Stromal upregulation of lateral epithelial adhesions : gene expression analysis of signalling pathways in prostate epithelium

BACKGROUND: Stromal signalling increases the lateral cell adhesions of prostate epithelial cells grown in 3D culture. The aim of this study was to use microarray analysis to identify significant epithelial signalling pathways and genes in this process. METHODS: Microarray analysis was used to identify genes that were differentially expressed when epithelial cells were grown in 3D Matrigel culture with stromal co-culture compared to without stroma. Two culture models were employed: primary epithelial cells (ten samples) and an epithelial cell line (three experiments). A separate microarray analysis was performed on each model system and then compared to identify tissue-relevant genes in a cell line model. RESULTS: TGF beta signalling was significantly ranked for both model systems and in both models the TGF beta signalling gene SOX4 was significantly down regulated. Analysis of all differentially expressed genes to identify genes that were common to both models found several morphology related gene clusters; actin binding (DIAPH2, FHOD3, ABLIM1, TMOD4, MYH10), GTPase activator activity (BCR, MYH10), cytoskeleton (MAP2, MYH10, TMOD4, FHOD3), protein binding (ITGA6, CD44), proteinaceous extracellular matrix (NID2, CILP2), ion channel/ ion transporter activity (CACNA1C, CACNB2, KCNH2, SLC8A1, SLC39A9) and genes associated with developmental pathways (POFUT1, FZD2, HOXA5, IRX2, FGF11, SOX4, SMARCC1). CONCLUSIONS: In 3D prostate cultures, stromal cells increase lateral epithelial cell adhesions. We show that this morphological effect is associated with gene expression changes to TGF beta signalling, cytoskeleton and anion activity.

Seed-based biclustering of gene expression data

Background Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner. Methods In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns: (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty. Conclusions This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.

Bayesian survival analysis using gene expression

This thesis developed and applied Bayesian models for the analysis of survival data. The gene expression was considered as explanatory variables within the Bayesian survival model which can be considered the new contribution in the analysis of such data. The censoring factor that is inherent of survival data has also been addressed in terms of its impact on the fitting of a finite mixture of Weibull distribution with and without covariates. To investigate this, simulation study were carried out under several censoring percentages. Censoring percentage as high as 80% is acceptable here as the work involved high dimensional data. Lastly the Bayesian model averaging approach was developed to incorporate model uncertainty in the prediction of survival.

Gene expression studies in multiple sclerosis

Multiple sclerosis (MS) is a serious neurological disorder affecting young Caucasian individuals, usually with an age of onset at 18 to 40 years old. Females account for approximately 60x of MS cases and the manifestation and course of the disease is highly variable from patient to patient. The disorder is characterised by the development of plaques within the central nervous system (CNS). Many gene expression studies have been undertaken to look at the specific patterns of gene transcript levels in MS. Human tissues and experimental mice were used in these gene-profiling studies and a very valuable and interesting set of data has resulted from these various expression studies. In general, genes showing variable expression include mainly immunological and inflammatory genes, stress and antioxidant genes, as well as metabolic and central nervous system markers. Of particular interest are a number of genes localised to susceptible loci previously shown to be in linkage with MS. However due to the clinical complexity of the disease, the heterogeneity of the tissues used in expression studies, as well as the variable DNA chips/membranes used for the gene profiling, it is difficult to interpret the available information. Although this information is essential for the understanding of the pathogenesis of MS, it is difficult to decipher and define the gene pathways involved in the disorder. Experiments in gene expression profiling in MS have been numerous and lists of candidates are now available for analysis. Researchers have investigated gene expression in peripheral mononuclear white blood cells (PBMCs), in MS animal models Experimental Allergic Encephalomyelitis (EAE) and post mortem MS brain tissues. This review will focus on the results of these studies.

