Nck1 Deficiency Impairs Adipogenesis by Activation of PDGFRα in Preadipocytes
Bibliographic record
Abstract
Obesity results from an excessive expansion of white adipose tissue (WAT), which is still poorly understood from an etiologic-mechanistic perspective. Here, we report that Nck1, a Src homology domain-containing adaptor, is upregulated during WAT expansion and in vitro adipogenesis. In agreement, Nck1 mRNA correlates positively with peroxisome proliferator-activated receptor (PPAR) γ and adiponectin mRNAs in the WAT of obese humans, whereas Nck1-deficient mice display smaller WAT depots with reduced number of adipocyte precursors and accumulation of extracellular matrix. Furthermore, silencing Nck1 in 3T3-L1 preadipocytes increases the proliferation and expression of genes encoding collagen, whereas it decreases the expression of adipogenic markers and impairs adipogenesis. Silencing Nck1 in 3T3-L1 preadipocytes also promotes the expression of platelet-derived growth factor (PDGF)-A and platelet-derived growth factor receptor (PDGFR) α activation and signaling. Preventing PDGFRα activation using imatinib, or through PDGF-A or PDGFRα deficiency, inhibits collagen expression in Nck1-deficient preadipocytes. Finally, imatinib rescues differentiation of Nck1-deficient preadipocytes. Altogether, our findings reveal that Nck1 modulates WAT development through PDGFRα-dependent remodeling of preadipocytes.
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 | 0.000 | 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".