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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Bystander Effects of Ionizing Radiation

The objectives of this grant renewal are to provide administrative support and travel funds to allow the continued participation of the principal investigator (Dr. John B. Little) as an advisor to research initiated by several research fellows from his laboratory. The actual research will be carried out under the direction of Dr. Hatsumi Nagasawa with the collaboration of Dr. Joel Bedford at the Colorado State University, and by Drs. Edouard Azzam and Sonia de Toledo at the University of Medicine and Dentistry of New Jersey. Dr. Little will advise on the planning of experiments and development of experimental protocols, the analysis of data, and the preparation of manuscripts for publication. The Specific Aims for several of the planned experiments include: 1) to extend studies of the role of recombinational repair in the bystander effect by examining other genes in this pathway and cell lines deficient in excision repair; 2) to continue studies to determine the nature of the damage signal transmitted to bystander cells including the expression of several connexins in the bystander response, and the extent to which the enhanced oxidative metabolism observed in bystander cells may relate to the nature of the transmitted bystander signal; 3) to utilize a genome-wide approach to examine the genetic basis for the hypersensitivity to ionization we have observed in unaffected parents of patients with hereditary retinoblastoma, as well as from a group of apparently normal individuals that show similar radiosensitivity; 4) to complete studies concerning the induction of high frequencies of cells with massive chromosome damage in clonal derivatives of p53 and p21 knockout mouse cell lines; in particular to examine the role of telomere changes in this phenomenon. Overall, the results of these studies should enhance our understanding of the risk of low-dose exposures to ionizing radiation, including human populations to residential radon as well as occupational exposures.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2019

Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae

Abstract Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Among 43,301 ITRs across the four species, 34,460 (80%) are species-specific. ITRs found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could accurately distinguish between phenotype genes and pseudogenes (area under curve-receiver operating characteristic = 0.94). Based on the models, 584 (8%) and 4391 (61%) rice ITRs are classified as likely functional and nonfunctional with high confidence, respectively. ITRs with conserved expression and ancient retained duplicates, features that were not part of the model, are frequently classified as likely-functional, suggesting these characteristics could serve as pragmatic rules of thumb for identifying candidate sequences likely to be under selection. This study also provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Quantitative multiplexed analysis of gene and protein expression patterns in Yarrowia lipolytica

In this report, we present coordinated observations of protein and mRNA transcript counts at the single-cell level in the oleaginous yeast model Yarrowia lipolytica. The transcription factor Xbp1p regulates entry into a quiescent state, representing a shift of resources to sequestration of nutrients rather than cell division. We observed the responses of wild-type and Δxbp1 cells to protein (by fluorescence) and transcript quantification and localization at both single-cell and population-averaged levels. Data were collected via single-molecule fluorescence in situ hybridization (smFISH) and qPCR under nitrogen depletion, a condition that drives lipid accumulation. These techniques reveal a complex and heterogeneous population of Xbp1p dynamics and downstream regulation. Our findings highlight the need for single-cell resolution analyses to describe cellular dynamics and regulatory processes.

Yarrowia lipolytica↗
DOE OSTI2022

Comparative genomic and transcriptomic analyses of trans-kingdom pathogen Fusarium solani species complex reveal degrees of compartmentalization

