of the processes of adaptation to rapid environmental changes. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Log-Normal Distribution in a Growing System with Weighted and Multiplicatively Interacting Particles

Science.gov (United States)

Fujihara, Akihiro; Tanimoto, Satoshi; Yamamoto, Hiroshi; Ohtsuki, Toshiya

2018-03-01

A growing system with weighted and multiplicatively interacting particles is investigated. Each particle has a quantity that changes multiplicatively after a binary interaction, with its growth rate controlled by a weight parameter in a homogeneous symmetric kernel. We consider the system using moment inequalities and analytically derive the log-normal-type tail in the probability distribution function of quantities when the parameter is negative, which is different from the result for single-body multiplicative processes. We also find that the system approaches a winner-take-all state when the parameter is positive.

C/EBPβ Mediates Growth Hormone-Regulated Expression of Multiple Target Genes

Science.gov (United States)

Cui, Tracy X.; Lin, Grace; LaPensee, Christopher R.; Calinescu, Anda-Alexandra; Rathore, Maanjot; Streeter, Cale; Piwien-Pilipuk, Graciela; Lanning, Nathan; Jin, Hui; Carter-Su, Christin; Qin, Zhaohui S.

2011-01-01

Regulation of c-Fos transcription by GH is mediated by CCAAT/enhancer binding protein β (C/EBPβ). This study examines the role of C/EBPβ in mediating GH activation of other early response genes, including Cyr61, Btg2, Socs3, Zfp36, and Socs1. C/EBPβ depletion using short hairpin RNA impaired responsiveness of these genes to GH, as seen for c-Fos. Rescue with wild-type C/EBPβ led to GH-dependent recruitment of the coactivator p300 to the c-Fos promoter. In contrast, rescue with C/EBPβ mutated at the ERK phosphorylation site at T188 failed to induce GH-dependent recruitment of p300, indicating that ERK-mediated phosphorylation of C/EBPβ at T188 is required for GH-induced recruitment of p300 to c-Fos. GH also induced the occupancy of phosphorylated C/EBPβ and p300 on Cyr61, Btg2, and Socs3 at predicted C/EBP-cAMP response element-binding protein motifs in their promoters. Consistent with a role for ERKs in GH-induced expression of these genes, treatment with U0126 to block ERK phosphorylation inhibited their GH-induced expression. In contrast, GH-dependent expression of Zfp36 and Socs1 was not inhibited by U0126. Thus, induction of multiple early response genes by GH in 3T3-F442A cells is mediated by C/EBPβ. A subset of these genes is regulated similarly to c-Fos, through a mechanism involving GH-stimulated ERK 1/2 activation, phosphorylation of C/EBPβ, and recruitment of p300. Overall, these studies suggest that C/EBPβ, like the signal transducer and activator of transcription proteins, regulates multiple genes in response to GH. PMID:21292824

Extracting Gene Networks for Low-Dose Radiation Using Graph Theoretical Algorithms

Energy Technology Data Exchange (ETDEWEB)

Voy, Brynn H [ORNL; Scharff, Jon [University of Tennessee, Knoxville (UTK); Perkins, Andy [University of Tennessee, Knoxville (UTK); Saxton, Arnold [University of Tennessee, Knoxville (UTK); Borate, Bhavesh [University of Tennessee, Knoxville (UTK); Chesler, Elissa J [ORNL; Branstetter, Lisa R [ORNL; Langston, Michael A [University of Tennessee, Knoxville (UTK)

2006-01-01

Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from the graph correspond to sets of genes for which significant edges are present between all members of the set, representing potential members of common or interacting pathways. Clique membership can be used to infer function about poorly annotated genes, based on the known functions of better-annotated genes with which they share clique membership (i.e., ''guilt-by-association''). We illustrate our method by applying it to microarray data collected from the spleens of mice exposed to low-dose ionizing radiation. Differential analysis is used to identify sets of genes whose interactions are impacted by radiation exposure. The correlation graph is also queried independently of clique to extract edges that are impacted by radiation. We present several examples of multiple gene interactions that are altered by radiation exposure and thus represent potential molecular pathways that mediate the radiation response.

Extracting gene networks for low-dose radiation using graph theoretical algorithms.

Directory of Open Access Journals (Sweden)

Brynn H Voy

2006-07-01

Full Text Available Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from the graph correspond to sets of genes for which significant edges are present between all members of the set, representing potential members of common or interacting pathways. Clique membership can be used to infer function about poorly annotated genes, based on the known functions of better-annotated genes with which they share clique membership (i.e., "guilt-by-association". We illustrate our method by applying it to microarray data collected from the spleens of mice exposed to low-dose ionizing radiation. Differential analysis is used to identify sets of genes whose interactions are impacted by radiation exposure. The correlation graph is also queried independently of clique to extract edges that are impacted by radiation. We present several examples of multiple gene interactions that are altered by radiation exposure and thus represent potential molecular pathways that mediate the radiation response.

Association and Interaction Effect of AGTR1 and AGTR2 Gene Polymorphisms with Dietary Pattern on Metabolic Risk Factors of Cardiovascular Disease in Malaysian Adults.

Science.gov (United States)

Yap, Roseline Wai Kuan; Shidoji, Yoshihiro; Yap, Wai Sum; Masaki, Motofumi

2017-08-09

Gene-diet interaction using a multifactorial approach is preferred to study the multiple risk factors of cardiovascular disease (CVD). This study examined the association and gene-diet interaction effects of the angiotensin II type 1 receptor ( AGTR1 ) gene (rs5186), and type 2 receptor ( AGTR2 ) gene (rs1403543) polymorphisms on metabolic risk factors of CVD in Malaysian adults. CVD parameters (BMI, blood pressure, glycated hemoglobin, total cholesterol (TC), triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), and TC/HDL-C ratio), and constructed dietary patterns "vegetables, fruits, and soy diet" (VFSD), and "rice, egg, and fish diet" (REFD) were obtained from previous studies. Genotyping analysis was performed by real-time PCR using Taqman probes. The subjects were 507 adults (151 Malays; 179 Chinese; and 177 Indians). Significant genetic associations were obtained on blood lipids for rs5186 in Malays and Chinese, and rs1403543 in Chinese females. The significant gene-diet interaction effects after adjusting for potential confounders were: rs5186 × VFSD on blood pressure in Malays ( p = 0.016), and in Chinese on blood lipids for rs5186 × REFD ( p = 0.009-0.023), and rs1403543 × VFSD in female subjects ( p = 0.001-0.011). Malays and Chinese showed higher risk for blood pressure and/or lipids involving rs5186 and rs1403543 SNPs together with gene-diet interactions, but not Indians.

Visual Comparison of Multiple Gene Expression Datasets in a Genomic Context

Directory of Open Access Journals (Sweden)

Borowski Krzysztof

2008-06-01

Full Text Available The need for novel methods of visualizing microarray data is growing. New perspectives are beneficial to finding patterns in expression data. The Bluejay genome browser provides an integrative way of visualizing gene expression datasets in a genomic context. We have now developed the functionality to display multiple microarray datasets simultaneously in Bluejay, in order to provide researchers with a comprehensive view of their datasets linked to a graphical representation of gene function. This will enable biologists to obtain valuable insights on expression patterns, by allowing them to analyze the expression values in relation to the gene locations as well as to compare expression profiles of related genomes or of di erent experiments for the same genome.

DNMT1-interacting RNAs block gene specific DNA methylation

Science.gov (United States)

Di Ruscio, Annalisa; Ebralidze, Alexander K.; Benoukraf, Touati; Amabile, Giovanni; Goff, Loyal A.; Terragni, Joylon; Figueroa, Maria Eugenia; De Figureido Pontes, Lorena Lobo; Alberich-Jorda, Meritxell; Zhang, Pu; Wu, Mengchu; D’Alò, Francesco; Melnick, Ari; Leone, Giuseppe; Ebralidze, Konstantin K.; Pradhan, Sriharsa; Rinn, John L.; Tenen, Daniel G.

2013-01-01

Summary DNA methylation was described almost a century ago. However, the rules governing its establishment and maintenance remain elusive. Here, we present data demonstrating that active transcription regulates levels of genomic methylation. We identified a novel RNA arising from the CEBPA gene locus critical in regulating the local DNA methylation profile. This RNA binds to DNMT1 and prevents CEBPA gene locus methylation. Deep sequencing of transcripts associated with DNMT1 combined with genome-scale methylation and expression profiling extended the generality of this finding to numerous gene loci. Collectively, these results delineate the nature of DNMT1-RNA interactions and suggest strategies for gene selective demethylation of therapeutic targets in disease. PMID:24107992

Meta-Analysis of Multiple Sclerosis Microarray Data Reveals Dysregulation in RNA Splicing Regulatory Genes

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Elvezia Maria Paraboschi

2015-09-01

Full Text Available Abnormalities in RNA metabolism and alternative splicing (AS are emerging as important players in complex disease phenotypes. In particular, accumulating evidence suggests the existence of pathogenic links between multiple sclerosis (MS and altered AS, including functional studies showing that an imbalance in alternatively-spliced isoforms may contribute to disease etiology. Here, we tested whether the altered expression of AS-related genes represents a MS-specific signature. A comprehensive comparative analysis of gene expression profiles of publicly-available microarray datasets (190 MS cases, 182 controls, followed by gene-ontology enrichment analysis, highlighted a significant enrichment for differentially-expressed genes involved in RNA metabolism/AS. In detail, a total of 17 genes were found to be differentially expressed in MS in multiple datasets, with CELF1 being dysregulated in five out of seven studies. We confirmed CELF1 downregulation in MS (p = 0.0015 by real-time RT-PCRs on RNA extracted from blood cells of 30 cases and 30 controls. As a proof of concept, we experimentally verified the unbalance in alternatively-spliced isoforms in MS of the NFAT5 gene, a putative CELF1 target. In conclusion, for the first time we provide evidence of a consistent dysregulation of splicing-related genes in MS and we discuss its possible implications in modulating specific AS events in MS susceptibility genes.

Multiple independent insertions of 5S rRNA genes in the spliced-leader gene family of trypanosome species.

Science.gov (United States)

Beauparlant, Marc A; Drouin, Guy

2014-02-01

Analyses of the 5S rRNA genes found in the spliced-leader (SL) gene repeat units of numerous trypanosome species suggest that such linkages were not inherited from a common ancestor, but were the result of independent 5S rRNA gene insertions. In trypanosomes, 5S rRNA genes are found either in the tandemly repeated units coding for SL genes or in independent tandemly repeated units. Given that trypanosome species where 5S rRNA genes are within the tandemly repeated units coding for SL genes are phylogenetically related, one might hypothesize that this arrangement is the result of an ancestral insertion of 5S rRNA genes into the tandemly repeated SL gene family of trypanosomes. Here, we use the types of 5S rRNA genes found associated with SL genes, the flanking regions of the inserted 5S rRNA genes and the position of these insertions to show that most of the 5S rRNA genes found within SL gene repeat units of trypanosome species were not acquired from a common ancestor but are the results of independent insertions. These multiple 5S rRNA genes insertion events in trypanosomes are likely the result of frequent founder events in different hosts and/or geographical locations in species having short generation times.

Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes

KAUST Repository

AlShahrani, Mona; Hoehndorf, Robert

2018-01-01

In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease's (or patient's) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results: We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprising of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.

Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes

KAUST Repository

Alshahrani, Mona

2018-04-30

In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease\\'s (or patient\\'s) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results: We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprising of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.

Gene by Environment Interaction and Resilience: Effects of Child Maltreatment and Serotonin, Corticotropin Releasing Hormone, Dopamine, and Oxytocin Genes

Science.gov (United States)

Cicchetti, Dante; Rogosch, Fred A.

2013-01-01

In this investigation, gene-environment interaction effects in predicting resilience in adaptive functioning among maltreated and nonmaltreated low-income children (N = 595) were examined. A multi-component index of resilient functioning was derived and levels of resilient functioning were identified. Variants in four genes, 5-HTTLPR, CRHR1, DRD4 -521C/T, and OXTR, were investigated. In a series of ANCOVAs, child maltreatment demonstrated a strong negative main effect on children’s resilient functioning, whereas no main effects for any of the genotypes of the respective genes were found. However, gene-environment interactions involving genotypes of each of the respective genes and maltreatment status were obtained. For each respective gene, among children with a specific genotype, the relative advantage in resilient functioning of nonmaltreated compared to maltreated children was stronger than was the case for nonmaltreated and maltreated children with other genotypes of the respective gene. Across the four genes, a composite of the genotypes that more strongly differentiated resilient functioning between nonmaltreated and maltreated children provided further evidence of genetic variations influencing resilient functioning in nonmaltreated children, whereas genetic variation had a negligible effect on promoting resilience among maltreated children. Additional effects were observed for children based on the number of subtypes of maltreatment children experienced, as well as for abuse and neglect subgroups. Finally, maltreated and nonmaltreated children with high levels of resilience differed in their average number of differentiating genotypes. These results suggest that differential resilient outcomes are based on the interaction between genes and developmental experiences. PMID:22559122

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...

Gene-environment interaction: Does fluoride influence the reproductive hormones in male farmers modified by ERα gene polymorphisms?

Science.gov (United States)

Ma, Qiang; Huang, Hui; Sun, Long; Zhou, Tong; Zhu, Jingyuan; Cheng, Xuemin; Duan, Lijv; Li, Zhiyuan; Cui, Liuxin; Ba, Yue

2017-12-01

The occurrence of endemic fluorosis is derived from high fluoride levels in drinking water and industrial fumes or dust. Reproductive disruption is also a major harm caused by fluoride exposure besides dental and skeletal lesions. However, few studies focus on the mechanism of fluoride exposure on male reproductive function, especially the possible interaction of fluoride exposure and gene polymorphism on male reproductive hormones. Therefore, we conducted a cross-sectional study in rural areas of Henan province in China to explore the interaction between the estrogen receptor alpha (ERα) gene and fluoride exposure on reproductive hormone levels in male farmers living in the endemic fluorosis villages. The results showed that fluoride exposure significantly increased the serum level of estradiol in the hypothalamic-pituitary-testicular (HPT) axis in male farmers. Moreover, the observations indicated that fluoride exposure and genetic markers had an interaction on serum concentration of follicle-stimulating hormone and estradiol, and the interaction among different loci of the ERα gene could impact the serum testosterone level. Findings in the present work suggest that chronic fluoride exposure in drinking water could modulate the levels of reproductive hormones in males living in endemic fluorosis areas, and the interaction between fluoride exposure and ERα polymorphisms might affect the serum levels of hormones in the HPT axis in male farmers. Copyright © 2017 Elsevier Ltd. All rights reserved.

The SH2D2A gene and susceptibility to multiple sclerosis

DEFF Research Database (Denmark)

Lorentzen, A.R.; Smestad, C.; Lie, B.A.

2008-01-01

We previously reported an association between the SH2D2A gene encoding TSAd and multiple sclerosis (MS). Here a total of 2128 Nordic MS patients and 2004 controls were genotyped for the SH2D2A promoter GA repeat polymorphism and rs926103 encoding a serine to asparagine substitution at amino acid...... that the SH2D2A gene may contribute to susceptibility to MS Udgivelsesdato: 2008/7/15...

Pareto-Zipf law in growing systems with multiplicative interactions

Science.gov (United States)

Ohtsuki, Toshiya; Tanimoto, Satoshi; Sekiyama, Makoto; Fujihara, Akihiro; Yamamoto, Hiroshi

2018-06-01

Numerical simulations of multiplicatively interacting stochastic processes with weighted selections were conducted. A feedback mechanism to control the weight w of selections was proposed. It becomes evident that when w is moderately controlled around 0, such systems spontaneously exhibit the Pareto-Zipf distribution. The simulation results are universal in the sense that microscopic details, such as parameter values and the type of control and weight, are irrelevant. The central ingredient of the Pareto-Zipf law is argued to be the mild control of interactions.

Finding gene-environment interactions for Phobias

OpenAIRE

Gregory, Alice M.; Lau, Jennifer Y. F.; Eley, Thalia C.

2008-01-01

Phobias are common disorders causing a great deal of suffering. Studies of gene-environment interaction (G × E) have revealed much about the complex processes underlying the development of various psychiatric disorders but have told us little about phobias. This article describes what is already known about genetic and environmental influences upon phobias and suggests how this information can be used to optimise the chances of discovering G × Es for phobias. In addition to the careful concep...

Two-Way Gene Interaction From Microarray Data Based on Correlation Methods.

Science.gov (United States)

Alavi Majd, Hamid; Talebi, Atefeh; Gilany, Kambiz; Khayyer, Nasibeh

2016-06-01

Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity. Networks offer a natural way to model interactions between genes, and extracting gene network information from high-throughput genomic data is an important and difficult task. The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients. The first step in constructing a Gene Co-expression Network is to score all pairs of gene vectors. The second step is to select a score threshold and connect all gene pairs whose scores exceed this value. In the foundation-application study, we constructed two-way gene networks using nonparametric methods, such as Spearman's rank correlation coefficient and Blomqvist's measure, and compared them with Pearson's correlation coefficient. We surveyed six genes of venous thrombosis disease, made a matrix entry representing the score for the corresponding gene pair, and obtained two-way interactions using Pearson's correlation, Spearman's rank correlation, and Blomqvist's coefficient. Finally, these methods were compared with Cytoscape, based on BIND, and Gene Ontology, based on molecular function visual methods; R software version 3.2 and Bioconductor were used to perform these methods. Based on the Pearson and Spearman correlations, the results were the same and were confirmed by Cytoscape and GO visual methods; however, Blomqvist's coefficient was not confirmed by visual methods. Some results of the correlation coefficients are not the same with visualization. The reason may be due to the small number of data.

