Integrating omics approaches to provide a systems-level view of microbial community responses in benthic ecosystems affected by the Deepwater Horizon oil spill

The Deepwater Horizon (DWH) oil spill in 2010, one of the largest environmental accidents in history, had pronounced impacts affecting vast areas of the open ocean, deep sea, and coastal ecosystems. Biodegradation mediated by a complex network of microorganisms and their interaction with their physicochemical environment ultimately dictates the fate of these hydrocarbons. Most of these interactions remain elusive due to the limitations of traditional, culture-based approaches, but the advent of next generation sequencing has enabled new opportunities to mine the “microbial dark matter”, and thus provide new insights into these issues. The impacts on coastal ecosystems, in particular, remain comparatively less understood due to the stochasticity and complexity of ecosystem processes and lack of appropriate model microorganisms. To close these knowledge gaps, this thesis integrated taxonomic, genetic and oil degradation rate data from laboratory advective flow chambers that simulated the temporal oxic-anoxic cycles observed in the natural beach sand environment. Hydrocarbon quantification and metatranscriptomics analyses showed that oil biodegradation was not severely limited in the absence of oxygen, with sulfate and to a lesser extent, nitrate, serving as alternative electron acceptors in the anoxic phases. Interestingly, microbial activities during the oxic phases further promoted the anaerobic biodegradation by re-oxidizing (and/or detoxifying) the (reduced) alternative electron acceptors and providing nitrogen, a limiting nutrient, through biological nitrogen fixation. The thesis also generated reliable biomarkers to screen for oil degradation potential in marine ecosystems which are essential in determining if an ecosystem is more “primed” for oil biodegradation. Using genome-resolved metagenomic approaches, the key hydrocarbon degrading and nitrogen-fixing microorganism in these laboratory incubations, which made up ~30% of the total microbial community, was isolated and characterized. This organism, provisionally named Candidatus Macondimonas diazotrophica, represents a previously overlooked family of hydrocarbon degraders that are major responders to oil spills in coastal environments worldwide. A new, divergent clade of alkane monooxygenase (alkB) specialized to crude oil, as opposed to algae-derived, hydrocarbons was also discovered. Therefore, this thesis provided reliable biomarkers of the different phases of oil biodegradation (e.g., oxic/anoxic and early/late) and novel organisms for bioaugmentation that should be useful for managing future oil spills. All underlying genomic, metagenomic and associated metadata were organized into an interactive and searchable webserver, called “Genome repository of oiled systems” or GROS (http://microbial-genomes.org/projects/GROS). GROS should facilitate future studies to further understand the interactions among microbial community members and their chemical environment that ultimately control the fate of oil spills.Ph.D

Wastewater Surveillance for SARS-CoV-2 on College Campuses: Initial Efforts, Lessons Learned, and Research Needs

Wastewater surveillance for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging approach to help identify the risk of a coronavirus disease (COVID-19) outbreak. This tool can contribute to public health surveillance at both community (wastewater treatment system) and institutional (e.g., colleges, prisons, and nursing homes) scales. This paper explores the successes, challenges, and lessons learned from initial wastewater surveillance efforts at colleges and university systems to inform future research, development and implementation. We present the experiences of 25 college and university systems in the United States that monitored campus wastewater for SARS-CoV-2 during the fall 2020 academic period. We describe the broad range of approaches, findings, resources, and impacts from these initial efforts. These institutions range in size, social and political geographies, and include both public and private institutions. Our analysis suggests that wastewater monitoring at colleges requires consideration of local information needs, sewage infrastructure, resources for sampling and analysis, college and community dynamics, approaches to interpretation and communication of results, and follow-up actions. Most colleges reported that a learning process of experimentation, evaluation, and adaptation was key to progress. This process requires ongoing collaboration among diverse stakeholders including decision-makers, researchers, faculty, facilities staff, students, and community members

Microbes, Oil Spills and Beyond: Using Microbes to Predict the Impact of Oil Spills

Presented on November 14, 2019 at 6:00 p.m. in the Global Learning Conference Center, Room 236.Smruthi Karthikeyan is a Ph.D student in the School of Civil and Environmental Engineering at Georgia Tech. She won second place in the Ph.D category.Runtime: 03:02 minutesThe Deepwater Horizon oil spill was the largest accidental marine oil spill in history and affected the benthic ecosystems, as well as vast areas of the open ocean and coastal wetlands along the Gulf of Mexico. Biodegradation mediated by a complex network of microorganisms dictates the ultimate fate of the majority of oil hydrocarbons that enter the marine environment. There is a fundamental lack of baseline environmental data and understanding of the rate of microbial oil degradation that could be used to formulate effective responses to an environmental disaster of this magnitude. Previous models only focused on culture based microbial techniques, but these microbes make up less than 1% of these environmental systems, thus leaving a vast majority of the “microbial dark matter” unexplored. The ecosystem level interactions that dictate microbial community structuring are highly complex and culture independent DNA/RNA analyses can help unravel these complex interactions. We leveraged terabytes of microbial “omics” data (which harnesses the power of computational biology and machine learning) along with engineered “real-time” systems to produce oil degradation models that can help environmental managers with future oil spill response plans. Furthermore, we curated a comprehensive and searchable database documenting microbial indicators that responded to accidental or natural oil spills across a range of global ecosystems along with their underlying physicochemical data, geocoded via GIS to reveal their biogeographic distribution patterns. This interactive repository can help provide a predictive understanding of the microbial response to oil perturbations and identify biomarkers that can universally predict ecosystem recovery

Immobilized Biocatalyst for Detection and Destruction of the Insensitive Explosive, 2,4-Dinitroanisole (DNAN)

Accurate and convenient detection of explosive components is vital for a wide spectrum of applications ranging from national security and demilitarization to environmental monitoring and restoration. With the increasing use of DNAN as. a replacement for 2,4,6-trinitrotoluene (TNT) in insensitive explosive formulations, there has been a growing interest in strategies to minimize its release and to understand and predict its behavior in the environment. Consequently, a convenient tool. for its detection and destruction could enable development of More effective decontamination and demilitarization strategies. Biosensors and biocatalysts have limited applicability to the more traditional explosives because of the inherent limitations of the relevant enzymes. Here, we report a highly specific, convenient and robust biocatalyst based on a novel ether hydrolase enzyme, DNAN demethylase (that requires no cofactors), from a Nocardioides strain that can mineralize DNAN. Biogenic silica encapsulation was used to stabilize the enzyme and enable it to be packed into a Model microcolumn for application as a biosensor or as a bioreactor for continuous destruction of DNAN. The immobilized enzyme was stable and not inhibited by other insensitive munitions constituents. An alternative method for DNAN detection involved coating the encapsulated enzyme on cellulose filter paper. The hydrolase based biocatalyst could provide the basis for a wide spectrum of applications including detection, identification; destruction Or inertion of explosives containing DNAN (demilitarization operations), and for environmental restorations

Wastewater surveillance for public health

Wastewater contains information on pathogen spread, evolution, and outbreak risk

High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County.

Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead-based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-min run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10 ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect 1 asymptomatic individual in a building of 415 residents. Using the high-throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego County (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates.IMPORTANCE Wastewater monitoring has a lot of potential for revealing coronavirus disease 2019 (COVID-19) outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples and show its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and 3 weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics