Exploring the Spatial Distribution of Persistent SARS-CoV-2 Mutations - Leveraging mobility data for targeted sampling (preprint)

Given the rapid cross-country spread of SARS-CoV-2 and the resulting difficulty in tracking lineage spread, we investigated the potential of combining mobile service data and fine-granular metadata (such as postal codes and genomic data) to advance integrated genomic surveillance of the pandemic in the federal state of Thuringia, Germany. We sequenced over 6,500 SARS-CoV-2 Alpha genomes (B.1.1.7) across seven months within Thuringia while collecting patients' isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes and mobile service data for Thuringia. We identified the existence and spread of nine persistent mutation variants within the Alpha lineage, seven of which formed separate phylogenetic clusters with different spreading patterns in Thuringia. The remaining two are sub-clusters. Mobile service data can indicate these clusters' spread and highlight a potential sampling bias, especially of low-prevalence variants. Thereby, mobile service data can be used either retrospectively to assess surveillance coverage and efficiency from already collected data or to actively guide part of a surveillance sampling process to districts where these variants are expected to emerge. The latter concept proved successful as we introduced a mobility-guided sampling strategy for the surveillance of Omicron sublineage BQ.1.1. The combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.

Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data (preprint)

Sequencing of SARS-CoV-2 RNA from wastewater samples has emerged as a valuable tool for detecting the presence and relative abundances of SARS-CoV-2 variants in a community. By analyzing the viral genetic material present in wastewater, public health officials can gain early insights into the spread of the virus and inform timely intervention measures. The construction of reference datasets from known SARS-CoV-2 lineages and their mutation profiles has become state-of-the-art for assigning viral lineages and their relative abundances from wastewater sequencing data. However, the selection of reference sequences or mutations directly affects the predictive power. Here, we show the impact of a mutation- and sequence-based reference reconstruction for SARS-CoV-2 abundance estimation. We benchmark three data sets: 1) synthetic 'spike-in' mixtures, 2) German samples from early 2021, mainly comprising Alpha, and 3) samples obtained from wastewater at an international airport in Germany from the end of 2021, including first signals of Omicron. The two approaches differ in sub-lineage detection, with the marker-mutation-based method, in particular, being challenged by the increasing number of mutations and lineages. However, the estimations of both approaches depend on selecting representative references and optimized parameter settings. By performing parameter escalation experiments, we demonstrate the effects of reference size and alternative allele frequency cutoffs for abundance estimation. We show how different parameter settings can lead to different results for our test data sets, and illustrate the effects of virus lineage composition of wastewater samples and references. Here, we compare a mutation- and sequence-based reference construction and assignment for SARS-CoV-2 abundance estimation from wastewater samples. Our study highlights current computational challenges, focusing on the general reference design, which significantly and directly impacts abundance allocations. We illustrate advantages and disadvantages that may be relevant for further developments in the wastewater community and in the context of higher standardization.

Functional comparisons of the virus sensor RIG-I from humans, the microbat Myotis daubentonii, and the megabat Rousettus aegyptiacus, and their response to SARS-CoV-2 infection (preprint)

Bats (order Chiroptera) are a major reservoir for emerging and re-emerging zoonotic viruses. Their tolerance towards highly pathogenic human viruses led to the hypothesis that bats may possess an especially active antiviral interferon (IFN) system. Here, we cloned and functionally characterized the virus RNA sensor, Retinoic Acid-Inducible Gene-I (RIG-I), from the "microbat" Myotis daubentonii (suborder Yangochiroptera) and the "megabat" Rousettus aegyptiacus (suborder Yinpterochiroptera), and compared them to the human ortholog. Our data show that the overall sequence and domain organization is highly conserved and that all three RIG-I orthologs can mediate a similar IFN induction in response to viral RNA at 37{degrees} and 39{degrees}C, but not at 30{degrees}C. Like human RIG-I, bat RIG-Is were optimally activated by double stranded RNA containing a 5'-triphosphate end and required Mitochondrial Antiviral-Signalling Protein (MAVS) for antiviral signalling. Moreover, the RIG-I orthologs of humans and of R. aegyptiacus, but not of M. daubentonii, enable innate immune sensing of SARS-CoV-2 infection. Our results thus show that microbats and megabats express a RIG-I that is not substantially different from the human counterpart with respect to function, temperature dependency, antiviral signaling, and RNA ligand properties, and that human and megabat RIG-I are able to sense SARS-CoV-2 infection.