A map of directional genetic interactions in a metazoan cell.

Science.gov (United States)

Fischer, Bernd; Sandmann, Thomas; Horn, Thomas; Billmann, Maximilian; Chaudhary, Varun; Huber, Wolfgang; Boutros, Michael

2015-03-06

Gene-gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene-gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.

Scaling of chaotic multiplicity: A new observation in high-energy interactions

International Nuclear Information System (INIS)

Ghosh, D.; Ghosh, P.; Roy, J.

1990-01-01

We analyze high-energy-interaction data to study the dependence of chaotic multiplicity on the pseudorapidity window and propose a new scaling function bar Ψ(bar z)=left-angle n 1 right-angle/left-angle n right-angle max where left-angle n 1 right-angle is the chaotic multiplicity and bar z=left-angle n right-angle/left-angle n right-angle max is the reduced multiplicity, following the quantum-optical concept of particle production. It has been observed that the proposed ''chaotic multiplicity scaling'' is obeyed by pp, p bar p, and AA collisions at different available energies

Multiple Syntrophic Interactions in a Terephthalate-Degrading Methanogenic Consortium

Energy Technology Data Exchange (ETDEWEB)

Lykidis, Athanasios; Chen, Chia-Lung; Tringe, Susannah G.; McHardy, Alice C.; Copeland, Alex 5; Kyrpides, Nikos C.; Hugenholtz, Philip; Liu, Wen-Tso

2010-08-05

Terephthalate (TA) is one of the top 50 chemicals produced worldwide. Its production results in a TA-containing wastewater that is treated by anaerobic processes through a poorly understood methanogenic syntrophy. Using metagenomics, we characterized the methanogenic consortium tinside a hyper-mesophilic (i.e., between mesophilic and thermophilic), TA-degrading bioreactor. We identified genes belonging to dominant Pelotomaculum species presumably involved in TA degradation through decarboxylation, dearomatization, and modified ?-oxidation to H{sub 2}/CO{sub 2} and acetate. These intermediates are converted to CH{sub 4}/CO{sub 2} by three novel hyper-mesophilic methanogens. Additional secondary syntrophic interactions were predicted in Thermotogae, Syntrophus and candidate phyla OP5 and WWE1 populations. The OP5 encodes genes capable of anaerobic autotrophic butyrate production and Thermotogae, Syntrophus and WWE1 have the genetic potential to oxidize butyrate to COsub 2}/H{sub 2} and acetate. These observations suggest that the TA-degrading consortium consists of additional syntrophic interactions beyond the standard H{sub 2}-producing syntroph ? methanogen partnership that may serve to improve community stability.

Functional modules by relating protein interaction networks and gene expression.

Science.gov (United States)

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.

Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks.

Science.gov (United States)

Wu, Mengmeng; Lin, Zhixiang; Ma, Shining; Chen, Ting; Jiang, Rui; Wong, Wing Hung

2017-12-01

Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.

Cross-species multiple environmental stress responses: An integrated approach to identify candidate genes for multiple stress tolerance in sorghum (Sorghum bicolor (L. Moench and related model species.

Directory of Open Access Journals (Sweden)

Adugna Abdi Woldesemayat

Full Text Available Crop response to the changing climate and unpredictable effects of global warming with adverse conditions such as drought stress has brought concerns about food security to the fore; crop yield loss is a major cause of concern in this regard. Identification of genes with multiple responses across environmental stresses is the genetic foundation that leads to crop adaptation to environmental perturbations.In this paper, we introduce an integrated approach to assess candidate genes for multiple stress responses across-species. The approach combines ontology based semantic data integration with expression profiling, comparative genomics, phylogenomics, functional gene enrichment and gene enrichment network analysis to identify genes associated with plant stress phenotypes. Five different ontologies, viz., Gene Ontology (GO, Trait Ontology (TO, Plant Ontology (PO, Growth Ontology (GRO and Environment Ontology (EO were used to semantically integrate drought related information.Target genes linked to Quantitative Trait Loci (QTLs controlling yield and stress tolerance in sorghum (Sorghum bicolor (L. Moench and closely related species were identified. Based on the enriched GO terms of the biological processes, 1116 sorghum genes with potential responses to 5 different stresses, such as drought (18%, salt (32%, cold (20%, heat (8% and oxidative stress (25% were identified to be over-expressed. Out of 169 sorghum drought responsive QTLs associated genes that were identified based on expression datasets, 56% were shown to have multiple stress responses. On the other hand, out of 168 additional genes that have been evaluated for orthologous pairs, 90% were conserved across species for drought tolerance. Over 50% of identified maize and rice genes were responsive to drought and salt stresses and were co-located within multifunctional QTLs. Among the total identified multi-stress responsive genes, 272 targets were shown to be co-localized within QTLs

Global changes in gene expression during compatible and incompatible interactions of cowpea (Vigna unguiculata L.) with the root parasitic angiosperm Striga gesnerioides.

Science.gov (United States)

Huang, Kan; Mellor, Karolina E; Paul, Shom N; Lawson, Mark J; Mackey, Aaron J; Timko, Michael P

2012-08-17

Cowpea, Vigna unguiculata L. Walp., is one of the most important food and forage legumes in the semi-arid tropics. While most domesticated forms of cowpea are susceptible to the root parasitic weed Striga gesnerioides, several cultivars have been identified that show race-specific resistance. Cowpea cultivar B301 contains the RSG3-301 gene for resistance to S. gesnerioides race SG3, but is susceptible to race SG4z. When challenged by SG3, roots of cultivar B301 develop a strong resistance response characterized by a hypersensitive reaction and cell death at the site of parasite attachment. In contrast, no visible response occurs in B301 roots parasitized by SG4z. Gene expression in the roots of the cowpea cultivar B301 during compatible (susceptible) and incompatible (resistant) interactions with S. gesnerioides races SG4z and SG3, respectively, were investigated at the early (6 days post-inoculation (dpi)) and late (13 dpi) stages of the resistance response using a Nimblegen custom design cowpea microarray. A total of 111 genes were differentially expressed in B301 roots at 6 dpi; this number increased to 2102 genes at 13 dpi. At 13 dpi, a total of 1944 genes were differentially expressed during compatible (susceptible) interactions of B301 with SG4z. Genes and pathways involved in signal transduction, programmed cell death and apoptosis, and defense response to biotic and abiotic stress were differentially expressed in the early resistance response; at the later time point, enrichment was primarily for defense-related gene expression, and genes encoding components of lignifications and secondary wall formation. In compatible interactions (B301-SG4z), multiple defense pathways were repressed, including those involved in lignin biosynthesis and secondary cell wall modifications, while cellular transport processes for nitrogen and sulfur were increased. Distinct changes in global gene expression profiles occur in host roots following successful and unsuccessful

VEGF-A isoforms differentially regulate ATF-2-dependent VCAM-1 gene expression and endothelial-leukocyte interactions.

Science.gov (United States)

Fearnley, Gareth W; Odell, Adam F; Latham, Antony M; Mughal, Nadeem A; Bruns, Alexander F; Burgoyne, Nicholas J; Homer-Vanniasinkam, Shervanthi; Zachary, Ian C; Hollstein, Monica C; Wheatcroft, Stephen B; Ponnambalam, Sreenivasan

2014-08-15

Vascular endothelial growth factor A (VEGF-A) regulates many aspects of vascular physiology. VEGF-A stimulates signal transduction pathways that modulate endothelial outputs such as cell migration, proliferation, tubulogenesis, and cell-cell interactions. Multiple VEGF-A isoforms exist, but the biological significance of this is unclear. Here we analyzed VEGF-A isoform-specific stimulation of VCAM-1 gene expression, which controls endothelial-leukocyte interactions, and show that this is dependent on both ERK1/2 and activating transcription factor-2 (ATF-2). VEGF-A isoforms showed differential ERK1/2 and p38 MAPK phosphorylation kinetics. A key feature of VEGF-A isoform-specific ERK1/2 activation and nuclear translocation was increased phosphorylation of ATF-2 on threonine residue 71 (T71). Using reverse genetics, we showed ATF-2 to be functionally required for VEGF-A-stimulated endothelial VCAM-1 gene expression. ATF-2 knockdown blocked VEGF-A-stimulated VCAM-1 expression and endothelial-leukocyte interactions. ATF-2 was also required for other endothelial cell outputs, such as cell migration and tubulogenesis. In contrast, VCAM-1 was essential only for promoting endothelial-leukocyte interactions. This work presents a new paradigm for understanding how soluble growth factor isoforms program complex cellular outputs and responses by modulating signal transduction pathways. © 2014 Fearnley et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Evidence for multiple stressor interactions and effects on coral reefs.

Science.gov (United States)

Ban, Stephen S; Graham, Nicholas A J; Connolly, Sean R

2014-03-01

Concern is growing about the potential effects of interacting multiple stressors, especially as the global climate changes. We provide a comprehensive review of multiple stressor interactions in coral reef ecosystems, which are widely considered to be one of the most sensitive ecosystems to global change. First, we synthesized coral reef studies that examined interactions of two or more stressors, highlighting stressor interactions (where one stressor directly influences another) and potentially synergistic effects on response variables (where two stressors interact to produce an effect that is greater than purely additive). For stressor-stressor interactions, we found 176 studies that examined at least 2 of the 13 stressors of interest. Applying network analysis to analyze relationships between stressors, we found that pathogens were exacerbated by more costressors than any other stressor, with ca. 78% of studies reporting an enhancing effect by another stressor. Sedimentation, storms, and water temperature directly affected the largest number of other stressors. Pathogens, nutrients, and crown-of-thorns starfish were the most-influenced stressors. We found 187 studies that examined the effects of two or more stressors on a third dependent variable. The interaction of irradiance and temperature on corals has been the subject of more research (62 studies, 33% of the total) than any other combination of stressors, with many studies reporting a synergistic effect on coral symbiont photosynthetic performance (n = 19). Second, we performed a quantitative meta-analysis of existing literature on this most-studied interaction (irradiance and temperature). We found that the mean effect size of combined treatments was statistically indistinguishable from a purely additive interaction, although it should be noted that the sample size was relatively small (n = 26). Overall, although in aggregate a large body of literature examines stressor effects on coral reefs and coral

Interactive Visualization of DGA Data Based on Multiple Views

Science.gov (United States)

Geng, Yujie; Lin, Ying; Ma, Yan; Guo, Zhihong; Gu, Chao; Wang, Mingtao

2017-01-01

The commission and operation of dissolved gas analysis (DGA) online monitoring makes up for the weakness of traditional DGA method. However, volume and high-dimensional DGA data brings a huge challenge for monitoring and analysis. In this paper, we present a novel interactive visualization model of DGA data based on multiple views. This model imitates multi-angle analysis by combining parallel coordinates, scatter plot matrix and data table. By offering brush, collaborative filter and focus + context technology, this model provides a convenient and flexible interactive way to analyze and understand the DGA data.

Iron-related gene variants and brain iron in multiple sclerosis and healthy individuals

Directory of Open Access Journals (Sweden)

Jesper Hagemeier

2018-01-01

Full Text Available Brain iron homeostasis is known to be disturbed in multiple sclerosis (MS, yet little is known about the association of common gene variants linked to iron regulation and pathological tissue changes in the brain. In this study, we investigated the association of genetic determinants linked to iron regulation with deep gray matter (GM magnetic susceptibility in both healthy controls (HC and MS patients. Four hundred (400 patients with MS and 150 age- and sex-matched HCs were enrolled and obtained 3 T MRI examination. Three (3 single nucleotide polymorphisms (SNPs associated with iron regulation were genotyped: two SNPs in the human hereditary hemochromatosis protein gene HFE: rs1800562 (C282Y mutation and rs1799945 (H63D mutation, as well as the rs1049296 SNP in the transferrin gene (C2 mutation. The effects of disease and genetic status were studied using quantitative susceptibility mapping (QSM voxel-based analysis (VBA and region-of-interest (ROI analysis of the deep GM. The general linear model framework was used to compare groups. Analyses were corrected for age and sex, and adjusted for false discovery rate. We found moderate increases in susceptibility in the right putamen of participants with the C282Y (+6.1 ppb and H63D (+6.9 ppb gene variants vs. non-carriers, as well as a decrease in thalamic susceptibility of progressive MS patients with the C282Y mutation (left: −5.3 ppb, right: −6.7 ppb, p < 0.05. Female MS patients had lower susceptibility in the caudate (−6.0 ppb and putamen (left: −3.9 ppb, right: −4.6 ppb than men, but only when they had a wild-type allele (p < 0.05. Iron-gene linked increases in putamen susceptibility (in HC and relapsing remitting MS and decreases in thalamus susceptibility (in progressive MS, coupled with apparent sex interactions, indicate that brain iron in healthy and disease states may be influenced by genetic factors.

Genetic interaction analysis of point mutations enables interrogation of gene function at a residue-level resolution

Science.gov (United States)

Braberg, Hannes; Moehle, Erica A.; Shales, Michael; Guthrie, Christine; Krogan, Nevan J.

2014-01-01

We have achieved a residue-level resolution of genetic interaction mapping – a technique that measures how the function of one gene is affected by the alteration of a second gene – by analyzing point mutations. Here, we describe how to interpret point mutant genetic interactions, and outline key applications for the approach, including interrogation of protein interaction interfaces and active sites, and examination of post-translational modifications. Genetic interaction analysis has proven effective for characterizing cellular processes; however, to date, systematic high-throughput genetic interaction screens have relied on gene deletions or knockdowns, which limits the resolution of gene function analysis and poses problems for multifunctional genes. Our point mutant approach addresses these issues, and further provides a tool for in vivo structure-function analysis that complements traditional biophysical methods. We also discuss the potential for genetic interaction mapping of point mutations in human cells and its application to personalized medicine. PMID:24842270

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

Science.gov (United States)

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.

Electron-electron interaction in Multiple Quantum Wells

Science.gov (United States)

Zybert, M.; Marchewka, M.; Tomaka, G.; Sheregii, E. M.

2012-07-01

The complex investigation of the magneto-transport effects in structures containing multiple quantum well (MQWs) based on the GaAs/AlGaAs-heterostructures has been performed. The MQWs investigated have different electron densities in QWs. The parameters of 2DEG in MQWs were determined from the data of the Integer Quantum Hall Effect (IQHE) and Shubnikov-de Haas oscillations (SdH) observed at low temperatures (0.6-4.2 K). The method of calculation of the electron states energies in MQWs has been developed which is based on the splitting of these states due to the exchange interaction (SAS-splitting, see D. Płoch et al., Phys. Rev. B 79 (2009) 195434) including the screening of this interaction. The IQHE and SdH observed in these multilayer structures with the third degree of freedom for electrons are interpreted from this.

[Dopamine and excessive alcohol consumption: how genes interact with their environment

NARCIS (Netherlands)

Schellekens, A.F.A.; Scholte, R.H.J.; Engels, R.C.M.E.; Verkes, R.J.

2013-01-01

SUMMARY BACKGROUND: Hereditary factors account for approximately 50% of the risk of developing alcohol dependence. Genes that affect the dopamine function in the brain have been extensively studied as candidate genes. AIM: To present the results of recent Dutch studies on the interaction between

Differential gene expression and Hog1 interaction with osmoresponsive genes in the extremely halotolerant black yeast Hortaea werneckii

Directory of Open Access Journals (Sweden)

Plemenitaš Ana

2007-08-01

Full Text Available Abstract Background Fluctuations in external salinity force eukaryotic cells to respond by changes in the gene expression of proteins acting in protective biochemical processes, thus counteracting the changing osmotic pressure. The high-osmolarity glycerol (HOG signaling pathway is essential for the efficient up-regulation of the osmoresponsive genes. In this study, the differential gene expression of the extremely halotolerant black yeast Hortaea werneckii was explored. Furthermore, the interaction of mitogen-activated protein kinase HwHog1 and RNA polymerase II with the chromatin in cells adapted to an extremely hypersaline environment was analyzed. Results A cDNA subtraction library was constructed for H. werneckii, adapted to moderate salinity or an extremely hypersaline environment of 4.5 M NaCl. An uncommon osmoresponsive set of 95 differentially expressed genes was identified. The majority of these had not previously been connected with the adaptation of salt-sensitive S. cerevisiae to hypersaline conditions. The transcriptional response in hypersaline-adapted and hypersaline-stressed cells showed that only a subset of the identified genes responded to acute salt-stress, whereas all were differentially expressed in adapted cells. Interaction with HwHog1 was shown for 36 of the 95 differentially expressed genes. The majority of the identified osmoresponsive and HwHog1-dependent genes in H. werneckii have not been previously reported as Hog1-dependent genes in the salt-sensitive S. cerevisiae. The study further demonstrated the co-occupancy of HwHog1 and RNA polymerase II on the chromatin of 17 up-regulated and 2 down-regulated genes in 4.5 M NaCl-adapted H. werneckii cells. Conclusion Extremely halotolerant H. werneckii represents a suitable and highly relevant organism to study cellular responses to environmental salinity. In comparison with the salt-sensitive S. cerevisiae, this yeast shows a different set of genes being expressed at

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

Science.gov (United States)

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.

