Genome-scale evaluation of the transcriptional and metabolic network in Pseudomonas putida KT2440 for its industrial applications

Abstract

Pseudomonas putida KT2440 has gained increasing attention as an industrially relevant microbial chassis due to its metabolic versatility, solvent tolerance, and robust stress response. However, despite its widespread use in biotechnology, the regulatory mechanisms coordinating transcriptional control and metabolism in P. putida remain incompletely characterized. This thesis addresses this gap by integrating systems biology approaches with reproducible computational workflow to investigate transcriptional regulation and its metabolic consequences. Chapter I provides a background on P. putida, including its physiological traits, current and emerging industrial applications, and existing limitations in regulatory annotation. This chapter introduces systems biology as a framework for studying complex biological networks and reviews the experimental and computational techniques used throughout the thesis, including RNA-seq, ChIP-seq, ChIP-exo, i-modulon analysis, and genome-scale metabolic modeling. In Chapter II, the transcription factor HexR is investigated as a regulator of central carbon metabolism through integrated ChIP-exo and RNA-seq analyses. Genome-wide binding profiles reveal both canonical and previously uncharacterized HexR targets, while transcriptomic data demonstrate that HexR-mediated regulation extends beyond carbohydrate metabolism to include stress-associated transcriptional programs. Analysis using i-modulons further elucidates coordinated regulatory responses linking carbon assimilation with global cellular states. Chapter III focuses on sigma factor mediated regulation, with emphasis on RpoN-dependent transcription under alternative nitrogen sources, the roles of the housekeeping sigma factor RpoD, and the stationary phase sigma factor RpoS. Comparative ChIP-seq analyses indicate that RpoN binding locations are largely conserved across conditions, whereas condition-specific regulatory responses are achieved through substantial variation in binding intensity. Integration with gene expression data further reveals a redistribution of promoter occupancy between RpoD and RpoS, in which RpoS activation during stress or nutrient limitation reduces RpoD-driven transcription. Chapter IV presents a modular, Snakemake-based computational workflow designed for reproducible and scalable multi-omics analysis. The workflow automates processing of RNA-seq, ChIP-seq, and ChIP-exo data, integrates i-modulon inference, and generates standardized outputs compatible with genome-scale metabolic modeling. Its applicability is demonstrated using datasets from both P. putida KT2440 and E. coli MG1655, highlighting its flexibility, transparency, and potential for extension to other bacterial systems. Together, this thesis provides a systems-level view of transcriptional regulation in P. putida. By coupling biological insight with reproducible computational infrastructure, this work advances both fundamental understanding and practical analysis of regulatory metabolism in non-model, industrially relevant bacteria.