Recombination Signal in Mycobacterium tuberculosis Stems from Reference-guided Assemblies and Alignment Artefacts

Area of Science:

• Microbial genomics
• Evolutionary biology
• Molecular genetics

Background:

• Genetic recombination facilitates beneficial mutation accumulation and genome stability.
• Recombination prevalence varies across prokaryotes, dependent on DNA transfer mechanisms.
• The human pathogen Mycobacterium tuberculosis (MTB) lacks characterized DNA transfer mechanisms, casting doubt on its recombination.
• Previous studies have reported recombination in MTB, but its existence remains debated.

Purpose of the Study:

• To investigate the presence and extent of genetic recombination in Mycobacterium tuberculosis (MTB).
• To critically evaluate evidence for recombination in MTB genomes and identify potential sources of artifactual signals.
• To assess the impact of genome assembly and alignment methodologies on phylogenetic reconstructions in MTB.

Main Methods:

• Analysis of completely assembled Mycobacterium tuberculosis (MTB) genomes.
• Identification and characterization of putative recombination events.
• Evaluation of assembly and alignment quality in genomic regions.
• Comparison of phylogenetic signals derived from different assembly approaches.

Main Results:

• Putative recombination events were found to be enriched in strains reconstructed by reference-guided assembly.
• Regions with unreliable alignments showed a higher incidence of artifactual recombination signals.
• Assembly and alignment artifacts generated phylogenetic signals conflicting with established MTB phylogeny.
• No reliable signal of genetic recombination was detected in the analyzed MTB genomes.

Conclusions:

• Reported recombination events in Mycobacterium tuberculosis (MTB) are likely attributable to methodological artifacts.
• Reference-guided genome assembly poses limitations for accurate phylogenetic reconstruction, especially in low-diversity species.
• High-quality, de novo assembled genomes are essential for distinguishing true evolutionary signals from noise in MTB.