8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Phagocytosis of Plasmodium falciparum ring-stage parasites predicts protection against malaria

      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

          Ring-infected erythrocytes are the predominant asexual stage in the peripheral circulation but are rarely investigated in the context of acquired immunity against Plasmodium falciparum malaria. Here we compare antibody-dependent phagocytosis of ring-infected parasite cultures in samples from a controlled human malaria infection (CHMI) study (NCT02739763). Protected volunteers did not develop clinical symptoms, maintained parasitaemia below a predefined threshold of 500 parasites/μl and were not treated until the end of the study. Antibody-dependent phagocytosis of both ring-infected and uninfected erythrocytes from parasite cultures was strongly correlated with protection. A surface proteomic analysis revealed the presence of merozoite proteins including erythrocyte binding antigen-175 and −140 on ring-infected and uninfected erythrocytes, providing an additional antibody-mediated protective mechanism for their activity beyond invasion-inhibition. Competition phagocytosis assays support the hypothesis that merozoite antigens are the key mediators of this functional activity. Targeting ring-stage parasites may contribute to the control of parasitaemia and prevention of clinical malaria.

          Abstract

          Here the authors show that antibody-dependent phagocytosis of ring-stage P. falciparum parasites is mediated by merozoite antigens and is a strong predictor of protection following challenge in a controlled human malaria infection study in semi-immune Kenyan adults.

          Related collections

          Most cited references54

          • Record: found
          • Abstract: found
          • Article: not found

          The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

          MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *

            Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              In-gel digestion for mass spectrometric characterization of proteins and proteomes.

              In-gel digestion of proteins isolated by gel electrophoresis is a cornerstone of mass spectrometry (MS)-driven proteomics. The 10-year-old recipe by Shevchenko et al. has been optimized to increase the speed and sensitivity of analysis. The protocol is for the in-gel digestion of both silver and Coomassie-stained protein spots or bands and can be followed by MALDI-MS or LC-MS/MS analysis to identify proteins at sensitivities better than a few femtomoles of protein starting material.
                Bookmark

                Author and article information

                Contributors
                f.osier@imperial.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 July 2022
                14 July 2022
                2022
                : 13
                : 4098
                Affiliations
                [1 ]GRID grid.5253.1, ISNI 0000 0001 0328 4908, Centre for Infectious Diseases, , Heidelberg University Hospital, ; Heidelberg, Germany
                [2 ]GRID grid.33058.3d, ISNI 0000 0001 0155 5938, Centre for Geographic Medicine Research (Coast), , Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, ; Kilifi, Kenya
                [3 ]GRID grid.449481.4, ISNI 0000 0004 0427 2011, Department of Biotechnology, , Hochschule Rhein-Waal, ; Kleve, Germany
                [4 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Genomics and Proteomics Core Facility, , German Cancer Research Center, ; Heidelberg, Germany
                [5 ]GRID grid.11951.3d, ISNI 0000 0004 1937 1135, Epidemiology and Biostatistics Division, School of Public Health, , University of the Witwatersrand, ; Johannesburg, South Africa
                [6 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Division of B Cell Immunology, , German Cancer Research Center, ; Heidelberg, Germany
                [7 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Life Sciences, , Imperial College London, ; London, UK
                [8 ]GRID grid.280962.7, Sanaria Inc., ; Rockville, MD USA
                [9 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, , University Oxford, ; Oxford, UK
                [10 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Pathology, , University of Cambridge, ; Cambridge, UK
                [11 ]GRID grid.33058.3d, ISNI 0000 0001 0155 5938, Centre for Clinical Research, , Kenya Medical Research Institute, ; Kisumu, Kenya
                [12 ]GRID grid.449370.d, ISNI 0000 0004 1780 4347, Pwani University, ; P. O. Box 195-80108, Kilifi, Kenya
                [13 ]GRID grid.442494.b, ISNI 0000 0000 9430 1509, Center for Research in Therapeutic Sciences, , Strathmore University, ; Nairobi, Kenya
                [14 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Medicine, , Imperial College London, ; London, UK
                Author information
                http://orcid.org/0000-0002-7757-7516
                http://orcid.org/0000-0002-0266-1414
                http://orcid.org/0000-0003-3921-5933
                http://orcid.org/0000-0001-7133-5375
                Article
                31640
                10.1038/s41467-022-31640-6
                9281573
                35835738
                fde37faf-8d30-459e-8b1d-27670ae76a9b
                © 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
                : 8 February 2021
                : 27 June 2022
                Funding
                Funded by: German Academic Exchange Service (DAAD) Funding Programme 57214224, ST-32-PKZ 91608705
                Funded by: Alexander von Humboldt Foundation grant number 3.2 - 1184811 - KEN - SKP
                Funded by: FundRef https://doi.org/10.13039/501100000272, DH | National Institute for Health Research (NIHR);
                Award ID: TIBA-16/136/33
                Award Recipient :
                Funded by: DELTAS Africa Initiative grant numbers 107754/Z/15/Z and DEL-15-003
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 107499
                Award ID: 107499
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010269, Wellcome Trust (Wellcome);
                Award ID: 107499
                Award Recipient :
                Funded by: Sofja Kovalevskaja Award from the Alexander von Humboldt Foundation (3.2 - 1184811 - KEN - SKP) and an EDCTP Senior Fellowship (TMA 2015 SF1001)
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                malaria,predictive markers
                Uncategorized
                malaria, predictive markers

                Comments

                Comment on this article