SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Disclosed herein is Arrowland, a web-based software tool for inputting, managing and viewing multiomics data, such as transcriptomics, proteomics, metabolomics and fluxomics data in an interactive, intuitive and multiscale system.
The annual Workshop on Workflows in Support of Large-Scale Science (WORKS) is a premier venue for the scientific workflow community to present the latest advances in research and development on the many facets of scientific workflows throughout their life-cycle. The Lightning Talks at WORKS focus on describing a novel tool, scientific workflow, or concept, which are work-in-progress and address emerging technologies and frameworks to foster discussion in the community. This paper summarizes the lightning talks at the 2023 edition of WORKS, covering five topics: leveraging large language models to build and execute workflows; developing a common workflow scheduler interface; scaling uncertainty workflow applications on exascale computing systems; evaluating a transcriptomics workflow for cloud vs. HPC systems; and best practices in migrating legacy workflows to workflow management systems.
Iron oxyhydroxides are extremely reactive components of environmental systems, and therefore exert a strong influence on biogeochemical cycles. These oxyhydroxides strongly adsorb many biologically-relevant elements, including organic carbon and phosphate, as well as a wide range of metals including uranium and actinide species. Thus, the formation mechanism of iron oxyhydroxides are key to understanding both nutrient and contaminant cycling. Microorganisms can catalyze iron oxidation and promote the formation of Fe biominerals and thus are increasingly recognized as important players in biogeochemical cycling. However, it is completely unknown how much of environmental iron oxidation is biologically mediated versus abiotic, and various challenges in studying microbial iron oxidation have hindered accurate incorporation into hydrobiogeochemical models. The overarching goal of our work was to quantify and constrain microbial iron oxidation rates and use ‘omics to gain insight into the controls on this process, while developing tools to enable integration of biotic iron oxidation into hydrobiogeochemical models. Our work focused on the Savannah River Site (SRS) in South Carolina, where extensive microbial iron oxidation has been observed. At Tims Branch, part of the Argonne National Laboratory Wetland Hydrobiogeochemistry Science Focus Area (Argonne SFA), where groundwater discharges into a stream, iron-oxidizing microbial mats form and appear to be a major sink of uranium. In the wetlands that surround Tims Branch, there are wide swaths of iron microbial mats and flocs (mobilized mat). We measured biotic and abiotic iron oxidation rates using mats and water sampled from these sites and found that iron oxidation is primarily carried out by chemolithotrophic microorganisms. The resulting rate constants can be incorporated into models. These mats were characterized by metagenomics and metatranscriptomics, which showed that aerobic chemolithotrophs were the dominant iron-oxidizing bacteria (FeOB), and these included Gallionellaceae and Leptothrix, and possibly Rhodoferax, which is known as an Fe-reducer but may also oxidize Fe(II). This demonstrated that diverse FeOB can coexist and suggests that there are a range of niches and therefore drivers of chemolithotrophic iron oxidation. Analysis of reconstructed genomes strongly suggests that a major factor in diversity is carbon source, as genomes contained varied pathways for autotrophy and heterotrophy. We performed an in-depth analysis of Leptothrix ochracea genomes, since this sheath-former is one of the primary mat builders, yet its physiology remained unresolved. A combination of genomics, transcriptomics, and metabolic modeling suggest that L. ochracea grows mixotrophically using a combination of Fe(II) and organics for energy and both inorganic and organic carbon to create biomass. This contrasts with the largely autotrophic Gallionellaceae (Gallionella, Sideroxydans, and Ferriphaselus) also present in the mats and flocs. Remarkably, multiple FeOB, both Leptothrix and Gallionellaceae, showed activity in response to Fe(II) in live mat incubations. We tracked the gene expression of individual MAGs to Fe(II) and found that various autotrophic and heterotrophic FeOB responded to Fe(II), increasing expression of both carbon fixation and organic utilization genes. The results of the integrated field, kinetics, and omics studies give detailed insight into 1) the taxa that oxidize Fe, and 2) how they connect Fe, C, and N cycles. Towards the goal of connecting omics data to hydrobiogeochemical models, we worked with the KBase team to create a template metabolic model for chemolithotrophic iron oxidation. We initially modeled the well-characterized isolate Gallionellaceae Sideroxydans lithotrophicus, and also applied the model to the mixotroph L. ochracea. In all, we have characterized diverse FeOB in a representative wetland system and solved key problems that enable better incorporation of iron-oxidizing microbes into hydrobiogeochemical models.
