Nicholas J. Bouskill

Metagenomics reveals sediment microbial community response to Deepwater Horizon oil spill

The Deepwater Horizon (DWH) oil spill in the spring of 2010 resulted in an input of ∼4.1 million barrels of oil to the Gulf of Mexico; >22% of this oil is unaccounted for, with unknown environmental consequences. Here we investigated the impact of oil deposition on microbial communities in surface sediments collected at 64 sites by targeted sequencing of 16S rRNA genes, shotgun metagenomic sequencing of 14 of these samples and mineralization experiments using 14C-labeled model substrates. The 16S rRNA gene data indicated that the most heavily oil-impacted sediments were enriched in an uncultured Gammaproteobacterium and a Colwellia species, both of which were highly similar to sequences in the DWH deep-sea hydrocarbon plume. The primary drivers in structuring the microbial community were nitrogen and hydrocarbons. Annotation of unassembled metagenomic data revealed the most abundant hydrocarbon degradation pathway encoded genes involved in degrading aliphatic and simple aromatics via butane monooxygenase. The activity of key hydrocarbon degradation pathways by sediment microbes was confirmed by determining the mineralization of 14C-labeled model substrates in the following order: propylene glycol, dodecane, toluene and phenanthrene. Further, analysis of metagenomic sequence data revealed an increase in abundance of genes involved in denitrification pathways in samples that exceeded the Environmental Protection Agency (EPA)’s benchmarks for polycyclic aromatic hydrocarbons (PAHs) compared with those that did not. Importantly, these data demonstrate that the indigenous sediment microbiota contributed an important ecosystem service for remediation of oil in the Gulf. However, PAHs were more recalcitrant to degradation, and their persistence could have deleterious impacts on the sediment ecosystem.

Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust

Biological soil crusts (BSCs) cover extensive portions of the earth’s deserts. In order to survive desiccation cycles and utilize short periods of activity during infrequent precipitation, crust microorganisms must rely on the unique capabilities of vegetative cells to enter a dormant state and be poised for rapid resuscitation upon wetting. To elucidate the key events involved in the exit from dormancy, we performed a wetting experiment of a BSC and followed the response of the dominant cyanobacterium, Microcoleus vaginatus, in situ using a whole-genome transcriptional time course that included two diel cycles. Immediate, but transient, induction of DNA repair and regulatory genes signaled the hydration event. Recovery of photosynthesis occurred within 1 h, accompanied by upregulation of anabolic pathways. Onset of desiccation was characterized by the induction of genes for oxidative and photo-oxidative stress responses, osmotic stress response and the synthesis of C and N storage polymers. Early expression of genes for the production of exopolysaccharides, additional storage molecules and genes for membrane unsaturation occurred before drying and hints at preparedness for desiccation. We also observed signatures of preparation for future precipitation, notably the expression of genes for anaplerotic reactions in drying crusts, and the stable maintenance of mRNA through dormancy. These data shed light on possible synchronization between this cyanobacterium and its environment, and provides key mechanistic insights into its metabolism in situ that may be used to predict its response to climate, and or, land-use driven perturbations.

Trait-Based Representation of Biological Nitrification: Model Development, Testing, and Predicted Community Composition

Trait-based microbial models show clear promise as tools to represent the diversity and activity of microorganisms across ecosystem gradients. These models parameterize specific traits that determine the relative fitness of an “organism” in a given environment, and represent the complexity of biological systems across temporal and spatial scales. In this study we introduce a microbial community trait-based modeling framework (MicroTrait) focused on nitrification (MicroTrait-N) that represents the ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB) using traits related to enzyme kinetics and physiological properties. We used this model to predict nitrifier diversity, ammonia (NH3) oxidation rates, and nitrous oxide (N2O) production across pH, temperature, and substrate gradients. Predicted nitrifier diversity was predominantly determined by temperature and substrate availability, the latter was strongly influenced by pH. The model predicted that transient N2O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N2O by AOB. However, cumulative N2O production (over 6 month simulations) is maximized in a system where the relationship between AOB and NOB is maintained. When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH3 oxidation and N2O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH3 oxidation rates and the relative ratio of AOA:AOB biomass. The predicted community structure and activity indicate (a) parameterization of a small number of traits may be sufficient to broadly characterize nitrifying community structure and (b) changing decadal trends in climate and edaphic conditions could impact nitrification rates in ways that are not captured by extant biogeochemical models.

