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      In Silico Identification of a Candidate Synthetic Peptide (Tsgf1 18–43) to Monitor Human Exposure to Tsetse Flies in West Africa

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          Abstract

          Background

          The analysis of humoral responses directed against the saliva of blood-sucking arthropods was shown to provide epidemiological biomarkers of human exposure to vector-borne diseases. However, the use of whole saliva as antigen presents several limitations such as problems of mass production, reproducibility and specificity. The aim of this study was to design a specific biomarker of exposure to tsetse flies based on the in silico analysis of three Glossina salivary proteins (Ada, Ag5 and Tsgf1) previously shown to be specifically recognized by plasma from exposed individuals.

          Methodology/Principal Findings

          Synthetic peptides were designed by combining several linear epitope prediction methods and Blast analysis. The most specific peptides were then tested by indirect ELISA on a bank of 160 plasma samples from tsetse infested areas and tsetse free areas. Anti-Tsgf1 18–43 specific IgG levels were low in all three control populations (from rural Africa, urban Africa and Europe) and were significantly higher (p<0.0001) in the two populations exposed to tsetse flies (Guinean HAT foci, and South West Burkina Faso). A positive correlation was also found between Anti-Tsgf1 18–43 IgG levels and the risk of being infected by Trypanosoma brucei gambiense in the sleeping sickness foci of Guinea.

          Conclusion/Significance

          The Tsgf1 18–43 peptide is a suitable and promising candidate to develop a standardize immunoassay allowing large scale monitoring of human exposure to tsetse flies in West Africa. This could provide a new surveillance indicator for tsetse control interventions by HAT control programs.

          Author Summary

          Increasing interest is paid to blood-sucking arthropod's salivary antigens to develop host direct biomarkers of exposure. Nevertheless use of whole saliva is problematic both because of mass production and specificity issues. Here, we describe an in silico approach we used to identify potential epitopes on the amino acid sequence of three tsetse salivary proteins (Ada, Ag5 and Tsgf1) that were previously shown to be specifically recognized by antibodies from exposed individuals. Three candidate peptides were synthesized and evaluated on a set of plasma collected in different tsetse-infested and tsetse-free areas. The Tsgf1 18–43 synthetic peptide appeared as a promising candidate to assess human exposure to tsetse flies as antibody responses were low in all three control groups and were significantly higher in our two exposed groups. Significantly higher anti- Tsgf1 18–43 responses were also observed in sleeping sickness patients as compared to uninfected controls suggesting that Tsgf1 18–43 may be used both to assess human tsetse contacts and the risk of infection by trypanosomes. This new sero-epidemiological tool could thus help National Control Programs to quickly map human exposure levels in order to better target vector control efforts and monitor vector control efficiency.

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          Most cited references40

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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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            A generic method for assignment of reliability scores applied to solvent accessibility predictions

            Background Estimation of the reliability of specific real value predictions is nontrivial and the efficacy of this is often questionable. It is important to know if you can trust a given prediction and therefore the best methods associate a prediction with a reliability score or index. For discrete qualitative predictions, the reliability is conventionally estimated as the difference between output scores of selected classes. Such an approach is not feasible for methods that predict a biological feature as a single real value rather than a classification. As a solution to this challenge, we have implemented a method that predicts the relative surface accessibility of an amino acid and simultaneously predicts the reliability for each prediction, in the form of a Z-score. Results An ensemble of artificial neural networks has been trained on a set of experimentally solved protein structures to predict the relative exposure of the amino acids. The method assigns a reliability score to each surface accessibility prediction as an inherent part of the training process. This is in contrast to the most commonly used procedures where reliabilities are obtained by post-processing the output. Conclusion The performance of the neural networks was evaluated on a commonly used set of sequences known as the CB513 set. An overall Pearson's correlation coefficient of 0.72 was obtained, which is comparable to the performance of the currently best public available method, Real-SPINE. Both methods associate a reliability score with the individual predictions. However, our implementation of reliability scores in the form of a Z-score is shown to be the more informative measure for discriminating good predictions from bad ones in the entire range from completely buried to fully exposed amino acids. This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability. For this subset, values of 0.79 and 0.74 are obtained using our and the compared method, respectively. This tendency is true for any selected subset.
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              The Atlas of human African trypanosomiasis: a contribution to global mapping of neglected tropical diseases

