Avian Expression Patterns and Genomic Mapping Implicate Leptin in Digestion and TNF in Immunity, Suggesting That Their Interacting Adipokine Role Has Been Acquired Only in Mammals
Bibliographic record
Abstract
In mammals, leptin and tumor-necrosis factor (TNF) are prominent interacting adipokines mediating appetite control and insulin sensitivity. While TNF pleiotropically functions in immune defense and cell survival, leptin is largely confined to signaling energy stores in adipocytes. Knowledge about the function of avian leptin and TNF is limited and they are absent or lowly expressed in adipose, respectively. Employing radiation-hybrid mapping and FISH-TSA, we mapped TNF and its syntenic genes to chicken chromosome 16 within the major histocompatibility complex (MHC) region. This mapping position suggests that avian TNF has a role in regulating immune response. To test its possible interaction with leptin within the immune system and beyond, we compared the transcription patterns of TNF, leptin and their cognate receptors obtained by meta-analysis of GenBank RNA-seq data. While expression of leptin and its receptor (LEPR) were detected in the brain and digestive tract, TNF and its receptor mRNAs were primarily found in viral-infected and LPS-treated leukocytes. We confirmed leptin expression in the duodenum by immunohistochemistry staining. Altogether, we suggest that whereas leptin and TNF interact as adipokines in mammals, in birds, they have distinct roles. Thus, the interaction between leptin and TNF may be unique to mammals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
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".