Know more

Our use of cookies

Cookies are a set of data stored on a user’s device when the user browses a web site. The data is in a file containing an ID number, the name of the server which deposited it and, in some cases, an expiry date. We use cookies to record information about your visit, language of preference, and other parameters on the site in order to optimise your next visit and make the site even more useful to you.

To improve your experience, we use cookies to store certain browsing information and provide secure navigation, and to collect statistics with a view to improve the site’s features. For a complete list of the cookies we use, download “Ghostery”, a free plug-in for browsers which can detect, and, in some cases, block cookies.

Ghostery is available here for free: https://www.ghostery.com/fr/products/

You can also visit the CNIL web site for instructions on how to configure your browser to manage cookie storage on your device.

In the case of third-party advertising cookies, you can also visit the following site: http://www.youronlinechoices.com/fr/controler-ses-cookies/, offered by digital advertising professionals within the European Digital Advertising Alliance (EDAA). From the site, you can deny or accept the cookies used by advertising professionals who are members.

It is also possible to block certain third-party cookies directly via publishers:

Cookie type

Means of blocking

Analytical and performance cookies

Realytics
Google Analytics
Spoteffects
Optimizely

Targeted advertising cookies

DoubleClick
Mediarithmics

The following types of cookies may be used on our websites:

Mandatory cookies

Functional cookies

Social media and advertising cookies

These cookies are needed to ensure the proper functioning of the site and cannot be disabled. They help ensure a secure connection and the basic availability of our website.

These cookies allow us to analyse site use in order to measure and optimise performance. They allow us to store your sign-in information and display the different components of our website in a more coherent way.

These cookies are used by advertising agencies such as Google and by social media sites such as LinkedIn and Facebook. Among other things, they allow pages to be shared on social media, the posting of comments, and the publication (on our site or elsewhere) of ads that reflect your centres of interest.

Our EZPublish content management system (CMS) uses CAS and PHP session cookies and the New Relic cookie for monitoring purposes (IP, response times).

These cookies are deleted at the end of the browsing session (when you log off or close your browser window)

Our EZPublish content management system (CMS) uses the XiTi cookie to measure traffic. Our service provider is AT Internet. This company stores data (IPs, date and time of access, length of the visit and pages viewed) for six months.

Our EZPublish content management system (CMS) does not use this type of cookie.

For more information about the cookies we use, contact INRA’s Data Protection Officer by email at cil-dpo@inra.fr or by post at:

INRA
24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal Université de Liège Université de Lille Université Picardie Jules Verne Associated institutions

UMR Transfrontalière BioEcoAgro (www.bioecoagro.eu)

An untargeted LC‐MS based workflow for the structural characterization of plant polyesters

An untargeted LC‐MS based workflow for the structural characterization of plant polyesters
A team of researchers, including Rebecca DAUWE, Assistant Professor at BIOPI - UPJV, has just published in The Plant Journal a publication entitled "An untargeted LC‐MS based workflow for the structural characterization of plant polyesters”.

This work was developed with the financial support of the Picardie Region Research Council (project COMET).

Cell wall localized heterogeneous polyesters are widespread in land plants. The composition of these polyesters, such as cutin, suberin, or more plant specific forms such as the flax seed coat lignan macromolecule, can be determined after total hydrolysis of the ester linkages. The main bottleneck in the structural characterization of these macromolecules, however, resides in the determination of the higher order monomer sequences. Partial hydrolysates of the polyesters release a complex mixture of fragments of different lengths, each present in low abundance and therefore challenging to structurally characterize.

Here, a method is presented by which liquid chromatography‐mass spectrometry (LC‐MS) profiles of such partial hydrolysates are searched for pairs of related fragments. LC‐MS peaks that show a mass difference corresponding to the addition of one or more macromolecule monomers were connected in a network. Starting from the lowest molecular weight peaks in the network, the annotation of the connections as the addition of one or more polyester monomers allow the prediction of consecutive and increasingly complex adjacent peaks.

MSn experiments further help to reject, corroborate, and sometimes refine the structures predicted by the network. As a proof of concept, this procedure was applied to partial hydrolysates of the flax seed coat lignan macromolecule, and allowed to characterize 120 distinct oligo‐esters, consisting of up to 6 monomers, and containing monomers and linkages of which the incorporation in the lignan macromolecule had not been described before. These results show the capacity of the approach to advance the structural elucidation of complex plant polyesters.

Benjamin Thiombiano, Eric Gontier, Roland Molinié, Paulo Marcelo, François Mesnard, Rebecca Dauwe

See also

First published: 14 January 2020  https://doi.org/10.1111/tpj.14686