Background: The Fusarium solani species complex (FSSC) comprises fungal pathogens responsible for mortality in a diverse range of animals and plants, but their genome diversity and transcriptome responses in animal pathogenicity remain to be elucidated. We sequenced, assembled and annotated six chromosome-level FSSC clade 3 genomes of aquatic animal and plant host origins. We established a pathosystem and investigated the expression data of F. falciforme and F. keratoplasticum in Chinese softshell turtle (Pelodiscus sinensis) host. Results: Comparative analyses between the FSSC genomes revealed a spectrum of conservation patterns in chromosomes categorised into three compartments: core, fast-core (FC), and lineage-specific (LS). LS chromosomes contribute to variations in genomes size, with up to 42.2% of variations between F. vanettenii strains. Each chromosome compartment varied in structural architectures, with FC and LS chromosomes contain higher proportions of repetitive elements with genes enriched in functions related to pathogenicity and niche expansion. We identified differences in both selection in the coding sequences and DNA methylation levels between genome features and chromosome compartments which suggest a multi-speed evolution that can be traced back to the last common ancestor of Fusarium. We further demonstrated that F. falciforme and F. keratoplasticum are opportunistic pathogens by inoculating P. sinensis eggs and identified differentially expressed genes also associated with plant pathogenicity. These included the most upregulated genes encoding the CFEM (Common in Fungal Extracellular Membrane) domain. Conclusions: The high-quality genome assemblies provided new insights into the evolution of FSSC chromosomes, which also serve as a resource for studies of fungal genome evolution and pathogenesis. This study also establishes an animal model for fungal pathogens of trans-kingdom hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution is a reliable way to disentangle the diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA-seq) rather than protein levels in tumor cells. While gene expression is less expensive to measure, protein levels provide a more accurate view of immune markers. To facilitate the development as well as improve the reproducibility and reusability of multi-omic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic data sets. Decomprolute incorporates the large-scale multiomic data sets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. The platform consists of modular architecture and it comes with well-defined input and output formats at each module. As a result, it is robust and extendable easily with additional algorithms or analyses. The platform is available for access and use at http://pnnl-compbio.github.io/decomprolute.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

The origin of aerobic methanotrophy within the Proteobacteria

ABSTRACT Aerobic methanotrophs play critical roles in the global carbon cycle, but despite their environmental ubiquity, they are phylogenetically restricted. Via bioinformatic analyses, it is shown that methanotrophy likely arose from methylotrophy from the lateral gene transfer of either of the two known forms of methane monooxygenase (particulate and soluble methane monooxygenases). Moreover, it appears that both known forms of pyrroloquinoline quinone-dependent methanol dehydrogenase (MeDH) found in methanotrophs—the calcium-containing Mxa-MeDH and the rare earth element-containing Xox-MeDH—were likely encoded in the genomes before the acquisition of the methane monooxygenases (MMOs), but that some methanotrophs subsequently received an additional copy of Xox-MeDH-encoding genes via lateral gene transfer. Further, data are presented that indicate the evolution of methanotrophy from methylotrophy not only required lateral transfer of genes encoding for methane monooxygenases, but also likely the pre-existence of a means of collecting copper. Given the emerging interest in valorizing methane via biological platforms, it is recommended that future strategies for heterologous expression of methane monooxygenase for conversion of methane to methanol also include cloning of genes encoding mechanism(s) of copper uptake, especially for expression of particulate methane monooxygenase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Glycosyl transferase GT2 genes mediate the biosynthesis of an unusual (1,3;1,4)‐β‐glucan exopolysaccharide in the bacterium Sarcina ventriculi

Abstract Linear, unbranched (1,3;1,4)‐β‐glucans (mixed‐linkage glucans or MLGs) are commonly found in the cell walls of grasses, but have also been detected in basal land plants, algae, fungi and bacteria. Here we show that two family GT2 glycosyltransferases from the Gram‐positive bacteriumSarcina ventriculiare capable of synthesizing MLGs. Immunotransmission electron microscopy demonstrates that MLG is secreted as an exopolysaccharide, where it may play a role in organizing individual cells into packets that are characteristic ofSarcinaspecies. Heterologous expression of these two genes shows that they are capable of producing MLGsin planta, including an MLG that is chemically identical to the MLG secreted fromS. ventriculicells but which has regularly spaced (1,3)‐β‐linkages in a structure not reported previously for MLGs. The tandemly arranged, paralogous pair of genes are designatedSvBmlgs1andSvBmlgs2. The data indicate that MLG synthases have evolved different enzymic mechanisms for the incorporation of (1,3)‐β‐ and (1,4)‐β‐glucosyl residues into a single polysaccharide chain. Amino acid variants associated with the evolutionary switch from (1,4)‐β‐glucan (cellulose) to MLG synthesis have been identified in the active site regions of the enzymes. The presence of MLG synthesis in bacteria could prove valuable for large‐scale production of MLG for medical, food and beverage applications.