Multiplicative interaction of functional inflammasome genetic variants in determining the risk of gout.

Science.gov (United States)

McKinney, Cushla; Stamp, Lisa K; Dalbeth, Nicola; Topless, Ruth K; Day, Richard O; Kannangara, Diluk Rw; Williams, Kenneth M; Janssen, Matthijs; Jansen, Timothy L; Joosten, Leo A; Radstake, Timothy R; Riches, Philip L; Tausche, Anne-Kathrin; Lioté, Frederic; So, Alexander; Merriman, Tony R

2015-10-13

The acute gout flare results from a localised self-limiting innate immune response to monosodium urate (MSU) crystals deposited in joints in hyperuricaemic individuals. Activation of the caspase recruitment domain-containing protein 8 (CARD8) NOD-like receptor pyrin-containing 3 (NLRP3) inflammasome by MSU crystals and production of mature interleukin-1β (IL-1β) is central to acute gouty arthritis. However very little is known about genetic control of the innate immune response involved in acute gouty arthritis. Therefore our aim was to test functional single nucleotide polymorphism (SNP) variants in the toll-like receptor (TLR)-inflammasome-IL-1β axis for association with gout. 1,494 gout cases of European and 863 gout cases of New Zealand (NZ) Polynesian (Māori and Pacific Island) ancestry were included. Gout was diagnosed by the 1977 ARA gout classification criteria. There were 1,030 Polynesian controls and 10,942 European controls including from the publicly-available Atherosclerosis Risk in Communities (ARIC) and Framingham Heart (FHS) studies. The ten SNPs were either genotyped by Sequenom MassArray or by Affymetrix SNP array or imputed in the ARIC and FHS datasets. Allelic association was done by logistic regression adjusting by age and sex with European and Polynesian data combined by meta-analysis. Sample sets were pooled for multiplicative interaction analysis, which was also adjusted by sample set. Eleven SNPs were tested in the TLR2, CD14, IL1B, CARD8, NLRP3, MYD88, P2RX7, DAPK1 and TNXIP genes. Nominally significant (P gout were detected at CARD8 rs2043211 (OR = 1.12, P = 0.007), IL1B rs1143623 (OR = 1.10, P = 0.020) and CD14 rs2569190 (OR = 1.08; P = 0.036). There was significant multiplicative interaction between CARD8 and IL1B (P = 0.005), with the IL1B risk genotype amplifying the risk effect of CARD8. There is evidence for association of gout with functional variants in CARD8, IL1B and CD14. The gout-associated allele of IL1B increases

Interaction dynamics of multiple mobile robots with simple navigation strategies

Science.gov (United States)

Wang, P. K. C.

1989-01-01

The global dynamic behavior of multiple interacting autonomous mobile robots with simple navigation strategies is studied. Here, the effective spatial domain of each robot is taken to be a closed ball about its mass center. It is assumed that each robot has a specified cone of visibility such that interaction with other robots takes place only when they enter its visibility cone. Based on a particle model for the robots, various simple homing and collision-avoidance navigation strategies are derived. Then, an analysis of the dynamical behavior of the interacting robots in unbounded spatial domains is made. The article concludes with the results of computer simulations studies of two or more interacting robots.

Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network.

Science.gov (United States)

Song, Xian-Dong; Song, Xian-Xu; Liu, Gui-Bo; Ren, Chun-Hui; Sun, Yuan-Bo; Liu, Ke-Xin; Liu, Bo; Liang, Shuang; Zhu, Zhu

2018-03-01

The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein-protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.

A spatial theory for emergent multiple predator-prey interactions in food webs.

Science.gov (United States)

Northfield, Tobin D; Barton, Brandon T; Schmitz, Oswald J

2017-09-01

Predator-prey interaction is inherently spatial because animals move through landscapes to search for and consume food resources and to avoid being consumed by other species. The spatial nature of species interactions necessitates integrating spatial processes into food web theory and evaluating how predators combine to impact their prey. Here, we present a spatial modeling approach that examines emergent multiple predator effects on prey within landscapes. The modeling is inspired by the habitat domain concept derived from empirical synthesis of spatial movement and interactions studies. Because these principles are motivated by synthesis of short-term experiments, it remains uncertain whether spatial contingency principles hold in dynamical systems. We address this uncertainty by formulating dynamical systems models, guided by core habitat domain principles, to examine long-term multiple predator-prey spatial dynamics. To describe habitat domains, we use classical niche concepts describing resource utilization distributions, and assume species interactions emerge from the degree of overlap between species. The analytical results generally align with those from empirical synthesis and present a theoretical framework capable of demonstrating multiple predator effects that does not depend on the small spatial or temporal scales typical of mesocosm experiments, and help bridge between empirical experiments and long-term dynamics in natural systems.

Gene expression profiling reveals multiple toxicity endpoints induced by hepatotoxicants

Energy Technology Data Exchange (ETDEWEB)

Huang Qihong; Jin Xidong; Gaillard, Elias T.; Knight, Brian L.; Pack, Franklin D.; Stoltz, James H.; Jayadev, Supriya; Blanchard, Kerry T

2004-05-18

Microarray technology continues to gain increased acceptance in the drug development process, particularly at the stage of toxicology and safety assessment. In the current study, microarrays were used to investigate gene expression changes associated with hepatotoxicity, the most commonly reported clinical liability with pharmaceutical agents. Acetaminophen, methotrexate, methapyrilene, furan and phenytoin were used as benchmark compounds capable of inducing specific but different types of hepatotoxicity. The goal of the work was to define gene expression profiles capable of distinguishing the different subtypes of hepatotoxicity. Sprague-Dawley rats were orally dosed with acetaminophen (single dose, 4500 mg/kg for 6, 24 and 72 h), methotrexate (1 mg/kg per day for 1, 7 and 14 days), methapyrilene (100 mg/kg per day for 3 and 7 days), furan (40 mg/kg per day for 1, 3, 7 and 14 days) or phenytoin (300 mg/kg per day for 14 days). Hepatic gene expression was assessed using toxicology-specific gene arrays containing 684 target genes or expressed sequence tags (ESTs). Principal component analysis (PCA) of gene expression data was able to provide a clear distinction of each compound, suggesting that gene expression data can be used to discern different hepatotoxic agents and toxicity endpoints. Gene expression data were applied to the multiplicity-adjusted permutation test and significantly changed genes were categorized and correlated to hepatotoxic endpoints. Repression of enzymes involved in lipid oxidation (acyl-CoA dehydrogenase, medium chain, enoyl CoA hydratase, very long-chain acyl-CoA synthetase) were associated with microvesicular lipidosis. Likewise, subsets of genes associated with hepatotocellular necrosis, inflammation, hepatitis, bile duct hyperplasia and fibrosis have been identified. The current study illustrates that expression profiling can be used to: (1) distinguish different hepatotoxic endpoints; (2) predict the development of toxic endpoints; and

5C analysis of the Epidermal Differentiation Complex locus reveals distinct chromatin interaction networks between gene-rich and gene-poor TADs in skin epithelial cells.

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Krzysztof Poterlowicz

2017-09-01

Full Text Available Mammalian genomes contain several dozens of large (>0.5 Mbp lineage-specific gene loci harbouring functionally related genes. However, spatial chromatin folding, organization of the enhancer-promoter networks and their relevance to Topologically Associating Domains (TADs in these loci remain poorly understood. TADs are principle units of the genome folding and represents the DNA regions within which DNA interacts more frequently and less frequently across the TAD boundary. Here, we used Chromatin Conformation Capture Carbon Copy (5C technology to characterize spatial chromatin interaction network in the 3.1 Mb Epidermal Differentiation Complex (EDC locus harbouring 61 functionally related genes that show lineage-specific activation during terminal keratinocyte differentiation in the epidermis. 5C data validated by 3D-FISH demonstrate that the EDC locus is organized into several TADs showing distinct lineage-specific chromatin interaction networks based on their transcription activity and the gene-rich or gene-poor status. Correlation of the 5C results with genome-wide studies for enhancer-specific histone modifications (H3K4me1 and H3K27ac revealed that the majority of spatial chromatin interactions that involves the gene-rich TADs at the EDC locus in keratinocytes include both intra- and inter-TAD interaction networks, connecting gene promoters and enhancers. Compared to thymocytes in which the EDC locus is mostly transcriptionally inactive, these interactions were found to be keratinocyte-specific. In keratinocytes, the promoter-enhancer anchoring regions in the gene-rich transcriptionally active TADs are enriched for the binding of chromatin architectural proteins CTCF, Rad21 and chromatin remodeler Brg1. In contrast to gene-rich TADs, gene-poor TADs show preferential spatial contacts with each other, do not contain active enhancers and show decreased binding of CTCF, Rad21 and Brg1 in keratinocytes. Thus, spatial interactions between gene

Community Structure Analysis of Gene Interaction Networks in Duchenne Muscular Dystrophy.

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Tejaswini Narayanan

Full Text Available Duchenne Muscular Dystrophy (DMD is an important pathology associated with the human skeletal muscle and has been studied extensively. Gene expression measurements on skeletal muscle of patients afflicted with DMD provides the opportunity to understand the underlying mechanisms that lead to the pathology. Community structure analysis is a useful computational technique for understanding and modeling genetic interaction networks. In this paper, we leverage this technique in combination with gene expression measurements from normal and DMD patient skeletal muscle tissue to study the structure of genetic interactions in the context of DMD. We define a novel framework for transforming a raw dataset of gene expression measurements into an interaction network, and subsequently apply algorithms for community structure analysis for the extraction of topological communities. The emergent communities are analyzed from a biological standpoint in terms of their constituent biological pathways, and an interpretation that draws correlations between functional and structural organization of the genetic interactions is presented. We also compare these communities and associated functions in pathology against those in normal human skeletal muscle. In particular, differential enhancements are observed in the following pathways between pathological and normal cases: Metabolic, Focal adhesion, Regulation of actin cytoskeleton and Cell adhesion, and implication of these mechanisms are supported by prior work. Furthermore, our study also includes a gene-level analysis to identify genes that are involved in the coupling between the pathways of interest. We believe that our results serve to highlight important distinguishing features in the structural/functional organization of constituent biological pathways, as it relates to normal and DMD cases, and provide the mechanistic basis for further biological investigations into specific pathways differently regulated

Common sources of bias in gene-lifestyle interaction studies of cardiometabolic disease

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Oskari Kilpeläinen, Tuomas

2013-01-01

The role of gene x lifestyle interactions in the development of cardiometabolic diseases is often highlighted, but very few robustly replicated examples of interactions exist in the literature. The slow pace of discoveries may largely be due to interaction effects being generally small in magnitude...

Gene environment interaction studies in depression and suicidal behavior: An update.

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Mandelli, Laura; Serretti, Alessandro

2013-12-01

Increasing evidence supports the involvement of both heritable and environmental risk factors in major depression (MD) and suicidal behavior (SB). Studies investigating gene-environment interaction (G × E) may be useful for elucidating the role of biological mechanisms in the risk for mental disorders. In the present paper, we review the literature regarding the interaction between genes modulating brain functions and stressful life events in the etiology of MD and SB and discuss their potential added benefit compared to genetic studies only. Within the context of G × E investigation, thus far, only a few reliable results have been obtained, although some genes have consistently shown interactive effects with environmental risk in MD and, to a lesser extent, in SB. Further investigation is required to disentangle the direct and mediated effects that are common or specific to MD and SB. Since traditional G × E studies overall suffer from important methodological limitations, further effort is required to develop novel methodological strategies with an interdisciplinary approach. Copyright © 2013 Elsevier Ltd. All rights reserved.

The SKP1-like gene family of Arabidopsis exhibits a high degree of differential gene expression and gene product interaction during development.

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Mohammad H Dezfulian

Full Text Available The Arabidopsis thaliana genome encodes several families of polypeptides that are known or predicted to participate in the formation of the SCF-class of E3-ubiquitin ligase complexes. One such gene family encodes the Skp1-like class of polypeptide subunits, where 21 genes have been identified and are known to be expressed in Arabidopsis. Phylogenetic analysis based on deduced polypeptide sequence organizes the family of ASK proteins into 7 clades. The complexity of the ASK gene family, together with the close structural similarity among its members raises the prospect of significant functional redundancy among select paralogs. We have assessed the potential for functional redundancy within the ASK gene family by analyzing an expanded set of criteria that define redundancy with higher resolution. The criteria used include quantitative expression of locus-specific transcripts using qRT-PCR, assessment of the sub-cellular localization of individual ASK:YFP auto-fluorescent fusion proteins expressed in vivo as well as the in planta assessment of individual ASK-F-Box protein interactions using bimolecular fluorescent complementation techniques in combination with confocal imagery in live cells. The results indicate significant functional divergence of steady state transcript abundance and protein-protein interaction specificity involving ASK proteins in a pattern that is poorly predicted by sequence-based phylogeny. The information emerging from this and related studies will prove important for defining the functional intersection of expression, localization and gene product interaction that better predicts the formation of discrete SCF complexes, as a prelude to investigating their molecular mode of action.

The development and application of a multiple gene co-silencing system using endogenous URA3 as a reporter gene in Ganoderma lucidum.

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Dashuai Mu

Full Text Available Ganoderma lucidum is one of the most important medicinal mushrooms; however, molecular genetics research on this species has been limited due to a lack of reliable reverse genetic tools. In this study, the endogenous orotidine 5'-monophosphate decarboxylase gene (URA3 was cloned as a silencing reporter, and four gene-silencing methods using hairpin, sense, antisense, and dual promoter constructs, were introduced into G. lucidum through a simple electroporation procedure. A comparison and evaluation of silencing efficiency demonstrated that all of the four methods differentially suppressed the expression of URA3. Our data unequivocally indicate that the dual promoter silencing vector yields the highest rate of URA3 silencing compared with other vectors (up to 81.9%. To highlight the advantages of the dual promoter system, we constructed a co-silencing system based on the dual promoter method and succeeded in co-silencing URA3 and laccase in G. lucidum. The reduction of the mRNA levels of the two genes were correlated. Thus, the screening efficiency for RNAi knockdown of multiple genes may be improved by the co-silencing of an endogenous reporter gene. The molecular tools developed in this study should facilitate the isolation of genes and the characterization of the functions of multiple genes in this pharmaceutically important species, and these tools should be highly useful for the study of other basidiomycetes.

Development of a multiple-gene-loading method by combining multi-integration system-equipped mouse artificial chromosome vector and CRISPR-Cas9.

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Kazuhisa Honma

Full Text Available Mouse artificial chromosome (MAC vectors have several advantages as gene delivery vectors, such as stable and independent maintenance in host cells without integration, transferability from donor cells to recipient cells via microcell-mediated chromosome transfer (MMCT, and the potential for loading a megabase-sized DNA fragment. Previously, a MAC containing a multi-integrase platform (MI-MAC was developed to facilitate the transfer of multiple genes into desired cells. Although the MI system can theoretically hold five gene-loading vectors (GLVs, there are a limited number of drugs available for the selection of multiple-GLV integration. To overcome this issue, we attempted to knock out and reuse drug resistance genes (DRGs using the CRISPR-Cas9 system. In this study, we developed new methods for multiple-GLV integration. As a proof of concept, we introduced five GLVs in the MI-MAC by these methods, in which each GLV contained a gene encoding a fluorescent or luminescent protein (EGFP, mCherry, BFP, Eluc, and Cluc. Genes of interest (GOI on the MI-MAC were expressed stably and functionally without silencing in the host cells. Furthermore, the MI-MAC carrying five GLVs was transferred to other cells by MMCT, and the resultant recipient cells exhibited all five fluorescence/luminescence signals. Thus, the MI-MAC was successfully used as a multiple-GLV integration vector using the CRISPR-Cas9 system. The MI-MAC employing these methods may resolve bottlenecks in developing multiple-gene humanized models, multiple-gene monitoring models, disease models, reprogramming, and inducible gene expression systems.

Cell-Specific PEAR1 Methylation Studies Reveal a Locus that Coordinates Expression of Multiple Genes

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Benedetta Izzi

2018-04-01

Full Text Available Chromosomal interactions connect distant enhancers and promoters on the same chromosome, activating or repressing gene expression. PEAR1 encodes the Platelet-Endothelial Aggregation Receptor 1, a contact receptor involved in platelet function and megakaryocyte and endothelial cell proliferation. PEAR1 expression during megakaryocyte differentiation is controlled by DNA methylation at its first CpG island. We identified a PEAR1 cell-specific methylation sensitive region in endothelial cells and megakaryocytes that showed strong chromosomal interactions with ISGL20L2, RRNAD1, MRLP24, HDGF and PRCC, using available promoter capture Hi-C datasets. These genes are involved in ribosome processing, protein synthesis, cell cycle and cell proliferation. We next studied the methylation and expression profile of these five genes in Human Umbilical Vein Endothelial Cells (HUVECs and megakaryocyte precursors. While cell-specific PEAR1 methylation corresponded to variability in expression for four out of five genes, no methylation change was observed in their promoter regions across cell types. Our data suggest that PEAR1 cell-type specific methylation changes may control long distance interactions with other genes. Further studies are needed to show whether such interaction data might be relevant for the genome-wide association data that showed a role for non-coding PEAR1 variants in the same region and platelet function, platelet count and cardiovascular risk.

Candidate genes and their interactions with other genetic/environmental risk factors in the etiology of schizophrenia.

