Novel conditional mouse models for targeted kidney research : Emphasis on the analysis of the biological function of nephrin

Nephrin is a transmembrane protein belonging to the immunoglobulin superfamily and is expressed primarily in the podocytes, which are highly differentiated epithelial cells needed for primary urine formation in the kidney. Mutations leading to nephrin loss abrogate podocyte morphology, and result in massive protein loss into urine and consequent early death in humans carrying specific mutations in this gene. The disease phenotype is closely replicated in respective mouse models. The purpose of this thesis was to generate novel inducible mouse-lines, which allow targeted gene deletion in a time and tissue-specific manner. A proof of principle model for succesful gene therapy for this disease was generated, which allowed podocyte specific transgene replacement to rescue gene deficient mice from perinatal lethality. Furthermore, the phenotypic consequences of nephrin restoration in the kidney and nephrin deficiency in the testis, brain and pancreas in rescued mice were investigated. A novel podocyte-specific construct was achieved by using standard cloning techniques to provide an inducible tool for in vitro and in vivo gene targeting. Using modified constructs and microinjection procedures two novel transgenic mouse-lines were generated. First, a mouse-line with doxycycline inducible expression of Cre recombinase that allows podocyte-specific gene deletion was generated. Second, a mouse-line with doxycycline inducible expression of rat nephrin, which allows podocyte-specific nephrin over-expression was made. Furthermore, it was possible to rescue nephrin deficient mice from perinatal lethality by cross-breeding them with a mouse-line with inducible rat nephrin expression that restored the missing endogenous nephrin only in the kidney after doxycycline treatment. The rescued mice were smaller, infertile, showed genital malformations and developed distinct histological abnormalities in the kidney with an altered molecular composition of the podocytes. Histological changes were also found in the testis, cerebellum and pancreas. The expression of another molecule with limited tissue expression, densin, was localized to the plasma membranes of Sertoli cells in the testis by immunofluorescence staining. Densin may be an essential adherens junction protein between Sertoli cells and developing germ cells and these junctions share similar protein assembly with kidney podocytes. This single, binary conditional construct serves as a cost- and time-efficient tool to increase the understanding of podocyte-specific key proteins in health and disease. The results verified a tightly controlled inducible podocyte-specific transgene expression in vitro and in vivo as expected. These novel mouse-lines with doxycycline inducible Cre recombinase and with rat nephrin expression will be useful for conditional gene targeting of essential podocyte proteins and to study in detail their functions in the adult mice. This is important for future diagnostic and pharmacologic development platforms.

A dynamic programming model for optimising feeding and slaughter decisions regarding fattening pigs

Costs of purchasing new piglets and of feeding them until slaughter are the main variable expenditures in pig fattening. They both depend on slaughter intensity, the nature of feeding patterns and the technological constraints of pig fattening, such as genotype. Therefore, it is of interest to examine the effect of production technology and changes in input and output prices on feeding and slaughter decisions. This study examines the problem by using a dynamic programming model that links genetic characteristics of a pig to feeding decisions and the timing of slaughter and takes into account how these jointly affect the quality-adjusted value of a carcass. The model simulates the growth mechanism of a pig under optional feeding and slaughter patterns and then solves the optimal feeding and slaughter decisions recursively. The state of nature and the genotype of a pig are known in the analysis. The main contribution of this study is the dynamic approach that explicitly takes into account carcass quality while simultaneously optimising feeding and slaughter decisions. The method maximises the internal rate of return to the capacity unit. Hence, the results can have vital impact on competitiveness of pig production, which is known to be quite capital-intensive. The results suggest that producer can significantly benefit from improvements in the pig's genotype, because they improve efficiency of pig production. The annual benefits from obtaining pigs of improved genotype can be more than €20 per capacity unit. The annual net benefits of animal breeding to pig farms can also be considerable. Animals of improved genotype can reach optimal slaughter maturity quicker and produce leaner meat than animals of poor genotype. In order to fully utilise the benefits of animal breeding, the producer must adjust feeding and slaughter patterns on the basis of genotype. The results suggest that the producer can benefit from flexible feeding technology. The flexible feeding technology segregates pigs into groups according to their weight, carcass leanness, genotype and sex and thereafter optimises feeding and slaughter decisions separately for these groups. Typically, such a technology provides incentives to feed piglets with protein-rich feed such that the genetic potential to produce leaner meat is fully utilised. When the pig approaches slaughter maturity, the share of protein-rich feed in the diet gradually decreases and the amount of energy-rich feed increases. Generally, the optimal slaughter weight is within the weight range that pays the highest price per kilogram of pig meat. The optimal feeding pattern and the optimal timing of slaughter depend on price ratios. Particularly, an increase in the price of pig meat provides incentives to increase the growth rates up to the pig's biological maximum by increasing the amount of energy in the feed. Price changes and changes in slaughter premium can also have large income effects. Key words: barley, carcass composition, dynamic programming, feeding, genotypes, lean, pig fattening, precision agriculture, productivity, slaughter weight, soybeans

