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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Contrasting Metabolism in Perenniating Structures of Upland and Lowland Switchgrass Plants Late in the Growing Season

Background: Switchgrass (Panicum virgatum L.) is being developed as a bioenergy crop for many temperate regions of the world. One way to increase biomass yields is to move southern adapted lowland cultivars to more northern latitudes. However, many southerly adapted switchgrass germplasm can suffer significant winter kill in northerly climes. Materials and Methods: Here, we have applied next-generation sequencing in combination with biochemical analyses to query the metabolism of crowns and rhizomes obtained from two contrasting switchgrass cultivars. Crowns and rhizomes from field-grown lowland (cv Kanlow) and upland (cv Summer) switchgrass cultivars were collected from three randomly selected post-flowering plants. Summer plants were senescing, whereas Kanlow plants were not at this harvest date. Results: Principal component analysis (PCA) differentiated between both the Summer and Kanlow transcriptomes and metabolomes. Significant differences in transcript abundances were detected for 8,050 genes, including transcription factors such as WRKYs and those associated with phenylpropanoid biosynthesis. Gene-set enrichment analyses showed that a number of pathways were differentially up-regulated in the two populations. For both populations, protein levels and enzyme activities agreed well with transcript abundances for genes involved in the phenylpropanoid pathway that were upregulated in Kanlow crowns and rhizomes. The combination of these datasets suggests that dormancy-related mechanisms had been triggered in the crowns and rhizomes of the Summer plants, whereas the crowns and rhizomes of Kanlow plants had yet to enter dormancy. Conclusions: Delayed establishment of dormancy at more northerly latitudes could be one factor that reduces wintersurvival in the high-yielding Kanlow plants. Understanding the cellular signatures that accompany the transition to dormancy can be used in the future to select plants with improved winter hardiness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

‘Candidatus Liberibacter asiaticus’, causal agent of citrus Huanglongbing, is reduced by treatment with Brassinosteroids

Huanglongbing (HLB) constitutes the most destructive disease of citrus worldwide, yet no established efficient management measures exist for it. Brassinosteroids, a family of plant steroidal compounds, are essential for plant growth, development and stress tolerance. As a possible control strategy for HLB, epibrassinolide was applied to as a foliar spray to citrus plants infected with the causal agent of HLB, ‘Candidatus Liberibacter asiaticus’. The bacterial titers were reduced after treatment with epibrassinolide under both greenhouse and field conditions but were stronger in the greenhouse. Known defense genes were induced in leaves by epibrassinolide. With the SuperSAGE technology combined with next generation sequencing, induction of genes known to be associated with defense response to bacteria and hormone transduction pathways were identified. Lastly, the results demonstrate that epibrassinolide may provide a useful tool for the management of HLB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Developmental Pathway of the MPER-Directed HIV-1-Neutralizing Antibody 10E8

Antibody 10E8 targets the membrane-proximal external region (MPER) of HIV-1 gp41, neutralizes >97% of HIV-1 isolates, and lacks the auto-reactivity often associated with MPER-directed antibodies. The developmental pathway of 10E8 might therefore serve as a promising template for vaccine design, but samples from time-of-infection—often used to infer the B cell record—are unavailable. In this study, we used crystallography, next-generation sequencing (NGS), and functional assessments to infer the 10E8 developmental pathway from a single time point. Mutational analysis indicated somatic hypermutation of the 2nd-heavy chain-complementarity determining region (CDR H2) to be critical for neutralization, and structures of 10E8 variants with V-gene regions reverted to genomic origin for heavy-and-light chains or heavy chain-only showed structural differences >2 Å relative to mature 10E8 in the CDR H2 and H3. To understand these developmental changes, we used bioinformatic sieving, maximum likelihood, and parsimony analyses of immunoglobulin transcripts to identify 10E8-lineage members, to infer the 10E8-unmutated common ancestor (UCA), and to calculate 10E8-developmental intermediates. We were assisted in this analysis by the preservation of a critical D-gene segment, which was unmutated in most 10E8-lineage sequences. UCA and early intermediates weakly bound a 26-residue-MPER peptide, whereas HIV-1 neutralization and epitope recognition in liposomes were only observed with late intermediates. Antibody 10E8 thus develops from a UCA with weak MPER affinity and substantial differences in CDR H2 and H3 from the mature 10E8; only after extensive somatic hypermutation do 10E8-lineage members gain recognition in the context of membrane and HIV-1 neutralization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Genetic stability of foot-and-mouth disease virus during long-term infections in natural hosts

