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The Cassava Source-Sink project: Opportunities and challenges for crop improvement by metabolic engineering

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Fernie,  A. R.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Citation

Sonnewald, U., Fernie, A. R., Gruissem, W., Schläpfer, P., Anjanappa, R. B., Chang, S.-H., et al. (2020). The Cassava Source-Sink project: Opportunities and challenges for crop improvement by metabolic engineering. The Plant Journal, 103(5), 1655-1665. doi:10.1111/tpj.14865.


Cite as: https://hdl.handle.net/21.11116/0000-0006-F5D0-A
Abstract
Abstract Cassava (Manihot esculenta Crantz) is one of the important staple foods in Sub-Saharan Africa. It produces starchy storage roots that provide food and income for several hundred million people, mainly in tropical agriculture zones. Increasing cassava storage root and starch yield is one of the major breeding targets to secure the future food supply for the growing population of Sub-Saharan Africa. The Cassava Source-Sink (CASS) project aims at increasing cassava storage root and starch yield by strategically integrating approaches from different disciplines. We present our perspective and progress on cassava as an applied research organism and provide insight into the CASS strategy, which can serve as a blueprint for the improvement of other root and tuber crops. Extensive profiling of different field-grown cassava genotypes generates information for leaf, phloem and root metabolic and physiological processes that are relevant for biotechnological improvements. A multi-national pipeline for genetic engineering of cassava plants covers all steps from gene discovery, cloning, transformation, molecular and biochemical characterization, confined field trials (CFTs) and phenotyping of the seasonal dynamics of shoot traits under field conditions. Together, the CASS project generates comprehensive data to facilitate conventional breeding strategies for high-yielding cassava genotypes. It also builds the foundation for genome-scale metabolic modelling to predict targets and bottlenecks in metabolic pathways. This information is used to engineer cassava genotypes with improved source-sink relations and increased yield potential.