Correction: Prioritizing surveillance of Nipah virus in India.

[This corrects the article DOI: 10.1371/journal.pntd.0007393.].

The microbiome of cryospheric ecosystems.

The melting of the cryosphere is among the most conspicuous consequences of climate change, with impacts on microbial life and related biogeochemistry. However, we are missing a systematic understanding of microbiome structure and function across cryospheric ecosystems. Here, we present a global inventory of the microbiome from snow, ice, permafrost soils, and both coastal and freshwater ecosystems under glacier influence. Combining phylogenetic and taxonomic approaches, we find that these cryospheric ecosystems, despite their particularities, share a microbiome with representatives across the bacterial tree of life and apparent signatures of early and constrained radiation. In addition, we use metagenomic analyses to define the genetic repertoire of cryospheric bacteria. Our work provides a reference resource for future studies on climate change microbiology.

Microdiversity characterizes prevalent phylogenetic clades in the glacier-fed stream microbiome.

Glacier-fed streams (GFSs) are extreme and rapidly vanishing ecosystems, and yet they harbor diverse microbial communities. Although our understanding of the GFS microbiome has recently increased, we do not know which microbial clades are ecologically successful in these ecosystems, nor do we understand potentially underlying mechanisms. Ecologically successful clades should be more prevalent across GFSs compared to other clades, which should be reflected as clade-wise distinctly low phylogenetic turnover. However, methods to assess such patterns are currently missing. Here we developed and applied a novel analytical framework, "phyloscore analysis", to identify clades with lower spatial phylogenetic turnover than other clades in the sediment microbiome across twenty GFSs in New Zealand. These clades constituted up to 44% and 64% of community α-diversity and abundance, respectively. Furthermore, both their α-diversity and abundance increased as sediment chlorophyll a decreased, corroborating their ecological success in GFS habitats largely devoid of primary production. These clades also contained elevated levels of putative microdiversity than others, which could potentially explain their high prevalence in GFSs. This hitherto unknown microdiversity may be threatened as glaciers shrink, urging towards further genomic and functional exploration of the GFS microbiome.

Estimating viral prevalence with data fusion for adaptive two-phase pooled sampling.

The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second-phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second-phase samples.To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two-phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.The manuscript presents guidance on implementing the first-phase and second-phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.

Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England.

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

Patterns and Drivers of Extracellular Enzyme Activity in New Zealand Glacier-Fed Streams.

Glacier-fed streams (GFSs) exhibit near-freezing temperatures, variable flows, and often high turbidities. Currently, the rapid shrinkage of mountain glaciers is altering the delivery of meltwater, solutes, and particulate matter to GFSs, with unknown consequences for their ecology. Benthic biofilms dominate microbial life in GFSs, and play a major role in their biogeochemical cycling. Mineralization is likely an important process for microbes to meet elemental budgets in these systems due to commonly oligotrophic conditions, and extracellular enzymes retained within the biofilm enable the degradation of organic matter and acquisition of carbon (C), nitrogen (N), and phosphorus (P). The measurement and comparison of these extracellular enzyme activities (EEA) can in turn provide insight into microbial elemental acquisition effort relative to environmental availability. To better understand how benthic biofilm communities meet resource demands, and how this might shift as glaciers vanish under climate change, we investigated biofilm EEA in 20 GFSs varying in glacier influence from New Zealand's Southern Alps. Using turbidity and distance to the glacier snout normalized for glacier size as proxies for glacier influence, we found that bacterial abundance (BA), chlorophyll a (Chl a), extracellular polymeric substances (EPS), and total EEA per gram of sediment increased with decreasing glacier influence. Yet, when normalized by BA, EPS decreased with decreasing glacier influence, Chl a still increased, and there was no relationship with total EEA. Based on EEA ratios, we found that the majority of GFS microbial communities were N-limited, with a few streams of different underlying bedrock geology exhibiting P-limitation. Cell-specific C-acquiring EEA was positively related to the ratio of Chl a to BA, presumably reflecting the utilization of algal exudates. Meanwhile, cell-specific N-acquiring EEA were positively correlated with the concentration of dissolved inorganic nitrogen (DIN), and both N- and P-acquiring EEA increased with greater cell-specific EPS. Overall, our results reveal greater glacier influence to be negatively related to GFS biofilm biomass parameters, and generally associated with greater microbial N demand. These results help to illuminate the ecology of GFS biofilms, along with their biogeochemical response to a shifting habitat template with ongoing climate change.

