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Background Microorganisms in urban sanitary sewers exhibit community properties that suggest sewers are a novel ecosystem. Sewer microorganisms present both an opportunity as a control point for wastewater treatment and a risk to human health. If treatment processes are to be improved and health risks quantified, then it is necessary to understand microbial distributions and dynamics within this community. Here, we use 16S rRNA gene sequencing to characterize raw influent wastewater bacterial communities in a 5-year time series from two wastewater treatment plants in Milwaukee, WI; influent wastewater from 77 treatment plants across the USA; and wastewater in 12 Milwaukee residential sewers. Results In Milwaukee, we find that in transit from residences to treatment plants, the human bacterial component of wastewater decreases in proportion and exhibits stochastic temporal variation. In contrast, the resident sewer community increases in abundance during transit and cycles seasonally according to changes in wastewater temperature. The result is a bacterial community that assembles into two distinct community states each year according to the extremes in wastewater temperature. Wastewater bacterial communities from other northern US cities follow temporal trends that mirror those in Milwaukee, but southern US cities have distinct community compositions and differ in their seasonal patterns. Conclusions Our findings provide evidence that environmental conditions associated with seasonal change and climatic differences related to geography predictably structure the bacterial communities residing in below-ground sewer pipes. DoZVvrVoCyPBLkvDJ_Cd4M Video abstract

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

Molecular characterizations of the gut microbiome from individual human stool samples have identified community patterns that correlate with age, disease, diet, and other human characteristics, but resources for marker gene studies that consider microbiome trends among human populations scale with the number of individuals sampled from each population. As an alternative strategy for sampling populations, we examined whether sewage accurately reflects the microbial community of a mixture of stool samples. We used oligotyping of high-throughput 16S rRNA gene sequence data to compare the bacterial distribution in a stool data set to a sewage influent data set from 71 U.S. cities. On average, only 15% of sewage sample sequence reads were attributed to human fecal origin, but sewage recaptured most (97%) human fecal oligotypes. The most common oligotypes in stool matched the most common and abundant in sewage. After informatically separating sequences of human fecal origin, sewage samples exhibited ~3× greater diversity than stool samples. Comparisons among municipal sewage communities revealed the ubiquitous and abundant occurrence of 27 human fecal oligotypes, representing an apparent core set of organisms in U.S. populations. The fecal community variability among U.S. populations was significantly lower than among individuals. It clustered into three primary community structures distinguished by oligotypes from either: Bacteroidaceae , Prevotellaceae , or Lachnospiraceae/Ruminococcaceae . These distribution patterns reflected human population variation and predicted whether samples represented lean or obese populations with 81 to 89% accuracy. Our findings demonstrate that sewage represents the fecal microbial community of human populations and captures population-level traits of the human microbiome. IMPORTANCE The gut microbiota serves important functions in healthy humans. Numerous projects aim to define a healthy gut microbiome and its association with health states. However, financial considerations and privacy concerns limit the number of individuals who can be screened. By analyzing sewage from 71 cities, we demonstrate that geographically distributed U.S. populations share a small set of bacteria whose members represent various common community states within U.S. adults. Cities were differentiated by their sewage bacterial communities, and the community structures were good predictors of a city's estimated level of obesity. Our approach demonstrates the use of sewage as a means to sample the fecal microbiota from millions of people and its potential to elucidate microbiome patterns associated with human demographics. The gut microbiota serves important functions in healthy humans. Numerous projects aim to define a healthy gut microbiome and its association with health states. However, financial considerations and privacy concerns limit the number of individuals who can be screened. By analyzing sewage from 71 cities, we demonstrate that geographically distributed U.S. populations share a small set of bacteria whose members represent various common community states within U.S. adults. Cities were differentiated by their sewage bacterial communities, and the community structures were good predictors of a city's estimated level of obesity. Our approach demonstrates the use of sewage as a means to sample the fecal microbiota from millions of people and its potential to elucidate microbiome patterns associated with human demographics.

