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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Transcriptome-Wide Cleavage Site Mapping on Cellular mRNAs Reveals Features Underlying Sequence-Specific Cleavage by the Viral Ribonuclease SOX

Many viruses express factors that reduce host gene expression through widespread degradation of cellular mRNA. An example of this class of proteins is the mRNA-targeting endoribonuclease SOX from the gamma-herpesvirus Kaposi’s sarcoma-associated herpesvirus (KSHV). Previous studies indicated that cleavage of messenger RNAs (mRNA) by SOX occurs at specific locations defined by the sequence of the target RNA, which is at odds with the down-regulation of a large portion of cellular transcripts. In this study, we address this paradox by using high-throughput sequencing of cleavage intermediates combined with a custom bioinformatics-based analysis pipeline to identify SOX cleavage sites across the mRNA transcriptome. These data, coupled with targeted mutagenesis, reveal that while cleavage sites are specific and reproducible, they are defined by a degenerate sequence motif containing a small number of conserved residues rather than a strong consensus sequence. This degenerate element is well represented in both human and KSHV mRNA, and its presence correlates with RNA destabilization by SOX. This represents a new endonuclease targeting strategy, in which use of a degenerate targeting element enables RNA cleavage at specific locations without restricting the range of targets. Furthermore, it shows that strong target selectivity can be achieved without a high degree of sequence specificity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

A lincRNA connected to cell mortality and epigenetically-silenced in most common human cancers

Immortality is an essential characteristic of human carcinoma cells. We recently developed an efficient, reproducible method that immortalizes human mammary epithelial cells (HMEC) in the absence of gross genomic changes by targeting 2 critical senescence barriers. Consistent transcriptomic changes associated with immortality were identified using microarray analysis of isogenic normal finite pre-stasis, abnormal finite post-stasis, and immortal HMECs from 4 individuals. A total of 277 genes consistently changed in cells that transitioned from post-stasis to immortal. Gene ontology analysis of affected genes revealed biological processes significantly altered in the immortalization process. These immortalization-associated changes showed striking similarity to the gene expression changes seen in The Cancer Genome Atlas (TCGA) clinical breast cancer data. The most dramatic change in gene expression seen during the immortalization step was the downregulation of an unnamed, incompletely annotated transcript that we called MORT, for mortality, since its expression was closely associated with the mortal, finite lifespan phenotype. We show here that MORT (ZNF667-AS1) is expressed in all normal finite lifespan human cells examined to date and is lost in immortalized HMEC. MORT gene silencing at the mortal/immortal boundary was due to DNA hypermethylation of its CpG island promoter. This epigenetic silencing is also seen in human breast cancer cell lines and in a majority of human breast tumor tissues. The functional importance of DNA hypermethylation in MORT gene silencing is supported by the ability of 5-aza-2'- deoxycytidine to reactivate MORT expression. Analysis of TCGA data revealed deregulation of MORT expression due to DNA hypermethylation in 15 out of the 17 most common human cancers. In conclusion, the epigenetic silencing of MORT in a large majority of the common human cancers suggests a potential fundamental role in cellular immortalization during human carcinogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Contrasting Metabolism in Perenniating Structures of Upland and Lowland Switchgrass Plants Late in the Growing Season

Background: Switchgrass (Panicum virgatum L.) is being developed as a bioenergy crop for many temperate regions of the world. One way to increase biomass yields is to move southern adapted lowland cultivars to more northern latitudes. However, many southerly adapted switchgrass germplasm can suffer significant winter kill in northerly climes. Materials and Methods: Here, we have applied next-generation sequencing in combination with biochemical analyses to query the metabolism of crowns and rhizomes obtained from two contrasting switchgrass cultivars. Crowns and rhizomes from field-grown lowland (cv Kanlow) and upland (cv Summer) switchgrass cultivars were collected from three randomly selected post-flowering plants. Summer plants were senescing, whereas Kanlow plants were not at this harvest date. Results: Principal component analysis (PCA) differentiated between both the Summer and Kanlow transcriptomes and metabolomes. Significant differences in transcript abundances were detected for 8,050 genes, including transcription factors such as WRKYs and those associated with phenylpropanoid biosynthesis. Gene-set enrichment analyses showed that a number of pathways were differentially up-regulated in the two populations. For both populations, protein levels and enzyme activities agreed well with transcript abundances for genes involved in the phenylpropanoid pathway that were upregulated in Kanlow crowns and rhizomes. The combination of these datasets suggests that dormancy-related mechanisms had been triggered in the crowns and rhizomes of the Summer plants, whereas the crowns and rhizomes of Kanlow plants had yet to enter dormancy. Conclusions: Delayed establishment of dormancy at more northerly latitudes could be one factor that reduces wintersurvival in the high-yielding Kanlow plants. Understanding the cellular signatures that accompany the transition to dormancy can be used in the future to select plants with improved winter hardiness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Human Host Cellular Response to HCoV-229E Infection Proteomics (ACS-JM-DP2)

