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Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
The SARS-CoV-2 genome encodes for approximately 30 proteins. Within the international project COVID19-NMR, we distribute the spectroscopic analysis of the viral proteins and RNA. Here, we report NMR chemical shift assignments for the protein Nsp3b, a domain of Nsp3. The 217-kDa large Nsp3 protein contains multiple structurally independent, yet functionally related domains including the viral papain-like protease and Nsp3b, a macrodomain (MD). In general, the MDs of SARS-CoV and MERS-CoV were suggested to play a key role in viral replication by modulating the immune response of the host. The MDs are structurally conserved. They most likely remove ADP-ribose, a common posttranslational modification, from protein side chains. This de-ADP ribosylating function has potentially evolved to protect the virus from the anti-viral ADP-ribosylation catalyzed by poly-ADP-ribose polymerases (PARPs), which in turn are triggered by pathogen-associated sensing of the host immune system. This renders the SARS-CoV-2 Nsp3b a highly relevant drug target in the viral replication process. We here report the near-complete NMR backbone resonance assignment (1H, 13C, 15N) of the putative Nsp3b MD in its apo form and in complex with ADP-ribose. Furthermore, we derive the secondary structure of Nsp3b in solution. In addition, 15N-relaxation data suggest an ordered, rigid core of the MD structure. These data will provide a basis for NMR investigations targeted at obtaining small-molecule inhibitors interfering with the catalytic activity of Nsp3b.
The highly infectious disease COVID-19 caused by the Betacoronavirus SARS-CoV-2 poses a severe threat to humanity and demands the redirection of scientific efforts and criteria to organized research projects. The international COVID19-NMR consortium seeks to provide such new approaches by gathering scientific expertise worldwide. In particular, making available viral proteins and RNAs will pave the way to understanding the SARS-CoV-2 molecular components in detail. The research in COVID19-NMR and the resources provided through the consortium are fully disclosed to accelerate access and exploitation. NMR investigations of the viral molecular components are designated to provide the essential basis for further work, including macromolecular interaction studies and high-throughput drug screening. Here, we present the extensive catalog of a holistic SARS-CoV-2 protein preparation approach based on the consortium’s collective efforts. We provide protocols for the large-scale production of more than 80% of all SARS-CoV-2 proteins or essential parts of them. Several of the proteins were produced in more than one laboratory, demonstrating the high interoperability between NMR groups worldwide. For the majority of proteins, we can produce isotope-labeled samples of HSQC-grade. Together with several NMR chemical shift assignments made publicly available on covid19-nmr.com, we here provide highly valuable resources for the production of SARS-CoV-2 proteins in isotope-labeled form.
1H, 13C, and 15N backbone chemical shift assignments of coronavirus-2 non-structural protein Nsp10
(2020)
The international Covid19-NMR consortium aims at the comprehensive spectroscopic characterization of SARS-CoV-2 RNA elements and proteins and will provide NMR chemical shift assignments of the molecular components of this virus. The SARS-CoV-2 genome encodes approximately 30 different proteins. Four of these proteins are involved in forming the viral envelope or in the packaging of the RNA genome and are therefore called structural proteins. The other proteins fulfill a variety of functions during the viral life cycle and comprise the so-called non-structural proteins (nsps). Here, we report the near-complete NMR resonance assignment for the backbone chemical shifts of the non-structural protein 10 (nsp10). Nsp10 is part of the viral replication-transcription complex (RTC). It aids in synthesizing and modifying the genomic and subgenomic RNAs. Via its interaction with nsp14, it ensures transcriptional fidelity of the RNA-dependent RNA polymerase, and through its stimulation of the methyltransferase activity of nsp16, it aids in synthesizing the RNA cap structures which protect the viral RNAs from being recognized by the innate immune system. Both of these functions can be potentially targeted by drugs. Our data will aid in performing additional NMR-based characterizations, and provide a basis for the identification of possible small molecule ligands interfering with nsp10 exerting its essential role in viral replication.