Comparing Mycobacterium tuberculosis genomes using genome topology networks

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Comparative genomic analysis reveals genomic structural variations (SVs) contribute to diverse phenotypes in closely related bacterial strains.
• Investigating genes affected by SVs enhances understanding of microbial relationships, particularly in pathogenic bacteria.

Purpose of the Study:

• Introduce a novel Genome Topology Network (GTN) method for analyzing genomic SVs and performing phylogenetic analysis.
• Propose the concept of 'unfixed orthologs' affected by SVs to improve SV recognition precision.

Main Methods:

• Developed the Genome Topology Network (GTN) method using gene homology and location data.
• Applied strategies for detecting annotation differences and completing gene annotation for enhanced 'unfixed ortholog' recognition.
• Analyzed thirteen complete Mycobacterium tuberculosis genomes as a case study, utilizing Clusters of Orthologous Groups (COG) and orthoMCL methods.

Main Results:

• Constructed GTNs and phylogenetic trees, providing insights into whole-genome-based phylogenetic analysis.
• Identified 24 unfixable COG groups, with many members associated with immunogenicity and drug resistance (e.g., PPE-repeat proteins, TetR family).

Conclusions:

• The GTN method, implemented in PERL and available online, facilitates re-annotation of 'lost' genes and analysis of SV-affected genes.
• The GTN tool aids in exploring SVs' connection to bacterial biological features and enhances whole-genome phylogenetic analysis.
• Demonstrated the GTN method's utility in identifying immunogenic and drug resistance-related genes affected by SVs in M. tuberculosis.