The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.
Small signaling peptides (SSPs) are important regulators of plant growth, development, and responses to biotic and abiotic stress, yet their role in the C4 grass Sorghum bicolor is largely uncharacterized. To help fill this knowledge gap, 219 sorghum genes that encode SSPs were identified based on SSP sequences previously identified in Arabidopsis thaliana, Zea mays, Oryza sativa, Triticum aestivum , and Brachypodium distachyon . The 219 sorghum SSP-encoding genes were assigned to 19 gene families, analyzed for the presence of motifs, and aligned with genes that encode SSPs in other plants using phylogenetic analysis. Sorghum genes in 12 of the 19 SSP gene families had not been previously characterized. Expression of the 219 SSP-encoding genes in sorghum organs, during stem development, and in stem tissues and cell types revealed distinct spatial, temporal, and developmental patterns of expression. Genes associated with the SbCEP and SbRGF families were preferentially expressed in roots, whereas SbEPF genes were expressed in stem epidermal and pith parenchyma cells and panicles. The expression of genes during bioenergy sorghum stem growth and development was investigated because stems account for ~80% of harvested biomass and serve as conduits for water and nutrient transport between leaves and roots. During stem development, 28 SSP genes in several families ( CLE, EPF, CEP, GASS, PSY, ES, PSK, CAPE, POE ) were expressed at higher levels in zones of cell proliferation. For example, the TDIF homologs SbCLE41 and SbCLE42 were expressed at high levels in nascent stem nodes where they may regulate vascular bundle cambial activity and cell differentiation. A different set of 15 genes in the CIF, POE, CAPE, PSY, CEP, RALF , and CLE families were expressed at higher levels in zones of stem tissue differentiation highlighted by elevated expression of five SbRALFR s in the stem nodal plexus. Cell type–specific expression of many sorghum genes that encode SSPs was observed in fully elongated internodes indicating gene expression is regulated with high spatial resolution. Overall, the results provide a foundation of information for analysis of SSP function in sorghum that can be integrated with knowledge of sorghum gene regulatory networks to modulate traits important for production of sorghum crops.
Despite the tremendous potential of algae to contribute to a future bioeconomy, there are practical and theoretical limitations to how well naturally sourced species and strains can perform in an outdoor setting. The application of biotechnology to modulate and engineer algae metabolism or to increase performance, resilience, or produce novel compounds, offers opportunities to overcome some of the major commercialization barriers. There are numerous approaches reported in the literature having variable success on genetic engineering of algae with non-model algae often presenting unique challenges to genetic engineering. We report here on successful nuclear and chloroplast genomic integration of selection marker resistance in the non-model alga Desmodesmus armatus. Nuclear transformation was accomplished using both electroporation and Agrobacterium-mediated approaches. However, in all surviving transformants, DNA integration was accompanied by excision and/or rearrangement of the gene of interest and fluorescence reporter coding sequences. Similarly, chloroplast transformation was successfully accomplished using a biolistic DNA delivery method. For these transformants, we also observed off-target mutations in the chloroplast genome, not previously observed in other, more routinely used, algae species. Finally, we present insights into potential mechanisms for these observed truncations, rearrangements, and mutations in D. armatus.
Microalgae that are of interest for biofuel production must be able to tolerate environmental changes that occur in outdoor cultivation systems. While algal cultures may experience daily temperature fluctuations and seasonal environmental changes, the underlying mechanisms that control and regulate physiological responses and adaptation to environmental pressures are largely unknown. Systems-level characterization enabled by functional genomics can help identify biochemical pathways that promote stability and productivity of algae in various environmental conditions. Monoraphidium minutum 26B-AM, a freshwater green microalga, was identified as a top performer in biomass production in winter season screens. We sequenced the genome of M. minutum 26B-AM and applied our multi-omics pipeline to profile this high potential strain under high salt and cold temperature perturbations. Through comparative analysis, including other green algae in the class Chlorophyceae, we identified gene families unique to the genus Monoraphidium, including a desaturase that has been linked to cold tolerance in plants. We observed that osmolytes, such as trehalose, proline and betaine, accumulate under salt stress, coinciding with upregulation of genes involved in biosynthesis of these metabolites. From the genome annotation, we reconstructed a metabolic model to provide a detailed map of the metabolic pathways and can be used to simulate growth and reaction fluxes. This multi-omics analysis provides a foundation to explore algal strain potential for biofuel applications, guides strain engineering, and expands our understanding of metabolic and regulatory mechanisms of algae in applied systems.
