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      NRF2-dependent regulation of the prostacyclin receptor PTGIR drives CD8 T cell exhaustion

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          Abstract

          The progressive decline of CD8 T cell effector function—also known as terminal exhaustion—is a major contributor to immune evasion in cancer. Yet, the molecular mechanisms that drive CD8 T cell dysfunction remain poorly understood. Here, we report that the Kelch-like ECH-associated protein 1 (KEAP1)-Nuclear factor erythroid 2-related factor 2 (NRF2) signaling axis, which mediates cellular adaptations to oxidative stress, directly regulates CD8 T cell exhaustion. Transcriptional profiling of dysfunctional CD8 T cells from chronic infection and cancer reveals enrichment of NRF2 activity in terminally exhausted (Tex term) CD8 T cells. Increasing NRF2 activity in CD8 T cells (via conditional deletion of KEAP1) promotes increased glutathione production and antioxidant defense yet accelerates the development of terminally exhausted (PD-1 +TIM-3 +) CD8 T cells in response to chronic infection or tumor challenge. Mechanistically, we identify PTGIR, a receptor for the circulating eicosanoid prostacyclin, as an NRF2-regulated protein that promotes CD8 T cell dysfunction. Silencing PTGIR expression restores the anti-tumor function of KEAP1-deficient T cells. Moreover, lowering PTGIR expression in CD8 T cells both reduces terminal exhaustion and enhances T cell effector responses (i.e. IFN-γ and granzyme production) to chronic infection and cancer. Together, these results establish the KEAP1-NRF2 axis as a metabolic sensor linking oxidative stress to CD8 T cell dysfunction and identify the prostacyclin receptor PTGIR as an NRF2-regulated immune checkpoint that regulates CD8 T cell fate decisions between effector and exhausted states.

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          The KEAP1-NRF2 pathway is hyperactivated in terminally exhausted CD8 T cells and drives T cell dysfunction via transcriptional regulation of the prostacyclin receptor, Ptgir.

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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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            Hallmarks of Cancer: The Next Generation

            The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: InvestigationRole: Funding acquisitionRole: Writing – original draftRole: Writing – review & editing
                Role: Investigation
                Role: Investigation
                Role: Writing – original draft
                Role: VisualizationRole: Methodology
                Role: Investigation
                Role: Investigation
                Role: Project administration
                Role: Methodology
                Role: Methodology
                Role: Methodology
                Role: VisualizationRole: Methodology
                Role: MethodologyRole: Writing – original draft
                Role: MethodologyRole: Writing – original draft
                Role: Conceptualization
                Role: Supervision
                Role: Supervision
                Role: ConceptualizationRole: MethodologyRole: Funding acquisitionRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                2692-8205
                28 June 2024
                : 2024.06.23.600279
                Affiliations
                [1 ]Department of Metabolism and Nutritional Programming, Van Andel Institute, Grand Rapids, MI, USA.
                [2 ]Metabolism and Nutrition (MeNu) Program, Van Andel Institute, Grand Rapids, MI, USA.
                [3 ]Bioinformatics and Biostatistics Core Facility, Van Andel Institute, Grand Rapids, MI, USA.
                [4 ]Mass Spectrometry Core Facility, Van Andel Institute, Grand Rapids, MI, USA.
                [5 ]Department of Immunology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
                [6 ]Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
                [7 ]Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
                Author notes
                [* ]Corresponding author: Russell G. Jones, russell.jones@ 123456vai.org
                Author information
                http://orcid.org/0000-0002-2468-5986
                Article
                10.1101/2024.06.23.600279
                11230227
                38979360
                cbd2cbe5-9b52-4cf2-a6c9-ef7140c32c49

                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.

                History
                Funding
                Funded by: National Institute of Allergy and Infectious Diseases
                Award ID: R21AI153997
                Award ID: R01AI165722
                Funded by: Paul G. Allen Frontiers Group Distinguished Investigator Program, Chan Zuckerberg Initiative
                Funded by: VAI Metabolism & Nutrition (MeNu) Program Pathway-to-Independence (P2i) Award
                Funded by: Canadian Institutes of Health Research (CIHR) Fellowship
                Award ID: MFE-181903
                Funded by: VAI MeNu Program P2i Award
                Funded by: Fonds de recherche du Québec-Santé (FRQS) Postdoctoral Fellowship
                Award ID: 0000289124
                Funded by: CIHR Fellowship
                Award ID: MFE-403514
                Funded by: NIH
                Award ID: AI154450
                Award ID: AG056524
                Award ID: AI158294
                Funded by: CPRIT
                Award ID: RR210035
                Funded by: V Scholar Award, an AFAR Grant for Junior Faculty, a Clinic & Laboratory Integration Program Grant (Cancer Research Institute)
                Funded by: startup funds from UTSW
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