Ohio Coronavirus Wastewater Monitoring Network: Implementation of Statewide Monitoring for Protecting Public Health.

CONTEXT: Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community. PROGRAM: Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020. IMPLEMENTATION: The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard. EVALUATION: This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring. DISCUSSION: Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic.

Analysis of metatranscriptomic methods to enable wastewater-based biosurveillance of all infectious diseases.

Introduction: Wastewater-based surveillance emerged during the COVID-19 pandemic as an efficient way to quickly screen large populations, monitor infectious disease transmission over time, and identify whether more virulent strains are becoming more prevalent in the region without burdening the health care system with individualized testing. Ohio was one of the first states to implement wastewater monitoring through its Ohio Coronavirus Wastewater Monitoring Network (OCWMN), originally tracking the prevalence of COVID-19 by quantitative qPCR from over 67 sites across the state. The OCWMN evolved along with the pandemic to include sequencing the SARS-CoV-2 genome to assess variants of concern circulating within the population. As the pandemic wanes, networks such as OCWMN can be expanded to monitor other infectious diseases and outbreaks of interest to the health department to reduce the burden of communicable diseases. However, most surveillance still utilizes qPCR based diagnostic tests for individual pathogens, which is hard to scale for surveillance of multiple pathogens. Methods: Here we have tested several genomic methods, both targeted and untargeted, for wastewater-based biosurveillance to find the most efficient procedure to detect and track trends in reportable infectious diseases and outbreaks of known pathogens as well as potentially novel pathogens or variants on the rise in our communities. RNA extracts from the OCWMN were provided weekly from 10 sites for 6 weeks. Total RNA was sequenced from the samples on the Illumina NextSeq and on the MinION to identify pathogens present. The MinION long read platform was also used to sequence SARS-CoV-2 with the goal of reducing the complexity of variant calling in mixed populations as occurs with short Illumina reads. Finally, a targeted hybridization approach was tested for compatibility with wastewater RNA samples. Results and discussion: The data analyzed here provides a baseline assessment that demonstrates that wastewater is a rich resource for infectious disease epidemiology and identifies technology gaps and potential solutions to enable this resource to be used by public health laboratories to monitor the infectious disease landscape of the regions they serve.

UltraSEQ, a Universal Bioinformatic Platform for Information-Based Clinical Metagenomics and Beyond.

Applied metagenomics is a powerful emerging capability enabling the untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of-its-kind accurate and scalable metagenomic bioinformatic tool for potential clinical diagnostics and biosurveillance utility. Here, we present the results of the evaluation of our novel UltraSEQ pipeline using an in silico-synthesized metagenome, mock microbial community data sets, and publicly available clinical data sets from samples of different infection types, including both short-read and long-read sequencing data. Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community data set. For clinical data sets, even without requiring data set-specific configuration setting changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Furthermore, as an initial demonstration with a limited patient sample set, we show UltraSEQ's ability to provide antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above-described results demonstrate that the UltraSEQ platform offers a transformative approach for microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for the classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infections. IMPORTANCE Traditional clinical microbiology-based diagnostic tests rely on targeted methods that can detect only one to a few preselected organisms or slow, culture-based methods. Although widely used today, these methods have several limitations, resulting in rates of cases of an unknown etiology of infection of >50% for several disease types. Massive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, but current offerings are limited to a specific disease type or sequencer workflow and/or require laboratory-specific controls. The limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. In this paper, we demonstrate that UltraSEQ uses a novel, information-based approach that enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample.

Feasibility of neighborhood and building scale wastewater-based genomic epidemiology for pathogen surveillance.

