News & Media
Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions
Report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions
Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report from a working group dedicated to the U.S. Department of Energy’s (DOE’s) capabilities and future in artificial intelligence (AI) and machine learning. PNNL researchers’ expertise is prominent throughout DOE’s AI efforts, particularly in the areas of data sciences and national security.
Based largely on input from DOE sponsors, the report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions. Key highlights include:
- Studying how AI affects the global landscape for securing nuclear materials, potentially using deep learning to enhance physical and digital protections against material concealment, delivery, theft, and sabotage.
- Describing how the United States and its partners might employ deep learning to combat attack efforts for enhanced nuclear security.
- Designing advanced deep learning models to characterize operations with buildings, using electrical signatures on power lines, enabling new designs for energy-efficient buildings in addition to enhanced security features for nuclear facilities.
- Leading the nuclear explosive monitoring project with data scientists working to significantly lower detection thresholds of low-yield, evasive underground nuclear explosions without increasing time-to-detection or the amount of human analysis.
- Co-design of advanced accelerator, memory and data movement concepts to support convergence of AI and machine learning methods with other forms of data analytics and traditional scientific high performance computing (HPC).
The report highlights PNNL’s support to the National Nuclear Security Administration, featuring joint laboratory collaborations between PNNL and others, including the Y-12 National Security Complex, Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory. Additionally, PNNL is working as part of DOE’s comparative advantages in AI, providing the Office of Energy Efficiency and Renewable Energy access to AI subject matter experts.
Study Shows Coastal Wetlands Aid in Carbon Sequestration
Sea-level rise impacts will likely decrease ecosystem carbon stocks
Tidal marshes, seagrass beds, and tidal forests are exceptional at absorbing and storing carbon. They are referred to as total ecosystem carbon stocks, yet little data exists quantifying how much carbon is absorbed and stored by tidal wetlands in the Pacific Northwest (PNW). Knowing this information is valuable, particularly in the context of sea level rise and with the associated need for Earth system modeling to predict changes at the coast.
Researchers found that the average total ecosystem carbon stock in the PNW is higher than in other areas of the U.S. and other parts of the world. Marsh carbon stocks, in particular, are twice the global average. Researchers found progressive increases in total ecosystem carbon stocks along the elevation gradient of coastal wetland types common in the PNW: seagrass, low marshes, high marshes, and tidal forests. Total carbon also increased along the salinity gradient, with more carbon occurring in lower salinity areas.
Additionally, this research showed that common methods used to estimate soil carbon actually underestimate soil carbon stocks in coastal wetlands. Soil carbon storage below the depth of 100 centimeters proved to be an important carbon pool in PNW tidal wetlands.
The results suggest that long-term sea-level rise impacts, such as tidal inundation and increased soil salinity, will likely decrease ecosystem carbon stocks. This is a concern if wetlands can’t migrate with increased sea level due to being bound by topography and human development.
This research arose from the Pacific Northwest Blue Carbon Working Group, of which Amy Borde and Heida Diefenderfer of Pacific Northwest National Laboratory’s Coastal Sciences Division are members. The team studied 28 tidal ecosystems across the PNW coast, from Humboldt Bay, California, to Padilla Bay, Washington. They sampled common coastal wetland types that occur along broad gradients of elevation, salinity, and tidal influences, collecting the data necessary to calculate total carbon stocks in both above ground biomass and the soil profile.
In three years of study, the researchers found that most carbon is in the wetland soils not aboveground, and much of it is deeper than one meter—a typical lower limit of sampling. Total ecosystem carbon stocks progressively increased along the terrestrial-aquatic gradient of coastal wetland ecosystems common in the temperate zone including seagrass, low marshes, high marshes, and tidal forests. The findings were reported in “Total Ecosystem Carbon Stocks at the Marine-Terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, USA,” published in the August 2020 online edition of Global Change Biology (DOI: 10.1111/gcb.15248).
Research Team: PNNL’s Amy Borde and Heida Diefenderfer, along with J. Boone Kauffman, Leila Giovanonni, James Kelly, Nicholas Dunstan, and Christopher Janousek (Oregon State University); Craig Cornu and Laura Brophy (Institute for Applied Ecology/Estuary Technical Group); and Jude Apple (Padilla Bay National Estuarine Research Reserve).
