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DOE OSTI2021

Machine learning application to single channel design of molten salt reactor

This study proposes a robust approach to quickly design a nuclear reactor core and explores the best performing machine learning (ML) technique for predicting feature parameters of the core. Here we implemented the approach into a hypothetical channel of molten salt reactors to demonstrate the applicability of the method. We prepared a Python tool, named Plankton, which couples to a reactor physics code and an optimization tool, and imports ML methods. The tool performs three consecutive phases: reactor database generation, machine learning application, and design optimization. We identified the extra trees method as the best performing estimator. With the estimator, we found nine optimum designs in total, one for each fuel-salt pair, and estimated all the performance metrics of the designs with a <5% prediction error compared to their actual values. U-Pu-NaCl fuel-salt gave promising results with the highest conversion ratio, the most negative feedback coefficient, and the lowest fast flux.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility

Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Furthermore, our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Breaking the loop: Tackling homoacetogenesis by chloroform to halt hydrogen production-consumption loop in single chamber microbial electrolysis cells

The presence of homoacetogenesis and hydrogen production-consumption loop significantly hinders the efficient hydrogen recovery in single chamber microbial electrolysis cells (MECs). Furthermore, no effective inhibition method has been proposed against homoacetogens hitherto. In this study, we demonstrated the effectiveness of chloroform as a homoacetogen inhibitor in MECs. In small MECs (320 ml) operated in batch mode, chloroform with concentrations of 0.02% (v/v) and 0.03% (v/v) ceased homoacetogenesis with nonfermentable substrate (acetate), enhancing hydrogen yield and cathodic hydrogen recovery from 21% and 14% to 94% and 90%, respectively. Electrochemical performance was not significantly affected as current density (based on anode surface area) only decreased from 18 A/m 2 to 13 A/m 2 with chloroform concentration increased from 0% (v/v) to 0.03% (v/v). However, when using a fermentable substrate (glucose), the inhibition against homoacetogens by 0.01% (v/v) chloroform led to a significant decrease in current density possibly due to the decreased production of acetate, which reduced the substrate concentration for exoelectrogenesis. Nevertheless, in a larger MEC (10 L), using an immobilized sludge culture and higher glucose concentration (56mM), 0.02% (v/v) chloroform enhanced hydrogen production rate from 0 L/L/D to 4.9 L/L/D and current density from 12-16 A/m 2 to 18-21 A/m 2 . Microbial community analysis revealed that the amount of homoacetogenic Acetobacterium spp. was eliminated in the cathodic biofilms and planktonic cells by chloroform. Future improvement of this method, through reducing the residual chloroform concentration and developing more environment friendly inhibitor using similar mechanisms, may lead to the practical application in MECs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Structural and antibacterial properties of NSAID-loaded ultra-high molecular weight polyethylene

Peri-articular delivery of pain medication in joint arthroplasty has the potential to address post-operative pain and decrease the use of systemic opioids. Additionally, the sustained release from a delivery vehicle may be especially impactful in the initial post-operative period when pain is most severe. We recently described a novel approach for the sustained intra-articular delivery of an analgesic (bupivacaine) from an ultra-high molecular weight polyethylene (UHMWPE) bearing surface. A secondary but very significant effect of incorporating bupivacaine in UHMWPE was its antibacterial effect against Staphylococcal species, which are associated with the majority of periprosthetic joint infections. Here, we report on the structure, properties, elution characteristics and antibacterial properties of UHMWPE loaded with the non-steroidal anti-inflammatory drug (NSAID) — tolfenamic acid. We showed that 6 wt% drug loading was optimal for eluting clinically relevant intra-articular and systemic concentrations, for maintaining mechanical properties at a clinically relevant level, and for providing anti-colonization potential against low risk (planktonic) and high risk (biofilm forming) Staphylococcal species. We determined the osteoblast response to the tolfenamic acid-loaded UHMWPE to evaluate the cytotoxicity profile of this novel material. Tolfenamic acid-loaded UHMWPE has the potential to be used as a part of multimodal pain management therapy following arthroplasty and may provide prophylaxis for periprosthetic infection.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Probing Nanoscale Chemical Environments of Zinc in Diatoms

