The study identified mutations in the atpE gene, which encodes the C subunit of the ATP synthase, as a mechanism of resistance to R207910 in Mycobacterium tuberculosis.

The study identified multiple AMR genes and mutations in Mycobacterium tuberculosis variant bovis strains from Mato Grosso, Brazil, including resistance to pyrazinamide, isoniazid, rifampicin, streptomycin, ethambutol, ethionamide, fluoroquinolones, kanamycin, capreomycin, paraminosalicylic acid, cycloserine, bedaquiline, linezolid, and delamanid.

The study established a direct genotype-phenotype association for bedaquiline resistance in Mycobacterium abscessus through targeted chromosomal barcoding, confirming that the D29A and A64P mutations in the atpE gene are responsible for resistance.

The study identified 265 genomic variants implicated in bedaquiline resistance, primarily in the Rv0678 gene, which regulates the MmpS5-MmpL5 efflux pump. Variants in atpE were also found to contribute to bedaquiline resistance. A large-scale genomic rearrangement involving Rv0678 was discovered as a new resistance mechanism.

Compounds 4 and 5 inhibit ATP synthase in Pseudomonas aeruginosa, with compound 5 showing potent antibacterial activity against drug-resistant strains. Mutations in the H+ binding site of the c-subunit of ATP synthase affect the inhibition by these compounds.

The study identifies several genes and mutations associated with bedaquiline resistance in non-tuberculous mycobacteria (NTM) species, including atpE, mmpT5, MAB_4384, MAB_2299c, MAV_2152, rpoB, and prpE. These findings highlight the genetic mechanisms underlying bedaquiline resistance in NTM.

The study identifies resistance mutations in commensal bacteria that contribute to microbiome resilience during MDR TB treatment, highlighting the role of antimicrobial resistance in maintaining microbiome stability.

The study introduces TRACeR-TB, a rapid drug susceptibility testing method for Mycobacterium tuberculosis that uses RNA biomarkers to detect resistance. It identifies specific mRNA biomarkers for different antibiotics and demonstrates improved sensitivity and specificity compared to traditional methods.

The study introduces LLMTB, a large language model that predicts antibiotic resistance in Mycobacterium tuberculosis by analyzing genomic data. It identifies key resistance genes and intergenic regions associated with various antibiotics, demonstrating superior performance compared to existing methods.

The study presents OTN-FMCA, a novel one-tube nested fluorescence melting curve analysis for rapid detection of drug-resistant Mycobacterium tuberculosis. It identifies 44 mutations in six key genes (rpoB, katG, inhA, gyrA, gyrB, and atpE) associated with resistance to rifampicin, isoniazid, fluoroquinolones, and bedaquiline.