Motivation Predicting the binding between T-cell receptor (TCR) and peptide presented by HLA molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed. Results We collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of > 3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3β sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with AUC (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy. Availability epiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR). Supplementary information Supplementary data are available at Bioinformatics online.

No abstract

Transient receptor potential vanilloid 4 (TRPV4) is a non-selective mechanosensitive ion channel expressed by various macrophage populations. Recent reports have characterized the role of TRPV4 in shaping the activity and phenotype of macrophages to influence the innate immune response to pathogen exposure and inflammation. TRPV4 has been studied extensively in the context of inflammation and inflammatory pain. Although TRPV4 activity has been generally described as pro-inflammatory, emerging evidence suggests a more complex role where this channel may also contribute to anti-inflammatory activities. However, detailed understanding of how TRPV4 may influence the initiation, maintenance, and resolution of inflammatory disease remains limited. This review highlights recent insights into the cellular processes through which TRPV4 contributes to pathological conditions and immune processes, with a focus on macrophage biology. The potential use of high-throughput and omics methods as an unbiased approach for studying the functional outcomes of TRPV4 activation is also discussed.

In Xenopus laevis eight tRNA genes are located in a 3.18 kb tandemly repeated unit. There are 150 copies of the unit at a single locus near the long arm telomere of one of the acrocentric chromosomes in the 14-17 group. Two additional classes of tRNA gene-containing repeats have been isolated (defined by clones p3.1 and p3.2) that have structures related to that of the 3.18 kb unit. Using in situ hybridization at the electron microscopic level, the p3.2 repeats are found clustered at a single locus in the subtelomeric region on one of the submetacentric chromosomes, whereas the p3.1 repeats are clustered at a locus indistinguishable from that containing the 3.18 kb repeats. This suggests that these tDNA tandem repeats can diverge in sequence from each other without being at distantly separated loci.