              Background Following World Health Assembly resolutions 50.36 in 1997 and 56.7 in 2003, the World Health Organization (WHO) committed itself to supporting human African trypanosomiasis (HAT)-endemic countries in their efforts to remove the disease as a public health problem. Mapping the distribution of HAT in time and space has a pivotal role to play if this objective is to be met. For this reason WHO launched the HAT Atlas initiative, jointly implemented with the Food and Agriculture Organization of the United Nations, in the framework of the Programme Against African Trypanosomosis. Results The distribution of HAT is presented for 23 out of 25 sub-Saharan countries having reported on the status of sleeping sickness in the period 2000 - 2009. For the two remaining countries, i.e. Angola and the Democratic Republic of the Congo, data processing is ongoing. Reports by National Sleeping Sickness Control Programmes (NSSCPs), Non-Governmental Organizations (NGOs) and Research Institutes were collated and the relevant epidemiological data were entered in a database, thus incorporating (i) the results of active screening of over 2.2 million people, and (ii) cases detected in health care facilities engaged in passive surveillance. A total of over 42 000 cases of HAT and 6 000 different localities were included in the database. Various sources of geographic coordinates were used to locate the villages of epidemiological interest. The resulting average mapping accuracy is estimated at 900 m. Conclusions Full involvement of NSSCPs, NGOs and Research Institutes in building the Atlas of HAT contributes to the efficiency of the mapping process and it assures both the quality of the collated information and the accuracy of the outputs. Although efforts are still needed to reduce the number of undetected and unreported cases, the comprehensive, village-level mapping of HAT control activities over a ten-year period ensures a detailed and reliable representation of the known geographic distribution of the disease. Not only does the Atlas serve research and advocacy, but, more importantly, it provides crucial evidence and a valuable tool for making informed decisions to plan and monitor the control of sleeping sickness.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                plosntds
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, USA )
                1935-2727
                1935-2735
                September 2013
                26 September 2013
                : 7
                : 9
                : e2455
                Affiliations
                [1 ]Centre International de Recherche-Développement sur l'Elevage en zone Subhumide (CIRDES), Bobo-Dioulasso, Burkina Faso
                [2 ]Institut de Recherche pour le Développement (IRD), UMR MIVEGEC IRD 224-CNRS 5290-Université de Montpellier 1 et Montpellier 2, Montpellier, France
                [3 ]Centre de Recherche Entomologique de Cotonou (CREC), Cotonou, Bénin
                [4 ]Programme National de Lutte contre la Trypanosomose Humaine Africaine en Guinée, Conakry, Guinée
                [5 ]Institut de Recherche pour le Développement, Unité Mixte de Recherche IRD-CIRAD 177, Campus International de Baillarguet, Montpellier, France
                [6 ]Université Polytechnique de Bobo-Dioulasso, Bobo-Dioulasso, Burkina Faso
                Lancaster University, United Kingdom
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: ED SC MC AP FR ZB AMGB BB. Performed the experiments: ED SC MBS FR EEN. Analyzed the data: ED SC MBS AP EEN BB. Contributed reagents/materials/analysis tools: FR PS HI ZB. Wrote the paper: ED SC AP VJ PS FR AMGB BB. Participated in field work and sample collection: ED MC VJ BB HI EEN AP.

                Article
                PNTD-D-13-00573
                10.1371/journal.pntd.0002455
                3784472
                24086785
                19b09b24-a1f6-493d-8b4b-b07289f4815c
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 April 2013
                : 12 August 2013
                Page count
                Pages: 8
                Funding
                Medical surveys and laboratory work were supported by the French Ministry of Foreign Affairs (FSP/REFS and Aires-SUD projects), the World Health Organization (WHO) and the International Atomic Energy Agency (IAEA). ED was a recipient of an Institut de Recherche pour le Développement (IRD) PhD fellowship. We would also like to thank the Targeting Tsetse Project funded by the Bill and Melinda Gates Foundation who paid for the synthetic peptides. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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