Proteomics

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Rauvolfia tetraphylla latex proteome


ABSTRACT: Monoterpene indole alkaloids (MIAs) are a structurally diverse family of specialized metabolites mainly produced in Gentianales to cope with environmental challenges. Due to their pharmacological properties, the biosynthetic modalities of several MIA types have been elucidated but not that of the yohimbanes. Here, we combine transcriptomics and genome sequencing of Rauvolfia tetraphylla with machine learning to discover the unexpected multiple actors of this natural product's synthesis. We identify a medium chain dehydrogenase/reductase (MDR) that produces a mixture of four diastereomers of yohimbanes including the well-known yohimbine and rauwolscine. In addition to this multifunctional yohimbane synthase (YOS), an MDR synthesizing mainly heteroyohimbanes and the short chain dehydrogenase vitrosamine synthase also display a yohimbane synthase side activity. Lastly, we establish that the combination of geissoschizine synthase with at least three other MDRs also produces a yohimbane mixture thus shedding light on the complex mechanisms evolved for the synthesis of these plant bioactives.

INSTRUMENT(S): Q Exactive Plus

ORGANISM(S): Rauvolfia Tetraphylla

TISSUE(S): Latex

SUBMITTER: Clément Cuello  

LAB HEAD: Vincent Courdavault

PROVIDER: PXD046315 | Pride | 2024-01-26

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
20190617_Courdavault_55_01.mzXML Mzxml
20190617_Courdavault_55_02.mzXML Mzxml
20190617_Courdavault_55_03.mzXML Mzxml
20190617_Courdavault_55_04.mzXML Mzxml
20190617_Courdavault_55_05.mzXML Mzxml
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Publications


Monoterpene indole alkaloids (MIAs) are a structurally diverse family of specialized metabolites mainly produced in Gentianales to cope with environmental challenges. Due to their pharmacological properties, the biosynthetic modalities of several MIA types have been elucidated but not that of the yohimbanes. Here, we combine metabolomics, proteomics, transcriptomics and genome sequencing of Rauvolfia tetraphylla with machine learning to discover the unexpected multiple actors of this natural pro  ...[more]

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