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.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

The main topic of this thesis is confounding in linear regression models. It arises when a relationship between an observed process, the covariate, and an outcome process, the response, is influenced by an unmeasured process, the confounder, associated with both. Consequently, the estimators for the regression coefficients of the measured covariates might be severely biased, less efficient and characterized by misleading interpretations. Confounding is an issue when the primary target of the work is the estimation of the regression parameters. The central point of the dissertation is the evaluation of the sampling properties of parameter estimators. This work aims to extend the spatial confounding framework to general structured settings and to understand the behaviour of confounding as a function of the data generating process structure parameters in several scenarios focusing on the joint covariate-confounder structure. In line with the spatial statistics literature, our purpose is to quantify the sampling properties of the regression coefficient estimators and, in turn, to identify the most prominent quantities depending on the generative mechanism impacting confounding. Once the sampling properties of the estimator conditionally on the covariate process are derived as ratios of dependent quadratic forms in Gaussian random variables, we provide an analytic expression of the marginal sampling properties of the estimator using Carlson’s R function. Additionally, we propose a representative quantity for the magnitude of confounding as a proxy of the bias, its first-order Laplace approximation. To conclude, we work under several frameworks considering spatial and temporal data with specific assumptions regarding the covariance and cross-covariance functions used to generate the processes involved. This study allows us to claim that the variability of the confounder-covariate interaction and of the covariate plays the most relevant role in determining the principal marker of the magnitude of confounding.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.