Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.

Cathepsin D Deficiency - Molecular and Cellular Mechanisms of Neurodegeneration

Cathepsin D (CTSD) is a lysosomal protease, the deficiency of which is fatal and associated with neurodegeneration. CTSD knock-out mice, which die at the age of four weeks, show intestinal necrosis, loss of lymphoid cells and moderate pathological changes in the brain. An active-site mutation in the CTSD gene underlies a neurodegenerative disease in newborn sheep, characterized by brain atrophy without any changes to visceral tissues. The CTSD deficiences belong to the group of neuronal ceroid-lipofuscinoses (NCLs), severe neurodegenerative lysosomal storage disorders. The aim of this thesis was to examine the molecular and cellular mechanisms behind neurodegeneration in CTSD deficiency. We found the developmental expression pattern of CTSD to resemble that of synaptophysin and the increasing expression of CTSD to coincide with the active period of myelination in the rat brain, suggesting a role for CTSD in early rat brain development. An active-site mutation underlying the congenital ovine NCL not only affected enzymatic activity, but also changed the stability, processing and transport of the mutant protein, possibly contributing to the disease pathogenesis. We also provide CTSD deficiency as a first molecular explanation for human congenital NCL, a lysosomal storage disorder, characterized by neuronal loss and demyelination in the central nervous system. Finally, we show the first evidence for synaptic abnormalities and thalamocortical changes in CTSD-deficient mice at the molecular and ultrastructural levels. Keywords: cathepsin D, congenital, cortex, lysosomal storage disorder, lysosome, mutation, neurodegeneration, neuronal ceroid-lipofuscinosis, overexpression, synapse, thalamus