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

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The JA‐responsive MYC2‐ BADH ‐ like transcriptional regulatory module in Poncirus trifoliata contributes to cold tolerance by modulation of glycine betaine biosynthesis

Summary Glycine betaine (GB) is known to accumulate in plants exposed to cold, but the underlying molecular mechanisms and associated regulatory network remain unclear. Here, we demonstrated that PtrMYC2 of Poncirus trifoliata integrates the jasmonic acid (JA) signal to modulate cold‐induced GB accumulation by directly regulating PtrBADH‐l , a betaine aldehyde dehydrogenase (BADH)‐like gene. PtrBADH‐l was identified based on transcriptome and expression analysis in P. trifoliata . Overexpression and VIGS (virus‐induced gene silencing)‐mediated knockdown showed that PtrBADH‐l plays a positive role in cold tolerance and GB synthesis. Yeast one‐hybrid library screening using PtrBADH‐l promoter as baits unraveled PtrMYC2 as an interacting candidate. PtrMYC2 was confirmed to directly bind to two G‐box cis ‐acting elements within PtrBADH‐l promoter and acts as a transcriptional activator. In addition, PtrMYC2 functions positively in cold tolerance through modulation of GB synthesis by regulating PtrBADH‐l expression. Interestingly, we found that GB accumulation under cold stress was JA‐dependent and that PtrMYC2 orchestrates JA‐mediated PtrBADH‐l upregulation and GB accumulation. This study sheds new light on the roles of MYC2 homolog in modulating GB synthesis. In particular, we propose a transcriptional regulatory module PtrMYC2‐PtrBADH‐l to advance the understanding of molecular mechanisms underlying the GB accumulation under cold stress.

Ming, Ruhong↗
DOE OSTI2021

Heterospecific Neighbor Plants Impact Root Microbiome Diversity and Molecular Function of Root Fungi

Within the forest community, competition and facilitation between adjacent-growing conspecific and heterospecific plants are mediated by interactions involving common mycorrhizal networks. The ability of plants to alter their neighbor’s microbiome is well documented, but the molecular biology of plant-fungal interactions during competition and facilitation has not been previously examined. We used a common soil-plant bioassay experiment to study molecular plant-microbial interactions among rhizosphere communities associated with Pinus taeda (native host) and Populus trichocarpa (non-native host). Gene expression of interacting fungal and bacterial rhizosphere communities was compared among three plant-pairs: Populus growing with Populus , Populus with Pinus , and Pinus with Pinus . Our results demonstrate that heterospecific plant partners affect the assembly of root microbiomes, including the changes in the structure of host specific community. Comparative metatranscriptomics reveals that several species of ectomycorrhizal fungi (EMF) and saprotrophic fungi exhibit different patterns of functional and regulatory gene expression with these two plant hosts. Heterospecific plants affect the transcriptional expression pattern of EMF host-specialists (e.g., Pinus -associated Suillus spp.) on both plant species, mainly including the genes involved in the transportation of amino acids, carbohydrates, and inorganic ions. Alteration of root microbiome by neighboring plants may help regulate basic plant physiological processes via modulation of molecular functions in the root microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

Fibronectin gene expression, synthesis and accumulation during in vitro differentiation of chicken osteoblasts

A well-defined chicken osteoblast culture system(18) has been used to examine fibronectin (FN) mRNA levels, synthesis, and accumulation during in vitro differentiation and matrix mineralization. Immunofluorescent staining of cells after 6 or 18 days in culture revealed that FN was initially associated with the cell surface and in partial coalignment with cytoskeletal elements while at the latter time most FN was associated with the extracellular matrix as a ubiquitous fibrillar network. Western blot analysis of total cell-associated proteins also detected FN at all culture times. However, when results were normalized to cellular DNA, FN levels increased until 12-16 and remained relatively constant thereafter. Similarly, FN synthesis as measured by [35S]-methionine labeling, and immunoprecipitation was greatest in early cultures (culture day 3) and then declined such that synthesis decreased 60% at day 18 and 94% after 24-31 days. FN mRNA levels as measured by Northern blot analysis were well correlated with FN synthesis. These results clearly show that FN is made by primary osteoblasts during their in vitro maturation. In contrast to other osteoblast markers such as alkaline phosphatase, osteocalcin, and osteopontin, whose expression increases as cells differentiate, FN accumulates in the matrix during periods of early cell growth and attachment and then remains proportional to cell number. Results with FN differ from those obtained with collagen which continues to accumulate in the extracellular matrix during osteoblast maturation. These results are consistent with FN being important for the initial attachment of early osteoblasts or osteoblast precursors to the pericellular matrix.

