DNA methylation and gene expression regulation associated with vascularization in Sorghum bicolor

Abstract

Summary Plant secondary cell walls constitute the majority of plant biomass. They are predominantly found in xylem cells, which are derived from vascular initials during vascularization. Little is known about these processes in grass species despite their emerging importance as biomass feedstocks. The targeted biofuel crop Sorghum bicolor has a sequenced and well‐annotated genome, making it an ideal monocot model for addressing vascularization and biomass deposition.Here we generated tissue‐specific transcriptome and DNA methylome data from sorghum shoots, roots and developing root vascular and nonvascular tissues.Many genes associated with vascular development in other species show enriched expression in developing vasculature. However, several transcription factor families varied in vascular expression in sorghum compared with Arabidopsis and maize. Furthermore, differential expression of genes associated with DNA methylation were identified between vascular and nonvascular tissues, implying that changes in DNA methylation are a feature of sorghum root vascularization, which we confirmed using tissue‐specific DNA methylome data. Roots treated with a DNA methylation inhibitor also showed a significant decrease in root length.Tissues and organs can be discriminated based on their genomic methylation patterns and methylation context. Consequently, tissue‐specific changes in DNA methylation are part of the normal developmental process.

Integrated Analysis of DNA Methylome and Transcriptome Reveals Epigenetic Regulation of CAM Photosynthesis in Pineapple

“…1d). We found that the mC distribution in pineapple was different than the previously reported distribution in rice and sorghum [30,31], and there were also differences between the green and white pineapple leaf tissues (10am as an example, Supplemental Fig. 2).…”

Co-expression and Transcriptome Analysis of Marchantia polymorpha Transcription Factors Supports Class C ARFs as Independent Actors of an Ancient Auxin Regulatory Module

“…Transcriptome co-expression studies can highlight how specification of complex phenotypes depends on the activity of coordinated batteries of regulatory genes. In plants, many co-expression analyses focused on secondary metabolite production ( Ruprecht et al, 2011 ; Ruprecht and Persson, 2012 ; Cassan-Wang et al, 2013 ; Ferreira et al, 2016 ; Turco et al, 2017 ), although co-expressed modules for hormonal genes ( Ruprecht et al, 2017b ; Singh et al, 2017 ), or developmental factors have also been described ( Ichihashi et al, 2014 ). The functional significance of co-expression modules has been tested by further differential gene expression (DGE), protein-protein interaction ( Kemmeren et al, 2002 ; Ichihashi et al, 2014 ), epigenetic ( Turco et al, 2017 ) or functional analyses ( Yokoyama et al, 2007 ; Ruprecht et al, 2011 ; Cassan-Wang et al, 2013 ; Ichihashi et al, 2014 ).…”

“…In plants, many co-expression analyses focused on secondary metabolite production ( Ruprecht et al, 2011 ; Ruprecht and Persson, 2012 ; Cassan-Wang et al, 2013 ; Ferreira et al, 2016 ; Turco et al, 2017 ), although co-expressed modules for hormonal genes ( Ruprecht et al, 2017b ; Singh et al, 2017 ), or developmental factors have also been described ( Ichihashi et al, 2014 ). The functional significance of co-expression modules has been tested by further differential gene expression (DGE), protein-protein interaction ( Kemmeren et al, 2002 ; Ichihashi et al, 2014 ), epigenetic ( Turco et al, 2017 ) or functional analyses ( Yokoyama et al, 2007 ; Ruprecht et al, 2011 ; Cassan-Wang et al, 2013 ; Ichihashi et al, 2014 ). Furthermore, studies have corroborated conservation of sets of co-expressed genes in closely related species ( Ficklin and Feltus, 2011 ; Ichihashi et al, 2014 ; Netotea et al, 2014 ; Ferreira et al, 2016 ) as well as in deep evolutionary time ( Stuart et al, 2003 ; Gerstein et al, 2014 ; Ruprecht et al, 2017a ).…”

Profiling Alternative 3′ Untranslated Regions in Sorghum using RNA-seq Data

“…Its primary applications are to measure the gene expression levels and to calculate differential gene expression (DGE). Like in model species and major crops, RNA-seq has recently been widely employed in sorghum research and advanced our understanding in many aspects of sorghum including development (Davidson et al, 2012;Kebrom et al, 2017;Turco et al, 2017;Leiboff and Hake, 2019), accumulation of sugar/biomass (McKinley et al, 2016(McKinley et al, , 2018Mizuno et al, 2016Mizuno et al, , 2018Zhang L. M. et al, 2018Li et al, 2019aZhang et al, 2019;Hennet et al, 2020), stress responses and tolerance (Dugas et al, 2011;Gelli et al, 2014;Sui et al, 2015;Fracasso et al, 2016;Yang et al, 2018;Varoquaux et al, 2019), senescence (Johnson et al, 2015;Wu et al, 2016a), and regulation of miRNA and long non-coding RNA (Calvino et al, 2011;Sun et al, 2020).…”