MicroRNA modulation of aldosterone production in the adrenal gland

Hypertension is the major risk factor for coronary disease worldwide. Primary hypertension is idiopathic in origin but is thought to arise from multiple risk factors including genetic, lifestyle and environmental influences. Secondary hypertension has a more definite aetiology; its major single cause is primary aldosteronism (PA), the greatest proportion of which is caused by aldosteroneproducing adenoma (APA), where aldosterone is synthesized at high levels by an adenoma of the adrenal gland. There is strong evidence to show that high aldosterone levels cause adverse effects on cardiovascular, cerebrovascular, renal and other systems. Extensive studies have been conducted to analyse the role that regulation of CYP11B2, the gene encoding the aldosterone synthase enzyme plays in determining aldosterone production and the development of hypertension. One significant regulatory factor that has only recently emerged is microRNA (miRNA). miRNAs are small non-coding RNAs, synthesized by a series of enzymatic processes, that negatively regulate gene expression at the posttranscriptional level. Detection and manipulation of miRNA is now known to be a viable method in the treatment, prevention and prognosis of certain diseases. The aim of the present study was to identify miRNAs likely to have a role in the regulation of corticosteroid biosynthesis. To achieve this, the miRNA profile of APA and normal human adrenal tissue was compared, as was the H295R adrenocortical cell line model of adrenocortical function, under both basal conditions and following stimulation of aldosterone production. Key differentially-expressed miRNAs were then identified and bioinformatic tools used to identify likely mRNA targets and pathways for these miRNAs, several of which were investigated and validated using in vitro methods. The background to this study is set out in Chapter 1 of this thesis, followed by a description of the major technical methods employed in Chapter 2. Chapter 3 presents the first of the study results, analysing differences in miRNA profile between APA and normal human adrenal tissue. Microarray was implemented to detect the expression of miRNAs in these two tissue types and several miRNAs were found to vary significantly and consistently between them. Furthermore, members of several miRNA clusters exhibited similar changes in expression pattern between the two tissues e.g. members of cluster miR-29b-1 (miR-29a-3p and miR-29b-3p) and of cluster miR-29b-2 (miR-29b-3p and miR-29c- 3p) are downregulated in APA, while members of cluster let-7a-1 (let-7a-5p and let-7d-5p), cluster let-7a-3 (let-7a-5p and let-7b-5p) and cluster miR-134 (miR- 134 and miR-382) are upregulated. Further bioinformatic analysis explored the possible biological function of these miRNAs using Ingenuity® Systems Pathway Analysis software. This led to the identification of validated mRNAs already known to be targeted by these miRNAs, as well as the prediction of other mRNAs that are likely targets and which are involved in processes relevant to APA pathology including cholesterol synthesis (HMGCR) and corticosteroidogenesis (CYP11B2). It was therefore hypothesised that increases in miR-125a-5p or miR- 335-5p would reduce HMGCR and CYP11B2 expression. Chapter 4 describes the characterisation of H295R cells of different strains and sources (H295R Strain 1, 2, 3 and HAC 15). Expression of CYP11B2 was assessed following application of 3 different stimulants: Angio II, dbcAMP and KCl. The most responsive strain to stimulation was Strain 1 at lower passage numbers. Furthermore, H295R proliferation increased following Angio II stimulation. In Chapter 5, the hypothesis that increases in miR-125a-5p or miR-335-5p reduces HMGCR and CYP11B2 expression was tested using realtime quantitative RT-PCR and transfection of miRNA mimics and inhibitors into the H295R cell line model of adrenocortical function. In this way, miR-125a-5p and miR-335-5p were shown to downregulate CYP11B2 and HMGCR expression, thereby validating certain of the bioinformatic predictions generated in Chapter 3. The study of miRNA profile in the H295R cell lines was conducted in Chapter 6, analysing how it changes under conditions that increase aldosterone secretion, including stimulation Angiotensin II, potassium chloride or dibutyryl cAMP (as a substitute for adrenocorticotropic hormone). miRNA profiling identified 7 miRNAs that are consistently downregulated by all three stimuli relative to basal cells: miR-106a-5p, miR-154-3p, miR-17-5p, miR-196b-5p, miR-19a-3p, miR-20b- 5p and miR-766-3p. These miRNAs include those derived from cluster miR-106a- 5p/miR-20b-5p and cluster miR-17-5p/miR-19a-3p, each producing a single polycistronic transcript. IPA bioinformatic analysis was again applied to identify experimentally validated and predicted mRNA targets of these miRNAs and the key biological pathways likely to be affected. This predicted several interactions between miRNAs derived from cluster miR-17-5p/miR-19a-3p and important mRNAs involved in cholesterol biosynthesis: LDLR and ABCA1. These predictions were investigated by in vitro experiment. miR-17-5p/miR-106a-p and miR-20b-5p were found to be consistently downregulated by stimulation of aldosterone biosynthesis. Moreover, miR-766-3p was upregulation throughout. Furthermore, I was able to validate the downregulation of LDLR by miR-17 transfection, as predicted by IPA. In summary, this study identified key miRNAs that are differentially-expressed in vivo in cases of APA or in vitro following stimulation of aldosterone biosynthesis. The many possible biological actions these miRNAs could have were filtered by bioinformatic analysis and selected interactions validated in vitro. While direct actions of these miRNAs on steroidogenic enzymes were identified, cholesterol handling also emerged as an important target and may represent a useful point of intervention in future therapies designed to modulate aldosterone biosynthesis and reduce its harmful effects.

