CliPME: The clinical pathogenic bacteria mutation and expression database.

Area of Science:

• Bacterial genomics
• Microbial evolution
• Bioinformatics

Background:

• Pathogenic bacteria evolve rapidly due to human interactions.
• Whole-genome sequencing (WGS) aids mutation tracking, but tools for analyzing bacterial mutation patterns and functional impacts are limited.
• Existing bioinformatics tools lack comprehensive solutions for pathogenic bacteria.

Purpose of the Study:

• To present CliPME, an integrated platform for bacterial genomics.
• To combine mutation detection, effect prediction, and regulatory network analysis for pathogenic bacteria.
• To provide a resource for decoding bacterial pathogen evolution and informing antimicrobial strategies.

Main Methods:

• Development of CliPME, a platform integrating mutation detection, effect prediction, and regulatory network analysis.
• Creation of qMut, an R package for large-scale mutation profiling.
• Implementation of functional modules: MutFinder, MutAnalyzer, and ExpMiner for population-level analysis, functional prediction, and gene expression relationships.
• Case study using Mycobacterium tuberculosis (Mtb).

Main Results:

• CliPME successfully identified functionally significant mutations in the Mtb transcription factor Rv0324.
• Experimental validation demonstrated a link between Rv0324 genetic variation and potential adaptive phenotypes.
• The platform integrates population-level mutation analysis, functional predictions, and gene expression insights.

Conclusions:

• CliPME provides a comprehensive solution for analyzing bacterial genomic mutations and their functional consequences.
• The platform aids in understanding evolutionary mechanisms in bacterial pathogens.
• CliPME can accelerate the development of novel antimicrobial strategies by translating genomic insights.