Pre-exposure to drought increases the resistance of tropical forest soil bacterial communities to extended drought.

Global climate models project a decrease in the magnitude of precipitation in tropical regions. Changes in rainfall patterns have important implications for the moisture content and redox status of tropical soils, yet little is known about how these changes may affect microbial community structure. Specifically, does exposure to prior stress confer increased resistance to subsequent perturbation? Here we reduced the quantity of precipitation throughfall to tropical forest soils in the Luquillo Mountains, Puerto Rico. Treatments included newly established throughfall exclusion plots (de novo excluded), plots undergoing reduction for a second time (pre-excluded) and ambient control plots. Ten months of throughfall exclusion led to a small but statistically significant decline in soil water potential and bacterial populations clearly adapted to increased osmotic stress. Although the water potential decline was small and microbial biomass did not change, phylogenetic diversity in the de novo-excluded plots decreased by ∼40% compared with the control plots, yet pre-excluded plots showed no significant change. On the other hand, the relative abundances of bacterial taxa in both the de novo-excluded and pre-excluded plots changed significantly with throughfall exclusion compared with control plots. Changes in bacterial community structure could be explained by changes in soil pore water chemistry and suggested changes in soil redox. Soluble iron declined in treatment plots and was correlated with decreased soluble phosphorus concentrations, which may have significant implications for microbial productivity in these P-limited systems.

Metabolite Identification in Synechococcus sp. PCC 7002 Using Untargeted Stable Isotope Assisted Metabolite Profiling

Metabolite profiling using mass spectrometry provides an attractive approach for the interrogation of cellular metabolic capabilities. Untargeted metabolite profiling has the potential to identify numerous novel metabolites; however, de novo identification of metabolites from spectral features remains a challenge. Here we present an integrated workflow for metabolite identification using uniform stable isotope labeling. Metabolite profiling of cell and growth media extracts of unlabeled control, (15)N, and (13)C-labeled cultures of the cyanobacterium, Synechococcus sp. PCC 7002 was performed using normal phase liquid chromatography coupled to mass spectrometry (LC-MS). Visualization of three-way comparisons of raw data sets highlighted characteristic labeling patterns for metabolites of biological origin allowing exhaustive identification of corresponding spectral features. Additionally, unambiguous assignment of chemical formulas was greatly facilitated by the use of stable isotope labeling. Chemical formulas of metabolites responsible for redundant spectral features were determined and fragmentation (MS/MS) spectra for these metabolites were collected. Analysis of acquired MS/MS spectra against spectral database records led to the identification of a number of metabolites absent not only from the reconstructed draft metabolic network of Synechococcus sp. PCC 7002 but not included in databases of metabolism (MetaCyc or KEGG).

Belowground Response to Drought in a Tropical Forest Soil. I. Changes in Microbial Functional Potential and Metabolism

Global climate models predict a future of increased severity of drought in many tropical forests. Soil microbes are central to the balance of these systems as sources or sinks of atmospheric carbon (C), yet how they respond metabolically to drought is not well-understood. We simulated drought in the typically aseasonal Luquillo Experimental Forest, Puerto Rico, by intercepting precipitation falling through the forest canopy. This approach reduced soil moisture by 13% and water potential by 0.14 MPa (from -0.2 to -0.34). Previous results from this experiment have demonstrated that the diversity and composition of these soil microbial communities are sensitive to even small changes in soil water. Here, we show prolonged drought significantly alters the functional potential of the community and provokes a clear osmotic stress response, including the production of compatible solutes that increase intracellular C demand. Subsequently, a microbial population emerges with a greater capacity for extracellular enzyme production targeting macromolecular carbon. Significantly, some of these drought-induced functional shifts in the soil microbiota are attenuated by prior exposure to a short-term drought suggesting that acclimation may occur despite a lack of longer-term drought history.

