High-throughput transgenic mouse phenotyping using microscopic-MRI

With the completion of the human genome sequence in 2003, efforts have shifted towards elucidating gene function. Such phenotypic investigations are aided by advances in techniques for genetic modification of mice, with whom we share ~99% of genes. Mice are key models for both examination of basic gene function and translational study of human conditions. Furthering these efforts, ambitious programmes are underway to produce knockout mice for the ~25,000 mouse genes. In the coming years, methods to rapidly phenotype mouse morphology will be in great demand. This thesis demonstrates the development of non-invasive microscopic magnetic resonance imaging (\muMRI) methods for high-resolution ex-vivo phenotyping of mouse embryo and mouse brain morphology. It then goes on to show the application of computational atlasing techniques to these datasets, enabling automated analysis of phenotype. First, the issue of image quality in high-throughput embryo MRI was addressed. After investigating preparation and imaging parameters, substantial gains in signal- and contrast-to-noise were achieved. This protocol was applied to a study of Chd7+/- mice (a model of CHARGE syndrome), identifying cardiac defects. Combining this protocol with automated segmentation-propagation techniques, phenotypic differences were shown between three groups of mice in a volumetric analysis involving a number of organ systems. Focussing on the mouse brain, the optimal preparation and imaging parameters to maximise image quality and structural contrast were investigated, producing a high-resolution in-skull imaging protocol. Enhanced delineation of hippocampal and cerebellar structures was observed, correlating well to detailed histological comparisons. Subsequently this protocol was applied to a phenotypic investigation of the Tc1 model of Down syndrome. Using both visual inspection and automated, tensor based morphometry, novel phenotypic findings were identified in brain and inner ear structures. It is hoped that a combination of \muMRI with computational analysis techniques, as presented in this work, may help ease the burden of current phenotyping efforts

The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015

Monocarboxylate transporter 1 deficiency and ketone utilization

Ketoacidosis is a potentially lethal condition caused by the imbalance between hepatic production and extrahepatic utilization of ketone bodies. We performed exome sequencing in a patient with recurrent, severe ketoacidosis and identified a homozygous frameshift mutation in the gene encoding monocarboxylate transporter 1 (SLC16A1, also called MCT1). Genetic analysis in 96 patients suspected of having ketolytic defects yielded seven additional inactivating mutations in MCT1, both homozygous and heterozygous. Mutational status was found to be correlated with ketoacidosis severity, MCT1 protein levels, and transport capacity. Thus, MCT1 deficiency is a novel cause of profound ketoacidosis; the present work suggests that MCT1-mediated ketone-body transport is needed to maintain acid-base balanc

Sex differences in oncogenic mutational processes

Funder: Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology); doi: https://doi.org/10.13039/501100002790Funder: Genome Canada (Génome Canada); doi: https://doi.org/10.13039/100008762Funder: Canada Foundation for Innovation (Fondation canadienne pour l'innovation); doi: https://doi.org/10.13039/501100000196Funder: Terry Fox Research Institute (Institut de Recherche Terry Fox); doi: https://doi.org/10.13039/501100004376Abstract: Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research