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      Genetic polymorphisms in malaria vaccine candidate Plasmodium falciparum reticulocyte-binding protein homologue-5 among populations in Lagos, Nigeria

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          Abstract

          Background

          Vaccines are the most reliable alternative to elicit sterile immunity against malaria but their development has been hindered by polymorphisms and strain-specificity in previously studied antigens. New vaccine candidates are therefore urgently needed. Highly conserved Plasmodium falciparum reticulocyte-binding protein homologue-5 (PfRH5) has been identified as a potential candidate for anti-disease vaccine development. PfRH5 is essential for erythrocyte invasion by merozoites and crucial for parasite survival. However, there is paucity of data on the extent of genetic variations on PfRH5 in field isolates of Plasmodium falciparum. This study described genetic polymorphisms at the high affinity binding polypeptides (HABPs) 36718, 36727, 36728 of PfRH5 in Nigerian isolates of P. falciparum. This study tested the hypothesis that only specific conserved B and T cell epitopes on PfRH5 HABPs are crucial for vaccine development.

          Methods

          One hundred and ninety-five microscopically confirmed P. falciparum samples collected in a prospective cross-sectional study of three different populations in Lagos, Nigeria. Genetic diversity and haplotype construct of Pfrh5 gene were determined using bi-directional sequencing approach. Tajima’s D and the ratio of nonsynonymous vs synonymous mutations were utilized to estimate the extent of balancing and directional selection in the pfrh5 gene.

          Results

          Sequence analysis revealed three haplotypes of PfRH5 with negative Tajima’s D and dN/dS value of − 1.717 and 0.011 ± 0.020, respectively. A single nucleotide polymorphism, SNP (G → A) at position 608 was observed, which resulted in a change of the amino acid cysteine at position 203 to tyrosine. Haplotype and nucleotide diversities were 0.318 ± 0.016 and 0.0046 ± 0.0001 while inter-population genetic differentiation ranged from 0.007 to 0.037. Five polypeptide variants were identified, the most frequent being KTKYH with a frequency of 51.3%. One B-cell epitope, 151 major histocompatibility complex (MHC) class II T-cell epitopes, four intrinsically unstructured regions (IURs) and six MHC class I T-cell epitopes were observed in the study. Phylogenetic analysis of the sequences showed clustering and evidence of evolutionary relationship with 3D7, PAS-2 and FCB-2 RH5 sequences.

          Conclusions

          This study has revealed low level of genetic polymorphisms in PfRH5 antigen with B- and T-cell epitopes in intrinsically unstructured regions along the PfRH5 gene in Lagos, Nigeria. A broader investigation is however required in other parts of the country to support the possible inclusion of PfRH5 in a cross-protective multi-component vaccine.

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          Most cited references14

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          Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.

          B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/. Proteins 2006. (c) 2006 Wiley-Liss, Inc.
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            Estimating divergence times in large molecular phylogenies.

            Molecular dating of species divergences has become an important means to add a temporal dimension to the Tree of Life. Increasingly larger datasets encompassing greater taxonomic diversity are becoming available to generate molecular timetrees by using sophisticated methods that model rate variation among lineages. However, the practical application of these methods is challenging because of the exorbitant calculation times required by current methods for contemporary data sizes, the difficulty in correctly modeling the rate heterogeneity in highly diverse taxonomic groups, and the lack of reliable clock calibrations and their uncertainty distributions for most groups of species. Here, we present a method that estimates relative times of divergences for all branching points (nodes) in very large phylogenetic trees without assuming a specific model for lineage rate variation or specifying any clock calibrations. The method (RelTime) performed better than existing methods when applied to very large computer simulated datasets where evolutionary rates were varied extensively among lineages by following autocorrelated and uncorrelated models. On average, RelTime completed calculations 1,000 times faster than the fastest Bayesian method, with even greater speed difference for larger number of sequences. This speed and accuracy will enable molecular dating analysis of very large datasets. Relative time estimates will be useful for determining the relative ordering and spacing of speciation events, identifying lineages with significantly slower or faster evolutionary rates, diagnosing the effect of selected calibrations on absolute divergence times, and estimating absolute times of divergence when highly reliable calibration points are available.
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              Evolutionary insights into host–pathogen interactions from mammalian sequence data

