MON-725 Transcriptome Profiling in Postnatal Pituitary Gland Identifies Cell Type-Driven Sex-Specific Changes
Bibliographic record
Abstract
Abstract The pituitary gland is integral to the regulation of growth, metabolism, puberty, reproduction, and stress responses. Previously, we found that many genes associated with age-at-menarche in genome-wide association studies (GWAS) displayed increasingly sex-biased expression across the pubertal transition in the mouse pituitary. However, whether this trend exists beyond puberty-related genes was not known. In addition, the regulatory mechanisms underlying these gene expression changes remained to be explored. To answer these questions, we profiled the transcriptome, including microRNAs, of mouse pituitary in both sexes across pubertal transition in an unbiased manner and leveraged a recently published pituitary single cell transcriptome to explore cellular composition changes. We found that the most dynamic temporal changes in both mRNA and miRNA expression occur prior to puberty, underscoring a role for regulation of early pituitary postnatal development. We also observed ~900 genes displaying sex-biased expression patterns, arising during early development and becoming increasingly biased across puberty, including known sex-biased genes such as Fshb and Lhb. However, sex differences in miRNA expression are less pronounced, only 13 miRNAs were found to be sex-biased, suggesting lesser contribution of miRNAs to sex-biased gene expression relative to other forms of regulation. To assess whether pituitary cellular composition could underlie changes in gene expression across pubertal transition, we performed single cell deconvolution of our bulk pituitary gland gene expression. Interestingly, we found that sex differences in cell proportions were estimated to emerge across puberty: a greater proportion of lactotropes was found among females, and greater proportions of gonadotropes and somatotropes were found among males. We observed sex-biased expression patterns of marker genes for these cell types, including Prl, Fshb, and Gh. This finding suggests that cell proportion differences between sexes likely contribute to whole pituitary transcriptome changes we observed, however, to what extent remains to be studied. Together our study indicates that miRNAs play a substantial role in regulation of pituitary postnatal development but that differences in cellular composition may contribute more robustly to sex-biased gene expression.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".