Biochemistry & Molecular Biology↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
DOE OSTI2017

Cross‐species complementation reveals conserved functions for EARLY FLOWERING 3 between monocots and dicots

Abstract Plant responses to the environment are shaped by external stimuli and internal signaling pathways. In both the model plant Arabidopsis thaliana ( Arabidopsis ) and crop species, circadian clock factors are critical for growth, flowering, and circadian rhythms. Outside of Arabidopsis, however, little is known about the molecular function of clock gene products. Therefore, we sought to compare the function of Brachypodium distachyon ( Brachypodium ) and Setaria viridis ( Setaria ) orthologs of EARLY FLOWERING 3, a key clock gene in Arabidopsis . To identify both cycling genes and putative ELF 3 functional orthologs in Setaria , a circadian RNA ‐seq dataset and online query tool (Diel Explorer) were generated to explore expression profiles of Setaria genes under circadian conditions. The function of ELF 3 orthologs from Arabidopsis, Brachypodium, and Setaria was tested for complementation of an elf3 mutation in Arabidopsis . We find that both monocot orthologs were capable of rescuing hypocotyl elongation, flowering time, and arrhythmic clock phenotypes. Using affinity purification and mass spectrometry, our data indicate that Bd ELF 3 and Sv ELF 3 could be integrated into similar complexes in vivo as At ELF 3. Thus, we find that, despite 180 million years of separation, Bd ELF 3 and Sv ELF 3 can functionally complement loss of ELF 3 at the molecular and physiological level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Crybb2 associates with Tmsb4X and is crucial for dendrite morphogenesis

Highlights: • A structural protein of ocular lens, Crybb2, has function in neuronal development. • Crybb2 contributes to dendritogenesis in vitro and in vivo. • Tmsb4X up-regulated in Crybb2 mutants mouse brain. • Overexpression of Tmsb4X inhibits dendritogenesis phenocopied the Crybb2 knock-down in hippocampal neuron. Dendrite morphogenesis is a complex but well-orchestrated process. Various studies reported the involvement of alteration in dendrite morphology in different brain disorders, including neuropsychiatric disorders. Initially, βB2-crystallin (gene symbol: Crybb2/CRYBB2) has been described as a structural protein of the ocular lens. Mutations of the corresponding gene, Crybb2, lead to cataract. Recent studies in mice suggested that mutations in Crybb2 cause alterations in hippocampal morphology and function, albeit its function in hippocampal neuron development remained elusive. In the current study, we found that Crybb2 contributes to dendritogenesis in vitro and in vivo. Furthermore, screening of previous data on differential expression-arrays, we found Tmsb4X up-regulated in Crybb2 mutants mouse brain. Additionally, Tmsb4X was co-expressed with Crybb2 at actin-enriched cell ruffles. Over-expression of Tmsb4X in cultured hippocampal neurons inhibited dendritogenesis, which phenocopied Crybb2 knock-down. The current study uncovers a new function of Crybb2 in brain development, especially in dendritogenesis, and the possible interplay partner Tmsb4X involved in this process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

MicroRNA-98 regulates hepatic cholesterol metabolism via targeting sterol regulatory element-binding protein 2

Highlights: • The expression of miR-98 decreased while the expression of SREBP-2 increased in hypercholesterolemic patients. • SREBP-2 is a direct target gene of miR-98. • The effect of miR-98 on cholesterol metabolism is mediated by SREBP-2. • Overexpression of miR-98 reduced cholesterol level in vitro and in vivo. Hypercholesterolemia is an important risk factor for coronary heart disease. Although a lot of research has been conducted, the regulation of cholesterol metabolism is still largely unknown. Some miRNAs have been found to play critical role in the cholesterol metabolism. MiR-98 is a miRNA whose function has been reported mainly in tumorigenesis. In this study, we elucidate a novel role of miR-98 in cholesterol metabolism. We found that the expression of miR-98 was decreased significantly in hypercholesterolemic patients compared with healthy control subjects. Furthermore, we identified that SREBP-2, an important transcriptional factor in cholesterol metabolism, was a direct target of miR-98. Overexpression of miR-98 significantly repressed the 3′-UTR reporter activities of SREBP-2 in a dose-dependent manner in HepG2 cells, while the effect of miR-98 was blocked when the binding site of miR-98 within the SREBP-2 3′-UTR was mutated. And overexpression of miR-98 reduced both the mRNA and protein levels of HMGCR and LDLR significantly in vitro, which are two target genes of SREBP-2. Furthermore, MiR-98 overexpression reduced the intracellular total cholesterol levels dramatically. Moreover, we overexpressed the miR-98 by lentiviral tail vein injection in vivo. Compared with the control mice, the miR-98 overexpression mice showed lower serum cholesterol level and decreased SREBP-2, HMGCR as well as LDLR expression. Our data confirmed that reduced expression of miR-98 potentially contributes to disturbance of cholesterol metabolism. MiR-98 might be a novel therapeutic target to hypercholesterolemia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

