Transcriptome-Wide Identification of Preferentially Expressed Genes in the Hypothalamus and Pituitary Gland
Bibliographic record
Abstract
To identify preferentially expressed genes in the central endocrine organs of the hypothalamus and pituitary gland, we generated transcriptome-wide mRNA profiles of the hypothalamus, pituitary gland, and parietal cortex in male mice (12-15 weeks old) using serial analysis of gene expression (SAGE). Total counts of SAGE tags for the hypothalamus, pituitary gland, and parietal cortex were 165824, 126688, and 161045 tags, respectively. This represented 59244, 45151, and 55131 distinct tags, respectively. Comparison of these mRNA profiles revealed that 22 mRNA species, including three potential novel transcripts, were preferentially expressed in the hypothalamus. In addition to well-known hypothalamic transcripts, such as hypocretin, several genes involved in hormone function, intracellular transduction, metabolism, protein transport, steroidogenesis, extracellular matrix, and brain disease were identified as preferentially expressed hypothalamic transcripts. In the pituitary gland, 106 mRNA species, including 60 potential novel transcripts, were preferentially expressed. In addition to well-known pituitary genes, such as growth hormone and thyroid stimulating hormone beta, a number of genes classified to function in transport, amino acid metabolism, intracellular transduction, cell adhesion, disulfide bond formation, stress response, transcription, protein synthesis, and turnover, cell differentiation, the cell cycle, and in the cytoskeleton and extracellular matrix were also preferentially expressed. In conclusion, the current study identified not only well-known hypothalamic and pituitary transcripts but also a number of new candidates likely to be involved in endocrine homeostatic systems regulated by the hypothalamus and pituitary gland.
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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) | 0.001 | 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".