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Palma M, Roque FDC, Guerreiro JF, Mira NP, Queiroz L, Sá-Correia I. Search for genes responsible for the remarkably high acetic acid tolerance of a Zygosaccharomyces bailii-derived interspecies hybrid strain. BMC Genomics 2015; 16:1070. [PMID: 26673744 PMCID: PMC4681151 DOI: 10.1186/s12864-015-2278-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 12/08/2015] [Indexed: 11/30/2022] Open
Abstract
Background Zygosaccharomyces bailii is considered the most problematic acidic food spoilage yeast species due to its exceptional capacity to tolerate high concentrations of weak acids used as fungistatic preservatives at low pH. However, the mechanisms underlying its intrinsic remarkable tolerance to weak acids remain poorly understood. The identification of genes and mechanisms involved in Z. bailii acetic acid tolerance was on the focus of this study. For this, a genomic library from the highly acetic acid tolerant hybrid strain ISA1307, derived from Z. bailii and a closely related species and isolated from a sparkling wine production plant, was screened for acetic acid tolerance genes. This screen was based on the transformation of an acetic acid susceptible Saccharomyces cerevisiae mutant deleted for the gene encoding the acetic acid resistance determinant transcription factor Haa1. Results The expression of 31 different DNA inserts from ISA1307 strain genome was found to significantly increase the host cell tolerance to acetic acid. The in silico analysis of these inserts was facilitated by the recently available genome sequence of this strain. In total, 65 complete or truncated ORFs were identified as putative determinants of acetic acid tolerance and an S. cerevisiae gene homologous to most of them was found. These include genes involved in cellular transport and transport routes, protein fate, protein synthesis, amino acid metabolism and transcription. The role of strong candidates in Z. bailii and S. cerevisiae acetic acid tolerance was confirmed based on homologous and heterologous expression analyses. Conclusions ISA1307 genes homologous to S. cerevisiae genes GYP8, WSC4, PMT1, KTR7, RKR1, TIF3, ILV3 and MSN4 are proposed as strong candidate determinants of acetic acid tolerance. The ORF ZBAI_02295 that contains a functional domain associated to the uncharacterised integral membrane proteins of unknown function of the DUP family is also suggested as a relevant tolerance determinant. The genes ZbMSN4 and ZbTIF3, encoding a putative stress response transcription factor and a putative translation initiation factor, were confirmed as determinants of acetic acid tolerance in both Z. bailii and S. cerevisiae. This study provides valuable indications on the cellular components, pathways and processes to be targeted in order to control food spoilage by the highly acetic acid tolerant Z. bailii and Z. bailii-derived strains. Additionally, this information is essential to guide the improvement of yeast cells robustness against acetic acid if the objective is their use as cell factories. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2278-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Margarida Palma
- Department of Bioengineering, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
| | - Filipa de Canaveira Roque
- Department of Bioengineering, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
| | - Joana Fernandes Guerreiro
- Department of Bioengineering, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
| | - Nuno Pereira Mira
- Department of Bioengineering, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
| | - Lise Queiroz
- Department of Bioengineering, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
| | - Isabel Sá-Correia
- Department of Bioengineering, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.
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Monti M, Cozzolino M, Cozzolino F, Vitiello G, Tedesco R, Flagiello A, Pucci P. Puzzle of protein complexesin vivo: a present and future challenge for functional proteomics. Expert Rev Proteomics 2014; 6:159-69. [DOI: 10.1586/epr.09.7] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Lan A, Ziv-Ukelson M, Yeger-Lotem E. A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks. Bioinformatics 2013; 29:i210-6. [PMID: 23812986 PMCID: PMC3694656 DOI: 10.1093/bioinformatics/btt240] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
MOTIVATION A major challenge in systems biology is to reveal the cellular pathways that give rise to specific phenotypes and behaviours. Current techniques often rely on a network representation of molecular interactions, where each node represents a protein or a gene and each interaction is assigned a single static score. However, the use of single interaction scores fails to capture the tendency of proteins to favour different partners under distinct cellular conditions. RESULTS Here, we propose a novel context-sensitive network model, in which genes and protein nodes are assigned multiple contexts based on their gene ontology annotations, and their interactions are associated with multiple context-sensitive scores. Using this model, we developed a new approach and a corresponding tool, ContextNet, based on a dynamic programming algorithm for identifying signalling paths linking proteins to their downstream target genes. ContextNet finds high-ranking context-sensitive paths in the interactome, thereby revealing the intermediate proteins in the path and their path-specific contexts. We validated the model using 18 348 manually curated cellular paths derived from the SPIKE database. We next applied our framework to elucidate the responses of human primary lung cells to influenza infection. Top-ranking paths were much more likely to contain infection-related proteins, and this likelihood was highly correlated with path score. Moreover, the contexts assigned by the algorithm pointed to putative, as well as previously known responses to viral infection. Thus, context sensitivity is an important extension to current network biology models and can be efficiently used to elucidate cellular response mechanisms. AVAILABILITY ContextNet is publicly available at http://netbio.bgu.ac.il/ContextNet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexander Lan
- Department of Computer Science, National Center for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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Simple topological features reflect dynamics and modularity in protein interaction networks. PLoS Comput Biol 2013; 9:e1003243. [PMID: 24130468 PMCID: PMC3794914 DOI: 10.1371/journal.pcbi.1003243] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 08/14/2013] [Indexed: 11/30/2022] Open