NASA Discipline Musculoskeletal↗
DOE OSTI2021

Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Transcriptional network orchestrating regional patterning of cortical progenitors

We uncovered a transcription factor (TF) network that regulates cortical regional patterning in radial glial stem cells. Screening the expression of hundreds of TFs in the developing mouse cortex identified 38 TFs that are expressed in gradients in the ventricular zone (VZ). We tested whether their cortical expression was altered in mutant mice with known patterning defects (Emx2, Nr2f1, and Pax6), which enabled us to define a cortical regionalization TF network (CRTFN). To identify genomic programming underlying this network, we performed TF ChIP-seq and chromatin-looping conformation to identify enhancer–gene interactions. To map enhancers involved in regional patterning of cortical progenitors, we performed assays for epigenomic marks and DNA accessibility in VZ cells purified from wild-type and patterning mutant mice. This integrated approach has identified a CRTFN and VZ enhancers involved in cortical regional patterning in the mouse.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Decoding crops one cell at a time: from cell atlases to single-cell genetics

Understanding the mechanisms underlying key agricultural traits remains a central challenge in crop research, but recent advances in technologies are providing powerful tools to address this issue. Among these, single-cell and spatial transcriptomics have revealed tissue heterogeneity and spatial organization, offering unique insights into cellular gene expression dynamics and the coordinated activity of multiple cell types. These approaches help uncover how specific cell types contribute to agricultural traits and refine candidate loci lists through integration with trait-associated loci. Additionally, single-cell and spatial transcriptomics have the potential to serve as cell-level readout platforms integrating cellular perturbations, enabling high-throughput discovery of causal relationships between genotype and gene expression at the cellular level in plants. Successful implementation will accelerate the identification of key genetic variants for crop improvement. Furthermore we review lessons learned from application of single-cell screening in mammalian cells, highlight major technical and biological barriers to its use in plants, and outline potential strategies to overcome these challenges. Together, the widespread application and integration of single-cell and spatial transcriptomics with other technologies enable not only the descriptive cataloging of cell states but also the causal interrogation of sequence functions and regulatory networks at cell type resolution, ultimately advancing gene function studies and accelerating crop improvement.

Cellular heterogeneity↗
DOE OSTI2023

A conserved gene regulatory network controls root epidermal cell patterning in superrosid species

Summary In superrosid species, root epidermal cells differentiate into root hair cells and nonhair cells. In some superrosids, the root hair cells and nonhair cells are distributed randomly (Type I pattern), and in others, they are arranged in a position‐dependent manner (Type III pattern). The model plant Arabidopsis ( Arabidopsis thaliana ) adopts the Type III pattern, and the gene regulatory network (GRN) that controls this pattern has been defined. However, it is unclear whether the Type III pattern in other species is controlled by a similar GRN as in Arabidopsis, and it is not known how the different patterns evolved. In this study, we analyzed superrosid species Rhodiola rosea , Boehmeria nivea , and Cucumis sativus for their root epidermal cell patterns. Combining phylogenetics, transcriptomics, and cross‐species complementation, we analyzed homologs of the Arabidopsis patterning genes from these species. We identified R. rosea and B. nivea as Type III species and C. sativus as Type I species. We discovered substantial similarities in structure, expression, and function of Arabidopsis patterning gene homologs in R. rosea and B. nivea , and major changes in C. sativus . We propose that in superrosids, diverse Type III species inherited the patterning GRN from a common ancestor, whereas Type I species arose by mutations in multiple lineages.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Evolutionary Variation in MADS Box Dimerization Affects Floral Development and Protein Abundance in Maize

