A DNA microarray survey of gene expression in normal human tissues

Disease signatures are robust across tissues and experiments

Meta-analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta-analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large-scale analysis of disease-associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta-analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi-tissue systems models of disease pathology using public data.

The B7 family member B7-H6 is a tumor cell ligand for the activating natural killer cell receptor NKp30 in humans

Cancer development is often associated with the lack of specific and efficient recognition of tumor cells by the immune system. Natural killer (NK) cells are lymphocytes of the innate immune system that participate in the elimination of tumors. We report the identification of a tumor cell surface molecule that binds NKp30, a human receptor which triggers antitumor NK cell cytotoxicity and cytokine secretion. This previously unannotated gene belongs to the B7 family and, hence, was designated B7-H6. B7-H6 triggers NKp30-mediated activation of human NK cells. B7-H6 was not detected in normal human tissues but was expressed on human tumor cells, emphasizing that the expression of stress-induced self-molecules associated with cell transformation serves as a mode of cell recognition in innate immunity.

Genes that mediate breast cancer metastasis to the brain

The molecular basis for breast cancer metastasis to the brain is largely unknown. Brain relapse typically occurs years after the removal of a breast tumour, suggesting that disseminated cancer cells must acquire specialized functions to take over this organ. Here we show that breast cancer metastasis to the brain involves mediators of extravasation through non-fenestrated capillaries, complemented by specific enhancers of blood-brain barrier crossing and brain colonization. We isolated cells that preferentially infiltrate the brain from patients with advanced disease. Gene expression analysis of these cells and of clinical samples, coupled with functional analysis, identified the cyclooxygenase COX2 (also known as PTGS2), the epidermal growth factor receptor (EGFR) ligand HBEGF, and the alpha2,6-sialyltransferase ST6GALNAC5 as mediators of cancer cell passage through the blood-brain barrier. EGFR ligands and COX2 were previously linked to breast cancer infiltration of the lungs, but not the bones or liver, suggesting a sharing of these mediators in cerebral and pulmonary metastases. In contrast, ST6GALNAC5 specifically mediates brain metastasis. Normally restricted to the brain, the expression of ST6GALNAC5 in breast cancer cells enhances their adhesion to brain endothelial cells and their passage through the blood-brain barrier. This co-option of a brain sialyltransferase highlights the role of cell-surface glycosylation in organ-specific metastatic interactions.

Genetic control of human brain transcript expression in Alzheimer disease

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

Impact of breed and sex on porcine endocrine transcriptome: a bayesian biometrical analysis

Background

Transcriptome variability is due to genetic and environmental causes, much like any other complex phenotype. Ascertaining the transcriptome differences between individuals is an important step to understand how selection and genetic drift may affect gene expression. To that end, extant divergent livestock breeds offer an ideal genetic material.

Results

We have analyzed with microarrays five tissues from the endocrine axis (hypothalamus, adenohypophysis, thyroid gland, gonads and fat tissue) of 16 pigs from both sexes pertaining to four extreme breeds (Duroc, Large White, Iberian and a cross with SinoEuropean hybrid line). Using a Bayesian linear model approach, we observed that the largest breed variability corresponded to the male gonads, and was larger than at the remaining tissues, including ovaries. Measurement of sex hormones in peripheral blood at slaughter did not detect any breed-related differences. Not unexpectedly, the gonads were the tissue with the largest number of sex biased genes. There was a strong correlation between sex and breed bias expression, although the most breed biased genes were not the most sex biased genes. A combined analysis of connectivity and differential expression suggested three biological processes as being primarily different between breeds: spermatogenesis, muscle differentiation and several metabolic processes.

Conclusion

These results suggest that differences across breeds in gene expression of the male gonads are larger than in other endocrine tissues in the pig. Nevertheless, the strong presence of breed biased genes in the male gonads cannot be explained solely by changes in spermatogenesis nor by differences in the reproductive tract development.

Expression profile of mammalian microRNAs in endometrioid adenocarcinoma

MicroRNAs (miRNAs) are small noncoding RNAs of 19-24 nucleotides in length and involved in gene expression regulation. They are associated with cell proliferation, differentiation, apoptosis, and carcinogenesis. The specific expression profiles of miRNAs have been found in many human cancers, but there are few studies on endometrioid adenocarcinoma. We found the miRNA expression profile in 10 pairs of endometrioid adenocarcinoma and adjacent nontumorous endometrium using human miRNA microarray. Seventeen miRNAs exhibited higher expression and six miRNAs exhibited lower expression in endometrioid adenocarcinoma samples than those in the nontumorous samples in the microarray. Of those, the miR-205, miR-449, and miR-429 were greatly enriched; in contrast the miR-204, miR-99b, and miR-193b were greatly downregulated in adenocarcinoma tissues. The expressions of these six miRNAs were validated using real time reverse transcription-PCR. This information may provide the candidate miRNA genome for further confirming the role of miRNAs in carcinogenesis of endometrioid adenocarcinoma and potentially serving as a diagnostic or therapeutic tool in endometrioid adenocarcinoma.

Network-based prediction of human tissue-specific metabolism

Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.

Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification

Background

Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.

Results

Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response.

Conclusion

Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.

Mapping key features of transcriptional regulatory circuitry in embryonic stem cells

The process by which a single fertilized egg develops into a human being with more than 200 cell types--each with a distinct gene expression pattern controlling its cellular state--is poorly understood. Knowledge of the transcriptional regulatory circuitry that establishes and maintains gene expression programs in mammalian cells is fundamental to understanding development and should provide the foundation for improved diagnosis and treatment of disease. Although it is not yet feasible to map the entirety of this circuitry in vertebrate cells, recent work in embryonic stem (ES) cells has demonstrated that core features of the circuitry can be discovered through studies involving selected regulators. Here, we highlight the fundamental insights that have emerged from studies that examined the role of transcription factors, chromatin regulators, signaling pathways, and noncoding RNAs in the regulatory circuitry of ES cells. Maps of regulatory circuitry and the insights that have emerged from these studies have improved our understanding of global gene expression and are facilitating efforts to reprogram cells for disease therapeutics and regenerative medicine.

