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PubMed2004

Selective acquisition of individual cell types in the vestibular periphery for molecular biology studies.

OBJECTIVES: To develop a method for characterizing the transcriptome of individual cell types in the inner ear sensory epithelia. STUDY DESIGN: We employed the technique of laser capture microdissection to obtain enriched populations of hair cells and supporting cells. The respective mRNAs were extracted, reverse transcribed, and amplified using PCR. RESULTS: We were able to isolate RNAs with good integrity from enriched cell populations obtained with laser capture microscopy and amplify specific mRNA targets. CONCLUSIONS: We can now investigate the molecular differences between the different cell types in the inner ear sensory epithelia as identified by morphological criteria. SIGNIFICANCE: Analysis of gene expression profiles in the inner ear cell types has been hampered by the small size of this tissue and by the compact histoarchitecture of the sensory epithelia; however, the present technique offers new possibilities for the analysis of transcriptomes in the vestibular periphery using available high-throughput gene expression analysis methods.

Animals↗
PubMed2004

Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana.

Plant metabolism is a complex set of processes that produce a wide diversity of foods, woods, and medicines. With the genome sequences of Arabidopsis and rice in hands, postgenomics studies integrating all "omics" sciences can depict precise pictures of a whole-cellular process. Here, we present, to our knowledge, the first report of investigation for gene-to-metabolite networks regulating sulfur and nitrogen nutrition and secondary metabolism in Arabidopsis, with integration of metabolomics and transcriptomics. Transcriptome and metabolome analyses were carried out, respectively, with DNA macroarray and several chemical analytical methods, including ultra high-resolution Fourier transform-ion cyclotron MS. Mathematical analyses, including principal component analysis and batch-learning self-organizing map analysis of transcriptome and metabolome data suggested the presence of general responses to sulfur and nitrogen deficiencies. In addition, specific responses to either sulfur or nitrogen deficiency were observed in several metabolic pathways: in particular, the genes and metabolites involved in glucosinolate metabolism were shown to be coordinately modulated. Understanding such gene-to-metabolite networks in primary and secondary metabolism through integration of transcriptomics and metabolomics can lead to identification of gene function and subsequent improvement of production of useful compounds in plants.

Arabidopsis↗
PubMed2005

Avoiding inconsistencies over time and tracking difficulties in Applied Biosystems AB1700/Panther probe-to-gene annotations.

BACKGROUND: Significant inconsistencies between probe-to-gene annotations between different releases of probe set identifiers by commercial microarray platform solutions have been reported. Such inconsistencies lead to misleading or ambiguous interpretation of published gene expression results. RESULTS: We report here similar inconsistencies in the probe-to-gene annotation of Applied Biosystems AB1700 data, demonstrating that this is not an isolated concern. Moreover, the online information source PANTHER does not provide information required to track such inconsistencies, hence, even correctly annotated datasets, when resubmitted after PANTHER was updated to a new probe-to-gene annotation release, will generate differing results without any feedback on the origin of the change. CONCLUSION: The importance of unequivocal annotation of microarray experiments can not be underestimated. Inconsistencies greatly diminish the usefulness of the technology. Novel methods in the analysis of transcriptome profiles often rely on large disparate datasets stemming from multiple sources. The predictive and analytic power of such approaches rapidly diminishes if only least-common subsets can be used for analysis. We present here the information that needs to be provided together with the raw AB1700 data, and the information required together with the biologic interpretation of such data to avoid inconsistencies and tracking difficulties.

Algorithms↗
PubMed2005

Selection-driven transcriptome polymorphism in Escherichia coli/Shigella species.

