Microbiome Analysis for Wastewater Surveillance during COVID-19.

Wastewater surveillance (WS), when coupled with advanced molecular techniques, offers near real-time monitoring of community-wide transmission of SARS-CoV-2 and allows assessing and mitigating COVID-19 outbreaks, by evaluating the total microbial assemblage in a community. Composite wastewater samples (24 h) were collected weekly from a manhole between December 2020 and November 2021 in Maryland, USA. RT-qPCR results showed concentrations of SARS-CoV-2 RNA recovered from wastewater samples reflected incidence of COVID-19 cases. When a drastic increase in COVID-19 was detected in February 2021, samples were selected for microbiome analysis (DNA metagenomics, RNA metatranscriptomics, and targeted SARS-CoV-2 sequencing). Targeted SARS-CoV-2 sequencing allowed for detection of important genetic mutations, such as spike: K417N, D614G, P681H, T716I, S982A, and D1118H, commonly associated with increased cell entry and reinfection. Microbiome analysis (DNA and RNA) provided important insight with respect to human health-related factors, including detection of pathogens and their virulence/antibiotic resistance genes. Specific microbial species comprising the wastewater microbiome correlated with incidence of SARS-CoV-2 RNA, suggesting potential association with SARS-CoV-2 infection. Climatic conditions, namely, temperature, were related to incidence of COVID-19 and detection of SARS-CoV-2 in wastewater, having been monitored as part of an environmental risk score assessment carried out in this study. In summary, the wastewater microbiome provides useful public health information, and hence, a valuable tool to proactively detect and characterize pathogenic agents circulating in a community. In effect, metagenomics of wastewater can serve as an early warning system for communicable diseases, by providing a larger source of information for health departments and public officials. IMPORTANCE Traditionally, testing for COVID-19 is done by detecting SARS-CoV-2 in samples collected from nasal swabs and/or saliva. However, SARS-CoV-2 can also be detected in feces of infected individuals. Therefore, wastewater samples can be used to test all individuals of a community contributing to the sewage collection system, i.e., the infrastructure, such as gravity pipes, manholes, tanks, lift stations, control structures, and force mains, that collects used water from residential and commercial sources and conveys the flow to a wastewater treatment plant. Here, we profile community wastewater collected from a manhole, detect presence of SARS-CoV-2, identify genetic mutations of SARS-CoV-2, and perform COVID-19 risk score assessment of the study area. Using metagenomics analysis, we also detect other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Results show that by analyzing all microorganisms present in wastewater, pathogens circulating in a community can provide an early warning for contagious diseases.

Distributions of waterborne pathogens in raw wastewater based on a 14-month, multi-site monitoring campaign.

The California State Water Resources Control Board is the first regulatory body in the United States to develop statewide regulations for direct potable reuse (DPR). To support this effort, a pathogen monitoring campaign was undertaken to develop and implement an optimized standard operating protocol to better characterize the concentration of human pathogens in raw wastewater. Methods to detect relevant viral and protozoan pathogens in raw wastewater were optimized and implemented during a 14-month monitoring campaign. Over 120 samples were collected from five wastewater treatment plants treating a quarter of California's population. Samples were analyzed for two protozoa (Cryptosporidium and Giardia) using microscopy methods, three enteric viruses (enterovirus, adenovirus, and norovirus) using culture and/or molecular methods, and male-specific coliphage using culture methods. The method recovery efficiency was measured in every protozoa sample and every other virus sample to confirm minimum recovery efficiencies were achieved and to correct the concentrations for pathogen losses during sample processing. The results from this study provide the industry with a large, high-quality dataset as demonstrated by the high degree of method sensitivity, method recovery, and QA/QC steps. Such high-quality data on pathogen concentrations in raw wastewater are critical for confirming the level of treatment needed to reduce pathogen concentrations down to acceptable levels for potable water in DPR projects.

Metagenomic Sequencing and Quantitative Real-Time PCR for Fecal Pollution Assessment in an Urban Watershed.

Microbial contamination of recreation waters is a major concern globally, with pollutants originating from many sources, including human and other animal wastes often introduced during storm events. Fecal contamination is traditionally monitored by employing culture methods targeting fecal indicator bacteria (FIB), namely E. coli and enterococci, which provides only limited information of a few microbial taxa and no information on their sources. Host-associated qPCR and metagenomic DNA sequencing are complementary methods for FIB monitoring that can provide enhanced understanding of microbial communities and sources of fecal pollution. Whole metagenome sequencing (WMS), quantitative real-time PCR (qPCR), and culture-based FIB tests were performed in an urban watershed before and after a rainfall event to determine the feasibility and application of employing a multi-assay approach for examining microbial content of ambient source waters. Cultivated E. coli and enterococci enumeration confirmed presence of fecal contamination in all samples exceeding local single sample recreational water quality thresholds (E. coli, 410 MPN/100 mL; enterococci, 107 MPN/100 mL) following a rainfall. Test results obtained with qPCR showed concentrations of E. coli, enterococci, and human-associated genetic markers increased after rainfall by 1.52-, 1.26-, and 1.11-fold log10 copies per 100 mL, respectively. Taxonomic analysis of the surface water microbiome and detection of antibiotic resistance genes, general FIB, and human-associated microorganisms were also employed. Results showed that fecal contamination from multiple sources (human, avian, dog, and ruminant), as well as FIB, enteric microorganisms, and antibiotic resistance genes increased demonstrably after a storm event. In summary, the addition of qPCR and WMS to traditional surrogate techniques may provide enhanced characterization and improved understanding of microbial pollution sources in ambient waters.

A comparative analysis of drinking water employing metagenomics.

The microbiological content of drinking water traditionally is determined by employing culture-dependent methods that are unable to detect all microorganisms, especially those that are not culturable. High-throughput sequencing now makes it possible to determine the microbiome of drinking water. Thus, the natural microbiota of water and water distribution systems can now be determined more accurately and analyzed in significantly greater detail, providing comprehensive understanding of the microbial community of drinking water applicable to public health. In this study, shotgun metagenomic analysis was performed to determine the microbiological content of drinking water and to provide a preliminary assessment of tap, drinking fountain, sparkling natural mineral, and non-mineral bottled water. Predominant bacterial species detected were members of the phyla Actinobacteria and Proteobacteria, notably the genera Alishewanella, Salmonella, and Propionibacterium in non-carbonated non-mineral bottled water, Methyloversatilis and Methylibium in sparkling natural mineral water, and Mycobacterium and Afipia in tap and drinking fountain water. Fecal indicator bacteria, i.e., Escherichia coli or enterococci, were not detected in any samples examined in this study. Bacteriophages and DNA encoding a few virulence-associated factors were detected but determined to be present only at low abundance. Antibiotic resistance markers were detected only at abundance values below our threshold of confidence. DNA of opportunistic plant and animal pathogens was identified in some samples and these included bacteria (Mycobacterium spp.), protozoa (Acanthamoeba mauritaniensis and Acanthamoeba palestinensis), and fungi (Melampsora pinitorqua and Chryosporium queenslandicum). Archaeal DNA (Candidatus Nitrosoarchaeum) was detected only in sparkling natural mineral water. This preliminary study reports the complete microbiome (bacteria, viruses, fungi, and protists) of selected types of drinking water employing whole-genome high-throughput sequencing and bioinformatics. Investigation into activity and function of the organisms detected is in progress.

Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data.

Microorganisms are ubiquitous in the biosphere, playing a crucial role in both biogeochemistry of the planet and human health. However, identifying these microorganisms and defining their function are challenging. Widely used approaches in comparative metagenomics, 16S amplicon sequencing and whole genome shotgun sequencing (WGS), have provided access to DNA sequencing analysis to identify microorganisms and evaluate diversity and abundance in various environments. However, advances in parallel high-throughput DNA sequencing in the past decade have introduced major hurdles, namely standardization of methods, data storage, reproducible interoperability of results, and data sharing. The National Ecological Observatory Network (NEON), established by the National Science Foundation, enables all researchers to address queries on a regional to continental scale around a variety of environmental challenges and provide high-quality, integrated, and standardized data from field sites across the U.S. As the amount of metagenomic data continues to grow, standardized procedures that allow results across projects to be assessed and compared is becoming increasingly important in the field of metagenomics. We demonstrate the feasibility of using publicly available NEON soil metagenomic sequencing datasets in combination with open access Metagenomics Rapid Annotation using the Subsystem Technology (MG-RAST) server to illustrate advantages of WGS compared to 16S amplicon sequencing. Four WGS and four 16S amplicon sequence datasets, from surface soil samples prepared by NEON investigators, were selected for comparison, using standardized protocols collected at the same locations in Colorado between April-July 2014. The dominant bacterial phyla detected across samples agreed between sequencing methodologies. However, WGS yielded greater microbial resolution, increased accuracy, and allowed identification of more genera of bacteria, archaea, viruses, and eukaryota, and putative functional genes that would have gone undetected using 16S amplicon sequencing. NEON open data will be useful for future studies characterizing and quantifying complex ecological processes associated with changing aquatic and terrestrial ecosystems.

