Source Tracking Tools Research Articles

Integrating source tracking tools for marine shellfish tracing: Metataxonomic and isotopic analyses

Shellfish farming is a significant activity in the Northern Adriatic Sea, particularly in Italy, where it plays a crucial role in the European industry of live bivalve mollusks. This study aimed to exploit different analyses, including metataxonomy and isotopic profiling, to address these issues. Isotopic analysis showed differences between habitats, with lower δ15N in sea samples, likely indicating lesser anthropogenic impact compared to lagoon environments. Additionally, seasonal variations in δ15N, δ13C and C/N highlighted differences in primary productivity and organic matter sources across seasons. Metataxonomic analysis revealed significant variability in microbial taxa across harvesting habitats and seasons. While no marked differences were observed in alpha and beta diversity based on habitat, distinct seasonal clustering indicated the influence of environmental factors on microbial community structure. Gammaproteobacteria were prevalent across habitats and seasons, with notable fluctuations in other taxa, such as Alphaproteobacteria, Epsilonproteobacteria, Flavobacteria, and Bacteroidia, particularly in lagoon habitats during winter. Overall, this study highlights the potential of microbial and isotopic characterization for tracing the origin of shellfish products. Metataxonomic analysis identified microbial signatures indicative of habitat and season, with implications for ecological dynamics and consumer safety. Isotopic analysis provided information on habitat characteristics and harvesting seasons, enhancing product traceability.

Pepper mild mottle virus as an effective tool in microbial source tracking for deficient domestic on-site water treatment systems

Pollution from domestic on-site wastewater treatment systems (OWTS) is a significant contaminant pressure in many rural catchments. However, due to their design, and dispersed proliferation, it is difficult to assess their impact. Water testing methodologies employ bacterial culturing methods and chemical analysis which may lose resolution and/or specificity being confounded by diffuse agricultural sources within a rural environment. In this study, we successfully assessed the applicability of Pepper Mild Mottle Virus (PMMoV) as a human faecal source tracker for deficient on-site wastewater treatment systems. The transport of PMMoV was first studied in the effluent of a 30 cm deep soil column which was dosed for 510 days with primary influent from a conventional septic system. The removal of PMMoV through the 30 cm deep soil column was quantified with a 5-day seeding trial employing primary influent mixed with PMMoV sourced from Tabasco pepper product ®. The trial was then carried out at field scale with the seeding solution dosed into an operational percolation trench receiving septic tank effluent which had been instrumented for porewater sampling. Samples were taken at depths of 10 cm, 30 cm, and 50 cm across the length of the trench at distances of 1 m, 7.5 m, and 17.5 m from the inlet of the trench. PMMoV was detected on all days of the trial, with a peak concentration of 1 × 106 found at the rear of the trench on day 2 of the seeding trial. Finally, to assess the effectiveness of PPMoV as a microbial source tracking tool from a water receptor perspective, three rural catchments with high densities of OWTSs were sampled and analysed for hourly variations in biological parameters which included total coliforms, Escherichia coli, PMMoV, and chemical parameters total organic carbon, total nitrogen, and total carbon. PMMoV was detected in all river samples over a 24-hour period, thereby indicating its suitability as a tracer of human wastewater effluent in such environments with multiple diffuse sources.

Integrated assessment of groundwater contamination: A multi-marker approach for comprehensive water quality monitoring

Abstract This study investigated the presence of adenoviral markers (Library-Independent microbial source tracking tool) in the groundwater of Ludhiana, Punjab, India, a hitherto unexplored area. While deep aquifers post-chlorination were adenovirus free, shallow aquifers near farm wastewater pits exhibited human adenovirus and bovine adenovirus. Coliform-negative samples also harboured pathogens, highlighting the limitations of conventional indicators. Surface water displayed higher viral contamination, potentially impacting groundwater. The use of farm wastewater for irrigation and open pit disposal emerged as key contributors, advocating for sustainable wastewater management. Physiochemical and microbial analyses revealed variations across sites, emphasising regional and temporal variations. The weighted arithmetic water quality index ranged from good to very poor, with deep aquifers showing better quality than shallow ones. A novel approach incorporating graphical representations of adenovirus estimations alongside water quality index provided a more comprehensive understanding. Intriguingly, the study revealed the presence of coliforms irrespective of water quality grade, questioning its reliability as a sole indicator. Correlations between specific water quality grades and adenovirus types suggested targeted control measures. The lack of significant correlations between viral markers and conventional parameters in groundwater compared to surface water studies highlighted the unique dynamics of groundwater contamination.

