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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Transcript abundance on its own cannot be used to infer fluxes in central metabolism

An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism, some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. Lastly, this limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.

13C-metabolic flux analysis↗
DOE OSTI2021

New Insights Into Lignification via Network and Multi-Omics Analyses of Arogenate Dehydratase Knock-Out Mutants in Arabidopsis thaliana

Multiple Arabidopsis arogenate dehydratase (ADT) knock-out (KO) mutants, with phenotypes having variable lignin levels (up to circa 70% reduction), were studied to investigate how differential reductions in ADTs perturb its overall plant systems biology. Integrated "omics" analyses (metabolome, transcriptome, and proteome) of wild type (WT), single and multiple ADT KO lines were conducted. Transcriptome and proteome data were collapsed into gene ortholog (GO) data, with this allowing for enzymatic reaction and metabolome cross-comparisons to uncover dominant or likely metabolic biosynthesis reactions affected. Network analysis of enzymes-highly correlated to stem lignin levels-deduced the involvement of novel putative lignin related proteins or processes. These included those associated with ribosomes, the spliceosome, mRNA transport, aminoacyl tRNA biosynthesis, and phosphorylation. While prior work helped explain lignin biosynthesis regulation at the transcriptional level, our data here provide support for a new hypothesis that there are additional post-transcriptional and translational level processes that need to be considered. These findings are anticipated to lead to development of more accurate depictions of lignin/phenylpropanoid biosynthesis models in situ, with new protein targets identified for further biochemical analysis and/or plant bioengineering. Additionally, using KEGG defined functional categorization of proteomics and transcriptomics analyses, we detected significant changes to glucosinolate, a-linolenic acid, nitrogen, carotenoid, aromatic amino acid, phenylpropanoid, and photosynthesis-related metabolic pathways in ADT KO mutants. Metabolomics results also revealed that putative carotenoid and galactolipid levels were generally increased in amount, whereas many glucosinolates and phenylpropanoids (including flavonoids and lignans) were decreased in the KO mutants.

Arabidopsis, arogenate dehydratases, lignin, multi↗
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↗
DOE OSTI2021

A Systems Biology Approach to Identify Essential Epigenetic Regulators for Specific Biological Processes in Plants

Upon sensing developmental or environmental cues, epigenetic regulators transform the chromatin landscape of a network of genes to modulate their expression and dictate adequate cellular and organismal responses. Knowledge of the specific biological processes and genomic loci controlled by each epigenetic regulator will greatly advance our understanding of epigenetic regulation in plants. To facilitate hypothesis generation and testing in this domain, we present EpiNet, an extensive gene regulatory network (GRN) featuring epigenetic regulators. EpiNet was enabled by (i) curated knowledge of epigenetic regulators involved in DNA methylation, histone modification, chromatin remodeling, and siRNA pathways; and (ii) a machine-learning network inference approach powered by a wealth of public transcriptome datasets. We applied GENIE3, a machine-learning network inference approach, to mine public Arabidopsis transcriptomes and construct tissue-specific GRNs with both epigenetic regulators and transcription factors as predictors. The resultant GRNs, named EpiNet, can now be intersected with individual transcriptomic studies on biological processes of interest to identify the most influential epigenetic regulators, as well as predicted gene targets of the epigenetic regulators. We demonstrate the validity of this approach using case studies of shoot and root apical meristem development.

root apical meristem↗
DOE OSTI2015

An expanded maize gene expression atlas based on RNA sequencing and its use to explore root development

