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      KILchip v1.0: A Novel Plasmodium falciparum Merozoite Protein Microarray to Facilitate Malaria Vaccine Candidate Prioritization


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          Passive transfer studies in humans clearly demonstrated the protective role of IgG antibodies against malaria. Identifying the precise parasite antigens that mediate immunity is essential for vaccine design, but has proved difficult. Completion of the Plasmodium falciparum genome revealed thousands of potential vaccine candidates, but a significant bottleneck remains in their validation and prioritization for further evaluation in clinical trials. Focusing initially on the Plasmodium falciparum merozoite proteome, we used peer-reviewed publications, multiple proteomic and bioinformatic approaches, to select and prioritize potential immune targets. We expressed 109 P. falciparum recombinant proteins, the majority of which were obtained using a mammalian expression system that has been shown to produce biologically functional extracellular proteins, and used them to create KILchip v1.0: a novel protein microarray to facilitate high-throughput multiplexed antibody detection from individual samples.

          The microarray assay was highly specific; antibodies against P. falciparum proteins were detected exclusively in sera from malaria-exposed but not malaria-naïve individuals. The intensity of antibody reactivity varied as expected from strong to weak across well-studied antigens such as AMA1 and RH5 (Kruskal–Wallis H test for trend: p < 0.0001). The inter-assay and intra-assay variability was minimal, with reproducible results obtained in re-assays using the same chip over a duration of 3 months. Antibodies quantified using the multiplexed format in KILchip v1.0 were highly correlated with those measured in the gold-standard monoplex ELISA [median (range) Spearman's R of 0.84 (0.65–0.95)]. KILchip v1.0 is a robust, scalable and adaptable protein microarray that has broad applicability to studies of naturally acquired immunity against malaria by providing a standardized tool for the detection of antibody correlates of protection. It will facilitate rapid high-throughput validation and prioritization of potential Plasmodium falciparum merozoite-stage antigens paving the way for urgently needed clinical trials for the next generation of malaria vaccines.

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          Most cited references71

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          Adjusting batch effects in microarray expression data using empirical Bayes methods.

          Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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            Dose-Response Analysis Using R

            Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.
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              Using circular dichroism spectra to estimate protein secondary structure.

              Circular dichroism (CD) is an excellent tool for rapid determination of the secondary structure and folding properties of proteins that have been obtained using recombinant techniques or purified from tissues. The most widely used applications of protein CD are to determine whether an expressed, purified protein is folded, or if a mutation affects its conformation or stability. In addition, it can be used to study protein interactions. This protocol details the basic steps of obtaining and interpreting CD data, and methods for analyzing spectra to estimate the secondary structural composition of proteins. CD has the advantage that measurements may be made on multiple samples containing < or =20 microg of proteins in physiological buffers in a few hours. However, it does not give the residue-specific information that can be obtained by x-ray crystallography or NMR.

                Author and article information

                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                11 December 2018
                : 9
                : 2866
                [1] 1KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research—Coast , Kilifi, Kenya
                [2] 2Centre for Infectious Diseases, Parasitology, Heidelberg University Hospital , Heidelberg, Germany
                [3] 3Department of Biochemistry, Pwani University , Kilifi, Kenya
                [4] 4Arrayjet, Innovative Microarray Solutions , Edinburgh, United Kingdom
                [5] 5Department of Pathology, University of Cape Town , Cape Town, South Africa
                [6] 6Department of Tropical and Infectious Diseases, Institute of Primate Research , Nairobi, Kenya
                [7] 7Cellular and Molecular Immunology, Vrije Universiteit Brussels , Brussels, Belgium
                [8] 8Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford , Oxford, United Kingdom
                [9] 9Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet , Stockholm, Sweden
                [10] 10Department of Infectious Diseases, Karolinska University Hospital , Stockholm, Sweden
                [11] 11Immunology and Infection Department, London School of Hygiene and Tropical Medicine , London, United Kingdom
                [12] 12Burnet Institute , Melbourne, VIC, Australia
                [13] 13Central Clinical School, Monash University , Melbourne, VIC, Australia
                [14] 14Department of Medicine, University of Melbourne , Melbourne, VIC, Australia
                [15] 15Pathogen Molecular Biology Department, London School of Hygiene and Tropical Medicine , London, United Kingdom
                [16] 16African Academy of Sciences , Nairobi, Kenya
                [17] 17Wellcome Sanger Institute , Hinxton, Cambridge, United Kingdom
                Author notes

                Edited by: Ashraful Haque, QIMR Berghofer Medical Research Institute, Australia

                Reviewed by: Giampietro Corradin, Université de Lausanne, Switzerland; Takafumi Tsuboi, Ehime University, Japan

                This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology

                †These authors have contributed equally to this work

                Copyright © 2018 Kamuyu, Tuju, Kimathi, Mwai, Mburu, Kibinge, Chong Kwan, Hawkings, Yaa, Chepsat, Njunge, Chege, Guleid, Rosenkranz, Kariuki, Frank, Kinyanjui, Murungi, Bejon, Färnert, Tetteh, Beeson, Conway, Marsh, Rayner and Osier.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 26 June 2018
                : 21 November 2018
                Page count
                Figures: 8, Tables: 0, Equations: 1, References: 74, Pages: 16, Words: 10374
                Original Research

                plasmodium falciparum,merozoite,antibodies,vaccine candidates,protein microarray,bioinformatics


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