In vitro and in vivo MMP gene expression localisation by In Situ-RT-PCR in cell culture and paraffin embedded human breast cancer cell line xenografts

Background Members of the matrix metalloproteinase (MMP) family of proteases are required for the degradation of the basement membrane and extracellular matrix in both normal and pathological conditions. In vitro, MT1-MMP (MMP-14, membrane type-1-MMP) expression is higher in more invasive human breast cancer (HBC) cell lines, whilst in vivo its expression has been associated with the stroma surrounding breast tumours. MMP-1 (interstitial collagenase) has been associated with MDA-MB-231 invasion in vitro, while MMP-3 (stromelysin-1) has been localised around invasive cells of breast tumours in vivo. As MMPs are not stored intracellularly, the ability to localise their expression to their cells of origin is difficult. Methods We utilised the unique in situ-reverse transcription-polymerase chain reaction (IS-RT-PCR) methodology to localise the in vitro and in vivo gene expression of MT1-MMP, MMP-1 and MMP-3 in human breast cancer. In vitro, MMP induction was examined in the MDA-MB-231 and MCF-7 HBC cell lines following exposure to Concanavalin A (Con A). In vivo, we examined their expression in archival paraffin embedded xenografts derived from a range of HBC cell lines of varied invasive and metastatic potential. Mouse xenografts are heterogenous, containing neoplastic human parenchyma with mouse stroma and vasculature and provide a reproducible in vivo model system correlated to the human disease state. Results In vitro, exposure to Con A increased MT1-MMP gene expression in MDA-MB-231 cells and decreased MT1-MMP gene expression in MCF-7 cells. MMP-1 and MMP-3 gene expression remained unchanged in both cell lines. In vivo, stromal cells recruited into each xenograft demonstrated differences in localised levels of MMP gene expression. Specifically, MDA-MB-231, MDA-MB-435 and Hs578T HBC cell lines are able to influence MMP gene expression in the surrounding stroma. Conclusion We have demonstrated the applicability and sensitivity of IS-RT-PCR for the examination of MMP gene expression both in vitro and in vivo. Induction of MMP gene expression in both the epithelial tumour cells and surrounding stromal cells is associated with increased metastatic potential. Our data demonstrate the contribution of the stroma to epithelial MMP gene expression, and highlight the complexity of the role of MMPs in the stromal-epithelial interactions within breast carcinoma.

An assessment of MMP and TIMP gene expression in cell lines and stroma : tumour differences in microdissected breast cancer biopsies

To examine matrix metalloproteinase (MMP) and tissue inhibitor of metalloproteinases (TIMP) mRNA levels in archival breast cancer biopsies, we employed microdissection to separate tumour tissue from the surrounding breast tissue, or stroma and RT-PCR to determine gross qualitative and small quantitative differences in the patterns of expression. In this study, a significant correlation (p < 0.05, by Mann-Whitney U analysis) between TIMP-2 expression and lymph node involvement was identified, while MMP-11 and TIMP-1 expression patterning also significantly (p < 0.05) differed between those tumours showing calcification and those that did not. When compared by Spearmans’ ρ correlation analysis, a significant association (p < 0.05, ρ = 0.404) was identified in the pattern of MMP-2 and MMP-9 gene expression. In this study, the use of microdissection and a systematic strategy of RT-PCR analysis have allowed us to investigate localized MMP and MMP inhibitor expression within breast tumours. We have identified patterns of gene expression that may further reveal aspects of breast carcinogenesis, and a robust method for examining changes in clinically important genes using archival biopsies and across stroma-tumour boundaries.

Quantitative and qualitative changes in gene expression patterns characterize the activity of plaques in multiple sclerosis

Multiple sclerosis (MS) is a complex autoimmune disorder of the CNS with both genetic and environmental contributing factors. Clinical symptoms are broadly characterized by initial onset, and progressive debilitating neurological impairment. In this study, RNA from MS chronic active and MS acute lesions was extracted, and compared with patient matched normal white matter by fluorescent cDNA microarray hybridization analysis. This resulted in the identification of 139 genes that were differentially regulated in MS plaque tissue compared to normal tissue. Of these, 69 genes showed a common pattern of expression in the chronic active and acute plaque tissues investigated (Pvalue<0.0001, ρ=0.73, by Spearman's ρ analysis); while 70 transcripts were uniquely differentially expressed (≥1.5-fold) in either acute or chronic active tissues. These results included known markers of MS such as the myelin basic protein (MBP) and glutathione S-transferase (GST) M1, nerve growth factors, such as nerve injury-induced protein 1 (NINJ1), X-ray and excision DNA repair factors (XRCC9 and ERCC5) and X-linked genes such as the ribosomal protein, RPS4X. Primers were then designed for seven array-selected genes, including transferrin (TF), superoxide dismutase 1 (SOD1), glutathione peroxidase 1 (GPX1), GSTP1, crystallin, alpha-B (CRYAB), phosphomannomutase 1 (PMM1) and tubulin β-5 (TBB5), and real time quantitative (Q)-PCR analysis was performed. The results of comparative Q-PCR analysis correlated significantly with those obtained by array analysis (r=0.75, Pvalue<0.01, by Pearson's bivariate correlation). Both chronic active and acute plaques shared the majority of factors identified suggesting that quantitative, rather than gross qualitative differences in gene expression pattern may define the progression from acute to chronic active plaques in MS.

