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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens

Abstract Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3–29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%–97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%–94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or “molecular tipping points” that are predictive of later-life health outcomes.

Hill, III,, Thomas↗
DOE OSTI2023

Vertical and horizontal gene transfer shaped plant colonization and biomass degradation in the fungal genus Armillaria

The fungal genus Armillaria contains necrotrophic pathogens and some of the largest terrestrial organisms that cause tremendous losses in diverse ecosystems, yet how they evolved pathogenicity in a clade of dominantly non-pathogenic wood degraders remains elusive. Here we show that Armillaria species, in addition to gene duplications and de novo gene origins, acquired at least 1,025 genes via 124 horizontal gene transfer events, primarily from Ascomycota. Horizontal gene transfer might have affected plant biomass degrading and virulence abilities of Armillaria, and provides an explanation for their unusual, soft rot-like wood decay strategy. Furthermore, combined multi-species expression data revealed extensive regulation of horizontally acquired and wood-decay related genes, putative virulence factors and two novel conserved pathogenicity-induced small secreted proteins, which induced necrosis in planta. Overall, this study details how evolution knitted together horizontally and vertically inherited genes in complex adaptive traits of plant biomass degradation and pathogenicity in important fungal pathogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Significant accumulation of persistent organic pollutants and dysregulation in multiple DNA damage repair pathways in the electronic-waste-exposed populations

Electronic waste (e-waste) has created a worldwide environmental and health problem, by generating a diverse group of hazardous compounds such as persistent organic pollutants (POPs). Our previous studies demonstrated that populations from e-waste exposed region have a significantly higher level of chromosomal aberrancy and incidence of DNA damage. In this study, we further demonstrated that various POPs persisted at a significantly higher concentration in the exposed group than those in the unexposed group. The level of reactive oxygen species and micronucleus rate were also significantly elevated in the exposed group. RNA sequencing analysis revealed 31 genes in DNA damage responses and repair pathways that were differentially expressed between the two groups (Log 2 ratio >1 or <−1). Our data demonstrated that both females and males of the exposed group have activated a series of DNA damage response genes; however many important DNA repair pathways have been dysregulated. Expressions of NEIL1/3 and RPA3, which are critical in initiating base pair and nucleotide excision repairs respectively, have been downregulated in both females and males of the exposed group. In contrast, expression of RNF8, an E3 ligase involved in an error prone non-homologous end joining repair for DNA double strand break, was upregulated in both genders of the exposed group. The other genes appeared to be differentially expressed only when the males or females of the two groups were compared respectively. Importantly, the expression of cell cycle regulatory gene CDC25A that has been implicated in multiple kinds of malignant transformation was significantly upregulated among the exposed males while downregulated among the exposed females. In conclusion, our studies have demonstrated significant correlations between e-waste disposing and POPs accumulation, DNA lesions and dysregulation of multiple DNA damage repair mechanisms in the residents of the e-waste exposed region. - Highlights: • We compared concentration of POPs, ROS and micronucleus rate in POPs exposed area. • Significant accumulation of POPs homologous in the e-waste exposed residents. • DNA damage and DNA damage repair pathways have been differentially activated. • Females and males in the exposed group have different responses to the DNA damage. • Exposed males may be more prone to undergo malignant transformation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the respective standard approach. Of the methods tested, standard ComBat and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

GeneLab↗
NASA NTRSDate not supplied

Combining RNA-SEQ Datasets from NASA GENELAB: An Evaluation of Correction Methods

Background: Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. Methods: In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, the median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. Results: The results showed that the reference-based approach introduced several additional (and likely artificial) differentially expressed genes when compared with the respective standard approach. Conclusions: Of the methods tested, standard ComBat_seq and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

Finsam Samson↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets such as sex or age of the model organism used. In the present study, NASA GeneLab-hosted RNAseq datasets from rodent liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC, to determine statistical differences between datasets before and after correction, Principal Component Analysis, to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the standard approach. Thus, the most robust standard correction will be implemented in the GeneLab Visualization 2.0 platform when datasets are combined.

