Since their discovery in the 1980s, it has gradually become apparent that there are several functional classes of naturally occurring ribozymes. These include ribozymes that mediate RNA splicing (the Group I and Group II introns, and possibly the RNA components of the spliceosome), RNA processing ribozymes (RNase P, which cleaves precursor tRNAs and other structural RNA precursors), the peptidyl transferase center of the ribosome, and small, self-cleaving genomic ribozymes (including the hammerhead, hairpin, HDV and VS ribozymes). The most recently discovered functional class of ribozymes include those that are embedded in the untranslated regions of mature mRNAs that regulate the gene's translational expression. These include the prokaryotic glmS ribozyme, a bacterial riboswitch, and a variant of the hammerhead ribozyme, which has been found embedded in mammalian mRNAs. With the discovery of a mammalian riboswitch ribozyme, the question of how an embedded hammerhead ribozyme's switching mechanism works becomes a compelling question. Recent structural results suggest several possibilities.

The crystallographic phase problem is the primary bottleneck encountered when attempting to solve macromolecular structures for which no close crystallographic structural homologues are known. Typically, isomorphous "heavy-atom" replacement and/or anomalous dispersion methods must be used in such cases to obtain experimentally-determined phases. Even three-dimensional NMR structures of the same macromolecule are often not sufficient to solve the crystallographic phase problem. RNA crystal structures present additional challenges due to greater difficulty in obtaining suitable heavy-atom derivatives. We present a unique approach to solve the phase problem for novel RNA crystal structures that has enjoyed a reasonable degree of success. This approach involves modeling only those portions of the RNA sequence whose structure can be predicted readily, i.e., the individual A-form helical regions and well-known stem-loop sub-structures. We have found that no prior knowledge of how the helices and other structural elements are arranged with respect to one another in three-dimensional space, or in some cases, even the sequence, is required to obtain a useable solution to the phase problem, using simultaneous molecular replacement of a set of generic helical RNA fragments.

We have obtained precatalytic (enzyme-substrate complex) and postcatalytic (enzyme-product complex) crystal structures of an active full-length hammerhead RNA that cleaves in the crystal. Using the natural satellite tobacco ringspot virus hammerhead RNA sequence, the self-cleavage reaction was modulated by substituting the general base of the ribozyme, G12, with A12, a purine variant with a much lower pKa that does not significantly perturb the ribozyme's atomic structure. The active, but slowly cleaving, ribozyme thus permitted isolation of enzyme-substrate and enzyme-product complexes without modifying the nucleophile or leaving group of the cleavage reaction, nor any other aspect of the substrate. The predissociation enzyme-product complex structure reveals RNA and metal ion interactions potentially relevant to transition-state stabilization that are absent in precatalytic structures.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

Purpose

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.

Patients and methods

We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.

Results

We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.

Conclusion

We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.