Functional Analysis of MrpL55, Vig and Mat1 Acting at the Crossroad of Cell Cycle, Transcription and Metabolism Regulation

Cell proliferation, transcription and metabolism are regulated by complex partly overlapping signaling networks involving proteins in various subcellular compartments. The objective of this study was to increase our knowledge on such regulatory networks and their interrelationships through analysis of MrpL55, Vig, and Mat1 representing three gene products implicated in regulation of cell cycle, transcription, and metabolism. Genome-wide and biochemical in vitro studies have previously revealed MrpL55 as a component of the large subunit of the mitochondrial ribosome and demonstrated a possible role for the protein in cell cycle regulation. Vig has been implicated in heterochromatin formation and identified as a constituent of the RNAi-induced silencing complex (RISC) involved in cell cycle regulation and RNAi-directed transcriptional gene silencing (TGS) coupled to RNA polymerase II (RNAPII) transcription. Mat1 has been characterized as a regulatory subunit of cyclin-dependent kinase 7 (Cdk7) complex phosphorylating and regulating critical targets involved in cell cycle progression, energy metabolism and transcription by RNAPII. The first part of the study explored whether mRpL55 is required for cell viability or involved in a regulation of energy metabolism and cell proliferation. The results revealed a dynamic requirement of the essential Drosophila mRpL55 gene during development and suggested a function of MrpL55 in cell cycle control either at the G1/S or G2/M transition prior to cell differentiation. This first in vivo characterization of a metazoan-specific constituent of the large subunit of mitochondrial ribosome also demonstrated forth compelling evidence of the interconnection of nuclear and mitochondrial genomes as well as complex functions of the evolutionarily young metazoan-specific mitochondrial ribosomal proteins. In studies on the Drosophila RISC complex regulation, it was noted that Vig, a protein involved in heterochromatin formation, unlike other analyzed RISC associated proteins Argonaute2 and R2D2, is dynamically phosphorylated in a dsRNA-independent manner. Vig displays similarity with a known in vivo substrate for protein kinase C (PKC), human chromatin remodeling factor Ki-1/57, and is efficiently phosphorylated by PKC on multiple sites in vitro. These results suggest that function of the RISC complex protein Vig in RNAi-directed TGS and chromatin modification may be regulated through dsRNA-independent phosphorylation by PKC. In the third part of this study the role of Mat1 in regulating RNAPII transcription was investigated using cultured murine immortal fibroblasts with a conditional allele of Mat1. The results demonstrated that phosphorylation of the carboxy-terminal domain (CTD) of the large subunit of RNAPII in the heptapeptide YSPTSPS repeat in Mat-/- cells was over 10-fold reduced on Serine-5 and subsequently on Serine-2. Occupancy of the hypophosphorylated RNAPII in gene bodies was detectably decreased, whereas capping, splicing, histone methylation and mRNA levels were generally not affected. However, a subset of transcripts in absence of Mat1 was repressed and associated with decreased occupancy of RNAPII at promoters as well as defective capping. The results identify the Cdk7-CycH-Mat1 kinase submodule of TFIIH as a stimulatory non-essential regulator of transcriptional elongation and a genespecific essential factor for stable binding of RNAPII at the promoter region and capping. The results of these studies suggest important roles for both MrpL55 and Mat1 in cell cycle progression and their possible interplay at the G2/M stage in undifferentiated cells. The identified function of Mat1 and of TFIIH kinase complex in gene-specific transcriptional repression is challenging for further studies in regard to a possible link to Vig and RISC-mediated transcriptional gene silencing.

