Endocrine, Metabolic, Cardiovascular and Immunological Aspects of Sex Chromosome Abnormalities in Relation to Genotype

Overview

Observational study of 160 patients with sex-chromosome abnormalities and 160 matched controls. Blood, fat, muscle, skin, buccal swaps, urine will be collected and analyzed for DNA, RNA and methylation patterns. The goal is to associated genotype and epigenetic changes with the phenotype of patients with sex-chromosome abnormalities. Patients participate in questionaries, dexa-scan of bones, fibroscan of liver, ultra sound of testicles and blood will be analyzed for organ specific blood work as well as immunological and coagulation components.

Background: The most prevalent SCAs are Klinefelter syndrome (KS; 47, XXY), 47,XXX, 47,XYY and Turner syndrome (TS; 45,X) with a prevalence of 85-250, 84, 98 and 50 per 100,000 liveborn boys/girls, respectively. The majority of SCAs can suffer from a range of diseases including congenital malformations, metabolic diseases, hypergonadotropic hypogonadism and infertility, autoimmune disease and psychiatric diseases. However, the genetic mechanisms causing these phenotypes are largely unexplained. The phenotypes have been suggested to arise from alterations in DNA methylation and RNA-expression. The methylome and transcriptome in peripheral blood samples from persons with KS, 47,XXX and TS have been found to be altered in comparison with controls. These genes are now starting to be found ex. SHOX, located in the pseudo autosomal region of the X and Y chromosome, escapes X-inactivation and is therefore equivalent to the number of sex chromosomes. Altered expression of SHOX in SCAs has been associated with the altered height seen in these patients. Hypotheses: 1. The methylome and transcriptome of SCAs is altered compared to karyotypical normal female and males, and a unique methylation profile and RNA expression profile is seen for the different SCAs subgroups. 2. The methylation profile and the RNA expression profile show temporal alterations. 3. The DNA methylation profile and the RNA expression profile are tissue-specific. 3\. The phenotype and the increased risk of diseases seen in patients with SCAs are associated with the altered RNA-expression and DNA methylation profile. Materials: Blood, fat, muscle, skin, buccal swaps, urine, will be collected from 60 klinefelter, 60 Turner syndrome patient, 20: 47, XXX and 20: 47, XYY and 80 male and female matched controls. Methods: Analysis of DNA-methylation using Whole Genome Bisulfite Sequencing (WGBS). Genomic DNA will be bisulfite-converted and sequenced on an Illumina Novaseq System. Sequence data pre-processors of software pipeline MethylStar. Analyzed using R. Gene expression analysis (RNA) RNA will be cleaned and sequenced with a sequence depth of 30 million reads. Processing of sequence data using FastQC (quality control), HISAT2 (mapping) and featureCounts (gene-expression). Differences in gene-expression will be analyzed in R. The extracted biopsies will be dissociated to singular cells RNA from these singular cells will be individually sequenced. For miRNA analysis we will isolate small non-coding RNAs and analyze these by next generation sequencing. Chromatin re-modelling can be analyzed through "footprints" left by histones on DNA-strand. Mapping of footprints along the whole X-chromosome is done using a single assay with chromatin-immunoprecipitation (CHIP) in combination med deep sequencing (chIPseq). Genotype-Phenotype association analysis with weighted correlation network analysis (WGCNA) we will uncover the patterns in which genes behave and divide them into modules where genes react dependent of each other. These modules will afterwards be associated with the clinical data, enabling identification of the "hub" genes with the strongest associations to the phenotype. These gene-modules, and the gene expression data itself, can furthermore be included in "deep-phenotyping" using artificial intelligence Perspectives A characterization of the methylome and transcriptome from different target tissue from patients with SCAs would not just be of significance to these patients but could lead to a larger understanding of similar diseases in patients without SCAs. Using SCAs as disease models and identify changes in DNA methylation and RNA-expression related to co-morbidity such as the metabolic syndrome, congenital heart disease or psychiatric diseases could increase the understanding of these diseases in general and potentially improve treatment in other patients groups with similar diseases. In addition, the data collection will expand our biobank and will enable future research projects about SCAs.