Mycobacterium tuberculosis manipulates LINC02528 in macrophages to modulate anti-tuberculosis metabolic immunity
Bibliographic record
Abstract
Host defenses are crucial in deciding the fate of Mycobacterium tuberculosis (Mtb) infections, as less than 10% of infected individuals develop tuberculosis. Oxidative stress plays a critical role in the host defense against Mtb. However, the mechanisms by which Mtb modulates redox homeostasis to evade immune responses remain poorly understood. In this study, we primarily identified a pathogen-responsive long noncoding RNA, LINC02528, which was selectively upregulated in peripheral blood mononuclear cells (PBMCs) from tuberculosis (TB) patients. In Mtb-infected macrophages, LINC02528 dynamically relocalizes from the nucleus to the cytoplasm. Functionally, CRISPR-Cas9-mediated knockout (KO) of LINC02528 in macrophages resulted in reduced Mtb survival concurrent with an elevated IL-1β expression. Importantly, these antimicrobial effects were abrogated by IL-1 receptor antagonist (IL-RA) treatment. Interestingly, LINC02528 was found to directly bind to TOMM22, a mitochondrial outer membrane translocase, as validated by co-localization analysis using in situ hybridization of lung tissue sections from a TB patient. The ECAR results revealed that LINC02528 deficiency significantly increased glycolysis and elevated Mtb-induced mitochondrial ROS (mtROS) production. Notably, TOMM22 knockdown phenocopied LINC02528 deletion effects, suggesting functional interdependence in modulating mitochondrial dynamics and the host's anti-TB immunity. Collectively, our findings reveal a novel strategy wherein Mtb hijacks the lncRNA-mitochondrial axis to rewire redox-metabolic checkpoints to favor immune evasion. Targeting LINC02528 could dually disrupt the pathogen-permissive redox balance and activate mtROS-IL-1β-mediated antimicrobial defense, offering novel therapeutic avenues for 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".