Mitochondrial DNA in Urinary Large Extracellular Vesicles as a Marker of Relapse in Children with Nephrotic Syndrome
Bibliographic record
Abstract
Background: Nephrotic Syndrome (NS) is one of the most common causes of glomerulopathy in children. There has been an increased focus on reactive oxygen species (ROS) and mitochondrial injury in podocytes as drivers of proteinuric disease. We recently showed that podocyte-specific large extracellular vesicles (LEVs; 0.1-1.0 um) are increased in the urine of children with NS. The aim of this study was to characterize LEV mitochondrial DNA (mtDNA) content from those same children. We investigated this relationship further using cultured podocytes exposed to toxins in vitro. Methods: We analyzed urine samples from a prospective cohort enrolling children 1-18y with NS. Podocyte specific LEVs were quantified using flow cytometry and nanoparticle tracking (NTA). Urinary LEV mtDNA was assessed using qPCR. Human immortalized podocytes (hPod) were used in cell culture experiments. Puromycin aminonucleoside (PAN; 25 ug/mL; 24 hours) and lipopolysaccharide (LPS; 25 ug/mL; 24 hours) were used as podocyte toxins. Results: We analyzed 28 samples from 14 patients. Podocyte LEVs were significantly lower in remission vs. nephrosis (p<0.01). Urine protein to creatinine ratio correlated with elevated LEVs (p=0.0005). Patient urinary LEV mtDNA was higher in NS relapse compared to remission (p=0.04). In hPod cells, PAN treatment resulted in a 2.5-fold increase in hPod LEVs (p=0.03) while LPS caused a 3.5-fold increase (p=0.0004). The impact of PAN and LPS treatment on in vitro LEV production was abrogated by the antioxidant MITO-Tempol. Following treatment with PAN or LPS in vitro, we observed an ˜25-fold increase in LEV mtDNA content (p<0.01). Conclusions: In summary, LEVs may serve as a biomarker of podocyte injury in nephrotic syndrome in children and their mtDNA content can differentiate remission from relapse. hPods show similar characteristics when treated with common podocyte toxins, and ongoing studies aim to characterize this further.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".