Foot-and-mouth disease (FMD) is a severe infection caused by a picornavirus that affects livestock and wildlife. Persistence in ruminants is a well-documented feature of Foot-andmouth disease virus (FMDV) pathogenesis and a major concern for disease control. Persistently infected animals harbor virus for extended periods, providing a unique opportunity to study within-host virus evolution. This study investigated the genetic dynamics of FMDV during persistent infections of naturally infected Asian buffalo. Using next-generation sequencing (NGS) we obtained 21 near complete FMDV genome sequences from 12 sub-clinically infected buffalo over a period of one year. Four animals yielded only one virus isolate and one yielded two isolates of different serotype suggesting a serial infection. Seven persistently infected animals yielded more than one virus of the same serotype showing a longterm intra-host viral genetic divergence at the consensus level of less than 2.5%. Quasi-species analysis showed few nucleotide variants and non-synonymous substitutions of progeny virus despite intra-host persistence of up to 152 days. Phylogenetic analyses of serotype Asia-1 VP1 sequences clustered all viruses from persistent animals with Group VII viruses circulating in Pakistan in 2011, but distinct from those circulating on 2008–2009. Furthermore, signature amino acid (aa) substitutions were found in the antigenically relevant VP1 of persistent viruses compared with viruses from 2008–2009. Intra-host purifying selective pressure was observed, with few codons in structural proteins undergoing positive selection. However, FMD persistent viruses did not show a clear pattern of antigenic selection. Our findings provide insight into the evolutionary dynamics of FMDV populations within naturally occurring subclinical and persistent infections that may have implications to vaccination strategies in the region.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Association of microbial dynamics with urinary estrogens and estrogen metabolites in patients with endometriosis

Endometriosis is an estrogen dependent gynecological disease associated with altered microbial phenotypes. The association among endogenous estrogen, estrogen metabolites, and microbial dynamics on disease pathogenesis has not been fully investigated. Here, we identified estrogen metabolites as well as microbial phenotypes in non-diseased patients (n = 9) and those with pathologically confirmed endometriosis (P-EOSIS, n = 20), on day of surgery (DOS) and ~1–3 weeks post-surgical intervention (PSI). Then, we examined the effects of surgical intervention with or without hormonal therapy (OCPs) on estrogen and microbial profiles of both study groups. For estrogen metabolism analysis, liquid chromatography/tandem mass spectrometry was used to quantify urinary estrogens. The microbiome data assessment was performed with Next generation sequencing to V4 region of 16S rRNA. Surgical intervention and hormonal therapy altered gastrointestinal (GI), urogenital (UG) microbiomes, urinary estrogen and estrogen metabolite levels in P-EOSIS. At DOS, 17β-estradiol was enhanced in P-EOSIS treated with OCPs. At PSI, 16-keto-17β-estradiol was increased in P-EOSIS not receiving OCPs while 2-hydroxyestradiol and 2-hydroxyestrone were decreased in P-EOSIS receiving OCPs. GI bacterial α-diversity was greater for controls and P-EOSIS that did not receive OCPs. P-EOSIS not utilizing OCPs exhibited a decrease in UG bacterial α-diversity and differences in dominant taxa, while P-EOSIS utilizing OCPs had an increase in UG bacterial α-diversity. P-EOSIS had a strong positive correlation between the GI/UG bacteria species and the concentrations of urinary estrogen and its metabolites. These results indicate an association between microbial dysbiosis and altered urinary estrogens in P-EOSIS, which may impact disease progression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Systems level investigation of uranium response and regulation by Caulobacter crescentus