Soil Bacterial and Fungal Richness Forecast Patterns of Early Pine Litter Decomposition.

Discovering widespread microbial processes that drive unexpected variation in carbon cycling may improve modeling and management of soil carbon (Prescott, 2010; Wieder et al., 2015a, 2018). A first step is to identify community features linked to carbon cycle variation. We addressed this challenge using an epidemiological approach with 206 soil communities decomposing Ponderosa pine litter in 618 microcosms. Carbon flow from litter decomposition was measured over a 6-week incubation. Cumulative CO2 from microbial respiration varied two-fold among microcosms and dissolved organic carbon (DOC) from litter decomposition varied five-fold, demonstrating large functional variation despite constant environmental conditions where strong selection is expected. To investigate microbial features driving DOC concentration, two microbial community cohorts were delineated as "high" and "low" DOC. For each cohort, communities from the original soils and from the final microcosm communities after the 6-week incubation with litter were taxonomically profiled. A logistic model including total biomass, fungal richness, and bacterial richness measured in the original soils or in the final microcosm communities predicted the DOC cohort with 72 (P < 0.05) and 80 (P < 0.001) percent accuracy, respectively. The strongest predictors of the DOC cohort were biomass and either fungal richness (in the original soils) or bacterial richness (in the final microcosm communities). Successful forecasting of functional patterns after lengthy community succession in a new environment reveals strong historical contingencies. Forecasting future community function is a key advance beyond correlation of functional variance with end-state community features. The importance of taxon richness-the same feature linked to carbon fate in gut microbiome studies-underscores the need for increased understanding of biotic mechanisms that can shape richness in microbial communities independent of physicochemical conditions.

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.

Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States.

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections to date has relied heavily on reverse transcription polymerase chain reaction testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or subclinical infections have resulted in an undercounting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with coronavirus disease 2019 (COVID-19) case counts across states. If one-third of patients infected with SARS-CoV-2 in the United States sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the United States during the 3-week period from 8 to 28 March 2020. Combining excess ILI counts with the date of onset of community transmission in the United States, we also show that the early epidemic in the United States was unlikely to have been doubling slower than every 4 days. Together, these results suggest a conceptual model for the COVID-19 epidemic in the United States characterized by rapid spread across the United States with more than 80% infected individuals remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases.

Identifying Suspect Bat Reservoirs of Emerging Infections.

Bats host a number of pathogens that cause severe disease and onward transmission in humans and domestic animals. Some of these pathogens, including henipaviruses and filoviruses, are considered a concern for future pandemics. There has been substantial effort to identify these viruses in bats. However, the reservoir hosts for Ebola virus are still unknown and henipaviruses are largely uncharacterized across their distribution. Identifying reservoir species is critical in understanding the viral ecology within these hosts and the conditions that lead to spillover. We collated surveillance data to identify taxonomic patterns in prevalence and seroprevalence and to assess sampling efforts across species. We systematically collected data on filovirus and henipavirus detections and used a machine-learning algorithm, phylofactorization, in order to search the bat phylogeny for cladistic patterns in filovirus and henipavirus infection, accounting for sampling efforts. Across sampled bat species, evidence for filovirus infection was widely dispersed across the sampled phylogeny. We found major gaps in filovirus sampling in bats, especially in Western Hemisphere species. Evidence for henipavirus infection was clustered within the Pteropodidae; however, no other clades have been as intensely sampled. The major predictor of filovirus and henipavirus exposure or infection was sampling effort. Based on these results, we recommend expanding surveillance for these pathogens across the bat phylogenetic tree.

Ecological and evolutionary drivers of haemoplasma infection and bacterial genotype sharing in a Neotropical bat community.