The benefits of wastewater-based epidemiology (WBE) for tracking the viral load of SARS-CoV-2, the causative agent of COVID-19, have become apparent since the start of the pandemic. However, most sampling occurs at the wastewater treatment plant influent and therefore monitors the entire catchment, encompassing multiple municipalities, and is conducted using quantitative polymerase chain reaction (qPCR), which only quantifies one target. Sequencing methods provide additional strain information and also can identify other pathogens, broadening the applicability of WBE to beyond the COVID-19 pandemic. Here we demonstrate feasibility of sampling at the neighborhood or building complex level using qPCR, targeted sequencing, and untargeted metatranscriptomics (total RNA sequencing) to provide a refined understanding of the local dynamics of SARS-CoV-2 strains and identify other pathogens circulating in the community. We demonstrate feasibility of tracking SARS-CoV-2 at the neighborhood, hospital, and nursing home level with the ability to detect one COVID-19 positive out of 60 nursing home residents. The viral load obtained was correlative with the number of COVID-19 patients being treated in the hospital. Targeted wastewater-based sequencing over time demonstrated that nonsynonymous mutations fluctuate in the viral population. Clades and shifts in mutation profiles within the community were monitored and could be used to determine if vaccine or diagnostics need to be adapted to ensure continued efficacy. Furthermore, untargeted RNA sequencing identified several other pathogens in the samples. Therefore, untargeted RNA sequencing could be used to identify new outbreaks or emerging pathogens beyond the COVID-19 pandemic.

Sequence-based autosomal STR characterization in four US populations using PowerSeq™ Auto/Y system.

The forensic science community is poised to utilize modern advances in massively parallel sequencing (MPS) technologies to better characterize biological samples with higher resolution. A critical component towards the advancement of forensic DNA analysis with these technologies is a comprehensive understanding of the diversity and population distribution of sequence-based short tandem repeat (STR) alleles. Here we analyzed 786 samples of individuals from different population groups, including four of the mostly commonly encountered in forensic casework in the USA. DNA samples were amplified with the PowerSeq™ Auto/Y System Prototype Kit (Promega Corp.), and sequencing was performed on an Illumina® MiSeq instrument. Sequence data were analyzed using a bioinformatics processing tool, Altius. For additional data analysis and profile comparison, capillary electrophoresis (CE) size-based STR genotypes were generated for a subset of individuals, and where possible, also with a second commercially available MPS STR assay. Autosomal STR loci were analyzed and frequencies were calculated based on sequence composition. Also, population genetics studies were performed, with Hardy-Weinberg equilibrium, polymorphic information content (PIC), and observed and expected heterozygosity all assessed. Overall, sequence-based allelic variants of the repeat region were observed in 20 out of 22 different STR loci commonly used in forensic DNA genotyping, with the highest number of sequence variation observed at locus D12S391. The highest increase in allelic diversity and in PIC through sequence-based genotyping was observed at loci D3S1358 and D8S1179. Such detailed sequence analysis, as the one performed in the present study, is important to help understand the diversity of sequence-based STR alleles across different populations and to demonstrate how such allelic variation can improve statistics used for forensic casework.

Application of a multiple lines of evidence approach to document natural attenuation of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in groundwater.

This study utilized innovative analyses to develop multiple lines of evidence for natural attenuation of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in groundwater at the U.S. Department of Energy's Pantex Plant. RDX, as well as the degradation product 4-nitro-2,4-diazabutanal (NDAB; produced by aerobic biodegradation or alkaline hydrolysis) were detected in a large portion of the plume, with lower concentrations of the nitroso-containing metabolites produced during anaerobic biodegradation. 16S metagenomic sequencing detected the presence of bacteria known to aerobically degrade RDX (e.g., Gordonia, Rhodococcus) and NDAB (Methylobacterium), as well as the known anoxic RDX degrader Pseudomonas fluorescens I-C. Proteomic analysis detected both the aerobic RDX degradative enzyme XplA, and the anoxic RDX degradative enzyme XenB. Groundwater enrichment cultures supplied with low concentrations of labile carbon confirmed the potential of the extant groundwater community to aerobically degrade RDX and produce NDAB. Compound-specific isotope analysis (CSIA) of RDX collected at the site showed fractionation of nitrogen isotopes with δ15N values ranging from approximately -5‰ to +9‰, providing additional evidence of RDX degradation. Taken together, these results provide evidence of in situ RDX degradation in the Pantex Plant groundwater. Furthermore, they demonstrate the benefit of multiple lines of evidence in supporting natural attenuation assessments, especially with the application of innovative isotopic and -omic technologies.