Abstract
The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the S. cerevisiae interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes. A better understanding of protein interaction networks would be a great aid in furthering our knowledge of the molecular biology of the cell. Towards this end, large-scale protein-protein physical interaction data have been determined for organisms across the evolutionary spectrum. However, the resulting networks give a static view of interactomes, and our knowledge about protein interactions is rarely time or context specific. A previous prominent but controversial attempt to characterize the dynamic modularity of the interactome was based on integrating physical interaction data with gene activity measurements from transcript expression data. This analysis distinguished between proteins that are co-expressed with their interacting partners and those that are not, and argued that the former are intramodular and the latter are intermodular. By analyzing the interactomes of five organisms, we largely confirm the biological significance of this characterization through a variety of statistical tests and computational experiments. Surprisingly, however, we find that similar results can be obtained using just network information without additionally integrating expression data, suggesting that purely topological characteristics of interaction networks strongly reflect certain aspects of the dynamics and modularity of interactomes.
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Abstract
High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.
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Affiliation(s)
- Bonnie Berger
- Department of Mathematics and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
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Song J, Singh M. From hub proteins to hub modules: the relationship between essentiality and centrality in the yeast interactome at different scales of organization. PLoS Comput Biol 2013; 9:e1002910. [PMID: 23436988 PMCID: PMC3578755 DOI: 10.1371/journal.pcbi.1002910] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2012] [Accepted: 12/21/2012] [Indexed: 11/22/2022] Open
Abstract
Numerous studies have suggested that hub proteins in the S. cerevisiae physical interaction network are more likely to be essential than other proteins. The proposed reasons underlying this observed relationship between topology and functioning have been subject to some controversy, with recent work suggesting that it arises due to the participation of hub proteins in essential complexes and processes. However, do these essential modules themselves have distinct network characteristics, and how do their essential proteins differ in their topological properties from their non-essential proteins? We aimed to advance our understanding of protein essentiality by analyzing proteins, complexes and processes within their broader functional context and by considering physical interactions both within and across complexes and biological processes. In agreement with the view that essentiality is a modular property, we found that the number of intracomplex or intraprocess interactions that a protein has is a better indicator of its essentiality than its overall number of interactions. Moreover, we found that within an essential complex, its essential proteins have on average more interactions, especially intracomplex interactions, than its non-essential proteins. Finally, we built a module-level interaction network and found that essential complexes and processes tend to have higher interaction degrees in this network than non-essential complexes and processes; that is, they exhibit a larger amount of functional cross-talk than their non-essential counterparts.
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Affiliation(s)
- Jimin Song
- Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Mona Singh
- Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
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Capra JA, Pollard KS, Singh M. Novel genes exhibit distinct patterns of function acquisition and network integration. Genome Biol 2010; 11:R127. [PMID: 21187012 PMCID: PMC3046487 DOI: 10.1186/gb-2010-11-12-r127] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2010] [Revised: 11/18/2010] [Accepted: 12/27/2010] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Genes are created by a variety of evolutionary processes, some of which generate duplicate copies of an entire gene, while others rearrange pre-existing genetic elements or co-opt previously non-coding sequence to create genes with 'novel' sequences. These novel genes are thought to contribute to distinct phenotypes that distinguish organisms. The creation, evolution, and function of duplicated genes are well-studied; however, the genesis and early evolution of novel genes are not well-characterized. We developed a computational approach to investigate these issues by integrating genome-wide comparative phylogenetic analysis with functional and interaction data derived from small-scale and high-throughput experiments. RESULTS We examine the function and evolution of new genes in the yeast Saccharomyces cerevisiae. We observed significant differences in the functional attributes and interactions of genes created at different times and by different mechanisms. Novel genes are initially less integrated into cellular networks than duplicate genes, but they appear to gain functions and interactions more quickly than duplicates. Recently created duplicated genes show evidence of adapting existing functions to environmental changes, while young novel genes do not exhibit enrichment for any particular functions. Finally, we found a significant preference for genes to interact with other genes of similar age and origin. CONCLUSIONS Our results suggest a strong relationship between how and when genes are created and the roles they play in the cell. Overall, genes tend to become more integrated into the functional networks of the cell with time, but the dynamics of this process differ significantly between duplicate and novel genes.
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Affiliation(s)
- John A Capra
- Gladstone Institutes, University of California, San Francisco, 1650 Owens St, San Francisco, CA 94158, USA.
