A new model defines the minimal set of polymorphism in HLA-DQ and -DR that determines susceptibility and resistance to autoimmune diabetes.
BACKGROUND
The mechanism underlying autoimmune diabetes has been difficult to define. There is a strong genetic contribution and numerous studies associate the major histocompatibility complex, especially the class II region, with predisposition or resistance. However, how these molecules are implicated remains obscure.


OBJECTIVE
We have supplemented structural analysis with computational biophysical and sequence analyses and propose an heuristic for distinguishing between human leukocyte antigen molecules that predispose to insulin dependent diabetes mellitus and those that are protective. Polar residues at both beta37 and beta9 suffice to distinguish accurately between class II alleles that predispose to type 1 diabetes and those that do not. The electrostatic potential within the peptide binding pocket exerts a strong influence on diabetogenic epitopes with basic residues. Diabetes susceptibility alleles are predicted to bind autoantigens strongly with tight affinity, prolonged association and altered cytokine expression profile. Protective alleles bind moderately, and neutral alleles poorly or not at all. Non-Asp beta57 is a modifier that supplements disease risk but only in the presence of the polymorphic, polar pair at beta9 and beta37. The nature of beta37 determines resistance on one hand, and susceptibility or dominant protection on the other.


CONCLUSIONS
The proposed ideas are illustrated with structural, functional and population studies from the literature. The hypothesis, in turn, rationalizes their results. A plausible mechanism of immune mediated diabetes based on binding affinity and peptide kinetics is discussed. The number of the polymorphic markers present correlates with onset of disease and severity. The molecular elucidation of disease susceptibility and resistance paves the way for risk prediction, treatment and prevention of disease based on analogue peptides.


METHODS
This article was reviewed by Eugene V. Koonin, Michael Lenardo, Hossam Ashour, and Bhagirath Singh. For the full reviews, please go to the Reviewers' comments section.
Computational Biophysics Section, Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892-9314, USA. csparry@helix.nih.gov
Christian S
CS
Parry
Bernard R
BR
Brooks
D000483:Alleles; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000818:Animals; D016000:Cluster Analysis; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics* / Q000276:immunology*; D004198:Disease Susceptibility; D006683:HLA-DQ Antigens / Q000737:chemistry / Q000235:genetics*; D006684:HLA-DR Antigens / Q000737:chemistry / Q000235:genetics*; D006860:Hydrogen Bonding; D007113:Immunity, Innate; D007328:Insulin / Q000737:chemistry; D051379:Mice; D008957:Models, Genetic; D018448:Models, Immunological; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry; D011110:Polymorphism, Genetic; D017433:Protein Structure, Secondary; D055672:Static Electricity
D006683:HLA-DQ Antigens; D006684:HLA-DR Antigens; D007328:Insulin; D010455:Peptides