Characterization of the interaction of yeast enolase with polynucleotides.

Science.gov (United States)

al-Giery, A G; Brewer, J M

1992-09-23

Yeast enolase is inhibited under certain conditions by DNA. The enzyme binds to single-stranded DNA-cellulose. Inhibition was used for routine characterization of the interaction. The presence of the substrate 2-phospho-D-glycerate reduces inhibition and binding. Both yeast enolase isozymes behave similarly. Impure yeast enolase was purified by adsorption onto a single-stranded DNA-cellulose column followed by elution with substrate. Interaction with RNA, double-stranded DNA, or degraded DNA results in less inhibition, suggesting that yeast enolase preferentially binds single-stranded DNA. However, yeast enolase is not a DNA-unwinding protein. The enzyme is inhibited by the short synthetic oligodeoxynucleotides G6, G8 and G10 but not T8 or T6, suggesting some base specificity in the interaction. The interaction is stronger at more acid pH values, with an apparent pK of 5.6. The interaction is prevented by 0.3 M KCl, suggesting that electrostatic factors are important. Histidine or lysine reverse the inhibition at lower concentrations, while phosphate is still more effective. Binding of single-stranded DNA to enolase reduces the reaction of protein histidyl residues with diethylpyrocarbonate. The inhibition of yeast enolase by single-stranded DNA is not total, and suggests the active site is not directly involved in the interaction. Binding of substrate may induce a conformational change in the enzyme that interferes with DNA binding and vice versa.

Network Compression as a Quality Measure for Protein Interaction Networks

Science.gov (United States)

Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

2012-01-01

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

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

Science.gov (United States)

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

2006-03-01

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

Interactions between yeasts, fungicides and apple fruit russeting

NARCIS (Netherlands)

Gildemacher, P.R.; Heijne, B.; Silvestri, M.; Houbraken, J.; Hoekstra, E.; Theelen, B.; Boekhout, T.

2006-01-01

The effect of inoculations with yeasts occurring on apple surfaces and fungicide treatments on the russeting of Elstar apples was studied. Captan, dithianon and a water treatment were implemented to study the interaction between the fungicides, the inoculated yeast species and Aureobasidium

Improving functional modules discovery by enriching interaction networks with gene profiles

KAUST Repository

Salem, Saeed; Alroobi, Rami; Banitaan, Shadi; Seridi, Loqmane; Aljarah, Ibrahim; Brewer, James

2013-01-01

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

Unveiling protein functions through the dynamics of the interaction network.

Directory of Open Access Journals (Sweden)

Irene Sendiña-Nadal

Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

Graph theoretic analysis of protein interaction networks of eukaryotes

Science.gov (United States)

Goh, K.-I.; Kahng, B.; Kim, D.

2005-11-01

Owing to the recent progress in high-throughput experimental techniques, the datasets of large-scale protein interactions of prototypical multicellular species, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have been assayed. The datasets are obtained mainly by using the yeast hybrid method, which contains false-positive and false-negative simultaneously. Accordingly, while it is desirable to test such datasets through further wet experiments, here we invoke recent developed network theory to test such high-throughput datasets in a simple way. Based on the fact that the key biological processes indispensable to maintaining life are conserved across eukaryotic species, and the comparison of structural properties of the protein interaction networks (PINs) of the two species with those of the yeast PIN, we find that while the worm and yeast PIN datasets exhibit similar structural properties, the current fly dataset, though most comprehensively screened ever, does not reflect generic structural properties correctly as it is. The modularity is suppressed and the connectivity correlation is lacking. Addition of interologs to the current fly dataset increases the modularity and enhances the occurrence of triangular motifs as well. The connectivity correlation function of the fly, however, remains distinct under such interolog additions, for which we present a possible scenario through an in silico modeling.

Interactions between Drosophila and its natural yeast symbionts-Is Saccharomyces cerevisiae a good model for studying the fly-yeast relationship?

Science.gov (United States)

Hoang, Don; Kopp, Artyom; Chandler, James Angus

2015-01-01

Yeasts play an important role in the biology of the fruit fly, Drosophila melanogaster. In addition to being a valuable source of nutrition, yeasts affect D. melanogaster behavior and interact with the host immune system. Most experiments investigating the role of yeasts in D. melanogaster biology use the baker's yeast, Saccharomyces cerevisiae. However, S. cerevisiae is rarely found with natural populations of D. melanogaster or other Drosophila species. Moreover, the strain of S. cerevisiae used most often in D. melanogaster experiments is a commercially and industrially important strain that, to the best of our knowledge, was not isolated from flies. Since disrupting natural host-microbe interactions can have profound effects on host biology, the results from D. melanogaster-S. cerevisiae laboratory experiments may not be fully representative of host-microbe interactions in nature. In this study, we explore the D. melanogaster-yeast relationship using five different strains of yeast that were isolated from wild Drosophila populations. Ingested live yeasts have variable persistence in the D. melanogaster gastrointestinal tract. For example, Hanseniaspora occidentalis persists relative to S. cerevisiae, while Brettanomyces naardenensis is removed. Despite these differences in persistence relative to S. cerevisiae, we find that all yeasts decrease in total abundance over time. Reactive oxygen species (ROS) are an important component of the D. melanogaster anti-microbial response and can inhibit S. cerevisiae growth in the intestine. To determine if sensitivity to ROS explains the differences in yeast persistence, we measured yeast growth in the presence and absence of hydrogen peroxide. We find that B. naardenesis is completely inhibited by hydrogen peroxide, while H. occidentalis is not, which is consistent with yeast sensitivity to ROS affecting persistence within the D. melanogaster gastrointestinal tract. We also compared the feeding preference of D

Sirtuins as regulators of the yeast metabolic network

Directory of Open Access Journals (Sweden)

Markus eRalser

2012-03-01

Full Text Available There is growing evidence that the metabolic network is an integral regulator of cellularphysiology. Dynamic changes in metabolite concentrations, metabolic flux, or networktopology act as reporters of biological or environmental signals, and are required for the cellto trigger an appropriate biological reaction. Changes in the metabolic network are recognizedby specific sensory macromolecules and translated into a transcriptional or translationalresponse. The protein family of sirtuins, discovered more than 30 years ago as regulators ofsilent chromatin, seems to fulfill the role of a metabolic sensor during aging and conditions ofcaloric restriction. NAD+/NADH interconverting metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase and alcohol dehydrogenase, as well as enzymes involved inNAD(H, synthesis provide or deprive NAD+ in close proximity to Sir2. This influence sirtuinactivity, and facilitates a dynamic response of the metabolic network to changes inmetabolism with effects on physiology and aging. The molecular network downstream Sir2,however, is complex. In just two orders, Sir2’s metabolism-related interactions span half ofthe yeast proteome, and are connected with virtually every physiological process. Thus,although it is fundamental to analyze single molecular mechanisms, it is at the same timecrucial to consider this genome-scale complexity when correlating single molecular eventswith phenotypes such as aging, cell growth, or stress resistance.

Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

Science.gov (United States)

Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

2014-01-01

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891

Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

DEFF Research Database (Denmark)

Usaite, Renata; Jewett, Michael Christopher; Soberano de Oliveira, Ana Paula

2009-01-01

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite...

Network thermodynamic curation of human and yeast genome-scale metabolic models.

Science.gov (United States)

Martínez, Verónica S; Quek, Lake-Ee; Nielsen, Lars K

2014-07-15

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

DEFF Research Database (Denmark)

Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala

2016-01-01

All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae......, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While...... demonstrate that a new receptor-ligand pair can evolve through network-altering mutations independently of receptor-ligand binding, and suggest a potential role for such mutations in disease....

Environmental influences on organotin-yeast interactions

OpenAIRE

White, Jane S.

2002-01-01

As a consequence of the widespread industrial and agricultural applications of organotin compounds, contamination of various ecosystems has occurred in recent decades. Understanding how these compounds interact with cellular membranes is essential in assessing the risks of organotin pollution. The organotins, tributyltin (TBT) and trimethyltin (TMT) and inorganic tin, Sn(IV), were investigated for their physical interactions with non-metabolising cells and protoplasts of the yeast, Candida ma...

Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools.

Science.gov (United States)

Felgueiras, Juliana; Silva, Joana Vieira; Fardilha, Margarida

2018-01-16

"A man is known by the company he keeps" is a popular expression that perfectly fits proteins. A common approach to characterize the function of a target protein is to identify its interacting partners and thus infer its roles based on the known functions of the interactors. Protein-protein interaction networks (PPINs) have been created for several organisms, including humans, primarily as results of high-throughput screenings, such as yeast two-hybrid (Y2H). Their unequivocal use to understand events underlying human pathophysiology is promising in identifying genes and proteins associated with diseases. Therefore, numerous opportunities have emerged for PPINs as tools for clinical management of diseases: network-based disease classification systems, discovery of biomarkers and identification of therapeutic targets. Despite the great advantages of PPINs, their use is still unrecognised by several researchers who generate high-throughput data to generally characterize interactions in a certain model or to select an interaction to study in detail. We strongly believe that both approaches are not exclusive and that we can use PPINs as a complementary methodology and rich-source of information to the initial study proposal. Here, we suggest a pipeline to deal with Y2H results using bioinformatics tools freely available for academics. Yeast two-hybrid is widely-used to identify protein-protein interactions. Conventionally, the positive clones that result from a yeast two-hybrid screening are sequenced to identify the interactors of the protein of interest (also known as bait protein), and few interactions, thought as potentially relevant for the model in study, are selected for further validation using biochemical methods (e.g. co-immunoprecipitation and co-localization). The huge amount of data that is potentially lost during this conservative approach motivated us to write this tutorial-like review, so that researchers feel encouraged to take advantage of

A human protein interaction network shows conservation of aging processes between human and invertebrate species.

Directory of Open Access Journals (Sweden)

Russell Bell

2009-03-01

Full Text Available We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

Science.gov (United States)

Usaite, Renata; Jewett, Michael C; Oliveira, Ana Paula; Yates, John R; Olsson, Lisbeth; Nielsen, Jens

2009-01-01

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases. PMID:19888214

Influence of degree correlations on network structure and stability in protein-protein interaction networks

Directory of Open Access Journals (Sweden)

Zimmer Ralf

2007-08-01

Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

The function of communities in protein interaction networks at multiple scales

Directory of Open Access Journals (Sweden)

Jones Nick S

2010-07-01

Full Text Available Abstract Background If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

The Protein Interaction Network of Bacteriophage Lambda with Its Host, Escherichia coli

Science.gov (United States)

Blasche, Sonja; Wuchty, Stefan; Rajagopala, Seesandra V.

2013-01-01

Although most of the 73 open reading frames (ORFs) in bacteriophage λ have been investigated intensively, the function of many genes in host-phage interactions remains poorly understood. Using yeast two-hybrid screens of all lambda ORFs for interactions with its host Escherichia coli, we determined a raw data set of 631 host-phage interactions resulting in a set of 62 high-confidence interactions after multiple rounds of retesting. These links suggest novel regulatory interactions between the E. coli transcriptional network and lambda proteins. Targeted host proteins and genes required for lambda infection are enriched among highly connected proteins, suggesting that bacteriophages resemble interaction patterns of human viruses. Lambda tail proteins interact with both bacterial fimbrial proteins and E. coli proteins homologous to other phage proteins. Lambda appears to dramatically differ from other phages, such as T7, because of its unusually large number of modified and processed proteins, which reduces the number of host-virus interactions detectable by yeast two-hybrid screens. PMID:24049175

The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

DEFF Research Database (Denmark)

Liu, Guodong; Marras, Antonio; Nielsen, Jens

2014-01-01

regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network (TRN) for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model......Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional....... While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate...

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.

Update History of This Database - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Yeast Interacting Proteins Database Update History of This Database Date Update contents 201...0/03/29 Yeast Interacting Proteins Database English archive site is opened. 2000/12/4 Yeast Interacting Proteins Database...( http://itolab.cb.k.u-tokyo.ac.jp/Y2H/ ) is released. About This Database Database Description... Download License Update History of This Database Site Policy | Contact Us Update History of This Database... - Yeast Interacting Proteins Database | LSDB Archive ...

Binding properties of SUMO-interacting motifs (SIMs) in yeast.

Science.gov (United States)

Jardin, Christophe; Horn, Anselm H C; Sticht, Heinrich

2015-03-01

Small ubiquitin-like modifier (SUMO) conjugation and interaction play an essential role in many cellular processes. A large number of yeast proteins is known to interact non-covalently with SUMO via short SUMO-interacting motifs (SIMs), but the structural details of this interaction are yet poorly characterized. In the present work, sequence analysis of a large dataset of 148 yeast SIMs revealed the existence of a hydrophobic core binding motif and a preference for acidic residues either within or adjacent to the core motif. Thus the sequence properties of yeast SIMs are highly similar to those described for human. Molecular dynamics simulations were performed to investigate the binding preferences for four representative SIM peptides differing in the number and distribution of acidic residues. Furthermore, the relative stability of two previously observed alternative binding orientations (parallel, antiparallel) was assessed. For all SIMs investigated, the antiparallel binding mode remained stable in the simulations and the SIMs were tightly bound via their hydrophobic core residues supplemented by polar interactions of the acidic residues. In contrary, the stability of the parallel binding mode is more dependent on the sequence features of the SIM motif like the number and position of acidic residues or the presence of additional adjacent interaction motifs. This information should be helpful to enhance the prediction of SIMs and their binding properties in different organisms to facilitate the reconstruction of the SUMO interactome.

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

Science.gov (United States)

Reguly, Teresa; Breitkreutz, Ashton; Boucher, Lorrie; Breitkreutz, Bobby-Joe; Hon, Gary C; Myers, Chad L; Parsons, Ainslie; Friesen, Helena; Oughtred, Rose; Tong, Amy; Stark, Chris; Ho, Yuen; Botstein, David; Andrews, Brenda; Boone, Charles; Troyanskya, Olga G; Ideker, Trey; Dolinski, Kara; Batada, Nizar N; Tyers, Mike

2006-01-01

Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID () and SGD () databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. PMID:16762047

Defining the protein interaction network of human malaria parasite Plasmodium falciparum

KAUST Repository

Ramaprasad, Abhinay

2012-02-01

Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225. million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network. © 2011 Elsevier Inc.

Interactions of Condensed Tannins with Saccharomyces cerevisiae Yeast Cells and Cell Walls: Tannin Location by Microscopy.

Science.gov (United States)

Mekoue Nguela, Julie; Vernhet, Aude; Sieczkowski, Nathalie; Brillouet, Jean-Marc

2015-09-02

Interactions between grape tannins/red wine polyphenols and yeast cells/cell walls was previously studied within the framework of red wine aging and the use of yeast-derived products as an alternative to aging on lees. Results evidenced a quite different behavior between whole cells (biomass grown to elaborate yeast-derived products, inactivated yeast, and yeast inactivated after autolysis) and yeast cell walls (obtained from mechanical disruption of the biomass). Briefly, whole cells exhibited a high capacity to irreversibly adsorb grape and wine tannins, whereas only weak interactions were observed for cell walls. This last point was quite unexpected considering the literature and called into question the real role of cell walls in yeasts' ability to fix tannins. In the present work, tannin location after interactions between grape and wine tannins and yeast cells and cell walls was studied by means of transmission electron microscopy, light epifluorescence, and confocal microscopy. Microscopy observations evidenced that if tannins interact with cell walls, and especially cell wall mannoproteins, they also diffuse freely through the walls of dead cells to interact with their plasma membrane and cytoplasmic components.

Information processing in the transcriptional regulatory network of yeast: Functional robustness

Directory of Open Access Journals (Sweden)

Dehmer Matthias

2009-03-01

Full Text Available Abstract Background Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism. Results In this paper we study the functional robustness of the transcriptional regulatory network of S. cerevisiae. We model the information processing in the network as a first order Markov chain and study the influence of single gene perturbations on the global, asymptotic communication among genes. Modification in the communication is measured by an information theoretic measure allowing to predict genes that are 'fragile' with respect to single gene knockouts. Our results demonstrate that the predicted set of fragile genes contains a statistically significant enrichment of so called essential genes that are experimentally found to be necessary to ensure vital yeast. Further, a structural analysis of the transcriptional regulatory network reveals that there are significant differences between fragile genes, hub genes and genes with a high betweenness centrality value. Conclusion Our study does not only demonstrate that a combination of graph theoretical, information theoretical and statistical methods leads to meaningful biological results but also that such methods allow to study information processing in gene networks instead of just their structural properties.

Effect of dataset selection on the topological interpretation of protein interaction networks

Directory of Open Access Journals (Sweden)

Robertson David L

2005-09-01

Full Text Available Abstract Background Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. Results We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. Conclusion When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected

p53 inhibits autophagy by interacting with the human ortholog of yeast Atg17, RB1CC1/FIP200.

Science.gov (United States)

Morselli, Eugenia; Shen, Shensi; Ruckenstuhl, Christoph; Bauer, Maria Anna; Mariño, Guillermo; Galluzzi, Lorenzo; Criollo, Alfredo; Michaud, Mickael; Maiuri, Maria Chiara; Chano, Tokuhiro; Madeo, Frank; Kroemer, Guido

2011-08-15

The tumor suppressor protein p53 tonically suppresses autophagy when it is present in the cytoplasm. This effect is phylogenetically conserved from mammals to nematodes, and human p53 can inhibit autophagy in yeast, as we show here. Bioinformatic investigations of the p53 interactome in relationship to the autophagy-relevant protein network underscored the possible relevance of a direct molecular interaction between p53 and the mammalian ortholog of the essential yeast autophagy protein Atg17, namely RB1-inducible coiled-coil protein 1 (RB1CC1), also called FAK family kinase-interacting protein of 200 KDa (FIP200). Mutational analyses revealed that a single point mutation in p53 (K382R) abolished its capacity to inhibit autophagy upon transfection into p53-deficient human colon cancer or yeast cells. In conditions in which wild-type p53 co-immunoprecipitated with RB1CC1/FIP200, p53 (K382R) failed to do so, underscoring the importance of the physical interaction between these proteins for the control of autophagy. In conclusion, p53 regulates autophagy through a direct molecular interaction with RB1CC1/FIP200, a protein that is essential for the very apical step of autophagy initiation.

Yeasts in foods and beverages: impact on product quality and safety.

Science.gov (United States)

Fleet, Graham H

2007-04-01

The role of yeasts in food and beverage production extends beyond the well-known bread, beer and wine fermentations. Molecular analytical technologies have led to a major revision of yeast taxonomy, and have facilitated the ecological study of yeasts in many other products. The mechanisms by which yeasts grow in these ecosystems and impact on product quality can now be studied at the level of gene expression. Their growth and metabolic activities are moderated by a network of strain and species interactions, including interactions with bacteria and other fungi. Some yeasts have been developed as agents for the biocontrol of food spoilage fungi, and others are being considered as novel probiotic organisms. The association of yeasts with opportunistic infections and other adverse responses in humans raises new issues in the field of food safety.

Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks

Science.gov (United States)

LAVALLÉE-ADAM, MATHIEU; COULOMBE, BENOIT; BLANCHETTE, MATHIEU

2015-01-01

High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com. PMID:20377456

Yeast-yeast interactions revealed by aromatic profile analysis of Sauvignon Blanc wine fermented by single or co-culture of non-Saccharomyces and Saccharomyces yeasts.

Science.gov (United States)

Sadoudi, Mohand; Tourdot-Maréchal, Raphaëlle; Rousseaux, Sandrine; Steyer, Damien; Gallardo-Chacón, Joan-Josep; Ballester, Jordi; Vichi, Stefania; Guérin-Schneider, Rémi; Caixach, Josep; Alexandre, Hervé

2012-12-01

There has been increasing interest in the use of selected non-Saccharomyces yeasts in co-culture with Saccharomyces cerevisiae. The main reason is that the multistarter fermentation process is thought to simulate indigenous fermentation, thus increasing wine aroma complexity while avoiding the risks linked to natural fermentation. However, multistarter fermentation is characterised by complex and largely unknown interactions between yeasts. Consequently the resulting wine quality is rather unpredictable. In order to better understand the interactions that take place between non-Saccharomyces and Saccharomyces yeasts during alcoholic fermentation, we analysed the volatile profiles of several mono-culture and co-cultures. Candida zemplinina, Torulaspora delbrueckii and Metschnikowia pulcherrima were used to conduct fermentations either in mono-culture or in co-culture with S. cerevisiae. Up to 48 volatile compounds belonging to different chemical families were quantified. For the first time, we show that C. zemplinina is a strong producer of terpenes and lactones. We demonstrate by means of multivariate analysis that different interactions exist between the co-cultures studied. We observed a synergistic effect on aromatic compound production when M. pulcherrima was in co-culture with S. cerevisiae. However a negative interaction was observed between C. zemplinina and S. cerevisiae, which resulted in a decrease in terpene and lactone content. These interactions are independent of biomass production. The aromatic profiles of T. delbrueckii and S. cerevisiae in mono-culture and in co-culture are very close, and are biomass-dependent, reflecting a neutral interaction. This study reveals that a whole family of compounds could be altered by such interactions. These results suggest that the entire metabolic pathway is affected by these interactions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Interaction Between Yeasts and Zinc

Science.gov (United States)

Nicola, Raffaele De; Walker, Graeme

Zinc is an essential trace element in biological systems. For example, it acts as a cellular membrane stabiliser, plays a critical role in gene expression and genome modification and activates nearly 300 enzymes, including alcohol dehydrogenase. The present chapter will be focused on the influence of zinc on cell physiology of industrial yeast strains of Saccharomyces cerevisiae, with special regard to the uptake and subsequent utilisation of this metal. Zinc uptake by yeast is metabolism-dependent, with most of the available zinc translocated very quickly into the vacuole. At cell division, zinc is distributed from mother to daughter cells and this effectively lowers the individual cellular zinc concentration, which may become zinc depleted at the onset of the fermentation. Zinc influences yeast fermentative performance and examples will be provided relating to brewing and wine fermentations. Industrial yeasts are subjected to several stresses that may impair fermentation performance. Such stresses may also impact on yeast cell zinc homeostasis. This chapter will discuss the practical implications for the correct management of zinc bioavailability for yeast-based biotechnologies aimed at improving yeast growth, viability, fermentation performance and resistance to environmental stresses

Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation

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Shen Li

2010-09-01

Full Text Available Abstract Background An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task. Results Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes. Conclusions This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.

Database Description - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Yeast Interacting Proteins Database Database Description General information of database Database... name Yeast Interacting Proteins Database Alternative name - DOI 10.18908/lsdba.nbdc00742-000 Creator C...-ken 277-8561 Tel: +81-4-7136-3989 FAX: +81-4-7136-3979 E-mail : Database classif...s cerevisiae Taxonomy ID: 4932 Database description Information on interactions and related information obta...l Acad Sci U S A. 2001 Apr 10;98(8):4569-74. Epub 2001 Mar 13. External Links: Original website information Database

Interactions between yeasts and bacteria in the smear surface-ripened cheeses.

Science.gov (United States)

Corsetti, A; Rossi, J; Gobbetti, M

2001-09-19

In the initial phase of ripening, the microflora of bacterial smear surface-ripened cheeses such as Limburger, Taleggio, Brick, Münster and Saint-Paulin and that of surface mould-ripened cheeses such as Camembert and Brie may be similar, but at the end of the ripening, bacteria such as Brevibacterium spp., Arthrobacter spp., Micrococcus spp., Corynebacterium spp. and moulds such as Penicillium camemberti are, respectively, the dominant microorganisms. Yeasts such as Candida spp., Cryptococcus spp., Debaryomyces spp., Geotrichum candidum, Pichia spp., Rhodotorula spp., Saccharomyces spp. and Yarrowia lipolytica are often and variably isolated from the smear surface-ripened cheeses. Although not dominant within the microorganisms of the smear surface-ripened cheeses, yeasts establish significant interactions with moulds and especially bacteria, including surface bacteria and lactic acid bacteria. Some aspects of the interactions between yeasts and bacteria in such type of cheeses are considered in this paper.

Investigation of Fanconi anemia protein interactions by yeast two-hybrid analysis.

Science.gov (United States)

Huber, P A; Medhurst, A L; Youssoufian, H; Mathew, C G

2000-02-05

Fanconi anemia is a chromosomal breakage disorder with eight complementation groups (A-H), and three genes (FANCA, FANCC, and FANCG) have been identified. Initial investigations of the interaction between FANCA and FANCC, principally by co-immunoprecipitation, have proved controversial. We used the yeast two-hybrid assay to test for interactions of the FANCA, FANCC, and FANCG proteins. No activation of the reporter gene was observed in yeast co-expressing FANCA and FANCC as hybrid proteins, suggesting that FANCA does not directly interact with FANCC. However, a high level of activation was found when FANCA was co-expressed with FANCG, indicating strong, direct interaction between these proteins. Both FANCA and FANCG show weak but consistent interaction with themselves, suggesting that their function may involve dimerisation. The site of interaction of FANCG with FANCA was investigated by analysis of 12 mutant fragments of FANCG. Although both N- and C-terminal fragments did interact, binding to FANCA was drastically reduced, suggesting that more than one region of the FANCG protein is required for proper interaction with FANCA. Copyright 2000 Academic Press.

Transcriptional robustness and protein interactions are associated in yeast

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Conant Gavin C

2011-05-01

Full Text Available Abstract Background Robustness to insults, both external and internal, is a characteristic feature of life. One level of biological organization for which noise and robustness have been extensively studied is gene expression. Cells have a variety of mechanisms for buffering noise in gene expression, but it is not completely clear what rules govern whether or not a given gene uses such tools to maintain appropriate expression. Results Here, we show a general association between the degree to which yeast cells have evolved mechanisms to buffer changes in gene expression and whether they possess protein-protein interactions. We argue that this effect bears an affinity to epistasis, because yeast appears to have evolved regulatory mechanisms such that distant changes in gene copy number for a protein-protein interaction partner gene can alter a gene's expression. This association is not unexpected given recent work linking epistasis and the deleterious effects of changes in gene dosage (i.e., the dosage balance hypothesis. Using gene expression data from artificial aneuploid strains of bakers' yeast, we found that genes coding for proteins that physically interact with other proteins show less expression variation in response to aneuploidy than do other genes. This effect is even more pronounced for genes whose products interact with proteins encoded on aneuploid chromosomes. We further found that genes targeted by transcription factors encoded on aneuploid chromosomes were more likely to change in expression after aneuploidy. Conclusions We suggest that these observations can be best understood as resulting from the higher fitness cost of misexpression in epistatic genes and a commensurate greater regulatory control of them.

Dictyostelium discoideum as a novel host system to study the interaction between phagocytes and yeasts

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Barbara Koller

2016-10-01

Full Text Available The social amoeba Dictyostelium discoideum is a well-established model organism to study the interaction between bacteria and phagocytes. In contrast, research using D. discoideum as a host model for fungi is rare. We describe a comprehensive study, which uses D. discoideum as a host model system to investigate the interaction with apathogenic (Saccharomyces cerevisiae and pathogenic (Candida sp. yeast. We show that Dictyostelium can be co-cultivated with yeasts on solid media, offering a convenient test to study the interaction between fungi and phagocytes. We demonstrate that a number of D. discoideum mutants increase (atg1-, kil1-, kil2- or decrease (atg6- the ability of the amoebae to predate yeast cells. On the yeast side, growth characteristics, reduced phagocytosis rate, as well as known virulence factors of C. albicans (EFG1, CPH1, HGC1, ICL1 contribute to the resistance of yeast cells against predation by the amoebae. Investigating haploid C. albicans strains, we suggest using the amoebae plate test for screening purposes after random mutagenesis. Finally, we discuss the potential of our adapted amoebae plate test to use D. discoideum for risk assessment of yeast strains.

Application of random matrix theory to biological networks

Energy Technology Data Exchange (ETDEWEB)

Luo Feng [Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, SC 29634 (United States); Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhong Jianxin [Department of Physics, Xiangtan University, Hunan 411105 (China) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhongjn@ornl.gov; Yang Yunfeng [Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Scheuermann, Richard H. [Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhou Jizhong [Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019 (United States) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhouj@ornl.gov

2006-09-25

We show that spectral fluctuation of interaction matrices of a yeast protein-protein interaction network and a yeast metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson distribution. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at network scale. The transition point provides a new objective approach for the identification of functional modules.

Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast.

Science.gov (United States)

Abd-Rabbo, Diala; Michnick, Stephen W

2017-03-16

Kinases and phosphatases (KP) form complex self-regulating networks essential for cellular signal processing. In spite of having a wealth of data about interactions among KPs and their substrates, we have very limited models of the structures of the directed networks they form and consequently our ability to formulate hypotheses about how their structure determines the flow of information in these networks is restricted. We assembled and studied the largest bona fide kinase-phosphatase network (KP-Net) known to date for the yeast Saccharomyces cerevisiae. Application of the vertex sort (VS) algorithm on the KP-Net allowed us to elucidate its hierarchical structure in which nodes are sorted into top, core and bottom layers, forming a bow tie structure with a strongly connected core layer. Surprisingly, phosphatases tend to sort into the top layer, implying they are less regulated by phosphorylation than kinases. Superposition of the widest range of KP biological properties over the KP-Net hierarchy shows that core layer KPs: (i), receive the largest number of inputs; (ii), form bottlenecks implicated in multiple pathways and in decision-making; (iii), and are among the most regulated KPs both temporally and spatially. Moreover, top layer KPs are more abundant and less noisy than those in the bottom layer. Finally, we showed that the VS algorithm depends on node degrees without biasing the biological results of the sorted network. The VS algorithm is available as an R package ( https://cran.r-project.org/web/packages/VertexSort/index.html ). The KP-Net model we propose possesses a bow tie hierarchical structure in which the top layer appears to ensure highest fidelity and the core layer appears to mediate signal integration and cell state-dependent signal interpretation. Our model of the yeast KP-Net provides both functional insight into its organization as we understand today and a framework for future investigation of information processing in yeast and eukaryotes

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

Science.gov (United States)

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

Construction and evaluation of yeast expression networks by database-guided predictions

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Katharina Papsdorf

2016-05-01

Full Text Available DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. We performed microarray experiments to elucidate the transcriptional networks, which are up- or down-regulated in response to the expression of toxic polyglutamine proteins in yeast. Such experiments initially generate hit lists containing differentially expressed genes. To look into transcriptional responses, we constructed networks from these genes. We therefore developed an algorithm, which is capable of dealing with very small numbers of microarrays by clustering the hits based on co-regulatory relationships obtained from the SPELL database. Here, we evaluate this algorithm according to several criteria and further develop its statistical capabilities. Initially, we define how the number of SPELL-derived co-regulated genes and the number of input hits influences the quality of the networks. We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. We find that this approach is useful for our own experimental data sets and also for many other data sets which we tested from the SPELL microarray database. Furthermore, the clusters obtained by the described algorithm greatly improve the assignment to biological processes and transcription factors for the individual clusters. Thus, the described clustering approach, which will be available through the ClusterEx web interface, and the evaluation parameters derived from it represent valuable tools for the fast and informative analysis of yeast microarray data.

