High-Resolution Spatiotemporal Proteogenomic Profiling Of Host–Pathogen Interactions During Bacterial Infection

Abstract

Integrated molecular analyses including spatial and temporal resolution and the dynamics between host tissues and bacterial pathogens are necessary for understanding dynamic interactions between bacterial pathogens and host tissues. In this study, spatiotemporal proteogenomic analysis of host-pathogen interactions during bacterial infection was provided by integrating spatial transcriptomics, quantitative proteomics and network-based systems biology analysis in a high-dimensional framework of a high-resolution spatiotemporal matrix. Multi-omics datasets were curated for public release at various infection stages and went through a common, standardized quality control, normalization, and computational integration process. To identify infection associated molecular signatures and regulatory mechanisms, spatial clustering, differential expression analysis, protein–protein interaction network construction, pathway enrichment and temporal trajectory analyses were carried out. A machine learning modelling was then used to prioritise candidate biomarkers and assess the biomarkers predictive performance. The proposed framework succeeded in identifying stage-specific immune responses, spatially localized host defense mechanisms, and key genes and proteins that are important to the progression of bacterial infections. The analysis of these data also presents in-depth insights into infection biology and an example of the potential of proteogenomic integration for biomarker design and therapeutic targets identification. The proposed computational framework could be a scalable way for conducting precision infectious disease research, and for future translational applications.This type of abstract can be enhanced with specific quantitative results when your analyses are finished if you are targeting a high-level life sciences journal.