SARS-CoV-2 monitoring at three sewersheds of different scales and complexity demonstrates distinctive relationships between wastewater measurements and COVID-19 case data.

Wastewater monitoring of SARS-CoV-2 presents a means of tracking COVID-19 community infection dynamics on a broader geographic scale. However, accounting for environmental and sample-processing losses may be necessary for wastewater measurements to readily inform our understanding of infection prevalence. Here, we present measurements of the SARS-CoV-2 N1 and N2 gene targets from weekly wastewater samples at three sites in Hamilton County, Ohio, during an increase and subsequent decline of COVID-19 infections. The concentration of N1 or N2 RNA in wastewater, measured over the course of six months, ranged from below the detection limit to over 104 gene copies/l, and correlated with case data at two wastewater treatment plants, but not at a sub-sewershed-level sampling site. We also evaluated the utility of a broader range of variables than has been reported consistently in previous work, in improving correlations of SARS-CoV-2 concentrations with case data. These include a spiked matrix recovery control (OC43), flow-normalization, and assessment of fecal loading using endogenous fecal markers (HF183, PMMoV, crAssphage). We found that adjusting for recovery, flow, and fecal indicators increased these correlations for samples from a larger sewershed (serving ~488,000 people) with greater industrial and stormwater inputs, but raw N1/N2 concentrations corresponded better with case data at a smaller, residential-oriented sewershed. Our results indicate that the optimal adjustment factors for correlating wastewater and clinical case data moving forward may not be generalizable to all sewersheds.

Human- and infrastructure-associated bacteria in greywater.

Greywater, the wastewater from sinks, showers and laundry, is an understudied environment for bacterial communities. Most greywater studies focus on quantifying pathogens, often via proxies used in other wastewater, like faecal indicator bacteria; there is a need to identify more greywater-appropriate surrogates, like Staphylococcus sp. Sequencing-based studies have revealed distinct communities in different types of greywater as well as in different parts of greywater infrastructure, including biofilms on pipes, holding tanks and filtration systems. The use of metagenomic sequencing provides high resolution on both the taxa and genes present, which may be of interest in cases like identifying pathogens and surrogates relevant to different matrices, monitoring antibiotic resistance genes and understanding metabolic processes occurring in the system. Here, we review what is known about bacterial communities in different types of greywater and its infrastructure. We suggest that wider adoption of environmental sequencing in greywater research is important because it can describe the entire bacterial community along with its metabolic capabilities, including pathways for removal of nutrients and organic materials. We briefly describe a metagenomic dataset comparing different types of greywater samples in a college dormitory building to highlight the type of questions these methods can address. Metagenomic sequencing can help further the understanding of greywater treatment for reuse because it allows for identification of new pathogens or genes of concern.

Twenty-first century molecular methods for analyzing antimicrobial resistance in surface waters to support One Health assessments.

Antimicrobial resistance (AMR) in the environment is a growing global health concern, especially the dissemination of AMR into surface waters due to human and agricultural inputs. Within recent years, research has focused on trying to understand the impact of AMR in surface waters on human, agricultural and ecological health (One Health). While surface water quality assessments and surveillance of AMR have historically utilized culture-based methods, culturing bacteria has limitations due to difficulty in isolating environmental bacteria and the need for a priori information about the bacteria for selective isolation. The use of molecular techniques to analyze AMR at the genetic level has helped to overcome the difficulties with culture-based techniques since they do not require advance knowledge of the bacterial population and can analyze uncultivable environmental bacteria. The aim of this review is to provide an overview of common contemporary molecular methods available for analyzing AMR in surface waters, which include high throughput real-time polymerase chain reaction (HT-qPCR), metagenomics, and whole genome sequencing. This review will also feature how these methods may provide information on human and animal health risks. HT-qPCR works at the nanoliter scale, requires only a small amount of DNA, and can analyze numerous gene targets simultaneously, but may lack in analytical sensitivity and the ability to optimize individual assays compared to conventional qPCR. Metagenomics offers more detailed genomic information and taxonomic resolution than PCR by sequencing all the microbial genomes within a sample. Its open format allows for the discovery of new antibiotic resistance genes; however, the quantity of DNA necessary for this technique can be a limiting factor for surface water samples that typically have low numbers of bacteria per sample volume. Whole genome sequencing provides the complete genomic profile of a single environmental isolate and can identify all genetic elements that may confer AMR. However, a main disadvantage of this technique is that it only provides information about one bacterial isolate and is challenging to utilize for community analysis. While these contemporary techniques can quickly provide a vast array of information about AMR in surface waters, one technique does not fully characterize AMR nor its potential risks to human, animal, or ecological health. Rather, a combination of techniques (including both molecular- and culture-based) are necessary to fully understand AMR in surface waters from a One Health perspective.

Characterization of the relative importance of human- and infrastructure-associated bacteria in grey water: a case study.

AIMS: Development of efficacious grey water (GW) treatment systems would benefit from detailed knowledge of the bacterial composition of GW. Thus, the aim of this study was to characterize the bacterial composition from (i) various points throughout a GW recycling system that collects shower and sink handwash (SH) water into an equalization tank (ET) prior to treatment and (ii) laundry (LA) water effluent of a commercial-scale washer. METHODS AND RESULTS: Bacterial composition was analysed by high-throughput pyrosequencing of the 16S rRNA gene. LA was dominated by skin-associated bacteria, with Corynebacterium, Staphylococcus, Micrococcus, Propionibacterium and Lactobacillus collectively accounting for nearly 50% of the total sequences. SH contained a more evenly distributed community than LA, with some overlap (e.g. Propionibacterium), but also contained distinct genera common to wastewater infrastructure (e.g. Zoogloea). The ET contained many of these same wastewater infrastructure-associated bacteria, but was dominated by genera adapted for anaerobic conditions. CONCLUSIONS: The data indicate that a relatively consistent set of skin-associated genera are the dominant human-associated bacteria in GW, but infrastructure-associated bacteria from the GW collection system and ET used for transient storage will be the most common bacteria entering GW treatment and reuse systems. SIGNIFICANCE AND IMPACT OF THE STUDY: This study is the first to use high-throughput sequencing to identify the bacterial composition of various GW sources.

Detection of multiple waterborne pathogens using microsequencing arrays.

AIMS: A microarray was developed to simultaneously detect Cryptosporidium parvum, Cryptosporidium hominis, Enterococcus faecium, Bacillus anthracis and Francisella tularensis in water. METHODS AND RESULTS: A DNA microarray was designed to contain probes that specifically detected C. parvum, C. hominis, Ent. faecium, B. anthracis and F. tularensis. The microarray was then evaluated with samples containing target and nontarget DNA from near-neighbour micro-organisms, and tap water spiked with multiple organisms. Results demonstrated that the microarray consistently detected Ent. faecium, B. anthracis, F. tularensis and C. parvum when present in samples. Cryptosporidium hominis was only consistently detected through the use of shared probes between C. hominis and C. parvum. CONCLUSIONS: This study successfully developed and tested a microarray-based assay that can specifically detect faecal indicator bacteria and human pathogens in tap water. SIGNIFICANCE AND IMPACT OF THE STUDY: The use of indicator organisms has become a practical solution for monitoring for water quality. However, they do not always correlate well with the presence of many microbial pathogens, thus necessitating direct monitoring for most pathogens. This microarray can be used to simultaneously detect multiple organisms in a single sample. More importantly, it can provide occurrence information that may be used in assessing potential exposure risks to waterborne pathogens.