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At least 1,531 records · Page 85Linked to original sources
PubMed2005

Frequent overexpression of ETS-related gene-1 (ERG1) in prostate cancer transcriptome.

Transcription factors encoded by the ETS family of genes are central in integrating signals that regulate cell growth and differentiation, stress responses, and tumorigenesis. This study, analysing laser microdissected paired benign and malignant prostate epithelial cells from prostate cancer (CaP) patients (n=114; 228 specimen) by GeneChip and quantitative real-time RT-PCR, identifies ETS-related gene (ERG), a member of the ETS transcription factor family, as the most frequently overexpressed proto-oncogene in the transcriptome of malignant prostate epithelial cells. Combined quantitative expression analysis of ERG with two other genes commonly overexpressed in CaP, AMACR and DD3, revealed overexpression of at least one of these three genes in virtually all CaP specimen (54 of 55). Comprehensive evaluation of quantitative ERG1 expression with clinicopathological features also suggested that ERG1 expression level in prostate tumor cells relative to benign epithelial cells is indicator of disease-free survival after radical prostatectomy.

Antigens, Neoplasm↗
PubMed2005

Sex and tissue-specific differences in low-dose radiation-induced oncogenic signaling.

PURPOSE: The possible adverse health effects of low-dose radiation (LDR) exposure constitute a growing concern. Clinically and environmentally relevant exposures occur predominantly under chronic conditions, notwithstanding that most studies of LDR effects have been performed using a single acute exposure. Sex- and tissue-specificity of the LDR-induced changes have not been considered before. We investigated LDR-related expression patterns in muscle, liver and spleen of male and female mice subjected to acute and chronic LDR exposure. Genes involved in oncogenic signaling were of specific interest, as radiation is a well-known carcinogen. MATERIALS AND METHODS: We analyzed the expression pattern of genes coding for growth factors and growth-factor receptors, cytoplasmic serine/threonine protein kinases, G-proteins and nuclear DNA-binding proteins, and other important components of oncogenic signaling. RESULTS: We found sex- and tissue-specific changes in the expression of Ras superfamily members (Nras, Rab2, Rab34, Vav2), protein kinase C (PKC) isoforms (PKCbeta, PKCmu), AP-1 factor components (Jun, JunB and FosB), Wnt signaling pathway members as well as in a variety of other cellular proto-oncogenes and oncogenes. Importantly, Western blot analysis of JunB, PKCmu and Rab2 proteins supported the transcriptomic data. CONCLUSIONS: Substantially different protein levels were observed in all three tissues (muscle, spleen and liver) of acutely and chronically irradiated female and male animals. Based on the obtained data and available literature, we discuss several possible mechanisms that may contribute to radiation-induced carcinogenesis in various tissues of males and females. From our results we could identify the genes that may serve as sex- and tissue-specific biomarkers of the LDR exposure.

Adaptation, Physiological↗
PubMed2006

Role of mRNA stability in growth phase regulation of gene expression in the group A streptococcus.

The impressive disease spectrum of Streptococcus pyogenes (the group A streptococcus [GAS]) is believed to be determined by its ability to modify gene expression in response to environmental stimuli. Virulence gene expression is controlled tightly by several different transcriptional regulators in this organism. In addition, expression of most, if not all, GAS genes is determined by a global mechanism dependent on growth phase. To begin an analysis of growth-phase regulation, we compared the transcriptome 2 h into stationary phase to that in late exponential phase of a serotype M3 GAS strain. We identified the arc transcript as more abundant in stationary phase in addition to the sag and sda transcripts that had been previously identified. We found that in stationary phase, the stability of sagA, sda, and arcT transcripts increased dramatically. We found that polynucleotide phosphorylase (PNPase [encoded by pnpA]) is rate limiting for decay of sagA and sda transcripts in late exponential phase, since the stability of these mRNAs was greater in a pnpA mutant, while stability of control mRNAs was unaffected by this mutation. Complementation restored the wild-type decay rate. Furthermore, in a pnpA mutant, the sagA mRNA appeared to be full length, as determined by Northern hybridization. It seems likely that mRNAs abundant in stationary phase are insensitive to the normal decay enzyme(s) and instead require PNPase for this process. It is possible that PNPase activity is limited in stationary phase, allowing persistence of these important virulence factor transcripts at this phase of growth.