GeneLab, RNA-seq, Batch Correction↗
DOE OSTI2020

Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations

The soil environment is constantly changing due to shifts in soil moisture, nutrient availability and other conditions. To contend with these changes, soil microorganisms have evolved a variety of ways to adapt to environmental perturbations, including regulation of gene expression. However, it is challenging to untangle the complex phenotypic response of the soil to environmental change, partly due to the absence of predictive modeling frameworks that can mechanistically link molecular-level changes in soil microorganisms to a community’s functional phenotypes (or metaphenome). Towards filling this gap, we performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responded to changes in soil moisture and nutrient conditions and to determine which genes were expressed under a given condition. Our integrated modeling approach revealed previously unknown, but critically important aspects of the soil microbiomes’ response to environmental perturbations. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended conditions. A subsequent gene co-expression network analysis revealed that drought-associated genes occupied more central positions in a network model of the soil community, compared to the genes associated with wet, and glycine-amended conditions. These results indicate the occurrence of system-wide metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, the approach that we demonstrate here to analyze large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which multi-omics data are available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Hexokinase 1 is required for glucose-induced repression of bZIP63 , At5g22920 , and BT2 in Arabidopsis

Simple sugars, like glucose (Glc) and sucrose (Suc), act as signals to modulate the expression of hundreds of genes in plants. Frequently, however, it remains unclear whether this regulation is induced by the sugars themselves or by their derivatives generated in the course of carbohydrate (CH) metabolism. In the present study, we tested the relevance of different CH metabolism and allocation pathways affecting expression patterns of five selected sugar-responsive genes ( bZIP63 , At5g22920 , BT2 , MGD2 , and TPS9 ) in Arabidopsis thaliana . In general, the expression followed diurnal changes in the overall sugar availability. However, under steady growth conditions, this response was hardly impaired in the mutants for CH metabolizing/ transporting proteins ( adg1 , sex1 , sus1-4 , sus5/6 , and tpt2 ), including also hexokinase1 (HXK1) loss- and gain-of-function plants— gin2.1 and oe3.2 , respectively. In addition, transgenic plants carrying pbZIP63::GUS showed no changes in reporter-gene-expression when grown on sugar under steady-state conditions. In contrast, short-term treatments of agar-grown seedlings with 1% Glc or Suc induced pbZIP63::GUS repression, which became even more apparent in seedlings grown in liquid media. Subsequent analyses of liquid-grown gin2.1 and oe3.2 seedlings revealed that Glc -dependent regulation of the five selected genes was not affected in gin2.1, whereas it was enhanced in oe3.2 plants for bZIP63 , At5g22920 , and BT . The sugar treatments had no effect on ATP/ADP ratio, suggesting that changes in gene expression were not linked to cellular energy status. Altogether, the data suggest that HXK1 does not act as Glc sensor controlling bZIP63 , At5g22920 , and BT2 expression, but it is nevertheless required for the production of a downstream metabolic signal regulating their expression

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Spatial distribution of marker gene activity in the mouse lung during alveolarization

Here, this data is a curated collection of visual images of gene expression patterns from the pre- and post-natal mouse lung, accompanied by associated mRNA probe sequences and RNA-Seq expression profiles. Mammalian lungs undergo significant growth and cellular differentiation before and after the transition to breathing air. Documenting normal lung development is an important step in understanding abnormal lung development, as well as the challenges faced during a preterm birth. Images in this dataset indicate the spatial distribution of mRNA transcripts for over 500 different genes that are active during lung development, as initially determined via RNA-Seq. Images were systematically acquired using high-throughput in situ hybridization with non-radioactive digoxigenin-labeled mRNA probes across mouse lungs from developmental time points E16.5, E18.5, P7, and P28.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Cryptic glucocorticoid receptor-binding sites pervade genomic NF-κB response elements

Glucocorticoids (GCs) are potent repressors of NF-κB activity, making them a preferred choice for treatment of inflammation-driven conditions. Despite the widespread use of GCs in the clinic, current models are inadequate to explain the role of the glucocorticoid receptor (GR) within this critical signaling pathway. GR binding directly to NF-κB itself—tethering in a DNA binding-independent manner—represents the standing model of how GCs inhibit NF-κB-driven transcription. We demonstrate that direct binding of GR to genomic NF-κB response elements (κBREs) mediates GR-driven repression of inflammatory gene expression. We report five crystal structures and solution NMR data of GR DBD-κBRE complexes, which reveal that GR recognizes a cryptic response element between the binding footprints of NF-κB subunits within κBREs. These cryptic sequences exhibit high sequence and functional conservation, suggesting that GR binding to κBREs is an evolutionarily conserved mechanism of controlling the inflammatory response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The landscape of Chlamydomonas histone H3 lysine 4 methylation reveals both constant features and dynamic changes during the diurnal cycle

