Rebekah Henry

Introduction: Monitoring of drinking water sources is an essential component of broader public health practise. However, routine water monitoring programmes that follow established methodological standards, such as low-volume grab sampling with standard filtration, have limitations in being representative. Particularly for protected source waters where (wildlife introduced) pathogens are in low concentration and are not evenly distributed. Microbial source tracking (MST) offers a promising approach to close this gap, enabling more precise identification of faecal contamination sources and their associated risk. However, as with other culture- and molecular-based approaches, the sensitivity of MST is constrained by sample capture methodology, limited by sample volume, timing, and randomness of grab sampling. Methods: This study investigated the application of a high-volume sample concentration method (EasyElute ultrafiltration) to enhance microbial recovery from source water. All evaluation was conducted alongside standard grab sampling and filtration methods. Post-concentration analyses combined traditional culture-based quantification of faecal indicator organisms (FIOs) and reference pathogens, with 16S rRNA amplicon MST to provide an integrated approach to surveillance of animal-derived microbial risks in forested water supply catchments. Results: The results demonstrated that high-volume ultrafiltration enhanced bacterial recovery from source water samples, although turbidity was observed to limit overall efficiency, highlighting potential operational challenges. Comparative analysis demonstrated that amplicon-based MST produced consistent faecal source attribution across both standard and ultrafiltration methods, showing greater sensitivity at increasing volumes. Discussion: This study advances MST methodology by demonstrating the feasibility and added sensitivity achievable through high-volume, concentrated sample collection approaches. This is particularly relevant where water samples are expected to carry low microbial loads, ultimately offering a practical approach for improving faecal source tracking and risk assessment for water sources to protect public health in water supply catchments.

Background/Objectives: This exploratory study aimed to evaluate the impact of an 80 km ultra-marathon trail running event on changes in faecal bacterial composition, and to investigate whether any correlations exist between exercise-associated gastrointestinal symptoms (Ex-GIS) with faecal bacterial profiles. Such events represent a unique physiological stressor and may impact the composition of the gut microbiota. Studying this impact may provide insights into acute (i.e., n = 7 males, n = 6 females) aged 41 ± 8 years completed the 80 km Margaret River (Australia) ultra-marathon race in 2022. Faecal samples were collected pre- and post-race. Faecal bacterial profile, as per relative abundance (RA) of operational taxonomic units and the determination of α-diversity (Shannon Equitability Index (SEI)), was achieved by 16S rRNA amplicon gene sequencing. Changes in RA% and SEI pre- to post-race were assessed by the Wilcoxon signed-rank test. Correlations between Ex-GIS with bacterial profile and changes pre-, during, and post-ultra-marathon race were determined by Spearman’s rank correlation coefficients. Results: Bacterial calculations of phyla (n = 5), family (n = 23), and genus (n = 41) were detected for RA (≥0.5%). A significant decrease pre- to post-race of Actinobacteriota (p = 0.035) phyla, Bifidobacteriaceae (p = 0.007), and Clostridiaceae (p = 0.010) family, and Blautia (p = 0.039) and Subdoligranulum (p = 0.023) genus was determined; meanwhile, Oscillospiraceae (p = 0.016) and Monoglobaceae (p = 0.039) family significantly increased pre- to post-race. No other bacterial group changes were observed. No correlations were observed between pre- to post-ultra-marathon RA change and Ex-GIS. Conclusions: The completion of an 80 km ultra-marathon did not invoke substantial changes in the gut microbiota as determined by faecal bacterial profiling. Very strong and strong correlations were observed between certain bacterial groups and Ex-GIS; however, no significant correlations were observed between pre- to post-ultra-marathon changes in RA ≥ 0.5% and Ex-GIS.

Integration of water monitoring and disease reporting systems is crucial for addressing the interconnected challenges of human, animal, and environmental health. Enteric pathogens introduced into water systems via faecal contamination pose persistent public health risks, particularly in recreational and drinking water sources. Using Australia as a case study, this review examines data collection protocols, surveillance efforts, and reporting guidelines across health and water sectors, identifying gaps and opportunities for improving collaboration nationally and internationally. We collated national and jurisdictional notifiable disease guidelines and notification forms to evaluate data collection and reporting. Drinking and recreational water guidelines and public-facing water surveillance programmes were also reviewed to evaluate monitoring practices, data collection, and accessibility. Findings revealed disparities in pathogen reporting between water and health sectors. Most monitoring guidelines relied on outdated data, failing to reflect contemporary risks and analytical advancements. Clinical disease reporting also lacked adequate waterborne pathogen metadata collection, limiting outbreak tracking. We identified opportunities to enhance data integration and cross-sector collaboration, potentially enabling clinicians, decision-makers, and the public to utilise data on recreational water environments and waterborne infections more effectively. This review underscores the need for greater coordination between water and health sectors, with broader implications for global health surveillance.

