Fast and accurate variant identification tool for sequencing-based studies

BACKGROUND

Accurate identification of genetic variants, such as point mutations and insertions/deletions (indels), is crucial for various genetic studies into epidemic tracking, population genetics, and disease diagnosis. Genetic studies into microbiomes often require processing numerous sequencing datasets, necessitating variant identifiers with high speed, accuracy, and robustness.

RESULTS

We present QuickVariants, a bioinformatics tool that effectively summarizes variant information from read alignments and identifies variants. When tested on diverse bacterial sequencing data, QuickVariants demonstrates a ninefold higher median speed than bcftools, a widely used variant identifier, with higher accuracy in identifying both point mutations and indels. This accuracy extends to variant identification in virus samples, including SARS-CoV-2, particularly with significantly fewer false negative indels than bcftools. The high accuracy of QuickVariants is further demonstrated by its detection of a greater number of Omicron-specific indels (5 versus 0) and point mutations (61 versus 48-54) than bcftools in sewage metagenomes predominated by Omicron variants. Much of the reduced accuracy of bcftools was attributable to its misinterpretation of indels, often producing false negative indels and false positive point mutations at the same locations.

CONCLUSIONS

We introduce QuickVariants, a fast, accurate, and robust bioinformatics tool designed for identifying genetic variants for microbial studies. QuickVariants is available at https://github.com/caozhichongchong/QuickVariants .

Longitudinal microbiome changes in children exposed to proton pump inhibitors

INTRODUCTION

Proton pump inhibitor (PPI) use has been associated with an increased risk of gastrointestinal and upper respiratory infections in children. There are limited longitudinal data on the effect of PPI in children. The goal of this prospective observational study was to compare the stool and oropharyngeal microbiome of children before and after starting PPIs.

METHODS

We prospectively recruited participants from a gastroenterology clinic. Consented pariticpants provided stool samples and oropharyngeal swabs at baseline and after eight weeks of PPI therapy. Microbiome changes were measured by analyzing 16S sequencing from both body sites at both timepoints.

RESULTS

Thirty-four participants completed the study and provided samples both at baseline and after eight weeks on PPI therapy. Of those, 24 participants had sufficient sequencing from both stool and oropharyngeal samples at both time points. There were no differences between the pre- vs post-PPI samples using beta-diversity metrics in either the oropharynx or stool. There were, however, significant changes in specific taxa. There was an enrichment of Streptococcus in the stool in after PPI-use and a reduction in the relative abundance of Bifidobacterium, Peptostreptococcus and Turicibacter (p-values < 0.01). Furthermore, there was an increase in the relative abundance of oropharyngeal bacteria in the stool after PPI therapy. This enrichment of oropharyngeal bacteria in the stool was most prominent in younger participants.

DISCUSSION

Further investigation is needed to determine the clinical and microbial factors that predispose or protect against microbiome changes due to PPI-use, and why young children are more susceptible to this PPI effect.

An immune-competent human gut microphysiological system enables inflammation-modulation by Faecalibacterium prausnitzii

Crosstalk of microbes with human gut epithelia and immune cells is crucial for gut health. However, there is no existing system for a long-term co-culture of human innate immune cells with epithelium and oxygen-intolerant commensal microbes, hindering the understanding of microbe-immune interactions in a controlled manner. Here, we established a gut epithelium-microbe-immune (GuMI) microphysiological system to maintain the long-term continuous co-culture of Faecalibacterium prausnitzii/Faecalibacterium duncaniae with colonic epithelium, antigen-presenting cells (APCs, herein dendritic cells and macrophages), and CD4+ naive T cells circulating underneath the colonic epithelium. In GuMI-APC condition, multiplex cytokine assays suggested that APCs contribute to the elevated level of cytokines and chemokines secreted into both apical and basolateral compartments compared to GuMI condition that lacks APC. In GuMI-APC with F. prausnitzii (GuMI-APC-FP), F. prausnitzii increased the transcription of pro-inflammatory genes such as toll-like receptor 1 (TLR1) and interferon alpha 1 (IFNA1) in the colonic epithelium, without a significant effect on cytokine secretion, compared to the GuMI-APC without bacteria (GuMI-APC-NB). In contrast, in the presence of CD4+ naive T cells (GuMI-APCT-FP), TLR1, IFNA1, and IDO1 transcription levels decreased with a simultaneous increase in F. prausnitzii-induced secretion of pro-inflammatory cytokines (e.g., IL8) compared to GuMI-APC-FP that lacks T cells. These results highlight the contribution of individual innate immune cells in regulating the immune response triggered by the gut commensal F. prausnitzii. The integration of defined populations of immune cells in the gut microphysiological system demonstrated the usefulness of GuMI physiomimetic platform to study microbe-epithelial-immune interactions in healthy and disease conditions.

Temporal dynamics of gut microbiomes in non-industrialized urban Amazonia

Increasing levels of industrialization have been associated with changes in gut microbiome structure and loss of features thought to be crucial for maintaining gut ecological balance. The stability of gut microbial communities over time within individuals seems to be largely affected by these changes but has been overlooked among transitioning populations from low- to middle-income countries. Here, we used metagenomic sequencing to characterize the temporal dynamics in gut microbiomes of 24 individuals living an urban non-industrialized lifestyle in the Brazilian Amazon. We further contextualized our data with 165 matching longitudinal samples from an urban industrialized and a rural non-industrialized population. We show that gut microbiome composition and diversity have greater variability over time among non-industrialized individuals when compared to industrialized counterparts and that taxa may present diverse temporal dynamics across human populations. Enterotype classifications show that community types are generally stable over time despite shifts in microbiome structure. Furthermore, by tracking genomes over time, we show that levels of bacterial population replacements are more frequent among Amazonian individuals and that non-synonymous variants accumulate in genes associated with degradation of host dietary polysaccharides. Taken together, our results suggest that the stability of gut microbiomes is influenced by levels of industrialization and that tracking microbial population dynamics is important to understand how the microbiome will adapt to these transitions.IMPORTANCEThe transition from a rural or non-industrialized lifestyle to urbanization and industrialization has been linked to changes in the structure and function of the human gut microbiome. Understanding how the gut microbiomes changes over time is crucial to define healthy states and to grasp how the gut microbiome interacts with the host environment. Here, we investigate the temporal dynamics of gut microbiomes from an urban and non-industrialized population in the Amazon, as well as metagenomic data sets from urban United States and rural Tanzania. We showed that healthy non-industrialized microbiomes experience greater compositional shifts over time compared to industrialized individuals. Furthermore, bacterial strain populations are more frequently replaced in non-industrialized microbiomes, and most non-synonymous mutations accumulate in genes associated with the degradation of host dietary components. This indicates that microbiome stability is affected by transitions to industrialization, and that strain tracking can elucidate the ecological dynamics behind such transitions.

