Mengshi Zhang, QBioS Thesis Defense

In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Quantitative Biosciences
in the School of Biological Sciences

Mengshi Zhang

Defends her thesis:
Probing mRNA-protein relationships across prokaryotes: From Pseudomonas to Sulfolobus

Wednesday, July 10, 2024
10:00am Eastern
Location: Klaus 2456

Zoom: https://gatech.zoom.us/j/99164094069?pwd=SFhDVzNjaDUwZitsTEE0alBlMDc5Zz09
Meeting ID: 991 6409 4069
Passcode: 349897

Advisor:
Dr. Marvin Whiteley
School of Biological Sciences
Georgia Institute of Technology

Committee:
Dr. Steve Diggle
School of Biological Sciences
Georgia Institute of Technology

Dr. Peter Yunker
School of Physics
Georgia Institute of Technology

Dr. Sam Brown
School of Biological Sciences
Georgia Institute of Technology

Dr. David Weiss
School of Medicine
Emory University

Abstract:      
     Understanding the biology of native microbial communities is hindered by the lack of robust functional data for the microbes within these communities. One way to tackle this problem is to quantify gene expression in native communities and use this data to infer microbial function. Although RNA-seq has been widely used to study bacterial physiology in situ, a critical concern arises regarding whether mRNA levels accurately predict protein levels, which are the primary functional units of a cell. Here, we addressed this challenge systematically by using comprehensive transcriptome and proteome datasets from Gram-negative bacteria, Gram-positive bacteria, and an archaea. This thesis explores three questions: (i) How does growth rate impact mRNA-protein correlations in the human pathogen Pseudomonas aeruginosa?; (ii) How do mRNA-protein correlations change across six prokaryotes?; (iii) Can protein level prediction from mRNA levels be improved? Here, we discovered that the overall correlation of mRNA and protein is similar across different growth rates in P. aeruginosa and across diverse prokaryotes, with mRNA and protein positively correlated. However, genes essential for viability have higher mRNA-protein correlations, and both mRNAs and proteins from these essential genes are produced at higher levels compared to non-essential genes. We used statistical methods to identify ‘outlier’ genes in which mRNA and protein were poorly correlated in six prokaryotes and showed that RNA-to-protein (RTP) conversion factors can be used to improve the predictivity of protein levels across strains and growth conditions. Indeed, RTP conversion factors calculated from bacteria were shown to improve protein predictivity in a hyperthermophilic archaea, providing proof-of-principle that this approach is robust across domains of life. Collectively, our results provide new insights into mRNA-protein relationships and provide valuable tools for inferring in situ bacterial function from transcriptome data.