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      Systems-based approaches to study immunometabolism

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          Abstract

          Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

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          Most cited references135

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease.

            Alzheimer's disease (AD) is a detrimental neurodegenerative disease with no effective treatments. Due to cellular heterogeneity, defining the roles of immune cell subsets in AD onset and progression has been challenging. Using transcriptional single-cell sorting, we comprehensively map all immune populations in wild-type and AD-transgenic (Tg-AD) mouse brains. We describe a novel microglia type associated with neurodegenerative diseases (DAM) and identify markers, spatial localization, and pathways associated with these cells. Immunohistochemical staining of mice and human brain slices shows DAM with intracellular/phagocytic Aβ particles. Single-cell analysis of DAM in Tg-AD and triggering receptor expressed on myeloid cells 2 (Trem2)(-/-) Tg-AD reveals that the DAM program is activated in a two-step process. Activation is initiated in a Trem2-independent manner that involves downregulation of microglia checkpoints, followed by activation of a Trem2-dependent program. This unique microglia-type has the potential to restrict neurodegeneration, which may have important implications for future treatment of AD and other neurodegenerative diseases. VIDEO ABSTRACT.
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              The Warburg Effect: How Does it Benefit Cancer Cells?

              Cancer cells rewire their metabolism to promote growth, survival, proliferation, and long-term maintenance. The common feature of this altered metabolism is the increased glucose uptake and fermentation of glucose to lactate. This phenomenon is observed even in the presence of completely functioning mitochondria and, together, is known as the 'Warburg Effect'. The Warburg Effect has been documented for over 90 years and extensively studied over the past 10 years, with thousands of papers reporting to have established either its causes or its functions. Despite this intense interest, the function of the Warburg Effect remains unclear. Here, we analyze several proposed explanations for the function of Warburg Effect, emphasize their rationale, and discuss their controversies.
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                Author and article information

                Contributors
                vkuchroo@evergrande.hms.harvard.edu
                Journal
                Cell Mol Immunol
                Cell Mol Immunol
                Cellular and Molecular Immunology
                Nature Publishing Group UK (London )
                1672-7681
                2042-0226
                4 February 2022
                4 February 2022
                March 2022
                : 19
                : 3
                : 409-420
                Affiliations
                [1 ]GRID grid.38142.3c, ISNI 000000041936754X, Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA 02115 USA
                [2 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Broad Institute of MIT and Harvard, ; Cambridge, MA 02141 USA
                [3 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Electrical Engineering and Computer Science, , University of California, ; Berkeley, CA 94720 USA
                [4 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Center for Computational Biology, , University of California, ; Berkeley, CA 94720 USA
                Author information
                http://orcid.org/0000-0001-8093-5540
                Article
                783
                10.1038/s41423-021-00783-9
                8891302
                35121805
                e1bfacc1-3375-4fb6-8ef7-3623fd92e35e
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 August 2021
                : 17 September 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100005440, U.S. Department of Health & Human Services | NIH | Center for Scientific Review (NIH Center for Scientific Review);
                Award ID: 1R01AI139536-01
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000025, U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH);
                Award ID: NIH5U19MH114821
                Award ID: NIH5U19MH114821
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
                Categories
                Review Article
                Custom metadata
                © The Author(s), under exclusive licence to CSI and USTC 2022

                Immunology
                immunometabolism,metabolic techniques,gsmm,metabolic modeling,systems biology,immunology,cell biology

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