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.

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.