Identification and analysis of evolutionarily cohesive functional modules in protein networks

The European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.
Genome Research (Impact Factor: 13.85). 04/2006; 16(3):374-82. DOI: 10.1101/gr.4336406
Source: PubMed

ABSTRACT The increasing number of sequenced genomes makes it possible to infer the evolutionary history of functional modules, i.e., groups of proteins that contribute jointly to the same cellular function in a given species. Here we identify and analyze those prokaryotic functional modules, whose composition remains largely unchanged during evolution, and study their properties. Such "cohesive" modules have a large number of internal functional connections, encode genes that tend to be in close proximity in prokaryotic genomes, and correspond to physical complexes or complex functional systems like the flagellar apparatus. Cohesive modules are enriched in processes such as energy and amino acid metabolism, cell motility, and intracellular trafficking, or secretion. By grouping genes into modules we achieve a more precise estimate of their age and find that the young modules are often horizontally transferred between species and are enriched in functions involved in interactions with the environment, implying that they play an important role in the adaptation of species to new environments.

  • Source
    • "We also confirmed that orthogroupbased profiling increases predictive power over a pairwise BBH approach restricted to genes not assigned to gene families (Figure S3A). Figure 3C plots the cumulative fraction (upper panel) and number of predicted interactions (lower panel) as a function of PCS at mp = 0.6, after filters were applied to exclude orthogroups that either appeared too recently or contained too many genes to produce useful functional predictions (Figure S3B; Supplemental Experimental Procedures). We found that that the strongest co-evolving pairs (2,101 unique genes, PCS R 10) were strongly enriched for large protein complexes, metabolic pathways, and some organelles but devoid of genes involved in canonical signaling, immune responses and transcriptional control (Reactome pathways; see Figure 3D and Table S1), closely mirroring trends in bacteria (Campillos et al., 2006). This observation argues for a generalizable tendency for cellular networks with strong internal coupling (protein complexes and metabolic pathways) to form evolutionary modules over interlinked signaling and transcriptional pathways. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Functional links between genes can be predicted using phylogenetic profiling, by correlating the appearance and loss of homologs in subsets of species. However, effective genome-wide phylogenetic profiling has been hindered by the large fraction of human genes related to each other through historical duplication events. Here, we overcame this challenge by automatically profiling over 30,000 groups of homologous human genes (orthogroups) representing the entire protein-coding genome across 177 eukaryotic species (hOP profiles). By generating a full pairwise orthogroup phylogenetic co-occurrence matrix, we derive unbiased genome-wide predictions of functional modules (hOP modules). Our approach predicts functions for hundreds of poorly characterized genes. The results suggest evolutionary constraints that lead components of protein complexes and metabolic pathways to co-evolve while genes in signaling and transcriptional networks do not. As a proof of principle, we validated two subsets of candidates experimentally for their predicted link to the actin-nucleating WASH complex and cilia/basal body function. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
    Cell Reports 02/2015; 10(6). DOI:10.1016/j.celrep.2015.01.025 · 7.21 Impact Factor
  • Source
    • "Network module , a group of proteins that are connected with each other to carry out a function [5], will be more accurate because a loss or gain of interaction will not break down the module structure . Modules have been applied to predict protein function [6] and disease genes [7] and trace the evolutionary history of networks [8] [9] [10]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Since organism development and many critical cell biology processes are organized in modular patterns, many algorithms have been proposed to detect modules. In this study, a new method, MOfinder, was developed to detect overlapping modules in a protein-protein interaction (PPI) network. We demonstrate that our method is more accurate than other 5 methods. Then, we applied MOfinder to yeast and human PPI network and explored the overlapping information. Using the overlapping modules of human PPI network, we constructed the module-module communication network. Functional annotation showed that the immune-related and cancer-related proteins were always together and present in the same modules, which offer some clues for immune therapy for cancer. Our study around overlapping modules suggests a new perspective on the analysis of PPI network and improves our understanding of disease.
    BioMed Research International 02/2012; 2012(1110-7243):103702. DOI:10.1155/2012/103702 · 2.71 Impact Factor
  • Source
    • "Comparative network analysis can help us take advantage of the available biological data and knowledge encoded in biological networks—which include the fast growing list of functional biomolecular entities within cells; their composition, structure, and annotated functions; and the interactions among these entities—in an integrative manner. As noted in [55], [56], this may expedite the genomescale functional annotation of biomolecules at a relatively low cost. Furthermore, computational analysis of biomolecular interaction networks can help us better understand the functional organization of biological networks and elucidate the similarities and differences among networks that belong to different species. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The diverse cellular mechanisms that sustain the life of living organisms are carried out by numerous biomolecules, such as deoxyribonucleic acids (DNAs), ribonucleic acids (RNAs), and proteins. Over the past few decades, significant research efforts have been made to sequence the genomes of various species and to search these genomes to track down genes that give rise to proteins and noncoding RNAs (ncRNAs) [1], [2]. As a result, the catalog of known functional molecules in cells has experienced a rapid expansion.
    IEEE Signal Processing Magazine 01/2012; 29(1):22-34. DOI:10.1109/MSP.2011.942819 · 4.48 Impact Factor
Show more


Available from