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

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ZHANG-DISSERTATION-2024.pdf (4.8 MB)
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Zhang, Mengshi
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School of Biological Sciences
School established in 2016 with the merger of the Schools of Applied Physiology and Biology
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https://hdl.handle.net/1853/78570
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 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.
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2024-07-22
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