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Przytycka TM, Singh M, Slonim DK. Toward the dynamic interactome: it's about time. Brief Bioinform 2010; 11:15-29. [PMID: 20061351 PMCID: PMC2810115 DOI: 10.1093/bib/bbp057] [Citation(s) in RCA: 147] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2009] [Revised: 11/01/2009] [Indexed: 11/14/2022] Open
Abstract
Dynamic molecular interactions play a central role in regulating the functioning of cells and organisms. The availability of experimentally determined large-scale cellular networks, along with other high-throughput experimental data sets that provide snapshots of biological systems at different times and conditions, is increasingly helpful in elucidating interaction dynamics. Here we review the beginnings of a new subfield within computational biology, one focused on the global inference and analysis of the dynamic interactome. This burgeoning research area, which entails a shift from static to dynamic network analysis, promises to be a major step forward in our ability to model and reason about cellular function and behavior.
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Affiliation(s)
- Teresa M Przytycka
- National Center of Biotechnology Information, NLM, NIH, 8000 Rockville Pike, Bethesda MD 20814, USA.
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Abstract
BACKGROUND One of the most recent and important developments in drug discovery is a new drug development approach of building and analyzing networks that contain relationships among drugs and targets, diseases, genes and other components. These networks and their integrations provide useful information for finding new targets as well as new drugs. OBJECTIVE This review article aims to review recent developments in various types of networks and suggest the future direction of these network studies for drug discovery. METHODS Databases and networks are integrated into a more complete network to better present the relationships among drugs, targets, genes, phenotypes and diseases. After discussing the limitations and obstacles of the recent research, we suggest several strategies to build a successful and practical drug-target network. RESULTS/CONCLUSION A useful, integrated network can be built from various databases and networks by resolving several issues, such as limited coverage and inconsistency. This integrated network can be completed by the prediction of missing links, biological network comparison and drug target identification. Possible applications are multi-target drug development, drug repurposing, estimation of drug effect on target perturbations in the whole system and extraction of the suitable purpose of the drug-target sub-network.
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Affiliation(s)
- Soyoung Lee
- KAIST, Department of Bio and Brain Engineering, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701 Korea, Republic of Korea +82 42 350 4317 ; +82 42 350 4310 ;
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The capabilities of chaos and complexity. Int J Mol Sci 2009; 10:247-291. [PMID: 19333445 PMCID: PMC2662469 DOI: 10.3390/ijms10010247] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2008] [Revised: 12/27/2008] [Accepted: 01/04/2009] [Indexed: 11/17/2022] Open
Abstract
To what degree could chaos and complexity have organized a Peptide or RNA World of crude yet necessarily integrated protometabolism? How far could such protolife evolve in the absence of a heritable linear digital symbol system that could mutate, instruct, regulate, optimize and maintain metabolic homeostasis? To address these questions, chaos, complexity, self-ordered states, and organization must all be carefully defined and distinguished. In addition their cause-and-effect relationships and mechanisms of action must be delineated. Are there any formal (non physical, abstract, conceptual, algorithmic) components to chaos, complexity, self-ordering and organization, or are they entirely physicodynamic (physical, mass/energy interaction alone)? Chaos and complexity can produce some fascinating self-ordered phenomena. But can spontaneous chaos and complexity steer events and processes toward pragmatic benefit, select function over non function, optimize algorithms, integrate circuits, produce computational halting, organize processes into formal systems, control and regulate existing systems toward greater efficiency? The question is pursued of whether there might be some yet-to-be discovered new law of biology that will elucidate the derivation of prescriptive information and control. “System” will be rigorously defined. Can a low-informational rapid succession of Prigogine’s dissipative structures self-order into bona fide organization?
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Banks E, Nabieva E, Peterson R, Singh M. NetGrep: fast network schema searches in interactomes. Genome Biol 2008; 9:R138. [PMID: 18801179 PMCID: PMC2592716 DOI: 10.1186/gb-2008-9-9-r138] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2008] [Revised: 08/22/2008] [Accepted: 09/18/2008] [Indexed: 11/10/2022] Open
Abstract
NetGrep (http://genomics.princeton.edu/singhlab/netgrep/) is a system for searching protein interaction networks for matches to user-supplied 'network schemas'. Each schema consists of descriptions of proteins (for example, their molecular functions or putative domains) along with the desired topology and types of interactions among them. Schemas can thus describe domain-domain interactions, signaling and regulatory pathways, or more complex network patterns. NetGrep provides an advanced graphical interface for specifying schemas and fast algorithms for extracting their matches.
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Affiliation(s)
- Eric Banks
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Lab, Princeton, NJ 08544, USA
| | - Elena Nabieva
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Lab, Princeton, NJ 08544, USA
| | - Ryan Peterson
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
- Current address: Department of Computer Science, Cornell University, 4130 Upson Hall, Ithaca, NY 14853, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Lab, Princeton, NJ 08544, USA
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