The CRL4Cdt2 Ubiquitin Ligase Mediates the Proteolysis of Cyclin-Dependent Kinase Inhibitor Xic1 through a Direct Association with PCNA ▿

During DNA polymerase switching, the Xenopus laevis Cip/Kip-type cyclin-dependent kinase inhibitor Xic1 associates with trimeric proliferating cell nuclear antigen (PCNA) and is recruited to chromatin, where it is ubiquitinated and degraded. In this study, we show that the predominant E3 for Xic1 in the egg is the Cul4-DDB1-XCdt2 (Xenopus Cdt2) (CRL4Cdt2) ubiquitin ligase. The addition of full-length XCdt2 to the Xenopus extract promotes Xic1 turnover, while the N-terminal domain of XCdt2 (residues 1 to 400) cannot promote Xic1 turnover, despite its ability to bind both Xic1 and DDB1. Further analysis demonstrated that XCdt2 binds directly to PCNA through its C-terminal domain (residues 401 to 710), indicating that this interaction is important for promoting Xic1 turnover. We also identify the cis-acting sequences required for Xic1 binding to Cdt2. Xic1 binds to Cdt2 through two domains (residues 161 to 170 and 179 to 190) directly flanking the Xic1 PCNA binding domain (PIP box) but does not require PIP box sequences (residues 171 to 178). Similarly, human p21 binds to human Cdt2 through residues 156 to 161, adjacent to the p21 PIP box. In addition, we identify five lysine residues (K180, K182, K183, K188, and K193) immediately downstream of the Xic1 PIP box and within the second Cdt2 binding domain as critical sites for Xic1 ubiquitination. Our studies suggest a model in which both the CRL4Cdt2 E3- and PIP box-containing substrates, like Xic1, are recruited to chromatin through independent direct associations with PCNA

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use