The proteogenomic path towards biomarker discovery

The desire for biomarkers for diagnosis and prognosis of diseases has never been greater. With the availability of genome data and an increased availability of proteome data, the discovery of biomarkers has become increasingly feasible. However, the task is daunting and requires collaborations among researchers working in the fields of transplantation, immunology, genetics, molecular biology, biostatistics and bioinformatics. With the advancement of high throughput omic techniques such as genomics and proteomics (collectively known as proteogenomics), efforts have been made to develop diagnostic tools from new and to-be discovered biomarkers. Yet biomarker validation, particularly in organ transplantation, remains challenging because of the lack of a true gold standard for diagnostic categories and analytical bottlenecks that face high-throughput data deconvolution. Even though microarray technique is relatively mature, proteomics is still growing with regards to data normalization and analysis methods. Study design, sample selection and rigorous data analysis are the critical issues for biomarker discovery using high-throughput proteogenomic technologies that combine the use and strengths of both genomics and proteomics. In this review, we look into the current status and latest developments in the field of biomarker discovery using genomics and proteomics related to organ transplantation, with an emphasis on the evolution of proteomic technologies.

Fast approximate hierarchical clustering using similarity heuristics

Background

Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers.

Results

We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings.

Conclusion

The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL

Tissue-specific expression and post-translational modification of histone H3 variants

Analyses of histone H3 from 10 rat tissues using a Middle Down proteomics platform revealed tissue-specific differences in their expression and global PTM abundance. ESI/FTMS with electron capture dissociation showed that, in general, these proteins were hypomodified in heart, liver and testes. H3.3 was hypermodified compared to H3.2 in some, but not all tissues. In addition, a novel rat testes-specific H3 protein was identified with this approach.

The multifaceted roles of the receptor tyrosine kinase ROS in development and cancer

The proto-oncogene receptor tyrosine kinase ROS was originally discovered through the identification of oncogenic variants isolated from tumors. These discoveries spearheaded a body of work aimed at elucidating the function of this evolutionarily conserved receptor in development and cancer. Through genetic and biochemical approaches, progress in the characterization of ROS points to distinctive roles in the program of epithelial cell differentiation during the development of a variety of organs. Although substantial, these advances remain hampered by the absence of an identified ligand, making ROS one of the last two remaining orphan receptor tyrosine kinases. Recent studies on the oncogenic activation of ROS as a result of different chromosomal rearrangements found in brain and lung cancers have shed light on the molecular mechanisms underlying ROS transforming activities. ROS and its oncogenic variants therefore constitute clinically relevant targets for cancer therapeutic intervention. This review highlights the various roles that this receptor plays in multiple system networks in normalcy and disease and points to future directions towards the elucidation of ROS function in the context of ligand identification, signaling pathways and clinical applications.

Identification of candidate prostate cancer genes through comparative expression-profiling of seminal vesicle

BACKGROUND

Prostate cancer is the most frequently diagnosed cancer among men in the United States. In contrast, cancer of the seminal vesicle is exceedingly rare, despite that the prostate and seminal vesicle share similar histology, secretory function, androgen dependency, blood supply, and (in part) embryonic origin. We hypothesized that gene-expression differences between prostate and seminal vesicle might inform mechanisms underlying the higher incidence of prostate cancer.

METHODS

Whole-genome DNA microarrays were used to profile gene expression of 11 normal prostate and 7 seminal vesicle specimens (including six matched pairs) obtained from radical prostatectomy. Supervised analysis was used to identify genes differentially expressed between normal prostate and seminal vesicle, and this list was then cross-referenced to genes differentially expressed between normal and cancerous prostate. Expression patterns of selected genes were confirmed by immunohistochemistry using a tissue microarray.

RESULTS

We identified 32 genes that displayed a highly statistically significant expression pattern with highest levels in seminal vesicle, lower levels in normal prostate, and lowest levels in prostate cancer. Among these genes was the known candidate prostate tumor suppressor GSTP1 (involved in xenobiotic detoxification). The expression pattern of GSTP1 and four other genes, ABCG2 (xenobiotic transport), CRABP2 (retinoic acid signaling), GATA3 (lineage-specific transcription), and SLPI (immune response), was confirmed by immunohistochemistry.

CONCLUSIONS

Our findings identify candidate prostate cancer genes whose reduced expression in prostate (compared to seminal vesicle) may be permissive to prostate cancer initiation. Such genes and their pathways may inform mechanisms of prostate carcinogenesis, and suggest new opportunities for prostate cancer prevention.

Research resource: nuclear hormone receptor expression in the endocrine pancreas

The endocrine pancreas comprises the islets of Langerhans, tiny clusters of cells that contribute only about 2% to the total pancreas mass. However, this little endocrine organ plays a critical role in maintaining glucose homeostasis by the regulated secretion of insulin (by beta-cells) and glucagon (by alpha-cells). The rapid increase in the incidence of diabetes worldwide has spurred renewed interest in islet cell biology. Some of the most widely prescribed oral drugs for treating type 2 diabetes include agents that bind and activate the nuclear hormone receptor, peroxisome proliferator-activated receptor-gamma. As a first step in addressing potential roles of peroxisome proliferator-activated receptor-gamma and other nuclear hormone receptors (NHRs) in the biology of the endocrine pancreas, we have used quantitative real-time PCR to profile the expression of all 49 members of the mouse NHR superfamily in primary islets, and cell lines that represent alpha-cells (alphaTC1) and beta-cells (betaTC6 and MIN6). In summary, 19 NHR members were highly expressed in both alpha- and beta-cell lines, 13 receptors showed predominant expression (at least an 8-fold difference) in alpha- vs. beta-cell lines, and 10 NHRs were not expressed in the endocrine pancreas. In addition we evaluated the relative expression of these transcription factors during hyperglycemia and found that 16 NHRs showed significantly altered mRNA levels in mouse islets. A similar survey was conducted in primary human islets to reveal several significant differences in NHR expression between mouse and man. These data identify potential therapeutic targets in the endocrine pancreas for the treatment of diabetes mellitus.

Systematic evaluation of candidate blood markers for detecting ovarian cancer

BACKGROUND

Epithelial ovarian cancer is a significant cause of mortality both in the United States and worldwide, due largely to the high proportion of cases that present at a late stage, when survival is extremely poor. Early detection of epithelial ovarian cancer, and of the serous subtype in particular, is a promising strategy for saving lives. The low prevalence of ovarian cancer makes the development of an adequately sensitive and specific test based on blood markers very challenging. We evaluated the performance of a set of candidate blood markers and combinations of these markers in detecting serous ovarian cancer.

