HPLC Measurement of the DNA Oxidation Biomarker, 8-oxo-7,8-dihydro-2’-deoxyguanosine, in Cultured Cells and Animal Tissues
Bibliographic record
Abstract
Oxidative stress is associated with many physiological and pathological processes, as well as xenobiotic metabolism, leading to the oxidation of biomacromolecules, including DNA. Therefore, efficient detection of DNA oxidation is important for a variety of research disciplines, including medicine and toxicology. A common biomarker of oxidatively damaged DNA is 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxo-dGuo; often erroneously referred to as 8-hydroxy-2'-deoxyguanosine (8-OH-dGuo or 8-oxo-dG)). Several protocols for 8-oxo-dGuo measurement by high pressure liquid chromatography with electrochemical detection (HPLC-ED) have been described. However, these were mainly applied to purified DNA treated with pro-oxidants. In addition, due to methodological differences between laboratories, mainly due to differences in analytical equipment, the adoption of published methods for detection of 8-oxo-dGuo by HPLC-ED requires careful optimization by each laboratory. A comprehensive protocol, describing such an optimization process, is lacking. Here, a detailed protocol is described for the detection of 8-oxo-dGuo by HPLC-ED, in DNA from cultured cells or animal tissues. It illustrates how DNA sample preparation can be easily and rapidly optimized to minimize undesirable DNA oxidation that can occur during sample preparation. This protocol shows how to detect 8-oxo-dGuo in cultured human alveolar adenocarcinoma cells (i.e., A549 cells) treated with the oxidizing agent KBrO3, and from the spleen of mice exposed to the polycyclic aromatic hydrocarbon dibenzo(def,p)chrysene (DBC, formerly known as dibenzo(a,l)pyrene, DalP). Overall, this work illustrates how an HPLC-ED methodology can be readily optimized for the detection of 8-oxo-dGuo in biological samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".