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Vernalization Alters Sink and Source Identities and Reverses Phloem Translocation from Taproots to Shoots in Sugar Beet.

Cristina Martins RodriguesChristina MüdsamIsabel KellerWolfgang ZiererOlaf CzarneckiJosé María CorralFrank ReinhardtPetra NieberlKarin Fiedler-WiechersFrederik SommerMichael SchrodaTimo MühlhausKarsten HarmsUlf-Ingo FlüggeUwe SonnewaldWolfgang KochFrank LudewigHorst Ekkehard NeuhausBenjamin Pommerrenig
Published in: The Plant cell (2020)
During their first year of growth, overwintering biennial plants transport Suc through the phloem from photosynthetic source tissues to storage tissues. In their second year, they mobilize carbon from these storage tissues to fuel new growth and reproduction. However, both the mechanisms driving this shift and the link to reproductive growth remain unclear. During vegetative growth, biennial sugar beet (Beta vulgaris) maintains a steep Suc concentration gradient between the shoot (source) and the taproot (sink). To shift from vegetative to generative growth, they require a chilling phase known as vernalization. We studied sugar beet sink-source dynamics upon vernalization and showed that before flowering, the taproot underwent a reversal from a sink to a source of carbohydrates. This transition was induced by transcriptomic and functional reprogramming of sugar beet tissue, resulting in a reversal of flux direction in the phloem. In this transition, the vacuolar Suc importers and exporters TONOPLAST SUGAR TRANSPORTER2;1 and SUCROSE TRANSPORTER4 were oppositely regulated, leading to the mobilization of sugars from taproot storage vacuoles. Concomitant changes in the expression of floral regulator genes suggest that these processes are a prerequisite for bolting. Our data will help both to dissect the metabolic and developmental triggers for bolting and to identify potential targets for genome editing and breeding.
Keyphrases
  • genome editing
  • crispr cas
  • gene expression
  • transcription factor
  • big data
  • long non coding rna
  • climate change
  • bioinformatics analysis