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      Comparative proteomics of vesicles essential for the egress of Plasmodium falciparum gametocytes from red blood cells

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          Abstract

          Transmission of malaria parasites to the mosquito is mediated by sexual precursor cells, the gametocytes. Upon entering the mosquito midgut, the gametocytes egress from the enveloping erythrocyte while passing through gametogenesis. Egress follows an inside‐out mode during which the membrane of the parasitophorous vacuole (PV) ruptures prior to the erythrocyte membrane. Membrane rupture requires exocytosis of specialized egress vesicles of the parasites; that is, osmiophilic bodies (OBs) involved in rupturing the PV membrane, and vesicles that harbor the perforin‐like protein PPLP2 (here termed P‐EVs) required for erythrocyte lysis. While some OB proteins have been identified, like G377 and MDV1/Peg3, the majority of egress vesicle‐resident proteins is yet unknown. Here, we used high‐resolution imaging and BioID methods to study the two egress vesicle types in Plasmodium falciparum gametocytes. We show that OB exocytosis precedes discharge of the P‐EVs and that exocytosis of the P‐EVs, but not of the OBs, is calcium sensitive. Both vesicle types exhibit distinct proteomes with the majority of proteins located in the OBs. In addition to known egress‐related proteins, we identified novel components of OBs and P‐EVs, including vesicle‐trafficking proteins. Our data provide insight into the immense molecular machinery required for the inside‐out egress of P. falciparum gametocytes.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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              MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

              Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Molecular Microbiology
                Molecular Microbiology
                Wiley
                0950-382X
                1365-2958
                July 26 2023
                Affiliations
                [1 ] Division of Cellular and Applied Infection Biology Institute of Zoology, RWTH Aachen University Aachen Germany
                [2 ] Core Facility for Mass Spectrometry Institute of Immunology, University Medical Centre of the Johannes‐Gutenberg University Mainz Germany
                [3 ] Department of Biochemistry and Molecular Biology, Huck Center for Malaria Research The Pennsylvania State University University Park Pennsylvania USA
                [4 ] Centre for Structural Systems Biology Hamburg Germany
                [5 ] Bernhard Nocht Institute for Tropical Medicine Hamburg Germany
                [6 ] Biology Department University of Hamburg Hamburg Germany
                Article
                10.1111/mmi.15125
                34549769-b35c-4f61-a381-ef3244bb3895
                © 2023

                http://creativecommons.org/licenses/by-nc/4.0/

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