Sequence introgression from exogenous lineages underlies genomic and biological differences among Cryptosporidium parvum IOWA lines

The IOWA strain of Cryptosporidium parvum is widely used in studies of the biology and detection of the waterborne pathogens Cryptosporidium spp. While several lines of the strain have been sequenced, IOWA-II, the only reference of the original subtype (IIaA15G2R1), exhibits significant assembly errors. Here we generated a fully assembled genome of IOWA-CDC of this subtype using PacBio and Illumina technologies. In comparative analyses of seven IOWA lines maintained in different laboratories (including two sequenced in this study) and 56 field isolates, IOWA lines (IIaA17G2R1) with less virulence had mixed genomes closely related to IOWA-CDC but with multiple sequence introgressions from IOWA-II and unknown lineages. In addition, the IOWA-IIaA17G2R1 lines showed unique nucleotide substitutions and loss of a gene associated with host infectivity, which were not observed in other isolates analyzed. These genomic differences among IOWA lines could be the genetic determinants of phenotypic traits in C. parvum. These data provide a new reference for comparative genomic analyses of Cryptosporidium spp. and rich targets for the development of advanced source tracking tools.

Fate, risk and sources of antibiotic resistome and its attenuation dynamics in the river water-sediment system: Field and microcosm study

Antibiotic resistance genes (ARGs) in rivers have received widespread attentions. Deciphering the fate and spread mechanisms of ARGs in river system can contribute to the design of effective strategies for reducing resistome risk in the environment. Although some studies have reported the prevalence and distribution of ARGs in rivers worldwide, few have systematically explored their fates, sources and risks in river water-sediment system. Also, the role of natural sunlight on the attenuation and fate of ARGs in river remains to be demonstrated. To fill the gaps, field investigation and microcosm experiment have been conducted in this study to reveal the fate, risk, source-sink relationship and attenuation dynamics of ARGs in an urban river water-sediment system, by utilizing high-throughput sequencing-based metagenomic assembly analysis and machine-learning-based source tracking tool. In all, 527 unique ARGs belonging to 29 antimicrobial classes were identified in the river. Relatively, the level of ARGs in the sediments were significantly higher (p < 0.05) than that in the waters. Variance partitioning analysis indicated the biotic and abiotic factors co-governed the riverine resistome, totally explaining 76% and 83% variations of ARGs in the waters and sediments, respectively. Microcosm experiment under natural light and dark condition showed that light induced the decay of ARGs in the waters and might promote their transfers from waters to sediments, which were also confirmed by the attenuation dynamics of bacteria in the experimental water-sediment system. Notably, the co-occurrences of ARGs with MGEs and VFs on the same contigs suggested resistome risk in the river, and relatively, the risk scores in the sediments were significantly higher (p < 0.05) than those in the waters. Source apportionment with metagenomic resistome signatures showed the Wenyu River was the most dominant contributor of ARGs in the downstream, with average contributions of 44.5% and 40.8% in the waters and sediments, respectively.

Evaluation of phenotypical and genotypical methods for the identification and typing of Stenotrophomonas maltophilia isolated from a pharmaceutical facility.

Evaluate methods for identification and typing of Stenotrophomonas maltophilia isolated from a pharmaceutical facility. From 270 S. maltophilia strains identified by VITEK®2, 40 were selected and submitted to MALDI TOF-MS, 16S and 23S rRNA gene analysis, ERIC-PCR, and antimicrobial susceptibility profile. 16S rRNA sequencing was able to identify 39 (97.5%) strains as Stenotrophomonas spp. and one (2.5%) as Luteimonas huabeiensis. MALDI TOF-MS identified 37 (92.5%) strains as S. maltophilia, and three (7.5%) were not identified. PCR targeting 23S rRNA yielded a positive result for 39 (97.5%) strains. However, after sequencing, two strains were identified as Stenotrophomonas rhizophila, showing false-positive results. The confirmed S. maltophilia strains (n=37) showed 35 distinct ERIC-PCR profiles and exhibited sensitivity to minocycline and levofloxacin, and six (16.3%) showed intermediate resistance to sulfamethoxazole-trimethoprim. MALDI-TOF MS was a satisfactory methodology for the identification of S. maltophilia, but expansion of the database is necessary for the identification of other species. 16S rDNA sequencing showed low resolution for Stenotrophomonas species differentiation. PCR targeting 23S rRNA could not differentiate S. maltophilia from S. rhizophila. ERIC-PCR was shown to be a useful tool for the microbial source tracking of S. maltophilia.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.Graphical abstract

