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Identification of putative rhamnogalacturonan-II specific glycosyltransferases in Arabidopsis using a combination of bioinformatics approaches.
PLoS ONE
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Rhamnogalacturonan-II (RG-II) is a complex plant cell wall polysaccharide that is composed of an ?(1,4)-linked homogalacturonan backbone substituted with four side chains. It exists in the cell wall in the form of a dimer that is cross-linked by a borate di-ester. Despite its highly complex structure, RG-II is evolutionarily conserved in the plant kingdom suggesting that this polymer has fundamental functions in the primary wall organisation. In this study, we have set up a bioinformatics strategy aimed at identifying putative glycosyltransferases (GTs) involved in RG-II biosynthesis. This strategy is based on the selection of candidate genes encoding type II membrane proteins that are tightly coexpressed in both rice and Arabidopsis with previously characterised genes encoding enzymes involved in the synthesis of RG-II and exhibiting an up-regulation upon isoxaben treatment. This study results in the final selection of 26 putative Arabidopsis GTs, including 10 sequences already classified in the CAZy database. Among these CAZy sequences, the screening protocol allowed the selection of ?-galacturonosyltransferases involved in the synthesis of ?4-GalA oligogalacturonides present in both homogalacturonans and RG-II, and two sialyltransferase-like sequences previously proposed to be involved in the transfer of Kdo and/or Dha on the pectic backbone of RG-II. In addition, 16 non-CAZy GT sequences were retrieved in the present study. Four of them exhibited a GT-A fold. The remaining sequences harbored a GT-B like fold and a fucosyltransferase signature. Based on homologies with glycosyltransferases of known functions, putative roles in the RG-II biosynthesis are proposed for some GT candidates.
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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.