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      TCF-1 limits intraepithelial lymphocyte antitumor immunity in colorectal carcinoma

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Intraepithelial lymphocytes (IELs), including αβ and γδ T cells (T-IELs), constantly survey and play a critical role in maintaining the gastrointestinal epithelium. We show that cytotoxic molecules important for defense against cancer were highly expressed by T-IELs in the small intestine. In contrast, abundance of colonic T-IELs was dependent on the microbiome and displayed higher expression of TCF-1/ TCF7 and a reduced effector and cytotoxic profile, including low expression of granzymes. Targeted deletion of TCF-1 in γδ T-IELs induced a distinct effector profile and reduced colon tumor formation in mice. In addition, TCF-1 expression was significantly reduced in γδ T-IELs present in human colorectal cancers (CRCs) compared with normal healthy colon, which strongly correlated with an enhanced γδ T-IEL effector phenotype and improved patient survival. Our work identifies TCF-1 as a colon-specific T-IEL transcriptional regulator that could inform new immunotherapy strategies to treat CRC.

          Abstract

          Intraepithelial T lymphocytes in the colon are distinct, and TCF-1 acts to suppress their antitumor immunity.

          Editor’s summary

          The colon is the most common site of gastrointestinal (GI) cancer, as well as functionally and immunologically distinct from other regions of the GI tract. Intraepithelial T lymphocytes (T-IELs) contribute to host defense by surveilling the epithelium, with specific subsets of gamma-delta (γδ) T-IELs providing protection against colorectal cancer. Yakou et al . used single cell transcriptomics to profile the composition of murine T-IELs from different regions of the GI tract, finding that colon IELs have a suppressed cytotoxic phenotype and express high levels of the transcription factor T cell factor 1 (TCF-1). TCF-1 was required for maintenance of multiple colon T-IEL subtypes yet also suppressed antitumor effector functions among γδ T-IELs. These findings identify distinct features of colon IELs that may suppress immunity against colorectal tumors. —Claire Olingy

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          Most cited references69

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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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                Journal
                Science Immunology
                Sci. Immunol.
                American Association for the Advancement of Science (AAAS)
                2470-9468
                October 13 2023
                October 13 2023
                : 8
                : 88
                Affiliations
                [1 ]Olivia Newton-John Cancer Research Institute and La Trobe University School of Cancer Medicine, Heidelberg, Victoria 3084, Australia.
                [2 ]Centre for Molecular Therapeutics, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, Queensland, Australia.
                [3 ]Center for Discovery and Innovation, Hackensack University Medical Center, Nutley, NJ, USA.
                [4 ]New Jersey Veterans Affairs Health Care System, East Orange, NJ, USA.
                [5 ]University of Queensland Frazer Institute, Faculty of Medicine, University of Queensland, Woolloongabba, Queensland 4102, Australia.
                [6 ]Centre for Cardiovascular Biology and Disease Research; Department of Microbiology, Anatomy, Physiology and Pharmacology; and School of Agriculture, Biomedicine, and Environment, La Trobe University, Bundoora, Victoria, Australia.
                [7 ]Department of Anatomical Pathology, Austin Health, Heidelberg, Victoria, Australia.
                Article
                10.1126/sciimmunol.adf2163
                37801516
                b606ea64-783a-477c-b01c-d7b304452b1d
                © 2023

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