Development of a colorimetric α-ketoglutarate detection assay for prolyl hydroxylase domain (PHD) proteins
Bibliographic record
Abstract
Since the discovery of the prolyl hydroxylases domain (PHD) proteins and their canonical hypoxia-inducible factor (HIF) substrate two decades ago, a number of in vitro hydroxylation (IVH) assays for PHD activity have been developed to measure the PHD–HIF interaction. However, most of these assays either require complex proteomics mass spectrometry methods that rely on the specific PHD–HIF interaction or require the handling of radioactive material, as seen in the most commonly used assay measuring [ 14 C]O 2 release from labeled [ 14 C]α-ketoglutarate. Here, we report an alternative rapid, cost-effective assay in which the consumption of α-ketoglutarate is monitored by its derivatization with 2,4-dinitrophenylhydrazine (2,4-DNPH) followed by treatment with concentrated base. We extensively optimized this 2,4-DNPH α-ketoglutarate assay to maximize the signal-to-noise ratio and demonstrated that it is robust enough to obtain kinetic parameters of the well-characterized PHD2 isoform comparable with those in published literature. We further showed that it is also sensitive enough to detect and measure the IC 50 values of pan-PHD inhibitors and several PHD2 inhibitors in clinical trials for chronic kidney disease (CKD)-induced anemia. Given the efficiency of this assay coupled with its multiwell format, the 2,4-DNPH α-KG assay may be adaptable to explore non-HIF substrates of PHDs and potentially to high-throughput assays.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".