The grant award was administered by the Institute of Applied Ecology, and other partners included Oregon State University and the Padilla Bay National Estuarine Research Reserve. This research was supported by the National Oceanic and Atmospheric Administration, through a cooperative agreement with the University of Michigan.
Kauffman, J Boone, Leila Giovanonni, James Kelly, Nicholas Dunstan, Amy Borde, Heida Diefenderfer, Craig Cornu, Christopher Janousek, Jude Apple, and Laura Brophy. “Total Ecosystem Carbon Stocks at the Marine‐terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, United States.” Global change biology, no. 0 (August 11, 2020). DOI: 10.1111/GCB.15248
Integrative Network Modeling Reveals Key Drought-Associated Genes Key in the Soil Microbiome
Molecular and gene networks were combined to better understand how soil microbial communities respond to changes in water levels and nutrient sources
Harnessing the soil microbiome to enhance ecosystem services, like plant productivity or bioenergy production, requires understanding how soil microbiomes respond to environmental stresses, such as flood, drought, or changing nutrient levels. In this study, researchers examined how the soil microbiome responds at genetic and metabolic levels to changes in water content and nutrient sources. This integrated network analysis identified unique sets of genes and metabolic reactions that are expressed only under wet, dry, or high nutrient conditions. When focusing on these unique genes and pathways, the analysis showed that genes associated with dry soil conditions are central to the soil microbiome's response to environmental shifts.
The soil microbiome promotes plant health and affects the cycling of carbon through the ecosystem. Here, researchers examined how many different genes and molecules expressed by soil microbes are related to each other and to certain environmental conditions. They were also able to identify how individual genes occupied key positions in a functional network. The researchers found that the soil microbiome particularly responded to dry conditions. This knowledge will help in future efforts to harness the soil microbiome for optimizing plant productivity under drought conditions.
It is challenging to untangle the complex response of the soil microbial community to environmental change, partly due to the absence of modeling frameworks that can predict how environmental changes in soil can lead to changes in the microbial community’s function and role in promoting soil health. To fill this gap, researchers performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responds to changes in moisture and nutrient conditions. The integrated modeling approach revealed previously unknown, but critically important, aspects of the soil microbiomes’ response to environmental perturbations, including soil desiccation. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended treatments. A subsequent gene co-expression network analysis revealed that dry-associated genes, in particular, are central to the network; this means they are especially critical to the soil microbial community’s response to changing conditions. These results indicate the occurrence of a system-wide microbiome metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, this approach to analyzing large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which genomic and metabolite data are available.
Janet K. Jansson, Pacific Northwest National Laboratory, firstname.lastname@example.org
Kirsten Hofmockel, Pacific Northwest National Laboratory, email@example.com
This research was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, as part of the Genomic Science Program, and is a contribution of the Pacific Northwest National Laboratory Soil Microbiome Scientific Focus Area "Phenotypic Response of the Soil Microbiome to Environmental Perturbations."
R.S. McClure, et al., “Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations.” Scientific Reports 10, 10882 (2020). [DOI: 10.1038/s41598-020-67878-7]
Revealing an Unexplored Mechanism for Microbial Metabolism in River Sediment
Laboratory experiment is the first to show organic matter thermodynamics govern aerobic respiration rates in ecosystems with low carbon to nutrient ratios
River corridors have major influences on the Earth system by transforming organic matter into substances that impact water quality, contaminants, and climate. It has long been thought that the microbial metabolism underlying these transformations are controlled by temperature and the concentration of carbon-containing molecules. However, recent field experiments suggest thermodynamics, or the amount of chemical energy in the system available for organic matter decomposition, plays a key role in controlling microbial metabolism within river corridors, particularly in areas where groundwater and surface water mix. Now researchers have performed controlled laboratory experiments using river sediment to test organic matter thermodynamics as a mechanism of metabolic control in these environments. They find that organic matter thermodynamics control metabolism in oxygen rich environments in ways that depend on the concentration of nutrients and organic matter.
