Late-onset spinal motor neuronopathy- a new neuromuscular disease

The aim of this study was to clarify the clinical phenotype of late-onset spinal motor neuronopathy (LOSMoN), an adult-onset autosomal dominant lower motor neuron disorder identified first in two families in Eastern Finland, in order to clarify its genetic background. Motor neuron disorders (MNDs) are characterized by dysfunction and premature death of motor neurons in the brain and spinal cord. MNDs can manifest at any age of the human lifespan, ranging from pre- or neonatal forms such as spinal muscular atrophy type I (SMA I) to those preferentially affecting the older age groups exemplified by sporadic amyotrophic lateral sclerosis (ALS). With a combination of genetic linkage analysis and genome sequencing using DNA from a total of 55 affected members of 17 families and a whole genome scan, we were able to show that LOSMoN is caused by the c.197G>T p.G66V mutation in the gene CHCHD10. This study showed that LOSMoN has very characteristic features that help to differentiate it from other more malignant forms of motor neuron disease, such as ALS, which was erroneously diagnosed in many patients in our cohort. Lack of fibrillations in the first dorsal interosseus muscle on EMG and extensive grouping of non-atrophic type IIA/2A fibers on muscle biopsy were shown to be common findings in LOSMoN, but rare or absent in ALS patients. The results of this study will help clinicians recognize the characteristic phenotype of LOSMoN disease and thus improve their diagnostic accuracy, and will also allow physicians to provide adequate genetic counseling for patients.

Pulsatile Blood Flow Simulations in Computed Tomography(CT) Scan-Based and Idealized Geometries of Human Aorta

Blood flow in human aorta is an unsteady and complex phenomenon. The complex patterns are related to the geometrical features like curvature, bends, and branching and pulsatile nature of flow from left ventricle of heart. The aim of this work was to understand the effect of aorta geometry on the flow dynamics. To achieve this, 3D realistic and idealized models of descending aorta were reconstructed from Computed Tomography (CT) images of a female patient. The geometries were reconstructed using medical image processing code. The blood flow in aorta was assumed to be laminar and incompressible and the blood was assumed to be Newtonian fluid. A time dependent pulsatile and parabolic boundary condition was deployed at inlet. Steady and unsteady blood flow simulations were performed in real and idealized geometries of descending aorta using a Finite Volume Method (FVM) code. Analysis of Wall Shear Stress (WSS) distribution, pressure distribution, and axial velocity profiles were carried out in both geometries at steady and unsteady state conditions. The results obtained in thesis work reveal that the idealization of geometry underestimates the values of WSS especially near the region with sudden change of diameter. However, the resultant pressure and velocity in idealized geometry are close to those in real geometry

Characterization of novel genes predominantly expressed in the epididymis – from genome analyses to genetically modified mice

In mammals, post-testicular sperm maturation taking place in the epididymis is required for the spermatozoa to acquire the abilities required to fertilize the egg in vivo. The epididymal epithelial cells secrete proteins and other small molecules into the lumen, where they interact with the spermatozoa and enable necessary maturational changes. In this study different in silico, in vitro and in vivo approaches were utilized in order to find novel genes responsible for the function of the epididymis and post-testicular sperm maturation in the mouse. Available online genomic databases were analyzed to identify genes potentially expressed in the epididymis, gene expression profiling was performed by studying their expression in different mouse tissues, and significance of certain genes to fertility was assessed by generating genetically modified mouse models. A recently discovered Pate (prostate and testis expression) gene family was found to be predominantly expressed in the epididymis. It represents one of the largest known gene families expressed in the epididymis, and the members code for proteins potentially involved in defense against microorganisms. Through genetically modified mouse models CRISP4 (cysteine-rich secretory protein 4) was identified to regulate sperm acrosome reaction, and BMYC to inhibit the expression of the Myc proto-oncogene in the developing testis. A mouse line expressing iCre recombinase specifically in the epididymis was also generated. This model can be used to generate conditional, epididymis-specific knock-out models, and will be a valuable tool in fertility studies.

Along-scan radiometric gradient in Landsat images: understanding the relation between floristic and remote sensing information in tropical forests

The along-scan radiometric gradient causes severe interpretation problems in Landsat images of tropical forests. It creates a decreasing trend in pixel values with the column number of the image. In practical applications it has been corrected assuming the trend to be linear within structurally similar forests. This has improved the relation between floristic and remote sensing information, but just in some cases. I use 3 Landsat images and 105 floristic inventories to test the assumption of linearity, and to examine how the gradient and linear corrections affect the relation between floristic and Landsat data. Results suggest the gradient to be linear in infrared bands. Also, the relation between floristic and Landsat data could be conditioned by the distribution of the sampling sites and the direction in which images are mosaicked. Additionally, there seems to be a conjunction between the radiometric gradient and a natural east-west vegetation gradient common in Western Amazonia. This conjunction might have enhanced artificially correlations between field and remotely-sensed information in previous studies. Linear corrections may remove such artificial enhancement, but along with true and relevant spectral information about floristic patterns, because they can´t separate the radiometric gradient from a natural one.

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.

Characterisation of Cladonia rangiferina transcriptome and genome, and the effects of dehydration and rehydration on its gene expression

Lichens are symbiotic organisms, which consist of the fungal partner and the photosynthetic partner, which can be either an alga or a cyanobacterium. In some lichen species the symbiosis is tripartite, where the relationship includes both an alga and a cyanobacterium alongside the primary symbiont, fungus. The lichen symbiosis is an evolutionarily old adaptation to life on land and many extant fungal species have evolved from lichenised ancestors. Lichens inhabit a wide range of habitats and are capable of living in harsh environments and on nutrient poor substrates, such as bare rocks, often enduring frequent cycles of drying and wetting. Most lichen species are desiccation tolerant, and they can survive long periods of dehydration, but can rapidly resume photosynthesis upon rehydration. The molecular mechanisms behind lichen desiccation tolerance are still largely uncharacterised and little information is available for any lichen species at the genomic or transcriptomic level. The emergence of the high-throughput next generation sequencing (NGS) technologies and the subsequent decrease in the cost of sequencing new genomes and transcriptomes has enabled non-model organism research on the whole genome level. In this doctoral work the transcriptome and genome of the grey reindeer lichen, Cladonia rangiferina, were sequenced, de novo assembled and characterised using NGS and traditional expressed sequence tag (EST) technologies. RNA extraction methods were optimised to improve the yield and quality of RNA extracted from lichen tissue. The effects of rehydration and desiccation on C. rangiferina gene expression on whole transcriptome level were studied and the most differentially expressed genes were identified. The secondary metabolites present in C. rangiferina decreased the quality – integrity, optical characteristics and utility for sensitive molecular biological applications – of the extracted RNA requiring an optimised RNA extraction method for isolating sufficient quantities of high-quality RNA from lichen tissue in a time- and cost-efficient manner. The de novo assembly of the transcriptome of C. rangiferina was used to produce a set of contiguous unigene sequences that were used to investigate the biological functions and pathways active in a hydrated lichen thallus. The de novo assembly of the genome yielded an assembly containing mostly genes derived from the fungal partner. The assembly was of sufficient quality, in size similar to other lichen-forming fungal genomes and included most of the core eukaryotic genes. Differences in gene expression were detected in all studied stages of desiccation and rehydration, but the largest changes occurred during the early stages of rehydration. The most differentially expressed genes did not have any annotations, making them potentially lichen-specific genes, but several genes known to participate in environmental stress tolerance in other organisms were also identified as differentially expressed.