101
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Gardy JL, Lynn DJ, Brinkman FSL, Hancock REW. Enabling a systems biology approach to immunology: focus on innate immunity. Trends Immunol 2009; 30:249-62. [PMID: 19428301 DOI: 10.1016/j.it.2009.03.009] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2009] [Revised: 03/27/2009] [Accepted: 03/31/2009] [Indexed: 12/15/2022]
Abstract
Immunity is not simply the product of a series of discrete linear signalling pathways; rather it is comprised of a complex set of integrated responses arising from a dynamic network of thousands of molecules subject to multiple influences. Its behaviour often cannot be explained or predicted solely by examining its components. Here, we review recently developed resources for the systems-level investigation of immunity. Although innate immunity is emphasized here, its considerable overlap with adaptive immunity makes many of these resources relevant to both arms of the immune response. We discuss recent studies implementing these approaches and illustrate the potential of systems biology to generate novel insights into the complexities of innate immunity.
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Affiliation(s)
- Jennifer L Gardy
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada
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102
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Tuller T, Kupiec M, Ruppin E. Co-evolutionary networks of genes and cellular processes across fungal species. Genome Biol 2009; 10:R48. [PMID: 19416514 PMCID: PMC2718514 DOI: 10.1186/gb-2009-10-5-r48] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2009] [Revised: 02/24/2009] [Accepted: 05/05/2009] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The introduction of measures such as evolutionary rate and propensity for gene loss have significantly advanced our knowledge of the evolutionary history and selection forces acting upon individual genes and cellular processes. RESULTS We present two new measures, the 'relative evolutionary rate pattern' (rERP), which records the relative evolutionary rates of conserved genes across the different branches of a species' phylogenetic tree, and the 'copy number pattern' (CNP), which quantifies the rate of gene loss of less conserved genes. Together, these measures yield a high-resolution study of the co-evolution of genes in 9 fungal species, spanning 3,540 sets of orthologs. We find that the evolutionary tempo of conserved genes varies in different evolutionary periods. The co-evolution of genes' Gene Ontology categories exhibits a significant correlation with their functional distance in the Gene Ontology hierarchy, but not with their location on chromosomes, showing that cellular functions are a more important driving force in gene co-evolution than their chromosomal proximity. Two fundamental patterns of co-evolution of conserved genes, cooperative and reciprocal, are identified; only genes co-evolving cooperatively functionally back each other up. The co-evolution of conserved and less conserved genes exhibits both commonalities and differences; DNA metabolism is positively correlated with nuclear traffic, transcription processes and vacuolar biology in both analyses. CONCLUSIONS Overall, this study charts the first global network view of gene co-evolution in fungi. The future application of the approach presented here to other phylogenetic trees holds much promise in characterizing the forces that shape cellular co-evolution.
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Affiliation(s)
- Tamir Tuller
- School of Computer Sciences, Tel Aviv University, Ramat Aviv 69978, Israel
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Ramat Aviv 69978, Israel
- School of Medicine, Tel Aviv University, Ramat Aviv 69978, Israel
| | - Martin Kupiec
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Ramat Aviv 69978, Israel
| | - Eytan Ruppin
- School of Computer Sciences, Tel Aviv University, Ramat Aviv 69978, Israel
- School of Medicine, Tel Aviv University, Ramat Aviv 69978, Israel
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103
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Little DY, Chen L. Identification of coevolving residues and coevolution potentials emphasizing structure, bond formation and catalytic coordination in protein evolution. PLoS One 2009; 4:e4762. [PMID: 19274093 PMCID: PMC2651771 DOI: 10.1371/journal.pone.0004762] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2008] [Accepted: 02/19/2009] [Indexed: 11/30/2022] Open
Abstract
The structure and function of a protein is dependent on coordinated interactions between its residues. The selective pressures associated with a mutation at one site should therefore depend on the amino acid identity of interacting sites. Mutual information has previously been applied to multiple sequence alignments as a means of detecting coevolutionary interactions. Here, we introduce a refinement of the mutual information method that: 1) removes a significant, non-coevolutionary bias and 2) accounts for heteroscedasticity. Using a large, non-overlapping database of protein alignments, we demonstrate that predicted coevolving residue-pairs tend to lie in close physical proximity. We introduce coevolution potentials as a novel measure of the propensity for the 20 amino acids to pair amongst predicted coevolutionary interactions. Ionic, hydrogen, and disulfide bond-forming pairs exhibited the highest potentials. Finally, we demonstrate that pairs of catalytic residues have a significantly increased likelihood to be identified as coevolving. These correlations to distinct protein features verify the accuracy of our algorithm and are consistent with a model of coevolution in which selective pressures towards preserving residue interactions act to shape the mutational landscape of a protein by restricting the set of admissible neutral mutations.