Diatoms impact the biogeochemical cycling of zinc (Zn) due to elevated cellular Zn levels over other phytoplankton, and their strong biological uptake of Zn in the Southern Ocean sets the global distribution of Zn in ocean water columns. Past studies have revealed the abundance and spatial distribution of Zn in individual diatoms, while others have shown diverse Zn chemical species in whole-cell aggregates. However, intracellular and intercellular variations in Zn chemical forms remained unknown. For the first time, we applied a synchrotron X-ray nanoprobe to cultured diatoms (Phaeodactylum tricornutum and Chaetoceros muelleri) and resolved spatial heterogeneities of Zn chemistry. The result shows the partitioning of Zn between intracellular contents (soft parts) and frustules (mainly in the intracellular contents for P. tricornutum and similar Zn concentrations in the intracellular contents and frustules for C. muelleri). We found multiple Zn chemical species unevenly distributed within individual cells, where Zn–phosphoryl complexes were the most abundant followed by cysteine, histidine, biogenic silica, and carboxyl complexes. Cellular Zn species varied between individual organisms and were influenced by Zn availability. To our knowledge, this work presents the first X-ray measurements of trace metal speciation at sub-100 nm resolution in biological samples in their natural state. In conclusion, this research improves our ability to examine the biogeochemistry of Zn on the nanoscale and can help us understand the role of Zn in plankton growth and the Zn cycle in ocean waters.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Abiotic selection of microbial genome size in the global ocean

Abstract Strong purifying selection is considered a major evolutionary force behind small microbial genomes in the resource-poor photic ocean. However, very little is currently known about how the size of prokaryotic genomes evolves in the global ocean and whether patterns reflect shifts in resource availability in the epipelagic and relatively stable deep-sea environmental conditions. Using 364 marine microbial metagenomes, we investigate how the average genome size of uncultured planktonic prokaryotes varies across the tropical and polar oceans to the hadal realm. We find that genome size is highest in the perennially cold polar ocean, reflecting elongation of coding genes and gene dosage effects due to duplications in the interior ocean microbiome. Moreover, the rate of change in genome size due to temperature is 16-fold higher than with depth up to 200 m. Our results demonstrate how environmental factors can influence marine microbial genome size selection and ecological strategies of the microbiome.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A high-throughput and low-waste viability assay for microbes

Abstract Counting viable cells is a universal practice in microbiology. The colony-forming unit (CFU) assay has remained the gold standard to measure viability across disciplines, but it is time-intensive and resource-consuming. Here we describe the geometric viability assay (GVA) that replicates CFU measurements over 6 orders of magnitude while reducing over 10-fold the time and consumables required. GVA computes a sample’s viable cell count on the basis of the distribution of embedded colonies growing inside a pipette tip. GVA is compatible with Gram-positive and Gram-negative planktonic bacteria ( Escherichia coli, Pseudomonas aeruginosa and Bacillus subtilis ), biofilms and fungi ( Saccharomyces cerevisiae ). Laborious CFU experiments such as checkerboard assays, treatment time-courses and drug screens against slow-growing cells are simplified by GVA. The ease and low cost of GVA evinces that it can replace existing viability assays and enable viability measurements at previously impractical scales.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Metabolites from intact phage-infected Synechococcus chemotactically attract heterotrophic marine bacteria

Chemical cues mediate interactions between marine phytoplankton and bacteria, underpinning ecosystem-scale processes including nutrient cycling and carbon fixation. Phage infection alters host metabolism, stimulating the release of chemical cues from intact plankton, but how these dynamics impact ecology and biogeochemistry is poorly understood. Here we determine the impact of phage infection on dissolved metabolite pools from marine cyanobacteria and the subsequent chemotactic response of heterotrophic bacteria using time-resolved metabolomics and microfluidics. Metabolites released from intact, phage-infected Synechococcus elicited strong chemoattraction from Vibrio alginolyticus and Pseudoalteromonas haloplanktis, especially during early infection stages. Sustained bacterial chemotaxis occurred towards live-infected Synechococcus, contrasted by no discernible chemotaxis towards uninfected cyanobacteria. High-throughput microfluidics identified 5′-deoxyadenosine and 5′-methylthioadenosine as key attractants. Our findings establish that, before lysis, phage-infected picophytoplankton release compounds that attract motile heterotrophic bacteria, suggesting a mechanism for resource transfer that might impact carbon and nutrient fluxes across trophic levels.