Analysis of Causes and Sources of Summer Ozone Pollution in Rizhao Based on CMAQ and HYSPLIT Models

As a typical coastal city, O3 pollution in Rizhao has become increasingly serious in recent years. In order to explore the causes and sources of O3 pollution, IPR process analysis and ISAM source tracking tools based on the CMAQ model were used, respectively, to quantify the contributions of different physicochemical processes and different source tracking areas to O3 in Rizhao. Additionally, by comparing the differences between O3-exceeding days and non-exceeding days, combined with the HYSPLIT model, the regional transportation path of O3 in Rizhao was explored. The results showed that the concentrations of O3, NOx, and VOCs near the coastal areas of Rizhao and Lianyungang were significantly increased on O3 exceedance days compared with those on non-exceedance days. This was mainly because Rizhao was the convergence zone of western, southwestern, and eastern winds on exceedance days, which facilitated the transport and accumulation of pollutants. Process analysis showed that the transport process (TRAN) contribution to the near-surface O3 near the coastal areas of Rizhao and Lianyungang increased significantly on the exceedance days, whereas the contribution to most areas to the west of Linyi decreased. Photochemical reaction (CHEM) had a positive contribution to the O3 concentration in Rizhao during the daytime at all heights, and TRAN had a positive contribution at 0-60 m above the ground, and mainly had a negative contribution above 60 m. The contributions of CHEM and TRAN at 0-60 m above the ground would increase significantly on exceedance days, which was approximately twice that on the non-exceedance days. Source analysis showed that the local sources in Rizhao were the main contribution sources of NOx and VOCs, with the contribution rates of 47.5% and 58.0%, respectively. O3 mainly came from the contribution outside the simulation area (67.5%). The O3 and precursor contributions of the western cities of Rizhao (Weifang, Linyi, etc.) and the southern cities (Lianyungang, etc.) would increase significantly on the days of exceeding the standard. The transportation path analysis showed that the number of exceedances accounted for the largest proportion (11.8%) in the path from the west of Rizhao, which was the main transportation channel of O3 and precursors in Rizhao. This was verified through process analysis and source tracking results, and such trajectories accounted for 13.0% of the total number of trajectories, and their main routes were in the Shaanxi, Shanxi, Hebei, and Shandong regions.

Tracking the sources of dissolved organic matter under bio- and photo-transformation conditions using fluorescence spectrum-based machine learning techniques

Dissolved organic matter (DOM) from various sources can lead to environmental issues such as eutrophication in agricultural watersheds. Effective source-tracking tools are needed to implement proper management practices. Fluorescence excitation–emission matrix (EEM) spectroscopy has been widely used to probe DOM composition. We explored optimal fluorescence EEM-based machine learning (ML) tools to quantify the proportions of different DOM sources in mixture samples under natural transformation conditions. Bulk DOM samples were prepared from soil and compost at various ratios and treated to simulate biogeochemical transformations. ML models based on all the EEM data outperformed those based on defined fluorescence indices. The trained support vector regression model (SVR) outperformed the conventional source tracking method of end-member mixing analysis (EMMA) with an R2 of 0.88 versus 0.83. Among the five suitable ML algorithms tested, SVR explained 90% and 85% of the variability in the proportions of soil and compost sources in the DOM mixture, with the mean squared errors of 0.004 and 0.007, respectively. The predicted capacity revealed a close relationship or causality between the specific mixing ratios of the bulk samples and the EEM spectra. The ML technique with EEM data was not constrained by the identification of all major sources, which is a required condition for the EMMA method. This study highlights the significant potential of EEM-based ML for tracing the source of DOM and establishes a basis for the future development of EEM data-driven models capable of tracking multiple DOM sources, even in the absence of all possible end-members.

