Juan C. Castro

Defends his thesis:

Probing genomic, metabolic, and phenotypic evolution in microbes using comparative and experimental evolution data

Thursday, January 12, 2023

10:00 AM Eastern Time

Marcus Nanotechnology Building Conference Room (room #1117-1118)

Zoom link: https://gatech.zoom.us/j/99028783106?pwd=RUpZd1RnWDFrMXUzdzhkaFJtR1I4Zz09

Thesis Advisor:

Dr. Sam P. Brown

School of Biological Sciences

Georgia Institute of Technology

Committee Members:

Dr. Marvin Whiteley

School of Biological Sciences

Georgia Institute of Technology

Dr. Neha Garg

School of Chemistry & Biochemistry

Georgia Institute of Technology

Dr. Peng Qiu

Coulter Department of Biomedical Engineering

Georgia Institute of Technology

Dr. Timothy D. Read

School of Medicine

Emory University

Summary:

Microbial model systems offer unique opportunities for evolutionary biologists, due to the ability to probe evolutionary dynamics using both comparative and experimental evolution techniques. This thesis leverages these opportunities to address questions on genomic, metabolic, and phenotypic evolution in bacteria.

First, we exploit the growing availability of closed genomes for model bacteria (E. coli and P. aeruginosa) to build pan-genomes where we can track the physical linkage of all genes. Through a combination of evolutionary simulations and data-analysis, we ask how mutation, selection and gene interactions combine to shape genome structural organization (linkage) and variation (co-segregation) across strains. We show that co-segregation networks are modular, associate with physical linkage, and map to metabolic (for P. aeruginosa) and regulatory networks (for E. coli). The results imply that modular gene interactions are sufficient to guide the evolution of persistent gene clusters and are the primary force shaping genome structural evolution.

Next, we focus on metabolic network evolution, and assess whether we can predict the metabolic wiring of P. aeruginosa, both before and after experimental evolution in defined environments. Standard flux-balance analysis (FBA) models have weak predictive value for ancestral strains both before and after experimental evolution adaptation to a novel defined environment. We reasoned that FBA models are limited by their focus on primary metabolic processes, and therefore fail to capture adaptation of secondary metabolism. By incorporating Tn-seq data on gene essentiality into our FBA model predictions we build metabolic predictions spanning primary and secondary metabolism. Our enhanced FBA models show (1) consistent predictive improvements following experimental evolution, and (2) highest predictive performance in the specific environment in which the Tn-seq data was generated.

Finally, we turn to a phenotypic scale of evolutionary analysis, with a focus on biofilm production. Using a combination of theory and comparative data, we ask how biofilm investment strategies vary across strains of P. aeruginosa and are shaped by population dynamical processes and phylogenetic constraints. Our data illustrates substantial variation in biofilm allocation, with the proportion of biofilm cells varying from ~5 to 55%. Our data analysis allows us to reject a simple allocation tradeoff model and favors the ‘growth engine’ model introduced in earlier work (Lowery ref). Under the growth engine model, maximal biofilm production requires robust planktonic growth, generating a hump-shaped relationship between the total abundance of biofilm cells and planktonic cells. Finally, our heritability analysis indicates that biofilm phenotypic variation is substantially determined by phylogeny.