Genetic Factors in the Regulation of Striatal and Extrastriatal Dopamine D2 Receptor Expression

Positron emission tomography (PET) studies on healthy individuals have revealed a marked interindividual variability in striatal dopamine D2 receptor density that can be partly accounted for by genetic factors. The examination of the extrastriatal lowdensity D2 receptor populations has been impeded by the lack of suitable tracers. However, the quantification of these D2 receptor populations is now feasible with recently developed PET radioligands. The objective of this thesis was to study brain neurobiological correlates of common functional genetic variants residing in candidate genes relevant for D2 receptor functioning. For this purpose, healthy subjects were studied with PET imaging using [₁₁C]raclopride and [₁₁C]FLB457 as radioligands. The candidate genes examined in this work were the human D2 receptor gene (DRD2) and the catechol-Omethyltransferase gene (COMT). The region-specific genotypic influences were explored by comparing D2 receptor binding properties in the striatum, the cortex and the thalamus. As an additional study objective, the relationship between cortical D2 receptor density and a cognitive phenotype i.e. verbal memory and learning was assessed. The main finding of this study was that DRD2 C957T genotype altered markedly D2 receptor density in the cortex and the thalamus whereas in the striatum the C957T genotype affected D2 receptor affinity, but not density. Furthermore, the A1 allele of the DRD2-related TaqIA polymorphism showed increased cortical and thalamic D2 receptor density, but had the opposite effect on striatal D2 receptor density. The DRD2 –141C Ins/Del or the COMT Val158Met genotypes did not change D2 receptor binding properties. Finally, unlike previously reported, cortical D2 receptor density did not show any significant correlation with verbal memory function. The results of this study suggest that the C957T and the TaqIA genotypes have region-specific neurobiological correlates in brain dopamine D2 receptor availability in vivo. The biological mechanisms underlying these findings are unclear, but they may be related to the region-specific regulation of dopamine neurotranssion, gene/receptor expression and epigenesis. These findings contribute to the understanding of the genetic regulation of dopamine and D2 receptor-related brain functions in vivo in man. In addition, the results provide potentially useful endophenotypes for genetic research on psychiatric and neurological disorders.

Methods of genetic diversity creation and functional display for directed evolution experiments

Protein engineering aims to improve the properties of enzymes and affinity reagents by genetic changes. Typical engineered properties are affinity, specificity, stability, expression, and solubility. Because proteins are complex biomolecules, the effects of specific genetic changes are seldom predictable. Consequently, a popular strategy in protein engineering is to create a library of genetic variants of the target molecule, and render the population in a selection process to sort the variants by the desired property. This technique, called directed evolution, is a central tool for trimming protein-based products used in a wide range of applications from laundry detergents to anti-cancer drugs. New methods are continuously needed to generate larger gene repertoires and compatible selection platforms to shorten the development timeline for new biochemicals. In the first study of this thesis, primer extension mutagenesis was revisited to establish higher quality gene variant libraries in Escherichia coli cells. In the second study, recombination was explored as a method to expand the number of screenable enzyme variants. A selection platform was developed to improve antigen binding fragment (Fab) display on filamentous phages in the third article and, in the fourth study, novel design concepts were tested by two differentially randomized recombinant antibody libraries. Finally, in the last study, the performance of the same antibody repertoire was compared in phage display selections as a genetic fusion to different phage capsid proteins and in different antibody formats, Fab vs. single chain variable fragment (ScFv), in order to find out the most suitable display platform for the library at hand. As a result of the studies, a novel gene library construction method, termed selective rolling circle amplification (sRCA), was developed. The method increases mutagenesis frequency close to 100% in the final library and the number of transformants over 100-fold compared to traditional primer extension mutagenesis. In the second study, Cre/loxP recombination was found to be an appropriate tool to resolve the DNA concatemer resulting from error-prone RCA (epRCA) mutagenesis into monomeric circular DNA units for higher efficiency transformation into E. coli. Library selections against antigens of various size in the fourth study demonstrated that diversity placed closer to the antigen binding site of antibodies supports generation of antibodies against haptens and peptides, whereas diversity at more peripheral locations is better suited for targeting proteins. The conclusion from a comparison of the display formats was that truncated capsid protein three (p3Δ) of filamentous phage was superior to the full-length p3 and protein nine (p9) in obtaining a high number of uniquely specific clones. Especially for digoxigenin, a difficult hapten target, the antibody repertoire as ScFv-p3Δ provided the clones with the highest affinity for binding. This thesis on the construction, design, and selection of gene variant libraries contributes to the practical know-how in directed evolution and contains useful information for scientists in the field to support their undertakings.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Sedentary behaviour and health with special reference to obesity and fatty liver in early midlife. The Cardiovascular risk in Young Finns Study

Background: Physical inactivity and positive energy balance pose a risk to health. They increase the risk of obesity and associated non-communicable diseases. Recently, also sedentary behaviour has been associated with obesity and non-communicable diseases. Nevertheless, it has been unclear which type of sedentary behaviour is the most harmful. It is also unknown whether the relationship of sedentary behaviour with obesity is truly independent of other factors, for example physical activity and diet. Longitudinal data are limited, and the direction of causality and the mechanism of action are still unknown. Aims: The aim of this study was 1) to identify the type of sedentary behaviour having the strongest association with obesity, 2) to explore the causal relationship of sedentary behaviour and weight increase, and 3) to additionally, investigate the relationship of sedentary behaviour with fatty liver. These were studied in cross-sectional and/or longitudinal settings using data from the Cardiovascular Risk in Young Finns Study. Special emphasis was put on the evaluation of a wide range of other lifestyle factors and risks for obesity and fatty liver. Subjects: 2,060 subjects (aged 33-50 years in 2011, of which 55 % were female) from the Cardiovascular Risk in Young Finns Study participating in follow-ups in 2001, 2007, and 2011. Measures: Self-reported time spent in various types of sedentary behaviour (I), or TV viewing time (I-III). Measured body weight, height and waist circumference (I-III), and genetic variants for high BMI (I). Fasting plasma concentrations of gamma-glutamyltransferase enzyme and triglyceride, calculated Fatty Liver Index (based on gamma-glutamyltransferase and triglyceride concentration, BMI and waist circumference), and the amount of intrahepatic fat measured with ultrasound (III). Self-reported leisure-time physical activity and active commuting, occupational physical activity, energy intake, diet, alcohol consumption, smoking, socioeconomic status, and sleep duration as possible confounders were considered (I-III). Results: TV viewing is the sedentary behaviour type that has the strongest association with obesity. Sedentary behaviour (TV viewing) precedes weight increase, and not the other way around. Sedentary behaviour (TV viewing) is associated with increased risk of fatty liver. Conclusions: Sedentary behaviour (especially high TV viewing time) is associated with increased risks of obesity and fatty liver. Intervention studies are needed to assess whether reduction of TV time would prevent obesity and fatty liver.