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Prasad, K M; Talkowski, M E; Chowdari, K V; McClain, L; Yolken, R H; Nimgaonkar, V L

2010-09-30

Identification of causative factors for common, chronic disorders is a major focus of current human health science research. These disorders are likely to be caused by multiple etiological agents. Available evidence also suggests that interactions between the risk factors may explain some of their pathogenic effects. While progress in genomics and allied biological research has brought forth powerful analytic techniques, the predicted complexity poses daunting analytic challenges. The search for pathogenesis of schizophrenia shares most of these challenges. We have reviewed the analytic and logistic problems associated with the search for pathogenesis. Evidence for pathogenic interactions is presented for selected diseases and for schizophrenia. We end by suggesting 'recursive analyses' as a potential design to address these challenges. This scheme involves initial focused searches for interactions motivated by available evidence, typically involving identified individual risk factors, such as candidate gene variants. Putative interactions are tested rigorously for replication and for biological plausibility. Support for the interactions from statistical and functional analyses motivates a progressively larger array of interactants that are evaluated recursively. The risk explained by the interactions is assessed concurrently and further elaborate searches may be guided by the results of such analyses. By way of example, we summarize our ongoing analyses of dopaminergic polymorphisms, as well as infectious etiological factors in schizophrenia genesis. Copyright © 2009 Elsevier Inc. All rights reserved.

AURKA Phe31Ile polymorphism interacted with use of alcohol, betel quid, and cigarettes at multiplicative risk of oral cancer occurrence.

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Lee, Chi-Pin; Chiang, Shang-Lun; Lee, Chien-Hung; Tsai, Yi-Shan; Wang, Zhi-Hong; Hua, Chun-Hung; Chen, Yuan-Chien; Tsai, Eing-Mei; Ko, Ying-Chin

2015-11-01

The expression levels of two DNA repair genes (CHAF1A and CHAF1B) and a chromosome segregation gene (AURKA) were susceptible to arecoline exposure, a major alkaloid of areca nut. We hypothesize that genetic variants of these genes might also be implicated in the risk of oral cancer and could be modified by substance use of betel quid or alcohol and cigarettes. A case-control study, which included 507 patients with oral cancer and 717 matched controls, was performed in order to evaluate the cancer susceptibility by the tagging single-nucleotide polymorphisms (tagSNPs) in AURKA, CHAF1A, and CHAF1B using a genotyping assay and gene-environment interaction analysis. The Phe31Ile polymorphism (rs2273535, T91A) of AURKA was significantly associated with an increased risk of oral cancer (odds ratio (OR) = 2.1, 95% confidence interval (CI) 1.2-3.5). The gene dosage of the 91A allele also showed a significant trend in risk of oral cancer (P = 0.008). Furthermore, we found the AURKA 91AA homozygote was modifiable by substance use of alcohol, betel quid, and cigarettes (ABC), leading to increased risk of oral cancer in an additive or a multiplicative model (combined effect indexes = 1.2-4.0 and 1.5-2.2, respectively). However, no association was observed between the genetic variants of CHAF1A or CHAF1B and oral cancer risk in the study. These findings reveal the functional Phe31Ile polymorphism tagSNP of AURKA may be a strong susceptibility gene in ABC-related oral cancer occurrence. The results of this betel-related oral cancer study provide the evidence of environment-gene interaction for early prediction and molecular diagnosis.

Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment.

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Yang, Yi; Maxwell, Andrew; Zhang, Xiaowei; Wang, Nan; Perkins, Edward J; Zhang, Chaoyang; Gong, Ping

2013-01-01

Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting. Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways. Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach

Molecular evolution of the Paramyxoviridae and Rhabdoviridae multiple-protein-encoding P gene.

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Jordan, I K; Sutter, B A; McClure, M A

2000-01-01

Presented here is an analysis of the molecular evolutionary dynamics of the P gene among 76 representative sequences of the Paramyxoviridae and Rhabdoviridae RNA virus families. In a number of Paramyxoviridae taxa, as well as in vesicular stomatitis viruses of the Rhabdoviridae, the P gene encodes multiple proteins from a single genomic RNA sequence. These products include the phosphoprotein (P), as well as the C and V proteins. The complexity of the P gene makes it an intriguing locus to study from an evolutionary perspective. Amino acid sequence alignments of the proteins encoded at the P and N loci were used in independent phylogenetic reconstructions of the Paramyxoviridae and Rhabdoviridae families. P-gene-coding capacities were mapped onto the Paramyxoviridae phylogeny, and the most parsimonious path of multiple-coding-capacity evolution was determined. Levels of amino acid variation for Paramyxoviridae and Rhabdoviridae P-gene-encoded products were also analyzed. Proteins encoded in overlapping reading frames from the same nucleotides have different levels of amino acid variation. The nucleotide architecture that underlies the amino acid variation was determined in order to evaluate the role of selection in the evolution of the P gene overlapping reading frames. In every case, the evolution of one of the proteins encoded in the overlapping reading frames has been constrained by negative selection while the other has evolved more rapidly. The integrity of the overlapping reading frame that represents a derived state is generally maintained at the expense of the ancestral reading frame encoded by the same nucleotides. The evolution of such multicoding sequences is likely a response by RNA viruses to selective pressure to maximize genomic information content while maintaining small genome size. The ability to evolve such a complex genomic strategy is intimately related to the dynamics of the viral quasispecies, which allow enhanced exploration of the adaptive

Interacting Multiple Model (IMM Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

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Hua Liu

2017-06-01

Full Text Available For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF. The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF, the interacting multiple model cubature Kalman filter (IMMCKF and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF.

Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

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Liu, Hua; Wu, Wen

2017-06-13

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).

Positive selection of Plasmodium falciparum parasites with multiple var2csa-type PfEMP1 genes during the course of infection in pregnant women

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Sander, Adam F; Salanti, Ali; Lavstsen, Thomas

2011-01-01

multiple genes coding for different VAR2CSA proteins, and parasites with >1 var2csa gene appear to be more common in pregnant women with placental malaria than in nonpregnant individuals. We present evidence that, in pregnant women, parasites containing multiple var2csa-type genes possess a selective...... advantage over parasites with a single var2csa gene. Accumulation of parasites with multiple copies of the var2csa gene during the course of pregnancy was also correlated with the development of antibodies involved in blocking VAR2CSA adhesion. The data suggest that multiplicity of var2csa-type genes...

Antisocial peer affiliation and externalizing disorders: Evidence for Gene × Environment × Development interaction.

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Samek, Diana R; Hicks, Brian M; Keyes, Margaret A; Iacono, William G; McGue, Matt

2017-02-01

Gene × Environment interaction contributes to externalizing disorders in childhood and adolescence, but little is known about whether such effects are long lasting or present in adulthood. We examined gene-environment interplay in the concurrent and prospective associations between antisocial peer affiliation and externalizing disorders (antisocial behavior and substance use disorders) at ages 17, 20, 24, and 29. The sample included 1,382 same-sex twin pairs participating in the Minnesota Twin Family Study. We detected a Gene × Environment interaction at age 17, such that additive genetic influences on antisocial behavior and substance use disorders were greater in the context of greater antisocial peer affiliation. This Gene × Environment interaction was not present for antisocial behavior symptoms after age 17, but it was for substance use disorder symptoms through age 29 (though effect sizes were largest at age 17). The results suggest adolescence is a critical period for the development of externalizing disorders wherein exposure to greater environmental adversity is associated with a greater expression of genetic risk. This form of Gene × Environment interaction may persist through young adulthood for substance use disorders, but it appears to be limited to adolescence for antisocial behavior.

Antisocial Peer Affiliation and Externalizing Disorders: Evidence for Gene × Environment × Development Interaction

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Samek, Diana R.; Hicks, Brian M.; Keyes, Margaret A.; Iacono, William G.; McGue, Matt

2016-01-01

Gene × environment interaction contributes to externalizing disorders in adolescence, but little is known about whether such effects are long-lasting or present in adulthood. We examined gene-environment interplay in the concurrent and prospective associations between antisocial peer affiliation and externalizing disorders (antisocial behavior and substance use disorders) at ages 17, 20, 24, and 29. The sample included 1,382 same-sex twin pairs participating in the Minnesota Twin Family Study. We detected a gene × environment interaction at age 17, such that additive genetic influences on antisocial behavior and substance use disorders were greater in the context of greater antisocial peer affiliation. This gene × environment interaction was not present for antisocial behavior symptoms after age 17, but was for substance use disorder symptoms through age 29 (though effect sizes were largest at age 17). Results suggest adolescence is a critical period for the development of externalizing disorders wherein exposure to greater environmental adversity is associated with a greater expression of genetic risk. This form of gene × environment interaction may persist through young adulthood for substance use disorders, but is limited to adolescence for antisocial behavior. PMID:27580681

Interaction of two photoreceptors in the regulation of bacterial photosynthesis genes.

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Metz, Sebastian; Haberzettl, Kerstin; Frühwirth, Sebastian; Teich, Kristin; Hasewinkel, Christian; Klug, Gabriele

2012-07-01

The expression of photosynthesis genes in the facultatively photosynthetic bacterium Rhodobacter sphaeroides is controlled by the oxygen tension and by light quantity. Two photoreceptor proteins, AppA and CryB, have been identified in the past, which are involved in this regulation. AppA senses light by its N-terminal BLUF domain, its C-terminal part binds heme and is redox-responsive. Through its interaction to the transcriptional repressor PpsR the AppA photoreceptor controls expression of photosynthesis genes. The cryptochrome-like protein CryB was shown to affect regulation of photosynthesis genes, but the underlying signal chain remained unknown. Here we show that CryB interacts with the C-terminal domain of AppA and modulates the binding of AppA to the transcriptional repressor PpsR in a light-dependent manner. Consequently, binding of the transcription factor PpsR to its DNA target is affected by CryB. In agreement with this, all genes of the PpsR regulon showed altered expression levels in a CryB deletion strain after blue-light illumination. These results elucidate for the first time how a bacterial cryptochrome affects gene expression.

Genetic interactions between planar cell polarity genes cause diverse neural tube defects in mice

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Jennifer N. Murdoch

2014-10-01

Full Text Available Neural tube defects (NTDs are among the commonest and most severe forms of developmental defect, characterized by disruption of the early embryonic events of central nervous system formation. NTDs have long been known to exhibit a strong genetic dependence, yet the identity of the genetic determinants remains largely undiscovered. Initiation of neural tube closure is disrupted in mice homozygous for mutations in planar cell polarity (PCP pathway genes, providing a strong link between NTDs and PCP signaling. Recently, missense gene variants have been identified in PCP genes in humans with NTDs, although the range of phenotypes is greater than in the mouse mutants. In addition, the sequence variants detected in affected humans are heterozygous, and can often be detected in unaffected individuals. It has been suggested that interactions between multiple heterozygous gene mutations cause the NTDs in humans. To determine the phenotypes produced in double heterozygotes, we bred mice with all three pairwise combinations of Vangl2Lp, ScribCrc and Celsr1Crsh mutations, the most intensively studied PCP mutants. The majority of double-mutant embryos had open NTDs, with the range of phenotypes including anencephaly and spina bifida, therefore reflecting the defects observed in humans. Strikingly, even on a uniform genetic background, variability in the penetrance and severity of the mutant phenotypes was observed between the different double-heterozygote combinations. Phenotypically, Celsr1Crsh;Vangl2Lp;ScribCrc triply heterozygous mutants were no more severe than doubly heterozygous or singly homozygous mutants. We propose that some of the variation between double-mutant phenotypes could be attributed to the nature of the protein disruption in each allele: whereas ScribCrc is a null mutant and produces no Scrib protein, Celsr1Crsh and Vangl2Lp homozygotes both express mutant proteins, consistent with dominant effects. The variable outcomes of these genetic

Interaction of the ADRB2 gene polymorphism with childhood trauma in predicting adult symptoms of posttraumatic stress disorder.

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Liberzon, Israel; King, Anthony P; Ressler, Kerry J; Almli, Lynn M; Zhang, Peng; Ma, Sean T; Cohen, Gregory H; Tamburrino, Marijo B; Calabrese, Joseph R; Galea, Sandro

2014-10-01

Posttraumatic stress disorder (PTSD), while highly prevalent (7.6% over a lifetime), develops only in a subset of trauma-exposed individuals. Genetic risk factors in interaction with trauma exposure have been implicated in PTSD vulnerability. To examine the association of 3755 candidate gene single-nucleotide polymorphisms with PTSD development in interaction with a history of childhood trauma. Genetic association study in an Ohio National Guard longitudinal cohort (n = 810) of predominantly male soldiers of European ancestry, with replication in an independent Grady Trauma Project (Atlanta, Georgia) cohort (n = 2083) of predominantly female African American civilians. Continuous measures of PTSD severity, with a modified (interview) PTSD checklist in the discovery cohort and the PTSD Symptom Scale in the replication cohort. Controlling for the level of lifetime adult trauma exposure, we identified the novel association of a single-nucleotide polymorphism within the promoter region of the ADRB2 (Online Mendelian Inheritance in Man 109690) gene with PTSD symptoms in interaction with childhood trauma (rs2400707, P = 1.02 × 10-5, significant after correction for multiple comparisons). The rs2400707 A allele was associated with relative resilience to childhood adversity. An rs2400707 × childhood trauma interaction predicting adult PTSD symptoms was replicated in the independent predominantly female African American cohort. Altered adrenergic and noradrenergic function has been long believed to have a key etiologic role in PTSD development; however, direct evidence of this link has been missing. The rs2400707 polymorphism has been linked to function of the adrenergic system, but, to our knowledge, this is the first study to date linking the ADRB2 gene to PTSD or any psychiatric disorders. These findings have important implications for PTSD etiology, chronic pain, and stress-related comorbidity, as well as for both primary prevention and treatment

Multiple episodes of convergence in genes of the dim light vision pathway in bats.

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Yong-Yi Shen

Full Text Available The molecular basis of the evolution of phenotypic characters is very complex and is poorly understood with few examples documenting the roles of multiple genes. Considering that a single gene cannot fully explain the convergence of phenotypic characters, we choose to study the convergent evolution of rod vision in two divergent bats from a network perspective. The Old World fruit bats (Pteropodidae are non-echolocating and have binocular vision, whereas the sheath-tailed bats (Emballonuridae are echolocating and have monocular vision; however, they both have relatively large eyes and rely more on rod vision to find food and navigate in the night. We found that the genes CRX, which plays an essential role in the differentiation of photoreceptor cells, SAG, which is involved in the desensitization of the photoactivated transduction cascade, and the photoreceptor gene RH, which is directly responsible for the perception of dim light, have undergone parallel sequence evolution in two divergent lineages of bats with larger eyes (Pteropodidae and Emballonuroidea. The multiple convergent events in the network of genes essential for rod vision is a rare phenomenon that illustrates the importance of investigating pathways and networks in the evolution of the molecular basis of phenotypic convergence.

Detailed assessment of gene activation levels by multiple hypoxia-responsive elements under various hypoxic conditions.

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Takeuchi, Yasuto; Inubushi, Masayuki; Jin, Yong-Nan; Murai, Chika; Tsuji, Atsushi B; Hata, Hironobu; Kitagawa, Yoshimasa; Saga, Tsuneo

2014-12-01

HIF-1/HRE pathway is a promising target for the imaging and the treatment of intractable malignancy (HIF-1; hypoxia-inducible factor 1, HRE; hypoxia-responsive element). The purposes of our study are: (1) to assess the gene activation levels resulting from various numbers of HREs under various hypoxic conditions, (2) to evaluate the bidirectional activity of multiple HREs, and (3) to confirm whether multiple HREs can induce gene expression in vivo. Human colon carcinoma HCT116 cells were transiently transfected by the constructs containing a firefly luciferase reporter gene and various numbers (2, 4, 6, 8, 10, and 12) of HREs (nHRE+, nHRE-). The relative luciferase activities were measured under various durations of hypoxia (6, 12, 18, and 24 h), O2 concentrations (1, 2, 4, 8, and 16 %), and various concentrations of deferoxamine mesylate (20, 40, 80, 160, and 320 µg/mL growth medium). The bidirectional gene activation levels by HREs were examined in the constructs (dual-luc-nHREs) containing firefly and Renilla luciferase reporter genes at each side of nHREs. Finally, to test whether the construct containing 12HRE and the NIS reporter gene (12HRE-NIS) can induce gene expression in vivo, SPECT imaging was performed in a mouse xenograft model. (1) gene activation levels by HREs tended to increase with increasing HRE copy number, but a saturation effect was observed in constructs with more than 6 or 8 copies of an HRE, (2) gene activation levels by HREs increased remarkably during 6-12 h of hypoxia, but not beyond 12 h, (3) gene activation levels by HREs decreased with increasing O2 concentrations, but could be detected even under mild hypoxia at 16 % O2, (4) the bidirectionally proportional activity of the HRE was confirmed regardless of the hypoxic severity, and (5) NIS expression driven by 12 tandem copies of an HRE in response to hypoxia could be visualized on in vivo SPECT imaging. The results of this study will help in the understanding and assessment of

A Hox Gene, Antennapedia, Regulates Expression of Multiple Major Silk Protein Genes in the Silkworm Bombyx mori.

