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  • br Materials and methods br Results br Discussion In the

    2019-07-10


    Materials and methods
    Results
    Discussion In the past decade, GWAS have attempted to identify genetic variants that confer risk for many human diseases, whose inherited components remain unexplained (Manolio CAY10603 et al., 2009). In a few cases risk variants identified by GWAS have paved the way for a molecular understanding of disease causes. Crohn\'s disease, ulcerative colitis, and type I diabetes are all examples where deep sequencing follow-up on GWAS hits have revealed specific disease causing mutations (Lee et al., 2013, Nejentsev et al., 2009). However, experience from these studies and others indicates that when individual gene effects are relatively small, as would be the case for neuropsychiatric diseases such as BD, data from tens of thousands of patients is required in order for GWAS results to be meaningful (Craddock and Sklar, 2009). Only in recent years have patient databases become large and diagnostically detailed enough to confidently identify individual risk CAY10603 as well as complex genetic pathways (Cross-Disorder Group of the Psychiatric Genomics, C, 2013, Psychiatric, G.C.B.D.W.G., 2011). An additional difficulty, making many GWAS hits challenging to follow up functionally, is the absence of comprehensive transcriptional, and protein coding maps for genes in the region of the identified risk loci. Without knowing the gene products potentially altered by GWAS identified variations, mechanistic testing of disease processes is impossible. Further, knowledge of the human gene products is essential for evaluating the relevance of studies using animal models to human disease. While annotated human sequence databases, such as the UCSC Genome database, are a good starting point, they suffer from several limitations. Most annotated database sequences are unverified, in particular in relation to their transcript start and stop sites. Due to the way most database sequences are generated from poly-A-primed cDNA templates, transcript ends are frequently a result of early termination of cDNA synthesis and lack the true 5′ terminus. This is particularly true for the long transcripts typical of brain tissue (Zylka et al., 2015). In addition, transcript priming can arise from genomic poly-A stretches, thus denoting false 3′ termini. Further, current databases are by no means comprehensive and it is likely that additional unreported transcripts exist, especially in the case of genes with differential tissue specific expression patterns. Given these concerns, the first step in evaluating the potential significance of various mutations and polymorphisms that fall near GWAS risk loci is mapping of the transcriptional and protein coding region of interest. Here, we map the SYNE1 gene, a large gene with a complex transcriptional profile, which has been implicated in a variety of diseases. The first group includes muscular dystrophies; Emery–Dreifuss type muscular dystrophy and autosomal recessive arthrogryposis, which are conditions that severely affect skeletal and cardiac muscle function and might be associated with muscle specific transcripts of SYNE1 (Attali et al., 2009, Puckelwartz et al., 2009, Zhang et al., 2001, Zhang et al., 2007a). The mutations for these degenerative neuromuscular diseases have been mapped to the SYNE1 3′ end (Rajgor and Shanahan, 2013). Mice with deletion of the Syne1 3′ end encoding the Klarsicht, ANC-1, Syne1 Homology (KASH) domain, a consensus sequence for nuclear envelope binding, display disrupted anchoring of nuclei to the cytoskeleton in skeletal muscle, which significantly affects motor nerve innervation (Zhang et al., 2010). Both SYNE1 and the homologous gene SYNE2 are expressed in skeletal muscle cells and have overlapping cellular functions (Rajgor et al., 2012, Zhang et al., 2005). Syne1; Syne2 double-knockout mice die of respiratory failure shortly after birth, suggesting that these genes\' role in anchoring myonuclei is crucial for respiratory muscle innervation and function (Banerjee et al., 2014, Zhang et al., 2007b).