OBJECTIVE-The Type 1 Diabetes Genetics Consortium has collected type 1 diabetic families worldwide for genetic analysis. The major genetic determinants of type 1 diabetes are alleles at the HLA-DRB1 and DQB1 loci, with both susceptible and protective DR-DQ haplotypes present in all human populations. The aim of this study is to estimate the risk conferred by specific DR-DQ haplotypes and genotypes.RESEARCH DESIGN AND METHODS:-Six hundred and seven Caucasian families and 38 Asian families were typed at high resolution for the DRB1, DQA1, and DQB1 loci. The association analysis was performed by comparing the frequency of DR-DQ haplotypes among the chromosomes transmitted to an affected child with the frequency of chromosomes not transmitted to any affected child.RESULTS-A number of susceptible, neutral, and protective DR-DQ haplotypes have been identified, and a statistically significant hierarchy of type 1 diabetes risk has been established. The most susceptible haplotypes are the DRB1*0301-DQA1*0501-DQB1*0201 (odds ratio [OR] 3.64) and the

An operon encoding four proteins required for bacterial cellulose biosynthesis (bcs) in Acetobacter xylinum was isolated via genetic complementation with strains lacking cellulose synthase activity. Nucleotide sequence analysis indicated that the cellulose synthase operon is 9217 base pairs long and consists of four genes. The four genes-bcsA, bcsB, bcsC, and bcsD-appear to be translationally coupled and transcribed as a polycistronic mRNA with an initiation site 97 bases upstream of the coding region of the first gene (besA) in the operon. Results from genetic complementation tests and gene disruption analyses demonstrate that all four genes in the operon are required for maximal bacterial cellulose synthesis in A. xylinum. The calculated molecular masses of the proteins encoded by bcsA, bcsB, bcsC, and bcsD are 84.4, 85.3, 141.0, and 17.3 kDa, respectively. The second gene in the operon (bcsB) encodes the catalytic subunit of cellulose synthase. The functions of the bcsA, bcsC, and bcsD gene products are unknown. Bacterial strains mutated in the bcsA locus were found to be deficient in cellulose synthesis due to the lack of cellulose synthase and diguanylate cyclase activities. Mutants in the bcsC and bcsD genes were impaired in cellulose production in vivo, even though they had the capacity to make all the necessary metabolic precursors and cyclic diguanylic acid, the activator of cellulose synthase, and exhibit cellulose synthase activity in vitro. When the entire operon was present on a multicopy plasmid in the bacterial cell, both cellulose synthase activity and cellulose biosynthesis increased. When the promoter of the cellulose synthase operon was replaced on the chromosome by E. coli tac or lac promoters, cellulose production was reduced in parallel with decreased cellulose synthase activity. These observations suggest that the expression of the bcs operon is rate-limiting for cellulose synthesis in A. xylinum.

Cyclic di-GMP (c-di-GMP) is the specific nucleotide regulator of β-1,4-glucan (cellulose) synthase in Acetobacter xylinum. The enzymes controlling turnover of c-di-GMP are diguanylate cyclase (DGC), which catalyzes its formation, and phosphodiesterase A (PDEA), which catalyzes its degradation. Following biochemical purification of DGC and PDEA, genes encoding isoforms of these enzymes have been isolated and found to be located on three distinct yet highly homologous operons for cyclic diguanylate, cdg1, cdg2, andcdg3. Within each cdg operon, apdeA gene lies upstream of a dgc gene.cdg1 contains two additional flanking genes,cdg1a and cdg1d. cdg1a encodes a putative transcriptional activator, similar to AadR of Rhodopseudomonas palustris and FixK proteins of rhizobia. The deduced DGC and PDEA proteins have an identical motif structure of two lengthy domains in their C-terminal regions. These domains are also present in numerous bacterial proteins of undefined function. The N termini of the DGC and PDEA deduced proteins contain putative oxygen-sensing domains, based on similarity to domains on bacterial NifL and FixL proteins, respectively. Genetic disruption analyses demonstrated a physiological hierarchy among the cdg operons, such that cdg1contributes 80% of cellular DGC and PDEA activities andcdg2 and cdg3 contribute 15 and 5%, respectively. Disruption of dgc genes markedly reduced in vivo cellulose production, demonstrating that c-di-GMP controls this process.