Comprehensive and systematic transcriptome profiling provides valuable insight into biological and developmental processes that occur throughout the life cycle of a plant. We have enhanced our previously published microarray-based gene atlas of maize ( Zea mays L.) inbred B73 to now include 79 distinct replicated samples that have been interrogated using RNA sequencing (RNA-seq). The current version of the atlas includes 50 original array-based gene atlas samples, a time-course of 12 stalk and leaf samples postflowering, and an additional set of 17 samples from the maize seedling and adult root system. The entire dataset contains 4.6 billion mapped reads, with an average of 20.5 million mapped reads per biological replicate, allowing for detection of genes with lower transcript abundance. As the new root samples represent key additions to the previously examined tissues, we highlight insights into the root transcriptome, which is represented by 28,894 (73.2%) annotated genes in maize. Additionally, we observed remarkable expression differences across both the longitudinal (four zones) and radial gradients (cortical parenchyma and stele) of the primary root supported by fourfold differential expression of 9353 and 4728 genes, respectively. Among the latter were 1110 genes that encode transcription factors, some of which are orthologs of previously characterized transcription factors known to regulate root development in Arabidopsis thaliana (L.) Heynh., while most are novel, and represent attractive targets for reverse genetics approaches to determine their roles in this important organ. As a result, this comprehensive transcriptome dataset is a powerful tool toward understanding maize development, physiology, and phenotypic diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for Sugar Accumulation Enhancement in Sorghum Stem is Associated with Reduced Reproductive Sink Strength and Increased Phloem Unloading Activity

Sweet sorghum has emerged as a promising source of bioenergy mainly due to its high biomass and high soluble sugar yield in stems. Studies have shown that loss-of-function Dry locus alleles have been selected during sweet sorghum domestication, and decapitation can further boost sugar accumulation in sweet sorghum, indicating that the potential for improving sugar yields is yet to be fully realized. To maximize sugar accumulation, it is essential to gain a better understanding of the mechanism underlying the massive accumulation of soluble sugars in sweet sorghum stems in addition to the Dry locus. We performed a transcriptomic analysis upon decapitation of near-isogenic lines for mutant (d, juicy stems, and green leaf midrib) and functional (D, dry stems and white leaf midrib) alleles at the Dry locus. Our analysis revealed that decapitation suppressed photosynthesis in leaves, but accelerated starch metabolic processes in stems. SbbHLH093 negatively correlates with sugar levels supported by genotypes (DD vs. dd), treatments (control vs. decapitation), and developmental stages post anthesis (3d vs.10d). D locus gene SbNAC074A and other programmed cell death-related genes were down regulated by decapitation, while sugar transporter-encoding gene SbSWEET1A was induced. Both SbSWEET1A and Invertase 5 were detected in phloem companion cells by RNA in situ assay. Loss of the SbbHLH093 homolog, AtbHLH093, in Arabidopsis led to a sugar accumulation increase. This study provides new insights into sugar accumulation enhancement in bioenergy crops, which can be potentially achieved by reducing reproductive sink strength and enhancing phloem unloading.

Transcriptomics↗
DOE OSTI2026

Carbon source-induced changes in the transcriptional landscape of Novacetimonas hansenii

Novacetimonas hansenii is a model organism for bacterial cellulose (BC) production, a biopolymer with broad industrial applications. To better understand its carbon source-dependent metabolism, we conducted the first time-resolved transcriptomic analysis of N. hansenii ATCC 23769 grown on five carbon sources (glucose, fructose, mannitol, galactose, and sucrose) across five time points (24–120h). Glucose, fructose, and mannitol supported growth and efficient BC production, whereas galactose and sucrose resulted in poor yields, likely due to limited transport and enzymatic capacity. Solid-state NMR revealed that cellulose crystallinity and polymorphism were carbon source-dependent, with reduced bcsD expression correlating with lower crystallinity. Transcriptomic profiling showed distinct metabolic responses. Cultures containing glucose and mannitol activated core metabolic pathways, fructose induced a delayed adaptive shift, and galactose and sucrose triggered stress-related gene expression. These results highlight the metabolic plasticity of N. hansenii and offer insights for enhancing BC production through carbon source optomization.

Bacterial cellulose↗
DOE OSTI2025

A network-enabled pipeline for gene discovery and validation in non-model plant species

Identifying key regulators of important genes in non-model crop species is challenging due to limited multi-omics resources. To address this, we introduce the network-enabled gene discovery pipeline NEEDLE, a user-friendly tool that systematically generates coexpression gene network modules, measures gene connectivity, and establishes network hierarchy to pinpoint key transcriptional regulators from dynamic transcriptome datasets. After validating its accuracy with two independent datasets, we applied NEEDLE to identify transcription factors (TFs) regulating the expression of cellulose synthase-like F6 ( CSLF6 ), a crucial cell wall biosynthetic gene, in Brachypodium and sorghum. Our analyses uncover regulators of CSLF6 and also shed light on the evolutionary conservation or divergence of gene regulatory elements among grass species. These results highlight NEEDLE’s capability to provide biologically relevant TF predictions and demonstrate its value for non-model plant species with dynamic transcriptome datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Examining the Relationship Between the Testate Amoeba Hyalosphenia papilio (Arcellinida, Amoebozoa) and its Associated Intracellular Microalgae Using Molecular and Microscopic Methods

