Doxycycline and HIV Infection Suppress Tuberculosis-induced Matrix Metalloproteinases
Bibliographic record
Abstract
RATIONALE: Tuberculosis kills more than 1.5 million people per year, and standard treatment has remained unchanged for more than 30 years. Tuberculosis (TB) drives matrix metalloproteinase (MMP) activity to cause immunopathology. In advanced HIV infection, tissue destruction is reduced, but underlying mechanisms are poorly defined and no current antituberculous therapy reduces host tissue damage. OBJECTIVES: To investigate MMP activity in patients with TB with and without HIV coinfection and to determine the potential of doxycycline to inhibit MMPs and decrease pathology. METHODS: Concentrations of MMPs and cytokines were analyzed by Luminex array in a prospectively recruited cohort of patients. Modulation of MMP secretion and Mycobacterium tuberculosis growth by doxycycline was studied in primary human cells and TB-infected guinea pigs. MEASUREMENTS AND MAIN RESULTS: HIV coinfection decreased MMP concentrations in induced sputum of patients with TB. MMPs correlated with clinical markers of tissue damage, further implicating dysregulated protease activity in TB-driven pathology. In contrast, cytokine concentrations were no different. Doxycycline, a licensed MMP inhibitor, suppressed TB-dependent MMP-1 and -9 secretion from primary human macrophages and epithelial cells by inhibiting promoter activation. In the guinea pig model, doxycycline reduced lung TB colony forming units after 8 weeks in a dose-dependent manner compared with untreated animals, and in vitro doxycycline inhibited mycobacterial proliferation. CONCLUSIONS: HIV coinfection in patients with TB reduces concentrations of immunopathogenic MMPs. Doxycycline decreases MMP activity in a cellular model and suppresses mycobacterial growth in vitro and in guinea pigs. Adjunctive doxycycline therapy may reduce morbidity and mortality in TB.
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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) | 0.001 | 0.000 |
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".