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) nuclear extracts, immortalized human lung fibroblasts cells (MRC5) (MOI5) nuclear extracts, and primary human airway epithelial (HAE) (MOI 3) cells from lung tissue and processed for proteome analysis. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files and supporting metadata materials. Experimental proteomics samples were prepared using Limited Proteolysis (LiP) methods for Label-free quantification (LFQ) and global proteomic evaluation. Sample data was acquired using a Q-Exactive HF-X mass spectrometer and was processed and compiled using MaxQuant software (v.1.6.17.0). Processed proteomic data downloads include a sample naming key, processed MaxQuant results/parameters, and protein annotated relative abundance files. See corresponding primary data accessions below and Viral Experiment LiP Analysis source code supporting data transparency and reuse. Experimental transcriptomics samples were collected in parallel and processed for RNA sequencing (RNA-Seq) as summarized under ACS-DP1 (https://data.pnnl.gov/group/nodes/dataset/34069).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

Background: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. Methods: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. Results: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. Conclusions: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

60 APPLIED LIFE SCIENCES↗
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 OSTI2012

Polycyclic aromatic hydrocarbons as skin carcinogens: Comparison of benzo[a]pyrene, dibenzo[def,p]chrysene and three environmental mixtures in the FVB/N mouse

The polycyclic aromatic hydrocarbon (PAH), benzo[a]pyrene (BaP), was compared to dibenzo[def,p]chrysene (DBC) and combinations of three environmental PAH mixtures (coal tar, diesel particulate and cigarette smoke condensate) using a two stage, FVB/N mouse skin tumor model. DBC (4 nmol) was most potent, reaching 100% tumor incidence with a shorter latency to tumor formation, less than 20 weeks of 12-O-tetradecanoylphorbol-13-acetate (TPA) promotion compared to all other treatments. Multiplicity was 4 times greater than BaP (400 nmol). Both PAHs produced primarily papillomas followed by squamous cell carcinoma and carcinoma in situ. Diesel particulate extract (1 mg SRM 1650b; mix 1) did not differ from toluene controls and failed to elicit a carcinogenic response. Addition of coal tar extract (1 mg SRM 1597a; mix 2) produced a response similar to BaP. Further addition of 2 mg of cigarette smoke condensate (mix 3) did not alter the response with mix 2. PAH-DNA adducts measured in epidermis 12 h post initiation and analyzed by {sup 32}P post‐labeling, did not correlate with tumor incidence. PAH‐dependent alteration in transcriptome of skin 12 h post initiation was assessed by microarray. Principal component analysis (sum of all treatments) of the 922 significantly altered genes (p < 0.05), showed DBC and BaP to cluster distinct from PAH mixtures and each other. BaP and mixtures up-regulated phase 1 and phase 2 metabolizing enzymes while DBC did not. The carcinogenicity with DBC and two of the mixtures was much greater than would be predicted based on published Relative Potency Factors (RPFs). -- Highlights: ► Dibenzo[def,p]chrysene (DBC), 3 PAH mixtures, benzo[a]pyrene (BaP) were compared. ► DBC and 2 PAH mixtures were more potent than Relative Potency Factor estimates. ► Transcriptome profiles 12 hours post initiation were analyzed by microarray. ► Principle components analysis of alterations revealed treatment-based clustering. ► DBC gave a unique pattern of gene alterations compared to BaP and PAH mixtures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Transcriptomics of Temporal- versus Substrate-Specific Wood Decay in the Brown-Rot Fungus Fibroporia radiculosa

Brown-rot fungi lack many enzymes associated with complete wood degradation, such as lignin-attacking peroxidases, and have developed alternative mechanisms for rapid wood breakdown. To identify the effects of culture conditions and wood substrates on gene expression, we grew Fibroporia radiculosa in submerged cultures containing Wiley milled wood (5 days) and solid wood wafers (30 days), using aspen, pine, and spruce as a substrate. The comparative analysis revealed that wood species had a limited effect on the transcriptome: <3% of genes were differentially expressed between different wood species substrates. The comparison between gene expression during growth on milled wood and wood wafer conditions, however, indicated that the genes encoding plant cell wall-degrading enzymes, such as glycoside hydrolases and peptidases, were activated during growth on wood wafers, confirming previous reports. On the other hand, it was shown for the first time that the genes encoding Fenton chemistry enzymes, such as hydroquinone biosynthesis enzymes and oxidoreductases, were activated during submerged growth on ground wood. This illustrates the diversity of wood-decay reactions encoded in fungi and activated at different stages of this process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Genomics Characterization of an Engineered Corynebacterium glutamicum in Bioreactor Cultivation Under Ionic Liquid Stress

