Bone Marrow Mesenchymal Stem Cells Provide an Antibiotic-Protective Niche for Persistent Viable Mycobacterium tuberculosis that Survive Antibiotic Treatment
Bibliographic record
Abstract
During tuberculosis (TB), some Mycobacterium tuberculosis bacilli persist in the presence of an active immunity and antibiotics that are used to treat the disease. Herein, by using the Cornell model of TB persistence, we further explored our recent finding that suggested that M. tuberculosis can escape therapy by residing in the bone marrow (BM) mesenchymal stem cells. We initially showed that M. tuberculosis rapidly disseminates to the mouse BM after aerosol exposure and maintained a stable burden for at least 220 days. In contrast, in the lungs, the M. tuberculosis burden peaked at 28 days and subsequently declined approximately 10-fold. More important, treatment of the mice with the antibiotics rifampicin and isoniazid, as expected, resulted in effective clearance of M. tuberculosis from the lungs and spleen. In contrast, M. tuberculosis persisted, albeit at low numbers, in the BM of antibiotic-treated mice. Moreover, most viable M. tuberculosis was recovered from the bone marrow CD271(+)CD45(-)-enriched cell fraction, and only few viable bacteria could be isolated from the CD271(-)CD45(+) cell fraction. These results clearly show that BM mesenchymal stem cells provide an antibiotic-protective niche for M. tuberculosis and suggest that unraveling the mechanisms underlying this phenomenon will enhance our understanding of M. tuberculosis persistence in treated TB patients.
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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 | 0.001 |
| 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".