Differential gene expression in breast cancer cell lines and stroma-tumor differences in microdissected breast cancer biopsies revealed by display array analysis

To examine gene-expression patterning in late-stage breast cancer biopsies, we used a microdissection technique to separate tumor from the surrounding breast tissue or stroma. A DD-PCR protocol was then used to amplify expressed products, which were resolved using PAGE and used as probe to hybridize with representative human arrays and cDNA libraries. The probe derived from the tumor–stroma comparison was hybridized with a gene array and an arrayed cDNA library derived from a GCT of bone; 21 known genes or expressed sequence tags were detected, of which 17 showed differential expression. These included factors associated with epithelial to mesenchymal transition (vimentin), the cargo selection protein (TIP47) and the signal transducer and activator of transcription (STAT3). Northern blot analysis was used to confirm those genes also expressed by representative breast cancer cell lines. Notably, 6 genes of unknown function were restricted to tumor while the majority of stroma-associated genes were known. When applied to transformed breast cancer cell lines (MDA-MB-435 and T47D) that are known to have different metastatic potential, DD array analysis revealed a further 20 genes; 17 of these genes showed differential expression. Use of microdissection and the DD-PCR array protocol allowed us to identify factors whose localized expression within the breast may play a role in abnormal breast development or breast carcinogenesis.

Gene expression profiling in human breast cancer – toward personalised therapeutics?

The most integrated approach toward understanding the multiple molecular events and mechanisms by which cancer may develop is the application of gene expression profiling using microarray technologies. As molecular alterations in breast cancer are complex and involve cross-talk between multiple cellular signalling pathways, microarray technology provides a means of capturing and comparing the expression patterns of the entire genome across multiple samples in a high throughput manner. Since the development of microarray technologies, together with the advances in RNA extraction methodologies, gene expression studies have revolutionised the means by which genes suitable as targets for drug development and individualised cancer treatment can be identified. As of the mid-1990s, expression microarrays have been extensively applied to the study of cancer and no cancer type has seen as much genomic attention as breast cancer. The most abundant area of breast cancer genomics has been the clarification and interpretation of gene expression patterns that unite both biological and clinical aspects of tumours. It is hoped that one day molecular profiling will transform diagnosis and therapeutic selection in human breast cancer toward more individualised regimes. Here, we review a number of prominent microarray profiling studies focussed on human breast cancer and examine their strengths, their limitations, clinical implications including prognostic relevance and gene signature significance along with potential improvements for the next generation of microarray studies.

A transcription factor map as revealed by a genome-wide gene expression analysis of whole-blood mRNA transcriptome in multiple sclerosis

Background Several lines of evidence suggests that transcription factors are involved in the pathogenesis of Multiple Sclerosis (MS) but a complete mapping the whole network has been elusive. One of the reasons is that there are several clinical subtypes of MS and transcription factors which may be involved in one subtype may not be in others. We investigated the possibility that this network could be mapped using microarray technologies and modern bioinformatics methods on a dataset from whole blood in 99 untreated MS patients (36 Relapse Remitting MS, 43 Primary Progressive MS, and 20 Secondary Progressive MS) and 45 age-matched healthy controls, Methodology/Principal Findings We have used two different analytical methodologies: a differential expression analysis and a differential co-expression analysis, which have converged on a significant number of regulatory motifs that seem to be statistically overrepresented in genes which are either differentially expressed (or differentially co-expressed) in cases and controls (e.g. V$KROX_Q6, p-value < 3.31E-6; V$CREBP1_Q2, p-value < 9.93E-6, V$YY1_02, p-value < 1.65E-5). Conclusions/significance: Our analysis uncovered a network of transcription factors that potentially dysregulate several genes in MS or one or more of its disease subtypes. Analysing the published literature we have found that these transcription factors are involved in the early T-lymphocyte specification and commitment as well as in oligodendrocytes dedifferentiation and development. The most significant transcription factors motifs were for the Early Growth response EGR/KROX family, ATF2, YY1 (Yin and Yang 1), E2F-1/DP-1 and E2F-4/DP-2 heterodimers, SOX5, and CREB and ATF families.