Gene Expression Regulation, Bacterial↗
PubMed2004

Proteomics in pancreatic disease.

Proteomics represents a novel methodological approach to investigate the expression of all proteins by a cell or organism in its entireness, similar to global strategies for DNA (genomics) and RNA (transcriptomics). This review focuses on the history of protein analysis, which made up the golden age of pancreatic physiology, the current methodology for proteomics (2D gel electrophoresis, mass spectrometry) and the few published experiences with proteomics in the field of pancreatology until now. Finally, potential applications of proteomics for the pancreas, in concert with other techniques, are cited.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2003

Gene expression profiling in CD34 cells to identify differences between aplastic anemia patients and healthy volunteers.

An immune pathophysiology for acquired aplastic anemia (AA) has been inferred from the responsiveness of the patients to immunosuppressive therapies and experimental laboratory data. To address the transcriptome of hematopoietic cells in AA, we undertook GeneChip analysis of the extremely limited numbers of progenitor and stem cells in the marrow of patients with this disease. We pooled total RNA from highly enriched bone marrow CD34 cells of 36 patients with newly diagnosed AA and 12 healthy volunteers for analysis on oligonucleotide chips. A large number of genes implicated in apoptosis and cell death showed markedly increased expression in AA CD34 cells, and negative proliferation control genes also had increased activity. Conversely, cell cycle progress-enhancing genes showed low expression in AA. Cytokine/chemokine signal transducer genes, stress response genes, and defense/immune response genes were up-regulated, as anticipated from other evidence of the heightened immune activity in AA patients' marrow. In summary, detailed genetic analysis of small numbers of hematopoietic progenitor cells is feasible even in marrow failure states where such cells are present in very small numbers. The gene expression profile of primary human CD34 hematopoietic stem cells from AA was consistent with a stressed, dying, and immunologically activated target cell population. Many of the genes showing differential expression in AA deserve further detailed analysis, including comparison with other marrow failure states and autoimmune disease.

Adolescent↗
PubMed2004

SAGE is far more sensitive than EST for detecting low-abundance transcripts.

BACKGROUND: Isolation of low-abundance transcripts expressed in a genome remains a serious challenge in transcriptome studies. The sensitivity of the methods used for analysis has a direct impact on the efficiency of the detection. We compared the EST method and the SAGE method to determine which one is more sensitive and to what extent the sensitivity is great for the detection of low-abundance transcripts. RESULTS: Using the same low-abundance transcripts detected by both methods as the targeted sequences, we observed that the SAGE method is 26 times more sensitive than the EST method for the detection of low-abundance transcripts. CONCLUSIONS: The SAGE method is more efficient than the EST method in detecting the low-abundance transcripts.

3' Untranslated Regions↗
PubMed2002

Human members of the eukaryotic protein kinase family.

BACKGROUND: Eukaryotic protein kinases (EPKs) constitute one of the largest recognized protein families represented in the human genome. EPKs, which are similar to each other in sequence, structure and biochemical properties, are important players in virtually every signaling pathway involved in normal development and disease. Near completion of projects to sequence the human genome and transcriptome provide an opportunity to identify and perform sequence analysis on a nearly complete set of human EPKs. RESULTS: Publicly available genetic sequence data were searched for human sequences that potentially represent EPK family members. After removal of duplicates, splice variants and pseudogenes, this search yielded 510 sequences with recognizable similarity to the EPK family. Protein sequences of putative EPK catalytic domains identified in the search were aligned, and a phonogram was constructed based on the alignment. Representative sequence records in GenBank were identified, and derived information about gene mapping and nomenclature was summarized. CONCLUSIONS: This work represents a nearly comprehensive census and early bioinformatics overview of the EPKs encoded in the human genome. Evaluation of the sequence relationships between these proteins contributes contextual information that enhances understanding of individual family members. This curation of human EPK sequences provides tools and a framework for the further characterization of this important class of enzymes.

