Analysis of a genetic region affecting mouse body weight
Bibliographic record
Abstract
Genetic factors affect an individual’s risk of developing obesity, but in most cases each genetic variant has a small effect. Discovery of genes that regulate obesity may provide clues about its underlying biological processes and point to new ways the disease can be treated. Preclinical animal models facilitate genetic discovery in obesity because environmental factors can be better controlled compared with the human population. We studied inbred mouse strains to identify novel genes affecting obesity and glucose metabolism. BTBR T+ Itpr3 tf /J (BTBR) mice are fatter and more glucose intolerant than C57BL/6J (B6) mice. Prior genetic studies of these strains identified an obesity locus on chromosome 2. Using congenic mice, we found that obesity was affected by a ∼316 kb region, with only two known genes, pyruvate dehydrogenase kinase 1 ( Pdk1) and integrin α 6 ( Itga6). Both genes had mutations affecting their amino acid sequence and reducing mRNA levels. Both genes have known functions that could modulate obesity, lipid metabolism, insulin secretion, and/or glucose homeostasis. We hypothesized that genetic variation in or near Pdk1 or Itga6 causing reduced Pdk1 and Itga6 expression would promote obesity and impaired glucose tolerance. We used knockout mice lacking Pdk1 or Itga6 fed an obesigenic diet to test this hypothesis. Under the conditions we studied, we were unable to detect an individual contribution of either Pdk1 or Itga6 to body weight. During our studies, with conditions outside our control, we were unable to reproduce some of our previous body weight data. However, we identified a previously unknown role for Pdk1 in cardiac cholesterol metabolism providing the basis for future investigations. The studies described in this paper highlight the importance and the challenge using physiological outcomes to study obesity genes in mice.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".