The expression, responsiveness and regulation of mouse Toll-like receptors (TLRs) in bone marrow-derived macrophages (BM-Ø) were investigated prior to and following the development of diabetes. Expression of TLR3 and TLR5 was significantly higher in newly diabetic non-obese diabetic (NOD) mice when compared with pre-diabetic and control strains of mice. The TLR3 ligand poly(I)poly(C) triggered up-regulation of its own receptor in NOR and pre-diabetic NOD, but TLR3 was already highly expressed in diabetic NOD mice. Expression levels of TLR3 correlated with poly(I)poly(C)-triggered IFN activity. LPS triggered down-regulation of TLR4 in pre-diabetic NOD, NOR and BALB/c, while levels of TLR4 remained consistently elevated in type 1 diabetic NOD and type 2 diabetic NZL mice. Dysregulation of TLR4 expression in the diabetic state correlated with increased nuclear factor kappa B (NF-kappaB) activation in response to the TLR4 ligand LPS and higher expression of IL-12p40, tumor necrosis factor alpha (TNFalpha), IL-6 and inducible nitric oxide synthase but lowered expression of IL-10. Exposure of bone marrow precursor cells from NOD mice to a hyperglycemic environment during differentiation into macrophages resulted in elevated levels of TLR2 and TLR4 and the cytokine TNFalpha. The results indicate that macrophage precursors are influenced by systemic changes in diabetes favoring altered TLR expression and sensitivity that may influence susceptibility to macrophage-mediated diabetes complications and explain inappropriate responses to infection in diabetes.

The proton-coupled intestinal dipeptide transporter, PepT1, has 707 amino acids, 12 putative transmembrane domains (TMD), and is of importance in the transport of nutritional di- and tripeptides and structurally related drugs, such as penicillins and cephalosporins. By using a combination of molecular modeling and site-directed mutagenesis, we have identified several key amino acid residues that effect catalytic transport properties of PepT1. Our molecular model of the transporter was examined by dividing it into four sections, parallel to the membrane, starting from the extracellular side. The molecular model revealed a putative transport channel and the approximate locations of several aromatic and charged amino acid residues that were selected as targets for mutagenesis. Wild type or mutagenized human PepT1 cDNA was transfected into human embryonic kidney (HEK293) cells, and the uptake of tritiated glycylsarcosine [3H]-(Gly-Sar) was measured. Michaelis-Menton analysis of the wild-type and mutated transporters revealed the following results for site-directed mutagenesis. Mutation of Tyr-12 or Arg-282 into alanine has only a very modest effect on Gly-Sar uptake. By contrast, mutation of Trp-294 or Glu-595 into alanine reduced Gly-Sar uptake by 80 and 95%, respectively, and mutation of Tyr-167 reduced Gly-Sar uptake to the level of mock-transfected cells. In addition, preliminary data from fluorescence microscopy following the expression of N-terminal-GFP-labeled PepT1Y167A in HEK cells indicates that the Y167A mutation was properly inserted into the plasma membrane but has a greatly reduced Vmax.

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Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.

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