Expression Profile of Rat Hippocampal Neurons Treated with the Neuroprotective Compound 2,4-Dinitrophenol: Up-Regulation of cAMP Signaling Genes

2,4-Dinitrophenol (DNP) is classically known as a mitochondrial uncoupler and, at high concentrations, is toxic to a variety of cells. However, it has recently been shown that, at subtoxic concentrations, DNP protects neurons against a variety of insults and promotes neuronal differentiation and neuritogenesis. The molecular and cellular mechanisms underlying the beneficial neuroactive properties of DNP are still largely unknown. We have now used DNA microarray analysis to investigate changes in gene expression in rat hippocampal neurons in culture treated with low micromolar concentrations of DNP. Under conditions that did not affect neuronal viability, high-energy phosphate levels or mitochondrial oxygen consumption, DNP induced up-regulation of 275 genes and down-regulation of 231 genes. Significantly, several up-regulated genes were linked to intracellular cAMP signaling, known to be involved in neurite outgrowth, synaptic plasticity, and neuronal survival. Differential expression of specific genes was validated by quantitative RT-PCR using independent samples. Results shed light on molecular mechanisms underlying neuroprotection by DNP and point to possible targets for development of novel therapeutics for neurodegenerative disorders.

Expression of human papillomavirus type 16 E7 oncoprotein alters keratinocytes expression profile in response to tumor necrosis factor-alpha

Acute expression of E7 oncogene from human papillomavirus (HPV) 16 or HPV18 is sufficient to overcome tumor necrosis factor (TNF)-alpha cytostatic effect on primary human keratinocytes. In the present study, we investigated the molecular basis of E7-induced TNF resistance through a comparative analysis of the effect of this cytokine on the proliferation and global gene expression of normal and E7-expressing keratinocytes. Using E7 functional mutants, we show that E7-induced TNF resistance correlates with its ability to mediate pRb degradation and cell transformation. On the other hand, this effect does not depend on E7 sequences required to override DNA damage-induced cell cycle arrest or extend keratinocyte life span. Furthermore, we identified a group of 66 genes whose expression pattern differs between normal and E7-expressing cells upon cytokine treatment. These genes are mainly involved in cell cycle regulation suggesting that their altered expression may contribute to sustained cell proliferation even in the presence of a cytostatic stimulus. Differential expression of TCN1 (transcobalamin I), IFI44 (Interferon-induced protein 44), HMGB2 (high-mobility group box 2) and FUS [Fusion (involved in t(12; 16) in malignant liposarcoma)] among other genes were further confirmed by western-blot and/or real-time polymerase chain reaction. Moreover, FUS upregulation was detected in HPV-positive cervical high-grade squamous intraepithelial lesions when compared with normal cervical tissue. Further evaluation of the role of such genes in TNF resistance and HPVassociated disease development is warranted.

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyse microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods cannot be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes) to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches.

Exequibility of differential gene expression analysis by DDRT-PCR in murine bone marrow cells

Model of study: Experimental study. Introduction: Recently, stem cell research has generated great interest due to its applicability in regenerative medicine. Bone marrow is considered the most important source of adult stem cells and the establishment of new methods towards gene expression analysis regarding stem cells has become necessary. Thus Differential Display Reverse Transcription Polymerase Chain Reaction (DDRT-PCR) may be an accessible tool to investigate small differences in the gene expression of different stem cells in distinct situations. Aim: In the present study, we investigated the exequibility of DDRT-PCR to identify differences in global gene expression of mice bone marrow cells under two conditions. Methods: First, bone marrow cells were isolated fresh and a part was cultivated during one week without medium replacement. Afterwards, both bone marrow cells (fresh and cultivated) were submitted to gene expression analyses by DDRT-PCR. Results: Initially, it was possible to observe in one week-cultured bone marrow cells, changes in morphology (oval cells to fibroblastic-like cells) and protein profile, which was seen through differences in band distribution in SDS-Page gels. Finally through gene expression analysis, we detected three bands (1300, 1000 and 225 bp) exclusively expressed in the fresh bone marrow group and two bands (400 and 300 bp) expressed specifically in the cultivated bone marrow cell group. Conclusions: In summary, the DDRT-PCR method was proved efficient towards the identification of small differences in gene expression of bone marrow cells in two defined conditions. Thus, we expect that DDRT-PCR can be fast and efficiently designed to analyze differential gene expression in several stem cell types under distinct conditions.

