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Ollivier, S.

Publications and source records attributed to Ollivier, S..

2 recordsLinked to original sources

TBL38 is an atypical homogalacturonan acetylesterase with a peculiar cell wall microdomain localization in Arabidopsis seed mucilage secretory cells

Plant cell walls are made of complex polysaccharidic/proteinaceous network whose biosynthesis and dynamics implicate several cell compartments and impact plant development. The synthesis and remodeling of homogalacturonan pectins is associated with multiple developmental processes ranging from growth to response to biotic/abiotic stress. It encompasses Golgi-localized methylation and acetylation and subsequent demethylation and deacetylation in the cell wall. In the last decade, our comprehension of plant polysaccharides acetylation has increased significantly thanks to the study of the TRICHOME BIREFRINGENCE-LIKE (TBL) protein family. TBLs are mostly described as Golgi-localized acetyltransferases specifically targeting diverse hemicelluloses or pectins. Various tbl mutants showed altered wall mechanical properties and dynamics. Here, we study TBL38 that is co-expressed with PECTIN METHYLESTERASE INHIBITOR6 (PMEI6) and PEROXIDASE 36 (PRX36) during the development of Arabidopsis seed mucilage secretory cells (MSCs). We demonstrate the atypical TBL38 cell wall localization restricted to the PMEI6/PRX36 MSC cell wall microdomain. A tbl38 mutant displays an intriguing homogalacturonan immunological phenotype in this cell wall microdomain and in a MSC surface-enriched abrasion powder. This fraction was further characterized by mass spectrometry oligosaccharide profiling revealing an increased homogalacturonan acetylation phenotype. Finally, a recombinant TBL38 is shown to display pectin acetylesterase activity in vitro. These results indicate that TBL38 is an atypical cell wall-localized TBL that displays a homogalacturonan acetylesterase activity rather than a Golgi-localized acetyltransferase activity as observed in previously studied TBLs. TBL38 function during seed development is discussed.

plant biology↗

From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

In the context of untargeted metabolomics, molecular networking is a popular and efficient tool which organizes and simplifies mass spectrometry fragmentation data (LC-MS/MS), by clustering ions based on a cosine similarity score. However, the nature of the ion species is rarely taken into account, causing redundancy as a single compound may be present in different forms throughout the network. Taking advantage of the presence of such redundant ions, we developed a new method named MolNotator. Using the different ion species produced by a molecule during ionization (adducts, dimers, trimers, in-source fragments), a predicted molecule node (or neutral node) is created by triangulation, and ultimately computing the associated molecules calculated mass. These neutral nodes provide researchers with several advantages. Firstly, each molecule is then represented in its ionization context, connected to all produced ions and indirectly to some coeluted compounds, thereby also highlighting unexpected widely present adduct species. Secondly, the predicted neutrals serve as anchors to merge the complementary positive and negative ionization modes into a single network. Lastly, the dereplication is improved by the use of all available ions connected to the neutral nodes, and the computed molecular masses can be used for exact mass dereplication. MolNotator is available as a Python library and was validated using the lichen database spectra acquired on an Orbitrap, computing neutral molecules for >90% of the 156 molecules in the dataset. By focusing on actual molecules instead of ions, MolNotator greatly facilitates the selection of molecules of interest. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/473622v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@730ecaorg.highwire.dtl.DTLVardef@1d012e1org.highwire.dtl.DTLVardef@187a1e5org.highwire.dtl.DTLVardef@195c53f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