An investigation of the relationship between the components of tumour microenvironment, signal transduction pathways and outcome in breast cancer

Breast cancer, the most commonly diagnosed type of cancer in women, is a major cause of morbidity and mortality in the western world. Well-established risk factors of breast cancer are mostly related to women’s reproductive history, such as early menarche, late first pregnancy and late menopause. Survival rates have improved due to a combination of factors, including better health education, early detection with large-scale use of screening mammogram, improved surgical techniques, as well as widespread use of adjuvant therapy. At initial presentation, clinicopathological features of breast cancer such as age, nodal status, tumour size, tumour grade, and hormonal receptor status are considered to be the standard prognostic and predictive markers of patient survival, and are used to guide appropriate treatment strategies. Lymphovascular invasion (LBVI), including lymphatic (LVI) and blood (BVI) vessel invasion, has been reported to be prognostic and merit accurate evaluation, particularly in patients with node negative tumours who might benefit from adjuvant chemotherapy. There is a lack of standard assessment and agreement on distinguishing LVI from BVI despite the major challenges in the field. A systematic review of the literatures, examining methods of detection and the prognostic significance of LBVI, LVI and BVI, was carried out. The majority of studies used haematoxylin and eosin (H&E) and classical histochemistry to identify LVI and BVI. Only few recent studies used immunohistochemistry (IHC) staining of the endothelium lining lymphatic and blood vessels, and were able to show clear differences between LVI and BVI. The prognostic significance of LBVI and LVI was well-documented and strongly associated with aggressive features of breast tumours, while the prognostic value and the optimal detection method of BVI were unclear. Assessment and prognostic value of LBVI on H&E sections (LBVIH&E) was examined and compared to that of LVI and BVI detected using IHC with D2-40 for LVI (LVID2–40) and Factor VIII for BVI (BVIFVIII) in patients with breast cancer including node negative and triple negative patients (n=360). LBVIH&E, LVID2–40 and BVIFVIII were present in 102 (28%), 127 (35%) and 59 (16%) patients respectively. In node negative patients (206), LBVIH&E, LVID2–40 and BVIFVIII were present in 41 (20%), 53 (26%) and 21 (10%) respectively. In triple negative patients (102), LBVIH&E, LVID2–40 and BVIFVIII were present in 35 (29%), 36 (35%) and 14 (14%) respectively. LBVIH&E, LVID2–40 and BVIFVIII were all significantly associated with tumour recurrence in all cohorts. On multivariate survival analysis, only LVID2–40 and BVIFVIII were independent predictors of cancer specific survival (CSS) in the whole cohort (P=0.022 and P<0.001 respectively), node negative (P=0.008 and P=0.001 respectively) and triple negative patients (P=0.014 and P<0.001 respectively). Assessment of LVI and BVI by IHC, using D2-40 and Factor VIII, improves prediction of outcome in patients with node negative and triple negative breast cancer and was superior to the conventional detection method. Breast cancer is recognised as a complex molecular disease and histologically identical tumours may have highly variable outcomes, including different responses to therapy. Therefore, there is a compelling need for new prognostic and predictive markers helpful of selecting patients at risk and patients with aggressive diseases who might benefit from adjuvant and targeted therapy. It is increasingly recognised that the development and progression of human breast cancer is not only determined by genetically abnormal cells, but also dependent on complex interactions between malignant cells and the surrounding microenvironment. This has led to reconsider the features of tumour microenvironment as potential predictive and prognostic markers. Among these markers, tumour stroma percentage (TSP) and tumour budding, as well as local tumour inflammatory infiltrate have received recent attention. In particular, the local environment of cytokines, proteases, angiogenic and growth factors secreted by inflammatory cells and stromal fibroblasts has identified crucial roles in facilitating tumour growth, and metastasis of cancer cells through lymphatic and/or blood vessel invasion. This might help understand the underlying process promoting tumour invasion into these vessels. An increase in the proportion of tumour stroma and an increase in the dissociation of tumour cells have been associated with poorer survival in a number of solid tumours, including breast cancer. However, the interrelationship between these variables and other features of the tumour microenvironment in different subgroups of breast cancer are not clear. Also, whether their prognostic values are independent of other components of the tumour microenvironment have yet to be identified. Therefore, the relationship between TSP, clinicopathological characteristics and outcome in patients with invasive ductal breast cancer, in particular node negative and triple negative disease was examined in patients with invasive ductal breast cancer (n=361). The TSP was assessed on the haematoxylin and eosin-stained tissue sections. With a cut-off value of 50% TSP, patients with ≤50% stroma were classified as the low-TSP group and those with >50% stroma were classified as the high-TSP group. A total of 109 (30%) patients had high TSP. Patients with high TSP were old age (P=0.035), had involved lymph node (P=0.049), Her-2 positive tumours (P=0.029), low-grade peri-tumoural inflammatory infiltrate (P=0.034), low CD68+ macrophage infiltrate (P<0.001), low CD4+ (P=0.023) and low CD8+ T-lymphocytes infiltrate (P=0.017), tumour recurrence (P=0.015) and shorter CSS (P<0.001). In node negative patients (n=207), high TSP was associated with low CD68+ macrophage infiltrate (P=0.001), low CD4+ (P=0.040) and low CD8+ T-lymphocytes infiltrate (P=0.016) and shorter CSS (P=0.005). In triple negative patients (n=103), high TSP was associated with increased tumour size (P=0.017) high tumour grade (P=0.014), low CD8+ T-lymphocytes infiltrate (P=0.048) and shorter CSS (P=0.041). The 15-year cancer specific survival rate was 79% vs 21% in the low-TSP group vs high-TSP group. On multivariate survival analysis, a high TSP was associated with reduced CSS in the whole cohort (P=0.007), node negative patients (P=0.005) and those who received systemic adjuvant therapy (P=0.016), independent of other pathological characteristics including local host inflammatory responses. Therefore, a high TSP in invasive ductal breast cancer was associated with recurrence and poorer long-term survival. The inverse relation with the tumour inflammatory infiltrate highlights the importance of the amount of tumour stroma on immunological response in patients with invasive ductal breast cancer. Implementing this simple and reproducible parameter in routine pathological examination may help optimise risk stratification in patients with breast cancer. Similarly, the relationship between tumour budding, clinicopathological characteristics and outcome was examined in patients with invasive ductal breast cancer (n=474), using routine pathological sections. Tumour budding was associated with several adverse pathological characteristics, including positive lymph node (P=0.009), presence of LVI (P<0.001), and high TSP (P=0.001) and low-grade general peri-tumural inflammatory infiltrative (P=0.002). In node negative patients, a high tumour budding was associated with presence of LVI (P<0.001) and low-grade general peri-tumural inflammatory infiltrative (P=0.038). On multivariate survival analysis, tumour budding was associated with reduced CSS (P=0.001), independent of nodal status, tumour necrosis, CD8+ and CD138+ inflammatory cells infiltrate, LVI, BVI and TSP. Furthermore, tumour budding was independently associated with reduced CSS in node negative patients (P=0.004) and in those who have low TSP (P=0.003) and high-grade peri-tumoural inflammatory infiltrative (P=0.012). A high tumour budding was significantly associated with shorter CSS in luminal B and triple negative breast cancer subtypes (all P<0.001). Therefore, tumour budding was a significant predictor of poor survival in patients with invasive ductal breast cancer, independent of adverse pathological characteristics and components of tumour microenvironment. These results suggest that tumour budding may promote disease progression through a direct effect on local and distant invasion into lymph nodes and lymphatic vessels. Therefore, detection of tumour buds at the stroma invasive front might therefore represent a morphologic link between tumour progression, lymphatic invasion, spread of tumour cells to regional lymph nodes, and the establishment of metastatic dissemination. Given the potential importance of the tumour microenvironment, the characterisation of intracellular signalling pathways is important in the tumour microenvironment and is of considerable interest. One plausible signalling molecule that links tumour stroma, inflammatory cell infiltrate and tumour budding is the signal transducer and activator of transcription (STAT). The relationship between total and phosphorylated STAT1 (ph-STAT1), and total and ph-STAT3 tumour cell expression, components of tumour microenvironment and survival in patients with invasive ductal breast cancer was examined. IHC of total and ph-STAT1/STAT3 was performed on tissue microarray of 384 breast cancer specimens. Cellular STAT1 and cellular STAT3 expression at both cytoplasmic and nuclear locations were combined and identified as STAT1/STAT3 tumour cell expression. These results were then related to CSS and phenotypic features of the tumour and host. A high ph-STAT1 and a high ph-STAT3 tumour cell expression was associated with increased ER (P=0.001 and P<0.001 respectively) and PR (all P<0.05), reduced tumour grade (P=0.015 and P<0.001 respectively) and necrosis (all P=0.001). Ph-STAT1 was associated with increased general peri-tumoural inflammatory infiltrate (P=0.007) and ph-STAT3 was associated with lower CD4+ T-lymphocyte infiltrate (P=0.024). On multivariate survival analysis, including both ph-STAT1 and ph-STAT3 tumour cell expression, only high ph-STAT3 tumour cell expression was significantly associated with improved CSS (P=0.010) independent of other tumour and host-based factors. In patients with high necrosis grade, high ph-STAT3 tumour cell expression was independent predictor of improved CSS (P=0.021). Ph-STAT1 and ph-STAT3 were also significantly associated with improved cancer specific survival in luminal A and B subtypes. STAT1 and STAT3 tumour cell expression appeared to be an important determinant of favourable outcome in patients with invasive ductal breast cancer. The present results suggest that STATs may affect disease outcome through direct impact on tumour cells, and the surrounding microenvironment. The above observations of the present thesis point to the importance of the tumour microenvironment in promoting tumour budding, LVI and BVI. The observations from STATs work may suggest that an important driving mechanism for the above associations is the presence of tumour necrosis, probably secondary to hypoxia. Further work is needed to examine the interaction of other molecular pathways involved in the tumour microenvironment, such as HIF and NFkB in patients with invasive ductal breast cancer.