Yeast systems biology to unravel the network of life

DEFF Research Database (Denmark)

Mustacchi, Roberta; Hohmann, S; Nielsen, Jens

2006-01-01

Systems biology focuses on obtaining a quantitative description of complete biological systems, even complete cellular function. In this way, it will be possible to perform computer-guided design of novel drugs, advanced therapies for treatment of complex diseases, and to perform in silico design....... Furthermore, it serves as an industrial workhorse for production of a wide range of chemicals and pharmaceuticals. Systems biology involves the combination of novel experimental techniques from different disciplines as well as functional genomics, bioinformatics and mathematical modelling, and hence no single...... laboratory has access to all the necessary competences. For this reason the Yeast Systems Biology Network (YSBN) has been established. YSBN will coordinate research efforts, in yeast systems biology and, through the recently obtained EU funding for a Coordination Action, it will be possible to set...

Physical Interactions between Yeast Pichia guilliermondii and Post-Harvest Fruit Pathogen Penicillium expansum

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SRI WIDYASTUTI

2008-03-01

Full Text Available Attachment of yeast cells or bacteria on fungal hyphae have been observed in various antagonisms between microorganisms. Physical interactions between yeast Pichia guilliermondii and postharvest fruit pathogen Penicillium expansum in culture were studied in detail using light and transmission electron microscope to give better understanding on their mode of antagonism. Both organisms were co-cultured for 24-hr on potato dextrose agar. Light microscopy observations on the co-culture showed that the yeast cells attached firmly on the fungal hyphae. This attachment was inhibited by several substances such as enzymes degrading protein (protease or trypsin, a respiration inhibitor (sodium azide, an acid (hydrochloric acid or an alkali (sodium hydroxide. Although autoclaved hyphae did not affect the attachment, but boiled enzymes and autoclaved yeast cells totally abolished the attachment. These evidences suggested that the attachment might be an active process mediated by certain protein from live yeast cells. Transmission electron micrographs on the ultrastructure of the co-culture revealed that the hyphae showed abnormalities in their structure and organelles, and a degree of obvious damage. Physical interactions observed in this study could be contributed to the mechanism of antagonism between P. guilliermondii and P. expansum.

Detection of Protein Interactions in T3S Systems Using Yeast Two-Hybrid Analysis.

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Nilles, Matthew L

2017-01-01

Two-hybrid systems, sometimes termed interaction traps, are genetic systems designed to find and analyze interactions between proteins. The most common systems are yeast based (commonly Saccharomyces cerevisae) and rely on the functional reconstitution of the GAL4 transcriptional activator. Reporter genes, such as the lacZ gene of Escherichia coli (encodes β-galactosidase), are placed under GAL4-dependent transcriptional control to provide quick and reliable detection of protein interactions. In this method the use of a yeast-based two-hybrid system is described to study protein interactions between components of type III secretion systems.

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

Directory of Open Access Journals (Sweden)

Arthur Brady

Full Text Available As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all. We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

Science.gov (United States)

Wuchty, Stefan

2006-05-23

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

Protein complex prediction in large ontology attributed protein-protein interaction networks.

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Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

2013-01-01

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach

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Luan Yihui

2009-09-01

Full Text Available Abstract Background Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Results Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Conclusion Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach.

Science.gov (United States)

Wang, Wenhui; Nunez-Iglesias, Juan; Luan, Yihui; Sun, Fengzhu

2009-09-03

Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

Yeast Interacting Proteins Database: YFR015C, YFR015C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available yeast homolog; expression induced by glucose limitation, nitrogen starvation, environmental stress, and entr...ression induced by glucose limitation, nitrogen starvation, environmental stress, and entry into stationary ...tion, nitrogen starvation, environmental stress, and entry into stationary phase Rows with this bait as bait..., the more highly expressed yeast homolog; expression induced by glucose limitation, nitrogen starvation, environmental

Physicochemical and biochemical interactions in yeast immobilization by adhesion to a cellulose based support

OpenAIRE

Kurec, M.; Brányik, Tomáš; Mota, André; Domingues, Lucília; Teixeira, J. A.

2008-01-01

An important quality of yeast cell wall is the ability to adhere to other cell walls or solid surfaces. This feature of yeast is responsible for technologically important phenomena such as flocculation at the end of beer fermentation and cell adhesion to immobilization supports e.g. spent grains, DEAE-cellulose etc. Physicochemical properties of yeast surfaces, e.g. hydrophobicity and surface charge, have a substantial impact on cell adhesion and flocculation. The interaction e...

Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

Directory of Open Access Journals (Sweden)

Wuchty Stefan

2006-05-01

Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

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.

Divergent Evolution of the Transcriptional Network Controlled by Snf1-Interacting Protein Sip4 in Budding Yeasts.

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Constance Mehlgarten

Full Text Available Cellular responses to starvation are of ancient origin since nutrient limitation has always been a common challenge to the stability of living systems. Hence, signaling molecules involved in sensing or transducing information about limiting metabolites are highly conserved, whereas transcription factors and the genes they regulate have diverged. In eukaryotes the AMP-activated protein kinase (AMPK functions as a central regulator of cellular energy homeostasis. The yeast AMPK ortholog SNF1 controls the transcriptional network that counteracts carbon starvation conditions by regulating a set of transcription factors. Among those Cat8 and Sip4 have overlapping DNA-binding specificity for so-called carbon source responsive elements and induce target genes upon SNF1 activation. To analyze the evolution of the Cat8-Sip4 controlled transcriptional network we have compared the response to carbon limitation of Saccharomyces cerevisiae to that of Kluyveromyces lactis. In high glucose, S. cerevisiae displays tumor cell-like aerobic fermentation and repression of respiration (Crabtree-positive while K. lactis has a respiratory-fermentative life-style, respiration being regulated by oxygen availability (Crabtree-negative, which is typical for many yeasts and for differentiated higher cells. We demonstrate divergent evolution of the Cat8-Sip4 network and present evidence that a role of Sip4 in controlling anabolic metabolism has been lost in the Saccharomyces lineage. We find that in K. lactis, but not in S. cerevisiae, the Sip4 protein plays an essential role in C2 carbon assimilation including induction of the glyoxylate cycle and the carnitine shuttle genes. Induction of KlSIP4 gene expression by KlCat8 is essential under these growth conditions and a primary function of KlCat8. Both KlCat8 and KlSip4 are involved in the regulation of lactose metabolism in K. lactis. In chromatin-immunoprecipitation experiments we demonstrate binding of both, KlSip4 and

Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

DEFF Research Database (Denmark)

Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

2010-01-01

proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. CONCLUSIONS: The reported fully annotated interaction model serves as a platform for integrated systems biology studies...

Tombusvirus-yeast interactions identify conserved cell-intrinsic viral restriction factors

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Zsuzsanna eSasvari

2014-08-01

Full Text Available To combat viral infections, plants possess innate and adaptive immune pathways, such as RNA silencing, R gene and recessive gene-mediated resistance mechanisms. However, it is likely that additional cell-intrinsic restriction factors (CIRF are also involved in limiting plant virus replication. This review discusses novel CIRFs with antiviral functions, many of them RNA-binding proteins or affecting the RNA binding activities of viral replication proteins. The CIRFs against tombusviruses have been identified in yeast (Saccharomyces cerevisiae, which is developed as an advanced model organism. Grouping of the identified CIRFs based on their known cellular functions and subcellular localization in yeast reveals that TBSV replication is limited by a wide variety of host gene functions. Yeast proteins with the highest connectivity in the network map include the well-characterized Xrn1p 5’-3’ exoribonuclease, Act1p actin protein and Cse4p centromere protein. The protein network map also reveals an important interplay between the pro-viral Hsp70 cellular chaperone and the antiviral co-chaperones, and possibly key roles for the ribosomal or ribosome-associated factors. We discuss the antiviral functions of selected CIRFs, such as the RNA binding nucleolin, ribonucleases, WW-domain proteins, single- and multi-domain cyclophilins, TPR-domain co-chaperones and cellular ion pumps. These restriction factors frequently target the RNA-binding region in the viral replication proteins, thus interfering with the recruitment of the viral RNA for replication and the assembly of the membrane-bound viral replicase. Although many of the characterized CIRFs act directly against TBSV, we propose that the TPR-domain co-chaperones function as guardians of the cellular Hsp70 chaperone system, which is subverted efficiently by TBSV for viral replicase assembly in the absence of the TPR-domain co-chaperones.

Screening for proteins interacting with MCM7 in human lung cancer library using yeast two hybrid system

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Yuchen HAN

2008-08-01

Full Text Available Background and objective MCM7 is a subunit of the MCM complex that plays a key role in DNA replication initiation. But little is known about its interaction proteins. In this study yeast two hybrid screening was used to identify the MCM7 interacting proteins. Methods Yeast expression vector containing human full length MCM7-pGBKT7 plasmid was constructed, and with a library of cDNAs from human lung cancer-pACT2 plasmid was transformed into yeast strain AH109, and was electively grew in X-a-gal auxotrophy medium SD/-Trp-Leu-His-Ade, and the blue colonies were picked up, the plasmid of the yeast colonies was extracted , and transformed into E. Coli to extract DNA and performed sequence analysis. Results Eleven proteins were identified which could specifically interact with MCM7 proteins, among these five were cytoskeleton proteins, six were enzymes, kinases and related receptors. Conclusion The investigation provides functional clues for further exploration of MCM7 gene.

Pathway Detection from Protein Interaction Networks and Gene Expression Data Using Color-Coding Methods and A* Search Algorithms

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Cheng-Yu Yeh

2012-01-01

Full Text Available With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73GHz and 1GB main memory running under windows operating system.

Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

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Neszt Michael

2008-07-01

Full Text Available Abstract Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q. Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.

Interactions of grape tannins and wine polyphenols with a yeast protein extract, mannoproteins and β-glucan

OpenAIRE

Mekoue Nguela, Julie; Poncet-Legrand, Celine; Sieczkowski, N.; Vernhet, Aude

2016-01-01

At present, there is a great interest in enology for yeast derived products to replace aging on lees in winemaking or as an alternative for wine fining. These are yeast protein extracts (YPE), cell walls and mannoproteins. Our aim was to further understand the mechanisms that drive interactions between these components and red wine polyphenols. To this end, interactions between grape skin tannins or wine polyphenols or tannins and a YPE, a mannoprotein fraction and a β-glucan were monitored b...

The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis.

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Matthew D Dyer

2010-08-01

Full Text Available Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion.In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity.These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions.

Tombusviruses upregulate phospholipid biosynthesis via interaction between p33 replication protein and yeast lipid sensor proteins during virus replication in yeast

International Nuclear Information System (INIS)

Barajas, Daniel; Xu, Kai; Sharma, Monika; Wu, Cheng-Yu; Nagy, Peter D.

2014-01-01

Positive-stranded RNA viruses induce new membranous structures and promote membrane proliferation in infected cells to facilitate viral replication. In this paper, the authors show that a plant-infecting tombusvirus upregulates transcription of phospholipid biosynthesis genes, such as INO1, OPI3 and CHO1, and increases phospholipid levels in yeast model host. This is accomplished by the viral p33 replication protein, which interacts with Opi1p FFAT domain protein and Scs2p VAP protein. Opi1p and Scs2p are phospholipid sensor proteins and they repress the expression of phospholipid genes. Accordingly, deletion of OPI1 transcription repressor in yeast has a stimulatory effect on TBSV RNA accumulation and enhanced tombusvirus replicase activity in an in vitro assay. Altogether, the presented data convincingly demonstrate that de novo lipid biosynthesis is required for optimal TBSV replication. Overall, this work reveals that a (+)RNA virus reprograms the phospholipid biosynthesis pathway in a unique way to facilitate its replication in yeast cells. - Highlights: • Tombusvirus p33 replication protein interacts with FFAT-domain host protein. • Tombusvirus replication leads to upregulation of phospholipids. • Tombusvirus replication depends on de novo lipid synthesis. • Deletion of FFAT-domain host protein enhances TBSV replication. • TBSV rewires host phospholipid synthesis

Unravelling Protein-Protein Interaction Networks Linked to Aliphatic and Indole Glucosinolate Biosynthetic Pathways in Arabidopsis

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Sebastian J. Nintemann

2017-11-01

Full Text Available Within the cell, biosynthetic pathways are embedded in protein-protein interaction networks. In Arabidopsis, the biosynthetic pathways of aliphatic and indole glucosinolate defense compounds are well-characterized. However, little is known about the spatial orchestration of these enzymes and their interplay with the cellular environment. To address these aspects, we applied two complementary, untargeted approaches—split-ubiquitin yeast 2-hybrid and co-immunoprecipitation screens—to identify proteins interacting with CYP83A1 and CYP83B1, two homologous enzymes specific for aliphatic and indole glucosinolate biosynthesis, respectively. Our analyses reveal distinct functional networks with substantial interconnection among the identified interactors for both pathway-specific markers, and add to our knowledge about how biochemical pathways are connected to cellular processes. Specifically, a group of protein interactors involved in cell death and the hypersensitive response provides a potential link between the glucosinolate defense compounds and defense against biotrophic pathogens, mediated by protein-protein interactions.

Yeast Interacting Proteins Database: YFR015C, YJL137C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available yeast homolog; expression induced by glucose limitation, nitrogen starvation, environmental stress, and entr...pression induced by glucose limitation, nitrogen starvation, environmental stress, and entry into stationary

Systematic Analysis of the DNA Damage Response Network in Telomere Defective Budding Yeast

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Eva-Maria Holstein

2017-07-01

Full Text Available Functional telomeres are critically important to eukaryotic genetic stability. Scores of proteins and pathways are known to affect telomere function. Here, we report a series of related genome-wide genetic interaction screens performed on budding yeast cells with acute or chronic telomere defects. Genetic interactions were examined in cells defective in Cdc13 and Stn1, affecting two components of CST, a single stranded DNA (ssDNA binding complex that binds telomeric DNA. For comparison, genetic interactions were also examined in cells with defects in Rfa3, affecting the major ssDNA binding protein, RPA, which has overlapping functions with CST at telomeres. In more complex experiments, genetic interactions were measured in cells lacking EXO1 or RAD9, affecting different aspects of the DNA damage response, and containing a cdc13-1 induced telomere defect. Comparing fitness profiles across these data sets helps build a picture of the specific responses to different types of dysfunctional telomeres. The experiments show that each context reveals different genetic interactions, consistent with the idea that each genetic defect causes distinct molecular defects. To help others engage with the large volumes of data, the data are made available via two interactive web-based tools: Profilyzer and DIXY. One particularly striking genetic interaction observed was that the chk1∆ mutation improved fitness of cdc13-1 exo1∆ cells more than other checkpoint mutations (ddc1∆, rad9∆, rad17∆, and rad24∆, whereas, in cdc13-1 cells, the effects of all checkpoint mutations were similar. We show that this can be explained by Chk1 stimulating resection—a new function for Chk1 in the eukaryotic DNA damage response network.

Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

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Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

2011-11-01

A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

Hsp12p and PAU genes are involved in ecological interactions between natural yeast strains.

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Rivero, Damaríz; Berná, Luisa; Stefanini, Irene; Baruffini, Enrico; Bergerat, Agnes; Csikász-Nagy, Attila; De Filippo, Carlotta; Cavalieri, Duccio

2015-08-01

The coexistence of different yeasts in a single vineyard raises the question on how they communicate and why slow growers are not competed out. Genetically modified laboratory strains of Saccharomyces cerevisiae are extensively used to investigate ecological interactions, but little is known about the genes regulating cooperation and competition in ecologically relevant settings. Here, we present evidences of Hsp12p-dependent altruistic and contact-dependent competitive interactions between two natural yeast isolates. Hsp12p is released during cell death for public benefit by a fast-growing strain that also produces a killer toxin to inhibit growth of a slow grower that can enjoy the benefits of released Hsp12p. We also show that the protein Pau5p is essential in the defense against the killer effect. Our results demonstrate that the combined action of Hsp12p, Pau5p and a killer toxin is sufficient to steer a yeast community. © 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd.

Yeast for virus research

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Zhao, Richard Yuqi

2017-01-01

Budding yeast (Saccharomyces cerevisiae) and fission yeast (Schizosaccharomyces pombe) are two popular model organisms for virus research. They are natural hosts for viruses as they carry their own indigenous viruses. Both yeasts have been used for studies of plant, animal and human viruses. Many positive sense (+) RNA viruses and some DNA viruses replicate with various levels in yeasts, thus allowing study of those viral activities during viral life cycle. Yeasts are single cell eukaryotic organisms. Hence, many of the fundamental cellular functions such as cell cycle regulation or programed cell death are highly conserved from yeasts to higher eukaryotes. Therefore, they are particularly suited to study the impact of those viral activities on related cellular activities during virus-host interactions. Yeasts present many unique advantages in virus research over high eukaryotes. Yeast cells are easy to maintain in the laboratory with relative short doubling time. They are non-biohazardous, genetically amendable with small genomes that permit genome-wide analysis of virologic and cellular functions. In this review, similarities and differences of these two yeasts are described. Studies of virologic activities such as viral translation, viral replication and genome-wide study of virus-cell interactions in yeasts are highlighted. Impacts of viral proteins on basic cellular functions such as cell cycle regulation and programed cell death are discussed. Potential applications of using yeasts as hosts to carry out functional analysis of small viral genome and to develop high throughput drug screening platform for the discovery of antiviral drugs are presented. PMID:29082230

Interaction between lactic acid bacteria and yeasts in airag, an alcoholic fermented milk.

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Sudun; Wulijideligen; Arakawa, Kensuke; Miyamoto, Mari; Miyamoto, Taku

2013-01-01

The interaction between nine lactic acid bacteria (LAB) and five yeast strains isolated from airag of Inner Mongolia Autonomic Region, China was investigated. Three representative LAB and two yeasts showed symbioses were selected and incubated in 10% (w/v) reconstituted skim milk as single and mixed cultures to measure viable count, titratable acidity, ethanol and sugar content every 24 h for 1 week. LAB and yeasts showed high viable counts in the mixed cultures compared to the single cultures. Titratable acidity of the mixed cultures was obviously enhanced compared with that of the single cultures, except for the combinations of Lactobacillus reuteri 940B3 with Saccharomyces cerevisiae 4C and Lactobacillus helveticus 130B4 with Candida kefyr 2Y305. C. kefyr 2Y305 produced large amounts of ethanol (maximum 1.35 g/L), whereas non-lactose-fermenting S. cerevisiae 4C produced large amounts of ethanol only in the mixed cultures. Total glucose and galactose content increased while lactose content decreased in the single cultures of Leuconostoc mesenteroides 6B2081 and Lb. helveticus 130B4. However, both glucose and galactose were completely consumed and lactose was markedly reduced in the mixed cultures with yeasts. The result suggests that yeasts utilize glucose and galactose produced by LAB lactase to promote cell growth. © 2012 The Authors. Animal Science Journal © 2012 Japanese Society of Animal Science.

Nucleo-mitochondrial interaction of yeast in response to cadmium sulfide quantum dot exposure

International Nuclear Information System (INIS)

Pasquali, Francesco; Agrimonti, Caterina; Pagano, Luca; Zappettini, Andrea; Villani, Marco; Marmiroli, Marta; White, Jason C.; Marmiroli, Nelson

2017-01-01

Highlights: • CdS QDs induce oxidative stress in yeast. • CdS QDs disrupt mitochondrial membrane potentials and morphology. • CdS QDs do not affect mtDNA content. • CdS QDs modify the expression of genes involved in mitochondrial organization and function. • Deletion of some of these genes induces either tolerant or sensitive phenotypes to CdS QDs. - Abstract: Cell sensitivity to quantum dots (QDs) has been attributed to a cascade triggered by oxidative stress leading to apoptosis. The role and function of mitochondria in animal cells are well understood but little information is available on the complex genetic networks that regulate nucleo-mitochondrial interaction. The effect of CdS QD exposure in yeast Saccharomyces cerevisiae was assessed under conditions of limited lethality (<10%), using cell physiological and morphological endpoints. Whole-genomic array analysis and the screening of a deletion mutant library were also carried out. The results showed that QDs: increased the level of reactive oxygen species (ROS) and decreased the level of reduced vs oxidized glutathione (GSH/GSSG); reduced oxygen consumption and the abundance of respiratory cytochromes; disrupted mitochondrial membrane potentials and affected mitochondrial morphology. Exposure affected the capacity of cells to grow on galactose, which requires nucleo-mitochondrial involvement. However, QDs exposure did not materially induce respiratory deficient (RD) mutants but only RD phenocopies. All of these cellular changes were correlated with several key nuclear genes, including TOM5 and FKS1, involved in the maintenance of mitochondrial organization and function. The consequences of these cellular effects are discussed in terms of dysregulation of cell function in response to these “pathological mitochondria”.

Nucleo-mitochondrial interaction of yeast in response to cadmium sulfide quantum dot exposure

Energy Technology Data Exchange (ETDEWEB)

Pasquali, Francesco; Agrimonti, Caterina [Department of Life Sciences, University of Parma, Parma (Italy); Pagano, Luca [Department of Life Sciences, University of Parma, Parma (Italy); Stockbridge school of Agriculture, University of Massachusetts, Amherst, MA (United States); The Connecticut Agricultural Experiment Station, New Haven, CT (United States); Zappettini, Andrea; Villani, Marco [IMEM-CNR - Istituto dei Materiali per l' Elettronica ed il Magnetismo, Parma (Italy); Marmiroli, Marta [Department of Life Sciences, University of Parma, Parma (Italy); White, Jason C. [The Connecticut Agricultural Experiment Station, New Haven, CT (United States); Marmiroli, Nelson, E-mail: nelson.marmiroli@unipr.it [Department of Life Sciences, University of Parma, Parma (Italy); CINSA - Consorzio Interuniversitario Nazionale per le Scienze Ambientali, University of Parma, Parma (Italy)

2017-02-15

Highlights: • CdS QDs induce oxidative stress in yeast. • CdS QDs disrupt mitochondrial membrane potentials and morphology. • CdS QDs do not affect mtDNA content. • CdS QDs modify the expression of genes involved in mitochondrial organization and function. • Deletion of some of these genes induces either tolerant or sensitive phenotypes to CdS QDs. - Abstract: Cell sensitivity to quantum dots (QDs) has been attributed to a cascade triggered by oxidative stress leading to apoptosis. The role and function of mitochondria in animal cells are well understood but little information is available on the complex genetic networks that regulate nucleo-mitochondrial interaction. The effect of CdS QD exposure in yeast Saccharomyces cerevisiae was assessed under conditions of limited lethality (<10%), using cell physiological and morphological endpoints. Whole-genomic array analysis and the screening of a deletion mutant library were also carried out. The results showed that QDs: increased the level of reactive oxygen species (ROS) and decreased the level of reduced vs oxidized glutathione (GSH/GSSG); reduced oxygen consumption and the abundance of respiratory cytochromes; disrupted mitochondrial membrane potentials and affected mitochondrial morphology. Exposure affected the capacity of cells to grow on galactose, which requires nucleo-mitochondrial involvement. However, QDs exposure did not materially induce respiratory deficient (RD) mutants but only RD phenocopies. All of these cellular changes were correlated with several key nuclear genes, including TOM5 and FKS1, involved in the maintenance of mitochondrial organization and function. The consequences of these cellular effects are discussed in terms of dysregulation of cell function in response to these “pathological mitochondria”.

Dissecting Fission Yeast Shelterin Interactions via MICro-MS Links Disruption of Shelterin Bridge to Tumorigenesis

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Jinqiang Liu

2015-09-01

Full Text Available Shelterin, a six-member complex, protects telomeres from nucleolytic attack and regulates their elongation by telomerase. Here, we have developed a strategy, called MICro-MS (Mapping Interfaces via Crosslinking-Mass Spectrometry, that combines crosslinking-mass spectrometry and phylogenetic analysis to identify contact sites within the complex. This strategy allowed identification of separation-of-function mutants of fission yeast Ccq1, Poz1, and Pot1 that selectively disrupt their respective interactions with Tpz1. The various telomere dysregulation phenotypes observed in these mutants further emphasize the critical regulatory roles of Tpz1-centered shelterin interactions in telomere homeostasis. Furthermore, the conservation between fission yeast Tpz1-Pot1 and human TPP1-POT1 interactions led us to map a human melanoma-associated POT1 mutation (A532P to the TPP1-POT1 interface. Diminished TPP1-POT1 interaction caused by hPOT1-A532P may enable unregulated telomere extension, which, in turn, helps cancer cells to achieve replicative immortality. Therefore, our study reveals a connection between shelterin connectivity and tumorigenicity.

Statistical Mechanics of Temporal and Interacting Networks

Science.gov (United States)

Zhao, Kun

In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide

Strong FANCA/FANCG but weak FANCA/FANCC interaction in the yeast 2-hybrid system.

Science.gov (United States)

Reuter, T; Herterich, S; Bernhard, O; Hoehn, H; Gross, H J

2000-01-15

Three of at least 8 Fanconi anemia (FA) genes have been cloned (FANCA, FANCC, FANCG), but their functions remain unknown. Using the yeast 2-hybrid system and full-length cDNA, the authors found a strong interaction between FANCA and FANCG proteins. They also obtained evidence for a weak interaction between FANCA and FANCC. Neither FANCA nor FANCC was found to interact with itself. These results support the notion of a functional association between the FA gene products. (Blood. 2000;95:719-720)

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

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Recep Colak

2010-10-01

Full Text Available Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

Science.gov (United States)

Colak, Recep; Moser, Flavia; Chu, Jeffrey Shih-Chieh; Schönhuth, Alexander; Chen, Nansheng; Ester, Martin

2010-10-25

Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large

Yeast two-hybrid screening of proteins interacting with plasmin receptor subunit: C-terminal fragment of annexin A2.

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Li, Qun; Laumonnier, Yves; Syrovets, Tatiana; Simmet, Thomas

2011-11-01

To identify proteins that interact with the C-terminal fragment of annexin A2 (A2IC), generated by plasmin cleavage of the plasmin receptor, a heterotetramer (AA2t) containing annexin A2. The gene that encodes the A2IC fragment was obtained from PCR-amplified cDNA isolated from human monocytes, and was ligated into the pBTM116 vector using a DNA ligation kit. The resultant plasmid (pBTM116-A2IC) was sequenced with an ABI PRISM 310 Genetic Analyzer. The expression of an A2IC bait protein fused with a LexA-DNA binding domain (BD) was determined using Western blot analysis. The identification of proteins that interact with A2IC and are encoded in a human monocyte cDNA library was performed using yeast two-hybrid screening. The DNA sequences of the relevant cDNAs were determined using an ABI PRISM BigDye terminator cycle sequencing ready reaction kit. Nucleotide sequence databases were searched for homologous sequences using BLAST search analysis (http://www.ncbi.nlm.nih.gov). Confirmation of the interaction between the protein LexA-A2IC and each of cathepsin S and SNX17 was conducted using a small-scale yeast transformation and X-gal assay. The yeast transformed with plasmids encoding the bait proteins were screened with a human monocyte cDNA library by reconstituting full-length transcription factors containing the GAL4-active domain (GAL4-AD) as the prey in a yeast two-hybrid approach. After screening 1×10(7) clones, 23 independent β-Gal-positive clones were identified. Sequence analysis and a database search revealed that 15 of these positive clones matched eight different proteins (SNX17, ProCathepsin S, RPS2, ZBTB4, OGDH, CCDC32, PAPD4, and actin which was already known to interact with annexin A2). A2IC A2IC interacts with various proteins to form protein complexes, which may contribute to the molecular mechanism of monocyte activation induced by plasmin. The yeast two-hybrid system is an efficient approach for investigating protein interactions.

PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

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Greenblatt Jack

2006-07-01

Full Text Available Abstract Background Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions. Results Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30 and YMR135C (gid8 yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c. The observed interaction was confirmed by tandem affinity purification (TAP tag, verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not

Interactions between yeast lees and wine polyphenols during simulation of wine aging: I. Analysis of remnant polyphenolic compounds in the resulting wines.

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Mazauric, Jean-Paul; Salmon, Jean-Michel

2005-07-13

Wine aging on yeast lees is a traditional enological practice used during the manufacture of wines. This technique has increased in popularity in recent years for the aging of red wines. Although wine polyphenols interact with yeast lees to a limited extent, such interactions have a large effect on the reactivity toward oxygen of wine polyphenolic compounds and yeast lees. Various domains of the yeast cell wall are protected by wine polyphenols from the action of extracellular hydrolytic enzymatic activities. Polysaccharides released during autolysis are thought to exert a significant effect on the sensory qualities of wine. We studied the chemical composition of polyphenolic compounds remaining in solution or adsorbed on yeast lees after various contact times during the simulation of wine aging. The analysis of the remnant polyphenols in the wine indicated that wine polyphenols adsorption on yeast lees follows biphasic kinetics. An initial and rapid fixation is followed by a slow, constant, and saturating fixation that reaches its maximum after about 1 week. Only very few monomeric phenolic compounds remained adsorbed on yeast lees, and no preferential adsorption of low or high polymeric size tannins occurred. The remnant condensed tannins in the wine contained fewer epigallocatechin units than the initial tannins, indicating that polar condensed tannins were preferentially adsorbed on yeast lees. Conversely, the efficiency of anthocyanin adsorption on yeast lees was unrelated to its polarity.

A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.

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Teeraphan Laomettachit

Full Text Available To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast.

Yeast Interacting Proteins Database: YFR015C, YLR258W [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available yeast homolog; expression induced by glucose limitation, nitrogen starvation, environmental stress, and entr...n synthase, similar to Gsy1p; expression induced by glucose limitation, nitrogen ...; expression induced by glucose limitation, nitrogen starvation, environmental stress, and entry into statio...ogen synthase, similar to Gsy1p; expression induced by glucose limitation, nitrogen starvation, heat shock,

Transcriptional Waves in the Yeast Cell Cycle

OpenAIRE

Oliva, Anna; Rosebrock, Adam; Ferrezuelo, Francisco; Pyne, Saumyadipta; Chen, Haiying; Skiena, Steve; Futcher, Bruce; Leatherwood, Janet

2005-01-01

Many genes are regulated as an innate part of the eukaryotic cell cycle, and a complex transcriptional network helps enable the cyclic behavior of dividing cells. This transcriptional network has been studied in Saccharomyces cerevisiae (budding yeast) and elsewhere. To provide more perspective on these regulatory mechanisms, we have used microarrays to measure gene expression through the cell cycle of Schizosaccharomyces pombe (fission yeast). The 750 genes with the most significant oscillat...

Spatial organization of the budding yeast genome in the cell nucleus and identification of specific chromatin interactions from multi-chromosome constrained chromatin model.

