Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “transcriptome analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Omics-driven onboarding of the carotenoid producing red yeast Xanthophyllomyces dendrorhous CBS 6938

Transcriptomics is a powerful approach for functional genomics and systems biology, yet it can also be used for genetic part discovery. Here, we derive constitutive and light-regulated promoters directly from transcriptomics data of the basidiomycete red yeast Xanthophyllomyces dendrorhous CBS 6938 (anamorph Phaffia rhodozyma) and use these promoters with other genetic elements to create a modular synthetic biology parts collection for this organism. X. dendrorhous is currently the sole biotechnologically relevant yeast in the Tremellomycete class-it produces large amounts of astaxanthin, especially under oxidative stress and exposure to light. Thus, we performed transcriptomics on X. dendrorhous under different wavelengths of light (red, green, blue, and ultraviolet) and oxidative stress. Differential gene expression analysis (DGE) revealed that terpenoid biosynthesis was primarily upregulated by light through crtI, while oxidative stress upregulated several genes in the pathway. Further gene ontology (GO) analysis revealed a complex survival response to ultraviolet (UV) where X. dendrorhous upregulates aromatic amino acid and tetraterpenoid biosynthesis and downregulates central carbon metabolism and respiration. The DGE data was also used to identify 26 constitutive and regulated genes, and then, putative promoters for each of the 26 genes were derived from the genome. Simultaneously, a modular cloning system for X. dendrorhous was developed, including integration sites, terminators, selection markers, and reporters. Each of the 26 putative promoters were integrated into the genome and characterized by luciferase assay in the dark and under UV light. The putative constitutive promoters were constitutive in the synthetic genetic context, but so were many of the putative regulated promoters. Notably, one putative promoter, derived from a hypothetical gene, showed ninefold activation upon UV exposure. Thus, this study reveals metabolic pathway regulation and develops a genetic parts collection for X. dendrorhous from transcriptomic data. Therefore, this study demonstrates that combining systems biology and synthetic biology into an omics-to-parts workflow can simultaneously provide useful biological insight and genetic tools for nonconventional microbes, particularly those without a related model organism. This approach can enhance current efforts to engineer diverse microbes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Bioavailable iron titrations reveal oceanic Synechococcus ecotypes optimized for different iron availabilities

Abstract The trace metal iron (Fe) controls the diversity and activity of phytoplankton across the surface oceans, a paradigm established through decades of in situ and mesocosm experimental studies. Despite widespread Fe-limitation within high-nutrient, low chlorophyll (HNLC) waters, significant contributions of the cyanobacterium Synechococcus to the phytoplankton stock can be found. Correlations among differing strains of Synechococcus across different Fe-regimes have suggested the existence of Fe-adapted ecotypes. However, experimental evidence of high- versus low-Fe adapted strains of Synechococcus is lacking, and so we investigated the transcriptional responses of microbial communities inhabiting the HNLC, sub-Antarctic region of the Southern Ocean during the Spring of 2018. Analysis of metatranscriptomes generated from on-deck incubation experiments reflecting a gradient of Fe-availabilities reveal transcriptomic signatures indicative of co-occurring Synechococcus ecotypes adapted to differing Fe-regimes. Functional analyses comparing low-Fe and high-Fe conditions point to various Fe-acquisition mechanisms that may allow persistence of low-Fe adapted Synechococcus under Fe-limitation. Comparison of in situ surface conditions to the Fe-titrations indicate ecological relevance of these mechanisms as well as persistence of both putative ecotypes within this region. This Fe-titration approach, combined with transcriptomics, highlights the short-term responses of the in situ phytoplankton community to Fe-availability that are often overlooked by examining genomic content or bulk physiological responses alone. These findings expand our knowledge about how phytoplankton in HNLC Southern Ocean waters adapt and respond to changing Fe supply.

