Showing papers on "Gene expression profiling published in 2006"

A microRNA expression signature of human solid tumors defines cancer gene targets

Abstract: Small noncoding microRNAs (miRNAs) can contribute to cancer development and progression and are differentially expressed in normal tissues and cancers From a large-scale miRnome analysis on 540 samples including lung, breast, stomach, prostate, colon, and pancreatic tumors, we identified a solid cancer miRNA signature composed by a large portion of overexpressed miRNAs Among these miRNAs are some with well characterized cancer association, such as miR-17-5p, miR-20a, miR-21, miR-92, miR-106a, and miR-155 The predicted targets for the differentially expressed miRNAs are significantly enriched for protein-coding tumor suppressors and oncogenes (P < 00001) A number of the predicted targets, including the tumor suppressors RB1 (Retinoblastoma 1) and TGFBR2 (transforming growth factor, beta receptor II) genes were confirmed experimentally Our results indicate that miRNAs are extensively involved in cancer pathogenesis of solid tumors and support their function as either dominant or recessive cancer genes

CellProfiler: image analysis software for identifying and quantifying cell phenotypes

Abstract: Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

Abstract: To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.

NF-κB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses

Abstract: Activation of mammalian innate and acquired immune responses must be tightly regulated by elaborate mechanisms to control their onset and termination. MicroRNAs have been implicated as negative regulators controlling diverse biological processes at the level of posttranscriptional repression. Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-κB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3′ UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.

ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

Abstract: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors. ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.

The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells.

Abstract: Oct4 and Nanog are transcription factors required to maintain the pluripotency and self-renewal of embryonic stem (ES) cells. Using the chromatin immunoprecipitation paired-end ditags method, we mapped the binding sites of these factors in the mouse ES cell genome. We identified 1,083 and 3,006 high-confidence binding sites for Oct4 and Nanog, respectively. Comparative location analyses indicated that Oct4 and Nanog overlap substantially in their targets, and they are bound to genes in different configurations. Using de novo motif discovery algorithms, we defined the cis-acting elements mediating their respective binding to genomic sites. By integrating RNA interference-mediated depletion of Oct4 and Nanog with microarray expression profiling, we demonstrated that these factors can activate or suppress transcription. We further showed that common core downstream targets are important to keep ES cells from differentiating. The emerging picture is one in which Oct4 and Nanog control a cascade of pathways that are intricately connected to govern pluripotency, self-renewal, genome surveillance and cell fate determination.

Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression

Abstract: Comprehensive analysis of the gene expression profiles associated with human monocyte-to-macrophage differentiation and polarization toward M1 or M2 phenotypes led to the following main results: 1) M-CSF-driven monocyte-to-macrophage differentiation is associated with activation of cell cycle genes, substantiating the underestimated proliferation potential of monocytes. 2) M-CSF leads to expression of a substantial part of the M2 transcriptome, suggesting that under homeostatic conditions a default shift toward M2 occurs. 3) Modulation of genes involved in metabolic activities is a prominent feature of macrophage differentiation and polarization. 4) Lipid metabolism is a main category of modulated transcripts, with expected up-regulation of cyclo-oxygenase 2 in M1 cells and unexpected cyclo-oxygenase 1 up-regulation in M2 cells. 5) Each step is characterized by a different repertoire of G protein-coupled receptors, with five nucleotide receptors as novel M2-associated genes. 6) The chemokinome of polarized macrophages is profoundly diverse and new differentially expressed chemokines are reported. Thus, transcriptome profiling reveals novel molecules and signatures associated with human monocyte-to-macrophage differentiation and polarized activation which may represent candidate targets in pathophysiology.

Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

Abstract: The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

GenePattern 2.0.

