ABSTRACT
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.
ABSTRACT
Genomic sequences from rapidly evolving pathogens, sampled over time, hold information on disease origin, transmission, and evolution. Together with their sampling times, sequences can be used to estimate the rates of molecular evolution and date evolutionary events through molecular tip-dating. The validity of this approach, however, depends on whether detectable levels of genetic variation have accumulated over the given sampling interval, generating temporal signal. Moreover, different molecular dating methods have demonstrated varying degrees of systematic biases under different biologically realistic scenarios, such as the presence of phylo-temporal clustering. Chronic SARS-CoV-2 infection in immunocompromised patients has been linked to remarkably higher intra-host molecular rates than those of global lineages, facilitating the emergence of novel viral lineages. Yet, most studies reporting accelerated rates lack the evaluation of temporal signal or comparison of multiple methods of inference, both required to reliably estimate molecular rates. In this study, we use 26 previously published longitudinally sampled sequence series obtained from chronically infected immunocompromised patients to re-evaluate the rate of SARS-CoV-2 intrahost evolution. Using a range of methods, we analyse the strength of temporal signal and infer evolutionary rates from tip-calibrated phylogenies. Regardless of heterogeneity in rate estimates between sample series and methods, we find within-host rates to be in good agreement with rates derived from host-to-host transmission chains. Our findings suggest that when certain limitations of the methodology are disregarded, such as the underlying assumption of phylogenetic independence or the methods sensitivity to phylo- temporal grouping, evolutionary rates can be substantially overestimated. We demonstrate that estimating within-host rates is a challenging question necessitating careful interpretation of findings. While our results do not support faster evolution across the complete viral genome during chronic SARS-CoV-2 infection, prolonged viral shedding together with relapsing viral load dynamics may nevertheless promote the emergence of new viral variants in immunocompromised patients. AUTHOR SUMMARYThe evolutionary origin of SARS-CoV-2 variants of concern (VOC) is a longstanding point of controversy, with multiple proposed explanations. Observations of immunocompromised individuals being at a greater risk of developing a prolonged SARS-CoV-2 infection have led to the Chronic infection hypothesis, suggesting that these cases may contribute to the emergence of VOCs. Correspondingly, many studies have reported accelerated viral evolution of SARS-CoV-2 within immunocompromised individuals with respect to the viral background population. However, many of these findings have not been validated with appropriate analytical methods. In this study we re-evaluate the rate of intrahost viral evolution of SARS- CoV-2 within immunocompromised patients utilising a range of methods. We assess the performance of different methodologies and compare our results to published estimates of SARS-CoV-2 evolutionary rates. Our systematic comparison showed no evidence supporting the previous claims of elevated levels of intrahost evolution in immunocompromised patients with chronic SARS-CoV-2. Instead, our findings exemplify the complexity of within-host viral dynamics, suggesting that a more comprehensive understanding of SARS-CoV-2 evolutionary processes would be derived from concurrent evaluation of viral genomic data together with patients clinical information.
Subject(s)
COVID-19ABSTRACT
SARS-CoV-2 can infect human cells through the recognition of the human angiotensin-converting enzyme 2 (ACE2) receptors. This affinity is given by six amino acid located in the receptor binding domain (RBD) region within the Spike protein. Genetic recombination involving bat and pangolin Sarbecoviruses, and natural selection have been proposed as possible explanations for the acquisition of these amino acids. In this study we employed Bayesian phylogenetics to jointly reconstruct the phylogeny of the RBD among human, bat and pangolin Sarbecoviruses and detect recombination events affecting this region of the genome. A recombination event involving RaTG13, the closest relative of SARS-CoV-2 that lacks five of the six residues, and an unsampled Sarbecovirus lineage was detected. This result suggests that the key amino acids were likely present in the common ancestor of SARS-CoV-2 and RaTG13, with the latter losing five of the amino acids as the result of recombination.
ABSTRACT
By May 2021, over 160 million SARS-CoV-2 diagnoses have been reported worldwide. Yet, the true number of infections is unknown and believed to exceed the reported numbers by several fold. National testing policies, in particular, can strongly affect the proportion of undetected cases. Here, we propose a novel method (GInPipe) that reconstructs SARS-CoV-2 incidence profiles within minutes, solely from publicly available, time-stamped viral genomes. We validated GInPipe against in silico generated outbreak data and elaborate phylodynamic analyses. We apply the method to reconstruct incidence histories from sequence data for Denmark, Scotland, Switzerland, and Victoria (Australia). GInPipe reconstructs the different pandemic waves robustly and remarkably accurate. We demonstrate how the method can be used to investigate the effects of changing testing policies on the probability to diagnose and report infected individuals. Specifically, we find that under-reporting was highest in mid 2020 in parts of Europe, coinciding with changes towards more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. We anticipate that the method is particularly useful in settings where diagnostic and reporting infrastructures are insufficient. In ‘post-pandemic’ times, when diagnostic efforts are decreased, GInPipe may facilitate the detection of hidden infection dynamics.
ABSTRACT
In May 2021, over 160 million SARS-CoV-2 infections have been reported worldwide. Yet, the true amount of infections is unknown and believed to exceed the reported numbers by several fold, depending on national testing policies that can strongly affect the proportion of undetected cases. To overcome this testing bias and better assess SARS-CoV-2 transmission dynamics, we propose a genome-based computational pipeline, GInPipe, to reconstruct the SARS-CoV-2 incidence dynamics through time. After validating GInPipe against in silico generated outbreak data, as well as more complex phylodynamic analyses, we use the pipeline to reconstruct incidence histories in Denmark, Scotland, Switzerland, and Victoria (Australia) solely from viral sequence data. The proposed method robustly reconstructs the different pandemic waves in the investigated countries and regions, does not require phylodynamic reconstruction, and can be directly applied to publicly deposited SARS-CoV-2 sequencing data sets. We observe differences in the relative magnitude of reconstructed versus reported incidences during times with sparse availability of diagnostic tests. Using the reconstructed incidence dynamics, we assess how testing policies may have affected the probability to diagnose and report infected individuals. We find that under-reporting was highest in mid 2020 in all analysed countries, coinciding with liberal testing policies at times of low test capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic and evaluate testing policies. The method executes within minutes on very large data sets and is freely available as a fully automated pipeline from https://github.com/KleistLab/GInPipe.
Subject(s)
Severe Acute Respiratory SyndromeABSTRACT
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.