Darrin Lemmer

We aimed to characterize SARS-CoV-2 infection in companion animals living in households with COVID-19-positive people and understand the dynamics surrounding how these animals become infected. Public health investigators contacted households with at least one confirmed, symptomatic person with COVID-19 for study recruitment. Blood, nasal, and rectal swab specimens were collected from pet dogs and cats and a questionnaire was completed. Specimens were tested for SARS-CoV-2 by RT-PCR, and for neutralizing antibodies; genomic sequencing was performed on viral-positive samples. A total of 36.4% of 110 pets enrolled had evidence of infection with SARS-CoV-2. Pets were more likely to test positive if the pet was immunocompromised, and if more than one person in the home was positive for COVID-19. Among 12 multi-pet households where at least one pet was positive, 10 had at least one other pet test positive. Whole-genome sequencing revealed the genomes of viral lineages circulating in the community during the time of sample collection. Our findings suggest a high likelihood of viral transmission in households with multiple pets and when pets had very close interactions with symptomatic humans. Further surveillance studies are needed to characterize how new variants impact animals and to understand opportunities for infection and spillover in susceptible species.

Genomic epidemiology has proven successful for real-time and retrospective monitoring of small and large-scale outbreaks. Here, we report two genomic sequencing and analysis strategies for rapid-turnaround or high-throughput processing of metagenomic samples. The rapid-turnaround method was designed to provide a quick phylogenetic snapshot of samples at the heart of active outbreaks, and has a total turnaround time of <48 hours from raw sample to analyzed data. The high-throughput method, first reported here for SARS-CoV2, was designed for semi-retrospective data analysis, and is both cost effective and highly scalable. Though these methods were developed and utilized for the SARS-CoV-2 pandemic response in Arizona, U.S, we envision their use for infectious disease epidemiology in the 21st Century.

Genomic epidemiology has proven successful for real-time and retrospective monitoring of small and large-scale outbreaks. Here, we report two genomic sequencing and analysis strategies for rapid-turnaround or high-throughput processing of metagenomic samples. The rapid-turnaround method was designed to provide a quick phylogenetic snapshot of samples at the heart of active outbreaks, and has a total turnaround time of <48 hours from raw sample to analyzed data. The high-throughput method was designed for semi-retrospective data analysis, and is both cost effective and highly scalable. Though these methods were developed and utilized for the SARS-CoV-2 pandemic response in Arizona, U.S, and we envision their use for infectious disease epidemiology in the 21st Century.

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2’s unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet, there is no current consensus on how to measure genomic variation. To test the effect of the variant-identification approach on transmission inferences for Mycobacterium tuberculosis, we conducted an experiment in which five genomic epidemiology groups applied variant-identification pipelines to the same outbreak sequence data. We compared the variants identified by each group in addition to transmission and phylogenetic inferences made with each variant set. To measure the performance of commonly used variant-identification tools, we simulated an outbreak. We compared the performance of three mapping algorithms, five variant callers and two variant filters in recovering true outbreak variants. Finally, we investigated the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant-calling approaches used by different groups do not recover consistent sets of variants, which can lead to conflicting transmission inferences. Further, performance in recovering true variation varied widely across approaches. While no single variant-identification approach outperforms others in both recovering true genome-wide and outbreak-level variation, variant-identification algorithms calibrated upon real sequence data or that incorporate local reassembly outperform others in recovering true pairwise differences between isolates. The choice of variant filters contributed to extensive differences across pipelines, and applying increasingly stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. Commonly used approaches to identify M. tuberculosis genomic variation have variable performance, particularly when predicting potential transmission links from pairwise genetic distances. Phylogenetic reconstruction may be improved by less stringent variant filtering. Approaches that improve variant identification in repetitive, hypervariable regions, such as long-read assemblies, may improve transmission inference.

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2’s unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

In December of 2019, a novel coronavirus, SARS-CoV-2, emerged in the city of Wuhan, China causing severe morbidity and mortality. Since then, the virus has swept across the globe causing millions of confirmed infections and hundreds of thousands of deaths. To better understand the nature of the pandemic and the introduction and spread of the virus in Arizona, we sequenced viral genomes from clinical samples tested at the TGen North Clinical Laboratory, provided to us by the Arizona Department of Health Services, and at Arizona State University and the University of Arizona, collected as part of community surveillance projects. Phylogenetic analysis of 79 genomes we generated from across Arizona revealed a minimum of 9 distinct introductions throughout February and March. We show that >80% of our sequences descend from clades that were initially circulating widely in Europe but have since dominated the outbreak in the United States. In addition, we show that the first reported case of community transmission in Arizona descended from the Washington state outbreak that was discovered in late February. Notably, none of the observed transmission clusters are epidemiologically linked to the original travel-related cases in the state, suggesting successful early isolation and quarantine. Finally, we use molecular clock analyses to demonstrate a lack of identifiable, widespread cryptic transmission in Arizona prior to the middle of February 2020.

Altered intestinal microbiota is associated with systemic and intestinal diseases, such as inflammatory bowel disease (IBD). Dysbiotic microbiota with enhanced proinflammatory capacity is characterized by depletion of anaerobic commensals, increased proportion of facultatively anaerobic bacteria, as well as reduced diversity and stability. In this study, we developed a high-throughput in vitro screening assay to isolate intestinal commensal bacteria with anti-inflammatory capacity from a healthy fecal microbiota transplantation donor. Freshly isolated gut bacteria were screened for their capacity to attenuate Escherichia coli lipopolysaccharide (LPS)-induced interleukin 8 (IL-8) release from HT-29 cells. The screen yielded a number of Bacteroides and Parabacteroides isolates, which were identified as P. distasonis, B. caccae, B. intestinalis, B. uniformis, B. fragilis, B. vulgatus and B. ovatus using whole genome sequencing. We observed that a cell-cell contact with the epithelium was not necessary to alleviate in vitro inflammation as spent culture media from the isolates were also effective and the anti-inflammatory action did not correlate with the enterocyte adherence capacity of the isolates. The anti-inflammatory isolates also exerted enterocyte monolayer reinforcing action and lacked essential genes to synthetize hexa-acylated, proinflammatory lipid A, part of LPS. Yet, the anti-inflammatory effector molecules remain to be identified. The Bacteroides strains isolated and characterized in this study have potential to be used as so-called next-generation probiotics.