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

Abstract

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.

Metabolomic and transcriptomic insights into how cotton fiber transitions to secondary wall synthesis, represses lignification, and prolongs elongation

“…Cotton fiber shows relatively weak expression of a homolog of AtCSLA2 and even lower expression of an AtCSLA9 homolog: both of the encoded proteins have ß-1,4-mannan and/or glucomannan synthase activity in vitro [ 72 ]. Mannan has been detected in extracts of tightly bound cell wall matrix polymers of 6 to 30 DPA Gh fiber and mature Gh and Gb fibers that are still surrounded by a primary wall [ 25 , 73 ]. Tentatively, it may contribute positively to the ‘elongation’ parameter, or the extent of fiber stretching before breaking [ 73 ].…”

Interactions and functionalities of the gut revealed by computational approaches

“…Since the data originated from an animal experiment that was not designed for the detection of genetically and/or dietarily induced differences in external phenotypes, we only focused on the connectivity between 5 intermediate phenotypic levels. Some studies have reported pairwise data integration of two (Benis et al 2015;Lu et al 2014;Rajasundaram et al 2014 or three data sets (Adourian et al 2008. But this is, to the best of our knowledge, the first time that an integration of such heterogeneous datatypes from different tissues, arising from a single experiment, has been reported.…”

“…gration of microbiota with gene expression data(Benis et al 2015;Steegenga et al 2016, and measurements on cell wall polysaccharides of fibers with phenotypic characterizations of fibers in cotton balls(Rajasundaram et al 2014. We performed pairwise integration of the datasets, resulting in ten networks with varying spreads of correlation values. Deciding on a threshold to distinguish genuine from spurious correlations is a major bottleneck for the definition of correlation networks.…”

FeedOmics, an approach to evaluate the functional properties of protein containing feed ingredients

“…Since the data originated from an animal experiment that was not designed for the detection of genetically and/or dietary induced differences in external phenotypes, we only focused on the connectivity between 5 intermediate phenotypic levels. Some studies have reported pairwise data integration of two (Benis et al, 2015;Lu et al, 2014;Rajasundaram et al, 2014) or three data sets (Adourian et al, 2008). But this is, to the best of our knowledge, the first time that an integration of such heterogeneous data-types from different tissues, arising from a single experiment, has been reported.…”

“…This method can also handle the dimensionality problem of biological datasets where the number of variables is usually higher than the number of samples. sPLS has been previously used for integration of microbiota with gene expression data (Benis et al, 2015;Steegenga et al, 2016) and measurements on cell wall polysaccharides of fibers with phenotypic characterizations of fibers in cotton balls (Rajasundaram et al, 2014).…”