MCA: Genomic algorithms and statistical models for gene transfer in naturally transformable bacteria

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Bacteria, which often live in complex multi-species communities, are not limited to acquiring their genes from prior generations. Using a process called natural transformation, bacteria can also acquire new genetic elements from neighbors and even from free DNA in the environment. It is currently unknown which genetic sequences are most readily transferred via transformation. Understanding natural transformation at the genome and community scale requires development and application of novel algorithms to address this previously unexplored space. This project will develop computational methods to identify and predict which genetic functions are likely to spread through bacterial populations by natural transformation. Application of these computational methods will help fill a void in understanding genetic evolution in bacterial communities which may be overlooked by taxonomic identification. This in turn can lead to better assessment of risks or benefits associated with bacterial community members and improved planning of synthetic bacterial communities. The project workflow will enable new capstone research experiences for undergraduates from multiple STEM majors on campus. The connections between molecular and biological parameters that drive genome-wide gene transfer preferences during bacterial natural transformation have been unexplored due to lack of genome level experimental and computational analyses. The PI’s laboratory has developed experimental molecular genetic tools to collect and map natural transformation gene transfer sites across diverse pairs of donor and recipient bacteria. However, the computational methods to quantify the driving functional parameters and ultimately to predict gene transfer dynamics associated with natural transformation do not yet exist. Collaboration with the partner will use the experimental data to generate genome scale statistical algorithms and computational tools for (i) global analysis of molecular and biological parameters governing gene transfer events, and (ii) prediction of gene transfer events that result specifically from natural transformation. This project will enable expansion of the work beyond current molecular genetic analysis of natural transformation to the genome and community scale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.