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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Robust Power System Stability Assessment Against Adversarial Machine Learning-Based Cyberattacks via Online Purification

The increasing complexity associated with renewable generation brings more challenges to power system stability assessment (SA). Data-driven approaches based on machine learning (ML) techniques for stability assessment have received significant research interest and shown their promising performance. However, ML-based models are recognized to be vulnerable to adversarial disturbances, where a slight perturbation to power system measurements could lead to unacceptable errors. To address this issue, this paper develops a novel lightweight mitigation strategy, i.e., robust online stability assessment (ROSA), to enhance the ML-based assessment model against both white-box and the black-box adversarial disturbances (i.e., purification) in the online implementation. The ROSA involves a supervised learning-based module for the primary stability assessment and a self-supervised learning-based module. Further, the two modules are trained jointly with different objective (loss) functions and implemented in sequence. A suitable purification objective and various time-series data augmentation methods are designed for SA applications to tackle adversarial disturbances adaptively. Case studies are performed, and the comparative results have clearly illustrated the competitive, robust accuracy against various adversarial scenarios and verified the effectiveness of the proposed online purification strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Applying Noether's theorem to matter in the Milky Way: evidence for external perturbations and non-steady-state effects from Gaia Data Release 2

We apply Noether's theorem to observations of main-sequence stars from the Gaia Data Release 2 archive to probe the matter distribution function of the Galaxy. That is, we examine the axial symmetry of stars at vertical heights $z$, $0.2 \le |z| \le 3$ kpc, to probe the quality of the angular momentum $L_z$ as an integral of motion. The failure of this symmetry test would speak to a Milky Way, in both its visible and dark matter, that is not isolated and/or not in steady state. The left-right symmetry-breaking pattern we have observed, north and south, reveals both effects, with a measured deviation from symmetry of typically 0.5%. We show that a prolate form of the gravitational distortion of the Milky Way by the Large Magellanic Cloud, determined from fits to the Orphan stream by Erkal et al., 2019, is compatible with the size and sign of the axial-symmetry-breaking effects we have discovered in our sample of up to 14.4 million main-sequence stars, speaking to a distortion of an emergent, rather than static, nature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

The main sequences of star-forming galaxies and active galactic nuclei at high redshift

We provide a novel, unifying physical interpretation on the origin, average shape, scatter, and cosmic evolution for the main sequences of star-forming galaxies and active galactic nuclei (AGNs) at high redshift z≳1. We achieve this goal in a model-independent way by exploiting: (i) the redshift-dependent star formation rate functions based on the latest UV/far-IR data from HST/Herschel, and related statistics of strong gravitationally lensed sources; (ii) deterministic evolutionary tracks for the history of star formation and black hole accretion, gauged on a wealth of multiwavelength observations including the observed Eddington ratio distribution. We further validate these ingredients by showing their consistency with the observed galaxy stellar mass functions and AGN bolometric luminosity functions at different redshifts via the continuity equation approach. Our analysis of the main sequence for high-redshift galaxies and AGNs highlights that the present data are consistently interpreted in terms of an in situ coevolution scenario for star formation and black hole accretion, envisaging these as local, time-coordinated processes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗
DOE OSTI2016

Unification of [FeFe]-hydrogenases into three structural and functional groups

[FeFe]-hydrogenases (Hyd) are structurally diverse enzymes that catalyze the reversible oxidation of hydrogen (H 2 ). Recent biochemical data demonstrate new functional roles for these enzymes, including those that function in electron bifurcation where an exergonic reaction is coupled with an endergonic reaction to drive the reversible oxidation/production of H 2 . To identify the structural determinants that underpin differences in enzyme functionality, a total of 714 homologous sequences of the catalytic subunit, HydA, were compiled. Bioinformatics approaches informed by biochemical data were then used to characterize differences in inferred quaternary structure, HydA active site protein environment, accessory iron-sulfur clusters in HydA, and regulatory proteins encoded in HydA gene neighborhoods. HydA homologs were clustered into one of three classification groups, Group 1 (G1), Group 2 (G2), and Group 3 (G3). G1 enzymes were predicted to be monomeric while those in G2 and G3 were predicted to be multimeric and include HydB, HydC (G2/G3) and HydD (G3) subunits. Variation in the HydA active site and accessory iron-sulfur clusters did not vary by group type. Group-specific regulatory genes were identified in the gene neighborhoods of both G2 and G3 Hyd. Analyses of purified G2 and G3 enzymes by mass spectrometry strongly suggests that they are post-translationally modified by phosphorylation. In conclusion, these results suggest that bifurcation capability is dictated primarily by the presence of both HydB and HydC in Hyd complexes, rather than by variation in HydA.