RNAi-Mediated Manipulation of Cuticle Coloration Genes in Lygus hesperus Knight (Hemiptera: Miridae)

Cuticle coloration in insects is a consequence of the accumulation of pigments in a species-specific pattern. Numerous genes are involved in regulating the underlying processes of melanization and sclerotization, and their manipulation can be used to create externally visible markers of successful gene editing. To clarify the roles for many of these genes and examine their suitability as phenotypic markers in Lygus hesperus Knight (western tarnished plant bug), transcriptomic data were screened for sequences exhibiting homology with the Drosophila melanogaster proteins. Complete open reading frames encoding putative homologs for six genes (aaNAT, black, ebony, pale, tan, and yellow) were identified, with two variants for black. Sequence and phylogenetic analyses supported preliminary annotations as cuticle pigmentation genes. In accord with observable difference in color patterning, expression varied for each gene by developmental stage, adult age, body part, and sex. Knockdown by injection of dsRNA for each gene produced varied effects in adults, ranging from the non-detectable (black 1, yellow), to moderate decreases (pale, tan) and increases (black 2, ebony) in darkness, to extreme melanization (aaNAT). Based solely on its expression profile and highly visible phenotype, aaNAT appears to be the best marker for tracking transgenic L. hesperus.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Evolution and development of the vertebrate ear

This review outlines major aspects of development and evolution of the ear, specifically addressing issues of cell fate commitment and the emerging molecular governance of these decisions. Available data support the notion of homology of subsets of mechanosensors across phyla (proprioreceptive mechanosensory neurons in insects, hair cells in vertebrates). It is argued that this conservation is primarily related to the specific transducing environment needed to achieve mechanosensation. Achieving this requires highly conserved transcription factors that regulate the expression of the relevant structural genes for mechanosensory transduction. While conserved at the level of some cell fate assignment genes (atonal and its mammalian homologue), the ear has also radically reorganized its development by implementing genes used for cell fate assignment in other parts of the developing nervous systems (e.g., neurogenin 1) and by evolving novel sets of genes specifically associated with the novel formation of sensory neurons that contact hair cells (neurotrophins and their receptors). Numerous genes have been identified that regulate morphogenesis, but there is only one common feature that emerges at the moment: the ear appears to have co-opted genes from a large variety of other parts of the developing body (forebrain, limbs, kidneys) and establishes, in combination with existing transcription factors, an environment in which those genes govern novel, ear-related morphogenetic aspects. The ear thus represents a unique mix of highly conserved developmental elements combined with co-opted and newly evolved developmental elements.

Review↗
DOE OSTI2016

Beneficial effects of vitamin C treatment on pregnant rats exposed to formaldehyde: Reversal of immunosuppression in the offspring

Inhalation of formaldehyde (FA) during the pregnancy induces oxidative stress in the uterus, and here we hypothesized that this mechanism may be responsible for the impaired immune response detected in the offspring. In order to investigate the protective effects of Vitamin C on the oxidative stress induced by FA in the uterine microenvironment, pregnant Wistar rats were treated with vitamin C (150 mg/kg, gavage) or vehicle (distilled water, gavage) 1 h before FA exposure (0.92 mg/m{sup 3}, 1 h/day, 5 days/week), for 21 days, and the 30 days old offspring were submitted to LPS injection (Salmonella abortus equi, 5 mg/kg, i.p.). The enhanced gene expression of iNOS, COX-1 and COX-2 and decreased gene expression of SOD-2 in the uterus of FA exposed mothers was rescued by Vit C treatment. Moreover, vitamin C rescued the impaired immune response elicited by LPS in the offspring from FA exposed mothers, by increasing the number of blood and bone marrow leukocytes, and augmenting gene expression of IL-6 and reducing mRNA levels of IL-10 and IFN in the lungs. Vitamin C treatment did not rescue the impaired TLR4-NF-kB pathway in the lung of the offspring, suggesting that FA-induced uterine oxidative stress affects other inflammatory pathways activated by LPS in the offspring. Together, data obtained here confirm our hypothesis that FA-induced oxidative stress in the uterine microenvironment modifies the programming mechanisms of the immune defenses of offspring, leading to an impaired host defense. - Highlights: • FA exposure during pregnancy induces oxidative stress in the uterus. • Vitamin C treatment blunted the oxidative stress in uterus induced by FA exposure. • Oxidative stress in uterus after FA exposure impairs the immune response of offspring. • Vitamin C in pregnant rats rescued the impaired immune response in the offspring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Glucocorticoids activate Yes-associated protein in human vocal fold fibroblasts