To explore the role of transcriptome polymorphism in adaptation of organisms to their environment, we evaluated this parameter for the Escherichia coli/Shigella bacterial species, which is composed of well-characterized phylogenetic groups that exhibit characteristic life styles ranging from commensalism to intracellular pathogenicity. Both the genomic content and the transcriptome of 10 strains representative of the major E. coli/Shigella phylogenetic groups were evaluated using macroarrays displaying the 4290 K12-MG1655 open reading frames (ORFs). Although Shigella and enteroinvasive E. coli (EIEC) are not monophyletic, phylogenetic analysis of the binary coded (presence/absence) gene content data showed that these organisms group together due to similar patterns of undetectable K12-MG1655 genes. The variation in transcript abundance was then analyzed using a core genome of 2880 genes present in all strains, after adjusting RNA hybridization signals for DNA hybridization signals. Nonrandom changes in gene expression during the evolution of the E. coli/Shigella species were evidenced. Phylogenetic analysis of transcriptome data again showed that Shigella and EIEC strains group together in terms of gene expression, and this convergence involved groups of genes displaying biologically coherent patterns of functional divergence. Unlike the other E. coli strains evaluated, Shigella and EIEC are intracellular pathogens, and therefore face similar selective pressures. Thus, within the E. coli/Shigella species, strains exhibiting a particular life style have converged toward a specific gene expression pattern in a subset of genes common to the species, revealing the role of selection in shaping transcriptome polymorphism.

Caco-2 Cells↗
PubMed2003

Transcriptional analysis of product-concentration driven changes in cellular programs of recombinant Clostridium acetobutylicumstrains.

Antisense RNA (asRNA) downregulation alters protein expression without changing the regulation of gene expression. Downregulation of primary metabolic enzymes possibly combined with overexpression of other metabolic enzymes may result in profound changes in product formation, and this may alter the large-scale transcriptional program of the cells. DNA-array based large-scale transcriptional analysis has the potential to elucidate factors that control cellular fluxes even in the absence of proteome data. These themes are explored in the study of large-scale transcriptional analysis programs and the in vivo primary-metabolism fluxes of several related recombinant C. acetobutylicum strains: C. acetobutylicum ATCC 824(pSOS95del) (plasmid control; produces high levels of butanol snd acetone), 824(pCTFB1AS) (expresses antisense RNA against CoA transferase (ctfb1-asRNA); produces very low levels of butanol and acetone), and 824(pAADB1) (expresses ctfb1-asRNA and the alcohol-aldehyde dahydrogenase gene (aad); produce high alcohol and low acetone levels). DNA-array based transcriptional analysis revealed that the large changes in product concentrations (snd notably butanol concentration) due to ctfb1-asRNA expression alone and in combination with aad overexpression resulted in dramatic changes of the cellular transcriptome. Cluster analysis and gene expression patterns of established and putative operons involved in stress response, motility, sporulation, and fatty-acid biosynthesis indicate that these simple genetic changes dramatically alter the cellular programs of C. acetobutylicum. Comparison of gene expression and flux analysis data may point to possible flux-controling steps and suggest unknown regulatory mechanisms.

Adaptation, Physiological↗
PubMed2024

Genetic association between epilepsy and gliomas: Insights from Mendelian randomization and single-cell transcriptomic analyses.

BACKGROUND: Seizures are prevalent in glioma patients, especially in those with low-grade gliomas. The interaction between gliomas and epilepsy involves complex biological mechanisms that are not fully understood. METHODS: We collected Genome-Wide Association Study data for epilepsy and gliomas, performed differential expression analysis, and conducted Gene Ontology (GO) enrichment analysis on the identified genes. Single-cell RNA sequencing data (scRNA-seq) from GSE221534 dataset in Gene Expression Omnibus (GEO) were used to analyze cell-cell interactions within glioma samples from patients with and without epilepsy. RESULTS: Mendelian Randomization (MR) analysis revealed significant associations between genetic variants related to epilepsy and glioma risk, suggesting a potential causal relationship, especially in astrocytomas. Differential expression analysis identified epilepsy-related genes that were significantly upregulated in astrocytoma tissues compared to normal brain tissues. GO enrichment analysis indicated that these genes are involved in critical biological processes such as neurogenesis and cellular signaling. The scRNA-seq analysis showed, compared to non-epileptic samples, glioma stem cells, microglia, and NK cells are increased in the core regions of astrocytomas in epileptic patients. Additionally, intercellular communication between tumor cells and other non-tumor cells is markedly enhanced in astrocytoma samples from epileptic patients. CONCLUSION: This study provides evidence of a genetic association between epilepsy and gliomas and elucidates the biological mechanisms through which epilepsy may influence glioma progression.

