Xenbase: Facilitating the Use of Xenopus to Model Human Disease

At a fundamental level most genes, signaling pathways, biological functions and organ systems are highly conserved between man and all vertebrate species. Leveraging this conservation, researchers are increasingly using the experimental advantages of the amphibian Xenopus to model human disease. The online Xenopus resource, Xenbase, enables human disease modeling by curating the Xenopus literature published in PubMed and integrating these Xenopus data with orthologous human genes, anatomy, and more recently with links to the Online Mendelian Inheritance in Man resource (OMIM) and the Human Disease Ontology (DO). Here we review how Xenbase supports disease modeling and report on a meta-analysis of the published Xenopus research providing an overview of the different types of diseases being modeled in Xenopus and the variety of experimental approaches being used. Text mining of over 50,000 Xenopus research articles imported into Xenbase from PubMed identified approximately 1,000 putative disease- modeling articles. These articles were manually assessed and annotated with disease ontologies, which were then used to classify papers based on disease type. We found that Xenopus is being used to study a diverse array of disease with three main experimental approaches: cell-free egg extracts to study fundamental aspects of cellular and molecular biology, oocytes to study ion transport and channel physiology and embryo experiments focused on congenital diseases. We integrated these data into Xenbase Disease Pages to allow easy navigation to disease information on external databases. Results of this analysis will equip Xenopus researchers with a suite of experimental approaches available to model or dissect a pathological process. Ideally clinicians and basic researchers will use this information to foster collaborations necessary to interrogate the development and treatment of human diseases

Genome evolution in the allotetraploid frog Xenopus laevis

To explore the origins and consequences of tetraploidy in the African clawed frog, we sequenced the Xenopus laevis genome and compared it to the related diploid X. tropicalis genome. We characterize the allotetraploid origin of X. laevis by partitioning its genome into two homoeologous subgenomes, marked by distinct families of ???fossil??? transposable elements. On the basis of the activity of these elements and the age of hundreds of unitary pseudogenes, we estimate that the two diploid progenitor species diverged around 34 million years ago (Ma) and combined to form an allotetraploid around 17-18 Ma. More than 56% of all genes were retained in two homoeologous copies. Protein function, gene expression, and the amount of conserved flanking sequence all correlate with retention rates. The subgenomes have evolved asymmetrically, with one chromosome set more often preserving the ancestral state and the other experiencing more gene loss, deletion, rearrangement, and reduced gene expression.ope

Evaluating coexpression analysis for gene function prediction

Microarray expression data sets vary in size, data quality and other features, but most methods for selecting coexpressed gene pairs use a ‘one size fits all’ approach. There have been many different procedures for selecting coexpressed gene pairs of high functional similarity from an expression dataset. However, it is not clear which procedure performs best as there are few studies reporting comparisons of these approaches. The goal of this thesis is to develop a set of “best practices” in order to select coexpression links of high functional similarity from an expression dataset, along which methods for identifying datasets likely to yield poor information. With these goals, we hope to improve the quality of gene function predictions produced by coexpression analysis. Using 80 human expression datasets we examined the impact of different thresholds, correlation metrics, expression data filtering and transformation procedures on performance in functional prediction. We also investigated the relationship between data quality and other features of expression datasets and their performance in functional prediction. We used the annotations of the Gene Ontology as a primary metric to measure similarity in gene function, and employ additional functional metrics for validation. Our results show that several dataset features have a greater influence on the performance in functional prediction than others. Expression datasets which produce coexpressed gene pairs of poor functional quality can be identified by a similar set of data features. Some procedures used in coexpression analysis have a negligible effect on the quality of functional predictions while others are essential to achieving the best performance in the algorithm. We also find that some procedures interact greatly with features of expression datasets and that these interactions increase the number of high quality coexpressed gene pairs retrieved through coexpression analysis. This thesis uncovers important information on the many intrinsic and extrinsic factors that influence the performance in functional prediction of coexpression analysis. The information summarized here will help guide future studies using coexpression analysis and improve the quality of gene function predictions.Science, Faculty ofGraduat

Can Genetic Pleiotropy Replicate Common Clinical Constellations of Cardiovascular Disease and Risk?