Symbiotic relationships between heterotrophic and phototrophic partners are common in microbial eukaryotes. Among Arcellinida (Amoebozoa) several species are associated with microalgae of the genus Chlorella (Archaeplastida). So far, these symbioses were assumed to be stable and mutualistic, yet details of the interactions are limited. Here, we analyzed 22 single-cell transcriptomes and 36 partially-sequenced genomes of the Arcellinida morphospecies Hyalosphenia papilio, which contains Chlorella algae, to shed light on the amoeba-algae association. By characterizing the genetic diversity of associated Chlorella, we detected two distinct clades that can be linked to host genetic diversity, yet at the same time show a biogeographic signal across sampling sites. Fluorescence and transmission electron microscopy showed the presence of intact algae cells within the amoeba cell. Yet analysis of transcriptome data suggested that the algal nuclei are inactive, implying that instead of a stable, mutualistic relationship, the algae may be temporarily exploited for photosynthetic activity before being digested. Furthermore, differences in gene expression of H. papilio and Hyalosphenia elegans demonstrated increased expression of genes related to oxidative stress. Together, our analyses increase knowledge of this host-symbiont association and reveal 1) higher diversity of associated algae than previously characterized, 2) a transient association between H. papilio and Chlorella with unclear benefits for the algae, 3) algal-induced gene expression changes in the host.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Data integration reveals key homeostatic mechanisms following low dose radiation exposure

The goal of this study was to define pathways regulated by low dose radiation to understand how biological systems respond to subtle perturbations in their environment and prioritize pathways for human health assessment. Using an in vitro 3-D human full thickness skin model, we have examined the temporal response of dermal and epidermal layers to 10 cGy X-ray using transcriptomic, proteomic, phosphoproteomic and metabolomic platforms. Bioinformatics analysis of each dataset independently revealed potential signaling mechanisms affected by low dose radiation, and integrating data shed additional insight into the mechanisms regulating low dose responses in human tissue. We examined direct interactions among datasets (top down approach) and defined several hubs as significant regulators, including transcription factors (YY1, MYC and CREB1), kinases (CDK2, PLK1) and a protease (MMP2). These data indicate a shift in response across time — with an increase in DNA repair, tissue remodeling and repression of cell proliferation acutely (24–72 h). Pathway-based integration (bottom up approach) identified common molecular and pathway responses to low dose radiation, including oxidative stress, nitric oxide signaling and transcriptional regulation through the SP1 factor that would not have been identified by the individual data sets. Significant regulation of key downstream metabolites of nitrative stress was measured within these pathways. Among the features identified in our study, the regulation of MMP2 and SP1 was experimentally validated. Our results demonstrate the advantage of data integration to broadly define the pathways and networks that represent the mechanisms by which complex biological systems respond to perturbation. - Highlights: • Low dose ionizing radiation altered homeostasis in 3D skin tissue model. • Global gene/protein/metabolite data integrated using complementary statistical approaches • Time and location-specific change in matrix regulation, ROS/RNS and DNA repair pathways detected • Low dose exposure alters metabolites involved in nitric oxide biosynthesis and wound healing. • Computationally predicted regulators of primary mechanisms were experimentally validated.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and don’t always predict therapeutic response. Here we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measured the proteome and phosphoproteome of 210 patients and combined them with genomics and transcriptomic measurements to identify four proteogenomic subtypes that complemented existing genetic subtypes. We then built a predictor to classify samples into subtypes based on 147 molecular features and mapped them to a ‘landscape’. Each region of this landscape corresponded to specific drug response patterns. We then built a drug response prediction model to identify drugs that target distinct subtypes. We can ultimately use these models to predict drug treatment response and prioritize treatments. Finally, we extended our models and mapped a series of cell lines representing various stages of quizartinib resistance into our subtype landscape, predicting and experimentally validating a switch in sensitivity to venetoclax to panobinostat, two drugs with very different mechanisms than quizartinib. Our results show how multi-omics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2020