In the past year, we have fully started this project both in terms of laboratory setup and staffing. We have outfitted a new laboratory that hosts most of the equipment needed for the project, and we hired two postdoctoral fellows (Drs. Cho and Ma) who work on the project full time. Dr. Mimi Cho received her Ph.D. in the laboratory of Dr. JoAnne Stubbe at MIT in early 2012. Her graduate work focused on the biochemistry of polyhydroxybutyrate granule formation in Ralstonia eutropha . She has expertise in protein purification and enzyme characterization, which will be useful in our biochemical studies on the putative phytase. Additionally, she has experience in both fluorescence microscopy and TEM of bacterial cells, which will help us to characterize the physiology of C. crescentus under uranium stress. Dr. Jincai Ma has a Ph.D. in environmental sciences from Rutgers University. During his graduate studies, he employed a combination of molecular genetics, chemical kinetics, and spectroscopy to elucidate the mechanisms of selenium reduction by Enterobacter cloacae . After receiving his Ph.D., he studied the persistence and survival of the food pathogen, E. coli O157:H7, in soils of leafy greens at the US Salinity Laboratory (U.C. Riverside) for his postdoctoral training. Dr. Ma’s role in the current project is to investigate the proteomics of C. crescentus in response to various stresses and to examine microbe-uranium interaction. Dr. Cho started at LLNL in March 2012 and Dr. Ma in June 2012. It should be noted that the PI, Dr. Jiao, took a 3-month maternity leave (Nov 2011- Jan 2012). As a result, the pace of the progress in the last year was not as fast as we had hoped. Nevertheless, we have begun several lines of experimental approaches towards the overall goal of our research: deciphering the mechanism of uranium (U) resistance in Caulobacter crescentus . In the past year, our research has focused on one question: what are the genes and proteins that are involved in U (and other heavy metal) resistance in C. crescentus and how do these genetic elements function to confer resistance? We have started to test several of our hypotheses through a targeted approach, in which we have focused on the roles of a putative phytase and an S-layer protein. In addition, we employed three unbiased approaches towards an understanding of the fundamental processes that underpin U resistance, including Tn-seq, proteomic profiling, and Fosmid library screening. For these studies, we have integrated bacterial culturing, genetics, biochemistry, spectroscopy, electron microscopy, and next-generation sequencing. Our major accomplishments for the first year of the ECRP are listed below, categorized by specific aims.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Integrated Detection of Pathogens and Host Biomarkers for Wounds

The increasing incidence and complications arising from combat wounds has necessitated a reassessment of methods for effective treatment. Infection, excessive inflammation, and incidence of drug-resistant organisms all contribute toward negative outcomes for afflicted individuals. The organisms and host processes involved in wound progression, however, are not well understood. We therefore set out, using our unique technical resources, to construct a profile of combat wounds. We employed the Lawrence Livermore Microbial Detection Array and next-generation sequencing to identify all microorganisms present in wound samples, building complete profiles of wound infection. These methods allowed for comprehensive assessment of the wound bioburden, revealing pathogens that could not be detected by standard microbiology. Further, we developed and applied proteomics technology and identified host biomarkers associated with wound resolution. In combination with our pathogen data, these biomarker discovery efforts will provide a profile that will assist in clinical outcome classification and will provide vital insight for treatment of these complex cases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Enhanced vector borne disease surveillance of California Culex mosquito populations reveals spatial and species-specific barriers of infection

Monitoring infections in vectors such as mosquitoes,sand flies, tsetse flies, and ticks to identify human pathogens may serve as an early warning detection system to direct local government disease preventive measures. One major hurdle in detection is the ability to screen large numbers of vectors for human pathogens without the use of genotype-specific molecular techniques. Next generation sequencing (NGS) provides an unbiased platform capable of identifying known and unknown pathogens circulating within a vector population, but utilizing this technology is time-consuming and costly for vector-borne disease surveillance programs. To address this we developed cost-effective Ilumina® RNA-Seq library preparation methodologiesin conjunction with an automated computational analysis pipeline to characterize the microbial populations circulating in Culex mosquitoes (Culex quinquefasciatus, Culex quinquefasciatus/pipiens complex hybrids, and Culex tarsalis) throughout California. We assembled 20 novel and well-documented arboviruses representing members of Bunyaviridae, Flaviviridae, Ifaviridae, Mesoniviridae, Nidoviridae, Orthomyxoviridae, Parvoviridae, Reoviridae, Rhabdoviridae, Tymoviridae, as well as several unassigned viruses. In addition, we mapped mRNA species to divergent species of trypanosoma and plasmodium eukaryotic parasites and characterized the prokaryotic microbial composition to identify bacterial transcripts derived from wolbachia, clostridium, mycoplasma, fusobacterium and campylobacter bacterial species. We utilized these microbial transcriptomes present in geographically defined Culex populations to define spatial and mosquito species-specific barriers of infection. The virome and microbiome composition identified in each mosquito pool provided sufficient resolution to determine both the mosquito species and the geographic region in California where the mosquito pool originated. This data provides insight into the complexity of microbial species circulating in medically important Culex mosquitoes and their potential impact on the transmission of vector-borne human/veterinary pathogens in California.