Assembly process and source tracking of microbial communities in sediments of Dongting Lake

Sediment source tracing can accurately provide a theoretical basis for controlling soil erosion effectively, by identifying the most serious types of land use. Traditional sediment tracing methods are based on physical, chemical, biological, and composite fingerprinting, which have not included microbes. As high-throughput sequencing becomes more prevalent, microorganisms can provide more information than what we think. Thus, whether the microorganism can also be used as a special fingerprint factor for sediment source identification during soil erosion, we have tested it by using microbial source tracking tool FEAST to quantify the microbe contribution from five types of eroded land (including dryland, urban, paddy field, forest and grassland) to the depositional areas (Niubitan) in the Yuanjiang basin. The source microbial community in the erosive area was heterogeneous, and assembly process analysis further demonstrated that the source tracking results could reach higher accuracy. The results of FEAST showed that dryland (35.50%), urban (17.21%), paddy field (8.14%), and forest (1.07%) were the major contributors to Niubitan. Our results follow the general soil erosion rules and prove its validity. Taken together, a new perspective is provided by these results for tracing sediment sources in erosion-sedimentary systems.

Prevalence and Characterization of CRISPR Locus 2.1 Spacers in Escherichia coli Isolates Obtained from Feces of Animals and Humans.

The clustered regularly interspaced short palindromic repeat (CRISPR) has been studied as an immune system in prokaryotes for the survival of bacteriophages. The CRISPR system in prokaryotes records the invasion of bacteriophages or other genetic materials in CRISPR loci. Accordingly, CRISPR loci can reveal a history of infection records of bacteriophages and other genetic materials. Therefore, identification of the CRISPR array may help trace the events that bacteria have undergone. In this study, we characterized and identified the spacers of the CRISPR loci in Escherichia coli isolates obtained from the feces of animals and humans. Most CRISPR spacers were found to stem from phages. Although we did not find any patterns in CRISPR spacers according to sources, our results showed that phage-derived spacers mainly originated from the families Inoviridae, Myoviridae, Podoviridae, and Siphoviridae and the order Caudovirales, whereas plasmid-derived CRISPR spacers were mainly from the Enterobacteriaceae family. In addition, it is worth noting that the isolates from each animal and human source harbored source-specific spacers. Considering that some of these taxa are likely found in the gut of mammalian animals, CRISPR spacers identified in these E. coli isolates were likely derived from the bacteriophageome and microbiome in closed gut environments. Although the bacteriophageome database limits the characterization of CRISPR arrays, the present study showed that some spacers were specifically found in both animal and human sources. Thus, this finding may suggest the possible use of E. coli CRISPR spacers as a microbial source tracking tool. IMPORTANCE We characterized spacers of CRISPR locus 2.1 in E. coli isolates obtained from the feces of various sources. Phage-derived CRISPR spacers are mainly acquired from the order Caudovirales, and plasmid-derived CRISPR spacers are mostly from the Enterobacteriaceae family. This is thought to reflect the microbiome and phageome of the gut environment of the sources. Hence, spacers may help track the encounter of bacterial cells with bacterial cells, viruses, or other genetic materials. Interestingly, source-specific spacers are also observed. The identification of source-specific spacers is thought to help develop the methodology of microbial source tracking and understanding the interactions between viruses and bacteria. However, very few spacers have been uncovered to track where they originate. The accumulation of genome sequences can help identify the hosts of spacers and can be applied for microbial source tracking.

Investigation of Human and Animal Viruses in Water Matrices from a Rural Area in Southeastern Region of Brazil and Their Potential Use as Microbial Source-Tracking Markers.

This study assessed the sources of contamination of water matrices in a rural area using detection of a host-specific virus (human adenovirus [HAdV], porcine adenovirus [PAdV] and bovine polyomaviruses [BoPyV]) as potential microbial source-tracking tool, and rotavirus A [RVA], given its epidemiological importance in Brazil. From July 2017 to June 2018, 92 samples were collected from eight points (P1-P8) of surface and raw waters in southeastern region of Brazil. Fifty-five (59.8%) were positive for HAdV, 41 (44.5%) for RVA, 10 (10.9%) for PAdV and four (4.3%) for BoPyV. HAdV and RVA were detected at all sites, and over the entire sampling period, PAdV was detected at a porcine breeding area and at Guarda River site, presenting high concentrations up to 2.6 × 109 genome copies per liter [GC/L], and viral concentrations ranging from 9.6 × 101 to 7.1 × 107, while BoPyV (1.5 × 104 GC/L-9.2 × 105 GC/L) was only detected in samples from the bovine breeding areas. The combination of human and animal virus circulation presents a potential impact in the environment due to raw sewage discharge from regional communities, as well as potential hazard to human and animal health.

