PME58 plays a role in pectin distribution during seed coat mucilage extrusion through homogalacturonan modification.
Amélie TurbantFrançoise FournetMichelle LequartLuciane ZabijakKarine PageauSophie BoutonOlivier Van WuytswinkelPublished in: Journal of experimental botany (2016)
Pectins are major components of plant primary cell walls. They include homogalacturonans (HGs), which are the most abundant pectin and can be the target of apoplastic enzymes like pectin methylesterases (PMEs) that control their methylesterification level. Several PMEs are expressed in the seed coat of Arabidopsis thaliana, particularly in mucilage secretory cells (MSCs). On the basis of public transcriptomic data, seven PME genes were selected and checked for their seed-specific expression by quantitative reverse transcription PCR. Of these, PME58 presented the highest level of expression and was specifically expressed in MSCs at the early stages of seed development. pme58 mutants presented two discrete phenotypes: (i) their adherent mucilage was less stained by ruthenium red when compared to wild-type seeds, but only in the presence of EDTA, a Ca(2+)chelator; and (ii) the MSC surface area was decreased. These phenotypes are the consequence of an increase in the degree of HG methylesterification connected to a decrease in PME activity. Analysis of the sugar composition of soluble and adherent mucilage showed that, in the presence of EDTA, sugars of adherent mucilage were more readily extracted in pme58 mutants. Immunolabelling with LM19, an antibody that preferentially recognizes unesterified HGs, also showed that molecular interactions with HGs were modified in the adherent mucilage of pme58 mutants, suggesting a role of PME58 in mucilage structure and organization. In conclusion, PME58 is the first PME identified to play a direct role in seed mucilage structure.
Keyphrases
- wild type
- poor prognosis
- mesenchymal stem cells
- arabidopsis thaliana
- healthcare
- mental health
- emergency department
- single cell
- umbilical cord
- induced apoptosis
- gene expression
- binding protein
- mass spectrometry
- machine learning
- big data
- deep learning
- bone marrow
- cell death
- electronic health record
- transcription factor
- protein kinase