Humans↗
PubMed2025

Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.

BACKGROUND: Cell clustering is an essential step in uncovering cellular architectures in single-cell RNA sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. RESULTS: Here, we develop a multiscale clustering (MSC) approach to construct a sparse cell-cell correlation network for unsupervised identification of de novo cell types and subtypes across multiple resolutions. Based upon simulated silver- and gold-standard data as well as real scRNA-seq data in diseases, MSC demonstrates significantly improved performance compared to established benchmark methods and reveals a biologically meaningful cell hierarchy to facilitate the discovery of novel disease-associated cell subtypes and mechanisms. CONCLUSIONS: We present MSC as a new single-cell multiscale clustering framework as a powerful tool for advancing discoveries in disease-associated cell populations using single-cell sequencing data.

Single-Cell Analysis↗
PubMed2005

Analysis of the female gametophyte transcriptome of Arabidopsis by comparative expression profiling.

The extensive data on the transcription of the plant genome are derived primarily from the sporophytic generation. There currently is little information on genes that are expressed during female gametophyte development in angiosperms, and it is not known whether the female gametophyte transcriptome contains a major set of genes that are not expressed in the sporophyte or whether it is primarily a subset of the sporophytic transcriptome. Because the embryo sac is embedded within the maternal ovule tissue, we have utilized the Arabidopsis (Arabidopsis thaliana) mutant sporocyteless that produces ovules without embryo sacs, together with the ATH1 Arabidopsis whole-genome oligonucleotide array, to identify genes that are preferentially or specifically expressed in female gametophyte development. From analysis of the datasets, 225 genes are identified as female gametophyte genes, likely a lower limit as stringent criteria were used for the analysis, eliminating many low expressed genes. Nearly 45% of the identified genes were not previously detected by sporophytic expression profiling, suggesting that the embryo sac transcriptome may contain a significant fraction of transcripts restricted to the gametophyte. Validation of six candidate genes was performed using promoterbeta-glucuronidase fusions, and all of these showed embryo sac-specific expression in the ovule. The unfiltered expression data from this study can be used to evaluate the possibility of female gametophytic expression for any gene in the ATH1 array, and contribute to identification of the functions of the component of the Arabidopsis genome not represented in studies of sporophytic expression and function.

Arabidopsis↗
PubMed2004

Transcriptome of mouse uterus by serial analysis of gene expression (SAGE): comparison with skeletal muscle.

The aim of this study was to identify the transcriptome of the normal mouse uterus by Serial Analysis of Gene Expression method. mRNA was extracted from the uterus and also from the gastrocnemius muscle of mice. Short sequences (tags), each one usually corresponding to a distinct transcript, were isolated and concatemerized into long DNA molecules which were cloned and sequenced. We detected 44,484 tags for the uterus and 42,518 tags for the muscle, representing 14,543 and 14,958 potential transcript species, respectively. Seventy-five and sixty-nine genes were expressed at more than 0.1%, thus corresponding to 37 and 34% of the mRNA population detected in the respective tissues. In both cases, the most highly expressed genes are especially involved in muscle contraction, energy metabolism, and protein synthesis. Compared to skeletal muscle, some differentially expressed genes in the uterus are likely to correspond to its specific reproductive functions. The majority of these genes remain to be characterized. More than 70% of the different tags detected in the uterus did not match any sequence in the public databases and can represent novel or poorly identified genes. This study is the first quantitative description of the transcriptome of the uterus.

Animals↗
PubMed2026

RNF43 Mutations Are Associated With the Classical Molecular Subtype, Vigorous Antitumor Immune Responses, and Prolonged Survival in Pancreatic Adenocarcinoma.

