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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 OSTI2014

Exome-wide mutation profile in benzo[a]pyrene-derived post-stasis and immortal human mammary epithelial cells

Genetic mutations are known to drive cancer progression and certain tumors have mutation signatures that reflect exposures to environmental carcinogens. Benzo[a]pyrene (BaP) has a known mutation signature and has proven capable of inducing changes to DNA sequence that drives normal pre-stasis human mammary epithelial cells (HMEC) past a first tumor suppressor barrier (stasis) and toward immortality. We analyzed normal, pre-stasis HMEC, three independent BaP-derived post-stasis HMEC strains (184Aa, 184Be, 184Ce) and two of their immortal derivatives(184A1 and 184BE1) by whole exome sequencing. The independent post-stasis strains exhibited between 93 and 233 BaP-induced mutations in exons. Seventy percent of the mutations were C:G>A:T transversions, consistent with the known mutation spectrum of BaP. Mutations predicted to impact protein function occurred in several known and putative cancer drivers including p16, PLCG1, MED12, TAF1 in 184Aa; PIK3CG, HSP90AB1, WHSC1L1, LCP1 in 184Be and FANCA, LPP in 184Ce. Biological processes that typically harbor cancer driver mutations such as cell cycle, regulation of cell death and proliferation, RNA processing, chromatin modification and DNA repair were found to have mutations predicted to impact function in each of the post-stasis strains. Spontaneously immortalized HMEC lines derived from two of the BaP-derived post-stasis strains shared greater than 95% of their BaP-induced mutations with their precursor cells. These immortal HMEC had 10 or fewer additional point mutations relative to their post-stasis precursors, but acquired chromosomal anomalies during immortalization that arose independent of BaP. In conclusion, the results of this study indicate that acute exposures of HMEC to high dose BaP recapitulate mutation patterns of human tumors and can induce mutations in a number of cancer driver genes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Deep Learning Prediction of Protein Complex Structures

Proteins interact to form protein complex to carry out biological functions such as catalytic chemical reaction. Therefore, it is important to develop computational methods to predict protein-protein interaction and the structures of protein complexes to study and enhance protein function. In this project, we successfully developed several deep learning methods to predict inter-protein contacts and the reinforcement learning and optimization methods to reconstruct protein complex structures from predicted inter-chain contacts. The methods were integrated with the MULTICOM protein complex structure prediction system and applied to predict the complex structures of biomass production-related proteins of green algae. During the two and a half years of research and development, all the specific milestones of the project were achieved successfully. 16 publications/manuscripts were produced. 10 software tools were developed. A patent application was submitted. Our MULTICOM predictors leveraging some tools developed in this project were ranked among the top predictors in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Abstract Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities

Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, in this work, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Comparative and Functional Algal Genomics

Over 100 whole-genome sequences from algae are published or soon to be published. The rapidly increasing availability of these fundamental resources is precipitating a paradigm shift in the way we understand one of the most diverse, complex, and understudied groups of photosynthetic eukaryotes. Genome sequences provide a window into the functional potential of each disparate alga, with phylogenomics and functional genomics as tools for contextualizing and transferring knowledge from reference organisms into less well-characterized systems. Remarkably, over half of the proteins encoded by algal genomes are of unknown function, highlighting the volume of unique functional capabilities yet to be discovered. We provide an overview of publicly available algal genomes, their associated protein inventories, and their quality, with a summary of the statuses of protein function understanding and prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Genome-wide analysis of cytochrome P450s of Trichoderma spp.: annotation and evolutionary relationships

Background Cytochrome P450s form an important group of enzymes involved in xenobiotics degradation and metabolism, both primary and secondary. These enzymes are also useful in industry as biotechnological tools for bioconversion and a few are reported to be involved in pathogenicity. Trichoderma spp. are widely used in industry and agriculture and are known for their biosynthetic potential of a large number of secondary metabolites. For realising the full biosynthetic potential of an organism, it is important to do a genome-wide annotation and cataloguing of these enzymes. Results Here, we have studied the genomes of seven species (T. asperellum, T. atroviride, T. citrinoviride, T. longibrachiatum, T. reesei , T. harzianum and T. virens) and identified a total of 477 cytochrome P450s. We present here the classification, evolution and structure as well as predicted function of these proteins. This study would pave the way for functional characterization of these groups of enzymes and will also help in realization of their full economic potential. Conclusion Our CYPome annotation and evolutionary studies of the seven Trichoderma species now provides opportunities for exploration of research-driven strategies to select Trichoderma species for various applications especially in relation to secondary metabolism and degradation of environmental pollutants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Data from: Diversity and population structure of northern switchgrass as revealed through exome capture sequencing