cDNA microarray analysis of differentially expressed genes in penile tissue after treatment with tadalafil

To evaluate differential gene expression in penile tissue after treatment with the phosphodiesterase 5 (PDE5) inhibitor tadalafil, as of the three clinically available PDE5 inhibitors (sildenafil, tadalafil, and vardenafil) used for the treatment of erectile dysfunction (ED), tadalafil has a long half-life and low incidence of side-effects. In all, 32 adult rats were divided into two groups. The control group received 0.5 mL of drinking water alone, while the tadalafil group was treated with tadalafil at a dose of 0.27 mg/kg. At 4 h after treatment with water or tadalafil the rats were killed and the penile tissue was removed. The total RNA was isolated from the penile tissue from both groups and differentially expressed genes were identified by cDNA microarray analysis. To validate the expression data from the microarray analysis, quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry were used. In all, 153 genes were differentially expressed between the control group and the tadalafil group. We validated the microarray results by quantitative PCR for the insulin-like growth factor binding protein 6 (IGFBP-6) gene and the neuronal calcium sensor 1 (NCS-1) gene, both of which were up-regulated in the tadalafil group, and for the natriuretic peptide receptor 1 (NPR-1) gene that was down-regulated in this group. Immunohistochemistry showed localization of the NCS-1 protein in sinusoid trabeculae of the corpus cavernosum in control and tadalafil-treated rats. There was differential expression in 153 genes after tadalafil treatment. Some of these genes such as IGFBP-6, NPR-1 and NCS-1, might result in new targets in the treatment of ED.