Binding Sites↗
PubMed2026

Drug repurposing using transcriptomics: principles and unmet needs in cardiovascular disease.

Although cardiovascular disease is the leading cause of death globally, therapeutic development in this field is slow. Given the high cost of developing new drugs and running clinical trials for cardiovascular disease, repurposing of drugs with approved safety profiles is an attractive strategy for therapeutic development that can significantly reduce the time and cost investment before phase II clinical trials. In the era of "Omics," various new methods and several large databases have been developed to enable the use of transcriptomics data for drug repurposing. This review summarizes the principles and workflow of signature mapping, which forms the foundation of statistical models used for transcriptome-based drug repurposing. We highlight the features of different analysis pipelines and databases that have been developed for signature mapping. These analysis pipelines prioritize genes that are statistically important, an approach that fundamentally differs from the pharmacological approach of identifying disease-driving and therapeutically targetable pathways. Outcomes of signature mapping pipelines are sensitive to the quality of input data, and results are not always reproducible. Moreover, all widely used RNA-seq databases are derived from cancer research and lack high-quality molecular data for cardiovascular disease. These unmet needs call for interdisciplinary collaboration and large networks of cardiovascular research-oriented biobanks to create the databases needed for transcriptomic-based signature mapping for drug repurposing efforts.

Drug Repositioning↗
PubMed2005

Effective transcriptome amplification for expression profiling on sense-oriented oligonucleotide microarrays.

Gene expression analysis using microarrays of synthetic long oligonucleotides is limited in that it requires substantial amounts of RNA. To obtain these quantities from minute amounts of starting material, protocols were developed that linearly amplify mRNA by cDNA synthesis and in vitro transcription. Since orientation of the product is antisense (aRNA), it is inapplicable for dye-labelling by reverse transcription and hybridization to sense-oriented oligonucleotide arrays. Here, we introduce a novel protocol in which aRNA labelling is achieved by a combination of two reverse and one forward transcription reactions followed by dye-incorporation using Klenow fragment, generating fluorescent antisense cDNA. We demonstrate high fidelity in arrays using up to 10(5)-fold amplification, starting from 2 ng total RNA. The generated data are highly reproducible and maintain relative gene expression levels between samples. These results demonstrate that our protocol describes an efficient and reliable technique to expand the applicability of oligonucleotide arrays to studies where RNA is the limited source material.

DNA, Antisense↗
PubMed2005

From targets to leads: the importance of advanced data analysis for decision support in drug discovery.

Lead discovery is a complex process that is intimately linked to chemistry, but which is also increasingly driven by biological sciences. In an industrial pharmaceutical research environment the process is defined by highly automated technologies for target identification and validation, compound library screening, and compound efficacy assessment. The huge volumes and complex dependencies of data produced by such large-scale experiments have led to a reassessment of data analysis processes, resulting in the development of novel data analysis strategies tailored to drug discovery. In this review, recent progress in data-driven research applications is reported, focusing on the use and processing of transcriptomics, proteomics and high-throughput screening data. The successful application of specialized data analysis procedures in many companies is discussed, which has resulted in significant improvements in decision-making processes for progressing therapeutic targets to promising leads.

Animals↗
PubMed2006

Transcriptome coexpression map of human embryonic stem cells.

BACKGROUND: Human embryonic stem (ES) cells hold great promise for medicine and science. The transcriptome of human ES cells has been studied in detail in recent years. However, no systematic analysis has yet addressed whether gene expression in human ES cells may be regulated in chromosomal domains, and no chromosomal domains of coexpression have been identified. RESULTS: We report the first transcriptome coexpression map of the human ES cell and the earliest stage of ES differentiation, the embryoid body (EB), for the analysis of how transcriptional regulation interacts with genomic structure during ES self-renewal and differentiation. We determined the gene expression profiles from multiple ES and EB samples and identified chromosomal domains showing coexpression of adjacent genes on the genome. The coexpression domains were not random, with significant enrichment in chromosomes 8, 11, 16, 17, 19, and Y in the ES state, and 6, 11, 17, 19 and 20 in the EB state. The domains were significantly associated with Giemsa-negative bands in EB, yet showed little correlation with known cytogenetic structures in ES cells. Different patterns of coexpression were revealed by comparative transcriptome mapping between ES and EB. CONCLUSION: The findings and methods reported in this investigation advance our understanding of how genome organization affects gene expression in human ES cells and help to identify new mechanisms and pathways controlling ES self-renewal or differentiation.

Animals↗
PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗
PubMed2002

Contributions of proteome profiling to the molecular analysis of cancer.

The proteome is the most functional compartment encoded for in the genome. Technologies for protein separation and quantitation, coupled with mass spectrometry for protein identification, have provided the means for proteome profiling of tumor cell lines and tissues that complement genomic and transcriptomic profiling. The application of established and novel proteomic technologies to the molecular analysis of cancer is reviewed.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2002

Yellow pages to the transcriptome.

Transcriptomics has become an important tool for the large-scale analysis of biological processes. This review aims to provide sufficient criteria to make an appropriate choice among the variety of 'closed' systems, represented by DNA microarrays, and 'open' systems like fragment display, tag sequencing and subtractive hybridization, depending on the biological system under investigation. The most important technologies currently available are presented, their strengths and weaknesses are discussed and companies active in the field are listed. The potential of transcriptomics in the pharmaceutical research and development process is highlighted by applications in oncology, research on neurological diseases, and predictive toxicology. Finally, a prognosis for future developments of the technologies is given.

Animals↗
PubMed2025

Genomic and Transcriptomic Profiling of Radiation-Resistant, Locally Recurrent Prostate Cancer.

PURPOSE: The biology of locally radiorecurrent prostate cancer (LRR-PCa) is poorly understood. METHODS AND MATERIALS: We sought to explore the genomic and transcriptomic landscape of LRR-PCa with targeted DNA sequencing and RNA expression analysis from 41 biopsy-proven LRR-PCa tumors from 36 unique patients who had a recurrence at a median interval of 84 months (IQR, 70-124 months). Genomic alteration frequencies and transcriptomic data were compared between the LRR-PCa cohort and treatment-na&#xef;ve patients from the Cancer Genome Atlas (genomic; n = 496) and Gleason grade-at-recurrence-matched patients from the Decipher Genomics Resource for Intelligent Discovery (transcriptomic; n = 22,320). RESULTS: Twenty-five patients (69%) had pathologic upgrading at recurrence (17% vs 64% with Gleason grade 4-5 disease; P < .001). The LRR-PCa cohort demonstrated significantly greater single-nucleotide variations in 29 genes known to be associated with prostate cancer, including several associated with increased aggressiveness and DNA repair: FAT1 (58.5% vs 1.0%), RAD51B (36.6% vs 0.4%), POLQ (34.1% vs 1.4%), KMT2C (34.1% vs 4.9%), BRCA2 (29.3% vs 1.8%), ATRX (26.8% vs 0.8%), and BRCA1 (24.4% vs 0.4%) (Pvalues < .001 for all). The LRR-PCa cohort had a significantly higher Decipher score (median, 0.80 vs 0.66; P = .05) and demonstrated significantly greater basal subtype based on PAM50 (56% vs 20%; P < .001) and lower androgen receptor activity (61% for LRR vs 9%; P < .001). CONCLUSIONS: Overall, these results suggest that LRR-PCa has a distinct genomic and transcriptomic landscape from de novo prostate cancer. Specifically, LRR-PCa has an enrichment in SNVs in genes associated with tumor aggressiveness and/or DNA repair, has higher Decipher scores, a more basal subtype, and has transcriptomic evidence of lower androgen receptor activity and loss of tumor suppressor genes.

