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At least 451 records · Page 25Linked to original sources

PubMed2004

NASCArrays: a repository for microarray data generated by NASC's transcriptomics service.

NASC operates an Affymetrix 'GeneChip' (microarray) service for the Arabidopsis thaliana community. All data produced by the service are publicly available through our microarray data base 'NASCArrays' published at http://affymetrix. arabidopsis.info. The data are accessible through text searching and a series of data mining tools. All data are annotated with sample preparation details, and the original Affymetrix data are available for download. The database aims to be MIAME supportive and provide a coordinated resource for re searchers interested in the transcriptome of Arabidopsis. Using this database, data produced will be shared with other databases worldwide.

Arabidopsis↗

PubMed2003

Small amplified RNA-SAGE: an alternative approach to study transcriptome from limiting amount of mRNA.

Serial analysis of gene expression (SAGE) is a widely used and powerful technique to characterize and compare transcriptomes. Although several modifications have been proposed to the initial protocol with the aim of reducing the amount of starting material, unless additional PCR steps are added, the technique is still limited by the need for at least 1 micro g of total RNA. As extra PCR amplification might introduce representation biases, current SAGE protocols are not fully suitable for the study of small, microdissected tissue samples. We propose here an alternative method involving the linear amplification of small mRNA fragments containing the SAGE tags. The procedure allows preparation of libraries of over 100 000 tags from as few as 2500 cells. A satisfactory correlation was observed between a microSAGE library made from 5 microg of total thyroid RNA, and a library prepared from 50 ng of the same RNA preparation according to the present protocol.

Animals↗

PubMed2026

Genomic and Transcriptomic Correlates of Deep PSA Response in Patients with Metastatic Androgen Pathway Modulation-Sensitive Prostate Cancer.

BACKGROUND: Despite advances in metastatic androgen pathway modulation-sensitive prostate cancer (mAPMS) treatment, outcomes remain heterogeneous. Achieving a post-treatment undetectable prostate specific antigen (PSA) is a strong prognostic marker. We aimed to identify genomic and transcriptomic determinants of PSA response in a real-world clinical-genomic cohort. PATIENTS AND METHODS: Patients with mAPMS who underwent DNA (Tempus xT) and, in a subset, RNA (Tempus xR) sequencing were identified from the Tempus Lens database. Inclusion required stage IV disease within 90 days of sample collection and samples obtained within 12 months before or 3 months after treatment initiation. Patients with PSA at 6 months (n = 525) were classified as PSA-low (<0.1 ng/mL, n = 240) or PSA-high (≥0.1 ng/mL, n = 285). Overall survival (OS) was assessed by 6-month landmark analysis with delayed-entry adjustment. Logistic and Cox models were adjusted for clinical variables. Sensitivity analyses used a relative definition of > 95% PSA decline from baseline. RESULTS: Baseline PSA was lower in PSA-low versus PSA-high patients (24 vs 36 ng/mL, p = 0.01). SPOP (17% vs 11%) and ZFHX3 (2.5% vs 6%) alterations differed between groups, but neither persisted after adjustment. Using the relative definition, ZMYM3 and JAK1 alterations were independently associated with failure to achieve a deep PSA response. Expression of PSMA, TROP2, B7-H3, and STEAP1 did not differ between groups. PSA-low status was independently associated with improved OS, as was deep relative response. CONCLUSION: Deep PSA response at 6 months correlates with improved OS in mAPMS. Integrating molecular markers with PSA response may inform treatment intensification or de-escalation strategies.

Biomarkers↗

PubMed2003

Transcriptome profiling and the pathogenesis of diabetic complications.

Diabetes is an escalating problem worldwide and a major cause of vascular disease, renal failure, and blindness, among other complications. The cellular mediators of high glucose-induced injury include activation of protein kinase C, accumulation of cell sorbitol from increased flux through the aldose reductase pathway, and generation of advanced glycosylation end products and reactive oxygen species, among others. Current strategies for preventing and slowing the progression of the macrovascular and microvascular complications of diabetes include optimization of glycemic control and BP, angiotensin-converting enzyme inhibitors and angiotensin II blockers, and HMG CoA reductase inhibitors. However, there is an urgent need to develop new therapeutic strategies, as these interventions, although they may slow, rarely halt the progression of diabetic complications. Central to this process is the elucidation of the molecular events that drive this complex disease and that are potential therapeutic targets. This review discusses the promise offered in this regard by global monitoring of cellular or tissue mRNA expression (so-called transcriptomics) and illustrates the potential of this approach by focusing on recent studies on the pathogenesis of diabetic nephropathy.

Actins↗

PubMed2026

Tertiary lymphoid structure transcriptomic signatures show limited and cohort-dependent value for predicting axillary nodal involvement in oestrogen receptor-positive luminal breast cancer.

Tertiary lymphoid structures (TLS) are associated with prognosis in solid tumours. Their value for predicting axillary nodal involvement in oestrogen receptor-positive luminal breast cancer remains uncertain. Three published TLS signatures were scored by single-sample gene set enrichment analysis in oestrogen receptor-positive luminal tumours. The Cancer Genome Atlas Breast Invasive Carcinoma cohort (TCGA-BRCA) included 632 cases, of which 379 met strict consensus. METABRIC included 1086 cases, of which 663 met strict consensus. Logistic models adjusted for age and pathological tumour stage. Strict consensus, majority vote, and continuous scores were compared. Performance assessment included bootstrapped changes in area under the receiver-operating-characteristic curve, Brier scores, calibration, and decision-curve analysis. Survival was evaluated in METABRIC and explored in TCGA-BRCA. Strict-consensus TLS status was not associated with nodal positivity in TCGA-BRCA (adjusted odds ratio: 0.95, 95% confidence interval: 0.62-1.45, P = 0.822). METABRIC was similar (odds ratio: 0.76, 95% confidence interval: 0.55-1.06, P = 0.105). Full-cohort METABRIC analyses detected small majority-vote and continuous-score associations, absent in TCGA-BRCA. Across specifications, bootstrapped changes in area under the receiver-operating-characteristic curve ranged from 0.0002 to 0.0089, with minimal Brier-score improvement and no stable decision-curve benefit. In METABRIC, the univariable overall survival association attenuated after age adjustment (hazard ratio: 1.33-1.10). TCGA-BRCA survival analyses were nonsignificant. TLS transcriptomic signals showed small, cohort-dependent associations with nodal status but no reproducible or clinically meaningful incremental predictive value. These data do not support replacing sentinel lymph node biopsy with a TLS signature in oestrogen receptor-positive luminal breast cancer.

breast cancer↗

PubMed2004

Transcriptome and proteome analysis of Bacillus subtilis gene expression in response to superoxide and peroxide stress.

The Gram-positive soil bacterium Bacillus subtilis responds to oxidative stress by the activation of different cellular defence mechanisms. These are composed of scavenging enzymes as well as protection and repair systems organized in highly sophisticated networks. In this study, the peroxide and the superoxide stress stimulons of B. subtilis were characterized by means of transcriptomics and proteomics. The results demonstrate that oxidative-stress-responsive genes can be classified into two groups. One group encompasses genes which show similar expression patterns in the presence of both reactive oxygen species. Examples are members of the PerR and the Fur regulon which were induced by peroxide and superoxide stress. Similarly, both kinds of stress stimulated the activation of the stringent response. The second group is composed of genes primarily responding to one stimulus, like the members of the SOS regulon which were particularly upregulated in the presence of peroxide, and many genes involved in sulfate assimilation and methionine biosynthesis which were only induced by superoxide. Several genes encoding proteins of unknown function could be assigned to one of these groups.

Bacillus subtilis↗

PubMed2003

Transcriptome profile of murine gammaherpesvirus-68 lytic infection.

The murine gammaherpesvirus-68 genome encodes 73 protein-coding open reading frames with extensive similarities to human gamma(2) herpesviruses, as well as unique genes and cellular homologues. We performed transcriptome analysis of stage-specific viral RNA during permissive infection using an oligonucleotide-based microarray. Using this approach, M4, K3, ORF38, ORF50, ORF57 and ORF73 were designated as immediate-early genes based on cycloheximide treatment. The microarray analysis also identified 10 transcripts with early expression kinetics, 32 transcripts with early-late expression kinetics and 29 transcripts with late expression kinetics. The latter group consisted mainly of structural proteins, and showed high expression levels relative to other viral transcripts. Moreover, we detected all eight tRNA-like transcripts in the presence of cycloheximide and phosphonoacetic acid. Lytic infection with MHV-68 also resulted in a significant reduction in the expression of cellular transcripts included in the DNA chip. This global approach to viral transcript analysis offers a powerful system for examining molecular transitions between lytic and latent virus infections associated with disease pathogenesis.

Animals↗

PubMed2023

Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type and sex specificity of gene expression with novel genetic risk for MERTK in female.

Alzheimer's disease, the most common age-related neurodegenerative disease, is closely associated with both amyloid-ß plaque and neuroinflammation. Two thirds of Alzheimer's disease patients are females and they have a higher disease risk. Moreover, women with Alzheimer's disease have more extensive brain histological changes than men along with more severe cognitive symptoms and neurodegeneration. To identify how sex difference induces structural brain changes, we performed unbiased massively parallel single nucleus RNA sequencing on Alzheimer's disease and control brains focusing on the middle temporal gyrus, a brain region strongly affected by the disease but not previously studied with these methods. We identified a subpopulation of selectively vulnerable layer 2/3 excitatory neurons that that were RORB-negative and CDH9-expressing. This vulnerability differs from that reported for other brain regions, but there was no detectable difference between male and female patterns in middle temporal gyrus samples. Disease-associated, but sex-independent, reactive astrocyte signatures were also present. In clear contrast, the microglia signatures of diseased brains differed between males and females. Combining single cell transcriptomic data with results from genome-wide association studies (GWAS), we identified MERTK genetic variation as a risk factor for Alzheimer's disease selectively in females. Taken together, our single cell dataset revealed a unique cellular-level view of sex-specific transcriptional changes in Alzheimer's disease, illuminating GWAS identification of sex-specific Alzheimer's risk genes. These data serve as a rich resource for interrogation of the molecular and cellular basis of Alzheimer's disease.

Journal Article↗

PubMed2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Preprint↗

PubMed2024

Transcriptome-Wide Root Causal Inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Journal Article↗

PubMed2025

GSK3B inhibition partially reverses brain ethanol-induced transcriptomic changes in C57BL/6J mice: Expression network co-analysis with human genome-wide association studies.

Alcohol use disorder (AUD) is a chronic behavioral disease with greater than 50% of its risk due to complex genetic contributions. Existing pharmacological and behavioral treatments for AUD are minimally effective and underutilized. Animal model behavioral genetics and human genome-wide association studies have begun to identify individual genes contributing to the progressive compulsive consumption of ethanol that occurs with AUD, promising possible new therapeutic targets. Our laboratory has previously identified Gsk3b as a central member in a network of ethanol-responsive genes in mouse prefrontal cortex, which altered ethanol consumption with genetic manipulation and was also significantly associated with risk for alcohol dependence in human genome-wide association studies. Here we perform detailed brain RNA sequencing transcriptomic studies to characterize a highly specific and clinically available GSK3B pharmacological inhibitor, tideglusib, as a possible therapeutic for clinical trials on treatment of AUD. A model of chronic intermittent ethanol consumption was used to study gene expression changes in prefrontal cortex and nucleus accumbens in the presence or absence of tideglusib treatment. Multivariate analysis of differentially expressed genes showed that tideglusib largely reversed ethanol- induced expression changes for two prominent clusters of genes in both prefrontal cortex and nucleus accumbens. Bioinformatic analysis showed these genes to have prominent roles in neuronal functioning and synaptic activity. Additionally, mouse brain differential gene expression data was analyzed together with human protein-protein interaction and genome-wide association studies on AUD to derive networks responding to tideglusib and relevant to human genetic risk for alcohol dependence. These studies identified discrete networks significantly enriched with genes provisionally associated with AUD, and provide key information on central hubs of such networks. Together these studies document tideglusib as a major modulator of chronic ethanol consumption-evoked brain gene expression signatures, and identify possible new targets for therapeutic modulation of AUD.