Structured community transitions explain the switching capacity of microbial systems

Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space-which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions: one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.

Environmental stress mediates groundwater microbial community assembly

Community assembly describes how different ecological processes shape microbial community composition and structure. How environmental factors impact community assembly remains elusive. Here we sampled microbial communities and >200 biogeochemical variables in groundwater at the Oak Ridge Field Research Center, a former nuclear waste disposal site, and developed a theoretical framework to conceptualize the relationships between community assembly processes and environmental stresses. We found that stochastic assembly processes were critical (>60% on average) in shaping community structure, but their relative importance decreased as stress increased. Dispersal limitation and 'drift' related to random birth and death had negative correlations with stresses, whereas the selection processes leading to dissimilar communities increased with stresses, primarily related to pH, cobalt and molybdenum. Assembly mechanisms also varied greatly among different phylogenetic groups. Our findings highlight the importance of microbial dispersal limitation and environmental heterogeneity in ecosystem restoration and management.

Low-level resource partitioning supports coexistence among functionally redundant bacteria during successional dynamics

Members of microbial communities can substantially overlap in substrate use. However, what enables functionally redundant microorganisms to coassemble or even stably coexist remains poorly understood. Here, we show that during unstable successional dynamics on complex, natural organic matter, functionally redundant bacteria can coexist by partitioning low-concentration substrates even though they compete for one simple, dominant substrate. We allowed ocean microbial communities to self-assemble on leachates of the brown seaweed Fucus vesiculosus and then analyzed the competition among 10 taxonomically diverse isolates representing two distinct stages of the succession. All, but two isolates, exhibited an average of 90% ± 6% pairwise overlap in resource use, and functional redundancy of isolates from the same assembly stage was higher than that from between assembly stages, leading us to construct a simpler four-isolate community with two isolates from each of the early and late stages. We found that, although the short-term dynamics of the four-isolate communities in F. vesiculosus leachate was dependent on initial isolate ratios, in the long term, the four isolates stably coexist in F. vesiculosus leachate, albeit with some strains at low abundance. We therefore explored the potential for nonredundant substrate use by genomic content analysis and RNA expression patterns. This analysis revealed that the four isolates mainly differed in peripheral metabolic pathways, such as the ability to degrade pyrimidine, leucine, and tyrosine, as well as aromatic substrates. These results highlight the importance of fine-scale differences in metabolic strategies for supporting the frequently observed coexistence of large numbers of rare organisms in natural microbiomes.

An immune-competent human gut microphysiological system enables inflammation-modulation of Faecalibacterium prausnitzii

Crosstalk of microbes with human gut epithelia and immune cells is crucial for gut health. However, there is no existing system for a long-term co-culture of human innate immune cells with epithelium and oxygen-intolerant commensal microbes, hindering the understanding of microbe-immune interactions in a controlled manner. Here, we establish a gut epithelium-microbe-immune microphysiological system to maintain the long-term continuous co-culture of Faecalibacterium prausnitzii/Faecalibacterium duncaniae with colonic epithelium, antigen-presenting cells (APCs, herein dendritic cells and macrophages), with CD4+ naïve T cells circulating underneath the colonic epithelium. Multiplex cytokine assays suggested that APCs contribute to the elevated level of cytokines and chemokines being secreted into both apical and basolateral compartments. In contrast, the absence of APCs does not allow reliable detection of these cytokines. In the presence of APCs, F. prausnitzii increased the transcription of pro-inflammatory genes such as toll-like receptor 1 (TLR1) and interferon alpha 1 (IFNA1) in the colonic epithelium, but no significant change on the secreted cytokines. In contrast, integration of CD4+ naïve T cells reverses this effect by decreasing the transcription of TLR1, IFNA1, and indoleamine 2,3-dioxygenase, and increasing the F. prausnitzii-induced secretion of pro-inflammatory cytokines such as IL-8, MCP-1/CCL2, and IL1A. These results highlight the contribution of individual innate immune cells in the regulation of the immune response triggered by the gut commensal F. prausnitzii. The successful integration of defined populations of immune cells in this gut microphysiological system demonstrated the usefulness of the GuMI physiomimetic platform to study microbe-epithelial-immune interactions in health and disease.

Lectin-Seq: A method to profile lectin-microbe interactions in native communities

Soluble human lectins are critical components of innate immunity. Genetic models suggest that lectins influence host-resident microbiota, but their specificity for commensal and mutualist species is understudied. Elucidating lectins' roles in regulating microbiota requires an understanding of which microbial species they bind within native communities. To profile human lectin recognition, we developed Lectin-Seq. We apply Lectin-Seq to human fecal microbiota using the soluble mannose-binding lectin (MBL) and intelectin-1 (hItln1). Although each lectin binds a substantial percentage of the samples (10 to 20%), the microbial interactomes of MBL and hItln1 differ markedly in composition and diversity. MBL binding is highly selective for a small subset of species commonly associated with humans. In contrast, hItln1's interaction profile encompasses a broad range of lower-abundance species. Our data uncover stark differences in the commensal recognition properties of human lectins.

