Selective inhibition of IFNG-induced autophagy by <i>Mir155</i> - and <i>Mir31</i> -responsive WNT5A and SHH signaling
Bibliographic record
Abstract
Autophagy is one of the major immune mechanisms engaged to clear intracellular infectious agents. However, several pathogens have evolved strategies to evade autophagy. Here, we demonstrated that Mycobacteria, Shigella, and Listeria but not Klebsiella, Staphylococcus, and Escherichia inhibit IFNG-induced autophagy in macrophages by evoking selective and robust activation of WNT and SHH pathways via MTOR. Utilization of gain- or loss-of-function analyses as well as mir155-null macrophages emphasized the role of MTOR-responsive epigenetic modifications in the induction of Mir155 and Mir31. Importantly, cellular levels of PP2A, a phosphatase, were regulated by Mir155 and Mir31 to fine-tune autophagy. Diminished expression of PP2A led to inhibition of GSK3B, thus facilitating the prolonged activation of WNT and SHH signaling pathways. Sustained WNT and SHH signaling effectuated the expression of anti-inflammatory lipoxygenases, which in tandem inhibited IFNG-induced JAK-STAT signaling and contributed to evasion of autophagy. Altogether, these results established a role for new host factors and inhibitory mechanisms employed by the pathogens to limit autophagy, which could be targeted for therapeutic interventions.
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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".