Assessment of allelic diversity in intron-containing Mal d 1 genes and their association to apple allergenicity

<p>Abstract</p> <p>Background</p> <p>Mal d 1 is a major apple allergen causing food allergic symptoms of the oral allergy syndrome (OAS) in birch-pollen sensitised patients. The <it>Mal d 1 </it>gene family is known to have at least 7 intron-containing and 11 intronless members that have been mapped in clusters on three linkage groups. In this study, the allelic diversity of the seven intron-containing <it>Mal d 1 </it>genes was assessed among a set of apple cultivars by sequencing or indirectly through pedigree genotyping. Protein variant constitutions were subsequently compared with <b>S</b>kin <b>P</b>rick <b>T</b>est (SPT) responses to study the association of deduced protein variants with allergenicity in a set of 14 cultivars.</p> <p>Results</p> <p>From the seven intron-containing <it>Mal d 1 </it>genes investigated, <it>Mal d 1.01 </it>and <it>Mal d 1.02 </it>were highly conserved, as nine out of ten cultivars coded for the same protein variant, while only one cultivar coded for a second variant. <it>Mal d 1.04</it>, <it>Mal d 1.05 </it>and <it>Mal d 1.06 A, B </it>and <it>C </it>were more variable, coding for three to six different protein variants. Comparison of <it>Mal d 1 </it>allelic composition between the high-allergenic cultivar Golden Delicious and the low-allergenic cultivars Santana and Priscilla, which are linked in pedigree, showed an association between the protein variants coded by the <it>Mal d 1.04 </it>and <it>-1.06A </it>genes (both located on linkage group 16) with allergenicity. This association was confirmed in 10 other cultivars. In addition, <it>Mal d 1.06A </it>allele dosage effects associated with the degree of allergenicity based on prick to prick testing. Conversely, no associations were observed for the protein variants coded by the <it>Mal d 1.01 </it>(on linkage group 13), -<it>1.02</it>, -<it>1.06B, -1.06C </it>genes (all on linkage group 16), nor by the <it>Mal d 1.05 </it>gene (on linkage group 6).</p> <p>Conclusion</p> <p>Protein variant compositions of Mal d 1.04 and -1.06A and, in case of <it>Mal d 1.06A</it>, allele doses are associated with the differences in allergenicity among fourteen apple cultivars. This information indicates the involvement of qualitative as well as quantitative factors in allergenicity and warrants further research in the relative importance of quantitative and qualitative aspects of <it>Mal d 1 </it>gene expression on allergenicity. Results from this study have implications for medical diagnostics, immunotherapy, clinical research and breeding schemes for new hypo-allergenic cultivars.</p

Enhancing Biological and Biomechanical Fixation of Osteochondral Scaffold: A Grand Challenge

Osteoarthritis (OA) is a degenerative joint disease, typified by degradation of cartilage and changes in the subchondral bone, resulting in pain, stiffness and reduced mobility. Current surgical treatments often fail to regenerate hyaline cartilage and result in the formation of fibrocartilage. Tissue engineering approaches have emerged for the repair of cartilage defects and damages to the subchondral bones in the early stage of OA and have shown potential in restoring the joint's function. In this approach, the use of three-dimensional scaffolds (with or without cells) provides support for tissue growth. Commercially available osteochondral (OC) scaffolds have been studied in OA patients for repair and regeneration of OC defects. However, some controversial results are often reported from both clinical trials and animal studies. The objective of this chapter is to report the scaffolds clinical requirements and performance of the currently available OC scaffolds that have been investigated both in animal studies and in clinical trials. The findings have demonstrated the importance of biological and biomechanical fixation of the OC scaffolds in achieving good cartilage fill and improved hyaline cartilage formation. It is concluded that improving cartilage fill, enhancing its integration with host tissues and achieving a strong and stable subchondral bone support for overlying cartilage are still grand challenges for the early treatment of OA

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

Colocalization of GWAS and eQTL signals detects target genes

The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci

Colocalization of GWAS and eQTL signals detects target genes

The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci