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Plant virome ecology in African farming systems: A genomics and bioinformatics framework for high-throughput virus detection and pathogen discovery
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Plant virome ecology in African farming systems: A genomics and bioinformatics framework for high-throughput virus detection and pathogen discovery

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Poster prepared by Francesca Stomeo, Mark Wamalwa, Jagger Harvey, Douglas W. Miano, Neil Boonham, Dora Kilalo, Ian Adams and Appolinaire Djikeng) for the ILRI APM 2013, Addis Ababa, 15-17 May 2013 …

Poster prepared by Francesca Stomeo, Mark Wamalwa, Jagger Harvey, Douglas W. Miano, Neil Boonham, Dora Kilalo, Ian Adams and Appolinaire Djikeng) for the ILRI APM 2013, Addis Ababa, 15-17 May 2013

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  • 1. Mobilizing biosciences for Africa’s developmentThis document is licensed for use under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License May2013Plant virome ecology in African farming systems:A genomics and bioinformatics framework for high-throughputVirus detection and Pathogen DiscoveryThis document is licensed for use under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License May20131Biosciences Eastern and Central Africa (BecA) - ILRI Hub, Nairobi, PO Box 30709, 00100, Kenya (f.stomeo@cgiar.org; m.wamalwa@cgiar.org) - 2Kenya Agricultural ResearchInstitute (KARI), Nairobi, PO Box 14733-00800, Kenya - 3Food and Environment Research Agency (FERA), Sand Hutton, York, YO41 1LZ, UK - 4University of Nairobi, KenyaFrancesca Stomeo1, Mark Wamalwa1, Jagger Harvey1, Douglas W. Miano2, Neil Boonham3,Dora Kilalo4, Ian Adams3, Appolinaire Djikeng1This project is funded by the Swedish Ministry for Foreign Affairs through SIDA. For more info: http://hub.africabiosciences.org/Outputs• Confirmation of known diseases/pathogens• Pathogen Discovery• Host range and vector information• Risk analysis based on AEZs and dynamics of disease spread/climate change• Information to help guide decisions and activities of policy makers, donors andresearchers• Application of these methodologies to viruses will make it possible to explore viraldiversity through automatically constructed time-measured phylogenies and performcomparison against their viromes.IntroductionCrop diseases are one of the major constraints to crop production of sub-Saharan Africa (SSA) small-scale farmers. Small farm ecosystems are a complex mix of crop, non-crop plants,insects, vectors, fungal, bacterial and virus pathogens. The maize mixed farming system, typically including maize and a selection of different crops (potatoes, banana, rice, sorghum,cassava, etc.), is among the most common small farming systems in SSA. These ecosystems support greater pathogen (and vector) diversity. This project aims to assess the diversity ofviruses thriving in the maize mixed farming systems in Kenya through a combined genomics – bioinformatics approach. Metagenomics sequencing offers significant advantages overtraditional diagnostics and presents a novel opportunity for understanding virus evolution and the genetic diversity present in these environments, and allows outbreaks to bemonitored in detail. The identification of emerging diseases and associated risks is paramount for improving African sustainability and ensuring food security, especially in the face ofclimate change.• In order to elucidate the presence of pathogens in the soils and theircharacteristics, soils were collected from the two farms.• Genomics and Bioinformatics approaches will be used to gain a betterunderstanding of the potential factors influencing the spread of viruses (inspace and time) in this ecosystem. Next generation sequencing (NGS) will becarried out to elucidate the complex mix of hosts, vectors, and viruses.• Total RNA/siRNA/ds-RNA and DNA will be extracted and sequenced usingNGS techniques (Illumina MiSeq) in order to elucidate the most efficientnucleic acid class for virus discovery.• Furthermore, a 16S rRNA gene metagenomics approach will be conductedto elucidate the diversity of pathogens thriving in the selectedenvironments (plants and soils) and shade light into their relationships.Materials and Methods• Selected crops (maize, beans, irish potatoes, sorghum, sweet potato, millet,etc.) vegetables (cabbage, onions, etc.), pastures (Napier grass and kikuyugrass, etc.) and potential vectors (aphids, beetles, etc.) will be sampled fromthree Kenyan agro-ecological zones: Bomet, Narok and Trans Nzoia/UasinGishu, representing different climatic zones. To date, samples have beencollected from the Bomet area in the lower highlands (Figure 1a), from twofarms, characterized by mixed cropping systems and different crops diseases.• Moreover, our effort will concentrate on farming systems affected by theMaize lethal necrosis (MLN) triggered by a combination of Maize Chloroticmottle virus (MCMV) and Sugarcane mosaic virus (SMV) that is causingsevere losses in Kenya (Figure 1b).Aims and Objectives• Assessment of the overall diversity of viruses thriving in the maize mixed‘farming systems in Kenya.• Virome comparisons, geographical distribution and spatial characterizationthrough a viral metagenome analysis pipeline.• Development of methods for pathogens detection.• To make biological data available to scientists and policy makers.Figure 1 a: Map showing main crop zones of Kenya. 1b: The Maize Lethal Necrosis (MLN) disease in maizeplantations in the Bomet district (Kenya).bPhoto: CIMMYTBometaSymptomatic and asymptomatic crops and pastures leaves were collectedtogether with the potential vectors (aphids and beetles) responsible for diseasestransmission.ResultsComplete metagenomes are in the pipeline for sequencing with the MiSeq Illuminasystem. A preliminary analysis of 16S rRNA gene T-RFLP profiles suggest that plant and soilecosystems are characterized by different microbial communities and can be groupedinto two separate clusters.A semi-automated sample tracking interface that tracks the progress of viral samples fromacquisition to GenBank submission was created (Figure 2) using Drupal version 7.15 andMySQL database.A prototype web framework for high-throughput virus detection and pathogen discoverywith a customizable web server for fast metagenomic analysis was developed. Thewebserver includes commonly used tools (quality control, tRNA and rRNA prediction,taxonomic analysis and functional annotation) and provides users with rapidmetagenomic data analysis using published tools (Figure 3). The webserver is temporarilyavailable at http://localhost:8080/Drupal7/. We are yet to benchmark this tool to otherssuch as MEGAN and MG-RAST.Figure 3. Automated sample processing and annotation workflowFigure 2. A semi-automated workflow that tracks the progress of samples fromacquisition through to NCBI submission

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