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Recent advances in γδ T cell biology: New ligands, new functions, and new translational perspectives

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889197842 Year: Pages: 269 DOI: 10.3389/978-2-88919-784-2 Language: English
Publisher: Frontiers Media SA
Subject: Medicine (General) --- Allergy and Immunology
Added to DOAB on : 2016-04-07 11:22:02
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Abstract

Gamma/delta (γδ) T-cells are a small subset of T-lymphocytes in the peripheral circulation but constitute a major T-cell population at other anatomical localizations such as the epithelial tissues. In contrast to conventional a/ß T-cells, the available number of germline genes coding for T-cell receptor (TCR) variable elements of γδ T-cells is very small. Moreover, there is a prefential localization of γδ T-cells expressing given Vgamma and Vdelta genes in certain tissues. In humans, γδ T-cells expressing the Vg9Vd2-encoded TCR account for anywhere between 50 and >95% of peripheral blood γδ T-cells, whereas cells expressing non-Vd2 genes dominate in mucosal tissues. In mice, there is an ordered appearance of γδ T-cell „waves“ during embryonic development, resulting in preferential localization of γδ T-cells expressing distinct VgammaVdelta genes in the skin, the reproductive organs, or gut epithelia. The major function of γδ T-cells resides in local immunosurveillance and immune defense against infection and malignancy. This is supported by the identification of ligands that are selectively recognized by the γδ TCR. As an example, human Vgamma9Vdelta2 T-cells recognize phosphorylated metabolites („phosphoantigens“) that are secreted by many pathogens but can also be overproduced by tumor cells, providing a basis for a role of these γδ T-cells in both anti-infective and anti-tumor immunity. Similarly, the recognition of endothelial protein C receptor by human non-Vdelta2 γδ T-cells has recently been identified to provide a link for the role for such γδ T-cells in immunity against epithelial tumor cells and cytomegalovirus-infected endothelial cells. In addition to „classical“ functions such as cytokine production and cytotoxicity, recent studies suggest that subsets of γδ T-cells can exert additional functions such as regulatory activity and – quite surpisingly – „professional“ antigen-presenting capacity. It is currently not well known how this tremendous extent of functional plasticity is regulated and what is the extent of γδ TCR ligand diversity. Due to their non-MHC-restricted recognition of unusual stress-associated ligands, γδ T-cells have raised great interest as to their potential translational application in cell-based immunotherapy. Topics of this Research Focus include: Molecular insights into the activation and differentiation requirements of γδ T-cells, role of pyrophosphates and butyrophilin molecules for the activation of human γδ T-cells, role of γδ T-cells in tumor immunity and in other infectious and non-infectious diseases, and many others. We are most grateful to all colleagues who agreed to write a manuscript. Thanks to their contributions, this E-book presents an up-to-date overview on many facets of the still exciting γδ T-cells.

Application of Bioinformatics in Cancers

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ISBN: 9783039217885 / 9783039217892 Year: Pages: 418 DOI: 10.3390/books978-3-03921-789-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Biotechnology
Added to DOAB on : 2019-12-09 11:49:16
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Abstract

This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.

Keywords

comorbidity score --- mortality --- locoregionally advanced --- HNSCC --- curative surgery --- traditional Chinese medicine --- health strengthening herb --- cancer treatment --- network pharmacology --- network target --- high-throughput analysis --- brain metastases --- colorectal cancer --- KRAS mutation --- PD-L1 --- tumor infiltrating lymphocytes --- drug resistance --- gefitinib --- erlotinib --- biostatistics --- bioinformatics --- Bufadienolide-like chemicals --- molecular mechanism --- anti-cancer --- bioinformatics --- cancer --- brain --- pathophysiology --- imaging --- machine learning --- extreme learning --- deep learning --- neurological disorders --- pancreatic cancer --- TCGA --- curation --- DNA --- RNA --- protein --- single-biomarkers --- multiple-biomarkers --- cancer-related pathways --- colorectal cancer --- DNA sequence profile --- Monte Carlo --- mixture of normal distributions --- somatic mutation --- tumor --- mutable motif --- activation induced deaminase --- AID/APOBEC --- transcriptional signatures --- copy number variation --- copy number aberration --- TCGA mining --- cancer CRISPR --- firehose --- gene signature extraction --- gene loss biomarkers --- gene inactivation biomarkers --- biomarker discovery --- chemotherapy --- microarray --- ovarian cancer --- predictive model --- machine learning --- overall survival --- observed survival interval --- skin cutaneous melanoma --- The Cancer Genome Atlas --- omics --- breast cancer prognosis --- artificial intelligence --- machine learning --- decision support systems --- cancer prognosis --- independent prognostic power --- omics profiles --- histopathological imaging features --- cancer --- intratumor heterogeneity --- genomic instability --- epigenetics --- mitochondrial metabolism --- miRNAs --- cancer biomarkers --- breast cancer detection --- machine learning --- feature selection --- classification --- denoising autoencoders --- breast cancer --- feature extraction and interpretation --- concatenated deep feature --- cancer modeling --- interaction --- histopathological imaging --- clinical/environmental factors --- oral cancer --- miRNA --- bioinformatics --- datasets --- biomarkers --- TCGA --- GEO DataSets --- hormone sensitive cancers --- breast cancer --- StAR --- estrogen --- steroidogenic enzymes --- hTERT --- telomerase --- telomeres --- alternative splicing --- network analysis --- hierarchical clustering analysis --- differential gene expression analysis --- cancer biomarker --- diseases genes --- variable selection --- false discovery rate --- knockoffs --- bioinformatics --- copy number variation --- cell-free DNA --- methylation --- mutation --- next generation sequencing --- self-organizing map --- head and neck cancer --- treatment de-escalation --- HP --- molecular subtypes --- tumor microenvironment --- Bioinformatics tool --- R package --- machine learning --- meta-analysis --- biomarker signature --- gene expression analysis --- survival analysis --- functional analysis --- bioinformatics --- machine learning --- artificial intelligence --- Network Analysis --- single-cell sequencing --- circulating tumor DNA (ctDNA) --- Neoantigen Prediction --- precision medicine --- Computational Immunology

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