Journal Article↗

PubMed2025

Multi-ancestry genome-wide and transcriptome-wide association analyses identified new risk loci and genes for inflammatory bowel disease.

To advance genetic understanding of inflammatory bowel disease (IBD), we conducted genome-wide association meta-analyses of 63,415 IBD cases of European and East Asian descendants and identified 90 previously unknown risk loci. Integrating multi-ancestry transcriptome-wide association studies (TWAS), cell type-specific TWAS, alternative splicing (AS-WAS), and alternative polyadenylation (APA-WAS) analyses using RNA-seq data from normal colon tissues of 707 European and 364 East Asian individuals, we uncovered 506 high-confidence IBD risk genes, including 384 not previously reported. These genes converge on immune regulation, microbial interaction, and other pathways central to IBD pathogenesis, with over half showing transcriptional dysregulation supported by single-cell and spatial omics analyses. Notably, 46 risk genes are targeted by 225 drugs that have been approved or in Phase II/III trials, including sulfasalazine already used in IBD therapy. Our study findings deepen the understanding of IBD genetics and support the development of precision medicine for its prevention and treatment.

GWAS↗

PubMed2003

Identification and analysis of chromodomain-containing proteins encoded in the mouse transcriptome.

The chromodomain is 40-50 amino acids in length and is conserved in a wide range of chromatic and regulatory proteins involved in chromatin remodeling. Chromodomain-containing proteins can be classified into families based on their broader characteristics, in particular the presence of other types of domains, and which correlate with different subclasses of the chromodomains themselves. Hidden Markov model (HMM)-generated profiles of different subclasses of chromodomains were used here to identify sequences encoding chromodomain-containing proteins in the mouse transcriptome and genome. A total of 36 different loci encoding proteins containing chromodomains, including 17 novel loci, were identified. Six of these loci (including three apparent pseudogenes, a novel HP1 ortholog, and two novel Msl-3 transcription factor-like proteins) are not present in the human genome, whereas the human genome contains four loci (two CDY orthologs and two apparent CDY pseudogenes) that are not present in mouse. A number of these loci exhibit alternative splicing to produce different isoforms, including 43 novel variants, some of which lack the chromodomain. The likely functions of these proteins are discussed in relation to the known functions of other chromodomain-containing proteins within the same family.

Acetyltransferases↗

PubMed2003

Systematic expression profiling of the mouse transcriptome using RIKEN cDNA microarrays.

The number of known mRNA transcripts in the mouse has been greatly expanded by the RIKEN Mouse Gene Encyclopedia project. Validation of their reproducible expression in a tissue is an important contribution to the study of functional genomics. In this report, we determine the expression profile of 57,931 clones on 20 mouse tissues using cDNA microarrays. Of these 57,931 clones, 22,928 clones correspond to the FANTOM2 clone set. The set represents 20,234 transcriptional units (TUs) out of 33,409 TUs in the FANTOM2 set. We identified 7206 separate clones that satisfied stringent criteria for tissue-specific expression. Gene Ontology terms were assigned for these 7206 clones, and the proportion of 'molecular function' ontology for each tissue-specific clone was examined. These data will provide insights into the function of each tissue. Tissue-specific gene expression profiles obtained using our cDNA microarrays were also compared with the data extracted from the GNF Expression Atlas based on Affymetrix microarrays. One major outcome of the RIKEN transcriptome analysis is the identification of numerous nonprotein-coding mRNAs. The expression profile was also used to obtain evidence of expression for putative noncoding RNAs. In addition, 1926 clones (70%) of 2768 clones that were categorized as "unknown EST," and 1969 (58%) clones of 3388 clones that were categorized as "unclassifiable" were also shown to be reproducibly expressed.

Animals↗

PubMed2003

Quantitative assessment of transcriptome differences between brain territories.

Transcriptome analysis of mammalian brain structures is a potentially powerful approach in addressing the diversity of cerebral functions. Here, we used a microassay for serial analysis of gene expression (SAGE) to generate quantitative mRNA expression profiles of normal adult mouse striatum, nucleus accumbens, and somatosensory cortex. Comparison of these profiles revealed 135 transcripts heterogeneously distributed in the brain. Among them, a majority (78), although matching a registered sequence, are novel regional markers. To improve the anatomical resolution of our analysis, we performed in situ hybridization and observed unique expression patterns in discrete brain regions for a number of candidates. We assessed the distribution of the new markers in peripheral tissues using quantitative RT-PCR, Northern hybridization, and published SAGE data. In most cases, expression was higher in the brain than in peripheral tissues. Because the markers were selected according to their expression level, without reference to prior knowledge, our studies provide an unbiased, comprehensive molecular signature for various mammalian brain structures that can be used to investigate their plasticity under a variety of circumstances.

Animals↗

PubMed2003

The human transcriptome map reveals extremes in gene density, intron length, GC content, and repeat pattern for domains of highly and weakly expressed genes.

The chromosomal gene expression profiles established by the Human Transcriptome Map (HTM) revealed a clustering of highly expressed genes in about 30 domains, called ridges. To physically characterize ridges, we constructed a new HTM based on the draft human genome sequence (HTMseq). Expression of 25,003 genes can be analyzed online in a multitude of tissues (http://bioinfo.amc.uva.nl/HTMseq). Ridges are found to be very gene-dense domains with a high GC content, a high SINE repeat density, and a low LINE repeat density. Genes in ridges have significantly shorter introns than genes outside of ridges. The HTMseq also identifies a significant clustering of weakly expressed genes in domains with fully opposite characteristics (antiridges). Both types of domains are open to tissue-specific expression regulation, but the maximal expression levels in ridges are considerably higher than in antiridges. Ridges are therefore an integral part of a higher order structure in the genome related to transcriptional regulation.

Base Composition↗

PubMed2026

Dual-contrastive learning for spatial domain identification in spatial transcriptomics with STAMGC.

Spatial transcriptomics (STs) have become a valuable approach for understanding the growth and development of organisms. Despite the recent emergence of numerous ST models, accurately identifying spatial domains remains challenging owing to the trade-off between preserving local details and reducing noise. Here, we introduce STAMGC, which is a dual-contrastive learning framework built upon graph convolutional networks. This model leverages regional and topological contrastive learning to jointly optimize the model, effectively reducing the noise in spatial domain identification and enhancing the extraction of detailed features. In this study, Gaussian smoothing, originally developed in the image processing field, is introduced to process ST data, providing a foundation for region-level contrastive learning by mitigating spatial discontinuities of gene expression signals. Experimental results indicate that STAMGC outperforms existing methods across multiple data sets according to comprehensive evaluations. Furthermore, STAMGC not only identifies finer structures in the mouse brain but also brings new discoveries for human breast cancer research.

Journal Article↗

PubMed2003

Systematic characterization of the zinc-finger-containing proteins in the mouse transcriptome.

Zinc-finger-containing proteins can be classified into evolutionary and functionally divergent protein families that share one or more domains in which a zinc ion is tetrahedrally coordinated by cysteines and histidines. The zinc finger domain defines one of the largest protein superfamilies in mammalian genomes;46 different conserved zinc finger domains are listed in InterPro (http://www.ebi.ac.uk/InterPro). Zinc finger proteins can bind to DNA, RNA, other proteins, or lipids as a modular domain in combination with other conserved structures. Owing to this combinatorial diversity, different members of zinc finger superfamilies contribute to many distinct cellular processes, including transcriptional regulation, mRNA stability and processing, and protein turnover. Accordingly, mutations of zinc finger genes lead to aberrations in a broad spectrum of biological processes such as development, differentiation, apoptosis, and immunological responses. This study provides the first comprehensive classification of zinc finger proteins in a mammalian transcriptome. Specific detailed analysis of the SP/Krüppel-like factors and the E3 ubiquitin-ligase RING-H2 families illustrates the importance of such an analysis for a more comprehensive functional classification of large protein families. We describe the characterization of a new family of C2H2 zinc-finger-containing proteins and a new conserved domain characteristic of this family, the identification and characterization of Sp8, a new member of the Sp family of transcriptional regulators, and the identification of five new RING-H2 proteins.

Alternative Splicing↗