Gene expression profiling in leiomyosarcomas and undifferentiated pleomorphic sarcomas: SRC as a new diagnostic marker

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of gene expression SAP5, LIP9, and PLB2 of candida albicans biofilms after photodynamic inactivation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Gene Expression in Bovine Oocytes and Cumulus Cells After Meiotic Inhibition with the Cyclin-Dependent Kinase Inhibitor Butyrolactone I

Contents The aim of this study was to determine the effect of temporary inhibition of meiosis using the cyclin-dependent kinase inhibitor butyrolactone I (BLI) on gene expression in bovine oocytes and cumulus cells. Immature bovine cumulusoocyte complexes (COCs) were assigned to groups: (i) Control COCs collected immediately after recovery from the ovary or (ii) after in vitro maturation (IVM) for 24 h, (iii) Inhibited COCs collected 24 h after incubation with 100 mu m BLI or (iv) after meiotic inhibition for 24 h followed by IVM for a further 22 h. For mRNA relative abundance analysis, pools of 10 denuded oocytes and respective cumulus cells were collected. Transcripts related to cell cycle regulation and oocyte competence were evaluated in oocytes and cumulus cells by quantitative real-time PCR (qPCR). Most of the examined transcripts were downregulated (p < 0.05) after IVM in control and inhibited oocytes (19 of 35). Nine transcripts remained stable (p > 0.05) after IVM in control oocytes; only INHBA did not show this pattern in inhibited oocytes. Seven genes were upregulated after IVM in control oocytes (p < 0.05), and only PLAT, RBP1 and INHBB were not upregulated in inhibited oocytes after IVM. In cumulus cells, six genes were upregulated (p < 0.05) after IVM and eight were downregulated (p < 0.05). Cells from inhibited oocytes showed the same pattern of expression regarding maturation profile, but were affected by the temporary meiosis inhibition of the oocyte when the same maturation stages were compared between inhibited and control groups. In conclusion, changes in transcript abundance in oocytes and cumulus cells during maturation in vitro were mostly mirrored after meiotic inhibition followed by maturation.

Stage, Grade and Behavior of Bladder Urothelial Carcinoma Defined by the MicroRNA Expression Profile

Purpose: We identified miRNA expression profiles in urothelial carcinoma that are associated with grade, stage, and recurrence-free and disease specific survival. Materials and Methods: The expression of 14 miRNAs was evaluated by quantitative reverse transcriptase-polymerase chain reaction in surgical specimens from 30 patients with low grade, noninvasive (pTa) and 30 with high grade, invasive (pT2-3) urothelial carcinoma. Controls were normal bladder tissue from 5 patients who underwent surgical treatment for benign prostatic hyperplasia. Endogenous controls were RNU-43 and RNU-48. miRNA profiles were compared and Kaplan-Meier curves were constructed to analyze disease-free and disease specific survival. Results: miR-100 was under expressed in 100% of low grade pTa specimens (p <0.001) and miR-10a was over expressed in 73.3% (p <0.001). miR-21 and miR-205 were over expressed in high grade pT2-3 disease (p = 0.02 and <0.001, respectively). The other miRNAs were present at levels similar to those of normal bladder tissue or under expressed in each tumor group. miR-21 over expression (greater than 1.08) was related to shorter disease-free survival in patients with low grade pTa urothelial carcinoma. Higher miR-10a levels (greater than 2.30) were associated with shorter disease-free and disease specific survival in patients with high grade pT2-3 urothelial carcinoma. Conclusions: Four miRNAs were differentially expressed in the 2 urothelial carcinoma groups. miR-100 and miR-10a showed under expression and over expression, respectively, in low grade pTa tumors. miR-21 and miR-205 were over expressed in pT2-3 disease. In addition, miR-10a and miR-21 over expression was associated with shorter disease-free and disease specific survival. miRNAs could be incorporated into the urothelial carcinoma molecular pathway. These miRNAs could also serve as new diagnostic or prognostic markers and new target drugs.

