The data indicate that operating and maximum gs of non-stressed leaves maintained under stable conditions deviate considerably (by 45-91 %), because stomatal size inadequately reflects operating pore area (R(2) = 0·46). Furthermore, it was found that variation between ILs in both stomatal sensitivity to desiccation and operating gs is associated with features of individual stoma. In contrast, genotypic variation in gs partitioning depends on the distribution of stomata between the leaf adaxial and abaxial epidermis.

Cell adhesion in plants is mediated predominantly by pectins, a group of complex cell wall associated polysaccharides. An Arabidopsis mutant, friable1 (frb1), was identified through a screen of T-DNA insertion lines that exhibited defective cell adhesion. Interestingly, the frb1 plants displayed both cell and organ dissociations and also ectopic defects in organ separation. The FRB1 gene encodes a Golgi-localized, plant specific protein with only weak sequence similarities to known proteins (DUF246). Unlike other cell adhesion deficient mutants, frb1 mutants do not have reduced levels of adhesion related cell wall polymers, such as pectins. Instead, FRB1 affects the abundance of galactose- and arabinose-containing oligosaccharides in the Golgi. Furthermore, frb1 mutants displayed alteration in pectin methylesterification, cell wall associated extensins and xyloglucan microstructure. We propose that abnormal FRB1 action has pleiotropic consequences on wall architecture, affecting both the extensin and pectin matrices, with consequent changes to the biomechanical properties of the wall and middle lamella, thereby influencing cell-cell adhesion.

Whole genome transcript correlation-based approaches have been shown to be enormously useful for candidate gene detection. Consequently, simple Pearson correlation has been widely applied in several web based tools. That said, several more sophisticated methods based on e.g. mutual information or Bayesian network inference have been developed and have been shown to be theoretically superior but are not yet commonly applied. Here, we propose the application of a recently developed statistical regression technique, the LASSO, to detect novel candidates from high throughput transcriptomic datasets. We apply the LASSO to a tissue specific dataset in the model plant Arabidopsis thaliana to identify novel players in Arabidopsis thaliana seed coat mucilage synthesis. We built LASSO models based on a list of genes known to be involved in a sub-pathway of Arabidopsis mucilage synthesis. After identifying a putative transcription factor, we verified its involvement in mucilage synthesis by obtaining knock-out mutants for this gene. We show that a loss of function of this putative transcription factor leads to a significant decrease in mucilage pectin.