09 BIOMASS FUELS↗
DOE OSTI2019

Unified rational protein engineering with sequence-based deep representation learning

Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. Lastly, UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Myotube-derived factor promotes oligodendrocyte precursor cell proliferation

Highlights: • Myotube-derived factors promote proliferation of oligodendrocyte precursor cells (OPCs). • Myotube-derived factors do not promote OPC migration and differentiation. • Candidate genes which promote OPC proliferation were identified by RNA sequence analysis. • Molecules released by myotubes may be involved in OPC proliferation. Muscle cells secrete numerous molecules that function as endocrine hormones and regulate the functions of distant organs. Myelination in the central nervous system (CNS) is regulated by peripheral hormones. However, the effects of muscle-derived molecules on myelination have not been sufficiently analyzed. In this study, we show that muscle-releasing factors promote proliferation of oligodendrocyte precursor cells (OPCs), which is an element of myelination process. Supernatants of mouse myotube cultures stimulated bromodeoxyuridine (BrdU) incorporation into mouse OPCs. Mouse myotube supernatants did not enhance mouse OPC transmigration and myelin basic protein (MBP) expression. RNA sequencing identified candidate genes with hormonal functions that were expressed in mouse myotubes. These data support the possibility that hormonal molecules secreted by myotubes contribute to OPC proliferation and myelination.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Discovery and Characterization of FMN-Binding β-Glucuronidases in the Human Gut Microbiome

The human gut microbiota encodes β-glucuronidases (GUSs) that play key roles in health and disease via the metabolism of glucuronate-containing carbohydrates and drugs. Hundreds of putative bacterial GUS enzymes have been identified by metagenomic analysis of the human gut microbiome, but less than 10% have characterized structures and functions. Here in this paper we describe a set of unique gut microbial GUS enzymes that bind flavin mononucleotide (FMN). First, we show using mass spectrometry, isothermal titration calorimetry, and x-ray crystallography that a purified GUS from the gut commensal microbe Faecalibacterium prausnitzii binds to FMN on a surface groove located 30 Å away from the active site. Second, utilizing structural and functional data from this FMN-binding GUS, we analyzed the 279 unique GUS sequences from the Human Microbiome Project database and identified 14 putative FMN-binding GUSs. We characterized four of these hits and solved the structure of two, the GUSs from Ruminococcus gnavus and Roseburia hominis, which confirmed that these are FMN binders. Third, binding and kinetic analysis of the FMN-binding site mutants of these five GUSs show that they utilize a conserved site to bind FMN that is not essential for GUS activity, but can affect KM. Lastly, a comprehensive structural review of the PDB reveals that the FMN-binding site employed by these enzymes is unlike any structurally characterized FMN binders to date. These findings reveal the first instance of an FMN-binding glycoside hydrolase and suggest a potential link between FMN and carbohydrate metabolism in the human gut microbiota.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Nucleus-specific expression in the multinuclear mushroom-forming fungus Agaricus bisporus reveals different nuclear regulatory programs

Many fungi are polykaryotic, containing multiple nuclei per cell. In the case of heterokaryons, there are different nuclear types within a single cell. It is unknown what the different nuclear types contribute in terms of mRNA expression levels in fungal heterokaryons. Each cell of the mushroom Agaricus bisporus contains two to 25 nuclei of two nuclear types originating from two parental strains. Using RNA-sequencing data, we assess the differential mRNA contribution of individual nuclear types and its functional impact. We studied differential expression between genes of the two nuclear types, P1 and P2, throughout mushroom development in various tissue types. P1 and P2 produced specific mRNA profiles that changed through mushroom development. Differential regulation occurred at the gene level, rather than at the locus, chromosomal, or nuclear level. P1 dominated mRNA production throughout development, and P2 showed more differentially up-regulated genes in important functional groups. In the vegetative mycelium, P2 up-regulated almost threefold more metabolism genes and carbohydrate active enzymes (cazymes) than P1, suggesting phenotypic differences in growth. We identified widespread transcriptomic variation between the nuclear types of A. bisporus. Our method enables studying nucleus-specific expression, which likely influences the phenotype of a fungus in a polykaryotic stage. Our findings have a wider impact to better understand gene regulation in fungi in a heterokaryotic state. This work provides insight into the transcriptomic variation introduced by genomic nuclear separation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

PNNL-CompBio/perseq

Per sequence functional classification and Functional annotation in addition to linking those functions to taxonomic groups presents a challenge due to many established tools performing one task and not the other creating disparate downstream data. PerSeq aims to address these challenges by performing local alignments against a reference database annotated for both aspects, function and taxonomy, to annotate sequences individually. Downstream results are merged into count tables facilitating complex analyses based on observed function or functional potential in addition to being able to separate out function by taxonomic level to determine metabolic contributions by present organisms.

