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, Advisor

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:

A major challenge in understanding the mechanisms controlling colonization and persistence of bacterial pathogens is a lack of functional data regarding their physiology during human infection. One way to tackle this problem is to quantify gene expression during human infection and use this data to infer microbial function. Although RNA-seq has been widely used to study bacterial physiology during infection, 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 aims to explore the mRNA-protein relationship in two aspects, 1) How growth rate impacts mRNA-protein relationships in the model system P. aeruginosa; 2) How the mRNA-protein relationships change across diverse prokaryotes. 

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. In addition, essential genes have higher mRNA-protein correlations with both mRNAs and proteins produced at higher levels compared to non-essential genes in S. aureus and P. aeruginosa. We used statistical methods to identify ‘outlier’ genes in which mRNA and protein were poorly correlated in each species. Additionally, we found that protein-to-RNA ratios are often conserved across strains and environments, enabling the calculation of RNA-to-protein (RTP) conversion factors that improved predictivity of protein levels across strains and growth conditions. This advancement allows for improvement of the accuracy of protein prediction from mRNA of distantly related microbes. Our results provide new insights into mRNA-protein relationships and provide valuable tools for a deeper understanding of bacterial physiology from transcriptome data.