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Nature ; 609(7925): 101-108, 2022 09.
Article in English | MEDLINE | ID: covidwho-1921636


As SARS-CoV-2 continues to spread and evolve, 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 and/or sequencing capacity, which can also introduce biases1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. 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 developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified 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.

COVID-19 , SARS-CoV-2 , Waste Water , Wastewater-Based Epidemiological Monitoring , COVID-19/epidemiology , COVID-19/transmission , COVID-19/virology , Humans , RNA, Viral/analysis , RNA, Viral/genetics , SARS-CoV-2/classification , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Sequence Analysis, RNA , Waste Water/virology
Am J Physiol Lung Cell Mol Physiol ; 323(1): L84-L92, 2022 07 01.
Article in English | MEDLINE | ID: covidwho-1891936


Increased plasma mitochondrial DNA concentrations are associated with poor outcomes in multiple critical illnesses, including COVID-19. However, current methods of cell-free mitochondrial DNA quantification in plasma are time-consuming and lack reproducibility. Here, we used next-generation sequencing to characterize the size and genome location of circulating mitochondrial DNA in critically ill subjects with COVID-19 to develop a facile and optimal method of quantification by droplet digital PCR. Sequencing revealed a large percentage of small mitochondrial DNA fragments in plasma with wide variability in coverage by genome location. We identified probes for the mitochondrial DNA genes, cytochrome B and NADH dehydrogenase 1, in regions of relatively high coverage that target small sequences potentially missed by other methods. Serial assessments of absolute mitochondrial DNA concentrations were then determined in plasma from 20 critically ill subjects with COVID-19 without a DNA isolation step. Mitochondrial DNA concentrations on the day of enrollment were increased significantly in patients with moderate or severe acute respiratory distress syndrome (ARDS) compared with those with no or mild ARDS. Comparisons of mitochondrial DNA concentrations over time between patients with no/mild ARDS who survived, patients with moderate/severe ARDS who survived, and nonsurvivors showed the highest concentrations in patients with more severe disease. Absolute mitochondrial DNA quantification by droplet digital PCR is time-efficient and reproducible; thus, we provide a valuable tool and rationale for future studies evaluating mitochondrial DNA as a real-time biomarker to guide clinical decision-making in critically ill subjects with COVID-19.

COVID-19 , Respiratory Distress Syndrome , COVID-19/diagnosis , COVID-19/genetics , Critical Illness , DNA, Mitochondrial/genetics , Humans , Intensive Care Units , Polymerase Chain Reaction , Reproducibility of Results , Respiratory Distress Syndrome/diagnosis , Respiratory Distress Syndrome/genetics
Sci Rep ; 12(1): 5077, 2022 03 24.
Article in English | MEDLINE | ID: covidwho-1815587


Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in < 10 min, with no user intervention. ViReflow's simplicity, flexibility, and scalability make it an ideal tool for viral molecular epidemiological efforts.

COVID-19 , Software , COVID-19/epidemiology , Genome, Viral/genetics , Humans , Pandemics , SARS-CoV-2/genetics