MStrain: strain-level identification of Yersinia pestis using metagenomic data.

High-resolution target pathogen detection using metagenomic sequencing data represents a major challenge due to the low concentration of target pathogens in samples. We introduced mStrain, a novel Yesinia pestis strain/lineage-level identification tool that utilizes metagenomic data. mStrain successfully identified Y. pestis at the strain/lineage level by extracting sufficient information regarding single-nucleotide polymorphisms (SNPs), which can therefore be an effective tool for identification and source tracking of Y. pestis based on metagenomic data during a plague outbreak. . Assigning the reads in the metagenomic sequencing data to an exactly known or most closely representative Y. pestis strain. Assigning the reads in the metagenomic sequencing data to a specific lineage on the phylogenetic tree. The unique and typical SNPs present in all representative strains. An SNP is defined as the ancestor state when consistent with the allele of Yersinia pseudotuberculosis strain IP32953; otherwise, the SNP is defined as the derived state. The code for running mStrain, the test dataset, and instructions for running the code can be found at the following GitHub repository: https://github.com/xwqian1123/mStrain.

Shiga Toxin Subtypes, Serogroups, Phylogroups, RAPD Genotypic Diversity, and Select Virulence Markers of Shiga-Toxigenic Escherichia coli Strains from Goats in Mid-Atlantic US.

Understanding Shiga toxin subtypes in E. coli from reservoir hosts may give insight into their significance as human pathogens. The data also serve as an epidemiological tool for source tracking. We characterized Shiga toxin subtypes in 491 goat E. coli isolates (STEC) from the mid-Atlantic US region (stx1 = 278, stx2 = 213, and stx1/stx2 = 95). Their serogroups, phylogroups, M13RAPD genotypes, eae (intimin), and hly (hemolysin) genes were also evaluated. STEC-positive for stx1 harbored Stx1c (79%), stx1a (21%), and stx a/c (4%). Those positive for Stx2 harbored stx2a (55%) and Stx2b (32%), while stx2a/stx2d and stx2a/stx2b were each 2%. Among the 343 STEC that were serogrouped, 46% (n = 158) belonged to O8, 20% (n = 67) to 076, 12% (n = 42) to O91, 5% (n = 17) to O5, and 5% (n = 18) to O26. Less than 5% belonged to O78, O87, O146, and O103. The hly and eae genes were detected in 48% and 14% of STEC, respectively. Most belonged to phylogroup B1 (73%), followed by D (10%), E (8%), A (4%), B2 (4%), and F (1%). M13RAPD genotyping revealed clonality of 091, O5, O87, O103, and O78 but higher diversity in the O8, O76, and O26 serogroups. These results indicate goat STEC belonged to important non-O157 STEC serogroups, were genomically diverse, and harbored Shiga toxin subtypes associated with severe human disease.

Characterizing the chemical and microbial fingerprint of unsheltered homelessness in an urban watershed

Unsheltered homelessness is rapidly becoming a critical issue in many cities worldwide. The worsening situation not only highlights the socioeconomic plight, but it also raises awareness of ancillary issues such as the potential implications for urban water quality. The objective of this study was to simultaneously leverage diverse source tracking tools to develop a chemical and microbial fingerprint describing the relative contribution of direct human inputs into Las Vegas' tributary washes. By evaluating a wide range of urban water matrices using general water quality parameters, fecal indicator bacteria (FIB), human-associated microbial markers [e.g., HF183, crAssphage, and pepper mild mottle virus (PMMoV)], 16S rRNA gene sequencing data, and concentrations of 52 anthropogenic trace organic compounds (TOrCs), this study was able to differentiate principal sources of these constituents, including contributions from unsheltered homelessness. For example, HF183 (31% vs. 0%), crAssphage (61% vs. 5%), and PMMoV (72% vs. 55%) were more frequently detected in tributary washes with higher homeless census counts vs. ‘control’ tributary washes. Illicit drugs or their metabolites (e.g., heroin, acetylmorphine, amphetamine, and cocaine) and select TOrCs (e.g., acetaminophen, caffeine, ibuprofen, and naproxen) were also detected more frequently and at higher concentrations in the more anthropogenically-impacted washes. These data can be used to raise awareness of the shared interests between the broader community and those who are experiencing homelessness, notably the importance of protecting environmental health and water quality. Ultimately, this may lead to more rapid adoption of proven strategies for achieving functional zero homelessness, or at least additional resources for unsheltered individuals.

