Autoreactive T cells, responsible for the destruction of pancreatic β cells in type 1 diabetes, are known to have a skewed TCR repertoire in the NOD mouse. To define the autoreactive T cell repertoire in human diabetes, we searched for intraislet monoclonal expansions from a recent onset in human pancreas to then trace them down to the patient’s peripheral blood and spleen. Islet infiltration was diverse, but five monoclonal TCR β-chain variable expansions were detected for Vβ1, Vβ7, Vβ11, Vβ17, and Vβ22 families. To identify any sequence bias in the TCRs from intrapancreatic T cells, we analyzed 139 different CDR3 sequences. We observed amino acid preferences in the NDN region that suggested a skewed TCR repertoire within infiltrating T cells. The monoclonal expanded TCR sequences contained amino acid combinations that fit the observed bias. Using these CDR3 sequences as a marker, we traced some of these expansions in the spleen. There, we identified a Vβ22 monoclonal expansion with identical CDR3 sequence to that found in the islets within a polyclonal TCR β-chain variable repertoire. The same Vβ22 TCR was detected in the patient’s PBMCs, making a cross talk between the pancreas and spleen that was reflected in peripheral blood evident. No other pancreatic monoclonal expansions were found in peripheral blood or the spleen, suggesting that the Vβ22 clone may have expanded or accumulated in situ by an autoantigen present in both the spleen and pancreas. Thus, the patient’s spleen might be contributing to disease perpetuation by expanding or retaining some autoreactive T cells.

The objective of this study was to characterize the peptide-binding motif of the major histocompatibility complex (MHC) class II HLA-DR8 molecule included in the type 1 diabetes-associated haplotype DRB1*0801-DQA1*0401/DQB1*0402 (DR8-DQ4), and compare it with that of other diabetes-associated MHC class II alleles; DR8-bound peptides were eluted from an HLA-DR homozygous lymphoblastoid cell line. The repertoire was characterized by peptide sequencing using a LTQ ion trap mass spectrometer coupled to a multidimensional liquid chromatography system. After validation of the spectra identification, the definition of the HLA-DR8 peptide-binding motif was achieved from the analysis of 486 natural ligands, based on serial alignments of all possible HLA-DR-binding cores. The DR8 motif showed a strong similarity with the peptide-binding motifs of other MHC class II diabetes-associated alleles, HLA-DQ8 and H-2 I-A g7 . Similar to HLA-DQ8 and H-2 I-A g7 , HLA-DR8 preferentially binds peptides with an acidic residue at position P9 of the binding core, indicating that DR8 is the susceptibility component of the DR8-DQ4 haplotype. Indeed, some DR8 peptides were identical to peptides previously identified as DQ8-or I-A g7 ligands, and several diabetes-specific peptides associated with DQ8 or I-A g7 could theoretically bind to HLA-DR8. These data further strengthen the association of HLA-DR8 with type I diabetes.

BackgroundMass spectrometry (MS) are a group of a high-throughput techniques used to increase knowledge about biomolecules. They produce a large amount of data which is presented as a list of hundreds or thousands of proteins. Filtering those data efficiently is the first step for extracting biologically relevant information. The filtering may increase interest by merging previous data with the data obtained from public databases, resulting in an accurate list of proteins which meet the predetermined conditions.ResultsIn this article we present msBiodat Analysis Tool, a web-based application thought to approach proteomics to the big data analysis. With this tool, researchers can easily select the most relevant information from their MS experiments using an easy-to-use web interface. An interesting feature of msBiodat analysis tool is the possibility of selecting proteins by its annotation on Gene Ontology using its Gene Id, ensembl or UniProt codes.ConclusionThe msBiodat analysis tool is a web-based application that allows researchers with any programming experience to deal with efficient database querying advantages. Its versatility and user-friendly interface makes easy to perform fast and accurate data screening by using complex queries. Once the analysis is finished, the result is delivered by e-mail. msBiodat analysis tool is freely available at http://msbiodata.irb.hrElectronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0104-6) contains supplementary material, which is available to authorized users.

Hereditary hemochromatosis (HH) is an iron metabolism disease clinically characterized by excessive iron deposition in parenchymal organs such as liver, heart, pancreas, and joints. It is caused by mutations in at least five different genes. HFE hemochromatosis is the most common type of hemochromatosis, while non-HFE related hemochromatosis are rare cases. Here, we describe six new patients of non-HFE related HH from five different families. Two families (Family 1 and 2) have novel nonsense mutations in the HFE2 gene have novel nonsense mutations (p.Arg63Ter and Asp36ThrfsTer96). Three families have mutations in the TFR2 gene, one case has one previously unreported mutation (Family A—p.Asp680Tyr) and two cases have known pathogenic mutations (Family B and D—p.Trp781Ter and p.Gln672Ter respectively). Clinical, biochemical, and genetic data are discussed in all these cases. These rare cases of non-HFE related hereditary hemochromatosis highlight the importance of an earlier molecular diagnosis in a specialized center to prevent serious clinical complications.

Molecular dynamics (MD) simulations have become a standard tool to correlate the structure and function of biomolecules and significant advances have been made in the study of proteins and their complexes. A major drawback of conventional MD simulations is the difficulty and cost of obtaining converged results, especially when exploring potential energy surfaces containing considerable energy barriers. This limits the wide use of MD calculations to determine the thermodynamic properties of biomolecular processes. Indeed, this is true when considering the conformational entropy of such processes, which is ultimately critical in assessing the simulations' convergence. Alternatively, a wide range of structure-based models (SBMs) has been used in the literature to unravel the basic mechanisms of biomolecular dynamics. These models introduce simplifications that focus on the relevant aspects of the physical process under study. Because of this, SBMs incorporate the need to modify the force field definition and parameters to target specific biophysical simulations. Here we introduce SBMOpenMM, a Python library to build force fields for SBMs, that uses the OpenMM framework to create and run SBM simulations. The code is flexible and user-friendly and profits from the high customizability and performance provided by the OpenMM platform.