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Effect of progesterone on Candida albicans biofilm formation under acidic conditions: A transcriptomic analysis
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Clinicopathologic and Transcriptomic Analysis of Radiation-Induced Lung Injury in Nonhuman Primates
Radiation-induced lung injury (RILI) is a progressive condition with an early phase (radiation pneumonitis) and a late phase (lung fibrosis). RILI may occur after partial-body ionizing radiation exposures or internal radioisotope exposure, with wide individual variability in timing and extent of lung injury. This study aimed to provide new insights into the pathogenesis and progression of RILI in the nonhuman primate (NHP) rhesus macaque model.
Genome-wide association studies and transcriptome analysis reveal the genetic mechanisms contributing to thermotolerance in Neurospora discreta
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Transcriptome analysis and immune gene expression of channel catfish ( Ictalurus punctatus ) fed diets with inclusion of frass from black soldier fly larvae
The larval waste, exoskeleton shedding, and leftover feed components of the black soldier fly and its larvae make up the by-product known as frass. In this study, we subjected channel catfish (Ictalurus punctatus) to a 10-week feeding trial to assess how different dietary amounts of frass inclusion would affect both systemic and mucosal tissue gene expression, especially in regard to growth and immune-related genes. Fish were divided in quadruplicate aquaria, and five experimental diets comprising 0, 50, 100, 200, and 300 g of frass per kilogram of feed were fed twice daily. At the end of the trial, liver, head kidney, gill, and intestine samples were collected for gene expression analyses. First, liver and intestine samples from fish fed with a no frass inclusion diet (control), low-frass (50 g/kg) inclusion diet, or a high-frass (300 g/kg) inclusion diet were subjected to Illumina RNA sequencing to determine global differential gene expression among diet groups. Differentially expressed genes (DEGs) included the upregulation of growth-related genes such as glucose-6-phosphatase and myostatin, as well as innate immune receptors and effector molecules such as toll-like receptor 5, apolipoprotein A1, C-type lectin, and lysozyme. Based on the initial screenings of low/high frass using RNA sequencing, a more thorough evaluation of immune gene expression of all tissues sampled, and all levels of frass inclusion, was further conducted. Using targeted quantitative PCR panels for both innate and adaptive immune genes from channel catfish, differential expression of genes was identified, which included innate receptors (TLR1, TLR5, TLR9, and TLR20A), proinflammatory cytokines (IL-1β type a, IL-1β type b, IL-17, IFN-γ, and TNFα), chemokines (CFC3 and CFD), and hepcidin in both systemic (liver and head kidney) and mucosal (gill and intestine) tissues. Overall, frass from black soldier fly larvae inclusion in formulated diets was found to alter global gene expression and activate innate and adaptive immunity in channel catfish, which has the potential to support disease resistance in this species in addition to demonstrated growth benefits.
A Pilot Study of Whole-Blood Transcriptomic Analysis to Identify Genes Associated with Repetitive Low-Level Blast Exposure in Career Breachers
Repetitive low-level blast exposure is one of the major occupational health concerns among US military service members and law enforcement. This study seeks to identify gene expression using microRNA and RNA sequencing in whole-blood samples from experienced breachers and unexposed controls. We performed experimental RNA sequencing using Illumina’s HiSeq 2500 Sequencing System, and microRNA analysis using NanoString Technology nCounter miRNA expression panel in whole-blood total RNA samples from 15 experienced breachers and 14 age-, sex-, and race-matched unexposed controls. We identified 10 significantly dysregulated genes between experienced breachers and unexposed controls, with FDR corrected <0.05: One upregulated gene, LINC00996 (long intergenic non-protein coding RNA 996); and nine downregulated genes, IGLV3-16 (immunoglobulin lambda variable 3-16), CD200 (CD200 molecule), LILRB5 (leukocyte immunoglobulin-like receptor B5), ZNF667-AS1 (ZNF667 antisense RNA 1), LMOD1 (leiomodin 1), CNTNAP2 (contactin-associated protein 2), EVPL (envoplakin), DPF3 (double PHD fingers 3), and IGHV4-34 (immunoglobulin heavy variable 4-34). The dysregulated gene expressions reported here have been associated with chronic inflammation and immune response, suggesting that these pathways may relate to the risk of lasting neurological symptoms following high exposures to blast over a career.
Single-Cell Transcriptomic Analysis of Tumor-Derived Fibroblasts and Normal Tissue-Resident Fibroblasts Reveals Fibroblast Heterogeneity in Breast Cancer
Cancer-associated fibroblasts (CAFs) are a prominent stromal cell type in solid tumors and molecules secreted by CAFs play an important role in tumor progression and metastasis. CAFs coexist as heterogeneous populations with potentially different biological functions. Although CAFs are a major component of the breast cancer stroma, molecular and phenotypic heterogeneity of CAFs in breast cancer is poorly understood. In this study, we investigated CAF heterogeneity in triple-negative breast cancer (TNBC) using a syngeneic mouse model, BALB/c-derived 4T1 mammary tumors. Using single-cell RNA sequencing (scRNA-seq), we identified six CAF subpopulations in 4T1 tumors including: 1) myofibroblastic CAFs, enriched for α-smooth muscle actin and several other contractile proteins; 2) ‘inflammatory’ CAFs with elevated expression of inflammatory cytokines; and 3) a CAF subpopulation expressing major histocompatibility complex (MHC) class II proteins that are generally expressed in antigen-presenting cells. Comparison of 4T1-derived CAFs to CAFs from pancreatic cancer revealed that these three CAF subpopulations exist in both tumor types. Interestingly, cells with inflammatory and MHC class II-expressing CAF profiles were also detected in normal breast/pancreas tissue, suggesting that these phenotypes are not tumor microenvironment-induced. This work enhances our understanding of CAF heterogeneity, and specifically targeting these CAF subpopulations could be an effective therapeutic approach for treating highly aggressive TNBCs.
Genomic and transcriptomic analysis of Saccharomyces eubayanus isolates
WGS and RNA-seq of Saccharomyces eubayanus strain yHRVM108 and derivatives
Transcriptomic Analysis of Mouse Retina Exposed to Low-Dose Irradiation and Modeled Microgravity
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Transcriptomic analysis links hepatocellular carcinoma (HCC) in HZE ion irradiated mice to a human HCC subtype with favorable outcomes
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Transcriptomic Network Analysis of Cyanobacterial-Methylotroph Interactions in Coculture and Axenic Conditions
A previous study demonstrated the potential for Cyanobacterial-Methylotroph cocultures to facilitate biogas processing as well as to be used in other biotechnological applications. To advance this technology, we investigated potential interactions between Cyanobacterium stanieri HL-69 (HL69) and Methylotuvimicrobium alkaliphilum 20Z (20Z) by inferring and analyzing gene co-expression networks under co-culture and axenic conditions. Five different co-expression networks were examined. These networks were inferred using gene expression profiles for 20Z axenic condition, HL-69 axenic, HL-69, 20Z coculture, HL-69 coculture, and cross-species HL-69-20Z coculture. Through the analysis of node (gene) betweenness and node normalized degree values in all five network cases, we compared adjustments in gene expression between growth conditions (axenic vs co-culture) as well as identify biological functions relevant to interspecies interactions. This analysis was done to distinguish between gene interactions within an organism and gene interactions between two organisms. Moreover, for all five cases we investigated two different network cutoff levels of 3,000 and 10,000. By shedding light on inter- and intra- species interactions, we hope to gain a better understanding of how these two organisms interact. This research will allow the investigation of further biotechnological applications of coculture systems and optimization of such applications for biotechnological purposes.
Optimal dimensionality selection for independent component analysis of transcriptomic data
Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. We computed independent components across a range of dimensionalities for four gene expression datasets with varying dimensions (both in terms of number of genes and number of samples). We computed the correlation between independent components across different dimensionalities to understand how the overall structure evolves as the number of user-defined components increases. We then measured how well the resulting gene clusters reflected known regulatory mechanisms, and developed a set of metrics to assess the accuracy of the decomposition at a given dimension. We found that over-decomposition results in many independent components dominated by a single gene, whereas under-decomposition results in independent components that poorly capture the known regulatory structure. From these results, we developed a new method, called OptICA, for finding the optimal dimensionality that controls for both over- and under-decomposition. Specifically, OptICA selects the highest dimension that produces a low number of components that are dominated by a single gene. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organism’s underlying transcriptional regulatory network.
Mechanisms of metabolic adaptation in the duckweed Lemna gibba: an integrated metabolic, transcriptomic and flux analysis
Abstract Background Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba cultures to different nutritional conditions. Results To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13 C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13 C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts. Conclusions Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.
A multi-omic characterization of the physiological responses to salt stress in Scenedesmus obliquus UTEX393
Scenedesmus obliquus UTEX393 is a promising microalgal candidate for sustainable biomanufacturing but its limited halotolerance hinders large-scale cultivation in saline environments. To investigate the molecular basis of salt stress responses, we conducted a comprehensive multi-omic analysis integrating genomics, transcriptomics, proteomics, lipidomics, metabolomics, and DNA affinity purification sequencing (DAP-seq). An improved nuclear genome assembly and annotation yielded 19,017 gene models and a 97% BUSCO completeness score, enabling construction of a genome-scale metabolic model. Comparing 15 ppt salinity stress to 5 ppt control, growth and productivity were significantly reduced, accompanied by widespread transcriptomic and proteomic changes. Transcriptomic analysis revealed downregulation of photosynthetic machinery and energy conservation genes, and upregulation of stress-responsive elements such as expansins, flavodoxins, and osmoprotectants. Lipidomic profiling showed accumulation of triacylglycerols (TAGs) and degradation of galactosyl lipids, consistent with a shift toward lipid biosynthesis to mitigate redox imbalance. Depletion of key polar metabolites and branched-chain amino acids suggested a rerouting of central carbon metabolism under stress. DAP-seq identified key transcription factors, including LHY1 and SPL12, that target central metabolic enzymes involved in redox balancing, such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and malate dehydrogenase (MDH). These findings establish a regulatory-metabolic framework linking redox stress to lipid accumulation and reveal potential engineering targets to enhance salt tolerance. Overall, the multi-omic analysis supports the “overflow” hypothesis, where impaired photosynthesis results in excess reducing equivalents being diverted into TAG synthesis and highlights transcriptional regulators as candidates for improving algal robustness in brackish environments.