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Gürsoy, Gamze; Xu, Yun; Liang, Jie

2017-07-01

Nuclear landmarks and biochemical factors play important roles in the organization of the yeast genome. The interaction pattern of budding yeast as measured from genome-wide 3C studies are largely recapitulated by model polymer genomes subject to landmark constraints. However, the origin of inter-chromosomal interactions, specific roles of individual landmarks, and the roles of biochemical factors in yeast genome organization remain unclear. Here we describe a multi-chromosome constrained self-avoiding chromatin model (mC-SAC) to gain understanding of the budding yeast genome organization. With significantly improved sampling of genome structures, both intra- and inter-chromosomal interaction patterns from genome-wide 3C studies are accurately captured in our model at higher resolution than previous studies. We show that nuclear confinement is a key determinant of the intra-chromosomal interactions, and centromere tethering is responsible for the inter-chromosomal interactions. In addition, important genomic elements such as fragile sites and tRNA genes are found to be clustered spatially, largely due to centromere tethering. We uncovered previously unknown interactions that were not captured by genome-wide 3C studies, which are found to be enriched with tRNA genes, RNAPIII and TFIIS binding. Moreover, we identified specific high-frequency genome-wide 3C interactions that are unaccounted for by polymer effects under landmark constraints. These interactions are enriched with important genes and likely play biological roles.

A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

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Mehranfar, Adele; Ghadiri, Nasser; Kouhsar, Morteza; Golshani, Ashkan

2017-09-01

Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Network class superposition analyses.

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Carl A B Pearson

Full Text Available Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., ≈ 10(30 for the yeast cell cycle process, considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix T, which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for T derived from boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying T to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with T. We show how to generate Derrida plots based on T. We show that T-based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on T. We motivate all of these results in terms of a popular molecular biology boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for T, for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses.

Network Physiology: How Organ Systems Dynamically Interact

Science.gov (United States)

Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

2015-01-01

We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

Network Physiology: How Organ Systems Dynamically Interact.

Science.gov (United States)

Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch

2015-01-01

We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.

Scaling laws and universality for the strength of genetic interactions in yeast

Science.gov (United States)

Velenich, Andrea; Dai, Mingjie; Gore, Jeff

2012-02-01

Genetic interactions provide a window to the organization of the thousands of biochemical reactions in living cells. If two mutations affect unrelated cellular functions, the fitness effects of their combination can be easily predicted from the two separate fitness effects. However, because of interactions, for some pairs of mutations their combined fitness effect deviates from the naive prediction. We study genetic interactions in yeast cells by analyzing a publicly available database containing experimental growth rates of 5 million double mutants. We show that the characteristic strength of genetic interactions has a simple power law dependence on the fitness effects of the two interacting mutations and that the probability distribution of genetic interactions is a universal function. We further argue that the strength of genetic interactions depends only on the fitness effects of the interacting mutations and not on their biological origin in terms of single point mutations, entire gene knockouts or even more complicated physiological perturbations. Finally, we discuss the implications of the power law scaling of genetic interactions on the ruggedness of fitness landscapes and the consequent evolutionary dynamics.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization.

Science.gov (United States)

Tuikkala, Johannes; Vähämaa, Heidi; Salmela, Pekka; Nevalainen, Olli S; Aittokallio, Tero

2012-03-26

Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

Yeast ribonuclease III uses a network of multiple hydrogen bonds for RNA binding and cleavage.

Science.gov (United States)

Lavoie, Mathieu; Abou Elela, Sherif

2008-08-19

Members of the bacterial RNase III family recognize a variety of short structured RNAs with few common features. It is not clear how this group of enzymes supports high cleavage fidelity while maintaining a broad base of substrates. Here we show that the yeast orthologue of RNase III (Rnt1p) uses a network of 2'-OH-dependent interactions to recognize substrates with different structures. We designed a series of bipartite substrates permitting the distinction between binding and cleavage defects. Each substrate was engineered to carry a single or multiple 2'- O-methyl or 2'-fluoro ribonucleotide substitutions to prevent the formation of hydrogen bonds with a specific nucleotide or group of nucleotides. Interestingly, introduction of 2'- O-methyl ribonucleotides near the cleavage site increased the rate of catalysis, indicating that 2'-OH are not required for cleavage. Substitution of nucleotides in known Rnt1p binding site with 2'- O-methyl ribonucleotides inhibited cleavage while single 2'-fluoro ribonucleotide substitutions did not. This indicates that while no single 2'-OH is essential for Rnt1p cleavage, small changes in the substrate structure are not tolerated. Strikingly, several nucleotide substitutions greatly increased the substrate dissociation constant with little or no effect on the Michaelis-Menten constant or rate of catalysis. Together, the results indicate that Rnt1p uses a network of nucleotide interactions to identify its substrate and support two distinct modes of binding. One mode is primarily mediated by the dsRNA binding domain and leads to the formation of stable RNA/protein complex, while the other requires the presence of the nuclease and N-terminal domains and leads to RNA cleavage.

Interactive optical trapping shows that confinement is a determinant of growth in a mixed yeast culture

DEFF Research Database (Denmark)

Arneborg, N.; Siegumfeldt, H.; Andersen, G.H.

2005-01-01

Applying a newly developed user-interactive optical trapping system, we controllably surrounded individual cells of one yeast species, Hanseniaspora uvarum, with viable cells of another yeast species, Saccharomyces cerevisiae, thus creating a confinement of the former. Growth of surrounded and non......-surrounded H. uvarum cells was followed under a microscope by determining their generation time. The average generation time of surrounded H. uvarum cells was 15% higher than that of non-surrounded cells thereby showing that the confinement imposed by viable S. cerevisiae cells on H. uvarum inhibits growth...

Screening and identification of host proteins interacting with Theileria annulata cysteine proteinase (TaCP by yeast-two-hybrid system

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Shuaiyang Zhao

2017-10-01

Full Text Available Abstract Background Theileria annulata can infect monocytes/macrophages and B lymphocytes and causes severe lymphoproliferative disease in ruminants. Meanwhile, infection by T. annulata leads to the permanent proliferation of cell population through regulating signaling pathways of host cells. Cysteine proteinases (CPs are one kind of protein hydrolase and usually play critical roles in parasite virulence, host invasion, nutrition and host immune response. However, the biological function of T. annulata CP (TaCP is still unclear. In this study, a yeast-two-hybrid assay was performed to screen host proteins interacting with TaCP, to provide information to help our understanding of the molecular mechanisms between T. annulata and host cells. Methods The cDNA from purified bovine B cells was inserted into pGADT7-SfiI vector (pGADT7-SfiI-BcDNA, Prey plasmid for constructing the yeast two-hybrid cDNA library. TaCP was cloned into the pGBKT7 vector (pGBKT7-TaCP and was considered as bait plasmid after evaluating the expression, auto-activation and toxicity tests in the yeast strain Y2HGold. The yeast two-hybrid screening was carried out via co-transforming bait and prey plasmids into yeast strain Y2HGold. Sequences of positive preys were analyzed using BLAST, Gene Ontology, UniProt and STRING. Results Two host proteins, CRBN (Bos taurus cereblon transcript variant X2 and Ppp4C (Bos indicus protein phosphatase 4 catalytic subunit were identified to interact with TaCP. The results of functional analysis showed that the two proteins were involved in many cellular processes, such as ubiquitylation regulation, microtubule organization, DNA repair, cell apoptosis and maturation of spliceosomal snRNPs. Conclusions This study is the first to screen the host proteins of bovine B cells interacting with TaCP, and 2 proteins, CRBN and Ppp4C, were identified using yeast two-hybrid technique. The results of functional analysis suggest that the two proteins are

Regulatory networks and connected components of the neutral space. A look at functional islands

Science.gov (United States)

Boldhaus, G.; Klemm, K.

2010-09-01

The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality - typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into ≈ 4.7 × 108 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.

s-core network decomposition: A generalization of k-core analysis to weighted networks

Science.gov (United States)

Eidsaa, Marius; Almaas, Eivind

2013-12-01

A broad range of systems spanning biology, technology, and social phenomena may be represented and analyzed as complex networks. Recent studies of such networks using k-core decomposition have uncovered groups of nodes that play important roles. Here, we present s-core analysis, a generalization of k-core (or k-shell) analysis to complex networks where the links have different strengths or weights. We demonstrate the s-core decomposition approach on two random networks (ER and configuration model with scale-free degree distribution) where the link weights are (i) random, (ii) correlated, and (iii) anticorrelated with the node degrees. Finally, we apply the s-core decomposition approach to the protein-interaction network of the yeast Saccharomyces cerevisiae in the context of two gene-expression experiments: oxidative stress in response to cumene hydroperoxide (CHP), and fermentation stress response (FSR). We find that the innermost s-cores are (i) different from innermost k-cores, (ii) different for the two stress conditions CHP and FSR, and (iii) enriched with proteins whose biological functions give insight into how yeast manages these specific stresses.

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

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Guo Hao

2011-05-01

Full Text Available Abstract Background High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance. Results Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes. Conclusions Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

Hazard interactions and interaction networks (cascades) within multi-hazard methodologies

Science.gov (United States)

Gill, Joel C.; Malamud, Bruce D.

2016-08-01

This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability

Evaluation of clustering algorithms for protein-protein interaction networks

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van Helden Jacques

2006-11-01

Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This

Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins.

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Raffi Tonikian

2009-10-01

Full Text Available SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast, Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools.

Science.gov (United States)

Miryala, Sravan Kumar; Anbarasu, Anand; Ramaiah, Sudha

2018-02-05

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks. Copyright © 2017 Elsevier B.V. All rights reserved.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

Directory of Open Access Journals (Sweden)

Tuikkala Johannes

2012-03-01

Full Text Available Abstract Background Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. Methods We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. Results The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. Conclusions By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

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

Potyvirus helper component-proteinase self-interaction in the yeast two-hybrid system and delineation of the interaction domain involved.

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Urcuqui-Inchima, S; Walter, J; Drugeon, G; German-Retana, S; Haenni, A L; Candresse, T; Bernardi, F; Le Gall, O

1999-05-25

Using the yeast two-hybrid system, a screen was performed for possible interactions between the proteins encoded by the 5' region of potyviral genomes [P1, helper component-proteinase (HC-Pro), and P3]. A positive self-interaction involving HC-Pro was detected with lettuce mosaic virus (LMV) and potato virus Y (PVY). The possibility of heterologous interaction between the HC-Pro of LMV and of PVY was also demonstrated. No interaction involving either the P1 or the P3 proteins was detected. A series of ordered deletions from either the N- or C-terminal end of the LMV HC-Pro was used to map the domain involved in interaction to the 72 N-terminal amino acids of the protein, a region known to be dispensable for virus viability but necessary for aphid transmission. A similar but less detailed analysis mapped the interacting domain to the N-terminal half of the PVY HC-Pro. Copyright 1999 Academic Press.

Game theory in communication networks cooperative resolution of interactive networking scenarios

CERN Document Server

Antoniou, Josephina

2012-01-01

A mathematical tool for scientists and researchers who work with computer and communication networks, Game Theory in Communication Networks: Cooperative Resolution of Interactive Networking Scenarios addresses the question of how to promote cooperative behavior in interactive situations between heterogeneous entities in communication networking scenarios. It explores network design and management from a theoretical perspective, using game theory and graph theory to analyze strategic situations and demonstrate profitable behaviors of the cooperative entities. The book promotes the use of Game T

The growth, properties and interactions of yeasts and bacteria associated with the maturation of Camembert and blue-veined cheeses.

Science.gov (United States)

Addis, E; Fleet, G H; Cox, J M; Kolak, D; Leung, T

2001-09-19

The growth of yeasts and bacteria were monitored during the maturation of Camembert and blue-veined cheese produced in Australia. Yeasts were prominent throughout maturation, growing to 10(5)-10(9)/g, depending on the manufacturer. Debaryomyces hansenii predominated, but there were lesser, inconsistent contributions from Yarrowia lipolytica. Of the non-lactic acid bacteria, Acinetobacter species were significant during the maturation of Camembert but not blue-veined cheeses, and grew to 10(6)-10(8) cfu/g. Staphylococcus and Micrococcus species were consistently isolated from the cheeses with Staphylococcus xylosus growing to 10(5)-10(9) cfu/g, depending on the product. Lactic acid bacteria (10(7)-10(9) cfu/g) were present throughout maturation but were not identified. Interactions between the various yeasts and bacterial isolates were examined. Several strains of D. hansenii exhibited killer activity but not against Y. lipolytica. None of the yeasts were antagonistic towards the bacteria but some strains of D. hansenii enhanced the growth of Y. lipolytica and S. xylosus. The yeast and bacterial isolates exhibited various degrees of extracellular proteolytic and lipolytic activities.

[Yeast urinary tract infections. Multicentre study in 14 hospitals belonging to the Buenos Aires City Mycology Network].

Science.gov (United States)

Maldonado, Ivana; Arechavala, Alicia; Guelfand, Liliana; Relloso, Silvia; Garbasz, Claudia

2016-01-01

Urinary tract infections are a frequent ailment in patients in intensive care units. Candida and other yeasts cause 5-12% of these infections. The value of the finding of any yeast is controversial, and there is no consensus about which parameters are adequate for differentiating urinary infections from colonization or contamination. To analyse the epidemiological characteristics of patients with funguria, to determine potential cut-off points in cultures (to distinguish an infection from other conditions), to identify the prevalent yeast species, and to determine the value of a second urine sample. A multicentre study was conducted in intensive care units of 14 hospitals in the Buenos Aires City Mycology Network. The first and second samples of urine from every patient were cultured. The presence of white cells and yeasts in direct examination, colony counts, and the identification of the isolated species, were evaluated. Yeasts grew in 12.2% of the samples. There was no statistical correlation between the number of white cells and the fungal colony-forming units. Eighty five percent of the patients had indwelling catheters. Funguria was not prevalent in women or in patients over the age of 65. Candida albicans, followed by Candida tropicalis, were the most frequently isolated yeasts. Candida parapsilosis and Candida glabrata appeared less frequently. The same species were isolated in 70% of second samples, and in 23% of the cases the second culture was negative. It was not possible to determine a useful cut-off point for colony counts to help in the diagnosis of urinary infections. As in other publications, C. albicans, followed by C. tropicalis, were the most prevalent species. Copyright © 2015 Asociación Española de Micología. Published by Elsevier Espana. All rights reserved.

License - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

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Full Text Available List Contact us Yeast Interacting Proteins Database License to Use This Database Last updated : 2010/02/15 You may use this database...nal License described below. The Standard License specifies the license terms regarding the use of this database... and the requirements you must follow in using this database. The Additional ...the Standard License. Standard License The Standard License for this database is the license specified in th...e Creative Commons Attribution-Share Alike 2.1 Japan . If you use data from this database

Interactions of grape tannins and wine polyphenols with a yeast protein extract, mannoproteins and β-glucan.

Science.gov (United States)

Mekoue Nguela, J; Poncet-Legrand, C; Sieczkowski, N; Vernhet, A

2016-11-01

At present, there is a great interest in enology for yeast derived products to replace aging on lees in winemaking or as an alternative for wine fining. These are yeast protein extracts (YPE), cell walls and mannoproteins. Our aim was to further understand the mechanisms that drive interactions between these components and red wine polyphenols. To this end, interactions between grape skin tannins or wine polyphenols or tannins and a YPE, a mannoprotein fraction and a β-glucan were monitored by binding experiments, ITC and DLS. Depending on the tannin structure, a different affinity between the polyphenols and the YPE was observed, as well as differences in the stability of the aggregates. This was attributed to the mean degree of polymerization of tannins in the polyphenol fractions and to chemical changes that occur during winemaking. Much lower affinities were found between polyphenols and polysaccharides, with different behaviors between mannoproteins and β-glucans. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Yeast Retrograde Response as a Model of Intracellular Signaling of Mitochondrial Dysfunction

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S. Michal eJazwinski

2012-05-01

Full Text Available Mitochondrial dysfunction activates intracellular signaling pathways that impact yeast longevity, and the best known of these pathways is the retrograde response. More recently, similar responses have been discerned in other systems, from invertebrates to human cells. However, the identity of the signal transducers is either unknown or apparently diverse, contrasting with the well-established signaling module of the yeast retrograde response. On the other hand, it has become equally clear that several other pathways and processes interact with the retrograde response, embedding it in a network responsive to a variety of cellular states. An examination of this network supports the notion that the master regulator NFkB aggregated a variety of mitochondria-related cellular responses at some point in evolution and has become the retrograde transcription factor. This has significant consequences for how we view some of the deficits associated with aging, such as inflammation. The support for NFkB as the retrograde response transcription factor is not only based on functional analyses. It is bolstered by the fact that NFkB can regulate Myc-Max, which is activated in human cells with dysfunctional mitochondria and impacts cellular metabolism. Myc-Max is homologous to the yeast retrograde response transcription factor Rtg1-Rtg3. Further research will be needed to disentangle the pro-aging from the anti-aging effects of NFkB. Interestingly, this is also a challenge for the complete understanding of the yeast retrograde response.

Unraveling spurious properties of interaction networks with tailored random networks.

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Stephan Bialonski

Full Text Available We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

Yeast modulation of human dendritic cell cytokine secretion: an in vitro study.

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Ida M Smith

Full Text Available Probiotics are live microorganisms which when administered in adequate amounts confer a health benefit on the host. The concept of individual microorganisms influencing the makeup of T cell subsets via interactions with intestinal dendritic cells (DCs appears to constitute the foundation for immunoregulatory effects of probiotics, and several studies have reported probiotic strains resulting in reduction of intestinal inflammation through modulation of DC function. Consequent to a focus on Saccharomyces boulardii as the fundamental probiotic yeast, very little is known about hundreds of non-Saccharomyces yeasts in terms of their interaction with the human gastrointestinal immune system. The aim of the present study was to evaluate 170 yeast strains representing 75 diverse species for modulation of inflammatory cytokine secretion by human DCs in vitro, as compared to cytokine responses induced by a S. boulardii reference strain with probiotic properties documented in clinical trials. Furthermore, we investigated whether cytokine inducing interactions between yeasts and human DCs are dependent upon yeast viability or rather a product of membrane interactions regardless of yeast metabolic function. We demonstrate high diversity in yeast induced cytokine profiles and employ multivariate data analysis to reveal distinct clustering of yeasts inducing similar cytokine profiles in DCs, highlighting clear species distinction within specific yeast genera. The observed differences in induced DC cytokine profiles add to the currently very limited knowledge of the cross-talk between yeasts and human immune cells and provide a foundation for selecting yeast strains for further characterization and development toward potentially novel yeast probiotics. Additionally, we present data to support a hypothesis that the interaction between yeasts and human DCs does not solely depend on yeast viability, a concept which may suggest a need for further classifications

Yeast Modulation of Human Dendritic Cell Cytokine Secretion: An In Vitro Study

Science.gov (United States)

Smith, Ida M.; Christensen, Jeffrey E.; Arneborg, Nils; Jespersen, Lene

2014-01-01

Probiotics are live microorganisms which when administered in adequate amounts confer a health benefit on the host. The concept of individual microorganisms influencing the makeup of T cell subsets via interactions with intestinal dendritic cells (DCs) appears to constitute the foundation for immunoregulatory effects of probiotics, and several studies have reported probiotic strains resulting in reduction of intestinal inflammation through modulation of DC function. Consequent to a focus on Saccharomyces boulardii as the fundamental probiotic yeast, very little is known about hundreds of non-Saccharomyces yeasts in terms of their interaction with the human gastrointestinal immune system. The aim of the present study was to evaluate 170 yeast strains representing 75 diverse species for modulation of inflammatory cytokine secretion by human DCs in vitro, as compared to cytokine responses induced by a S. boulardii reference strain with probiotic properties documented in clinical trials. Furthermore, we investigated whether cytokine inducing interactions between yeasts and human DCs are dependent upon yeast viability or rather a product of membrane interactions regardless of yeast metabolic function. We demonstrate high diversity in yeast induced cytokine profiles and employ multivariate data analysis to reveal distinct clustering of yeasts inducing similar cytokine profiles in DCs, highlighting clear species distinction within specific yeast genera. The observed differences in induced DC cytokine profiles add to the currently very limited knowledge of the cross-talk between yeasts and human immune cells and provide a foundation for selecting yeast strains for further characterization and development toward potentially novel yeast probiotics. Additionally, we present data to support a hypothesis that the interaction between yeasts and human DCs does not solely depend on yeast viability, a concept which may suggest a need for further classifications beyond the current

Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks.

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Eric A Yen

2014-05-01

Full Text Available Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and

A Physical Interaction Network of Dengue Virus and Human Proteins*

Science.gov (United States)

Khadka, Sudip; Vangeloff, Abbey D.; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S.; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J.; Perera, Rushika; LaCount, Douglas J.

2011-01-01

Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection. PMID:21911577

A physical interaction network of dengue virus and human proteins.

Science.gov (United States)

Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

2011-12-01

Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

Control activity of yeast geranylgeranyl diphosphate synthase from dimer interface through H-bonds and hydrophobic interaction.

Science.gov (United States)

Chang, Chih-Kang; Teng, Kuo-Hsun; Lin, Sheng-Wei; Chang, Tao-Hsin; Liang, Po-Huang

2013-04-23

Previously we showed that yeast geranylgeranyl diphosphate synthase (GGPPS) becomes an inactive monomer when the first N-terminal helix involved in dimerization is deleted. This raises questions regarding why dimerization is required for GGPPS activity and which amino acids in the dimer interface are essential for dimerization-mediated activity. According to the GGPPS crystal structure, three amino acids (N101, N104, and Y105) located in the helix F of one subunit are near the active site of the other subunit. As presented here, when these residues were replaced individually with Ala caused insignificant activity changes, N101A/Y105A and N101A/N104A but not N104A/Y105A showed remarkably decreased k(cat) values (200-250-fold). The triple mutant N101A/N104A/Y105A displayed no detectable activity, although dimer was retained in these mutants. Because N101 and Y105 form H-bonds with H139 and R140 in the other subunit, respectively, we generated H139A/R140A double mutant and found it was inactive and became monomeric. Therefore, the multiple mutations apparently influence the integrity of the catalytic site due to the missing H-bonding network. Moreover, Met111, also on the highly conserved helix F, was necessary for dimer formation and enzyme activity. When Met111 was replaced with Glu, the negative-charged repulsion converted half of the dimer into a monomer. In conclusion, the H-bonds mainly through N101 for maintaining substrate binding stability and the hydrophobic interaction of M111 in dimer interface are essential for activity of yeast GGPPS.

Cellular Ubc2/Rad6 E2 ubiquitin-conjugating enzyme facilitates tombusvirus replication in yeast and plants

International Nuclear Information System (INIS)

Imura, Yoshiyuki; Molho, Melissa; Chuang, Chingkai; Nagy, Peter D.

2015-01-01

Mono- and multi-ubiquitination alters the functions and subcellular localization of many cellular and viral proteins. Viruses can co-opt or actively manipulate the ubiquitin network to support viral processes or suppress innate immunity. Using yeast (Saccharomyces cerevisiae) model host, we show that the yeast Rad6p (radiation sensitive 6) E2 ubiquitin-conjugating enzyme and its plant ortholog, AtUbc2, interact with two tombusviral replication proteins and these E2 ubiquitin-conjugating enzymes could be co-purified with the tombusvirus replicase. We demonstrate that TBSV RNA replication and the mono- and bi-ubiquitination level of p33 is decreased in rad6Δ yeast. However, plasmid-based expression of AtUbc2p could complement both defects in rad6Δ yeast. Knockdown of UBC2 expression in plants also decreases tombusvirus accumulation and reduces symptom severity, suggesting that Ubc2p is critical for virus replication in plants. We provide evidence that Rad6p is involved in promoting the subversion of Vps23p and Vps4p ESCRT proteins for viral replicase complex assembly. - Highlights: • Tombusvirus p33 replication protein interacts with cellular RAD6/Ubc2 E2 enzymes. • Deletion of RAD6 reduces tombusvirus replication in yeast. • Silencing of UBC2 in plants inhibits tombusvirus replication. • Mono- and bi-ubiquitination of p33 replication protein in yeast and in vitro. • Rad6p promotes the recruitment of cellular ESCRT proteins into the tombusvirus replicase

Cellular Ubc2/Rad6 E2 ubiquitin-conjugating enzyme facilitates tombusvirus replication in yeast and plants

Energy Technology Data Exchange (ETDEWEB)

Imura, Yoshiyuki, E-mail: imura@brs.nihon-u.ac.jp; Molho, Melissa; Chuang, Chingkai; Nagy, Peter D., E-mail: pdnagy2@uky.edu

2015-10-15

Mono- and multi-ubiquitination alters the functions and subcellular localization of many cellular and viral proteins. Viruses can co-opt or actively manipulate the ubiquitin network to support viral processes or suppress innate immunity. Using yeast (Saccharomyces cerevisiae) model host, we show that the yeast Rad6p (radiation sensitive 6) E2 ubiquitin-conjugating enzyme and its plant ortholog, AtUbc2, interact with two tombusviral replication proteins and these E2 ubiquitin-conjugating enzymes could be co-purified with the tombusvirus replicase. We demonstrate that TBSV RNA replication and the mono- and bi-ubiquitination level of p33 is decreased in rad6Δ yeast. However, plasmid-based expression of AtUbc2p could complement both defects in rad6Δ yeast. Knockdown of UBC2 expression in plants also decreases tombusvirus accumulation and reduces symptom severity, suggesting that Ubc2p is critical for virus replication in plants. We provide evidence that Rad6p is involved in promoting the subversion of Vps23p and Vps4p ESCRT proteins for viral replicase complex assembly. - Highlights: • Tombusvirus p33 replication protein interacts with cellular RAD6/Ubc2 E2 enzymes. • Deletion of RAD6 reduces tombusvirus replication in yeast. • Silencing of UBC2 in plants inhibits tombusvirus replication. • Mono- and bi-ubiquitination of p33 replication protein in yeast and in vitro. • Rad6p promotes the recruitment of cellular ESCRT proteins into the tombusvirus replicase.

Defaunation leads to interaction deficits, not interaction compensation, in an island seed dispersal network.

Science.gov (United States)

Fricke, Evan C; Tewksbury, Joshua J; Rogers, Haldre S

2018-01-01

Following defaunation, the loss of interactions with mutualists such as pollinators or seed dispersers may be compensated through increased interactions with remaining mutualists, ameliorating the negative cascading impacts on biodiversity. Alternatively, remaining mutualists may respond to altered competition by reducing the breadth or intensity of their interactions, exacerbating negative impacts on biodiversity. Despite the importance of these responses for our understanding of the dynamics of mutualistic networks and their response to global change, the mechanism and magnitude of interaction compensation within real mutualistic networks remains largely unknown. We examined differences in mutualistic interactions between frugivores and fruiting plants in two island ecosystems possessing an intact or disrupted seed dispersal network. We determined how changes in the abundance and behavior of remaining seed dispersers either increased mutualistic interactions (contributing to "interaction compensation") or decreased interactions (causing an "interaction deficit") in the disrupted network. We found a "rich-get-richer" response in the disrupted network, where remaining frugivores favored the plant species with highest interaction frequency, a dynamic that worsened the interaction deficit among plant species with low interaction frequency. Only one of five plant species experienced compensation and the other four had significant interaction deficits, with interaction frequencies 56-95% lower in the disrupted network. These results do not provide support for the strong compensating mechanisms assumed in theoretical network models, suggesting that existing network models underestimate the prevalence of cascading mutualism disruption after defaunation. This work supports a mutualist biodiversity-ecosystem functioning relationship, highlighting the importance of mutualist diversity for sustaining diverse and resilient ecosystems. © 2017 John Wiley & Sons Ltd.

Social Interaction in Learning Networks

NARCIS (Netherlands)

Sloep, Peter

2009-01-01

The original publication is available from www.springerlink.com. Sloep, P. (2009). Social Interaction in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp 13-15). Berlin, Germany: Springer Verlag.

Statistical physics of interacting neural networks

Science.gov (United States)

Kinzel, Wolfgang; Metzler, Richard; Kanter, Ido

2001-12-01

Recent results on the statistical physics of time series generation and prediction are presented. A neural network is trained on quasi-periodic and chaotic sequences and overlaps to the sequence generator as well as the prediction errors are calculated numerically. For each network there exists a sequence for which it completely fails to make predictions. Two interacting networks show a transition to perfect synchronization. A pool of interacting networks shows good coordination in the minority game-a model of competition in a closed market. Finally, as a demonstration, a perceptron predicts bit sequences produced by human beings.

Lager Yeast Comes of Age

Science.gov (United States)

2014-01-01

Alcoholic fermentations have accompanied human civilizations throughout our history. Lager yeasts have a several-century-long tradition of providing fresh beer with clean taste. The yeast strains used for lager beer fermentation have long been recognized as hybrids between two Saccharomyces species. We summarize the initial findings on this hybrid nature, the genomics/transcriptomics of lager yeasts, and established targets of strain improvements. Next-generation sequencing has provided fast access to yeast genomes. Its use in population genomics has uncovered many more hybridization events within Saccharomyces species, so that lager yeast hybrids are no longer the exception from the rule. These findings have led us to propose network evolution within Saccharomyces species. This “web of life” recognizes the ability of closely related species to exchange DNA and thus drain from a combined gene pool rather than be limited to a gene pool restricted by speciation. Within the domesticated lager yeasts, two groups, the Saaz and Frohberg groups, can be distinguished based on fermentation characteristics. Recent evidence suggests that these groups share an evolutionary history. We thus propose to refer to the Saaz group as Saccharomyces carlsbergensis and to the Frohberg group as Saccharomyces pastorianus based on their distinct genomes. New insight into the hybrid nature of lager yeast will provide novel directions for future strain improvement. PMID:25084862

[Identification of C(2)M interacting proteins by yeast two-hybrid screening].

Science.gov (United States)

Yue, Shan-shan; Xia, Lai-xin

2015-11-01

The synaptonemal complex (SC) is a huge structure which assembles between the homologous chromosomes during meiotic prophase I. Drosophila germ cell-specific nucleoprotein C(2)M clustering at chromosomes can induce SC formation. To further study the molecular function and mechanism of C(2)M in meiosis, we constructed a bait vector for C(2)M and used the yeast two-hybrid system to identify C(2)M interacting proteins. Forty interacting proteins were obtained, including many DNA and histone binding proteins, ATP synthases and transcription factors. Gene silencing assays in Drosophila showed that two genes, wech and Psf1, may delay the disappearance of SC. These results indicate that Wech and Psf1 may form a complex with C(2)M to participate in the formation or stabilization of the SC complex.

Yeast diversity and native vigor for flavor phenotypes.

