title: Detecting rare gene transfer events in bacterial populations.
abstract: Horizontal gene transfer (HGT) enables bacteria to access, share, and recombine genetic variation, resulting in genetic diversity that cannot be obtained through mutational processes alone. In most cases, the observation of evolutionary successful HGT events relies on the outcome of initially rare events that lead to novel functions in the new host, and that exhibit a positive effect on host fitness. Conversely, the large majority of HGT events occurring in bacterial populations will go undetected due to lack of replication success of transformants. Moreover, other HGT events that would be highly beneficial to new hosts can fail to ensue due to lack of physical proximity to the donor organism, lack of a suitable gene transfer mechanism, genetic compatibility, and stochasticity in tempo-spatial occurrence. Experimental attempts to detect HGT events in bacterial populations have typically focused on the transformed cells or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to reach relative population sizes that will allow their immediate identification; the exception being the unusually strong positive selection conferred by antibiotics. Most HGT events are not expected to alter the likelihood of host survival to such an extreme extent, and will confer only minor changes in host fitness. Due to the large population sizes of bacteria and the time scales involved, the process and outcome of HGT are often not amenable to experimental investigation. Population genetic modeling of the growth dynamics of bacteria with differing HGT rates and resulting fitness changes is therefore necessary to guide sampling design and predict realistic time frames for detection of HGT, as it occurs in laboratory or natural settings. Here we review the key population genetic parameters, consider their complexity and highlight knowledge gaps for further research.
authors.affiliation: Department of Pharmacy, Faculty of Health Sciences, University of Tromsø Tromsø, Norway ; GenØk-Centre for Biosafety, The Science Park Tromsø, Norway.
authors.forename: Kaare M
authors.initials: KM
authors.lastname: Nielsen
authors.affiliation: Department of Pharmacy, Faculty of Health Sciences, University of Tromsø Tromsø, Norway ; GenØk-Centre for Biosafety, The Science Park Tromsø, Norway.
authors.forename: Thomas
authors.initials: T
authors.lastname: Bøhn
authors.affiliation: Department of Biostatistics, Yale University New Haven, CT, USA ; Program in Computational Biology and Bioinformatics, Yale University New Haven, CT, USA ; Program in Microbiology, Yale University New Haven, CT, USA.
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Townsend
keywords: DNA uptake; GMO; antibiotic resistance; biosafety; lateral or horizontal gene transfer; modeling; monitoring; sampling