Gene expression profile analysis of primary glioblastomas and non-neoplastic brain tissue: identification of potential target genes by oligonucleotide microarray and real-time quantitative PCR

The prognosis of glioblastomas is still extremely poor and the discovery of novel molecular therapeutic targets can be important to optimize treatment strategies. Gene expression analyses comparing normal and neoplastic tissues have been used to identify genes associated with tumorigenesis and potential therapeutic targets. We have used this approach to identify differentially expressed genes between primary glioblastomas and non-neoplastic brain tissues. We selected 20 overexpressed genes related to cell cycle, cellular movement and growth, proliferation and cell-to-cell signaling and analyzed their expression levels by real time quantitative PCR in cDNA obtained from microdissected fresh tumor tissue from 20 patients with primary glioblastomas and from 10 samples of non-neoplastic white matter tissue. The gene expression levels were significantly higher in glioblastomas than in non-neoplastic white matter in 18 out of 20 genes analyzed: P < 0.00001 for CDKN2C, CKS2, EEF1A1, EMP3, PDPN, BNIP2, CA12, CD34, CDC42EP4, PPIE, SNAI2, GDF15 and MMP23b; and NFIA (P: 0.0001), GPS1 (P: 0.0003), LAMA1 (P: 0.002), STIM1 (P: 0.006), and TASP1 (P: 0.01). Five of these genes are located in contiguous loci at 1p31-36 and 2 at 17q24-25 and 8 of them encode surface membrane proteins. PDPN and CD34 protein expression were evaluated by immunohistochemistry and they showed concordance with the PCR results. The present results indicate the presence of 18 overexpressed genes in human primary glioblastomas that may play a significant role in the pathogenesis of these tumors and that deserve further functional investigation as attractive candidates for new therapeutic targets.

Tissue-specific gene expression in soybean (Glycine max) detected by cDNA microarray analysis

We have constructed cDNA microarrays for soybean (Glycine max L. Merrill), containing approximately 4,100 Unigene ESTs derived from axenic roots, to evaluate their application and utility for functional genomics of organ differentiation in legumes. We assessed microarray technology by conducting studies to evaluate the accuracy of microarray data and have found them to be both reliable and reproducible in repeat hybridisations. Several ESTs showed high levels (>50 fold) of differential expression in either root or shoot tissue of soybean. A small number of physiologically interesting, and differentially expressed sequences found by microarray analysis were verified by both quantitative real-time RT-PCR and Northern blot analysis. There was a linear correlation (r(2) = 0.99, over 5 orders of magnitude) between microarray and quantitative real-time RT-PCR data. Microarray analysis of soybean has enormous potential not only for the discovery of new genes involved in tissue differentiation and function, but also to study the expression of previously characterised genes, gene networks and gene interactions in wild-type, mutant or transgenic; plants.

A microarray study of post-mortem mRNA degradation in mouse brain tissue

Background: Although there is evidence that post-mortem interval (PMI) is not a major contributor to reduced overall RNA integrity, it may differentially affect a subgroup of gene transcripts that are susceptible to PMI-related degradation. This would particularly have ramifications for microarray studies that include a broad spectrum of genes. Method: Brain tissue was removed from adult mice at 0, 6, 12, 18, 24,36 and 48 h post-mortem. RNA transcript abundance was measured by hybridising RNA from the zero time point with test RNA from each PMI time point, and differential gene expression was assessed using cDNA microarrays. Sequence and ontological analyses were performed on the group of RNA transcripts showing greater than two-fold reduction. Results: Increasing PMI was associated with decreased tissue pH and increased RNA degradation as indexed by 28S/18S ribosomal RNA ratio. Approximately 12% of mRNAs detected on the arrays displayed more than a two-fold decrease in abundance by 48 It post-mortem. An analysis of nucleotide composition provided evidence that transcripts with the AUUUA motif in the 3' untranslated region (3'UTR) were more susceptible to PMI-related RNA degradation, compared to transcripts not carrying the 3'UTR AUUUA motif. Consistent with this finding, ontological analysis showed transcription factors and elements to be over-represented in the group of transcripts susceptible to degradation. Conclusion: A subgroup of mammalian mRNA transcripts are particularly susceptible to PMI-related degradation, and as a group, they are more likely to carry the YUTR AUUUA motif. PMI should be controlled for in human and animal model post-mortem brain studies, particularly those including a broad spectrum of mRNA transcripts. (c) 2005 Elsevier B.V. All rights reserved.