Langevin, Stanley↗
DOE OSTI2015

Scalable Parallel Methods for Analyzing Metagenomics Data at Extreme Scale

The field of bioinformatics and computational biology is currently experiencing a data revolution. The exciting prospect of making fundamental biological discoveries is fueling the rapid development and deployment of numerous cost-effective, high-throughput next-generation sequencing technologies. The result is that the DNA and protein sequence repositories are being bombarded with new sequence information. Databases are continuing to report a Moore’s law-like growth trajectory in their database sizes, roughly doubling every 18 months. In what seems to be a paradigm-shift, individual projects are now capable of generating billions of raw sequence data that need to be analyzed in the presence of already annotated sequence information. While it is clear that data-driven methods, such as sequencing homology detection, are becoming the mainstay in the field of computational life sciences, the algorithmic advancements essential for implementing complex data analytics at scale have mostly lagged behind. Sequence homology detection is central to a number of bioinformatics applications including genome sequencing and protein family characterization. Given millions of sequences, the goal is to identify all pairs of sequences that are highly similar (or “homologous”) on the basis of alignment criteria. While there are optimal alignment algorithms to compute pairwise homology, their deployment for large-scale is currently not feasible; instead, heuristic methods are used at the expense of quality. In this dissertation, we present the design and evaluation of a parallel implementation for conducting optimal homology detection on distributed memory supercomputers. Our approach uses a combination of techniques from asynchronous load balancing (viz. work stealing, dynamic task counters), data replication, and exact-matching filters to achieve homology detection at scale. Results for a collection of 2.56M sequences show parallel efficiencies of ~75-100% on up to 8K cores, representing a time-to-solution of 33 seconds. We extend this work with a detailed analysis of single-node sequence alignment performance using the latest CPU vector instruction set extensions. Preliminary results reveal that current sequence alignment algorithms are unable to fully utilize widening vector registers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

The response of soil carbon storage and microbially mediated carbon turnover to simulated climatic disturbance in a northern peatland forest. Revisiting the concept of soil organic matter recalcitrance

The goal of this project was to investigate changes in the structure of dissolved and solid phase organic matter, the production of CO 2 and CH 4 , and the composition of decomposer microbial communities in response to the climatic forcing of environmental processes that determine the balance between carbon gas production versus storage and sequestration in peatlands. Cutting-edge analytical chemistry and next generation sequencing of microbial genes were been applied to habitats at the Marcell Experimental Forest (MEF), where the US DOE’s Oak Ridge National Laboratory and the USDA Forest Service are constructing a large-scale ecosystem study entitled, “Spruce and Peatland Responses Under Climatic and Environmental Change”(SPRUCE). Our study represented a comprehensive characterization of the sources, transformation, and decomposition of organic matter in the S1 bog at MEF. Multiple lines of evidence point to distinct, vertical zones of organic matter transformation: 1) the acrotelm consisting of living mosses, root material, and newly formed litter (0-30 cm), 2) the mesotelm, a mid-depth transition zone (30-75 cm) characterized by labile organic C compounds and intense decomposition, and 3) the underlying catotelm (below 75cm) characterized by refractory organic compounds as well as relatively low decomposition rates. These zones are in part defined by physical changes in hydraulic conductivity and water table depth. O-alkyl-C, which represents the carbohydrate fraction in the peat, was shown to be an excellent proxy for soil decomposition rates. The carbon cycle in deep peat was shown to be fueled by modern carbon sources further indicating that hydrology and surface vegetation play a role in belowground carbon cycling. We provide the first metagenomic study of an ombrotrophic peat bog, with novel insights into microbial specialization and functions in this unique terrestrial ecosystem. Vertical structuring of microbial communities closely paralleled the chemical evolution of peat, with large shifts in microbial populations occurring in the biogeochemical hotspot, the mesotelm, where the highest rates of decomposition were detected. Stable isotope geochemistry and potential rates of methane production paralleled vertical changes in methanogen community composition to indicate a predominance of acetoclastic methanogenesis mediated by the Methanosarcinales in the mesotelm, while hydrogen-utilizing methanogens dominated in the deeper catotelm. Evidence pointed to the availability of phosphorus as well as nitrogen limiting the microbially-mediated turnover of organic carbon at MEF. Prior to initiation of the experimental treatments, our study provided key baseline data for the SPRUCE site on the vertical stratification of peat decomposition, key enzymatic pathways, and microbial taxa containing these pathways. The sensitivity of soil carbon turnover to climate change is strongly linked to recalcitrant carbon stocks and the temperature sensitivity of decomposition is thought to increase with increasing molecular complexity of carbon substrates. This project delivered results on how climate change perturbations impact the microbially-mediated turnover of recalcitrant organic matter in peatland forest soils, both under controlled conditions in the laboratory and at the ecosystem-scale in the field. This project revisited the concept of “recalcitrance” in the regulation of soil carbon turnover using a combination of natural abundance radiocarbon and optical spectroscopic measurements on bulk DOM, and high resolution molecular characterization of DOM. The project elucidated how organic matter reactivity and decomposition will respond to climate change in a both a qualitative (organic matter lability) and quantitiative (increased rates) manner. An Aromaticity Index was developed to represent a more direct and accurate parameter for modeling of DOM reactivity in peatlands. The abundance and community composition of soil microorganisms that mediate C cycling were interrogated with depth in the peat, with season, and in manipulated climate enclosures at unprecedented resolution. Therefore this project delivered strategic new insights on the functioning of peatland ecosystems that collectively store approximately one-third of the world's soil carbon. Furthermore, results from the detailed characterization of DOM lability and microbial community structure/ function will be employed to further develop biogeochemical models to include microbial respiration pathways as well as to track carbon flow with a term that incorporates relative reactivity based on aromaticity index. As it stands now, detailed soil organic matter structure and microbial parameters are not included in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Abiotic stress networks converging on FT2 to control growth in Populus

Regulation of growth, quiescence and dormancy by seasonally recurring and episodic environmental factors is of central importance to the productivity of woody plants. Woody biomass feedstock plants such as Populus show substantial natural variation in growth responses to various environmental conditions; however, the complex genetic nature of these traits slows breeding for optimal biomass production in specific climates and on marginal lands. These growth responses are controlled by complex gene regulatory networks that are poorly understood. Elucidating the mechanisms that determine periods of growth and quiescence will identify specific genes and modules that can be manipulated to maximize growth in different environments. The main goal of the project was to develop transcriptional networks that describe spatially and temporally explicit regulatory relationships for both daylength and nutrient-mediated quiescence. We used next generation sequencing to characterize genome-wide gene expression changes to these two abiotic signals in shoot apices, leaf, root and vascular cambium. Overall, we sequenced 270 mRNAseq libraries and 54 smallRNAseq libraries from Populus trichocarpa and 58 RNAseq libraries from Populus nigra. We found both organ-specific transcriptional responses as well as responses common to multiple organs. Similarly, we uncovered both divergent and convergent gene regulatory networks that control growth responses to changes in daylength and nutrient stress. Thus, this work provides a foundation for developing approaches to optimize tree growth and biomass yield in response to specific as well as multiple abiotic conditions. Finally, we validated the functions of candidate regulators of growth in response to photoperiod and nutrient availability by introducing mutations via gene editing and transgenic methods. This data will be available to the public through journal publications and national/international genomic data archives.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Metagenomic Analysis of Three Samples from the MCU Process

