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The Cassava Source–Sink project: opportunities and challenges for crop improvement by metabolic engineering
Author(s) -
Sonnewald Uwe,
Fernie Alisdair R.,
Gruissem Wilhelm,
Schläpfer Pascal,
Anjanappa Ravi B.,
Chang ShuHeng,
Ludewig Frank,
Rascher Uwe,
Muller Onno,
Doorn Anna M.,
Rabbi Ismail Y.,
Zierer Wolfgang
Publication year - 2020
Publication title -
the plant journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.058
H-Index - 269
eISSN - 1365-313X
pISSN - 0960-7412
DOI - 10.1111/tpj.14865
Subject(s) - biology , microbiology and biotechnology , agriculture , population , starch , staple food , agronomy , tropical agriculture , ecology , biochemistry , demography , sociology
Summary 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 with respect to securing the future food supply for the growing population of Sub‐Saharan Africa. The Cassava Source–Sink (CASS) project aims to increase 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, 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 aiming 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.