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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 OSTI2024

Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances

Purpose: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results: Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions: Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Diverse signatures of convergent evolution in cactus-associated yeasts

Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through the same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset of genomic and phenotypic data from 1,049 yeast species in the subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) to explore signatures of convergent evolution in cactophilic yeasts, ecological specialists associated with cacti. We inferred that the ecological association of yeasts with cacti arose independently approximately 17 times. Using a machine learning–based approach, we further found that cactophily can be predicted with 76% accuracy from both functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which we found to be likely associated with altered evolutionary rates of genes impacting the cell envelope in several cactophilic lineages. We also identified horizontal gene transfer and duplication events of plant cell wall–degrading enzymes in distantly related cactophilic clades, suggesting that putatively adaptive traits evolved independently through disparate molecular mechanisms. Notably, we found that multiple cactophilic species and their close relatives have been reported as emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle—and perhaps more generally lifestyles favoring thermotolerance—might preadapt yeasts to cause human disease. This work underscores the potential of a multifaceted approach involving high-throughput genomic and phenotypic data to shed light onto ecological adaptation and highlights how convergent evolution to wild environments could facilitate the transition to human pathogenicity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Genome Sequence and Analysis of the Flavinogenic Yeast Candida membranifaciens IST 626

The ascomycetous yeast Candida membranifaciens has been isolated from diverse habitats, including humans, insects, and environmental sources, exhibiting a remarkable ability to use different carbon sources that include pentoses, melibiose, and inulin. In this study, we isolated four C. membranifaciens strains from soil and investigated their potential to overproduce riboflavin. C. membranifaciens IST 626 was found to produce the highest concentrations of riboflavin. The volumetric production of this vitamin was higher when C. membranifaciens IST 626 cells were cultured in a commercial medium without iron and when xylose was the available carbon source compared to the same basal medium with glucose. Supplementation of the growth medium with 2 g/L glycine favored the metabolization of xylose, leading to biomass increase and consequent enhancement of riboflavin volumetric production that reached 120 mg/L after 216 h of cultivation. To gain new insights into the molecular basis of riboflavin production and carbon source utilization in this species, the first annotated genome sequence of C. membranifaciens is reported in this article, as well as the result of a comparative genomic analysis with other relevant yeast species. A total of 5619 genes were predicted to be present in C. membranifaciens IST 626 genome sequence (11.5 Mbp). Among them are genes involved in riboflavin biosynthesis, iron homeostasis, and sugar uptake and metabolism. This work put forward C. membranifaciens IST 626 as a riboflavin overproducer and provides valuable molecular data for future development of superior producing strains capable of using the wide range of carbon sources, which is a characteristic trait of the species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Single-Cell Genomics of Novel Actinobacteria With the Wood–Ljungdahl Pathway Discovered in a Serpentinizing System