Three Modular Caustic-Side Solvent Extraction (CSSX) Unit (MCU) Strip Effluent Feed samples were sent from Savannah River National Laboratory (SRNL) to Microbial Insights (MI), Knoxville, TN, for molecular analysis. The objective of the analysis was to profile and identify dominant members of the microbial community in these samples. More effective control of microbial growth in the MCU depends on the fundamental understanding of the microorganisms responsible for system colonization that leads to biofilm formation and coalescer impairment. This analysis entailed Next-generation sequencing (NGS) of DNA, a collective term for advanced technologies that allow the determination of microbial species present. NGS, also commonly called metagenomics, provides for the identification of microorganisms down to the genus level. Although function, e.g. biofilm formation or corrosion, cannot always be predicted from identification, knowledge of the microorganisms present can offer insight into the potential microbial processes that may influence system function occurring in the MCU. Information on microbial identification can also influence control measures, e.g. operational/maintenance parameters or the choice of biocides to employ. This knowledge can help control undesired microbial activity as conventional cleaning and disinfection regimens may also contribute to inefficient biofilm control and dissemination of microbial resistance to remediation efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

COVID-19 biomarkers based on respiratory microbiome content

COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Ice Nucleating Particles, Aerosols and Clouds over the Higher Latitude Southern Ocean

Improved understanding of aerosol-cloud-precipitation interactions is a key goal of the DOE-ASR program. Ice Nucleating Particles (INPs) are rare aerosol particles that trigger ice formation in clouds. By doing so they may initiate precipitation in clouds, and this impacts cloud lifetime. Nowhere may the role of INPs be manifested so dramatically as in the Southern Ocean (SO). This vast region was the focus of the DOE-ARM MARCUS (Measurement of Aerosols, Radiation and CloUds over the Southern Oceans) and MICRE (Macquarie Island Cloud and Radiation Experiment) campaigns. In the SO, a deficit of INPs has been a leading hypothesis for why liquid clouds persist, which underlays the bias of global climate models in predicting excess shortwave radiation reaching the ocean surface between 55°S and Antarctica. In this study, we used the 6-month acquisition of INP data on four MARCUS cruises from Hobart, Tasmania, to Antarctica, and the single-location full annual INP cycle observed in MICRE, to gain a holistic, quantitative picture of the INP number concentrations as a function of temperature and their variability over SO latitudes from 43°S to Antarctica. Augmenting initial processing of filter collections of aerosol particles during MARCUS and MICRE, additional freezing studies following thermal treatment (to removes INPs associated with microbes) and peroxide treatment (to remove all organics), as well as ionic chemistry and total organic carbon analysis, were applied to samples to improve time and space resolution, and differentiate marine from terrestrial contributions. We also performed Next Generation Sequencing to determine that bacterial composition can tag periods of enhanced and degraded marine organic INPs and the relative lack of occurrence of land-sourced bio-particles. Through these and other analyses, INP data were categorized into representative source types (e.g., marine versus terrestrially-influenced). The products of our analyses are being used to parameterize ice formation for use in numerical modeling studies to determine if, and when, ocean-derived INPs control the microphysical composition and radiative balance of SO clouds in a manner not presently captured by global models. The specific objectives and approaches proposed were largely accomplished, including: 1) Additional and value-added chemical and biological analyses of archived samples of aerosols were completed to categorize and quantify INP types and concentrations over the SO. 2) Analyses were completed to place INP data in meteorological and aerosol context. 3) Sea spray aerosol INPs were demonstrated to dominate the SO region in all but episodic events. 4) New parameterizations for INP sources relevant to the SO were constructed. 5) Collaborative modeling studies demonstrating the crucial role of INPs in determining cloud radiative and precipitation properties were conducted with MARCUS and MICRE partners. Collaborative publication submissions in this regard ensued within the two year study. This work will advance the science of interactions of aerosols, clouds and precipitation, to improve their representation in regional and global climate models. Results have informed representation of primary ice crystal nucleation, give inference to the role of secondary processes, and improve investigations of aerosol influences on clouds via ice nucleation over SO high latitudes and similar vast ocean regions. Application will improve representation of such interactions for clouds in both regional and global climate models. The data base and analyses methods applied will continue to serve research studies in the future.

