CD8 kinetically promotes ligand binding to the T-cell antigen receptor.

The mechanism of CD8 cooperation with the TCR in antigen recognition was studied on live T cells. Fluorescence correlation measurements yielded evidence of the presence of two TCR and CD8 subpopulations with different lateral diffusion rate constants. Independently, evidence for two subpopulations was derived from the experimentally observed two distinct association phases of cognate peptide bound to class I MHC (pMHC) tetramers and the T cells. The fast phase rate constant ((1.7 +/- 0.2) x 10(5) M(-1) s(-1)) was independent of examined cell type or MHC-bound peptides' structure. Its value was much faster than that of the association of soluble pMHC and TCR ((7.0 +/- 0.3) x 10(3) M(-1) s(-1)), and close to that of the association of soluble pMHC with CD8 ((1-2) x 10(5) M(-1) s(-1)). The fast binding phase disappeared when CD8-pMHC interaction was blocked by a CD8-specific mAb. The latter rate constant was slowed down approximately 10-fold after cells treatment with methyl-beta-cyclodextrin. These results suggest that the most efficient pMHC-cell association route corresponds to a fast tetramer binding to a colocalized CD8-TCR subpopulation, which apparently resides within membrane rafts: the reaction starts by pMHC association with the CD8. This markedly faster step significantly increases the probability of pMHC-TCR encounters and thereby promotes pMHC association with CD8-proximal TCR. The slow binding phase is assigned to pMHC association with a noncolocalized CD8-TCR subpopulation. Taken together with results of cytotoxicity assays, our data suggest that the colocalized, raft-associated CD8-TCR subpopulation is the one capable of inducing T-cell activation.

Genetic variation in IL28B is associated with chronic hepatitis C and treatment failure: a genome-wide association study.

BACKGROUND & AIMS: Hepatitis C virus (HCV) induces chronic infection in 50% to 80% of infected persons; approximately 50% of these do not respond to therapy. We performed a genome-wide association study to screen for host genetic determinants of HCV persistence and response to therapy. METHODS: The analysis included 1362 individuals: 1015 with chronic hepatitis C and 347 who spontaneously cleared the virus (448 were coinfected with human immunodeficiency virus [HIV]). Responses to pegylated interferon alfa and ribavirin were assessed in 465 individuals. Associations between more than 500,000 single nucleotide polymorphisms (SNPs) and outcomes were assessed by multivariate logistic regression. RESULTS: Chronic hepatitis C was associated with SNPs in the IL28B locus, which encodes the antiviral cytokine interferon lambda. The rs8099917 minor allele was associated with progression to chronic HCV infection (odds ratio [OR], 2.31; 95% confidence interval [CI], 1.74-3.06; P = 6.07 x 10(-9)). The association was observed in HCV mono-infected (OR, 2.49; 95% CI, 1.64-3.79; P = 1.96 x 10(-5)) and HCV/HIV coinfected individuals (OR, 2.16; 95% CI, 1.47-3.18; P = 8.24 x 10(-5)). rs8099917 was also associated with failure to respond to therapy (OR, 5.19; 95% CI, 2.90-9.30; P = 3.11 x 10(-8)), with the strongest effects in patients with HCV genotype 1 or 4. This risk allele was identified in 24% of individuals with spontaneous HCV clearance, 32% of chronically infected patients who responded to therapy, and 58% who did not respond (P = 3.2 x 10(-10)). Resequencing of IL28B identified distinct haplotypes that were associated with the clinical phenotype. CONCLUSIONS: The association of the IL28B locus with natural and treatment-associated control of HCV indicates the importance of innate immunity and interferon lambda in the pathogenesis of HCV infection.

Variants in MTNR1B influence fasting glucose levels.

To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 x 10(-50)) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 x 10(-15)). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 x 10(-7)) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 x 10(-57)) and GCK (rs4607517, P = 1.0 x 10(-25)) loci.

Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study.

Adiponectin has a variety of metabolic effects on obesity, insulin sensitivity, and atherosclerosis. To identify genes influencing variation in plasma adiponectin levels, we performed genome-wide linkage and association scans of adiponectin in two cohorts of subjects recruited in the Genetic Epidemiology of Metabolic Syndrome Study. The genome-wide linkage scan was conducted in families of Turkish and southern European (TSE, n = 789) and Northern and Western European (NWE, N = 2,280) origin. A whole genome association (WGA) analysis (500K Affymetrix platform) was carried out in a set of unrelated NWE subjects consisting of approximately 1,000 subjects with dyslipidemia and 1,000 overweight subjects with normal lipids. Peak evidence for linkage occurred at chromosome 8p23 in NWE subjects (lod = 3.10) and at chromosome 3q28 near ADIPOQ, the adiponectin structural gene, in TSE subjects (lod = 1.70). In the WGA analysis, the single-nucleotide polymorphisms (SNPs) most strongly associated with adiponectin were rs3774261 and rs6773957 (P < 10(-7)). These two SNPs were in high linkage disequilibrium (r(2) = 0.98) and located within ADIPOQ. Interestingly, our fourth strongest region of association (P < 2 x 10(-5)) was to an SNP within CDH13, whose protein product is a newly identified receptor for high-molecular-weight species of adiponectin. Through WGA analysis, we confirmed previous studies showing SNPs within ADIPOQ to be strongly associated with variation in adiponectin levels and further observed these to have the strongest effects on adiponectin levels throughout the genome. We additionally identified a second gene (CDH13) possibly influencing variation in adiponectin levels. The impact of these SNPs on health and disease has yet to be determined.