On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

This paper considers a model-based approach to the clustering of tissue samples of a very large number of genes from microarray experiments. It is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. Frequently in practice, there are also clinical data available on those cases on which the tissue samples have been obtained. Here we investigate how to use the clinical data in conjunction with the microarray gene expression data to cluster the tissue samples. We propose two mixture model-based approaches in which the number of components in the mixture model corresponds to the number of clusters to be imposed on the tissue samples. One approach specifies the components of the mixture model to be the conditional distributions of the microarray data given the clinical data with the mixing proportions also conditioned on the latter data. Another takes the components of the mixture model to represent the joint distributions of the clinical and microarray data. The approaches are demonstrated on some breast cancer data, as studied recently in van't Veer et al. (2002).

Selection bias in gene extraction on the basis of microarray gene-expression data

In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called. 632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.

Coordinated plant defense responses in Arabidopsis revealed by microarray analysis

Disease resistance is associated with a plant defense response that involves an integrated set of signal transduction pathways. Changes in the expression patterns of 2.375 selected genes were examined simultaneously by cDNA microarray analysis in Arabidopsis thaliana after inoculation with an incompatible fungal pathogen Alternaria brassicicola or treatment with the defense-related signaling molecules salicylic acid (SA), methyl jasmonate (MJ), or ethylene, Substantial changes (up- and down-regulation) in the steady-state abundance of 705 mRNAs were observed in response to one or more of the treatments, including known and putative defense-related genes and 106 genes with no previously described function or homology, In leaf tissue inoculated with A. brassicicola, the abundance of 168 mRNAs was increased more than 2.5-fold, whereas that of 39 mRNAs was reduced. Similarly, the abundance of 192, 221, and 55 mRNAs was highly (>2.5-fold) increased after treatment with SA, MJ, and ethylene, respectively. Data analysis revealed a surprising level of coordinated defense responses, including 169 mRNAs regulated by multiple treatments/defense pathways. The largest number of genes coinduced (one of four induced genes) and corepressed was found after treatments with SA and MJ. In addition, 50% of the genes induced by ethylene treatment were also induced by MJ treatment. These results indicated the existence of a substantial network of regulatory interactions and coordination occurring during plant defense among the different defense signaling pathways, notably between the salicylate and jasmonate pathways that were previously thought to act in an antagonistic fashion.

The Role of Prostate Specific Membrane Antigen and Pepsinogen C Tissue Expression as an Adjunctive Method to Prostate Cancer Diagnosis

Purpose: The diagnosis of prostate cancer in men with persistently increased prostate specific antigen after a negative prostate biopsy has become a great challenge for urologists and pathologists. We analyzed the diagnostic value of 6 genes in the tissue of patients with prostate cancer. Materials and Methods: The study was comprised of 50 patients with localized disease who underwent radical prostatectomy. Gene selection was based on a previous microarray analysis. Among 4,147 genes with different expressions between 2 pools of patients 6 genes (PSMA, TMEFF2, GREB1, TH1L, IgH3 and PGC) were selected. These genes were tested for diagnostic value using the quantitative reverse transcription polymerase chain reaction method. Initially malignant tissue samples from 33 patients were analyzed and in the second part of the study we analyzed benign tissue samples from the other 17 patients with prostate cancer. The control group was comprised of tissue samples of patients with benign prostatic hyperplasia. Results: Analysis of malignant prostatic tissue demonstrated that prostate specific membrane antigen was over expressed (mean 9 times) and pepsinogen C was under expressed (mean 1.3 X 10(-4) times) in all cases compared to benign prostatic hyperplasia. The other 4 tested genes showed a variable expression pattern not allowing for differentiation between benign and malignant cases. When we tested these results in the benign prostate tissues from patients with cancer, pepsinogen C maintained the expression pattern. In terms of prostate specific membrane antigen, despite over expression in most cases (mean 12 times), 2 cases (12%) presented with under expression. Conclusions: Pepsinogen C tissue expression may constitute a powerful adjunctive method to prostate biopsy in the diagnosis of prostate cancer cases.

