Dataset Information

An endogenous positively selecting peptide enhances mature T cell responses and becomes an autoantigen in the absence of microRNA miR-181a.

ABSTRACT: Thymic positive selection is based on the interactions of T cell antigen receptors (TCRs) with self peptide-major histocompatibility complex (MHC) ligands, but the identity of selecting peptides for MHC class II-restricted TCRs and the functional consequences of this peptide specificity are not clear. Here we identify several endogenous self peptides that positively selected the MHC class II-restricted 5C.C7 TCR. The most potent of these also enhanced mature T cell activation, which supports the hypothesis that one function of positive selection is to produce T cells that can use particular self peptide-MHC complexes for activation and/or homeostasis. We also show that inhibiting the microRNA miR-181a resulted in maturation of T cells that overtly reacted toward these erstwhile positively selecting peptides. Therefore, miR-181a helps to guarantee the clonal deletion of particular moderate-affinity clones by modulating the TCR signaling threshold of thymocytes.

SUBMITTER: Ebert PJ

PROVIDER: S-EPMC3762483 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An endogenous positively selecting peptide enhances mature T cell responses and becomes an autoantigen in the absence of microRNA miR-181a.

Ebert Peter J R PJ Jiang Shan S Xie Jianming J Li Qi-Jing QJ Davis Mark M MM

Nature immunology 20091004 11

Thymic positive selection is based on the interactions of T cell antigen receptors (TCRs) with self peptide-major histocompatibility complex (MHC) ligands, but the identity of selecting peptides for MHC class II-restricted TCRs and the functional consequences of this peptide specificity are not clear. Here we identify several endogenous self peptides that positively selected the MHC class II-restricted 5C.C7 TCR. The most potent of these also enhanced mature T cell activation, which supports the ...[more]

PMID: 19801983

Similar Datasets

Project description:BackgroundMicroRNAs are known to be generated from primary transcripts mainly through the sequential cleavages by two enzymes, Drosha and Dicer. The sequence of a mature microRNA, especially the 'seeding sequence', largely determines its binding ability and specificity to target mRNAs. Therefore, methods that predict mature microRNA sequences with high accuracy will benefit the identification and characterization of novel microRNAs and their targets, and contribute to inferring the post-transcriptional regulation network at a genome scale.Methodology/principal findingsWe have developed a method, MiRmat, to predict the mature microRNA sequence. MiRmat is essentially composed of two parts: the prediction of Drosha processing site and the identification of Dicer processing site. Based on the analysis of microRNAs from 12 species, we found that the patterns of free energy profiles are conserved among vertebrate microRNA hairpins. Therefore, we introduced in our method the free energy distribution pattern of the downstream part of pri-microRNA secondary structure and Random Forest algorithm to predict the mature microRNA sequence. Based on the evaluation on an independent test dataset from 10 vertebrates, MiRmat was shown to identify 77.8% of the Drosha processing sites and 92.8% of the Dicer sites within a deviation of 2 nt. In a more stringent evaluation by excluding the microRNAs sharing the same family between the training set and test set, MiRmat kept a rather well performance of 71.9% and 87.2% of the identification rate on the Drosha and Dicer site respectively, which represents the ability to deal with the novel microRNA family. MiRmat outperforms other state-of-the-art methods and has a high degree of efficacy for the prediction of mature microRNA sequences of vertebrates.ConclusionMiRmat was developed for identifying microRNA mature sequence(s) by introducing the free energy distribution of RNA stem-loop structure and the Random Forest algorithm. We prove that MiRmat has better performance than the existing tools and is applicable among vertebrates. MiRmat is freely available at http://mcube.nju.edu.cn/jwang/lab/soft/MiRmat/.

Dataset Information

An endogenous positively selecting peptide enhances mature T cell responses and becomes an autoantigen in the absence of microRNA miR-181a.

Publications

An endogenous positively selecting peptide enhances mature T cell responses and becomes an autoantigen in the absence of microRNA miR-181a.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets