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University of Greenwich

Mining disease resistance genes in cassava using next-generation sequencing

Abstract

dc:description.abstract

Cassava brown streak disease (CBSD) remains a major threat to cassava productivity hence to food security and livelihood of over half a billion people in sub-Saharan Africa. Exploitation of natural resistance is generally accepted as the most sustainable means to control the disease. Most of the existing resistance sources have been identified based on evaluation of resistance to infection in the field where escapes is not uncommon. This limitation alongside the need to enhance knowledge on the currently poorly understood molecular processes underlying CBSD resistance and susceptibility gave impetus to this project. The project was therefore designed around identifying new sources of resistance to Cassava brown streak virus (CBSV) and understanding molecular mechanisms underlying natural resistance. A multiplex real time PCR method was developed for quantification of CBSVs alongside the DNA viruses of cassava – African cassava mosaic virus and East African cassava mosaic virus – in a single tube. The method was highly sensitive and reliably quantified cassava viruses and multiplexing did not diminish sensitivity or accuracy. Evaluation of responses to controlled CBSV infection classified cassava accessions as CBSD resistant, tolerant or susceptible based on foliar and root CBSV quantities. Average CBSV quantity were up to 45 times lower in resistant compared to susceptible cassava. Resistance to CBSV inoculation in the two accessions – Mkumba and Pwani was demonstrated for the first time. Transcriptome analysis of 48 samples comprising eight CBSV- and mock-inoculated cassava accessions sampled at one, five and eight weeks after inoculation showed that the cassava transcriptome is very dynamic. About 68% of the expressed genes were found to change over time. Transcription of genes encoding antioxidant defense, pathogenesis-related and cell expansion functions were positively modulated by CBSV infection, in susceptible cassava but repressed in the resistant ones. Genes which function in plant adaptive response to abiotic stress were induced in both accessions but substantially more so in susceptible accessions. Unique transcriptional activity of CBSD-resistant cassava was defined by overexpression of nucleotide binding site / leucine-rich repeat (NBS-LRR) resistance genes. Data from RNA-sequencing of the cassava samples was also applied, for the first time, to the analysis of allele expression at individual single nucleotide polymorphic (SNP) loci. Higher proportion of loci were expressed as heterozygous alleles in resistant compared to susceptible and tolerant cassava. This observation was associated with the introgression of alleles from the wild cassava – Manihot glaziovii. Genome segments ranging from 0.1 to 8 megabases in chromosomes 3, 4 and 13 were found to contain M. glaziovii haplotypes common and unique to CBSV-resistant accessions. A synthesis of results from analyses of allele and gene expression suggests that a more pronounced activity of the plant immunity pathway dissociated from hypersensitive response leads to quick control of CBSV replication upon infection. This, and peculiar genetic variations underlie the low virus quantity and under-expression of stress-associated genes characteristic of CBSD-resistant cassava.

Degree

thesis:*
Name dc:type.qualificationname
phd
Level dc:type.qualificationlevel
doctoral
Grantor dc:publisher.institution
University of Greenwich
Year dc:date.issued
2016

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Otti, Gerald Akachi
Advisors dc:contributor.advisor
  • Maruthi, Midatharahally Narasegowda
  • Bouvaine, Sophie

Subjects

dc:subject × 1

Rights

Language dc:language
en

Chain of custody

source
Harvested from
University of Greenwich
Base URL
gala.gre.ac.uk/cgi/oai2
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Otti, Gerald Akachi. Mining disease resistance genes in cassava using next-generation sequencing. doctoral thesis, University of Greenwich, 2016.