Characterization of a novel role of the Birt-Hogg-Dubé tumor suppressor protein in metabolism
Bibliographic record
Abstract
The Birt-Hogg-Dubé (BHD) syndrome is a hereditary human cancer syndrome that predisposes affected individuals to develop skin hamartomas, lung cysts and pneumothorax as well as renal carcinoma. BHD is caused by loss-of-function mutations in the folliculin (FLCN) gene. The molecular function of the FLCN gene product is still largely unknown; opposite and conflicting evidence of mTOR signaling activity has been reported. To further study the development of BHD malignancies, a novel conditional Pax8Cre FLCN floxed mouse model was generated. Pathological analysis revealed that the mice developed a unique and relevant phenotype of renal tubule hyperplasia, multifocal renal cysts, adenomas as well as histiocytic sarcoma. From this model, FLCN-null MEFs were generated to further investigate the molecular pathways by which FLCN suppresses tumorigenesis. Preliminary data showing lengthened survival of FLCN-null MEFs under metabolic stress supports a role for FLCN in nutrient sensing. Upon further investigation, FLCN-null MEFs show an increase in mitochondrial biogenesis as denoted by an increase in AMP:ATP ratio, respiration rates, lactate levels, mitochondrial numbers and PGC1-alpha/beta transcripts. We hypothesize that the previously shown indirect physical interaction between AMPK and FLCN could provide a rationale for the striking metabolic phenotype. To this end, FLCN-null MEFs with stably knocked down levels of AMPKalpha1/2 isoforms were generated in an attempt to rescue the novel metabolic phenotype. Altogether, these studies are aimed to further elucidate the metabolic function of FLCN in order to better understand its role as a tumor suppressor.
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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) | 0.001 | 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".