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Results for “gene expression networks”

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

IPD3, a master regulator of arbuscular mycorrhizal symbiosis, affects genes for immunity and metabolism of non-host Arabidopsis when restored long after its evolutionary loss

Abstract Arbuscular mycorrhizal symbiosis (AM) is a beneficial trait originating with the first land plants, which has subsequently been lost by species scattered throughout the radiation of plant diversity to the present day, including the model Arabidopsis thaliana . To explore if elements of this apparently beneficial trait are still present and could be reactivated we generated Arabidopsis plants expressing a constitutively active form of Interacting Protein of DMI3 , a key transcription factor that enables AM within the Common Symbiosis Pathway, which was lost from Arabidopsis along with the AM host trait. We characterize the transcriptomic effect of expressing IPD3 in Arabidopsis with and without exposure to the AM fungus (AMF) Rhizophagus irregularis , and compare these results to the AM model Lotus japonicus and its ipd3 knockout mutant cyclops-4 . Despite its long history as a non-AM species, restoring IPD3 in the form of its constitutively active DNA-binding domain to Arabidopsis altered expression of specific gene networks. Surprisingly, the effect of expressing IPD3 in Arabidopsis and knocking it out in Lotus was strongest in plants not exposed to AMF, which is revealed to be due to changes in IPD3 genotype causing a transcriptional state, which partially mimics AMF exposure in non-inoculated plants. Our results indicate that molecular connections to symbiosis machinery remain in place in this nonAM species, with implications for both basic science and the prospect of engineering this trait for agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Transcriptomic and metabolomic changes in postharvest sugarbeet roots reveal widespread metabolic changes in storage and identify genes potentially responsible for respiratory sucrose loss

Endogenous metabolism is primarily responsible for losses in sucrose content and processing quality in postharvest sugarbeet roots. The genes responsible for this metabolism and the transcriptional changes that regulate it, however, are largely unknown. To identify genes and metabolic pathways that participate in postharvest sugarbeet root metabolism and the transcriptional changes that contribute to their regulation, transcriptomic and metabolomic profiles were generated for sugarbeet roots at harvest and after 12, 40 and 120 d storage at 5 and 12°C and gene expression and metabolite concentration changes related to storage duration or temperature were identified. During storage, 8656 genes, or 34% of all expressed genes, and 225 metabolites, equivalent to 59% of detected metabolites, were altered in expression or concentration, indicating extensive transcriptional and metabolic changes in stored roots. These genes and metabolites contributed to a wide range of cellular and molecular functions, with carbohydrate metabolism being the function to which the greatest number of genes and metabolites classified. Because respiration has a central role in postharvest metabolism and is largely responsible for sucrose loss in sugarbeet roots, genes and metabolites involved in and correlated to respiration were identified. Seventy-five genes participating in respiration were differentially expressed during storage, including two bidirectional sugar transporter SWEET17 genes that highly correlated with respiration rate. Weighted gene co-expression network analysis identified 1896 additional genes that positively correlated with respiration rate and predicted a pyruvate kinase gene to be a central regulator or biomarker for respiration rate. Overall, these results reveal the extensive and diverse physiological and metabolic changes that occur in stored sugarbeet roots and identify genes with potential roles as regulators or biomarkers for respiratory sucrose loss.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Transcriptomic and Proteomic Insights into Host Immune Responses in Pediatric Severe Malarial Anemia: Dysregulation in HSP60-70-TLR2/4 Signaling and Altered Glutamine Metabolism

Severe malarial anemia (SMA, Hb < 6.0 g/dL) is a leading cause of childhood morbidity and mortality in holoendemic Plasmodium falciparum transmission zones. This study explored the entire expressed human transcriptome in whole blood from 66 Kenyan children with non-SMA (Hb ≥ 6.0 g/dL, n = 41) and SMA (n = 25), focusing on host immune response networks. RNA-seq analysis revealed 6862 differentially expressed genes, with equally distributed up-and down-regulated genes, indicating a complex host immune response. Deconvolution analyses uncovered leukocytic immune profiles indicative of a diminished antigenic response, reduced immune priming, and polarization toward cellular repair in SMA. Weighted gene co-expression network analysis revealed that immune-regulated processes are central molecular distinctions between non-SMA and SMA. A top dysregulated immune response signaling network in SMA was the HSP60-HSP70-TLR2/4 signaling pathway, indicating altered pathogen recognition, innate immune activation, stress responses, and antigen recognition. Validation with high-throughput gene expression from a separate cohort of Kenyan children (n = 50) with varying severities of malarial anemia (n = 38 non-SMA and n = 12 SMA) confirmed the RNA-seq findings. Proteomic analyses in 35 children with matched transcript and protein abundance (n = 19 non-SMA and n = 16 SMA) confirmed dysregulation in the HSP60-HSP70-TLR2/4 signaling pathway. Additionally, glutamine transporter and glutamine synthetase genes were differentially expressed, indicating altered glutamine metabolism in SMA. This comprehensive analysis underscores complex immune dysregulation and novel pathogenic features in SMA.

