Glutathione-Specific Gamma-Glutamylcyclotransferase 1 (Chac-1) Is a CKD Risk Gene
Bibliographic record
Abstract
Background: Genome-wide association studies (GWAS) have identified more than 300 loci where genetic variants associated with CKD development, however the causal variant, gene, cell type and the disease mechanism remain mostly unknown Methods: We used expression of quantitative trait loci (eQTL) and computational integration coloc and transcriptome wide association analysis, to identify target genes for GWAS variants. We integrated human kidney single cell RNA and ATAC-seq to fine map likely causal variants. Using CRISPR technology we generated mice with genetic deletion of Chac1. Kidney injury was induced by folic acid injection, and uninephrectomy followed by streptozotocin injection. We have also analyzed primary tubule cells isolated from control (Chac1 +/+) and heterozygous (Chac1 +/-) mice. Results: Integration of GWAS and human kidney eQTL dataset prioritized Chac1 as potential kidney disease risk gene. Lower CHAC1 level was protective. Single cell and immunofluorescence studies highlighted strong Chac1 expression in kidney tubules. Mice with a heterozygous deletion in C showed no phenotypic differences at baseline, however exhibited less fibrosis both in the folic acid and diabetic injury models compared to wild type animals. In vitro, Chac1 heterozygous cells showed improved survival following cisplatin treatment compared to wild type cells, but no difference in apoptosis or necroptosis. Chac1 +/- cells showed protection from cisplatin induced ferroptosis, including preserved cell viability, less lipid peroxidation and higher expression of ferroptosis inhibitors such as Aifm2 and Gpx4. Gluthathione levels were also higher in kidneys of Chac1 heterozygous mice when compared to controls potentially explaining their ferroptosis resistance. Conclusions: Via the integration of kidney function GWAS and eQTL, mouse model and cell culture studies we identified Chac1 as a new kidney disease risk gene. Funding: Other NIH Support - Pediatric Scientist Development Program
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".