Online assessment of sand filter performance for bacterial removal in a full-scale drinking water treatment plant.

Microbiological risks associated with drinking water can be minimized by providing enhanced integrity monitoring of bacterial removal by water treatment processes. This study aimed to evaluate the efficacy of real-time bacteriological counters for continuously assessing the performance of a full-scale sand filter to remove bacteria. Over the course of an 8-day evaluation, online counting of bacteria was successfully performed, providing continuous bacterial counts in the sand filter influent and effluent over approximate ranges from 17 × 104 to 94 × 104 and from 0.2 × 104 to 1.3 × 104 counts/mL, respectively. Periodic variations were observed with online bacterial counts in the sand filter influent because of the changes in the performance of flocculation and sedimentation processes. Overall, online removal rates of bacteria determined during the full-scale test were 95.2-99.3% (i.e., 1.3-2.2-log), indicating that online bacterial counting can continuously demonstrate over 1.3-log removal in the sand filter. Real-time bacteriological counting technology can be a useful tool for assessing variability and detecting bacterial breakthrough. It can be integrated with other online water quality measurements to evaluate underlying trends and the performance of sand filters for bacterial removal, which can enhance the safety of drinking water.

Assessment of online bacterial particle counts for monitoring the performance of reverse osmosis membrane process in potable reuse.

Safety of potable reuse can be enhanced by improved water quality monitoring techniques for assessing water treatment processes. This study evaluated the efficacy of online bacterial counting for continuous monitoring of reverse osmosis (RO) membranes to remove bacteria using real-time bacteriological commercial counters and an on-site pilot-scale RO system. Prior to on-site assessments, the online bacterial counting was verified by comparing the measurement of fluorescent particles in water with flow cytometry. During a seven day pilot test of RO treatment at a water reclamation plant, online bacterial counts in RO permeate were monitored below 15 counts/mL; whereas the bacterial counts in RO feed water were approximately 2500 to 10,000 counts/mL. Removal rates of bacterial counts ranged from 2.6 to 3.1-log (average = 2.9-log) by continuously monitoring bacterial removal. This is greater than a 2-log reduction frequently determined using other water quality surrogates (i.e., electrical conductivity). Overall, the continuous monitoring of bacteria in RO feed and permeate can be implemented without the addition of chemicals to provide near real-time bacterial counts to measure their reduction after RO treatment. This can be developed for continuous performance monitoring of the RO process, providing greater assurance of microbial water quality after RO treatment.

Characterization of the Microbiome at the World's Largest Potable Water Reuse Facility.

Conventional water resources are not sufficient in many regions to meet the needs of growing populations. Due to cyclical weather cycles, drought, and climate change, water stress has increased worldwide including in Southern California, which serves as a model for regions that integrate reuse of wastewater for both potable and non-potable use. The Orange County Water District (OCWD) Advanced Water Purification Facility (AWPF) is a highly engineered system designed to treat and produce up to 100 million gallons per day (MGD) of purified water from a municipal wastewater source for potable reuse. Routine facility microbial water quality analysis is limited to standard indicators at this and similar facilities. Given recent advances in high throughput DNA sequencing techniques, complete microbial profiling of communities in water samples is now possible. By using 16S/18S rRNA gene sequencing, metagenomic and metatranscriptomic sequencing coupled to a highly accurate identification method along with 16S rRNA gene qPCR, we describe a detailed view of the total microbial community throughout the facility. The total bacterial load of the water at stages of the treatment train ranged from 3.02 × 106 copies in source, unchlorinated wastewater feed to 5.49 × 101 copies of 16S rRNA gene/mL after treatment (consisting of microfiltration, reverse osmosis, and ultraviolet/advanced oxidation). Microbial diversity and load decreased by several orders of magnitude after microfiltration and reverse osmosis treatment, falling to almost non-detectable levels that more closely resembled controls of molecular grade laboratory water than the biomass detected in the source water. The presence of antibiotic resistance genes and viruses was also greatly reduced. Overall, system design performance was achieved, and comprehensive microbial community analysis was found to enable a more complete characterization of the water/wastewater microbial signature.

