The Composition of Microbial Communities in Six Streams, and Its Association With Environmental Conditions, and Foodborne Pathogen Isolation

Nature-based bioreactors: Tackling antibiotic resistance in urban wastewater treatment

The overuse and misuse of antibiotics have accelerated the selection of antibiotic-resistant bacteria, significantly impacting human, animal, and environmental health. As aquatic environments are vulnerable to antibiotic resistance, suitable management practices should be adopted to tackle this phenomenon. Here we show an effective, nature-based solution for reducing antibiotic resistance from actual wastewater. We utilize a bioreactor that relies on benthic (biofilms) and planktonic microbial communities to treat secondary effluent from a small urban wastewater treatment plant (<10,000 population equivalent). This treated effluent is eventually released into the local aquatic ecosystem. We observe high removal efficiency for genes that provide resistance to commonly used antibiotic families, as well as for mobile genetic elements that could potentially aid in their spread. Importantly, we notice a buildup of sulfonamide (sul1 and sul2) and tetracycline (tet(C), tet(G), and tetR) resistance genes specifically in biofilms. This advancement marks the initial step in considering this bioreactor as a nature-based, cost-effective tertiary treatment option for small UWWTPs facing antibiotic resistance challenges.

Prevalence of STEC virulence markers and Salmonella as a function of abiotic factors in agricultural water in the southeastern United States

Fresh produce can be contaminated by enteric pathogens throughout crop production, including through contact with contaminated agricultural water. The most common outbreaks and recalls in fresh produce are due to contamination by Salmonella enterica and Shiga toxin-producing E. coli (STEC). Thus, the objectives of this study were to investigate the prevalence of markers for STEC (wzy, hly, fliC, eaeA, rfbE, stx-I, stx-II) and Salmonella (invA) in surface water sources (n = 8) from produce farms in Southwest Georgia and to determine correlations among the prevalence of virulence markers for STEC, water nutrient profile, and environmental factors. Water samples (500 mL) from eight irrigation ponds were collected from February to December 2021 (n = 88). Polymerase chain reaction (PCR) was used to screen for Salmonella and STEC genes, and Salmonella samples were confirmed by culture-based methods. Positive samples for Salmonella were further serotyped. Particularly, Salmonella was detected in 6/88 (6.81%) water samples from all ponds, and the following 4 serotypes were detected: Saintpaul 3/6 (50%), Montevideo 1/6 (16.66%), Mississippi 1/6 (16.66%), and Bareilly 1/6 (16.66%). Salmonella isolates were only found in the summer months (May-Aug.). The most prevalent STEC genes were hly 77/88 (87.50%) and stx-I 75/88 (85.22%), followed by fliC 54/88 (61.63%), stx-II 41/88 (46.59%), rfbE 31/88 (35.22%), and eaeA 28/88 (31.81%). The wzy gene was not detected in any of the samples. Based on a logistic regression analysis, the odds of codetection for STEC virulence markers (stx-I, stx-II, and eaeA) were negatively correlated with calcium and relative humidity (p < 0.05). A conditional forest analysis was performed to assess predictive performance (AUC = 0.921), and the top predictors included humidity, nitrate, calcium, and solar radiation. Overall, information from this research adds to a growing body of knowledge regarding the risk that surface water sources pose to produce grown in subtropical environmental conditions and emphasizes the importance of understanding the use of abiotic factors as a holistic approach to understanding the microbial quality of water.

Unraveling the rate-limiting step in microorganisms' mediation of denitrification and phosphorus absorption/transport processes in a highly regulated river-lake system

River-lake ecosystems are indispensable hubs for water transfers and flow regulation engineering, which have frequent and complex artificial hydrological regulation processes, and the water quality is often unstable. Microorganisms usually affect these systems by driving the nutrient cycling process. Thus, understanding the key biochemical rate-limiting steps under highly regulated conditions was critical for the water quality stability of river-lake ecosystems. This study investigated how the key microorganisms and genes involving nitrogen and phosphorus cycling contributed to the stability of water by combining 16S rRNA and metagenomic sequencing using the Dongping river-lake system as the case study. The results showed that nitrogen and phosphorus concentrations were significantly lower in lake zones than in river inflow and outflow zones (p < 0.05). Pseudomonas, Acinetobacter, and Microbacterium were the key microorganisms associated with nitrate and phosphate removal. These microorganisms contributed to key genes that promote denitrification (nirB/narG/narH/nasA) and phosphorus absorption and transport (pstA/pstB/pstC/pstS). Partial least squares path modeling (PLS-PM) revealed that environmental factors (especially flow velocity and COD concentration) have a significant negative effect on the key microbial abundance (p < 0.001). Our study provides theoretical support for the effective management and protection of water transfer and the regulation function of the river-lake system.

Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams

The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.

Microbiome response in an urban river system is dominated by seasonality over wastewater treatment upgrades

BACKGROUND

Microorganisms such as coliform-forming bacteria are commonly used to assess freshwater quality for drinking and recreational use. However, such organisms do not exist in isolation; they exist within the context of dynamic, interactive microbial communities which vary through space and time. Elucidating spatiotemporal microbial dynamics is imperative for discriminating robust community changes from ephemeral ecological trends, and for improving our overall understanding of the relationship between microbial communities and ecosystem health. We conducted a seven-year (2013-2019) microbial time-series investigation in the Chicago Area Waterways (CAWS): an urban river system which, in 2016, experienced substantial upgrades to disinfection processes at two wastewater reclamation plants (WRPs) that discharge into the CAWS and improved stormwater capture, to improve river water quality and reduce flooding. Using culture-independent and culture-dependent approaches, we compared CAWS microbial ecology before and after the intervention.

RESULTS

Examinations of time-resolved beta distances between WRP-adjacent sites showed that community similarity measures were often consistent with the spatial orientation of site locations to one another and to the WRP outfalls. Fecal coliform results suggested that upgrades reduced coliform-associated bacteria in the effluent and the downstream river community. However, examinations of whole community changes through time suggest that the upgrades did little to affect overall riverine community dynamics, which instead were overwhelmingly driven by yearly patterns consistent with seasonality.

CONCLUSIONS

This study presents a systematic effort to combine 16S rRNA gene amplicon sequencing with traditional culture-based methods to evaluate the influence of treatment innovations and systems upgrades on the microbiome of the Chicago Area Waterway System, representing the longest and most comprehensive characterization of the microbiome of an urban waterway yet attempted. We found that the systems upgrades were successful in improving specific water quality measures immediately downstream of wastewater outflows. Additionally, we found that the implementation of the water quality improvement measures to the river system did not disrupt the overall dynamics of the downstream microbial community, which remained heavily influenced by seasonal trends. Such results emphasize the dynamic nature of microbiomes in open environmental systems such as the CAWS, but also suggest that the seasonal oscillations remain consistent even when perturbed.

It’s a Long Way to the Tap: Microbiome and DNA-Based Omics at the Core of Drinking Water Quality

Microbial communities interact with us and affect our health in ways that are only beginning to be understood. Microorganisms have been detected in every ecosystem on Earth, as well as in any built environment that has been investigated. Drinking water sources, drinking water treatment plants and distribution systems provide peculiar microbial ecological niches, dismantling the belief of the “biological simplicity” of drinking water. Nevertheless, drinking water microbiomes are understudied compared to other microbiomes. Recent DNA sequencing and meta-omics advancements allow a deeper understanding of drinking water microbiota. Thus, moving beyond the limits of day-to-day testing for specific pathogenic microbes, new approaches aim at predicting microbiome changes driven by disturbances at the macro-scale and overtime. This will foster an effective and proactive management of water sources, improving the drinking water supply system and the monitoring activities to lower public health risk. Here, we want to give a new angle on drinking water microbiome research. Starting from a selection of 231 scientific publications on this topic, we emphasize the value of biodiversity in drinking water ecosystems and how it can be related with industrialization. We then discuss how microbiome research can support sustainable drinking water management, encouraging collaborations across sectors and involving the society through responsible research and innovation.

Aquatic Ecosystems of the Anthropocene: Limnology and Microbial Ecology of Mine Pit Lakes

Mine pit lakes ('pit lakes') are new aquatic ecosystems of the Anthropocene. Potentially hundreds of meters deep, these lakes are prominent in the landscape and in the public consciousness. However, the ecology of pit lakes is underrepresented in the literature. The broad goal of this research was to determine the environmental drivers of pelagic microbe assemblages in Australian coal pit lakes. The overall experimental design was four lakes sampled three times, top and bottom, in 2019. Instrument chains were installed in lakes and measurements of in situ water quality and water samples for metals, metalloids, nutrients and microbe assemblage were collected. Lakes were monomictic and the timing of mixing was influenced by high rainfall events. Water quality and microbial assemblages varied significantly across space and time, and most taxa were rare. Lakes were moderately saline and circumneutral; Archeans were not prevalent. Richness also varied by catchment. Microbial assemblages correlated to environmental variables, and no one variable was consistently significant, spatially or temporally. Study lakes were dominated by 'core' taxa exhibiting temporal turnover likely driven by geography, water quality and interspecific competition, and the presence of water chemistry associated with an artificial aquifer likely influenced microbial community composition. Pit lakes are deceptively complex aquatic ecosystems that host equally complex pelagic microbial communities. This research established links between microbial assemblages and environmental variables in pit lakes and determined core communities; the first steps towards developing a monitoring program using microbes.