This work challenges a long-held belief about processes that govern organic matter metabolism in freshwater ecosystems. It is the first study to provide direct evidence for thermodynamic regulation of organic matter metabolism under oxygen-rich conditions in a controlled laboratory setting. Improving representations of river corridors with refined mechanisms of nutrient processing could improve predictive models of local to regional to global biogeochemical cycling used to help manage ecosystems and predict changes to the integrated Earth system.
Researchers gathered sediment from the Columbia River in areas where groundwater and surface water mix. In the laboratory, they added four different organic compounds to the sediment at one of three different concentrations. Then the researchers measured the rate of metabolism and used mass spectrometry to characterize the organic molecules that remained after incubation using an ultrahigh resolution technique. Using the molecular formulas of the observed molecules, the researchers calculated the amount of energy required to oxidize these molecules as a way of capturing thermodynamic favorability for decomposition. They found that organic matter thermodynamics govern aerobic microbial metabolism when organic carbon is at low concentration. As the concentration of organic carbon increased, thermodynamic controls became less influential and nutrient availability became the key factor governing metabolic rates. Although this study is of a single ecosystem, it provides a proof-of-concept that can be applied to experiments in more diverse ecosystems. It also demonstrates that thermodynamic constraints, in addition to the kinetic constraints of temperature and substrate concentration, can govern aerobic metabolism. Finally, the work proposes a new conceptual model in which organic matter thermodynamic and nutrient limitations dually control aerobic metabolism. Understanding microbial metabolism at a finer resolution, as well as from a variety of mechanistic perspectives, can help improve models of local to regional to global biogeochemical cycling used to help manage ecosystems and predict changes to the integrated Earth system.
Emily Graham, Pacific Northwest National Laboratory, firstname.lastname@example.org
This research was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, as part of Subsurface Biogeochemical Research Program’s Scientific Focus Area at the Pacific Northwest National Laboratory. A portion of the research was performed at Environmental Molecular Science Laboratory User Facility.
V. A. Garayburu-Caruso, et al., “Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism.” Environmental Science & Technology Letters 7, 517-524 (2020). [DOI: 10.1021/acs.estlett.0c00258]
Deep Learning to Predict Interspecies Spatial Interactions from Microbial Assembly Patterns
Deep learning enables incorporating microscopic images as a new data source to predict microbial spatial interactions
Interactions between different species in a microbial community govern how members self-assemble in specific spatial patterns. However, methods that use physical features of ecological assembly to predict microbial interactions do not exist. Now researchers trained deep neural networks to accurately predict microbial interactions captured by fluorescence microscopy. They trained the networks using data collected experimentally and simulated from modeling.
This work shows how data-driven modeling can leverage visualization techniques to tackle key science questions in microbial ecology. The developed deep learning workflow can significantly improve understanding of how microorganisms colonize habitats and interact with each other in spatially varied environments such as soils.
Rapid advancement in experimental and instrumental technologies is enabling the generation of high-resolution and high-throughput microscopic images that reveal the spatial distribution of microorganisms. These spatial interactions are key to carrying out coordinated metabolic reactions within microbial communities, but the use of spatial patterns for predicting microbial interactions is currently lacking. Conventional population-based computational methods that use species abundance data as a primary input to predict interspecies interactions have yet to be extended to incorporate spatial organizations of microorganisms. To fill this gap, the research team proposed supervised deep learning as a new network inference tool.
Currently, developing deep neural networks directly from experimental microscopy image data is infeasible due to unknown input-output relationships and insufficient amounts of training data. The team overcame these limitations by using high-fidelity agent-based models to perform 5000 simulations of the growth of two interacting microorganisms. This generated usable image data to effectively train deep learning networks. The resulting neural networks accurately predicted microbial interactions and their spatial variations not only from in silico images, but also from actual microscopic images obtained through carefully co-designed experiments. Therefore, the combined use of the agent-based model, machine learning algorithms, and experiments successfully demonstrated how to infer microbial interactions from spatially distributed data. This combination of techniques is a useful tool to reveal key—but previously unknown—interaction mechanisms in complex microbial communities that have been underexplored to date.
Janet K. Jansson, Pacific Northwest National Laboratory, email@example.com
Kirsten Hofmockel, Pacific Northwest National Laboratory, firstname.lastname@example.org
This research was supported by the U.S. Department of Energy Office of Science, Biological and Environmental Research Program, and is a contribution of the Scientific Focus Area "Phenotypic response of the soil microbiome to environmental perturbations" at the Pacific Northwest National Laboratory. A portion of the research described in this paper was also performed at EMSL- the Environmental Molecular Sciences Laboratory.
