Hidden evolutionary constraints dictate the retention of coronavirus accessory genes (preprint)

Genetic innovation is fundamental to the ability of viruses to adapt in the face of host immunity. Coronaviruses exhibit many mechanisms of innovation given flexibility in genomic composition relative to most RNA virus families (1-5). Examples include the acquisition of unique accessory genes that can originate by capture of cellular genes or through duplication and divergence of existing viral genes (6-8). Accessory genes may be influential in dictating viral host range and cellular tropism, but little is known about how selection acts on these variable regions of virus genomes. We used experimental evolution of mouse hepatitis virus (MHV) with an inactive native phosphodiesterase, NS2, that encodes a complementing cellular AKAP7 gene (9), to simulate the capture of a host gene and found hidden patterns of constraint that determine the fate of coronavirus accessory genes. After courses of serial infection, AKAP7 was retained under strong selection but rapidly lost under relaxed selection. In contrast, the gene encoding inactive NS2, ORF2, remained intact, suggesting it is under cryptic evolutionary constraint. Guided by the retention of ORF2 and hints of similar patterns in related betacoronaviruses, we analyzed the evolution of SARS-CoV-2 ORF8, which arose via gene duplication (6) and contains premature stop codons in several globally successful lineages. As with MHV ORF2, the coding-defective SARS-CoV-2 ORF8 gene remains largely intact, mirroring patterns observed during MHV experimental evolution and extending these findings to viruses currently adapting to humans. Retention of inactive genes challenges assumptions on the dynamics of gene loss in virus genomes and can help guide evolutionary analysis of emerging and pandemic coronaviruses.

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic (preprint)

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Wholesale Seafood Market, the site with the most reported wildlife vendors in the city of Wuhan, China. Here, we analyze publicly available qPCR and sequencing data from environmental samples collected in the Huanan market in early 2020. We demonstrate that the SARS-CoV-2 genetic diversity linked to this market is consistent with market emergence, and find increased SARS-CoV-2 positivity near and within a particular wildlife stall. We identify wildlife DNA in all SARS-CoV-2 positive samples from this stall. This includes species such as civets, bamboo rats, porcupines, hedgehogs, and one species, raccoon dogs, known to be capable of SARS-CoV-2 transmission. We also detect other animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them to those from other markets. This analysis provides the genetic basis for a short list of potential intermediate hosts of SARS-CoV-2 to prioritize for retrospective serological testing and viral sampling.

Single-linkage molecular clustering of viral pathogens (preprint)

Introduction: Public health faces the ongoing mission of safeguarding the population's health against various infectious diseases caused by a great number of pathogens. Epidemiology is an essential discipline in this field. With the rise of more advanced technologies, new tools are emerging to enhance the capability to intervene and control an epidemic. Among these approaches, molecular clustering comes forth as a promising option. However, appropriate genetic distance thresholds for defining clusters are poorly explored in contexts outside of Human Immunodeficiency Virus-1 (HIV-1). Methods: In this work, using the well-used pairwise Tamura-Nei 93 (TN93) distance threshold of 0.015 for HIV-1 as a point of reference for molecular cluster properties of interest, we perform molecular clustering on whole genome sequence datasets from HIV-1, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Zaire ebolavirus, and Mpox virus, to explore potential pairwise distances thresholds for these other viruses. Results: We found the following pairwise TN93 distance thresholds as potential candidates for use in molecular clustering: 0.00014 (4 mutations) for SARS-CoV-2, 0.00016 (3 mutations) for Ebola, and 0.0000051 (1 mutation) for Mpox. Conclusion: This study provides valuable information for epidemic control strategies, and public health efforts in managing infectious diseases caused by these viruses. The identified pairwise distance thresholds for molecular clustering can serve as a foundation for future research and intervention to combat epidemics effectively.

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics (preprint)

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of SARS-CoV-2 lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of 'local' when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

ViReaDB: A user-friendly database for compactly storing viral sequence data and rapidly computing consensus genome sequences (preprint)

Motivation In viral molecular epidemiology, reconstruction of consensus genomes from sequence data is critical for tracking mutations and variants of concern. However, storage of the raw sequence data can become prohibitively large, and computing consensus genome from sequence data can be slow and requires bioinformatics expertise. Results ViReaDB is a user-friendly database system for compactly storing viral sequence data and rapidly computing consensus genome sequences. From a dataset of 1 million trimmed mapped SARS-CoV-2 reads, it is able to compute the base counts and the consensus genome in 16 minutes, store the reads alongside the base counts and consensus in 50 MB, and optionally store just the base counts and consensus (without the reads) in 300 KB. Availability ViReaDB is freely available on PyPI ( https://pypi.org/project/vireadb ) and on GitHub ( https://github.com/niemasd/ViReaDB ) as an open-source Python software project. Contact niema@ucsd.edu

Wastewater sequencing uncovers early, cryptic SARS-CoV-2 variant transmission (preprint)

As SARS-CoV-2 becomes an endemic pathogen, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Wastewater and surface monitoring to detect COVID-19 in elementary school settings: The Safer at School Early Alert project (preprint)

Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. While wastewater monitoring has been implemented to mitigate outbreak risk in universities and residential settings, its effectiveness in community K-12 sites is unknown. We implemented a wastewater and surface monitoring system to detect SARS-CoV-2 in nine elementary schools in San Diego County. Ninety-three percent of identified cases were associated with either a positive wastewater or surface sample; 67% were associated with a positive wastewater sample, and 40% were associated with a positive surface sample. The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Passive environmental surveillance can complement approaches that require individual consent, particularly in communities with limited access and/or high rates of testing hesitancy.

Timing the SARS-CoV-2 Index Case in Hubei Province (preprint)

Understanding when SARS-CoV-2 emerged is critical to evaluating our current approach to monitoring novel zoonotic pathogens and understanding the failure of early containment and mitigation efforts for COVID-19. We employed a coalescent framework to combine retrospective molecular clock inference with forward epidemiological simulations to determine how long SARS-CoV-2 could have circulated prior to the time of the most recent common ancestor. Our results define the period between mid-October and mid-November 2019 as the plausible interval when the first case of SARS-CoV-2 emerged in Hubei province. By characterizing the likely dynamics of the virus before it was discovered, we show that over two-thirds of SARS-CoV-2-like zoonotic events would be self-limited, dying out without igniting a pandemic. Our findings highlight the shortcomings of zoonosis surveillance approaches for detecting highly contagious pathogens with moderate mortality rates.

An analysis of clinical and geographical metadata of over 75,000 records in the GISAID COVID-19 database (preprint)

During the SARS-CoV-2 outbreak that caused the coronavirus pandemic it is important now more than ever that scientists and public health officials work side-by-side and use their available resources to track patient information from those that have been affected by the novel coronavirus. The ability to track the disease helps identify possible trends and patterns that can be used by public health officials to make more informed decisions. Tracking data like this may be the key to helping states and countries safely re-open. However, when analyzing large collections of data there is the occurrence of confounding factors such as biases in patient sampling. In this project, a massive collection of COVID-19 data was analyzed, and explored potential biases in patient sampling were explored.

An Analysis of SARS-CoV-2 Using ViReport (preprint)

The ongoing outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in millions of cases and hundreds of thousands of deaths. Given the current lack of treatments or vaccines available, it may be useful to trace the evolu-tion and spread of the virus to better develop methods of preventative intervention. In this study, we analyzed over 4,000 full genome sequences of human SARS-CoV-2 using novel tool ViReport [13], an automated workflow for performing phylogenetic analyses on viral sequences and generating comprehensive molecular epidemiologi-cal reports. The complete ViReport output can be found at https://github.com/mirandajsong/ViReport-SARS-CoV-2.