Interactions between MADS-box transcription factors are critical in the regulation of floral development, and shifting MADS-box protein-protein interactions are predicted to have influenced floral evolution. However, precisely how evolutionary variation in protein-protein interactions affects MADS-box protein function remains unknown. To assess the impact of changing MADS-box protein-protein interactions on transcription factor function, we turned to the grasses, where interactions between B-class MADS-box proteins vary. We tested the functional consequences of this evolutionary variability using maize as an experimental system. We found that differential B-class dimerization was associated with subtle, quantitative differences in stamen shape. In contrast, differential dimerization resulted in large-scale changes to downstream gene expression. Differential dimerization also affected B-class complex composition and abundance, independent of transcript levels. This indicates that differential B-class dimerization affects protein degradation, revealing an important consequence for evolutionary variability in MADS-box interactions. Our results highlight complexity in the evolution of developmental gene networks - changing protein-protein interactions could affect not only the composition of transcription factor complexes, but also their degradation and persistence in developing flowers. Here, our results also show how coding change in a pleiotropic master regulator could have small, quantitative effects on development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Targeting the Y-box Binding Protein-1 Axis to Overcome Radiochemotherapy Resistance in Solid Tumors

Multifunctional Y-box binding protein-1 (YB-1) is highly expressed in different human solid tumors and is involved in various cellular processes. DNA damage is the major mechanism by which radiochemotherapy (RCT) induces cell death. On induction of DNA damage, a multicomponent signal transduction network, known as the DNA damage response, is activated to induce cell cycle arrest and initiate DNA repair, which protects cells against damage. YB-1 regulates nearly all cancer hallmarks described to date by participating in DNA damage response, gene transcription, mRNA splicing, translation, and tumor stemness. YB-1 lacks kinase activity, and p90 ribosomal S6 kinase and AKT are the key kinases within the RAS/mitogen-activated protein kinase and phosphoinositide 3-kinase pathways that directly activate YB-1. Thus, the molecular targeting of ribosomal S6 kinase and AKT is thought to be the most effective strategy for blocking the cellular function of YB-1 in human solid tumors. In this review, after describing the prosurvival effect of YB-1 with a focus on DNA damage repair and cancer cell stemness, clinical evidence will be provided indicating an inverse correlation between YB-1 expression and the treatment outcome of solid tumors after RCT. In the interest of being concise, YB-1 signaling cascades will be briefly discussed and the current literature on YB-1 posttranslational modifications will be summarized. Finally, the current status of targeting the YB-1 axis, especially in combination with RCT, will be highlighted.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Differential Gene Expression in Host Ubiquitination Processes in Childhood Malarial Anemia

Background: Malaria remains one of the leading global causes of childhood morbidity and mortality. In holoendemic Plasmodium falciparum transmission regions, such as western Kenya, severe malarial anemia [SMA, hemoglobin (Hb) < 6.0 g/dl] is the primary form of severe disease. Ubiquitination is essential for regulating intracellular processes involved in innate and adaptive immunity. Although dysregulation in ubiquitin molecular processes is central to the pathogenesis of multiple human diseases, the expression patterns of ubiquitination genes in SMA remain unexplored. Methods: To examine the role of the ubiquitination processes in pathogenesis of SMA, differential gene expression profiles were determined in Kenyan children ( n = 44, aged <48 mos) with either mild malarial anemia (M l MA; Hb ≥9.0 g/dl; n = 23) or SMA (Hb <6.0 g/dl; n = 21) using the Qiagen Human Ubiquitination Pathway RT 2 Profiler PCR Array containing a set of 84 human ubiquitination genes. Results: In children with SMA, 10 genes were down-regulated ( BRCC3 , FBXO3 , MARCH5 , RFWD2 , SMURF2 , UBA6 , UBE2A , UBE2D1 , UBE2L3 , UBR1 ), and five genes were up-regulated ( MDM2 , PARK2 , STUB1 , UBE2E3 , UBE2M ). Enrichment analyses revealed Ubiquitin-Proteasomal Proteolysis as the top disrupted process, along with altered sub-networks involved in proteasomal, protein, and ubiquitin-dependent catabolic processes. Conclusion: Collectively, these novel results show that protein coding genes of the ubiquitination processes are involved in the pathogenesis of SMA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Adaptive laboratory evolution and metabolic engineering of Cupriavidus necator for improved catabolism of volatile fatty acids