Data for The Stem Cell-Type Transcriptome of Bioenergy Sorghum Reveals the Spatial Regulation of Secondary Cell Wall Networks
Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum’s 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type specific gene regulatory networks (GRNs) revealed that unique TF families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with a stem developmental transcriptome dataset to identify the GRN that differentially activates the secondary cell wall (SCW) formation in stem xylem sclerenchyma and epidermal cells. The cell-type transcriptomic dataset provides a valuable source of information about the function of sorghum stem cell types and GRNs that will enable the engineering of bioenergy sorghum stems.
Bioenergy sorghum stem growth regulation: intercalary meristem localization, development, and gene regulatory network analysis
SUMMARY Bioenergy sorghum is a highly productive drought tolerant C 4 grass that accumulates 80% of its harvestable biomass in approximately 4 m length stems. Stem internode growth is regulated by development, shading, and hormones that modulate cell proliferation in intercalary meristems (IMs). In this study, sorghum stem IMs were localized above the pulvinus at the base of elongating internodes using magnetic resonance imaging, microscopy, and transcriptome analysis. A change in cell morphology/organization occurred at the junction between the pulvinus and internode where LATERAL ORGAN BOUNDARIES ( SbLOB ), a boundary layer gene, was expressed. Inactivation of an AGCVIII kinase in DDYM ( dw2 ) resulted in decreased SbLOB expression, disrupted IM localization, and reduced internode cell proliferation. Transcriptome analysis identified approximately 1000 genes involved in cell proliferation, hormone signaling, and other functions selectively upregulated in the IM compared with a non‐meristematic stem tissue. This cohort of genes is expressed in apical dome stem tissues before localization of the IM at the base of elongating internodes. Gene regulatory network analysis identified connections between genes involved in hormone signaling and cell proliferation. The results indicate that gibberellic acid induces accumulation of growth regulatory factors (GRFs) known to interact with ANGUSTIFOLIA (SbAN3), a master regulator of cell proliferation. GRF:AN3 was predicted to induce SbARF3/ETT expression and regulate SbAN3 expression in an auxin‐dependent manner. GRFs and ARFs regulate genes involved in cytokinin and brassinosteroid signaling and cell proliferation. The results provide a molecular framework for understanding how hormone signaling regulates the expression of genes involved in cell proliferation in the stem IM.
Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics
Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.
Peeling back the many layers of competitive exclusion
Baby chicks administered a fecal transplant from adult chickens are resistant to Salmonella colonization by competitive exclusion. A two-pronged approach was used to investigate the mechanism of this process. First, Salmonella response to an exclusive (Salmonella competitive exclusion product, Aviguard ® ) or permissive microbial community (chicken cecal contents from colonized birds containing 7.85 Log 10 Salmonella genomes/gram) was assessed ex vivo using a S. typhimurium reporter strain with fluorescent YFP and CFP gene fusions to rrn and hilA operon, respectively. Second, cecal transcriptome analysis was used to assess the cecal communities’ response to Salmonella in chickens with low (≤5.85 Log 10 genomes/g) or high (≥6.00 Log 10 genomes/g) Salmonella colonization. The ex vivo experiment revealed a reduction in Salmonella growth and hilA expression following co-culture with the exclusive community. The exclusive community also repressed Salmonella’s SPI-1 virulence genes and LPS modification, while the anti-virulence/inflammatory gene avrA was upregulated. Salmonella transcriptome analysis revealed significant metabolic disparities in Salmonella grown with the two different communities. Propanediol utilization and vitamin B12 synthesis were central to Salmonella metabolism co-cultured with either community, and mutations in propanediol and vitamin B12 metabolism altered Salmonella growth in the exclusive community. There were significant differences in the cecal community’s stress response to Salmonella colonization. Cecal community transcripts indicated that antimicrobials were central to the type of stress response detected in the low Salmonella abundance community, suggesting antagonism involved in Salmonella exclusion. This study indicates complex community interactions that modulate Salmonella metabolism and pathogenic behavior and reduce growth through antagonism may be key to exclusion.