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Tsubota, Takuya; Tomita, Shuichiro; Uchino, Keiro; Kimoto, Mai; Takiya, Shigeharu; Kajiwara, Hideyuki; Yamazaki, Toshimasa; Sezutsu, Hideki

2016-03-25

Hoxgenes play a pivotal role in the determination of anteroposterior axis specificity during bilaterian animal development. They do so by acting as a master control and regulating the expression of genes important for development. Recently, however, we showed that Hoxgenes can also function in terminally differentiated tissue of the lepidopteranBombyx mori In this species,Antennapedia(Antp) regulates expression of sericin-1, a major silk protein gene, in the silk gland. Here, we investigated whether Antpcan regulate expression of multiple genes in this tissue. By means of proteomic, RT-PCR, and in situ hybridization analyses, we demonstrate that misexpression of Antpin the posterior silk gland induced ectopic expression of major silk protein genes such assericin-3,fhxh4, and fhxh5 These genes are normally expressed specifically in the middle silk gland as is Antp Therefore, the evidence strongly suggests that Antpactivates these silk protein genes in the middle silk gland. The putativesericin-1 activator complex (middle silk gland-intermolt-specific complex) can bind to the upstream regions of these genes, suggesting that Antpdirectly activates their expression. We also found that the pattern of gene expression was well conserved between B. moriand the wild species Bombyx mandarina, indicating that the gene regulation mechanism identified here is an evolutionarily conserved mechanism and not an artifact of the domestication of B. mori We suggest that Hoxgenes have a role as a master control in terminally differentiated tissues, possibly acting as a primary regulator for a range of physiological processes. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Multiplicities of secondary hadrons produced in vp and overlinevp charged current interactions

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Grässler, H.; Lanske, D.; Schulte, R.; Jones, G. T.; Middleton, R. P.; O'Neale, S. W.; Böckmann, K.; Gebel, W.; Geich-Gimbel, C.; Nellen, B.; Grant, A.; Klein, H.; Morrison, D. R. O.; Schmid, P.; Wachsmuth, H.; Chima, J. S.; Mobayyen, M. M.; Talebzadeh, M.; Villalobos-Baillie, O.; Aderholz, M.; Deck, L.; Schmitz, N.; Settles, R.; Wernhard, K. L.; Wittek, W.; Corrigan, G.; Myatt, G.; Radojicić, D.; Saitta, B.; Wells, J.; Aachen-Birmingham-Bonn-CERN-Imperial College-München (MPI)-Oxford Collaboration

1983-08-01

In an experiment with the hydrogen bubble chamber BEBC at CERN multiplicities of hadrons produced in νp and overlinevp interactions have been investigated. Results are presented on the multiplicities of charged hadrons and neutral pions, forward and backward multiplicities of charged hadrons and correlations between forward and backward multiplicities. Comparisons are made with hadronic reactions and e +e - annihilation. In the framework of the quark-parton model the data imply similar charged multiplicities for the fragments of a u- and a d-quark, and a larger multiplicities for the fragments of a uu- than for a ud-diquark. The correlation data suggest independent fragmentation of the quark and diquark for hadronic masses above ˜ 7 GeV and local charge compensation within an event.

Cre/lox-based multiple markerless gene disruption in the genome of the extreme thermophile Thermus thermophilus.

Science.gov (United States)

Togawa, Yoichiro; Nunoshiba, Tatsuo; Hiratsu, Keiichiro

2018-02-01

Markerless gene-disruption technology is particularly useful for effective genetic analyses of Thermus thermophilus (T. thermophilus), which have a limited number of selectable markers. In an attempt to develop a novel system for the markerless disruption of genes in T. thermophilus, we applied a Cre/lox system to construct a triple gene disruptant. To achieve this, we constructed two genetic tools, a loxP-htk-loxP cassette and cre-expressing plasmid, pSH-Cre, for gene disruption and removal of the selectable marker by Cre-mediated recombination. We found that the Cre/lox system was compatible with the proliferation of the T. thermophilus HB27 strain at the lowest growth temperature (50 °C), and thus succeeded in establishing a triple gene disruptant, the (∆TTC1454::loxP, ∆TTC1535KpnI::loxP, ∆TTC1576::loxP) strain, without leaving behind a selectable marker. During the process of the sequential disruption of multiple genes, we observed the undesired deletion and inversion of the chromosomal region between multiple loxP sites that were induced by Cre-mediated recombination. Therefore, we examined the effects of a lox66-htk-lox71 cassette by exploiting the mutant lox sites, lox66 and lox71, instead of native loxP sites. We successfully constructed a (∆TTC1535::lox72, ∆TTC1537::lox72) double gene disruptant without inducing the undesired deletion of the 0.7-kbp region between the two directly oriented lox72 sites created by the Cre-mediated recombination of the lox66-htk-lox71 cassette. This is the first demonstration of a Cre/lox system being applicable to extreme thermophiles in a genetic manipulation. Our results indicate that this system is a powerful tool for multiple markerless gene disruption in T. thermophilus.

Effects of multiple modes interaction on the resistive wall mode instability

International Nuclear Information System (INIS)

Chen, Longxi; Lei, Wenqing; Ma, Zhiwei; Wu, Bin

2013-01-01

The effects of multiple modes interaction on the resistive wall mode (RWM) are studied in a slab geometry with and without plasma flow. The modes interaction can have a large effect on both the linear growth rate and the nonlinear saturation level of the RWM. We found that modes interaction can suppress the linear growth rate for the most unstable mode. The plasma flow can also help to control the growth of the RWM. The RWM can be stabilized completely by a plasma flow when considering the modes interaction. The effect of modes interaction on the RWM is stronger for the mode rational surface in the vacuum than that in the plasma. The modes interaction results in a substantially lowered saturation level for the most unstable RWM. (paper)

Trends in gastrectomy and ADH1B and ALDH2 genotypes in Japanese alcoholic men and their gene-gastrectomy, gene-gene and gene-age interactions for risk of alcoholism.

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Yokoyama, Akira; Yokoyama, Tetsuji; Matsui, Toshifumi; Mizukami, Takeshi; Kimura, Mitsuru; Matsushita, Sachio; Higuchi, Susumu; Maruyama, Katsuya

2013-01-01

The life-time drinking profiles of Japanese alcoholics have shown that gastrectomy increases susceptibility to alcoholism. We investigated the trends in gastrectomy and alcohol dehydrogenase-1B (ADH1B) and aldehyde dehydrogenase-2 (ALDH2) genotypes and their interactions in alcoholics. This survey was conducted on 4879 Japanese alcoholic men 40 years of age or older who underwent routine gastrointestinal endoscopic screening during the period 1996-2010. ADH1B/ALDH2 genotyping was performed in 3702 patients. A history of gastrectomy was found in 508 (10.4%) patients. The reason for the gastrectomy was peptic ulcer in 317 patients and gastric cancer in 187 patients. The frequency of gastrectomy had gradually decreased from 13.3% in 1996-2000 to 10.5% in 2001-2005 and to 7.8% in 2006-2010 (P alcoholism-susceptibility genotypes, ADH1B*1/*1 and ALDH2*1/*1, modestly but significantly tended not to occur in the same individual (P = 0.026). The frequency of ADH1B*1/*1 decreased with ascending age groups. The high frequency of history of gastrectomy suggested that gastrectomy is still a risk factor for alcoholism, although the percentage decreased during the period. The alcoholism-susceptibility genotype ADH1B*1/*1 was less frequent in the gastrectomy group, suggesting a competitive gene-gastrectomy interaction for alcoholism. A gene-gene interaction and gene-age interactions regarding the ADH1B genotype were observed.

Genomewide Expression and Functional Interactions of Genes under Drought Stress in Maize

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Nepolean Thirunavukkarasu

2017-01-01

Full Text Available A genomewide transcriptome assay of two subtropical genotypes of maize was used to observe the expression of genes at seedling stage of drought stress. The number of genes expressed differentially was greater in HKI1532 (a drought tolerant genotype than in PC3 (a drought sensitive genotype, indicating primary differences at the transcriptional level in stress tolerance. The global coexpression networks of the two genotypes differed significantly with respect to the number of modules and the coexpression pattern within the modules. A total of 174 drought-responsive genes were selected from HKI1532, and their coexpression network revealed key correlations between different adaptive pathways, each cluster of the network representing a specific biological function. Transcription factors related to ABA-dependent stomatal closure, signalling, and phosphoprotein cascades work in concert to compensate for reduced photosynthesis. Under stress, water balance was maintained by coexpression of the genes involved in osmotic adjustments and transporter proteins. Metabolism was maintained by the coexpression of genes involved in cell wall modification and protein and lipid metabolism. The interaction of genes involved in crucial biological functions during stress was identified and the results will be useful in targeting important gene interactions to understand drought tolerance in greater detail.

Novel functional polymorphism in IGF-1 gene associated with multiple sclerosis: A new insight to MS.

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Shahbazi, Majid; Abdolmohammadi, Reza; Ebadi, Hamid; Farazmandfar, Touraj

2017-04-01

Interactions between several genes and environment may play a role in susceptibility to multiple sclerosis (MS). The IGF-1 plays a key role in proliferation, maintenance and survival of nerve cells. Therefore, we hypothesized that IGF-1 may be a target for prediction and control MS. We aimed to analysis IGF-1 gene promoter sequence, to investigate the effect of the single nucleotide variants on IGF-1 expression and its association with MS. We enrolled 339 MS patients and 431 healthy controls. A specific region in IGF-1 gene promoter was investigated by SSCP analysis. All samples were genotyped by SSP-PCR. In-vitro and in-vivo IGF-1 production was measured by ELISA assay. IGF-1 expression in PBMCs was measured using real-time PCR. We identified a T to C single nucleotide substitution at position -1089 and a C to T at position -383 from transcription start site in the IGF-1 gene promoter. There was a significant association between MS and genotypes IGF-1(-383) C/T (p=0.001) and IGF-1(-383) C/C (pMS (p=0.001). In-vitro and in-vivo IGF-1 level showed that IGF-1 production in samples with genotype IGF-1(-383) C/C significantly was less than T/T (p=0.004) but not T/C (p=0.220). According to IGF-1 roles in CNS and our results, this study suggests that low IGF-1 level may be associated with susceptibility to MS. Copyright © 2017 Elsevier B.V. All rights reserved.

Art27 interacts with GATA4, FOG2 and NKX2.5 and is a novel co-repressor of cardiac genes.

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Daniel R Carter

Full Text Available Transcription factors play a crucial role in regulation of cardiac biology. FOG-2 is indispensable in this setting, predominantly functioning through a physical interaction with GATA-4. This study aimed to identify novel co-regulators of FOG-2 to further elaborate on its inhibitory activity on GATA-4. The Art27 transcription factor was identified by a yeast-2-hybrid library screen to be a novel FOG-2 protein partner. Characterisation revealed that Art27 is co-expressed with FOG-2 and GATA-4 throughout cardiac myocyte differentiation and in multiple structures of the adult heart. Art27 physically interacts with GATA-4, FOG-2 and other cardiac transcription factors and by this means, down-regulates their activity on cardiac specific promoters α-myosin heavy chain, atrial natriuretic peptide and B-type natriuretic peptide. Regulation of endogenous cardiac genes by Art27 was shown using microarray analysis of P19CL6-Mlc2v-GFP cardiomyocytes. Together these results suggest that Art27 is a novel transcription factor that is involved in downregulation of cardiac specific genes by physically interacting and inhibiting the activity of crucial transcriptions factors involved in cardiac biology.

Social Peer Interactions in Persons with Profound Intellectual and Multiple Disabilities: A Literature Review

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Nijs, Sara; Maes, Bea

2014-01-01

Social interactions may positively influence developmental and quality of life outcomes. Research in persons with profound intellectual and multiple disabilities (PIMD) mostly investigated interactions with caregivers. This literature review focuses on peer interactions of persons with PIMD. A computerized literature search of three databases was…

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

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Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan

2018-04-01

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

An ancestry-based approach for detecting interactions.

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Park, Danny S; Eskin, Itamar; Kang, Eun Yong; Gamazon, Eric R; Eng, Celeste; Gignoux, Christopher R; Galanter, Joshua M; Burchard, Esteban; Ye, Chun J; Aschard, Hugues; Eskin, Eleazar; Halperin, Eran; Zaitlen, Noah

2018-02-01

Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies. In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at Pancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits. © 2017 WILEY PERIODICALS, INC.

Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity

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Sucheston Lara

2010-09-01

Full Text Available Abstract Background Multifactorial diseases such as cancer and cardiovascular diseases are caused by the complex interplay between genes and environment. The detection of these interactions remains challenging due to computational limitations. Information theoretic approaches use computationally efficient directed search strategies and thus provide a feasible solution to this problem. However, the power of information theoretic methods for interaction analysis has not been systematically evaluated. In this work, we compare power and Type I error of an information-theoretic approach to existing interaction analysis methods. Methods The k-way interaction information (KWII metric for identifying variable combinations involved in gene-gene interactions (GGI was assessed using several simulated data sets under models of genetic heterogeneity driven by susceptibility increasing loci with varying allele frequency, penetrance values and heritability. The power and proportion of false positives of the KWII was compared to multifactor dimensionality reduction (MDR, restricted partitioning method (RPM and logistic regression. Results The power of the KWII was considerably greater than MDR on all six simulation models examined. For a given disease prevalence at high values of heritability, the power of both RPM and KWII was greater than 95%. For models with low heritability and/or genetic heterogeneity, the power of the KWII was consistently greater than RPM; the improvements in power for the KWII over RPM ranged from 4.7% to 14.2% at for α = 0.001 in the three models at the lowest heritability values examined. KWII performed similar to logistic regression. Conclusions Information theoretic models are flexible and have excellent power to detect GGI under a variety of conditions that characterize complex diseases.

The interaction of BDNF and NTRK2 gene increases the susceptibility of paranoid schizophrenia.

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Zheng Lin

Full Text Available The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2 is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445 and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445 and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605, in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605 may be involved in the susceptibility to paranoid

The interaction of BDNF and NTRK2 gene increases the susceptibility of paranoid schizophrenia.

Science.gov (United States)

Lin, Zheng; Su, Yousong; Zhang, Chengfang; Xing, Mengjuan; Ding, Wenhua; Liao, Liwei; Guan, Yangtai; Li, Zezhi; Cui, Donghong

2013-01-01

The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2) is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia) and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445) and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445) and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605), in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605) may be involved in the susceptibility to paranoid schizophrenia.

Large Display Interaction via Multiple Acceleration Curves and Multifinger Pointer Control

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Andrey Esakia

2014-01-01

Full Text Available Large high-resolution displays combine high pixel density with ample physical dimensions. The combination of these factors creates a multiscale workspace where interactive targeting of on-screen objects requires both high speed for distant targets and high accuracy for small targets. Modern operating systems support implicit dynamic control-display gain adjustment (i.e., a pointer acceleration curve that helps to maintain both speed and accuracy. However, large high-resolution displays require a broader range of control-display gains than a single acceleration curve can usably enable. Some interaction techniques attempt to solve the problem by utilizing multiple explicit modes of interaction, where different modes provide different levels of pointer precision. Here, we investigate the alternative hypothesis of using a single mode of interaction for continuous pointing that enables both (1 standard implicit granularity control via an acceleration curve and (2 explicit switching between multiple acceleration curves in an efficient and dynamic way. We evaluate a sample solution that augments standard touchpad accelerated pointer manipulation with multitouch capability, where the choice of acceleration curve dynamically changes depending on the number of fingers in contact with the touchpad. Specifically, users can dynamically switch among three different acceleration curves by using one, two, or three fingers on the touchpad.

Finding gene-environment interactions for phobias.

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Gregory, Alice M; Lau, Jennifer Y F; Eley, Thalia C

2008-03-01

Phobias are common disorders causing a great deal of suffering. Studies of gene-environment interaction (G x E) have revealed much about the complex processes underlying the development of various psychiatric disorders but have told us little about phobias. This article describes what is already known about genetic and environmental influences upon phobias and suggests how this information can be used to optimise the chances of discovering G x Es for phobias. In addition to the careful conceptualisation of new studies, it is suggested that data already collected should be re-analysed in light of increased understanding of processes influencing phobias.

Temporal scale dependent interactions between multiple environmental disturbances in microcosm ecosystems.

Science.gov (United States)

Garnier, Aurélie; Pennekamp, Frank; Lemoine, Mélissa; Petchey, Owen L

2017-12-01

Global environmental change has negative impacts on ecological systems, impacting the stable provision of functions, goods, and services. Whereas effects of individual environmental changes (e.g. temperature change or change in resource availability) are reasonably well understood, we lack information about if and how multiple changes interact. We examined interactions among four types of environmental disturbance (temperature, nutrient ratio, carbon enrichment, and light) in a fully factorial design using a microbial aquatic ecosystem and observed responses of dissolved oxygen saturation at three temporal scales (resistance, resilience, and return time). We tested whether multiple disturbances combine in a dominant, additive, or interactive fashion, and compared the predictability of dissolved oxygen across scales. Carbon enrichment and shading reduced oxygen concentration in the short term (i.e. resistance); although no other effects or interactions were statistically significant, resistance decreased as the number of disturbances increased. In the medium term, only enrichment accelerated recovery, but none of the other effects (including interactions) were significant. In the long term, enrichment and shading lengthened return times, and we found significant two-way synergistic interactions between disturbances. The best performing model (dominant, additive, or interactive) depended on the temporal scale of response. In the short term (i.e. for resistance), the dominance model predicted resistance of dissolved oxygen best, due to a large effect of carbon enrichment, whereas none of the models could predict the medium term (i.e. resilience). The long-term response was best predicted by models including interactions among disturbances. Our results indicate the importance of accounting for the temporal scale of responses when researching the effects of environmental disturbances on ecosystems. © 2017 The Authors. Global Change Biology Published by John Wiley

HSD3B and gene-gene interactions in a pathway-based analysis of genetic susceptibility to bladder cancer.

