Establishment of the epithelial-specific transcriptome of normal and malignant human breast cells based on MPSS and array expression data.

INTRODUCTION: Diverse microarray and sequencing technologies have been widely used to characterise the molecular changes in malignant epithelial cells in breast cancers. Such gene expression studies to identify markers and targets in tumour cells are, however, compromised by the cellular heterogeneity of solid breast tumours and by the lack of appropriate counterparts representing normal breast epithelial cells. METHODS: Malignant neoplastic epithelial cells from primary breast cancers and luminal and myoepithelial cells isolated from normal human breast tissue were isolated by immunomagnetic separation methods. Pools of RNA from highly enriched preparations of these cell types were subjected to expression profiling using massively parallel signature sequencing (MPSS) and four different genome wide microarray platforms. Functional related transcripts of the differential tumour epithelial transcriptome were used for gene set enrichment analysis to identify enrichment of luminal and myoepithelial type genes. Clinical pathological validation of a small number of genes was performed on tissue microarrays. RESULTS: MPSS identified 6,553 differentially expressed genes between the pool of normal luminal cells and that of primary tumours substantially enriched for epithelial cells, of which 98% were represented and 60% were confirmed by microarray profiling. Significant expression level changes between these two samples detected only by microarray technology were shown by 4,149 transcripts, resulting in a combined differential tumour epithelial transcriptome of 8,051 genes. Microarray gene signatures identified a comprehensive list of 907 and 955 transcripts whose expression differed between luminal epithelial cells and myoepithelial cells, respectively. Functional annotation and gene set enrichment analysis highlighted a group of genes related to skeletal development that were associated with the myoepithelial/basal cells and upregulated in the tumour sample. One of the most highly overexpressed genes in this category, that encoding periostin, was analysed immunohistochemically on breast cancer tissue microarrays and its expression in neoplastic cells correlated with poor outcome in a cohort of poor prognosis estrogen receptor-positive tumours. CONCLUSION: Using highly enriched cell populations in combination with multiplatform gene expression profiling studies, a comprehensive analysis of molecular changes between the normal and malignant breast tissue was established. This study provides a basis for the identification of novel and potentially important targets for diagnosis, prognosis and therapy in breast cancer.

Involvement of microRNAs in the cytotoxic effects exerted by proinflammatory cytokines on pancreatic beta-cells.

OBJECTIVE: Pancreatic beta-cells exposed to proinflammatory cytokines display alterations in gene expression resulting in defective insulin secretion and apoptosis. MicroRNAs are small noncoding RNAs emerging as key regulators of gene expression. Here, we evaluated the contribution of microRNAs to cytokine-mediated beta-cell cytotoxicity. RESEARCH DESIGN AND METHODS: We used global microarray profiling and real-time PCR analysis to detect changes in microRNA expression in beta-cells exposed to cytokines and in islets of pre-diabetic NOD mice. We assessed the involvement of the microRNAs affected in cytokine-mediated beta-cell failure by modifying their expression in insulin-secreting MIN6 cells. RESULTS: We found that IL-1beta and TNF-alpha induce the expression of miR-21, miR-34a, and miR-146a both in MIN6 cells and human pancreatic islets. We further show an increase of these microRNAs in islets of NOD mice during development of pre-diabetic insulitis. Blocking miR-21, miR-34a, or miR-146a function using antisense molecules did not restore insulin-promoter activity but prevented the reduction in glucose-induced insulin secretion observed upon IL-1beta exposure. Moreover, anti-miR-34a and anti-miR-146a treatment protected MIN6 cells from cytokine-triggered cell death. CONCLUSIONS: Our data identify miR-21, miR-34a, and miR-146a as novel players in beta-cell failure elicited in vitro and in vivo by proinflammatory cytokines, notably during the development of peri-insulitis that precedes overt diabetes in NOD mice.

Identification of ectodysplasin target genes reveals the involvement of chemokines in hair development.

Ectodysplasin (Eda), a member of the tumor necrosis factor (Tnf) family, regulates skin appendage morphogenesis via its receptor Edar and transcription factor NF-κB. In humans, inactivating mutations in the Eda pathway components lead to hypohidrotic ectodermal dysplasia (HED), a syndrome characterized by sparse hair, tooth abnormalities, and defects in several cutaneous glands. A corresponding phenotype is observed in Eda-null mice, where failure in the initiation of the first wave of hair follicle development is a hallmark of HED pathogenesis. In an attempt to discover immediate target genes of the Eda/NF-κB pathway, we performed microarray profiling of genes differentially expressed in embryonic skin explants after a short exposure to recombinant Fc-Eda protein. Upregulated genes included components of the Wnt, fibroblast growth factor, transforming growth factor-β, Tnf, and epidermal growth factor families, indicating that Eda modulates multiple signaling pathways implicated in skin appendage development. Surprisingly, we identified two ligands of the chemokine receptor cxcR3, cxcl10 and cxcl11, as new hair-specific transcriptional targets of Eda. Deficiency in cxcR3 resulted in decreased primary hair follicle density but otherwise normal hair development, indicating that chemokine signaling influences the patterning of primary hair placodes only.