Abstract: Stephen J Chapman1,2, Chiea C Khor1, Fredrik O Vannberg1, Nicholas A Maskell2, Christopher WH Davies3, Emma L Hedley2, Shelley Segal4, Catrin E Moore4, Kyle Knox5, Nicholas P Day6, Stephen H Gillespie7, Derrick W Crook5, Robert JO Davies2 & Adrian VS Hill1 1The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. 2Oxford Centre for Respiratory Medicine, Churchill Hospital Site, Oxford Radcliffe Hospital, Oxford OX3 7LJ, UK. 3Department of Respiratory Medicine, Royal Berkshire Hospital, Reading RG1 5AN, UK. 4Department of Paediatrics, John Radcliffe Hospital, Oxford OX3 9DU, UK. 5Department of Microbiology, John Radcliffe Hospital, Oxford OX3 9DU, UK. 6Centre for Clinical Vaccinology and Tropical Medicine, Oxford OX3 9DU, UK. 7Centre for Medical Microbiology, Department of Infection, University College London, London NW1 2BU, UK. e-mail: adrian.hill@well.ox.ac.uk

STEM: a tool for the analysis of short time series gene expression data

Abstract: Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at http://www.cs.cmu.edu/~jernst/stem .

Comprehensive analysis of microRNA expression patterns in hepatocellular carcinoma and non-tumorous tissues

Abstract: MicroRNAs (miRNAs) are a non-coding family of genes involved in post-transcriptional gene regulation. These transcripts are associated with cell proliferation, cell differentiation, cell death and carcinogenesis. We analysed the miRNA expression profiles in 25 pairs of hepatocellular carcinoma (HCC) and adjacent non-tumorous tissue (NT) and nine additional chronic hepatitis (CH) specimens using a human miRNA microarray. Targets and references samples were co-hybridized to a microarray containing whole human mature and precursor miRNA sequences. Whereas three miRNAs exhibited higher expression in the HCC samples than that in the NT samples, five miRNAs demonstrated lower expression in the HCC samples than in the NT samples (P<0.0001). Classification of samples as HCC or NT by using support vector machine algorithms based on these data provided an overall prediction accuracy of 97.8% (45/46). In addition, the expression levels of four miRNAs were inversely correlated with the degree of HCC differentiation (P<0.01). A comparison of CH and liver cirrhosis samples revealed significantly different pattern of miRNA expression (P<0.01). There were no differences, however, between hepatitis B-positive and hepatitis C-positive samples. This information may help clarify the molecular mechanisms involved in the progression of liver disease, potentially serving as a diagnostic tool of HCC.

microRNAs exhibit high frequency genomic alterations in human cancer

Abstract: MicroRNAs (miRNAs) are endogenous noncoding RNAs, which negatively regulate gene expression. To determine genomewide miRNA DNA copy number abnormalities in cancer, 283 known human miRNA genes were analyzed by high-resolution array-based comparative genomic hybridization in 227 human ovarian cancer, breast cancer, and melanoma specimens. A high proportion of genomic loci containing miRNA genes exhibited DNA copy number alterations in ovarian cancer (37.1%), breast cancer (72.8%), and melanoma (85.9%), where copy number alterations observed in >15% tumors were considered significant for each miRNA gene. We identified 41 miRNA genes with gene copy number changes that were shared among the three cancer types (26 with gains and 15 with losses) as well as miRNA genes with copy number changes that were unique to each tumor type. Importantly, we show that miRNA copy changes correlate with miRNA expression. Finally, we identified high frequency copy number abnormalities of Dicer1, Argonaute2, and other miRNA-associated genes in breast and ovarian cancer as well as melanoma. These findings support the notion that copy number alterations of miRNAs and their regulatory genes are highly prevalent in cancer and may account partly for the frequent miRNA gene deregulation reported in several tumor types.