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Angeline S Andrew

Full Text Available Bladder cancer is the 4(th most common cancer among men in the U.S. We analyzed variant genotypes hypothesized to modify major biological processes involved in bladder carcinogenesis, including hormone regulation, apoptosis, DNA repair, immune surveillance, metabolism, proliferation, and telomere maintenance. Logistic regression was used to assess the relationship between genetic variation affecting these processes and susceptibility in 563 genotyped urothelial cell carcinoma cases and 863 controls enrolled in a case-control study of incident bladder cancer conducted in New Hampshire, U.S. We evaluated gene-gene interactions using Multifactor Dimensionality Reduction (MDR and Statistical Epistasis Network analysis. The 3'UTR flanking variant form of the hormone regulation gene HSD3B2 was associated with increased bladder cancer risk in the New Hampshire population (adjusted OR 1.85 95%CI 1.31-2.62. This finding was successfully replicated in the Texas Bladder Cancer Study with 957 controls, 497 cases (adjusted OR 3.66 95%CI 1.06-12.63. The effect of this prevalent SNP was stronger among males (OR 2.13 95%CI 1.40-3.25 than females (OR 1.56 95%CI 0.83-2.95, (SNP-gender interaction P = 0.048. We also identified a SNP-SNP interaction between T-cell activation related genes GATA3 and CD81 (interaction P = 0.0003. The fact that bladder cancer incidence is 3-4 times higher in males suggests the involvement of hormone levels. This biologic process-based analysis suggests candidate susceptibility markers and supports the theory that disrupted hormone regulation plays a role in bladder carcinogenesis.

Clustering gene expression data based on predicted differential effects of GV interaction.

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Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

2005-02-01

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

EasyClone: method for iterative chromosomal integration of multiple genes in Saccharomyces cerevisiae

DEFF Research Database (Denmark)

Jensen, Niels Bjerg; Strucko, Tomas; Kildegaard, Kanchana Rueksomtawin

2014-01-01

of multiple genes with an option of recycling selection markers. The vectors combine the advantage of efficient uracil excision reaction-based cloning and Cre-LoxP-mediated marker recycling system. The episomal and integrative vector sets were tested by inserting genes encoding cyan, yellow, and red...... fluorescent proteins into separate vectors and analyzing for co-expression of proteins by flow cytometry. Cells expressing genes encoding for the three fluorescent proteins from three integrations exhibited a much higher level of simultaneous expression than cells producing fluorescent proteins encoded...... on episomal plasmids, where correspondingly 95% and 6% of the cells were within a fluorescence interval of Log10 mean ± 15% for all three colors. We demonstrate that selective markers can be simultaneously removed using Cre-mediated recombination and all the integrated heterologous genes remain...

Numerical Simulations for Nonlinear Waves Interaction with Multiple Perforated Quasi-Ellipse Caissons

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Xiaozhong Ren

2015-01-01

Full Text Available A three-dimensional numerical flume is developed to study cnoidal wave interaction with multiple arranged perforated quasi-ellipse caissons. The continuity equation and the Navier-Stokes equations are used as the governing equation, and the VOF method is adopted to capture the free surface elevation. The equations are discretized on staggered cells and then solved using a finite difference method. The generation and propagation of cnoidal waves in the numerical flume are tested first. And the ability of the present model to simulate interactions between waves and structures is verified by known experimental results. Then cnoidal waves with varying incident wave height and period are generated and interact with multiple quasi-ellipse caissons with and without perforation. It is found that the perforation plays an effective role in reducing wave runup/rundown and wave forces on the caissons. The wave forces on caissons reduce with the decreasing incident wave period. The influence of the transverse distance of multiple caissons on wave forces is also investigated. A closer transverse distance between caissons can produce larger wave forces. But when relative adjacent distance L/D (L is the transverse distance and D is the width of the quasi-ellipse caisson is larger than 3, the effect of adjacent distance is limited.

Interaction of Multiple Particles with a Solidification Front: From Compacted Particle Layer to Particle Trapping.

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Saint-Michel, Brice; Georgelin, Marc; Deville, Sylvain; Pocheau, Alain

2017-06-13

The interaction of solidification fronts with objects such as particles, droplets, cells, or bubbles is a phenomenon with many natural and technological occurrences. For an object facing the front, it may yield various fates, from trapping to rejection, with large implications regarding the solidification pattern. However, whereas most situations involve multiple particles interacting with each other and the front, attention has focused almost exclusively on the interaction of a single, isolated object with the front. Here we address experimentally the interaction of multiple particles with a solidification front by performing solidification experiments of a monodisperse particle suspension in a Hele-Shaw cell with precise control of growth conditions and real-time visualization. We evidence the growth of a particle layer ahead of the front at a close-packing volume fraction, and we document its steady-state value at various solidification velocities. We then extend single-particle models to the situation of multiple particles by taking into account the additional force induced on an entering particle by viscous friction in the compacted particle layer. By a force balance model this provides an indirect measure of the repelling mean thermomolecular pressure over a particle entering the front. The presence of multiple particles is found to increase it following a reduction of the thickness of the thin liquid film that separates particles and front. We anticipate the findings reported here to provide a relevant basis to understand many complex solidification situations in geophysics, engineering, biology, or food engineering, where multiple objects interact with the front and control the resulting solidification patterns.

Gene-environment interaction and male reproductive function

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Axelsson, Jonatan; Bonde, Jens Peter; Giwercman, Yvonne L.; Rylander, Lars; Giwercman, Aleksander

2010-01-01

As genetic factors can hardly explain the changes taking place during short time spans, environmental and lifestyle-related factors have been suggested as the causes of time-related deterioration of male reproductive function. However, considering the strong heterogeneity of male fecundity between and within populations, genetic variants might be important determinants of the individual susceptibility to the adverse effects of environment or lifestyle. Although the possible mechanisms of such interplay in relation to the reproductive system are largely unknown, some recent studies have indicated that specific genotypes may confer a larger risk of male reproductive disorders following certain exposures. This paper presents a critical review of animal and human evidence on how genes may modify environmental effects on male reproductive function. Some examples have been found that support this mechanism, but the number of studies is still limited. This type of interaction studies may improve our understanding of normal physiology and help us to identify the risk factors to male reproductive malfunction. We also shortly discuss other aspects of gene-environment interaction specifically associated with the issue of reproduction, namely environmental and lifestyle factors as the cause of sperm DNA damage. It remains to be investigated to what extent such genetic changes, by natural conception or through the use of assisted reproductive techniques, are transmitted to the next generation, thereby causing increased morbidity in the offspring. PMID:20348940

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

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Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

2009-03-11

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene

Interaction cross sections and pions minus multiplicities on nucleus-nucleus collisions at 4.2 GeV/c per nucleon

International Nuclear Information System (INIS)

Angelov, N.; Ahababyan, N.; Anoshin, A.I.

1979-01-01

Data are presented on inelastic cross sections and multiplicities of pions minus produced on collisions of protons, deuterons, helium-4 and carbon-12 nuclei at 4.2 GeV/c per nucleon in the propane bubble chamber witn tantalum and carbon plates. Average multiplicities and dispersions of multiplicity distributions are compared with those in nucleon-nucleon interactions. Deviation of C-Ta interaction data from universal dependence of dispersion on multiplicity is observed. Pion multiplicities are found proportional to the number of nucleons from incident nucleus which interacted in the target. The results are not in contradiction with the assumption that nucleons from the incident nucleus interact independently in the target. For C-Ta interactions the average radius of the pion emission volume has been determined by the interference method to be r=(3.3+-0.6) fm

Interactions of renin-angiotensin system gene polymorphisms and antihypertensive effect of benazepril in Chinese population.

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Chen, Qing; Yu, Can-Qing; Tang, Xun; Chen, Da-Fang; Tian, Jun; Cao, Yang; Fan, Wen-Yi; Cao, Wei-Hua; Zhan, Si-Yan; Lv, Jun; Guo, Xiao-Xia; Hu, Yong-Hua; Lee, Li-Ming

2011-05-01

Angiotensin-converting enzyme inhibitors are widely used antihypertensive drugs with individual response variation. We studied whether interactions of AGT, AGTR1 and ACE2 gene polymorphisms affect this response. Our study is based on a 3-year field trial with 1831 hypertensive patients prescribed benazepril. Generalized multifactor dimensionality reduction was used to explore interaction models and logistic regressions were used to confirm them. A two-locus model involving the AGT and ACE2 genes was found in males, the sensitive genotypes showed an odds ratio (OR) of 1.9 (95% CI: 1.3-2.8) when compared with nonsensitive genotypes. Two AGT-AGTR1 models were found in females, with an OR of 3.5 (95% CI: 2.0-5.9) and 3.1 (95% CI: 1.8-5.3). Gender-specific gene-gene interactions of the AGT, AGTR1 and ACE2 genes were associated with individual variation of response to benazepril. Further studies are needed to confirm this finding.

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

NARCIS (Netherlands)

Broyl, Annemiek; Hose, Dirk; Lokhorst, Henk; de Knegt, Yvonne; Peeters, Justine; Jauch, Anna; Bertsch, Uta; Buijs, Arjan; Stevens-Kroef, Marian; Beverloo, H. Berna; Vellenga, Edo; Zweegman, Sonja; Kersten, Marie-Josée; van der Holt, Bronno; el Jarari, Laila; Mulligan, George; Goldschmidt, Hartmut; van Duin, Mark; Sonneveld, Pieter

2010-01-01

To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138(+) plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6

Permethrin induction of multiple cytochrome P450 genes in insecticide resistant mosquitoes, Culex quinquefasciatus.

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Gong, Youhui; Li, Ting; Zhang, Lee; Gao, Xiwu; Liu, Nannan

2013-01-01

The expression of some insect P450 genes can be induced by both exogenous and endogenous compounds and there is evidence to suggest that multiple constitutively overexpressed P450 genes are co-responsible for the development of resistance to permethrin in resistant mosquitoes. This study characterized the permethrin induction profiles of P450 genes known to be constitutively overexpressed in resistant mosquitoes, Culex quinquefasciatus. The gene expression in 7 of the 19 P450 genes CYP325K3v1, CYP4D42v2, CYP9J45, (CYP) CPIJ000926, CYP325G4, CYP4C38, CYP4H40 in the HAmCqG8 strain, increased more than 2-fold after exposure to permethrin at an LC50 concentration (10 ppm) compared to their acetone treated counterpart; no significant differences in the expression of these P450 genes in susceptible S-Lab mosquitoes were observed after permethrin treatment. Eleven of the fourteen P450 genes overexpressed in the MAmCqG6 strain, CYP9M10, CYP6Z12, CYP9J33, CYP9J43, CYP9J34, CYP306A1, CYP6Z15, CYP9J45, CYPPAL1, CYP4C52v1, CYP9J39, were also induced more than doubled after exposure to an LC50 (0.7 ppm) dose of permethrin. No significant induction in P450 gene expression was observed in the susceptible S-Lab mosquitoes after permethrin treatment except for CYP6Z15 and CYP9J39, suggesting that permethrin induction of these two P450 genes are common to both susceptible and resistant mosquitoes while the induction of the others are specific to insecticide resistant mosquitoes. These results demonstrate that multiple P450 genes are co-up-regulated in insecticide resistant mosquitoes through both constitutive overexpression and induction mechanisms, providing additional support for their involvement in the detoxification of insecticides and the development of insecticide resistance.

Multiple and variable NHEJ-like genes are involved in resistance to DNA damage in Streptomyces ambofaciens

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Grégory Hoff

2016-11-01

Full Text Available Non homologous end-joining (NHEJ is a double strand break (DSB repair pathway which does not require any homologous template and can ligate two DNA ends together. The basic bacterial NHEJ machinery involves two partners: the Ku protein, a DNA end binding protein for DSB recognition and the multifunctional LigD protein composed a ligase, a nuclease and a polymerase domain, for end processing and ligation of the broken ends. In silico analyses performed in the 38 sequenced genomes of Streptomyces species revealed the existence of a large panel of NHEJ-like genes. Indeed, ku genes or ligD domain homologues are scattered throughout the genome in multiple copies and can be distinguished in two categories: the core NHEJ gene set constituted of conserved loci and the variable NHEJ gene set constituted of NHEJ-like genes present in only a part of the species. In Streptomyces ambofaciens ATCC 23877, not only the deletion of core genes but also that of variable genes led to an increased sensitivity to DNA damage induced by electron beam irradiation. Multiple mutants of ku, ligase or polymerase encoding genes showed an aggravated phenotype compared to single mutants. Biochemical assays revealed the ability of Ku-like proteins to protect and to stimulate ligation of DNA ends. RT-qPCR and GFP fusion experiments suggested that ku-like genes show a growth phase dependent expression profile consistent with their involvement in DNA repair during spores formation and/or germination.

Rice-arsenate interactions in hydroponics: a three-gene model for tolerance.

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Norton, Gareth J; Nigar, Meher; Williams, Paul N; Dasgupta, Tapash; Meharg, Andrew A; Price, Adam H

2008-01-01

In this study, the genetic mapping of the tolerance of root growth to 13.3 muM arsenate [As(V)] using the BalaxAzucena population is improved, and candidate genes for further study are identified. A remarkable three-gene model of tolerance is advanced, which appears to involve epistatic interaction between three major genes, two on chromosome 6 and one on chromosome 10. Any combination of two of these genes inherited from the tolerant parent leads to the plant having tolerance. Lists of potential positional candidate genes are presented. These are then refined using whole genome transcriptomics data and bioinformatics. Physiological evidence is also provided that genes related to phosphate transport are unlikely to be behind the genetic loci conferring tolerance. These results offer testable hypotheses for genes related to As(V) tolerance that might offer strategies for mitigating arsenic (As) accumulation in consumed rice.

Rice–arsenate interactions in hydroponics: a three-gene model for tolerance

Science.gov (United States)

Norton, Gareth J.; Nigar, Meher; Dasgupta, Tapash; Meharg, Andrew A.; Price, Adam H.

2008-01-01

In this study, the genetic mapping of the tolerance of root growth to 13.3 μM arsenate [As(V)] using the Bala×Azucena population is improved, and candidate genes for further study are identified. A remarkable three-gene model of tolerance is advanced, which appears to involve epistatic interaction between three major genes, two on chromosome 6 and one on chromosome 10. Any combination of two of these genes inherited from the tolerant parent leads to the plant having tolerance. Lists of potential positional candidate genes are presented. These are then refined using whole genome transcriptomics data and bioinformatics. Physiological evidence is also provided that genes related to phosphate transport are unlikely to be behind the genetic loci conferring tolerance. These results offer testable hypotheses for genes related to As(V) tolerance that might offer strategies for mitigating arsenic (As) accumulation in consumed rice. PMID:18453529

Gene-diet-interactions in folate-mediated one-carbon metabolism modify colon cancer risk.

Science.gov (United States)

Liu, Amy Y; Scherer, Dominique; Poole, Elizabeth; Potter, John D; Curtin, Karen; Makar, Karen; Slattery, Martha L; Caan, Bette J; Ulrich, Cornelia M

2013-04-01

The importance of folate-mediated one-carbon metabolism (FOCM) in colorectal carcinogenesis is emphasized by observations that high dietary folate intake is associated with decreased risk of colon cancer (CC) and its precursors. Additionally, polymorphisms in FOCM-related genes have been repeatedly associated with risk, supporting a causal relationship between folate and colorectal carcinogenesis. We investigated ten candidate polymorphisms with defined or probable functional impact in eight FOCM-related genes (SHMT1, DHFR, DNMT1, MTHFD1, MTHFR, MTRR, TCN2, and TDG) in 1609 CC cases and 1974 controls for association with CC risk and for interaction with dietary factors. No polymorphism was statistically significantly associated with overall risk of CC. However, statistically significant interactions modifying CC risk were observed for DNMT1 I311V with dietary folate, methionine, vitamin B2 , and vitamin B12 intake and for MTRR I22M with dietary folate, a predefined one-carbon dietary pattern, and vitamin B6 intake. We observed statistically significant gene-diet interactions with five additional polymorphisms. Our results provide evidence that FOCM-related dietary intakes modify the association between CC risk and FOCM allelic variants. These findings add to observations showing that folate-related gene-nutrient interactions play an important role in modifying the risk of CC. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Clock genes × stress × reward interactions in alcohol and substance use disorders.

Science.gov (United States)

Perreau-Lenz, Stéphanie; Spanagel, Rainer

2015-06-01

Adverse life events and highly stressful environments have deleterious consequences for mental health. Those environmental factors can potentiate alcohol and drug abuse in vulnerable individuals carrying specific genetic risk factors, hence producing the final risk for alcohol- and substance-use disorders development. The nature of these genes remains to be fully determined, but studies indicate their direct or indirect relation to the stress hypothalamo-pituitary-adrenal (HPA) axis and/or reward systems. Over the past decade, clock genes have been revealed to be key-players in influencing acute and chronic alcohol/drug effects. In parallel, the influence of chronic stress and stressful life events in promoting alcohol and substance use and abuse has been demonstrated. Furthermore, the reciprocal interaction of clock genes with various HPA-axis components, as well as the evidence for an implication of clock genes in stress-induced alcohol abuse, have led to the idea that clock genes, and Period genes in particular, may represent key genetic factors to consider when examining gene × environment interaction in the etiology of addiction. The aim of the present review is to summarize findings linking clock genes, stress, and alcohol and substance abuse, and to propose potential underlying neurobiological mechanisms. Copyright © 2015 Elsevier Inc. All rights reserved.

