Methods to Study Intracellular Movement and Localization of the Nucleotide Excision Repair Proteins at the DNA Lesions in Mammalian Cells
Bibliographic record
Abstract
Nucleotide excision repair (NER) is the most versatile DNA repair pathway that removes a wide variety of DNA lesions caused by different types of physical and chemical agents, such as ultraviolet radiation (UV), environmental carcinogen benzo[a]pyrene and anti-cancer drug carboplatin. The mammalian NER utilizes more than 30 proteins, in a multi-step process that begins with the lesion recognition within seconds of DNA damage to completion of repair after few hours to several days. The core proteins and their biochemical reactions are known from in vitro DNA repair assays using purified proteins, but challenge was to understand the dynamics of their rapid recruitment and departure from the lesion site and their coordination with other proteins and post-translational modifications to execute the sequential steps of repair. Here, we provide a brief overview of various techniques developed by different groups over last 20 years to overcome these challenges. However, more work is needed for a comprehensive knowledge of all aspects of mammalian NER. With this aim, here we provide detailed protocols of three simple yet innovative methods developed by many teams that range from local UVC irradiation to in situ extraction and sub-cellular fractionation that will permit study of endogenous as well as exogenous NER proteins in any cellular model. These methods do not require unique reagents or specialized instruments, and will allow many more laboratories to explore this repair pathway in different models. These techniques would reveal intracellular movement of these proteins to the DNA lesion site, their interactions with other proteins during repair and the effect of post-translational modifications on their functions. We also describe how these methods led us to identify hitherto unexpected role of poly(ADP-ribose) polymerase-1 (PARP1) in NER. Collectively these three simple techniques can provide an initial assessment of the functions of known and unknown proteins in the core or auxiliary events associated with mammalian NER. The results from these techniques could serve as a solid foundation and a justification for more detailed studies in NER using specialized reagents and more sophisticated tools. They can also be suitably modified to study other cellular processes beyond DNA repair.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".