Dynamic nature of proteins - interpretation of residual dipolar couplings

Protein conformations and dynamics can be studied by nuclear magnetic resonance spectroscopy using dilute liquid crystalline samples. This work clarifies the interpretation of residual dipolar coupling data yielded by the experiments. It was discovered that unfolded proteins without any additional structure beyond that of a mere polypeptide chain exhibit residual dipolar couplings. Also, it was found that molecular dynamics induce fluctuations in the molecular alignment and doing so affect residual dipolar couplings. The finding clarified the origins of low order parameter values observed earlier. The work required the development of new analytical and computational methods for the prediction of intrinsic residual dipolar coupling profiles for unfolded proteins. The presented characteristic chain model is able to reproduce the general trend of experimental residual dipolar couplings for denatured proteins. The details of experimental residual dipolar coupling profiles are beyond the analytical model, but improvements are proposed to achieve greater accuracy. A computational method for rapid prediction of unfolded protein residual dipolar couplings was also developed. Protein dynamics were shown to modulate the effective molecular alignment in a dilute liquid crystalline medium. The effects were investigated from experimental and molecular dynamics generated conformational ensembles of folded proteins. It was noted that dynamics induced alignment is significant especially for the interpretation of molecular dynamics in small, globular proteins. A method of correction was presented. Residual dipolar couplings offer an attractive possibility for the direct observation of protein conformational preferences and dynamics. The presented models and methods of analysis provide significant advances in the interpretation of residual dipolar coupling data from proteins.

Essays on the Modeling and Prediction of Volatility and Higher Moments of Stock Returns (summary section only)

One of the most fundamental and widely accepted ideas in finance is that investors are compensated through higher returns for taking on non-diversifiable risk. Hence the quantification, modeling and prediction of risk have been, and still are one of the most prolific research areas in financial economics. It was recognized early on that there are predictable patterns in the variance of speculative prices. Later research has shown that there may also be systematic variation in the skewness and kurtosis of financial returns. Lacking in the literature so far, is an out-of-sample forecast evaluation of the potential benefits of these new more complicated models with time-varying higher moments. Such an evaluation is the topic of this dissertation. Essay 1 investigates the forecast performance of the GARCH (1,1) model when estimated with 9 different error distributions on Standard and Poor’s 500 Index Future returns. By utilizing the theory of realized variance to construct an appropriate ex post measure of variance from intra-day data it is shown that allowing for a leptokurtic error distribution leads to significant improvements in variance forecasts compared to using the normal distribution. This result holds for daily, weekly as well as monthly forecast horizons. It is also found that allowing for skewness and time variation in the higher moments of the distribution does not further improve forecasts. In Essay 2, by using 20 years of daily Standard and Poor 500 index returns, it is found that density forecasts are much improved by allowing for constant excess kurtosis but not improved by allowing for skewness. By allowing the kurtosis and skewness to be time varying the density forecasts are not further improved but on the contrary made slightly worse. In Essay 3 a new model incorporating conditional variance, skewness and kurtosis based on the Normal Inverse Gaussian (NIG) distribution is proposed. The new model and two previously used NIG models are evaluated by their Value at Risk (VaR) forecasts on a long series of daily Standard and Poor’s 500 returns. The results show that only the new model produces satisfactory VaR forecasts for both 1% and 5% VaR Taken together the results of the thesis show that kurtosis appears not to exhibit predictable time variation, whereas there is found some predictability in the skewness. However, the dynamic properties of the skewness are not completely captured by any of the models.