Developing a novel Bifidobacterium phage quantitative polymerase chain reaction-based assay for tracking untreated wastewater

Microbial source tracking (MST) tools provide insights on fecal pollution levels in aquatic environments using predominantly quantitative PCR (qPCR) assays that target host-associated molecular marker genes. Existing wastewater-associated marker genes have shown limited or significant cross-reactions with non-human fecal samples. In this study, we mined the current Gut Phage Database (GPD) and designed a novel untreated wastewater-specific Bifidobacterium phage qPCR assay (i.e., Bifi assay). The sensitivity and specificity of the Bifi marker genes were assessed by collectively analyzing untreated (n = 33) and treated (n = 15) wastewater and non-human fecal samples (i.e., Rabbit, mouse, cow, horse, pig, chicken, sheep, dog, deer, kangaroos; n = 113) in Shenzhen, Guangdong Province, China and Brisbane, Australia. Bifi assay revealed 100% host-specificity against non-human fecal samples collected from Shenzhen and Brisbane. Furthermore, this marker gene was also detected in all untreated and treated wastewater samples, whose concentrations ranged from 5.54 to 6.83 log10 GC/L. In Shenzhen, the concentrations of Bifi marker gene were approximately two orders of magnitude lower than Bacteroides (HF183/BacR287 assay) and CrAssphage (CPQ_56 assay). The concentration of Bifi marker gene in untreated wastewater from Brisbane was 1.35 log10 greater than those in Shenzhen. Our results suggest that Bifi marker gene has the potential to detect and quantify the levels of human fecal pollution in Shenzhen and Brisbane. If additional detection sensitivity is required for environmental studies, Bifi marker gene should be paired with either CrAssphage or HF183/BacR287 marker genes.

A Combined Digital PCR and Next Generation DNA-Sequencing Based Approach for Tracking Nearshore Pollutant Dynamics Along the Southwest United States/Mexico Border.

Ocean currents, multiple fecal bacteria input sources, and jurisdictional boundaries can complicate pollution source tracking and associated mitigation and management efforts within the nearshore coastal environment. In this study, multiple microbial source tracking tools were employed to characterize the impact and reach of an ocean wastewater treatment facility discharge in Mexico northward along the coast and across the Southwest United States- Mexico Border. Water samples were evaluated for fecal indicator bacteria (FIB), Enterococcus by culture-based methods, and human-associated genetic marker (HF183) and Enterococcus by droplet digital polymerase chain reaction (ddPCR). In addition, 16S rRNA gene sequence analysis was performed and the SourceTracker algorithm was used to characterize the bacterial community of the wastewater treatment plume and its contribution to beach waters. Sampling dates were chosen based on ocean conditions associated with northern currents. Evidence of a gradient in human fecal pollution that extended north from the wastewater discharge across the United States/Mexico border from the point source was observed using human-associated genetic markers and microbial community analysis. The spatial extent of fecal contamination observed was largely dependent on swell and ocean conditions. These findings demonstrate the utility of a combination of molecular tools for understanding and tracking specific pollutant sources in dynamic coastal water environments.

Occurrence and distribution of microbial pollutants in coastal areas of the Adriatic Sea influenced by river discharge

The transport of a variety of pollutants from agricultural, industrial and urbanised areas makes rivers major contributors to the contamination of coastal marine environments. Too little is known of their role in carrying pathogens to the coast. We used DNA-based metabarcoding data to describe the microbial community composition in seawater and sediment collected in front of the estuary of the Tronto, the Chienti and the Esino, three Italian rivers with different pollution levels that empty into the north-central Adriatic Sea, and to detect and measure within these communities the relative abundance of microbial pollutants, including traditional faecal indicators and alternative faecal and sewage-associated pollutants. We then applied the FORENSIC algorithm to distinguish human from non-human sources of microbial pollution and FAPROTAX to map prokaryotic clades to established metabolic or other ecologically relevant functions. Finally, we searched the dataset for other common pathogenic taxa. Seawater and sediment contained numerous potentially pathogenic bacteria, mainly faecal and sewage-associated. The samples collected in front of the Tronto estuary showed the highest level of contamination, likely sewage-associated. The pathogenic signature showed a weak but positive correlation with some nutrients and strong correlations with some polycyclic aromatic hydrocarbons. This study confirms that rivers transport pathogenic bacteria to the coastal sea and highlights the value of expanding the use of HTS data, source tracking and functional identification tools to detect microbial pollutants and identify their sources with a view to gaining a better understanding of the pathways of sewage-associated discharges to the sea.