Not Available
Not Available
We report biological organisms are multifaceted, intricate systems where slight perturbations can result in extensive changes in gene expression, protein abundance and/or activity, and metabolic flux. These changes occur at different timescales, spatially across cells of heterogeneous origins, and within single-cells. Hence, multimodal measurements at the smallest biological scales are necessary to capture dynamic changes in heterogeneous biological systems. Of the analytical techniques used to measure biomolecules, mass spectrometry (MS) has proven to be a powerful option due to its sensitivity, robustness, and flexibility with regard to the breadth of biomolecules that can be analyzed. Recently, many studies have coupled MS to other analytical techniques with the goal of measuring multiple modalities from the same single-cell. It is with these concepts in mind that we focus this review on MS-enabled multiomic measurements at single-cell or near-single- cell resolution.
To evaluate the impact of individual sugar catabolic pathways on the physiology of A. niger when using plant biomass as a carbon source, key pathways converting plant biomass derived monomers were blocked. The resulting deletion mutants were analyzed using wheat bran and sugar beet pulp as substrates. On both substrates, the strongest affected single-pathway mutants were the pentose catabolic pathway (PCP) (Δ xkiA ) and glycolysis (Δ hxkA Δ glkA ) deficient mutants. On wheat bran, which is rich in pentose sugars, blocking the PCP by deletion of xkiA strongly impacted both growth and gene expression. However, the effect was even stronger in Δ hxkA Δ glkA and similar to a strain in which all pathways were blocked, highlighting the crucial role of glycolysis and/or carbon catabolite repression in A. niger physiology. These results demonstrate the complexity of A. niger metabolism during growth on plant biomass and provide insights into aspects to consider during metabolic engineering to obtain fungal cell factories.
Cellular senescence is a driver of many age-related pathologies. There is an active search for pharmaceuticals termed senolytics that can mitigate or remove senescent cells in vivo by targeting genes that promote the survival of senescent cells. We utilized single-cell RNA sequencing to identify CRYAB as a robust senescence-induced gene and potential target for senolysis. Using chemical inhibitor screening for CRYAB disruption, we identified 25-hydroxycholesterol (25HC), an endogenous metabolite of cholesterol biosynthesis, as a potent senolytic. We then validated 25HC as a senolytic in mouse and human cells in culture and in vivo in mouse skeletal muscle. Thus, 25HC represents a potential class of senolytics, which may be useful in combating diseases or physiologies in which cellular senescence is a key driver.
Not Available
Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.
Whether synthetic genomes can power life has attracted broad interest in the synthetic biology field. Here, we report de novo synthesis of the largest eukaryotic chromosome thus far, synIV, a 1,454,621-bp yeast chromosome resulting from extensive genome streamlining and modification. We developed megachunk assembly combined with a hierarchical integration strategy, which significantly increased the accuracy and flexibility of synthetic chromosome construction. Besides the drastic sequence changes, we further manipulated the 3D structure of synIV to explore spatial gene regulation. Surprisingly, we found few gene expression changes, suggesting that positioning inside the yeast nucleoplasm plays a minor role in gene regulation. Lastly, we tethered synIV to the inner nuclear membrane via its hundreds of loxPsym sites and observed transcriptional repression of the entire chromosome, demonstrating chromosome-wide transcription manipulation without changing the DNA sequences. Our manipulation of the spatial structure of synIV sheds light on higher-order architectural design of the synthetic genomes.
The need for convenient tools to express transgenes over a large dynamic range is pervasive throughout plant synthetic biology; however, current efforts are largely limited by the heavy reliance on a small set of strong promoters, precluding more nuanced and refined engineering endeavors in planta . To address this technical gap, we characterize a suite of constitutive promoters that span a wide range of transcriptional levels and develop a GoldenGate-based plasmid toolkit named PCONS, optimized for versatile cloning and rapid testing of transgene expression at varying strengths. We demonstrate how easy access to a stepwise gradient of expression levels can be used for optimizing synthetic transcriptional systems and the production of small molecules in planta . We also systematically investigate the potential of using PCONS as an internal standard in plant biology experimental design, establishing the best practices for signal normalization in experiments. Although our library has primarily been developed for optimizing expression in N. benthamiana , we demonstrate the translatability of our promoters across distantly related species using a multiplexed reporter assay with barcoded transcripts. In conclusion, our findings showcase the advantages of the PCONS library as an invaluable toolkit for plant synthetic biology.
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.