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      Engineering cGAS-agonistic oligonucleotides as therapeutics and vaccine adjuvants for cancer immunotherapy

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          Abstract

          Current cancer immunotherapy (e.g., immune checkpoint blockade (ICB)) has only benefited a small subset of patients. Cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) activation holds the potential to improve cancer immunotherapy by eliciting type-I interferon (IFN-I) responses in cancer cells and myeloid cells. Yet, current approaches to this end, mostly by targeting STING, have marginal clinical therapeutic efficacy. Here, we report a cGAS-specific agonistic oligonucleotide, Svg3, as a novel approach to cGAS-STING activation for versatile cancer immunotherapy. Featured with a hairpin structure with consecutive guanosines flanking the stem, Svg3 binds to cGAS and enhances cGAS-Svg3 phase separation to form liquid-like droplets. This results in cGAS activation by Svg3 for robust and dose-dependent IFN-I responses, which outperforms several state-of-the-art STING agonists in murine and human immune cells, and human tumor tissues. Nanocarriers efficiently delivers Svg3 to tissues, cells, and cytosol where cGAS is located. Svg3 reduces tumor immunosuppression and potentiates ICB therapeutic efficacy of multiple syngeneic tumors, in wildtype but neither cGas −/− nor goldenticket Sting −/− mice. Further, as an immunostimulant adjuvant, Svg3 enhances the immunogenicity of peptide antigens to elicit potent T cell responses for robust ICB combination immunotherapy of tumors. Overall, cGAS-agonistic Svg3 is promising for versatile cancer combination immunotherapy.

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          Most cited references38

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                13 July 2023
                : 2023.07.13.548237
                Affiliations
                [1 ]Department of Pharmaceutical Sciences, College of Pharmacy; Biointerfaces Institute. University of Michigan. Ann Arbor, MI 48109, USA
                [2 ]Department of Pharmaceutics and Center for Pharmaceutical Engineering and Sciences, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA
                [3 ]Department of Biostatistics, School of Medicine; Bioinformatics Shared Resource, Massey Cancer Center; Virginia Commonwealth University, Richmond, VA 23298, USA
                Author notes
                [* ] Corresponding author ( guizhiz@ 123456umich.edu )
                Article
                10.1101/2023.07.13.548237
                10369979
                37502970
                48cecf72-573c-4019-ab03-1b821801b71f

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                cgas,oligonucleotide therapeutics,immunostimulant,cancer vaccines,combination immunotherapy

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