Science.gov (United States)

Carrau, Francisco; Gaggero, Carina; Aguilar, Pablo S

2015-03-01

Saccharomyces cerevisiae, the yeast used widely for beer, bread, cider, and wine production, is the most resourceful eukaryotic model used for genetic engineering. A typical concern about using engineered yeasts for food production might be negative consumer perception of genetically modified organisms. However, we believe the true pitfall of using genetically modified yeasts is their limited capacity to either refine or improve the sensory properties of fermented foods under real production conditions. Alternatively, yeast diversity screening to improve the aroma and flavors could offer groundbreaking opportunities in food biotechnology. We propose a 'Yeast Flavor Diversity Screening' strategy which integrates knowledge from sensory analysis and natural whole-genome evolution with information about flavor metabolic networks and their regulation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Topology-function conservation in protein-protein interaction networks.

Science.gov (United States)

Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

2015-05-15

Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

Specific non-monotonous interactions increase persistence of ecological networks.

Science.gov (United States)

Yan, Chuan; Zhang, Zhibin

2014-03-22

The relationship between stability and biodiversity has long been debated in ecology due to opposing empirical observations and theoretical predictions. Species interaction strength is often assumed to be monotonically related to population density, but the effects on stability of ecological networks of non-monotonous interactions that change signs have not been investigated previously. We demonstrate that for four kinds of non-monotonous interactions, shifting signs to negative or neutral interactions at high population density increases persistence (a measure of stability) of ecological networks, while for the other two kinds of non-monotonous interactions shifting signs to positive interactions at high population density decreases persistence of networks. Our results reveal a novel mechanism of network stabilization caused by specific non-monotonous interaction types through either increasing stable equilibrium points or reducing unstable equilibrium points (or both). These specific non-monotonous interactions may be important in maintaining stable and complex ecological networks, as well as other networks such as genes, neurons, the internet and human societies.

An integrated approach to elucidate the intra-viral and viral-cellular protein interaction networks of a gamma-herpesvirus.

Directory of Open Access Journals (Sweden)

Shaoying Lee

2011-10-01

Full Text Available Genome-wide yeast two-hybrid (Y2H screens were conducted to elucidate the molecular functions of open reading frames (ORFs encoded by murine γ-herpesvirus 68 (MHV-68. A library of 84 MHV-68 genes and gene fragments was generated in a Gateway entry plasmid and transferred to Y2H vectors. All possible pair-wise interactions between viral proteins were tested in the Y2H assay, resulting in the identification of 23 intra-viral protein-protein interactions (PPIs. Seventy percent of the interactions between viral proteins were confirmed by co-immunoprecipitation experiments. To systematically investigate virus-cellular protein interactions, the MHV-68 Y2H constructs were screened against a cellular cDNA library, yielding 243 viral-cellular PPIs involving 197 distinct cellar proteins. Network analyses indicated that cellular proteins targeted by MHV-68 had more partners in the cellular PPI network and were located closer to each other than expected by chance. Taking advantage of this observation, we scored the cellular proteins based on their network distances from other MHV-68-interacting proteins and segregated them into high (Y2H-HP and low priority/not-scored (Y2H-LP/NS groups. Significantly more genes from Y2H-HP altered MHV-68 replication when their expression was inhibited with siRNAs (53% of genes from Y2H-HP, 21% of genes from Y2H-LP/NS, and 16% of genes randomly chosen from the human PPI network; p<0.05. Enriched Gene Ontology (GO terms in the Y2H-HP group included regulation of apoptosis, protein kinase cascade, post-translational protein modification, transcription from RNA polymerase II promoter, and IκB kinase/NFκB cascade. Functional validation assays indicated that PCBP1, which interacted with MHV-68 ORF34, may be involved in regulating late virus gene expression in a manner consistent with the effects of its viral interacting partner. Our study integrated Y2H screening with multiple functional validation approaches to create �

Evolution of a protein domain interaction network

International Nuclear Information System (INIS)

Li-Feng, Gao; Jian-Jun, Shi; Shan, Guan

2010-01-01

In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship between biological organisms. Firstly, we construct a Pfam domain interaction network for each of the 470 completely sequenced organisms, and therefore each organism is correlated with a specific Pfam domain interaction network; secondly, we infer the evolutionary relationship of these organisms with the nearest neighbour joining method; thirdly, we use the evolutionary relationship between organisms constructed in the second step as the evolutionary course of the Pfam domain interaction network constructed in the first step. This analysis of the evolutionary course shows: (i) there is a conserved sub-network structure in network evolution; in this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as a hub is attached preferentially to new added nodes; (ii) few nodes are conserved as hubs; most of the other nodes are conserved as one with very low degree; (iii) in the course of network evolution, new nodes are added to the network either individually in most cases or as clusters with relative high clustering coefficients in a very few cases. (general)

Topology of molecular interaction networks

NARCIS (Netherlands)

Winterbach, W.; Van Mieghem, P.; Reinders, M.; Wang, H.; De Ridder, D.

2013-01-01

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over

The control of translational accuracy is a determinant of healthy ageing in yeast.

Science.gov (United States)

von der Haar, Tobias; Leadsham, Jane E; Sauvadet, Aimie; Tarrant, Daniel; Adam, Ilectra S; Saromi, Kofo; Laun, Peter; Rinnerthaler, Mark; Breitenbach-Koller, Hannelore; Breitenbach, Michael; Tuite, Mick F; Gourlay, Campbell W

2017-01-01

Life requires the maintenance of molecular function in the face of stochastic processes that tend to adversely affect macromolecular integrity. This is particularly relevant during ageing, as many cellular functions decline with age, including growth, mitochondrial function and energy metabolism. Protein synthesis must deliver functional proteins at all times, implying that the effects of protein synthesis errors like amino acid misincorporation and stop-codon read-through must be minimized during ageing. Here we show that loss of translational accuracy accelerates the loss of viability in stationary phase yeast. Since reduced translational accuracy also reduces the folding competence of at least some proteins, we hypothesize that negative interactions between translational errors and age-related protein damage together overwhelm the cellular chaperone network. We further show that multiple cellular signalling networks control basal error rates in yeast cells, including a ROS signal controlled by mitochondrial activity, and the Ras pathway. Together, our findings indicate that signalling pathways regulating growth, protein homeostasis and energy metabolism may jointly safeguard accurate protein synthesis during healthy ageing. © 2017 The Authors.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses.

Science.gov (United States)

Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

2018-01-01

Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL ( MeCBL ) and 26 CIPK ( MeCIPK ) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBL s and CIPK s. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10 , and Na + /H + antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses

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Chunyan Mo

2018-03-01

Full Text Available Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL protein and CBL-interacting protein kinase (CIPK is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL (MeCBL and 26 CIPK (MeCIPK genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBLs and CIPKs. Reverse-transcriptase polymerase chain reaction (RT-PCR analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR, and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10, and Na+/H+ antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

Catalytic site interactions in yeast OMP synthase

DEFF Research Database (Denmark)

Hansen, Michael Riis; Barr, Eric W.; Jensen, Kaj Frank

2014-01-01

45 (2006) 5330-5342]. This behavior was investigated in the yeast enzyme by mutations in the conserved catalytic loop and 5-phosphoribosyl-1-diphosphate (PRPP) binding motif. Although the reaction is mechanistically sequential, the wild-type (WT) enzyme shows parallel lines in double reciprocal...

The role of the Parkinson's disease gene PARK9 in essential cellular pathways and the manganese homeostasis network in yeast.

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Alessandra Chesi

Full Text Available YPK9 (Yeast PARK9; also known as YOR291W is a non-essential yeast gene predicted by sequence to encode a transmembrane P-type transport ATPase. However, its substrate specificity is unknown. Mutations in the human homolog of YPK9, ATP13A2/PARK9, have been linked to genetic forms of early onset parkinsonism. We previously described a strong genetic interaction between Ypk9 and another Parkinson's disease (PD protein α-synuclein in multiple model systems, and a role for Ypk9 in manganese detoxification in yeast. In humans, environmental exposure to toxic levels of manganese causes a syndrome similar to PD and is thus an environmental risk factor for the disease. How manganese contributes to neurodegeneration is poorly understood. Here we describe multiple genome-wide screens in yeast aimed at defining the cellular function of Ypk9 and the mechanisms by which it protects cells from manganese toxicity. In physiological conditions, we found that Ypk9 genetically interacts with essential genes involved in cellular trafficking and the cell cycle. Deletion of Ypk9 sensitizes yeast cells to exposure to excess manganese. Using a library of non-essential gene deletions, we screened for additional genes involved in tolerance to excess manganese exposure, discovering several novel pathways involved in manganese homeostasis. We defined the dependence of the deletion strain phenotypes in the presence of manganese on Ypk9, and found that Ypk9 deletion modifies the manganese tolerance of only a subset of strains. These results confirm a role for Ypk9 in manganese homeostasis and illuminates cellular pathways and biological processes in which Ypk9 likely functions.

Interaction of human laminin receptor with Sup35, the [PSI⁺] prion-forming protein from S. cerevisiae: a yeast model for studies of LamR interactions with amyloidogenic proteins.

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Christine Pampeno

Full Text Available The laminin receptor (LamR is a cell surface receptor for extracellular matrix laminin, whereas the same protein within the cell interacts with ribosomes, nuclear proteins and cytoskeletal fibers. LamR has been shown to be a receptor for several bacteria and viruses. Furthermore, LamR interacts with both cellular and infectious forms of the prion protein, PrP(C and PrP(Sc. Indeed, LamR is a receptor for PrP(C. Whether LamR interacts with PrP(Sc exclusively in a capacity of the PrP receptor, or LamR specifically recognizes prion determinants of PrP(Sc, is unclear. In order to explore whether LamR has a propensity to interact with prions and amyloids, we examined LamR interaction with the yeast prion-forming protein, Sup35. Sup35 is a translation termination factor with no homology or functional relationship to PrP. Plasmids expressing LamR or LamR fused with the green fluorescent protein (GFP were transformed into yeast strain variants differing by the presence or absence of the prion conformation of Sup35, respectively [PSI⁺] and [psi⁻]. Analyses by immunoprecipitation, centrifugal fractionation and fluorescent microscopy reveal interaction between LamR and Sup35 in [PSI⁺] strains. The presence of [PSI⁺] promotes LamR co-precipitation with Sup35 as well as LamR aggregation. In [PSI⁺] cells, LamR tagged with GFP or mCherry forms bright fluorescent aggregates that co-localize with visible [PSI⁺] foci. The yeast prion model will facilitate studying the interaction of LamR with amyloidogenic prions in a safe and easily manipulated system that may lead to a better understanding and treatment of amyloid diseases.

Interaction Networks: Generating High Level Hints Based on Network Community Clustering

Science.gov (United States)

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, Yarrowia lipolytica.

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Pengcheng Pan

Full Text Available With the emergence of energy scarcity, the use of renewable energy sources such as biodiesel is becoming increasingly necessary. Recently, many researchers have focused their minds on Yarrowia lipolytica, a model oleaginous yeast, which can be employed to accumulate large amounts of lipids that could be further converted to biodiesel. In order to understand the metabolic characteristics of Y. lipolytica at a systems level and to examine the potential for enhanced lipid production, a genome-scale compartmentalized metabolic network was reconstructed based on a combination of genome annotation and the detailed biochemical knowledge from multiple databases such as KEGG, ENZYME and BIGG. The information about protein and reaction associations of all the organisms in KEGG and Expasy-ENZYME database was arranged into an EXCEL file that can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for 619 genes, 843 metabolites and 1,142 reactions including 236 transport reactions, 125 exchange reactions and 13 spontaneous reactions. The in silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, single gene knockouts were also simulated to predict the essential genes and partially essential genes. In addition, flux variability analysis was applied to design new mutant strains that will redirect fluxes through the network and may enhance the production of lipid. This genome-scale metabolic model of Y. lipolytica can facilitate system-level metabolic analysis as well as strain development for improving the production of biodiesels and other valuable products by Y. lipolytica and other closely related oleaginous yeasts.

A Method to Design Synthetic Cell-Cycle Networks

International Nuclear Information System (INIS)

Ke-Ke, Miao

2009-01-01

The interactions among proteins, DNA and RNA in an organism form elaborate cell-cycle networks which govern cell growth and proliferation. Understanding the common structure of cell-cycle networks will be of great benefit to science research. Here, inspired by the importance of the cell-cycle regulatory network of yeast which has been studied intensively, we focus on small networks with 11 nodes, equivalent to that of the cell-cycle regulatory network used by Li et al. [Proc. Natl. Acad. Sci. USA 101(2004)4781] Using a Boolean model, we study the correlation between structure and function, and a possible common structure. It is found that cascade-like networks with a great number of interactions between nodes are stable. Based on these findings, we are able to construct synthetic networks that have the same functions as the cell-cycle regulatory network. (condensed matter: structure, mechanical and thermal properties)

Structure of the human chromosome interaction network.

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Sergio Sarnataro

Full Text Available New Hi-C technologies have revealed that chromosomes have a complex network of spatial contacts in the cell nucleus of higher organisms, whose organisation is only partially understood. Here, we investigate the structure of such a network in human GM12878 cells, to derive a large scale picture of nuclear architecture. We find that the intensity of intra-chromosomal interactions is power-law distributed. Inter-chromosomal interactions are two orders of magnitude weaker and exponentially distributed, yet they are not randomly arranged along the genomic sequence. Intra-chromosomal contacts broadly occur between epigenomically homologous regions, whereas inter-chromosomal contacts are especially associated with regions rich in highly expressed genes. Overall, genomic contacts in the nucleus appear to be structured as a network of networks where a set of strongly individual chromosomal units, as envisaged in the 'chromosomal territory' scenario derived from microscopy, interact with each other via on average weaker, yet far from random and functionally important interactions.

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

Exploring Protein Interactions on a Minimal Type II Polyketide Synthase Using a Yeast Two-Hybrid System

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Gaetano Castaldo

2005-01-01

Full Text Available Interactions between proteins that form the ’minimal’ type II polyketide synthase in the doxorubicin producing biosynthetic pathway from Streptomyces peucetius were investigated using a yeast two-hybrid system (Y2H. Proteins that function as the so called ’chain length factor’ (DpsB and putative transacylase (DpsD were found to interact with the ketosynthase subunit (DpsA, which can also interact with itself. On the basis of these results we propose a head-to-tail homodimeric structure, which is consistent with previously published in vivo mutagenesis studies. No interactions were found between the acyl-carrier protein (DpsG and any of the other constituents of the complex, however, transient interactions, not detectable using the Y2H system, cannot be discounted and warrant further investigation.

Inferring the effective TOR-dependent network: a computational study in yeast.

Science.gov (United States)

Mohammadi, Shahin; Subramaniam, Shankar; Grama, Ananth

2013-08-30

Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR. In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies.Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition. Our approach, unlike experimental methods, is not limited to specific aspects of cellular response. Rather, it predicts transcriptional

Utilizing Biotinylated Proteins Expressed in Yeast to Visualize DNA–Protein Interactions at the Single-Molecule Level

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Huijun Xue

2017-10-01

Full Text Available Much of our knowledge in conventional biochemistry has derived from bulk assays. However, many stochastic processes and transient intermediates are hidden when averaged over the ensemble. The powerful technique of single-molecule fluorescence microscopy has made great contributions to the understanding of life processes that are inaccessible when using traditional approaches. In single-molecule studies, quantum dots (Qdots have several unique advantages over other fluorescent probes, such as high brightness, extremely high photostability, and large Stokes shift, thus allowing long-time observation and improved signal-to-noise ratios. So far, however, there is no convenient way to label proteins purified from budding yeast with Qdots. Based on BirA–Avi and biotin–streptavidin systems, we have established a simple method to acquire a Qdot-labeled protein and visualize its interaction with DNA using total internal reflection fluorescence microscopy. For proof-of-concept, we chose replication protein A (RPA and origin recognition complex (ORC as the proteins of interest. Proteins were purified from budding yeast with high biotinylation efficiency and rapidly labeled with streptavidin-coated Qdots. Interactions between proteins and DNA were observed successfully at the single-molecule level.

Interactions between yeast lees and wine polyphenols during simulation of wine aging. II. Analysis of desorbed polyphenol compounds from yeast lees.

Science.gov (United States)

Mazauric, Jean-Paul; Salmon, Jean-Michel

2006-05-31

In the first part of this work, the analysis of the polyphenolic compounds remaining in the wine after different contact times with yeast lees during simulation of red wine aging was undertaken. To achieve a more precise view of the wine polyphenols adsorbed on lees during red wine aging and to establish a clear balance between adsorbed and remnant polyphenol compounds, the specific analysis of the chemical composition of the adsorbed polyphenolic compounds (condensed tannins and anthocyanins) after their partial desorbtion from yeast lees by denaturation treatments was realized in the second part of the study. The total recovery of polyphenol compounds from yeast lees was not complete, since a rather important part of the initial wine colored polyphenols, especially those with a dominant blue color component, remained strongly adsorbed on yeast lees, as monitored by color tristimulus and reflectance spectra measurements. All anthocyanins were recovered at a rather high percentage (about 62%), and it was demonstrated that they were not adsorbed in relation with their sole polarity. Very few monomeric phenolic compounds were extracted from yeast lees. With the use of drastic denaturing treatments, the total recovery of condensed tannins reached 83%. Such tannins extracted from yeast lees exhibited very high polymeric size and a rather high percentage of galloylated residues by comparison with initial wine tannins, indicating that nonpolar tannins were preferentially desorbed from yeast lees by the extraction treatments.

Do networks of social interactions reflect patterns of kinship?

Institute of Scientific and Technical Information of China (English)

Joah R. MADDEN; Johanna F. NIEL SEN; Tim H. CLUTTON-BROCK

2012-01-01

The underlying kin structure of groups of animals may be glimpsed from patterns of spatial position or temporal association between individuals,and is presumed to facilitate inclusive fitness benefits.Such structure may be evident at a finer,behavioural,scale with individuals preferentially interacting with kin.We tested whether kin structure within groups of meerkats Suricata suricatta matched three forms of social interaction networks:grooming,dominance or foraging competitions.Networks of dominance interactions were positively related to networks of kinship,with close relatives engaging in dominance interactions with each other.This relationship persisted even after excluding the breeding dominant pair and when we restricted the kinship network to only include links between first order kin,which are most likely to be able to discern kin through simple rules of thumb.Conversely,we found no relationship between kinship networks and either grooming networks or networks of foraging competitions.This is surprising because a positive association between kin in a grooming network,or a negative association between kin in a network of foraging competitions offers opportunities for inclusive fitness benefits.Indeed,the positive association between kin in a network of dominance interactions that we did detect does not offer clear inclusive fitness benefits to group members.We conclude that kin structure in behavioural interactions in meerkats may be driven by factors other than indirect fitness benefits,and that networks of cooperative behaviours such as grooming may be driven by direct benefits accruing to individuals perhaps through mutualism or manipulation [Current Zoology 58 (2):319-328,2012].

Do networks of social interactions reflect patterns of kinship?

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Joah R. MADDEN, Johanna F. NIELSEN, Tim H. CLUTTON-BROCK

2012-04-01

Full Text Available The underlying kin structure of groups of animals may be glimpsed from patterns of spatial position or temporal association between individuals, and is presumed to facilitate inclusive fitness benefits. Such structure may be evident at a finer, behavioural, scale with individuals preferentially interacting with kin. We tested whether kin structure within groups of meerkats Suricata suricatta matched three forms of social interaction networks: grooming, dominance or foraging competitions. Networks of dominance interactions were positively related to networks of kinship, with close relatives engaging in dominance interactions with each other. This relationship persisted even after excluding the breeding dominant pair and when we restricted the kinship network to only include links between first order kin, which are most likely to be able to discern kin through simple rules of thumb. Conversely, we found no relationship between kinship networks and either grooming networks or networks of foraging competitions. This is surprising because a positive association between kin in a grooming network, or a negative association between kin in a network of foraging competitions offers opportunities for inclusive fitness benefits. Indeed, the positive association between kin in a network of dominance interactions that we did detect does not offer clear inclusive fitness benefits to group members. We conclude that kin structure in behavioural interactions in meerkats may be driven by factors other than indirect fitness benefits, and that networks of cooperative behaviours such as grooming may be driven by direct benefits accruing to individuals perhaps through mutualism or manipulation [Current Zoology 58 (2: 319-328, 2012].

Functional Interaction Network Construction and Analysis for Disease Discovery.

Science.gov (United States)

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Yeast lipids can phase separate into micrometer-scale membrane domains

DEFF Research Database (Denmark)

Klose, Christian; Ejsing, Christer S; Garcia-Saez, Ana J

2010-01-01

The lipid raft concept proposes that biological membranes have the potential to form functional domains based on a selective interaction between sphingolipids and sterols. These domains seem to be involved in signal transduction and vesicular sorting of proteins and lipids. Although there is bioc......The lipid raft concept proposes that biological membranes have the potential to form functional domains based on a selective interaction between sphingolipids and sterols. These domains seem to be involved in signal transduction and vesicular sorting of proteins and lipids. Although...... there is biochemical evidence for lipid raft-dependent protein and lipid sorting in the yeast Saccharomyces cerevisiae, direct evidence for an interaction between yeast sphingolipids and the yeast sterol ergosterol, resulting in membrane domain formation, is lacking. Here we show that model membranes formed from yeast...... total lipid extracts possess an inherent self-organization potential resulting in Ld-Lo phase coexistence at physiologically relevant temperature. Analyses of lipid extracts from mutants defective in sphingolipid metabolism as well as reconstitution of purified yeast lipids in model membranes of defined...

Global patterns of interaction specialization in bird-flower networks

DEFF Research Database (Denmark)

Zanata, Thais B.; Dalsgaard, Bo; Passos, Fernando C.

2017-01-01

, such as plant species richness, asymmetry, latitude, insularity, topography, sampling methods and intensity. Results: Hummingbird–flower networks were more specialized than honeyeater–flower networks. Specifically, hummingbird–flower networks had a lower proportion of realized interactions (lower C), decreased...... in the interaction patterns with their floral resources. Location: Americas, Africa, Asia and Oceania/Australia. Methods: We compiled interaction networks between birds and floral resources for 79 hummingbird, nine sunbird and 33 honeyeater communities. Interaction specialization was quantified through connectance...... (C), complementary specialization (H2′), binary (QB) and weighted modularity (Q), with both observed and null-model corrected values. We compared interaction specialization among the three types of bird–flower communities, both independently and while controlling for potential confounding variables...

Interactive Network Exploration with Orange

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Miha Štajdohar

2013-04-01

Full Text Available Network analysis is one of the most widely used techniques in many areas of modern science. Most existing tools for that purpose are limited to drawing networks and computing their basic general characteristics. The user is not able to interactively and graphically manipulate the networks, select and explore subgraphs using other statistical and data mining techniques, add and plot various other data within the graph, and so on. In this paper we present a tool that addresses these challenges, an add-on for exploration of networks within the general component-based environment Orange.

A conserved mammalian protein interaction network.

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Åsa Pérez-Bercoff

Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

Drug-domain interaction networks in myocardial infarction.

Science.gov (United States)

Wang, Haiying; Zheng, Huiru; Azuaje, Francisco; Zhao, Xing-Ming

2013-09-01

It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p drugs, their known targets, and seemingly unrelated proteins can be revealed.

Specificity and evolvability in eukaryotic protein interaction networks.

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Pedro Beltrao

2007-02-01

Full Text Available Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5 interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3 interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

Enhancing the Functional Content of Eukaryotic Protein Interaction Networks

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Pandey, Gaurav; Arora, Sonali; Manocha, Sahil; Whalen, Sean

2014-01-01

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks. PMID:25275489

Construction of ontology augmented networks for protein complex prediction.

Science.gov (United States)

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Structural stability of interaction networks against negative external fields

Science.gov (United States)

Yoon, S.; Goltsev, A. V.; Mendes, J. F. F.

2018-04-01

We explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. We study how the agents support the activity of each other to confront the negative field, which suppresses the activity of agents and can lead to collapse of the whole network. The competition between the interactions and the field shape the structure of stable states of the system. In unweighted networks (uniform interactions) the stable states have the structure of k -cores of the interaction network. The interplay between the topology and the distribution of weights (heterogeneous interactions) impacts strongly the structural stability against a negative field, especially in the case of fat-tailed distributions of weights. We show that apart from critical slowing down there is also a critical change in the system structure that precedes the network collapse. The change can serve as an early warning of the critical transition. To characterize changes of network structure we develop a method based on statistical analysis of the k -core organization and so-called "corona" clusters belonging to the k -cores.

Identification of brain-specific angiogenesis inhibitor 2 as an interaction partner of glutaminase interacting protein

International Nuclear Information System (INIS)

Zencir, Sevil; Ovee, Mohiuddin; Dobson, Melanie J.; Banerjee, Monimoy; Topcu, Zeki; Mohanty, Smita

2011-01-01

Highlights: → Brain-specific angiogenesis inhibitor 2 (BAI2) is a new partner protein for GIP. → BAI2 interaction with GIP was revealed by yeast two-hybrid assay. → Binding of BAI2 to GIP was characterized by NMR, CD and fluorescence. → BAI2 and GIP binding was mediated through the C-terminus of BAI2. -- Abstract: The vast majority of physiological processes in living cells are mediated by protein-protein interactions often specified by particular protein sequence motifs. PDZ domains, composed of 80-100 amino acid residues, are an important class of interaction motif. Among the PDZ-containing proteins, glutaminase interacting protein (GIP), also known as Tax Interacting Protein TIP-1, is unique in being composed almost exclusively of a single PDZ domain. GIP has important roles in cellular signaling, protein scaffolding and modulation of tumor growth and interacts with a number of physiological partner proteins, including Glutaminase L, β-Catenin, FAS, HTLV-1 Tax, HPV16 E6, Rhotekin and Kir 2.3. To identify the network of proteins that interact with GIP, a human fetal brain cDNA library was screened using a yeast two-hybrid assay with GIP as bait. We identified brain-specific angiogenesis inhibitor 2 (BAI2), a member of the adhesion-G protein-coupled receptors (GPCRs), as a new partner of GIP. BAI2 is expressed primarily in neurons, further expanding GIP cellular functions. The interaction between GIP and the carboxy-terminus of BAI2 was characterized using fluorescence, circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy assays. These biophysical analyses support the interaction identified in the yeast two-hybrid assay. This is the first study reporting BAI2 as an interaction partner of GIP.

Investigating physics learning with layered student interaction networks

DEFF Research Database (Denmark)

Bruun, Jesper; Traxler, Adrienne

Centrality in student interaction networks (SINs) can be linked to variables like grades [1], persistence [2], and participation [3]. Recent efforts in the field of network science have been done to investigate layered - or multiplex - networks as mathematical objects [4]. These networks can be e......, this study investigates how target entropy [5,1] and pagerank [6,7] are affected when we take time and modes of interaction into account. We present our preliminary models and results and outline our future work in this area....

Predicting and validating protein interactions using network structure.

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Pao-Yang Chen

2008-07-01

Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

Multiple genetic interaction experiments provide complementary information useful for gene function prediction.

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Magali Michaut

Full Text Available Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.

Copula-based modeling of degree-correlated networks

International Nuclear Information System (INIS)

Raschke, Mathias; Schläpfer, Markus; Trantopoulos, Konstantinos

2014-01-01

Dynamical processes on complex networks such as information exchange, innovation diffusion, cascades in financial networks or epidemic spreading are highly affected by their underlying topologies as characterized by, for instance, degree–degree correlations. Here, we introduce the concept of copulas in order to generate random networks with an arbitrary degree distribution and a rich a priori degree–degree correlation (or ‘association’) structure. The accuracy of the proposed formalism and corresponding algorithm is numerically confirmed, while the method is tested on a real-world network of yeast protein–protein interactions. The derived network ensembles can be systematically deployed as proper null models, in order to unfold the complex interplay between the topology of real-world networks and the dynamics on top of them. (paper)

Interactions Between Industrial Yeasts and Chemical Contaminants in Grape Juice Affect Wine Composition Profile

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Etjen Bizaj

2014-01-01

Full Text Available The interaction between four industrial wine yeast strains and grape juice chemical contaminants during alcoholic fermentation was studied. Industrial strains of Saccharomyces cerevisiae (AWRI 0838, S. cerevisiae mutant with low H2S production phenotype (AWRI 1640, interspecies hybrid of S. cerevisiae and S. kudriavzevii (AWRI 1539 and a hybrid of AWRI 1640 and AWRI 1539 (AWRI 1810 were exposed separately to fungicides pyrimethanil (Pyr, 10 mg/L and fenhexamid (Fhx, 10 mg/L, as well as to the most common toxin produced by moulds on grapes, ochratoxin A (OTA, 5 μg/L, during alcoholic fermentation of Vitis vinifera L. cv. Sauvignon blanc juice. Contaminants were found to strongly impair fermentation performance and metabolic activity of all yeast strains studied. The chemical profile of wine was analyzed by HPLC (volatile acidity, concentrations of ethanol, fructose, glucose, glycerol and organic acids and the aromatic profile was analyzed using a stable isotope dilution technique using GC/MS (ethyl esters, acetates and aromatic alcohols and Kitagawa tubes (H2S. The chemical composition of wine with added contaminants was in all cases significantly different from the control. Of particular note is that the quantity of aromatic compounds produced by yeast was significantly lower. Yeast’s capacity to remove contaminants from wine at the end of the alcoholic fermentation, and after extended contact (7 days was determined. All the strains were able to remove contaminants from the media, moreover, after extended contact, the concentration of contaminants was in most cases lower.

Evidence of probabilistic behaviour in protein interaction networks

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Reifman Jaques

2008-01-01

Full Text Available Abstract Background Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. Results We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. Conclusion The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

Protein interaction networks by proteome peptide scanning.

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Christiane Landgraf

2004-01-01

Full Text Available A substantial proportion of protein interactions relies on small domains binding to short peptides in the partner proteins. Many of these interactions are relatively low affinity and transient, and they impact on signal transduction. However, neither the number of potential interactions mediated by each domain nor the degree of promiscuity at a whole proteome level has been investigated. We have used a combination of phage display and SPOT synthesis to discover all the peptides in the yeast proteome that have the potential to bind to eight SH3 domains. We first identified the peptides that match a relaxed consensus, as deduced from peptides selected by phage display experiments. Next, we synthesized all the matching peptides at high density on a cellulose membrane, and we probed them directly with the SH3 domains. The domains that we have studied were grouped by this approach into five classes with partially overlapping specificity. Within the classes, however, the domains display a high promiscuity and bind to a large number of common targets with comparable affinity. We estimate that the yeast proteome contains as few as six peptides that bind to the Abp1 SH3 domain with a dissociation constant lower than 100 microM, while it contains as many as 50-80 peptides with corresponding affinity for the SH3 domain of Yfr024c. All the targets of the Abp1 SH3 domain, identified by this approach, bind to the native protein in vivo, as shown by coimmunoprecipitation experiments. Finally, we demonstrate that this strategy can be extended to the analysis of the entire human proteome. We have developed an approach, named WISE (whole interactome scanning experiment, that permits rapid and reliable identification of the partners of any peptide recognition module by peptide scanning of a proteome. Since the SPOT synthesis approach is semiquantitative and provides an approximation of the dissociation constants of the several thousands of interactions that are

Generation and comprehensive analysis of an influenza virus polymerase cellular interaction network.

