Wastewater genomic surveillance tracks the spread of the SARS-CoV-2 Omicron variant across England (preprint)

Background Many countries have moved into a new stage of managing the SARS-CoV-2 pandemic with minimal restrictions and reduced testing in the population, leading to reduced genomic surveillance of virus variants in individuals. Wastewater-based epidemiology (WBE) can provide an alternative means of tracking virus variants in the population but is lacking verifications of its comparability to individual testing data. Methods We analysed more than 19,000 samples from 524 wastewater sites across England at least twice a week between November 2021 and February 2022, capturing sewage from >70% of the English population. We used amplicon-based sequencing and the phylogeny based de-mixing tool Freyja to estimate SARS-CoV-2 variant frequencies and compared these to the variant dynamics observed in individual testing data from clinical and community settings. Findings We show that wastewater data can reconstruct the spread of the Omicron variant across England since November 2021 in close detail and aligns closely with epidemiological estimates from individual testing data. We also show the temporal and spatial spread of Omicron within London. Our wastewater data further reliably track the transition between Omicron subvariants BA1 and BA2 in February 2022 at regional and national levels. Interpretation Our demonstration that WBE can track the fast-paced dynamics of SARS-CoV-2 variant frequencies at a national scale and closely match individual testing data in time shows that WBE can reliably fill the monitoring gap left by reduced individual testing in a more affordable way.

Comparison of variant callers for wastewater-based epidemiology (preprint)

Wastewater-based epidemiology (WBE) has been extensively used during the COVID-19 pandemic to detect and monitor the spread of the SARS-CoV-2 virus and its variants. It has also proven to be an excellent tool to complement and support insights gained from reported clinical data. Globally, many groups have developed bioinformatics pipelines to analyse sequencing data from wastewater. Accurate calling of mutations from RNA extracted from wastewater samples is key in supporting clinical data to make informed decisions on the prevalence of variants, as well as in the use of WBE as a molecular surveillance tool. However, wastewater samples can be challenging to extract and sequence, and performance of variant-calling algorithms in this context has, so far, not been investigated. Analysis of the data and assignment of circulating variants depends heavily on the accuracy of the variant caller, particularly given the degraded nature of the viral RNA and the heterogeneous nature of metagenomic samples. To address this, we compared the performance of six variant callers (VarScan, iVAR, GATK, FreeBayes, LoFreq and BCFtools), used widely in bioinformatics pipelines, on 19 synthetic samples with a known mix of three different SARS-CoV-2 variant genomes (Alpha, Beta and Delta), as well as 13 wastewater samples collected in London between the 15 th and 18 th December 2021. Using the Quasimodo benchmarking tool to compare the six variant callers, we assessed the fundamental parameters of recall (sensitivity) and precision (specificity) in confirming the presence of a variant within the population. Our results show that BCFtools, FreeBayes and VarScan called the expected mutations with higher precision and recall than iVAR or GATK, although the latter identified more expected defining mutations. LoFreq gave the least reliable results due to the high number of false positive mutations detected, resulting in lower precision. Similar results were obtained for both the synthetic and wastewater samples.

Post-COVID-19 illness trajectory: a multisystem investigation. (preprint)

Background: The pathophysiology and trajectory of multiorgan involvement in post-COVID-19 syndrome is uncertain. Methods: : A prospective, multicenter, longitudinal, cohort study involving post-COVID-19 patients enrolled in-hospital or early post-discharge (visit 1) and re-evaluated 28-60 days post-discharge (visit 2). Multisystem investigations included chest computed tomography with pulmonary and coronary angiography, cardiovascular and renal magnetic resonance imaging, digital electrocardiography, and multisystem biomarkers. The primary outcome was the adjudicated likelihood of myocarditis. Results: : 161 patients (mean age 55 years, 43% female) and 27 controls with similar age, sex, ethnicity, and vascular risk factors were enrolled from 22 May 2020 to 2 July 2021 and had a primary outcome evaluation. Compared to controls, at 28-60 days post-discharge, patients with COVID-19 had persisting evidence of cardio-renal involvement, systemic inflammation, and hemostasis pathway activation. Myocarditis was adjudicated as being not likely (n=17; 10%), unlikely (n=56; 35%), probable (n=67; 42%) or very likely (n=21; 13%). Acute kidney injury (odds ratio, 95% confidence interval: 3.40 (1.13, 11.84); p=0.038) and low hemoglobin A1c (0.26 (0.07, 0.87); p=0.035) were multivariable associates of adjudicated myocarditis. During convalescence, compared to controls, COVID-19 was associated with worse health-related quality of life (EQ5D-5L) (p<0.001), illness perception (p<0.001), anxiety and depression (p<0.001), physical activity (p<0.001) and predicted maximal oxygen utilization (ml/kg/min) (p<0.001). These measures were associated with adjudicated myocarditis. Conclusions: : The illness trajectory of COVID-19 includes persisting cardio-renal inflammation, lung damage and hemostasis activation. Adjudicated myocarditis occurred in one in eight hospitalized patients and was associated with impairments in health status, physical and psychological wellbeing during community convalescence. Public registration ClinicalTrials.gov identifier is NCT04403607.

