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<p>Different mutations within the <em>NALP3</em> gene are thought to be associated with development of several types of hereditary auto inflammatory disorders such as neonatal onset multisystem inflammatory disorder (NOMID) and muckle-wells syndrome (MWS). In this work two separate mutations E688K and G569R were supposed to be constructed by site-directed mutagenesis in the cloned wild type <em>NALP3</em> genes and further expressed in bacterial and mammalian host cells for functional studies in protein -protein interaction models.</p>

Different mutations within the NALP3 gene are thought to be associated with development of several types of hereditary auto inflammatory disorders such as neonatal onset multisystem inflammatory disorder (NOMID) and muckle-wells syndrome (MWS). In this work two separate mutations E688K and G569R were supposed to be constructed by site-directed mutagenesis in the cloned wild type NALP3 genes and further expressed in bacterial and mammalian host cells for functional studies in protein -protein interaction models.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20282号 / 医博第4241号 / 新制||医||1021(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 三森 経世, 教授 鈴木 茂彦, 教授 生田 宏一 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

<p>Complex disorders are caused by multiple genetic, environmental, and lifestyle factors, and their interactions. Most human diseases are complex, including many psychiatric, autoimmune, neurodegenerative, and cardiovascular disorders. Understanding their genetic background is an essential step toward developing effective preventive and therapeutic interventions for these disorders. In this dissertation, we present an overview of state-of-the-art methodology that is used to help elucidate the genetic basis of complex diseases and apply these methods to understand the genetic background of different complex disorders. First, we carried out a GWAS for myasthenia gravis (MG), a rare autoimmune disorder, and detected a novel risk locus, AGRN, which encodes a protein, involved in neuromuscular junction activation. Additionally, we observed significant genetic correlation between MG and ADs, and variants with pleiotropic effects. Second, we explored the genetic and phenotypic relationships among 11 different autoimmune disorders (ADs), using GWAS results o to calculate polygenic risk scores (PRS) and performing a PRS- phenome-wide association study (PheWAS) analysis with 3,281 phenotypes available in the UK Biobank. We observed associations of ADs PRS with phenotypes in multiple categories, including lifestyle, biomarkers, mental and physical health. We also explored the shared genetic components among the ADs, through genetic correlation and cross-disorder meta-analysis approaches, where we</p> <p>identified pleiotropic variants among the correlated ADs. Finally, we performed a meta-analysis GWAS of Tourette Syndrome (TS) followed by post-GWAS analyses including biological annotation of the results, and association tests of TS PRS with brain volumes. We detected a novel locus, NR2F1, associated with TS, supported by eQTL and Hi-C data. TS PRS was significantly associated with right and left thalamus volumes and right putamen volume. Overall, our work demonstrates the power of GWAS and related methods to help disentangle the genetic basis of complex disease and provides important insights into the genetic basis of the specific disorders that are the focus of our studies.</p>

Systemic Lupus Erythematosus (SLE) and Mixed Connective Tissue Disease (MCTD) are chronic, autoimmune disorders that target overlapping autoantigens and exhibit similar clinical manifestations. Despite 40 years of research, a reliable biomarker capable of diagnosing these syndromes has yet to be identified. Previous studies have confirmed that components of the U1 small nuclear ribonucleoprotein complex (U1 snRNP) such as U1A are 1000 fold more autoantigenic than any other nuclear component in SLE patients. Based on these findings, I hypothesize that models derived from the U1 snRNP autoantigenic properties could distinguish SLE from MCTD patients. To test this hypothesis, 30 peptides corresponding to protein regions of the U1 snRNP were tested in triplicates by indirect ELISA in sera from SLE or MCTD subjects. In addition laboratory tests and clinical manifestations data from these patients were included and analyzed in this investigation. Statistical classification methods as well as bioinformatics pattern recognition strategy were employed to determine which combination, if any, of all the variables included in this study provide the best segregation power for SLE and MCTD. The results confirmed that the IgM reactivity for U1 snRNP and U1A have the power to significantly distinguish SLE from MTCD patients as well as identify kidney and lung malfunctions for these subjects (p ≤ 0.05). Furthermore, the data analysis revealed eight novel classification rules for the segregation of SLE and MCTD which are a better classification tool than any of the currently available methods (p ≤ 0.05). Consequently, the results derived from this study support that SLE and MCTD are indeed separate disorders and pioneer the description of eight novel classification criteria capable of significantly discerning between SLE and MCTD patients (p ≤ 0.05).