Identification of the principal transcriptional regulators for low-fat and high-fat meal responsive genes in small intestine
Bibliographic record
Abstract
BACKGROUND: High-fat (HF) diet is a well-known cause of obesity. To identify principle transcriptional regulators that could be therapeutic targets of obesity, we investigated transcriptomic modulation in the duodenal mucosa following low-fat (LF) and HF meal ingestion. METHODS: Whereas one group of mice was sacrificed after fasting, the others were fed ad libitum with LF or HF meal, and sacrificed 30 min, 1 h and 3 h after the beginning of the meal. A transcriptome analysis of the duodenal mucosa of the 7 groups was conducted using both microarray and serial analysis of gene expression (SAGE) method followed by an Ingenuity Pathways Analysis (IPA). RESULTS: SAGE and microarray showed that the modulation of a total of 896 transcripts in the duodenal mucosa after LF and/or HF meal, compared to the fasting condition. The IPA identified lipid metabolism, molecular transport, and small molecule biochemistry as top three molecular and cellular functions for the HF-responsive, HF-specific, HF-delay, and LF-HF different genes. Moreover, the top transcriptional regulator for the HF-responsive and HF-specific genes was peroxisome proliferator-activated receptor alpha (PPARα). On the other hand, the LF-responsive and LF-specific genes were related to carbohydrate metabolism, cellular function and maintenance, and cell death/cellular growth and proliferation, and the top transcriptional regulators were forkhead box protein O1 (FOXO1) and cAMP response element binding protein 1 (CREB1), respectively. CONCLUSIONS: These results will help to understand the molecular mechanisms of intestinal response after LF and HF ingestions, and contribute to identify therapeutic targets for obesity and obesity-related diseases.
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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) | 0.002 | 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".