Modulating intestinal neuroimmune VIPergic signaling attenuates the reduction in ILC3-derived IL-22 and hepatic steatosis in MASLD
Bibliographic record
Abstract
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as NAFLD) is a major driver of cirrhosis and liver-related mortality. However, therapeutic options for MASLD, including prevention of liver steatosis, are limited. We previously described that vasoactive intestinal peptide-producing neurons (VIP-neurons) regulate the efficiency of intestinal dietary fat absorption and IL-22 production by type 3 innate lymphoid cells (ILC3) in the intestine. Given the described hepatoprotective role of IL-22, we hypothesize that modulation of this neuroimmune circuit could potentially be an innovative approach for the control of liver steatosis. METHODS: We used a model of diet-induced MASLD by exposing mice to a high-fat diet (HFD) for 16 weeks, when the development of liver steatosis was first observed in our animals. We characterized IL-22 production by intestinal ILC3 at this dietary endpoint. We then evaluated whether communication between VIP-neurons and ILC3 affected IL-22 production and MASLD development by exposing mice with a conditional genetic deletion of Vipr2 in ILC3 (Rorc(t)CreVipr2fl/fl) to the HFD. We also performed intermittent global inhibition of VIP-neurons using a chemogenetic inhibitory approach (VipIres-CrehM4DiLSL) in HFD-fed mice. RESULTS: Production of IL-22 by intestinal ILC3 is reduced in steatotic mice that were exposed to an HFD for 16 weeks. Targeted deletion of VIP receptor 2 in ILC3 resulted in higher production of IL-22 in ILC3 and was associated with a significant reduction in liver steatosis in mice under HFD. Global inhibition of VIP-producing neurons also resulted in a significant reduction in liver steatosis. CONCLUSIONS: Modulating VIPergic neuroimmune signaling can ameliorate the development of hepatic steatosis induced by a surplus of fat ingestion in the diet. This neuroimmune pathway should be further investigated as a potential therapeutic avenue in MASLD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".