DESCRIPTION (provided by applicant):
Like all forms of life, bacteria undergo evolution. However, unlike many other organisms, bacterial evolution is not one of strict vertical descent. Horizontal (or lateral) gene transfer (HGT), a process by which genetic material is transferred among distantly related species, is ubiquitous in the prokaryotic branch of the Tree of Life. In the presence of HGT, the evolutionary history of a set of organisms is not treelike; rather, it is reticulate. HGT plays a major role in microbial genome diversification, and is claimed to be rampant among various groups of genes in bacteria. Further, it is a major mechanism by which bacteria develop resistance to antibiotics.
Two major challenges that face studies of bacterial genomes involve estimating the extent of horizontally transferred genes in them, and reconstructing their evolutionary history. The former bears great significance on understanding the evolutionary role HGT plays and making predictions about its occurrence, and the latter amounts to detecting the donors and recipients of horizontally transferred genes, helping to understand how bacteria acquire antibiotic resistance and how to develop more effective ones. Attempts at addressing the first challenge have led to conflicting results, whereas attempts at the second challenge have been limited. Estimates as to the extent of HGT in bacteria range from one extreme (HGT is so rampant, rendering a bacterial phylogenetic tree useless) to another (HGT is mere background noise overridden by the lineal descent signal). As for the challenge of reconstructing reticulate evolutionary histories, the progress is even less satisfactory, mainly due to the lack of accurate and efficient methods for reconstructing phylogenetic networks. Our objective is to develop computational tools for high-throughput genome-wide evolutionary analysis of bacterial genomes, with focus on the detection and reconstruction of HGT. To achieve this objective, we will develop:
(1) Protocols for micro-level analyses of bacterial genomes, to estimate HGT rates.
(2) A stochastic framework that combined population genetics and phylogenetics theories for medium-level analyses.
(3) Algorithmic techniques for phylogenetic (macro-level) analyses of genomes for detecting HGT.
(4) Software tools that implement the protocols and methodologies and make them available to the research community.