Article

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.

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    • "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. "
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    Nucleic Acids Research 06/2013; 41(Web Server issue). DOI:10.1093/nar/gkt471 · 9.11 Impact Factor
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    • "Phylogeny-based examples include methods that employ the maximum parsimony framework (e.g. Campillos et al., 2006; Cordero et al., 2008) and methods that rely on explicit evolutionary models of co-evolution (e.g. Barker et al., 2007). "
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