Genome-wide analysis of gene transcription in the hypothalamus
Bibliographic record
Abstract
As the genomic regions containing loci predisposing to obesity-related traits are mapped in human population screens and mouse genetic studies, identification of susceptibility genes will increasingly be facilitated by bioinformatic methods. We hypothesized that candidate genes can be prioritized by their expression levels in tissues of central importance in obesity. Our objective was to develop a combined bioinformatics and molecular paradigm to identify novel genes as candidates for murine or human obesity genetic modifiers based on their differential expression patterns in the hypothalamus compared with other murine tissues. We used bioinformatics tools to search publicly available gene expression databases using criteria designed to identify novel genes differentially expressed in the hypothalamus. We used RNA methods to determine their expression sites and levels of expression in the hypothalamus of the murine brain. We identified the chromosomal location of the novel genes in mice and in humans and compared these locations with those of genetic loci predisposing to obesity-related traits. We developed a search strategy that correctly identified a set of genes known to be important in hypothalamic function as well as a candidate gene for Prader-Willi syndrome that was not previously identified as differentially expressed in the hypothalamus. Using this same strategy, we identified and characterized a set of 11 genes not previously known to be differentially expressed in the murine hypothalamus. Our results demonstrate the feasibility of combined bioinformatics and molecular approaches to the identification of genes that are candidates for obesity-related disorders in humans and 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) | 0.000 | 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) | 0.000 | 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 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".