Use of omic tools to assess methyl tert-butyl ether (MTBE) degradation in groundwater.

This study employed innovative technologies to evaluate multiple lines of evidence for natural attenuation (NA) of methyl tertiary-butyl ether (MTBE) in groundwater at the 22 Area of Marine Corps Base (MCB) Camp Pendleton after decommissioning of a biobarrier system. For comparison, data from the 13 Area Gas Station where active treatment of MTBE is occurring was used to evaluate the effectiveness of omic techniques in assessing biodegradation. Overall, the 22 Area Gas Station appeared to be anoxic. MTBE was detected in large portion of the plume. In comparison, concentrations of MTBE at the 13 Area Gas Station were much higher (42,000 µg/L to 2800 µg/L); however, none of the oxygenates were detected. Metagenomic analysis of the indigenous groundwater microbial community revealed the presence of bacterial strains known to aerobically and anaerobically degrade MTBE at both sites. While proteomic analysis at the 22 Area Gas Station showed the presence of proteins of MTBE degrading microorganisms, the MTBE degradative proteins were only found at the 13 Area Gas Station. Taken together, these results provide evidence for previous NA of MTBE in the groundwater at 22 Area Gas Station and demonstrate the effectiveness of innovative-omic technologies to assist monitored NA assessments.

Biodegradation of dispersed Macondo crude oil by indigenous Gulf of Mexico microbial communities.

Because of the extreme conditions of the Deepwater Horizon (DWH) release (turbulent flow at 1500m depth and 5°C water temperature) and the sub-surface application of dispersant, small but neutrally buoyant oil droplets <70µm were formed, remained in the water column and were subjected to in-situ biodegradation processes. In order to investigate the biodegradation of Macondo oil components during the release, we designed and performed an experiment to evaluate the interactions of the indigenous microbial communities present in the deep waters of the Gulf of Mexico (GOM) with oil droplets of two representative sizes (10µm and 30µm median volume diameter) created with Macondo source oil in the presence of Corexit 9500 using natural seawater collected at the depth of 1100-1300m in the vicinity of the DWH wellhead. The evolution of the oil was followed in the dark and at 5°C for 64days by collecting sacrificial water samples at fixed intervals and analyzing them for a wide range of chemical and biological parameters including volatile components, saturated and aromatic hydrocarbons, dispersant markers, dissolved oxygen, nutrients, microbial cell counts and microbial population dynamics. A one phase exponential decay from a plateau model was used to calculate degradation rates and lag times for more than 150 individual oil components. Calculations were normalized to a conserved petroleum biomarker (30αß-hopane). Half-lives ranged from about 3days for easily degradable compounds to about 60days for higher molecular weight aromatics. Rapid degradation was observed for BTEX, 2-3 ring PAHs, and n-alkanes below n-C23. The results in this experimental study showed good agreement with the n-alkane (n-C13 to n-C26) half-lives (0.6-9.5days) previously reported for the Deepwater Horizon plume samples and other laboratory studies with chemically dispersed Macondo oil conducted at low temperatures (<8°C). The responses of the microbial populations also were consistent with what was reported during the actual oil release, e.g. Colwellia, Cycloclasticus and Oceanospirillales (including the specific DWH Oceanospirillales) were present and increased in numbers indicating that they were degrading components of the oil. The consistency of the field and laboratory data indicate that these results could be used, in combination with other field and model data to characterize the dissipation of Macondo oil in the deepwater environment as part of the risk assessment estimations.

Oil Biodegradation and Oil-Degrading Microbial Populations in Marsh Sediments Impacted by Oil from the Deepwater Horizon Well Blowout.