Genome-Wide Constitutively Expressed Gene Analysis and New Reference Gene Selection Based on Transcriptome Data: A Case Study from Poplar/Canker Disease Interaction

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Jiaping Zhao

2017-10-01

Full Text Available A number of transcriptome datasets for differential expression (DE genes have been widely used for understanding organismal biology, but these datasets also contain untapped information that can be used to develop more precise analytical tools. With the use of transcriptome data generated from poplar/canker disease interaction system, we describe a methodology to identify candidate reference genes from high-throughput sequencing data. This methodology will improve the accuracy of RT-qPCR and will lead to better standards for the normalization of expression data. Expression stability analysis from xylem and phloem of Populus bejingensis inoculated with the fungal canker pathogen Botryosphaeria dothidea revealed that 729 poplar transcripts (1.11% were stably expressed, at a threshold level of coefficient of variance (CV of FPKM < 20% and maximum fold change (MFC of FPKM < 2.0. Expression stability and bioinformatics analysis suggested that commonly used house-keeping (HK genes were not the most appropriate internal controls: 70 of the 72 commonly used HK genes were not stably expressed, 45 of the 72 produced multiple isoform transcripts, and some of their reported primers produced unspecific amplicons in PCR amplification. RT-qPCR analysis to compare and evaluate the expression stability of 10 commonly used poplar HK genes and 20 of the 729 newly-identified stably expressed transcripts showed that some of the newly-identified genes (such as SSU_S8e, LSU_L5e, and 20S_PSU had higher stability ranking than most of commonly used HK genes. Based on these results, we recommend a pipeline for deriving reference genes from transcriptome data. An appropriate candidate gene should have a unique transcript, constitutive expression, CV value of expression < 20% (or possibly 30% and MFC value of expression <2, and an expression level of 50–1,000 units. Lastly, when four of the newly identified HK genes were used in the normalization of expression data for 20

Systemic virus-induced gene silencing allows functional characterization of maize genes during biotrophic interaction with Ustilago maydis.

Science.gov (United States)

van der Linde, Karina; Kastner, Christine; Kumlehn, Jochen; Kahmann, Regine; Doehlemann, Gunther

2011-01-01

Infection of maize (Zea mays) plants with the corn smut fungus Ustilago maydis leads to the formation of large tumors on the stem, leaves and inflorescences. In this biotrophic interaction, plant defense responses are actively suppressed by the pathogen, and previous transcriptome analyses of infected maize plants showed massive and stage-specific changes in host gene expression during disease progression. To identify maize genes that are functionally involved in the interaction with U. maydis, we adapted a virus-induced gene silencing (VIGS) system based on the brome mosaic virus (BMV) for maize. Conditions were established that allowed successful U. maydis infection of BMV-preinfected maize plants. This set-up enabled quantification of VIGS and its impact on U. maydis infection using a quantitative real-time PCR (qRT-PCR)-based readout. In proof-of-principle experiments, an U. maydis-induced terpene synthase was shown to negatively regulate disease development while a protein involved in cell death inhibition was required for full virulence of U. maydis. The results suggest that this system is a versatile tool for the rapid identification of maize genes that determine compatibility with U. maydis. © (2010) Max Planck Society. Journal compilation © New Phytologist Trust (2010).

Modeling Gene-Environment Interactions With Quasi-Natural Experiments.

Science.gov (United States)

Schmitz, Lauren; Conley, Dalton

2017-02-01

This overview develops new empirical models that can effectively document Gene × Environment (G×E) interactions in observational data. Current G×E studies are often unable to support causal inference because they use endogenous measures of the environment or fail to adequately address the nonrandom distribution of genes across environments, confounding estimates. Comprehensive measures of genetic variation are incorporated into quasi-natural experimental designs to exploit exogenous environmental shocks or isolate variation in environmental exposure to avoid potential confounders. In addition, we offer insights from population genetics that improve upon extant approaches to address problems from population stratification. Together, these tools offer a powerful way forward for G×E research on the origin and development of social inequality across the life course. © 2015 Wiley Periodicals, Inc.

Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality

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Ye Ping

2005-12-01

Full Text Available Abstract Background Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. Results We have developed Genetic Interaction Motif Finding (GIMF, an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast. Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. Conclusion GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic

The wheat resistance gene Lr34 results in the constitutive induction of multiple defense pathways in transgenic barley.

Science.gov (United States)

Chauhan, Harsh; Boni, Rainer; Bucher, Rahel; Kuhn, Benjamin; Buchmann, Gabriele; Sucher, Justine; Selter, Liselotte L; Hensel, Goetz; Kumlehn, Jochen; Bigler, Laurent; Glauser, Gaëtan; Wicker, Thomas; Krattinger, Simon G; Keller, Beat

2015-10-01

The wheat gene Lr34 encodes an ABCG-type transporter which provides durable resistance against multiple pathogens. Lr34 is functional as a transgene in barley, but its mode of action has remained largely unknown both in wheat and barley. Here we studied gene expression in uninfected barley lines transgenic for Lr34. Genes from multiple defense pathways contributing to basal and inducible disease resistance were constitutively active in seedlings and mature leaves. In addition, the hormones jasmonic acid and salicylic acid were induced to high levels, and increased levels of lignin as well as hordatines were observed. These results demonstrate a strong, constitutive re-programming of metabolism by Lr34. The resistant Lr34 allele (Lr34res) encodes a protein that differs by two amino acid polymorphisms from the susceptible Lr34sus allele. The deletion of a single phenylalanine residue in Lr34sus was sufficient to induce the characteristic Lr34-based responses. Combination of Lr34res and Lr34sus in the same plant resulted in a reduction of Lr34res expression by 8- to 20-fold when the low-expressing Lr34res line BG8 was used as a parent. Crosses with the high-expressing Lr34res line BG9 resulted in an increase of Lr34sus expression by 13- to 16-fold in progenies that inherited both alleles. These results indicate an interaction of the two Lr34 alleles on the transcriptional level. Reduction of Lr34res expression in BG8 crosses reduced the negative pleiotropic effects of Lr34res on barley growth and vigor without compromising disease resistance, suggesting that transgenic combination of Lr34res and Lr34sus can result in agronomically useful resistance. © 2015 The Authors The Plant Journal © 2015 John Wiley & Sons Ltd.

The "11K" gene family members sf68, sf95 and sf138 modulate transmissibility and insecticidal properties of Spodoptera frugiperda multiple nucleopolyhedrovirus.

Science.gov (United States)

Beperet, Inés; Simón, Oihane; Williams, Trevor; López-Ferber, Miguel; Caballero, Primitivo

2015-05-01

The "11K" gene family is notable for having homologs in both baculoviruses and entomopoxviruses and is classified as either type 145 or type 150, according to their similarity with the ac145 or ac150 genes of Autographa californica multiple nucleopolyhedrovirus (AcMNPV). One homolog of ac145 (sf138) and two homologs of ac150 (sf68 and sf95) are present in Spodoptera frugiperda multiple nucleopolyhedrovirus (SfMNPV). Recombinant bacmids lacking sf68, sf95 or sf138 (Sf68null, Sf95null and Sf138null, respectively) and the respective repair bacmids were generated from a bacmid comprising the complete virus genome. Occlusion bodies (OBs) of the Sf138null virus were ∼15-fold less orally infective to insects, which was attributed to a 100-fold reduction in ODV infectious titer. Inoculation of insects with Sf138null OBs in mixtures with an optical brightener failed to restore the pathogenicity of Sf138null OBs to that of the parental virus, indicating that the effects of sf138 deletion on OB pathogenicity were unlikely to involve an interaction with the gut peritrophic matrix. In contrast, deletion of sf68 and sf95 resulted in a slower speed-of-kill by 9h, and a concurrent increase in the yield of OBs. Phylogenetic analysis indicated that sf68 and sf95 were not generated after a duplication event of an ancestral gene homologous to the ac150 gene. We conclude that type 145 genes modulate the primary infection process of the virus, whereas type 150 genes appear to have a role in spreading systemic infection within the insect. Copyright © 2015 Elsevier Inc. All rights reserved.

N-cadherin-mediated interaction with multiple myeloma cells inhibits osteoblast differentiation

NARCIS (Netherlands)

Groen, Richard W. J.; de Rooij, Martin F. M.; Kocemba, Kinga A.; Reijmers, Rogier M.; de Haan-Kramer, Anneke; Overdijk, Marije B.; Aalders, Linda; Rozemuller, Henk; Martens, Anton C. M.; Bergsagel, P. Leif; Kersten, Marie José; Pals, Steven T.; Spaargaren, Marcel

2011-01-01

Multiple myeloma is a hematologic malignancy characterized by a clonal expansion of malignant plasma cells in the bone marrow, which is accompanied by the development of osteolytic lesions and/or diffuse osteopenia. The intricate bi-directional interaction with the bone marrow microenvironment plays

N-cadherin-mediated interaction with multiple myeloma cells inhibits osteoblast differentiation

NARCIS (Netherlands)

Groen, R.W.J.; de Rooij, M.F.M.; Kocemba, K.A.; Reijmers, R.M.; de Haan-Kramer, A.; Overdijk, M.B.; Aalders, L.; Rozemuller, H.; Martens, A.C.M.; Bergsagel, P.L.; Kersten, M.J.; Pals, S.T.; Spaargaren, M.

2011-01-01

Background Multiple myeloma is a hematologic malignancy characterized by a clonal expansion of malignant plasma cells in the bone marrow, which is accompanied by the development of osteolytic lesions and/or diffuse osteopenia. The intricate bi-directional interaction with the bone marrow

Exclusive description of multiple production on nuclei in the additive quark model. Multiplicity distributions in interactions with heavy nuclei

International Nuclear Information System (INIS)

Levchenko, B.B.; Nikolaev, N.N.

1985-01-01

In the framework of the additive quark model of multiple production on nuclei we calculate the multiplicity distributions of secondary particles and the correlations between secondary particles in πA and pA interactions with heavy nuclei. We show that intranuclear cascades are responsible for up to 50% of the nuclear increase of the multiplicity of fast particles. We analyze the sensitivity of the multiplicities and their correlations to the choice of the quark-hadronization function. We show that with good accuracy the yield of relativistic secondary particles from heavy and intermediate nuclei depends only on the number N/sub p/ of protons knocked out of the nucleus, and not on the mass number of the nucleus (N/sub p/ scaling)

Multiplicity distributions of charged hadrons in vp and charged current interactions

Science.gov (United States)

Jones, G. T.; Jones, R. W. L.; Kennedy, B. W.; Morrison, D. R. O.; Mobayyen, M. M.; Wainstein, S.; Aderholz, M.; Hantke, D.; Katz, U. F.; Kern, J.; Schmitz, N.; Wittek, W.; Borner, H. P.; Myatt, G.; Radojicic, D.; Burke, S.

1992-03-01

Using data on vp andbar vp charged current interactions from a bubble chamber experiment with BEBC at CERN, the multiplicity distributions of charged hadrons are investigated. The analysis is based on ˜20000 events with incident v and ˜10000 events with incidentbar v. The invariant mass W of the total hadronic system ranges from 3 GeV to ˜14 GeV. The experimental multiplicity distributions are fitted by the binomial function (for different intervals of W and in different intervals of the rapidity y), by the Levy function and the lognormal function. All three parametrizations give acceptable values for X 2. For fixed W, forward and backward multiplicities are found to be uncorrelated. The normalized moments of the charged multiplicity distributions are measured as a function of W. They show a violation of KNO scaling.

Gene-Diet Interaction and Precision Nutrition in Obesity

Directory of Open Access Journals (Sweden)

Yoriko Heianza

2017-04-01

Full Text Available The rapid rise of obesity during the past decades has coincided with a profound shift of our living environment, including unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Growing studies have found that changes in adiposity and metabolic response to low-calorie weight loss diets might be modified by genetic variants related to obesity, metabolic status and preference to nutrients. This review summarized data from recent studies of gene-diet interactions, and discussed integration of research of metabolomics and gut microbiome, as well as potential application of the findings in precision nutrition.

Multiplicity and transverse momentum fluctuations in inelastic proton-proton interactions at the CERN Super Proton Synchrotron

International Nuclear Information System (INIS)

Aduszkiewicz, A.; Dominik, W.; Kuich, M.; Matulewicz, T.; Posiadala, M.; Ali, Y.; Brzychczyk, J.; Larsen, D.; Planeta, R.; Richter-Was, E.; Staszel, P.; Wyszynski, O.; Andronov, E.; Feofilov, G.A.; Igolkin, S.; Kondratiev, V.P.; Seryakov, A.; Vechernin, V.V.; Vinogradov, L.; Anticic, T.; Kadija, K.; Susa, T.; Antoniou, N.; Christakoglou, P.; Davis, N.; Diakonos, F.; Kapoyannis, A.; Panagiotou, A.D.; Vassiliou, M.; Baatar, B.; Bunyatov, S.A.; Kolesnikov, V.I.; Krasnoperov, A.; Lyubushkin, V.V.; Malakhov, A.I.; Matveev, V.; Melkumov, G.L.; Tereshchenko, V.; Bay, F.; Di Luise, S.; Rubbia, A.; Sgalaberna, D.; Blondel, A.; Bravar, A.; Debieux, S.; Haesler, A.; Korzenev, A.; Ravonel, M.; Bluemer, J.; Dembinski, H.; Engel, R.; Herve, A.; Mathes, H.J.; Roth, M.; Szuba, M.; Ulrich, R.; Unger, M.; Veberic, D.; Bogomilov, M.; Kolev, D.; Tsenov, R.; Busygina, O.; Golubeva, M.; Guber, F.; Ivashkin, A.; Kurepin, A.; Morozov, S.; Petukhov, O.; Sadovsky, A.; Cirkovic, M.; Manic, D.; Puzovic, J.; Czopowicz, T.; Dynowski, K.; Grebieszkow, K.; Mackowiak-Pawlowska, M.; Maksiak, B.; Sarnecki, R.; Slodkowski, M.; Tefelska, A.; Tefelski, D.; Deveaux, M.; Koziel, M.; Renfordt, R.; Stroebele, H.; Dumarchez, J.; Robert, A.; Ereditato, A.; Hierholzer, M.; Nirkko, M.; Pistillo, C.; Redij, A.; Fodor, Z.; Garibov, A.; Gazdzicki, M.; Grzeszczuk, A.; Kaptur, E.; Kisiel, J.; Kowalski, S.; Pulawski, S.; Schmidt, K.; Wilczek, A.; Hasegawa, T.; Kobayashi, T.; Nakadaira, T.; Nishikawa, K.; Sakashita, K.; Sekiguchi, T.; Shibata, M.; Tada, M.; Kowalik, K.; Rondio, E.; Stepaniak, J.; Laszlo, A.; Marton, K.; Vesztergombi, G.; Lewicki, M.; Naskret, M.; Turko, L.; Marcinek, A.; Mrowczynski, S.; Rybczynski, M.; Seyboth, P.; Stefanek, G.; Wlodarczyk, Z.; Wojtaszek-Szwarc, A.; Pavin, M.; Popov, B.A.; Rauch, W.; Roehrich, D.; Rustamov, A.; Zambelli, L.

2016-01-01

Measurements of multiplicity and transverse momentum fluctuations of charged particles were performed in inelastic p+p interactions at 20, 31, 40, 80, and 158 GeV/c beam momentum. Results for the scaled variance of the multiplicity distribution and for three strongly intensive measures of multiplicity and transverse momentum fluctuations Δ[P_T,N], Σ[P_T,N] and Φ_p__T are presented. For the first time the results on fluctuations are fully corrected for experimental biases. The results on multiplicity and transverse momentum fluctuations significantly deviate from expectations for the independent particle production. They also depend on charges of selected hadrons. The string-resonance Monte Carlo models Epos and Urqmd do not describe the data. The scaled variance of multiplicity fluctuations is significantly higher in inelastic p+p interactions than in central Pb+Pb collisions measured by NA49 at the same energy per nucleon. This is in qualitative disagreement with the predictions of the Wounded Nucleon Model. Within the statistical framework the enhanced multiplicity fluctuations in inelastic p+p interactions can be interpreted as due to event-by-event fluctuations of the fireball energy and/or volume. (orig.)