Serpentinite-hosted systems represent modern-day analogs of early Earth environments. In these systems, water-rock interactions generate highly alkaline and reducing fluids that can contain hydrogen, methane, and low-molecular-weight hydrocarbons-potent reductants capable of fueling microbial metabolism. In this study, we investigated the microbiota of Hakuba Happo hot springs (~50°C; pH~10.5–11), located in Nagano (Japan), which are impacted by the serpentinization process. Analysis of the 16S rRNA gene amplicon sequences revealed that the bacterial community comprises Nitrospirae (47%), “Parcubacteria” (19%), Deinococcus-Thermus (16%), and Actinobacteria (9%), among others. Notably, only 57 amplicon sequence variants (ASV) were detected, and fifteen of these accounted for 90% of the amplicons. Among the abundant ASVs, an early-branching, uncultivated actinobacterial clade identified as RBG-16-55-12 in the SILVA database was detected. Ten single-cell genomes (average pairwise nucleotide identity: 0.98–1.00; estimated completeness: 33–93%; estimated genome size: ~2.3 Mb) that affiliated with this clade were obtained. Taxonomic classification using single copy genes indicates that the genomes belong to the actinobacterial class-level clade UBA1414 in the Genome Taxonomy Database. Based on metabolic pathway predictions, these actinobacteria are anaerobes, capable of glycolysis, dissimilatory nitrate reduction and CO 2 fixation via the Wood–Ljungdahl (WL) pathway. Several other genomes within UBA1414 and two related class-level clades also encode the WL pathway, which has not yet been reported for the Actinobacteria phylum. For the Hakuba actinobacterium, the energy metabolism related to the WL pathway is likely supported by a combination of the Rnf complex, group 3b and 3d [NiFe]-hydrogenases, [FeFe]-hydrogenases, and V-type (H + /Na + pump) ATPase. The genomes also harbor a form IV ribulose 1,5-bisphosphate carboxylase/oxygenase (RubisCO) complex, also known as a RubisCO-like protein, and contain signatures of interactions with viruses, including clustered regularly interspaced short palindromic repeat (CRISPR) regions and several phage integrases. This is the first report and detailed genome analysis of a bacterium within the Actinobacteria phylum capable of utilizing the WL pathway. The Hakuba actinobacterium is a member of the clade UBA1414/RBG-16-55-12, formerly within the group “OPB41.” We propose to name this bacterium ‘Candidatus Hakubanella thermoalkaliphilus.'

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response

Motivation: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems such as protein complexes, metabolic reactions, and signal transduction pathways. Hypergraphs are generalizations of graphs that naturally model multi-way interactions in data, and we therefore seek to understand how they can more faithfully identify, and potentially predict, complex relationships in genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges are differentially expressed genes and vertices represent conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Our results demonstrate the utility of using hypergraphs to represent complex biological systems, and highlight potentially interesting biological results about host response to highly pathogenic viruses.

systems biology, hypergraph, viral infection, biol↗
DOE OSTI2025

Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

Abstract The genome-scale model of metabolism and gene expression (ME-model) forPseudomonas putidaKT2440,iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model,iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization inP. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus,iPpu1676-ME offers valuable insights intoP. putida’s proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

Mathematical & Computational Biology↗
DOE OSTI2024

Predicting the Identities of su(met-2) and met-3 in Neurospora crassa by Genome Resequencing

A significant number of classical genetic Neurospora crassa biochemical mutants remain anonymous, unassociated with a physical genome locus. By utilizing short read next-generation sequencing methods, it is possible to sequence the genomes of mutant strains rapidly and economically for the purpose of identifying genes associated with mutant phenotypes. We have taken this approach to connect genes and mutations to “methionineless” phenotypes in N. crassa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Atomistic measurement and modeling of intrinsic fracture toughness of two-dimensional materials

Quantifying the intrinsic mechanical properties of two-dimensional (2D) materials is essential to predict the long-term reliability of materials and systems in emerging applications ranging from energy to health to next-generation sensors and electronics. Currently, measurements of fracture toughness and identification of associated atomistic mechanisms remain challenging. Herein, we report an integrated experimental–computational framework in which in-situ high-resolution transmission electron microscopy (HRTEM) measurements of the intrinsic fracture energy of monolayer MoS 2 and MoSe 2 are in good agreement with atomistic model predictions based on an accurately parameterized interatomic potential. Changes in crystalline structures at the crack tip and crack edges, as observed in in-situ HRTEM crack extension tests, are properly predicted. Such a good agreement is the result of including large deformation pathways and phase transitions in the parameterization of the inter-atomic potential. The established framework emerges as a robust approach to determine the predictive capabilities of molecular dynamics models employed in the screening of 2D materials, in the spirit of the materials genome initiative. Moreover, it enables device-level predictions with superior accuracy (e.g., fatigue lifetime predictions of electro- and opto-electronic nanodevices).

