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Monozygotic twins discordant for neurofibromatosis 1.

ABSTRACT: We present monozygotic twins discordant for the autosomal dominant disorder neurofibromatosis type 1 (NF1). The affected twin was diagnosed with NF1 at age 12, based upon accepted clinical criteria for the disorder. Both twins were re-examined at ages 35 and 57, at which times the unaffected twin continued to show no clinical manifestations of NF1. Short tandem repeat marker (STR) genotyping at 10 loci on chromosome 17 and 10 additional loci dispersed across the genome revealed identical genotypes for the twins, confirming their monozygosity. The affected twin has three children, two of whom also have NF1, while the unaffected twin has two children, both unaffected. Using lymphoblastoid, fibroblast, and buccal cell samples collected from both twins and from other family members in three generations, we discovered a pathogenic nonsense mutation in exon 40 of the NF1 gene. This mutation was found in all cell samples from the affected twin and her affected daughter, and in lymphoblastoid and buccal cells but not fibroblasts from the unaffected twin. We also found a novel non-synonymous change in exon 16 of the NF1 gene that was transmitted from the unaffected mother to both twins and co-segregated with the pathogenic mutation in the ensuing generation. All cells from the twins were heterozygous for this apparent exon 16 polymorphism and for single nucleotide polymorphisms (SNPs) within 2.5 kb flanking the site of the exon 40 nonsense mutation. This suggests that the NF1 gene of the unaffected twin differed in the respective lymphoblastoid cells and fibroblasts only at the mutation site itself, making post-zygotic mutation leading to mosaicism the most likely mechanism of phenotypic discordance. Although the unaffected twin is a mosaic, the distribution of the mutant allele among different cells and tissues appears to be insufficient to cause overt clinical manifestations of NF1.

SUBMITTER: Kaplan L

PROVIDER: S-EPMC2830382 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Monozygotic twins discordant for neurofibromatosis 1.

Kaplan Lee L Foster Rosemary R Shen Yiping Y Parry Dilys M DM McMaster Mary L ML O'Leary Melanie Collins MC Gusella James F JF

American journal of medical genetics. Part A 20100301 3

We present monozygotic twins discordant for the autosomal dominant disorder neurofibromatosis type 1 (NF1). The affected twin was diagnosed with NF1 at age 12, based upon accepted clinical criteria for the disorder. Both twins were re-examined at ages 35 and 57, at which times the unaffected twin continued to show no clinical manifestations of NF1. Short tandem repeat marker (STR) genotyping at 10 loci on chromosome 17 and 10 additional loci dispersed across the genome revealed identical genotyp ...[more]

PMID: 20186797

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Project description:BackgroundRheumatoid arthritis is a common autoimmune disorder influenced by both genetic and environmental factors. Epigenome-wide association studies can identify environmentally mediated epigenetic changes such as altered DNA methylation, which may also be influenced by genetic factors. To investigate possible contributions of DNA methylation to the aetiology of rheumatoid arthritis with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs.MethodsGenome-wide DNA methylation was assessed in 79 monozygotic twin pairs discordant for rheumatoid arthritis using the HumanMethylation450 BeadChip array (Illumina). Discordant twins were tested for both differential DNA methylation and methylation variability between rheumatoid arthritis and healthy twins. The methylation variability signature was then compared with methylation variants from studies of other autoimmune diseases and with an independent healthy population.ResultsWe have identified a differentially variable DNA methylation signature that suggests multiple stress response pathways may be involved in the aetiology of the disease. This methylation variability signature also highlighted potential epigenetic disruption of multiple RUNX3 transcription factor binding sites as being associated with disease development. Comparison with previously performed epigenome-wide association studies of rheumatoid arthritis and type 1 diabetes identified shared pathways for autoimmune disorders, suggesting that epigenetics plays a role in autoimmunity and offering the possibility of identifying new targets for intervention.ConclusionsThrough genome-wide analysis of DNA methylation in disease-discordant monozygotic twins, we have identified a differentially variable DNA methylation signature, in the absence of differential methylation in rheumatoid arthritis. This finding supports the importance of epigenetic variability as an emerging component in autoimmune disorders.

Project description:Although monozygotic (MZ) twins share the majority of their genetic makeup, they can be phenotypically discordant on several traits and diseases. DNA methylation is an epigenetic mechanism that can be influenced by genetic, environmental and stochastic events and may have an important impact on individual variability. In this study we explored epigenetic differences in peripheral blood samples in three MZ twin studies on major depressive disorder (MDD). Epigenetic data for twin pairs were collected as part of a previous study using 8.1-K-CpG microarrays tagging DNA modification in white blood cells from MZ twins discordant for MDD. Data originated from three geographical regions: UK, Australia and the Netherlands. Ninety-seven MZ pairs (194 individuals) discordant for MDD were included. Different methods to address non independently-and-identically distributed (non-i.i.d.) data were evaluated. Machine-learning methods with feature selection centered on support vector machine and random forest were used to build a classifier to predict cases and controls based on epivariations. The most informative variants were mapped to genes and carried forward for network analysis. A mixture approach using principal component analysis (PCA) and Bayes methods allowed to combine the three studies and to leverage the increased predictive power provided by the larger sample. A machine-learning algorithm with feature reduction classified affected from non-affected twins above chance levels in an independent training-testing design. Network analysis revealed gene networks centered on the PPAR-γ (NR1C3) and C-MYC gene hubs interacting through the AP-1 (c-Jun) transcription factor. PPAR-γ (NR1C3) is a drug target for pioglitazone, which has been shown to reduce depression symptoms in patients with MDD. Using a data-driven approach we were able to overcome challenges of non-i.i.d. data when combining epigenetic studies from MZ twins discordant for MDD. Individually, the studies yielded negative results but when combined classification of the disease state from blood epigenome alone was possible. Network analysis revealed genes and gene networks that support the inflammation hypothesis of MDD.

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Monozygotic twins discordant for neurofibromatosis 1.

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Monozygotic twins discordant for neurofibromatosis 1.

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