Wastewater surveillance for SARS-CoV-2 provides an approach for assessing the infection burden across a sewer service area. For these data to be useful for public health, measurement variability and the relationship to case data need to be established. We determined SARS-CoV-2 RNA concentrations in the influent of 12 wastewater treatment plants from August 2020 to January 2021. Technical replicates for N1 gene concentrations showed a relative standard deviation of 24%, suggesting it is possible to track relatively small (∼30%) changes in SARS-CoV-2 concentrations over time. COVID-19 cases were correlated significantly (ρ ≥ 0.70) to wastewater SARS-CoV-2 RNA concentrations across large and small service areas, with weaker relationships (ρ ≥ 0.59) in two communities. SARS-CoV-2 concentrations normalized to per capita slightly improved correlations to COVID-19 incidence, but normalizing to a spiked recovery control (BCoV) or a fecal marker (PMMoV or HF183) reduced correlations for a number of plants. Daily sampling demonstrated that a minimum of two samples collected per week were needed to maintain accuracy in trend analysis. The differences in the strength of SARS-CoV-2 relationships to COVID-19 incidence and the effect of normalization on these data among communities demonstrate that rigorous validation should be performed at individual sites where wastewater surveillance programs are implemented.

BackgroundClostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species. As a result, members of Clostridiales and Bacteroidales are particularly suitable for identifying sources of fecal contamination in environmental samples. However, a comprehensive evaluation of their predictive power and development of computational approaches is lacking. Given the global public health concern for waterborne disease, accurate identification of fecal pollution sources is essential for effective risk assessment and management. Here, we use random forest algorithm and 16S rRNA gene amplicon sequences assigned to Clostridiales and Bacteroidales to identify common fecal pollution sources. We benchmarked the accuracy, consistency, and sensitivity of our classification approach using fecal, environmental, and artificial in silico generated samples.ResultsClostridiales and Bacteroidales classifiers were composed mainly of sequences that displayed differential distributions (host-preferred) among sewage, cow, deer, pig, cat, and dog sources. Each classifier correctly identified human and individual animal sources in approximately 90% of the fecal and environmental samples tested. Misclassifications resulted mostly from false-positive identification of cat and dog fecal signatures in host animals not used to build the classifiers, suggesting characterization of additional animals would improve accuracy. Random forest predictions were highly reproducible, reflecting the consistency of the bacterial signatures within each of the animal and sewage sources. Using in silico generated samples, we could detect fecal bacterial signatures when the source dataset accounted for as little as ~ 0.5% of the assemblage, with ~ 0.04% of the sequences matching the classifiers. Finally, we developed a proxy to estimate proportions among sources, which allowed us to determine which sources contribute the most to observed fecal pollution.ConclusionRandom forest classification with 16S rRNA gene amplicons offers a rapid, sensitive, and accurate solution for identifying host microbial signatures to detect human and animal fecal contamination in environmental samples.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0568-3) contains supplementary material, which is available to authorized users.

No abstract

Over the past decade, neutral theory has gained attention and recognition for its capacity to explain bacterial community structure (BCS) in addition to deterministic processes. However, no clear consensus has been drawn so far on their relative importance. In a metacommunity analysis, we explored at the regional and local scale the effects of these processes on the bacterial community assembly within the water column of 49 freshwater lakes. The BCS was assessed using terminal restriction fragment length polymorphism (T-RFLP) of the 16S rRNA genes. At the regional scales, results indicated that the neutral community model well predicted the spatial community structure (R(2) mean = 76%) compared with the deterministic factors - which explained only a small fraction of the BCS total variance (less than 14%). This suggests that the bacterial compartment was notably driven by stochastic processes, through loss and gain of taxa. At the local scale, the bacterial community appeared to be spatially structured by stochastic processes (R(2) mean = 65%) and temporally governed by the water temperature, a deterministic factor, even if some bacterial taxa were driven by neutral dynamics. Therefore, at both regional and local scales the neutral community model appeared to be relevant in explaining the bacterial assemblage structure.