Bioconversion of high-volume waste streams into value-added products will be an integral component of the growing bioeconomy. Volatile fatty acids (VFAs) (e.g., butyrate, valerate, and hexanoate) are an emerging and promising waste-derived feedstock for microbial carbon upcycling. Cupriavidus necator H16 is a favorable host for conversion of VFAs into various bioproducts due to its diverse carbon metabolism, ease of metabolic engineering, and use at industrial scales. Here, in this study, we report that a common strategy to improve product titers in C. necator, deletion of the polyhydroxybutyrate (PHB) biosynthetic operon, results in a significant growth defect on VFA substrates. Using adaptive laboratory evolution, we identify mutations to the regulator gene phaR, the two-component response regulator-histidine kinase pair encoded by H16_A1372/H16_A1373, and the tripartite transporter assembly encoded by H16_A2296-A2298 as causative for improved growth on VFA substrates. Deletion of phaR and H16_A1373 led to significantly reduced NADH abundance accompanied by large changes to expression of genes involved in carbon metabolism, balance of electron carriers, and oxidative stress tolerance that may be responsible for improved growth of these engineered strains. These results provide insight into the role of PHB biosynthesis in carbon and energy metabolism and highlight a key role for the regulator PhaR in global regulatory networks. By combining mutations, we generated platform strains with significant growth improvements on VFAs, which can enable improved conversion of waste-derived VFA substrates to target bioproducts.

09 BIOMASS FUELS↗
DOE OSTI2021

Spurious regulatory connections dictate the expression-fitness landscape of translation factors

During steady-state cell growth, individual enzymatic fluxes can be directly inferred from growth rate by mass conservation, but the inverse problem remains unsolved. Perturbing the flux and expression of a single enzyme could have pleiotropic effects that may or may not dominate the impact on cell fitness. Here, we quantitatively dissect the molecular and global responses to varied expression of translation termination factors (peptide release factors, RFs) in the bacterium Bacillus subtilis. While endogenous RF expression maximizes proliferation, deviations in expression lead to unexpected distal regulatory responses that dictate fitness reduction. Molecularly, RF depletion causes expression imbalance at specific operons, which activates master regulators and detrimentally overrides the transcriptome. Through these spurious connections, RF abundances are thus entrenched by focal points within the regulatory network, in one case located at a single stop codon. Such regulatory entrenchment suggests that predictive bottom-up models of expression-fitness landscapes will require near-exhaustive characterization of parts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The mycorrhizal symbiosis: research frontiers in genomics, ecology, and agricultural application

Summary Mycorrhizal symbioses between plants and fungi are vital for the soil structure, nutrient cycling, plant diversity, and ecosystem sustainability. More than 250 000 plant species are associated with mycorrhizal fungi. Recent advances in genomics and related approaches have revolutionized our understanding of the biology and ecology of mycorrhizal associations. The genomes of 250+ mycorrhizal fungi have been released and hundreds of genes that play pivotal roles in regulating symbiosis development and metabolism have been characterized. rDNA metabarcoding and metatranscriptomics provide novel insights into the ecological cues driving mycorrhizal communities and functions expressed by these associations, linking genes to ecological traits such as nutrient acquisition and soil organic matter decomposition. Here, we review genomic studies that have revealed genes involved in nutrient uptake and symbiosis development, and discuss adaptations that are fundamental to the evolution of mycorrhizal lifestyles. We also evaluated the ecosystem services provided by mycorrhizal networks and discuss how mycorrhizal symbioses hold promise for sustainable agriculture and forestry by enhancing nutrient acquisition and stress tolerance. Overall, unraveling the intricate dynamics of mycorrhizal symbioses is paramount for promoting ecological sustainability and addressing current pressing environmental concerns. This review ends with major frontiers for further research.

Plant Sciences↗
DOE OSTI2020

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model’s rule set. The model’s internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine

The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

One hundred years into the study of ecotypes, new advances are being made through large-scale field experiments in perennial plant systems

A hundred years after Turesson first clearly described how locally adaptive variation is distributed within species, plant biologists are making major breakthroughs in our understanding of mechanisms underlying adaptation from local populations to the scale of continents. Although the genetics of local adaptation has typically been studied in smaller reciprocal transplant experiments, here it is being evaluated with whole genomes in large-scale networks of common garden experiments with perennial switchgrass and poplar trees. These studies support the hypothesis that a complex combination of loci, both with and without adaptive trade-offs, underlies local adaptation and that hybridization and adaptive introgression play a key role in the evolution of these species. Future studies incorporating high-throughput phenotyping, gene expression, and modeling will be used to predict responses of these species to climate change.

Trade-off↗
DOE OSTI2021

Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy

Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.

60 APPLIED LIFE SCIENCES↗