Fangqiong Ling

Methods to quantitatively synthesize findings across multiple studies is an emerging need in wastewater-based epidemiology (WBE), where disease tracking through wastewater analysis is performed at broad geographical locations using various techniques to facilitate public health responses. Meta-analysis provides a rigorous statistical procedure for research synthesis, yet the manual process of screening large volumes of literature remains a hurdle for its application in timely evidence-based public health responses. Here, we evaluated the performance of GPT-3, GPT-3.5, and GPT-4 models in automated screening of publications for meta-analysis in the WBE literature. We show that the chat completion model in GPT-4 accurately differentiates papers that contain original data from those that did not with texts of the Abstract as the input at a Precision of 0.96 and Recall of 1.00, exceeding current quality standards for manual screening (Recall = 0.95) while costing less than $0.01 per paper. GPT models performed less accurately in detecting studies reporting relevant sampling location, highlighting the value of maintaining human intervention in AI-assisted literature screening. Importantly, we show that certain formulation and model choices generated nonsensical answers to the screening tasks, while others did not, urging the attention to robustness when employing AI-assisted literature screening. This study provided novel performance evaluation data on GPT models for document screening as a step in meta-analysis, suggesting AI-assisted literature screening a useful complementary technique to speed up research synthesis in WBE.

The hospital environmental microbiome, which can affect patients’ and healthcare workers’ health, is highly variable and the drivers of this variability are not well understood. In this study, we collected 37 surface samples from the neonatal intensive care unit (NICU) in an inpatient hospital before and after the operation began. Additionally, healthcare workers collected 160 surface samples from five additional areas of the hospital. All samples were analyzed using 16S rRNA gene amplicon sequencing, and the samples collected by healthcare workers were cultured. The NICU samples exhibited similar alpha and beta diversities before and after opening, which indicated that the microbiome there was stable over time. Conversely, the diversities of samples taken after opening varied widely by area. Principal coordinate analysis (PCoA) showed the samples clustered into two distinct groups: high alpha diversity [the pediatric intensive care unit (PICU), pathology lab, and microbiology lab] and low alpha diversity [the NICU, pediatric surgery ward, and infection prevention and control (IPAC) office]. Least absolute shrinkage and selection operator (LASSO) classification models identified 156 informative amplicon sequence variants (ASVs) for predicting the sample’s area of origin. The testing accuracy ranged from 86.37% to 100%, which outperformed linear and radial support vector machine (SVM) and random forest models. ASVs of genera that contain emerging pathogens were identified in these models. Culture experiments had identified viable species among the samples, including potential antibiotic-resistant bacteria. Though area type differences were not noted in the culture data, the prevalences and relative abundances of genera detected positively correlated with 16S sequencing data. This study brings to light the microbial community temporal and spatial variation within the hospital and the importance of pathogenic and commensal bacteria to understanding dispersal patterns for infection control. IMPORTANCE: We sampled surface samples from a newly built inpatient hospital in multiple areas, including areas accessed by only healthcare workers. Our analysis of the neonatal intensive care unit (NICU) showed that the microbiome was stable before and after the operation began, possibly due to access restrictions. Of the high-touch samples taken after opening, areas with high diversity had more potential external seeds (long-term patients and clinical samples), and areas with low diversity and had fewer (short-term or newborn patients). Classification models performed at high accuracy and identified biomarkers that could be used for more targeted surveillance and infection control. Though culturing data yielded viability and antibiotic-resistance information, it disproportionately detected the presence of genera relative to 16S data. This difference reinforces the utility of 16S sequencing in profiling hospital microbiomes. By examining the microbiome over time and in multiple areas, we identified potential drivers of the microbial variation within a hospital.