Chromatin modifications are epigenetic regulatory features with major roles in various cellular events, yet they remain understudied in algae. Here, we interrogated the genome-wide distribution pattern of mono- and trimethylated histone H3 lysine 4 (H3K4) using chromatin-immunoprecipitation followed by deep-sequencing (ChIP-seq) during key phases of the Chlamydomonas cell cycle: early G 1 phase, Zeitgeber Time 1 (ZT1), when cells initiate biomass accumulation, S/M phase (ZT13) when cells are replicating DNA and undergoing mitosis, and late G 0 phase (ZT23) when they are quiescent. Tri-methylated H3K4 was predominantly enriched at transcription start sites of the majority of protein coding genes (85%). The likelihood of a gene being marked by H3K4me3 correlated with it being transcribed at some point during the life cycle but not necessarily by continuous active transcription, as exemplified by early zygotic genes, which may remain transcriptionally dormant for thousands of generations between sexual cycles. The exceptions to this rule were around 120 loci, some of which encode non-poly-adenylated transcripts, such as small nuclear RNAs and replication-dependent histones that had H3K4me3 peaks only when they were being transcribed. Mono-methylated H3K4 was the default state for the vast majority of histones that were bound outside of transcription start sites and terminator regions of genes. A small fraction of the genome that was depleted of any H3 lysine 4 methylation was enriched for DNA cytosine methylation and the genes within these DNA methylation islands were poorly expressed. Besides marking protein coding genes, H3K4me3 ChIP-seq data served also as a annotation tool for validation of hundreds of long non-coding RNA genes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2014

Exposure to Microgravity for 30 Days Onboard Bion M1 Caused Muscle Atrophy and Decreased Regeneration in the Mouse Femoral Quadriceps

Mechanical unloading of muscle during spaceflight in microgravity is known to cause muscular atrophy, changes in muscle fiber type composition, gene expression, and reductions in regenerative muscle growth. Although limited data exists for long-term effects of microgravity in human muscle, these processes have mostly been studied in rodents for short periods of time. Here we report on how 30-day, long-term, mechanical unloading in microgravity affects mouse muscle of the femoral Quadriceps group. To conduct these studies we used muscle tissue from 6 mice from the NASA Biospecimen Sharing Program conducted in collaboration with the Institute for Biomedical Problems of the Russian Academy of Sciences, during the Russian Bion M1 biosatellite mission in 2013. Muscle morphology observed in histological sections shows signs of extensive atrophy and regenerative hypoplasia. Specifically, we observed a two-fold decrease in the number of myonuclei, their central location, low density of myofibers and myofibrils, in fragmentation and swelling of myofibers. Despite obvious atrophy, muscle regeneration nevertheless appears to have continued after 30 days in microgravity as evidenced by thin and short newly formed myofibers. Many of them however showed evidence of apoptotic, TUNEL positive cells and myofibrils degradation, suggesting long-term unloading in microgravity affects late stages of myofiber differentiation. Ground asynchronous and vivarium control animals showed normal, well-developed tissue structure with sufficient blood and nerve supply and evidence of regenerative formation of new myofibers free of apoptotic nuclei. Myonuclei stress response in spaceflight animals was detected by positive nuclear immunolocalization of c-jun and c-myc proteins. Regenerative activity of satellite cells in muscles is detected in mice of all animal groups, by pax7, MyoD, and myogenin immunostaining and myogenin PCR analysis. In summary, long-term spaceflight in microgravity causes significant atrophy and degeneration of the femoral Quadriceps muscle group, and it may interfere with muscle regenerative processes by inducing apoptosis in newly-formed myofibrils during their differentiation phase.

