<i>Mycobacterium tuberculosis</i> senses host Interferon-γ via the membrane protein MmpL10
Bibliographic record
Abstract
Abstract Mycobacterium tuberculosis (Mtb) is one of the most successful human pathogens and remains a leading cause of death from infectious disease. Interferon-γ (IFN-γ) is a central regulator of the immune defense against Mtb . Several cytokines have been shown to increase virulence of other bacterial pathogens, leading us to investigate whether IFN-γ has a direct effect on Mtb . We found that both recombinant and T-cell derived IFN-γ rapidly induced a dose-dependent increase in the oxygen consumption rate (OCR) of Mtb , consistent with increased bacterial respiration. This was also observed in clinical strains, but not in the vaccine strain Bacillus Calmette–Guérin (BCG), and did not occur for other cytokines tested, including TNF-α. IFN-γ binds to the cell surface of intact Mtb , but not BCG, whilst TNF-α binds to neither. Mass spectrometry analysis identified mycobacterial membrane protein large 10 (MmpL10) as the transmembrane binding partner. Consistent with this, IFN-γ binding and the OCR response was absent in a Mtb Δmmpl10 strain and restored by complementation of the mutant strain. RNA-sequencing of IFN-γ exposed Mtb revealed a distinct transcriptional profile, including genes involved in virulence and cholesterol catabolism. Finally, exposure of Mtb cells to IFN-γ resulted in sterilization of bacilli treated with isoniazid (INH), indicating clearance of phenotypically resistant bacteria that persist in the presence of INH alone. Our data suggest a novel mechanism allowing Mtb to respond to host immune activation that may be important in the immunopathogenesis of TB and have use in novel eradication strategies. One-Sentence Summary IFN-γ is a critical component of effective immune defense in human tuberculosis yet its causative agent, Mycobacterium tuberculosis it able to sense this cytokine and increase virulence and respiration in response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".