Lipocalin 2 Regulates Inflammation during Pulmonary Mycobacterial Infections
Bibliographic record
Abstract
Pulmonary tuberculosis (TB), caused by the intracellular bacteria Mycobacterium tuberculosis, is a worldwide disease that continues to kill more than 1.5 million people every year worldwide. The accumulation of lymphocytes mediates the formation of the tubercle granuloma in the lung and is crucial for host protection against M.tuberculosis infection. However, paradoxically the tubercle granuloma is also the basis for the immunopathology associated with the disease and very little is known about the regulatory mechanisms that constrain the inflammation associated with the granulomas. Lipocalin 2 (Lcn2) is a member of the lipocalin family of proteins and binds to bacterial siderophores thereby sequestering iron required for bacterial growth. Thus far, it is not known whether Lcn2 plays a role in the inflammatory response to mycobacterial pulmonary infections. In the present study, using models of acute and chronic mycobacterial pulmonary infections, we reveal a novel role for Lcn2 in constraining T cell lymphocytic accumulation and inflammation by inhibiting inflammatory chemokines, such as CXCL9. In contrast, Lcn2 promotes neutrophil recruitment during mycobacterial pulmonary infection, by inducing G-CSF and KC in alveolar macrophages. Importantly, despite a common role for Lcn2 in regulating chemokines during mycobacterial pulmonary infections, Lcn2 deficient mice are more susceptible to acute M.bovis BCG, but not low dose M.tuberculosis pulmonary infection.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".