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Tafforeau, Lionel; Chantier, Thibault; Pradezynski, Fabrine; Pellet, Johann; Mangeot, Philippe E; Vidalain, Pierre-Olivier; Andre, Patrice; Rabourdin-Combe, Chantal; Lotteau, Vincent

2011-12-01

The influenza virus transcribes and replicates its genome inside the nucleus of infected cells. Both activities are performed by the viral RNA-dependent RNA polymerase that is composed of the three subunits PA, PB1, and PB2, and recent studies have shown that it requires host cell factors to transcribe and replicate the viral genome. To identify these cellular partners, we generated a comprehensive physical interaction map between each polymerase subunit and the host cellular proteome. A total of 109 human interactors were identified by yeast two-hybrid screens, whereas 90 were retrieved by literature mining. We built the FluPol interactome network composed of the influenza virus polymerase (PA, PB1, and PB2) and the nucleoprotein NP and 234 human proteins that are connected through 279 viral-cellular protein interactions. Analysis of this interactome map revealed enriched cellular functions associated with the influenza virus polymerase, including host factors involved in RNA polymerase II-dependent transcription and mRNA processing. We confirmed that eight influenza virus polymerase-interacting proteins are required for virus replication and transcriptional activity of the viral polymerase. These are involved in cellular transcription (C14orf166, COPS5, MNAT1, NMI, and POLR2A), translation (EIF3S6IP), nuclear transport (NUP54), and DNA repair (FANCG). Conversely, we identified PRKRA, which acts as an inhibitor of the viral polymerase transcriptional activity and thus is required for the cellular antiviral response.

Prediction and characterization of protein-protein interaction networks in swine

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Wang Fen

2012-01-01

Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae.

Science.gov (United States)

Suzuki, Godai; Wang, Yang; Kubo, Karen; Hirata, Eri; Ohnuki, Shinsuke; Ohya, Yoshikazu

2018-02-20

The size of the phenotypic effect of a gene has been thoroughly investigated in terms of fitness and specific morphological traits in the budding yeast Saccharomyces cerevisiae, but little is known about gross morphological abnormalities. We identified 1126 holistic morphological effectors that cause severe gross morphological abnormality when deleted, and 2241 specific morphological effectors with weak holistic effects but distinctive effects on yeast morphology. Holistic effectors fell into many gene function categories and acted as network hubs, affecting a large number of morphological traits, interacting with a large number of genes, and facilitating high protein expression. Holistic morphological abnormality was useful for estimating the importance of a gene to morphology. The contribution of gene importance to fitness and morphology could be used to efficiently classify genes into functional groups. Holistic morphological abnormality can be used as a reproducible and reliable gene feature for high-dimensional morphological phenotyping. It can be used in many functional genomic applications.

Building a glaucoma interaction network using a text mining approach.

Science.gov (United States)

Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

2016-01-01

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of

Default network modulation and large-scale network interactivity in healthy young and old adults.

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Spreng, R Nathan; Schacter, Daniel L

2012-11-01

We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands.

Human cancer protein-protein interaction network: a structural perspective.

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Gozde Kar

2009-12-01

Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

Large scale genotype comparison of human papillomavirus E2-host interaction networks provides new insights for e2 molecular functions.

Science.gov (United States)

Muller, Mandy; Jacob, Yves; Jones, Louis; Weiss, Amélie; Brino, Laurent; Chantier, Thibault; Lotteau, Vincent; Favre, Michel; Demeret, Caroline

2012-01-01

Human Papillomaviruses (HPV) cause widespread infections in humans, resulting in latent infections or diseases ranging from benign hyperplasia to cancers. HPV-induced pathologies result from complex interplays between viral proteins and the host proteome. Given the major public health concern due to HPV-associated cancers, most studies have focused on the early proteins expressed by HPV genotypes with high oncogenic potential (designated high-risk HPV or HR-HPV). To advance the global understanding of HPV pathogenesis, we mapped the virus/host interaction networks of the E2 regulatory protein from 12 genotypes representative of the range of HPV pathogenicity. Large-scale identification of E2-interaction partners was performed by yeast two-hybrid screenings of a HaCaT cDNA library. Based on a high-confidence scoring scheme, a subset of these partners was then validated for pair-wise interaction in mammalian cells with the whole range of the 12 E2 proteins, allowing a comparative interaction analysis. Hierarchical clustering of E2-host interaction profiles mostly recapitulated HPV phylogeny and provides clues to the involvement of E2 in HPV infection. A set of cellular proteins could thus be identified discriminating, among the mucosal HPV, E2 proteins of HR-HPV 16 or 18 from the non-oncogenic genital HPV. The study of the interaction networks revealed a preferential hijacking of highly connected cellular proteins and the targeting of several functional families. These include transcription regulation, regulation of apoptosis, RNA processing, ubiquitination and intracellular trafficking. The present work provides an overview of E2 biological functions across multiple HPV genotypes.

Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

Energy Technology Data Exchange (ETDEWEB)

Shi, Cindy

2015-07-17

The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

Empirical evaluation of neutral interactions in host-parasite networks.

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Canard, E F; Mouquet, N; Mouillot, D; Stanko, M; Miklisova, D; Gravel, D

2014-04-01

While niche-based processes have been invoked extensively to explain the structure of interaction networks, recent studies propose that neutrality could also be of great importance. Under the neutral hypothesis, network structure would simply emerge from random encounters between individuals and thus would be directly linked to species abundance. We investigated the impact of species abundance distributions on qualitative and quantitative metrics of 113 host-parasite networks. We analyzed the concordance between neutral expectations and empirical observations at interaction, species, and network levels. We found that species abundance accurately predicts network metrics at all levels. Despite host-parasite systems being constrained by physiology and immunology, our results suggest that neutrality could also explain, at least partially, their structure. We hypothesize that trait matching would determine potential interactions between species, while abundance would determine their realization.

Speech networks at rest and in action: interactions between functional brain networks controlling speech production

Science.gov (United States)

Fuertinger, Stefan

2015-01-01

Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742

Deciphering microbial interactions and detecting keystone species with co-occurrence networks

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David eBerry

2014-05-01

Full Text Available Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics, construct co-occurrence networks, and evaluate how well networks reveal the underlying interactions, and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

Science.gov (United States)

Berry, David; Widder, Stefanie

2014-01-01

Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

End of Interactive Emailing from the Technical Network

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2006-01-01

According to the CNIC Security Policy for Control Systems (EDMS #584092), interactive emailing on PCs (and other devices) connected to the Technical Network is prohibited. Please note that from November 6th, neither reading emails nor sending emails interactively using e.g. Outlook or Pine mail clients on PCs connected to the Technical Network will be possible anymore. However, automatically generated emails will not be blocked and can still be sent off using CERNMX.CERN.CH as mail server. These restrictions DO NOT apply to PCs connected to any other network, like the General Purpose (or office) network. If you have questions, please do not hesitate to contact Uwe Epting, Pierre Charrue or Stefan Lueders (Technical-Network.Administrator@cern.ch). Your CNIC Working Group

Erv14 cargo receptor participates in yeast salt tolerance via its interaction with the plasma-membrane Nha1 cation/proton antiporter

Czech Academy of Sciences Publication Activity Database

Rosas-Santiago, P.; Zimmermannová, Olga; Vera-Estrella, R.; Sychrová, Hana; Pantoja, O.

2016-01-01

Roč. 1858, č. 1 (2016), s. 67-74 ISSN 0005-2736 Institutional support: RVO:67985823 Keywords : Erv14p * Nha1p * protein–protein interaction * mislocalization * salt-tolerance * yeast Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.498, year: 2016

Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

Directory of Open Access Journals (Sweden)

Peng Liu

2015-01-01

Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

Cluster Approach to Network Interaction in Pedagogical University

Science.gov (United States)

Chekaleva, Nadezhda V.; Makarova, Natalia S.; Drobotenko, Yulia B.

2016-01-01

The study presented in the article is devoted to the analysis of theory and practice of network interaction within the framework of education clusters. Education clusters are considered to be a novel form of network interaction in pedagogical education in Russia. The aim of the article is to show the advantages and disadvantages of the cluster…

Interactions of checkpoint-genes RAD9, RAD17, RAD24 and RAD53 determining radioresistance of Yeast Saccharomyces Cerevisiae

International Nuclear Information System (INIS)

Koltovaya, N.A.; Nikulushkina, Yu.V.; Roshchina, M.P.; Devin, A.B.

2007-01-01

The mechanisms of genetic control of progress through the division cell cycle (checkpoint-control) in yeast Saccharomyces cerevisiae have been studied intensively. To investigate the role of checkpoint-genes RAD9, RAD17, RAD24, RAD53 in cell radioresistance we have investigated cell sensitivity of double mutants to γ-ray. Double mutants involving various combinations with rad9Δ show epistatic interactions, i.e. the sensitivity of the double mutants to γ-ray was no greater than that of more sensitive of the two single mutants. This suggests that all these genes govern the same pathway. This group of genes was named RAD9-epistasis group. It is interesting to note that the genes RAD9 and RAD53 have positive effect but RAD17 and RAD24 have negative effect on radiosensitivity of yeast cells. Interactions between mutations may differ depending on the agent γ-ray or UV-light, for example mutations rad9Δ and rad24Δ show additive effect for γ-ray and epistatic effect for UV-light

Network traffic intelligence using a low interaction honeypot

Science.gov (United States)

Nyamugudza, Tendai; Rajasekar, Venkatesh; Sen, Prasad; Nirmala, M.; Madhu Viswanatham, V.

2017-11-01

Advancements in networking technology have seen more and more devices becoming connected day by day. This has given organizations capacity to extend their networks beyond their boundaries to remote offices and remote employees. However as the network grows security becomes a major challenge since the attack surface also increases. There is need to guard the network against different types of attacks like intrusion and malware through using different tools at different networking levels. This paper describes how network intelligence can be acquired through implementing a low-interaction honeypot which detects and track network intrusion. Honeypot allows an organization to interact and gather information about an attack earlier before it compromises the network. This process is important because it allows the organization to learn about future attacks of the same nature and allows them to develop counter measures. The paper further shows how honeypot-honey net based model for interruption detection system (IDS) can be used to get the best valuable information about the attacker and prevent unexpected harm to the network.

A scored human protein-protein interaction network to catalyze genomic interpretation

DEFF Research Database (Denmark)

Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B

2017-01-01

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In...

Online networks, social interaction and segregation: An evolutionary approach

OpenAIRE

Antoci, Angelo; Sabatini, Fabio

2018-01-01

There is growing evidence that face-to-face interaction is declining in many countries, exacerbating the phenomenon of social isolation. On the other hand, social interaction through online networking sites is steeply rising. To analyze these societal dynamics, we have built an evolutionary game model in which agents can choose between three strategies of social participation: 1) interaction via both online social networks and face-to-face encounters; 2) interaction by exclusive means of face...

Influences of brain development and ageing on cortical interactive networks.

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Zhu, Chengyu; Guo, Xiaoli; Jin, Zheng; Sun, Junfeng; Qiu, Yihong; Zhu, Yisheng; Tong, Shanbao

2011-02-01

To study the effect of brain development and ageing on the pattern of cortical interactive networks. By causality analysis of multichannel electroencephalograph (EEG) with partial directed coherence (PDC), we investigated the different neural networks involved in the whole cortex as well as the anterior and posterior areas in three age groups, i.e., children (0-10 years), mid-aged adults (26-38 years) and the elderly (56-80 years). By comparing the cortical interactive networks in different age groups, the following findings were concluded: (1) the cortical interactive network in the right hemisphere develops earlier than its left counterpart in the development stage; (2) the cortical interactive network of anterior cortex, especially at C3 and F3, is demonstrated to undergo far more extensive changes, compared with the posterior area during brain development and ageing; (3) the asymmetry of the cortical interactive networks declines during ageing with more loss of connectivity in the left frontal and central areas. The age-related variation of cortical interactive networks from resting EEG provides new insights into brain development and ageing. Our findings demonstrated that the PDC analysis of EEG is a powerful approach for characterizing the cortical functional connectivity during brain development and ageing. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Dynamics of Moment Neuronal Networks with Intra- and Inter-Interactions

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Xuyan Xiang

2015-01-01

Full Text Available A framework of moment neuronal networks with intra- and inter-interactions is presented. It is to show how the spontaneous activity is propagated across the homogeneous and heterogeneous network. The input-output firing relationship and the stability are first explored for a homogeneous network. For heterogeneous network without the constraint of the correlation coefficients between neurons, a more sophisticated dynamics is then explored. With random interactions, the network gets easily synchronized. However, desynchronization is produced by a lateral interaction such as Mexico hat function. It is the external intralayer input unit that offers a more sophisticated and unexpected dynamics over the predecessors. Hence, the work further opens up the possibility of carrying out a stochastic computation in neuronal networks.

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

Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

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Simonyan, Kristina; Fuertinger, Stefan

2015-04-01

Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. Copyright © 2015 the American Physiological Society.

Mapping DNA damage-dependent genetic interactions in yeast via party mating and barcode fusion genetics.

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Díaz-Mejía, J Javier; Celaj, Albi; Mellor, Joseph C; Coté, Atina; Balint, Attila; Ho, Brandon; Bansal, Pritpal; Shaeri, Fatemeh; Gebbia, Marinella; Weile, Jochen; Verby, Marta; Karkhanina, Anna; Zhang, YiFan; Wong, Cassandra; Rich, Justin; Prendergast, D'Arcy; Gupta, Gaurav; Öztürk, Sedide; Durocher, Daniel; Brown, Grant W; Roth, Frederick P

2018-05-28

Condition-dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State-of-the-art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double-mutant strains, does not scale readily to multi-condition studies. Here, we describe barcode fusion genetics to map genetic interactions (BFG-GI), by which double-mutant strains generated via en masse "party" mating can also be monitored en masse for growth to detect genetic interactions. By using site-specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG-GI enables multiplexed quantitative tracking of double mutants via next-generation sequencing. We applied BFG-GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4-nitroquinoline 1-oxide (4NQO), bleomycin, zeocin, and three other DNA-damaging environments. BFG-GI recapitulated known genetic interactions and yielded new condition-dependent genetic interactions. We validated and further explored a subnetwork of condition-dependent genetic interactions involving MAG1 , SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to an increase in the activation of the checkpoint protein kinase Rad53. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

DyNet: visualization and analysis of dynamic molecular interaction networks.

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Goenawan, Ivan H; Bryan, Kenneth; Lynn, David J

2016-09-01

: The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most 'rewired' nodes across many network states. DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet). david.lynn@sahmri.com Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data.

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Schweppe, Devin K; Huttlin, Edward L; Harper, J Wade; Gygi, Steven P

2018-01-05

The development of large-scale data sets requires a new means to display and disseminate research studies to large audiences. Knowledge of protein-protein interaction (PPI) networks has become a principle interest of many groups within the field of proteomics. At the confluence of technologies, such as cross-linking mass spectrometry, yeast two-hybrid, protein cofractionation, and affinity purification mass spectrometry (AP-MS), detection of PPIs can uncover novel biological inferences at a high-throughput. Thus new platforms to provide community access to large data sets are necessary. To this end, we have developed a web application that enables exploration and dissemination of the growing BioPlex interaction network. BioPlex is a large-scale interactome data set based on AP-MS of baits from the human ORFeome. The latest BioPlex data set release (BioPlex 2.0) contains 56 553 interactions from 5891 AP-MS experiments. To improve community access to this vast compendium of interactions, we developed BioPlex Display, which integrates individual protein querying, access to empirical data, and on-the-fly annotation of networks within an easy-to-use and mobile web application. BioPlex Display enables rapid acquisition of data from BioPlex and development of hypotheses based on protein interactions.

Hepatitis C virus infection protein network.

Science.gov (United States)

de Chassey, B; Navratil, V; Tafforeau, L; Hiet, M S; Aublin-Gex, A; Agaugué, S; Meiffren, G; Pradezynski, F; Faria, B F; Chantier, T; Le Breton, M; Pellet, J; Davoust, N; Mangeot, P E; Chaboud, A; Penin, F; Jacob, Y; Vidalain, P O; Vidal, M; André, P; Rabourdin-Combe, C; Lotteau, V

2008-01-01

A proteome-wide mapping of interactions between hepatitis C virus (HCV) and human proteins was performed to provide a comprehensive view of the cellular infection. A total of 314 protein-protein interactions between HCV and human proteins was identified by yeast two-hybrid and 170 by literature mining. Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HCV are enriched in highly central and interconnected proteins. A global analysis on the basis of functional annotation highlighted the enrichment of cellular pathways targeted by HCV. A network of proteins associated with frequent clinical disorders of chronically infected patients was constructed by connecting the insulin, Jak/STAT and TGFbeta pathways with cellular proteins targeted by HCV. CORE protein appeared as a major perturbator of this network. Focal adhesion was identified as a new function affected by HCV, mainly by NS3 and NS5A proteins.

Overexpression of O-methyltransferase leads to improved vanillin production in baker's yeast only when complemented with model-guided network engineering.

Science.gov (United States)

Brochado, Ana Rita; Patil, Kiran R

2013-02-01

Overproduction of a desired metabolite is often achieved via manipulation of the pathway directly leading to the product or through engineering of distant nodes within the metabolic network. Empirical examples illustrating the combined effect of these local and global strategies have been so far limited in eukaryotic systems. In this study, we compared the effects of overexpressing a key gene in de novo vanillin biosynthesis (coding for O-methyltransferase, hsOMT) in two yeast strains, with and without model-guided global network modifications. Overexpression of hsOMT resulted in increased vanillin production only in the strain with model-guided modifications, exemplifying advantage of using a global strategy prior to local pathway manipulation. Copyright © 2012 Wiley Periodicals, Inc.

SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets.

Science.gov (United States)

Gosline, Sara J C; Spencer, Sarah J; Ursu, Oana; Fraenkel, Ernest

2012-11-01

The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multiple assays (e.g. mRNA expression and phospho-proteomics) to measure a single condition. However, as experiments expand to accommodate various cellular conditions, proper analysis and interpretation of these data have become more challenging. Here we introduce a novel approach called SAMNet, for Simultaneous Analysis of Multiple Networks, that is able to interpret diverse assays over multiple perturbations. The algorithm uses a constrained optimization approach to integrate mRNA expression data with upstream genes, selecting edges in the protein-protein interaction network that best explain the changes across all perturbations. The result is a putative set of protein interactions that succinctly summarizes the results from all experiments, highlighting the network elements unique to each perturbation. We evaluated SAMNet in both yeast and human datasets. The yeast dataset measured the cellular response to seven different transition metals, and the human dataset measured cellular changes in four different lung cancer models of Epithelial-Mesenchymal Transition (EMT), a crucial process in tumor metastasis. SAMNet was able to identify canonical yeast metal-processing genes unique to each commodity in the yeast dataset, as well as human genes such as β-catenin and TCF7L2/TCF4 that are required for EMT signaling but escaped detection in the mRNA and phospho-proteomic data. Moreover, SAMNet also highlighted drugs likely to modulate EMT, identifying a series of less canonical genes known to be affected by the BCR-ABL inhibitor imatinib (Gleevec), suggesting a possible influence of this drug on EMT.

Myths on Bi-direction Communication of Web 2.0 Based Social Networks: Is Social Network Truly Interactive?

Science.gov (United States)

2011-03-10

more and more social interactions are happening on the on-line. Especially recent uptake of the social network sites (SNSs), such as Facebook (http...Smart phones • Live updates within social networks • Facebook & Twitters Solution: WebMon for Risk Management Need for New WebMon for Social Networks ...Title: Myths on bi-direction communication of Web 2.0 based social networks : Is social network truly interactive

Transcriptional Regulation and the Diversification of Metabolism in Wine Yeast Strains

Science.gov (United States)

Rossouw, Debra; Jacobson, Dan; Bauer, Florian F.

2012-01-01

Transcription factors and their binding sites have been proposed as primary targets of evolutionary adaptation because changes to single transcription factors can lead to far-reaching changes in gene expression patterns. Nevertheless, there is very little concrete evidence for such evolutionary changes. Industrial wine yeast strains, of the species Saccharomyces cerevisiae, are a geno- and phenotypically diverse group of organisms that have adapted to the ecological niches of industrial winemaking environments and have been selected to produce specific styles of wine. Variation in transcriptional regulation among wine yeast strains may be responsible for many of the observed differences and specific adaptations to different fermentative conditions in the context of commercial winemaking. We analyzed gene expression profiles of wine yeast strains to assess the impact of transcription factor expression on metabolic networks. The data provide new insights into the molecular basis of variations in gene expression in industrial strains and their consequent effects on metabolic networks important to wine fermentation. We show that the metabolic phenotype of a strain can be shifted in a relatively predictable manner by changing expression levels of individual transcription factors, opening opportunities to modify transcription networks to achieve desirable outcomes. PMID:22042577

Temporal stability in human interaction networks

Science.gov (United States)

Fabbri, Renato; Fabbri, Ricardo; Antunes, Deborah Christina; Pisani, Marilia Mello; de Oliveira, Osvaldo Novais

2017-11-01

This paper reports on stable (or invariant) properties of human interaction networks, with benchmarks derived from public email lists. Activity, recognized through messages sent, along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free trace, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös-Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, 45% are peripheral vertices. Similar results for the distribution of participants in the three sectors and for the relative importance of the topological metrics were obtained for 12 additional networks from Facebook, Twitter and ParticipaBR. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria.

Integrated multimedia information system on interactive CATV network

Science.gov (United States)

Lee, Meng-Huang; Chang, Shin-Hung

1998-10-01

In the current CATV system architectures, they provide one- way delivery of a common menu of entertainment to all the homes through the cable network. Through the technologies evolution, the interactive services (or two-way services) can be provided in the cable TV systems. They can supply customers with individualized programming and support real- time two-way communications. With a view to the service type changed from the one-way delivery systems to the two-way interactive systems, `on demand services' is a distinct feature of multimedia systems. In this paper, we present our work of building up an integrated multimedia system on interactive CATV network in Shih Chien University. Besides providing the traditional analog TV programming from the cable operator, we filter some channels to reserve them as our campus information channels. In addition to the analog broadcasting channel, the system also provides the interactive digital multimedia services, e.g. Video-On- Demand (VOD), Virtual Reality, BBS, World-Wide-Web, and Internet Radio Station. These two kinds of services are integrated in a CATV network by the separation of frequency allocation for the analog broadcasting service and the digital interactive services. Our ongoing work is to port our previous work of building up a VOD system conformed to DAVIC standard (for inter-operability concern) on Ethernet network into the current system.

Vulnerability of networks of interacting Markov chains.

Science.gov (United States)

Kocarev, L; Zlatanov, N; Trajanov, D

2010-05-13

The concept of vulnerability is introduced for a model of random, dynamical interactions on networks. In this model, known as the influence model, the nodes are arranged in an arbitrary network, while the evolution of the status at a node is according to an internal Markov chain, but with transition probabilities that depend not only on the current status of that node but also on the statuses of the neighbouring nodes. Vulnerability is treated analytically and numerically for several networks with different topological structures, as well as for two real networks--the network of infrastructures and the EU power grid--identifying the most vulnerable nodes of these networks.

Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

Science.gov (United States)

Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

2017-09-21

Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

Interacting neural networks

Science.gov (United States)

Metzler, R.; Kinzel, W.; Kanter, I.

2000-08-01

Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

Made for Each Other: Ascomycete Yeasts and Insects.

Science.gov (United States)

Blackwell, Meredith

2017-06-01

Fungi and insects live together in the same habitats, and many species of both groups rely on each other for success. Insects, the most successful animals on Earth, cannot produce sterols, essential vitamins, and many enzymes; fungi, often yeast-like in growth form, make up for these deficits. Fungi, however, require constantly replenished substrates because they consume the previous ones, and insects, sometimes lured by volatile fungal compounds, carry fungi directly to a similar, but fresh, habitat. Yeasts associated with insects include Ascomycota (Saccharomycotina, Pezizomycotina) and a few Basidiomycota. Beetles, homopterans, and flies are important associates of fungi, and in turn the insects carry yeasts in pits, specialized external pouches, and modified gut pockets. Some yeasts undergo sexual reproduction within the insect gut, where the genetic diversity of the population is increased, while others, well suited to their stable environment, may never mate. The range of interactions extends from dispersal of yeasts on the surface of insects (e.g., cactus- Drosophila -yeast and ephemeral flower communities, ambrosia beetles, yeasts with holdfasts) to extremely specialized associations of organisms that can no longer exist independently, as in the case of yeast-like symbionts of planthoppers. In a few cases yeast-like fungus-insect associations threaten butterflies and other species with extinction. Technical advances improve discovery and identification of the fungi but also inform our understanding of the evolution of yeast-insect symbioses, although there is much more to learn.

Mouse homologue of yeast Prp19 interacts with mouse SUG1, the regulatory subunit of 26S proteasome

International Nuclear Information System (INIS)

Sihn, Choong-Ryoul; Cho, Si Young; Lee, Jeong Ho; Lee, Tae Ryong; Kim, Sang Hoon

2007-01-01

Yeast Prp19 has been shown to involve in pre-mRNA splicing and DNA repair as well as being an ubiquitin ligase. Mammalian homologue of yeast Prp19 also plays on similar functional activities in cells. In the present study, we isolated mouse SUG1 (mSUG1) as binding partner of mouse Prp19 (mPrp19) by the yeast two-hybrid system. We confirmed the interaction of mPrp9 with mSUG1 by GST pull-down assay and co-immunoprecipitation assay. The N-terminus of mPrp19 including U-box domain was associated with the C-terminus of mSUG1. Although, mSUG1 is a regulatory subunit of 26S proteasome, mPrp19 was not degraded in the proteasome-dependent pathway. Interestingly, GFP-mPrp19 fusion protein was co-localized with mSUG1 protein in cytoplasm as the formation of the speckle-like structures in the presence of a proteasome inhibitor MG132. In addition, the activity of proteasome was increased in cells transfected with mPrp19. Taken together, these results suggest that mPrp19 involves the regulation of protein turnover and may transport its substrates to 26S proteasome through mSUG1 protein

Large scale genotype comparison of human papillomavirus E2-host interaction networks provides new insights for e2 molecular functions.

Directory of Open Access Journals (Sweden)

Mandy Muller

Full Text Available Human Papillomaviruses (HPV cause widespread infections in humans, resulting in latent infections or diseases ranging from benign hyperplasia to cancers. HPV-induced pathologies result from complex interplays between viral proteins and the host proteome. Given the major public health concern due to HPV-associated cancers, most studies have focused on the early proteins expressed by HPV genotypes with high oncogenic potential (designated high-risk HPV or HR-HPV. To advance the global understanding of HPV pathogenesis, we mapped the virus/host interaction networks of the E2 regulatory protein from 12 genotypes representative of the range of HPV pathogenicity. Large-scale identification of E2-interaction partners was performed by yeast two-hybrid screenings of a HaCaT cDNA library. Based on a high-confidence scoring scheme, a subset of these partners was then validated for pair-wise interaction in mammalian cells with the whole range of the 12 E2 proteins, allowing a comparative interaction analysis. Hierarchical clustering of E2-host interaction profiles mostly recapitulated HPV phylogeny and provides clues to the involvement of E2 in HPV infection. A set of cellular proteins could thus be identified discriminating, among the mucosal HPV, E2 proteins of HR-HPV 16 or 18 from the non-oncogenic genital HPV. The study of the interaction networks revealed a preferential hijacking of highly connected cellular proteins and the targeting of several functional families. These include transcription regulation, regulation of apoptosis, RNA processing, ubiquitination and intracellular trafficking. The present work provides an overview of E2 biological functions across multiple HPV genotypes.

Semantic integration to identify overlapping functional modules in protein interaction networks

Directory of Open Access Journals (Sweden)

Ramanathan Murali

2007-07-01

Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

The interaction of uranyl ions with inorganic pyrophosphatase from baker's yeast

International Nuclear Information System (INIS)

Bienwald, B.; Heitmann, P.

1978-01-01

The interaction of uranyl ions with inorganic pyrophosphatase from baker's yeast was investigated by measurement of their effect on the protein fluorescence. Fluorescence titrations of the native enzyme with uranyl nitrate show that there is a specific binding of uranyl ions to the enzyme. It was deduced that each subunit of the enzyme binds one uranyl ion. The binding constant was estimated to be in the order of 10 7 M -1 . The enzyme which contains a small number of chemically modified carboxyl groups was not able to bind uranyl ions specifically. The modification of carboxyl groups was carried out by use of a water soluble carbodiimide and the nucleophilic reagent N-(2,4-dinitro-phenyl)-hexamethylenediamine. The substrate analogue calcium pyrophosphate displaced the uranyl ions from their binding sites at the enzyme From the results it is concluded that carboxyl groups of the active site are the ligands for the binding of uranyl ions. (author)

The yeast replicative aging model.

Science.gov (United States)

He, Chong; Zhou, Chuankai; Kennedy, Brian K

2018-03-08

It has been nearly three decades since the budding yeast Saccharomyces cerevisiae became a significant model organism for aging research and it has emerged as both simple and powerful. The replicative aging assay, which interrogates the number of times a "mother" cell can divide and produce "daughters", has been a stalwart in these studies, and genetic approaches have led to the identification of hundreds of genes impacting lifespan. More recently, cell biological and biochemical approaches have been developed to determine how cellular processes become altered with age. Together, the tools are in place to develop a holistic view of aging in this single-celled organism. Here, we summarize the current state of understanding of yeast replicative aging with a focus on the recent studies that shed new light on how aging pathways interact to modulate lifespan in yeast. Copyright © 2018. Published by Elsevier B.V.

Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

Science.gov (United States)

de Vos, Marjon G. J.; Bollenbach, Tobias

2017-01-01

Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. PMID:28923953

Interaction of CSFV E2 protein with swine host factors as detected by yeast two-hybrid system.

Directory of Open Access Journals (Sweden)

Douglas P Gladue

Full Text Available E2 is one of the envelope glycoproteins of pestiviruses, including classical swine fever virus (CSFV and bovine viral diarrhea virus (BVDV. E2 is involved in several critical functions, including virus entry into target cells, induction of a protective immune response and virulence in swine. However, there is no information regarding any host binding partners for the E2 proteins. Here, we utilized the yeast two-hybrid system and identified fifty-seven host proteins as positive binding partners which bound E2 from both CSFV and BVDV with the exception of two proteins that were found to be positive for binding only to CSFV E2. Alanine scanning of CSFV E2 demonstrated that the binding sites for these cellular proteins on E2 are likely non-linear binding sites. The possible roles of the identified host proteins are discussed as the results presented here will be important for future studies to elucidate mechanisms of host protein-virus interactions during pestivirus infection. However, due to the limitations of the yeast two hybrid system, the proteins identified is not exhaustive and each interaction identified needs to be confirmed by independent experimental approaches in the context of virus-infected cells before any definitive conclusion can be drawn on relevance for the virus life cycle.

