Antibiotic resistance among pathogenic bacteria stands as a cumulative global threat, making new knowledge enabling development of next-generation antimicrobials urgently needed. Persistent bacterial infections are well-recognized clinical problems often resilient to treatment regimens, which adds to antibiotic overuse. The proposed research aims to elucidate molecular mechanisms critical for establishment and persistence of bacterial infections. The project relies on previous studies showing that it is possible to reveal gene expression of bacteria in mouse tissue, where data for persistent infections indicates importance of gene products involved in adaptation to tissue environments (1). By exploring gene expression patterns of 2 intestinal model pathogens with distinct virulence strategies Yersinia pseudotuberculosis (extracellular) and Salmonella enterica serovar Typhimurium (intracellular)—in mouse infection models (acute and chronic infections) and during in vitro stress conditions—we aim to identify new targets for antimicrobial therapies. The data will be generated through various omics approaches (in vivo bulk transcriptomics [1, 2], single-cell RNA-seq, spatial transcriptomics, Ribo-seq [3]). We will employ state-of-the-art machine learning and systems biology approaches to analyze transcriptomic profiles, combined with previous resources like the RNA atlas of human bacterial pathogenex (http://www.pathogenex.org) [4]. The project aligns with ongoing efforts at the Excellence Center for Modeling Adaptive Mechanisms in Living Systems under Stress (https://icelab.se/activities/research/stress-response-modeling-at-icelab/) and is linked to data generated through NAISS compute projects. While our previous studies have begun to reveal aspects of bacterial gene expression during infection, this project will build on that knowledge, using cutting-edge techniques like single-cell RNA-seq and spatial transcriptomics to provide unprecedented insights. Our focus on in vivo transcriptomics in mouse models and the use of machine learning for data integration are particularly innovative and challenging. The proposed research has the potential to significantly advance understanding of bacterial persistence by identifying novel gene products that could serve as targets for next-generation antimicrobial therapies. The project critically depends on high-performance computing (HPC) to manage and process the vast amounts of data generated from various sequencing techniques. This proposal highlights the necessity for large-scale data storage and HPC resources, equipped with bioinformatics tools similar to those available at UPPMAX, to efficiently execute computationally demanding tasks. These include processing spatial transcriptomics data and running machine learning algorithms to analyze complex gene expression networks, both requiring substantial computational power and storage capacity. 1. Avican K., et al., Reprogramming of Yersinia from virulent to persistent mode revealed by complex in vivo RNA-seq analysis. PLoS Pathog (2015) 11:e1004600. 2. Mahmud, A., et al., ProkSeq for complete analysis of RNA-Seq data from prokaryotes. Bioinformatics, 2021. 37(1):126 3. Choe, D., et al., STATR: A simple analysis pipeline of Ribo-Seq in bacteria. J Microbiol, 2020. 58:217. 4. Avican, K., et al., RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection. Nat Commun, 2021. 12:328