METHODS AND FINDINGS

We selected 14 candidate blood markers of serous ovarian cancer for which assays were available to measure their levels in serum or plasma, based on our analysis of global gene expression data and on literature searches. We evaluated the performance of these candidate markers individually and in combination by measuring them in overlapping sets of serum (or plasma) samples from women with clinically detectable ovarian cancer and women without ovarian cancer. Based on sensitivity at high specificity, we determined that 4 of the 14 candidate markers--MUC16, WFDC2, MSLN and MMP7--warrant further evaluation in precious serum specimens collected months to years prior to clinical diagnosis to assess their utility in early detection. We also reported differences in the performance of these candidate blood markers across histological types of epithelial ovarian cancer.

CONCLUSIONS

By systematically analyzing the performance of candidate blood markers of ovarian cancer in distinguishing women with clinically apparent ovarian cancer from women without ovarian cancer, we identified a set of serum markers with adequate performance to warrant testing for their ability to identify ovarian cancer months to years prior to clinical diagnosis. We argued for the importance of sensitivity at high specificity and of magnitude of difference in marker levels between cases and controls as performance metrics and demonstrated the importance of stratifying analyses by histological type of ovarian cancer. Also, we discussed the limitations of studies (like this one) that use samples obtained from symptomatic women to assess potential utility in detection of disease months to years prior to clinical detection.

Concordant gene expression in leukemia cells and normal leukocytes is associated with germline cis-SNPs

The degree to which gene expression covaries between different primary tissues within an individual is not well defined. We hypothesized that expression that is concordant across tissues is more likely influenced by genetic variability than gene expression which is discordant between tissues. We quantified expression of 11,873 genes in paired samples of primary leukemia cells and normal leukocytes from 92 patients with acute lymphoblastic leukemia (ALL). Genetic variation at >500,000 single nucleotide polymorphisms (SNPs) was also assessed. The expression of only 176/11,783 (1.5%) genes was correlated (p<0.008, FDR = 25%) in the two tissue types, but expression of a high proportion (20 of these 176 genes) was significantly related to cis-SNP genotypes (adjusted p<0.05). In an independent set of 134 patients with ALL, 14 of these 20 genes were validated as having expression related to cis-SNPs, as were 9 of 20 genes in a second validation set of HapMap cell lines. Genes whose expression was concordant among tissue types were more likely to be associated with germline cis-SNPs than genes with discordant expression in these tissues; genes affected were involved in housekeeping functions (GSTM2, GAPDH and NCOR1) and purine metabolism.

Transcriptome architecture across tissues in the pig

Background

Artificial selection has resulted in animal breeds with extreme phenotypes. As an organism is made up of many different tissues and organs, each with its own genetic programme, it is pertinent to ask: How relevant is tissue in terms of total transcriptome variability? Which are the genes most distinctly expressed between tissues? Does breed or sex equally affect the transcriptome across tissues?

Results

In order to gain insight on these issues, we conducted microarray expression profiling of 16 different tissues from four animals of two extreme pig breeds, Large White and Iberian, two males and two females. Mixed model analysis and neighbor – joining trees showed that tissues with similar developmental origin clustered closer than those with different embryonic origins. Often a sound biological interpretation was possible for overrepresented gene ontology categories within differentially expressed genes between groups of tissues. For instance, an excess of nervous system or muscle development genes were found among tissues of ectoderm or mesoderm origins, respectively. Tissue accounted for ~11 times more variability than sex or breed. Nevertheless, we were able to confidently identify genes with differential expression across tissues between breeds (33 genes) and between sexes (19 genes). The genes primarily affected by sex were overall different than those affected by breed or tissue. Interaction with tissue can be important for differentially expressed genes between breeds but not so much for genes whose expression differ between sexes.

Conclusion

Embryonic development leaves an enduring footprint on the transcriptome. The interaction in gene × tissue for differentially expressed genes between breeds suggests that animal breeding has targeted differentially each tissue's transcriptome.

Role of the TMPRSS2-ERG gene fusion in prostate cancer

TMPRSS2-ERG gene fusions are the predominant molecular subtype of prostate cancer. Here, we explored the role of TMPRSS2-ERG gene fusion product using in vitro and in vivo model systems. Transgenic mice expressing the ERG gene fusion product under androgen-regulation develop mouse prostatic intraepithelial neoplasia (PIN), a precursor lesion of prostate cancer. Introduction of the ERG gene fusion product into primary or immortalized benign prostate epithelial cells induced an invasion-associated transcriptional program but did not increase cellular proliferation or anchorage-independent growth. These results suggest that TMPRSS2-ERG may not be sufficient for transformation in the absence of secondary molecular lesions. Transcriptional profiling of ERG knockdown in the TMPPRSS2-ERG-positive prostate cancer cell line VCaP revealed decreased expression of genes over-expressed in prostate cancer versus PIN and genes overexpressed in ETS-positive versus -negative prostate cancers in addition to inhibiting invasion. ERG knockdown in VCaP cells also induced a transcriptional program consistent with prostate differentiation. Importantly, VCaP cells and benign prostate cells overexpressing ERG directly engage components of the plasminogen activation pathway to mediate cellular invasion, potentially representing a downstream ETS target susceptible to therapeutic intervention. Our results support previous work suggesting that TMPRSS2-ERG fusions mediate invasion, consistent with the defining histologic distinction between PIN and prostate cancer.

Using formal concept analysis for microarray data comparison

Microarray technologies, which can measure tens of thousands of gene expression values simultaneously in a single experiment, have become a common research method for biomedical researchers. Computational tools to analyze microarray data for biological discovery are needed. In this paper, we investigate the feasibility of using formal concept analysis (FCA) as a tool for microarray data analysis. The method of FCA builds a (concept) lattice from the experimental data together with additional biological information. For microarray data, each vertex of the lattice corresponds to a subset of genes that are grouped together according to their expression values and some biological information related to gene function. The lattice structure of these gene sets might reflect biological relationships in the dataset. Similarities and differences between experiments can then be investigated by comparing their corresponding lattices according to various graph measures. We apply our method to microarray data derived from influenza-infected mouse lung tissue and healthy controls. Our preliminary results show the promise of our method as a tool for microarray data analysis.