Brown, Joe↗
DOE OSTI2024

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems such as cell signaling and epigenetic regulation. Precisely defining the stoichiometry of PTMs has been challenging because, in the widely used bottom-up proteomics methods, the detection occurs at the peptide level and thus the link between peptides and their specific modification site is lost, resulting in proteoform ambiguity. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Proteins with positional isomers refers to proteoforms with identical total mass and set of modifications but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for relative quantification of PI within a single tool. We benchmarked IsoForma’s performance against two existing workflows and highlight the similarity of the results and improvements in speed. Overall, IsoForma provides a streamlined process, reduces the time of conducting isoform-based analyses, and offers an essential framework for developing customized proteoform analysis workflows. Finally, the software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

DDX1 regulates alternative splicing and insulin secretion in pancreatic β cells

DEAD-box helicase 1 (DDX1) is a multifunction protein involved in diverse cellular processes including transcription, viral replication, mRNA/miRNA processing, and tRNA splicing. Here, we report a novel function of DDX1 in mRNA alternative splicing in pancreatic β cells. By performing integrated data analysis of high-throughput RNA sequencing (RNA-Seq), and cross-linking and immunoprecipitation coupled with deep sequencing (CLIP-Seq), we identify hundreds of alternative splicing genes that are targeted by DDX1. These DDX1-targeted alternative splicing genes are mainly associated with calcium ion binding, high voltage-gated calcium channel, and transmembrane transporter. Functionally, silencing DDX1 impairs calcium influx and insulin secretion in the pancreatic β cells. These results reveal an important role for DDX1 in the regulation of gene alternative splicing and insulin secretion in pancreatic β cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

RB-TnSeq elucidates dicarboxylic-acid-specific catabolism in β-proteobacteria for improved plastic monomer upcycling

Dicarboxylic acids are key components of many polymers and plastics, making them a target for both engineered microbial degradation and sustainable bioproduction. In this study, we generated a comprehensive data set of functional evidence for the genetic basis of dicarboxylic and fatty acid metabolism using randomly barcoded transposon sequencing (RB-TnSeq). We identified four β-proteobacteria that displayed robust growth with dicarboxylic acid sole carbon source and cultured their mutant libraries with dicarboxylic and fatty acids with carbon chain lengths from C3 to C12. The resulting fitness data suggested that dicarboxylic and fatty acid metabolisms are largely distinct, and different sets of β-oxidation genes are required for catabolizing dicarboxylic versus fatty acids of the same carbon chain lengths. In addition, we identified transcriptional regulators and transporters with strong fitness phenotypes related to dicarboxylic acid utilization. In Ralstonia sp. UNC404CL21Col (R. CL21), we deleted two transcriptional repressors to improve its utilization of short-chain dicarboxylic acids. We exploited the diacid-utilizing catabolism of R. CL21 to upcycle a mock mixture of the dicarboxylic acids produced when polyethylene is oxidized. After introducing a heterologous indigoidine production pathway, this engineered Ralstonia produced 0.56 ± 0.02 g/L indigoidine from a mixture of dicarboxylic acids as a carbon source, demonstrating the potential of R. CL21 to upcycle plastic wastes to products derived from tricarboxylic acid (TCA) cycle intermediates. IMPORTANCE: Upcycling the carbon in plastic wastes to value-added products is a promising approach to address the plastic waste and climate crises, and dicarboxylic acid metabolism is an important facet of several approaches. Improving our understanding of the genetic basis of this metabolism has the potential to uncover new enzymes and genetic parts for engineered pathways involving dicarboxylic acids. Our data set is the most comprehensive interrogation of dicarboxylic acid catabolism to date, and this work will be of utility to researchers interested in both plastics bioproduction and upcycling applications.

Pearson, Allison N↗
DOE OSTI2024

Combinatorial transcription factor binding encodes cis -regulatory wiring of mouse forebrain GABAergic neurogenesis

Transcription factors (TFs) bind combinatorially to cis-regulatory elements, orchestrating transcriptional programs. Although studies of chromatin state and chromosomal interactions have demonstrated dynamic neurodevelopmental cis-regulatory landscapes, parallel understanding of TF interactions lags. To elucidate combinatorial TF binding driving mouse basal ganglia development, we integrated chromatin immunoprecipitation sequencing (ChIP-seq) for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and gene expression data, and functional enhancer assays. We identified sets of putative regulatory elements with shared TF binding (TF-pRE modules) that orchestrate distinct processes of GABAergic neurogenesis and suppress other cell fates. The majority of pREs were bound by one or two TFs; however, a small proportion were extensively bound. These sequences had exceptional evolutionary conservation and motif density, complex chromosomal interactions, and activity as in vivo enhancers. Our results provide insights into the combinatorial TF-pRE interactions that activate and repress expression programs during telencephalon neurogenesis and demonstrate the value of TF binding toward modeling developmental transcriptional wiring.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗
DOE OSTI2022

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

Abstract With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Summary Genome‐scale reconstructions of metabolism are computational species‐specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, i JN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock‐out library and Bar‐seq data; and (iv) allowed mapping of its network to 82 P . putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P . putida , which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas .

Microbiology↗