Expression profiling identifies microRNA signature in pancreatic cancer

Abstract: microRNAs are functional, 22 nt, noncoding RNAs that negatively regulate gene expression. Disturbance of microRNA expression may play a role in the initiation and progression of certain diseases. A microRNA expression signature has been identified that is associated with pancreatic cancer. This has been accomplished with the application of real-time PCR profiling of over 200 microRNA precursors on specimens of human pancreatic adenocarcinoma, paired benign tissue, normal pancreas, chronic pancreatitis and nine pancreatic cancer cell lines. Hierarchical clustering was able to distinguish tumor from normal pancreas, pancreatitis and cell lines. The PAM algorithm correctly classified 28 of 28 tumors, 6 of 6 normal pancreas and 11 of 15 adjacent benign tissues. One hundred microRNA precursors were aberrantly expressed in pancreatic cancer or desmoplasia (p < 0.01), including microRNAs previously reported as differentially expressed in other human cancers (miR-155, miR-21, miR-221 and miR-222) as well as those not previously reported in cancer (miR-376a and miR-301). Most of the top aberrantly expressed miRNAs displayed increased expression in the tumor. Expression of the active, mature microRNA was validated using a real-time PCR assay to quantify the mature microRNA and Northern blotting. Reverse transcription in situ PCR showed that three of the top differentially expressed miRNAs (miR-221, -376a and -301) were localized to tumor cells and not to stroma or normal acini or ducts. Aberrant microRNA expression may offer new clues to pancreatic tumorigenesis and may provide diagnostic biomarkers for pancreatic adenocarcinoma.

Identification by Real-time PCR of 13 mature microRNAs differentially expressed in colorectal cancer and non-tumoral tissues

Abstract: MicroRNAs (miRNAs) are short non-coding RNA molecules playing regulatory roles by repressing translation or cleaving RNA transcripts. Although the number of verified human miRNA is still expanding, only few have been functionally described. However, emerging evidences suggest the potential involvement of altered regulation of miRNA in pathogenesis of cancers and these genes are thought to function as both tumours suppressor and oncogenes. In our study, we examined by Real-Time PCR the expression of 156 mature miRNA in colorectal cancer. The analysis by several bioinformatics algorithms of colorectal tumours and adjacent non-neoplastic tissues from patients and colorectal cancer cell lines allowed identifying a group of 13 miRNA whose expression is significantly altered in this tumor. The most significantly deregulated miRNA being miR-31, miR-96, miR-133b, miR-135b, miR-145, and miR-183. In addition, the expression level of miR-31 was correlated with the stage of CRC tumor. Our results suggest that miRNA expression profile could have relevance to the biological and clinical behavior of colorectal neoplasia.

A novel prognostic subtype of human hepatocellular carcinoma derived from hepatic progenitor cells.

Abstract: The variability in the prognosis of individuals with hepatocellular carcinoma (HCC) suggests that HCC may comprise several distinct biological phenotypes. These phenotypes may result from activation of different oncogenic pathways during tumorigenesis and/or from a different cell of origin. Here we address whether the transcriptional characteristics of HCC can provide insight into the cellular origin of the tumor. We integrated gene expression data from rat fetal hepatoblasts and adult hepatocytes with HCC from human and mouse models. Individuals with HCC who shared a gene expression pattern with fetal hepatoblasts had a poor prognosis. The gene expression program that distinguished this subtype from other types of HCC included markers of hepatic oval cells, suggesting that HCC of this subtype may arise from hepatic progenitor cells. Analyses of gene networks showed that activation of AP-1 transcription factors in this newly identified HCC subtype might have key roles in tumor development.

NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth

Abstract: The NOTCH1 signaling pathway directly links extracellular signals with transcriptional responses in the cell nucleus and plays a critical role during T cell development and in the pathogenesis over 50% of human T cell lymphoblastic leukemia (T-ALL) cases. However, little is known about the transcriptional programs activated by NOTCH1. Using an integrative systems biology approach we show that NOTCH1 controls a feed-forward-loop transcriptional network that promotes cell growth. Inhibition of NOTCH1 signaling in T-ALL cells led to a reduction in cell size and elicited a gene expression signature dominated by down-regulated biosynthetic pathway genes. By integrating gene expression array and ChIP-on-chip data, we show that NOTCH1 directly activates multiple biosynthetic routes and induces c-MYC gene expression. Reverse engineering of regulatory networks from expression profiles showed that NOTCH1 and c-MYC govern two directly interconnected transcriptional programs containing common target genes that together regulate the growth of primary T-ALL cells. These results identify c-MYC as an essential mediator of NOTCH1 signaling and integrate NOTCH1 activation with oncogenic signaling pathways upstream of c-MYC.

