No association of two Fas gene polymorphisms with Hashimoto's thyroiditis and Graves' disease

BACKGROUND: Apoptosis is a joint pathogenic process underlying autoimmune thyroid disease. Increased programmed cell death in thyrocytes causes hypothyroidism in Hashimoto's thyroiditis, whereas in Graves' disease infiltrating lymphocytes undergo apoptosis while thyrocytes appear to proliferate under protection of anti-apoptotic signals. The Fas/Fas ligand cascade represents a major pathway initiating apoptosis. Its role in autoimmunity is well studied and genetic polymorphisms in gene loci of Fas and its ligand have been shown to be associated with autoimmune diseases. OBJECTIVE: Due to the functional relevance of the Fas pathway in autoimmune thyroid disease we were interested in the possible contribution of polymorphisms in the Fas gene to the genetic risk of thyroid autoimmunity, which so far is mainly, but incompletely, attributed to the HLA DQ region and polymorphisms in the CTLA-4 gene. DESIGN: We genotyped Caucasian families with at least one offspring affected by Hashimoto's thyroiditis (n=95) and Graves' disease (n=109) for two Fas gene polymorphisms (g-670 G-->A in the promoter region, g-154 C-->T in exon 7). METHODS: Extended transmission disequilibrium and chi(2) testing were performed. RESULTS: Neither polymorphism alone (P=0.44 and P=0.70) nor the promoter/exon 7 haplotypes (P=0.86) were associated with Hashimoto's thyroiditis. No association with Graves' disease was observed for the promoter polymorphism (P=0.91) and exon 7 (P=0.65) or the promoter/exon 7 haplotypes (P=0.80). CONCLUSION: In summary, our data do not suggest any significant contribution of common genetic Fas variants to the genetic risk of developing Hashimoto's thyroiditis or Graves' disease

Comprehensive Network Analysis of Anther-Expressed Genes in Rice by the Combination of 33 Laser Microdissection and 143 Spatiotemporal Microarrays

Co-expression networks systematically constructed from large-scale transcriptome data reflect the interactions and functions of genes with similar expression patterns and are a powerful tool for the comprehensive understanding of biological events and mining of novel genes. In Arabidopsis (a model dicot plant), high-resolution co-expression networks have been constructed from very large microarray datasets and these are publicly available as online information resources. However, the available transcriptome data of rice (a model monocot plant) have been limited so far, making it difficult for rice researchers to achieve reliable co-expression analysis. In this study, we performed co-expression network analysis by using combined 44 K agilent microarray datasets of rice, which consisted of 33 laser microdissection (LM)-microarray datasets of anthers, and 143 spatiotemporal transcriptome datasets deposited in RicexPro. The entire data of the rice co-expression network, which was generated from the 176 microarray datasets by the Pearson correlation coefficient (PCC) method with the mutual rank (MR)-based cut-off, contained 24,258 genes and 60,441 genes pairs. Using these datasets, we constructed high-resolution co-expression subnetworks of two specific biological events in the anther, “meiosis” and “pollen wall synthesis”. The meiosis network contained many known or putative meiotic genes, including genes related to meiosis initiation and recombination. In the pollen wall synthesis network, several candidate genes involved in the sporopollenin biosynthesis pathway were efficiently identified. Hence, these two subnetworks are important demonstrations of the efficiency of co-expression network analysis in rice. Our co-expression analysis included the separated transcriptomes of pollen and tapetum cells in the anther, which are able to provide precise information on transcriptional regulation during male gametophyte development in rice. The co-expression network data presented here is a useful resource for rice researchers to elucidate important and complex biological events