The metabotyping of an East African cassava diversity panel: A core collection for developing biotic stress tolerance in cassava

• Published in: PloS one 2020-11, Vol.15 (11), p.e0242245-e0242245
• Main Authors: Perez-Fons, Laura, Ovalle, Tatiana M, Maruthi, M N, Colvin, John, Lopez-Lavalle, Luis Augusto Becerra, Fraser, Paul D
• Format: Article
• Language: English
• Subjects: Agricultural production; Agricultural research; Animals; Bemisia tabaci; Biodiversity; Biology and Life Sciences; Breeding; Cassava; Cluster analysis; Clustering; Data analysis; Data processing; Discriminant analysis; Disease; Disease resistance (Plants); Donor materials; Food security; Genetic aspects; Genotyping; Geography; Germplasm; Hemiptera - physiology; Infestation; Manihot - genetics; Manihot - metabolism; Manihot - physiology; Metabolites; Metabolomics; Multivariate analysis; Natural resources; Phenotype; Physical Sciences; Physiological aspects; Plant hardiness; Principal components analysis; Research and Analysis Methods; Resistance factors; Stress, Physiological; Viruses
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0242245

Cassava will have a vital role to play, if food security is to be achieved in Sub-Saharan Africa, especially Central and East Africa. The whitefly Bemisia tabaci poses a major threat to cassava production by small holder farmers in part due to their role as a vector of cassava mosaic begomoviruses (CMBs) and cassava brown streak ipomoviruses (CBSIs). In the present study untargeted metabolomics has been used as a tool to assess natural variation, similarities and attempts to identify trait differentiators among an East African cassava diversity panel that displayed tolerance/resistance to the effects of Bemisia tabaci infestation. The metabolome captured, was represented by 1529 unique chemical features per accession. Principal component analysis (PCA) identified a 23% variation across the panel, with geographical origin/adaption the most influential classification factors. Separation based on resistance and susceptible traits to Bemisia tabaci could also be observed within the data and was corroborated by genotyping data. Thus the metabolomics pipeline represented an effective metabotyping approach. Agglomerative Hierarchical Clustering Analysis (HCA) of both the metabolomics and genotyping data was performed and revealed a high level of similarity between accessions. Specific differentiating features/metabolites were identified, including those potentially conferring vigour to whitefly tolerance on a constitutive manner. The implications of using these cassava varieties as parental breeding material and the future potential of incorporating more exotic donor material is discussed.