SUMMARYGeneralized arterial calcification of infancy (GACI), an autosomal recessive disorder, is characterized by early mineralization of blood vessels, often diagnosed by prenatal ultrasound and usually resulting in demise during the first year of life. It is caused in most cases by mutations in the ENPP1 gene, encoding an enzyme that hydrolyzes ATP to AMP and inorganic pyrophosphate, the latter being a powerful anti-mineralization factor. Recently, a novel mouse phenotype was recognized as a result of ENU mutagenesis – those mice developed stiffening of the joints, hence the mutant mouse was named ‘ages with stiffened joints’ (asj). These mice harbor a missense mutation, p.V246D, in the Enpp1 gene. Here we demonstrate that the mutant ENPP1 protein is largely absent in the liver of asj mice, and the lack of enzymatic activity results in reduced inorganic pyrophosphate (PPi) levels in the plasma, accompanied by extensive mineralization of a number of tissues, including arterial blood vessels. The progress of mineralization is highly dependent on the mineral composition of the diet, with significant shortening of the lifespan on a diet enriched in phosphorus and low in magnesium. These results suggest that the asj mouse can serve as an animal model for GACI.

Inbred mice provide a unique tool to study aging populations because of the genetic homogeneity within an inbred strain, their short life span, and the tools for analysis which are available. A large-scale longitudinal and cross-sectional aging study was conducted on 30 inbred strains to determine, using histopathology, the type and diversity of diseases mice develop as they age. These data provide tools that when linked with modern in silico genetic mapping tools, can begin to unravel the complex genetics of many of the common chronic diseases associated with aging in humans and other mammals. In addition, novel disease models were discovered in some strains, such as rhabdomyosarcoma in old A/J mice, to diseases affecting many but not all strains including pseudoxanthoma elasticum, pulmonary adenoma, alopecia areata, and many others. This extensive data set is now available online and provides a useful tool to help better understand strain-specific background diseases that can complicate interpretation of genetically engineered mice and other manipulatable mouse studies that utilize these strains.

Since the discovery of alpha-1 antitrypsin in the early 1960s, several new genes have been suggested to play a role in chronic obstructive pulmonary disease (COPD) pathogenesis. Yet, in spite of those advances, much about the genetic basis of COPD still remains to be discovered. Unbiased approaches, such as genome-wide association (GWA) studies, are critical to identify genes and pathways and to verify suggested genetic variants. Indeed, most of our current understanding about COPD candidate genes originates from GWA studies. Experiments in form of cross-study replications and advanced meta-analyses have propelled the field towards unravelling details about COPD's pathogenesis. Here, we review the discovery of genetic variants in association with COPD phenotypes by discussing the available approaches and current findings. Limitations of current studies are considered and future directions provided.

BackgroundThe Cancer Genome Atlas Project (TCGA) is a National Cancer Institute effort to profile at least 500 cases of 20 different tumor types using genomic platforms and to make these data, both raw and processed, available to all researchers. TCGA data are currently over 1.2 Petabyte in size and include whole genome sequence (WGS), whole exome sequence, methylation, RNA expression, proteomic, and clinical datasets. Publicly accessible TCGA data are released through public portals, but many challenges exist in navigating and using data obtained from these sites. We developed TCGA Expedition to support the research community focused on computational methods for cancer research. Data obtained, versioned, and archived using TCGA Expedition supports command line access at high-performance computing facilities as well as some functionality with third party tools. For a subset of TCGA data collected at University of Pittsburgh, we also re-associate TCGA data with de-identified data from the electronic health records. Here we describe the software as well as the architecture of our repository, methods for loading of TCGA data to multiple platforms, and security and regulatory controls that conform to federal best practices.ResultsTCGA Expedition software consists of a set of scripts written in Bash, Python and Java that download, extract, harmonize, version and store all TCGA data and metadata. The software generates a versioned, participant- and sample-centered, local TCGA data directory with metadata structures that directly reference the local data files as well as the original data files. The software supports flexible searches of the data via a web portal, user-centric data tracking tools, and data provenance tools. Using this software, we created a collaborative repository, the Pittsburgh Genome Resource Repository (PGRR) that enabled investigators at our institution to work with all TCGA data formats, and to interrogate these data with analysis pipelines, and associated tools. WGS data are especially challenging for individual investigators to use, due to issues with downloading, storage, and processing; having locally accessible WGS BAM files has proven invaluable.ConclusionOur open-source, freely available TCGA Expedition software can be used to create a local collaborative infrastructure for acquiring, managing, and analyzing TCGA data and other large public datasets.

Rationale: Because acute lung injury is a sporadic disease produced by heterogeneous precipitating factors, previous genetic analyses are mainly limited to candidate gene case-control studies. Objectives: To develop a genome-wide strategy in which single nucleotide polymorphism associations are assessed for functional consequences to survival during acute lung injury in mice. Methods: To identify genes associated with acute lung injury, 40 inbred strains were exposed to acrolein and haplotype association mapping, microarray, and DNA-protein binding were assessed. Measurements and Main Results:The mean survival time varied among mouse strains with polar strains differing approximately 2.5-fold. Associations were identified on chromosomes 1, 2, 4, 11, and 12. Seven genes (Acvr1, Cacnb4, Ccdc148, Galnt13, Rfwd2, Rpap2, and Tgfbr3) had single nucleotide polymorphism (SNP) associations within the gene. Because SNP associations may encompass ''blocks'' of associated variants, functional assessment was performed in 91 genes within 6 1 Mbp of each SNP association. Using 10% or greater allelic frequency and 10% or greater phenotype explained as threshold criteria, 16 genes were assessed by microarray and reverse realtime polymerase chain reaction. Microarray revealed several enriched pathways including transforming growth factor-b signaling. Transcripts for Acvr1, Arhgap15, Cacybp, Rfwd2, and Tgfbr3 differed between the strains with exposure and contained SNPs that could eliminate putative transcriptional factor recognition sites. Ccdc148, Fancl, and Tnn had sequence differences that could produce an amino acid substitution. Mycn and Mgat4a had a promoter SNP or 39untranslated region SNPs, respectively. Several genes were related and encoded receptors (ACVR1, TGFBR3), transcription factors (MYCN, possibly CCDC148), and ubiquitin-proteasome (RFWD2, FANCL, CACYBP) proteins that can modulate cell signaling. An Acvr1 SNP eliminated a putative ELK1 binding site and diminished DNA-protein binding. Conclusions: Assessment of genetic associations can be strengthened using a genetic/genomic approach. This approach identified several candidate genes, including Acvr1, associated with increased susceptibility to acute lung injury in mice.