Gene expression in cultured endometrium from women with different outcomes following IVF.

Estradiol and progesterone are crucial for the acquisition of receptivity and the change in transcriptional activity of target genes in the implantation window. The aim of this study was to differentiate the regulation of genes in the endometrium of patients with recurrent implantation failure (IF) versus those who became pregnant after in vitro fertilization (IVF) treatment. Moreover, the effect of embryo-derived factors on endometrial transcriptional activity was studied. Nine women with known IVF outcome (IF, M, miscarriage, OP, ongoing pregnancy) and undergoing hysteroscopy with endometrial biopsy were enrolled. Biopsies were taken during the midluteal phase. After culture in the presence of embryo-conditioned IVF media, total RNA was extracted and submitted to reverse transcription, target cDNA synthesis, biotin labelling, fragmentation and hybridization using the Affymetrix Human Genome U133A 2.0 Chip. Differential expression of selected genes was re-analysed by quantitative PCR, in which the results were calculated as threshold cycle differences between the groups and normalized to Glyceraldehyde phosphate dehydrogenase and beta-actin. Differences were seen for several genes from endometrial tissue between the IF and the pregnancy groups, and when comparing OP with M, 1875 up- and 1807 down-regulated genes were returned. Real-time PCR analysis confirmed up-regulation for somatostatin, PLAP-2, mucin 4 and CD163, and down-regulation of glycodelin, IL-24, CD69, leukaemia inhibitory factor and prolactin receptor between Op and M. When the different embryo-conditioned media were compared, no significant differential regulation could be demonstrated. Although microarray profiling may currently not be sensitive enough for studying the effects of embryo-derived factors on the endometrium, the observed differences in gene expression between M and OP suggest that it will become an interesting tool for the identification of fertility-relevant markers produced by the endometrium.

Impact of normalization on miRNA microarray expression profiling.

Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. We have developed a method to select nonchanging miRNAs (invariants) and use them to compute linear regression normalization coefficients or variance stabilizing normalization (VSN) parameters. We compared the invariants normalization to normalization by scaling, quantile, and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only a few miRNAs are affected (by p53 overexpression in squamous carcinoma cells versus control). All normalization methods performed better than no normalization. Normalization procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other data sets including those from one color miRNA microarray platforms, focused gene expression arrays, and gene expression analysis using quantitative PCR.

Single-cell gene-expression profiling reveals qualitatively distinct CD8 T cells elicited by different gene-based vaccines.

CD8 T cells play a key role in mediating protective immunity against selected pathogens after vaccination. Understanding the mechanism of this protection is dependent upon definition of the heterogeneity and complexity of cellular immune responses generated by different vaccines. Here, we identify previously unrecognized subsets of CD8 T cells based upon analysis of gene-expression patterns within single cells and show that they are differentially induced by different vaccines. Three prime-boost vector combinations encoding HIV Env stimulated antigen-specific CD8 T-cell populations of similar magnitude, phenotype, and functionality. Remarkably, however, analysis of single-cell gene-expression profiles enabled discrimination of a majority of central memory (CM) and effector memory (EM) CD8 T cells elicited by the three vaccines. Subsets of T cells could be defined based on their expression of Eomes, Cxcr3, and Ccr7, or Klrk1, Klrg1, and Ccr5 in CM and EM cells, respectively. Of CM cells elicited by DNA prime-recombinant adenoviral (rAd) boost vectors, 67% were Eomes(-) Ccr7(+) Cxcr3(-), in contrast to only 7% and 2% stimulated by rAd5-rAd5 or rAd-LCMV, respectively. Of EM cells elicited by DNA-rAd, 74% were Klrk1(-) Klrg1(-)Ccr5(-) compared with only 26% and 20% for rAd5-rAd5 or rAd5-LCMV. Definition by single-cell gene profiling of specific CM and EM CD8 T-cell subsets that are differentially induced by different gene-based vaccines will facilitate the design and evaluation of vaccines, as well as enable our understanding of mechanisms of protective immunity.

Functional sphere profiling reveals the complexity of neuroblastoma tumor-initiating cell model.

