Increasing researches reveal gut microbiota was associated with the development of tuberculosis (TB). How to prevent or reduce Mycobacterium tuberculosis colonization in the lungs is a key measure to prevent TB. However, the data on gut microbiota preventing Mycobacterium colonization in the lungs were scarce. Here, we established the clindamycin-inducing intestinal microbiome dysbiosis and fecal microbial transplantation models in mice to identify gut microbiota’s effect on Mycobacterium’s colonization in the mouse lungs and explore its potential mechanisms. The results showed that clindamycin treatment altered the diversity and composition of the intestinal bacterial and fungal microbiome, weakened the trans-kingdom network interactions between bacteria and fungi, and induced gut microbiome dysbiosis in the mice. Gut microbiota dysbiosis increases intestinal permeability and enhances the susceptibility of Mycobacterium colonization in the lungs of mice. The potential mechanisms were gut microbiota dysbiosis altered the lung transcriptome and increased Nos2 expression through the ‘gut–lung axis’. Nos2 high expression disrupts the intracellular antimicrobial and anti-inflammatory environment by increasing the concentration of nitric oxide, decreasing the levels of reactive oxygen species and Defb1 in the cells, and promoting Mycobacteria colonization in the lungs of mice. The present study raises a potential strategy for reducing the risks of Mycobacteria infections and transmission by regulating the gut microbiome balance.

Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance detection. This study presents a comprehensive benchmarking of variant calling accuracy in bacterial genomes using Oxford Nanopore Technologies (ONT) sequencing data. We evaluated three ONT basecalling models and both simplex (single-strand) and duplex (dual-strand) read types across 14 diverse bacterial species. Our findings reveal that deep learning-based variant callers, particularly Clair3 and DeepVariant, significantly outperform traditional methods and even exceed the accuracy of Illumina sequencing, especially when applied to ONT’s super-high accuracy model. ONT’s superior performance is attributed to its ability to overcome Illumina’s errors, which often arise from difficulties in aligning reads in repetitive and variant-dense genomic regions. Moreover, the use of high-performing variant callers with ONT’s super-high accuracy data mitigates ONT’s traditional errors in homopolymers. We also investigated the impact of read depth on variant calling, demonstrating that 10× depth of ONT super-accuracy data can achieve precision and recall comparable to, or better than, full-depth Illumina sequencing. These results underscore the potential of ONT sequencing, combined with advanced variant calling algorithms, to replace traditional short-read sequencing methods in bacterial genomics, particularly in resource-limited settings.