Fibroblast growth factor receptor 5 (FGFR5) is a co-receptor for FGFR1 that is up-regulated in beta-cells by cytokine-induced inflammation
Bibliographic record
Abstract
Fibroblast growth factor receptor-1 (FGFR1) activity at the plasma membrane is tightly controlled by the availability of co-receptors and competing receptor isoforms. We have previously shown that FGFR1 activity in pancreatic beta-cells modulates a wide range of processes, including lipid metabolism, insulin processing, and cell survival. More recently, we have revealed that co-expression of FGFR5, a receptor isoform that lacks a tyrosine-kinase domain, influences FGFR1 responses. We therefore hypothesized that FGFR5 is a co-receptor to FGFR1 that modulates responses to ligands by forming a receptor heterocomplex with FGFR1. We first show here increased FGFR5 expression in the pancreatic islets of nonobese diabetic (NOD) mice and also in mouse and human islets treated with proinflammatory cytokines. Using siRNA knockdown, we further report that FGFR5 and FGFR1 expression improves beta-cell survival. Co-immunoprecipitation and quantitative live-cell imaging to measure the molecular interaction between FGFR5 and FGFR1 revealed that FGFR5 forms a mixture of ligand-independent homodimers (∼25%) and homotrimers (∼75%) at the plasma membrane. Interestingly, co-expressed FGFR5 and FGFR1 formed heterocomplexes with a 2:1 ratio and subsequently responded to FGF2 by forming FGFR5/FGFR1 signaling complexes with a 4:2 ratio. Taken together, our findings identify FGFR5 as a co-receptor that is up-regulated by inflammation and promotes FGFR1-induced survival, insights that reveal a potential target for intervention during beta-cell pathogenesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".