Generation and Characterization of Knockout Mice and Analysis of Patient Material Demonstrates a Role for Tissue Inhibitor of Metalloproteinases 4 (Timp4) in the Cardiovascular System and Tumor Metastasis

This thesis focuses on tissue inhibitor of metalloproteinases 4 (TIMP4) which is the newest member of a small gene and protein family of four closely related endogenous inhibitors of extracellular matrix (ECM) degrading enzymes. Existing data on TIMP4 suggested that it exhibits a more restricted expression pattern than the other TIMPs with high expression levels in heart, brain, ovary and skeletal muscle. These observations and the fact that the ECM is of special importance to provide the cardiovascular system with structural strength combined with elasticity and distensibility, prompted the present molecular biologic investigation on TIMP4. In the first part of the study the murine Timp4 gene was cloned and characterized in detail. The structure of murine Timp4 genomic locus resembles that in other species and of the other Timps. The highest Timp4 expression was detected in heart, ovary and brain. As the expression pattern of Timp4 gives only limited information about its role in physiology and pathology, Timp4 knockout mice were generated next. The analysis of Timp4 knockout mice revealed that Timp4 deficiency has no obvious effect on the development, growth or fertility of mice. Therefore, Timp4 deficient mice were challenged using available cardiovascular models, i.e. experimental cardiac pressure overload and myocardial infarction. In the former model, Timp4 deficiency was found to be compensated by Timp2 overexpression, whereas in the myocardial infarct model, Timp4 deficiency resulted in increased mortality due to increased susceptibility for cardiac rupture. In the wound healing model, Timp4 deficiency was shown to result in transient retardation of re-epithelialization of cutaneous wounds. Melanoma tumor growth was similar in Timp4 deficient and control mice. Despite of this, lung metastasis of melanoma cells was significantly increased in Timp4 null mice. In an attempt to translate the current findings to patient material, TIMP4 expression was studied in human specimens representing different inflammatory cardiovascular pathologies, i.e. giant cell arteritis, atherosclerotic coronary arteries and heart allografts exhibiting signs of chronic rejection. The results showed that cardiovascular expression of TIMP4 is elevated particularly in areas exhibiting inflammation. The results of the present studies suggest that TIMP4 has a special role in the regulation of tissue repair processes in the heart, and also in healing wounds and metastases. Furthermore, evidence is provided suggesting the usefulness of TIMP4 as a novel systemic marker for vascular inflammation.

Identification and characterization of CIP2A as a novel oncogenic inhibitor of PP2A

Inhibition of the tumor suppressor protein phosphatase 2A (PP2A) activity has been identified as one of the five key alterations required for human cell transformation. Regardless of this crucial role in human cancer development, the detailed mechanisms by which PP2A inhibition occurs in human cancers remain largely uncharacterized. PP2A regulates a plethora of cellular signaling cascades. One of the targets of PP2A is Myc oncoprotein, which is destabilized and degraded in response to PP2A-mediated dephosphorylation of Myc serine 62. In this study we identify Cancerous Inhibitor of PP2A (CIP2A) as a previously uncharacterized endogenous inhibitor of PP2A in human cancer cells. CIP2A inhibits PP2A activity leading to subsequent stabilization of the Myc protein. CIP2A promotes malignant growth of cancer cells in vitro and xenograft tumor formation in vivo and is overexpressed in cancer. Moreover, we explored the effect of CIP2A on global transcriptional profiles and validated a CIP2A-dependent transcriptional signature. Analysis of the CIP2A signature revealed both Myc-dependent and -independent functions for CIP2A. Importantly, we demonstrate that the CIP2A signature has clinical relevance in human breast cancer subtypes. Finally, we identify the genes potentially mediating the long-term growth suppression in CIP2A depleted cancer cells. Taken together, this work identifies CIP2A as a novel human oncoprotein and describes its function in cancer cells. These results may open novel possibilities for patient stratification and therapeutic intervention of cancer.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.