Grigoryan, E. N.↗
NASA NTRS2002

Oxygen control of ethylene biosynthesis during seed development in Arabidopsis thaliana (L.) Heynh

An unforeseen side-effect on plant growth in reduced oxygen is the loss of seed production at concentrations around 25% atmospheric (50 mmol mol-1 O2). In this study, the model plant Arabidopsis thaliana (L.) Heynh. cv. 'Columbia' was used to investigate the effect of low oxygen on ethylene biosynthesis during seed development. Plants were grown in a range of oxygen concentrations (210 [equal to ambient], 160, 100, 50 and 25 mmol mol-1) with 0.35 mmol mol-1 CO2 in N2. Ethylene in full-sized siliques was sampled using gas chromatography, and viable seed production was determined at maturity. Molecular analysis of ethylene biosynthesis was accomplished using cDNAs encoding 1-aminocyclopropane-1-carboxylic acid (ACC) synthase and ACC oxidase in ribonuclease protection assays and in situ hybridizations. No ethylene was detected in siliques from plants grown at 50 and 25 mmol mol-1 O2. At the same time, silique ACC oxidase mRNA increased three-fold comparing plants grown under the lowest oxygen with ambient controls, whereas ACC synthase mRNA was unaffected. As O2 decreased, tissue-specific patterning of ACC oxidase and ACC synthase gene expression shifted from the embryo to the silique wall. These data demonstrate how low O2 modulates the activity and expression of the ethylene biosynthetic pathway during seed development in Arabidopsis.

NASA Discipline Plant Biology↗
DOE OSTI2024

Developing multi-gene CRISPRa/i programs to accelerate DBTL cycles in ABF hosts engineered for chemical production

This project developed and implemented a modular CRISPR activation and interference (CRISPRa/i) platform to accelerate strain optimization and pathway development for industrially relevant microbial hosts. By integrating multiplexed transcriptional perturbation tools with data-driven Design–Build–Test–Learn (DBTL) workflows, the team achieved reductions in cycle time and enhanced production of industrial aromatics, particularly 4-aminocinnamic acid (4-ACA), in Pseudomonas putida. Key accomplishments included: ● Development of a robust, tunable CRISPRa/i system in P. putida that enabled efficient multi-target gene regulation via guide RNA (gRNA) programs ● Completion of two full DBTL cycles, guided by machine learning (ML) models trained on transcriptomic and performance data, reducing engineering time by over 30% ● Optimization of multi-gene regulatory programs to balance expression of host and pathway modules, improve 4-ACA titers, and resolve metabolic bottlenecks ● Demonstration of system portability through a limited proof-of-concept extension in Acinetobacter baylyi, underscoring the generalizability of the approach ● Evaluation of strain performance on lignocellulosic biomass-derived substrates, demonstrating the feasibility of converting renewable carbon into aromatic building blocks These results illustrate the feasibility of applying ML-guided CRISPRa/i perturbation strategies to accelerate strain development in complex microbial systems. The resulting tools and datasets contribute to DOE objectives by improving platform predictability, reducing development costs, and enabling broader access to sustainable, economically viable bioproduction technologies.

09 BIOMASS FUELS↗
DOE OSTI2016

Transcriptome analysis in switchgrass discloses ecotype difference in photosynthetic efficiency

Background: Switchgrass, a warm-season perennial grass studied as a potential dedicated biofuel feedstock, is classified into two main taxa – lowland and upland ecotypes – that differ in morphology and habitat of adaptation. But there is limited information on their inherent molecular variations. Results: Transcriptome analysis by RNA-sequencing (RNA-Seq) was conducted for lowland and upland ecotypes to document their gene expression variations. Mapping of transcriptome to the reference genome (Panicum virgatum v1. 1) revealed that the lowland and upland ecotypes differ substantially in sets of genes transcribed as well as levels of expression. Differential gene expression analysis exhibited that transcripts related to photosynthesis efficiency and development and photosystem reaction center subunits were upregulated in lowlands compared to upland genotype. On the other hand, catalase isozymes, helix-loop-helix, late embryogenesis abundant group I, photosulfokinases, and S-adenosyl methionine synthase gene transcripts were upregulated in the upland compared to the lowlands. At ≥100x coverage and ≥5% minor allele frequency, a total of 25,894 and 16,979 single nucleotide polymorphism (SNP) markers were discovered for VS16 (upland ecotype) and K5 (lowland ecotype) against the reference genome. The allele combination of the SNPs revealed that the transition mutations are more prevalent than the transversion mutations. Conclusions: The gene ontology (GO) analysis of the transcriptome indicated lowland ecotype had significantly higher representation for cellular components associated with photosynthesis machinery controlling carbon fixation. In addition, using the transcriptome data, SNP markers were detected, which were distributed throughout the genome. The differentially expressed genes and SNP markers detected in this study would be useful resources for traits mapping and gene transfer across ecotypes in switchgrass breeding for increased biomass yield for biofuel conversion.