Neuroblastoma (NB) is a neural crest-derived childhood tumor characterized by a remarkable phenotypic diversity, ranging from spontaneous regression to fatal metastatic disease. Although the cancer stem cell (CSC) model provides a trail to characterize the cells responsible for tumor onset, the NB tumor-initiating cell (TIC) has not been identified. In this study, the relevance of the CSC model in NB was investigated by taking advantage of typical functional stem cell characteristics. A predictive association was established between self-renewal, as assessed by serial sphere formation, and clinical aggressiveness in primary tumors. Moreover, cell subsets gradually selected during serial sphere culture harbored increased in vivo tumorigenicity, only highlighted in an orthotopic microenvironment. A microarray time course analysis of serial spheres passages from metastatic cells allowed us to specifically "profile" the NB stem cell-like phenotype and to identify CD133, ABC transporter, and WNT and NOTCH genes as spheres markers. On the basis of combined sphere markers expression, at least two distinct tumorigenic cell subpopulations were identified, also shown to preexist in primary NB. However, sphere markers-mediated cell sorting of parental tumor failed to recapitulate the TIC phenotype in the orthotopic model, highlighting the complexity of the CSC model. Our data support the NB stem-like cells as a dynamic and heterogeneous cell population strongly dependent on microenvironmental signals and add novel candidate genes as potential therapeutic targets in the control of high-risk NB.

Versatile gene-specific sequence tags for Arabidopsis functional genomics: transcript profiling and reverse genetics applications.

Microarray transcript profiling and RNA interference are two new technologies crucial for large-scale gene function studies in multicellular eukaryotes. Both rely on sequence-specific hybridization between complementary nucleic acid strands, inciting us to create a collection of gene-specific sequence tags (GSTs) representing at least 21,500 Arabidopsis genes and which are compatible with both approaches. The GSTs were carefully selected to ensure that each of them shared no significant similarity with any other region in the Arabidopsis genome. They were synthesized by PCR amplification from genomic DNA. Spotted microarrays fabricated from the GSTs show good dynamic range, specificity, and sensitivity in transcript profiling experiments. The GSTs have also been transferred to bacterial plasmid vectors via recombinational cloning protocols. These cloned GSTs constitute the ideal starting point for a variety of functional approaches, including reverse genetics. We have subcloned GSTs on a large scale into vectors designed for gene silencing in plant cells. We show that in planta expression of GST hairpin RNA results in the expected phenotypes in silenced Arabidopsis lines. These versatile GST resources provide novel and powerful tools for functional genomics.

Selecting control genes for RT-QPCR using public microarray data.

Background: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e. g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. Results: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/similar to vpopovic/research/ Conclusion: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.

Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.

BACKGROUND: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profiles of breast cancers and whether such profiles could be used to improve histologic grading. METHODS: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. RESULTS: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P < .001, log-rank test). CONCLUSIONS: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value.

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

CATMA, a comprehensive genome-scale resource for silencing and transcript profiling of Arabidopsis genes.

BACKGROUND: The Complete Arabidopsis Transcript MicroArray (CATMA) initiative combines the efforts of laboratories in eight European countries 1 to deliver gene-specific sequence tags (GSTs) for the Arabidopsis research community. The CATMA initiative offers the power and flexibility to regularly update the GST collection according to evolving knowledge about the gene repertoire. These GST amplicons can easily be reamplified and shared, subsets can be picked at will to print dedicated arrays, and the GSTs can be cloned and used for other functional studies. This ongoing initiative has already produced approximately 24,000 GSTs that have been made publicly available for spotted microarray printing and RNA interference. RESULTS: GSTs from the CATMA version 2 repertoire (CATMAv2, created in 2002) were mapped onto the gene models from two independent Arabidopsis nuclear genome annotation efforts, TIGR5 and PSB-EuGène, to consolidate a list of genes that were targeted by previously designed CATMA tags. A total of 9,027 gene models were not tagged by any amplified CATMAv2 GST, and 2,533 amplified GSTs were no longer predicted to tag an updated gene model. To validate the efficacy of GST mapping criteria and design rules, the predicted and experimentally observed hybridization characteristics associated to GST features were correlated in transcript profiling datasets obtained with the CATMAv2 microarray, confirming the reliability of this platform. To complete the CATMA repertoire, all 9,027 gene models for which no GST had yet been designed were processed with an adjusted version of the Specific Primer and Amplicon Design Software (SPADS). A total of 5,756 novel GSTs were designed and amplified by PCR from genomic DNA. Together with the pre-existing GST collection, this new addition constitutes the CATMAv3 repertoire. It comprises 30,343 unique amplified sequences that tag 24,202 and 23,009 protein-encoding nuclear gene models in the TAIR6 and EuGène genome annotations, respectively. To cover the remaining untagged genes, we identified 543 additional GSTs using less stringent design criteria and designed 990 sequence tags matching multiple members of gene families (Gene Family Tags or GFTs) to cover any remaining untagged genes. These latter 1,533 features constitute the CATMAv4 addition. CONCLUSION: To update the CATMA GST repertoire, we designed 7,289 additional sequence tags, bringing the total number of tagged TAIR6-annotated Arabidopsis nuclear protein-coding genes to 26,173. This resource is used both for the production of spotted microarrays and the large-scale cloning of hairpin RNA silencing vectors. All information about the resulting updated CATMA repertoire is available through the CATMA database http://www.catma.org.

