An evolutionary functional genomics approach identifies novel candidate regions involved in isoniazid resistance in Mycobacterium tuberculosis

Abstract

Efforts to eradicate tuberculosis are hampered by the rise and spread of antibiotic resistance. Several large-scale projects have aimed to specifically link clinical mutations to resistance phenotypes, but they were limited in both their explanatory and predictive powers. Here, we combine functional genomics and phylogenetic associations using clinical strain genomes to decipher the architecture of isoniazid resistance and search for new resistance determinants. This approach has allowed us to confirm the main target route of the antibiotic, determine the clinical relevance of redox metabolism as an isoniazid resistance mechanism and identify novel candidate genes harboring resistance mutations in strains with previously unexplained isoniazid resistance. This approach can be useful for characterizing how the tuberculosis bacilli acquire resistance to new antibiotics and how to forestall them.

Gene evolutionary trajectories in Mycobacterium tuberculosis reveal temporal signs of selection

“…A gene set enrichment analysis of gene ontology (GO) functions of flanking genes of these intergenic regions demonstrated that the most overrepresented functions (hypergeometric test, Benjamini-Hochberg adjusted P value < 0.05) are responses to acid chemicals, oxidation-reduction (REDOX) processes, and regulation of DNA templated transcription. The identification of REDOX is in agreement with oxidative metabolism playing a role in macrophage survival and drug resistance ( 43 , 47 , 48 ). A previous study reported that changes in regulatory regions (mostly intergenic) could significantly affect the transcription rates of downstream genes ( 49 ).…”

“…3 B ): for example, Rv1730c associated with first-line treatments (Wald test, P value = 0.04 for streptomycin and P value = 1e-4 for ethambutol + pyrazinamide), treY associated with aminoglycoside injectable agents (Wald test, P value = 0.01), Rv1830 associated with isoniazid and rifampicin (Wald test, P value = 0.01), cyp128 associated with aminoglycosides (Wald test, P value = 0.002), and eccCa1 associated with d- cycloserine and aminoglycoside injectable agents (Wald test, P value = 0.01 and P value = 0.02, respectively). In order to support our results on Rv1830, we obtained data from a transposon mutant library derived from H37Rv in our laboratory ( 43 ). The library was grown in two isoniazid concentrations (0.18 and 0.2 μg/mL), and the frequency of Rv1830 mutants in these antibiotic-selected experiments was compared with controls.…”

“…We used TN-seq data described in Furió et al. ( 43 ) to further confirm the role of Rv1830 in isoniazid resistance. Briefly, a saturated transposon insertion library was generated and grown in the presence and absence of isoniazid (two concentrations, 0.18 and 0.2 μg/mL, close to the minimum inhibitory concentration for this drug).…”

Unraveling the mechanisms of intrinsic drug resistance in Mycobacterium tuberculosis

“…There are substantial data to support this model using both chemical and genetic disruption of the mycolic acid network to potentiate antibiotic uptake and activity (Liu and Nikaido, 1999;Larrouy-Maumus et al, 2016;Xu et al, 2017). The clinical implications of this phenomenon can be seen by the synergistic interaction between rifampicin, which inhibits RNA polymerase (Campbell et al, 2001), and ethambutol, which inhibits arabinogalactan and lipoarabinomannan (LAM) biosynthesis (Goude et al, 2009;McNeil et al, 2019;Zhang et al, 2020). Because arabinogalactan serves as an anchor for the mycolic acid layer, AG inhibitors like ethambutol also perturb the MOM (Kilburn and Takayama, 1981;Mikusováet al, 1995;Dulberger et al, 2020).…”

“…While chemical-genetic interactions can help inform which physiochemical properties and potentially which envelope structures are most important for intrinsic drug resistance, there are several limitations. For example, many antitubercular compounds target envelope biosynthesis either directly (Banerjee et al, 1994;Goude et al, 2009) or indirectly (Stover et al, 2000;Thiede et al, 2022). Let's assume that a CRISPRi knockdown strain against the essential arabinogalactan biosynthetic enzyme dprE1 renders Mtb more sensitive to a given compound.…”