Gilbert, Naomi E. (ORCID:0000000256915547)↗
DOE OSTI2019

Show me your secret(ed) weapons: a multifaceted approach reveals a wide arsenal of type III‐secreted effectors in the cucurbit pathogenic bacterium Acidovorax citrulli and novel effectors in the Acidovorax genus

Summary The cucurbit pathogenic bacterium Acidovorax citrulli requires a functional type III secretion system (T3SS) for pathogenicity. In this bacterium, as with Xanthomonas and Ralstonia spp . , an AraC‐type transcriptional regulator, HrpX, regulates expression of genes encoding T3SS components and type III‐secreted effectors (T3Es) . The annotation of a sequenced A. citrulli strain revealed 11 T3E genes. Assuming that this could be an underestimation, we aimed to uncover the T3E arsenal of the A. citrulli model strain, M6. Thorough sequence analysis revealed 51 M6 genes whose products are similar to known T3Es. Furthermore, we combined machine learning and transcriptomics to identify novel T3Es. The machine‐learning approach ranked all A. citrulli M6 genes according to their propensity to encode T3Es. RNA‐Seq revealed differential gene expression between wild‐type M6 and a mutant defective in HrpX: 159 and 28 genes showed significantly reduced and increased expression in the mutant relative to wild‐type M6, respectively. Data combined from these approaches led to the identification of seven novel T3E candidates that were further validated using a T3SS‐dependent translocation assay. These T3E genes encode hypothetical proteins that seem to be restricted to plant pathogenic Acidovorax species. Transient expression in Nicotiana benthamiana revealed that two of these T3Es localize to the cell nucleus and one interacts with the endoplasmic reticulum. This study places A. citrulli among the ‘richest’ bacterial pathogens in terms of T3E cargo. It also revealed novel T3Es that appear to be involved in the pathoadaptive evolution of plant pathogenic Acidovorax species.

Jiménez‐Guerrero, Irene↗
DOE OSTI2023

Constructing Regulatory Networks to Compare Axenic and Interspecies Microbial Gene Transcription

In this preliminary study, we constructed gene regulatory networks (GRNs) from transcriptional expression data of axenic and interspecies microbial cultures with the goal of predicting how cocultivation affected greenhouse gas respiration by these species. The specific strains of Methylotuvimicrobium alkaliphilum 20Z, a methylotroph, and Cyanobacterium stanieri HL-69, a phototroph, were chosen for their viability in industrial bioprocessing. We ranked directed interactions between gene pairs based on the ability of the input gene to predict the expression of a target gene relative to their transcriptomes. While we were able to identify topological differences between conditions, our initial findings require validation through experimental analysis and further modeling. We aimed to develop a systematic thresholding approach to optimize the accuracy of our networks. We filtered out trial networks separately from top gene interactions of the scored rankings. Parameters of unfiltered and filtered networks were used to test and develop thresholding approaches. Knee point detection of edge weight distributions was explored as an approach for separating significant interactions from insignificant interactions in unfiltered networks. While knee detection failed to produce analogous networks for broad cross-condition comparisons, the results informed us about the proportions of significant edges present in unfiltered networks. We also calculated the average mean degree for nodes in a selection of trial networks to find a thresholding value characteristic to all groups. While we did not reach a definitive conclusion, we gained insight into the coregulatory structures of our groups and made critical evaluations of systematic methods for filtering networks. We recommend an iterative process for the inference of GRNs, where the most significant results from preliminary explorations are used to improve the efficiency with which regulatory motifs are chosen for experimental characterization. Experimental results can then inform the framework of adjusted models to improve broad interpretations of GRNs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Human Primary Airway Epithelium +/- Macrophages Response to HCoV-229E Infection Transcriptomics (ACS-DP3)

The purpose of this experiment was to evaluate the human host cellular response to wild-type Human coronavirus strain 229E (HCoV-299E) infection. Sample data was obtained for mock and infected (MOI 3) primary human airway epithelial cells with and without macrophages and grown in air-liquid interface conditions. Sample data was acquired using an Illumina Hi-Seq 4000 sequencer system and further processed for RNA sequencing (RNA-Seq) expression analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Molecular Mechanisms of Nanomaterial-Bacterial Interactions Revealed by Omics—The Role of Nanomaterial Effect Level