Sensitive voltammetric detection of yeast RNA based on its interaction with Victoria Blue B

Directory of Open Access Journals (Sweden)

WEI SUN

2009-12-01

Full Text Available Voltammetric studies of the interaction of yeast RNA (y-RNA with Victoria Blue B (VBB are described in this paper. Furthermore, a linear sweep voltammetric method for the detection of y-RNA was established. The reaction conditions, such as acidity and amount of buffer solution, the concentration of VBB, the reaction time and temperature, etc., were carefully investigated by second order derivative linear sweep voltammetry. Under the optimal conditions, the reduction peak current of VBB at –0.75 V decreased greatly after the addition of y-RNA to the solution without any shift of the reduction peak potential. Based on the decrease of the peak current, a new quantitative method for the determination of y-RNA was developed. The effects of co-existing substances on the determination were carefully investigated and three synthetic samples were determined with satisfactory results. The stoichiometry of the VBB–y-RNA complex was calculated by linear sweep voltammetry and the interaction mechanism is discussed.

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.

Overexpression of O‐methyltransferase leads to improved vanillin production in baker's yeast only when complemented with model‐guided network engineering

DEFF Research Database (Denmark)

Brochado, Ana Rita; Patil, Kiran R.

2013-01-01

limited in eukaryotic systems. In this study, we compared the effects of overexpressing a key gene in de novo vanillin biosynthesis (coding for O‐methyltransferase, hsOMT) in two yeast strains, with and without model‐guided global network modifications. Overexpression of hsOMT resulted in increased...... vanillin production only in the strain with model‐guided modifications, exemplifying advantage of using a global strategy prior to local pathway manipulation. Biotechnol. Bioeng. 2013; 110: 656–659. © 2012 Wiley Periodicals, Inc....

NatalieQ: A web server for protein-protein interaction network querying

NARCIS (Netherlands)

El-Kebir, M.; Brandt, B.W.; Heringa, J.; Klau, G.W.

2014-01-01

Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks.

On the growth of scientific knowledge: yeast biology as a case study.

Directory of Open Access Journals (Sweden)

Xionglei He

2009-03-01

Full Text Available The tempo and mode of human knowledge expansion is an enduring yet poorly understood topic. Through a temporal network analysis of three decades of discoveries of protein interactions and genetic interactions in baker's yeast, we show that the growth of scientific knowledge is exponential over time and that important subjects tend to be studied earlier. However, expansions of different domains of knowledge are highly heterogeneous and episodic such that the temporal turnover of knowledge hubs is much greater than expected by chance. Familiar subjects are preferentially studied over new subjects, leading to a reduced pace of innovation. While research is increasingly done in teams, the number of discoveries per researcher is greater in smaller teams. These findings reveal collective human behaviors in scientific research and help design better strategies in future knowledge exploration.

Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae

DEFF Research Database (Denmark)

Chumnanpuen, Pramote; Hansen, Michael Adsetts Edberg; Smedsgaard, Jørn

2014-01-01

relies on analysis at a single time point. Using direct infusion-mass spectrometry (DI-MS), we could observe the dynamic metabolic footprinting in yeast S. cerevisiae BY4709 (wild type) cultured on 3 different C-sources (glucose, glycerol, and ethanol) and sampled along 10 time points with 5 biological...... replicates. In order to analyze the dynamic mass spectrometry data, we developed the novel analysis methods that allow us to perform correlation analysis to identify metabolites that significantly correlate over time during growth on the different carbon sources. Both positive and negative electrospray...... reconstructed an interaction map that provides information of how different metabolic pathways have correlated patterns during growth on the different carbon sources....

Diversity and killer activity of yeasts in Malaysian fermented food samples.

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Lim, S L; Tay, S T

2011-08-01

The biodiversity and the killer activity of yeasts isolated from various types of fermented food in Malaysia were investigated in this study. Of 252 yeasts isolated from 48 fermented food samples in this study, 19 yeast species were identified based on sequence analysis of the ITS1-5.8S-ITS2 partial fragments of the yeasts. A total of 29 (11.5%) of the yeast isolates demonstrated killer activity to at least one Candida species tested in this study; including 22 isolates of Trichosporon asahii, 4 isolates of Pichia anomala, and one isolate each of Pichia norvegensis, Pichia fermentans and Issatchenkia orientalis, respectively. The presence of killer yeasts reflects antagonism that occurs during microbial interaction in the fermented food, whereby certain yeasts produce killer toxins and possibly other toxic substances in competition for limited nutrients and space. The anti-Candida activity demonstrated by killer yeasts in this study should be further explored for development of alternative therapy against candidiasis.

Network Interactions in the Great Altai Region

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Lev Aleksandrovich Korshunov

2017-12-01

Full Text Available To improve the efficiency and competitiveness of the regional economy, an effective interaction between educational institutions in the Great Altai region is needed. The innovation growth can enhancing this interaction. The article explores the state of network structures in the economy and higher education in the border territories of the countries of Great Altai. The authors propose an updated approach to the three-level classification of network interaction. We analyze growing influence of the countries with emerging economies. We define the factors that impede the more stable and multifaceted regional development of these countries. Further, the authors determine indicators of the higher education systems and cooperation systems at the university level between the Shanghai Cooperation Organization countries (SCO and BRICS countries, showing the international rankings of the universities in these countries. The teaching language is important to overcome the obstacles in the interregional cooperation. The authors specify the problems of the development of the universities of the SCO and BRICS countries as global educational networks. The research applies basic scientific logical methods of analysis and synthesis, induction and deduction, as well as the SWOT analysis method. We have indentified and analyzed the existing economic and educational relations. To promote the economic innovation development of the border territories of the Great Altai, we propose a model of regional network university. Modern universities function in a new economic environment. Thus, in a great extent, they form the technological and social aspects of this environment. Innovative network structures contribute to the formation of a new network institutional environment of the regional economy, which impacts the macro- and microeconomic performance of the region as a whole. The results of the research can help to optimize the regional economies of the border

A global interaction network maps a wiring diagram of cellular function

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Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N.; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D.; Pelechano, Vicent; Styles, Erin B.; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S.; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F.; Li, Sheena C.; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; Luis, Bryan-Joseph San; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W.; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G.; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M.; Moore, Claire L.; Rosebrock, Adam P.; Caudy, Amy A.; Myers, Chad L.; Andrews, Brenda; Boone, Charles

2017-01-01

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing over 23 million double mutants, identifying ~550,000 negative and ~350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell. PMID:27708008

Downsides and benefits of unicellularity in budding yeast

Science.gov (United States)

Balazsi, Gabor; Chen, Lin; Kuzdzal-Fick, Jennie

Yeast cells that do not separate after cell division form clumps. Clumping was shown to aid utilization of certain sugars, but its effects in stressful conditions are unknown. Generally speaking, what are the costs and benefits of unicellularity versus clumping multicellularity in normal and stressful conditions? To address this question, we evolved clumping yeast towards unicellularity by continuously propagating only those cells that remain suspended in liquid culture after settling. Whole-genome sequencing indicated that mutations in the AMN1 (antagonist of mitotic exit network) gene underlie the changes from clumping to unicellular phenotypes in these evolved yeast cells. Simple models predict that clumping should hinder growth in normal conditions while being protective in stress. Accordingly, we find experimentally that yeast clumps are more resistant to freeze/thaw, hydrogen peroxide, and ethanol stressors than their unicellular counterparts. On the other hand, unicellularity seems to be advantageous in normal conditions. Overall, these results reveal the downsides and benefits of unicellularity in different environmental conditions and uncover its genetic bases in yeast. This research was supported by the NIH Director's New Innovator Award Program (1DP2 OD006481-01), by NSF/IOS 1021675 and the Laufer Center for Physical & Quantitative Biology.

Effect of interaction strength on robustness of controlling edge dynamics in complex networks

Science.gov (United States)

Pang, Shao-Peng; Hao, Fei

2018-05-01

Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network's degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability.

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

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Xiao-Dong Wang

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

Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

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

Brewing and volatiles analysis of three tea beers indicate a potential interaction between tea components and lager yeast.

Science.gov (United States)

Rong, Lei; Peng, Li-Juan; Ho, Chi-Tang; Yan, Shou-He; Meurens, Marc; Zhang, Zheng-Zhu; Li, Da-Xiang; Wan, Xiao-Chun; Bao, Guan-Hu; Gao, Xue-Ling; Ling, Tie-Jun

2016-04-15

Green tea, oolong tea and black tea were separately introduced to brew three kinds of tea beers. A model was designed to investigate the tea beer flavour character. Comparison of the volatiles between the sample of tea beer plus water mixture (TBW) and the sample of combination of tea infusion and normal beer (CTB) was accomplished by triangular sensory test and HS-SPME GC-MS analysis. The PCA of GC-MS data not only showed a significant difference between volatile features of each TBW and CTB group, but also suggested some key compounds to distinguish TBW from CTB. The results of GC-MS showed that the relative concentrations of many typical tea volatiles were significantly changed after the brewing process. More interestingly, the behaviour of yeast fermentation was influenced by tea components. A potential interaction between tea components and lager yeast could be suggested. Copyright © 2015 Elsevier Ltd. All rights reserved.

An analysis pipeline for the inference of protein-protein interaction networks

Energy Technology Data Exchange (ETDEWEB)

Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

2009-12-01

An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

Interaction in agent-based economics: A survey on the network approach

Science.gov (United States)

Bargigli, Leonardo; Tedeschi, Gabriele

2014-04-01

In this paper we aim to introduce the reader to some basic concepts and instruments used in a wide range of economic networks models. In particular, we adopt the theory of random networks as the main tool to describe the relationship between the organization of interaction among individuals within different components of the economy and overall aggregate behavior. The focus is on the ways in which economic agents interact and the possible consequences of their interaction on the system. We show that network models are able to introduce complex phenomena in economic systems by allowing for the endogenous evolution of networks.

Binding interactions between yeast tRNA ligase and a precursor transfer ribonucleic acid containing two photoreactive uridine analogues

International Nuclear Information System (INIS)

Tanner, N.K.; Hanna, M.M.; Abelson, J.

1988-01-01

Yeast tRNA ligase, from Saccharomyces cerevisiae, is one of the protein components that is involved in the splicing reaction of intron-containing yeast precursor tRNAs. It is an unusual protein because it has three distinct catalytic activities. It functions as a polynucleotide kinase, as a cyclic phosphodiesterase, and as an RNA ligase. We have studied the binding interactions between ligase and precursor tRNAs containing two photoreactive uridine analogues, 4-thiouridine and 5-bromouridine. When irradiated with long ultraviolet light, RNA containing these analogues can form specific covalent bonds with associated proteins. In this paper, we show that 4-thiouridine triphosphate and 5-bromouridine triphosphate were readily incorporated into a precursor tRNA(Phe) that was synthesized, in vitro, with bacteriophage T7 RNA polymerase. The analogue-containing precursor tRNAs were authentic substrates for the two splicing enzymes that were tested (endonuclease and ligase), and they formed specific covalent bonds with ligase when they were irradiated with long-wavelength ultraviolet light. We have determined the position of three major cross-links and one minor cross-link on precursor tRNA(Phe) that were located within the intron and near the 3' splice site. On the basis of these data, we present a model for the in vivo splicing reaction of yeast precursor tRNAs

Disentangling the co-structure of multilayer interaction networks: degree distribution and module composition in two-layer bipartite networks.

Science.gov (United States)

Astegiano, Julia; Altermatt, Florian; Massol, François

2017-11-13

Species establish different interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may affect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks, one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on flowers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not different from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect effects of species loss through interconnected modular networks.

Evaluating the interactions of vertebrate receptors with persistent pollutants and antifouling pesticides using recombinant yeast assays

Energy Technology Data Exchange (ETDEWEB)

Noguerol, Tania-Noelia; Boronat, Susanna; Casado, Marta; Pina, Benjamin [Institut de Biologia Molecular de Barcelona, CSIC, Department of Molecular Biology, Barcelona (Spain); Raldua, Demetrio [Laboratory of Environmental Toxicology, INTEXTER -UP, Terrassa (Spain); Barcelo, Damia [IIQAB-CSIC, Department of Environmental Chemistry, Barcelona (Spain)

2006-07-15

The development of in vitro methods for screening potentially harmful biological activities of new compounds is an extremely important way to increase not only their intrinsic environmental safety, but also the public perception of the safety standards associated with them. In this work we use two yeast systems to test the ability of different chemicals to bind and activate two vertebrate receptors which are intimately related to adverse biological effects of pollution in exposed fauna: the estrogen receptor (ER) and the aryl hydrocarbon receptor (AhR). The panel of compounds analysed here includes well-known pollutants, like PCBs, pp'-DDT and hexachlorobenzene, together with the less-known, emerging putative pollutants, such as Sea-Nine, Irgarol and diuron. Results show the ability of some of these compounds to interact with one or both receptors, provide hints about the relationship between structure and activity, and suggest mechanistic explanations for the biological activities already described in whole-animal experiments. In addition, we show that AhR may have an intrinsic ligand promiscuity comparable to that of ER, a feature not fully appreciated in the past due to the technical difficulties involved with testing highly lipophilic substances in yeast-based assays. (orig.)

EVALUATING AUSTRALIAN FOOTBALL LEAGUE PLAYER CONTRIBUTIONS USING INTERACTIVE NETWORK SIMULATION

Directory of Open Access Journals (Sweden)

Jonathan Sargent

2013-03-01

Full Text Available This paper focuses on the contribution of Australian Football League (AFL players to their team's on-field network by simulating player interactions within a chosen team list and estimating the net effect on final score margin. A Visual Basic computer program was written, firstly, to isolate the effective interactions between players from a particular team in all 2011 season matches and, secondly, to generate a symmetric interaction matrix for each match. Negative binomial distributions were fitted to each player pairing in the Geelong Football Club for the 2011 season, enabling an interactive match simulation model given the 22 chosen players. Dynamic player ratings were calculated from the simulated network using eigenvector centrality, a method that recognises and rewards interactions with more prominent players in the team network. The centrality ratings were recorded after every network simulation and then applied in final score margin predictions so that each player's match contribution-and, hence, an optimal team-could be estimated. The paper ultimately demonstrates that the presence of highly rated players, such as Geelong's Jimmy Bartel, provides the most utility within a simulated team network. It is anticipated that these findings will facilitate optimal AFL team selection and player substitutions, which are key areas of interest to coaches. Network simulations are also attractive for use within betting markets, specifically to provide information on the likelihood of a chosen AFL team list "covering the line".

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Science.gov (United States)

Shannon, Paul; Markiel, Andrew; Ozier, Owen; Baliga, Nitin S; Wang, Jonathan T; Ramage, Daniel; Amin, Nada; Schwikowski, Benno; Ideker, Trey

2003-11-01

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

Genomic signatures of adaptation to wine biological ageing conditions in biofilm-forming flor yeasts.

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Coi, A L; Bigey, F; Mallet, S; Marsit, S; Zara, G; Gladieux, P; Galeote, V; Budroni, M; Dequin, S; Legras, J L

2017-04-01

The molecular and evolutionary processes underlying fungal domestication remain largely unknown despite the importance of fungi to bioindustry and for comparative adaptation genomics in eukaryotes. Wine fermentation and biological ageing are performed by strains of S. cerevisiae with, respectively, pelagic fermentative growth on glucose and biofilm aerobic growth utilizing ethanol. Here, we use environmental samples of wine and flor yeasts to investigate the genomic basis of yeast adaptation to contrasted anthropogenic environments. Phylogenetic inference and population structure analysis based on single nucleotide polymorphisms revealed a group of flor yeasts separated from wine yeasts. A combination of methods revealed several highly differentiated regions between wine and flor yeasts, and analyses using codon-substitution models for detecting molecular adaptation identified sites under positive selection in the high-affinity transporter gene ZRT1. The cross-population composite likelihood ratio revealed selective sweeps at three regions, including in the hexose transporter gene HXT7, the yapsin gene YPS6 and the membrane protein coding gene MTS27. Our analyses also revealed that the biological ageing environment has led to the accumulation of numerous mutations in proteins from several networks, including Flo11 regulation and divalent metal transport. Together, our findings suggest that the tuning of FLO11 expression and zinc transport networks are a distinctive feature of the genetic changes underlying the domestication of flor yeasts. Our study highlights the multiplicity of genomic changes underlying yeast adaptation to man-made habitats and reveals that flor/wine yeast lineage can serve as a useful model for studying the genomics of adaptive divergence. © 2017 John Wiley & Sons Ltd.

L-GRAAL: Lagrangian graphlet-based network aligner.

Science.gov (United States)

Malod-Dognin, Noël; Pržulj, Nataša

2015-07-01

Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. n.malod-dognin@imperial.ac.uk Supplementary data are available at

DNA repair and the genetic control of radiosensitivity in yeast

International Nuclear Information System (INIS)

Haynes, R.H.

1975-01-01

The following topics are discussed: advantages of yeasts for easily manipulated model systems for studies on molecular biology of eukaryotes; induction of x-ray-resistant mutants by radiations and chemicals; genetics of uv-sensitive mutants; loci of genes affecting radiosensitivity; gene interactions in multiple mutants; liquid-holding recovery; mitotic and meiotic recombination; and repair of yeast mitochondrial DNA

Digital Ecology: Coexistence and Domination among Interacting Networks

Science.gov (United States)

Kleineberg, Kaj-Kolja; Boguñá, Marián

2015-05-01

The overwhelming success of Web 2.0, within which online social networks are key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of Web 2.0 services has allowed researchers to quantify large-scale social patterns for the first time. However, the mechanisms that determine the fate of networks at the system level are still poorly understood. For instance, the simultaneous existence of multiple digital services naturally raises questions concerning which conditions these services can coexist under. Analogously to the case of population dynamics, the digital world forms a complex ecosystem of interacting networks. The fitness of each network depends on its capacity to attract and maintain users’ attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits stable coexistence of several networks as well as the dominance of an individual one, in contrast to the competitive exclusion principle. Interestingly, our theory also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.

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

Non-criticality of interaction network over system's crises: A percolation analysis.

Science.gov (United States)

Shirazi, Amir Hossein; Saberi, Abbas Ali; Hosseiny, Ali; Amirzadeh, Ehsan; Toranj Simin, Pourya

2017-11-20

Extraction of interaction networks from multi-variate time-series is one of the topics of broad interest in complex systems. Although this method has a wide range of applications, most of the previous analyses have focused on the pairwise relations. Here we establish the potential of such a method to elicit aggregated behavior of the system by making a connection with the concepts from percolation theory. We study the dynamical interaction networks of a financial market extracted from the correlation network of indices, and build a weighted network. In correspondence with the percolation model, we find that away from financial crises the interaction network behaves like a critical random network of Erdős-Rényi, while close to a financial crisis, our model deviates from the critical random network and behaves differently at different size scales. We perform further analysis to clarify that our observation is not a simple consequence of the growth in correlations over the crises.

Some Remarks on Prediction of Drug-Target Interaction with Network Models.

Science.gov (United States)

Zhang, Shao-Wu; Yan, Xiao-Ying

2017-01-01

System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Photolabeling identifies an interaction between phosphatidylcholine and glycerol-3-phosphate dehydrogenase (Gut2p) in yeast mitochondria

DEFF Research Database (Denmark)

Janssen, Marjolein J F W; van Voorst, Frank; Ploeger, Ginette E J

2002-01-01

In search of mitochondrial proteins interacting with phosphatidylcholine (PC), a photolabeling approach was applied, in which photoactivatable probes were incorporated into isolated yeast mitochondria. Only a limited number of proteins were labeled upon photoactivation, using either the PC analogue......-dependent mitochondrial glycerol-3-phosphate dehydrogenase. This was confirmed by the lack of specific labeling in mitochondria from a gut2 deletion strain. Only under conditions where the inner membrane was accessible to the probe, Gut2p was labeled by [125I]TID-PC, in parallel with increased labeling of the phosphate...

Stochasticity in the yeast mating pathway

International Nuclear Information System (INIS)

Hong-Li, Wang; Zheng-Ping, Fu; Xin-Hang, Xu; Qi, Ouyang

2009-01-01

We report stochastic simulations of the yeast mating signal transduction pathway. The effects of intrinsic and external noise, the influence of cell-to-cell difference in the pathway capacity, and noise propagation in the pathway have been examined. The stochastic temporal behaviour of the pathway is found to be robust to the influence of inherent fluctuations, and intrinsic noise propagates in the pathway in a uniform pattern when the yeasts are treated with pheromones of different stimulus strengths and of varied fluctuations. In agreement with recent experimental findings, extrinsic noise is found to play a more prominent role than intrinsic noise in the variability of proteins. The occurrence frequency for the reactions in the pathway are also examined and a more compact network is obtained by dropping most of the reactions of least occurrence

Why does yeast ferment? A flux balance analysis study.

NARCIS (Netherlands)

Simeonides, E.; Murabito, E.; Smalbone, K.; Westerhoff, H.V.

2010-01-01

Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using

Limitation of degree information for analyzing the interaction evolution in online social networks

Science.gov (United States)

Shang, Ke-Ke; Yan, Wei-Sheng; Xu, Xiao-Ke

2014-04-01

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

Directory of Open Access Journals (Sweden)

Oyang Yen-Jen

2010-10-01

Full Text Available Abstract Background Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. Results We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. Conclusions We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.

Designing Networked Adaptive Interactive Hybrid Systems

NARCIS (Netherlands)

Kester, L.J.H.M.

2008-01-01

Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. In these systems humans and intelligent machines will, in close interaction, be able to reach their goals under

The ecology of the Drosophila-yeast mutualism in wineries

Science.gov (United States)

2018-01-01

The fruit fly, Drosophila melanogaster, is preferentially found on fermenting fruits. The yeasts that dominate the microbial communities of these substrates are the primary food source for developing D. melanogaster larvae, and adult flies manifest a strong olfactory system-mediated attraction for the volatile compounds produced by these yeasts during fermentation. Although most work on this interaction has focused on the standard laboratory yeast Saccharomyces cerevisiae, a wide variety of other yeasts naturally ferment fallen fruit. Here we address the open question of whether D. melanogaster preferentially associates with distinct yeasts in different, closely-related environments. We characterized the spatial and temporal dynamics of Drosophila-associated fungi in Northern California wineries that use organic grapes and natural fermentation using high-throughput, short-amplicon sequencing. We found that there is nonrandom structure in the fungal communities that are vectored by flies both between and within vineyards. Within wineries, the fungal communities associated with flies in cellars, fermentation tanks, and pomace piles are distinguished by varying abundances of a small number of yeast species. To investigate the origins of this structure, we assayed Drosophila attraction to, oviposition on, larval development in, and longevity when consuming the yeasts that distinguish vineyard microhabitats from each other. We found that wild fly lines did not respond differentially to the yeast species that distinguish winery habitats in habitat specific manner. Instead, this subset of yeast shares traits that make them attractive to and ensure their close association with Drosophila. PMID:29768432

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

Science.gov (United States)

Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

2016-05-01

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

Characterizing interactions in online social networks during exceptional events

Science.gov (United States)

Omodei, Elisa; De Domenico, Manlio; Arenas, Alex

2015-08-01

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

Global Diffusion of Interactive Networks. The Impact of Culture

OpenAIRE

Maitland, Carleen

1998-01-01

The Internet and other interactive networks are diffusing across the globe at rates that vary from country to country. Typically, economic and market structure variables are used to explain these differences. The addition of culture to these variables will provide a more robust understanding of the differences in Internet and interactive network diffusion. Existing analyses that identify culture as a predictor of diffusion do not adequately specificy the dimensions of culture and their imp...

Synchronization of glycolytic oscillations in a yeast cell population

DEFF Research Database (Denmark)

Dano, S.; Hynne, F.; De Monte, Silvia

2001-01-01

The mechanism of active phase synchronization in a suspension of oscillatory yeast cells has remained a puzzle for almost half a century. The difficulty of the problem stems from the fact that the synchronization phenomenon involves the entire metabolic network of glycolysis and fermentation, and...

Emergence of modularity and disassortativity in protein-protein interaction networks.

Science.gov (United States)

Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

2010-12-01

In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

Studies of Saccharomyces cerevisiae and Non-Saccharomyces Yeasts during Alcoholic Fermentation

DEFF Research Database (Denmark)

Kemsawasd, Varongsiri

The early death of non-Saccharomyces yeasts during mixed culture spontaneous wine fermentation has traditionally been attributed to the lower capacity of these yeast species to withstand high levels of ethanol, low pH, and other media properties that are a part of progressing fermentation. However......, other yeast-yeast interactions, such as cell-cell contact mediated growth arrest and/or toxininduced death may also be a significant factor in the relative fragility of these non-Saccharomyces yeasts in mixed culture fermentation. In the present work we evaluate the combined roles of cell-cell contact...... and/or antimicrobial peptides on the early death of Lachancea thermotolerans during mixed culture fermentations with Saccharomyces cerevisiae. Using a specially designed double compartment fermentation system, we established that both cell-to-cell contact and antimicrobial peptides contribute...

Finding low-conductance sets with dense interactions (FLCD) for better protein complex prediction.

Science.gov (United States)

Wang, Yijie; Qian, Xiaoning

2017-03-14

Intuitively, proteins in the same protein complexes should highly interact with each other but rarely interact with the other proteins in protein-protein interaction (PPI) networks. Surprisingly, many existing computational algorithms do not directly detect protein complexes based on both of these topological properties. Most of them, depending on mathematical definitions of either "modularity" or "conductance", have their own limitations: Modularity has the inherent resolution problem ignoring small protein complexes; and conductance characterizes the separability of complexes but fails to capture the interaction density within complexes. In this paper, we propose a two-step algorithm FLCD (Finding Low-Conductance sets with Dense interactions) to predict overlapping protein complexes with the desired topological structure, which is densely connected inside and well separated from the rest of the networks. First, FLCD detects well-separated subnetworks based on approximating a potential low-conductance set through a personalized PageRank vector from a protein and then solving a mixed integer programming (MIP) problem to find the minimum-conductance set within the identified low-conductance set. At the second step, the densely connected parts in those subnetworks are discovered as the protein complexes by solving another MIP problem that aims to find the dense subnetwork in the minimum-conductance set. Experiments on four large-scale yeast PPI networks from different public databases demonstrate that the complexes predicted by FLCD have better correspondence with the yeast protein complex gold standards than other three state-of-the-art algorithms (ClusterONE, LinkComm, and SR-MCL). Additionally, results of FLCD show higher biological relevance with respect to Gene Ontology (GO) terms by GO enrichment analysis.

Interactions between neural networks: a mechanism for tuning chaos and oscillations.

Science.gov (United States)

Wang, Lipo

2007-06-01

We show that chaos and oscillations in a higher-order binary neural network can be tuned effectively using interactions between neural networks. Our results suggest that network interactions may be useful as a means of adjusting the level of dynamic activities in systems that employ chaos and oscillations for information processing, or as a means of suppressing oscillatory behaviors in systems that require stability.

Flower-Visiting Social Wasps and Plants Interaction: Network Pattern and Environmental Complexity

Directory of Open Access Journals (Sweden)

Mateus Aparecido Clemente

2012-01-01

Full Text Available Network analysis as a tool for ecological interactions studies has been widely used since last decade. However, there are few studies on the factors that shape network patterns in communities. In this sense, we compared the topological properties of the interaction network between flower-visiting social wasps and plants in two distinct phytophysiognomies in a Brazilian savanna (Riparian Forest and Rocky Grassland. Results showed that the landscapes differed in species richness and composition, and also the interaction networks between wasps and plants had different patterns. The network was more complex in the Riparian Forest, with a larger number of species and individuals and a greater amount of connections between them. The network specialization degree was more generalist in the Riparian Forest than in the Rocky Grassland. This result was corroborated by means of the nestedness index. In both networks was found asymmetry, with a large number of wasps per plant species. In general aspects, most wasps had low niche amplitude, visiting from one to three plant species. Our results suggest that differences in structural complexity of the environment directly influence the structure of the interaction network between flower-visiting social wasps and plants.

Transport and cytotoxicity of the anticancer drug 3-bromopyruvate in the yeast Saccharomyces cerevisiae.

Science.gov (United States)

Lis, Paweł; Zarzycki, Marek; Ko, Young H; Casal, Margarida; Pedersen, Peter L; Goffeau, Andre; Ułaszewski, Stanisław

2012-02-01

We have investigated the cytotoxicity in Saccharomyces cerevisiae of the novel antitumor agent 3-bromopyruvate (3-BP). 3-BP enters the yeast cells through the lactate/pyruvate H(+) symporter Jen1p and inhibits cell growth at minimal inhibitory concentration of 1.8 mM when grown on non-glucose conditions. It is not submitted to the efflux pumps conferring Pleiotropic Drug Resistance in yeast. Yeast growth is more sensitive to 3-BP than Gleevec (Imatinib methanesulfonate) which in contrast to 3-BP is submitted to the PDR network of efflux pumps. The sensitivity of yeast to 3-BP is increased considerably by mutations or chemical treatment by buthionine sulfoximine that decrease the intracellular concentration of glutathione.

Distinct Domestication Trajectories in Top-Fermenting Beer Yeasts and Wine Yeasts.

Science.gov (United States)

Gonçalves, Margarida; Pontes, Ana; Almeida, Pedro; Barbosa, Raquel; Serra, Marta; Libkind, Diego; Hutzler, Mathias; Gonçalves, Paula; Sampaio, José Paulo

2016-10-24

Beer is one of the oldest alcoholic beverages and is produced by the fermentation of sugars derived from starches present in cereal grains. Contrary to lager beers, made by bottom-fermenting strains of Saccharomyces pastorianus, a hybrid yeast, ale beers are closer to the ancient beer type and are fermented by S. cerevisiae, a top-fermenting yeast. Here, we use population genomics to investigate (1) the closest relatives of top-fermenting beer yeasts; (2) whether top-fermenting yeasts represent an independent domestication event separate from those already described; (3) whether single or multiple beer yeast domestication events can be inferred; and (4) whether top-fermenting yeasts represent non-recombinant or recombinant lineages. Our results revealed that top-fermenting beer yeasts are polyphyletic, with a main clade composed of at least three subgroups, dominantly represented by the German, British, and wheat beer strains. Other beer strains were phylogenetically close to sake, wine, or bread yeasts. We detected genetic signatures of beer yeast domestication by investigating genes previously linked to brewing and using genome-wide scans. We propose that the emergence of the main clade of beer yeasts is related with a domestication event distinct from the previously known cases of wine and sake yeast domestication. The nucleotide diversity of the main beer clade more than doubled that of wine yeasts, which might be a consequence of fundamental differences in the modes of beer and wine yeast domestication. The higher diversity of beer strains could be due to the more intense and different selection regimes associated to brewing. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks

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Boucher Charles AB

2010-07-01

Full Text Available Abstract Background The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics. Results Our analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems. Conclusions HIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another.

msiDBN: A Method of Identifying Critical Proteins in Dynamic PPI Networks

Directory of Open Access Journals (Sweden)

Yuan Zhang

2014-01-01

Full Text Available Dynamics of protein-protein interactions (PPIs reveals the recondite principles of biological processes inside a cell. Shown in a wealth of study, just a small group of proteins, rather than the majority, play more essential roles at crucial points of biological processes. This present work focuses on identifying these critical proteins exhibiting dramatic structural changes in dynamic PPI networks. First, a comprehensive way of modeling the dynamic PPIs is presented which simultaneously analyzes the activity of proteins and assembles the dynamic coregulation correlation between proteins at each time point. Second, a novel method is proposed, named msiDBN, which models a common representation of multiple PPI networks using a deep belief network framework and analyzes the reconstruction errors and the variabilities across the time courses in the biological process. Experiments were implemented on data of yeast cell cycles. We evaluated our network construction method by comparing the functional representations of the derived networks with two other traditional construction methods. The ranking results of critical proteins in msiDBN were compared with the results from the baseline methods. The results of comparison showed that msiDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.