Zhang, Xu↗
DOE OSTI2022

$\mathrm{CROPSR}$: an automated platform for complex genome-wide $\mathrm{CRISPR}$ g$\mathrm{RNA}$ design and validation

CRISPR/Cas9 technology has become an important tool to generate targeted, highly specific genome mutations. The technology has great potential for crop improvement, as crop genomes are tailored to optimize specific traits over generations of breeding. Many crops have highly complex and polyploid genomes, particularly those used for bioenergy or bioproducts. The majority of tools currently available for designing and evaluating gRNAs for CRISPR experiments were developed based on mammalian genomes that do not share the characteristics or design criteria for crop genomes. We have developed an open source tool for genome-wide design and evaluation of gRNA sequences for CRISPR experiments, CROPSR. The genome-wide approach provides a significant decrease in the time required to design a CRISPR experiment, including validation through PCR, at the expense of an overhead compute time required once per genome, at the first run. To better cater to the needs of crop geneticists, restrictions imposed by other packages on design and evaluation of gRNA sequences were lifted. A new machine learning model was developed to provide scores while avoiding situations in which the currently available tools sometimes failed to provide guides for repetitive, A/T-rich genomic regions. We show that our gRNA scoring model provides a significant increase in prediction accuracy over existing tools, even in non-crop genomes. CROPSR provides the scientific community with new methods and a new workflow for performing CRISPR/Cas9 knockout experiments. CROPSR reduces the challenges of working in crops, and helps speed gRNA sequence design, evaluation and validation. We hope that the new software will accelerate discovery and reduce the number of failed experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Utilizing Amino Acid Composition and Entropy of Potential Open Reading Frames to Identify Protein-Coding Genes

One of the main steps in gene-finding in prokaryotes is determining which open reading frames encode for a protein, and which occur by chance alone. There are many different methods to differentiate the two; the most prevalent approach is using shared homology with a database of known genes. This method presents many pitfalls, most notably the catch that you only find genes that you have seen before. The four most popular prokaryotic gene-prediction programs (GeneMark, Glimmer, Prodigal, Phanotate) all use a protein-coding training model to predict protein-coding genes, with the latter three allowing for the training model to be created ab initio from the input genome. Different methods are available for creating the training model, and to increase the accuracy of such tools, we present here GOODORFS, a method for identifying protein-coding genes within a set of all possible open reading frames (ORFS). Our workflow begins with taking the amino acid frequencies of each ORF, calculating an entropy density profile (EDP), using KMeans to cluster the EDPs, and then selecting the cluster with the lowest variation as the coding ORFs. To test the efficacy of our method, we ran GOODORFS on 14,179 annotated phage genomes, and compared our results to the initial training-set creation step of four other similar methods (Glimmer, MED2, PHANOTATE, Prodigal). We found that GOODORFS was the most accurate (0.94) and had the best F1-score (0.85), while Glimmer had the highest precision (0.92) and PHANOTATE had the highest recall (0.96).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria

The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

In vivo human T cell engineering with enveloped delivery vehicles

Viruses and virally derived particles have the intrinsic capacity to deliver molecules to cells, but the difficulty of readily altering cell-type selectivity has hindered their use for therapeutic delivery. Here, we show that cell surface marker recognition by antibody fragments displayed on membrane-derived particles encapsulating CRISPR–Cas9 protein and guide RNA can deliver genome editing tools to specific cells. Compared to conventional vectors like adeno-associated virus that rely on evolved capsid tropisms to deliver virally encoded cargo, these Cas9-packaging enveloped delivery vehicles (Cas9-EDVs) leverage predictable antibody–antigen interactions to transiently deliver genome editing machinery selectively to cells of interest. Antibody-targeted Cas9-EDVs preferentially confer genome editing in cognate target cells over bystander cells in mixed populations, both ex vivo and in vivo. By using multiplexed targeting molecules to direct delivery to human T cells, Cas9-EDVs enable the generation of genome-edited chimeric antigen receptor T cells in humanized mice, establishing a programmable delivery modality with the potential for widespread therapeutic utility.

