Back

Isotopic tracing in Mycobacterium tuberculosis-infected alveolar epithelial cells indicates metabolic specificity to their virulence and drug-resistance status

Akram, S.; Masakapalli, S. K.; Nanda, R.

2024-12-04 biochemistry
10.1101/2024.12.03.625217 bioRxiv
Show abstract

Mycobacterium tuberculosis (Mtb), the causative organism of tuberculosis (TB), infects different host cells; however, studies on its metabolic impact on the type 2 alveolar epithelial cells are limited. In this study, comparative metabolic insights of A549 cells infected with laboratory (H37RV, H37Ra) and clinical isolates (drug-resistant: S6, S11 and sensitive: S4, S5) were derived from read out of the 13C-based proteinogenic amino acid kinetics. Mtb H37Rv (virulent) exhibited higher growth kinetics within A549 cells compared to H37Ra (avirulent), whereas drug-resistant clinical isolate S6 showed the highest intracellular growth. The viability of H37Rv-infected A549 cells was significantly low. Interestingly, the drug-resistant clinical Mtb isolates maintained better cell viability over time. A [13C6] glucose tracer and mass isotopomer distributions revealed that A549 cells infected with virulent Mtb strains exhibited higher dependency on the central carbon metabolism like glycolysis, the pentose phosphate pathway and the tricarboxylic acid cycle for de novo amino acid biosynthesis. Drug-resistant Mtb isolates infected A549 cells showed robust 13C incorporation in the proteinogenic Threonine, Methionine and Lysine, derived from the tricarboxylic acid cycle. A549 cells infected with Mtb, irrespective of their drug resistance status, showed 13C incorporation in the essential amino acids (lysine, methionine, threonine, valine), underlining the dependencies of the pathogen on the de novo host metabolic precursors. These findings elucidate the importance of deciphering the strain-specific host-pathogen metabolic interactions to develop targeted therapies against drug-resistant Mtb infections.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.