Microbiology↗
NASA NTRS2016

Network Security via Biometric Recognition of Patterns of Gene Expression

Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT (Information Technology) organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time assays of gene expression products.

cybersecurity↗
NASA NTRS2016

Network Security via Biometric Recognition of Patterns of Gene Expression

Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time expression and assay of gene expression products.

cybersecurity↗
DOE OSTI2024

Antimicrobial mechanisms of g‐C 3 N 4 @ ZnO against oomycetes Phytophthora capsici: from its metabolism, membrane structures and growth

Abstract BACKGROUND Phytophthora capsici , a refractory and model oomycete plant pathogen, especially threatens multiple vegetable crops. A limited number of chemical pesticides play a vital role in controlling oomycete plant diseases. However, this approach often leads to excessive use of chemical agent, exacerbates environmental issues and more and more drug‐resistant strains of oomycete. Therefore, it is imperative to devise innovative solutions that can effectively address the infection of oomycete while maintaining high levels of environmental sustainability and low toxicity. RESULTS In this study, g‐C 3 N 4 @ZnO heterostructure was synthesized and characterized. The g‐C 3 N 4 @ZnO showed higher toxicity on Phytophthora capsici than graphitic carbon nitride (g‐C 3 N 4 ) nanosheets and zinc oxide (ZnO) nanoparticles in vitro and in vivo . Except the hyphal growth of Phytophthora capsici , their germination rate of spores, sporangium formation and number of spores were all suppressed by g‐C 3 N 4 @ZnO heterostructure. Furthermore, we found that this g‐C 3 N 4 @ZnO heterostructure has higher photocatalytic activity under visible light, which potentially enhanced the reactive oxygen species (ROS) mediated stress on Phytophthora capsici . Ultrastructural morphology, global changes of gene expression and weighted gene co‐expression network analysis all supported that the anti‐oomycete activity of g‐C 3 N 4 @ZnO was manifested in the destruction of membrane system and inhibition of multiple metabolisms of Phytophthora capsici under visible irradiation, which also could be attributed to the ROS and zinc ion (Zn 2+ ) mediated stress. CONCLUSION This works offers a novel oomycete disease management strategy by using g‐C 3 N 4 @ZnO, which were attributed to the ROS stress, destruction of membrane system and inhibition of multiple metabolisms. © 2023 Society of Chemical Industry.

Cai, Lin↗
DOE OSTI2024

Genomes of multicellular algal sisters to land plants illuminate signaling network evolution

Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum . We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Codon2Vec v1.0

Background: Codon2Vec is an embedding neural network that predicts 'high' or 'low' gene expression directly from the protein-coding sequences. Embedding neural networks are commonly used for natural language processing (NLP) applications. Analogous to how an English sentence is a string of words, a gene can be thought of as a string of codons. Similar to how NLP neural networks model English sentences as a non-random sequence of words, we considered a coding sequence as a non-random non-overlapping array of codons (k-mers of length = 3). Value Proposition: - Codon2Vec achieved a high median AUC-ROC score of 83.8% when trained and applied to transcriptomic data from 300 fungal species - Unlike Codo2Vec, conventional methods predicting for expression based on codon usage rely on a priori knowledge of optimal codons or a set of reference genes. - Unlike Codon2vec, these methods do not account for the effect of codon order on gene expression. - Codon2Vec neural network bypasses the need for artisanal feature selection step that is necessary for traditional machine learning models.

Wint, Rhondene↗
DOE OSTI2026

A tissue‐resolved, network‐based transcriptomic framework for abiotic stress responses in sorghum

Developing climate‐resilient crops requires a detailed understanding of stress‐induced gene expression dynamics, as maladaptive responses can compromise their productivity and survival. Sorghum, a globally important cereal with exceptional tolerance to multiple abiotic stresses, provides a powerful system for investigating these dynamics. However, how stress type, tissue specificity, and temporal progression jointly shape transcriptomic responses in crops remains poorly understood. Here, we present a comparative, time‐resolved transcriptomic atlas of sorghum responses to drought, heat, and salinity stress across shoot and root tissues. Integrative analyses revealed that tissue specificity is the dominant determinant of abiotic stress‐induced gene reprogramming across all three stresses. Building on these global comparisons, we focused on heat stress, as it elicited the most coherent and pronounced transcriptional and regulatory responses, enabling deeper network‐level interrogation. Co‐expression network analysis identified tissue‐specific modules enriched for phytohormone‐responsive genes, while gene regulatory network (GRN) mapping and cistrome analyses uncovered transcription factors (TFs) controlling key hub genes within these modules. Together, this study provides a foundational transcriptomic and network‐based resource for dissecting the regulatory architecture of abiotic stress responses in sorghum and offers prioritized candidates for future functional validation and engineering of climate‐resilient crops.

