Project description:Non-tuberculous mycobacteria (NTM) are emerging pathogens with high intrinsic drug resistance. Among rapidly growing NTM species, Mycobacterium abscessus is among the most pathogenic. Standard of care therapy has led to unacceptable outcomes and demonstrates the urgent need to develop effective, broad-spectrum antimycobacterial regimens. Through synthetic modification of spectinomycin (SPC), an aminocyclitol antibiotic, we have identified a distinct structural subclass of ethylene linked aminomethyl spectinomycins (eAmSPC) that are up to 64-fold more potent against M. abscessus when compared to SPC. Lead eAmSPC retain activity against other NTM species and multi-drug resistant M. abscessus clinical isolates. Sequencing of eAmSPC-resistant mutants revealed nucleotide changes in the distinct helix-34 spectinomycin binding site and X-ray crystallography further demonstrated the derivatives mode of ribosomal inhibition remained on target. The eAmSPC displayed increased intracellular accumulation compared to SPC and transcriptional profiling indicate that eAmSPC’s induce whiB7 resistance responses, however, the series maintains potency despite its expression. These leads display favorable pharmacokinetic profiles and robust efficacy in M. abscessus mouse infection models. The results of these studies suggest that eAmSPCs have the potential to be developed into clinical treatments for M. abscessus and other NTM infections.
2023-01-09 | GSE222081 | GEO
Project description:whole genome sequencing of non-tuberculous mycobacteria isolated from South Korea
Project description:Detection of species-specific proteotypic peptides for accurate and easy characterization of infectious non-tuberculous mycobacteria such as Mycobacterium abscessus is essential. Therefore, we carried out reanalysis of publicly available M. abscessus proteomic dataset MSV000085363, PXD015680 and PXD022644 by proteome database search and followed by spectral library generation. The raw DDA data were searched against the M. abscessus proteome database using Proteome Discoverer and FragPipe. The resulting peptide spectrum matches were converted into a spectral library using BiblioSpec.