Combined analysis reveals a core set of cycling genes

The simultaneous analysis of expression data from multiple species reveals a core set of conserved cycling genes that is much larger than previously thought.

Background

Global transcript levels throughout the cell cycle have been characterized using microarrays in several species. Early analysis of these experiments focused on individual species. More recently, a number of studies have concluded that a surprisingly small number of genes conserved in two or more species are periodically transcribed in these species. Combining and comparing data from multiple species is challenging because of noise in expression data, the different synchronization and scoring methods used, and the need to determine an accurate set of homologs.

Results

To solve these problems, we developed and applied a new algorithm to analyze expression data from multiple species simultaneously. Unlike previous studies, we find that more than 20% of cycling genes in budding yeast have cycling homologs in fission yeast and 5% to 7% of cycling genes in each of four species have cycling homologs in all other species. These conserved cycling genes display much stronger cell cycle characteristics in several complementary high throughput datasets.

Essentiality analysis for yeast and human genes confirms these findings. Motif analysis indicates conservation in the corresponding regulatory mechanisms. Gene Ontology analysis and analysis of the genes in the conserved sets sheds light on the evolution of specific subfunctions within the cell cycle.

Conclusion

Our results indicate that the conservation in cyclic expression patterns is much greater than was previously thought. These genes are highly enriched for most cell cycle categories, and a large percentage of them are essential, supporting our claim that cross-species analysis can identify the core set of cycling genes.

Characterization of a newly developed chicken 44K Agilent microarray

Background

The development of microarray technology has greatly enhanced our ability to evaluate gene expression. In theory, the expression of all genes in a given organism can be monitored simultaneously. Sequencing of the chicken genome has provided the crucial information for the design of a comprehensive chicken transcriptome microarray. A long oligonucleotide microarray has been manually curated and designed by our group and manufactured using Agilent inkjet technology. This provides a flexible and powerful platform with high sensitivity and specificity for gene expression studies.

Results

A chicken 60-mer oligonucleotide microarray consisting of 42,034 features including the entire Marek's disease virus, two avian influenza virus (H5N2 and H5N3), and 150 chicken microRNAs has been designed and tested. In an important validation study, total RNA isolated from four major chicken tissues: cecal tonsil (C), ileum (I), liver (L), and spleen (S) were used for comparative hybridizations. More than 95% of spots had high signal noise ratio (SNR > 10). There were 2886, 2660, 358, 3208, 3355, and 3710 genes differentially expressed between liver and spleen, spleen and cecal tonsil, cecal tonsil and ileum, liver and cecal tonsil, liver and ileum, spleen and ileum (P < 10^-7), respectively. There were a number of tissue-selective genes for cecal tonsil, ileum, liver, and spleen identified (95, 71, 535, and 108, respectively; P < 10^-7). Another highlight of these data revealed that the antimicrobial peptides GAL1, GAL2, GAL6 and GAL7 were highly expressed in the spleen compared to other tissues tested.

Conclusion

A chicken 60-mer oligonucleotide 44K microarray was designed and validated in a comprehensive survey of gene expression in diverse tissues. The results of these tissue expression analyses have demonstrated that this microarray has high specificity and sensitivity, and will be a useful tool for chicken functional genomics. Novel data on the expression of putative tissue specific genes and antimicrobial peptides is highlighted as part of this comprehensive microarray validation study. The information for accessing and ordering this 44K chicken array can be found at

Transcriptional co-expression and co-regulation of genes coding for components of the oxidative phosphorylation system

Background

The mitochondrial oxidative phosphorylation (OXPHOS) is critical for energy (ATP) production in eukaryotic cells. It was previously shown that genes coding for mitochondrial proteins involved in energy production co-express at the RNA level. Because the OXPHOS enzymes are multimeric complexes, we tested the hypothesis that genes coding for components of specific complexes are also co-regulated at the transcriptional level, and share common regulatory elements in their promoters.

Results

We observed for the first time that, not only OXPHOS genes as a group co-express, but there is a co-expression of genes within each of the five OXPHOS enzyme complexes, showing a higher degree of complexity in gene co-regulation. In silico analysis of homologous promoter sequences in mammals identified the likely core promoter elements for most genes encoding OXPHOS subunits/assembly factors. The results included a significant abundance of previously identified sites (e.g. NRF1, NRF2, ERRA and YY1), as well as several sites that had not been previously detected. Although we identified patterns that correlated to OXPHOS gene expression, we did not detect an OXPHOS complex-specific arrangement of transcription factor binding sites within the core promoter that could explain the tight co-expression of these functionally related genes.

Conclusion

This study mapped the core promoters of most OXPHOS related genes and provided an example of gene expression regulation based on the final protein arrangement within a linear metabolic pathway.

Switch-like genes populate cell communication pathways and are enriched for extracellular proteins

BACKGROUND

Recent studies have placed gene expression in the context of distribution profiles including housekeeping, graded, and bimodal (switch-like). Single-gene studies have shown bimodal expression results from healthy cell signaling and complex diseases such as cancer, however developing a comprehensive list of human bimodal genes has remained a major challenge due to inherent noise in human microarray data. This study presents a two-component mixture analysis of mouse gene expression data for genes on the Affymetrix MG-U74Av2 array for the detection and annotation of switch-like genes. Two-component normal mixtures were fit to the data to identify bimodal genes and their potential roles in cell signaling and disease progression.

RESULTS

Seventeen percent of the genes on the MG-U74Av2 array (1519 out of 9091) were identified as bimodal or switch-like. KEGG pathways significantly enriched for bimodal genes included ECM-receptor interaction, cell communication, and focal adhesion. Similarly, the GO biological process "cell adhesion" and cellular component "extracellular matrix" were significantly enriched. Switch-like genes were found to be associated with such diseases as congestive heart failure, Alzheimer's disease, arteriosclerosis, breast neoplasms, hypertension, myocardial infarction, obesity, rheumatoid arthritis, and type I and type II diabetes. In diabetes alone, over two hundred bimodal genes were in a different mode of expression compared to normal tissue.

CONCLUSION

This research identified and annotated bimodal or switch-like genes in the mouse genome using a large collection of microarray data. Genes with bimodal expression were enriched within the cell membrane and extracellular environment. Hundreds of bimodal genes demonstrated alternate modes of expression in diabetic muscle, pancreas, liver, heart, and adipose tissue. Bimodal genes comprise a candidate set of biomarkers for a large number of disease states because their expressions are tightly regulated at the transcription level.

