Ph.D. Defense: Lina Kloub

Title: Computational Approaches for Inferring Units of Transfer and Modes of Integration for Horizontal Gene Transfer Events

Ph.D. Candidate: Lina Kloub

Major Advisor: Dr. Mukul Bansal

Associate Advisors: Dr. Peter Gogarten , Dr. Ion Mandoiu, Dr. Derek Aguiar
Date/Time: Thursday, June 3rd, 2021, 10:00 AM

Meeting link:
https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m0ba8c94ed463e89e502f97fb2485e40f

Meeting number:120 481 2004
Password: J3n2zE9aJYX

Abstract:

The transfer of genetic information between organisms that are not in a direct ancestor-descendant relationship, called Horizontal Gene Transfer (HGT), is a crucial process in microbial evolution. However, little is known about how horizontally transferred genes are integrated into recipient genomes or about the “scale” of individual HGT events. In this work, we develop new computational frameworks to answer two fundamental properties of HGT events.  The first property concerns the units of HGT events. An HGT event may involve the transfer of a gene fragment, an entire gene, or multiple genes and very little is currently known about the units of HGT events. The second property deals with the mode of any HGT event. In particular, when a gene is horizontally transferred, it may either add itself as a new gene to the recipient genome or replace an existing homologous gene. Currently, studies do not usually distinguish between “additive” and “replacing” HGTs, and their specific role and impact on microbial evolution is poorly understood.  

Here, we build upon recent computational advances in the detection of HGTs and leverage recent large-scale availability of microbial genomic datasets to develop new computational frameworks to study these two fundamental properties of HGT events. Our first method infers single-gene HGT events across the given set of species, uses several techniques to account for inference uncertainty, combines that information with gene order information, and uses statistical analysis to identify candidate horizontal multi-gene transfers (HMGTs). Our second method identifies high-confidence leaf-to-leaf HGTs and uses gene orderings along genomes and phylogenetic relationships between the microbial species under consideration, coupled with statistical analysis, to classify a subset of the identified HGTs as being either additive or replacing with high confidence. We apply both methods to a genome-scale dataset of over 22,000 gene families from 103 Aeromonas genomes. Our first method identifies a large number of plausible HMGTs of various scales at both small and large phylogenetic distances, and reveals interesting relationships between gene function, phylogenetic distance, and frequency of multi-gene transfer. The second method classifies a large fraction of the inferred leaf-to-leaf HGTs as being either additive or replacing with high confidence, and, again, uncovers interesting relationships between gene function, phylogenetic distance, and HGT type.