Neither glucagon-like peptide 1 receptors nor GDNF family receptor α-like neurons are required for aversive or anorectic response to deoxynivalenol (vomitoxin)
Bibliographic record
Abstract
Deoxynivalenol (DON), a type B trichothecene mycotoxin contaminating grains, promotes nausea, emesis and anorexia. With DON exposure, circulating levels of intestinally derived satiation hormones, including glucagon-like peptide 1 (GLP-1) are elevated. To directly test whether GLP-1 signaling mediates the effects of DON, we examined the response of GLP-1 or GLP-1R-deficient mice to DON injection. We found comparable anorectic and conditioned taste avoidance learning responses in GLP-1/GLP-1R deficient mice compared to control littermates, suggesting that GLP-1 is not necessary for the effects of DON on food intake and visceral illness. We then used our previously published data from translating ribosome affinity purification with RNA sequencing (TRAP-seq) analysis of area postrema neurons that express the receptor for the circulating cytokine growth differentiation factor (GDF15), growth differentiation factor a-like (GFRAL). Interestingly, this analysis showed that a cell surface receptor for DON, calcium sensing receptor (CaSR), is heavily enriched in GFRAL neurons. Given that GDF15 potently reduces food intake and can cause visceral illness by signaling through GFRAL neurons, we hypothesized that DON may also signal by activating CaSR on GFRAL neurons. Indeed, circulating GDF15 levels are elevated after DON administration but both GFRAL knockout and GFRAL neuron-ablated mice exhibited similar anorectic and conditioned taste avoidance responses compared to WT littermates. Thus, GLP-1 signaling and GFRAL signaling and neurons are not required for DON-induced visceral illness or anorexia.
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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".