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

1. Mobilizing biosciences for Africa’s development
This document is licensed for use under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License May
2013
Plant virome ecology in African farming systems:
A genomics and bioinformatics framework for high-throughput
Virus detection and Pathogen Discovery
This document is licensed for use under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License May
2013
1Biosciences Eastern and Central Africa (BecA) - ILRI Hub, Nairobi, PO Box 30709, 00100, Kenya (f.stomeo@cgiar.org; m.wamalwa@cgiar.org) - 2Kenya Agricultural Research
Institute (KARI), Nairobi, PO Box 14733-00800, Kenya - 3Food and Environment Research Agency (FERA), Sand Hutton, York, YO41 1LZ, UK - 4University of Nairobi, Kenya
Francesca Stomeo1, Mark Wamalwa1, Jagger Harvey1, Douglas W. Miano2, Neil Boonham3,Dora Kilalo4, Ian Adams3, Appolinaire Djikeng1
This 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 and
researchers
• Application of these methodologies to viruses will make it possible to explore viral
diversity through automatically constructed time-measured phylogenies and perform
comparison against their viromes.
Introduction
Crop 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 of
viruses thriving in the 'maize mixed' farming systems in Kenya through a combined genomics – bioinformatics approach. Metagenomics sequencing offers significant advantages over
traditional diagnostics and presents a novel opportunity for understanding virus evolution and the genetic diversity present in these environments, and allows outbreaks to be
monitored in detail. The identification of emerging diseases and associated risks is paramount for improving African sustainability and ensuring food security, especially in the face of
climate change.
• In order to elucidate the presence of pathogens in the soils and their
characteristics, soils were collected from the two farms.
• Genomics and Bioinformatics approaches will be used to gain a better
understanding of the potential factors influencing the spread of viruses (in
space and time) in this ecosystem. Next generation sequencing (NGS) will be
carried out to elucidate the complex mix of hosts, vectors, and viruses.
• Total RNA/siRNA/ds-RNA and DNA will be extracted and sequenced using
NGS techniques (Illumina MiSeq) in order to elucidate the most efficient
nucleic acid class for virus discovery.
• Furthermore, a 16S rRNA gene metagenomics approach will be conducted
to elucidate the diversity of pathogens thriving in the selected
environments (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 kikuyu
grass, etc.) and potential vectors (aphids, beetles, etc.) will be sampled from
three Kenyan agro-ecological zones: Bomet, Narok and Trans Nzoia/Uasin
Gishu, representing different climatic zones. To date, samples have been
collected from the Bomet area in the lower highlands (Figure 1a), from two
farms, characterized by mixed cropping systems and different crops diseases.
• Moreover, our effort will concentrate on farming systems affected by the
Maize lethal necrosis (MLN) triggered by a combination of Maize Chlorotic
mottle virus (MCMV) and Sugarcane mosaic virus (SMV) that is causing
severe 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 characterization
through 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 maize
plantations in the Bomet district (Kenya).
b
Photo: CIMMYT
Bomet
a
Symptomatic and asymptomatic crops and pastures leaves were collected
together with the potential vectors (aphids and beetles) responsible for diseases
transmission.
Results
Complete metagenomes are in the pipeline for sequencing with the MiSeq Illumina
system. A preliminary analysis of 16S rRNA gene T-RFLP profiles suggest that plant and soil
ecosystems are characterized by different microbial communities and can be grouped
into two separate clusters.
A semi-automated sample tracking interface that tracks the progress of viral samples from
acquisition to GenBank submission was created (Figure 2) using Drupal version 7.15 and
MySQL database.
A prototype web framework for high-throughput virus detection and pathogen discovery
with a customizable web server for fast metagenomic analysis was developed. The
webserver includes commonly used tools (quality control, tRNA and rRNA prediction,
taxonomic analysis and functional annotation) and provides users with rapid
metagenomic data analysis using published tools (Figure 3). The webserver is temporarily
available at http://localhost:8080/Drupal7/. We are yet to benchmark this tool to others
such as MEGAN and MG-RAST.
Figure 3. Automated sample processing and annotation workflow
Figure 2. A semi-automated workflow that tracks the progress of samples from
acquisition through to NCBI submission