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      The functional role of L-fucose on dendritic cell function and polarization

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          Abstract

          Despite significant advances in the development and refinement of immunotherapies administered to combat cancer over the past decades, a number of barriers continue to limit their efficacy. One significant clinical barrier is the inability to mount initial immune responses towards the tumor. As dendritic cells are central initiators of immune responses in the body, the elucidation of mechanisms that can be therapeutically leveraged to enhance their functions to drive anti-tumor immune responses is urgently needed. Here, we report that the dietary sugar L-fucose can be used to enhance the immunostimulatory activity of dendritic cells (DCs). L-fucose polarizes immature myeloid cells towards specific DC subsets, specifically cDC1 and moDC subsets. In vitro, L-fucose treatment enhances antigen uptake and processing of DCs. Furthermore, our data suggests that L-fucose-treated DCs increase stimulation of T cell populations. Consistent with our functional assays, single-cell RNA sequencing of intratumoral DCs from melanoma- and breast tumor-bearing mice confirmed transcriptional regulation and antigen processing as pathways that are significantly altered by dietary L-fucose. Together, this study provides the first evidence of the ability of L-fucose to bolster DC functionality and provides rational to further investigate how L-fucose can be used to leverage DC function in order to enhance current immunotherapy.

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          Most cited references113

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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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            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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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2601827Role: Role: Role: Role: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2601098Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2603100Role: Role:
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                URI : https://loop.frontiersin.org/people/886772Role: Role: Role:
                URI : https://loop.frontiersin.org/people/760543Role: Role:
                URI : https://loop.frontiersin.org/people/2132662Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1236343Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/816265Role: Role: Role: Role: Role: Role: Role: Role:
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                05 April 2024
                2024
                : 15
                : 1353570
                Affiliations
                [1] 1 Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute , Tampa, FL, United States
                [2] 2 Cancer Biology Ph.D. Program, University of South Florida , Tampa, FL, United States
                [3] 3 Immunology Program, H. Lee Moffitt Cancer Center & Research Institute , Tampa, FL, United States
                [4] 4 Molecular Medicine Program, H. Lee Moffitt Cancer Center & Research Institute , Tampa, FL, United States
                [5] 5 Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center & Research Institute , Tampa, FL, United States
                [6] 6 Department of Molecular Medicine, University of South Florida , Tampa, FL, United States
                [7] 7 Molecular Genomics Core, H. Lee Moffitt Cancer Center & Research Institute , Tampa, FL, United States
                [8] 8 Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute , Tampa, FL, United States
                Author notes

                Edited by: Sandra J. Van Vliet, VU Amsterdam, Netherlands

                Reviewed by: Sjoerd Schetters, Flanders Institute for Biotechnology, Belgium

                Efthalia Zervoudi, Cardiff University, United Kingdom

                *Correspondence: Eric K. Lau, Eric.Lau@ 123456Moffitt.org
                Article
                10.3389/fimmu.2024.1353570
                11026564
                38646527
                f5c1bced-c477-4aad-a686-49137250aebc
                Copyright © 2024 Burton, Bitaraf, Snyder, Zhang, Yoder, Avram, Du, Yu and Lau

                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
                : 10 December 2023
                : 21 February 2024
                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 113, Pages: 16, Words: 9006
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work has been supported in part by the Moffitt/USF Vivarium, Flow Cytometry, Molecular Genomics, and Biostatistics and Bioinformatics Shared Resources at the H. Lee Moffitt Cancer Center & Research Institute, an NCI designated Comprehensive Cancer Center (P30-CA076292). Support from a Florida Department of Health Bankhead Coley Grant 23B11 (to EL) is gratefully acknowledged.
                Categories
                Immunology
                Original Research
                Custom metadata
                Antigen Presenting Cell Biology

                Immunology
                dendritic cells,l-fucose,myeloid cells,antigen presentation,tumor immune microenvironment

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