Potential human pathogenic bacteria in a mixed urban watershed as revealed by pyrosequencing.

Current microbial source tracking (MST) methods for water depend on testing for fecal indicator bacterial counts or specific marker gene sequences to identify fecal contamination where potential human pathogenic bacteria could be present. In this study, we applied 454 high-throughput pyrosequencing to identify bacterial pathogen DNA sequences, including those not traditionally monitored by MST and correlated their abundances to specific sources of contamination such as urban runoff and agricultural runoff from concentrated animal feeding operations (CAFOs), recreation park area, waste-water treatment plants, and natural sites with little or no human activities. Samples for pyrosequencing were surface water, and sediment collected from 19 sites. A total of 12,959 16S rRNA gene sequences with average length of ≤400 bp were obtained, and were assigned to corresponding taxonomic ranks using ribosomal database project (RDP), Classifier and Greengenes databases. The percent of total potential pathogens were highest in urban runoff water (7.94%), agricultural runoff sediment (6.52%), and Prado Park sediment (6.00%), respectively. Although the numbers of DNA sequence tags from pyrosequencing were very high for the natural site, corresponding percent potential pathogens were very low (3.78-4.08%). Most of the potential pathogenic bacterial sequences identified were from three major phyla, namely, Proteobacteria, Bacteroidetes, and Firmicutes. The use of deep sequencing may provide improved and faster methods for the identification of pathogen sources in most watersheds so that better risk assessment methods may be developed to enhance public health.

Assimilable organic carbon (AOC) in soil water extracts using Vibrio harveyi BB721 and its implication for microbial biomass.

Assimilable organic carbon (AOC) is commonly used to measure the growth potential of microorganisms in water, but has not yet been investigated for measuring microbial growth potential in soils. In this study, a simple, rapid, and non-growth based assay to determine AOC in soil was developed using a naturally occurring luminous strain Vibrio harveyi BB721 to determine the fraction of low molecular weight organic carbon in soil water extract. Calibration of the assay was achieved by measuring the luminescence intensity of starved V. harveyi BB721 cells in the late exponential phase with a concentration range from 0 to 800 µg l(-1) glucose (equivalent to 0-16.0 mg glucose C kg(-1) soil) with the detection limit of 10 µg l(-1) equivalent to 0.20 mg glucose C kg(-1) soil. Results showed that bioluminescence was proportional to the concentration of glucose added to soil. The luminescence intensity of the cells was highly pH dependent and the optimal pH was about 7.0. The average AOC concentration in 32 soils tested was 2.9±2.2 mg glucose C kg(-1). Our data showed that AOC levels in soil water extracts were significantly correlated (P<0.05) with microbial biomass determined as microbial biomass carbon, indicating that the AOC concentrations determined by the method developed might be a good indicator of soil microbial biomass. Our findings provide a new approach that may be used to determine AOC in environmental samples using a non-growth bioluminescence based assay. Understanding the levels of AOC in soil water extract provides new insights into our ability to estimate the most available carbon pool to bacteria in soil that may be easily assimilated into cells for many metabolic processes and suggest possible the links between AOC, microbial regrowth potential, and microbial biomass in soils.

Bacterial community composition in low-flowing river water with different sources of pollutants.

Pollution of water resources is a major risk to human health and water quality throughout the world. The purpose of this study was to determine the influence of pollutant sources from agricultural activities, urban runoffs, and runoffs from wastewater treatment plants (WWTPs) on bacterial communities in a low-flowing river. Bacterial community structure was monitored using terminal restriction fragment length polymorphism (T-RFLP) and 16S rRNA gene clone library. The results were analyzed using nonmetric multidimensional scaling (NMDS) and UniFrac, coupled with principal coordinate analysis (PCoA) to compare diversity, abundance, community structure, and specific functional groups of bacteria in surface water affected by nonpoint sources. From all the sampling points, Bacteria were numerically dominated by three phyla ­ the Proteobacteria, Bacteroidetes, and Cyanobacteria ­ accounting for the majority of taxa detected. Overall results, using the b diversity measures UniFrac, coupled with PCoA, showed that bacterial contamination of the low-flowing river was not significantly different between agricultural activities and urban runoff.