To study hydrocarbon biodegradation in marsh sediments impacted by Macondo oil from the Deepwater Horizon well blowout, we collected sediment cores 18-36 months after the accident at the marshes in Bay Jimmy (Upper Barataria Bay), Louisiana, United States. The highest concentrations of oil were found in the top 2 cm of sediment nearest the waterline at the shorelines known to have been heavily oiled. Although petroleum hydrocarbons were detectable, Macondo oil could not be identified below 8 cm in 19 of the 20 surveyed sites. At the one site where oil was detected below 8 cm, concentrations were low. Residual Macondo oil was already highly weathered at the start of the study, and the concentrations of individual saturated hydrocarbons and polycyclic aromatic hydrocarbons continued to decrease over the course of the study due to biodegradation. Desulfococcus oleovorans, Marinobacter hydrocarbonoclasticus, Mycobacterium vanbaalenii, and related mycobacteria were the most abundant oil-degrading microorganisms detected in the top 2 cm at the oiled sites. Relative populations of these taxa declined as oil concentrations declined. The diversity of the microbial community was low at heavily oiled sites compared to that of the unoiled reference sites. As oil concentrations decreased over time, microbial diversity increased and approached the diversity levels of the reference sites. These trends show that the oil continues to be biodegraded, and microbial diversity continues to increase, indicating ongoing overall ecological recovery.

Microbial community profiling of human saliva using shotgun metagenomic sequencing.

Human saliva is clinically informative of both oral and general health. Since next generation shotgun sequencing (NGS) is now widely used to identify and quantify bacteria, we investigated the bacterial flora of saliva microbiomes of two healthy volunteers and five datasets from the Human Microbiome Project, along with a control dataset containing short NGS reads from bacterial species representative of the bacterial flora of human saliva. GENIUS, a system designed to identify and quantify bacterial species using unassembled short NGS reads was used to identify the bacterial species comprising the microbiomes of the saliva samples and datasets. Results, achieved within minutes and at greater than 90% accuracy, showed more than 175 bacterial species comprised the bacterial flora of human saliva, including bacteria known to be commensal human flora but also Haemophilus influenzae, Neisseria meningitidis, Streptococcus pneumoniae, and Gamma proteobacteria. Basic Local Alignment Search Tool (BLASTn) analysis in parallel, reported ca. five times more species than those actually comprising the in silico sample. Both GENIUS and BLAST analyses of saliva samples identified major genera comprising the bacterial flora of saliva, but GENIUS provided a more precise description of species composition, identifying to strain in most cases and delivered results at least 10,000 times faster. Therefore, GENIUS offers a facile and accurate system for identification and quantification of bacterial species and/or strains in metagenomic samples.

Short-read, high-throughput sequencing technology for STR genotyping.

DNA-based methods for human identification principally rely upon genotyping of short tandem repeat (STR) loci. Electrophoretic-based techniques for variable-length classification of STRs are universally utilized, but are limited in that they have relatively low throughput and do not yield nucleotide sequence information. High-throughput sequencing technology may provide a more powerful instrument for human identification, but is not currently validated for forensic casework. Here, we present a systematic method to perform high-throughput genotyping analysis of the Combined DNA Index System (CODIS) STR loci using short-read (150 bp) massively parallel sequencing technology. Open source reference alignment tools were optimized to evaluate PCR-amplified STR loci using a custom designed STR genome reference. Evaluation of this approach demonstrated that the 13 CODIS STR loci and amelogenin (AMEL) locus could be accurately called from individual and mixture samples. Sensitivity analysis showed that as few as 18,500 reads, aligned to an in silico referenced genome, were required to genotype an individual (>99% confidence) for the CODIS loci. The power of this technology was further demonstrated by identification of variant alleles containing single nucleotide polymorphisms (SNPs) and the development of quantitative measurements (reads) for resolving mixed samples.