J.-Y. Lee, et al., "Deep Learning Predicts Microbial Interactions from Self-organized Spatiotemporal Patterns." Computational and Structural Biotechnology Journal 18, 1259-1269 (2020). [DOI: 10.1016/j.csbj.2020.05.023]
This work has been published in Computational and Structural Biotechnology Journal as an invited paper for a special issue organized by the Editor-in-Chief.
Deconstructing the Soil Microbiome
Deconstruction of soil microbial communities into discrete functional groups enables piecing together the functional potential of the complex soil microbiome
The soil microbiome plays a major role in nutrient cycling and plant health. However, its inherent complexity, with a vast array of microbes that metabolize many different molecules, makes it challenging to effectively analyze ecosystem functions performed by interacting members of soil microbial communities. Researchers dissected the complex microbial community of a native Washington soil into reproducible, low-complexity communities called 'functional modules.' Because these subcommunities are easier to study than a bulk community, researchers could analyze microbial species and functions present in the soil in more depth than before.
By studying discrete functional components of the soil microbiome at high resolution, the researchers obtained a more complete picture of soil diversity compared to analysis of the entire soil community. They identified specific evolutionary relationships and biochemical characteristics of the soil microbiome that otherwise would have been hidden in previous community-scale genomic analyses. Improved understanding of the functions of the soil microbiome could help scientists harness beneficial aspects of the soil microbiome to increase soil health or crop productivity.
One gram of soil contains microbes from thousands of different evolutionary groups. These microbes also have a wide variety of metabolic functions that help them survive in different soil microenvironments. Analyzing the complete functional and taxonomic diversity of a soil microbiome requires a large amount of computing power, and it may fail to capture large populations of quiet or rare microbes.
To simplify the analysis of a soil microbial community, researchers incubated a parent soil microbiome under several different conditions to create different subcommunities of microbes with specific functions, or functional modules. The functional modules included: usage of simple and complex carbon substrates, antibiotic resistance, anaerobic growth with different redox acceptors, and stress resistance. For each functional module, the researchers performed 16S rRNA gene amplicon sequencing to determine the community composition and RNA sequencing to identify expressed functions. Approximately 27% of unique taxa present in the parent soil were found in the functional modules, in addition to 341 taxa not detected in the parent community. The functional modules had unique gene expression patterns that were also enriched for transcripts associated with functional characteristic of each module. By dissecting the soil microbiome into discrete components, the researchers obtained a more comprehensive and highly detailed view of a soil microbiome and its biochemical potential than through analysis of a soil microbiome as a whole.
Ryan McClure, Pacific Northwest National Laboratory, email@example.com
This research was supported by the U.S. Department of Energy’s Office of Science, Biological and Environmental Research Program and is a contribution of the Scientific Focus Area “Phenotypic response of the soil microbiome to environmental perturbations.”
D. Naylor, et al., “Deconstructing the Soil Microbiome into Reduced-Complexity Functional Modules.” mBio 11, e01349-20 (2020). [DOI: 10.1128/mBio.01349-20]
Microbial diversity influences nitrogen cycling in rivers
Seasonal changes affect microbiome communities, genes, and subsurface biogeochemical pathways differently
DOE researchers investigated the role of microbial genetic diversity in two major subsurface biogeochemical processes: nitrification and denitrification. Results show that across different seasons only a few microbe species, namely Nitrosoarchaeum, carry out nitrification functions—demonstrating high resistance to environmental change. However, denitrification genes, which are more broadly distributed in the community, displayed a variety of diversity patterns and abundance dynamics—demonstrating greater microbial interactions as conditions change.
There is little research connecting microbiomes at the genetic level to hydrobiogeochemical modeling. This research helps broaden collective knowledge for a better understanding of the pathways affected by environmental changes. For example, under extreme environmental conditions an entire biochemical pathway could be altered or eliminated if a single step has low genetic diversity such that its loss could not be replaced.