Erection induced by Tx2-6 toxin of Phoneutria nigriventer spider: Expression profile of genes in the nitric oxide pathway of penile tissue of mice

The peptides Tx2-5 and Tx2-6, isolated from the whole venom of ""armed-spider"" Phoneutria nigniventer venom, are directly linked with the induction of persistent and painful erection in the penis of mammals. The erection induced by Tx2-6 has been associated with the activation of nitric oxide synthases. There is a scarcity of studies focusing on the outcome of Tx2-6 at the molecular level, by this reason we evaluated the gene profile activity of this toxin at the nitric oxide (NO) pathway. After microarray analyses on cavernous tissue of mice inoculated with Tx2-6 we found that only 10.4% (10/96) of these genes were differentially expressed, showing a limited effect of the toxin on the NO pathway. We found the genes sparc, ednrb, junb, cdkn1a, bcl2, ccl5, abcc1 over-expressed and the genes sod1, s100a10 and fth1 under-expressed after inoculation of Tx2-6. The differential expressions of sparc and ednrb genes were further confirmed using real-time PCR. Interestingly, ednrb activates the L-arginine/NO/cGMP pathway that is involved in the relaxation of the cavernous body. Therefore the priapism induced by Tx2-6 is a consequence of a highly specific interference of this neurotoxin with the NO pathway. (C) 2009 Elsevier Ltd. All rights reserved.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Protein-Binding Microarray Analysis of Tumor Suppressor AP2α Target Gene Specificity.

Cheap and massively parallel methods to assess the DNA-binding specificity of transcription factors are actively sought, given their prominent regulatory role in cellular processes and diseases. Here we evaluated the use of protein-binding microarrays (PBM) to probe the association of the tumor suppressor AP2α with 6000 human genomic DNA regulatory sequences. We show that the PBM provides accurate relative binding affinities when compared to quantitative surface plasmon resonance assays. A PBM-based study of human healthy and breast tumor tissue extracts allowed the identification of previously unknown AP2α target genes and it revealed genes whose direct or indirect interactions with AP2α are affected in the diseased tissues. AP2α binding and regulation was confirmed experimentally in human carcinoma cells for novel target genes involved in tumor progression and resistance to chemotherapeutics, providing a molecular interpretation of AP2α role in cancer chemoresistance. Overall, we conclude that this approach provides quantitative and accurate assays of the specificity and activity of tumor suppressor and oncogenic proteins in clinical samples, interfacing genomic and proteomic assays.

“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, andmany others, offer tremendous promise for the personalized treatment of cancer. Thesetechnologies measure the amount of specific proteins, RNA, DNA or other moleculartargets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumorspecimens are heterogeneous; an individual specimen typically contains unknownamounts of multiple tissues types. Thus, the measured molecular concentrations resultfrom an unknown mixture of tissue types, and must be normalized to account for thecomposition of the mixture.For example, a breast tumor biopsy may contain normal, dysplastic and cancerousepithelial cells, as well as stromal components (fatty and connective tissue) and bloodand lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic andcancerous epithelial cells. The remaining tissue components serve to “contaminate”the signal of interest. The proportion of each of the tissue components changes asa function of patient characteristics (e.g., age), and varies spatially across the tumorregion. Because each of the tissue components produces a different molecular signature,and the amount of each tissue type is specimen dependent, we must estimate the tissuecomposition of the specimen, and adjust the molecular signal for this composition.Using the idea of a chemical mass balance, we consider the total measured concentrationsto be a weighted sum of the individual tissue signatures, where weightsare determined by the relative amounts of the different tissue types. We develop acompositional source apportionment model to estimate the relative amounts of tissuecomponents in a tumor specimen. We then use these estimates to infer the tissuespecificconcentrations of key molecular targets for sub-typing individual tumors. Weanticipate these specific measurements will greatly improve our ability to discriminatebetween different classes of tumors, and allow more precise matching of each patient tothe appropriate treatment