Most emerging pathogens can infect multiple species, underlining the importance of understanding the ecological and evolutionary factors that allow some hosts to harbour greater infection prevalence and share pathogens with other species. However, our understanding of pathogen jumps is based primarily around viruses, despite bacteria accounting for the greatest proportion of zoonoses. Because bacterial pathogens in bats (order Chiroptera) can have conservation and human health consequences, studies that examine the ecological and evolutionary drivers of bacterial prevalence and barriers to pathogen sharing are crucially needed. Here were studied haemotropic Mycoplasma spp. (i.e., haemoplasmas) across a species-rich bat community in Belize over two years. Across 469 bats spanning 33 species, half of individuals and two-thirds of species were haemoplasma positive. Infection prevalence was higher for males and for species with larger body mass and colony sizes. Haemoplasmas displayed high genetic diversity (21 novel genotypes) and strong host specificity. Evolutionary patterns supported codivergence of bats and bacterial genotypes alongside phylogenetically constrained host shifts. Bat species centrality to the network of shared haemoplasma genotypes was phylogenetically clustered and unrelated to prevalence, further suggesting rare-but detectable-bacterial sharing between species. Our study highlights the importance of using fine phylogenetic scales when assessing host specificity and suggests phylogenetic similarity may play a key role in host shifts not only for viruses but also for bacteria. Such work more broadly contributes to increasing efforts to understand cross-species transmission and the epidemiological consequences of bacterial pathogens.

A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome.

Understanding when and why new species are recruited into microbial communities is a formidable problem with implications for managing microbial systems, for instance by helping us better understand whether a probiotic or pathogen would be expected to colonize a human microbiome. Much theory in microbial temporal dynamics is focused on how phylogenetic relationships between microbes impact the order in which those microbes are recruited; for example, species that are closely related may competitively exclude each other. However, several recent human microbiome studies have observed closely related bacteria being recruited into microbial communities in short succession, suggesting that microbial community assembly is historically contingent, but competitive exclusion of close relatives may not be important. To address this, we developed a mathematical model that describes the order in which new species are detected in microbial communities over time within a phylogenetic framework. We use our model to test three hypothetical assembly modes: underdispersion (species recruitment is more likely if a close relative was previously detected), overdispersion (recruitment is more likely if a close relative has not been previously detected), and the neutral model (recruitment likelihood is not related to phylogenetic relationships among species). We applied our model to longitudinal human microbiome data, and found that for the individuals we analyzed, the human microbiome generally follows the underdispersion (i.e., nepotism) hypothesis. Exceptions were oral communities and the fecal communities of two infants that had undergone heavy antibiotic treatment. None of the datasets we analyzed showed statistically significant phylogenetic overdispersion.

Rare microbial taxa emerge when communities collide: freshwater and marine microbiome responses to experimental mixing.

Whole microbial communities regularly merge with one another, often in tandem with their environments, in a process called community coalescence. Such events impose substantial changes: abiotic perturbation from environmental blending and biotic perturbation of community merging. We used an aquatic mixing experiment to unravel the effects of these perturbations on the whole microbiome response and on the success of individual taxa when distinct freshwater and marine communities coalesce. We found that an equal mix of freshwater and marine habitats and blended microbiomes resulted in strong convergence of the community structure toward that of the marine microbiome. The enzymatic potential of these blended microbiomes in mixed media also converged toward that of the marine, with strong correlations between the multivariate response patterns of the enzymes and of community structure. Exposing each endmember inocula to an axenic equal mix of their freshwater and marine source waters led to a 96% loss of taxa from our freshwater microbiomes and a 66% loss from our marine microbiomes. When both inocula were added together to this mixed environment, interactions amongst the communities led to a further loss of 29% and 49% of freshwater and marine taxa, respectively. Under both the axenic and competitive scenarios, the diversity lost was somewhat counterbalanced by increased abundance of microbial taxa that were too rare to detect in the initial inocula. Our study emphasizes the importance of the rare biosphere as a critical component of microbial community responses to community coalescence.