Nanotechnology is employed across a wide range of antibacterial applications in clinical settings, food, pharmaceutical and textile industries, water treatment and consumer goods. Depending on type and concentration, engineered nanomaterials (ENMs) can also benefit bacteria in myriad contexts including within the human body, in biotechnology, environmental bioremediation, wastewater treatment, and agriculture. However, to realize the full potential of nanotechnology across broad applications, it is necessary to understand conditions and mechanisms of detrimental or beneficial effects of ENMs to bacteria. To study ENM effects, bacterial population growth or viability are commonly assessed. However, such endpoints alone may be insufficiently sensitive to fully probe ENM effects on bacterial physiology. To reveal more thoroughly how bacteria respond to ENMs, molecular-level omics methods such as transcriptomics, proteomics, and metabolomics are required. Because omics methods are increasingly utilized, a body of literature exists from which to synthesize state-of-the-art knowledge. Here we review relevant literature regarding ENM impacts on bacterial cellular pathways obtained by transcriptomic, proteomic, and metabolomic analyses across three growth and viability effect levels: inhibitory, sub-inhibitory or stimulatory. As indicated by our analysis, a wider range of pathways are affected in bacteria at sub-inhibitory vs. inhibitory ENM effect levels, underscoring the importance of ENM exposure concentration in elucidating ENM mechanisms of action and interpreting omics results. In addition, challenges and future research directions of applying omics approaches in studying bacterial-ENM interactions are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Human Host Cellular Response to HCoV-229E Infection Transcriptomics (ACS-DP1)

The purpose of this experiment was to evaluate the human host cellular response to wild-type Human coronavirus strain 229E (HCoV-229E) infection. Sample data was obtained for mock and infected immortalized human lung epithelial cells (A549) (MOI 5), immortalized human lung fibroblasts cells (MRC5) (MOI5), and primary human airway epithelial (HAE) (MOI 3) cells from lung tissue. Sample data was acquired using an Illumina HiSeq 2000 sequencer system and processed for RNA sequencing (RNA-Seq) expression analysis.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.

life sciences↗
DOE OSTI2019

A web-based tool for the prediction of rice transcription factor function

Transcription factors (TFs) are an important class of regulatory molecules. Despite their importance, only a small number of genes encoding TFs have been characterized in Oryza sativa (rice), often because gene duplication and functional redundancy complicate their analysis. To address this challenge, we developed a web-based tool called the Rice Transcription Factor Phylogenomics Database (RTFDB) and demonstrate its application for predicting TF function. The RTFDB hosts transcriptome and co-expression analyses. Sources include high-throughput data from oligonucleotide microarray (Affymetrix and Agilent) as well as RNA-Seq-based expression profiles. We used the RTFDB to identify tissue-specific and stress-related gene expression. Subsequently, 273 genes preferentially expressed in specific tissues or organs, 455 genes showing a differential expression pattern in response to 4 abiotic stresses, 179 genes responsive to infection of various pathogens and 512 genes showing differential accumulation in response to various hormone treatments were identified through the meta-expression analysis. Pairwise Pearson correlation coefficient analysis between paralogous genes in a phylogenetic tree was used to assess their expression collinearity and thereby provides a hint on their genetic redundancy. Integrating transcriptome with the gene evolutionary information reveals the possible functional redundancy or dominance played by paralog genes in a highly duplicated genome such as rice. With this method, we estimated a predominant role for 83.3% (65/78) of the TF or transcriptional regulator genes that had been characterized via loss-of-function studies. In this regard, the proposed method is applicable for functional studies of other plant species with annotated genome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Dis3- and exosome subunit-responsive 3 Prime mRNA instability elements

