Understanding the Relationship between Cotton Fiber Properties and Non-Cellulosic Cell Wall Polysaccharides

<div><p>A detailed knowledge of cell wall heterogeneity and complexity is crucial for understanding plant growth and development. One key challenge is to establish links between polysaccharide-rich cell walls and their phenotypic characteristics. It is of particular interest for some plant material, like cotton fibers, which are of both biological and industrial importance. To this end, we attempted to study cotton fiber characteristics together with glycan arrays using regression based approaches. Taking advantage of the comprehensive microarray polymer profiling technique (CoMPP), 32 cotton lines from different cotton species were studied. The glycan array was generated by sequential extraction of cell wall polysaccharides from mature cotton fibers and screening samples against eleven extensively characterized cell wall probes. Also, phenotypic characteristics of cotton fibers such as length, strength, elongation and micronaire were measured. The relationship between the two datasets was established in an integrative manner using linear regression methods. In the conducted analysis, we demonstrated the usefulness of regression based approaches in establishing a relationship between glycan measurements and phenotypic traits. In addition, the analysis also identified specific polysaccharides which may play a major role during fiber development for the final fiber characteristics. Three different regression methods identified a negative correlation between micronaire and the xyloglucan and homogalacturonan probes. Moreover, homogalacturonan and callose were shown to be significant predictors for fiber length. The role of these polysaccharides was already pointed out in previous cell wall elongation studies. Additional relationships were predicted for fiber strength and elongation which will need further experimental validation.</p></div

Profiling the Hydrolysis of Isolated Grape Berry Skin Cell Walls by Purified Enzymes

The unraveling of crushed grapes by maceration enzymes during winemaking is difficult to study because of the complex and rather undefined nature of both the substrate and the enzyme preparations. In this study we simplified both the substrate, by using isolated grape skin cell walls, and the enzyme preparations, by using purified enzymes in buffered conditions, to carefully follow the impact of the individual and combined enzymes on the grape skin cell walls. By using cell wall profiling techniques we could monitor the compositional changes in the grape cell wall polymers due to enzyme activity. Extensive enzymatic hydrolysis, achieved with a preparation of pectinases or pectinases combined with cellulase or hemicellulase enzymes, completely removed or drastically reduced levels of pectin polymers, whereas less extensive hydrolysis only opened up the cell wall structure and allowed extraction of polymers from within the cell wall layers. Synergistic enzyme activity was detectable as well as indications of specific cell wall polymer associations

Summary statistics of the five possible multiple regression models.

<p>Summary statistics of the five possible multiple regression models.</p

Redundancy analysis of dependent and independent variates for the first canonical function.

<p>Redundancy analysis of dependent and independent variates for the first canonical function.</p

Canonical weights, loadings, and cross-loadings for the first canonical function.

<p>Canonical weights, loadings, and cross-loadings for the first canonical function.</p

Canonical correlation analysis maximizes the correlation between the linear combination of the cell wall polysaccharides in the glycan array and the fiber properties.

<p>In this figure, given a linear combination of <i>X</i> variables: <i>U₁</i> = <i>f</i>_1×1+ <i>f</i>_2×2+ …+<i>f</i>_p<i>X</i>_p and a linear combination of <i>Y</i> variables: <i>V₁</i> = <i>g</i>₁<i>Y</i>₁+ <i>g</i>₂<i>Y</i>₂+ …+<i>g</i>_q<i>Y</i>_q, the first canonical correlation is the maximum correlation coefficient between <i>U₁</i> and <i>V₁</i>, for all <i>U₁</i> and <i>V₁.</i></p

Multivariate tests of significance for the canonical functions.

<p>Multivariate tests of significance for the canonical functions.</p

Graphical representation of the variables selected by sPLS on the first two dimensions predicts specific cell wall polysaccharides linked to the fiber properties.

<p>The coordinates of each variable are obtained by computing the correlation between the latent variable vectors and the original dataset. The selected variables are then projected onto correlation circles where highly correlated variables cluster together. These graphics help to identify association between the two datasets. The correlation between two variables is positive if the angle is sharp cos(α)>0, negative if the angle is obtuse cos(θ)<0, and null if the vectors are perpendicular cos(β)∼0.</p

Graphical representation of the cotton lines on the first two sPLS dimensions shows the trend in clustering of specific cotton lines across different species.

<p>Four different species of cotton are shown in different colors. <i>Gossypium hirsutum</i> is colored in magenta, <i>Gossypium barbadense</i> in green, <i>Gossypium herbaceum</i> in orange and <i>Gossypium arboreum</i> in red.</p

Canonical Correlation analysis relating probe signals and fiber characteristics with the measure of overall model fit.

<p>Canonical Correlation analysis relating probe signals and fiber characteristics with the measure of overall model fit.</p