RapTB: a lung-derived hemoglobin fragment with activity against Mycobacterium tuberculosis
Bibliographic record
Abstract
Tuberculosis (TB) remains difficult to treat due to the need for prolonged multidrug therapy and the global rise of drug-resistant Mycobacterium tuberculosis ( Mtb ) strains. Endogenous antimicrobial peptides (AMPs) have emerged as promising candidates for host-directed therapies. Given the pulmonary nature of TB, we hypothesized that human lung tissue contains peptides with intrinsic antimycobacterial activity. We screened a peptide library derived from human lung tissue and identified a 39-amino-acid C-terminal fragment of β-hemoglobin (HBB(112–147)), referred to as RapTB, with potent activity against Mtb . Recombinant RapTB exhibited dose-dependent inhibition of extracellular Mtb , reaching ~60% activity at 50 μM. Electron microscopy revealed mycobacterial cell wall disruption as a likely mechanism. RapTB was non-toxic to primary human macrophages and efficiently internalized by Mtb -infected cells. However, it did not co-localize with intracellular bacilli and failed to limit intracellular replication. HBB-derived fragments such as RapTB have previously been identified in human tissues and are known to exhibit broad-spectrum antimicrobial activity. Our findings extend this functional class to include antimycobacterial activity and suggest a potential role for RapTB in the early, extracellular phase of host defense against 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) | 0.002 | 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".