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Affiliation(s)
- Daniel Y. Little
- Department of Molecular and Cell Biology, University of California, Berkeley, California, United States of America
| | - Lu Chen
- Department of Molecular and Cell Biology, University of California, Berkeley, California, United States of America
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America
- * E-mail:
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104
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Rentzsch R, Orengo CA. Protein function prediction--the power of multiplicity. Trends Biotechnol 2009; 27:210-9. [PMID: 19251332 DOI: 10.1016/j.tibtech.2009.01.002] [Citation(s) in RCA: 88] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2008] [Revised: 01/21/2009] [Accepted: 01/23/2009] [Indexed: 01/07/2023]
Abstract
Advances in experimental and computational methods have quietly ushered in a new era in protein function annotation. This 'age of multiplicity' is marked by the notion that only the use of multiple tools, multiple evidence and considering the multiple aspects of function can give us the broad picture that 21st century biology will need to link and alter micro- and macroscopic phenotypes. It might also help us to undo past mistakes by removing errors from our databases and prevent us from producing more. On the downside, multiplicity is often confusing. We therefore systematically review methods and resources for automated protein function prediction, looking at individual (biochemical) and contextual (network) functions, respectively.
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Affiliation(s)
- Robert Rentzsch
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK.
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105
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Ciccarelli FD, Miklós I. Co-evolutionary Models for Reconstructing Ancestral Genomic Sequences: Computational Issues and Biological Examples. COMPARATIVE GENOMICS 2009. [PMCID: PMC7120581 DOI: 10.1007/978-3-642-04744-2_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The inference of ancestral genomes is a fundamental problem in molecular evolution. Due to the statistical nature of this problem, the most likely or the most parsimonious ancestral genomes usually include considerable error rates. In general, these errors cannot be abolished by utilizing more exhaustive computational approaches, by using longer genomic sequences, or by analyzing more taxa. In recent studies we showed that co-evolution is an important force that can be used for significantly improving the inference of ancestral genome content. In this work we formally define a computational problem for the inference of ancestral genome content by co-evolution. We show that this problem is NP-hard and present both a Fixed Parameter Tractable (FPT) algorithm, and heuristic approximation algorithms for solving it. The running time of these algorithms on simulated inputs with hundreds of protein families and hundreds of co-evolutionary relations was fast (up to four minutes) and it achieved an approximation ratio < 1.3. We use our approach to study the ancestral genome content of the Fungi. To this end, we implement our approach on a dataset of 33,931 protein families and 20,317 co-evolutionary relations. Our algorithm added and removed hundreds of proteins from the ancestral genomes inferred by maximum likelihood (ML) or maximum parsimony (MP) while slightly affecting the likelihood/parsimony score of the results. A biological analysis revealed various pieces of evidence that support the biological plausibility of the new solutions.
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Affiliation(s)
| | - István Miklós
- Rényi Institute, Hungarian Academy of Sciences, Reáltanoda utca 13-15, 1053 Budapest, Hungary
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106
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Abstract
Biological interaction networks have been in the scientific limelight for nearly a decade. Increasingly, the concept of network biology and its various applications are becoming more commonplace in the community. Recent years have seen networks move from pretty pictures with limited application to solid concepts that are increasingly used to understand the fundamentals of biology. They are no longer merely results of postgenome analysis projects, but are now the starting point of many of the most exciting new scientific developments. We discuss here recent progress in identifying and understanding interaction networks, new tools that use them in predictive ways in exciting areas of biology, and how they have become the focus of many efforts to study, design and tinker with biological systems, with applications in biomedicine, bioengineering, ecology and beyond.
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Affiliation(s)
- Robert B Russell
- European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
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107
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Andres Leon E, Ezkurdia I, García B, Valencia A, Juan D. EcID. A database for the inference of functional interactions in E. coli. Nucleic Acids Res 2008; 37:D629-35. [PMID: 19004873 PMCID: PMC2686479 DOI: 10.1093/nar/gkn853] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The EcID database (Escherichia coli Interaction Database) provides a framework for the integration of information on functional interactions extracted from the following sources: EcoCyc (metabolic pathways, protein complexes and regulatory information), KEGG (metabolic pathways), MINT and IntAct (protein interactions). It also includes information on protein complexes from the two E. coli high-throughput pull-down experiments and potential interactions extracted from the literature using the web services associated to the iHOP text-mining system. Additionally, EcID incorporates results of various prediction methods, including two protein interaction prediction methods based on genomic information (Phylogenetic Profiles and Gene Neighbourhoods) and three methods based on the analysis of co-evolution (Mirror Tree, In Silico 2 Hybrid and Context Mirror). EcID associates to each prediction a specifically developed confidence score. The two main features that make EcID different from other systems are the combination of co-evolution-based predictions with the experimental data, and the introduction of E. coli-specific information, such as gene regulation information from EcoCyc. The possibilities offered by the combination of the EcID database information are illustrated with a prediction of potential functions for a group of poorly characterized genes related to yeaG. EcID is available online at http://ecid.bioinfo.cnio.es.