Urban coasts receive watershed drainage from ecosystems that include highly developed lands with sewer and stormwater infrastructure. In these complex ecosystems, coastal waters are often contaminated with fecal pollution, where multiple delivery mechanisms that often contain multiple fecal sources make it difficult to mitigate the pollution. Here, we exploit bacterial community sequencing of the V6 and V6V4 hypervariable regions of the bacterial 16S rRNA gene to identify bacterial distributions that signal the presence of sewer, fecal, and human fecal pollution. The sequences classified to three sewer infrastructure-associated bacterial genera, Acinetobacter, Arcobacter, and Trichococcus, and five fecal-associated bacterial families, Bacteroidaceae, Porphyromonadaceae, Clostridiaceae, Lachnospiraceae, and Ruminococcaceae, served as signatures of sewer and fecal contamination, respectively. The human fecal signature was determined with the Bayesian source estimation program SourceTracker, which we applied to a set of 40 sewage influent samples collected in Milwaukee, WI, USA to identify operational taxonomic units (≥97 % identity) that were most likely of human fecal origin. During periods of dry weather, the magnitudes of all three signatures were relatively low in Milwaukee's urban rivers and harbor and nearly zero in Lake Michigan. However, the relative contribution of the sewer and fecal signature frequently increased to >2 % of the measured surface water communities following sewer overflows. Also during combined sewer overflows, the ratio of the human fecal pollution signature to the fecal pollution signature in surface waters was generally close to that of sewage, but this ratio decreased dramatically during dry weather and rain events, suggesting that nonhuman fecal pollution was the dominant source during these weather-driven scenarios. The qPCR detection of two human fecal indicators, human Bacteroides and Lachno2, confirmed the urban fecal footprint in this ecosystem extends to at least 8 km offshore.

Multiple models describe the formation and evolution of distinct microbial phylogenetic groups. These evolutionary models make different predictions regarding how adaptive alleles spread through populations and how genetic diversity is maintained. Processes predicted by competing evolutionary models, for example, genome-wide selective sweeps vs gene-specific sweeps, could be captured in natural populations using time-series metagenomics if the approach were applied over a sufficiently long time frame. Direct observations of either process would help resolve how distinct microbial groups evolve. Here, from a 9-year metagenomic study of a freshwater lake (2005–2013), we explore changes in single-nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in 30 bacterial populations. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied by >1000-fold among populations. SNP allele frequencies also changed dramatically over time within some populations. Interestingly, nearly all SNP variants were slowly purged over several years from one population of green sulfur bacteria, while at the same time multiple genes either swept through or were lost from this population. These patterns were consistent with a genome-wide selective sweep in progress, a process predicted by the ‘ecotype model’ of speciation but not previously observed in nature. In contrast, other populations contained large, SNP-free genomic regions that appear to have swept independently through the populations prior to the study without purging diversity elsewhere in the genome. Evidence for both genome-wide and gene-specific sweeps suggests that different models of bacterial speciation may apply to different populations coexisting in the same environment.

Delineating differences in gut microbiomes of human and animal hosts contributes towards understanding human health and enables new strategies for detecting reservoirs of waterborne human pathogens. We focused upon Blautia , a single microbial genus that is important for nutrient assimilation as preliminary work suggested host-related patterns within members of this genus. In our dataset of 57 M sequence reads of the V6 region of the 16S ribosomal RNA gene in samples collected from seven host species, we identified 200 high-resolution taxonomic units within Blautia using oligotyping. Our analysis revealed 13 host-specific oligotypes that occurred exclusively in fecal samples of humans (three oligotypes), swine (six oligotypes), cows (one oligotype), deer (one oligotype), or chickens (two oligotypes). We identified an additional 171 oligotypes that exhibited differential abundance patterns among all the host species. Blautia oligotypes in the human population obtained from sewage and fecal samples displayed remarkable continuity. Oligotypes from only 10 Brazilian human fecal samples collected from individuals in a rural village encompassed 97% of all Blautia oligotypes found in a Brazilian sewage sample from a city of three million people. Further, 75% of the oligotypes in Brazilian human fecal samples matched those in US sewage samples, implying that a universal set of Blautia strains may be shared among culturally and geographically distinct human populations. Such strains can serve as universal markers to assess human fecal contamination in environmental samples. Our results indicate that host-specificity and host-preference patterns of organisms within this genus are driven by host physiology more than dietary habits.

