13
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Metabolomics analysis reveals a modified amino acid metabolism that correlates with altered oxygen homeostasis in COVID-19 patients

      research-article

      Read this article at

      Bookmark
          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

          We identified the main changes in serum metabolites associated with severe (n = 46) and mild (n = 19) COVID-19 patients by gas chromatography coupled to mass spectrometry. The modified metabolic profiles were associated to an altered amino acid catabolism in hypoxic conditions. Noteworthy, three α-hydroxyl acids of amino acid origin increased with disease severity and correlated with altered oxygen saturation levels and clinical markers of lung damage. We hypothesize that the enzymatic conversion of α-keto-acids to α- hydroxyl-acids helps to maintain NAD recycling in patients with altered oxygen levels, highlighting the potential relevance of amino acid supplementation during SARS-CoV-2 infection.

          Related collections

          Most cited references54

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

          Abstract We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

            Background Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. Results A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. Conclusions MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease

              Abstract Background Since early December 2019, the 2019 novel coronavirus disease (COVID-19) has caused pneumonia epidemic in Wuhan, Hubei province of China. This study aims to investigate the factors affecting the progression of pneumonia in COVID-19 patients. Associated results will be used to evaluate the prognosis and to find the optimal treatment regimens for COVID-19 pneumonia. Methods Patients tested positive for the COVID-19 based on nucleic acid detection were included in this study. Patients were admitted to 3 tertiary hospitals in Wuhan between December 30, 2019, and January 15, 2020. Individual data, laboratory indices, imaging characteristics, and clinical data were collected, and statistical analysis was performed. Based on clinical typing results, the patients were divided into a progression group or an improvement/stabilization group. Continuous variables were analyzed using independent samples t-test or Mann-Whitney U test. Categorical variables were analyzed using Chi-squared test or Fisher's exact test. Logistic regression analysis was performed to explore the risk factors for disease progression. Results Seventy-eight patients with COVID-19-induced pneumonia met the inclusion criteria and were included in this study. Efficacy evaluation at 2 weeks after hospitalization indicated that 11 patients (14.1%) had deteriorated, and 67 patients (85.9%) had improved/stabilized. The patients in the progression group were significantly older than those in the disease improvement/stabilization group (66 [51, 70] vs. 37 [32, 41] years, U = 4.932, P = 0.001). The progression group had a significantly higher proportion of patients with a history of smoking than the improvement/stabilization group (27.3% vs. 3.0%, χ 2  = 9.291, P = 0.018). For all the 78 patients, fever was the most common initial symptom, and the maximum body temperature at admission was significantly higher in the progression group than in the improvement/stabilization group (38.2 [37.8, 38.6] vs. 37.5 [37.0, 38.4]°C, U = 2.057, P = 0.027). Moreover, the proportion of patients with respiratory failure (54.5% vs. 20.9%, χ 2  = 5.611, P = 0.028) and respiratory rate (34 [18, 48] vs. 24 [16, 60] breaths/min, U = 4.030, P = 0.004) were significantly higher in the progression group than in the improvement/stabilization group. C-reactive protein was significantly elevated in the progression group compared to the improvement/stabilization group (38.9 [14.3, 64.8] vs. 10.6 [1.9, 33.1] mg/L, U = 1.315, P = 0.024). Albumin was significantly lower in the progression group than in the improvement/stabilization group (36.62 ± 6.60 vs. 41.27 ± 4.55 g/L, U = 2.843, P = 0.006). Patients in the progression group were more likely to receive high-level respiratory support than in the improvement/stabilization group (χ 2  = 16.01, P = 0.001). Multivariate logistic analysis indicated that age (odds ratio [OR], 8.546; 95% confidence interval [CI]: 1.628–44.864; P = 0.011), history of smoking (OR, 14.285; 95% CI: 1.577–25.000; P = 0.018), maximum body temperature at admission (OR, 8.999; 95% CI: 1.036–78.147, P = 0.046), respiratory failure (OR, 8.772, 95% CI: 1.942–40.000; P = 0.016), albumin (OR, 7.353, 95% CI: 1.098–50.000; P = 0.003), and C-reactive protein (OR, 10.530; 95% CI: 1.224−34.701, P = 0.028) were risk factors for disease progression. Conclusions Several factors that led to the progression of COVID-19 pneumonia were identified, including age, history of smoking, maximum body temperature at admission, respiratory failure, albumin, C-reactive protein. These results can be used to further enhance the ability of management of COVID-19 pneumonia.
                Bookmark

                Author and article information

                Contributors
                luisllorentepeters57@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 March 2021
                18 March 2021
                2021
                : 11
                : 6350
                Affiliations
                [1 ]GRID grid.416850.e, ISNI 0000 0001 0698 4037, Red de Apoyo a la Investigación, , Universidad Nacional Autónoma de México e Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, ; Mexico City, Mexico
                [2 ]GRID grid.416850.e, ISNI 0000 0001 0698 4037, Emergency Medicine Department, , Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, ; Mexico City, Mexico
                [3 ]GRID grid.416850.e, ISNI 0000 0001 0698 4037, Departament of Immunology and Rheumatology, , Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, ; Vasco de Quiroga 15, Sección XVI, C.P. 14000 Tlalpan, Mexico City, Mexico
                [4 ]GRID grid.418275.d, ISNI 0000 0001 2165 8782, Departamento de Biomedicina Molecular, , Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, ; Mexico City, Mexico
                [5 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Facultad de Medicina, , Universidad Nacional Autónoma de México, ; Mexico City, Mexico
                [6 ]GRID grid.416850.e, ISNI 0000 0001 0698 4037, Department of Infectology and Microbiology, , Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, ; Mexico City, Mexico
                Article
                85788
                10.1038/s41598-021-85788-0
                7973513
                33737694
                91f612a0-5718-4afb-8c4d-7632d3f98f9d
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 October 2020
                : 2 March 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003141, Consejo Nacional de Ciencia y Tecnología;
                Award ID: F0005-2020-01-313252
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                metabolomics,viral infection,hypoxia
                Uncategorized
                metabolomics, viral infection, hypoxia

                Comments

                Comment on this article