Co-incidence of BA.1 and BA.2 at the start of Singapore's Omicron wave revealed by Community and University Campus wastewater surveillance

Wastewater surveillance (WWS) has been globally recognised to be a useful tool in quantifying SARS-CoV-2 RNA at the community and residential levels without biases associated with case-reporting. The emergence of variants of concern (VOCs) have given rise to an unprecedented number of infections even though populations are increasingly vaccinated. This is because VOCs have been reported to possess higher transmissibility and can evade host immune responses. The B.1.1.529 lineage (Omicron) has severely disrupted global plans to return to normalcy. In this study, we developed an allele-specific (AS) RT-qPCR assay which simultaneously targets the stretch of deletions and mutations in the spike protein from position 24-27 for quantitative detection of Omicron BA.2. Together with previous assays that detect mutations associated with Omicron BA.1 (deletion at position 69 and 70) and all Omicron (mutation at position 493 and 498), we report the validation and time series of these assays from September 2021 to May 2022 using influent samples from two wastewater treatment plants and across four University campus sites in Singapore. Viral RNA concentrations at the treatment plants corroborate with locally reported clinical cases, AS RT-qPCR assays revealed co-incidence of Omicron BA.1 and BA.2 on 12 January 2022, almost two months after initial BA.1 detection in South Africa and Botswana. BA.2 became the dominant variant by the end of January 2022 and completely displaced BA.1 by mid-March 2022. University campus sites were similarly positive for BA.1 and/or BA.2 in the same week as first detection at the treatment plants, where BA.2 became rapidly established as the dominant lineage within three weeks. These results corroborate clinical incidence of the Omicron lineages in Singapore and indicate minimal silent circulation prior to January 2022. The subsequent simultaneous spread of both variant lineages followed strategic relaxation of safe management measures upon meeting nationwide vaccination goals.

Higher alpha diversity and Lactobacillus blooms are associated with better engraftment after Fecal Microbiota Transplant in Inflammatory Bowel Disease

Background

Fecal Microbiota Transplant (FMT) has proven effective in treating recurrent Clostridioides difficile infection (rCDI) and has shown some success in treating inflammatory bowel diseases (IBD). There is emerging evidence that host engraftment of donor taxa is a tenet of successful FMT. However, there is little known regarding predictors of engraftment. We undertook a double-blind, randomized, placebo-controlled pilot study to characterize the response to FMT in children and young adults with mild to moderate active Crohn's disease (CD) and ulcerative colitis (UC).

Results

Subjects with CD or UC were randomized to receive antibiotics and weekly FMT or placebo in addition to baseline medications. The treatment arm received seven days of antibiotics followed by FMT enema and then capsules weekly for seven weeks. We enrolled four subjects with CD and 11 with UC, ages 14-29 years. Due to weekly stool sampling, we were able to create a time series of alpha diversity, beta diversity and engraftment as they related to clinical response. Subjects exhibited a wide range of microbial diversity and donor engraftment as FMT progressed. Specifically, engraftment ranged from 26% to 90% at week 2 and 3% to 92% at two months. Consistent with the current literature, increases over time of both alpha diversity (p< 0.05) and donor engraftment (p< 0.05) correlated with improved clinical response. Additionally, our weekly time series enabled an investigation into the clinical and microbial correlates of engraftment at various time points. We discovered that the post-antibiotic but pre-FMT time point, often overlooked in FMT trials, was rich in microbial correlates of eventual engraftment. Greater residual alpha diversity after antibiotic treatment was positively correlated with engraftment and subsequent clinical response. Interestingly, a transient rise in the relative abundance of Lactobacillus was also positively correlated with engraftment, a finding that we recapitulated with our analysis of another FMT trial with publicly available weekly sequencing data.

Conclusions

We found that higher residual alpha diversity and Lactobacillus blooms after antibiotic treatment correlated with improved engraftment and clinical response to FMT. Future studies should closely examine the host microbial communities pre-FMT and the impact of antibiotic preconditioning on engraftment and response.

Contextualizing Wastewater-Based surveillance in the COVID-19 vaccination era

SARS-CoV-2 wastewater-based surveillance (WBS) offers a tool for cost-effective oversight of a population's infections. In the past two years, WBS has proven to be crucial for managing the pandemic across different geographical regions. However, the changing context of the pandemic due to high levels of COVID-19 vaccination warrants a closer examination of its implication towards SARS-CoV-2 WBS. Two main questions were raised: 1) Does vaccination cause shedding of viral signatures without infection? 2) Does vaccination affect the relationship between wastewater and clinical data? To answer, we review historical reports of shedding from viral vaccines in use prior to the COVID-19 pandemic including for polio, rotavirus, influenza and measles infection and provide a perspective on the implications of different COVID-19 vaccination strategies with regard to the potential shedding of viral signatures into the sewershed. Additionally, we reviewed studies that looked into the relationship between wastewater and clinical data and how vaccination campaigns could have affected the relationship. Finally, analyzing wastewater and clinical data from the Netherlands, we observed changes in the relationship concomitant with increasing vaccination coverage and switches in dominant variants of concern. First, that no vaccine-derived shedding is expected from the current commercial pipeline of COVID-19 vaccines that may confound interpretation of WBS data. Secondly, that breakthrough infections from vaccinated individuals contribute significantly to wastewater signals and must be interpreted in light of the changing dynamics of shedding from new variants of concern.

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O'Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40-60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40-60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.

Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States

Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.

Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States

Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.

Monitoring human arboviral diseases through wastewater surveillance: Challenges, progress and future opportunities

Arboviral diseases are caused by a group of viruses spread by the bite of infected arthropods. Amongst these, dengue, Zika, west nile fever and yellow fever cause the greatest economic and social impact. Arboviral epidemics have increased in frequency, magnitude and geographical extent over the past decades and are expected to continue increasing with climate change and expanding urbanisation. Arboviral prevalence is largely underestimated, as most infections are asymptomatic, nevertheless existing surveillance systems are based on passive reporting of loosely defined clinical syndromes with infrequent laboratory confirmation. Wastewater-based surveillance (WBS), which has been demonstrated to be useful for monitoring diseases with significant asymptomatic populations including COVID19 and polio, could be a useful complement to arboviral surveillance. We review the current state of knowledge and identify key factors that affect the feasibility of monitoring arboviral diseases by WBS to include viral shedding loads by infected persons, the persistence of shed arboviruses and the efficiency of their recovery from sewage. We provide a simple model on the volume of wastewater that needs to be processed for detection of arboviruses, in face of lower arboviral shedding rates. In all, this review serves to reflect on the key challenges that need to be addressed and overcome for successful implementation of arboviral WBS.