Henshaw, Richard J↗

DOE OSTI2020

A microbiological survey of handwashing sinks in the hospital built environment reveals differences in patient room and healthcare personnel sinks

Handwashing sinks and their associated premise plumbing are an ideal environment for pathogen-harboring biofilms to grow and spread throughout facilities due to the connected system of wastewater plumbing. This study was designed to understand the distribution of pathogens and antibiotic resistant organisms (ARO) within and among handwashing sinks in healthcare settings, using culture-dependent methods to quantify Pseudomonas aeruginosa, opportunistic pathogens capable of growth on a cefotaxime-containing medium (OPP-C), and carbapenem-resistant Enterobacteriaceae (CRE). Isolates from each medium identified as P. aeruginosa or Enterobacteriaceae were tested for susceptibility to aztreonam, ceftazidime, and meropenem; Enterobacteriaceae were also tested against ertapenem and cefotaxime. Isolates exhibiting resistance or intermediate resistance were designated ARO. Pathogens were quantified at different locations within handwashing sinks and compared in quantity and distribution between healthcare personnel (HCP) and patient room (PR) sinks. ARO were compared between samples within a sink (biofilm vs planktonic samples) and between sink types (HCP vs. PR). The drain cover was identified as a reservoir within multiple sinks that was often colonized by pathogens despite daily sink cleaning. P. aeruginosa and OPP-C mean log 10 CFU/cm 2 counts were higher in p-trap and tail pipe biofilm samples from HCP compared to PR sinks (2.77 ± 2.39 vs. 1.23 ± 1.62 and 5.27 ± 1.10 vs. 4.74 ± 1.06) for P. aeruginosa and OPP-C, respectively. P. aeruginosa and OPP-C mean log 10 CFU/ml counts were also higher (p < 0.05) in HCP compared to PR sinks p-trap water (2.21 ± 1.52 vs. 0.89 ± 1.44 and 3.87 ± 0.78 vs. 3.21 ± 1.11) for P. aeruginosa and OPP-C, respectively. However, a greater percentage of ARO were recovered from PR sinks compared to HCP sinks (p < 0.05) for Enterobacteriaceae (76.4 vs. 32.9%) and P. aeruginosa (25.6 vs. 0.3%). This study supports previous work citing that handwashing sinks are reservoirs for pathogens and ARO and identifies differences in pathogen and ARO quantities between HCP and PR sinks, despite the interconnected premise plumbing.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Sponge exhalent metabolites influence coral reef picoplankton dynamics

Coral reef sponges efficiently take up particulate and dissolved organic matter (DOM) from the water column and release compounds such as nucleosides, amino acids, and other dissolved metabolites to the surrounding reef via their exhalent seawater, but the influence of this process on reef picoplankton and nutrient processing is relatively unexplored. Here we examined the impact of sponge exhalent on the reef picoplankon community and subsequent alterations to the reef dissolved metabolite pool. We exposed reef picoplankton communities to a sponge exhalent water mixture (Niphates digitalis and Xestospongia muta) or filtered reef seawater (control) in closed, container-based dark incubations. We used 16S rRNA gene sequencing and flow cytometry-based cell counts to examine the picoplankton community and metabolomics and other analyses to examine the dissolved metabolite pool. The initial sponge exhalent was enriched in adenosine, inosine, chorismate, humic-like and amino acid-like components, and ammonium. Following 48 h of exposure to sponge exhalent, the picoplankton differed in composition, were reduced in diversity, showed doubled (or higher) growth efficiencies, and harbored increased copiotrophic and denitrifying taxa (Marinomonas, Pontibacterium, Aliiroseovarius) compared to control, reef-water based incubations. Alongside these picoplankton alterations, the sponge treatments, relative to seawater controls, had decreased adenosine, inosine, tryptophan, and ammonium, metabolites that may support the observed higher picoplankton growth efficiencies. Sponge treatments also had a net increase in several monosaccharides and other metabolites including anthranilate, riboflavin, nitrite, and nitrate. Our work demonstrates a link between sponge exhalent-associated metabolites and the picoplankton community, with exhalent water supporting an increased abundance of efficient, copiotrophic taxa that catabolize complex nutrients. The copiotrophic taxa were often different from those observed in previous algae and coral studies. These results have implications for better understanding the multifaceted role of sponges on picoplankton biomass with subsequent potential impacts to coral and other planktonic feeders in oligotrophic reef environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

A hierarchal model for bacterial cell inactivation in solution by direct and indirect treatment using cold atmospheric plasmas

