MRTF: Basic Biology and Role in Kidney Disease
Bibliographic record
Abstract
A lesser known but crucially important downstream effect of Rho family GTPases is the regulation of gene expression. This major role is mediated via the cytoskeleton, the organization of which dictates the nucleocytoplasmic shuttling of a set of transcription factors. Central among these is myocardin-related transcription factor (MRTF), which upon actin polymerization translocates to the nucleus and binds to its cognate partner, serum response factor (SRF). The MRTF/SRF complex then drives a large cohort of genes involved in cytoskeleton remodeling, contractility, extracellular matrix organization and many other processes. Accordingly, MRTF, activated by a variety of mechanical and chemical stimuli, affects a plethora of functions with physiological and pathological relevance. These include cell motility, development, metabolism and thus metastasis formation, inflammatory responses and-predominantly-organ fibrosis. The aim of this review is twofold: to provide an up-to-date summary about the basic biology and regulation of this versatile transcriptional coactivator; and to highlight its principal involvement in the pathobiology of kidney disease. Acting through both direct transcriptional and epigenetic mechanisms, MRTF plays a key (yet not fully appreciated) role in the induction of a profibrotic epithelial phenotype (PEP) as well as in fibroblast-myofibroblast transition, prime pathomechanisms in chronic kidney disease and renal fibrosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".