A conserved Bacteroidetes antigen induces anti-inflammatory intestinal T lymphocytes

The microbiome contributes to the development and maturation of the immune system. In response to commensal bacteria, intestinal CD4⁺ T lymphocytes differentiate into functional subtypes with regulatory or effector functions. The development of small intestine intraepithelial lymphocytes that coexpress CD4 and CD8αα homodimers (CD4IELs) depends on the microbiota. However, the identity of the microbial antigens recognized by CD4⁺ T cells that can differentiate into CD4IELs remains unknown. We identified β-hexosaminidase, a conserved enzyme across commensals of the Bacteroidetes phylum, as a driver of CD4IEL differentiation. In a mouse model of colitis, β-hexosaminidase-specific lymphocytes protected against intestinal inflammation. Thus, T cells of a single specificity can recognize a variety of abundant commensals and elicit a regulatory immune response at the intestinal mucosa.

Rapid displacement of SARS-CoV-2 variant Delta by Omicron revealed by allele-specific PCR in wastewater

On November 26, 2021, the B.1.1.529 COVID-19 variant was classified as the Omicron variant of concern (VOC). Reports of higher transmissibility and potential immune evasion triggered flight bans and heightened health control measures across the world to stem its distribution. Wastewater-based surveillance has demonstrated to be a useful complement for clinical community-based tracking of SARS-CoV-2 variants. Using design principles of our previous assays that detect SARS-CoV-2 variants (Alpha and Delta), we developed an allele-specific RT-qPCR assay which simultaneously targets the stretch of mutations from Q493R to Q498R for quantitative detection of the Omicron variant in wastewater. We report their validation against 10-month longitudinal samples from the influent of a wastewater treatment plant in Italy. SARS-CoV-2 RNA concentrations and variant frequencies in wastewater determined using these variant assays agree with clinical cases, revealing rapid displacement of the Delta variant by the Omicron variant within three weeks. These variant trends, when mapped against vaccination rates, support clinical studies that found the rapid emergence of SARS-CoV-2 Omicron variant being associated with an infection advantage over Delta in vaccinated persons. These data reinforce the versatility, utility and accuracy of these open-sourced methods using allele-specific RT-qPCR for tracking the dynamics of variant displacement in communities through wastewater for informed public health responses.

Making waves: Wastewater surveillance of SARS-CoV-2 in an endemic future

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor the emergence and spread of SARS-CoV-2 infections in populations during the COVID-19 pandemic. Coincident with the global vaccination efforts, the world is also enduring new waves of SARS-CoV-2 variants. Reinfections and vaccine breakthroughs suggest an endemic future where SARS-CoV-2 continues to persist in the general population. In this treatise, we aim to explore the future roles of wastewater surveillance. Practically, WBS serves as a relatively affordable and non-invasive tool for mass surveillance of SARS-CoV-2 infection while minimizing privacy concerns, attributes that make it extremely suited for its long-term usage. In an endemic future, the utility of WBS will include 1) monitoring the trend of viral loads of targets in wastewater for quantitative estimate of changes in disease incidence; 2) sampling upstream for pinpointing infections in neighborhoods and at the building level; 3) integrating wastewater and clinical surveillance for cost-efficient population surveillance; and 4) genome sequencing wastewater samples to track circulating and emerging variants in the population. We further discuss the challenges and future developments of WBS to reduce inconsistencies in wastewater data worldwide, improve its epidemiological inference, and advance viral tracking and discovery as a preparation for the next viral pandemic.

Development of an efficient wastewater testing protocol for high-throughput country-wide SARS-CoV-2 monitoring

Wastewater-based surveillance has been widely used as a non-intrusive tool to monitor population-level transmission of COVID-19. Although various approaches are available to concentrate viruses from wastewater samples, scalable methods remain limited. Here, we sought to identify and evaluate SARS-CoV-2 virus concentration protocols for high-throughput wastewater testing. A total of twelve protocols for polyethylene glycol (PEG) precipitation and four protocols for ultrafiltration-based approaches were evaluated across two phases. The first phase entailed an initial evaluation using a small sample set, while the second phase further evaluated five protocols using wastewater samples of varying SARS-CoV-2 concentrations. Permutations in the pre-concentration, virus concentration and RNA extraction steps were evaluated. Among PEG-based methods, SARS-CoV-2 virus recovery was optimal with 1) the removal of debris prior to processing, 2) 2 h to 24 h incubation with 8% PEG at 4 °C, 3) 4000 xg or 14,000 xg centrifugation, and 4) a column-based RNA extraction method, yielding virus recovery of 42.4-52.5%. Similarly, the optimal protocol for ultrafiltration included 1) the removal of debris prior to processing, 2) ultrafiltration, and 3) a column-based RNA extraction method, yielding a recovery of 38.2%. This study also revealed that SARS-CoV-2 RNA recovery for samples with higher virus concentration were less sensitive to changes in the PEG method, but permutations in the PEG protocol could significantly impact virus yields when wastewater samples with lower SARS-CoV-2 RNA were used. Although both PEG precipitation and ultrafiltration methods resulted in similar SARS-CoV-2 RNA recoveries, the former method is more cost-effective while the latter method provided operational efficiency as it required a shorter turn-around-time (PEG precipitation, 9-23 h; Ultrafiltration, 5 h). The decision on which method to adopt will thus depend on the use-case for wastewater testing, and the need for cost-effectiveness, sensitivity, operational feasibility and scalability.

High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome

Characterizing complex microbial communities with single-cell resolution has been a long-standing goal of microbiology. We present Microbe-seq, a high-throughput method that yields the genomes of individual microbes from complex microbial communities. We encapsulate individual microbes in droplets with microfluidics and liberate their DNA, which we then amplify, tag with droplet-specific barcodes, and sequence. We explore the human gut microbiome, sequencing more than 20,000 microbial single-amplified genomes (SAGs) from a single human donor and coassembling genomes of almost 100 bacterial species, including several with multiple subspecies strains. We use these genomes to probe microbial interactions, reconstructing the horizontal gene transfer (HGT) network and observing HGT between 92 species pairs; we also identify a significant in vivo host-phage association between crAssphage and one strain of Bacteroides vulgatus. Microbe-seq contributes high-throughput culture-free capabilities to investigate genomic blueprints of complex microbial communities with single-microbe resolution.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Effect of antibiotic pretreatment on bacterial engraftment after Fecal Microbiota Transplant (FMT) in IBS-D