Ethanol exposure alters monoamine oxidase gene-expression in neuronal SH-SY5Y cells

Abstract: Monoamine Oxidase (MAO) enzymes catabolise, and thus modulate abundance of, neurotransmitters in the brain. Variation in MAO enzyme activity has been linked to alcohol abuse behaviour, although the molecular mechanisms underlying this association are not understood. The present study evaluated relative gene-transcript abundance of MAO-A and MAO-B in the SH-SY5Y human neuroblastoma cell-line in response to ethanol exposure and following ethanol withdrawal. We found that each isoform of MAO was significantly transcriptionally up-regulated 55-80% in response to 100mM ethanol exposure. This trend was maintained following prolonged exposures (24 h-72 h) and with short exposures (24 h) followed by a period of ethanol withdrawal, suggesting that the transcriptional regulation is the result of a cellular change occurring within the first 24 hours of ethanol exposure. These results suggest a role for MAO transcriptional regulation in the complex neurobiochemical changes underlying alcohol addiction.

Analysis of kinase gene expression in the frontal cortex of suicide victims: Implications of fear and stress

Suicide is a serious public health issue that results from an interaction between multiple risk factors including individual vulnerabilities to complex feelings of hopelessness, fear, and stress. Although kinase genes have been implicated in fear and stress, including the consolidation and extinction of fearful memories, expression profiles of those genes in the brain of suicide victims are less clear. Using gene expression microarray data from the Online Stanley Genomics Database 1 and a quantitative PCR, we investigated the expression profiles of multiple kinase genes including the calcium calmodulin-dependent kinase (CAMK), the cyclin-dependent kinase, the mitogen-activated protein kinase (MAPK), and the protein kinase C (PKC) in the prefrontal cortex (PFC) of mood disorder patients died with suicide (N = 45) and without suicide (N = 38). We also investigated the expression pattern of the same genes in the PFC of developing humans ranging in age from birth to 49 year (N = 46). The expression levels of CAMK2B, CDK5, MAPK9, and PRKCI were increased in the PFC of suicide victims as compared to non-suicide controls (false discovery rate, FDR-adjusted p < 0.05, fold change >1.1). Those genes also showed changes in expression pattern during the postnatal development (FDR-adjusted p < 0.05). These results suggest that multiple kinase genes undergo age-dependent changes in normal brains as well as pathological changes in suicide brains. These findings may provide an important link to protein kinases known to be important for the development of fear memory, stress associated neural plasticity, and up-regulation in the PFC of suicide victims. More research is needed to better understand the functional role of these kinase genes that may be associated with the pathophysiology of suicide

Design, construction, and analysis of cell line arrays and tissue microarrays for gene expression analysis

Cell line array (CMA) and tissue microarray (TMA) technologies are high-throughput methods for analysing both the abundance and distribution of gene expression in a panel of cell lines or multiple tissue specimens in an efficient and cost-effective manner. The process is based on Kononen's method of extracting a cylindrical core of paraffin-embedded donor tissue and inserting it into a recipient paraffin block. Donor tissue from surgically resected paraffin-embedded tissue blocks, frozen needle biopsies or cell line pellets can all be arrayed in the recipient block. The representative area of interest is identified and circled on a haematoxylin and eosin (H&E)-stained section of the donor block. Using a predesigned map showing a precise spacing pattern, a high density array of up to 1,000 cores of cell pellets and/or donor tissue can be embedded into the recipient block using a tissue arrayer from Beecher Instruments. Depending on the depth of the cell line/tissue removed from the donor block 100-300 consecutive sections can be cut from each CMA/TMA block. Sections can be stained for in situ detection of protein, DNA or RNA targets using immunohistochemistry (IHC), fluorescent in situ hybridisation (FISH) or mRNA in situ hybridisation (RNA-ISH), respectively. This chapter provides detailed methods for CMA/TMA design, construction and analysis with in-depth notes on all technical aspects including tips to deal with common pitfalls the user may encounter. © Springer Science+Business Media, LLC 2011.