Donor-Specific Effects of KDM6A in Regulating Proximal Tubular Epithelial Cell Metabolism: Implications for Diabetic Kidney Disease
Bibliographic record
Abstract
Background: Diabetic kidney disease (DKD), the leading cause of chronic kidney disease, involves metabolic dysfunction in proximal tubular epithelial cells (PTECs)—the kidney’s most abundant, metabolically active cells. High glucose shifts PTECs from fatty acid oxidation to glucose and glutamine metabolism, boosting glycolytic and TCA flux and accumulating pro-inflammatory, pro-fibrotic metabolites like lactate that drive DKD. KDM6A, a histone demethylase that removes repressive epigenetic marks, is upregulated in diabetic kidneys and enhances expression of metabolic enzymes and cytokines. While its inhibition mitigates glomerular lesions in DKD mouse models, KDM6A’s role in PTEC dysfunction remains unclear. We show KDM6A mediates mitochondrial dysfunction in PTECs under high glucose, modulated by donor sex. Methods: Primary human PTECs from different donors (Table 1) underwent 72h siRNA-mediated KDM6A inhibition, followed by 24h treatment with normal glucose (5.5 mM), high glucose (25 mM), or osmotic control (5.5 mM glucose + 19.5 mM mannitol). Results: High glucose significantly increased extracellular lactate levels in male-derived PTECs. KDM6A knockdown reduced lactate secretion in cells of all donors. In PTECs of one male donor, KDM6A silencing also prevented high glucose-induced upregulation of LDHA and MDH2, key enzymes in glycolysis and the TCA cycle. In all donors, KDM6A inhibition elevated extracellular glucose levels, especially in the donor most sensitive to KDM6A knockdown. Additionally, KDM6A inhibition prevented increased secretion of pro-inflammatory cytokines in PTECs of both male donors under high glucose, while lowering only IL-6 in female PTECs. Conclusion: Preliminary findings suggest that KDM6A promotes a high-glucose-induced metabolic switch in PTECs, particularly in male-derived cells. Given observed sex differences and donor-specific responses, future studies will include a broader panel of donor-derived PTECs to determine KDM6A’s role across individuals. This will clarify donor-specific effects and refine its potential as a personalized therapeutic target in DKD. Funding: Private Foundation Support, Government Support – Non-U.S. Donor Information - Donor Age (years) Sex Ethnicity/Race BMI Donor 1 45 years Male Hispanic 31 Donor 2 34 years Female Caucasian 18 Donor 3 31 years Male African American 26
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".