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      Kynurenic acid may underlie sex-specific immune responses to COVID-19

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          Abstract

          Compared to females, male COVID-19 patients have more kynurenic acid, which may underlie their poorer immune response.

          Sex-specific metabolism and COVID-19

          Males and females have different immune responses to SARS-CoV-2 infection, with male sex being a risk factor for mortality, particularly among older individuals. Cai et al. performed metabolomics analysis of serum from COVID-19 patients and uninfected health care workers and identified 17 metabolites that were associated with the disease. However, in male COVID-19 patients only, the amount of the tryptophan metabolite kynurenic acid (KA) correlated with age, inflammation, and disease outcome. KA inhibits glutamate release, and glutamate abundance was reduced in patients who deteriorated. Together, these findings indicate that KA is associated with sex-specific differences in immune responses to COVID-19, suggesting that it might be targeted in male patients.

          Abstract

          Coronavirus disease 2019 (COVID-19) has poorer clinical outcomes in males than in females, and immune responses underlie these sex-related differences. Because immune responses are, in part, regulated by metabolites, we examined the serum metabolomes of COVID-19 patients. In male patients, kynurenic acid (KA) and a high KA–to–kynurenine (K) ratio (KA:K) positively correlated with age and with inflammatory cytokines and chemokines and negatively correlated with T cell responses. Males that clinically deteriorated had a higher KA:K than those that stabilized. KA inhibits glutamate release, and glutamate abundance was lower in patients that clinically deteriorated and correlated with immune responses. Analysis of data from the Genotype-Tissue Expression (GTEx) project revealed that the expression of the gene encoding the enzyme that produces KA, kynurenine aminotransferase, correlated with cytokine abundance and activation of immune responses in older males. This study reveals that KA has a sex-specific link to immune responses and clinical outcomes in COVID-19, suggesting a positive feedback between metabolites and immune responses in males.

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          Most cited references42

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          Longitudinal analyses reveal immunological misfiring in severe COVID-19

          Recent studies have provided insights into the pathogenesis of coronavirus disease 2019 (COVID-19) 1–4 . However, the longitudinal immunological correlates of disease outcome remain unclear. Here we serially analysed immune responses in 113 patients with moderate or severe COVID-19. Immune profiling revealed an overall increase in innate cell lineages, with a concomitant reduction in T cell number. An early elevation in cytokine levels was associated with worse disease outcomes. Following an early increase in cytokines, patients with moderate COVID-19 displayed a progressive reduction in type 1 (antiviral) and type 3 (antifungal) responses. By contrast, patients with severe COVID-19 maintained these elevated responses throughout the course of the disease. Moreover, severe COVID-19 was accompanied by an increase in multiple type 2 (anti-helminths) effectors, including interleukin-5 (IL-5), IL-13, immunoglobulin E and eosinophils. Unsupervised clustering analysis identified four immune signatures, representing growth factors (A), type-2/3 cytokines (B), mixed type-1/2/3 cytokines (C), and chemokines (D) that correlated with three distinct disease trajectories. The immune profiles of patients who recovered from moderate COVID-19 were enriched in tissue reparative growth factor signature A, whereas the profiles of those with who developed severe disease had elevated levels of all four signatures. Thus, we have identified a maladapted immune response profile associated with severe COVID-19 and poor clinical outcome, as well as early immune signatures that correlate with divergent disease trajectories.
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            Statistics corner: A guide to appropriate use of correlation coefficient in medical research.

            M M Mukaka (2012)
            Correlation is a statistical method used to assess a possible linear association between two continuous variables. It is simple both to calculate and to interpret. However, misuse of correlation is so common among researchers that some statisticians have wished that the method had never been devised at all. The aim of this article is to provide a guide to appropriate use of correlation in medical research and to highlight some misuse. Examples of the applications of the correlation coefficient have been provided using data from statistical simulations as well as real data. Rule of thumb for interpreting size of a correlation coefficient has been provided.
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              A guide to immunometabolism for immunologists.

              In recent years a substantial number of findings have been made in the area of immunometabolism, by which we mean the changes in intracellular metabolic pathways in immune cells that alter their function. Here, we provide a brief refresher course on six of the major metabolic pathways involved (specifically, glycolysis, the tricarboxylic acid (TCA) cycle, the pentose phosphate pathway, fatty acid oxidation, fatty acid synthesis and amino acid metabolism), giving specific examples of how precise changes in the metabolites of these pathways shape the immune cell response. What is emerging is a complex interplay between metabolic reprogramming and immunity, which is providing an extra dimension to our understanding of the immune system in health and disease.
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                Author and article information

                Journal
                Sci Signal
                Sci Signal
                signaling
                sigtrans
                Science Signaling
                American Association for the Advancement of Science
                1945-0877
                1937-9145
                06 July 2021
                : 14
                : 690
                : eabf8483
                Affiliations
                [1 ]Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06510, USA.
                [2 ]Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China.
                [3 ]Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA.
                [4 ]Centre for Integrative Metabolomics and Computational Biology, School of Science, Edith Cowan University, Joondalup 6027, Australia.
                [5 ]Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA.
                [6 ]Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, UK.
                [7 ]Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA.
                [8 ]Department of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT 06520, USA.
                [9 ]Department of Surgery, Division of Surgical Oncology, Yale University School of Medicine, New Haven, CT 06520, USA.
                [10 ]Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
                Author notes
                [ * ]Corresponding author. Email: caroline.johnson@ 123456yale.edu
                Author information
                https://orcid.org/0000-0002-5701-3609
                https://orcid.org/0000-0002-1061-356X
                https://orcid.org/0000-0001-9001-4999
                https://orcid.org/0000-0002-3301-6143
                https://orcid.org/0000-0002-1308-8246
                https://orcid.org/0000-0002-3552-7684
                https://orcid.org/0000-0001-9251-8592
                https://orcid.org/0000-0002-1169-6395
                https://orcid.org/0000-0002-7824-9856
                https://orcid.org/0000-0002-5298-1299
                Article
                abf8483
                10.1126/scisignal.abf8483
                8432948
                34230210
                038e2ddf-ce14-44e3-af95-08a2f119e60e
                Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 November 2020
                : 16 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: F30CA236466
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32GM007205
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32GM136651
                Funded by: FundRef http://dx.doi.org/10.13039/100001229, G Harold and Leila Y. Mathers Foundation;
                Funded by: FundRef http://dx.doi.org/10.13039/100001229, G Harold and Leila Y. Mathers Foundation;
                Award ID: COVID-19 R
                Funded by: FundRef http://dx.doi.org/10.13039/100005326, Yale University;
                Award ID: NIH U19 AI08992
                Funded by: FundRef http://dx.doi.org/10.13039/100016220, Georgia Clinical and Translational Science Alliance;
                Award ID: UL1TR001863
                Funded by: YSPH Rapid Response Fund;
                Funded by: Women’s Health Research at Yale Pilot Project Program;
                Funded by: Fast Grant from Emergent Ventures at the Mercatus Center;
                Funded by: Ludwig Family Foundation;
                Funded by: Beatrice Kleinberg Neuwirth Fund;
                Categories
                Research Resource
                Research Resource
                STKE Research Resources
                Coronavirus
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                John F. Foley
                Kyle Solis

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