Switchgrass (Panicum virgatum L.) is a polyploid, perennial grass species that is native to North America, and is being developed as a future biofuels feedstock crop. Switchgrass is present primarily in two ecotypes: a northern upland ecotype composed of tetraploid and octoploid accessions, and a southern lowland ecotype composed of primarily tetraploid accessions. We employed high-coverage exome capture sequencing (~2.4 Tb) to genotype 537 individuals from 45 upland and 21 lowland populations. From these data, we identified ~27 million single nucleotide polymorphisms (SNPs), of which 1,590,653 high confidence SNPs were used in downstream analyses of diversity within and between the populations. From the 66 populations, we identified five primary population groups within the upland and lowland ecotypes, a result that was further supported through genetic distance analysis. We identified conserved, ecotype restricted non-synonymous SNPs that are predicted to impact protein function in genes that encode CONSTANS (CO) and EARLY HEADING DATE 1 (EHD1), key genes involved in flowering which may contribute to the phenotypic differences between the two ecotypes. We also identified, relative to the near-reference Kanlow population, 17,228 up-copy number variants (CNVs), 112,630 down-CNVs, and 14,430 presence/absence variants (PAV) impacting a total of 9,979 genes, including two upland-specific CNV-clusters. In total, 45,719 genes were impacted by a SNP, CNV, or a PAV across the panel providing a firm foundation to identify functional variation associated with phenotypic traits of interest for biofuel feedstock production.

09 BIOMASS FUELS↗
DOE OSTI2022

Expansion of the global RNA virome reveals diverse clades of bacteriophages

High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum Lenarviricota, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

SoyCSN: Soybean context-specific network analysis and prediction based on tissue-specific transcriptome data

The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA-Seq data generated in the project serve as a valuable resource for discovering tissue-specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context-specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context-specific networks at both transcriptome and interactome levels. BicMix and Cross-Conditions Cluster Detection algorithms were applied to detect modules based on co-expression relationships across all the tissues. Soybean context-specific interactomes were predicted by combining soybean tissue gene expression and protein–protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen-fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium–legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) at http://soykb.org/SoyCSN. SoyCSN provides a useful web-based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Phloem proteomics reveals new lipid-binding proteins with a putative role in lipid-mediated signaling

Global climate changes inversely affect our ability to grow the food required for an increasing world population. To combat future crop loss due to abiotic stress, we need to understand the signals responsible for changes in plant development and the resulting adaptations, especially the signaling molecules traveling long-distance through the plant phloem. Using a proteomics approach, we had identified several putative lipid-binding proteins in the phloem exudates. Simultaneously, we identified several complex lipids as well as jasmonates. These findings prompted us to propose that phloem (phospho-) lipids could act as long-distance developmental signals in response to abiotic stress, and that they are released, sensed, and moved by phloem lipid-binding proteins (Benning et al., 2012). Indeed, the proteins we identified include lipases that could release a signaling lipid into the phloem, putative receptor components, and proteins that could mediate lipid-movement. To test this possible protein-based lipid-signaling pathway, three of the proteins, which could potentially act in a relay, are characterized here: (I) a putative GDSL-motif lipase (II) a PIG-P-like protein, with a possible receptor-like function; (III) and PLAFP (phloem lipid-associated family protein), a predicted lipid-binding protein of unknown function. Here we show that all three proteins bind lipids, in particular phosphatidic acid (PtdOH), which is known to participate in intracellular stress signaling. Genes encoding these proteins are expressed in the vasculature, a prerequisite for phloem transport. Cellular localization studies show that the proteins are not retained in the endoplasmic reticulum but surround the cell in a spotted pattern that has been previously observed with receptors and plasmodesmatal proteins. Abiotic signals that induce the production of PtdOH also regulate the expression of GDSL-lipase and PLAFP, albeit in opposite patterns. Our findings suggest that while all three proteins are indeed lipid-binding and act in the vasculature possibly in a function related to long-distance signaling, the three proteins do not act in the same but rather in distinct pathways. Furthermore, it points toward PLAFP as a prime candidate to investigate long-distance lipid signaling in the plant drought response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

The SlZRT1 Gene Encodes a Plasma Membrane-Located ZIP (Zrt-, Irt-Like Protein) Transporter in the Ectomycorrhizal Fungus Suillus luteus

Zinc (Zn) is an essential micronutrient but may become toxic when present in excess. In Zn-contaminated environments, trees can be protected from Zn toxicity by their root-associated micro-organisms, in particular ectomycorrhizal fungi. The mechanisms of cellular Zn homeostasis in ectomycorrhizal fungi and their contribution to the host tree’s Zn status are however not yet fully understood. The aim of this study was to identify and characterize transporters involved in Zn uptake in the ectomycorrhizal fungus Suillus luteus, a cosmopolitan pine mycobiont. Zn uptake in fungi is known to be predominantly governed by members of the ZIP (Zrt/IrtT-like protein) family of Zn transporters. Four ZIP transporter encoding genes were identified in the S. luteus genome. By in silico and phylogenetic analysis, one of these proteins, SlZRT1, was predicted to be a plasma membrane located Zn importer. Heterologous expression in yeast confirmed the predicted function and localization of the protein. A gene expression analysis via RT-qPCR was performed in S. luteus to establish whether SlZRT1 expression is affected by external Zn concentrations. SlZRT1 transcripts accumulated almost immediately, though transiently upon growth in the absence of Zn. Exposure to elevated concentrations of Zn resulted in a significant reduction of SlZRT1 transcripts within the first hour after initiation of the exposure. Altogether, the data support a role as cellular Zn importer for SlZRT1 and indicate a key role in cellular Zn uptake of S. luteus. Further research is needed to understand the eventual contribution of SlZRT1 to the Zn status of the host plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A roadmap for the functional annotation of protein families: a community perspective