Fibrosis of the vocal folds poses a substantive clinical challenge potentially underlying the rapid proliferation of direct steroid injections into the upper airway. The variable clinical response to glucocorticoids (GCs) in the vocal folds is likely related to diversity inherent to GCs and patient-specific, and upstream, cell-specific responses to GCs. Broadly, we hypothesize the disparity in clinical outcomes are due to undesirable effects of GCs on resident fibroblasts. Transcriptome analysis identified significant GC-mediated modulation of Hippo signaling, a known regulator of fibrotic gene expression. Subsequent analysis confirmed GC-mediated YAP activation, a transcriptional co-factor in the Hippo signaling pathway. YAP inhibition attenuated ACTA2 expression in GC-treated human vocal fold fibroblasts. Nuclear localization and phosphorylation at Ser211, however, was not affected by YAP inhibition, suggesting nuclear translocation of YAP is indirectly driven by GR. RNA-seq analysis confirmed the influence of GCs on Wnt signaling, and canonical Wnt signaling target genes were upregulated by GCs. These data implicate YAP and its downstream targets as putative mediators of a pro-fibrotic response to GCs. Therapeutic YAP inhibition may ultimately be clinically relevant and warrants further consideration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine

Studies on gene expression in response to therapy have led to the discovery of pharmacogenomics biomarkers and advances in precision medicine. Whole transcriptome sequencing (RNA-seq) is an emerging tool for profiling gene expression and has received wide adoption in the biomedical research community. However, its value in regulatory decision making requires rigorous assessment and consensus between various stakeholders, including the research community, regulatory agencies, and industry. The FDA-led SEquencing Quality Control (SEQC) consortium has made considerable progress in this direction, and is the subject of this review. Specifically, three RNA-seq platforms (Illumina HiSeq, Life Technologies SOLiD, and Roche 454) were extensively evaluated at multiple sites to assess cross-site and cross-platform reproducibility. The results demonstrated that relative gene expression measurements were consistently comparable across labs and platforms, but not so for the measurement of absolute expression levels. As part of the quality evaluation several studies were included to evaluate the utility of RNA-seq in clinical settings and safety assessment. The neuroblastoma study profiled tumor samples from 498 pediatric neuroblastoma patients by both microarray and RNA-seq. RNA-seq offers more utilities than microarray in determining the transcriptomic characteristics of cancer. However, RNA-seq and microarray-based models were comparable in clinical endpoint prediction, even when including additional features unique to RNA-seq beyond gene expression. The toxicogenomics study compared microarray and RNA-seq profiles of the liver samples from rats exposed to 27 different chemicals representing multiple toxicity modes of action. Cross-platform concordance was dependent on chemical treatment and transcript abundance. Though both RNA-seq and microarray are suitable for developing gene expression based predictive models with comparable prediction performance, RNA-seq offers advantages over microarray in profiling genes with low expression. The rat BodyMap study provided a comprehensive rat transcriptomic body map by performing RNA-Seq on 320 samples from 11 organs in either sex of juvenile, adolescent, adult and aged Fischer 344 rats. Lastly, the transferability study demonstrated that signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development using a comprehensive approach with two large clinical data sets. This result suggests continued usefulness of legacy microarray data in the coming RNA-seq era. In conclusion, the SEQC project enhances our understanding of RNA-seq and provides valuable guidelines for RNA-seq based clinical application and safety evaluation to advance precision medicine.

59 BASIC BIOLOGICAL SCIENCES↗