Recent Advances in Autoimmune Thyroid Diseases

Autoimmune thyroid disease (AITD) includes hyperthyroid Graves disease, hypothyroid autoimmune thyroiditis, and subtle subclinical thyroid dysfunctions. AITD is caused by interactions between genetic and environmental predisposing factors and results in autoimmune deterioration. Data on polymorphisms in the AITD susceptibility genes, related environmental factors, and dysregulation of autoimmune processes have accumulated over time. Over the last decade, there has been progress in the clinical field of AITD with respect to the available diagnostic and therapeutic methods as well as clinical consensus. The updated clinical guidelines allow practitioners to identify the most reasonable and current approaches for proper management. In this review, we focus on recent advances in understanding the genetic and environmental pathogenic mechanisms underlying AITD and introduce the updated set of clinical guidelines for AITD management. We also discuss other aspects of the disease such as management of subclinical thyroid dysfunction, use of levothyroxine plus levotriiodothyronine in the treatment of autoimmune hypothyroidism, risk assessment of long-standing antithyroid drug therapy in recurrent Graves' hyperthyroidism, and future research needs.

StrokeBase: A Database of Cerebrovascular Disease-related Candidate Genes

Complex diseases such as stroke and cancer have two or more genetic loci and are affected by environmental factors that contribute to the diseases. Due to the complex characteristics of these diseases, identifying candidate genes requires a system-level analysis of the following: gene ontology, pathway, and interactions. A database and user interface, termed StrokeBase, was developed; StrokeBase provides queries that search for pathways, candidate genes, candidate SNPs, and gene networks. The database was developed by using in silico data mining of HGNC, ENSEMBL, STRING, RefSeq, UCSC, GO, HPRD, KEGG, GAD, and OMIM. Forty candidate genes that are associated with cerebrovascular disease were selected by human experts and public databases. The networked cerebrovascular disease gene maps also were developed; these maps describe genegene interactions and biological pathways. We identified 1127 genes, related indirectly to cerebrovascular disease but directly to the etiology of cerebrovascular disease. We found that a protein-protein interaction (PPI) network that was associated with cerebrovascular disease follows the power-law degree distribution that is evident in other biological networks. Not only was in silico data mining utilized, but also 250K Affymetrix SNP chips were utilized in the 320 control/disease association study to generate associated markers that were pertinent to the cerebrovascular disease as a genome- wide search. The associated genes and the genes that were retrieved from the in silico data mining system were compared and analyzed. We developed a well-curated cerebrovascular disease-associated gene network and provided bioinformatic resources to cerebrovascular disease researchers. This cerebrovascular disease network can be used as a frame of systematic genomic research, applicable to other complex diseases. Therefore, the ongoing database efficiently supports medical and genetic research in order to overcome cerebrovascular disease.