Epigenetic hereditary transcription profiles II, aging revisited

Background

Previously, we have shown that deviations from the average transcription profile of a group of functionally related genes can be epigenetically transmitted to daughter cells, thereby implicating nuclear programming as the cause. As a first step in further characterizing this phenomenon it was necessary to determine to what extent such deviations occur in non-tumorigenic tissues derived from normal individuals. To this end, a microarray database derived from 90 human donors aged between 22 to 87 years was used to study deviations from the average transcription profile of the proteasome genes.

Results

Increase in donor age was found to correlate with a decrease in deviations from the general transcription profile with this decline being gender-specific. The age-related index declined at a faster rate for males although it started from a higher level. Additionally, transcription profiles from similar tissues were more alike than those from different tissues, indicating that deviations arise during differentiation.

Conclusion

These findings suggest that aging and differentiation are related to epigenetic changes that alter the transcription profile of proteasomal genes. Since alterations in the structure and function of the proteasome are unlikely, such changes appear to occur without concomitant change in gene function.

These findings, if confirmed, may have a significant impact on our understanding of the aging process.

Open peer review

This article was reviewed by Nathan Bowen (nominated by I. King Jordan), Timothy E. Reddy (nominated by Charles DeLisi) and by Martijn Huynen. For the full reviews, please go to the Reviewers'comments section.

CD200: a putative therapeutic target in cancer

CD200 was recently described as a new prognosis factor in multiple myeloma and acute myeloid leukemia. CD200 is a membrane glycoprotein that imparts an immunoregulatory signal through CD200R, leading to the suppression of T-cell-mediated immune responses. We investigated the expression of CD200 in cancer using publicly available gene expression data. CD200 gene expression in normal or malignant human tissues or cell lines was obtained from the Oncomine Cancer Microarray database, Amazonia database and the ITTACA database. We found significant overexpression of CD200 in renal carcinoma, head and neck carcinoma, testicular cancer, malignant mesothelioma, colon carcinoma, MGUS/smoldering myeloma, and in chronic lymphocytic leukemia compared to their normal cells or their tissue counterparts. Moreover, we show that CD200 expression is associated with tumor progression in various cancers. Taken together, these data suggest that CD200 is a potential therapeutic target and prognostic factor for a large array of malignancies.

Microarray expression profiles of 20.000 genes across 23 healthy porcine tissues

BACKGROUND

Gene expression microarrays have been intensively applied to screen for genes involved in specific biological processes of interest such as diseases or responses to environmental stimuli. For mammalian species, cataloging of the global gene expression profiles in large tissue collections under normal conditions have been focusing on human and mouse genomes but is lacking for the pig genome.

METHODOLOGY/PRINCIPAL FINDINGS

Here we present the results from a large-scale porcine study establishing microarray cDNA expression profiles of approximately 20.000 genes across 23 healthy tissues. As expected, a large portion of the genes show tissue specific expression in agreement with mappings to gene descriptions, Gene Ontology terms and KEGG pathways. Two-way hierarchical clustering identified expected tissue clusters in accordance with tissue type and a number of cDNA clusters having similar gene expression patterns across tissues. For one of these cDNA clusters, we demonstrate that possible tissue associated gene function can be inferred for previously uncharacterized genes based on their shared expression patterns with functionally annotated genes. We show that gene expression in common porcine tissues is similar to the expression in homologous tissues of human.

CONCLUSIONS/SIGNIFICANCE

The results from this study constitute a valuable and publicly available resource of basic gene expression profiles in normal porcine tissues and will contribute to the identification and functional annotation of porcine genes.

AKAP13, a RhoA GTPase-specific guanine exchange factor, is a novel regulator of TLR2 signaling

Members of the guanine exchange factor (GEF) family of scaffold proteins are involved in the integration of signal flow downstream of many receptors in adaptive immunity. However, the full complement of GEFs that function downstream of Toll-like receptors (TLRs) requires further identification and functional understanding. By systematically integrating expression profiles from immune and epithelial cells with functional studies, we demonstrate that protein kinase A anchoring protein 13 (AKAP13), a scaffold protein with GEF activity, is an activator of NF-kappaB downstream of TLR2 signaling. Stimulation of the human macrophage cell line THP-1 and epithelial cells with a TLR2 ligand caused a significant up-regulation in AKAP13 mRNA, corresponding to an increase in protein expression. Analysis of TLR2 reporter cell lines deficient in AKAP13 expression revealed significantly reduced NF-kappaB activation and reduced secretion of interleukin-8 and MCP-1 in response to specific ligand stimulation. Furthermore, NF-kappaB activation was partially inhibited by a GEF-deficient AKAP13 mutant. AKAP13 was also involved in phosphorylation of JNK but not of extracellular signal-regulated kinase ERK1 and -2 following ligand stimulation. Together, our results suggest that AKAP13 plays a role in TLR2-mediated NF-kappaB activation and suggest that GEF-containing scaffold proteins may confer specificity to innate immune responses downstream of TLRs.

Automated discovery of functional generality of human gene expression programs

An important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-kappaB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal "cross-talk," and genes from high generality programs may maintain common physiological responses that go awry in disease states. Further, our method is multipurpose, and can be applied readily to novel compendia of biological data.

Cartilage-selective genes identified in genome-scale analysis of non-cartilage and cartilage gene expression

Background

Cartilage plays a fundamental role in the development of the human skeleton. Early in embryogenesis, mesenchymal cells condense and differentiate into chondrocytes to shape the early skeleton. Subsequently, the cartilage anlagen differentiate to form the growth plates, which are responsible for linear bone growth, and the articular chondrocytes, which facilitate joint function. However, despite the multiplicity of roles of cartilage during human fetal life, surprisingly little is known about its transcriptome. To address this, a whole genome microarray expression profile was generated using RNA isolated from 18–22 week human distal femur fetal cartilage and compared with a database of control normal human tissues aggregated at UCLA, termed Celsius.