              Key Points Infections are possibly the major selective pressure acting on humans, and host–pathogen interactions contribute to shaping the genetic diversity of both organisms. Comparisons among species provide a snapshot of selective events that have been unfolding over long timescales. These approaches use extant genetic diversity and phylogenetic relationships among species to identify positively selected sites. Positive selection often acts on a limited number of sites in a protein that is otherwise selectively constrained; one example is the localized signal of selection at Niemann–Pick C1 protein (NPC1), the receptor for the Ebola virus. As epitomized by the evolutionary history of tripartite motif-containing 5 (TRIM5), past infection events may leave a signature that affects the ability of extant species to fight emerging pathogens. Protein regions at the host–pathogen interface are expected to be targeted by the strongest selective pressure (this is the case for dipeptidyl peptidase 4 (DPP4) and angiotensin-converting enzyme 2 (ACE2), which act as receptors for coronaviruses). Other mammals host a wide range of viruses that are highly pathogenic for humans. Sequencing the genomes of these pathogens will be instrumental in refining our understanding of the process of host–pathogen interaction. Pathogen-driven natural selection is not limited to the immune system: genes that encode incidental pathogen receptors and components of the contact system and coagulation cascade can also be targeted. Supplementary information The online version of this article (doi:10.1038/nrg3905) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                sajibaye@yahoo.com
                Journal
                Malar J
                Malar. J
                Malaria Journal
                BioMed Central (London )
                1475-2875
                6 January 2020
                6 January 2020
                2020
                : 19
                : 6
                Affiliations
                [1 ]ISNI 0000 0001 0247 1197, GRID grid.416197.c, Department of Biochemistry & Nutrition, , Nigerian Institute of Medical Research, ; Yaba, Lagos, Nigeria
                [2 ]ISNI 0000 0004 1803 1817, GRID grid.411782.9, Department of Biochemistry, College of Medicine, , University of Lagos, ; Idi-Araba, Lagos, Nigeria
                [3 ]ISNI 0000 0004 1937 1493, GRID grid.411225.1, Department of Biochemistry, , Ahmadu Bello University, ; 2222 Zaria, Nigeria
                [4 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Department of Biomedical Chemistry, Graduate School of Medicine, , The University of Tokyo, ; 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033 Japan
                [5 ]ISNI 0000 0004 1803 1817, GRID grid.411782.9, Parasitology and Bioinformatics Unit, Department of Zoology, Faculty of Science, , University of Lagos, ; Akoka, Lagos, Nigeria
                [6 ]ISNI 0000 0004 0370 1101, GRID grid.136304.3, Department of Infection and Host Defense, Graduate School of Medicine, , Chiba University, ; 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670 Japan
                [7 ]ISNI 0000 0001 2325 1783, GRID grid.26597.3f, School of Health and Life Sciences, , Teesside University, ; Middlesbrough, TS1 3BX UK
                [8 ]ISNI 0000 0000 8902 2273, GRID grid.174567.6, School of Tropical Medicine and Global Health, , Nagasaki University, ; Nagasaki, 852-8523 Japan
                [9 ]Medical Research Council at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia
                Author information
                http://orcid.org/0000-0002-3888-2366
                Article
                3096
                10.1186/s12936-019-3096-0
                6945540
                31898492
                deabe301-b9ff-468c-9546-1794c1105f30
                © The Author(s) 2020

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 11 August 2019
                : 26 December 2019
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Infectious disease & Microbiology
                polymorphisms,reticulocyte-binding protein homologue-5,haplotypes,histocompatibility,gene flow,linkage,unstructured regions

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