Sexual Dimorphism of Rat Liver Nuclear Proteins
Bibliographic record
Abstract
Many genes are expressed in mammalian liver in a sexually dimorphic manner. DNA microarray analysis has shown that growth hormone (GH) and its sex-dependent pattern of pituitary secretion play a major role in establishing the sexually dimorphic patterns of liver gene expression. However, GH may exert effects on protein post-translational modification and nuclear localization that are not reflected at the mRNA level. To investigate these potential effects of GH, we used two-dimensional gel electrophoresis followed by LC-MS/MS to: 1) identify rat liver nuclear proteins whose abundance or state of post-translational modification displays sex-dependent differences; and 2) determine the role of the plasma GH profile in establishing these differences. Nuclear extracts prepared from livers of individual male (n=9) and female (n=5) adult rats, and from males given GH by continuous infusion for 7 days to feminize liver gene expression (n=5 rats), were resolved by two-dimensional electrophoresis. Image analysis of SYPRO Ruby-stained gels revealed 165 sexually dimorphic protein spots that differ in normalized volume between male and female groups by >1.5-fold at p<0.05. Sixty of these proteins exhibited female-like changes in spot abundance following continuous GH treatment. Comparison of male and GH-treated male groups revealed 130 proteins that displayed >1.5-fold differences in abundance, with 60 of these GH-responsive spots being sexually dimorphic. Thus, GH plays an important role in establishing the sex-dependent differences in liver nuclear protein content. Twenty-eight of the sexually dimorphic and/or GH-regulated protein spots were identified by LC-MS/MS. Proteins identified include regucalcin, nuclear factor 45, and heterogeneous nuclear ribonucleoproteins A3, D-like, and K, in addition to proteins such as GST, normally associated with cytosolic extracts but also reported to be localized in the nucleus.
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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 | 0.001 | 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".