Results

161 cartilage-selective genes were identified, defined as genes significantly expressed in cartilage with low expression and little variation across a panel of 34 non-cartilage tissues. Among these 161 genes were cartilage-specific genes such as cartilage collagen genes and 25 genes which have been associated with skeletal phenotypes in humans and/or mice. Many of the other cartilage-selective genes do not have established roles in cartilage or are novel, unannotated genes. Quantitative RT-PCR confirmed the unique pattern of gene expression observed by microarray analysis.

Conclusion

Defining the gene expression pattern for cartilage has identified new genes that may contribute to human skeletogenesis as well as provided further candidate genes for skeletal dysplasias. The data suggest that fetal cartilage is a complex and transcriptionally active tissue and demonstrate that the set of genes selectively expressed in the tissue has been greatly underestimated.

Characterization of microRNA expression profiles in normal human tissues

Background

Measuring the quantity of miRNAs in tissues of different physiological and pathological conditions is an important first step to investigate the functions of miRNAs. Matched samples from normal state can provide essential baseline references to analyze the variation of miRNA abundance.

Results

We provided expression data of 345 miRNAs in 40 normal human tissues, which identified universally expressed miRNAs, and several groups of miRNAs expressed exclusively or preferentially in certain tissue types. Many miRNAs with co-regulated expression patterns are located within the same genomic clusters, and candidate transcriptional factors that control the pattern of their expression may be identified by a comparative genomic strategy. Hierarchical clustering of normal tissues by their miRNA expression profiles basically followed the structure, anatomical locations, and physiological functions of the organs, suggesting that functions of a miRNA could be appreciated by linking to the biologies of the tissues in which it is uniquely expressed. Many predicted target genes of miRNAs that had specific reduced expression in brain and peripheral blood mononuclear cells are required for embryonic development of the nervous and hematopoietic systems based on database search.

Conclusion

We presented a global view of tissue distribution of miRNAs in relation to their chromosomal locations and genomic structures. We also described evidence from the cis-regulatory elements and the predicted target genes of miRNAs to support their tissue-specific functional roles to regulate the physiologies of the normal tissues in which they are expressed.

MicroRNA expression profiles in head and neck cancer cell lines

Non-coding RNA molecules such as microRNAs (miRNAs) may play an important role in human carcinogenesis. Their expression has been profiled in many human cancers but there are few published studies in head and neck cancer. In this study, the relative expression of 261 mature miRNA genes was determined in nine head and neck cancer cell lines using an oligonucleotide array platform. Thirty-three miRNAs in the array were found to be highly expressed and 22 showed low levels of expression in all cell lines. Notable was the high expression of miR-21 and miR-205. Expression of several miRNAs was validated using Northern blot analysis. Potential targets of validated miRNAs included tumor suppressor genes, kinesin family member 1B isoform alpha (KIF1B), and hypermethylated in cancer 2 (HIC2), and pleomorphic adenoma gene 1 (PLAG1). This study provides the largest genomewide survey of mature miRNA transcripts in head and neck cancer cell lines.

Single molecule transcription profiling with AFM

Established techniques for global gene expression profiling, such as microarrays, face fundamental sensitivity constraints. Due to greatly increasing interest in examining minute samples from micro-dissected tissues, including single cells, unorthodox approaches, including molecular nanotechnologies, are being explored in this application. Here, we examine the use of single molecule, ordered restriction mapping, combined with AFM, to measure gene transcription levels from very low abundance samples. We frame the problem mathematically, using coding theory, and present an analysis of the critical error sources that may serve as a guide to designing future studies. We follow with experiments detailing the construction of high density, single molecule, ordered restriction maps from plasmids and from cDNA molecules, using two different enzymes, a result not previously reported. We discuss these results in the context of our calculations.

Three methods for optimization of cross-laboratory and cross-platform microarray expression data

Microarray gene expression data becomes more valuable as our confidence in the results grows. Guaranteeing data quality becomes increasingly important as microarrays are being used to diagnose and treat patients (1–4). The MAQC Quality Control Consortium, the FDA's Critical Path Initiative, NCI's caBIG and others are implementing procedures that will broadly enhance data quality. As GEO continues to grow, its usefulness is constrained by the level of correlation across experiments and general applicability. Although RNA preparation and array platform play important roles in data accuracy, pre-processing is a user-selected factor that has an enormous effect. Normalization of expression data is necessary, but the methods have specific and pronounced effects on precision, accuracy and historical correlation. As a case study, we present a microarray calibration process using normalization as the adjustable parameter. We examine the impact of eight normalizations across both Agilent and Affymetrix expression platforms on three expression readouts: (1) sensitivity and power, (2) functional/biological interpretation and (3) feature selection and classification error. The reader is encouraged to measure their own discordant data, whether cross-laboratory, cross-platform or across any other variance source, and to use their results to tune the adjustable parameters of their laboratory to ensure increased correlation.

Gene expression patterns in pancreatic tumors, cells and tissues

Background

Cancers of the pancreas originate from both the endocrine and exocrine elements of the organ, and represent a major cause of cancer-related death. This study provides a comprehensive assessment of gene expression for pancreatic tumors, the normal pancreas, and nonneoplastic pancreatic disease.

Methods/Results

DNA microarrays were used to assess the gene expression for surgically derived pancreatic adenocarcinomas, islet cell tumors, and mesenchymal tumors. The addition of normal pancreata, isolated islets, isolated pancreatic ducts, and pancreatic adenocarcinoma cell lines enhanced subsequent analysis by increasing the diversity in gene expression profiles obtained. Exocrine, endocrine, and mesenchymal tumors displayed unique gene expression profiles. Similarities in gene expression support the pancreatic duct as the origin of adenocarcinomas. In addition, genes highly expressed in other cancers and associated with specific signal transduction pathways were also found in pancreatic tumors.

Conclusion

The scope of the present work was enhanced by the inclusion of publicly available datasets that encompass a wide spectrum of human tissues and enabled the identification of candidate genes that may serve diagnostic and therapeutic goals.

The use of transcriptomic biomarkers for personalized medicine

Microarrays are a high throughput technology that allows the quantification of tens of thousands of RNA transcripts in a single reaction. This new technology offers the promise of comprehensive study of disease at a genomic level, potentially identifying novel molecular abnormalities, developing novel clinical biomarkers, and investigating drug efficacy. The ability to develop a molecular profile corresponding to a therapeutic effect is the basis for the concept of drug repositioning. With regard to prediction of clinical events, microarray technology has the potential to contribute to the development of sophisticated new biomarkers useful as predictors of disease etiology, outcome, and responsiveness to therapy-so-called personalized medicine. Currently progress in the field is hampered by a degree of skepticism about the reliability of microarray data and its relevance for clinical applications. Here we discuss possible pitfalls of transcriptomic analysis, review current developments in the cardiovascular area and address the use of transcriptomics for clinical applications.

