Recognition and processing of a new repertoire of DNA substrates by human 3-methyladenine DNA glycosylase (AAG)

The human 3-methyladenine DNA glycosylase (AAG) recognizes and excises a broad range of purines damaged by alkylation and oxidative damage, including 3-methyladenine, 7-methylguanine, hypoxanthine (Hx), and 1,N[superscript 6]-ethenoadenine (εA). The crystal structures of AAG bound to εA have provided insights into the structural basis for substrate recognition, base excision, and exclusion of normal purines and pyrimidines from its substrate recognition pocket. In this study, we explore the substrate specificity of full-length and truncated Δ80AAG on a library of oligonucleotides containing structurally diverse base modifications. Substrate binding and base excision kinetics of AAG with 13 damaged oligonucleotides were examined. We found that AAG bound to a wide variety of purine and pyrimidine lesions but excised only a few of them. Single-turnover excision kinetics showed that in addition to the well-known εA and Hx substrates, 1-methylguanine (m1G) was also excised efficiently by AAG. Thus, along with εA and ethanoadenine (EA), m1G is another substrate that is shared between AAG and the direct repair protein AlkB. In addition, we found that both the full-length and truncated AAG excised 1,N[superscript 2]-ethenoguanine (1,N[superscript 2]-εG), albeit weakly, from duplex DNA. Uracil was excised from both single- and double-stranded DNA, but only by full-length AAG, indicating that the N-terminus of AAG may influence glycosylase activity for some substrates. Although AAG has been primarily shown to act on double-stranded DNA, AAG excised both εA and Hx from single-stranded DNA, suggesting the possible significance of repair of these frequent lesions in single-stranded DNA transiently generated during replication and transcription.United States. National Institutes of Health (grant ES05355)United States. National Institutes of Health (grant CA75576)United States. National Institutes of Health (grant CA55042)United States. National Institutes of Health (grant ES02109)United States. National Institutes of Health (grant T32-ES007020)United States. National Institutes of Health (grant CA80024)United States. National Institutes of Health (grant CA26731

Predicting major bleeding in patients with noncardioembolic stroke on antiplatelets

Objective: To develop and externally validate a prediction model for major bleeding in patients with a TIA or ischemic stroke on antiplatelet agents. Methods: We combined individual patient data from 6 randomized clinical trials (CAPRIE, ESPS-2, MATCH, CHARISMA, ESPRIT, and PRoFESS) investigating antiplatelet therapy after TIA or ischemic stroke. Cox regression analyses stratified by trial were performed to study the association between predictors and major bleeding. A risk prediction model was derived and validated in the PERFORM trial. Performance was assessed with the c statistic and calibration plots. Results: Major bleeding occurred in 1,530 of the 43,112 patients during 94,833 person-years of follow-up. The observed 3-year risk of major bleeding was 4.6% (95% confidence interval [CI] 4.4%–4.9%). Predictors were male sex, smoking, type of antiplatelet agents (aspirin-clopidogrel), outcome on modified Rankin Scale ≥3, prior stroke, high blood pressure, lower body mass index, elderly, Asian ethnicity, and diabetes (S2TOP-BLEED). The S2TOP-BLEED score had a c statistic of 0.63 (95% CI 0.60–0.64) and showed good calibration in the development data. Major bleeding risk ranged from 2% in patients aged 45–54 years without additional risk factors to more than 10% in patients aged 75–84 years with multiple risk factors. In external validation, the model had a c statistic of 0.61 (95% CI 0.59–0.63) and slightly underestimated major bleeding risk. Conclusions: The S2TOP-BLEED score can be used to estimate 3-year major bleeding risk in patients with a TIA or ischemic stroke who use antiplatelet agents, based on readily available characteristics. The discriminatory performance may be improved by identifying stronger predictors of major bleeding