MicroRNA Expression Abnormalities in Pancreatic Endocrine and Acinar Tumors Are Associated With Distinctive Pathologic Features and Clinical Behavior

Abstract: Purpose We investigated the global microRNA expression patterns in normal pancreas, pancreatic endocrine tumors and acinar carcinomas to evaluate their involvement in transformation and malignant progression of these tumor types. MicroRNAs are small noncoding RNAs that regulate gene expression by targeting specific mRNAs for degradation or translation inhibition. Recent evidence indicates that microRNAs can contribute to tumor development and progression and may have diagnostic and prognostic value in several human malignancies. Materials and Methods Using a custom microarray, we studied the global microRNA expression in 12 nontumor pancreas and 44 pancreatic primary tumors, including 12 insulinomas, 28 nonfunctioning endocrine tumors, and four acinar carcinomas. Results Our data showed that a common pattern of microRNA expression distinguishes any tumor type from normal pancreas, suggesting that this set of microRNAs might be involved in pancreatic tumorigenesis; the expression of miR-103 and miR-107, as...

Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies

Abstract: Recent studies indicate that microRNAs (miRNAs) are mechanistically involved in the development of various human malignancies, suggesting that they represent a promising new class of cancer biomarkers. However, previously reported methods for measuring miRNA expression consume large amounts of tissue, prohibiting high-throughput miRNA profiling from typically small clinical samples such as excision or core needle biopsies of breast or prostate cancer. Here we describe a novel combination of linear amplification and labeling of miRNA for highly sensitive expression microarray profiling requiring only picogram quantities of purified microRNA. Comparison of microarray and qRT-PCR measured miRNA levels from two different prostate cancer cell lines showed concordance between the two platforms (Pearson correlation R2 = 0.81); and extension of the amplification, labeling and microarray platform was successfully demonstrated using clinical core and excision biopsy samples from breast and prostate cancer patients. Unsupervised clustering analysis of the prostate biopsy microarrays separated advanced and metastatic prostate cancers from pooled normal prostatic samples and from a non-malignant precursor lesion. Unsupervised clustering of the breast cancer microarrays significantly distinguished ErbB2-positive/ER-negative, ErbB2-positive/ER-positive, and ErbB2-negative/ER-positive breast cancer phenotypes (Fisher exact test, p = 0.03); as well, supervised analysis of these microarray profiles identified distinct miRNA subsets distinguishing ErbB2-positive from ErbB2-negative and ER-positive from ER-negative breast cancers, independent of other clinically important parameters (patient age; tumor size, node status and proliferation index). In sum, these findings demonstrate that optimized high-throughput microRNA expression profiling offers novel biomarker identification from typically small clinical samples such as breast and prostate cancer biopsies.

Eotaxin-3 and a uniquely conserved gene-expression profile in eosinophilic esophagitis

Abstract: Eosinophilic esophagitis (EE) is an emerging disorder with a poorly understood pathogenesis. In order to define disease mechanisms, we took an empirical approach analyzing esophageal tissue by a genome-wide microarray expression analysis. EE patients had a striking transcript signature involving 1% of the human genome that was remarkably conserved across sex, age, and allergic status and was distinct from that associated with non-EE chronic esophagitis. Notably, the gene encoding the eosinophil-specific chemoattractant eotaxin-3 (also known as CCL26) was the most highly induced gene in EE patients compared with its expression level in healthy individuals. Esophageal eotaxin-3 mRNA and protein levels strongly correlated with tissue eosinophilia and mastocytosis. Furthermore, a single-nucleotide polymorphism in the human eotaxin-3 gene was associated with disease susceptibility. Finally, mice deficient in the eotaxin receptor (also known as CCR3) were protected from experimental EE. These results implicate eotaxin-3 as a critical effector molecule for EE and provide insight into disease pathogenesis.

