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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".