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T lymphocytes need less than 3 min to discriminate between peptide MHCs with similar TCR-binding parameters
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. T lymphocytes need to detect rare cognate foreign peptides among numerous foreign and self-peptides. This discrimination seems to be based on the kinetics of TCRs binding to their peptide-MHC (pMHC) ligands, but there is little direct information on the minimum time required for processing elementary signaling events and deciding to initiate activation. Here, we used interference reflection microscopy to study the early interaction between transfected human Jurkat T cells expressing the 1G4 TCR and surfaces coated with five different pMHC ligands of 1G4. The pMHC concentration required for inducing 50% maximal IFN-γ production by T cells, and 1G4-pMHC dissociation rates measured in soluble phase or on surface-bound molecules, displayed six- to sevenfold variation among pMHCs. When T cells were dropped onto pMHC-coated surfaces, rapid spreading occurred after a 2-min lag. The initial spreading rate measured during the first 45 s, and the contact area, were strongly dependent on the encountered TCR ligand. However, the lag duration did not significantly depend on encountered ligand. In addition, spreading appeared to be an all-or-none process, and the fraction of spreading cells was tightly correlated to the spreading rate and spreading area. Thus, T cells can discriminate between fairly similar TCR ligands within 2 min.
Synthesis of truncated analogues of the iNKT cell agonist, α-galactosyl ceramide (KRN7000), and their biological evaluation
Stimulation of iNKT cells by α-galactosyl ceramide (α-GalCer), also known as KRN7000, and its truncated analogue OCH induces both Th1- and Th2-cytokines, with OCH inducing a Th2-cytokine bias. Skewing of the iNKT cells' response towards either a Th1- or Th2-cytokine profile offers potential therapeutic benefits. The length of both the acyl and the sphingosine chains in α-galactosyl ceramides is known to influence the cytokine release profile. We have synthesized analogues of α-GalCer with truncated sphingosine chains for biological evaluation, with particular emphasis on the Th1/Th2 distribution. Starting from a common precursor, d-lyxose, the sphingosine derivatives were synthesised via a straightforward Wittig condensation. © 2010 Elsevier Ltd. All rights reserved.
Extensive alanine substitutions increase binding affinity of an influenza nucleoprotein peptide to HLA-Aw68 and do not abrogate peptide-specific CTL recognition.
Class I MHC molecules bind peptides in the endoplasmic reticulum and present them at the cell surface to circulating CD8+ T cells for analysis. We have examined binding of peptides and stabilization of HLA-Aw68 class I molecules using synthetic peptide variants of an influenza virus nucleoprotein peptide, NP91-99 (KTGGPIYKR). We have demonstrated that insertion of increasing numbers of alanines in the center of the peptide (between P and I), to increase a natural bulging out of the peptide-binding cleft, results in a large decrease in thermal stability. Although there is a great decrease in the t 1/2 of the MHC/peptide complex for NP-1A compared with NP91-99, a T cell line, stimulated by NP91-99, recognizes NP-1A efficiently. Peptide variants with three or more alanines do not show saturable binding to HLA-Aw68 and also are not recognized by the T cell line. Thermal studies show that polyalanine peptides with minimal anchors and nearly all TCR contact residues exchanged stabilized HLA-Aw68 to high temperatures. Additionally, some of these polyalanine peptides are recognized by T cell lines generated against NP91-99. Analysis of the peptide sequences show that the stabilization effects are not due to the hydrophobicity of the peptide. These data suggest that the strength of binding of peptides to HLA-Aw68 is not only dictated by the presence of anchor residues but also by the lack of unfavorable contacts between the peptide ligand and class I MHC-binding cleft.
Different developmental histories of beta-cells generate functional and proliferative heterogeneity during islet growth.
The proliferative and functional heterogeneity among seemingly uniform cells is a universal phenomenon. Identifying the underlying factors requires single-cell analysis of function and proliferation. Here we show that the pancreatic beta-cells in zebrafish exhibit different growth-promoting and functional properties, which in part reflect differences in the time elapsed since birth of the cells. Calcium imaging shows that the beta-cells in the embryonic islet become functional during early zebrafish development. At later stages, younger beta-cells join the islet following differentiation from post-embryonic progenitors. Notably, the older and younger beta-cells occupy different regions within the islet, which generates topological asymmetries in glucose responsiveness and proliferation. Specifically, the older beta-cells exhibit robust glucose responsiveness, whereas younger beta-cells are more proliferative but less functional. As the islet approaches its mature state, heterogeneity diminishes and beta-cells synchronize function and proliferation. Our work illustrates a dynamic model of heterogeneity based on evolving proliferative and functional beta-cell states.Βeta-cells have recently been shown to be heterogeneous with regard to morphology and function. Here, the authors show that β-cells in zebrafish switch from proliferative to functional states with increasing time since β-cell birth, leading to functional and proliferative heterogeneity.
Asterias: a parallelized web-based suite for the analysis of expression and aCGH data.
The analysis of expression and CGH arrays plays a central role in the study of complex diseases, especially cancer, including finding markers for early diagnosis and prognosis, choosing an optimal therapy, or increasing our understanding of cancer development and metastasis. Asterias (http://www.asterias.info) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI) and run on a server with 60 CPUs for computation; compared to a desktop or server-based but not parallelized application, parallelization provides speed ups of factors up to 50. Most of our applications allow the user to obtain additional information for user-selected genes (chromosomal location, PubMed ids, Gene Ontology terms, etc.) by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data (DNMAD); converting between different types of gene/clone and protein identifiers (IDconverter/IDClight); filtering and imputation (preP); finding differentially expressed genes related to patient class and survival data (Pomelo II); searching for models of class prediction (Tnasas); using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity (GeneSrF); searching for molecular signatures and predictive genes with survival data (SignS); detecting regions of genomic DNA gain or loss (ADaCGH). The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.
Network inference in matrix-variate Gaussian models with non-independent noise
Inferring a graphical model or network from observational data from a large number of variables is a well studied problem in machine learning and computational statistics. In this paper we consider a version of this problem that is relevant to the analysis of multiple phenotypes collected in genetic studies. In such datasets we expect correlations between phenotypes and between individuals. We model observations as a sum of two matrix normal variates such that the joint covariance function is a sum of Kronecker products. This model, which generalizes the Graphical Lasso, assumes observations are correlated due to known genetic relationships and corrupted with non-independent noise. We have developed a computationally efficient EM algorithm to fit this model. On simulated datasets we illustrate substantially improved performance in network reconstruction by allowing for a general noise distribution.