Quantitative Proteomics of Intestinal Mucosa From Male Mice Lacking Intestinal Epithelial Insulin Receptors
Bibliographic record
Abstract
The goal of the present study was to determine whether loss of the insulin receptor alters the molecular landscape of the intestinal mucosa, using intestinal-epithelial insulin receptor knockout (IE-irKO) mice and both genetic (IRfl/fl and Villin-cre) controls. Quantitative proteomic analysis by liquid chromatography mass spectrometry was applied to jejunal and colonic mucosa from mice fed a normal chow diet and mice fed a Western diet (WD). Jejunal mucosa from IE-irKO mice demonstrated alterations in all intestinal cell lineages: Paneth, goblet, absorptive, and enteroendocrine cells. Only goblet and absorptive cells were affected in the colon. Also, a marked effect of WD consumption was found on the gut proteome. A substantial reduction was detected in Paneth cell proteins with antimicrobial activity, including lysozyme C-1, angiogenin-4, cryptdin-related sequence 1C-3 and -2, α-defensin 17, and intelectin-1a. The key protein expressed by goblet cells, mucin-2, was also reduced in the IE-irKO mice. Proteins involved in lipid metabolism, including aldose reductase-related protein 1, 15-hydroxyprostaglandin dehydrogenase, apolipoprotein A-II, and pyruvate dehydrogenase kinase isozyme 4, were increased in the mucosa of WD-fed IE-irKO mice compared with controls. In contrast, expression of the nutrient-responsive gut hormones, glucose-dependent insulinotropic polypeptide and neurotensin, was reduced in the jejunal mucosa of IE-irKO mice, and the expression of proteins of the P-type adenosine triphosphatases and the solute carrier-transporter family was reduced in the colon of WD-fed IE-irKO mice. In conclusion, IE-irKO mice display a distinct molecular phenotype, suggesting a biological role of insulin and its receptor in determining differentiated cell specificity in the intestinal epithelium.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".