Spatial-temporal targeted and non-targeted surveys to assess microbiological composition of drinking water in Puerto Rico following Hurricane Maria

Long-term impact of Hurricane Maria on point-of-use drinking water quality in Puerto Rico and associated potential adverse health effects

Drinking water security in Puerto Rico (PR) is increasingly challenged by both regulated and emerging anthropogenic contaminants, which was exacerbated by the Hurricane Maria (HM) due to impaired regional water cycle and damaged water infrastructure. Leveraging the NIEHS PROTECT (Puerto Rico Testsite for Exploring Contamination Threats) cohort, this study assessed the long-term tap water (TW) quality changes from March 2018 to November 2018 after HM in PR, by innovatively integrating two different effect-based quantitative toxicity assays with a targeted analysis of 200 organic and 22 inorganic pollutants. Post-hurricane PR TW quality showed recovery after >6-month period as indicated by the decreased number of contaminants showing elevated average concentrations relative to pre-hurricane samples, with significant difference of both chemical and toxicity levels between northern and southern PR. Molecular toxicity profiling and correlation revealed that the HM-accelerated releases of certain pesticides and PPCPs could exert increased cellular oxidative and/or AhR (aryl hydrocarbon receptor)-mediated activities that may persist for more than six months after HM. Maximum cumulative ratio and adverse outcome pathway (AOP) assessment identified the top ranked detected TW contaminants (Cu, Sr, V, perfluorooctanoic acid) that potentially associated with different adverse health effects such as inflammation, impaired reproductive systems, cancers/tumors, and/or organ toxicity. These insights can be incorporated into the regulatory framework for post-disaster risk assessment, guiding water quality control and management for public health protection.

New Drinking Water Genome Catalog Identifies a Globally Distributed Bacterial Genus Adapted to Disinfected Drinking Water Systems

Genome-resolved insights into the structure and function of the drinking water microbiome can advance the effective management of drinking water quality. To enable this, we constructed and curated thousands of metagenome-assembled and isolate genomes from drinking water distribution systems globally to develop a Drinking Water Genome Catalog (DWGC). The current DWGC disproportionately represents disinfected drinking water systems due to a paucity of metagenomes from nondisinfected systems. Using the DWGC, we identify core genera of the drinking water microbiome including a genus (UBA4765) within the order Rhizobiales that is frequently detected and highly abundant in disinfected drinking water systems. We demonstrate that this genus has been widely detected but incorrectly classified in previous amplicon sequencing-based investigations of the drinking water microbiome. Further, we show that a single genome variant (genomovar) within this genus is detected in 75% of drinking water systems included in this study. We propose a name for this uncultured bacterium as "Raskinella chloraquaticus" and describe the genus as "Raskinella" (endorsed by SeqCode). Metabolic annotation and modeling-based predictions indicate that this bacterium is capable of necrotrophic growth, is able to metabolize halogenated compounds, proliferates in a biofilm-based environment, and shows clear indications of disinfection-mediated selection.

Are Pathogenic Leptospira Species Ubiquitous in Urban Recreational Parks in Sydney, Australia?

Leptospirosis is a zoonotic disease caused by the spirochete bacteria Leptospira spp. From December 2017 to December 2023, a total of 34 canine leptospirosis cases were reported in urban Sydney, Australia. During the same spatio-temporal frame, one locally acquired human case was also reported. As it was hypothesised that human residents and companion dogs might both be exposed to pathogenic Leptospira in community green spaces in Sydney, an environmental survey was conducted from December 2023 to January 2024 to detect the presence of pathogenic Leptospira DNA in multipurpose, recreational public parks in the council areas of the Inner West and City of Sydney, Australia. A total of 75 environmental samples were collected from 20 public parks that were easily accessible by human and canine visitors. Quantitative PCR (qPCR) testing targeting pathogenic and intermediate Leptospira spp. was performed, and differences in detection of Leptospira spp. between dog-allowed and dog-prohibited areas were statistically examined. The global Moran's Index was calculated to identify any spatial autocorrelation in the qPCR results. Pathogenic leptospires were detected in all 20 parks, either in water or soil samples (35/75 samples). Cycle threshold (Ct) values were slightly lower for water samples (Ct 28.52-39.10) compared to soil samples (Ct 33.78-39.77). The chi-squared test and Fisher's exact test results were statistically non-significant (p > 0.05 for both water and soil samples), and there was no spatial autocorrelation detected in the qPCR results (p > 0.05 for both sample types). Although further research is now required, our preliminary results indicate the presence of pathogenic Leptospira DNA and its potential ubiquity in recreational parks in Sydney.

A novel culture-enriched metagenomic sequencing strategy effectively guarantee the microbial safety of drinking water by uncovering the low abundance pathogens

Assessing the presence of waterborne pathogens and antibiotic resistance genes (ARGs) is crucial for managing the environmental quality of drinking water sources. However, detecting low abundance pathogens in such settings is challenging. In this study, a workflow was developed to enrich for broad spectrum pathogens from drinking water samples. A mock community was used to evaluate the effectiveness of various enrichment broths in detecting low-abundance pathogens. Monthly metagenomic surveillance was conducted in a drinking water source from May to September 2021, and water samples were subjected to five enrichment procedures for 6 h to recover the majority of waterborne bacterial pathogens. Oxford Nanopore Technology (ONT) was used for metagenomic sequencing of enriched samples to obtain high-quality pathogen genomes. The results showed that selective enrichment significantly increased the proportions of targeted bacterial pathogens. Compared to direct metagenomic sequencing of untreated water samples, targeted enrichment followed by ONT sequencing significantly improved the detection of waterborne pathogens and the quality of metagenome-assembled genomes (MAGs). Eighty-six high-quality MAGs, including 70 pathogen MAGs, were obtained from ONT sequencing, while only 12 MAGs representing 10 species were obtained from direct metagenomic sequencing of untreated water samples. In addition, ONT sequencing improved the recovery of mobile genetic elements and the accuracy of phylogenetic analysis. This study highlights the urgent need for efficient methodologies to detect and manage microbial risks in drinking water sources. The developed workflow provides a cost-effective approach for environmental management of drinking water sources with microbial risks. The study also uncovered pathogens that were not detected by traditional methods, thereby advancing microbial risk management of drinking water sources.

Infants exposed in utero to Hurricane Maria have gut microbiomes with reduced diversity and altered metabolic capacity

The gut microbiome is a potentially important mechanism that links prenatal disaster exposures with increased disease risks. However, whether prenatal disaster exposures are associated with alterations in the infant's gut microbiome remains unknown. We established a birth cohort study named Hurricane as the Origin of Later Alterations in Microbiome (HOLA) after Hurricane Maria struck Puerto Rico in 2017. We enrolled vaginally born Latino term infants aged 2 to 6 months, including n = 29 infants who were exposed in utero to Hurricane Maria in Puerto Rico and n = 34 infants who were conceived at least 5 months after the hurricane as controls. Shotgun metagenomic sequencing was performed on infant stool swabs. Infants exposed in utero to Hurricane Maria had a reduced diversity in their gut microbiome compared to the control infants, which was mainly seen in the exclusively formula-fed group (P = 0.02). Four bacterial species, including Bacteroides vulgatus, Clostridium innocuum, Bifidobacterium pseudocatenulatum, and Clostridium neonatale, were depleted in the exposure group compared to the control group. Compositional differences in the microbial community and metabolic genes between the exposure and control groups were significant, which were driven by the formula feeding group (P = 0.02 for the microbial community and P = 0.008 for the metabolic genes). Metabolic modules involved in carbohydrate metabolism were reduced in the exposure group. Prenatal maternal exposure to Hurricane Maria was associated with a reduced gut commensal and an altered microbial composition and metabolic potential in the offspring's gut. Breastfeeding can adjust the composition of the gut microbiomes of exposed infants. IMPORTANCE Climate change is a serious issue that is affecting human health. With more frequent and intense weather disasters due to climate change, there is an urgent need to evaluate and understand the impacts of prenatal disaster exposures on the offspring. The prenatal stage is a particularly vulnerable stage for disease origination. However, the impact of prenatal weather disaster exposures on the offspring's gut microbiome has not been evaluated. Our HOLA study starts to fill this knowledge gap and provides novel insights into the microbiome as a mechanism that links prenatal disaster exposures with elevated disease risks. Our major finding that reduced microbial diversity and altered metabolic capacity are associated with prenatal hurricane exposures warrants further studies to evaluate the impact of weather disasters on the unborn.

