Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool

Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope binding affinity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC comparable or superior to state-of-the-art methods, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system's predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section

Shannon entropy as a reliable score to diagnose human fibroelastic degenerative mitral chords: a micro-ct ex-vivo study

This paper is aimed at identifying by means of micro-CT the microstructural differences between normal and degenerative mitral marginal chordae tendineae. The control group is composed of 21 normal chords excised from 14 normal mitral valves from heart transplant recipients. The experimental group comprises 22 degenerative fibroelastic chords obtained at surgery from 11 pathological valves after mitral repair or replacement. In the control group the superficial endothelial cells and spongiosa layer remained intact, covering the wavy core collagen. In contrast, in the experimental group the collagen fibers were arranged as straightened thick bundles in a parallel configuration. 100 cross-sections were examined by micro-CT from each chord. Each image was randomized through the K-means machine learning algorithm and then, the global and local Shannon entropies were obtained. The optimum number of clusters, K, was estimated to maximize the differences between normal and degenerative chords in global and local Shannon entropy; the p-value after a nested ANOVA test was chosen as the parameter to be minimized. Optimum results were obtained with global Shannon entropy and 2≤K≤7, providing p < 0.01; for K=3, p = 2.86⋅10-³. These findings open the door to novel perioperative diagnostic methods in order to avoid or reduce postoperative mitral valve regurgitation recurrences.This work is supported by the “Ministerio de Economía, Industria y Competitividad” (MINECO) and the “Instituto de Salud Carlos III” (ISCIII) of Spain, through projects INTRACARDIO (DTS17/00056) and CIBER-BBN (co-financed by FEDER funds) and IDIVAL under project DiCuTen (INNVAL16/02). The technical contributions from the members of the DICUTEN and the financial contribution from the IDIVAL are gratefully acknowledged. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This study was approved by the Ethical Committee of Clinical Research of Cantabria – IDIVAL (Acta 02/2018)

Identification of an interactome network between lncRNAs and miRNAs in thyroid cancer reveals SPTY2D1-AS1 as a new tumor suppressor

Thyroid cancer is the most common primary endocrine malignancy in adults and its incidence is rapidly increasing. Long non-coding RNAs (lncRNAs), generally defined as RNA molecules longer than 200 nucleotides with no protein-encoding capacity, are highly tissue-specific molecules that serve important roles in gene regulation through a variety of different mechanisms, including acting as competing endogenous RNAs (ceRNAs) that ‘sponge’ microRNAs (miRNAs). In the present study, using an integrated approach through RNA-sequencing of paired thyroid tumor and non-tumor samples, we have identified an interactome network between lncRNAs and miRNAs and examined the functional consequences in vitro and in vivo of one of such interactions. We have identified a likely operative post-transcriptional regulatory network in which the downregulated lncRNA, SPTY2D1-AS1, is predicted to target the most abundant and upregulated miRNAs in thyroid cancer, particularly miR-221, a well-known oncomiRNA in cancer. Indeed, SPTY2D1-AS1 functions as a potent tumor suppressor in vitro and in vivo, it is downregulated in the most advanced stages of human thyroid cancer, and it seems to block the processing of the primary form of miR-221. Overall, our results link SPTY2D1-AS1 to thyroid cancer progression and highlight the potential use of this lncRNA as a therapeutic target of thyroid cancer.This work was supported by grants SAF2016-75531-R and PID2019-105303RB-I00/AEI/10.13039/501100011033 from Ministerio de Ciencia e Innovación (MICIN); Fondo Europeo de Desarrollo Regional, B2017/BMD-3724 from Comunidad de Madrid; GCB14142311CRES from Asociación Española contra el Cáncer (AECC); and PI14/01980 from Instituto de Salud Carlos III (Spain). JR-M holds a FPU fellowship from MECD (Spain

3DBionotes COVID-19 Edition

https://3dbionotes.cnb.csic.es/ws/covid19The web platform 3DBionotes-WS integrates multiple Web Services and an interactive Web Viewer to provide a unified environment in which biological annotations can be analyzed in their structural context. Since the COVID-19 outbreak, new structural data from many viral proteins have been provided at a very fast pace. This effort includes many cryogenic Electron Microscopy (cryo-EM) studies, together with more traditional ones (X-rays, NMR), using several modeling approaches and complemented with structural predictions. At the same time, a plethora of new genomics and interactomics information (including fragment screening and structure-based virtual screening efforts) have been made available from different servers. In this context we have developed 3DBionotes-COVID-19 as an answer to: (1) The need to explore multi-omics data in a unified context with a special focus on structural information and (2) the drive to incorporate quality measurements, especially in the form of advanced validation metrics for cryogenic Electron Microscopy.We acknowledge financial support from: CSIC (PIE/COVID-19 number 202020E079), the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Science and Innovation through projects (SEV 2017-0712, FPU-2015/264, PID2019-104757RB-I00 / AEI / 10.13039/501100011033), the Instituto de Salud Carlos III: PT17/0009/0010 (ISCIII-SGEFI / ERDF-) and the European Union and Horizon 2020 through grant: CORBEL (INFRADEV-01-2014- 1, Proposal 654248) and EOSC Life (INFRAEOSC-04-2018, Proposal: 824087). This work was supported by Instruct-ULTRA (Grant 731005), an EU H2020 project to further develop the services of Instruct-ERIC. Contributions from the Coronavirus Structural Task Force were supported by the German Federal Ministry of Education and Research [grant no. 05K19WWA] and Deutsche Forschungsgemeinschaft [project TH2135/2-1]. The authors acknowledge the support and the use of resources of Instruct, a Landmark ESFRI project.Peer reviewe