RNF43 mutations were correlated with microsatellite status in colorectal cancer and with fewer and later recurrences in pancreatic ductal adenocarcinoma (PDAC). Here, we undertake a detailed assessment of RNF43 mutations in PDAC. A total of 313 PDACs (308 microsatellite stable [MSS] and 5 microsatellite-instable [MSI] cases) underwent next-generation sequencing (Oncomine Tumor Mutation Load assay; Thermo Fisher). Spatial analyses (NanoString) classified PDACs according to their transcriptomic and proteomic immune signaling. Fluorescent imaging was used to define spatial compartments (tumor: pancytokeratin+/CD45- and leukocytes: pancytokeratin-/CD45+). Each of 20 PDACs with RNF43 mutations (RNF43mut) and without RNF43 mutations (RNF43wt) underwent multiplex immunofluorescence analysis to determine immune status. A total of 153 PDACs (22 RNF43mut and 131 RNF43wt cases) underwent bulk RNA sequencing to assign into molecular subtypes. Overall, 24 RNF43 mutations were identified (22 MSS PDACs and 2 MSI PDACs). The incidence of RNF43 mutations in MSS PDACs (7.1%) was consistent with The Cancer Genome Atlas (6.7%). However, RNF43 mutations were more frequent among MSI PDACs (40%). Additionally, RNF43mut had differential frequencies of other mutations (including Wnt pathway genes), higher tumor mutational burden values (5.5 mut/mb vs 1.67 mut/mb; P < .01), and significantly longer overall survival (47 vs 18 months; P < .0001) than RNF43wt. Moreover, RNF43mut exhibited significantly higher densities of CD8+ T lymphocytes, dendritic cells, and B lymphocytes (P < .001) and an upregulation of ITGAX, CD11c, CD8, and HLA-DR compared with RNF43wt. Patients with RNF43mut PDACs were more often of the classical molecular subtype (20/22, 90.9%). RNF43mut PDACs showed high tumor mutational burden values, suggesting increased neoantigen load coupled with an abundance of antigen-presenting immune cells and an upregulation of immune determinants promoting antigen presentation. All this contributes to stronger antitumor immune responses and improved clinical outcomes.

Humans↗
PubMed2006

Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks.

We introduce a mathematical framework that allows to test the compatibility between differential data and knowledge on genetic and metabolic interactions. Within this framework, a behavioral model is represented by a labeled oriented interaction graph; its predictions can be compared to experimental data. The comparison is qualitative and relies on a system of linear qualitative equations derived from the interaction graph. We show how to partially solve the qualitative system, how to identify incompatibilities between the model and the data, and how to detect competitions in the biological processes that are modeled. This approach can be used for the analysis of transcriptomic, metabolic or proteomic data.

Fatty Acids↗
PubMed2026

Predicting cellular responses to perturbation across diverse contexts with State.

While machine learning models offer potential for predicting transcriptomic effects of perturbation, they currently struggle to generalize across cellular contexts. Here, we introduce State, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. State is trained using single-cell gene expression data to predict perturbation effects across sets of cells. State improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling, and chemical perturbations with significantly improved accuracy compared with baselines. Its cell embeddings trained on observational data from 167 million cells enable the identification of strong perturbations in cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that can be used to evaluate future models. Overall, the performance and flexibility of State set the stage for scaling the development of AI models of cell state.

Machine Learning↗
PubMedDate not supplied

DNA microarrays: experimental issues, data analysis, and application to bacterial systems.

DNA microarrays are currently used to study the transcriptional response of many organisms to genetic and environmental perturbations. Although there is much room for improvement of this technology, its potential has been clearly demonstrated in the past 5 years. The general consensus is that the bottleneck is now located in the processing and analysis of transcriptome data and its use for purposes other than the quantification of changes in gene expression levels. In this article we discuss technological aspects of DNA microarrays, statistical and biological issues pertinent to the design of microarray experiments, and statistical tools for microarray data analysis. A review on applications of DNA microarrays in the study of bacterial systems is presented. Special attention is given to studies in the following areas: (1) bacterial response to environmental changes; (2) gene identification, genome organization, and transcriptional regulation; and (3) genetic and metabolic engineering. Soon, the use of DNA microarray technologies in conjunction with other genome/system-wide analyses (e.g., proteomics, metabolomics, fluxomics, phenomics, etc.) will provide a better assessment of genotype-phenotype relationships in bacteria, which serve as a basis for understanding similar processes in more complex organisms.

Algorithms↗
PubMed2026

Genome-wide identification and cold-stress-responsive expression analysis of the NOX gene family in Cucumis melo.