OBJECTIVEWe report here genotyping data and type 1 diabetes association analyses for HLA class I loci (A, B, and C) on 1,753 multiplex pedigrees from the Type 1 Diabetes Genetics Consortium (T1DGC), a large international collaborative study.RESEARCH DESIGN AND METHODSComplete eight-locus HLA genotyping data were generated. Expected patient class I (HLA-A, -B, and -C) allele frequencies were calculated, based on linkage disequilibrium (LD) patterns with observed HLA class II DRB1-DQA1-DQB1 haplotype frequencies. Expected frequencies were compared to observed allele frequencies in patients.RESULTSSignificant type 1 diabetes associations were observed at all class I HLA loci. After accounting for LD with HLA class II, the most significantly type 1 diabetes–associated alleles were B*5701 (odds ratio 0.19; P = 4 × 10−11) and B*3906 (10.31; P = 4 × 10−10). Other significantly type 1 diabetes–associated alleles included A*2402, A*0201, B*1801, and C*0501 (predisposing) and A*1101, A*3201, A*6601, B*0702, B*4403, B*3502, C*1601, and C*0401 (protective). Some alleles, notably B*3906, appear to modulate the risk of all DRB1-DQA1-DQB1 haplotypes on which they reside, suggesting a class I effect that is independent of class II. Other class I type 1 diabetes associations appear to be specific to individual class II haplotypes. Some apparent associations (e.g., C*1601) could be attributed to strong LD to another class I susceptibility locus (B*4403).CONCLUSIONSThese data indicate that HLA class I alleles, in addition to and independently from HLA class II alleles, are associated with type 1 diabetes.

Background Although human leukocyte antigen (HLA) DQ and DR loci appear to confer the strongest genetic risk for type 1 diabetes, more detailed information is required for other loci within the HLA region to understand causality and stratify additional risk factors. The Type 1 Diabetes Genetics Consortium (T1DGC) study design included high-resolution genotyping of HLA-A, B, C, DRB1, DQ, and DP loci in all affected sibling pair and trio families, and cases and controls, recruited from four networks worldwide, for analysis with clinical phenotypes and immunological markers.Purpose In this article, we present the operational strategy of training, classification, reporting, and quality control of HLA genotyping in four laboratories on three continents over nearly 5 years.Methods Methods to standardize HLA genotyping at eight loci included: central training and initial certification testing; the use of uniform reagents, protocols, instrumentation, and software versions; an automated data transfer; and the use of standardized nomenclature and allele databases. We implemented a rigorous and consistent quality control process, reinforced by repeated workshops, yearly meetings, and telephone conferences.Results A total of 15,246 samples have been HLA genotyped at eight loci to four-digit resolution; an additional 6797 samples have been HLA genotyped at two loci. The genotyping repeat rate decreased significantly over time, with an estimated unresolved Mendelian inconsistency rate of 0.21%. Annual quality control exercises tested 2192 genotypes (4384 alleles) and achieved 99.82% intra-laboratory and 99.68% inter-laboratory concordances.Limitations The chosen genotyping platform was unable to distinguish many allele combinations, which would require further multiple stepwise testing to resolve. For these combinations, a standard allele assignment was agreed upon, allowing further analysis if required.Conclusions High-resolution HLA genotyping can be performed in multiple laboratories using standard equipment, reagents, protocols, software, and communication to produce consistent and reproducible data with minimal systematic error. Many of the strategies used in this study are generally applicable to other large multi-center studies.