WNT6-ACC2-induced accumulation of triacylglycerol rich lipid droplets is exploited by <i>M. tuberculosis</i>
Bibliographic record
Abstract
Abstract In view of emerging drug-resistant tuberculosis, host directed therapies are urgently needed to improve treatment outcomes with currently available anti-tuberculosis therapies. One option is to interfere with the formation of lipid-laden “foamy” macrophages in the infected host. Here, we provide evidence that WNT6, a member of the evolutionary conserved WNT signaling pathway, promotes foam cell formation by regulating key lipid metabolic genes including acetyl-CoA carboxylase-2 (ACC2) during pulmonary TB. In addition, we demonstrate that Mycobacterium tuberculosis (Mtb) facilitates its intracellular growth and dissemination in the host by exploiting the WNT6-ACC2 pathway. Using genetic and pharmacological approaches, we show that lack of functional WNT6 or ACC2 significantly reduces intracellular TAG levels, Mtb growth and necrotic cell death of macrophages. In combination with the anti-TB drug isoniazid, pharmacological inhibition of ACC2 improved anti-mycobacterial treatment in vitro and in vivo . Therefore, we propose the WNT6-ACC2 signaling pathway as a promising target for a host-directed therapy to reduce intracellular replication of Mtb by modulating neutral lipid metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".