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Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.

Wheat and maize are increasingly used as alternative crops to sunflower monocultures that dominate the Hetao Irrigation District in China. Shifts from sunflower monocultures to alternate cropping systems may have significant effects on below-ground microbial communities which control nutrient cycling and influence plant productivity. In this research, rhizosphere bacterial communities were compared among sunflower, wheat and maize cropping systems by 454 pyrosequencing. These cropping systems included 2 years wheat (cultivar Yongliang 4) and maize (cultivar Sidan 19) monoculture, more than 20 years sunflower (cultivar 5009) monoculture, and wheat-sunflower and maize-sunflower rotation. In addition, we investigated rhizosphere bacterial communities of healthy and diseased plants at maturity to determine the relationship between plant health and rotation effect. The results revealed taxonomic information about the overall bacterial community. And significant differences in bacterial community structure were detected among these cropping systems. Eight of the most abundant groups including Proteobacteria, Bacteroidetes, Acidobacteria, Gemmatimonadetes, Chloroflexi, Actinobacteria, Planctomycetes and Firmicutes accounted for more than 85% of the sequences in each treatment. The wheat-wheat rhizosphere had the highest proportion of Acidobacteria, Bacteroidetes and the lowest proportion of unclassified bacteria. Wheat-sunflower cropping system showed more abundant Acidobacteria than maize-sunflower and sunflower monoculture, exhibiting some influences of wheat on the succeeding crop. Maize-maize rhizosphere had the highest proportion of γ-Proteobacteria, Pseudomonadales and the lowest proportion of Acidobacteria. Sunflower rotation with wheat and maize could increase the relative abundance of the Acidobacteria while decrease the relative abundance of the unclassified phyla, as was similar with the health plants. This suggests some positive impacts of rotation with wheat and maize on the bacterial communities within a single field. These results demonstrate that different crop rotation systems can have significant effects on rhizosphere microbiomes that potentially alter plant productivities in agricultural systems.

Because of the extreme conditions of the Deepwater Horizon (DWH) release (turbulent flow at 1500m depth and 5°C water temperature) and the sub-surface application of dispersant, small but neutrally buoyant oil droplets

A laboratory simulation revealed the succession patterns of oil degradation and microbial community changes during the Deepwater Horizon oil spill, one of the largest environmental catastrophes in human history (1). Hu et al. successfully recovered genomes of several oil-degrading bacteria, including one of “ Candidatus Bermanella macondoprimitus ,” with high identity to the dominant Oceanospirillales 16S rRNA gene sequences recovered from the deep-ocean hydrocarbon plumes (1, 2). Delmont and Eren (3) reanalyzed metagenomic data generated from these plumes (4) to argue that this Bermanella was not present. We find that Delmont and Eren (3) draw their conclusions from methods that are flawed. Importantly, the authors (3) use samples that underwent an emulsion PCR (4) [according to Mason et al. (4) overloaded by ∼20 molecules per droplet (5)], which likely resulted in a heavy distortion of the sequenced community and observed abundance patterns (6). Delmont and Eren (3) failed to use any metagenome assembler that attempts to account … [↵][1]1To whom correspondence should be addressed. Email: glandersen{at}lbl.gov. [1]: #xref-corresp-1-1

The Deepwater Horizon (DWH) accident released an estimated 4.1 million barrels of oil and 1010 mol of natural gas into the Gulf of Mexico, forming deep-sea plumes of dispersed oil droplets and dissolved gases that were largely degraded by bacteria. During the course of this 3-mo disaster a series of different bacterial taxa were enriched in succession within deep plumes, but the metabolic capabilities of the different populations that controlled degradation rates of crude oil components are poorly understood. We experimentally reproduced dispersed plumes of fine oil droplets in Gulf of Mexico seawater and successfully replicated the enrichment and succession of the principal oil-degrading bacteria observed during the DWH event. We recovered near-complete genomes, whose phylogeny matched those of the principal biodegrading taxa observed in the field, including the DWH Oceanospirillales (now identified as a Bermanella species), multiple species of Colwellia, Cycloclasticus, and other members of Gammaproteobacteria, Flavobacteria, and Rhodobacteria. Metabolic pathway analysis, combined with hydrocarbon compositional analysis and species abundance data, revealed substrate specialization that explained the successional pattern of oil-degrading bacteria. The fastest-growing bacteria used short-chain alkanes. The analyses also uncovered potential cooperative and competitive relationships, even among close relatives. We conclude that patterns of microbial succession following deep ocean hydrocarbon blowouts are predictable and primarily driven by the availability of liquid petroleum hydrocarbons rather than natural gases.