Highlights: Black-Right-Pointing-Pointer Successful use of a novel RNA-specific bioinformatic tool, RNA SCOPE. Black-Right-Pointing-Pointer Identified novel 3 Prime UTR cis-acting element that destabilizes a reporter mRNA. Black-Right-Pointing-Pointer Show exosome subunits are required for cis-acting element-mediated mRNA instability. Black-Right-Pointing-Pointer Define precise sequence requirements of novel cis-acting element. Black-Right-Pointing-Pointer Show that microarray-defined exosome subunit-regulated mRNAs have novel element. -- Abstract: Eukaryotic RNA turnover is regulated in part by the exosome, a nuclear and cytoplasmic complex of ribonucleases (RNases) and RNA-binding proteins. The major RNase of the complex is thought to be Dis3, a multi-functional 3 Prime -5 Prime exoribonuclease and endoribonuclease. Although it is known that Dis3 and core exosome subunits are recruited to transcriptionally active genes and to messenger RNA (mRNA) substrates, this recruitment is thought to occur indirectly. We sought to discover cis-acting elements that recruit Dis3 or other exosome subunits. Using a bioinformatic tool called RNA SCOPE to screen the 3 Prime untranslated regions of up-regulated transcripts from our published Dis3 depletion-derived transcriptomic data set, we identified several motifs as candidate instability elements. Secondary screening using a luciferase reporter system revealed that one cassette-harboring four elements-destabilized the reporter transcript. RNAi-based depletion of Dis3, Rrp6, Rrp4, Rrp40, or Rrp46 diminished the efficacy of cassette-mediated destabilization. Truncation analysis of the cassette showed that two exosome subunit-sensitive elements (ESSEs) destabilized the reporter. Point-directed mutagenesis of ESSE abrogated the destabilization effect. An examination of the transcriptomic data from exosome subunit depletion-based microarrays revealed that mRNAs with ESSEs are found in every up-regulated mRNA data set but are underrepresented or missing from the down-regulated data sets. Taken together, our findings imply a potentially novel mechanism of mRNA turnover that involves direct Dis3 and other exosome subunit recruitment to and/or regulation on mRNA substrates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

UV Laser-Induced, Time-Resolved Transcriptome Responses of Saccharomyces cerevisiae

We observed the effect on gene transcription of laser-mediated, long-wavelength UV-irradiation of Saccharomyces cerevisiae by RNAseq analysis at times T15, T30, and T60 minutes after recovery in growth medium. Laser-irradiated cells were viable, and the transcriptional response was transient, with over 400 genes differentially expressed at T15 or T30, returning to basal level transcription by T60. Identification of transcripts exhibiting enhanced differential expression that were unique to UV laser-irradiation were identified by imposing a stringent significance cut-off (p <0.05, log 2 difference >2) then filtering out genes known as environmental stress response (ESR) genes. Using these rigorous criteria, 56 genes were differentially expressed at T15; at T30 differential expression was observed for 57 genes, some of which persisted from T15. Among the highly up-regulated genes were those supporting amino acid metabolic processes sulfur amino acids, methionine, aspartate, cysteine, serine), sulfur regulation (hydrogen sulfite metabolic processes, sulfate assimilation, sulfate reduction), proteasome components, amino acid transporters, and the iron regulon. At T30, the expression profile shifted to expression of transcripts related to catabolic processes (oxidoreductase activity, peptidase activity). Transcripts common to both T15 and T30 suggested an up-regulation of catabolic events, including UV damage response genes, and protein catabolism via proteasome and peptidase activity. Specific genes encoding tRNAs were among the down-regulated genes adding to the suggestion that control of protein biosynthesis was a major response to long-wave UV laser irradiation. These transcriptional responses bring light to the remarkable ability of the yeast cell to respond to a UV-induced environmental insult.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Profiling of the Transcriptomic Responses of Clonostachys rosea Upon Treatment With Fusarium graminearum Secretome

