Proximity-based proteomics (BioID) uncovers the Rho GTPase interactome in kidney podocytes
Bibliographic record
Abstract
Introduction Podocyte injury causes proteinuria. Rho GTPases play critical roles in regulating the podocyte cytoskeleton, and their alteration leads to foot process effacement. Yet, their signaling networks remain poorly understood. Methodology To address this, we mapped the interactomes of RhoA, Rac1, and Cdc42 in human podocytes using proximity-dependent biotin identification (BioID) labeling. Results and discussion Our BioID analysis detected a total of 1927 interactions with AvgP ≥ 0.95. Approximately 50% of the interactions are unique to podocytes compared to interactions in HEK293 and HeLa cells, with enrichment in pathways related to cell adhesion and shape organization. KIAA1522 emerged as a Rac1/Cdc42 interactor. KIAA1522 knockout reduced cellular projection formation in podocytes, while KIAA1522 knockdown in zebrafish resulted in foot process effacement. Additionally, we identified 20 guanine nucleotide exchange factors (GEFs), with 11, 8, and 5 interacting with RhoA, Rac1, and Cdc42, respectively. Analysis of public scRNA-seq datasets identified RhoA regulators as highly enriched in podocytes. Knockout of most RhoA GEFs reduced RhoA activity, with ARHGEF12 having the greatest effect. Our study defined key upstream regulators and downstream effectors of Rho GTPases in podocytes, identifying KIAA1522 as a novel Cdc42 effector and ARHGEF12 as a key RhoA regulator.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".