MotifCombinator: a web-based tool to search for combinations of cis-regulatory motifs

Background

A combination of multiple types of transcription factors and cis-regulatory elements is often required for gene expression in eukaryotes, and the combinatorial regulation confers specific gene expression to tissues or environments. To reveal the combinatorial regulation, computational methods are developed that efficiently infer combinations of cis-regulatory motifs that are important for gene expression as measured by DNA microarrays. One promising type of computational method is to utilize regression analysis between expression levels and scores of motifs in input sequences. This type takes full advantage of information on expression levels because it does not require that the expression level of each gene be dichotomized according to whether or not it reaches a certain threshold level. However, there is no web-based tool that employs regression methods to systematically search for motif combinations and that practically handles combinations of more than two or three motifs.

Results

We here introduced MotifCombinator, an online tool with a user-friendly interface, to systematically search for combinations composed of any number of motifs based on regression methods. The tool utilizes well-known regression methods (the multivariate linear regression, the multivariate adaptive regression spline or MARS, and the multivariate logistic regression method) for this purpose, and uses the genetic algorithm to search for combinations composed of any desired number of motifs. The visualization systems in this tool help users to intuitively grasp the process of the combination search, and the backup system allows users to easily stop and restart calculations that are expected to require large computational time. This tool also provides preparatory steps needed for systematic combination search – i.e., selecting single motifs to constitute combinations and cutting out redundant similar motifs based on clustering analysis.

Conclusion

MotifCombinator helps users to systematically search for motif combinations that play an important role in gene expression as measured by microarrays.

Identification of N-acetyltransferase 2 (NAT2) transcription start sites and quantitation of NAT2-specific mRNA in human tissues

Human N-acetyltransferase 2 (NAT2) genetic polymorphism is associated with drug toxicity and/or carcinogenesis in various tissues. Knowledge of NAT2 gene structure and expression is critical for understanding these associations. Previous findings suggest that human NAT2 expression is highest in liver and gut but expressed at functional levels in other tissues. A sensitive and specific TaqMan reverse transcriptase-polymerase chain reaction (RT-PCR) assay with intron-spanning primers was developed and used, together with a second TaqMan RT-PCR assay based on amplification of a NAT2 open reading frame (ORF) exon segment, to measure NAT2 mRNA in 29 different human tissues. Cap-dependent amplification of mRNA 5' termini and review of public database information were done to more precisely define the NAT2 promoter(s) and to validate the quantitative RT-PCR assay design. The great majority (40/41) of NAT2 liver cDNAs had 5' termini between 8682 and 8752 nucleotides upstream of the NAT2 ORF exon, and 34 of 40 5' termini were at the -8711 and -8716 adenines. All 59 NAT2 cDNAs with 5' termini in this vicinity, including 40 of the liver isolates and 19 cDNAs in public databases from liver and other sources, showed direct splicing to the ORF exon, with no other noncoding exon detected. NAT2 mRNA was highest in liver, small intestine, and colon and was readily detected in most other tissues, albeit at much lower levels. NAT2 expression in diverse human tissues provides further mechanistic support underlying associations between NAT2 genetic polymorphism, drug toxicity, and/or chemical carcinogenesis.

Constructing human transcriptional regulatory subnets from crossgenome comparison and gene expression profile analysis

With the completion of Human Genome Project (HGP), understanding the complex interaction between trans- and cis-regulatory elements comprehensively and identifying these potential functional elements are fundamental problems in functional genomics. Although many computational approaches have been developed for lower eukaryotes and prokaryotes, most of them often do not generalize to vertebrates. Here, we use a decay function to characterize transcriptional behavior, and analyze correlations on gene expression profiles of human and mouse to construct coregulated gene groups. Using these two closely related species, we perform comparative genome analysis and identify target genes and conserved functional cis-regulatory elements by motif overrepresentation. Moreover, we presented experimental evidences (ChIP-Chip) for E2F to assert our findings.

MAIZEWALL. Database and developmental gene expression profiling of cell wall biosynthesis and assembly in maize

An extensive search for maize (Zea mays) genes involved in cell wall biosynthesis and assembly has been performed and 735 sequences have been centralized in a database, MAIZEWALL (http://www.polebio.scsv.ups-tlse.fr/MAIZEWALL). MAIZEWALL contains a bioinformatic analysis for each entry and gene expression data that are accessible via a user-friendly interface. A maize cell wall macroarray composed of a gene-specific tag for each entry was also constructed to monitor global cell wall-related gene expression in different organs and during internode development. By using this macroarray, we identified sets of genes that exhibit organ and internode-stage preferential expression profiles. These data provide a comprehensive fingerprint of cell wall-related gene expression throughout the maize plant. Moreover, an in-depth examination of genes involved in lignin biosynthesis coupled to biochemical and cytological data from different organs and stages of internode development has also been undertaken. These results allow us to trace spatially and developmentally regulated, putative preferential routes of monolignol biosynthesis involving specific gene family members and suggest that, although all of the gene families of the currently accepted monolignol biosynthetic pathway are conserved in maize, there are subtle differences in family size and a high degree of complexity in spatial expression patterns. These differences are in keeping with the diversity of lignified cell types throughout the maize plant.

Increased cortical expression of two synaptogenic thrombospondins in human brain evolution

Thrombospondins are extracellular-matrix glycoproteins implicated in the control of synaptogenesis and neurite growth. Previous microarray studies suggested that one gene of this family, thrombospondin 4 (THBS4), was upregulated during human brain evolution. Using independent techniques to examine thrombospondin expression patterns in adult brain samples, we report approximately 6-fold and approximately 2-fold greater expression of THBS4 and THBS2 messenger RNA (mRNA), respectively, in human cerebral cortex compared with chimpanzees and macaques, with corresponding differences in protein levels. In humans and chimpanzees, thrombospondin expression differences were observed in the forebrain (cortex and caudate), whereas the cerebellum and most nonbrain tissues exhibited similar levels of the 2 mRNAs. Histological examination revealed THBS4 mRNA and protein expression in numerous pyramidal and glial cells in the 3 species but humans also exhibited very prominent immunostaining of the synapse-rich cortical neuropil. In humans, additionally, THBS4 antibodies labeled beta-amyloid containing plaques in Alzheimer's cases and some control cases. This is the first detailed characterization of gene-expression changes in human evolution that involve specific brain regions, including portions of cerebral cortex. Increased expression of thrombospondins in human brain evolution could result in changes in synaptic organization and plasticity, and contribute to the distinctive cognitive abilities of humans, as well as to our unique vulnerability to neurodegenerative disease.