Phenotypic and functional characterization of corneal endothelial cells during in vitro expansion

The advent of cell culture-based methods for the establishment and expansion of human corneal endothelial cells (CEnC) has provided a source of transplantable corneal endothelium, with a significant potential to challenge the one donor-one recipient paradigm. However, concerns over cell identity remain, and a comprehensive characterization of the cultured CEnC across serial passages has not been performed. To this end, we compared two established CEnC culture methods by assessing the transcriptomic changes that occur during in vitro expansion. In confluent monolayers, low mitogenic culture conditions preserved corneal endothelial cell state identity better than culture in high mitogenic conditions. Expansion by continuous passaging induced replicative cell senescence. Transcriptomic analysis of the senescent phenotype identified a cell senescence signature distinct for CEnC. We identified activation of both classic and new cell signaling pathways that may be targeted to prevent senescence, a significant barrier to realizing the potential clinical utility of in vitro expansion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

The carbon starvation response of the ectomycorrhizal fungus Paxillus involutus

The amounts of carbon allocated to the fungal partner in ectomycorrhizal associations can vary substantially depending on the plant growth and the soil nutrient conditions, and the fungus may frequently be confronted with limitations in carbon. We used chemical analysis and transcriptome profiling to examine the physiological response of the ectomycorrhizal fungus Paxillus involutus to carbon starvation during axenic cultivation. Carbon starvation induced a decrease in the biomass. Concomitantly, ammonium, cell wall material (chitin) and proteolytic enzymes were released into the medium, which suggest autolysis. Compared with the transcriptome of actively growing hyphae, about 45% of the transcripts analyzed were differentially regulated during C-starvation. Induced during starvation were transcripts encoding extracellular enzymes such as peptidases, chitinases and laccases. In parallel, transcripts of N-transporters were upregulated, which suggest that some of the released nitrogen compounds were re-assimilated by the mycelium. The observed changes suggest that the carbon starvation response in P. involutus is associated with complex cellular changes that involves autolysis, recycling of intracellular compounds by autophagy and reabsorption of the extracellular released material. The study provides molecular markers that can be used to examine the role of autolysis for the turnover and survival of the ectomycorrhizal mycelium in soils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Comparative Genomics of Saccharomyces cerevisiae Natural Isolates for Bioenergy Production

Lignocellulosic plant material is a viable source of biomass to produce alternative energy including ethanol and other biofuels. However, several factors—including toxic by products from biomass pretreatment and poor fermentation of xylose and other pentose sugars—currently limit the efficiency of microbial biofuel production. To begin to understand the genetic basis of desirable traits, we characterized three strains of Saccharomyces cerevisiae with robust growth in a pretreated lignocellulosic hydrolysate or tolerance to stress conditions relevant to industrial biofuel production, through genome and transcriptome sequencing analysis. All stress resistant strains were highly mosaic, suggesting that genetic admixture may contribute to novel allele combinations underlying these phenotypes. Strain-specific gene sets not found in the lab strain were functionally linked to the tolerances of particular strains. Furthermore, genes with signatures of evolutionary selection were enriched for functional categories important for stress resistance and included stress-responsive signaling factors. Comparison of the strains’ transcriptomic responses to heat and ethanol treatment—two stresses relevant to industrial bioethanol production—pointed to physiological processes that were related to particular stress resistance profiles. Many of the genotype-by-environment expression responses occurred at targets of transcription factors with signatures of positive selection, suggesting that these strains have undergone positive selection for stress tolerance. Our results generate new insights into potential mechanisms of tolerance to stresses relevant to biofuel production, including ethanol and heat, present a backdrop for further engineering, and provide glimpses into the natural variation of stress tolerance in wild yeast strains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Genomics and Development of Lentinus tigrinus : A White-Rot Wood-Decaying Mushroom with Dimorphic Fruiting Bodies

