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A global gene evolution analysis on Vibrionaceae family using phylogenetic profile
Nicola Vitulo1, Alessandro Vezzi, Chiara Romualdi
1CRIBI Biotechnology Centre, Department of Biology, University of Padova, Padova, Italy. nicolav@cribi.unipd.it
BMC Bioinformatics
|April 14, 2007
Summary
This study analyzed gene content in Vibrionaceae bacteria to understand their evolution. Mobile genetic elements significantly influence bacterial evolution, even within the same family.
Area of Science:
- Microbiology
- Genomics
- Evolutionary Biology
Background:
- Vibrionaceae are abundant marine bacteria crucial for nutrient cycling, with some species being significant pathogens.
- Understanding Vibrionaceae evolution is vital due to their ecological and pathogenic roles.
Purpose of the Study:
- To analyze the evolution of the Vibrionaceae family using phylogenetic profiling based on gene content.
- To predict hypothetical protein functions and identify horizontally transferred genes within Vibrionaceae.
Main Methods:
- Phylogenetic profiling of 14 Vibrionaceae genomes by analyzing gene presence/absence patterns across bacterial proteomes.
- Utilizing amino acid substitution matrices to define gene phylogenetic profiles and clustering analysis to identify gene groups.
- Calculating Clusters of Orthologous Groups (COG) class enrichment for identified gene clusters.
Main Results:
- Identified "core genes" common across all organisms and specific gene clusters present in limited sets of organisms.
- Core gene clusters showed higher COG class enrichment compared to organism-specific clusters, which were enriched in DNA replication and repair.
- Mobile genetic elements exhibited heterogeneous profiles within Vibrionaceae, indicating significant evolutionary influence.
Conclusions:
- Mobile elements profoundly impact bacterial evolution, even within closely related species like Vibrionaceae.
- Hypothetical proteins correlating with mobile element profiles suggest potential horizontal gene transfer mechanisms.
- Phylogenetic profiling can elucidate the functions of unknown open reading frames (ORFs) by comparing them to characterized genes.
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