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      Ablation of NLRP3 inflammasome rewires MDSC function and promotes tumor regression

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          Abstract

          Myeloid-derived suppressor cells (MDSCs) are myeloid precursors that exert potent immunosuppressive properties in cancer. Despite the extensive knowledge on mechanisms implicated in mobilization, recruitment, and function of MDSCs, their therapeutic targeting remains an unmet need in cancer immunotherapy, suggesting that unappreciated mechanisms of MDSC-mediated suppression exist. Herein, we demonstrate an important role of NLRP3 inflammasome in the functional properties of MDSCs in tumor-bearing hosts. Specifically, Nlrp3-deficient mice exhibited reduced tumor growth compared to wild-type animals and induction of robust anti-tumor immunity, accompanied by re-wiring of the MDSC compartment. Interestingly, both monocytic (M-MDSCs) and granulocytic (G-MDSCs) subsets from Nlrp3 -/- mice displayed impaired suppressive activity and demonstrated significant transcriptomic alterations supporting the loss-of-function and associated with metabolic re-programming. Finally, therapeutic targeting of NLRP3 inhibited tumor development and re-programmed the MDSC compartment. These findings propose that targeting NLRP3 in MDSCs could overcome tumor-induced tolerance and may provide new checkpoints of cancer immunotherapy.

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          Most cited references67

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          HISAT: a fast spliced aligner with low memory requirements.

          HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                10 August 2022
                2022
                : 13
                : 889075
                Affiliations
                [1] 1 Laboratory of Immune Regulation and Tolerance, Division of Basic Sciences, University of Crete Medical School , Heraklion, Greece
                [2] 2 Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology , Heraklion, Greece
                [3] 3 Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation Academy of Athens , Athens, Greece
                [4] 4 JJP Biologics , Warsaw, Poland
                [5] 5 Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research , Braunschweig, Germany
                [6] 6 Institute for Clinical Chemistry and Laboratory Medicine, Faculty of Medicine, Technische Universität Dresden , Dresden, Germany
                Author notes

                Edited by: Darya Alizadeh, City of Hope, United States

                Reviewed by: Hiroaki Shime, Nagoya City University, Japan; Sabrin Albeituni, St. Jude Children’s Research Hospital, United States

                *Correspondence: Panayotis Verginis, pverginis@ 123456uoc.gr

                This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2022.889075
                9407017
                36032139
                4be6544a-9833-4cb0-9559-3358bce9eb39
                Copyright © 2022 Papafragkos, Grigoriou, Boon, Kloetgen, Hatzioannou and Verginis

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 March 2022
                : 07 July 2022
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 67, Pages: 19, Words: 9288
                Categories
                Immunology
                Original Research

                Immunology
                myeloid-derived suppressor cells (mdscs),inflammasome,cancer immunotherapy,tumor immunity,tumor resistance

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