Lentinus tigrinus is a species of wood-decaying fungi (Polyporales) that has an agaricoid form (a gilled mushroom) and a secotioid form (puffball-like, with enclosed spore-bearing structures). Previous studies suggested that the secotioid form is conferred by a recessive allele of a single locus. We sequenced the genomes of one agaricoid (Aga) strain and one secotioid (Sec) strain (39.53-39.88 Mb, with 15,581-15,380 genes, respectively). We mated the Sec and Aga monokaryons, genotyped the progeny, and performed bulked segregant analysis (BSA). We also fruited three Sec/Sec and three Aga/Aga dikaryons, and sampled transcriptomes at four developmental stages. Using BSA, we identified 105 top candidate genes with nonsynonymous SNPs that cosegregate with fruiting body phenotype. Transcriptome analyses of Sec/Sec versus Aga/Aga dikaryons identified 907 differentially expressed genes (DEGs) along four developmental stages. On the basis of BSA and DEGs, the top 25 candidate genes related to fruiting body development span 1.5 Mb (4% of the genome), possibly on a single chromosome, although the precise locus that controls the secotioid phenotype is unresolved. The top candidates include genes encoding a cytochrome P450 and an ATP-dependent RNA helicase, which may play a role in development, based on studies in other fungi.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Integrative analysis of CAM photosynthesis reveals its impact on primary metabolism in Yucca

Crassulacean Acid Metabolism (CAM) is an adaptation that temporally separates carbon uptake at night from photosynthesis during the day. CAM has evolved repeatedly across vascular plants, as its emergence may depend on simple regulatory changes to deeply conserved metabolic pathways. Modern CAM research relies heavily on interpretation of transcriptomic data, though regulation occurs at multiple levels following transcription. Additionally, while most research to date has focused on a handful of genes and metabolites in the core CAM pathway, the co-option of conserved regulatory and functional genes is bound to have wide-ranging effects on other aspects of primary metabolism. In this study, we integrate transcriptomic, proteomic, and metabolomic data to compare primary metabolism between the CAM species Yucca aloifolia and closely related C 3 species, Y. filamentosa. We observe minimal correlation between protein abundance and mRNA expression, suggesting significant post-transcriptional regulation in CAM species. We also find evidence of shifts in gene expression and metabolite accumulation outside of the central CAM pathway, suggesting that the shift to CAM has cascading effects across primary metabolism, especially nitrogen metabolism. Our findings provide insights into the metabolic shifts associated with CAM evolution, and highlight the complexity of its regulation at multiple biological levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Phytobiome and Transcriptional Adaptation of Populus deltoides to Acute Progressive Drought and Cyclic Drought

Plant drought stress causes systematic changes to photosynthesis, metabolism, growth, and potentially the phytobiome. Additionally, drought affects plants in both a species-specific and water-deficit-driven manner, causing the response to drought to be dependent both on how drought is being experienced and on any adaptation to prior drought exposure. Thus, understanding the effect of drought on plants requires assessing drought response in multiple conditions, such as progressive acute drought and recurrent cyclic drought, and at different levels of severity. In this study, we have utilized RNA sequencing to identify changes to the plant transcriptome and the phytobiome during both acute progressive drought and cyclic drought at multiple severities. Co-analysis of the plant and phytobiome, utilizing the same RNAseq data, allows for the identification of novel associations that would not be possible otherwise. We have identified that the drought response ranges from increased transcripts related to photosynthesis and metabolic activity in mild acute drought to decreased transcripts related to photosynthesis and metabolic impairment in severe drought. Moreover, while water deficit is a main driver of transcriptional responses in severe drought, there are increases in reactive oxygen species (ROS) metabolism and photosynthetic transcripts in cyclic severe drought compared with acute severe drought, independent of water deficit. The phytobiome exhibits alternate responses to drought when compared with the transcriptome. Specifically, the phytobiome is affected more by the cyclic or acute nature of the drought rather than the severity of the drought, with the phytobiome having an increase in taxa under cyclic drought that are often reported to have beneficial effects on the plants. Finally, we have identified associations between taxa in the phytobiome with expression of disease response, ROS metabolism, and photosynthesis transcripts suggesting interplay between the host plant and its phytobiome in response to drought.

59 BASIC BIOLOGICAL SCIENCES↗