A population based expression atlas provides insights into disease resistance and other physiological traits in cassava (Manihot esculenta Crantz)

Alex C Ogbonna,Leah Nandudu,Lukas A Mueller,Guillaume Bauchet,Adrian Powell,Punna Ramu,Nicolas Morales,Robert Kawuki,Williams Esuma,Jean-Luc Jannink

doi:10.1038/s41598-021-02794-y

Abstract

Cassava, a food security crop in Africa, is grown throughout the tropics and subtropics. Although cassava can provide high productivity in suboptimal conditions, the yield in Africa is substantially lower than in other geographies. The yield gap is attributable to many challenges faced by cassava in Africa, including susceptibility to diseases and poor soil conditions. In this study, we carried out 3’RNA sequencing on 150 accessions from the National Crops Resources Research Institute, Uganda for 5 tissue types, providing population-based transcriptomics resources to the research community in a web-based queryable cassava expression atlas. Differential expression and weighted gene co-expression network analysis were performed to detect 8820 significantly differentially expressed genes (DEGs), revealing similarity in expression patterns between tissue types and the clustering of detected DEGs into 18 gene modules. As a confirmation of data quality, differential expression and pathway analysis targeting cassava mosaic disease (CMD) identified 27 genes observed in the plant–pathogen interaction pathway, several previously identified CMD resistance genes, and two peroxidase family proteins different from the CMD2 gene. Present research work represents a novel resource towards understanding complex traits at expression and molecular levels for the development of resistant and high-yielding cassava varieties, as exemplified with CMD.

Highlights

Cassava (Manihot esculenta Crantz), a staple for over 800 million people worldwide, is cultivated across the tropics, with Africa accounting for over 50% of the total world production
The objectives of this study were to (1) quantify expression of transcripts across five tissues for 150 accessions, (2) make this data resource available to the community in a webbased queryable cassava expression atlas, (3) conduct differential gene expression analysis to detect differentially expressed genes (DEGs) across our population, with which we carried out weighted gene co-expression network analysis (WGCNA) and Gene Ontology (GO) analysis to characterize genes detected in different modules or co-expressed clusters, (iv) confirm data quality by differential gene expression and GO analysis carried out on clones differing for cassava mosaic disease (CMD) tolerance
While transcriptomics has relied on contrasting individuals, our study provides a population-based resource, unique to previously described available transcriptomics cassava resources

Summary

Introduction

Cassava (Manihot esculenta Crantz), a staple for over 800 million people worldwide, is cultivated across the tropics, with Africa accounting for over 50% of the total world production. Transcriptomics is an approach that uses deep sequencing technologies such as the RNA-seq to profile transcriptomes, representing the complete set of transcripts in a c ell[5] Techniques such as transcriptomics can be used to study plant diseases, such as Cassava mosaic disease (CMD). We present a population-based transcriptomic resource and expression atlas visualization for a population consisting of 150 cassava accessions sampled across five tissues (leaf, stem, fibrous root, storage root, flower) for studies of complex traits in the cassava community. The objectives of this study were to (1) quantify expression of transcripts across five tissues for 150 accessions, (2) make this data resource available to the community in a webbased queryable cassava expression atlas, (3) conduct differential gene expression analysis to detect differentially expressed genes (DEGs) across our population, with which we carried out weighted gene co-expression network analysis (WGCNA) and Gene Ontology (GO) analysis to characterize genes detected in different modules or co-expressed clusters, (iv) confirm data quality by differential gene expression and GO analysis carried out on clones differing for CMD tolerance. This work provides a population-based transcriptomics resource with a wide range of applications and can be leveraged for studies of simple and complex traits in cassava

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