NADPH oxidases (NOXs) are crucial enzymes for reactive oxygen species (ROS) generation in plants and play vital roles in growth, development, and stress responses. To elucidate the sequence characteristics of the NOX gene family and its low-temperature response patterns in melon (Cucumis melo L.), this study conducted genome-wide identification and expression profiling of NOX family members using bioinformatics analysis, RNA-seq transcriptome sequencing, and real-time quantitative PCR (RT-qPCR). The results revealed that eight NOX members were identified in the melon genome, distributed across six chromosomes. All members harbored conserved domains including Ferric_reductase, FAD_binding_8, NAD_binding_6, and NADPH_Ox, and the encoded proteins were generally basic and hydrophilic. Phylogenetic analysis classified the NOX proteins into five subgroups. Synteny analysis indicated the presence of only one pair of intraspecific duplicated genes in melon, which was under purifying selection. The promoter regions contained multiple hormone- and stress-responsive cis-acting elements, with CmNOX2 and CmNOX4 harboring low-temperature responsive elements. Following treatment at 4&#x2103; for 24 h and 48 h, leaf relative electrolyte leakage (REL) increased from 28.33% to 42.67% and 52.67%, respectively; transcriptome analysis identified 5,633 and 6,882 differentially expressed genes (DEGs), respectively. Cold-responsive genes exhibited significant differential expression, with SLAC1 and CPK19 showing sustained upregulation. RT-qPCR results demonstrated that the expression of CmNOX2, CmNOX5, CmNOX6, and CmNOX7 was significantly downregulated after low-temperature treatment, whereas CmNOX4 expression was significantly upregulated at 48 h. Integrating promoter elements and expression characteristics, CmNOX4 may represent an important candidate gene involved in melon low-temperature response. This study systematically characterized the structure, evolution, and expression patterns of the melon NOX gene family, identified candidate genes responsive to low temperature, and provides a reference for further investigation into the mechanisms underlying melon cold adaptation.

Cucumis melo↗
PubMed2005

Modulation of multigene expression and proteome profiles by chemopreventive agents.

Analysis of transcriptome and proteome profiles by microarray technologies provides a formidable, new tool in cancer chemoprevention research. An ideal chemopreventive agent should not excessively alter per se the basal make-up of multigene expression and protein synthesis and should at the same time be able to attenuate alterations induced by risk factors. In order to validate this working hypothesis, we previously performed a series of studies in animal models using the thiol N-acetyl-l-cysteine (NAC) and the nonsteroidal antiinflammatory drug sulindac. We report herein the results of new studies evaluating modulation of DNA adduct levels and expression of 4858 genes in lung and liver of Sprague-Dawley rats, either unexposed or exposed to environmental cigarette smoke (ECS). The tested chemopreventive agents included NAC, oltipraz (OPZ), 5,6-benzoflavone (5,6-BF), phenethyl isothiocyanate (PEITC), and indole 3-carbinol (I3C). Combinations of OPZ with NAC and of PEITC with I3C were also assayed. Excepting OPZ, all treatments inhibited by at least 50% the formation of bulky DNA adducts in the lung of ECS-exposed rats. Hierarchical cluster analysis and principal component analysis allowed us to classify the agents according to their influence on basal gene expression and their ability to attenuate ECS-induced transcriptome alterations. PEITC and I3C were the most effective but the least safe agents. 5,6-BF displayed intermediate patterns. OPZ was poorly effective in lung and considerably altered the basal gene expression in liver. NAC had a medium efficacy and was the safest agent, as also supported by the analysis of 518 proteins in rat lung.

Acetylcysteine↗
PubMed2025

Comparative analysis of DREB gene family in buckwheat: the role of FtDREB02 in the delphinidin biosynthesis and drought stress response.