Evaluation of Low-Cost Phage-Based Microbial Source Tracking Tools for Elucidating Human Fecal Contamination Pathways in Kolkata, India.

Phages, such as those infecting Bacteroides spp., have been proven to be reliable indicators of human fecal contamination in microbial source tracking (MST) studies, and the efficacy of these MST markers found to vary geographically. This study reports the application and evaluation of candidate MST methods (phages infecting previously isolated B. fragilis strain GB-124, newly isolated Bacteroides strains (K10, K29, and K33) and recently isolated Kluyvera intermedia strain ASH-08), along with non-source specific somatic coliphages (SOMCPH infecting strain WG-5) and indicator bacteria (Escherichia coli) for identifying fecal contamination pathways in Kolkata, India. Source specificity of the phage-based methods was first tested using 60 known non-human fecal samples from common animals, before being evaluated with 56 known human samples (municipal sewage) collected during both the rainy and dry season. SOMCPH were present in 40-90% of samples from different animal species and in 100% of sewage samples. Phages infecting Bacteroides strain GB-124 were not detected from the majority (95%) of animal samples (except in three porcine samples) and were present in 93 and 71% of the sewage samples in the rainy and dry season (Mean = 1.42 and 1.83 log10PFU/100mL, respectively), though at lower levels than SOMCPH (Mean = 3.27 and 3.02 log10PFU/100mL, respectively). Phages infecting strain ASH-08 were detected in 89 and 96% of the sewage samples in the rainy and dry season, respectively, but were also present in all animal samples tested (except goats). Strains K10, K29, and K30 were not found to be useful MST markers due to low levels of phages and/or co-presence in non-human sources. GB-124 and SOMCPH were subsequently deployed within two low-income neighborhoods to determine the levels and origin of fecal contamination in 110 environmental samples. E. coli, SOMCPH, and phages of GB-124 were detected in 68, 42, and 28% of the samples, respectively. Analyses of 166 wastewater samples from shared community toilets and 21 samples from sewage pumping stations from the same districts showed that SOMCPH were present in 100% and GB-124 phages in 31% of shared toilet samples (Median = 5.59 and <1 log10 PFU/100 mL, respectively), and both SOMCPH and GB-124 phages were detected in 95% of pumping station samples (Median = 5.82 and 4.04 log10 PFU/100 mL, respectively). Our findings suggest that GB-124 and SOMCPH have utility as low-cost fecal indicator tools which can facilitate environmental surveillance of enteric organisms, elucidate human and non-human fecal exposure pathways, and inform interventions to mitigate exposure to fecal contamination in the residential environment of Kolkata, India.

Evaluation of nanopore sequencing technology to identify Salmonella enterica Choleraesuis var. Kunzendorf and Orion var. 15+, 34+

Our previous study demonstrated that whole genome sequencing (WGS) data generated by Oxford Nanopore Technologies (ONT) can be used for rapid and accurate prediction of selected Salmonella serotypes. However, one limitation is that established methods for WGS-based serotype prediction, utilizing data from either ONT or Illumina, cannot differentiate certain serotypes and serotype variants with the same or closely related antigenic formulae. This study aimed to evaluate nanopore sequencing and additional data analysis for identification of Salmonella enterica Choleraesuis var. Kunzendorf and S. enterica Orion var. 15+, 34+, thus overcoming this limitation. Five workflows that combined different flow cells, library construction methods and basecaller models were evaluated and compared. The workflow that consisted of the R9 flow cell, rapid sequencing library construction kit and guppy basecaller with base modified model performed best for Single Nucleotide Polymorphism (SNP) analysis. With this workflow, 99.98% of matching identity between assembled genomes from ONT and that from Illumina was achieved. Less than five high-quality SNPs differed when comparing sequencing data between ONT and Illumina. SNP typing successfully identified Choleraesuis var. Kunzendorf. While prophage prediction further differentiated Orion var. 15+, 34+ from the other two Orion variants. Our study improves the readiness of ONT as a Salmonella subtyping and source tracking tool for food industry applications.