Investigation of a Limited but Explosive COVID-19 Outbreak in a German Secondary School (preprint)

Background The role of schools as a source of infection and driver in the coronavirus-pandemic has been controversial and is still not completely clarified. To prevent harm and disadvantages for children and adolescents, but also adults, detailed data on school outbreaks is needed, especially when talking about open schools employing evidence-based safety concepts. Here, we investigated the first significant COVID-19 school outbreak in Hamburg, Germany, after the re-opening of schools in 2020. Methods Using clinical, laboratory, and contact data and spatial measures for epidemiological and environmental studies combined with whole-genome sequencing (WGS) analysis, we examined the causes and the course of the secondary school outbreak. Findings The potential index case was identified by epidemiological tracking and the lessons in classrooms with presumably high virus spreading rates and further infection chains in the setting. Sequence analysis of samples detected one sample of a different virus lineage and 25 virus genomes with almost identical sequences, of which 21 showed 100% similarity. Most infections occurred in connection with two lesson units of the primary case. Likely, 31 students (12-14 years old), two staff members, and three family members were infected in the school or the typical household. Interpretations Sequence analysis revealed an outbreak cluster with a single source that was epidemiologically identified as a member of the educational staff. In lesson units, two superspreading events of varying degrees with airborne transmission took place. These were influenced by several parameters including the exposure times, the use of respiratory masks while speaking and spatial or structural conditions at that time.

Antibody escape and global spread of SARS-CoV-2 lineage A.27 (preprint)

In spring 2021, an increasing number of infections was observed caused by the hitherto rarely described SARS-CoV-2 variant A.27 in south-west Germany. From December 2020 to June 2021 this lineage has been detected in 31 countries. Phylogeographic analyses of A.27 sequences obtained from national and international databases reveal a global spread of this lineage through multiple introductions from its inferred origin in Western Africa. Variant A.27 is characterized by a mutational pattern in the spike gene that includes the L18F, L452R and N501Y spike amino acid substitutions found in various variants of concern but lacks the globally dominant D614G. Neutralization assays demonstrated an escape of A.27 from convalescent and vaccine-elicited antibody-mediated immunity. Moreover, the therapeutic monoclonal antibody Bamlanivimab and partially the REGN-COV2 cocktail failed to block infection by A.27. Our data emphasize the need for continued global monitoring of novel lineages because of the independent evolution of new escape mutations.

CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for molecular surveillance (preprint)

The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of molecular surveillance to understand the evolution of the virus and to monitor and plan the epidemiological responses. Quick analysis, easy visualization and convenient filtering of the latest viral sequences are essential for this purpose. We present CovRadar, a tool for molecular surveillance of the Corona spike protein. The spike protein contains the receptor binding domain (RBD) that is used as a target for most vaccine candidates. CovRadar consists of a workflow pipeline and a web application that enable the analysis and visualization of over 250,000 sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants, consensus sequences and phylogenetic trees and finally presents the results in an interactive PDF-like app, making reporting fast, easy and flexible. CovRadar is freely accessible at https://covradar.net, its open-source code is available at https://gitlab.com/dacs-hpi/covradar.

Computational Strategies to Combat COVID-19: Useful Tools to Accelerate SARS-CoV-2 and Coronavirus Research (preprint)

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding, and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are freely available online, either through web applications or public code repositories.