Dehydration response element binding (DREB) transcription factors play a pivotal role in plant abiotic stress responses, but its evolutionary and functional characterization in buckwheat remains unexplored. Here, we conducted a comprehensive analysis of the DREB gene family across three buckwheat species, revealing segmental duplication as the primary driver of family expansion and potential purifying selection during evolution. A FtDREB02 gene, classified as group A2, was identified through genome-wide association analysis (GWAS) on drought tolerance and delphinidin content. Functional validation in Arabidopsis thaliana and the hairy root of Tartary buckwheat (Fagopyrum tataricum) demonstrated that overexpression of this gene promotes delphinidin biosynthesis and enhances plant resistance to water scarcity. Through the integration of DAP-seq and PEG transcriptome cluster analysis, a FtANS candidate was screened. Functional studies showed that FtDREB02 regulates delphinidin content by binding directly to DRE elements of the FtANS promoter. This research identifies and comprehensively analyzes the DREB family within buckwheat species, elucidating the regulatory mechanisms of FtDREB02 in controlling flavonoid biosynthesis and drought resistance, providing potential genetic resources for breeding buckwheat varieties with excellent agronomic traits.

Anthocyanins↗
PubMed2005

New data analysis and mining approaches identify unique proteome and transcriptome markers of susceptibility to autoimmune diabetes.

Non-obese diabetic (NOD) mice spontaneously develop autoimmunity to the insulin producing beta cells leading to insulin-dependent diabetes. In this study we developed and used new data analysis and mining approaches on combined proteome and transcriptome (molecular phenotype) data to define pathways affected by abnormalities in peripheral leukocytes of young NOD female mice. Cells were collected before mice show signs of autoimmunity (age, 2-4 weeks). We extracted both protein and RNA from NOD and C57BL/6 control mice to conduct both proteome analysis by two-dimensional gel electrophoresis and transcriptome analysis on Affymetrix expression arrays. We developed a new approach to analyze the two-dimensional gel proteome data that included two-way analysis of variance, cluster analysis, and principal component analysis. Lists of differentially expressed proteins and transcripts were subjected to pathway analysis using a commercial service. From the list of 24 proteins differentially expressed between strains we identified two highly significant and interconnected networks centered around oncogenes (Myc and Mycn) and apoptosis-related genes (Bcl2 and Casp3). The 273 genes with significant strain differences in RNA expression levels created six interconnected networks with a significant over-representation of genes related to cancer, cell cycle, and cell death. They contained many of the same genes found in the proteome networks (including Myc and Mycn). The combination of the eight, highly significant networks created one large network of 272 genes of which 82 had differential expression between strains either at the protein or the RNA level. We conclude that new proteome data analysis strategies and combined information from proteome and transcriptome can enhance the insights gained from either type of data alone. The overall systems biology of prediabetic NOD mice points toward abnormalities in regulation of the opposing processes of cell renewal and cell death even before there are any clear signatures of immune system activation.

Analysis of Variance↗
PubMed2006

In vivo dissection of the Helicobacter pylori Fur regulatory circuit by genome-wide location analysis.

Iron homeostasis is particularly important in pathogenic bacteria, which need to compete with the host for this essential cofactor. In Helicobacter pylori, a causative agent of several gastric pathologies, iron uptake and storage genes are regulated at the transcriptional level by the ferric uptake regulator Fur. The regulatory circuit of Fur has recently come under focus because of an intimate interlink with a broader regulatory network governing metal homeostasis, acidic response, and virulence. To dissect the Fur regulatory circuit and identify in vivo targets of regulation, we developed a genome-wide location analysis protocol which allowed the identification of 200 genomic loci bound by Fur as well as the investigation of the binding efficiency of the protein to these loci in response to iron. Comparative analysis with transcriptomes of wild-type and fur deletion mutant strains allowed the distinction between targets associated with Fur regulation and genes indirectly influenced by the fur mutation. The Fur regulon includes 59 genes, 25 of which appear to be positively regulated. A case study conducted by primer extension analysis of two oppositely regulated genes, hpn2 and flaB, suggests that negative regulation as well as positive regulation occurs at the transcriptional level. Furthermore, the results revealed the existence of 13 Fur targeted loci within polycistronic operons, which were associated with transcript deregulation in the fur mutant strain. This study provides a systematic insight of Fur regulation at the genome-wide level in H. pylori and points to regulatory functions extending beyond the classical Fur repression paradigm.

Bacterial Proteins↗