Regulation of Cardiac Fibroblast GLS1 Expression by Scleraxis
Bibliographic record
Abstract
Fibrosis is an energy-intensive process requiring the activation of fibroblasts to myofibroblasts, resulting in the increased synthesis of extracellular matrix proteins. Little is known about the transcriptional control of energy metabolism in cardiac fibroblast activation, but glutaminolysis has been implicated in liver and lung fibrosis. Here we explored how pro-fibrotic TGFβ and its effector scleraxis, which drive cardiac fibroblast activation, regulate genes involved in glutaminolysis, particularly the rate-limiting enzyme glutaminase (GLS1). The GLS1 inhibitor CB-839 attenuated TGFβ-induced fibroblast activation. Cardiac fibroblast activation to myofibroblasts by scleraxis overexpression increased glutaminolysis gene expression, including GLS1, while cardiac fibroblasts from scleraxis-null mice showed reduced expression. TGFβ induced GLS1 expression and increased intracellular glutamine and glutamate levels, indicative of increased glutaminolysis, but in scleraxis knockout cells, these measures were attenuated, and the response to TGFβ was lost. The knockdown of scleraxis in activated cardiac fibroblasts reduced GLS1 expression by 75%. Scleraxis transactivated the human GLS1 promoter in luciferase reporter assays, and this effect was dependent on a key scleraxis-binding E-box motif. These results implicate scleraxis-mediated GLS1 expression as a key regulator of glutaminolysis in cardiac fibroblast activation, and blocking scleraxis in this process may provide a means of starving fibroblasts of the energy required for fibrosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".