Microorganisms play critical roles in Earth’s nitrogen cycle. In lakes, microorganisms called nitrifiers derive energy from reduced nitrogen compounds. Microbial nitrification is a critical process governing nitrogen availability in aquatic systems. Freshwater nitrifiers have received little attention, leaving many unanswered questions about their taxonomic distribution, functional potential, and ecological interactions. Here, we reconstructed genomes to infer the metabolism and ecology of free-living picoplanktonic nitrifiers across the Laurentian Great Lakes, a connected series of five of Earth’s largest lakes. Surprisingly, ammonia-oxidizing bacteria (AOB) related to Nitrosospira dominated over ammonia-oxidizing archaea (AOA) at nearly all stations, with distinct ecotypes prevailing in the transparent, oligotrophic upper lakes compared to Lakes Erie and Ontario. Unexpectedly, one ecotype of Nitrosospira encodes proteorhodopsin, which could enhance survival under conditions where ammonia oxidation is inhibited or substrate limited. Nitrite-oxidizing bacteria (NOB) “ Candidatus Nitrotoga” and Nitrospira fluctuated in dominance, with the latter prevailing in deeper, less-productive basins. Genome reconstructions reveal highly reduced genomes and features consistent with genome streamlining, along with diverse adaptations to sunlight and oxidative stress and widespread capacity for organic nitrogen use. Our findings expand the known functional diversity of nitrifiers and establish their ecological genomics in large lake ecosystems. By elucidating links between microbial biodiversity and biogeochemical cycling, our work also informs ecosystem models of the Laurentian Great Lakes, a critical freshwater resource experiencing rapid environmental change. IMPORTANCE Microorganisms play critical roles in Earth’s nitrogen cycle. In lakes, microorganisms called nitrifiers derive energy from reduced nitrogen compounds. In doing so, they transform nitrogen into a form that can ultimately be lost to the atmosphere by a process called denitrification, which helps mitigate nitrogen pollution from fertilizer runoff and sewage. Despite their importance, freshwater nitrifiers are virtually unexplored. To understand their diversity and function, we reconstructed genomes of freshwater nitrifiers across some of Earth’s largest freshwater lakes, the Laurentian Great Lakes. We discovered several new species of nitrifiers specialized for clear low-nutrient waters and distinct species in comparatively turbid Lake Erie. Surprisingly, one species may be able to harness light energy by using a protein called proteorhodopsin, despite the fact that nitrifiers typically live in deep dark water. Our work reveals the unique biodiversity of the Great Lakes and fills key gaps in our knowledge of an important microbial group, the nitrifiers.

Microorganisms associated with coastal sands serve as a natural biofilter, providing essential nutrient recycling in nearshore environments and acting to maintain coastal ecosystem health. Anthropogenic stressors often impact these ecosystems, but little is known about whether these disturbances can be identified through microbial community change. The blowout of the Macondo Prospect reservoir on April 20, 2010, which released oil hydrocarbons into the Gulf of Mexico, presented an opportunity to examine whether microbial community composition might provide a sensitive measure of ecosystem disturbance. Samples were collected on four occasions, beginning in mid-June, during initial beach oiling, until mid-November from surface sand and surf zone waters at seven beaches stretching from Bay St. Louis, MS to St. George Island, FL USA. Oil hydrocarbon measurements and NOAA shoreline assessments indicated little to no impact on the two most eastern beaches (controls). Sequence comparisons of bacterial ribosomal RNA gene hypervariable regions isolated from beach sands located to the east and west of Mobile Bay in Alabama demonstrated that regional drivers account for markedly different bacterial communities. Individual beaches had unique community signatures that persisted over time and exhibited spatial relationships, where community similarity decreased as horizontal distance between samples increased from one to hundreds of meters. In contrast, sequence analyses detected larger temporal and less spatial variation among the water samples. Superimposed upon these beach community distance and time relationships, was increased variability in bacterial community composition from oil hydrocarbon contaminated sands. The increased variability was observed among the core, resident, and transient community members, indicating the occurrence of community-wide impacts rather than solely an overprinting of oil hydrocarbon-degrading bacteria onto otherwise relatively stable sand population structures. Among sequences classified to genus, Alcanivorax, Alteromonas, Marinobacter, Winogradskyella, and Zeaxanthinibacter exhibited the largest relative abundance increases in oiled sands.

