Machine Learning and Metabolic Model Guided CRISPRi Reveals a Central Role for Phosphoglycerate Mutase in Chlamydia trachomatis Persistence

Authors

Niaz Bahar Chowdhury, University of Nebraska-LincolnFollow
Nick Pokorzynski, University of Nebraska Medical Center
Elizabeth A. Rucks, University of Nebraska Medical Center
Scot P. Ouellette, University of Nebraska Medical Center
Rey A. Carabeo, University of Nebraska Medical Center
Rajib Saha, National University of SingaporeFollow

Date of this Version

12-18-2023

Document Type

Article

Citation

doi: https://doi.org/10.1101/2023.12.18.572198

Comments

Used by permission.

Abstract

Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence is an adaptive response or lack of it. To understand that transcriptomics data were collected for nutrientsufficient and nutrient-starved CTL. Applying machine learning approaches on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions without having any global stress regulator. This indicated that CTL’s stress response is due to lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence. Later, pgm was found to have the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm and tryptophan starvation experiments revealed the importance of this gene in inducing persistence. Hence, this work, for the first time, introduced thermodynamics and enzyme-cost as tools to gain deeper understanding on CTL persistence.