Multiplicity and transverse momentum fluctuations in inelastic proton-proton interactions at the CERN Super Proton Synchrotron

Energy Technology Data Exchange (ETDEWEB)

Aduszkiewicz, A.; Dominik, W.; Kuich, M.; Matulewicz, T.; Posiadala, M. [University of Warsaw, Faculty of Physics, Warsaw (Poland); Ali, Y.; Brzychczyk, J.; Larsen, D.; Planeta, R.; Richter-Was, E.; Staszel, P.; Wyszynski, O. [Jagiellonian University, Cracow (Poland); Andronov, E.; Feofilov, G.A.; Igolkin, S.; Kondratiev, V.P.; Seryakov, A.; Vechernin, V.V.; Vinogradov, L. [St. Petersburg State University, St. Petersburg (Russian Federation); Anticic, T.; Kadija, K.; Susa, T. [Ruder Boskovic Institute, Zagreb (Croatia); Antoniou, N.; Christakoglou, P.; Davis, N.; Diakonos, F.; Kapoyannis, A.; Panagiotou, A.D.; Vassiliou, M. [University of Athens, Athens (Greece); Baatar, B.; Bunyatov, S.A.; Kolesnikov, V.I.; Krasnoperov, A.; Lyubushkin, V.V.; Malakhov, A.I.; Matveev, V.; Melkumov, G.L.; Tereshchenko, V. [Joint Institute for Nuclear Research, Dubna (Russian Federation); Bay, F.; Di Luise, S.; Rubbia, A.; Sgalaberna, D. [ETH Zuerich, Zurich (Switzerland); Blondel, A.; Bravar, A.; Debieux, S.; Haesler, A.; Korzenev, A.; Ravonel, M. [University of Geneva, Geneva (Switzerland); Bluemer, J.; Dembinski, H.; Engel, R.; Herve, A.; Mathes, H.J.; Roth, M.; Szuba, M.; Ulrich, R.; Unger, M.; Veberic, D. [Karlsruhe Institute of Technology, Karlsruhe (Germany); Bogomilov, M.; Kolev, D.; Tsenov, R. [University of Sofia, Faculty of Physics, Sofia (Bulgaria); Busygina, O.; Golubeva, M.; Guber, F.; Ivashkin, A.; Kurepin, A.; Morozov, S.; Petukhov, O.; Sadovsky, A. [Institute for Nuclear Research, Moscow (Russian Federation); Cirkovic, M.; Manic, D.; Puzovic, J. [University of Belgrade, Belgrade (Serbia); Czopowicz, T.; Dynowski, K.; Grebieszkow, K.; Mackowiak-Pawlowska, M.; Maksiak, B.; Sarnecki, R.; Slodkowski, M.; Tefelska, A.; Tefelski, D. [Warsaw University of Technology, Warsaw (Poland); Deveaux, M.; Koziel, M.; Renfordt, R.; Stroebele, H. [University of Frankfurt, Frankfurt (Germany); Dumarchez, J.; Robert, A. [University of Paris VI and VII, LPNHE, Paris (France); Ereditato, A.; Hierholzer, M.; Nirkko, M.; Pistillo, C.; Redij, A. [University of Bern, Bern (Switzerland); Fodor, Z. [Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Budapest (Hungary); University of Wroclaw, Wroclaw (Poland); Garibov, A. [National Nuclear Research Center, Baku (Azerbaijan); Gazdzicki, M. [University of Frankfurt, Frankfurt (Germany); Jan Kochanowski University in Kielce, Kielce (Poland); Grzeszczuk, A.; Kaptur, E.; Kisiel, J.; Kowalski, S.; Pulawski, S.; Schmidt, K.; Wilczek, A. [University of Silesia, Katowice (Poland); Hasegawa, T.; Kobayashi, T.; Nakadaira, T.; Nishikawa, K.; Sakashita, K.; Sekiguchi, T.; Shibata, M.; Tada, M. [Institute for Particle and Nuclear Studies, KEK, Tsukuba (Japan); Kowalik, K.; Rondio, E.; Stepaniak, J. [National Center for Nuclear Research, Warsaw (Poland); Laszlo, A.; Marton, K.; Vesztergombi, G. [Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Budapest (Hungary); Lewicki, M.; Naskret, M.; Turko, L. [University of Wroclaw, Wroclaw (Poland); Marcinek, A. [Jagiellonian University, Cracow (Poland); University of Wroclaw, Wroclaw (Poland); Mrowczynski, S.; Rybczynski, M.; Seyboth, P.; Stefanek, G.; Wlodarczyk, Z.; Wojtaszek-Szwarc, A. [Jan Kochanowski University in Kielce, Kielce (Poland); Pavin, M. [Ruder Boskovic Institute, Zagreb (Croatia); University of Paris VI and VII, LPNHE, Paris (France); Popov, B.A. [University of Paris VI and VII, LPNHE, Paris (France); Joint Institute for Nuclear Research, Dubna (RU); Rauch, W. [Fachhochschule Frankfurt, Frankfurt (DE); Roehrich, D. [University of Bergen, Bergen (NO); Rustamov, A. [National Nuclear Research Center, Baku (AZ); University of Frankfurt, Frankfurt (DE); Zambelli, L. [University of Paris VI and VII, LPNHE, Paris (FR); Institute for Particle and Nuclear Studies, KEK, Tsukuba (JP)

2016-11-15

Measurements of multiplicity and transverse momentum fluctuations of charged particles were performed in inelastic p+p interactions at 20, 31, 40, 80, and 158 GeV/c beam momentum. Results for the scaled variance of the multiplicity distribution and for three strongly intensive measures of multiplicity and transverse momentum fluctuations Δ[P{sub T},N], Σ[P{sub T},N] and Φ{sub p{sub T}} are presented. For the first time the results on fluctuations are fully corrected for experimental biases. The results on multiplicity and transverse momentum fluctuations significantly deviate from expectations for the independent particle production. They also depend on charges of selected hadrons. The string-resonance Monte Carlo models Epos and Urqmd do not describe the data. The scaled variance of multiplicity fluctuations is significantly higher in inelastic p+p interactions than in central Pb+Pb collisions measured by NA49 at the same energy per nucleon. This is in qualitative disagreement with the predictions of the Wounded Nucleon Model. Within the statistical framework the enhanced multiplicity fluctuations in inelastic p+p interactions can be interpreted as due to event-by-event fluctuations of the fireball energy and/or volume. (orig.)

Dynamic changes in the interchromosomal interaction of early histone gene loci during development of sea urchin.

Science.gov (United States)

Matsushita, Masaya; Ochiai, Hiroshi; Suzuki, Ken-Ichi T; Hayashi, Sayaka; Yamamoto, Takashi; Awazu, Akinori; Sakamoto, Naoaki

2017-12-15

The nuclear positioning and chromatin dynamics of eukaryotic genes are closely related to the regulation of gene expression, but they have not been well examined during early development, which is accompanied by rapid cell cycle progression and dynamic changes in nuclear organization, such as nuclear size and chromatin constitution. In this study, we focused on the early development of the sea urchin Hemicentrotus pulcherrimus and performed three-dimensional fluorescence in situ hybridization of gene loci encoding early histones (one of the types of histone in sea urchin). There are two non-allelic early histone gene loci per sea urchin genome. We found that during the morula stage, when the early histone gene expression levels are at their maximum, interchromosomal interactions were often formed between the early histone gene loci on separate chromosomes and that the gene loci were directed to locate to more interior positions. Furthermore, these interactions were associated with the active transcription of the early histone genes. Thus, such dynamic interchromosomal interactions may contribute to the efficient synthesis of early histone mRNA during the morula stage of sea urchin development. © 2017. Published by The Company of Biologists Ltd.

Diet-gene interactions between dietary fat intake and common polymorphisms in determining lipid metabolism

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Corella, Dolores

2009-03-01

Full Text Available Current dietary guidelines for fat intake have not taken into consideration the possible genetic differences underlying the individual variability in responsiveness to dietary components. Genetic variability has been identified in humans for all the known lipid metabolim-related genes resulting in a plethora of candidate genes and genetic variants to examine in diet-gene interaction studies focused on fat consumption. Some examples of fat-gene interaction are reviewed. These include: the interaction between total intake and the 514C/T in the hepatic lipase gene promoter in determining high-density lipoprotein cholesterol (HDL-C metabolism; the interaction between polyunsaturated fatty acids (PUFA and the 75G/A polymorphism in the APOA1 gene plasma HDL-C concentrations; the interaction between PUFA and the L162V polymorphism in the PPARA gene in determining triglycerides and APOC3 concentrations; and the interaction between PUFA intake and the 1131TC in the APOA5 gene in determining triglyceride metabolism. Although hundreds of diet-gene interaction studies in lipid metabolism have been published, the level of evidence to make specific nutritional recommendations to the population is still low and more research in nutrigenetics has to be undertaken.Las recomendaciones dietéticas actuales referentes al consumo de grasas en la dieta han sido realizadas sin tener en cuenta las posibles diferencias genéticas de las personas que podrían ser las responsables de las diferentes respuestas interindividuales que frecuentemente se observan ante la misma dieta. La presencia de variabilidad genética ha sido puesta de manifiesto para todos los genes relacionados con el metabolismo lipídico, por lo que existe un ingente número de genes y de variantes genéticas para ser incluidas en los estudios sobre interacciones dieta-genotipo en el ámbito específico del consumo de grasas y aceites. Se revisarán algunos ejemplos sobre interacciones grasa

Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates.

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Daniel Boloc

Full Text Available Retinitis pigmentosa (RP is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA. The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies.We have built an RP-specific network (RPGeNet by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space.In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates.

Gene interactions in the DNA damage-response pathway identified by genome-wide RNA-interference analysis of synthetic lethality

NARCIS (Netherlands)

van Haaften, Gijs; Vastenhouw, Nadine L; Nollen, Ellen A A; Plasterk, Ronald H A; Tijsterman, Marcel

2004-01-01

Here, we describe a systematic search for synthetic gene interactions in a multicellular organism, the nematode Caenorhabditis elegans. We established a high-throughput method to determine synthetic gene interactions by genome-wide RNA interference and identified genes that are required to protect

Multiplicities of secondary hadrons produced in vp and anti vp charged current interactions

International Nuclear Information System (INIS)

Graessler, H.; Lanske, D.; Schulte, R.; Chima, J.S.; Mobayyen, M.M.; Talebzadeh, M.; Villalobos-Baillie, O.; Corrigan, G.; Myatt, G.; Radojicic, D.; Saitta, B.; Wells, J.

1983-01-01

In an experiment with the hydrogen bubble chamber BEBC at CERN multiplicities of hadrons produced in vp and anti vp interactions have been investigated. Results are presented on the multiplicities of charged hadrons and neutral pions, forward and backward multiplicities of charged hadrons and correlations between forward and backward multiplicities. Comparisons are made with hadronic reactions and e + e - annihilation. In the framework of the quark-parton model the data imply similar charged multiplicities for the fragments of a u- and a d-quark, and larger multiplicities for the fragments of a uu- than for a ud-diquark. The correlation data suggest independent fragmentation of the quark and diquark for hadronic masses above approx.= 7 GeV and local charge compensation within an event. (orig.)

Gene-Diet Interactions in Type 2 Diabetes: The Chicken and Egg Debate

Science.gov (United States)

Ortega, Ángeles; Berná, Genoveva; Rojas, Anabel; Martín, Franz; Soria, Bernat

2017-01-01

Consistent evidence from both experimental and human studies indicates that Type 2 diabetes mellitus (T2DM) is a complex disease resulting from the interaction of genetic, epigenetic, environmental, and lifestyle factors. Nutrients and dietary patterns are important environmental factors to consider in the prevention, development and treatment of this disease. Nutritional genomics focuses on the interaction between bioactive food components and the genome and includes studies of nutrigenetics, nutrigenomics and epigenetic modifications caused by nutrients. There is evidence supporting the existence of nutrient-gene and T2DM interactions coming from animal studies and family-based intervention studies. Moreover, many case-control, cohort, cross-sectional cohort studies and clinical trials have identified relationships between individual genetic load, diet and T2DM. Some of these studies were on a large scale. In addition, studies with animal models and human observational studies, in different countries over periods of time, support a causative relationship between adverse nutritional conditions during in utero development, persistent epigenetic changes and T2DM. This review provides comprehensive information on the current state of nutrient-gene interactions and their role in T2DM pathogenesis, the relationship between individual genetic load and diet, and the importance of epigenetic factors in influencing gene expression and defining the individual risk of T2DM. PMID:28574454

Representing virus-host interactions and other multi-organism processes in the Gene Ontology.

Science.gov (United States)

Foulger, R E; Osumi-Sutherland, D; McIntosh, B K; Hulo, C; Masson, P; Poux, S; Le Mercier, P; Lomax, J

2015-07-28

The Gene Ontology project is a collaborative effort to provide descriptions of gene products in a consistent and computable language, and in a species-independent manner. The Gene Ontology is designed to be applicable to all organisms but up to now has been largely under-utilized for prokaryotes and viruses, in part because of a lack of appropriate ontology terms. To address this issue, we have developed a set of Gene Ontology classes that are applicable to microbes and their hosts, improving both coverage and quality in this area of the Gene Ontology. Describing microbial and viral gene products brings with it the additional challenge of capturing both the host and the microbe. Recognising this, we have worked closely with annotation groups to test and optimize the GO classes, and we describe here a set of annotation guidelines that allow the controlled description of two interacting organisms. Building on the microbial resources already in existence such as ViralZone, UniProtKB keywords and MeGO, this project provides an integrated ontology to describe interactions between microbial species and their hosts, with mappings to the external resources above. Housing this information within the freely-accessible Gene Ontology project allows the classes and annotation structure to be utilized by a large community of biologists and users.

Wide range of interacting partners of pea Gβ subunit of G-proteins suggests its multiple functions in cell signalling.

Science.gov (United States)

Bhardwaj, Deepak; Lakhanpaul, Suman; Tuteja, Narendra

2012-09-01

Climate change is a major concern especially in view of the increasing global population and food security. Plant scientists need to look for genetic tools whose appropriate usage can contribute to sustainable food availability. G-proteins have been identified as some of the potential genetic tools that could be useful for protecting plants from various stresses. Heterotrimeric G-proteins consisting of three subunits Gα, Gβ and Gγ are important components of a number of signalling pathways. Their structure and functions are already well studied in animals but their potential in plants is now gaining attention for their role in stress tolerance. Earlier we have reported that over expressing pea Gβ conferred heat tolerance in tobacco plants. Here we report the interacting partners (proteins) of Gβ subunit of Pisum sativum and their putative role in stress and development. Out of 90 transformants isolated from the yeast-two-hybrid (Y2H) screening, seven were chosen for further investigation due to their recurrence in multiple experiments. These interacting partners were confirmed using β-galactosidase colony filter lift and ONPG (O-nitrophenyl-β-D-galactopyranoside) assays. These partners include thioredoxin H, histidine-containing phosphotransfer protein 5-like, pathogenesis-related protein, glucan endo-beta-1, 3-glucosidase (acidic isoform), glycine rich RNA binding protein, cold and drought-regulated protein (corA gene) and soluble inorganic pyrophosphatase 1. This study suggests the role of pea Gβ subunit in stress signal transduction and development pathways owing to its capability to interact with a wide range of proteins of multiple functions. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Revealing molecular mechanisms by integrating high-dimensional functional screens with protein interaction data.

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Angela Simeone

2014-09-01

Full Text Available Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is the lack of computational methods for the systematic integration of multi-parametric phenotypic datasets with molecular interactions. Here, we present Integrative Multi Profile Analysis of Cellular Traits (IMPACT. The main goal of IMPACT is to identify the most consistent phenotypic profile among interacting genes. This approach utilizes two types of external information: sets of related genes (IMPACT-sets and network information (IMPACT-modules. Based on the notion that interacting genes are more likely to be involved in similar functions than non-interacting genes, this data is used as a prior to inform the filtering of phenotypic profiles that are similar among interacting genes. IMPACT-sets selects the most frequent profile among a set of related genes. IMPACT-modules identifies sub-networks containing genes with similar phenotype profiles. The statistical significance of these selections is subsequently quantified via permutations of the data. IMPACT (1 handles multiple profiles per gene, (2 rescues genes with weak phenotypes and (3 accounts for multiple biases e.g. caused by the network topology. Application to a genome-wide RNAi screen on endocytosis showed that IMPACT improved the recovery of known endocytosis-related genes, decreased off-target effects, and detected consistent phenotypes. Those findings were confirmed by rescreening 468 genes. Additionally we validated an unexpected influence of the IGF-receptor on EGF-endocytosis. IMPACT facilitates the selection of high-quality phenotypic profiles using different types of independent information, thereby supporting the molecular interpretation of functional screens.

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.

Gene-environment interaction and behavioral disorders: a developmental perspective based on endophenotypes.

Science.gov (United States)

Battaglia, Marco; Marino, Cecilia; Maziade, Michel; Molteni, Massimo; D'Amato, Francesca

2008-01-01

It has been observed that 'No aspect of human behavioral genetics has caused more confusion and generated more obscurantism than the analysis and interpretation of various types of non-additivity and non-independence of gene and environmental action and interaction' (Eaves LJ et al 1977 Br J Math Stat Psychol 30:1-42). On the other hand, a bulk of newly published studies appear to speak in favour of common and frequent interplay--and possibly interaction--between identified genetic polymorphisms and specified environmental variables in shaping behavior and behavioral disorders. Considerable interest has arisen from the introduction of putative functional 'endophenotypes' which would represent a more proximate biological link to genes, as well as an obligatory intermediate of behavior. While explicit criteria to identify valid endophenotypes have been offered, a number of new 'alternative phenotypes' are now being proposed as possible 'endophenotypes' for behavioral and psychiatric genetics research, sometimes with less than optimal stringency. Nonetheless, we suggest that some endophenotypes can be helpful in investigating several instances of gene-environment interactions and be employed as additional tools to reduce the risk for spurious results in this controversial area.