High-throughput Mutational Surveillance of the SARS-CoV-2 Spike Gene

SARS-CoV-2 has evolved rapidly towards higher infectivity and partial immune escape over the course of the pandemic. This evolution is driven by the enormous virus population, that has infected close to 200 million people by now. Therefore, cost effective and scalable methods are needed to monitor viral evolution globally. Mutation-specific PCR approaches have become inadequate to distinguish the variety of circulating SARS-CoV-2 variants and are unable to detect novel ones. Conversely, whole genome sequencing protocols remain too labor- and cost-intensive to monitor SARS-CoV-2 at the required density. By adapting SARSeq we present a simple, fast, and scalable S-gene tiling pipeline for focused sequencing of the S-gene encoding for the spike protein. This method reports on all sequence positions with known importance for infectivity and immunity, yet scales to >20K samples per run. S-gene tiling is used for nationwide surveillance of SARS-CoV-2 at a density of 10% to 50% of all cases of infection in Austria. SARSeq S-tiling uncovered several infection clusters with variants of concern such as the biggest known cluster of Beta/B.1.351 outside Africa and successfully informed public health measures in a timely manner, allowing their successful implementation. Our close monitoring of mutations further highlighted evolutionary constraints and freedom of the spike protein ectodomain and sheds light on foreseeable evolutionary trajectories.

Development and external validation of a logistic regression derived formula based on repeated routine hematological measurements predicting survival of hospitalized Covid-19 patients

BackgroundThe Covid-19 pandemic has become a global public health crisis and providing optimal patient care while preventing a collapse of the health care system is a principal objective worldwide. ObjectiveTo develop and validate a prognostic model based on routine hematological parameters to predict uncomplicated disease progression to support the decision for an earlier discharge. DesignDevelopment and refinement of a multivariable logistic regression model with subsequent external validation. The time course of several hematological variables until four days after admission were used as predictors. Variables were first selected based on subject matter knowledge; their number was further reduced using likelihood ratio-based backward elimination in random bootstrap samples. SettingModel development based on three Austrian hospitals, validation cohorts from two Austrian and one Swedish hospital. ParticipantsModel development based on 363 survivors and 78 non-survivors of Covid-19 hospitalized in Austria. External validation based on 492 survivors and 61 non-survivors hospitalized in Austria and Sweden. OutcomeIn-hospital death. Main ResultsThe final model includes age, fever upon admission, parameters derived from C-reactive protein (CRP) concentration, platelet count and creatinine concentration, approximating their baseline values (CRP, creatinine) and change over time (CRP, platelet count). In Austrian validation cohorts both discrimination and calibration of this model were good, with c indices of 0.93 (95% CI 0.90 - 0.96) in a cohort from Vienna and 0.93 (0.88 - 0.98) in one from Linz. The model performance seems independent of how long symptoms persisted before admission. In a small Swedish validation cohort, the model performance was poorer (p = 0.008) compared with Austrian cohorts with a c index of 0.77 (0.67 - 0.88), potentially due to substantial differences in patient demographics and clinical routine. ConclusionsHere we describe a formula, requiring only variables routinely acquired in hospitals, which allows to estimate death probabilities of hospitalized patients with Covid-19. The model could be used as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system. The model could further be used to monitor whether patients should be admitted to hospital in countries with health care systems with emphasis on outpatient care (e.g. Sweden).

SARSeq, a robust and highly multiplexed NGS assay for parallel detection of SARS-CoV2 and other respiratory infections

During a pandemic, mitigation as well as protection of system-critical or vulnerable institutions requires massively parallel, yet cost-effective testing to monitor the spread of agents such as the current SARS-CoV2 virus. Here we present SARSeq, saliva analysis by RNA sequencing, as an approach to monitor presence of SARS-CoV2 and other respiratory viruses performed on tens of thousands of samples in parallel. SARSeq is based on next generation sequencing of multiple amplicons generated in parallel in a multiplexed RT-PCR reaction. It relies on a two-dimensional unique dual indexing strategy using four indices in total, for unambiguous and scalable assignment of reads to individual samples. We calibrated this method using dilutions of synthetic RNA and virions to show sensitivity down to a few molecules, and applied it to hundreds of patient samples validating robust performance across various sample types. Double blinded benchmarking to gold-standard quantitative RT-PCR performed in a clinical setting and a human diagnostics laboratory showed robust performance up to a Ct of 36. The false positive rate, likely due to cross contamination during sample pipetting, was estimated at 0.04-0.1%. In addition to SARS-CoV2, SARSeq detects Influenza A and B viruses as well as human rhinovirus and can be easily expanded to include detection of other pathogens. In sum, SARSeq is an ideal platform for differential diagnostic of respiratory diseases at a scale, as is required during a pandemic.

Scalable, rapid and highly sensitive isothermal detection of SARS-CoV-2 for laboratory and home testing

Global efforts to combat the Covid-19 pandemic caused by SARS-CoV-2 still heavily rely on RT-qPCR-based diagnostic tests. However, their high cost, moderate throughput and reliance on sophisticated equipment limit widespread implementation. Loop-mediated isothermal amplification after reverse transcription (RT-LAMP) is an alternative detection method that has the potential to overcome these limitations. We present a rapid, robust, sensitive and versatile RT-LAMP based SARS-CoV-2 detection assay. Our forty-minute procedure bypasses a dedicated RNA isolation step, is insensitive to carry-over contamination, and uses a hydroxynaphthol blue (HNB)-based colorimetric readout, which allows robust SARS-CoV-2 detection from various sample types. Based on this assay, we have substantially increased sensitivity and scalability by a simple nucleic acid enrichment step (bead-LAMP), established a pipette-free version for home testing (HomeDip-LAMP), and developed open source enzymes that can be produced in any molecular biology setting. Our advanced, universally applicable RT-LAMP assay is a major step towards population-scale SARS-CoV-2 testing.