Corynebacterium glutamicum is an ideal microbial chassis for production of valuable bioproducts including amino acids and next generation biofuels. Here we resequence engineered isopentenol (IP) producing C. glutamicum BRC-JBEI 1.1.2 strain and assess differential transcriptional profiles using RNA sequencing under industrially relevant conditions including scale transition and compare the presence vs absence of an ionic liquid, cholinium lysinate ([Ch][Lys]). Analysis of the scale transition from shake flask to bioreactor with transcriptomics identified a distinct pattern of metabolic and regulatory responses needed for growth in this industrial format. These differential changes in gene expression corroborate altered accumulation of organic acids and bioproducts, including succinate, acetate, and acetoin that occur when cells are grown in the presence of 50 mM [Ch][Lys] in the stirred-tank reactor. This new genome assembly and differential expression analysis of cells grown in a stirred tank bioreactor clarify the cell response of an C. glutamicum strain engineered to produce IP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI’s cellular dynamics analysis, and the use of the fibrotic cocktail-treated human precision-cut lung slices confirms UNAGI’s predictions that nifedipine, an antihypertensive drug, may have anti-fibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including COVID, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fibrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.

Neural Network↗
DOE OSTI2018

Fractal dimension analysis reveals skeletal muscle disorganization in mdx mice

Duchenne Muscular Dystrophy (DMD) is characterized by muscle extracellular matrix disorganization due to the increased collagen deposition leading to fibrosis that significantly exacerbates disease progression. Fractal dimension analysis is a method that quantifies tissue/cellular disorganization and characterizes complex structures. The first objective of the present study was use fractal analysis to evaluate extracellular matrix disorganization in mdx mice soleus muscle. Next, we mimic a hyper-proliferation of fibrogenic cells by co-culturing NIH3T3 fibroblasts and C2C12 myoblasts to test whether fibroblasts induce disorganization in myoblast arrangement. Here, we show mdx presented high skeletal muscle disorganization as revealed by fractal analysis. Similarly, this method revealed that myoblasts co-cultured with fibroblast also presented cellular arrangement disorganization. We also reanalyzed skeletal muscle microarrays transcriptomic data from mdx and DMD patients that revealed transcripts related to extracellular matrix organization. This analysis also identified Osteoglycin, which was validated as a potential regulator of ECM organization in mdx dystrophic muscles. Our results demonstrate that fractal dimension is useful tool for the analysis of skeletal muscle disorganization in DMD and also reveal a fibroblast-myoblast cross-talk that contributes to “in vitro” myoblast disarrangement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity

Alzheimer’s disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data ( N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Directed Shotgun Proteomics Guided by Saturated RNA-seq Identifies a Complete Expressed Prokaryotic Proteome

Prokaryotes, due to their moderate complexity, are particularly amenable to the comprehensive identification of the protein repertoire expressed under different conditions. We applied a generic strategy to identify a complete expressed prokaryotic proteome, which is based on the analysis of RNA and proteins extracted from matched samples. Saturated transcriptome profiling by RNA-seq provided an endpoint estimate of the protein-coding genes expressed under two conditions which mimic the interaction of Bartonella henselae with its mammalian host. Directed shotgun proteomics experiments were carried out on four subcellular fractions. By specifically targeting proteins which are short, basic, low abundant, and membrane localized, we could eliminate their initial underrepresentation compared to the estimated endpoint. A total of 1250 proteins were identified with an estimated false discovery rate below 1%. This represents 85% of all distinct annotated proteins and ~90% of the expressed protein-coding genes. Genes that were detected at the transcript but not protein level, were found to be highly enriched in several genomic islands. Furthermore, genes that lacked an ortholog and a functional annotation were not detected at the protein level; these may represent examples of overprediction in genome annotations. A dramatic membrane proteome reorganization was observed, including differential regulation of autotransporters, adhesins, and hemin binding proteins. Particularly noteworthy was the complete membrane proteome coverage, which included expression of all members of the VirB/D4 type IV secretion system, a key virulence factor.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2026

Rhythmic Mechanisms Governing CAM Photosynthesis in Kalanchoe fedtschenkoi : High-Resolution Temporal Transcriptomics