Lipid raft involvement in yeast cell growth and death

Energy Technology Data Exchange (ETDEWEB)

Mollinedo, Faustino, E-mail: fmollin@usal.es [Instituto de Biología Molecular y Celular del Cáncer, Centro de Investigación del Cáncer, Consejo Superior de Investigaciones Científicas - Universidad de Salamanca, Salamanca (Spain)

2012-10-10

The notion that cellular membranes contain distinct microdomains, acting as scaffolds for signal transduction processes, has gained considerable momentum. In particular, a class of such domains that is rich in sphingolipids and cholesterol, termed as lipid rafts, is thought to compartmentalize the plasma membrane, and to have important roles in survival and cell death signaling in mammalian cells. Likewise, yeast lipid rafts are membrane domains enriched in sphingolipids and ergosterol, the yeast counterpart of mammalian cholesterol. Sterol-rich membrane domains have been identified in several fungal species, including the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe as well as the pathogens Candida albicans and Cryptococcus neoformans. Yeast rafts have been mainly involved in membrane trafficking, but increasing evidence implicates rafts in a wide range of additional cellular processes. Yeast lipid rafts house biologically important proteins involved in the proper function of yeast, such as proteins that control Na{sup +}, K{sup +}, and pH homeostasis, which influence many cellular processes, including cell growth and death. Membrane raft constituents affect drug susceptibility, and drugs interacting with sterols alter raft composition and membrane integrity, leading to yeast cell death. Because of the genetic tractability of yeast, analysis of yeast rafts could be an excellent model to approach unanswered questions of mammalian raft biology, and to understand the role of lipid rafts in the regulation of cell death and survival in human cells. A better insight in raft biology might lead to envisage new raft-mediated approaches to the treatment of human diseases where regulation of cell death and survival is critical, such as cancer and neurodegenerative diseases.

Lipid raft involvement in yeast cell growth and death

International Nuclear Information System (INIS)

Mollinedo, Faustino

2012-01-01

The notion that cellular membranes contain distinct microdomains, acting as scaffolds for signal transduction processes, has gained considerable momentum. In particular, a class of such domains that is rich in sphingolipids and cholesterol, termed as lipid rafts, is thought to compartmentalize the plasma membrane, and to have important roles in survival and cell death signaling in mammalian cells. Likewise, yeast lipid rafts are membrane domains enriched in sphingolipids and ergosterol, the yeast counterpart of mammalian cholesterol. Sterol-rich membrane domains have been identified in several fungal species, including the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe as well as the pathogens Candida albicans and Cryptococcus neoformans. Yeast rafts have been mainly involved in membrane trafficking, but increasing evidence implicates rafts in a wide range of additional cellular processes. Yeast lipid rafts house biologically important proteins involved in the proper function of yeast, such as proteins that control Na + , K + , and pH homeostasis, which influence many cellular processes, including cell growth and death. Membrane raft constituents affect drug susceptibility, and drugs interacting with sterols alter raft composition and membrane integrity, leading to yeast cell death. Because of the genetic tractability of yeast, analysis of yeast rafts could be an excellent model to approach unanswered questions of mammalian raft biology, and to understand the role of lipid rafts in the regulation of cell death and survival in human cells. A better insight in raft biology might lead to envisage new raft-mediated approaches to the treatment of human diseases where regulation of cell death and survival is critical, such as cancer and neurodegenerative diseases.

The yeast three-hybrid system as an experimental platform to identify proteins interacting with small signaling molecules in plant cells: Potential and limitations

Directory of Open Access Journals (Sweden)

Stéphanie eCottier

2011-12-01

Full Text Available Chemical genetics is a powerful scientific strategy that utilizes small bioactive molecules as experimental tools to unravel biological processes. Bioactive compounds occurring in nature represent an enormous diversity of structures that can be used to dissect functions of biological systems. Once the bioactivity of a natural or synthetic compound has been critically evaluated the challenge remains to identify its molecular target and mode of action, which usually is a time consuming and labor-intensive process. To facilitate this task, we decided to implement the yeast three-hybrid (Y3H technology as a general experimental platform to scan the whole Arabidopsis proteome for targets of small signaling molecules. The Y3H technology is based on the yeast two-hybrid system and allows direct cloning of proteins that interact in vivo with a synthetic hybrid ligand, which comprises the biologically active molecule of interest covalently linked to methotrexate (Mtx. In yeast nucleus the hybrid ligand connects two fusion proteins: the Mtx part binding to dihydrofolate reductase fused to a DNA binding domain (encoded in the yeast strain, and the bioactive molecule part binding to its potential protein target fused to a DNA activating domain (encoded on a cDNA expression vector. During cDNA library screening, the formation of this ternary, transcriptional activator complex leads to reporter gene activation in yeast cells, and thereby allows selection of the putative targets of small bioactive molecules of interest. Here we present the strategy and experimental details for construction and application of a Y3H platform, including chemical synthesis of different hybrid ligands, construction of suitable cDNA libraries, the choice of yeast strains, and appropriate screening conditions. Based on the results obtained and the current literature we discussed the perspectives and limitations of the Y3H approach for identifying targets of small bioactive molecules.

Prions in yeast

OpenAIRE

Bezdíčka, Martin

2013-01-01

The thesis describes yeast prions and their biological effects on yeast in general. It defines the basic characteristics of yeast prions, that distinguish prions from other proteins. The thesis introduces various possibilities of prion formation, and propagation as well as specific types of yeast prions, including various functions of most studied types of prions. The thesis also focuses on chaperones that affect the state of yeast prions in cells. Lastly, the thesis indicates similarities be...

Peptide microarrays to probe for competition for binding sites in a protein interaction network

NARCIS (Netherlands)

Sinzinger, M.D.S.; Ruttekolk, I.R.R.; Gloerich, J.; Wessels, H.; Chung, Y.D.; Adjobo-Hermans, M.J.W.; Brock, R.E.

2013-01-01

Cellular protein interaction networks are a result of the binding preferences of a particular protein and the entirety of interactors that mutually compete for binding sites. Therefore, the reconstruction of interaction networks by the accumulation of interaction networks for individual proteins

NASCENT: an automatic protein interaction network generation tool for non-model organisms.

Science.gov (United States)

Banky, Daniel; Ordog, Rafael; Grolmusz, Vince

2009-04-24

Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software. http://nascent.pitgroup.org.

Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities?

Science.gov (United States)

Freilich, Mara A; Wieters, Evie; Broitman, Bernardo R; Marquet, Pablo A; Navarrete, Sergio A

2018-03-01

Co-occurrence methods are increasingly utilized in ecology to infer networks of species interactions where detailed knowledge based on empirical studies is difficult to obtain. Their use is particularly common, but not restricted to, microbial networks constructed from metagenomic analyses. In this study, we test the efficacy of this procedure by comparing an inferred network constructed using spatially intensive co-occurrence data from the rocky intertidal zone in central Chile to a well-resolved, empirically based, species interaction network from the same region. We evaluated the overlap in the information provided by each network and the extent to which there is a bias for co-occurrence data to better detect known trophic or non-trophic, positive or negative interactions. We found a poor correspondence between the co-occurrence network and the known species interactions with overall sensitivity (probability of true link detection) equal to 0.469, and specificity (true non-interaction) equal to 0.527. The ability to detect interactions varied with interaction type. Positive non-trophic interactions such as commensalism and facilitation were detected at the highest rates. These results demonstrate that co-occurrence networks do not represent classical ecological networks in which interactions are defined by direct observations or experimental manipulations. Co-occurrence networks provide information about the joint spatial effects of environmental conditions, recruitment, and, to some extent, biotic interactions, and among the latter, they tend to better detect niche-expanding positive non-trophic interactions. Detection of links (sensitivity or specificity) was not higher for well-known intertidal keystone species than for the rest of consumers in the community. Thus, as observed in previous empirical and theoretical studies, patterns of interactions in co-occurrence networks must be interpreted with caution, especially when extending interaction

Responses to olfactory signals reflect network structure of flower-visitor interactions.

Science.gov (United States)

Junker, Robert R; Höcherl, Nicole; Blüthgen, Nico

2010-07-01

1. Network analyses provide insights into the diversity and complexity of ecological interactions and have motivated conclusions about community stability and co-evolution. However, biological traits and mechanisms such as chemical signals regulating the interactions between individual species--the microstructure of a network--are poorly understood. 2. We linked the responses of receivers (flower visitors) towards signals (flower scent) to the structure of a highly diverse natural flower-insect network. For each interaction, we define link temperature--a newly developed metric--as the deviation of the observed interaction strength from neutrality, assuming that animals randomly interact with flowers. 3. Link temperature was positively correlated to the specific visitors' responses to floral scents, experimentally examined in a mobile olfactometer. Thus, communication between plants and consumers via phytochemical signals reflects a significant part of the microstructure in a complex network. Negative as well as positive responses towards floral scents contributed to these results, where individual experience was important apart from innate behaviour. 4. Our results indicate that: (1) biological mechanisms have a profound impact on the microstructure of complex networks that underlies the outcome of aggregate statistics, and (2) floral scents act as a filter, promoting the visitation of some flower visitors, but also inhibiting the visitation of others.

A global genetic interaction network maps a wiring diagram of cellular function.

Science.gov (United States)

Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D; Pelechano, Vicent; Styles, Erin B; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F; Li, Sheena C; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; San Luis, Bryan-Joseph; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M; Moore, Claire L; Rosebrock, Adam P; Caudy, Amy A; Myers, Chad L; Andrews, Brenda; Boone, Charles

2016-09-23

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell. Copyright © 2016, American Association for the Advancement of Science.

Cell cycle control by a minimal Cdk network.

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Claude Gérard

2015-02-01

Full Text Available In present-day eukaryotes, the cell division cycle is controlled by a complex network of interacting proteins, including members of the cyclin and cyclin-dependent protein kinase (Cdk families, and the Anaphase Promoting Complex (APC. Successful progression through the cell cycle depends on precise, temporally ordered regulation of the functions of these proteins. In light of this complexity, it is surprising that in fission yeast, a minimal Cdk network consisting of a single cyclin-Cdk fusion protein can control DNA synthesis and mitosis in a manner that is indistinguishable from wild type. To improve our understanding of the cell cycle regulatory network, we built and analysed a mathematical model of the molecular interactions controlling the G1/S and G2/M transitions in these minimal cells. The model accounts for all observed properties of yeast strains operating with the fusion protein. Importantly, coupling the model's predictions with experimental analysis of alternative minimal cells, we uncover an explanation for the unexpected fact that elimination of inhibitory phosphorylation of Cdk is benign in these strains while it strongly affects normal cells. Furthermore, in the strain without inhibitory phosphorylation of the fusion protein, the distribution of cell size at division is unusually broad, an observation that is accounted for by stochastic simulations of the model. Our approach provides novel insights into the organization and quantitative regulation of wild type cell cycle progression. In particular, it leads us to propose a new mechanistic model for the phenomenon of mitotic catastrophe, relying on a combination of unregulated, multi-cyclin-dependent Cdk activities.

Yeast identification in floral nectar of Mimulus aurantiacus (Invited)

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Kyauk, C.; Belisle, M.; Fukami, T.

2009-12-01

Nectar is such a sugar-rich resource that serves as a natural habitat in which microbes thrive. As a result, yeasts arrive to nectar on the bodies of pollinators such as hummingbirds and bees. Yeasts use the sugar in nectar for their own needs when introduced. This research focuses on the identification of different types of yeast that are found in the nectar of Mimulus aurantiacus (commonly known as sticky monkey-flower). Unopened Mimulus aurantiacus flower buds were tagged at Jasper Ridge and bagged three days later. Floral nectar was then extracted and plated on potato dextrose agar. Colonies on the plates were isolated and DNA was extracted from each sample using QIAGEN DNeasy Plant Mini Kit. The DNA was amplified through PCR and ran through gel electrophoresis. The PCR product was used to clone the nectar samples into an E.coli vector. Finally, a phylogenetic tree was created by BLAST searching sequences in GenBank using the Internal Transcribed Space (ITS) locus. It was found that 18 of the 50 identified species were Candida magnifica, 14 was Candida rancensis, 6 were Crytococcus albidus and there were 3 or less of the following: Starmella bombicola, Candida floricola, Aureobasidium pullulans, Pichia kluyvera, Metschnikowa cibodaserisis, Rhodotorua colostri, and Malassezia globosa. The low diversity of the yeast could have been due to several factors: time of collection, demographics of Jasper Ridge, low variety of pollinators, and sugar concentration of the nectar. The results of this study serve as a necessary first step for a recently started research project on ecological interactions between plants, pollinators, and nectar-living yeast. More generally, this research studies the use of the nectar-living yeast community as a natural microcosm for addressing basic questions about the role of dispersal and competitive and facilitative interactions in ecological succession.

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

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Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

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Asa Thibodeau

2016-06-01

Full Text Available Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1 building and visualizing chromatin interaction networks, 2 annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3 querying network components based on gene name or chromosome location, and 4 utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

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Thibodeau, Asa; Márquez, Eladio J; Luo, Oscar; Ruan, Yijun; Menghi, Francesca; Shin, Dong-Guk; Stitzel, Michael L; Vera-Licona, Paola; Ucar, Duygu

2016-06-01

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

Chemical signaling and insect attraction is a conserved trait in yeasts.

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Becher, Paul G; Hagman, Arne; Verschut, Vasiliki; Chakraborty, Amrita; Rozpędowska, Elżbieta; Lebreton, Sébastien; Bengtsson, Marie; Flick, Gerhard; Witzgall, Peter; Piškur, Jure

2018-03-01

Yeast volatiles attract insects, which apparently is of mutual benefit, for both yeasts and insects. However, it is unknown whether biosynthesis of metabolites that attract insects is a basic and general trait, or if it is specific for yeasts that live in close association with insects. Our goal was to study chemical insect attractants produced by yeasts that span more than 250 million years of evolutionary history and vastly differ in their metabolism and lifestyle. We bioassayed attraction of the vinegar fly Drosophila melanogaster to odors of phylogenetically and ecologically distinct yeasts grown under controlled conditions. Baker's yeast Saccharomyces cerevisiae , the insect-associated species Candida californica , Pichia kluyveri and Metschnikowia andauensis , wine yeast Dekkera bruxellensis , milk yeast Kluyveromyces lactis , the vertebrate pathogens Candida albicans and Candida glabrata , and oleophilic Yarrowia lipolytica were screened for fly attraction in a wind tunnel. Yeast headspace was chemically analyzed, and co-occurrence of insect attractants in yeasts and flowering plants was investigated through a database search. In yeasts with known genomes, we investigated the occurrence of genes involved in the synthesis of key aroma compounds. Flies were attracted to all nine yeasts studied. The behavioral response to baker's yeast was independent of its growth stage. In addition to Drosophila , we tested the basal hexapod Folsomia candida (Collembola) in a Y-tube assay to the most ancient yeast, Y. lipolytica, which proved that early yeast signals also function on clades older than neopteran insects. Behavioral and chemical data and a search for selected genes of volatile metabolites underline that biosynthesis of chemical signals is found throughout the yeast clade and has been conserved during the evolution of yeast lifestyles. Literature and database reviews corroborate that yeast signals mediate mutualistic interactions between insects and yeasts

Data management of protein interaction networks

CERN Document Server

Cannataro, Mario

2012-01-01

Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate

Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.

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Carson M Andorf

Full Text Available Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH with one or two binding sites, or multiple-interface hubs (MIH with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations or party hubs (i.e., simultaneously interact with multiple partners.Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

Yeast Tdh3 (glyceraldehyde 3-phosphate dehydrogenase is a Sir2-interacting factor that regulates transcriptional silencing and rDNA recombination.

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Alison E Ringel

Full Text Available Sir2 is an NAD(+-dependent histone deacetylase required to mediate transcriptional silencing and suppress rDNA recombination in budding yeast. We previously identified Tdh3, a glyceraldehyde 3-phosphate dehydrogenase (GAPDH, as a high expression suppressor of the lethality caused by Sir2 overexpression in yeast cells. Here we show that Tdh3 interacts with Sir2, localizes to silent chromatin in a Sir2-dependent manner, and promotes normal silencing at the telomere and rDNA. Characterization of specific TDH3 alleles suggests that Tdh3's influence on silencing requires nuclear localization but does not correlate with its catalytic activity. Interestingly, a genetic assay suggests that Tdh3, an NAD(+-binding protein, influences nuclear NAD(+ levels; we speculate that Tdh3 links nuclear Sir2 with NAD(+ from the cytoplasm.

Unified Alignment of Protein-Protein Interaction Networks.

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Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

2017-04-19

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

Detection of protein complex from protein-protein interaction network using Markov clustering

International Nuclear Information System (INIS)

Ochieng, P J; Kusuma, W A; Haryanto, T

2017-01-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

The yeast Sks1p kinase signaling network regulates pseudohyphal growth and glucose response.

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Cole Johnson

2014-03-01

Full Text Available The yeast Saccharomyces cerevisiae undergoes a dramatic growth transition from its unicellular form to a filamentous state, marked by the formation of pseudohyphal filaments of elongated and connected cells. Yeast pseudohyphal growth is regulated by signaling pathways responsive to reductions in the availability of nitrogen and glucose, but the molecular link between pseudohyphal filamentation and glucose signaling is not fully understood. Here, we identify the glucose-responsive Sks1p kinase as a signaling protein required for pseudohyphal growth induced by nitrogen limitation and coupled nitrogen/glucose limitation. To identify the Sks1p signaling network, we applied mass spectrometry-based quantitative phosphoproteomics, profiling over 900 phosphosites for phosphorylation changes dependent upon Sks1p kinase activity. From this analysis, we report a set of novel phosphorylation sites and highlight Sks1p-dependent phosphorylation in Bud6p, Itr1p, Lrg1p, Npr3p, and Pda1p. In particular, we analyzed the Y309 and S313 phosphosites in the pyruvate dehydrogenase subunit Pda1p; these residues are required for pseudohyphal growth, and Y309A mutants exhibit phenotypes indicative of impaired aerobic respiration and decreased mitochondrial number. Epistasis studies place SKS1 downstream of the G-protein coupled receptor GPR1 and the G-protein RAS2 but upstream of or at the level of cAMP-dependent PKA. The pseudohyphal growth and glucose signaling transcription factors Flo8p, Mss11p, and Rgt1p are required to achieve wild-type SKS1 transcript levels. SKS1 is conserved, and deletion of the SKS1 ortholog SHA3 in the pathogenic fungus Candida albicans results in abnormal colony morphology. Collectively, these results identify Sks1p as an important regulator of filamentation and glucose signaling, with additional relevance towards understanding stress-responsive signaling in C. albicans.

LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

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MASLOV,S.SNEPPEN,K.

2003-11-17

Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.

Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p

DEFF Research Database (Denmark)

Moxley, Joel F.; Jewett, Michael Christopher; Antoniewicz, Maciek R.

2009-01-01

. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate m......RNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental C-13-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator...... of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow...

User-Centric Secure Cross-Site Interaction Framework for Online Social Networking Services

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Ko, Moo Nam

2011-01-01

Social networking service is one of major technological phenomena on Web 2.0. Hundreds of millions of users are posting message, photos, and videos on their profiles and interacting with other users, but the sharing and interaction are limited within the same social networking site. Although users can share some content on a social networking site…

Topological and functional properties of the small GTPases protein interaction network.

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Anna Delprato

Full Text Available Small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. A great deal of experimental evidence supports the existence of signaling cascades and feedback loops within and among the small GTPase subfamilies suggesting that these proteins function in a coordinated and cooperative manner. The interplay occurs largely through association with bi-partite regulatory and effector proteins but can also occur through the active form of the small GTPases themselves. In order to understand the connectivity of the small GTPases signaling routes, a systems-level approach that analyzes data describing direct and indirect interactions was used to construct the small GTPases protein interaction network. The data were curated from the Search Tool for the Retrieval of Interacting Genes (STRING database and include only experimentally validated interactions. The network method enables the conceptualization of the overall structure as well as the underlying organization of the protein-protein interactions. The interaction network described here is comprised of 778 nodes and 1943 edges and has a scale-free topology. Rac1, Cdc42, RhoA, and HRas are identified as the hubs. Ten sub-network motifs are also identified in this study with themes in apoptosis, cell growth/proliferation, vesicle traffic, cell adhesion/junction dynamics, the nicotinamide adenine dinucleotide phosphate (NADPH oxidase response, transcription regulation, receptor-mediated endocytosis, gene silencing, and growth factor signaling. Bottleneck proteins that bridge signaling paths and proteins that overlap in multiple small GTPase networks are described along with the functional annotation of all proteins in the network.

Building and analyzing protein interactome networks by cross-species comparisons

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Blackman Barron

2010-03-01

Full Text Available Abstract Background A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species. Results The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced. Conclusions Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website http://www.interologfinder.org provides research biologists intuitive access to this data.

Cell cycle commitment in budding yeast emerges from the cooperation of multiple bistable switches

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Zhang, Tongli; Schmierer, Bernhard; Novák, Béla

2011-01-01

The start-transition (START) in the G1 phase marks the point in the cell cycle at which a yeast cell initiates a new round of cell division. Once made, this decision is irreversible and the cell is committed to progressing through the entire cell cycle, irrespective of arrest signals such as pheromone. How commitment emerges from the underlying molecular interaction network is poorly understood. Here, we perform a dynamical systems analysis of an established cell cycle model, which has never been analysed from a commitment perspective. We show that the irreversibility of the START transition and subsequent commitment can be consistently explained in terms of the interplay of multiple bistable molecular switches. By applying an existing mathematical model to a novel problem and by expanding the model in a self-consistent manner, we achieve several goals: we bring together a large number of experimental findings into a coherent theoretical framework; we increase the scope and the applicability of the original model; we give a systems level explanation of how the START transition and the cell cycle commitment arise from the dynamical features of the underlying molecular interaction network; and we make clear, experimentally testable predictions. PMID:22645649

Network of microRNAs-mRNAs Interactions in Pancreatic Cancer

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Naderi, Elnaz; Mostafaei, Mehdi; Pourshams, Akram

2014-01-01

Background. MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death. Methods. 56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool. Results. This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated. Conclusion. Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches. PMID:24895587

Network of microRNAs-mRNAs Interactions in Pancreatic Cancer

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Elnaz Naderi

2014-01-01

Full Text Available Background. MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death. Methods. 56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool. Results. This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated. Conclusion. Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches.

Synergistic interactions promote behavior spreading and alter phase transitions on multiplex networks

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Liu, Quan-Hui; Wang, Wei; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-02-01

Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system.

Yeast-2-Hybrid data file showing progranulin interactions in human fetal brain and bone marrow libraries.

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Tegeder, Irmgard

2016-12-01

Progranulin deficiency in humans is associated with neurodegeneration. Its mechanisms are not yet fully understood. We performed a Yeast-2-Hybrid screen using human full-length progranulin as bait to assess the interactions of progranulin. Progranulin was screened against human fetal brain and human bone marrow libraries using the standard Matchmaker technology (Clontech). This article contains the full Y2H data table, including blast results and sequences, a sorted table according to selection criteria for likely positive, putatively positive, likely false and false preys, and tables showing the gene ontology terms associated with the likely and putative preys of the brain and bone marrow libraries. The interactions with autophagy proteins were confirmed and functionally analyzed in "Progranulin overexpression in sensory neurons attenuates neuropathic pain in mice: Role of autophagy" (C. Altmann, S. Hardt, C. Fischer, J. Heidler, H.Y. Lim, A. Haussler, B. Albuquerque, B. Zimmer, C. Moser, C. Behrends, F. Koentgen, I. Wittig, M.H. Schmidt, A.M. Clement, T. Deller, I. Tegeder, 2016) [1].

Cell-autonomous mechanisms of chronological aging in the yeast Saccharomyces cerevisiae

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Anthony Arlia-Ciommo

2014-05-01

Full Text Available A body of evidence supports the view that the signaling pathways governing cellular aging – as well as mechanisms of their modulation by longevity-extending genetic, dietary and pharmacological interventions - are conserved across species. The scope of this review is to critically analyze recent advances in our understanding of cell-autonomous mechanisms of chronological aging in the budding yeast Saccharomyces cerevisiae. Based on our analysis, we propose a concept of a biomolecular network underlying the chronology of cellular aging in yeast. The concept posits that such network progresses through a series of lifespan checkpoints. At each of these checkpoints, the intracellular concentrations of some key intermediates and products of certain metabolic pathways - as well as the rates of coordinated flow of such metabolites within an intricate network of intercompartmental communications - are monitored by some checkpoint-specific ′′master regulator′′ proteins. The concept envisions that a synergistic action of these master regulator proteins at certain early-life and late-life checkpoints modulates the rates and efficiencies of progression of such processes as cell metabolism, growth, proliferation, stress resistance, macromolecular homeostasis, survival and death. The concept predicts that, by modulating these vital cellular processes throughout lifespan (i.e., prior to an arrest of cell growth and division, and following such arrest, the checkpoint-specific master regulator proteins orchestrate the development and maintenance of a pro- or anti-aging cellular pattern and, thus, define longevity of chronologically aging yeast.

Cell-autonomous mechanisms of chronological aging in the yeast Saccharomyces cerevisiae.

Science.gov (United States)

Arlia-Ciommo, Anthony; Leonov, Anna; Piano, Amanda; Svistkova, Veronika; Titorenko, Vladimir I

2014-05-27

A body of evidence supports the view that the signaling pathways governing cellular aging - as well as mechanisms of their modulation by longevity-extending genetic, dietary and pharmacological interventions - are conserved across species. The scope of this review is to critically analyze recent advances in our understanding of cell-autonomous mechanisms of chronological aging in the budding yeast Saccharomyces cerevisiae . Based on our analysis, we propose a concept of a biomolecular network underlying the chronology of cellular aging in yeast. The concept posits that such network progresses through a series of lifespan checkpoints. At each of these checkpoints, the intracellular concentrations of some key intermediates and products of certain metabolic pathways - as well as the rates of coordinated flow of such metabolites within an intricate network of intercompartmental communications - are monitored by some checkpoint-specific "master regulator" proteins. The concept envisions that a synergistic action of these master regulator proteins at certain early-life and late-life checkpoints modulates the rates and efficiencies of progression of such processes as cell metabolism, growth, proliferation, stress resistance, macromolecular homeostasis, survival and death. The concept predicts that, by modulating these vital cellular processes throughout lifespan (i.e., prior to an arrest of cell growth and division, and following such arrest), the checkpoint-specific master regulator proteins orchestrate the development and maintenance of a pro- or anti-aging cellular pattern and, thus, define longevity of chronologically aging yeast.

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.

Integrated analysis of multiple data sources reveals modular structure of biological networks

International Nuclear Information System (INIS)

Lu Hongchao; Shi Baochen; Wu Gaowei; Zhang Yong; Zhu Xiaopeng; Zhang Zhihua; Liu Changning; Zhao, Yi; Wu Tao; Wang Jie; Chen Runsheng

2006-01-01

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks

Structural Modeling and Characteristics Analysis of Flow Interaction Networks in the Internet

International Nuclear Information System (INIS)

Wu Xiao-Yu; Gu Ren-Tao; Pan Zhuo-Ya; Jin Wei-Qi; Ji Yue-Feng

2015-01-01

Applying network duality and elastic mechanics, we investigate the interactions among Internet flows by constructing a weighted undirected network, where the vertices and the edges represent the flows and the mutual dependence between flows, respectively. Based on the obtained flow interaction network, we find the existence of ‘super flow’ in the Internet, indicating that some flows have a great impact on a huge number of other flows; moreover, one flow can spread its influence to another through a limited quantity of flows (less than 5 in the experimental simulations), which shows strong small-world characteristics like the social network. To reflect the flow interactions in the physical network congestion evaluation, the ‘congestion coefficient’ is proposed as a new metric which shows a finer observation on congestion than the conventional one. (paper)

Solution NMR study of the yeast cytochrome c peroxidase: cytochrome c interaction

Energy Technology Data Exchange (ETDEWEB)

Volkov, Alexander N., E-mail: ovolkov@vub.ac.be; Nuland, Nico A. J. van [Vrije Universiteit Brussel, Jean Jeener NMR Centre, Structural Biology Brussels (Belgium)

2013-07-15

Here we present a solution NMR study of the complex between yeast cytochrome c (Cc) and cytochrome c peroxidase (CcP), a paradigm for understanding the biological electron transfer. Performed for the first time, the CcP-observed heteronuclear NMR experiments were used to probe the Cc binding in solution. Combining the Cc- and CcP-detected experiments, the binding interface on both proteins was mapped out, confirming that the X-ray structure of the complex is maintained in solution. Using NMR titrations and chemical shift perturbation analysis, we show that the interaction is independent of the CcP spin-state and is only weakly affected by the Cc redox state. Based on these findings, we argue that the complex of the ferrous Cc and the cyanide-bound CcP is a good mimic of the catalytically-active Cc-CcP compound I species. Finally, no chemical shift perturbations due to the Cc binding at the low-affinity CcP site were observed at low ionic strength. We discuss possible reasons for the absence of the effects and outline future research directions.

Dynamic and interacting complex networks

Science.gov (United States)

Dickison, Mark E.

This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

Yeast and yeast-like fungi associated with dry indehiscent fruits of Nothofagus nervosa in Patagonia, Argentina.