Molecular microbial community analyses provide information on thousands of microorganisms simultaneously, and integrate biotic and abiotic perturbations caused by fecal contamination entering water bodies. A few studies have explored community methods as emerging approaches for microbial source tracking (MST), however, an evaluation of the current state of this approach is lacking. Here, we utilized three types of community-based methods with 64 blind, single- or dual-source, challenge samples generated from 12 sources, including: humans (feces), sewage, septage, dogs, pigs, deer, horses, cows, chickens, gulls, pigeons, and geese. Each source was a composite from multiple donors from four representative geographical regions in California. Methods evaluated included terminal restriction fragment polymorphism (TRFLP), phylogenetic microarray (PhyloChip), and next generation (Illumina) sequencing. These methods correctly identified dominant (or sole) sources in over 90% of the challenge samples, and exhibited excellent specificity regardless of source, rarely detecting a source that was not present in the challenge sample. Sensitivity, however, varied with source and community analysis method. All three methods distinguished septage from human feces and sewage, and identified deer and horse with 100% sensitivity and 100% specificity. Method performance improved if the composition of blind dual-source reference samples were defined by DNA contribution of each single source within the mixture, instead of by Enterococcus colony forming units. Data analysis approach also influenced method performance, indicating the need to standardize data interpretation. Overall, results of this study indicate that community analysis methods hold great promise as they may be used to identify any source, and they are particularly useful for sources that currently do not have, and may never have, a source-specific single marker gene.

Microbial source tracking (MST) methods were evaluated in the Source Identification Protocol Project (SIPP), in which 27 laboratories compared methods to identify host sources of fecal pollution from blinded water samples containing either one or two different fecal types collected from California. This paper details lessons learned from the SIPP study and makes recommendations to further advance the field of MST. Overall, results from the SIPP study demonstrated that methods are available that can correctly identify whether particular host sources including humans, cows and birds have contributed to contamination in a body of water. However, differences between laboratory protocols and data processing affected results and complicated interpretation of MST method performance in some cases. This was an issue particularly for samples that tested positive (non-zero Ct values) but below the limits of quantification or detection of a PCR assay. Although false positives were observed, such samples in the SIPP study often contained the fecal pollution source that was being targeted, i.e., the samples were true positives. Given these results, and the fact that MST often requires detection of targets present in low concentrations, we propose that such samples be reported and identified in a unique category to facilitate data analysis and method comparisons. Important data can be lost when such samples are simply reported as positive or negative. Actionable thresholds were not derived in the SIPP study due to limitations that included geographic scope, age of samples, and difficulties interpreting low concentrations of target in environmental samples. Nevertheless, the results of the study support the use of MST for water management, especially to prioritize impaired waters in need of remediation. Future integration of MST data into quantitative microbial risk assessments and other models could allow managers to more efficiently protect public health based on site conditions.

© 2017 Piceno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Recycling human waste for beneficial use has been practiced for millennia. Aerobic (thermophilic) composting of sewage sludge has been shown to reduce populations of opportunistically pathogenic bacteria and to inactivate both Ascaris eggs and culturable Escherichia coli in raw waste, but there is still a question about the fate of most fecal bacteria when raw material is composted directly. This study undertook a comprehensive microbial community analysis of composting material at various stages collected over 6 months at two composting facilities in Haiti. The fecal microbiota signal was monitored using a high-density DNA microarray (PhyloChip). Thermophilic composting altered the bacterial community structure of the starting material. Typical fecal bacteria classified in the following groups were present in at least half the starting material samples, yet were reduced below detection in finished compost: Prevotella and Erysipelotrichaceae (100% reduction of initial presence), Ruminococcaceae (98±99%), Lachnospiraceae (83±94%, primarily unclassified taxa remained), Escherichia and Shigella (100%). Opportunistic pathogens were reduced below the level of detection in the final product with the exception of Clostridium tetani, which could have survived in a spore state or been reintroduced late in the outdoor maturation process. Conversely, thermotolerant or thermophilic Actinomycetes and Firmicutes (e.g., Thermobifida, Bacillus, Geobacillus) typically found in compost increased substantially during the thermophilic stage. This community DNA-based assessment of the fate of human fecal microbiota during thermophilic composting will help optimize this process as a sanitation solution in areas where infrastructure and resources are limited.