The Pacific Northwest National Laboratory research team, led by Bill Nelson, found that major environmental processes—specifically nitrification and denitrification—are maintained through a variety of diversity strategies. Historically, the bulk of biogeochemical research has focused on microbial communities at the organismal level. But this research focused on the importance of genetic distribution and diversity.
In their recent PLoS ONE paper, the researchers discuss the roles microbes play in ecological functions; the novelty of the genetic makeup of these microbes; and future research opportunities to determine which organisms are genetically expressing nitrogen cycling functions.
The novelty of this study comes from examining the temporal dynamics of diversity at the gene level. To evaluate all genes in the nitrification and denitrification pathways, novel Hidden Markov Models (HMMs) were developed that can recognize the broad diversity found in environmental samples. They found that while different environmental conditions impair microbiome growth and the gene expression of some populations, at the same time, it can stimulate others. High biodiversity at the organism or genetic level creates more resiliency, and the microbiome community can respond more rapidly to environmental changes.
Bill Nelson, Pacific Northwest National Laboratory, William.Nelson@pnnl.gov
This research was supported by the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research Program, as part of the Subsurface Biogeochemical Research Scientific Focus Area (SFA) at Pacific Northwest National Laboratory (PNNL).
W.C. Nelson, E.B. Graham, A.R. Crump, S.J. Fansler, E.V. Arntzen, D.W. Kennedy, J.C. Stegen, “Distinct temporal diversity profiles for nitrogen cycling genes in a hyporheic microbiome”. PLoS ONE 15(1) e0228165 (2020). [DOI: 10.1371/ journal.pone.0228165]
Peeking Into the Lives of Soil Microbiomes
SoilBox provides in-depth imaging and characterization of soil microbial communities in their native environments.
To better characterize the vast diversity of soil microbes and their interactions, DOE researchers developed a high-tech simulated soil core called SoilBox. This 16.7-centimeter-deep box allows researchers to visualize soil microbes’ complex interactions using different imaging methods and facilitating, for the first time, visualization of the soil microbiome’s organization and community metabolism. Furthermore, SoilBox provides a tool for researchers to observe how soil microbial communities respond to environmental changes and perturbations.
The complexity of soil makes spatial imaging of soil microbial communities challenging. Using SoilBox, researchers can now visualize the diversity and metabolic
landscape of the soil microbiome under different environmental conditions, such as soil moisture and temperature. Understanding the basic biology of the soil microbiome is necessary for understanding how native soil systems respond to environmental perturbations such as drought, lack of nutrients, and fire.
Soil-dwelling microbes are key players in the overall health of soil ecosystems, performing critical functions like carbon and nutrient cycling. The interplay between the soil microbiome and the soil it inhabits is a dynamic relationship heavily influenced by factors such as soil acidity, organic content, and temperature. The size and distribution of soil particles also affects many soil characteristics, adding to the already complex challenge of accurately describing structure-function relationships of soil microbial communities.
To address the difficulties of studying the soil microbiome in its native state and at a microscale resolution, a team of researchers from Pacific Northwest National Laboratory, led by Arunima Bhattacharjee and Chris Anderton, developed SoilBox. This system represents a soil ecosystem by simulating an ~12-cm-deep soil core; several windows facilitate molecular and optical imaging measurements that are crucial to understanding the nuanced interactions between the soil microbiome and its environment. This novel imaging capability allows scientists to study the dynamic landscape of soil microbial communities as they relate to environmental changes, including nutrient cycling.
This work overcomes the challenge of visualizing the diversity of soil microbial communities in the complex and ever-changing environment of soil. SoilBox will be used in the near future to investigate soil microbial community dynamics.
Chris Anderton, Pacific Northwest National Laboratory, firstname.lastname@example.org
This research was supported by the Department of Energy (DOE) Office of Biological and Environmental Research (BER) and is a contribution of the Scientific Focus Area "Phenotypic response of the soil microbiome to environmental perturbations." Pacific Northwest National Laboratory (PNNL) is operated for the DOE by Battelle Memorial Institute under Contract DE-AC05-76RLO1830. A portion of the research was performed using EMSL, the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by BER and located at PNNL.
A. Bhattacharjee et al.,“Visualizing microbial community dynamics via a controllable soil environment.” mSystems 5, 1:e00645-19 (2020). https://doi.org/10.1128/mSystems.00645-19.