Fecal microbiota transplantation (FMT) is an attractive strategy to correct microbial dysbiosis in diarrhea-predominant irritable bowel syndrome (IBS-D). Although the mechanism of FMT is thought to be bacterial engraftment, the best approach to achieve engraftment after FMT in IBS-D (and other diseases) is not clear. We evaluated the effect of FMT (with or without pretreatment with antibiotics) on gut microbiome and symptoms in patients with IBS-D. In this randomized, placebo-controlled, single-center study, 44 patients with IBS-D with a least moderate severity (IBS severity scoring system, i.e., IBS-SSS, ≥175) were randomly assigned to one of four groups: single-dose oral FMT alone, single-dose oral FMT following a 7-day pretreatment course of Ciprofloxacin and Metronidazole (CM-FMT) or Rifaximin (R-FMT), or Placebo FMT. Primary endpoint was engraftment post-FMT and secondary endpoints were changes in IBS-SSS, and IBS-quality of life (IBS-QOL) at week 10. Median engraftment was significantly different among the three FMT groups (P = .013). Engraftment post-FMT was significantly higher in the FMT alone arm (15.5%) compared to that in R-FMT group (5%, P = .04) and CM-FMT group (2.4%, P = .002). The mean change in IBS-SSS and IBS-QOL from baseline were not significantly different among the four groups or between the three FMT groups combined vs. placebo at week 10. In summary, antibiotic pretreatment significantly reduced bacterial engraftment after FMT in patients with IBS-D.

Gut Ruminococcaceae levels at baseline correlate with risk of antibiotic-associated diarrhea

Antibiotic-associated diarrhea (AAD) affects a significant proportion of patients receiving antibiotics. We sought to understand if differences in the gut microbiome would influence the development of AAD. We administered a 3-day course of amoxicillin-clavulanate to 30 healthy adult volunteers, and analyzed their stool microbiome, using 16S rRNA gene sequencing, at baseline and up to 4 weeks post antibiotic administration. Lower levels of gut Ruminococcaceae were significantly and consistently observed from baseline until day 7 in participants who developed AAD. Overall, participants who developed AAD experienced a greater decrease in microbial diversity. The probability of AAD could be predicted based on qPCR-derived levels of Faecalibacterium prausnitzii at baseline. Our findings suggest that a lack of gut Ruminococcaceae influences development of AAD. Quantification of F. prausnitzii in stool prior to antibiotic administration may help identify patients at risk of AAD, and aid clinicians in devising individualized treatment regimens to minimize such adverse effects.

SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.

Sustained Ability of a Natural Microbial Community to Remove Nitrate from Groundwater

Microbial-mediated nitrate removal from groundwater is widely recognized as the predominant mechanism for nitrate attenuation in contaminated aquifers and is largely dependent on the presence of a carbon-bearing electron donor. The repeated exposure of a natural microbial community to an electron donor can result in the sustained ability of the community to remove nitrate; this phenomenon has been clearly demonstrated at the laboratory scale. However, in situ demonstrations of this ability are lacking. For this study, ethanol (electron donor) was repeatedly injected into a groundwater well (treatment) for six consecutive weeks to establish the sustained ability of a microbial community to remove nitrate. A second well (control) located upgradient was not injected with ethanol during this time. The treatment well demonstrated strong evidence of sustained ability as evident by ethanol, nitrate, and subsequent sulfate removal up to 21, 64, and 68%, respectively, as compared to the conservative tracer (bromide) upon consecutive exposures. Both wells were then monitored for six additional weeks under natural (no injection) conditions. During the final week, ethanol was injected into both treatment and control wells. The treatment well demonstrated sustained ability as evident by ethanol and nitrate removal up to 20 and 21%, respectively, as compared to bromide, whereas the control did not show strong evidence of nitrate removal (5% removal). Surprisingly, the treatment well did not indicate a sustained and selective enrichment of a microbial community. These results suggested that the predominant mechanism(s) of sustained ability likely exist at the enzymatic- and/or genetic-levels. The results of this study demonstrated the in situ ability of a microbial community to remove nitrate can be sustained in the prolonged absence of an electron donor.

"Waste Not, Want Not" - Leveraging Sewer Systems and Wastewater-Based Epidemiology for Drug Use Trends and Pharmaceutical Monitoring

During the current global COVID-19 pandemic and opioid epidemic, wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring public health trends by analysis of biomarkers including drugs, chemicals, and pathogens. Wastewater surveillance downstream at wastewater treatment plants provides large-scale population and regional-scale aggregation while upstream surveillance monitors locations at the neighborhood level with more precise geographic analysis. WBE can provide insights into dynamic drug consumption trends as well as environmental and toxicological contaminants. Applications of WBE include monitoring policy changes with cannabinoid legalization, tracking emerging illicit drugs, and early warning systems for potent fentanyl analogues along with the resurging wave of stimulants (e.g., methamphetamine, cocaine). Beyond drug consumption, WBE can also be used to monitor pharmaceuticals and their metabolites, including antidepressants and antipsychotics. In this manuscript, we describe the basic tenets and techniques of WBE, review its current application among drugs of abuse, and propose methods to scale and develop both monitoring and early warning systems with respect to measurement of illicit drugs and pharmaceuticals. We propose new frontiers in toxicological research with wastewater surveillance including assessment of medication assisted treatment of opioid use disorder (e.g., buprenorphine, methadone) in the context of other social burdens like COVID-19 disease.

Wastewater surveillance of SARS-CoV-2 across 40 U.S. states from February to June 2020

Wastewater-based disease surveillance is a promising approach for monitoring community outbreaks. Here we describe a nationwide campaign to monitor SARS-CoV-2 in the wastewater of 159 counties in 40 U.S. states, covering 13% of the U.S. population from February 18 to June 2, 2020. Out of 1,751 total samples analyzed, 846 samples were positive for SARS-CoV-2 RNA, with overall viral concentrations declining from April to May. Wastewater viral titers were consistent with, and appeared to precede, clinical COVID-19 surveillance indicators, including daily new cases. Wastewater surveillance had a high detection rate (>80%) of SARS-CoV-2 when the daily incidence exceeded 13 per 100,000 people. Detection rates were positively associated with wastewater treatment plant catchment size. To our knowledge, this work represents the largest-scale wastewater-based SARS-CoV-2 monitoring campaign to date, encompassing a wide diversity of wastewater treatment facilities and geographic locations. Our findings demonstrate that a national wastewater-based approach to disease surveillance may be feasible and effective.