The Deepwater Horizon oil spill in the Gulf of Mexico resulted in a deep-sea hydrocarbon plume that caused a shift in the indigenous microbial community composition with unknown ecological consequences. Early in the spill history, a bloom of uncultured, thus uncharacterized, members of the Oceanospirillales was previously detected, but their role in oil disposition was unknown. Here our aim was to determine the functional role of the Oceanospirillales and other active members of the indigenous microbial community using deep sequencing of community DNA and RNA, as well as single-cell genomics. Shotgun metagenomic and metatranscriptomic sequencing revealed that genes for motility, chemotaxis and aliphatic hydrocarbon degradation were significantly enriched and expressed in the hydrocarbon plume samples compared with uncontaminated seawater collected from plume depth. In contrast, although genes coding for degradation of more recalcitrant compounds, such as benzene, toluene, ethylbenzene, total xylenes and polycyclic aromatic hydrocarbons, were identified in the metagenomes, they were expressed at low levels, or not at all based on analysis of the metatranscriptomes. Isolation and sequencing of two Oceanospirillales single cells revealed that both cells possessed genes coding for n-alkane and cycloalkane degradation. Specifically, the near-complete pathway for cyclohexane oxidation in the Oceanospirillales single cells was elucidated and supported by both metagenome and metatranscriptome data. The draft genome also included genes for chemotaxis, motility and nutrient acquisition strategies that were also identified in the metagenomes and metatranscriptomes. These data point towards a rapid response of members of the Oceanospirillales to aliphatic hydrocarbons in the deep sea.

Conventional methods for fecal source tracking typically use single biomarkers to systematically identify or exclude sources. High-throughput DNA sequence analysis can potentially identify all sources of microbial contaminants in a single test by measuring the total diversity of fecal microbial communities. In this study, we used phylogenetic microarray analysis to determine the comprehensive suite of bacteria that define major sources of fecal contamination in coastal California. Fecal wastes were collected from 42 different populations of humans, birds, cows, horses, elk, and pinnipeds. We characterized bacterial community composition using a DNA microarray that probes for 16S rRNA genes of 59,316 different bacterial taxa. Cluster analysis revealed strong differences in community composition among fecal wastes from human, birds, pinnipeds, and grazers. Actinobacteria, Bacilli, and many Gammaproteobacteria taxa discriminated birds from mammalian sources. Diverse families within the Clostridia and Bacteroidetes taxa discriminated human wastes, grazers, and pinnipeds from each other. We found 1058 different bacterial taxa that were unique to either human, grazing mammal, or bird fecal wastes. These OTUs can serve as specific identifier taxa for these sources in environmental waters. Two field tests in marine waters demonstrate the capacity of phylogenetic microarray analysis to track multiple sources with one test.

Microbial communities can potentially mediate feedbacks between global change and ecosystem function, owing to their sensitivity to environmental change and their control over critical biogeochemical processes. Numerous ecosystem models have been developed to predict global change effects, but most do not consider microbial mechanisms in detail. In this idea paper, we examine the extent to which incorporation of microbial ecology into ecosystem models improves predictions of carbon (C) dynamics under warming, changes in precipitation regime, and anthropogenic nitrogen (N) enrichment. We focus on three cases in which this approach might be especially valuable: temporal dynamics in microbial responses to environmental change, variation in ecological function within microbial communities, and N effects on microbial activity. Four microbially-based models have addressed these scenarios. In each case, predictions of the microbial-based models differ—sometimes substantially—from comparable conventional models. However, validation and parameterization of model performance is challenging. We recommend that the development of microbial-based models must occur in conjunction with the development of theoretical frameworks that predict the temporal responses of microbial communities, the phylogenetic distribution of microbial functions, and the response of microbes to N enrichment.

Diving into Deep Water The Deepwater Horizon oil spill in the Gulf of Mexico was one of the largest oil spills on record. Its setting at the bottom of the sea floor posed an unanticipated risk as substantial amounts of hydrocarbons leaked into the deepwater column. Three separate cruises identified and sampled deep underwater hydrocarbon plumes that existed in May and June, 2010—before the well head was ultimately sealed. Camilli et al. (p. 201 ; published online 19 August) used an automated underwater vehicle to assess the dimensions of a stabilized, diffuse underwater plume of oil that was 22 miles long and estimated the daily quantity of oil released from the well, based on the concentration and dimensions of the plume. Hazen et al. (p. 204 ; published online 26 August) also observed an underwater plume at the same depth and found that hydrocarbon-degrading bacteria were enriched in the plume and were breaking down some parts of the oil. Finally, Valentine et al. (p. 208 ; published online 16 September) found that natural gas, including propane and ethane, were also present in hydrocarbon plumes. These gases were broken down quickly by bacteria, but primed the system for biodegradation of larger hydrocarbons, including those comprising the leaking crude oil. Differences were observed in dissolved oxygen levels in the plumes (a proxy for bacterial respiration), which may reflect differences in the location of sampling or the aging of the plumes.