Rapid urbanization leads to the growth of informal settlements, where inadequate sanitation infrastructure is common, thus promoting environmental contamination and risk of gastrointestinal infection. Soil contamination contributes to the transmission of enteropathogens, but traditional sampling approaches may poorly indicate public health risks due to limited spatial representation. This study compares traditional grab sampling of soil with a boot sock method, a composite technique designed to better reflect human-pathogen interactions. The boot sock method provided more sensitive detection of E. coli and lower inter-replicate variation compared to grab samples. Post hoc power analyses indicated that the boot sock technique required fewer samples to achieve adequate spatial representation across a sampling area than grab samples, potentially improving time and cost efficiency in pathogen exposure risk estimation.

Human fecal waste is a global health risk associated with diarrheal diseases, responsible for approximately 1.2 million deaths annually. Microbial Source Tracking (MST) is a molecular method that evaluates environmental sources of fecal contamination, aiding quantification of this contamination and associated health risks. However, reported variations in global human gut microbiomes and geographic performance of human-specific fecal markers suggest that current MST targets may not have broad applicability across populations. This systematic review quantified the performance of human-specific fecal markers to identify those suitable for use across various geographic regions. We evaluated data from primary research articles, published before 18^th October 2023, identified through PubMed, Scopus, and Web of Science using PRISMA guidelines. 103 studies published between 1995 and 2023, spanning 34 countries, 6 continents, and 4 climate zones met inclusion criteria, with quantifiable performance metrics (sensitivity, specificity or accuracy) and a geographic testing location. Extracted data was analyzed to establish marker performance across geographic locations, climate zones, and development status. Over 80% were conducted in High-Income Countries (HICs) and >50% in temperate zones, primarily in the USA (43%), Australia (24%), and Spain (19%). Bacteroides HF183 was the most commonly tested (n = 45 studies). However, no target consistently demonstrated sensitivity, specificity, and/or accuracy >80% across different settings. Consequently, a decision tree is presented supporting selection of appropriate human-specific markers for regional-specific baseline studies. This provides critical information to support new MST research, particularly in Low- and Middle-Income Countries (LMICs), assisting with informed decision and method selection for assessing risks of faecal derived pathogens.

Motivation: Understanding the quality of the source library prior to undertaking library-dependent microbial source-tracking (MST) is an essential, but often overlooked, primary analysis step. Results: We propose an assessment approach to validate the quality of amplicon-derived faecal source libraries. This approach was demonstrated on a faecal source library consisting of 16S rRNA paired-end amplicon sequences, obtained from various animal types in Victoria, Australia. First, a leave-one-out (LOO) analysis was performed to assess the accuracy of source category groupings by identifying the number of samples incorrectly assigned to a different source category (i.e. animal type). Following a quality control procedure to decide retaining/removing/grouping incorrectly assigned samples, we then assessed if the sample sizes for each source type were sufficient to properly characterize the source fingerprints. Results from LOO demonstrated 15.5% of samples were incorrectly assigned, with high error rates in birds and wallabies within our source library. Increasing the sample size improved source identification accuracy. However, accuracy eventually plateaued in a source-specific manner. Importantly, this highlights the importance of conducting thorough assessments to understand the quality and limitations of the source library prior to library-dependent MST applications. Availability and implementation: QIIME2 is available via https://qiime2.org/; SourceTracker v2.0.1 is available via https://github.com/caporaso-lab/sourcetracker2; Pipeline for LOO is available via https://github.com/MonashOWL/Bioinformatics-IlluminaMGI/tree/main/16S/LOO; Pipeline for sample size assessment is available via https://github.com/MonashOWL/Bioinformatics-IlluminaMGI/tree/main/16S/Source%20variability.

Enumeration of Campylobacter from environmental waters can be difficult due to its low concentrations, which can still pose a significant health risk. Spectrophotometry is an approach commonly used for fast detection of water-borne pollutants in water samples, but it has not been used for pathogen detection, which is commonly done through a laborious and time-consuming culture or qPCR Most Probable Number enumeration methods (i.e., MPN-PCR approaches). In this study, we proposed a new method, MPN-Spectro-ML, that can provide rapid evidence of Campylobacter detection and, hence, water concentrations. After an initial incubation, the samples were analysed using a spectrophotometer, and the spectrum data were used to train three machine learning (ML) models (i.e., supported vector machine ‐ SVM, logistic regression–LR, and random forest–RF). The trained models were used to predict the presence of Campylobacter in the enriched water samples and estimate the most probable number (MPN). Over 100 stormwater, river, and creek samples (including both fresh and brackish water) from rural and urban catchments were collected to test the accuracy of the MPN-Spectro-ML method under various scenarios and compared to a previously standardised MPN-PCR method. Differences in the spectrum were found between positive and negative control samples, with two distinctive absorbance peaks between 540-542nm and 575-576nm for positive samples. Further, the three ML models had similar performance irrespective of the scenario tested with average prediction accuracy (ACC) and false negative rates at 0.763 and 13.8%, respectively. However, the predicted MPN of Campylobacter from the new method varied from the traditional MPN-PCR method, with a maximum Nash-Sutcliffe coefficient of 0.44 for the urban catchment dataset. Nevertheless, the MPN values based on these two methods were still comparable, considering the confidence intervals and large uncertainties associated with MPN estimation. The study reveals the potential of this novel approach for providing interim evidence of the presence and levels of Campylobacter within environmental water bodies. This, in turn, decreases the time from risk detection to management for the benefit of public health.