Cold atmospheric plasma devices have shown promise for a variety of plasma medical applications, including wound healing and bacterial inactivation often performed in liquids. In the latter application, plasma-produced reactive oxygen and nitrogen species (RONS) interact with and damage bacterial cells, though the exact mechanism by which cell damage occurs is unclear. Computational models can help elucidate relationships between plasma-produced RONS and cell killing by enabling direct comparison between dissimilar plasma devices and by examining the effects of changing operating parameters in these devices. In biological applications, computational models of plasma-liquid interactions would be most effective in design and optimization of plasma devices if there is a corresponding prediction of the biological outcome. In this work, we propose a hierarchal model for planktonic bacterial cell inactivation by plasma produced RONS in liquid. A previously developed reaction mechanism for plasma induced modification of cysteine was extended to provide a basis for cell killing by plasma-produced RONS. Results from the model are compared to literature values to provide proof of concept. Differences in time to bacterial inactivation as a function of plasma operating parameters including gas composition and plasma source configuration are discussed. Results indicate that optimizing gas-phase reactive nitrogen species production may be key in the design of plasma devices for disinfection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Declines in ice cover are accompanied by light limitation responses and community change in freshwater diatoms

The rediscovery of diatom blooms embedded within and beneath the Lake Erie ice cover (2007–2012) ignited interest in psychrophilic adaptations and winter limnology. Subsequent studies determined the vital role ice plays in winter diatom ecophysiology as diatoms partition to the underside of ice, thereby fixing their location within the photic zone. Yet, climate change has led to widespread ice decline across the Great Lakes, with Lake Erie presenting a nearly “ice-free” state in several recent winters. It has been hypothesized that the resultant turbid, isothermal water column induces light limitation amongst winter diatoms and thus serves as a competitive disadvantage. To investigate this hypothesis, we conducted a physiochemical and metatranscriptomic survey that spanned spatial, temporal, and climatic gradients of the winter Lake Erie water column (2019–2020). Our results suggest that ice-free conditions decreased planktonic diatom bloom magnitude and altered diatom community composition. Diatoms increased their expression of various photosynthetic genes and iron transporters, which suggests that the diatoms are attempting to increase their quantity of photosystems and light-harvesting components (a well-defined indicator of light limitation). We identified two gene families which serve to increase diatom fitness in the turbid ice-free water column: proton-pumping rhodopsins (a potential second means of light-driven energy acquisition) and fasciclins (a means to “raft” together to increase buoyancy and co-locate to the surface to optimize light acquisition). With large-scale climatic changes already underway, our observations provide insight into how diatoms respond to the dynamic ice conditions of today and shed light on how they will fare in a climatically altered tomorrow.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Genome‐enabled exploration of microbial ecology and evolution in the sea: a rising tide lifts all boats

Summary As a young bacteriologist just launching my career during the early days of the ‘microbial revolution’ in the 1980s, I was fortunate to participate in some early discoveries, and collaborate in the development of cross‐disciplinary methods now commonly referred to as "metagenomics". My early scientific career focused on applying phylogenetic and genomic approaches to characterize ‘wild’ bacteria, archaea and viruses in their natural habitats, with an emphasis on marine systems. These central interests have not changed very much for me over the past three decades, but knowledge, methodological advances and new theoretical perspectives about the microbial world certainly have. In this invited ‘How we did it’ perspective, I trace some of the trajectories of my lab's collective efforts over the years, including phylogenetic surveys of microbial assemblages in marine plankton and sediments, development of microbial community gene‐ and genome‐enabled surveys, and application of genome‐guided, cultivation‐independent functional characterization of novel enzymes, pathways and their relationships to in situ biogeochemistry. Throughout this short review, I attempt to acknowledge, all the mentors, students, postdocs and collaborators who enabled this research. Inevitably, a brief autobiographical review like this cannot be fully comprehensive, so sincere apologies to any of my great colleagues who are not explicitly mentioned herein. I salute you all as well!

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multimodal freezing model

Sea spray aerosol (SSA) is a widely recognized important source of ice-nucleating particles (INPs) in the atmosphere. However, composition-specific identification, nucleation processes, and ice nucleation rates of SSA-INPs have not been well constrained. Microspectroscopic characterization of ambient and laboratory-generated SSA confirms that water-borne exudates from planktonic microorganisms composed of a mixture of proteinaceous and polysaccharidic compounds act as ice-nucleating agents (INAs). These data and data from previously published mesocosm and wave channel studies are subsequently used to further develop the stochastic freezing model (SFM) producing ice nucleation rate coefficients for SSA-INPs. The SFM simultaneously predicts immersion freezing and deposition and homogeneous ice nucleation by SSA particles under tropospheric conditions. Predicted INP concentrations agree with ambient and laboratory measurements. In addition, this holistic freezing model is independent of the source and exact composition of the SSA particles, making it well suited for implementation in cloud and climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Cyanobacteria newly isolated from marine volcanic seeps display rapid sinking and robust, high-density growth

