The Grapevine Expression Atlas Reveals a Deep Transcriptome Shift Driving the Entire Plant into a Maturation Program  

Abstract

We developed a genome-wide transcriptomic atlas of grapevine (Vitis vinifera) based on 54 samples representing green and woody tissues and organs at different developmental stages as well as specialized tissues such as pollen and senescent leaves. Together, these samples expressed ;91% of the predicted grapevine genes. Pollen and senescent leaves had unique transcriptomes reflecting their specialized functions and physiological status. However, microarray and RNA-seq analysis grouped all the other samples into two major classes based on maturity rather than organ identity, namely, the vegetative/ green and mature/woody categories. This division represents a fundamental transcriptomic reprogramming during the maturation process and was highlighted by three statistical approaches identifying the transcriptional relationships among samples (correlation analysis), putative biomarkers (O2PLS-DA approach), and sets of strongly and consistently expressed genes that define groups (topics) of similar samples (biclustering analysis). Gene coexpression analysis indicated that the mature/woody developmental program results from the reiterative coactivation of pathways that are largely inactive in vegetative/green tissues, often involving the coregulation of clusters of neighboring genes and global regulation based on codon preference. This global transcriptomic reprogramming during maturation has not been observed in herbaceous annual species and may be a defining characteristic of perennial woody plants.

MYB107 and MYB9 Homologs Regulate Suberin Deposition in Angiosperms

“…To this end, a collection of multitissue transcriptome data sets was identified from several species, each possessing an association with suberin deposition. In addition to (1) apple (i.e., russet clone) and (2) tomato (i.e., SlDCR-RNAi) fruit transcriptome data sets described above, this collection included gene expression data sets of (3) seed tissues during development in Arabidopsis (Le et al, 2010), (4) all major organs of grapevine (Vitis vinifera) (Fasoli et al, 2012), (5) multiple organs of tomato (Tomato Gene Consortium, 2012), (6) potato (Xu et al, 2011), and (7) the roots (A) Two Golden Delicious apple clones were analyzed: the normal skinned 'Reinders' clone and the russeted 'Rugiada' clone. 'Rugiada' displays a rough, brown, and cracked fruit surface.…”

Gene coexpression network for <i>trans</i>-1,4-polyisoprene biosynthesis involving mevalonate and methylerythritol phosphate pathways in <i>Eucommia ulmoides</i> Oliver

“…For PC3, hormone- treated fruit and female reproductive organs had positive scores. In a previous study, each plant tissue also showed convergence in the PCA plot and each PC represented organ-specific gene expression (Fasoli et al 2012). In E. ulmoides, accumulation of TPI is particularly observed in fruit (Nakazawa et al 2009), so PC3 might reflect fruitspecific genes including those related to TPI biosynthesis.…”

“…In contrast to model plants, progress in identifying the functions of genes in trees is slow because of the limited research approaches and the small number of researchers. Under such circumstances, microarray analysis has been used to analyze the global gene expression pattern in various plant organs, and functionally similar genes and organ-specific genes have been grouped by multivariate analysis, such as principal component analysis (PCA) (Chow et al 2007;Fasoli et al 2012). Based on PCA, unique functional genes in specific organs have been identified (Fasoli et al 2012).…”

“…In E. ulmoides, accumulation of TPI is particularly observed in fruit (Nakazawa et al 2009), so PC3 might reflect fruitspecific genes including those related to TPI biosynthesis. The global gene expression pattern provides fundamental insight into how specific genes are involved in a biological event (Fasoli et al 2012;Kang et al 2011), and gene grouping is also useful for isolating genes involved in the same biological process such as a metabolic pathway (Basso et al 2005;Hamada et al 2011;Srinivasasainagendra et al 2008). The multivariate analysis approach of PCA classifies multivariate gene expression data into mutually uncorrelated axes, and each of them has a different aspects of the samples (Ringnér 2008); thus, this method has been utilized for the grouping of the genes at the first step of classification of gene expression data without much computational (Ma and Dai 2011;Ringnér 2008).…”

“…Under such circumstances, microarray analysis has been used to analyze the global gene expression pattern in various plant organs, and functionally similar genes and organ-specific genes have been grouped by multivariate analysis, such as principal component analysis (PCA) (Chow et al 2007;Fasoli et al 2012). Based on PCA, unique functional genes in specific organs have been identified (Fasoli et al 2012). Furthermore, because genes in the same metabolic pathway are often coexpressed, genes of unknown function are sometimes identified as putatively related to the pathway based on coexpression network modeling (Hirai et al 2007;Wille et al 2004).…”