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach.

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundance and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the ‘High CO2+Nitrogen+Precipitation’ treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.

Belowground response to drought in a tropical forest soil. II. Change in microbial function impacts carbon composition

Climate model projections for tropical regions show clear perturbation of precipitation patterns leading to increased frequency and severity of drought in some regions. Previous work has shown declining soil moisture to be a strong driver of changes in microbial trait distribution, however, the feedback of any shift in functional potential on ecosystem properties related to carbon cycling are poorly understood. Here we show that drought-induced changes in microbial functional diversity and activity shape, and are in turn shaped by, the composition of dissolved and soil-associated carbon. We also demonstrate that a shift in microbial functional traits that favor the production of hygroscopic compounds alter the efflux of carbon dioxide following soil rewetting. Under drought the composition of the dissolved organic carbon pool changed in a manner consistent with a microbial metabolic response. We hypothesize that this microbial ecophysiological response to changing soil moisture elevates the intracellular carbon demand stimulating extracellular enzyme production, that prompts the observed decline in more complex carbon compounds (e.g., cellulose and lignin). Furthermore, a metabolic response to drought appeared to condition (biologically and physically) the soil, notably through the production of polysaccharides, particularly in experimental plots that had been pre-exposed to a short-term drought. This hysteretic response, in addition to an observed drought-related decline in phosphorus concentration, may have been responsible for a comparatively modest CO2 efflux following wet-up in drought plots relative to control plots.

Water Table Dynamics and Biogeochemical Cycling in a Shallow, Variably-Saturated Floodplain

Three-dimensional variably saturated flow and multicomponent biogeochemical reactive transport modeling, based on published and newly generated data, is used to better understand the interplay of hydrology, geochemistry, and biology controlling the cycling of carbon, nitrogen, oxygen, iron, sulfur, and uranium in a shallow floodplain. In this system, aerobic respiration generally maintains anoxic groundwater below an oxic vadose zone until seasonal snowmelt-driven water table peaking transports dissolved oxygen (DO) and nitrate from the vadose zone into the alluvial aquifer. The response to this perturbation is localized due to distinct physico-biogeochemical environments and relatively long time scales for transport through the floodplain aquifer and vadose zone. Naturally reduced zones (NRZs) containing sediments higher in organic matter, iron sulfides, and non-crystalline U(IV) rapidly consume DO and nitrate to maintain anoxic conditions, yielding Fe(II) from FeS oxidative dissolution, nitrite from denitrification, and U(VI) from nitrite-promoted U(IV) oxidation. Redox cycling is a key factor for sustaining the observed aquifer behaviors despite continuous oxygen influx and the annual hydrologically induced oxidation event. Depth-dependent activity of fermenters, aerobes, nitrate reducers, sulfate reducers, and chemolithoautotrophs (e.g., oxidizing Fe(II), S compounds, and ammonium) is linked to the presence of DO, which has higher concentrations near the water table.

Reactive Transport Model of Sulfur Cycling as Impacted by Perchlorate and Nitrate Treatments

Microbial souring in oil reservoirs produces toxic, corrosive hydrogen sulfide through microbial sulfate reduction, often accompanying (sea)water flooding during secondary oil recovery. With data from column experiments as constraints, we developed the first reactive-transport model of a new candidate inhibitor, perchlorate, and compared it with the commonly used inhibitor, nitrate. Our model provided a good fit to the data, which suggest that perchlorate is more effective than nitrate on a per mole of inhibitor basis. Critically, we used our model to gain insight into the underlying competing mechanisms controlling the action of each inhibitor. This analysis suggested that competition by heterotrophic perchlorate reducers and direct inhibition by nitrite produced from heterotrophic nitrate reduction were the most important mechanisms for the perchlorate and nitrate treatments, respectively, in the modeled column experiments. This work demonstrates modeling to be a powerful tool for increasing and testing our understanding of reservoir-souring generation, prevention, and remediation processes, allowing us to incorporate insights derived from laboratory experiments into a framework that can potentially be used to assess risk and design optimal treatment schemes.