Humans↗
PubMed2006

Epigenetic silencing of AXIN2 in colorectal carcinoma with microsatellite instability.

Mutation or epigenetic silencing of mismatch repair genes, such as MLH1 and MSH2, results in microsatellite instability (MSI) in the genome of a subset of colorectal carcinomas (CRCs). However, little is yet known of genes that directly contribute to tumor formation in such cancers. To characterize MSI-dependent changes in gene expression, we have now compared transcriptomes between fresh CRC specimens positive or negative for MSI (n=10 for each) with the use of high-density oligonucleotide microarrays harboring >44,000 probe sets. Correspondence analysis of the expression patterns of isolated MSI-associated genes revealed that the transcriptome of MSI+ CRCs is clearly distinct from that of MSI- CRCs. Such MSI-associated genes included that for AXIN2, an important component of the WNT signaling pathway. AXIN2 was silenced, apparently as a result of extensive methylation of its promoter region, specifically in MSI+ CRC specimens. Forced expression of AXIN2, either by treatment with 5'-azacytidine or by transfection with AXIN2 cDNA, resulted in rapid cell death in an MSI+ CRC cell line. These data indicate that epigenetic silencing of AXIN2 is specifically associated with carcinogenesis in MSI+ CRCs.

Adaptor Proteins, Signal Transducing↗
PubMed2005

Biological master games: using biologists' reasoning to guide algorithm development for integrated functional genomics.

We review some powerful new algorithms that build on the intuitive biological interpretation techniques for statistical analysis of functional genomics experiments. Although they were originally designed for transcriptomics, we argue that these algorithms are applicable to any type of -omics study (transcriptomics, proteomics, metabolomics). Rank Products (RP), a strictly non-parametric test statistic to detect differentially regulated elements (genes, proteins, metabolites) in genome-wide screens. RP is particularly powerful for noisy data and low numbers of replicates and makes full use of the availability of a large number of parallel measurements that is typical of modern large-scale experiments. Iterative Group Analysis (iGA), a statistical method that makes the transition from regulated single elements to significant classes of elements, and thus provides an automatic functional annotation of an experiment. Graph-based iGA (GiGA), an extension of iGA that combines experimental data with a broad variety of biological annotations to highlight physiologically relevant regions in a given "evidence graph" (e.g., metabolic networks, signaling pathway diagrams, protein interaction maps). The sequential application of these techniques yields an increasingly abstract interpretation of experimental data that is at the same time quantitative, statistically rigorous, and biologically significant. The results can be used either as helpful tools to guide data visualization and exploration, or as the input for downstream computational applications in a systems biology framework.

Algorithms↗
PubMed2003

The Schistosoma mansoni gene index: gene discovery and biology by reconstruction and analysis of expressed gene sequences.

Expressed sequence tag (EST) sequencing and analysis is a primary research tool to identify and characterize the Schistosoma mansoni transcriptome. As part of our gene discovery effort, a total of 5,793 ESTs have been generated from clones selected randomly from complementary DNA (cDNA) libraries constructed from male and female adult worms. Assembly analysis of all the 16,813 public S. mansoni ESTs has identified 1,920 distinct tentative consensus sequences (TCs) and 5,571 nonoverlapping ESTs (singletons). Of these, 376 TCs (20%) and 1,449 singletons (26%) are unique to the SUNY/TIGR sequencing effort. Tentative consensus sequences and singletons were distributed into various categories of biological roles associated with cell structure, metabolism, protein fate, signal transduction, transcription, protein synthesis, transporters, and cell growth. The TCs and singletons represent transcripts that can be used as a resource for functional annotation of genomic sequence data, comparative sequence analysis, and cDNA clone selection for microarray projects. The utility of EST analysis is demonstrated by identifying new protease genes, which may be involved in hemoglobin degradation.

Amino Acid Sequence↗