Temporal and spatial limitations in global surveillance for bat filoviruses and henipaviruses.

Sampling reservoir hosts over time and space is critical to detect epizootics, predict spillover and design interventions. However, because sampling is logistically difficult and expensive, researchers rarely perform spatio-temporal sampling of many reservoir hosts. Bats are reservoirs of many virulent zoonotic pathogens such as filoviruses and henipaviruses, yet the highly mobile nature of these animals has limited optimal sampling of bat populations. To quantify the frequency of temporal sampling and to characterize the geographical scope of bat virus research, we here collated data on filovirus and henipavirus prevalence and seroprevalence in wild bats. We used a phylogenetically controlled meta-analysis to next assess temporal and spatial variation in bat virus detection estimates. Our analysis shows that only one in four bat virus studies report data longitudinally, that sampling efforts cluster geographically (e.g. filovirus data are available across much of Africa and Asia but are absent from Latin America and Oceania), and that sampling designs and reporting practices may affect some viral detection estimates (e.g. filovirus seroprevalence). Within the limited number of longitudinal bat virus studies, we observed high heterogeneity in viral detection estimates that in turn reflected both spatial and temporal variation. This suggests that spatio-temporal sampling designs are important to understand how zoonotic viruses are maintained and spread within and across wild bat populations, which in turn could help predict and preempt risks of zoonotic viral spillover.

Percolation models of pathogen spillover.

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

The problem of scale in the prediction and management of pathogen spillover.

Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Prioritizing surveillance of Nipah virus in India.

The 2018 outbreak of Nipah virus in Kerala, India, highlights the need for global surveillance of henipaviruses in bats, which are the reservoir hosts for this and other viruses. Nipah virus, an emerging paramyxovirus in the genus Henipavirus, causes severe disease and stuttering chains of transmission in humans and is considered a potential pandemic threat. In May 2018, an outbreak of Nipah virus began in Kerala, > 1800 km from the sites of previous outbreaks in eastern India in 2001 and 2007. Twenty-three people were infected and 21 people died (16 deaths and 18 cases were laboratory confirmed). Initial surveillance focused on insectivorous bats (Megaderma spasma), whereas follow-up surveys within Kerala found evidence of Nipah virus in fruit bats (Pteropus medius). P. medius is the confirmed host in Bangladesh and is now a confirmed host in India. However, other bat species may also serve as reservoir hosts of henipaviruses. To inform surveillance of Nipah virus in bats, we reviewed and analyzed the published records of Nipah virus surveillance globally. We applied a trait-based machine learning approach to a subset of species that occur in Asia, Australia, and Oceana. In addition to seven species in Kerala that were previously identified as Nipah virus seropositive, we identified at least four bat species that, on the basis of trait similarity with known Nipah virus-seropositive species, have a relatively high likelihood of exposure to Nipah or Nipah-like viruses in India. These machine-learning approaches provide the first step in the sequence of studies required to assess the risk of Nipah virus spillover in India. Nipah virus surveillance not only within Kerala but also elsewhere in India would benefit from a research pipeline that included surveys of known and predicted reservoirs for serological evidence of past infection with Nipah virus (or cross reacting henipaviruses). Serosurveys should then be followed by longitudinal spatial and temporal studies to detect shedding and isolate virus from species with evidence of infection. Ecological studies will then be required to understand the dynamics governing prevalence and shedding in bats and the contacts that could pose a risk to public health.

Establishing microbial composition measurement standards with reference frames.

Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. Here, we demonstrate common pitfalls in comparing relative abundance across samples and identify two solutions that reveal microbial changes without the need to estimate total microbial load. We define the notion of "reference frames", which provide deep intuition about the compositional nature of microbiome data. In an oral time series experiment, reference frames alleviate false positives and produce consistent results on both raw and cell-count normalized data. Furthermore, reference frames identify consistent, differentially abundant microbes previously undetected in two independent published datasets from subjects with atopic dermatitis. These methods allow reassessment of published relative abundance data to reveal reproducible microbial changes from standard sequencing output without the need for new assays.