abiotic stress↗
DOE OSTI2022

Exploring drought-responsive crucial genes in Sorghum

Drought severely affects global food production. Sorghum is a typical drought-resistant model crop. Based on RNA-seq data for Sorghum with multiple time points and the gray correlation coefficient, this paper firstly selects candidate genes via mean variance test and constructs weighted gene differential co-expression networks (WGDCNs); then, based on guilt-by-rewiring principle, the WGDCNs and the hidden Markov random field model, drought-responsive crucial genes are identified for five developmental stages respectively. Enrichment and sequence alignment analysis reveal that the screened genes may play critical functional roles in drought responsiveness. A multilayer differential co-expression network for the screened genes reveals that Sorghum is very sensitive to pre-flowering drought. Furthermore, a crucial gene regulatory module is established, which regulates drought responsiveness via plant hormone signal transduction, MAPK cascades, and transcriptional regulations. The proposed method can well excavate crucial genes through RNA-seq data, which have implications in breeding of new varieties with improved drought tolerance.

60 APPLIED LIFE SCIENCES↗
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 OSTI2026

Protocol for applying a network-enabled gene discovery pipeline to non-model plant species

Identifying upstream regulators of key genes is essential for understanding gene regulatory mechanisms and translating these insights into functional targets. Here, we present a protocol for applying the network-enabled gene discovery pipeline (NEEDLE) to non-model plant species. We describe steps for environment setup, data preparation, computational analysis, expected outputs, and parameter considerations. NEEDLE integrates RNA sequencing (RNA-seq) processing, weighted gene co-expression analysis (WGCNA), Gene Network Inference with Ensemble of trees (GENIE3), and promoter conservation analysis to prioritize candidate transcriptional regulators.

Plant Sciences↗
DOE OSTI2022

Low-abundance populations distinguish microbiome performance in plant cell wall deconstruction

Abstract Background Plant cell walls are interwoven structures recalcitrant to degradation. Native and adapted microbiomes can be particularly effective at plant cell wall deconstruction. Although most understanding of biological cell wall deconstruction has been obtained from isolates, cultivated microbiomes that break down cell walls have emerged as new sources for biotechnologically relevant microbes and enzymes. These microbiomes provide a unique resource to identify key interacting functional microbial groups and to guide the design of specialized synthetic microbial communities. Results To establish a system assessing comparative microbiome performance, parallel microbiomes were cultivated on sorghum ( Sorghum bicolor L. Moench) from compost inocula. Biomass loss and biochemical assays indicated that these microbiomes diverged in their ability to deconstruct biomass. Network reconstructions from gene expression dynamics identified key groups and potential interactions within the adapted sorghum-degrading communities, including Actinotalea , Filomicrobium , and Gemmatimonadetes populations. Functional analysis demonstrated that the microbiomes proceeded through successive stages that are linked to enzymes that deconstruct plant cell wall polymers. The combination of network and functional analysis highlighted the importance of cellulose-degrading Actinobacteria in differentiating the performance of these microbiomes. Conclusions The two-tier cultivation of compost-derived microbiomes on sorghum led to the establishment of microbiomes for which community structure and performance could be assessed. The work reinforces the observation that subtle differences in community composition and the genomic content of strains may lead to significant differences in community performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network

The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1998

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Liang, Shoudan↗
DOE OSTI2023

Overexpression of REDUCED WALL ACETYLATION C increases xylan acetylation and biomass recalcitrance in Populus

Abstract Plant lignocellulosic biomass, i.e. secondary cell walls of plants, is a vital alternative source for bioenergy. However, the acetylation of xylan in secondary cell walls impedes the conversion of biomass to biofuels. Previous studies have shown that REDUCED WALL ACETYLATION (RWA) proteins are directly involved in the acetylation of xylan but the regulatory mechanism of RWAs is not fully understood. In this study, we demonstrate that overexpression of a Populus trichocarpa PtRWA-C gene increases the level of xylan acetylation and increases the lignin content and S/G ratio, ultimately yielding poplar woody biomass with reduced saccharification efficiency. Furthermore, through gene coexpression network and expression quantitative trait loci (eQTL) analysis, we found that PtRWA-C was regulated not only by the secondary cell wall hierarchical regulatory network but also by an AP2 family transcription factor HARDY (HRD). Specifically, HRD activates PtRWA-C expression by directly binding to the PtRWA-C promoter, which is also the cis-eQTL for PtRWA-C. Taken together, our findings provide insights into the functional roles of PtRWA-C in xylan acetylation and consequently saccharification and shed light on synthetic biology approaches to manipulate this gene and alter cell wall properties. These findings have substantial implications for genetic engineering of woody species, which could be used as a sustainable source of biofuels, valuable biochemicals, and biomaterials.

59 BASIC BIOLOGICAL SCIENCES↗