09 BIOMASS FUELS↗
DOE OSTI2017

Gene expression analysis of whole blood RNA from pigs infected with low and high pathogenic African swine fever viruses

African swine fever virus (ASFV) is a macrophage-tropic virus responsible for ASF, a transboundary disease that threatens swine production world-wide. Since there are no vaccines available to control ASF after an outbreak, obtaining an understanding of the virus-host interaction is important for developing new intervention strategies. In this study, a whole transcriptomic RNA-Seq method was used to characterize differentially expressed genes in pigs infected with a low pathogenic ASFV isolate, OUR T88/3 (OURT), or the highly pathogenic Georgia 2007/1 (GRG). After infection, pigs infected with OURT showed no or few clinical signs; whereas, GRG produced clinical signs consistent with acute ASF. RNA-Seq detected the expression of ASFV genes from the whole blood of the GRG, but not the OURT pigs, consistent with the pathotypes of these strains and the replication of GRG in circulating monocytes. Even though GRG and OURT possess different pathogenic properties, there was significant overlap in the most upregulated host genes. A small number of differentially expressed microRNAs were also detected in GRG and OURT pigs. These data confirm previous studies describing the response of macrophages and lymphocytes to ASFV infection, as well as reveal unique gene pathways upregulated in response to infection with GRG.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Spirometric traits show quantile-dependent heritability, which may contribute to their gene-environment interactions with smoking and pollution

Background: “Quantile-dependent expressivity” refers to a genetic effect that is dependent upon whether the phenotype (e.g., spirometric data) is high or low relative to its population distribution. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV 1 ), and the FEV 1 /FVC ratio are moderately heritable spirometric traits. The aim of the analyses is to test whether their heritability (h 2 ) is constant over all quantiles of their distribution. Methods: Quantile regression was applied to the mean age, sex, height and smoking-adjusted spirometric data over multiple visits in 9,993 offspring-parent pairs and 1,930 sibships from the Framingham Heart Study to obtain robust estimates of offspring-parent (β OP ), offspring-midparent (β OM ), and full-sib regression slopes (β FS ). Nonparametric significance levels were obtained from 1,000 bootstrap samples. β OP s were used as simple indicators of quantile-specific heritability (i.e.,h 2 = 2β OP /(1+r spouse ), where r spouse was the correlation between spouses). Results: β OP ± standard error (SE) decreased by 0.0009 ± 0.0003 (P = 0.003) with every one-percent increment in the population distribution of FEV 1 /FVC, i.e., β OP ± SE were: 0.182 ± 0.031, 0.152 ± 0.015; 0.136 ± 0.011; 0.121 ± 0.013; and 0.099 ± 0.013 at the 10th, 25th, 50th, 75th, and 90th percentiles of the FEV 1 /FVC distribution, respectively. These correspond to h 2 ± SEs of 0.350 ± 0.060 at the 10th, 0.292 ± 0.029 at the 25th, 0.262 ± 0.020 at the 50th, 0.234 ± 0.025 at the 75th, and 0.191 ± 0.025 at the 90th percentiles of the FEV 1 /FVC ratio. Maximum mid-expiratory flow (MMEF) h 2 ± SEs increased 0.0025 ± 0.0007 (P = 0.0004) with every one-percent increment in its distribution, i.e.: 0.467 ± 0.046, 0.467 ± 0.033, 0.554 ± 0.038, 0.615 ± 0.042, and 0.675 ± 0.060 at the 10th, 25th, 50th, 75th, and 90th percentiles of its distribution. This was due to forced expiratory flow at 75% of FVC (FEF75%), whose quantile-specific h 2 increased an average of 0.0042 ± 0.0008 for every one-percent increment in its distribution. It is speculated that previously reported gene-environment interactions may be partially attributable to quantile-specific h 2 , i.e., greater heritability in individuals with lower FEV 1 /FVC due to smoking or airborne particles exposure vs. nonsmoking, unexposed individuals. Conclusion: Heritabilities of FEV 1 /FVC, MMEF, and FEF75% from quantile-regression of offspring-parent and sibling spirometric data suggest their quantile-dependent expressivity.

59 BASIC BIOLOGICAL SCIENCES↗