Science.gov (United States)

Fernández, Natalia V; Mestre, M Cecilia; Marchelli, Paula; Fontenla, Sonia B

2012-04-01

Nothofagus nervosa (Raulí) is a native tree species that yields valuable timber. It was overexploited in the past and is currently included in domestication and conservation programs. Several research programs have focused on the characterization of epiphytic microorganisms because it has been demonstrated that they can affect plant-pathogen interactions and/or promote plant growth. Although the microbial ecology of leaves has been well studied, less is known about microorganisms occurring on seeds and noncommercial fruits. In this work, we analyzed the yeast and yeast-like fungi present on N. nervosa fruits destined for the propagation of this species, as well as the effects of fruit preservation and seed dormancy-breaking processes on fungal diversity. Morphological and molecular methods were used, and differences between fungal communities were analyzed using a similarity index. A total of 171 isolates corresponding to 17 species were recovered, most of which belong to the phylum Ascomycota. The majority of the species develop mycelia, produce pigments and mycosporines, and these adaptation strategies are discussed. It was observed that the preservation process considerably reduced yeast and yeast-like fungal diversity. This is the first study concerning microbial communities associated with this ecologically and economically important species, and the information presented is relevant to domestication programs. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Yeast Interacting Proteins Database: YNL189W, YLR377C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available phagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this prey as prey...ated or autophagy-mediated degradation depending on growth conditions; interacts

Nectar bacteria, but not yeast, weaken a plant-pollinator mutualism.

Science.gov (United States)

Vannette, Rachel L; Gauthier, Marie-Pierre L; Fukami, Tadashi

2013-02-07

Mutualistic interactions are often subject to exploitation by species that are not directly involved in the mutualism. Understanding which organisms act as such 'third-party' species and how they do so is a major challenge in the current study of mutualistic interactions. Here, we show that even species that appear ecologically similar can have contrasting effects as third-party species. We experimentally compared the effects of nectar-inhabiting bacteria and yeasts on the strength of a mutualism between a hummingbird-pollinated shrub, Mimulus aurantiacus, and its pollinators. We found that the common bacterium Gluconobacter sp., but not the common yeast Metschnikowia reukaufii, reduced pollination success, seed set and nectar consumption by pollinators, thereby weakening the plant-pollinator mutualism. We also found that the bacteria reduced nectar pH and total sugar concentration more greatly than the yeasts did and that the bacteria decreased glucose concentration and increased fructose concentration whereas the yeasts affected neither. These distinct changes to nectar chemistry may underlie the microbes' contrasting effects on the mutualism. Our results suggest that it is necessary to understand the determinants of microbial species composition in nectar and their differential modification of floral rewards to explain the mutual benefits that plants and pollinators gain from each other.

Nectar bacteria, but not yeast, weaken a plant–pollinator mutualism

Science.gov (United States)

Vannette, Rachel L.; Gauthier, Marie-Pierre L.; Fukami, Tadashi

2013-01-01

Mutualistic interactions are often subject to exploitation by species that are not directly involved in the mutualism. Understanding which organisms act as such ‘third-party’ species and how they do so is a major challenge in the current study of mutualistic interactions. Here, we show that even species that appear ecologically similar can have contrasting effects as third-party species. We experimentally compared the effects of nectar-inhabiting bacteria and yeasts on the strength of a mutualism between a hummingbird-pollinated shrub, Mimulus aurantiacus, and its pollinators. We found that the common bacterium Gluconobacter sp., but not the common yeast Metschnikowia reukaufii, reduced pollination success, seed set and nectar consumption by pollinators, thereby weakening the plant–pollinator mutualism. We also found that the bacteria reduced nectar pH and total sugar concentration more greatly than the yeasts did and that the bacteria decreased glucose concentration and increased fructose concentration whereas the yeasts affected neither. These distinct changes to nectar chemistry may underlie the microbes' contrasting effects on the mutualism. Our results suggest that it is necessary to understand the determinants of microbial species composition in nectar and their differential modification of floral rewards to explain the mutual benefits that plants and pollinators gain from each other. PMID:23222453

Fluctuating interaction network and time-varying stability of a natural fish community

Science.gov (United States)

Ushio, Masayuki; Hsieh, Chih-Hao; Masuda, Reiji; Deyle, Ethan R.; Ye, Hao; Chang, Chun-Wei; Sugihara, George; Kondoh, Michio

2018-02-01

Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

Yeast Interacting Proteins Database: YLR347C, YLR377C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available gy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this prey as prey (4...r autophagy-mediated degradation depending on growth conditions; interacts with V

Radiation stimulation of yeast crops for increasing output of alcohol and baker yeasts

International Nuclear Information System (INIS)

Vlad, E.; Marsheu, P.

1974-01-01

The purpose of this study was to stimulate by gamma radiation the existing commercial types of yeast so as to obtain yeasts that would better reflect the substrate and have improved reproductive capacity. The experiments were conducted under ordinary conditions using commercial yeasts received from one factory producing alcohol and bakery yeasts and isolated as pure cultures. Irradiating yeast cultures with small doses (up to 10 krad) was found to stimulate the reproduction and fermenting activity of yeast cells as manifested in increased accumulation of yeast biomass and greater yield of ethyl alcohol. (E.T.)

Yeast Interacting Proteins Database: YPR103W, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available tein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors...gulated gene expression; interacts with protein kinase Snf1p, glucose sensors Snf

Integration and visualization of non-coding RNA and protein interaction networks

OpenAIRE

Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian; Pan, Xiaoyong; Santos Delgado, Alberto; Anthon, Christian; Alkan, Ferhat; von Mering, Christian; Workman, Christopher; Jensen, Lars Juhl; Gorodkin, Jan

2015-01-01

Non-coding RNAs (ncRNAs) fulfill a diverse set of biological functions relying on interactions with other molecular entities. The advent of new experimental and computational approaches makes it possible to study ncRNAs and their associations on an unprecedented scale. We present RAIN (RNA Association and Interaction Networks) - a database that combines ncRNA-ncRNA, ncRNA-mRNA and ncRNA-protein interactions with large-scale protein association networks available in the STRING database. By int...

Yeast Interacting Proteins Database: YML064C, YLR377C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available d or autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this pre...er proteasome-mediated or autophagy-mediated degradation depending on growth conditions; interacts with Vid3

Major component analysis of dynamic networks of physiologic organ interactions

International Nuclear Information System (INIS)

Liu, Kang K L; Ma, Qianli D Y; Ivanov, Plamen Ch; Bartsch, Ronny P

2015-01-01

The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function. (paper)

Protein Inference from the Integration of Tandem MS Data and Interactome Networks.

Science.gov (United States)

Zhong, Jiancheng; Wang, Jianxing; Ding, Xiaojun; Zhang, Zhen; Li, Min; Wu, Fang-Xiang; Pan, Yi

2017-01-01

Since proteins are digested into a mixture of peptides in the preprocessing step of tandem mass spectrometry (MS), it is difficult to determine which specific protein a shared peptide belongs to. In recent studies, besides tandem MS data and peptide identification information, some other information is exploited to infer proteins. Different from the methods which first use only tandem MS data to infer proteins and then use network information to refine them, this study proposes a protein inference method named TMSIN, which uses interactome networks directly. As two interacting proteins should co-exist, it is reasonable to assume that if one of the interacting proteins is confidently inferred in a sample, its interacting partners should have a high probability in the same sample, too. Therefore, we can use the neighborhood information of a protein in an interactome network to adjust the probability that the shared peptide belongs to the protein. In TMSIN, a multi-weighted graph is constructed by incorporating the bipartite graph with interactome network information, where the bipartite graph is built with the peptide identification information. Based on multi-weighted graphs, TMSIN adopts an iterative workflow to infer proteins. At each iterative step, the probability that a shared peptide belongs to a specific protein is calculated by using the Bayes' law based on the neighbor protein support scores of each protein which are mapped by the shared peptides. We carried out experiments on yeast data and human data to evaluate the performance of TMSIN in terms of ROC, q-value, and accuracy. The experimental results show that AUC scores yielded by TMSIN are 0.742 and 0.874 in yeast dataset and human dataset, respectively, and TMSIN yields the maximum number of true positives when q-value less than or equal to 0.05. The overlap analysis shows that TMSIN is an effective complementary approach for protein inference.

Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

Science.gov (United States)

Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

2013-12-01

Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

Unravelling Darwin's entangled bank: architecture and robustness of mutualistic networks with multiple interaction types.

Science.gov (United States)

Dáttilo, Wesley; Lara-Rodríguez, Nubia; Jordano, Pedro; Guimarães, Paulo R; Thompson, John N; Marquis, Robert J; Medeiros, Lucas P; Ortiz-Pulido, Raul; Marcos-García, Maria A; Rico-Gray, Victor

2016-11-30

Trying to unravel Darwin's entangled bank further, we describe the architecture of a network involving multiple forms of mutualism (pollination by animals, seed dispersal by birds and plant protection by ants) and evaluate whether this multi-network shows evidence of a structure that promotes robustness. We found that species differed strongly in their contributions to the organization of the multi-interaction network, and that only a few species contributed to the structuring of these patterns. Moreover, we observed that the multi-interaction networks did not enhance community robustness compared with each of the three independent mutualistic networks when analysed across a range of simulated scenarios of species extinction. By simulating the removal of highly interacting species, we observed that, overall, these species enhance network nestedness and robustness, but decrease modularity. We discuss how the organization of interlinked mutualistic networks may be essential for the maintenance of ecological communities, and therefore the long-term ecological and evolutionary dynamics of interactive, species-rich communities. We suggest that conserving these keystone mutualists and their interactions is crucial to the persistence of species-rich mutualistic assemblages, mainly because they support other species and shape the network organization. © 2016 The Author(s).

Modeling interacting dynamic networks: II. Systematic study of the statistical properties of cross-links between two networks with preferred degrees

International Nuclear Information System (INIS)

Liu, Wenjia; Schmittmann, B; Zia, R K P

2014-01-01

In a recent work (Liu et al, 2013 J. Stat. Mech. P08001), we introduced dynamic networks with preferred degrees and presented simulation and analytic studies of a single, homogeneous system as well as two interacting networks. Here, we extend these studies to a wider range of parameter space, in a more systematic fashion. Though the interaction we introduced seems simple and intuitive, it produced dramatically different behavior in the single- and two-network systems. Specifically, partitioning the single network into two identical sectors, we find the cross-link distribution to be a sharply peaked Gaussian. In stark contrast, we find a very broad and flat plateau in the case of two interacting identical networks. A sound understanding of this phenomenon remains elusive. Exploring more asymmetric interacting networks, we discover a kind of ‘universal behavior’ for systems in which the ‘introverts’ (nodes with smaller preferred degree) are far outnumbered. Remarkably, an approximation scheme for their degree distribution can be formulated, leading to very successful predictions. (paper)

Linking plant specialization to dependence in interactions for seed set in pollination networks.

Science.gov (United States)

Tur, Cristina; Castro-Urgal, Rocío; Traveset, Anna

2013-01-01

Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

Linking plant specialization to dependence in interactions for seed set in pollination networks.

Directory of Open Access Journals (Sweden)

Cristina Tur

Full Text Available Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them. Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i linkage level (number of interactions, (ii diversity of interactions, and (iii closeness centrality (a measure of how much a species is connected to other plants via shared pollinators. Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

Yeast Interacting Proteins Database: YMR280C, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available olved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensor... glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, an

Yeast Interacting Proteins Database: YMR125W, YPL178W [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available so contains Sto1p, component of the spliceosomal commitment complex; interacts with Npl3p, possibly to packa...lso contains Sto1p, component of the spliceosomal commitment complex; interacts with Npl3p, possibly to pack

Architecture of the human and yeast general transcription and DNA repair factor TFIIH

Science.gov (United States)

Luo, Jie; Cimermancic, Peter; Viswanath, Shruthi; Ebmeier, Christopher C.; Kim, Bong; Dehecq, Marine; Raman, Vishnu; Greenberg, Charles H.; Pellarin, Riccardo; Sali, Andrej; Taatjes, Dylan J.; Hahn, Steven; Ranish, Jeff

2015-01-01

Summary TFIIH is essential for both RNA polymerase II transcription and DNA repair, and mutations in TFIIH can result in human disease. Here, we determine the molecular architecture of human and yeast TFIIH by an integrative approach using chemical crosslinking/mass spectrometry (CXMS) data, biochemical analyses, and previously published electron microscopy maps. We identified four new conserved “topological regions” that function as hubs for TFIIH assembly and more than 35 conserved topological features within TFIIH, illuminating a network of interactions involved in TFIIH assembly and regulation of its activities. We show that one of these conserved regions, the p62/Tfb1 Anchor region, directly interacts with the DNA helicase subunit XPD/Rad3 in native TFIIH and is required for the integrity and function of TFIIH. We also reveal the structural basis for defects in patients with Xeroderma pigmentosum and Trichothiodystrophy, with mutations found at the interface between the p62 Anchor region and the XPD subunit. PMID:26340423

Epidemic spreading in networks with nonrandom long-range interactions.

Science.gov (United States)

Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

2011-09-01

An "infection," understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both "close" contacts and "casual" encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called "conductance" controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

Yeast Interacting Proteins Database: YDL239C, YGR268C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...sembly of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spi

Yeast Interacting Proteins Database: YDL239C, YPL255W [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...ediate assembly of a Don1p-containing structure at the leading edge of the prospore membrane via interaction

Yeast Interacting Proteins Database: YDL239C, YOR324C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p... a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle pole

Yeast Interacting Proteins Database: YDL239C, YDR148C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...mbly of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spind

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

Science.gov (United States)

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

Development of Attention Networks and Their Interactions in Childhood

Science.gov (United States)

Pozuelos, Joan P.; Paz-Alonso, Pedro M.; Castillo, Alejandro; Fuentes, Luis J.; Rueda, M. Rosario

2014-01-01

In the present study, we investigated developmental trajectories of alerting, orienting, and executive attention networks and their interactions over childhood. Two cross-sectional experiments were conducted with different samples of 6-to 12-year-old children using modified versions of the attention network task (ANT). In Experiment 1 (N = 106),…

Synchronization unveils the organization of ecological networks with positive and negative interactions

Science.gov (United States)

Girón, Andrea; Saiz, Hugo; Bacelar, Flora S.; Andrade, Roberto F. S.; Gómez-Gardeñes, Jesús

2016-06-01

Network science has helped to understand the organization principles of the interactions among the constituents of large complex systems. However, recently, the high resolution of the data sets collected has allowed to capture the different types of interactions coexisting within the same system. A particularly important example is that of systems with positive and negative interactions, a usual feature appearing in social, neural, and ecological systems. The interplay of links of opposite sign presents natural difficulties for generalizing typical concepts and tools applied to unsigned networks and, moreover, poses some questions intrinsic to the signed nature of the network, such as how are negative interactions balanced by positive ones so to allow the coexistence and survival of competitors/foes within the same system? Here, we show that synchronization phenomenon is an ideal benchmark for uncovering such balance and, as a byproduct, to assess which nodes play a critical role in the overall organization of the system. We illustrate our findings with the analysis of synthetic and real ecological networks in which facilitation and competitive interactions coexist.

Synchronization unveils the organization of ecological networks with positive and negative interactions.

Science.gov (United States)

Girón, Andrea; Saiz, Hugo; Bacelar, Flora S; Andrade, Roberto F S; Gómez-Gardeñes, Jesús

2016-06-01

Network science has helped to understand the organization principles of the interactions among the constituents of large complex systems. However, recently, the high resolution of the data sets collected has allowed to capture the different types of interactions coexisting within the same system. A particularly important example is that of systems with positive and negative interactions, a usual feature appearing in social, neural, and ecological systems. The interplay of links of opposite sign presents natural difficulties for generalizing typical concepts and tools applied to unsigned networks and, moreover, poses some questions intrinsic to the signed nature of the network, such as how are negative interactions balanced by positive ones so to allow the coexistence and survival of competitors/foes within the same system? Here, we show that synchronization phenomenon is an ideal benchmark for uncovering such balance and, as a byproduct, to assess which nodes play a critical role in the overall organization of the system. We illustrate our findings with the analysis of synthetic and real ecological networks in which facilitation and competitive interactions coexist.

Visualization and quantification of three-dimensional distribution of yeast in bread dough.

Science.gov (United States)

Maeda, Tatsuro; DO, Gab-Soo; Sugiyama, Junichi; Araki, Tetsuya; Tsuta, Mizuki; Shiraga, Seizaburo; Ueda, Mitsuyoshi; Yamada, Masaharu; Takeya, Koji; Sagara, Yasuyuki

2009-07-01

A three-dimensional (3-D) bio-imaging technique was developed for visualizing and quantifying the 3-D distribution of yeast in frozen bread dough samples in accordance with the progress of the mixing process of the samples, applying cell-surface engineering to the surfaces of the yeast cells. The fluorescent yeast was recognized as bright spots at the wavelength of 520 nm. Frozen dough samples were sliced at intervals of 1 microm by an micro-slicer image processing system (MSIPS) equipped with a fluorescence microscope for acquiring cross-sectional images of the samples. A set of successive two-dimensional images was reconstructed to analyze the 3-D distribution of the yeast. The average shortest distance between centroids of enhanced green fluorescent protein (EGFP) yeasts was 10.7 microm at the pick-up stage, 9.7 microm at the clean-up stage, 9.0 microm at the final stage, and 10.2 microm at the over-mixing stage. The results indicated that the distribution of the yeast cells was the most uniform in the dough of white bread at the final stage, while the heterogeneous distribution at the over-mixing stage was possibly due to the destruction of the gluten network structure within the samples.

A network of paralogous stress response transcription factors in the human pathogen Candida glabrata.

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Jawad eMerhej

2016-05-01

Full Text Available The yeast Candida glabrata has become the second cause of systemic candidemia in humans. However, relatively few genome-wide studies have been conducted in this organism and our knowledge of its transcriptional regulatory network is quite limited. In the present work, we combined genome-wide chromatin immunoprecipitation (ChIP-seq, transcriptome analyses and DNA binding motif predictions to describe the regulatory interactions of the seven Yap (Yeast AP1 transcription factors of C. glabrata. We described a transcriptional network containing 255 regulatory interactions and 309 potential target genes. We predicted with high confidence the preferred DNA binding sites for 5 of the 7 CgYaps and showed a strong conservation of the Yap DNA binding properties between S. cerevisiae and C. glabrata. We provided reliable functional annotation for 3 of the 7 Yaps and identified for Yap1 and Yap5 a core regulon which is conserved in S. cerevisiae, C. glabrata and C. albicans. We uncovered new roles for CgYap7 in the regulation of iron-sulfur cluster biogenesis, for CgYap1 in the regulation of heme biosynthesis and for CgYap5 in the repression of GRX4 in response to iron starvation. These transcription factors define an interconnected transcriptional network at the cross-roads between redox homeostasis, oxygen consumption and iron metabolism.

Interaction of the RNP1 motif in PRT1 with HCR1 promotes 40S binding of eukaryotic initiation factor 3 in yeast

DEFF Research Database (Denmark)

Nielsen, Klaus H; Valásek, Leos; Sykes, Caroah

2006-01-01

We found that mutating the RNP1 motif in the predicted RRM domain in yeast eukaryotic initiation factor 3 (eIF3) subunit b/PRT1 (prt1-rnp1) impairs its direct interactions in vitro with both eIF3a/TIF32 and eIF3j/HCR1. The rnp1 mutation in PRT1 confers temperature-sensitive translation initiation...

Mutant allele of rna14 in fission yeast affects pre-mRNA splicing

Indian Academy of Sciences (India)

transcript. Rna14 protein in budding yeast has been implicated in cleavage and ... Subsequently, genetic interaction of Rna14 with prp1 and physical .... molecular yeast techniques as described by Moreno et al. ..... To elucidate the role of Rna14 in splicing, RT-PCR analysis ..... design principles of a dynamic RNP machine.

Nectar-living yeasts of a tropical host plant community: diversity and effects on community-wide floral nectar traits

Science.gov (United States)

2017-01-01

We characterize the diversity of nectar-living yeasts of a tropical host plant community at different hierarchical sampling levels, measure the associations between yeasts and nectariferous plants, and measure the effect of yeasts on nectar traits. Using a series of hierarchically nested sampling units, we extracted nectar from an assemblage of host plants that were representative of the diversity of life forms, flower shapes, and pollinator types in the tropical area of Yucatan, Mexico. Yeasts were isolated from single nectar samples; their DNA was identified, the yeast cell density was estimated, and the sugar composition and concentration of nectar were quantified using HPLC. In contrast to previous studies from temperate regions, the diversity of nectar-living yeasts in the plant community was characterized by a relatively high number of equally common species with low dominance. Analyses predict highly diverse nectar yeast communities in a relatively narrow range of tropical vegetation, suggesting that the diversity of yeasts will increase as the number of sampling units increases at the level of the species, genera, and botanical families of the hosts. Significant associations between specific yeast species and host plants were also detected; the interaction between yeasts and host plants impacted the effect of yeast cell density on nectar sugars. This study provides an overall picture of the diversity of nectar-living yeasts in tropical host plants and suggests that the key factor that affects the community-wide patterns of nectar traits is not nectar chemistry, but rather the type of yeasts interacting with host plants. PMID:28717591

Nectar-living yeasts of a tropical host plant community: diversity and effects on community-wide floral nectar traits

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Azucena Canto

2017-07-01

Full Text Available We characterize the diversity of nectar-living yeasts of a tropical host plant community at different hierarchical sampling levels, measure the associations between yeasts and nectariferous plants, and measure the effect of yeasts on nectar traits. Using a series of hierarchically nested sampling units, we extracted nectar from an assemblage of host plants that were representative of the diversity of life forms, flower shapes, and pollinator types in the tropical area of Yucatan, Mexico. Yeasts were isolated from single nectar samples; their DNA was identified, the yeast cell density was estimated, and the sugar composition and concentration of nectar were quantified using HPLC. In contrast to previous studies from temperate regions, the diversity of nectar-living yeasts in the plant community was characterized by a relatively high number of equally common species with low dominance. Analyses predict highly diverse nectar yeast communities in a relatively narrow range of tropical vegetation, suggesting that the diversity of yeasts will increase as the number of sampling units increases at the level of the species, genera, and botanical families of the hosts. Significant associations between specific yeast species and host plants were also detected; the interaction between yeasts and host plants impacted the effect of yeast cell density on nectar sugars. This study provides an overall picture of the diversity of nectar-living yeasts in tropical host plants and suggests that the key factor that affects the community-wide patterns of nectar traits is not nectar chemistry, but rather the type of yeasts interacting with host plants.

Yeast Interacting Proteins Database: YGL237C, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available ene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding prote... expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein

Yeast Interacting Proteins Database: YKL002W, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available ene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding prote...xpression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Sp

Completing sparse and disconnected protein-protein network by deep learning.

Science.gov (United States)

Huang, Lei; Liao, Li; Wu, Cathy H

2018-03-22

Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network

The fission yeast spindle orientation checkpoint: a model that generates tension?

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Gachet, Yannick; Reyes, Céline; Goldstone, Sherilyn; Tournier, Sylvie

2006-10-15

In all eukaryotes, the alignment of the mitotic spindle with the axis of cell polarity is essential for accurate chromosome segregation as well as for the establishment of cell fate, and thus morphogenesis, during development. Studies in invertebrates, higher eukaryotes and yeast suggest that astral microtubules interact with the cell cortex to position the spindle. These microtubules are thought to impose pushing or pulling forces on the spindle poles to affect the rotation or movement of the spindle. In the fission yeast model, where cell division is symmetrical, spindle rotation is dependent on the interaction of astral microtubules with the cortical actin cytoskeleton. In these cells, a bub1-dependent mitotic checkpoint, the spindle orientation checkpoint (SOC), is activated when the spindles fail to align with the cell polarity axis. In this paper we review the mechanism that orientates the spindle during mitosis in fission yeast, and discuss the consequences of misorientation on metaphase progression. Copyright 2006 John Wiley & Sons, Ltd.

A domain-based approach to predict protein-protein interactions

Directory of Open Access Journals (Sweden)

Resat Haluk

2007-06-01

Full Text Available Abstract Background Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins. Results DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms. Conclusion We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed

Bird-plant interaction networks: a study on frugivory in Brazilian urban areas

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Diego Silva Freitas Oliveira

2015-12-01

Full Text Available In Brazil, few studies compare the consumption of native and exotic fruits, especially in an urban environment. The Network Theory may be useful in such studies, because it allows evaluating many bird and plant species involved in interactions. The goals of this study were: evaluate a bird frugivory interaction network in an urban environment; checking the role played by native and exotic plants in the network and comparing the consumer assemblies of these two plant groups. A literature review on bird frugivory in Brazilian urban areas was conducted, as well as an analysis to create an interaction network on a regional scale. The analysis included 15 papers with 70 bird species eating fruits from 15 plant species (6 exotic and 9 native. The exotic and native fruit consumers did not form different groups and the interaction network was significantly nested (NODF = 0.30; p < 0.01 and not modular (M = 0.36; p = 0.16. Two exotic plant species are in the generalist core of the frugivory network (Ficus microcarpa and Michelia champaca. The results point out that a relatively diversified bird group eats fruits in Brazilian urban areas in an opportunistic way, with no preference for native or exotic plants.

Yeast Interacting Proteins Database: YLR447C, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available xpression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Sp...; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; act

Quantifying the dynamics of coupled networks of switches and oscillators.

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Matthew R Francis

Full Text Available Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled oscillators. However, many complex systems are composed of components with diverse dynamics whose interactions drive the system's evolution. We, therefore, introduce a new modeling framework that describes the dynamics of networks composed of both oscillators and switches. Both oscillator synchronization and switch stability are preserved in these heterogeneous, coupled networks. Furthermore, this model recapitulates the qualitative dynamics for the yeast cell cycle consistent with the hypothesized dynamics resulting from decomposition of the regulatory network into dynamic motifs. Introducing feedback into the cell-cycle network induces qualitative dynamics analogous to limitless replicative potential that is a hallmark of cancer. As a result, the proposed model of switch and oscillator coupling provides the ability to incorporate mechanisms that underlie the synchronized stimulus response ubiquitous in biochemical systems.

Antagonism Between Osmophilic Lactic Acid Bacteria and Yeasts in Brine Fermentation of Soy Sauce

OpenAIRE

Noda, Fumio; Hayashi, Kazuya; Mizunuma, Takeji

1980-01-01

Brine fermentation by osmophilic lactic acid bacteria and yeasts for long periods of time is essential to produce a good quality of shoyu (Japanese fermented soy sauce). It is well known that lactic acid fermentation by osmophilic lactic acid bacteria results in the depression of alcoholic fermentation by osmophilic yeasts, but the nature of the interaction between osmophilic lactic acid bacteria and yeasts in brine fermentation of shoyu has not been revealed. The inhibitory effect of osmophi...

The tumor suppressor homolog in fission yeast, myh1+, displays a strong interaction with the checkpoint gene rad1+

International Nuclear Information System (INIS)

Jansson, Kristina; Warringer, Jonas; Farewell, Anne; Park, Han-Oh; Hoe, Kwang-Lae; Kim, Dong-Uk; Hayles, Jacqueline; Sunnerhagen, Per

2008-01-01

The DNA glycosylase MutY is strongly conserved in evolution, and homologs are found in most eukaryotes and prokaryotes examined. This protein is implicated in repair of oxidative DNA damage, in particular adenine mispaired opposite 7,8-dihydro-8-oxoguanine. Previous investigations in Escherichia coli, fission yeast, and mammalian cells show an association of mutations in MutY homologs with a mutator phenotype and carcinogenesis. Eukaryotic MutY homologs physically associate with several proteins with a role in replication, DNA repair, and checkpoint signaling, specifically the trimeric 9-1-1 complex. In a genetic investigation of the fission yeast MutY homolog, myh1 + , we show that the myh1 mutation confers a moderately increased UV sensitivity alone and in combination with mutations in several DNA repair genes. The myh1 rad1, and to a lesser degree myh1 rad9, double mutants display a synthetic interaction resulting in enhanced sensitivity to DNA damaging agents and hydroxyurea. UV irradiation of myh1 rad1 double mutants results in severe chromosome segregation defects and visible DNA fragmentation, and a failure to activate the checkpoint. Additionally, myh1 rad1 double mutants exhibit morphological defects in the absence of DNA damaging agents. We also found a moderate suppression of the slow growth and UV sensitivity of rhp51 mutants by the myh1 mutation. Our results implicate fission yeast Myh1 in repair of a wider range of DNA damage than previously thought, and functionally link it to the checkpoint pathway

Epidemic spreading in networks with nonrandom long-range interactions

Science.gov (United States)

Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

2011-09-01

An “infection,” understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both “close” contacts and “casual” encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called “conductance” controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

Fatty acids from oleaginous yeasts and yeast-like fungi and their potential applications.

Science.gov (United States)

Xue, Si-Jia; Chi, Zhe; Zhang, Yu; Li, Yan-Feng; Liu, Guang-Lei; Jiang, Hong; Hu, Zhong; Chi, Zhen-Ming

2018-02-01

Oleaginous yeasts, fatty acids biosynthesis and regulation in the oleaginous yeasts and the fatty acids from the oleaginous yeasts and their applications are reviewed in this article. Oleaginous yeasts such as Rhodosporidium toruloides, Yarrowia lipolytica, Rhodotorula mucilaginosa, and Aureobasidium melanogenum, which can accumulate over 50% lipid of their cell dry weight, have many advantages over other oleaginous microorganisms. The fatty acids from the oleaginous yeasts have many potential applications. Many oleaginous yeasts have now been genetically modified to over-produce fatty acids and their derivatives. The most important features of the oleaginous yeasts are that they have special enzymatic systems for enhanced biosynthesis and regulation of fatty acids in their lipid particles. Recently, some oleaginous yeasts such as R. toruloides have been found to have a unique fatty acids synthetase and other oleaginous yeasts such as A. melanogenum have a unique highly reducing polyketide synthase (HR-PKS) involved in the biosynthesis of hydroxyl fatty acids. It is necessary to further enhance lipid biosynthesis using metabolic engineering and explore new applications of fatty acids in biotechnology.

Yeast: An Overlooked Component of Bactrocera tryoni (Diptera: Tephritidae) Larval Gut Microbiota.

Science.gov (United States)

Deutscher, Ania T; Reynolds, Olivia L; Chapman, Toni A

2017-02-01

Yeasts, often in hydrolyzed form, are key ingredients in the larval and adult diets of tephritid fruit fly colonies. However, very little is known about the presence or role of yeasts in the diets of tephritid fruit flies in nature. Previous studies have identified bacteria but not detected yeasts in the gut of Queensland fruit fly, Bactrocera tryoni (Froggatt), one of Australia's most economically damaging insect pests of horticultural crops and of significant biosecurity concern domestically and internationally. Here we demonstrate that cultivable yeasts are commonly found in the gut of B. tryoni larvae from fruit hosts. Analysis of the ITS1, 5.8S rRNA gene, and ITS2 sequences of randomly selected isolates identified yeasts and yeast-like fungi of the genera Aureobasidium, Candida, Cryptococcus, Hanseniaspora, Pichia, and Starmerella. The prevalence of these yeasts in fruits suggests that larvae consume the yeasts as part of their diet. This work highlights that yeasts should be considered in future tephritid larval gut microbiota studies. Understanding tephritid-microbial symbiont interactions will lead to improvements in artificial diets and the quality of mass-reared tephritids for the sterile insect technique. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.