Accessing and Mining Data from Large-Scale Mouse Phenotyping Projects

Evolution of Complexity. Molecular Aspects of Preassembly

An extension of neo-Darwinism, termed preassembly, states that genetic material required for many complex traits, such as echolocation, was present long before emergence of the traits. Assembly of genes and gene segments had occurred over protracted time-periods within large libraries of non-coding genes. Epigenetic factors ultimately promoted transfers from noncoding to coding genes, leading to abrupt formation of the trait via de novo genes. This preassembly model explains many observations that to this present day still puzzle biologists: formation of super-complexity in the absence of multiple fossil precursors, as with bat echolocation and flowering plants; major genetic and physical alterations occurring in just a few thousand years, as with housecat evolution; lack of precursors preceding lush periods of species expansion, as in the Cambrian explosion; and evolution of costly traits that exceed their need during evolutionary times, as with human intelligence. What follows in this paper is a mechanism that is not meant to supplant neo-Darwinism; instead, preassembly aims to supplement current ideas when complexity issues leave them struggling.

Identification of genes required for eye development by high-throughput screening of mouse knockouts

Despite advances in next generation sequencing technologies, determining the genetic basis of ocular disease remains a major challenge due to the limited access and prohibitive cost of human forward genetics. Thus, less than 4,000 genes currently have available phenotype information for any organ system. Here we report the ophthalmic findings from the International Mouse Phenotyping Consortium, a large-scale functional genetic screen with the goal of generating and phenotyping a null mutant for every mouse gene. Of 4364 genes evaluated, 347 were identified to influence ocular phenotypes, 75% of which are entirely novel in ocular pathology. This discovery greatly increases the current number of genes known to contribute to ophthalmic disease, and it is likely that many of the genes will subsequently prove to be important in human ocular development and disease.

Bret Moore et al. from the International Mouse Phenotyping Consortium report the identification of 347 mouse genes that influence ocular phenotypes when knocked out. 75% of the identified genes have not previously been associated with any ocular pathology.

Mitochondrial Calcium Handling in Physiology and Disease

Calcium (Ca²⁺) accumulation inside mitochondria represents a pleiotropic signal controlling a wide range of cellular functions, including key metabolic pathways and life/death decisions. This phenomenon has been first described in the 1960s, but the identity of the molecules controlling this process remained a mystery until just few years ago, when both mitochondrial Ca²⁺ uptake and release systems were genetically dissected. This finally opened the possibility to develop genetic models to directly test the contribution of mitochondrial Ca²⁺ homeostasis to cellular functions. Here we summarize our current understanding of the molecular machinery that controls mitochondrial Ca²⁺ handling and critically evaluate the physiopathological role of mitochondrial Ca²⁺ signaling, based on recent evidences obtained through in vitro and in vivo models.

Publishing and sharing multi-dimensional image data with OMERO

Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combination of binary pixel data and metadata that describe the acquisition process and any derived results. The OMERO image data management platform allows users to securely share image datasets according to specific permissions levels: data can be held privately, shared with a set of colleagues, or made available via a public URL. Users control access by assigning data to specific Groups with defined membership and access rights. OMERO’s Permission system supports simple data sharing in a lab, collaborative data analysis, and even teaching environments. OMERO software is open source and released by the OME Consortium at www.openmicroscopy.org.

Phenotype ontologies and cross-species analysis for translational research

The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.

Linking tissues to phenotypes using gene expression profiles

Despite great biological and computational efforts to determine the genetic causes underlying human heritable diseases, approximately half (3500) of these diseases are still without an identified genetic cause. Model organism studies allow the targeted modification of the genome and can help with the identification of genetic causes for human diseases. Targeted modifications have led to a vast amount of model organism data. However, these data are scattered across different databases, preventing an integrated view and missing out on contextual information. Once we are able to combine all the existing resources, will we be able to fully understand the causes underlying a disease and how species differ. Here, we present an integrated data resource combining tissue expression with phenotypes in mouse lines and bringing us one step closer to consequence chains from a molecular level to a resulting phenotype. Mutations in genes often manifest in phenotypes in the same tissue that the gene is expressed in. However, in other cases, a systems level approach is required to understand how perturbations to gene-networks connecting multiple tissues lead to a phenotype. Automated evaluation of the predicted tissue–phenotype associations reveals that 72–76% of the phenotypes are associated with disruption of genes expressed in the affected tissue. However, 55–64% of the individual phenotype-tissue associations show spatially separated gene expression and phenotype manifestation. For example, we see a correlation between ‘total body fat’ abnormalities and genes expressed in the ‘brain’, which fits recent discoveries linking genes expressed in the hypothalamus to obesity. Finally, we demonstrate that the use of our predicted tissue–phenotype associations can improve the detection of a known disease–gene association when combined with a disease gene candidate prediction tool. For example, JAK2, the known gene associated with Familial Erythrocytosis 1, rises from the seventh best candidate to the top hit when the associated tissues are taken into consideration. Database URL:http://www.sanger.ac.uk/resources/databases/phenodigm/phenotype/list