Expression profile of apoptosis-related genes in childhood adrenocortical tumors: low level of expression of BCL2 and TNF genes suggests a poor prognosis

Background: Impaired apoptosis has been implicated in the development of childhood adrenocortical tumors (ACT), although the expression of apoptosis-related gene expression in such tumors has not been reported. Methods: The mRNA expression levels of the genes CASP3, CASP8, CASP9, FAS, TNF, NFKB, and BCL2 were analyzed by quantitative real-time PCR in consecutive tumor samples obtained at diagnosis from 60 children with a diagnosis of ACT and in 11 non-neoplastic adrenal samples. BCL2 and TNF protein expression was analyzed by immunohistochemistry. Results: A significant association was observed between tumor size >= 100 g and lower expression levels of the BCL2 (P=0.03) and TNF (P=0.05) genes; between stage IV and lower expression levels of CASP3 (P=0.008), CASP9 (P=0.02), BCL2 (P=0.002), TNF (P=0.05), and NFKB (P=0.03); Weiss score >= 3 and lower expression of TNF (P=0.01); unfavorable event and higher expression values of CASP9 (P=0.01) and lower values of TNF (P=0.02); and death and lower expression of BCL2 (P=0.04). Underexpression of TNF was associated with lower event-free survival in uni- and multivariate analyses (P<0.01). Similar results were observed when patients with Weiss score <3 were excluded. Conclusion: This study supports the participation of apoptosis-related genes in the biology and prognosis of childhood ACT and suggests the complex role of these genes in the pathogenesis of this tumor.

Analysis of ripening-related gene expression in papaya using an Arabidopsis-based microarray

Abstract Background Papaya (Carica papaya L.) is a commercially important crop that produces climacteric fruits with a soft and sweet pulp that contain a wide range of health promoting phytochemicals. Despite its importance, little is known about transcriptional modifications during papaya fruit ripening and their control. In this study we report the analysis of ripe papaya transcriptome by using a cross-species (XSpecies) microarray technique based on the phylogenetic proximity between papaya and Arabidopsis thaliana. Results Papaya transcriptome analyses resulted in the identification of 414 ripening-related genes with some having their expression validated by qPCR. The transcription profile was compared with that from ripening tomato and grape. There were many similarities between papaya and tomato especially with respect to the expression of genes encoding proteins involved in primary metabolism, regulation of transcription, biotic and abiotic stress and cell wall metabolism. XSpecies microarray data indicated that transcription factors (TFs) of the MADS-box, NAC and AP2/ERF gene families were involved in the control of papaya ripening and revealed that cell wall-related gene expression in papaya had similarities to the expression profiles seen in Arabidopsis during hypocotyl development. Conclusion The cross-species array experiment identified a ripening-related set of genes in papaya allowing the comparison of transcription control between papaya and other fruit bearing taxa during the ripening process.

MicroRNA expression profile in head and neck cancer: HOX-cluster embedded microRNA-196a and microRNA-10b dysregulation implicated in cell proliferation

Abstract Background Current evidence implicates aberrant microRNA expression patterns in human malignancies; measurement of microRNA expression may have diagnostic and prognostic applications. Roles for microRNAs in head and neck squamous cell carcinomas (HNSCC) are largely unknown. HNSCC, a smoking-related cancer, is one of the most common malignancies worldwide but reliable diagnostic and prognostic markers have not been discovered so far. Some studies have evaluated the potential use of microRNA as biomarkers with clinical application in HNSCC. Methods MicroRNA expression profile of oral squamous cell carcinoma samples was determined by means of DNA microarrays. We also performed gain-of-function assays for two differentially expressed microRNA using two squamous cell carcinoma cell lines and normal oral keratinocytes. The effect of the over-expression of these molecules was evaluated by means of global gene expression profiling and cell proliferation assessment. Results Altered microRNA expression was detected for a total of 72 microRNAs. Among these we found well studied molecules, such as the miR-17-92 cluster, comprising potent oncogenic microRNA, and miR-34, recently found to interact with p53. HOX-cluster embedded miR-196a/b and miR-10b were up- and down-regulated, respectively, in tumor samples. Since validated HOX gene targets for these microRNAs are not consistently deregulated in HNSCC, we performed gain-of-function experiments, in an attempt to outline their possible role. Our results suggest that both molecules interfere in cell proliferation through distinct processes, possibly targeting a small set of genes involved in cell cycle progression. Conclusions Functional data on miRNAs in HNSCC is still scarce. Our data corroborate current literature and brings new insights into the role of microRNAs in HNSCC. We also show that miR-196a and miR-10b, not previously associated with HNSCC, may play an oncogenic role in this disease through the deregulation of cell proliferation. The study of microRNA alterations in HNSCC is an essential step to the mechanistic understanding of tumor formation and could lead to the discovery of clinically relevant biomarkers.