Clonostachys rosea strain ACM941 is a fungal bio-control agent patented against the causative agent of Fusarium Head Blight, Fusarium graminearum. Although the molecular details remain enigmatic, previous studies have suggested that C. rosea may secrete F. graminearum growth inhibitors. Further toward this, experiments described herein show that induction of C. rosea cultures by the addition of an aliquot of F. graminearum(Fg)-spent media (including macroconidia), yield C. rosea (Cr)-spent media that elicited higher anti-F. graminearum activity than either control or deoxynivalenol (DON)-induced Cr-spent media. To gain additional insight into the genetic and metabolic factors modulating this interaction, transcriptomic (RNAseq) profiles of C. rosea in response to DON and Fg-spent media treatment, were developed. This analysis revealed 24,112 C. rosea unigenes, of which 5,605 and 6,285 were differentially regulated by DON and F-spent media, respectively. More than half of these unigenes were up-regulated, with annotations, most notably in the Fg-spent media treatment data, suggesting enhancement of polyketide (PK) and non-ribosomal peptide (NRP) secondary metabolite precursor synthesis, and PK/NRP-like synthases. Four ABC transporters were also up-regulated in response to Fg-spent media. Further analysis showed that the PK and NRP-like synthases belong to three gene clusters that also include ABC transporters, and other genes known to tailor secondary metabolite biosynthesis. The RNAseq data was further validated using quantitative RT-qPCR. Taken together, these results show that C. rosea responds to the presence of Fg-spent media (and to a lesser extent, DON-alone) by up-regulating unique aspects of its secondary metabolism-related genetic repertoire. The identities and roles of C. rosea secondary metabolites produced by the targeted gene clusters are now under investigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

The Transcriptome and Flux Profiling of Crabtree-Negative Hydroxy Acid-Producing Strains of Saccharomyces cerevisiae Reveals Changes in the Central Carbon Metabolism

Saccharomyces cerevisiae (S. cerevisiae) is the yeast cell factory of choice for the production of many biobased chemicals. However, it is a Crabtree-positive yeast and so shuttles a large portion of carbon into ethanol. Ethanol formation can be eliminated by deleting pyruvate decarboxylase (PDC) activity. It is not yet well understood how PDC-negative yeasts are affected when engineered to produce other products than ethanol. In this study, pathways are introduced for the production of three hydroxy acids (lactic, malic, or 3-hydroxypropionic acid [3HP]) into an evolved PDC-negative strain. These strains are characterized via transcriptome and flux profiling to elucidate the effects that the production of these hydroxy acids has on the host strain. Expression of lactic and malic acid biosynthesis pathways improved the maximum specific growth rate (μ max ) of the strain by 64% and 20%, respectively, presumably due to nicotinamide adenine dinucleotide regeneration. All strains show a very high flux (> 90% of glucose uptake) into the oxidative pentose phosphate pathway under batch fermentation conditions. The study, for the first time, directly compares the flux and transcriptome profiles of several hydroxy acid-producing strains of an evolved PDC-negative S. cerevisiae and suggests directions for future metabolic engineering.

13C-based metabolic flux analysis↗
DOE OSTI2017

PCR and Omics Based Techniques to Study the Diversity, Ecology and Biology of Anaerobic Fungi: Insights, Challenges and Opportunities

Anaerobic fungi (phylum Neocallimastigomycota) are common inhabitants of the digestive tract of mammalian herbivores, and in the rumen, can account for up to 20% of the microbial biomass. Anaerobic fungi play a primary role in the degradation of lignocellulosic plant material. They also have a syntrophic interaction with methanogenic archaea, which increases their fiber degradation activity. To date, nine anaerobic fungal genera have been described, with further novel taxonomic groupings known to exist based on culture-independent molecular surveys. However, the true extent of their diversity may be even more extensively underestimated as anaerobic fungi continue being discovered in yet unexplored gut and non-gut environments. Additionally many studies are now known to have used primers that provide incomplete coverage of the Neocallimastigomycota. For ecological studies the internal transcribed spacer 1 region (ITS1) has been the taxonomic marker of choice, but due to various limitations the large subunit rRNA (LSU) is now being increasingly used. How the continued expansion of our knowledge regarding anaerobic fungal diversity will impact on our understanding of their biology and ecological role remains unclear; particularly as it is becoming apparent that anaerobic fungi display niche differentiation. As a consequence, there is a need to move beyond the broad generalization of anaerobic fungi as fiber-degraders, and explore the fundamental differences that underpin their ability to exist in distinct ecological niches. Application of genomics, transcriptomics, proteomics and metabolomics to their study in pure/mixed cultures and environmental samples will be invaluable in this process. To date the genomes and transcriptomes of several characterized anaerobic fungal isolates have been successfully generated. In contrast, the application of proteomics and metabolomics to anaerobic fungal analysis is still in its infancy. A central problem for all analyses, however, is the limited functional annotation of anaerobic fungal sequence data. There is therefore an urgent need to expand information held within publicly available reference databases. Finally, once this challenge is overcome, along with improved sample collection and extraction, the application of these techniques will be key in furthering our understanding of the ecological role and impact of anaerobic fungi in the wide range of environments they inhabit.

