Angiogenesis and angioregression gene expression analyses in swine corpus luteum

The corpus luteum (CL) lifespan is characterized by a rapid growth, differentiation and controlled regression of the luteal tissue, accompanied by an intense angiogenesis and angioregression. Indeed, the CL is one of the most highly vascularised tissue in the body with a proliferation rate of the endothelial cells 4- to 20-fold more intense than in some of the most malignant human tumours. This angiogenic process should be rigorously controlled to allow the repeated opportunities of fertilization. After a first period of rapid growth, the tissue becomes stably organized and prepares itself to switch to the phenotype required for its next apoptotic regression. In pregnant swine, the lifespan of the CLs must be extended to support embryonic and foetal development and vascularisation is necessary for the maintenance of luteal function. Among the molecules involved in the angiogenesis, Vascular Endothelial Growth Factor (VEGF) is the main regulator, promoting endothelial cells proliferation, differentiation and survival as well as vascular permeability and vessel lumen formation. During vascular invasion and apoptosis process, the remodelling of the extracellular matrix is essential for the correct evolution of the CL, particularly by the action of specific class of proteolytic enzymes known as matrix metalloproteinases (MMPs). Another important factor that plays a role in the processes of angiogenesis and angioregression during the CL formation and luteolysis is the isopeptide Endothelin-1 (ET-1), which is well-known to be a potent vasoconstrictor and mitogen for endothelial cells. The goal of the present thesis was to study the role and regulation of vascularisation in an adult vascular bed. For this purpose, using a precisely controlled in vivo model of swine CL development and regression, we determined the levels of expression of the members of VEGF system (VEGF total and specific isoforms; VEGF receptor-1, VEGFR-1; VEGF receptor-2, VEGFR-2) and ET- 1 system (ET-1; endothelin converting enzyme-1, ECE-1; endothelin receptor type A, ET-A) as well as the activity of the Ca++/Mg++-dependent endonucleases and gelatinases (MMP-2 and MMP-9). Three experiments were conducted to reach such objectives in CLs isolated from ovaries of cyclic, pregnant or fasted gilts. In the Experiment I, we evaluated the influence of acute fasting on VEGF production and VEGF, VEGFR-2, ET-1, ECE-1 and ET-A mRNA expressions in CLs collected on day 6 after ovulation (midluteal phase). The results indicated a down-regulation of VEGF, VEGFR-2, ET-1 and ECE-1 mRNA expression, although no change was observed for VEGF protein. Furthermore, we observed that fasting stimulated steroidogenesis by luteal cells. On the basis of the main effects of VEGF (stimulation of vessel growth and endothelial permeability) and ET-1 (stimulation of endothelial cell proliferation and vasoconstriction, as well as VEGF stimulation), we concluded that feed restriction possibly inhibited luteal vessel development. This could be, at least in part, compensated by a decrease of vasal tone due to a diminution of ET-1, thus ensuring an adequate blood flow and the production of steroids by the luteal cells. In the Experiment II, we investigated the relationship between VEGF, gelatinases and Ca++/Mg++-dependent endonucleases activities with the functional CL stage throughout the oestrous cycle and at pregnancy. The results demonstrated differential patterns of expression of those molecules in correspondence to the different phases of the oestrous cycle. Immediately after ovulation, VEGF mRNA/protein levels and MMP-9 activity are maximal. On days 5–14 after ovulation, VEGF expression and MMP-2 and -9 activities are at basal levels, while Ca++/Mg++-dependent endonuclease levels increased significantly in relation to day 1. Only at luteolysis (day 17), Ca++/Mg++-dependent endonuclease and MMP-2 spontaneous activity increased significantly. At pregnancy, high levels of MMP-9 and VEGF were observed. These results suggested that during the very early luteal phase, high MMPs activities coupled with high VEGF levels drive the tissue to an angiogenic phenotype, allowing CL growth under LH (Luteinising Hormone) stimulus, while during the late luteal phase, low VEGF and elevate MMPs levels may play a role in the apoptotic tissue and extracellular matrix remodelling during structural luteolysis. In the Experiment III, we described the expression patterns of all distinct VEGF isoforms throughout the oestrous cycle. Furthermore, the mRNA expression and protein levels of both VEGF receptors were also evaluated. Four novel VEGF isoforms (VEGF144, VEGF147, VEGF182, and VEGF164b) were found for the first time in swine and the seven identified isoforms presented four different patterns of expression. All isoforms showed their highest mRNA levels in newly formed CLs (day 1), followed by a decrease during mid-late luteal phase (days 10–17), except for VEGF182, VEGF188 and VEGF144 that showed a differential regulation during late luteal phase (day 14) or at luteolysis (day 17). VEGF protein levels paralleled the most expressed and secreted VEGF120 and VEGF164 isoforms. The VEGF receptors mRNAs showed a different pattern of expression in relation to their ligands, increasing between day 1 and 3 and gradually decreasing during the mid-late luteal phase. The differential regulation of some VEGF isoforms principally during the late luteal phase and luteolysis suggested a specific role of VEGF during tissue remodelling process that occurs either for CL maintenance in case of pregnancy or for noncapillary vessel development essential for tissue removal during structural luteolysis. In summary, our findings allow us to determine relationships among factors involved in the angiogenesis and angioregression mechanisms that take place during the formation and regression of the CL. Thus, CL provides a very interesting model for studying such factors in different fields of the basic research.

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.