Crassulacean acid metabolism (CAM) is a specialized photosynthetic pathway that enhances water-use efficiency by temporally separating nocturnal CO 2 uptake from daytime decarboxylation and carbon fixation. To uncover the regulatory mechanisms coordinating these temporal dynamics, we generated high-resolution, 48 h time-course transcriptomes for the CAM model Kalanchoe fedtschenkoi under both 12 h/12 h light/dark (LD) cycles and continuous light (LL). A rhythmicity analysis revealed that diel light cues are the dominant driver of transcript oscillations: 16,810 genes (54.3% of annotated genes) exhibited rhythmic expression only under LD, whereas just 399 genes (1.3%) remained rhythmic under LL. A smaller set of 3009 genes (9.7%) oscillated in both conditions, indicating that the intrinsic circadian clock sustains rhythmicity for a limited subset of the transcriptome. A gene co-expression network analysis revealed extensive integration between circadian clock components, core CAM pathway enzymes, and stomatal regulators, defining regulatory modules that coordinate metabolic and physiological timing. Notably, key hub genes associated with post-translational and post-transcriptional regulation, including the E3 ubiquitin ligase HUB2 and several pentatricopeptide repeat (PPR) proteins, act as central nodes in CAM-associated networks. This discovery implicates epigenetic and organellar regulation as previously unrecognized critical tiers of control in CAM. Together, our results support a regulatory model in which CAM rhythmicity is governed by both external light/dark cues and the endogenous circadian clock through multi-level control spanning transcriptional and protein-level regulation. To support community exploration, we also provide an interactive eFP (electronic Fluorescent Pictograph) browser for visualizing time-resolved gene expression profiles.

09 BIOMASS FUELS↗
DOE OSTI2020

A Complex Interplay between Nitric Oxide, Quorum Sensing, and the Unique Secondary Metabolite Tundrenone Constitutes the Hypoxia Response in Methylobacter

Methylobacter species, members of the Methylococcales, have recently emerged as some of the globally widespread, cosmopolitan species that play a key role in the environmental consumption of methane across gradients of dioxygen tensions. In this work, we approached the question of howMethylobacter copes with hypoxia, via laboratory manipulation. Through comparative transcriptomics of cultures grown under high dioxygen partial pressure versus cultures exposed to hypoxia, we identified a gene cluster encoding a hybrid cluster protein along with sensing and regulatory functions. Through mutant analysis, we demonstrated that this gene cluster is involved in the hypoxia stress response. Through additional transcriptomic analyses, we uncovered a complex interconnection between the NO-mediated stress response, quorum sensing, the secondary metabolite tundrenone, and methanol dehydrogenase functions. This novel and complex hypoxia stress response system is so far unique to Methylobacter species, and it may play a role in the environmental fitness of these organisms and in their cosmopolitan environmental distribution.Here, we describe a novel and complex hypoxia response system in a methanotrophic bacterium that involves modules of central carbon metabolism, denitrification, quorum sensing, and a secondary metabolite, tundrenone. This intricate stress response system, so far unique to Methylobacter species, may be responsible for the persistence and activity of these species across gradients of dioxygen tensions and for the cosmopolitan distribution of these organisms in freshwater and soil environments in the Northern Hemisphere, including the fast-melting permafrosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

COVID-19 biomarkers based on respiratory microbiome content

COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Bioinformatics Resource Manager: A systems biology web tool for microRNA and omics data integration

Background: The Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of miRNA-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers. Results: BRM has the capability to perform cross-species identifier lookups across common identifier types, directly integrate datasets across platform or species by performing identifier retrievals in the background, and retrieve miRNA targets from multiple databases simultaneously and integrate the resulting gene targets with experimental mRNA data. Here we use workflows provided in BRM to integrate RNAseq data across species to identify common biomarkers of exposure after treatment of human lung cells and zebrafish to benzo[a]pyrene (BAP). We further use the miRNA Target workflow to experimentally determine the role of miRNAs as regulators of BAP toxicity and identify the predicted functional consequences of miRNA-target regulation in our system. The output from BRM can easily and directly be uploaded to freely available visualization tools for further analysis. From these examples, we were able to identify an important role for several miRNAs as potential regulators of BAP toxicity in human lung cells associated with cell migration, cell communication, cell junction assembly and regulation of cell death. Conclusions: Overall, BRM provides bioinformatics tools to assist biologists having minimal programming skills with analysis and integration of high-content omics’ data from various transcriptomic and proteomic platforms. BRM workflows were developed in Java and other open-source technologies and are served publicly using Apache Tomcat at https://cbb.pnnl.gov/brm/.

bioinformatics, MicroRNA, systems biology, Genomic↗