Members of the marine Roseobacter lineage have been characterized as ecological generalists, suggesting that there will be challenges in assigning well-delineated ecological roles and biogeochemical functions to the taxon. To address this issue, genome sequences of 32 Roseobacter isolates were analyzed for patterns in genome characteristics, gene inventory, and individual gene/pathway distribution using three predictive frameworks: phylogenetic relatedness, lifestyle strategy and environmental origin of the isolate. For the first framework, a phylogeny containing five deeply branching clades was obtained from a concatenation of 70 conserved single-copy genes. Somewhat surprisingly, phylogenetic tree topology was not the best model for organizing genome characteristics or distribution patterns of individual genes/pathways, although it provided some predictive power. The lifestyle framework, established by grouping isolates according to evidence for heterotrophy, photoheterotrophy or autotrophy, explained more of the gene repertoire in this lineage. The environment framework had a weak predictive power for the overall genome content of each strain, but explained the distribution of several individual genes/pathways, including those related to phosphorus acquisition, chemotaxis and aromatic compound degradation. Unassembled sequences in the Global Ocean Sampling metagenomic data independently verified this global-scale geographical signal in some Roseobacter genes. The primary findings emerging from this comparative genome analysis are that members of the lineage cannot be easily collapsed into just a few ecologically differentiated clusters (that is, there are almost as many clusters as isolates); the strongest framework for predicting genome content is trophic strategy, but no single framework gives robust predictions; and previously unknown homologs to genes for H 2 oxidation, proteorhodopsin-based phototrophy, xanthorhodpsin-based phototrophy, and CO 2 fixation by Form IC ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) expand the possible mechanisms for energy and carbon acquisition in this remarkably versatile bacterial lineage.

Recirculating aquaculture systems (RAS) are agroecosystems for intensive on-land cultivation of products of fisheries. Practitioners that incorporate edible plant production into RAS refer to these facilities as aquaponic systems (AP). RAS have the potential to offset declining production levels of wild global fisheries while reducing waste and product distance to market, but system optimization is needed to reduce costs. Both RAS and AP rely on microbial consortia for maintaining water quality and promoting fish/plant health, but little is known about the microorganisms actually present. This lack of knowledge prevents optimization of designs and operational controls to target the growth of beneficial microbial species or consortia. The significance of our research is in identifying the common microorganisms that inhabit production RAS and AP and the operational factors that influence which microorganisms colonize and become abundant. Identifying these organisms is a first step toward advanced control of microbial activities that improve reproducibility and reduce costs. Flowthrough and pond aquaculture system microbiome management practices aim to mitigate fish disease and stress. However, the operational success of recirculating aquaculture systems (RAS) depends directly on system microbial community activities. In RAS, each component environment is engineered for a specific microbial niche for waste management, as the water continuously flowing through the system must be processed before returning to the rearing tank. In this study, we compared waste management component microbiomes (rearing tank water, pH correction tank, solid-waste clarifier, biofilter, and degassing tower) within a commercial-scale freshwater RAS by high-throughput 16S rRNA gene sequencing. To assess consistency among freshwater RAS microbiomes, we also compared the microbial community compositions of six aquaculture and aquaponic farms. Community assemblages reflected site and source water relationships, and the presence of a hydroponic subsystem was a major community determinant. In contrast to the facility-specific community composition, some sequence variants, mainly classified into Flavobacterium , Cetobacterium , the family Sphingomonadaceae , and nitrifying guilds of ammonia-oxidizing archaea and Nitrospira , were common across all facilities. The findings of this study suggest that, independently of system design, core taxa exist across RAS rearing similar fish species but that system design informs the individual aquatic microbiome assemblages. Future RAS design would benefit from understanding the roles of these core taxa and then capitalizing on their activities to further reduce system waste/added operational controls. IMPORTANCE Recirculating aquaculture systems (RAS) are agroecosystems for intensive on-land cultivation of products of fisheries. Practitioners that incorporate edible plant production into RAS refer to these facilities as aquaponic systems (AP). RAS have the potential to offset declining production levels of wild global fisheries while reducing waste and product distance to market, but system optimization is needed to reduce costs. Both RAS and AP rely on microbial consortia for maintaining water quality and promoting fish/plant health, but little is known about the microorganisms actually present. This lack of knowledge prevents optimization of designs and operational controls to target the growth of beneficial microbial species or consortia. The significance of our research is in identifying the common microorganisms that inhabit production RAS and AP and the operational factors that influence which microorganisms colonize and become abundant. Identifying these organisms is a first step toward advanced control of microbial activities that improve reproducibility and reduce costs.