Over the last 25 years, biology has entered the genomic era and is becoming a science of ‘big data’. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3–4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Diversity and population structure of northern switchgrass as revealed through exome capture sequencing

Panicum virgatum L. (switchgrass) is a polyploid, perennial grass species that is native to North America, and is being developed as a future biofuel feedstock crop. Switchgrass is present primarily in two ecotypes: a northern upland ecotype, composed of tetraploid and octoploid accessions, and a southern lowland ecotype, composed of primarily tetraploid accessions. We employed high-coverage exome capture sequencing (~2.4 Tb) to genotype 537 individuals from 45 upland and 21 lowland populations. From these data, we identified ~27 million single-nucleotide polymorphisms (SNPs), of which 1 590 653 high-confidence SNPs were used in downstream analyses of diversity within and between the populations. From the 66 populations, we identified five primary population groups within the upland and lowland ecotypes, a result that was further supported through genetic distance analysis. We identified conserved, ecotype-restricted, non-synonymous SNPs that are predicted to affect the protein function of CONSTANS (CO) and EARLY HEADING DATE 1 (EHD1), key genes involved in flowering, which may contribute to the phenotypic differences between the two ecotypes. We also identified, relative to the near-reference Kanlow population, 17 228 genes present in more copies than in the reference genome (up-CNVs), 112 630 genes present in fewer copies than in the reference genome (down-CNVs) and 14 430 presence/absence variants (PAVs), affecting a total of 9979 genes, including two upland-specific CNV clusters. In total, 45 719 genes were affected by an SNP, CNV, or PAV across the panel, providing a firm foundation to identify functional variation associated with phenotypic traits of interest for biofuel feedstock production.

09 BIOMASS FUELS↗
DOE OSTI2024

Interactive tools for functional annotation of bacterial genomes

Automated annotations of protein functions are error-prone because of our lack of knowledge of protein functions. For example, it is often impossible to predict the correct substrate for an enzyme or a transporter. Furthermore, much of the knowledge that we do have about the functions of proteins is missing from the underlying databases. We discuss how to use interactive tools to quickly find different kinds of information relevant to a protein’s function. Many of these tools are available via PaperBLAST (http://papers.genomics.lbl.gov). Combining these tools often allows us to infer a protein’s function. Ideally, accurate annotations would allow us to predict a bacterium’s capabilities from its genome sequence, but in practice, this remains challenging. We describe interactive tools that infer potential capabilities from a genome sequence or that search a genome to find proteins that might perform a specific function of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

Abstract Motivation Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. Results In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. Availability and implementation The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Serum Proteome Signature of Radiation Response: Upregulation of Inflammation-Related Factors and Downregulation of Apolipoproteins and Coagulation Factors in Cancer Patients Treated With Radiation Therapy—A Pilot Study

Purpose: Ionizing radiation affects the proteome of irradiated cells and tissue, yet data concerning changes induced during radiation therapy (RT) in human blood are fragmentary and inconclusive. We aimed to identify features of serum proteome and associated processes involved in response to partial body irradiation during cancer treatment. Methods and Materials: Twenty patients with head and neck squamous cell cancer (HNSCC) and 20 patients with prostate cancer received definitive intensity modulated RT. Blood samples were collected before RT, just after RT, and 1 month after the end of RT. Complete serum proteome was analyzed in individual samples, using a shotgun liquid chromatography-tandem mass spectrometry approach which allowed identification of approximately 450 proteins. Approximately 100 unique proteins were quantified in all samples after exclusion of immunoglobulins, and statistical significance of differences among consecutive samples was assessed. Processes associated with quantified proteins and their functional interactions were predicted using gene ontology tools. Results: RT-induced changes were marked in the HNSCC patient group: 22 upregulated and 33 downregulated proteins were detected in post-RT sera. Most of the changes reversed during follow-up, yet levels of some proteins remained affected 1 month after the end of RT. RT-upregulated proteins were associated with acute phase, inflammatory response, and complement activation. RT-downregulated proteins were associated with transport and metabolism of lipids (plasma apolipoproteins) and blood coagulation. RT-induced changes were much weaker in prostate cancer patients, which corresponded to differences in acute radiation toxicity observed in both groups. Nevertheless, general patterns of RT-induced sera proteome changes were similar in both of the groups of cancer patients. Conclusions: In this pilot study, we proposed to identify a molecular signature of radiation response, based on specific features of serum proteome. The signature included upregulation of factors involved in acute or inflammatory response but also downregulation of plasma apolipoproteins and factors involved in blood coagulation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Artificial Intelligence Transforming Post-Translational Modification Research

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