Hepatic leukocyte immunoglobulin‐like receptor B4 (LILRB4) attenuates nonalcoholic fatty liver disease via SHP1‐TRAF6 pathway
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is an increasingly prevalent liver pathology characterized by hepatic steatosis and commonly accompanied by systematic inflammation and metabolic disorder. Despite an accumulating number of studies, no pharmacological strategy is available to treat this condition in the clinic. In this study, we applied extensive gain- and loss-of-function approaches to identify the key immune factor leukocyte immunoglobulin-like receptor B4 (LILRB4) as a negative regulator of NAFLD. The hepatocyte-specific knockout of LILRB4 (LILRB4-HKO) exacerbated high-fat diet-induced insulin resistance, glucose metabolic imbalance, hepatic lipid accumulation, and systematic inflammation in mice, whereas LILRB4 overexpression in hepatocytes showed a completely opposite phenotype relative to that of LILRB4-HKO mice when compared with their corresponding controls. Further investigations of molecular mechanisms demonstrated that LILRB4 recruits SHP1 to inhibit TRAF6 ubiquitination and subsequent inactivation of nuclear factor kappa B and mitogen-activated protein kinase cascades. From a therapeutic perspective, the overexpression of LILRB4 in a genetic model of NAFLD, ob/ob mice, largely reversed the inherent hepatic steatosis, inflammation, and metabolic disorder. CONCLUSION: Targeting hepatic LILRB4 to improve its expression or activation represents a promising strategy for the treatment of NAFLD as well as related liver and metabolic diseases. (Hepatology 2018;67:1303-1319).
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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 | 0.001 |
| 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".