Making the invisible enemy visible (preprint)

During the COVID-19 pandemic, structural biologists rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 204 previously solved structures from SARS-CoV-1, 548 structures covering 16 of the SARS-CoV-2 viral proteins have been released in a span of only 6 months. These structural models serve as the basis for research to understand how the virus hijacks human cells, for structure-based drug design, and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and may be even more common among these structures, which were solved quickly and under immense pressure. The Coronavirus Structural Task Force has responded to this challenge by rapidly categorizing, evaluating and reviewing all of these experimental protein structures in order to help downstream users and original authors. In addition, the Task Force provided improved models for key structures online, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge and others.

Mass spectrometric based detection of protein nucleotidylation in the RNA polymerase of SARS-CoV-2 (preprint)

Coronaviruses, like SARS-CoV-2, encode a nucleotidyl transferase in the N-terminal NiRAN domain of the non-structural protein (nsp) 12 protein within the RNA dependent RNA polymerase (RdRP). Though the substrate targets of the viral nucleotidyl transferase are unknown, NiRAN active sites are highly conserved and essential for viral replication. We show, for the first time, the detection and sequence location of GMP-modified amino acids in nidovirus RdRP-associated proteins using heavy isotope-assisted MS and MS/MS peptide sequencing. We identified lys-143 in the equine arteritis virus (EAV) protein, nsp7, as a primary site of nucleotidylation in vitro that uses a phosphoramide bond to covalently attach with GMP. In SARS-CoV-2 replicase proteins, we demonstrate a unique O-linked GMP attachment on nsp7 ser-1, whose formation required the presence of nsp12. It is clear that additional nucleotidylation sites remain undiscovered, which includes the possibility that nsp12 itself may form a transient GMP adduct in the NiRAN active site that has eluted detection in these initial studies due to instability of the covalent attachment. Our results demonstrate new strategies for detecting GMP-peptide linkages that can be adapted for higher throughput screening using mass spectrometric technologies. These data are expected to be important for a rapid and timely characterization of a new enzymatic activity in SARS-CoV-2 that may be an attractive drug target aimed at limiting viral replication in infected patients.

Umbilical cord blood derived microglia-like cells to model COVID-19 exposure (preprint)

Microglia, the resident brain immune cells, play a critical role in normal brain development, and are impacted by the intrauterine environment, including maternal immune activation and inflammatory exposures. The COVID-19 pandemic presents a potential developmental immune challenge to the fetal brain, in the setting of maternal SARS-CoV-2 infection with its attendant potential for cytokine production and, in severe cases, cytokine storming. There is currently no biomarker or model for in utero microglial priming and function that might aid in identifying the neonates and children most vulnerable to neurodevelopmental morbidity, as microglia remain inaccessible in fetal life and after birth. This study aimed to generate patient-derived microglial-like cell models unique to each neonate from reprogrammed umbilical cord blood mononuclear cells, adapting and extending a novel methodology previously validated for adult peripheral blood mononuclear cells. We demonstrate that umbilical cord blood mononuclear cells can be used to create microglial-like cell models morphologically and functionally similar to microglia observed in vivo. We illustrate the application of this approach by generating microglia from cells exposed and unexposed to maternal SARS-CoV-2 infection. Our ability to create personalized neonatal models of fetal brain immune programming enables non-invasive insights into fetal brain development and potential childhood neurodevelopmental vulnerabilities for a range of maternal exposures, including COVID-19.