A DNA microarray survey of gene expression in normal human tissues

Identification and characterization of RTVP1/GLIPR1-like genes, a novel p53 target gene cluster

Our previous finding of RTVP1 (GLIPR1) as a p53 target gene with tumor suppressor functions prompted us to initiate a genome-wide sequence homology search for RTVP1/GLIPR1-like (GLIPR1L) genes. In this study we report the identification and characterization of a novel p53 target gene cluster that includes human RTVP1 (hRTVP-1) together with two GLIPR1L genes (GLIPR1L1 and GLIPR1L2) on human chromosome 12q21 and mouse Rtvp1 (mRTVP-1 or Glipr1) together with three Glipr1-like (Glipr1l) genes on mouse chromosome 10D1. GLIPR1L1 has two and GLIPR1L2 has five differentially spliced isoforms. Protein homology search revealed that hRTVP-1 gene cluster members share a high degree of identity and homology. GLIPR1L1 is testis-specific, whereas GLIPR1L2 is expressed in different types of tissues, including prostate and bladder. Like hRTVP-1, GLIPR1L1 and GLIPR1L2 are p53 target genes. The similarities of these novel p53 target gene cluster members in protein structure and their association with p53 suggest that these genes may have similar biological functions.

Cell-type specific gene expression profiles of leukocytes in human peripheral blood

Background

Blood is a complex tissue comprising numerous cell types with distinct functions and corresponding gene expression profiles. We attempted to define the cell type specific gene expression patterns for the major constituent cells of blood, including B-cells, CD4+ T-cells, CD8+ T-cells, lymphocytes and granulocytes. We did this by comparing the global gene expression profiles of purified B-cells, CD4+ T-cells, CD8+ T-cells, granulocytes, and lymphocytes using cDNA microarrays.

Results

Unsupervised clustering analysis showed that similar cell populations from different donors share common gene expression profiles. Supervised analyses identified gene expression signatures for B-cells (427 genes), T-cells (222 genes), CD8+ T-cells (23 genes), granulocytes (411 genes), and lymphocytes (67 genes). No statistically significant gene expression signature was identified for CD4+ cells. Genes encoding cell surface proteins were disproportionately represented among the genes that distinguished among the lymphocyte subpopulations. Lymphocytes were distinguishable from granulocytes based on their higher levels of expression of genes encoding ribosomal proteins, while granulocytes exhibited characteristic expression of various cell surface and inflammatory proteins.

Conclusion

The genes comprising the cell-type specific signatures encompassed many of the genes already known to be involved in cell-type specific processes, and provided clues that may prove useful in discovering the functions of many still unannotated genes. The most prominent feature of the cell type signature genes was the enrichment of genes encoding cell surface proteins, perhaps reflecting the importance of specialized systems for sensing the environment to the physiology of resting leukocytes.

Using endophenotypes for pathway clusters to map complex disease genes

Nature determines the complexity of disease etiology and the likelihood of revealing disease genes. While culprit genes for many monogenic diseases have been successfully unraveled, efforts to map major complex disease genes have not been as productive as hoped. The conceptual framework currently adopted to deal with the heterogeneous nature of complex diseases focuses on using homogeneous internal features of the disease phenotype for mapping. However, phenotypic homogeneity does not equal genotypic homogeneity. In this report, we advocate working with well-measured phenotypes portrayed by amounts of transcripts and activities of gene products or their metabolites, which are pertinent to relatively small pathway clusters. Reliable and controlled measures for oligogenic traits resulting from proper dissection efforts may enhance statistical power. The large amounts of information obtained on gene and protein expression from technological advances can add to the power of gene finding, particularly for diseases with unclear etiology. Data-mining tools for dimension reduction can assist biologists to reveal novel molecular endophenotypes. However, there are still hurdles to overcome, including high cost, relatively poor reproducibility and comparability among platforms, the cross-sectional nature of the information, and the accessibility of human tissues. Concerted efforts are required to carry out large-scale prospective studies that are integrated at the levels of phenotype characterization, high throughput experimental techniques, data analyses, and beyond.

Detecting and profiling tissue-selective genes

The widespread use of DNA microarray technologies has generated large amounts of data from various tissue and/or cell types. These data set the stage to answer the question of tissue specificity of human transcriptome in a comprehensive manner. Our focus is to uncover the tissue-gene relationship by identifying genes that are preferentially expressed in a small number of tissue types. The tissue selectivity would shed light on the potential physiological functions of these genes and provides an indispensable reference to compare against disease pathophysiology and to identify or validate tissue-specific drug targets. Here we describe a systematic computational and statistical approach to profile gene expression data to identify tissue-selective genes with the use of a more extensive data set and a well-established multiple-comparison procedure with error rate control. Expression data of 35,152 probe sets in 97 normal human tissue types were analyzed, and 3,919 genes were identified to be selective to one or a few tissue types. We presented results of these tissue-selective genes and compared them to those identified by other studies.