58 GEOSCIENCES↗
DOE OSTI2022

Prokaryotic and eukaryotic cell-free systems for prototyping (CRADA Final Report)

CRADA FP00008491 between Berkeley Lab and Synvitrobio, Inc. (now Tierra Biosciences) validated the use of cell-free phenotyping to conduct functional genomics. Currently, most phenotyping work occurs using cellular fermentation methods and cellular techniques. This limits functional genomics throughput, which increasingly cannot handle the wealth of genetic information developed from next-generation sequencing technologies. De-risking a cell-free phenotyping approach has the advantage of increasing multiple-fold the throughput of genetic information that can be explored and expanding the $3B market for protein synthesis and characterization, leading to the accelerated development of human therapeutics and new biologically based materials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High throughput single cell multiomics platform [Abstract]

In this collaborative project, PNNL and Scienion will co-develop an integrated microfluidic technology to co-measure the transcriptome and proteome in single cells. The technology will enable us to efficiently separate proteins from mRNA transcripts between two microchips, barcode the molecules, and measure them separately with next-generation sequencing and mass spectrometry, respectively. Multicellular organisms contain diverse cell types and tremendous cell-to-cell heterogeneity that dictates a multitude of biological functions in both physiological and pathological environments. Even in the case of microbes, these genetically identical organisms can randomly differentiate into many subpopulations that assume different roles for the survival of the community. Bulk-scale measurements are insufficient to resolve such complexities. The development and applications of high throughput single-cell omics technologies have transformed our understanding of cellular heterogeneities and their differential responses to internal signaling events or external stimulations. Despite these advances, most current single-cell omic technologies provide information on only one type of biomolecule. Perse, such measurements provide incomplete information because the cell phenotype is determined by multiple layers of biomolecules and the interplay between genome, epigenome, transcriptome, and proteome. For example, mRNA abundance in one cell can not be precisely referred to the corresponding DNA and protein in other cells because of the potential subtle difference in genotype (e.g., somatic mutation or copy number variation) or phenotype (external microenvironment and cell-cell interactions). As such, parallel measurement of multiple biomolecules in the same single cells can offer unique advantages compared with measuring them separately in different single cells. Scienion is a world-leading biotech company focusing on precision liquid handling and its application in single-cell whole-genome sequencing and RNA sequencing. PNNL is the leading institution in ultrasensitive mass spectrometry, microfluidics, and untargeted single-cell proteomics (scProteomics). This collaboration will facilitate a unique fusion between scTranscriptomics capability at Scienion and scProteomics capabilities at PNNL to, for the first time, perform both untargeted transcriptomics and proteomics from the same single cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High throughput, accurate gene annotation through AI and HPC-enabled structural analysis

With the advances in next generation sequencing technologies, the number of sequenced genomes is growing exponentially, resulting in a technology bottleneck for the translation of sequence information into usable hypotheses about the function of each gene. We have proposed leveraging our leadership high-performance computing (HPC) resources to help break this annotation bottleneck. Here we design an HPC-based framework to infer gene function from gene sequence by incorporating information about protein structure and interactions predicted by deep learning approaches. Accurate functional prediction and gene annotation using computational methods will facilitate breakthroughs in the genomic sciences essential to understanding and harnessing life processes in bacteria, fungi and plants. The development and applications of the state-of-the-art deep neural networks to protein structural modeling, interaction prediction, sequence comparison, and quality assessment of protein structural models will be made possible by leadership computational resources. These HPC-enabled bioinformatics and molecular modeling tools will provide powerful insights into molecular functions of genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modularization of EDGE Workflows Using Nextflow: Improving the Efficiency and Maintainability of Bioinformatics Software

EDGE is a bioinformatics platform developed in 2016 by researchers at Los Alamos National Laboratory (LANL) to facilitate the analysis of next-generation sequencing data by researchers with varying levels of experience in bioinformatics (Li et al., 2017). Users with single-end, paired-end or long-read sequencing data can provide their reads as input to EDGE and select the combination of workflows to run that are most useful for their research (e.g., quality control of reads, genome assembly, or the taxonomic classification of input reads). Table 1 summarizes the modules available in EDGE. EDGE is available as a web platform at https://edgebioinformatics.org, as installable source code maintained on GitHub under a GPLv3 license, and as a publicly hosted Docker image.

59 BASIC BIOLOGICAL SCIENCES↗