Human CLIP2 peptide

Human leukocyte antigen (HLA)-DQ2.5 (DQA1* 05/ DQB1* 02) is a class-II major histocompatibility complex protein associated with both type 1 diabetes and celiac disease. One unusual feature of DQ2.5 is its high class-II-associated invariant chain peptide (CLIP) content. Moreover, HLA-DQ2.5 preferentially binds the non-canonical CLIP2 over the canonical CLIP1. To better understand the structural basis of HLA-DQ2.5 ' s unusual CLIP association characteristics, better insight into the HLADQ2.5.CLIP complex structures is required. To this end, we determined the X-ray crystal structure of the HLA-DQ2.5.CLIP1 and HLA-DQ2.5.CLIP2 complexes at 2.73 and 2.20 angstrom, respectively. We found that HLA-DQ2.5 has an unusually large P4 pocket and a positively charged peptide-binding groove that together promote preferential binding of CLIP2 over CLIP1. An alpha 9-alpha 22-alpha 24-alpha 31-beta 86-beta 90 hydrogen bond network located at the bottom of the peptide-binding groove, spanning from the P1 to P4 pockets, renders the residues in this region relatively immobile. This hydrogen bond network, along with a deletion mutation at alpha 53, may lead to HLA-DM insensitivity in HLA-DQ2.5. A molecular dynamics simulation experiment reported here and recent biochemical studies by others support this hypothesis. The diminished HLA-DM sensitivity is the likely reason for the CLIP-rich phenotype of HLA-DQ2.5.

Purpose:Thyroid cancer of papillary histology (PTC) is the dominant type in radio-epidemiological cohorts established after nuclear accidents or warfare. Studies on post-Chernobyl PTC and on thyroid cancer in the life span study (LSS) of Japanese a-bomb survivors consistently revealed high radiation risk after exposure during childhood and adolescence. For post-Chernobyl risk assessment overexpression of the CLIP2 gene was proposed as molecular biomarker to separate radiogenic from sporadic PTC. Based on such binary marker a biologically-based risk model of PTC carcinogenesis has been developed for observational Chernobyl data. The model featured two independent molecular pathways of disease development, of which one was associated with radiation exposure. To gain credibility the concept for a mechanistic risk model must be based on general biological features which transcend findings in a single cohort. The purpose of the present study is therefore to demonstrate portability of the model concept by application to PTC incidence data in the LSS. By exploiting the molecular two-path concept we improve the determination of the probability of radiation causing cancer (POC). Materials and methods:The current analysis uses thyroid cancer incidence data of the LSS with thyroid cancer diagnoses and papillary histology (n = 292) from the follow-up period between 1958 and 2005. Risk analysis was performed with both descriptive and biologically-based models. Results:Judged by goodness-of-fit all applied models described the data almost equally well. They yielded similar risk estimates in cohorts post-Chernobyl and LSS. The preferred mechanistic model was selected by biological plausibility. It reflected important features of an imperfect radiation marker which are not easily addressed by descriptive models. Precise model predictions of marker prevalence in strata of epidemiological covariables can be tested by molecular measurements. Application of the radiation-related molecular pathway from our preferred model in retrospective risk assessment decreases the threshold dose for 50% POC from 0.33 (95% confidence interval (CI) 0.18; 0.64) Gy to 0.04 (95% CI 0.01; 0.19) Gy for females and from 0.43 (95% CI 0.17; 1.84) Gy to 0.19 (95% CI 0.05; 1.00) Gy for males. These improvements are still not sufficient to separate radiation-induced from sporadic PTC cases at very low doses Conclusions:Successful application of our preferred mechanistic model to LSS incidence data confirms and improves the biological two-path concept of radiation-induced PTC. Model predictions suggest further molecular validation studies to consolidate the basis of biologically-based risk estimation.