Dynamic Colonization of Microbes and Their Functions after Fecal Microbiota Transplantation for Inflammatory Bowel Disease

For fecal microbiota transplantation (FMT) to be successful in immune diseases like inflammatory bowel disease, it is assumed that therapeutic microbes and their beneficial functions and immune interactions must colonize a recipient patient and persist in sufficient quantity and for a sufficient period of time to produce a clinical benefit. Few studies, however, have comprehensively profiled the colonization and persistence of transferred microbes along with the transfer of their microbial functions and interactions with the host immune system. Using 16S, metagenomic, and immunoglobulin A (IgA) sequencing, we analyzed hundreds of longitudinal microbiome samples from a randomized controlled trial of 12 patients with ulcerative colitis who received fecal transplant or placebo for 12 weeks. We uncovered diverse competitive dynamics among donor and patient strains, showing that persistence of transferred microbes is far from static. Indeed, one patient experienced a dramatic loss of donor bacteria 10 weeks into the trial, coinciding with a bloom of pathogenic bacteria and worsening symptoms. We evaluated the transfer of microbial functions, including desired ones, such as butyrate production, and unintended ones, such as antibiotic resistance. By profiling bacteria coated with IgA, we identified bacteria associated with inflammation and found that microbial interactions with the host immune system can be transferred across people, which could play a role in gut microbiome therapeutics for immune-related diseases. Our findings shed light on the colonization dynamics of gut microbes and their functions in the context of FMT to treat a complex disease-information that may provide a foundation for developing more-targeted therapeutics. IMPORTANCE Fecal microbiota transplantation (FMT)-transferring fecal microbes from a healthy donor to a sick patient-has shown promise for gut diseases such as inflammatory bowel disease. Unlike pharmaceuticals, however, fecal transplants are complex mixtures of living organisms, which must then interact with the microbes and immune system of the recipient. We sought to understand these interactions by tracking the microbes of 12 inflammatory bowel disease patients who received fecal transplants for 12 weeks. We uncovered a range of dynamics. For example, one patient experienced successful transfer of donor bacteria, only to lose them after 10 weeks. We similarly evaluated transfer of microbial functions, including how they interacted with the recipient's immune system. Our findings shed light on the colonization dynamics of gut microbes, as well as their functions in the context of FMT-information that may provide a critical foundation for the development of more-targeted therapeutics.

An omics-based framework for assessing the health risk of antimicrobial resistance genes

Antibiotic resistance genes (ARGs) are widespread among bacteria. However, not all ARGs pose serious threats to public health, highlighting the importance of identifying those that are high-risk. Here, we developed an ‘omics-based’ framework to evaluate ARG risk considering human-associated-enrichment, gene mobility, and host pathogenicity. Our framework classifies human-associated, mobile ARGs (3.6% of all ARGs) as the highest risk, which we further differentiate as ‘current threats’ (Rank I; 3%) - already present among pathogens - and ‘future threats’ (Rank II; 0.6%) - novel resistance emerging from non-pathogens. Our framework identified 73 ‘current threat’ ARG families. Of these, 35 were among the 37 high-risk ARGs proposed by the World Health Organization and other literature; the remaining 38 were significantly enriched in hospital plasmids. By evaluating all pathogen genomes released since framework construction, we confirmed that ARGs that recently transferred into pathogens were significantly enriched in Rank II (‘future threats’). Lastly, we applied the framework to gut microbiome genomes from fecal microbiota transplantation donors. We found that although ARGs were widespread (73% of genomes), only 8.9% of genomes contained high-risk ARGs. Our framework provides an easy-to-implement approach to identify current and future antimicrobial resistance threats, with potential clinical applications including reducing risk of microbiome-based interventions.

Antibiotic resistance genes are common but not all are of high risk to human health. Here, the authors develop an omics-based framework for ranking genes by risk that incorporates level of enrichment in human associated environments, gene mobility, and host pathogenicity.

Functional heterogeneity in the fermentation capabilities of the healthy human gut microbiota

The human gut microbiota is known for its highly heterogeneous composition across different individuals. However, relatively little is known about functional differences in its ability to ferment complex polysaccharides. Through ex vivo measurements from healthy human donors, we show that individuals vary markedly in their microbial metabolic phenotypes (MMPs), mirroring differences in their microbiota composition, and resulting in the production of different quantities and proportions of Short Chain Fatty Acids (SCFAs) from the same inputs. We also show that aspects of these MMPs can be predicted from composition using 16S rRNA sequencing. From experiments performed using the same dietary fibers in vivo, we demonstrate that an ingested bolus of fiber is almost entirely consumed by the microbiota upon passage. We leverage our ex vivo data to construct a model of SCFA production and absorption in vivo, and argue that inter-individual differences in quantities of absorbed SCFA are directly related to differences in production. Though in vivo studies are required to confirm these data in the context of the gut, in addition to in vivo read outs of SCFAs produced in response to specific fiber spike-ins, these data suggest that optimizing SCFA production in a given individual through targeted fiber supplementation requires quantitative understanding of their MMP.

Daily, oral FMT for long-term maintenance therapy in ulcerative colitis: results of a single-center, prospective, randomized pilot study

Background

Fecal microbiota transplantation (FMT) is a promising new strategy in the treatment of Inflammatory Bowel Disease, but long-term delivery systems are lacking. This randomized study was designed as a safety and feasibility study of long-term FMT in subjects with mild to moderate UC using frozen, encapsulated oral FMT (cFMT).

Methods

Subjects were randomized 1:1 to receive FMT induction by colonoscopy, followed by 12 weeks of daily oral administration of frozen encapsulated cFMT or sham therpay. Subjects were followed for 36 weeks and longitudenal clinical assessments included multiple subjective and objective markers of disease severity. Ribosomal 16S bacterial sequencing was used to assess donor-induced changes in the gut microbiota. Changes in T regulatory (Treg) and mucosal associated invariant T (MAIT) cell populations were evaluated by flow cytometry as an exploratory endpoint.

Results

Twelve subjects with active UC were randomized: 6 subjects completed the full 12-week course of FMT plus cFMT, and 6 subjects received sham treatment by colonic installation and longitudinal oral placebo capules. Chronic administration of cFMT was found to be safe and well-tolerated but home storage concerns exist. Protocol adherence was high, and none of the study subjects experienced FMT-associated treatment emergent adverse events. Two subjects that received cFMT achieved clinical remission versus none in the placebo group (95% CI = 0.38-infinity, p = 0.45). cFMT was associated with sustained donor-induced shifts in fecal microbial composition. Changes in MAIT cell cytokine production were observed in cFMT recipients and correlated with treatment response.

Conclusion

These pilot data suggest that daily encapsulated cFMT may extend the durability of index FMT-induced changes in gut bacterial community structure and that an association between MAIT cell cytokine production and clinical response to FMT may exist in UC populations. Oral frozen encapsulated cFMT is a promising FMT delivery system and may be preferred for longterm treatment strategies in UC and other chronic diseases but further evaluations will have to address home storage concerns. Larger trials should be done to explore the benefits of cFMT and to determine its long-term impacts on the colonic microbiome. Trial registration: ClinicalTrials.gov (NCT02390726). Registered 17 March 2015, https://clinicaltrials.gov/ct2/show/NCT02390726?term=NCT02390726&draw=2&rank=1.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01856-9.

Ecogenomics of Groundwater Phages Suggests Niche Differentiation Linked to Specific Environmental Tolerance

Viruses are ubiquitous microbiome components, shaping ecosystems via strain-specific predation, horizontal gene transfer and redistribution of nutrients through host lysis. Viral impacts are important in groundwater ecosystems, where microbes drive many nutrient fluxes and metabolic processes; however, little is known about the diversity of viruses in these environments. We analyzed four groundwater plasmidomes (the entire plasmid content of an environment) and identified 200 viral sequences, which clustered into 41 genus-level viral clusters (approximately equivalent to viral genera) including 9 known and 32 putative new genera. We used publicly available bacterial whole-genome sequences (WGS) and WGS from 261 bacterial isolates from this groundwater environment to identify potential viral hosts. We linked 76 of the 200 viral sequences to a range of bacterial phyla, the majority associated with Proteobacteria, followed by Firmicutes, Bacteroidetes, and Actinobacteria. The publicly available WGS enabled mapping bacterial hosts to several viral sequences. The WGS of groundwater isolates increased the depth of host prediction by allowing host identification at the strain level. The latter included 4 viruses that were almost entirely (>99% query coverage, >99% identity) identified as integrated in the genomes of Pseudomonas, Acidovorax, and Castellaniella strains, resulting in high-confidence host assignments. Lastly, 21 of these viruses carried putative auxiliary metabolite genes for metal and antibiotic resistance, which might drive their infection cycles and/or provide selective advantage to infected hosts. Exploring the groundwater virome provides a necessary foundation for integration of viruses into ecosystem models where they are key players in microbial adaption to environmental stress. IMPORTANCE To our knowledge, this is the first study to identify the bacteriophage distribution in a groundwater ecosystem shedding light on their prevalence and distribution across metal-contaminated and background sites. Our study is uniquely based on selective sequencing of solely the extrachromosomal elements of a microbiome followed by analysis for viral signatures, thus establishing a more focused approach for phage identifications. Using this method, we detected several novel phage genera along with those previously established. Our approach of using the whole-genome sequences of hundreds of bacterial isolates from the same site enabled us to make host assignments with high confidence, several at strain levels. Certain phage genes suggest that they provide an environment-specific selective advantage to their bacterial hosts. Our study lays the foundation for future research on directed phage isolations using specific bacterial host strains to further characterize groundwater phages, their life cycles, and their effects on groundwater microbiome and biogeochemistry.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. We collected 24-hour composite wastewater samples from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and measured SARS-CoV-2 RNA concentrations using RT-qPCR. We show that the relationship between wastewater viral titers and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater viral titers and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. We find that the WC ratio increases after key events, providing insight into the balance between disease spread and public health response. We also find that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity. These three metrics could complement a framework for integrating wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Elevated rates of horizontal gene transfer in the industrialized human microbiome

Industrialization has impacted the human gut ecosystem, resulting in altered microbiome composition and diversity. Whether bacterial genomes may also adapt to the industrialization of their host populations remains largely unexplored. Here, we investigate the extent to which the rates and targets of horizontal gene transfer (HGT) vary across thousands of bacterial strains from 15 human populations spanning a range of industrialization. We show that HGTs have accumulated in the microbiome over recent host generations and that HGT occurs at high frequency within individuals. Comparison across human populations reveals that industrialized lifestyles are associated with higher HGT rates and that the functions of HGTs are related to the level of host industrialization. Our results suggest that gut bacteria continuously acquire new functionality based on host lifestyle and that high rates of HGT may be a recent development in human history linked to industrialization.

Wastewater Surveillance of SARS-CoV-2 across 40 U.S. states

Wastewater-based disease surveillance is a promising approach for monitoring community outbreaks. Here we describe a nationwide campaign to monitor SARS-CoV-2 in the wastewater of 159 counties in 40 U.S. states, covering 13% of the U.S. population from February 18 to June 2, 2020. Out of 1,751 total samples analyzed, 846 samples were positive for SARS-CoV-2 RNA, with overall viral concentrations declining from April to May. Wastewater viral titers were consistent with, and appeared to precede, clinical COVID-19 surveillance indicators, including daily new cases. Wastewater surveillance had a high detection rate (>80%) of SARS-CoV-2 when the daily incidence exceeded 13 per 100,000 people. Detection rates were positively associated with wastewater treatment plant catchment size. To our knowledge, this work represents the largest-scale wastewater-based SARS-CoV-2 monitoring campaign to date, encompassing a wide diversity of wastewater treatment facilities and geographic locations. Our findings demonstrate that a national wastewater-based approach to disease surveillance may be feasible and effective.