Conditional Moments in Asset Pricing

A better understanding of stock price changes is important in guiding many economic activities. Since prices often do not change without good reasons, searching for related explanatory variables has involved many enthusiasts. This book seeks answers from prices per se by relating price changes to their conditional moments. This is based on the belief that prices are the products of a complex psychological and economic process and their conditional moments derive ultimately from these psychological and economic shocks. Utilizing information about conditional moments hence makes it an attractive alternative to using other selective financial variables in explaining price changes. The first paper examines the relation between the conditional mean and the conditional variance using information about moments in three types of conditional distributions; it finds that the significance of the estimated mean and variance ratio can be affected by the assumed distributions and the time variations in skewness. The second paper decomposes the conditional industry volatility into a concurrent market component and an industry specific component; it finds that market volatility is on average responsible for a rather small share of total industry volatility — 6 to 9 percent in UK and 2 to 3 percent in Germany. The third paper looks at the heteroskedasticity in stock returns through an ARCH process supplemented with a set of conditioning information variables; it finds that the heteroskedasticity in stock returns allows for several forms of heteroskedasticity that include deterministic changes in variances due to seasonal factors, random adjustments in variances due to market and macro factors, and ARCH processes with past information. The fourth paper examines the role of higher moments — especially skewness and kurtosis — in determining the expected returns; it finds that total skewness and total kurtosis are more relevant non-beta risk measures and that they are costly to be diversified due either to the possible eliminations of their desirable parts or to the unsustainability of diversification strategies based on them.

Towards a Dynamic Communication Model: A Useful Tool in Relationship Communication

In order to bring insight into the emerging concept of relationship communication, concepts from two research traditions will be combined in this paper. Based on those concepts a new model, the dynamic relationship communication model, will be presented. Instead of a company perspective focusing on the integration of outgoing messages such as advertising, public relations and sales activities, it is suggested that the focus should be on factors integrated by the receiver. Such factors can be historical, future, external and internal factors. Thus, the model put a strong focus on the receiver in the communication process. The dynamic communication model is illustrated empirically using it as a tool on 78 short stories about communication. The empirical findings show that relationship communication occurs in some cases; in some cases it does not occur. The model is a useful tool in displaying relationship communication and how it differs from other communication. The importance of the time dimension, historical and future factors, in relationship communications is discussed. The possibility of reducing communications costs by the notion of relationship communication is discussed in managerial implications.

Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).

Analysis of forest soil chemistry and hydrology with a dynamic model ACIDIC.

In this study we analyze how the ion concentrations in forest soil solution are determined by hydrological and biogeochemical processes. A dynamic model ACIDIC was developed, including processes common to dynamic soil acidification models. The model treats up to eight interacting layers and simulates soil hydrology, transpiration, root water and nutrient uptake, cation exchange, dissolution and reactions of Al hydroxides in solution, and the formation of carbonic acid and its dissociation products. It includes also a possibility to a simultaneous use of preferential and matrix flow paths, enabling the throughfall water to enter the deeper soil layers in macropores without first reacting with the upper layers. Three different combinations of routing the throughfall water via macro- and micropores through the soil profile is presented. The large vertical gradient in the observed total charge was simulated succesfully. According to the simulations, gradient is mostly caused by differences in the intensity of water uptake, sulfate adsorption and organic anion retention at the various depths. The temporal variations in Ca and Mg concentrations were simulated fairly well in all soil layers. For H+, Al and K there were much more variation in the observed than in the simulated concentrations. Flow in macropores is a possible explanation for the apparent disequilibrium of the cation exchange for H+ and K, as the solution H+ and K concentrations have great vertical gradients in soil. The amount of exchangeable H+ increased in the O and E horizons and decreased in the Bs1 and Bs2 horizons, the net change in whole soil profile being a decrease. A large part of the decrease of the exchangeable H+ in the illuvial B horizon was caused by sulfate adsorption. The model produces soil water amounts and solution ion concentrations which are comparable to the measured values, and it can be used in both hydrological and chemical studies of soils.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.