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Affiliation(s)
- Eduardo Andres Leon
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre-CNIO and Computer Sciences Department, Universidad Carlos III de Madrid, Spain
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108
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Correlated evolution of interacting proteins: looking behind the mirrortree. J Mol Biol 2008; 385:91-8. [PMID: 18930732 DOI: 10.1016/j.jmb.2008.09.078] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2008] [Revised: 09/25/2008] [Accepted: 09/27/2008] [Indexed: 12/31/2022]
Abstract
It has been observed that the evolutionary distances of interacting proteins often display a higher level of similarity than those of noninteracting proteins. This finding indicates that interacting proteins are subject to common evolutionary constraints and constitutes the basis of a method to predict protein interactions known as mirrortree. It has been difficult, however, to identify the direct cause of the observed similarities between evolutionary trees. One possible explanation is the existence of compensatory mutations between partners' binding sites to maintain proper binding. This explanation, though, has been recently challenged, and it has been suggested that the signal of correlated evolution uncovered by the mirrortree method is unrelated to any correlated evolution between binding sites. We examine the contribution of binding sites to the correlation between evolutionary trees of interacting domains. We show that binding neighborhoods of interacting proteins have, on average, higher coevolutionary signal compared with the regions outside binding sites; however, when the binding neighborhood is removed, the remaining domain sequence still contains some coevolutionary signal. In conclusion, the correlation between evolutionary trees of interacting domains cannot exclusively be attributed to the correlated evolution of the binding sites or to common evolutionary pressure exerted on the whole protein domain sequence, each of which contributes to the signal measured by the mirrortree approach.
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109
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Pazos F, Valencia A. Protein co-evolution, co-adaptation and interactions. EMBO J 2008; 27:2648-55. [PMID: 18818697 PMCID: PMC2556093 DOI: 10.1038/emboj.2008.189] [Citation(s) in RCA: 124] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2008] [Accepted: 08/28/2008] [Indexed: 01/28/2023] Open
Abstract
Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.
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Affiliation(s)
- Florencio Pazos
- Structure of Macromolecules, Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
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110
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Chua HN, Wong L. Increasing the reliability of protein interactomes. Drug Discov Today 2008; 13:652-8. [DOI: 10.1016/j.drudis.2008.05.004] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2008] [Revised: 05/09/2008] [Accepted: 05/13/2008] [Indexed: 11/28/2022]
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111
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Gonzalez O, Zimmer R. Assigning functional linkages to proteins using phylogenetic profiles and continuous phenotypes. ACTA ACUST UNITED AC 2008; 24:1257-63. [PMID: 18381403 DOI: 10.1093/bioinformatics/btn106] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION A class of non-homology-based methods for protein function prediction relies on the assumption that genes linked to a phenotypic trait are preferentially conserved among organisms that share the trait. These methods typically compare pairs of binary strings, where one string encodes the phylogenetic distribution of a trait and the other of a protein. In this work, we extended the approach to automatically deal with continuous phenotypes. RESULTS Rather than use a priori rules, which can be very subjective, to construct binary profiles from continuous phenotypes, we propose to systematically explore thresholds which can meaningfully separate the phenotype values. We illustrate our method by analyzing optimal growth temperatures, and demonstrate its usefulness by automatically retrieving genes which have been associated with thermophilic growth. We also apply the general approach, for the first time, to optimal growth pH, and make novel predictions. Finally, we show that our method can also be applied to other properties which may not be classically considered as phenotypes. Specifically, we studied correlations between genome size and the distribution of genes.
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Affiliation(s)
- Orland Gonzalez
- Institute for Informatics, Ludwig-Maximilians-Universität München, Amalienstr. 17, 80333 Munich, Germany.
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112
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Harrington ED, Jensen LJ, Bork P. Predicting biological networks from genomic data. FEBS Lett 2008; 582:1251-8. [PMID: 18294967 DOI: 10.1016/j.febslet.2008.02.033] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2008] [Accepted: 02/13/2008] [Indexed: 12/27/2022]
Abstract
Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.
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Affiliation(s)
- Eoghan D Harrington
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, D-69117 Heidelberg, Germany
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113
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Juan D, Pazos F, Valencia A. Co-evolution and co-adaptation in protein networks. FEBS Lett 2008; 582:1225-30. [DOI: 10.1016/j.febslet.2008.02.017] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2008] [Accepted: 02/08/2008] [Indexed: 10/22/2022]
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114
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Protein-protein interactions: analysis and prediction. MODERN GENOME ANNOTATION 2008. [PMCID: PMC7120725 DOI: 10.1007/978-3-211-75123-7_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Proteins represent the tools and appliances of the cell — they assemble into larger structural elements, catalyze the biochemical reactions of metabolism, transmit signals, move cargo across membrane boundaries and carry out many other tasks. For most of these functions proteins cannot act in isolation but require close cooperation with other proteins to accomplish their task. Often, this collaborative action implies physical interaction of the proteins involved. Accordingly, experimental detection, in silico prediction and computational analysis of protein-protein interactions (PPI) have attracted great attention in the quest for discovering functional links among proteins and deciphering the complex networks of the cell.
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