A quantitative method for analyzing glycome profiles of plant cell walls. (7th August 2017)

• Record Type: Journal Article
• Authors: Pattathil, Sivakumar; Ingwers, Miles W.; Aubrey, Doug P.; Li, Zenglu; Dahlen, Joseph
• Is Part Of: Carbohydrate research. Volume 448(2017)
• Page Start: 128
• Page End: 135
• Publication Date: 2017-08-07
• Subjects: Cell walls -- Moisture stress -- Monoclonal antibodies -- Pinus taeda -- Xylan -- Pectin -- Xyloglucans; Carbohydrates -- Periodicals; Chemistry, Organic -- Periodicals; Biochemistry -- Periodicals; Carbohydrates -- Periodicals; Chimie organique -- Périodiques; Glucides -- Périodiques; Biochemistry; Carbohydrates; Chemistry, Organic; Periodicals; Electronic journals; 507.78
• DOI: 10.1016/j.carres.2017.06.009
• Languages: English
• ISSNs: 0008-6215

Abstract

Abstract: Glycome profiling allows for the characterization of plant cell wall ultrastructure via sequential extractions and subsequent detection of specific epitopes with a suite of glycan-specific monoclonal antibodies (mAbs). The data are often viewed as the amount of materials recovered and coinciding colored heatmaps of mAb binding are generated. Interpretation of these data can be considered qualitative in nature as it depends on detecting subtle visual differences in antibody binding strength. Here, we report a mixed model-based quantitative approach for glycome profile analyses, which accounts for the amount of materials recovered and displays the normalized values in revised heatmaps and statistical heatmaps depicting significant differences. The utility of this methodology was demonstrated on a previously published dataset investigating the effects of moisture stress on the roots and needles of Pinus taeda . An annotated R script for the quantitative methodology is included to allow future studies to utilize the same approach. Graphical abstract: Highlights: A quantitative methodology was developed to better analyze plant cell wall glycome profiles. The quantitative methodology proposed herein allows for better estimation of epitope abundance. The approach resulted in more in-depth findings when applied to a pre-existing dataset.