Award details

Rapid in-field Nanopore-based identification of plant and animal pathogens

Reference	BB/N023196/1
Principal Investigator / Supervisor	Dr Richard Leggett
Co-Investigators / Co-Supervisors	Dr Matthew Clark
Institution	Earlham Institute
Department	Research Faculty
Funding type	Research
Value (£)	150,956
Status	Completed
Type	Research Grant
Start date	03/01/2017
End date	02/04/2018
Duration	15 months

Abstract

Airborne crop pathogens pose a serious threat to food security and are responsible for devastating loss of yield and over-reliance on pesticides. Early detection enables farmers to take prophylactic action, drastically reducing damage and cost. Current detection regimes often rely on expert identification of the pathogen from plant damage. More recently, techniques have emerged utilising PCR or antibody-based assays. However, these methods suffer the same problems - being specific for a single species and a need for relatively large quantities of pathogenic material. Recently, TGAC has been working on an approach dubbed Air-seq that seeks to identify pathogens through sequencing of biological particles present in air. This overcomes both problems associated with current techniques as it is unbiased and requires very small quantities of material. Our ultimate aim is to put sample collection, sequencing and analysis in a single box that can be deployed in the field. Key to success is a compact sequencing technology and this has recently emerged in the form of Oxford Nanopore Technologies' (ONT) MinION. The MinION is a compact, low cost single molecule sequencing technology that offers multi-kilobase reads and a streamed mode of operation enabling analysis of data as it is generated. These attributes make it ideally suited to in-field use. However, ONT's basecalling utilises a cloud-based system in which pore electrical signal data is uploaded and basecalled sequence downloaded. For in-field deployment, this is unsatisfactory, as we cannot rely on high bandwidth data connections. We believe a completely new approach is required in which we utilise the raw signal data in order to identify species, instead of searching against basecalled sequence. In this project, we will develop a tool that searches Nanopore signal data looking for the characteristic signal traces of pathogens of interest, building up a report on abundance levels in the process.

Summary

Airborne crop diseases pose a serious threat to food security and are responsible for devastating loss of yield and over-reliance on pesticides. Early detection enables farmers to take preventative action, drastically reducing damage and cost. Current detection regimes often rely on expert identification of the pathogen from plant damage. More recently, other molecular techniques have emerged. However, these methods suffer the same problems - being specific for a single species and a need for relatively large quantities of pathogenic material. Recently, TGAC has been working on an approach dubbed Air-seq that seeks to identify pathogens through sequencing of biological particles present in air. This overcomes both problems associated with current techniques as it is unbiased (not limited by species) and requires very small quantities of material. Our ultimate aim is to put sample collection, sequencing and analysis in a single box that can be deployed in the field. Key to success is a compact sequencing technology and this has recently emerged in the form of Oxford Nanopore Technologies' (ONT) MinION. The MinION is a new compact, low-cost sequencing technology that offers long reads (thousands of bases of DNA) and a streamed mode of operation enabling analysis of data as it is generated. These attributes make it ideally suited to in-field use. However, part of the process of generating sequencing data involves converting an electrical signal from the DNA sensing pore into a sequence of bases (letters) and this is performed via an internet 'basecalling' service. For in-field deployment, this is unsatisfactory, as we cannot rely on high speed, reliable data connections. We believe a completely new approach is required in which we utilise the raw signal data in order to identify species, instead of searching against basecalled sequence. In this project, we will develop a tool that searches Nanopore signal data looking for the characteristic signal traces of pathogens of interest, building up a report on abundance levels in the process.

Impact Summary

Academic impact This work will result in the production of a valuable tool for scientists working on in-field uses of Nanopore sequencing. Our initial target is pathogen detection applications, but target sequences could be anything and this therefore widens the tools' usefulness to a wide range of clinical, ecological and conservation diagnostic and surveillance applications. The availability of an API widens use further, enabling others to build new tools tailored to specific applications that sit on top of the API. The development of the tool will generate new opportunities for collaborative work with R&D groups in industry and with academic institutions. We anticipate that it will also form part of an ongoing relationship with Dstl. The postdoctoral researcher employed for the project will gain important knowledge of bioinformatics, signal processing and Nanopore sequencing. They will develop extremely valuable skills in the use of high performance computing environments and will gain further opportunities to develop their written and verbal communication skills. Economic and societal impacts Early detection and quantification of crop pathogens has the potential to dramatically change agriculture in significant ways: 1. Reduction in the use of pesticides: by early harvesting or by spraying only when a) pathogen levels reach dangerous levels b) the pathogen race is a known to be able to overcome the cultivars resistance c) targeted use of pesticides the pathogen is not resistant to. The knock-on effect will be to reduce the economic impact of pesticides in non-target species, estimated to be around $8 billion annually (Aktar et al. 2009 PMID: 21217838). 2. Reduction in crop damage by ensuring spraying occurs before infection can take hold. 3. Improved results from crop spraying due to better selection of fungicides based on exact knowledge of pathogen levels and strain. 4. Detection of novel pathogens and new strains of known pathogens 5. Better modellingof disease epidemiology. Reducing the damage caused by crop disease and reducing the level of fungicide use will have positive economic impacts on farmers and will help to ensure UK food security. It will also allow (with wind direction) the early detection of pathogens that are carried from the continent or have just established a "bridgehead" in the UK. Within Dstl's remit, this project has the potential to revolutionise the detection of biological warfare agents in the field. This would have immediate implications for defending the UK against the use of biological weapons and against bioterrorism.

Committee	Research Committee A (Animal disease, health and welfare)
Research Topics	Technology and Methods Development
Research Priority	X – Research Priority information not available
Research Initiative	Tools and Resources Development Fund (TRDF) [2006-2015]
Funding Scheme	X – not Funded via a specific Funding Scheme

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