Adipokine Gene Expression in Brain and Pituitary Gland
Bibliographic record
Abstract
The brain has been recognized as a prominent site of peptide biosynthesis for more than 30 years, and many neuropeptides are now known to be common to gut and brain. With these precedents in mind it is remarkable that adipose-derived peptides like leptin have attracted minimal attention as brain-derived putative neuromodulators of energy balance. This review outlines the evidence that several adipose-specific genes are also expressed in the central nervous system and pituitary gland. We, and others, confirmed that the genes for leptin, resistin, adiponectin, FIAF (fasting-induced adipose factor) and adiponutrin are expressed and regulated in these tissues. For example, leptin mRNA was readily detectable in human, rat, sheep and pig brain, but not in the mouse. Leptin expression in rat brain and pituitary was regulated through development, by food restriction, and following traumatic brain injury. In contrast, hypothalamic resistin mRNA was unaffected by age or by fasting, but was significantly depleted by food restriction in mouse pituitary gland. Similar results were seen in the ob/ob mouse, and we noted a marked reduction in resistin-positive hypothalamic nerve fibres. Resistin and fiaf mRNA were also upregulated in hypoxic/ischaemic mouse brain. Our studies on the regulation of neuronal adipokines were greatly aided by the availability of clonal hypothalamic neuronal cell lines. One of these, N-1, expresses both rstn and fiaf together with several other neuropeptides and receptors involved in energy homeostasis. Selective silencing of rstn revealed an autocrine/paracrine regulatory system, mediated through socs-3 expression that may influence the feedback effects of insulin and leptin in vivo. A similar convergence of signals in the pituitary gland could also influence anterior pituitary hormone secretion. In conclusion, the evidence is suggestive that brain and pituitary-derived adipokines represent a local regulatory circuit that may fine tune the feedback effects of adipose hormones in the control of energy balance.
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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) | 0.002 | 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".