Prospective study of Candida auris nucleic acids in wastewater solids in 190 wastewater treatment plants in the United States suggests widespread occurrence.

Candida auris is an emerging, multidrug-resistant fungal pathogen that poses a significant public health threat in healthcare settings. Despite yearly clinical cases rapidly increasing from 77 to 8,131 in the last decade, surveillance data on its distribution and prevalence remain limited. We implemented a novel assay for C. auris detection on a nationwide scale prospectively from September 2023 to March 2024, analyzing a total of 13,842 samples from 190 wastewater treatment plants across 41 U.S. states. Assays were extensively validated through comparison to other known assays and internal controls. Of these 190 wastewater treatment plants, C. auris was detected in the wastewater solids of 65 of them (34.2%) with 1.45% of all samples having detectable levels of C. auris nucleic-acids. Detections varied seasonally, with 2.00% of samples positive in autumn vs 1.01% in winter (P < 0.0001). The frequency of detection in wastewater was significantly associated with states having older populations (P < 0.001), sewersheds containing more hospitals (P < 0.0001), and sewersheds containing more nursing homes (P < 0.001). These associations are in agreement with known C. auris epidemiology. This nationwide study demonstrates the viability of wastewater surveillance for C. auris surveillance and further highlights the value of wastewater surveillance when clinical testing is constrained. IMPORTANCE: This study highlights the viability of wastewater surveillance when dealing with emerging pathogens. By leveraging an existing framework of wastewater surveillance, we reveal the widespread presence of C. auris in the United States. We further demonstrate that these wastewater detections are consistent with demographic factors relevant to C. auris epidemiology like age and number of hospitals or nursing homes. As C. auris and other pathogens continue to emerge, the low-cost and rapid nature of wastewater surveillance will provide public health officials with the information necessary to enact targeted prevention and control strategies.

Observations of Respiratory Syncytial Virus (RSV) Nucleic Acids in Wastewater Solids Across the United States in the 2022-2023 Season: Relationships with RSV Infection Positivity and Hospitalization Rates.

Respiratory syncytial virus (RSV) is a leading cause of respiratory illness and hospitalization, but clinical surveillance detects only a minority of cases. Wastewater surveillance could determine the onset and extent of RSV circulation in the absence of sensitive case detection, but to date, studies of RSV in wastewater are few. We measured RSV RNA concentrations in wastewater solids from 176 sites during the 2022-2023 RSV season and compared those to publicly available RSV infection positivity and hospitalization rates. Concentrations ranged from undetectable to 107 copies per gram. RSV RNA concentration aggregated at state and national levels correlated with infection positivity and hospitalization rates. RSV season onset was determined using both wastewater and clinical positivity rates using independent algorithms for 14 states where both data were available at the start of the RSV season. In 4 of 14 states, wastewater and clinical surveillance identified RSV season onset during the same week; in 3 states, wastewater onset preceded clinical onset, and in 7 states, wastewater onset occurred after clinical onset. Wastewater concentrations generally peaked in the same week as hospitalization rates but after case positivity rates peaked. Differences in onset and peaks in wastewater versus clinical data may reflect inherent differences in the surveillance approaches.

Tunable Mesoscopic Collagen Island Architectures Modulate Stem Cell Behavior.

The extracellular matrix is the biophysical environment that scaffolds mammalian cells in the body. The main constituent is collagen. In physiological tissues, collagen network topology is diverse with complex mesoscopic features. While studies have explored the roles of collagen density and stiffness, the impact of complex architectures remains not well-understood. Developing in vitro systems that recapitulate these diverse collagen architectures is critical for understanding physiologically relevant cell behaviors. Here, methods are developed to induce the formation of heterogeneous mesoscopic architectures, referred to as collagen islands, in collagen hydrogels. These island-containing gels have highly tunable inclusions and mechanical properties. Although these gels are globally soft, there is regional enrichment in the collagen concentration at the cell-scale. Collagen-island architectures are utilized to study mesenchymal stem cell behavior, and it is demonstrated that cell migration and osteogenic differentiation are altered. Finally, induced pluripotent stem cells are cultured in island-containing gels, and it is shown that the architecture is sufficient to induce mesodermal differentiation. Overall, this work highlights complex mesoscopic tissue architectures as bioactive cues in regulating cell behavior and presents a novel collagen-based hydrogel that captures these features for tissue engineering applications.

Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques.

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.

Scaling SARS-CoV-2 wastewater concentrations to population estimates of infection.

Monitoring the progression of SARS-CoV-2 outbreaks requires accurate estimation of the unobservable fraction of the population infected over time in addition to the observed numbers of COVID-19 cases, as the latter present a distorted view of the pandemic due to changes in test frequency and coverage over time. The objective of this report is to describe and illustrate an approach that produces representative estimates of the unobservable cumulative incidence of infection by scaling the daily concentrations of SARS-CoV-2 RNA in wastewater from the consistent population contribution of fecal material to the sewage collection system.

Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification.

Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.

Occurrence of respiratory viruses on school desks.

BACKGROUND: Schools represent high occupancy environments and well-documented high-risk locations for the transmission of respiratory viruses. The goal of this study was to report on the area density, occurrence, and type of respiratory viruses on desks in primary school classrooms. METHODS: Quantitative reverse transcription polymerase chain reaction (qPCR) techniques were employed to measure nucleic acid area densities from a broad range of human adenoviruses and rhinoviruses, as well as coronavirus OC43, influenza A, and norovirus GI. Every two weeks, virus monitoring was conducted on the desks of four primary school classrooms in Colorado, USA, during the 2019 respiratory virus season. RESULTS: DNA and RNA from respiratory viruses and norovirus were recovered from more than 20% of the desks sampled; occurrence patterns that indicate a greater than 60% probability of encountering any virus, if more than five desks were occupied in a day. Rhinoviruses and adenoviruses were the most commonly detected viruses as judged by the composite of occurrence and number of gene copies recovered. Desktop adenosine triphosphate monitoring did not predict the recovery of viral genomic materials on desks. School desks can be commonly contaminated with respiratory viruses. CONCLUSIONS: Genomic surveys of the identity, distribution and abundance of human viruses on "high-touch" surfaces, can help inform risk assessments, design cleaning interventions, and may be useful for infection surveillance.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments.

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge.

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified and provide a 95% confidence interval estimate of the reproductive number R0 ≈ 2.4 ± 0.2. Sensitivity analysis accounting for alternative lag distributions from infection until hospitalization and sludge RNA concentration respectively suggests that the detection of viral RNA in sewage sludge leads hospital admissions by 3 to 5 days on average. The analysis suggests that stay-at-home restrictions plausibly removed 89% of the population from the risk of infection with the remaining 11% exposed to an unmitigated outbreak that infected 9.3% of the total population.

Measurement of SARS-CoV-2 RNA in wastewater tracks community infection dynamics.

We measured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in primary sewage sludge in the New Haven, Connecticut, USA, metropolitan area during the Coronavirus Disease 2019 (COVID-19) outbreak in Spring 2020. SARS-CoV-2 RNA was detected throughout the more than 10-week study and, when adjusted for time lags, tracked the rise and fall of cases seen in SARS-CoV-2 clinical test results and local COVID-19 hospital admissions. Relative to these indicators, SARS-CoV-2 RNA concentrations in sludge were 0-2 d ahead of SARS-CoV-2 positive test results by date of specimen collection, 0-2 d ahead of the percentage of positive tests by date of specimen collection, 1-4 d ahead of local hospital admissions and 6-8 d ahead of SARS-CoV-2 positive test results by reporting date. Our data show the utility of viral RNA monitoring in municipal wastewater for SARS-CoV-2 infection surveillance at a population-wide level. In communities facing a delay between specimen collection and the reporting of test results, immediate wastewater results can provide considerable advance notice of infection dynamics.