Specific MicroRNAs Modulate Embryonic Stem Cell–Derived Neurogenesis

Abstract: MicroRNAs (miRNAs) are recently discovered small non-coding transcripts with a broad spectrum of functions described mostly in invertebrates. As post-transcriptional regulators of gene expression, miRNAs trigger target mRNA degradation or translational repression. Although hundreds of miRNAs have been cloned from a variety of mammalian tissues and cells and multiple mRNA targets have been predicted, little is known about their functions. So far, a role of miRNA has only been described in hematopoietic, adipocytic, and muscle differentiation; regulation of insulin secretion; and potentially regulation of cancer growth. Here, we describe miRNA expression profiling in mouse embryonic stem (ES) cell– derived neurogenesis in vitro and show that a number of miRNAs are simultaneously co-induced during differentiation of neural progenitor cells to neurons and astrocytes. There was a clear correlation between miRNA expression profiles in ES cell– derived neurogenesis in vitro and in embryonal neurogenesis in vivo. Using both gain-of-function and loss-of-function approaches, we demonstrate that brain-specific miR-124a and miR-9 molecules affect neural lineage differentiation in the ES cell– derived cultures. In addition, we provide evidence that signal transducer and activator of transcription (STAT) 3, a member of the STAT family pathway, is involved in the function of these miRNAs. We conclude that distinct miRNAs play a functional role in the determination of neural fates in ES cell differentiation.

Small-molecule MDM2 antagonists reveal aberrant p53 signaling in cancer: implications for therapy.

Abstract: The p53 tumor suppressor retains its wild-type conformation and transcriptional activity in half of all human tumors, and its activation may offer a therapeutic benefit. However, p53 function could be compromised by defective signaling in the p53 pathway. Using a small-molecule MDM2 antagonist, nutlin-3, to probe downstream p53 signaling we find that the cell-cycle arrest function of the p53 pathway is preserved in multiple tumor-derived cell lines expressing wild-type p53, but many have a reduced ability to undergo p53-dependent apoptosis. Gene array analysis revealed attenuated expression of multiple apoptosis-related genes. Cancer cells with mdm2 gene amplification were most sensitive to nutlin-3 in vitro and in vivo, suggesting that MDM2 overexpression may be the only abnormality in the p53 pathway of these cells. Nutlin-3 also showed good efficacy against tumors with normal MDM2 expression, suggesting that many of the patients with wild-type p53 tumors may benefit from antagonists of the p53–MDM2 interaction.

Genomics Identifies Medulloblastoma Subgroups That Are Enriched for Specific Genetic Alterations

Abstract: Purpose Traditional genetic approaches to identify gene mutations in cancer are expensive and laborious. Nonetheless, if we are to avoid rejecting effective molecular targeted therapies, we must test these drugs in patients whose tumors harbor mutations in the drug target. We hypothesized that gene expression profiling might be a more rapid and cost-effective method of identifying tumors that contain specific genetic abnormalities. Materials and Methods Gene expression profiles of 46 samples of medulloblastoma were generated using the U133av2 Affymetrix oligonucleotide array and validated using real-time reverse transcriptase polymerase chain reaction (RT-PCR) and immunohistochemistry. Genetic abnormalities were confirmed using fluorescence in situ hybridization (FISH) and direct sequencing. Results Unsupervised analysis of gene expression profiles partitioned medulloblastomas into five distinct subgroups (subgroups A to E). Gene expression signatures that distinguished these subgroups predicted the prese...

Cell-type-specific signatures of microRNAs on target mRNA expression

Abstract: Although it is known that the human genome contains hundreds of microRNA (miRNA) genes and that each miRNA can regulate a large number of mRNA targets, the overall effect of miRNAs on mRNA tissue profiles has not been systematically elucidated. Here, we show that predicted human mRNA targets of several highly tissue-specific miRNAs are typically expressed in the same tissue as the miRNA but at significantly lower levels than in tissues where the miRNA is not present. Conversely, highly expressed genes are often enriched in mRNAs that do not have the recognition motifs for the miRNAs expressed in these tissues. Together, our data support the hypothesis that miRNA expression broadly contributes to tissue specificity of mRNA expression in many human tissues. Based on these insights, we apply a computational tool to directly correlate 3' UTR motifs with changes in mRNA levels upon miRNA overexpression or knockdown. We show that this tool can identify functionally important 3' UTR motifs without cross-species comparison.

Evaluation of DNA microarray results with quantitative gene expression platforms.

Abstract: We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.