MP008SILAC-BASED PROTEOMICS OF PRIMARY HUMAN RENAL CELLS REVEALS A NOVEL LINK BETWEEN MALE SEX HORMONES AND IMPAIRED ENERGY METABOLISM IN DIABETIC KIDNEY DISEASE
Bibliographic record
Abstract
Introduction and Aims: Male sex predisposes to chronic kidney disease (CKD). Since androgens have shown to exert deleterious effects in a variety of kidney cells, we hypothesized that dihydrotestosterone (DHT) would impair the biology of the renal tubular cell by inducing changes in the proteome. By employing stable isotope labeling with amino acids (SILAC) in an indirect spike-in fashion, we aimed to accurately quantify the proteome in DHT- and 17β-estradiol (EST)-treated human primary proximal tubular epithelial cells (PTEC). Methods: PTEC were serum starved for 18h and incubated with 100nM DHT (n = 4), 100nM EST (n = 3) or ethanol (CONT, n = 4) for 10min (control experiments) or 8h (proteome study). HK-2 cells were SILAC-labelled for 8 passages. For each replicate of PTEC, 150µg of total protein were mixed with 150µg of heavy proteins. After tryptic digestion, peptides were fractionated by strong cationic exchange chromatography and analyzed by online liquid chromatography tandem mass spectrometry (LC-MS/MS). Maxquant software was used for protein identification and H/L ratios calculation. Perseus and Cytoscape software were used for statistical and bioinformatics analyses. Western Blot was employed for pAKT and pERK determination in control experiments and for the in vitro and in vivo validation studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
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".