Research needs for optimising wastewater-based epidemiology monitoring for public health protection.

Wastewater-based epidemiology (WBE) is an unobtrusive method used to observe patterns in illicit drug use, poliovirus, and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The pandemic and need for surveillance measures have led to the rapid acceleration of WBE research and development globally. With the infrastructure available to monitor SARS-CoV-2 from wastewater in 58 countries globally, there is potential to expand targets and applications for public health protection, such as other viral pathogens, antimicrobial resistance (AMR), pharmaceutical consumption, or exposure to chemical pollutants. Some applications have been explored in academic research but are not used to inform public health decision-making. We reflect on the current knowledge of WBE for these applications and identify barriers and opportunities for expanding beyond SARS-CoV-2. This paper critically reviews the applications of WBE for public health and identifies the important research gaps for WBE to be a useful tool in public health. It considers possible uses for pathogenic viruses, AMR, and chemicals. It summarises the current evidence on the following: (1) the presence of markers in stool and urine; (2) environmental factors influencing persistence of markers in wastewater; (3) methods for sample collection and storage; (4) prospective methods for detection and quantification; (5) reducing uncertainties; and (6) further considerations for public health use.

Developing Surrogate Markers for Predicting Antibiotic Resistance "Hot Spots" in Rivers Where Limited Data Are Available.

Pinpointing environmental antibiotic resistance (AR) hot spots in low-and middle-income countries (LMICs) is hindered by a lack of available and comparable AR monitoring data relevant to such settings. Addressing this problem, we performed a comprehensive spatial and seasonal assessment of water quality and AR conditions in a Malaysian river catchment to identify potential "simple" surrogates that mirror elevated AR. We screened for resistant coliforms, 22 antibiotics, 287 AR genes and integrons, and routine water quality parameters, covering absolute concentrations and mass loadings. To understand relationships, we introduced standardized "effect sizes" (Cohen's D) for AR monitoring to improve comparability of field studies. Overall, water quality generally declined and environmental AR levels increased as one moved down the catchment without major seasonal variations, except total antibiotic concentrations that were higher in the dry season (Cohen's D > 0.8, P < 0.05). Among simple surrogates, dissolved oxygen (DO) most strongly correlated (inversely) with total AR gene concentrations (Spearman's ρ 0.81, P < 0.05). We suspect this results from minimally treated sewage inputs, which also contain AR bacteria and genes, depleting DO in the most impacted reaches. Thus, although DO is not a measure of AR, lower DO levels reflect wastewater inputs, flagging possible AR hot spots. DO measurement is inexpensive, already monitored in many catchments, and exists in many numerical water quality models (e.g., oxygen sag curves). Therefore, we propose combining DO data and prospective modeling to guide local interventions, especially in LMIC rivers with limited data.

Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance.

BACKGROUND: Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks. RESULTS: Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation. CONCLUSIONS: Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

Co-digestion of microalgae with potato processing waste and glycerol: effect of glycerol addition on methane production and the microbial community.

The production of methane-rich biogas from the anaerobic digestion (AD) of microalgae is limited by an unfavorable biomass carbon-to-nitrogen (C/N) ratio; however, this may be ameliorated using a co-digestion strategy with carbon-rich feedstocks. For reliable plant operation, and to improve the economics of the process, secure co-feedstock supply (ideally as a waste-stream) is important. To this end, this study investigated the feasibility of co-digesting microalgae (Chlorella vulgaris) with potato processing waste (potato discarded parts, PPWdp; potato peel, PPWp) and glycerol, while monitoring the response of the methanogenic community. In this semi-continuous study, glycerol (1 and 2% v/v) added to mixtures of C. vulgaris : PPWdp enhanced the specific methane yields the most, by 53-128%, whilst co-digestion with mixtures of C. vulgaris : PPWp enhanced the methane yields by 62-74%. The microbial communities diverged markedly over operational time, and to a lesser extent in response to glycerol addition. The acetoclast Methanosaeta was abundant in all treatments but was replaced by Methanosarcina in the potato peel with glycerol treatment due to volatile fatty acid (VFA) accumulation. Our findings demonstrate that the performance of microalgae co-digestion is substantially improved by the addition of glycerol as an additional co-feedstock. This should improve the economic case for anaerobically digesting microalgae as part of wastewater treatment processes and/or the terminal step of a microalgae biorefinery.

Molecular microbial ecology of stable versus failing rice straw anaerobic digesters.

Waste rice straw (RS) is generated in massive quantities around the world and is often burned, creating greenhouse gas and air quality problems. Anaerobic digestion (AD) may be a better option for RS management, but RS is presumed to be comparatively refractory under anaerobic conditions without pre-treatment or co-substrates. However, this presumption assumes frequent reactor feeding regimes but less frequent feeding may be better for RS due to slow hydrolysis rates. Here, we assess how feeding frequency (FF) and organic loading rate (OLR) impacts microbial communities and biogas production in RS AD reactors. Using 16S rDNA amplicon sequencing and bioinformatics, microbial communities from five bench-scale bioreactors were characterized. At low OLR (1.0 g VS l-1  day-1 ), infrequently fed units (once every 21 days) had higher specific biogas yields than more frequent feeding (five in 7 days), although microbial community diversities were statistically similar (P > 0.05; ANOVA with Tukey comparison). In contrast, an increase in OLR to 2.0 g VS l-1  day-1 significantly changed Archaeal and fermenting Eubacterial sub-communities and the least frequency fed reactors failed. 'Stable' reactors were dominated by Methanobacterium, Methanosarcina and diverse Bacteroidetes, whereas 'failed' reactors saw shifts towards Clostridia and Christensenellaceae among fermenters and reduced methanogen abundances. Overall, OLR impacted RS AD microbial communities more than FF. However, combining infrequent feeding and lower OLRs may be better for RS AD because of higher specific yields.