We report that iron-reducing bacteria are primary mediators of anaerobic carbon oxidation in upland tropical soils spanning a rainfall gradient (3500-5000 mm/yr) in northeast Puerto Rico. The abundant rainfall and high net primary productivity of these tropical forests provide optimal soil habitat for iron-reducing and iron-oxidizing bacteria. Spatially and temporally dynamic redox conditions make iron-transforming microbial communities central to the belowground carbon cycle in these wet tropical forests. The exceedingly high abundance of iron-reducing bacteria (up to 1.2 x 10(9) cells per gram soil) indicated that they possess extensive metabolic capacity to catalyze the reduction of iron minerals. In soils from the higher rainfall sites, measured rates of ferric iron reduction could account for up to 44% of organic carbon oxidation. Iron reducers appeared to compete with methanogens when labile carbon availability was limited. We found large numbers of bacteria that oxidize reduced iron at sites with high rates of iron reduction and large numbers of iron reducers. The coexistence of large populations of iron-reducing and iron-oxidizing bacteria is evidence for rapid iron cycling between its reduced and oxidized states and suggests that mutualistic interactions among these bacteria ultimately fuel organic carbon oxidation and inhibit CH4 production in these upland tropical forests.

Tropical forests are an important source of atmospheric methane (CH 4 ), and recent work suggests that CH 4 fluxes from humid tropical environments are driven by variations in CH 4 production, rather than by bacterial CH 4 oxidation. Competition for acetate between methanogenic archaea and Fe(III)-reducing bacteria is one of the principal controls on CH4 flux in many Fe-rich anoxic environments. Upland humid tropical forests are also abundant in Fe and are characterized by high organic matter inputs, steep soil oxygen (O 2 ) gradients, and fluctuating redox conditions, yielding concomitant methanogenesis and bacterial Fe(III) reduction. However, whether Fe(III)-reducing bacteria coexist with methanogens or competitively suppress methanogenic acetate use in wet tropical soils is uncertain. To address this question, we conducted a process-based laboratory experiment to determine if competition for acetate between methanogens and Fe(III)-reducing bacteria influenced CH 4 production and C isotope composition in humid tropical forest soils. We collected soils from a poor to moderately drained upland rain forest and incubated them with combinations of 13 C-bicarbonate, 13 C-methyl labeled acetate ( 13 CH 3 COO - ), poorly crystalline Fe(III), or fluoroacetate. CH 4 production showed a greater proportional increase than Fe 2+ production after competition for acetate was alleviated, suggesting that Fe(III)-reducing bacteria were suppressing methanogenesis. Methanogenesis increased by approximately 67 times while Fe 2+ production only doubled after the addition of 13 CH 3 COO - . Large increases in both CH 4 and Fe 2+ production also indicate that the two process were acetate limited, suggesting that acetate may be a key substrate for anoxic carbon (C) metabolism in humid tropical forest soils. C isotope analysis suggests that competition for acetate was not the only factor driving CH 4 production, as 13 C partitioning did not vary significantly between 13 CH 3 COO - and 13 CH 3 COO - + Fe(III) treatments. This suggests that dissimilatory Fe(III)-reduction suppressed both hydrogenotrophic and aceticlastic methanogenesis. These findings have implications for understanding the CH 4 biogeochemistry of highly weathered wet tropical soils, where CH 4 efflux is driven largely by CH4 production.

Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive., IMPORTANCE Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.