Modelling data across labs, genomes, space and time

Logical models and physical specifications provide the foundation for storage, management and analysis of complex sets of data, and describe the relationships between measured data elements and metadata - the contextual descriptors that define the primary data. Here, we use imaging applications to illustrate the purpose of the various implementations of data specifications and the requirement for open, standardized, data formats to facilitate the sharing of critical digital data and metadata.

Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways

BACKGROUND

Interpretation of lists of genes or proteins with altered expression is a critical and time-consuming part of microarray and proteomics research, but relatively little attention has been paid to methods for extracting biological meaning from these output lists. One powerful approach is to examine the expression of predefined biological pathways and gene sets, such as metabolic and signaling pathways and macromolecular complexes. Although many methods for measuring pathway expression have been proposed, a systematic analysis of the performance of multiple methods over multiple independent data sets has not previously been reported.

RESULTS

Five different measures of pathway expression were compared in an analysis of nine publicly available mRNA expression data sets. The relative sensitivity of the metrics varied greatly across data sets, and the biological pathways identified for each data set are also dependent on the choice of pathway activation metric. In addition, we show that removing incoherent pathways prior to analysis improves specificity. Finally, we create and analyze a public map of pathway expression in human tissues by gene-set analysis of a large compendium of human expression data.

CONCLUSION

We show that both the detection sensitivity and identity of pathways significantly perturbed in a microarray experiment are highly dependent on the analysis methods used and how incoherent pathways are treated. Analysts should thus consider using multiple approaches to test the robustness of their biological interpretations. We also provide a comprehensive picture of the tissue distribution of human gene pathways and a useful public archive of human pathway expression data.

Cell-line and tissue-specific signatures of androgen receptor-coregulator transcription

Normal genital skin fibroblasts (GSF) and the human prostate carcinoma cell line LNCaP have been used widely as cell culture models of genital origin to study androgen receptor (AR) signaling. We demonstrate that LNCaP shows a reproducible response to androgens as assessed using cDNA-microarrays representing approximately 32,000 unique human genes, whereas several independent GSF strains are virtually unresponsive. We show that LNCaP cells express markedly higher AR protein levels likely contributing to the observed differences of androgen responsiveness. However, previous data suggested that AR-expression levels alone do not determine androgen responsiveness of human GSF compared to LNCaP. We hypothesized that cell-specific differences in expression levels of AR coregulators might contribute to differences in androgen responsiveness and might be found by comparing LNCaP and GSFs. Using the Canadian McGill-database of AR coregulators ( http://www.mcgill.ca/androgendb ), we identified 61 AR-coregulator genes represented by 282 transcripts on our microarray platform that was used to measure transcript profiles of LNCaP and GSF cells. Baseline expression levels of 48 AR-coregulator transcripts representing 33 distinct genes showed significant differences between GSF and LNCaP, four of which we confirmed by reverse transcriptase polymerase chain reaction. Compared to LNCaP, GSFs displayed significant upregulation of AR coregulators that can function as repressors of AR-transactivation, such as caveolin 1. Analysis of a recently published comprehensive dataset of 115 microarrays representing 35 different human tissues revealed tissue-specific signatures of AR coregulators that segregated with ontogenetically related groups of tissues (e.g., lymphatic system and genital tissues, brain). Our data demonstrate the existence of cell-line and tissue-specific expression patterns of molecules with documented AR coregulatory functions. Therefore, differential expression patterns of AR coregulators could modify tissue-specificity and diversity of androgen actions in development, physiology, and disease.

Differentially profiling the low-expression transcriptomes of human hepatoma using a novel SSH/microarray approach

BACKGROUND

The main limitation in performing genome-wide gene-expression profiling is the assay of low-expression genes. Approaches with high throughput and high sensitivity for assaying low-expression transcripts are urgently needed for functional genomic studies. Combination of the suppressive subtractive hybridization (SSH) and cDNA microarray techniques using the subtracted cDNA clones as probes printed on chips has greatly improved the efficiency for fishing out the differentially expressed clones and has been used before. However, it remains tedious and inefficient sequencing works for identifying genes including the great number of redundancy in the subtracted amplicons, and sacrifices the original advantages of high sensitivity of SSH in profiling low-expression transcriptomes.

RESULTS

We modified the previous combination of SSH and microarray methods by directly using the subtracted amplicons as targets to hybridize the pre-made cDNA microarrays (named as "SSH/microarray"). mRNA prepared from three pairs of hepatoma and non-hepatoma liver tissues was subjected to the SSH/microarray assays, as well as directly to regular cDNA microarray assays for comparison. As compared to the original SSH and microarray combination assays, the modified SSH/microarray assays allowed for much easier inspection of the subtraction efficiency and identification of genes in the subtracted amplicons without tedious and inefficient sequencing work. On the other hand, 5015 of the 9376 genes originally filtered out by the regular cDNA microarray assays because of low expression became analyzable by the SSH/microarray assays. Moreover, the SSH/microarray assays detected about ten times more (701 vs. 69) HCC differentially expressed genes (at least a two-fold difference and P < 0.01), particularly for those with rare transcripts, than did the regular cDNA microarray assays. The differential expression was validated in 9 randomly selected genes in 18 pairs of hepatoma/non-hepatoma liver tissues using quantitative RT-PCR. The SSH/microarray approaches resulted in identifying many differentially expressed genes implicated in the regulation of cell cycle, cell death, signal transduction and cell morphogenesis, suggesting the involvement of multi-biological processes in hepato-carcinogenesis.

CONCLUSION

The modified SSH/microarray approach is a simple but high-sensitive and high-efficient tool for differentially profiling the low-expression transcriptomes. It is most adequate for applying to functional genomic studies.