Replication protein A (RPA) is a ubiquitous eukaryotic single-stranded DNA (ssDNA) binding protein necessary for all aspects of DNA metabolism involving an ssDNA intermediate, including DNA replication, repair, recombination, DNA damage response and checkpoint activation, and telomere maintenance [1], [2], [3]. The role of RPA in most of these reactions is to protect the ssDNA until it can be delivered to downstream enzymes. Therefore a crucial feature of RPA is that it must bind very tightly to ssDNA, but must also be easily displaced from ssDNA to allow other proteins to gain access to the substrate. Here we use total internal reflection fluorescence microscopy and nanofabricated DNA curtains to visualize the behavior of Saccharomyces cerevisiae RPA on individual strands of ssDNA in real-time. Our results show that RPA remains bound to ssDNA for long periods of time when free protein is absent from solution. In contrast, RPA rapidly dissociates from ssDNA when free RPA is present in solution allowing rapid exchange between the free and bound states. In addition, the S. cerevisiae DNA recombinase Rad51 and E. coli single-stranded binding protein (SSB) also promote removal of RPA from ssDNA. These results reveal an unanticipated exchange between bound and free RPA suggesting a binding mechanism that can confer exceptionally slow off rates, yet also enables rapid displacement through a direct exchange mechanism that is reliant upon the presence of free ssDNA-binding proteins in solution. Our results indicate that RPA undergoes constant microscopic dissociation under all conditions, but this is only manifested as macroscopic dissociation (i.e. exchange) when free proteins are present in solution, and this effect is due to mass action. We propose that the dissociation of RPA from ssDNA involves a partially dissociated intermediate, which exposes a small section of ssDNA allowing other proteins to access to the DNA.

Replication protein A (RPA) is a ubiquitous eukaryotic single-stranded DNA (ssDNA) binding protein necessary for all aspects of DNA metabolism involving an ssDNA intermediate, including DNA replication, repair, recombination, DNA damage response and checkpoint activation, and telomere maintenance [1,2,3]. The role of RPA in most of these reactions is to protect the ssDNA until it can be delivered to downstream enzymes. Therefore a crucial feature of RPA is that it must bind very tightly to ssDNA, but must also be easily displaced from ssDNA to allow other proteins to gain access to the substrate. Here we use total internal reflection fluorescence microscopy and nanofabricated DNA curtains to visualize the behavior of Saccharomyces cerevisiae RPA on individual strands of ssDNA in real-time. Our results show that RPA remains bound to ssDNA for long periods of time when free protein is absent from solution. In contrast, RPA rapidly dissociates from ssDNA when free RPA is present in solution allowing rapid exchange between the free and bound states. In addition, the S. cerevisiae DNA recombinase Rad51 and E. coli single-stranded binding protein (SSB) also promote removal of RPA from ssDNA. These results reveal an unanticipated exchange between bound and free RPA suggesting a binding mechanism that can confer exceptionally slow off rates, yet also enables rapid displacement through a direct exchange mechanism that is reliant upon the presence of free ssDNA-binding proteins in solution. Our results indicate that RPA undergoes constant microscopic dissociation under all conditions, but this is only manifested as macroscopic dissociation (i.e. exchange) when free proteins are present in solution, and this effect is due to mass action. We propose that the dissociation of RPA from ssDNA involves a partially dissociated intermediate, which exposes a small section of ssDNA allowing other proteins to access to the DNA.

Steroid-refractory (SR) acute graft-versus-host disease (GVHD) remains a major cause of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT), but its occurrence is not accurately predicted by pre-HCT clinical risk factors. The Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm probability (MAP) identifies patients who are at high risk for developing SR GVHD as early as 7 days after HCT based on the extent of intestinal crypt damage as measured by the concentrations of 2 serum biomarkers, suppressor of tumorigenesis 2 and regenerating islet-derived 3α. We conducted a multicenter proof-of-concept “preemptive” treatment trial of α-1-antitrypsin (AAT), a serine protease inhibitor with demonstrated activity against GVHD, in patients at high risk for developing SR GVHD. Patients were eligible if they possessed a high-risk MAP on day 7 after HCT or, if initially low risk, became high risk on repeat testing at day 14. Thirty high-risk patients were treated with twice-weekly infusions of AAT for a total of 16 doses, and their outcomes were compared with 90 high-risk near-contemporaneous MAGIC control patients. AAT treatment was well tolerated with few toxicities, but it did not lower the incidence of SR GVHD compared with controls (20% vs 14%, P = .56). We conclude that real-time biomarker-based risk assignment is feasible early after allogeneic HCT but that this dose and schedule of AAT did not change the incidence of SR acute GVHD. This trial was registered at www.clinicaltrials.gov as #NCT03459040.

The graft-versus-leukemia (GVL) effect after allogeneic hematopoietic cell transplant (HCT) can prevent relapse but the risk of severe graft-vs-host disease (GVHD) leads to prolonged intensive immunosuppression and possible blunting of the GVL effect. Strategies to reduce immunosuppression in order to prevent relapse have been offset by increases in severe GVHD and non-relapse mortality (NRM). We recently validated the MAGIC algorithm probability (MAP) that predicts the risk for severe GVHD and NRM in asymptomatic patients using serum biomarkers. In this study we tested whether the MAP could identify patients whose risk for relapse is higher than their risk for severe GVHD and NRM. The multicenter study population (n=1604) was divided into two cohorts: historical (2006–2015, n=702) and current (2015–2017, n=902) with similar non-relapse mortality, relapse, and survival. On day 28 post-HCT, patients who had not developed GVHD (75% of the population) and who possessed a low MAP were at much higher risk for relapse (24%) than severe GVHD and NRM (16% and 9%); this difference was even more pronounced in patients with a high disease risk index (relapse 33%, NRM 9%). Such patients are good candidates to test relapse prevention strategies that might enhance GVL.

We used a rigorous PRoBE (prospective-specimen collection, retrospective-blinded-evaluation) study design to compare the ability of biomarkers of systemic inflammation and biomarkers of gastrointestinal (GI) tissue damage to predict response to corticosteroid treatment, the incidence of clinically severe disease, 6-month nonrelapse mortality (NRM), and overall survival in patients with acute graft-versus-host disease (GVHD). We prospectively collected serum samples of newly diagnosed GVHD patients (n = 730) from 19 centers, divided them into training (n = 352) and validation (n = 378) cohorts, and measured TNFR1, TIM3, IL6, ST2, and REG3α via enzyme-linked immunosorbent assay. Performances of the 4 strongest algorithms from the training cohort (TNFR1 + TIM3, TNFR1 + ST2, TNFR1 + REG3α, and ST2 + REG3α) were evaluated in the validation cohort. The algorithm that included only biomarkers of systemic inflammation (TNFR1 + TIM3) had a significantly smaller area under the curve (AUC; 0.57) than the AUCs of algorithms that contained ≥1 GI damage biomarker (TNFR1 + ST2, 0.70; TNFR1 + REG3α, 0.73; ST2 + REG3α, 0.79; all P < .001). All 4 algorithms were able to predict short-term outcomes such as response to systemic corticosteroids and severe GVHD, but the inclusion of a GI damage biomarker was needed to predict long-term outcomes such as 6-month NRM and survival. The algorithm that included 2 GI damage biomarkers was the most accurate of the 4 algorithms for all endpoints.