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This article is part of the supplement: Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)

Open Access Proceedings

Horizontal gene transfer dynamics and distribution of fitness effects during microbial in silico evolution

Vadim Mozhayskiy and Ilias Tagkopoulos*

Author Affiliations

Department of Computer Science and UC Davis Genome Center, University of California Davis, Davis, California, 95616, USA

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BMC Bioinformatics 2012, 13(Suppl 10):S13  doi:10.1186/1471-2105-13-S10-S13

Published: 25 June 2012

Abstract

Background

Horizontal gene transfer (HGT) is a process that facilitates the transfer of genetic material between organisms that are not directly related, and thus can affect both the rate of evolution and emergence of traits. Recent phylogenetic studies reveal HGT events are likely ubiquitous in the Tree of Life. However, our knowledge of HGT's role in evolution and biological organization is very limited, mainly due to the lack of ancestral evolutionary signatures and the difficulty to observe complex evolutionary dynamics in a laboratory setting. Here, we utilize a multi-scale microbial evolution model to comprehensively study the effect of HGT on the evolution of complex traits and organization of gene regulatory networks.

Results

Large-scale simulations reveal a distinct signature of the Distribution of Fitness Effect (DFE) for HGT events: during evolution, while mutation fitness effects become more negative and neutral, HGT events result in a balanced effect distribution. In either case, lethal events are significantly decreased during evolution (33.0% to 3.2%), a clear indication of mutational robustness. Interestingly, evolution was accelerated when populations were exposed to correlated environments of increasing complexity, especially in the presence of HGT, a phenomenon that warrants further investigation. High HGT rates were found to be disruptive, while the average transferred fragment size was linked to functional module size in the underlying biological network. Network analysis reveals that HGT results in larger regulatory networks, but with the same sparsity level as those evolved in its absence. Observed phenotypic variability and co-existing solutions were traced to individual gain/loss of function events, while subsequent re-wiring after fragment integration was necessary for complex traits to emerge.