Gene expression patterns in human placenta

The placenta is the principal metabolic, respiratory, excretory, and endocrine organ for the first 9 months of fetal life. Its role in fetal and maternal physiology is remarkably diverse. Because of the central role that the placenta has in fetal and maternal physiology and development, the possibility that variation in placental gene expression patterns might be linked to important abnormalities in maternal or fetal health, or even variations in later life, warrants investigation. As an initial step, we used DNA microarrays to analyze gene expression patterns in 72 samples of amnion, chorion, umbilical cord, and sections of villus parenchyma from 19 human placentas from successful full-term pregnancies. The umbilical cord, chorion, amnion, and villus parenchyma samples were readily distinguished by differences in their global gene-expression patterns, many of which seemed to be related to physiology and histology. Differentially expressed genes have roles that include placental trophoblast secretion, signal transduction, metabolism, immune regulation, cell adhesion, and structure. We found interindividual differences in expression patterns in villus parenchyma and systematic differences between the maternal, fetal, and intermediate layers. A group of genes that was expressed in both the maternal and fetal villus parenchyma sections of placenta included genes that may be associated with preeclampsia. We identified sets of genes whose expression in placenta was significantly correlated with the sex of the fetus. This study provides a rich and diverse picture of the molecular variation in the placenta from healthy pregnancies.

Gene expression analyses reveal molecular relationships among 20 regions of the human CNS

Transcriptional profiling was performed to survey the global expression patterns of 20 anatomically distinct sites of the human central nervous system (CNS). Forty-five non-CNS tissues were also profiled to allow for comparative analyses. Using principal component analysis and hierarchical clustering, we were able to show that the expression patterns of the 20 CNS sites profiled were significantly different from all non-CNS tissues and were also similar to one another, indicating an underlying common expression signature. By focusing our analyses on the 20 sites of the CNS, we were able to show that these 20 sites could be segregated into discrete groups with underlying similarities in anatomical structure and, in many cases, functional activity. These findings suggest that gene expression data can help define CNS function at the molecular level. We have identified subsets of genes with the following patterns of expression: (1) across the CNS, suggesting homeostatic/housekeeping function; (2) in subsets of functionally related sites of the CNS identified by our unsupervised learning analyses; and (3) in single sites within the CNS, indicating their participation in distinct site-specific functions. By performing network analyses on these gene sets, we identified many pathways that are upregulated in particular sites of the CNS, some of which were previously described in the literature, validating both our dataset and approach. In summary, we have generated a database of gene expression that can be used to gain valuable insight into the molecular characterization of functions carried out by different sites of the human CNS.

Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization

Background

The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states.

Results

In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions.

Conclusion

The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.

Microarray analysis of the transcriptome as a stepping stone towards understanding biological systems: practical considerations and perspectives

DNA microarrays have been used to characterize plant transcriptomes to answer various biological questions. While many studies have provided significant insights, there has been great debate about the general reliability of the technology and data analysis. When compared to well-established transcript analysis technologies, such as RNA blot analysis or quantitative reverse transcription-PCR, discrepancies have frequently been observed. The reasons for these discrepancies often relate to the technical and experimental systems. This review-tutorial addresses common problems in microarray analysis and describes: (i) methods to maximize extraction of valuable biological information from the vast amount of microarray data and (ii) approaches to balance resource availability with high scientific standards and technological innovation with peer acceptability.

Cancer as a programmed death of an organism

The hypothesis introduces the idea that there is a critical level of mutagenesis that triggers a program of organism death by means of proliferation of killer cells. Similarly to apoptosis, which is an altruistic suicidal act of a faulty cell threatening the stability of a multicellular organism, a malignant tumor is an altruistic suicide of an individual carrier of harmful alleles threatening genetic stability of the population.

Genome-scale identification of membrane-associated human mRNAs

The subcellular localization of proteins is critical to their biological roles. Moreover, whether a protein is membrane-bound, secreted, or intracellular affects the usefulness of, and the strategies for, using a protein as a diagnostic marker or a target for therapy. We employed a rapid and efficient experimental approach to classify thousands of human gene products as either "membrane-associated/secreted" (MS) or "cytosolic/nuclear" (CN). Using subcellular fractionation methods, we separated mRNAs associated with membranes from those associated with the soluble cytosolic fraction and analyzed these two pools by comparative hybridization to DNA microarrays. Analysis of 11 different human cell lines, representing lymphoid, myeloid, breast, ovarian, hepatic, colon, and prostate tissues, identified more than 5,000 previously uncharacterized MS and more than 6,400 putative CN genes at high confidence levels. The experimentally determined localizations correlated well with in silico predictions of signal peptides and transmembrane domains, but also significantly increased the number of human genes that could be cataloged as encoding either MS or CN proteins. Using gene expression data from a variety of primary human malignancies and normal tissues, we rationally identified hundreds of MS gene products that are significantly overexpressed in tumors compared to normal tissues and thus represent candidates for serum diagnostic tests or monoclonal antibody-based therapies. Finally, we used the catalog of CN gene products to generate sets of candidate markers of organ-specific tissue injury. The large-scale annotation of subcellular localization reported here will serve as a reference database and will aid in the rational design of diagnostic tests and molecular therapies for diverse diseases.