Salmonella Genomics and Population Analyses Reveal High Inter- and Intraserovar Diversity in Freshwater

Freshwater can support the survival of the enteric pathogen Salmonella, though temporal Salmonella diversity in a large watershed has not been assessed. At 28 locations within the Susquehanna River basin, 10-liter samples were assessed in spring and summer over 2 years. Salmonella prevalence was 49%, and increased river discharge was the main driver of Salmonella presence. The amplicon-based sequencing tool, CRISPR-SeroSeq, was used to determine serovar population diversity and detected 25 different Salmonella serovars, including up to 10 serovars from a single water sample. On average, there were three serovars per sample, and 80% of Salmonella-positive samples contained more than one serovar. Serovars Give, Typhimurium, Thompson, and Infantis were identified throughout the watershed and over multiple collections. Seasonal differences were evident: serovar Give was abundant in the spring, whereas serovar Infantis was more frequently identified in the summer. Eight of the ten serovars most commonly associated with human illness were detected in this study. Crucially, six of these serovars often existed in the background, where they were masked by a more abundant serovar(s) in a sample. Serovars Enteritidis and Typhimurium, especially, were masked in 71 and 78% of samples where they were detected, respectively. Whole-genome sequencing-based phylogeny demonstrated that strains within the same serovar collected throughout the watershed were also very diverse. The Susquehanna River basin is the largest system where Salmonella prevalence and serovar diversity have been temporally and spatially investigated, and this study reveals an extraordinary level of inter- and intraserovar diversity.IMPORTANCESalmonella is a leading cause of bacterial foodborne illness in the United States, and outbreaks linked to fresh produce are increasing. Understanding Salmonella ecology in freshwater is of importance, especially where irrigation practices or recreational use occur. As the third largest river in the United States east of the Mississippi, the Susquehanna River is the largest freshwater contributor to the Chesapeake Bay, and it is the largest river system where Salmonella diversity has been studied. Rainfall and subsequent high river discharge rates were the greatest indicators of Salmonella presence in the Susquehanna and its tributaries. Several Salmonella serovars were identified, including eight commonly associated with foodborne illness. Many clinically important serovars were present at a low frequency within individual samples and so could not be detected by conventional culture methods. The technologies employed here reveal an average of three serovars in a 10-liter sample of water and up to 10 serovars in a single sample.

Correlation of Salmonella enterica and Listeria monocytogenes in Irrigation Water to Environmental Factors, Fecal Indicators, and Bacterial Communities

Outbreaks of foodborne illnesses linked to fresh fruits and vegetables have been key drivers behind a wide breadth of research aiming to fill data gaps in our understanding of the total ecology of agricultural water sources such as ponds and wells and the relationship of this ecology to foodborne pathogens such as Salmonella enterica and Listeria monocytogenes. Both S. enterica and L. monocytogenes can persist in irrigation water and have been linked to produce contamination events. Data describing the abundance of these organisms in specific agricultural water sources are valuable to guide water treatment measures. Here, we profiled the culture independent water microbiota of four farm ponds and wells correlated with microbiological recovery of S. enterica (prevalence: pond, 19.4%; well, 3.3%), L. monocytogenes (pond, 27.1%; well, 4.2%) and fecal indicator testing. Correlation between abiotic factors, including water parameters (temperature, pH, conductivity, dissolved oxygen percentage, oxidation reduction potential, and turbidity) and weather (temperature and rainfall), and foodborne pathogens were also evaluated. Although abiotic factors did not correlate with recovery of S. enterica or L. monocytogenes (p > 0.05), fecal indicators were positively correlated with incidence of S. enterica in well water. Bacterial taxa such as Sphingomonadaceae and Hymenobacter were positively correlated with the prevalence and population of S. enterica, and recovery of L. monocytogenes was positively correlated with the abundance of Rhizobacter and Comamonadaceae (p < 0.03). These data will support evolving mitigation strategies to reduce the risk of produce contamination by foodborne pathogens through irrigation.