Gene expression and phenotypic traits of Aggregatibacter actinomycetemcomitans in response to environmental changes

Background and Objective: Periodontopathogens experience several challenges in the oral cavity that may influence their transcription profile and resulting phenotype. This study evaluated the effect of environmental changes on phenotype and gene expression in a serotype b Aggregatibacter actinomycetemcomitans isolate. Material and Methods: Cultures in early exponential phase and at the start of stationary growth phase in microaerophilic and anaerobic atmospheres were evaluated. Cell hydrophobic properties were measured by adherence to n-hexadecane; in addition, adhesion to, and the ability to invade, KB cells was evaluated. Relative transcription of 12 virulence-associated genes was determined by real-time reverse transcritption quantitative PCR. Results: The culture conditions tested in this study were found to influence the phenotypic and genotypic traits of A. actinomycetemcomitans. Cells cultured in microaerophilic conditions were the most hydrophobic, reached the highest adhesion efficiency and showed up-regulation of omp100 (which encodes an adhesion) and pga (related to polysaccharide synthesis). Cells grown anaerobically were more invasive to epithelial cells and showed up-regulation of genes involved in host-cell invasion or apoptosis induction (such as apaH, omp29, cagE and cdtB) and in adhesion to extracellular matrix protein (emaA). Conclusion: Environmental conditions of different oral habitats may influence the expression of factors involved in the binding of A. actinomycetemcomitans to host tissues and the damage resulting thereby, and thus should be considered in in-vitro studies assessing its pathogenic potential.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Pioglitazone inhibits androgen production in NCI-H295R cells by regulating gene expression of CYP17 and HSD3B2

Thiazolidinediones (TZDs) such as pioglitazone and rosiglitazone are widely used as insulin sensitizers in the treatment of type 2 diabetes. In diabetic women with polycystic ovary syndrome, treatment with pioglitazone or rosiglitazone improves insulin resistance and hyperandrogenism, but the mechanism by which TZDs down-regulate androgen production is unknown. Androgens are synthesized in the human gonads as well as the adrenals. We studied the regulation of androgen production by analyzing the effect of pioglitazone and rosiglitazone on steroidogenesis in human adrenal NCI-H295R cells, an established in vitro model of steroidogenesis of the human adrenal cortex. Both TZDs changed the steroid profile of the NCI-H295R cells and inhibited the activities of P450c17 and 3betaHSDII, key enzymes of androgen biosynthesis. Pioglitazone but not rosiglitazone inhibited the expression of the CYP17 and HSD3B2 genes. Likewise, pioglitazone repressed basal and 8-bromo-cAMP-stimulated activities of CYP17 and HSD3B2 promoter reporters in NCI-H295R cells. However, pioglitazone did not change the activity of a cAMP-responsive luciferase reporter, indicating that it does not influence cAMP/protein kinase A/cAMP response element-binding protein pathway signaling. Although peroxisome proliferator-activated receptor gamma (PPARgamma) is the nuclear receptor for TZDs, suppression of PPARgamma by small interfering RNA technique did not alter the inhibitory effect of pioglitazone on CYP17 and HSD3B2 expression, suggesting that the action of pioglitazone is independent of PPARgamma. On the other hand, treatment of NCI-H295R cells with mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) inhibitor 2-(2-amino-3-methoxyphenyl)-4H-1-benzopyran-4-one (PD98059) enhanced promoter activity and expression of CYP17. This effect was reversed by pioglitazone treatment, indicating that the MEK/ERK signaling pathway plays a role in regulating androgen biosynthesis by pioglitazone.