Wastewater network infrastructure in public health: Applications and learnings from the COVID-19 pandemic

Accurate estimates of COVID-19 burden of infections in communities can inform public health strategy for the current pandemic. Wastewater based epidemiology (WBE) leverages sewer infrastructure to provide insights on rates of infection by measuring viral concentrations in wastewater. By accessing the sewer network at various junctures, important insights regarding COVID-19 disease activity can be gained. The analysis of sewage at the wastewater treatment plant level enables population-level surveillance of disease trends and virus mutations. At the neighborhood level, WBE can be used to describe trends in infection rates in the community thereby facilitating local efforts at targeted disease mitigation. Finally, at the building level, WBE can suggest the presence of infections and prompt individual testing. In this critical review, we describe the types of data that can be obtained through varying levels of WBE analysis, concrete plans for implementation, and public health actions that can be taken based on WBE surveillance data of infectious diseases, using recent and successful applications of WBE during the COVID-19 pandemic for illustration.

Culturing of "Unculturable" Subsurface Microbes: Natural Organic Carbon Source Fuels the Growth of Diverse and Distinct Bacteria From Groundwater

Recovery and cultivation of diverse environmentally-relevant microorganisms from the terrestrial subsurface remain a challenge despite recent advances in modern molecular technology. Here, we applied complex carbon (C) sources, i.e., sediment dissolved organic matter (DOM) and bacterial cell lysate, to enrich groundwater microbial communities for 30 days. As comparisons, we also included enrichments amended with simple C sources including glucose, acetate, benzoate, oleic acid, cellulose, and mixed vitamins. Our results demonstrate that complex C is far more effective in enriching diverse and distinct microorganisms from groundwater than simple C. Simple C enrichments yield significantly lower biodiversity, and are dominated by few phyla (e.g., Proteobacteria and Bacteroidetes), while microcosms enriched with complex C demonstrate significantly higher biodiversity including phyla that are poorly represented in published culture collections (e.g., Verrucomicrobia, Planctomycetes, and Armatimonadetes). Subsequent isolation from complex C enrichments yielded 228 bacterial isolates representing five phyla, 17 orders, and 56 distinct species, including candidate novel, rarely cultivated, and undescribed organisms. Results from this study will substantially advance cultivation and isolation strategies for recovering diverse and novel subsurface microorganisms. Obtaining axenic representatives of “once-unculturable” microorganisms will enhance our understanding of microbial physiology and function in different biogeochemical niches of terrestrial subsurface ecosystems.

Predicting human health from biofluid-based metabolomics using machine learning

Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.

Making waves: Wastewater surveillance of SARS-CoV-2 for population-based health management

Worldwide, clinical data remain the gold standard for disease surveillance and tracking. However, such data are limited due to factors such as reporting bias and inability to track asymptomatic disease carriers. Disease agents are excreted in the urine and feces of infected individuals regardless of disease symptom severity. Wastewater surveillance - that is, monitoring disease via human effluent - represents a valuable complement to clinical approaches. Because wastewater is relatively inexpensive and easy to collect and can be monitored at different levels of population aggregation as needed, wastewater surveillance can offer a real-time, cost-effective view of a community's health that is independent of biases associated with case-reporting. For SARS-CoV-2 and other disease-causing agents we envision an aggregate wastewater-monitoring system at the level of a wastewater treatment plant and exploratory or confirmatory monitoring of the sewerage system at the neighborhood scale to identify or confirm clusters of infection or assess impact of control measures where transmission has been established. Implementation will require constructing a framework with collaborating government agencies, public or private utilities, and civil society organizations for appropriate use of data collected from wastewater, identification of an appropriate scale of sample collection and aggregation to balance privacy concerns and risk of stigmatization with public health preservation, and consideration of the social implications of wastewater surveillance.

Prebiotics and Community Composition Influence Gas Production of the Human Gut Microbiota

Prebiotics confer benefits to human health, often by promoting the growth of gut bacteria that produce metabolites valuable to the human body, such as short-chain fatty acids (SCFAs). While prebiotic selection has strongly focused on maximizing the production of SCFAs, less attention has been paid to gases, a by-product of SCFA production that also has physiological effects on the human body. Here, we investigate how the content and volume of gas production by human gut microbiota are affected by the chemical composition of the prebiotic and the community composition of the microbiota. We first constructed a linear system model based on mass and electron balance and compared the theoretical product ranges of two prebiotics, inulin and pectin. Modeling shows that pectin is more restricted in product space, with less potential for H2 but more potential for CO2 production. An ex vivo experimental system showed pectin degradation produced significantly less H2 than inulin, but CO2 production fell outside the theoretical product range, suggesting fermentation of fecal debris. Microbial community composition also impacted results: methane production was dependent on the presence of Methanobacteria, while interindividual differences in H2 production during inulin degradation were driven by a Lachnospiraceae taxon. Overall, these results suggest that both the chemistry of the prebiotic and the composition of the microbiota are relevant to gas production. Metabolic processes that are relatively prevalent in the microbiome, such as H2 production, will depend more on substrate, while rare metabolisms such as methanogenesis depend more strongly on microbiome composition.IMPORTANCE Prebiotic fermentation in the gut often leads to the coproduction of short-chain fatty acids (SCFAs) and gases. While excess gas production can be a potential problem for those with functional gut disorders, gas production is rarely considered during prebiotic design. In this study, we combined the use of theoretical models and an ex vivo experimental platform to illustrate that both the chemical composition of the prebiotic and the community composition of the human gut microbiota can affect the volume and content of gas production during prebiotic fermentation. Specifically, more prevalent metabolic processes such as hydrogen production were strongly affected by the oxidation state of the probiotic, while rare metabolisms such as methane production were less affected by the chemical nature of the substrate and entirely dependent on the presence of Methanobacteria in the microbiota.

16S rRNA sequencing analysis: the devil is in the details

User-friendly computational tools for 16S ribosomal RNA (rRNA) sequencing analysis enable researchers who are not bioinformaticians to analyze and interpret sequencing data from microbial communities. These tools' easy-to-use interfaces belie the sophisticated and rapidly-evolving science of their underlying algorithms. When analyzing 16S data from a simple microbiome experiment, we found that superficially unimportant decisions about the bioinformatic pipeline led to results with radically different biological interpretations. We share these results as a cautionary tale whose moral is that, in 16S analysis, the devil is in the details. Wet bench researchers should therefore strongly consider partnering with bioinformaticians or computational biologists when analyzing 16S data.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases

Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases

Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases

Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases

Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases

Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases

Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.