(meta) transcriptomics↗
DOE OSTI2021

Asc-Seurat: analytical single-cell Seurat-based web application

Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. Summary We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat’s capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Constructing Networks of Organelle Functional Modules in Arabidopsis

With the rapid accumulation of gene expression data, gene functional module identification has become a widely used approach in functional analysis. However, tools to identify organelle functional modules and analyze their relationships are still missing. We present a soft thresholding approach to construct networks of functional modules using gene expression datasets, in which nodes are strongly co-expressed genes that encode proteins residing in the same subcellular localization, and links represent strong inter-module connections. Our algorithm has three steps. First, we identify functional modules by analyzing gene expression data. Next, we use a self-adaptive approach to construct a mixed network of functional modules and genes. Finally, we link functional modules that are tightly connected in the mixed network. Analysis of experimental data from Arabidopsis demonstrates that our approach is effective in improving the interpretability of high-throughput transcriptomic data and inferring function of unknown genes.

Arabidopsis thaliana↗
DOE OSTI2020

Francisella novicida Two-Component System Response Regulator BfpR Modulates iglC Gene Expression, Antimicrobial Peptide Resistance, and Biofilm Production

Response regulators are a critical part of the two-component system of gene expression regulation in bacteria, transferring a signal from a sensor kinase into DNA binding activity resulting in alteration of gene expression. In this study, we investigated a previously uncharacterized response regulator in Francisella novicida, FTN_1452 that we have named BfpR (Biofilm-regulating Francisella protein Regulator, FTN_1452). In contrast to another Francisella response regulator, QseB/PmrA, BfpR appears to be a negative regulator of biofilm production, and also a positive regulator of antimicrobial peptide resistance in this bacterium. The protein was crystallized and X-ray crystallography studies produced a 1.8 Å structure of the BfpR N-terminal receiver domain revealing interesting insight into its potential interaction with the sensor kinase. Structural analysis of BfpR places it in the OmpR/PhoP family of bacterial response regulators along with WalR and ResD. Proteomic and transcriptomic analyses suggest that BfpR overexpression affects expression of the critical Francisella virulence factor iglC, as well as other proteins in the bacterium. We demonstrate that mutation of bfpR is associated with an antimicrobial peptide resistance phenotype, a phenotype also associated with other response regulators, for the human cathelicidin peptide LL-37 and a sheep antimicrobial peptide SMAP-29. F. novicida with mutated bfpR replicated better than WT in intracellular infection assays in human-derived macrophages suggesting that the down-regulation of iglC expression in bfpR mutant may enable this intracellular replication to occur. Response regulators have been shown to play important roles in the regulation of bacterial biofilm production. We demonstrate that F. novicida biofilm formation was highly increased in the bfpR mutant, corresponding to altered glycogen synthesis. Waxworm infection experiments suggest a role of BfpR as a negative modulator of iglC expression with de-repression by Mg 2+ . In this study, we find that the response regulator BfpR may be a negative regulator of biofilm formation, and a positive regulator of antimicrobial peptide resistance in F. novicida.

59 BASIC BIOLOGICAL SCIENCES↗
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↗