[12] Genomic DNA as a General Cohybridization Standard for Ratiometric Microarrays

Feature variability on ratiometric microarrays is accommodated by simultaneous cohybridization of a labeled reference standard with a labeled experimental sample. An optimal reference standard would provide full and equal representation for all array features from a given genome so that it would function on any array, would represent all features with similar signal intensity, and would be highly reproducible-both technically and biologically-from preparation to preparation and laboratory to laboratory. A low cost and a good shelf life are also highly desirable. Finally, providing for straightforward recovery of RNA prevalence information and for integration of data across multiple, initially unrelated studies would be significant advances over current methods. For virtually all ratiometric array studies published to date the reference standard has been some kind of RNA sample assembled from a number of different cell lines, tissues, or experimental time points. These RNA references fall short of the desired universality, uniformity, and reproducibility criteria, which then affect data quality and integration across studies. Also, the various mixed RNA standards cannot be used to derive RNA prevalence information from an experimental sample. In contrast, genomic DNA is a natural choice to meet all the criteria, although it has not yet been widely exploited for eukaryotic array experiments. Principal stumbling blocks have been achieving high enough absolute signals for large mammalian and plant genomes and finding a way to stabilize labeled DNA so that it can be stored and used with ease. This chapter describes two genomic DNA-labeling methods that make it possible to use genomic DNA as a universal microarray cohybridization standard. The indirect labeling method permits production of a large quantity of a stable genomic DNA standard that can then be quality tested and stored frozen. This optimizes experimental consistency and significantly improves ease of use. This chapter also shows that the genomic DNA reference standard can deliver RNA prevalence measurements from ratiometric array platforms.

Genome-scale identification of membrane-associated human mRNAs

The subcellular localization of proteins is critical to their biological roles. Moreover, whether a protein is membrane-bound, secreted, or intracellular affects the usefulness of, and the strategies for, using a protein as a diagnostic marker or a target for therapy. We employed a rapid and efficient experimental approach to classify thousands of human gene products as either "membrane-associated/secreted" (MS) or "cytosolic/nuclear" (CN). Using subcellular fractionation methods, we separated mRNAs associated with membranes from those associated with the soluble cytosolic fraction and analyzed these two pools by comparative hybridization to DNA microarrays. Analysis of 11 different human cell lines, representing lymphoid, myeloid, breast, ovarian, hepatic, colon, and prostate tissues, identified more than 5,000 previously uncharacterized MS and more than 6,400 putative CN genes at high confidence levels. The experimentally determined localizations correlated well with in silico predictions of signal peptides and transmembrane domains, but also significantly increased the number of human genes that could be cataloged as encoding either MS or CN proteins. Using gene expression data from a variety of primary human malignancies and normal tissues, we rationally identified hundreds of MS gene products that are significantly overexpressed in tumors compared to normal tissues and thus represent candidates for serum diagnostic tests or monoclonal antibody-based therapies. Finally, we used the catalog of CN gene products to generate sets of candidate markers of organ-specific tissue injury. The large-scale annotation of subcellular localization reported here will serve as a reference database and will aid in the rational design of diagnostic tests and molecular therapies for diverse diseases.

Gene expression profiling differentiates germ cell tumors from other cancers and defines subtype-specific signatures

Germ cell tumors (GCTs) of the testis are the predominant cancer among young men. We analyzed gene expression profiles of 50 GCTs of various subtypes, and we compared them with 443 other common malignant tumors of epithelial, mesenchymal, and lymphoid origins. Significant differences in gene expression were found among major histological subtypes of GCTs, and between them and other malignancies. We identified 511 genes, belonging to several critical functional groups such as cell cycle progression, cell proliferation, and apoptosis, to be significantly differentially expressed in GCTs compared with other tumor types. Sixty-five genes were sufficient for the construction of a GCT class predictor of high predictive accuracy (100% training set, 96% test set), which might be useful in the diagnosis of tumors of unknown primary origin. Previously described diagnostic and prognostic markers were found to be expressed by the appropriate GCT subtype (AFP, POU5F1, POV1, CCND2, and KIT). Several additional differentially expressed genes were identified in teratomas (EGR1 and MMP7), yolk sac tumors (PTPN13 and FN1), and seminomas (NR6A1, DPPA4, and IRX1). Dynamic computation of interaction networks and mapping to existing pathways knowledge databases revealed a potential role of EGR1 in p21-induced cell cycle arrest and intrinsic chemotherapy resistance of mature teratomas.