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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 oildegrading 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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wu, Song, Dubinsky and Stewart.)

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.

Sources of fecal indicator bacteria are difficult to identify in watersheds that are impacted by a variety of non-point sources. We developed a molecular source tracking test using the PhyloChip microarray that detects and distinguishes fecal bacteria from humans, birds, ruminants, horses, pigs and dogs with a single test. The multiplexed assay targets 9001 different 25-mer fragments of 16S rRNA genes that are common to the bacterial community of each source type. Both random forests and SourceTracker were tested as discrimination tools, with SourceTracker classification producing superior specificity and sensitivity for all source types. Validation with 12 different mammalian sources in mixtures found 100% correct identification of the dominant source and 84-100% specificity. The test was applied to identify sources of fecal indicator bacteria in the Russian River watershed in California. We found widespread contamination by human sources during the wet season proximal to settlements with antiquated septic infrastructure and during the dry season at beaches during intense recreational activity. The test was more sensitive than common fecal indicator tests that failed to identify potential risks at these sites. Conversely, upstream beaches and numerous creeks with less reliance on onsite wastewater treatment contained no fecal signal from humans or other animals; however these waters did contain high counts of fecal indicator bacteria after rain. Microbial community analysis revealed that increased E. coli and enterococci at these locations did not co-occur with common fecal bacteria, but rather co-varied with copiotrophic bacteria that are common in freshwaters with high nutrient and carbon loading, suggesting runoff likely promoted the growth of environmental strains of E. coli and enterococci. These results indicate that machine-learning classification of PhyloChip microarray data can outperform conventional single marker tests that are used to assess health risks, and is an effective tool for distinguishing numerous fecal and environmental sources of pathogen indicators., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

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 <70μm were formed, remained in the water column and were subjected to in-situ biodegradation processes. In order to investigate the biodegradation of Macondo oil components during the release, we designed and performed an experiment to evaluate the interactions of the indigenous microbial communities present in the deep waters of the Gulf of Mexico (GOM) with oil droplets of two representative sizes (10μm and 30μm median volume diameter) created with Macondo source oil in the presence of Corexit 9500 using natural seawater collected at the depth of 1100-1300m in the vicinity of the DWH wellhead. The evolution of the oil was followed in the dark and at 5°C for 64days by collecting sacrificial water samples at fixed intervals and analyzing them for a wide range of chemical and biological parameters including volatile components, saturated and aromatic hydrocarbons, dispersant markers, dissolved oxygen, nutrients, microbial cell counts and microbial population dynamics. A one phase exponential decay from a plateau model was used to calculate degradation rates and lag times for more than 150 individual oil components. Calculations were normalized to a conserved petroleum biomarker (30αβ-hopane). Half-lives ranged from about 3days for easily degradable compounds to about 60days for higher molecular weight aromatics. Rapid degradation was observed for BTEX, 2-3 ring PAHs, and n-alkanes below n-C23. The results in this experimental study showed good agreement with the n-alkane (n-C13 to n-C26) half-lives (0.6-9.5days) previously reported for the Deepwater Horizon plume samples and other laboratory studies with chemically dispersed Macondo oil conducted at low temperatures (<8°C). The responses of the microbial populations also were consistent with what was reported during the actual oil release, e.g. Colwellia, Cycloclasticus and Oceanospirillales (including the specific DWH Oceanospirillales) were present and increased in numbers indicating that they were degrading components of the oil. The consistency of the field and laboratory data indicate that these results could be used, in combination with other field and model data to characterize the dissipation of Macondo oil in the deepwater environment as part of the risk assessment estimations., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Unlabelled: 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., (Copyright © 2015 Smith et al.)

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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 spill released over 4.1 million barrels of crude oil into the Gulf of Mexico. In an effort to mitigate large oil slicks, the dispersant Corexit 9500 was sprayed onto surface slicks and injected directly at the wellhead at water depth of 1,500 m. Several research groups were involved in investigating the fate of the MC-252 oil using newly advanced molecular tools to elucidate microbial interactions with oil, gases, and dispersant. Microbial community analysis by different research groups revealed that hydrocarbon degrading bacteria belonging to Oceanospirillales, Colwellia, Cycloclasticus, Rhodobacterales, Pseudoalteromonas, and methylotrophs were found enriched in the contaminated water column. Presented here is a comprehensive overview of the ecogenomics of microbial degradation of MC-252 oil and gases in the water column and shorelines. We also present some insight into the fate of the dispersant Corexit 9500 that was added to aid in oil dispersion process. Our results show the dispersant was not toxic to the indigenous microbes at concentrations added, and different bacterial species isolated in the aftermath of the spill were able to degrade the various components of Corexit 9500 that included hydrocarbons, glycols, and dioctyl sulfosuccinate.

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.

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.

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.

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.

The biological effects and expected fate of the vast amount of oil in the Gulf of Mexico from the Deepwater Horizon blowout are unknown owing to the depth and magnitude of this event. Here, we report that the dispersed hydrocarbon plume stimulated deep-sea indigenous γ-Proteobacteria that are closely related to known petroleum degraders. Hydrocarbon-degrading genes coincided with the concentration of various oil contaminants. Changes in hydrocarbon composition with distance from the source and incubation experiments with environmental isolates demonstrated faster-than-expected hydrocarbon biodegradation rates at 5°C. Based on these results, the potential exists for intrinsic bioremediation of the oil plume in the deep-water column without substantial oxygen drawdown.

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.