Gene expression profiling in the human pathogenic dermatophyte Trichophyton rubrum during growth on proteins.

Dermatophytes are highly specialized filamentous fungi which cause the majority of superficial mycoses in humans and animals. The high secreted proteolytic activity of these microorganisms during growth on proteins is assumed to be linked to their particular ability to exclusively infect keratinized host structures such as the skin stratum corneum, hair, and nails. Individual secreted dermatophyte proteases were recently described and linked with the in vitro digestion of keratin. However, the overall adaptation and transcriptional response of dermatophytes during protein degradation are largely unknown. To address this question, we constructed a cDNA microarray for the human pathogenic dermatophyte Trichophyton rubrum that was based on transcripts of the fungus grown on proteins. Profiles of gene expression during the growth of T. rubrum on soy and keratin protein displayed the activation of a large set of genes that encode secreted endo- and exoproteases. In addition, other specifically induced factors potentially implicated in protein utilization were identified, including heat shock proteins, transporters, metabolic enzymes, transcription factors, and hypothetical proteins with unknown functions. Of particular interest is the strong upregulation of key enzymes of the glyoxylate cycle in T. rubrum during growth on soy and keratin, namely, isocitrate lyase and malate synthase. This broad-scale transcriptional analysis of dermatophytes during growth on proteins reveals new putative pathogenicity-related host adaptation mechanisms of these human pathogenic fungi.

Transcript profiling suggests that differential metabolic adaptation of mice to a high fat diet is associated with changes in liver to muscle lipid fluxes.

Genetically homogenous C57Bl/6 mice display differential metabolic adaptation when fed a high fat diet for 9 months. Most become obese and diabetic, but a significant fraction remains lean and diabetic or lean and non-diabetic. Here, we performed microarray analysis of "metabolic" transcripts expressed in liver and hindlimb muscles to evaluate: (i) whether expressed transcript patterns could indicate changes in metabolic pathways associated with the different phenotypes, (ii) how these changes differed from the early metabolic adaptation to short term high fat feeding, and (iii) whether gene classifiers could be established that were characteristic of each metabolic phenotype. Our data indicate that obesity/diabetes was associated with preserved hepatic lipogenic gene expression and increased plasma levels of very low density lipoprotein and, in muscle, with an increase in lipoprotein lipase gene expression. This suggests increased muscle fatty acid uptake, which may favor insulin resistance. In contrast, the lean mice showed a strong reduction in the expression of hepatic lipogenic genes, in particular of Scd-1, a gene linked to sensitivity to diet-induced obesity; the lean and non-diabetic mice presented an additional increased expression of eNos in liver. After 1 week of high fat feeding the liver gene expression pattern was distinct from that seen at 9 months in any of the three mouse groups, thus indicating progressive establishment of the different phenotypes. Strikingly, development of the obese phenotype involved re-expression of Scd-1 and other lipogenic genes. Finally, gene classifiers could be established that were characteristic of each metabolic phenotype. Together, these data suggest that epigenetic mechanisms influence gene expression patterns and metabolic fates.

Proof of concept for microarray-based detection of DNA-binding oncogenes in cell extracts.

The function of DNA-binding proteins is controlled not just by their abundance, but mainly at the level of their activity in terms of their interactions with DNA and protein targets. Moreover, the affinity of such transcription factors to their target sequences is often controlled by co-factors and/or modifications that are not easily assessed from biological samples. Here, we describe a scalable method for monitoring protein-DNA interactions on a microarray surface. This approach was designed to determine the DNA-binding activity of proteins in crude cell extracts, complementing conventional expression profiling arrays. Enzymatic labeling of DNA enables direct normalization of the protein binding to the microarray, allowing the estimation of relative binding affinities. Using DNA sequences covering a range of affinities, we show that the new microarray-based method yields binding strength estimates similar to low-throughput gel mobility-shift assays. The microarray is also of high sensitivity, as it allows the detection of a rare DNA-binding protein from breast cancer cells, the human tumor suppressor AP-2. This approach thus mediates precise and robust assessment of the activity of DNA-binding proteins and takes present DNA-binding assays to a high throughput level.