Identifying Eukaryotes and Factors Influencing Their Biogeography in Drinking Water Metagenomes

The biogeography of eukaryotes in drinking water systems is poorly understood relative to that of prokaryotes or viruses, limiting the understanding of their role and management. A challenge with studying complex eukaryotic communities is that metagenomic analysis workflows are currently not as mature as those that focus on prokaryotes or viruses. In this study, we benchmarked different strategies to recover eukaryotic sequences and genomes from metagenomic data and applied the best-performing workflow to explore the factors affecting the relative abundance and diversity of eukaryotic communities in drinking water distribution systems (DWDSs). We developed an ensemble approach exploiting k-mer- and reference-based strategies to improve eukaryotic sequence identification and identified MetaBAT2 as the best-performing binning approach for their clustering. Applying this workflow to the DWDS metagenomes showed that eukaryotic sequences typically constituted small proportions (i.e., <1%) of the overall metagenomic data with higher relative abundances in surface water-fed or chlorinated systems with high residuals. The α and β diversities of eukaryotes were correlated with those of prokaryotic and viral communities, highlighting the common role of environmental/management factors. Finally, a co-occurrence analysis highlighted clusters of eukaryotes whose members' presence and abundance in DWDSs were affected by disinfection strategies, climate conditions, and source water types.

A snapshot of the global drinking water virome: Diversity and metabolic potential vary with residual disinfectant use

Viruses are important drivers of microbial community ecology and evolution, influencing microbial mortality, metabolism, and horizontal gene transfer. However, the effects of viruses remain largely unknown in many environments, including in drinking water systems. Drinking water metagenomic studies have offered a whole community perspective of bacterial impacts on water quality, but have not yet considered the influences of viruses. In this study, we address this gap by mining viral DNA sequences from publicly available drinking water metagenomes from distribution systems in six countries around the world. These datasets provide a snapshot of the taxonomic diversity and metabolic potential of the global drinking water virome; and provide an opportunity to investigate the effects of geography, climate, and drinking water treatment practices on viral diversity. Both environmental conditions and differences in sample processing were found to influence the viral composition. Using free chlorine as the residual disinfectant was associated with clear differences in viral taxonomic diversity and metabolic potential, with significantly fewer viral populations and less even viral community structures than observed in distribution systems without residual disinfectant. Additionally, drinking water viruses carry antibiotic resistance genes (ARGs), as well as genes to survive oxidative stress and nitrogen limitation. Through this study, we have demonstrated that viral communities are diverse across drinking water systems and vary with the use of residual disinfectant. Our findings offer directions for future research to develop a more robust understanding of how virus-bacteria interactions in drinking water distribution systems affect water quality.

Clean and Safe Drinking Water Systems via Metagenomics Data and Artificial Intelligence: State-of-the-Art and Future Perspective

The use of next-generation sequencing technologies in drinking water distribution systems (DWDS) has shed insight into the microbial communities' composition, and interaction in the drinking water microbiome. For the past two decades, various studies have been conducted in which metagenomics data have been collected over extended periods and analyzed spatially and temporally to understand the dynamics of microbial communities in DWDS. In this literature review, we outline the findings which were reported in the literature on what kind of occupancy-abundance patterns are exhibited in the drinking water microbiome, how the drinking water microbiome dynamically evolves spatially and temporally in the distribution networks, how different microbial communities co-exist, and what kind of clusters exist in the drinking water ecosystem. While data analysis in the current literature concerns mainly with confirmatory and exploratory questions pertaining to the use of metagenomics data for the analysis of DWDS microbiome, we present also future perspectives and the potential role of artificial intelligence (AI) and mechanistic models to address the predictive and mechanistic questions. The integration of meta-omics, AI, and mechanistic models transcends metagenomics into functional metagenomics, enabling deterministic understanding and control of DWDS for clean and safe drinking water systems of the future.