Nontuberculous mycobacteria (NTM) are any mycobacteria that do not cause tuberculosis or leprosy. While the majority of NTM are harmless and some of them are considered probiotic, a growing number of people are being diagnosed with NTM infections. Therefore, their detection in the environment is of interest to clinicians, environmental microbiologists, and water quality researchers alike. This review provides a tutorial on the foundational approaches for taxonomic classifications, with a focus on the phylogenetic relationships among NTM revealed by the 16S rRNA gene, rpoB gene, and hsp65 gene, and by genome-based approaches. Recent updates on the Mycobacterium genus taxonomy are also provided. A synthesis on the habitats of 189 mycobacterial species in a genome-based taxonomy framework was performed, with attention paid to environmental sources (e.g., drinking water, aquatic environments, and soil). The 16S rRNA gene-based classification accuracy for various regions was evaluated (V3, V3–V4, V3–V5, V4, V4–V5, and V1–V9), revealing overall excellent genus-level classification (up to 100% accuracy) yet only modest performance (up to 63.5% accuracy) at the species level. Future research quantifying NTM species in water systems, determining the effects of water treatment and plumbing conditions on their variations, developing high throughput species-level characterization tools for use in the environment, and incorporating the characterization of functions in a phylogenetic framework will likely fill critical knowledge gaps. We believe this tutorial will be useful for researchers new to the field of molecular or genome-based taxonomic profiling of environmental microbiomes. Experts may also find this review useful in terms of the selected key findings of the past 30 years, recent updates on phylogenomic analyses, as well as a synthesis of the ecology of NTM in a phylogenetic framework.

Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of the field blurred the boundary between measuring biomarkers for research activities and for pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process (or associated data management safeguards), introducing the potential for adverse outcomes for WBT professionals and community members. To address this deficiency, an interdisciplinary group developed a framework for a structured ethical review of WBT. The workshop employed a consensus approach to create this framework as a set of 11-questions derived from primarily public health guidance because of the common exemption of wastewater samples to human subject research considerations. This study retrospectively applied the set of questions to peer- reviewed published reports on SARS-CoV-2 monitoring campaigns covering the emergent phase of the pandemic from March 2020 to February 2022 (n=53). Overall, 43% of the responses to the questions were unable to be assessed because of lack of reported information. It is therefore hypothesized that a systematic framework would at a minimum improve the communication of key ethical considerations for the application of WBT. Consistent application of a standardized ethical review will also assist in developing an engaged practice of critically applying and updating approaches and techniques to reflect the concerns held by both those practicing and being monitored by WBT supported campaigns. Synopsis Development of a structured ethical review facilitates retrospective analysis of published studies and drafted scenarios in the context of wastewater-based testing.

Microbiomes play an essential role in promoting host health and fitness, but the factors affecting variation in gut microbiomes among individuals are not fully understood. Investigating the microbiome under different conditions is needed to link gut microbiomes to host physiology and potentially design manipulations to improve rearing success of captive species. In this study, we characterized the gut microbiomes of Atlantic salmon ( Salmo salar ) in individuals at different life stages, vaccination status, and hatchery origin. Microbiomes differed between age-0 sub-adults and adults, with sub-adults exhibiting higher diversity and more similar communities when compared to adults. We also found that vaccines against bacterial kidney disease reduced gut microbial diversity within individual sub-adult salmon, resulting in dissimilar gut microbial communities among individuals. The diversity and structure of microbiomes did not differ between groups of adults that were reared in two different hatcheries and sampled from the wild. Sub-adults, particularly unvaccinated sub-adults, displayed a strong core microbiome present in the majority of individuals. Our results suggest that life stage and vaccination status are essential factors in the gut microbiome development of salmon. Conditions experienced during early life stages appear to have a strong influence on the microbiome, but differences among individuals at early life stages may be lost due to environmental factors experienced later in life. The plasticity of the microbiome throughout the life of individuals may have important implications for understanding host health, with potential applications for improving the rearing and reintroduction success of the ecologically and economically important Atlantic salmon. IMPORTANCE: The Atlantic salmon ( Salmo salar ) is a globally important fisheries and aquaculture species, but the factors affecting gut microbiomes of hatchery-reared fish are not fully understood. Our study explores the influence of life stage, vaccination status, and hatchery origin on the composition and structure of the Atlantic salmon gut microbiome. We found that life stage is an important driver of gut microbiome diversity, likely driven by differences in habitat and diet. Vaccination against bacterial kidney disease led to marked declines in gut microbial diversity within individuals, resulting in highly distinct gut microbial communities among individuals. Hatchery origin did not have a strong influence on adult Atlantic salmon captured from the wild. These findings suggest that life stage and vaccination drive variation in Atlantic salmon microbiomes, but the stability and long-term implications of such variation on host health should be considered in future microbiome research.

The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxa’s relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome.