Mutations in the Gene That Encodes the F-Actin Binding Protein Anillin Cause FSGS
Bibliographic record
Abstract
FSGS is characterized by segmental scarring of the glomerulus and is a leading cause of kidney failure. Identification of genes causing FSGS has improved our understanding of disease mechanisms and points to defects in the glomerular epithelial cell, the podocyte, as a major factor in disease pathogenesis. Using a combination of genome-wide linkage studies and whole-exome sequencing in a kindred with familial FSGS, we identified a missense mutation R431C in anillin (ANLN), an F-actin binding cell cycle gene, as a cause of FSGS. We screened 250 additional families with FSGS and found another variant, G618C, that segregates with disease in a second family with FSGS. We demonstrate upregulation of anillin in podocytes in kidney biopsy specimens from individuals with FSGS and kidney samples from a murine model of HIV-1-associated nephropathy. Overexpression of R431C mutant ANLN in immortalized human podocytes results in enhanced podocyte motility. The mutant anillin displays reduced binding to the slit diaphragm-associated scaffold protein CD2AP. Knockdown of the ANLN gene in zebrafish morphants caused a loss of glomerular filtration barrier integrity, podocyte foot process effacement, and an edematous phenotype. Collectively, these findings suggest that anillin is important in maintaining the integrity of the podocyte actin cytoskeleton.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".