Ubiquilin 1 Promotes IFN-γ-Induced Xenophagy of Mycobacterium tuberculosis
Bibliographic record
Abstract
The success of Mycobacterium tuberculosis (Mtb) as a pathogen rests upon its ability to grow intracellularly in macrophages. Interferon-gamma (IFN-γ) is critical in host defense against Mtb and stimulates macrophage clearance of Mtb through an autophagy pathway. Here we show that the host protein ubiquilin 1 (UBQLN1) promotes IFN-γ-mediated autophagic clearance of Mtb. Ubiquilin family members have previously been shown to recognize proteins that aggregate in neurodegenerative disorders. We find that UBQLN1 can interact with Mtb surface proteins and associates with the bacilli in vitro. In IFN-γ activated macrophages, UBQLN1 co-localizes with Mtb and promotes the anti-mycobacterial activity of IFN-γ. The association of UBQLN1 with Mtb depends upon the secreted bacterial protein, EsxA, which is involved in permeabilizing host phagosomes. In autophagy-deficient macrophages, UBQLN1 accumulates around Mtb, consistent with the idea that it marks bacilli that traffic through the autophagy pathway. Moreover, UBQLN1 promotes ubiquitin, p62, and LC3 accumulation around Mtb, acting independently of the E3 ligase parkin. In summary, we propose a model in which UBQLN1 recognizes Mtb and in turn recruits the autophagy machinery thereby promoting intracellular control of Mtb. Thus, polymorphisms in ubiquilins, which are known to influence susceptibility to neurodegenerative illnesses, might also play a role in host defense against Mtb.
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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".