42 ENGINEERING↗
DOE OSTI2023

Reclassification of ASFV into 7 Biotypes Using Unsupervised Machine Learning

In 2007, an outbreak of African swine fever (ASF), a deadly disease of domestic swine and wild boar caused by the African swine fever virus (ASFV), occurred in Georgia and has since spread globally. Historically, ASFV was classified into 25 different genotypes. However, a newly proposed system recategorized all ASFV isolates into 6 genotypes exclusively using the predicted protein sequences of p72. However, ASFV has a large genome that encodes between 150–200 genes, and classifications using a single gene are insufficient and misleading, as strains encoding an identical p72 often have significant mutations in other areas of the genome. We present here a new classification of ASFV based on comparisons performed considering the entire encoded proteome. A curated database consisting of the protein sequences predicted to be encoded by 220 reannotated ASFV genomes was analyzed for similarity between homologous protein sequences. Weights were applied to the protein identity matrices and averaged to generate a genome-genome identity matrix that was then analyzed by an unsupervised machine learning algorithm, DBSCAN, to separate the genomes into distinct clusters. We conclude that all available ASFV genomes can be classified into 7 distinct biotypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Validation of a metabolite–GWAS network for Populus trichocarpa family 1 UDP-glycosyltransferases

Metabolite genome-wide association studies (mGWASs) are increasingly used to discover the genetic basis of target phenotypes in plants such as Populus trichocarpa , a biofuel feedstock and model woody plant species. Despite their growing importance in plant genetics and metabolomics, few mGWASs are experimentally validated. Here, we present a functional genomics workflow for validating mGWAS-predicted enzyme–substrate relationships. We focus on uridine diphosphate–glycosyltransferases (UGTs), a large family of enzymes that catalyze sugar transfer to a variety of plant secondary metabolites involved in defense, signaling, and lignification. Glycosylation influences physiological roles, localization within cells and tissues, and metabolic fates of these metabolites. UGTs have substantially expanded in P. trichocarpa , presenting a challenge for large-scale characterization. Using a high-throughput assay, we produced substrate acceptance profiles for 40 previously uncharacterized candidate enzymes. Assays confirmed 10 of 13 leaf mGWAS associations, and a focused metabolite screen demonstrated varying levels of substrate specificity among UGTs. A substrate binding model case study of UGT-23 rationalized observed enzyme activities and mGWAS associations, including glycosylation of trichocarpinene to produce trichocarpin, a major higher-order salicylate in P. trichocarpa. We identified UGTs putatively involved in lignan, flavonoid, salicylate, and phytohormone metabolism, with potential implications for cell wall biosynthesis, nitrogen uptake, and biotic and abiotic stress response that determine sustainable biomass crop production. Our results provide new support for in silico analyses and evidence-based guidance for in vivo functional characterization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Trichoderma harzianum transcriptome in response to the nematode Pratylenchus brachyurus

The root-lesion nematode Pratylenchus brachyurus causes extensive damage in several crops of economic importance. Fungi of Trichoderma genus have been highlighted as biopesticide agents in the control of several plant diseases. Although it is already widely used in agriculture, there are few studies, especially at the molecular level, that evaluate T. harzianum in the control of P. brachyurus . The aim of the present study was to investigate how interaction with the nematode P. brachyurus influences gene expression of T. harzianum , by using RNA-Seq analysis. Of the 13,932 predicted genes in the T. harzianum genome, 2,922 (21%) were differentially expressed in the presence of P. brachyurus , in relation to the absence of the nematode. Among the differentially expressed genes, we found genes encoding Carbohydrate Active EnZymes (CAZy), MEROPS peptidases, and proteins related to secondary metabolite synthesis. 118 pathways were identified as related to the biosynthesis of secondary metabolites. Additionally, the analysis identified 136 metabolic pathways related to these differentially expressed genes, among which we highlight: aminobenzoate degradation, xenobiotic metabolism by cytochrome P450, and sesquiterpenoid and triterpenoid biosynthesis. Our results contribute to a better understanding of the response of T. harzianum to the nematode P. bracyurus , the potential for biocontrol by this fungus.

59 BASIC BIOLOGICAL SCIENCES↗