<div><p>The relationship between obesity, diabetes, hyperlipidemia, hypertension, kidney disease and cardiovascular disease (CVD) is established when looked at from a clinical, epidemiological or pathophysiological perspective. Yet, when viewed from a genetic perspective, there is comparatively little data synthesis that these conditions have an underlying relationship. We sought to investigate the overlap of genetic variants independently associated with each of these commonly co-existing conditions from the NHGRI genome-wide association study (GWAS) catalog, in an attempt to replicate the established notion of shared pathophysiology and risk. We used pathway-based analyses to detect subsets of pleiotropic genes involved in similar biological processes. We identified 107 eligible GWAS studies related to CVD and its established comorbidities and risk factors and assigned genes that correspond to the associated signals based on their position. We found 44 positional genes shared across at least two CVD-related phenotypes that independently recreated the established relationship between the six phenotypes, but only if studies representing non-European populations were included. Seven genes revealed pleiotropy across three or more phenotypes, mostly related to lipid transport and metabolism. Yet, many genes had no relationship to each other or to genes with established functional connection. Whilst we successfully reproduced established relationships between CVD risk factors using GWAS findings, interpretation of biological pathways involved in the observed pleiotropy was limited. Further studies linking genetic variation to gene expression, as well as describing novel biological pathways will be needed to take full advantage of GWAS results.</p> </div

List of genes from all studies that showed three-way and four-way overlaps across CVD-related phenotypes.

<p>BP, blood pressure; CAD, coronary artery disease, T2D, type 2 diabetes.</p

A plot of 38 positional genes that overlapped between at least two CVD-related phenotypes.

<p>Plotted using VIZ-GRAIL <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046419#pone.0046419-Raychaudhuri2" target="_blank">[28]</a>. For each plot, phenotypic overlap is arranged along the outer circle; bold indicates three-way or four-way overlaps. Inner circle represents individual genes. The redness and thickness of lines connecting pairs of genes represent the strength of the connections with the thickness of the lines being inversely proportional to the probability that a literature-based connection would be seen by chance. Pathway-related links between 9 of 38 genes scored <i>P</i><0.05 using GRAIL. To accurately assess the statistical significance of this set of connections, we conducted simulations in which we selected 100 sets of 38 genes and scored them with GRAIL. We determined that the likelihood of observing 9 hits with P<0.05 by chance is about 7%.</p

Bubble Chart representing the genetic relationship of cardiovascular risk factors including GWAS positional genes across all populations.

<p>The size of the phenotype is representative of the percentage of genes studied attributed to that phenotype. Line thickness is representative of the number of intersecting genes between two phenotypes.</p

Bubble Chart representing the clinical and epidemiological relationship of cardiovascular risk factors.

<p>Connecting lines are unweighted (0/1) and indicate epidemiological relationships recreated from evidence presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046419#pone.0046419-Rexrode1" target="_blank">[1]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046419#pone.0046419-Navaneethan1" target="_blank">[15]</a>.</p

Number of studies, SNPs and genes by phenotype for all eligible studies.

*<p>includes a 4-SNP haplotype.</p

Genome wide identification of new genes and pathways in patients with both autoimmune thyroiditis and type 1 diabetes

Autoimmune thyroid diseases (AITD) and Type 1 diabetes (T1D) frequently occur in the same individual pointing to a strong shared genetic susceptibility. Indeed, the co-occurrence of T1D and AITD in the same individual is classified as a variant of the autoimmune polyglandular syndrome type 3 (designated APS3v). Our aim was to identify new genes and mechanisms causing the co-occurrence of T1D þ AITD (APS3v) in the same individual using a genome-wide approach. For our discovery set we analyzed 346 Caucasian APS3v patients and 727 gender and ethnicity matched healthy controls. Genotyping was performed using the Illumina Human660W-Quad.v1. The replication set included 185 APS3v patients and 340 controls. Association analyses were performed using the PLINK program, and pathway analyses were performed using the MAGENTA software.We identified multiple signals within the HLA region and conditioning studies suggested that a few of them contributed independently to the strong association of the HLA locus with APS3v. Outside the HLA region, variants in GPR103, a gene not suggested by previous studies of APS3v, T1D, or AITD, showed genome-wide significance (p < 5 _ 10_8). In addition, a locus on 1p13 containing the PTPN22 gene showed genome-wide significant associations. Pathway analysis demonstrated that cell cycle, B-cell development, CD40, and CTLA-4 signaling were the major pathways contributing to the pathogenesis of APS3v. These findings suggest that complex mechanisms involving Tcell and B-cell pathways are involved in the strong genetic association between AITD and T1D