DGAT2 Inhibition Potentiates Lipid Droplet Formation To Reduce Cytotoxicity in APOL1 Kidney Risk Variants
Bibliographic record
Abstract
Background Two variants in the gene encoding apolipoprotein L1 (APOL1) that are highly associated with African ancestry are major contributors to the large racial disparity in rates of human kidney disease. We previously demonstrated that recruitment of APOL1 risk variants G1 and G2 from the endoplasmic reticulum to lipid droplets leads to reduced APOL1-mediated cytotoxicity in human podocytes. Methods We used CRISPR-Cas9 gene editing of induced pluripotent stem cells to develop human-derived APOL1 G0/G0 and APOL1 G2/G2 kidney organoids on an isogenic background, and performed bulk RNA sequencing of organoids before and after treatment with IFN- γ . We examined the number and distribution of lipid droplets in response to treatment with inhibitors of diacylglycerol O-acyltransferases 1 and 2 (DGAT1 and DGAT2) in kidney cells and organoids. Results APOL1 was highly upregulated in response to IFN- γ in human kidney organoids, with greater increases in organoids of high-risk G1 and G2 genotypes compared with wild-type (G0) organoids. RNA sequencing of organoids revealed that high-risk APOL1 G2/G2 organoids exhibited downregulation of a number of genes involved in lipogenesis and lipid droplet biogenesis, as well as upregulation of genes involved in fatty acid oxidation. There were fewer lipid droplets in unstimulated high-risk APOL1 G2/G2 kidney organoids than in wild-type APOL1 G0/G0 organoids. Whereas DGAT1 inhibition reduced kidney organoid lipid droplet number, DGAT2 inhibition unexpectedly increased organoid lipid droplet number. DGAT2 inhibition promoted the recruitment of APOL1 to lipid droplets, with associated reduction in cytotoxicity. Conclusions Lipogenesis and lipid droplet formation are important modulators of APOL1-associated cytotoxicity. Inhibition of DGAT2 may offer a potential therapeutic strategy to attenuate cytotoxic effects of APOL1 risk variants.
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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 | 0.000 | 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 | 0.001 |
| 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".