The affinity of iron oxides and hydroxides for phosphorus is thought to contribute to phosphorus limitation to net primary productivity in humid tropical forests on acidic, highly weathered soils. Perennially warm, humid conditions and high biological activity in these soils can result in fluctuating redox potential that in turn leads to considerable iron reduction in the presence of labile carbon and humic substances. We investigated the effects of reducing conditions in combination with the addition of labile carbon substrates (glucose and acetate) and an electron shuttle compound on iron reduction and phosphorus release in a humid tropical forest soil. Glucose or acetate was added to soils as a single dose at the beginning of the experiment, and as pulsed inputs over time, which more closely mimics patterns in labile carbon availability. Iron reduction and phosphorus mobilization were weakly stimulated by a single low level addition of carbon, and the addition of the electron shuttle compound with or without added carbon. Pulsed labile carbon additions produced a significant increase in soil pH, soluble iron, and phosphorus concentrations. Pulsed labile carbon inputs also promoted the precipitation of ferrous hydroxide complexes which could increase the capacity for P sorption, although our results suggest that rates of P solubilization exceeded re-adsorption. Plant and microbial P demand are also likely to serve as an important sinks for released P, limiting the role of P re-adsorption. Our results suggest that reducing conditions coupled with periodic carbon inputs can stimulate iron reduction and a corresponding increase in soil phosphorus mobilization, which may provide a source of phosphorus to plants and microorganisms previously undocumented in these ecosystems.

The Deepwater Horizon oil spill produced large subsurface plumes of dispersed oil and gas in the Gulf of Mexico that stimulated growth of psychrophilic, hydrocarbon degrading bacteria. We tracked succession of plume bacteria before, during and after the 83-day spill to determine the microbial response and biodegradation potential throughout the incident. Dominant bacteria shifted substantially over time and were dependent on relative quantities of different hydrocarbon fractions. Unmitigated flow from the wellhead early in the spill resulted in the highest proportions of n-alkanes and cycloalkanes at depth and corresponded with dominance by Oceanospirillaceae and Pseudomonas. Once partial capture of oil and gas began 43 days into the spill, petroleum hydrocarbons decreased, the fraction of aromatic hydrocarbons increased, and Colwellia, Cycloclasticus, and Pseudoalteromonas increased in dominance. Enrichment of Methylomonas coincided with positive shifts in the δ(13)C values of methane in the plume and indicated significant methane oxidation occurred earlier than previously reported. Anomalous oxygen depressions persisted at plume depths for over six weeks after well shut-in and were likely caused by common marine heterotrophs associated with degradation of high-molecular-weight organic matter, including Methylophaga. Multiple hydrocarbon-degrading bacteria operated simultaneously throughout the spill, but their relative importance was controlled by changes in hydrocarbon supply.

The Deepwater Horizon oil spill in the Gulf of Mexico is the deepest and largest offshore spill in the United State history and its impacts on marine ecosystems are largely unknown. Here, we showed that the microbial community functional composition and structure were dramatically altered in a deep-sea oil plume resulting from the spill. A variety of metabolic genes involved in both aerobic and anaerobic hydrocarbon degradation were highly enriched in the plume compared with outside the plume, indicating a great potential for intrinsic bioremediation or natural attenuation in the deep sea. Various other microbial functional genes that are relevant to carbon, nitrogen, phosphorus, sulfur and iron cycling, metal resistance and bacteriophage replication were also enriched in the plume. Together, these results suggest that the indigenous marine microbial communities could have a significant role in biodegradation of oil spills in deep-sea environments.

There is an urgent need for rapid, accurate, and sensitive diagnostic platforms to confirm exposure to radiation and estimate the dose absorbed by individuals subjected to acts of radiological terrorism, nuclear power plant accidents, or nuclear warfare. Clinical symptoms and physical dosimeters, even when available, do not provide adequate diagnostic information to triage and treat life-threatening radiation injuries. We hypothesized that intestinal microbiota act as novel biomarkers of prior radiation exposure. Adult male Wistar rats (n = 5/group) received single or multiple fraction total-body irradiation of 10.0 Gy and 18.0 Gy, respectively. Fresh fecal pellets were obtained from each rat prior to (day 0) and at days 4, 11, and 21 post-irradiation. Fecal microbiota composition was determined using microarray and quantitative PCR (polymerase chain reaction) analyses. The radiation exposure biomarkers consisted of increased 16S rRNA levels of 12 members of the Bacteroidales, Lactobacillaceae, and Streptococcaceae after radiation exposure, unchanged levels of 98 Clostridiaceae and Peptostreptococcaceae, and decreased levels of 47 separate Clostridiaceae members; these biomarkers are present in human and rat feces. As a result of the ubiquity of these biomarkers, this biomarker technique is non-invasive; microbiota provide a sustained level of reporting signals that are increased several-fold following exposure to radiation, and intestinal microbiota that are unaffected by radiation serve as internal controls. We conclude that intestinal microbiota serve as novel biomarkers of prior radiation exposure, and may be able to complement conventional chromosome aberrational analysis to significantly enhance biological dose assessments.