Cyanobacteria are photosynthetic organisms that play important roles in carbon cycling and are promising bioproduction chassis. Here, we isolate two novel cyanobacteria with 4.6Mbp genomes, UTEX 3221 and UTEX 3222, from a unique marine environment with naturally elevated CO₂. We describe complete genome sequences for both isolates and, focusing on UTEX 3222 due to its planktonic growth in liquid, characterize biotechnologically relevant growth and biomass characteristics. UTEX 3222 outpaces other fast-growing model strains on a solid medium. It can double every 2.35 hours in a liquid medium and grows to high density (>31 g/L biomass dry weight) in batch culture, nearly double that of Synechococcus sp. PCC 11901, whose high-density growth was recently reported. In addition, UTEX 3222 sinks readily, settling more quickly than other fast-growing strains, suggesting favorable economics of harvesting UTEX 3222 biomass. These traits may make UTEX 3222 a compelling choice for marine carbon dioxide removal (CDR) and photosynthetic bioproduction from CO₂. Overall, we find that bio-prospecting in environments with naturally elevated CO₂ may uncover novel CO₂-metabolizing organisms with unique characteristics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Novel, active, and uncultured hydrocarbon-degrading microbes in the ocean

ABSTRACT Given the vast quantity of oil and gas input to the marine environment annually, hydrocarbon degradation by marine microorganisms is an essential ecosystem service. Linkages between taxonomy and hydrocarbon degradation capabilities are largely based on cultivation studies, leaving a knowledge gap regarding the intrinsic ability of uncultured marine microbes to degrade hydrocarbons. To address this knowledge gap, metagenomic sequence data from the Deepwater Horizon (DWH) oil spill deep-sea plume was assembled to which metagenomic and metatranscriptomic reads were mapped. Assembly and binning produced new DWH metagenome-assembled genomes that were evaluated along with their close relatives, all of which are from the marine environment (38 total). These analyses revealed globally distributed hydrocarbon-degrading microbes with clade-specific substrate degradation potentials that have not been reported previously. For example, methane oxidation capabilities were identified in all Cycloclasticus . Furthermore, all Bermanella encoded and expressed genes for non-gaseous n -alkane degradation; however, DWH Bermanella encoded alkane hydroxylase, not alkane 1-monooxygenase. All but one previously unrecognized DWH plume member in the SAR324 and UBA11654 have the capacity for aromatic hydrocarbon degradation. In contrast, Colwellia were diverse in the hydrocarbon substrates they could degrade. All clades encoded nutrient acquisition strategies and response to cold temperatures, while sensory and acquisition capabilities were clade specific. These novel insights regarding hydrocarbon degradation by uncultured planktonic microbes provides missing data, allowing for better prediction of the fate of oil and gas when hydrocarbons are input to the ocean, leading to a greater understanding of the ecological consequences to the marine environment. IMPORTANCE Microbial degradation of hydrocarbons is a critically important process promoting ecosystem health, yet much of what is known about this process is based on physiological experiments with a few hydrocarbon substrates and cultured microbes. Thus, the ability to degrade the diversity of hydrocarbons that comprise oil and gas by microbes in the environment, particularly in the ocean, is not well characterized. Therefore, this study aimed to utilize non-cultivation-based ‘omics data to explore novel genomes of uncultured marine microbes involved in degradation of oil and gas. Analyses of newly assembled metagenomic data and previously existing genomes from other marine data sets, with metagenomic and metatranscriptomic read recruitment, revealed globally distributed hydrocarbon-degrading marine microbes with clade-specific substrate degradation potentials that have not been previously reported. This new understanding of oil and gas degradation by uncultured marine microbes suggested that the global ocean harbors a diversity of hydrocarbon-degrading bacteria, which can act as primary agents regulating ecosystem health.

Howe, Kathryn L.↗

DOE OSTI2025

Sediment and groundwater metagenomes from subsurface microbial communities from the Oak Ridge National Laboratory Oak Ridge Reservation, Oak Ridge, Tennessee, USA

We report 26 subsurface sediment and 9 groundwater metagenomes from the Oak Ridge Reservation at Oak Ridge, TN, USA. Samples were collected from various depths and phases (attached vs planktonic) to study subsurface microbial metabolism, the effect of contamination on microbial communities, and differences across groundwater and sediment microbial communities.

Lui, Lauren M↗