Identification of genes involved in cell wall biogenesis in grasses by differential gene expression profiling of elongating and non-elongating maize internodes

Bosch, Marina; Mayer, Claus; Cookson, Alan; Donnison, Iain S.

doi:10.1093/jxb/err045

Cited by 117 publications

(107 citation statements)

References 102 publications

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…Several previous expression and QTL studies have focused on genes of lignin biosynthesis (Lorenz et al, 2010;Lorenzana et al, 2010;Thomas et al, 2010;Truntzler et al, 2010;Bosch et al, 2011). Consistent with these studies, we find homologs for almost every gene in the phenylpropanoid synthesis pathway present within at least one of our lignin abundance-or saccharification yieldrelated QTL regions (Supplemental Table S6).…”

Section: Phenotype Variation Can Arise From Differential Expression Osupporting

confidence: 88%

Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population

et al. 2014

View full text Add to dashboard Cite

Biotechnological approaches to reduce or modify lignin in biomass crops are predicated on the assumption that it is the principal determinant of the recalcitrance of biomass to enzymatic digestion for biofuels production. We defined quantitative trait loci (QTL) in the Intermated B73 3 Mo17 recombinant inbred maize (Zea mays) population using pyrolysis molecular-beam mass spectrometry to establish stem lignin content and an enzymatic hydrolysis assay to measure glucose and xylose yield. Among five multiyear QTL for lignin abundance, two for 4-vinylphenol abundance, and four for glucose and/or xylose yield, not a single QTL for aromatic abundance and sugar yield was shared. A genome-wide association study for lignin abundance and sugar yield of the 282-member maize association panel provided candidate genes in the 11 QTL of the B73 and Mo17 parents but showed that many other alleles impacting these traits exist among this broader pool of maize genetic diversity. B73 and Mo17 genotypes exhibited large differences in gene expression in developing stem tissues independent of allelic variation. Combining these complementary genetic approaches provides a narrowed list of candidate genes. A cluster of SCARECROW-LIKE9 and SCARECROW-LIKE14 transcription factor genes provides exceptionally strong candidate genes emerging from the genome-wide association study. In addition to these and genes associated with cell wall metabolism, candidates include several other transcription factors associated with vascularization and fiber formation and components of cellular signaling pathways. These results provide new insights and strategies beyond the modification of lignin to enhance yields of biofuels from genetically modified biomass.

show abstract

Section: Phenotype Variation Can Arise From Differential Expression Osupporting

confidence: 88%

Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The classic function of glycosyl hydrolases into hydrolyze plant wall polysaccharides. A role for these proteins in cell wall biosynthesis and remodelling has also recently been reported (Bosch et al 2011;Lopez-Casado et al 2008). Upregulation of genes encoding zinc finger proteins has been observed in cotton protoplasts undergoing wall regeneration as well (Yang et al 2008).…”

Section: Genes Involved In Cell Wall Regenerationmentioning

confidence: 90%

Transcriptional dynamics during cell wall removal and regeneration reveals key genes involved in cell wall development in rice

et al. 2011

View full text Add to dashboard Cite

TitleTranscriptional dynamics during cell wall removal and regeneration reveals key genes involved in cell wall development in rice. Abstract Efficient and cost-effective conversion of plant biomass to usable forms of energy requires a thorough understanding of cell wall biosynthesis, modification and degradation. To elucidate these processes, we assessed the expression dynamics during enzymatic removal and regeneration of rice cell walls in suspension cells over time. In total, 928 genes exhibited significant up-regulation during cell wall removal, whereas, 79 genes were up-regulated during cell wall regeneration. Both gene sets are enriched for kinases, transcription factors and genes predicted to be involved in cell wall-related functions. Integration of the gene expression datasets with a catalog of known and/or predicted biochemical pathways from rice, revealed metabolic and hormonal pathways involved in cell wall degradation and regeneration. Rice lines carrying Tos17 mutations in genes up-regulated during cell wall removal exhibit dwarf phenotypes. Many of the genes up-regulated during cell wall development are also up-regulated in response to infection and environmental perturbations indicating a coordinated response to diverse types of stress.

show abstract

“…Sixteen well-characterized components of cell wall biosynthesis were selected as guide genes (Supplemental Table S8), including five cellulose synthase genes, seven cellulose synthase-like genes, three glycosyl hydrolase genes, and one glycosidase gene (Penning et al, 2009;Bosch et al, 2011). Collectively, 214 genes containing 377 edges were extracted from the network with the 16 guide genes (Fig.…”

Section: Case Study: Cell Wall Biosynthesis and Regulationmentioning

confidence: 99%

“…Sixteen well-characterized (Penning et al, 2009;Bosch et al, 2011) components of cell wall biosynthesis (Supplemental Table S8) were chosen as query genes to search against CORNET Maize (https://bioinformatics.psb.ugent.be/ cornet/versions/cornet_maize1.0/) on its website and the STRING database using the Cytoscape stringApp (http://apps.cytoscape.org/apps/stringapp). The parameters for searching CORNET database were: Method = Pearson, Correlation coefficient = 0.75, P value # 0.05, and Top genes = 50.…”

Section: Databases Comparison On Cell Wall Pathwaymentioning

confidence: 99%

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

View full text Add to dashboard Cite

ORCID IDs: 0000-0002-6182-800X (J.H.); 0000-0001-6612-3570 (S.V.); 0000-0002-9564-8146 (K.M.M.).With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.Maize (Zea mays) is the most widely produced crop in United States, and U.S. agriculture accounted for 36% of world maize production in 2015 (USDA, 2016). Maize has also been in the center of genetics research for more than 100 years, including McClintock's pioneering work with transposable elements (reviewed by McClintock, 1983;Fedoroff, 2012). Due to recent technological advances in nucleic acid sequencing and the availability of the maize genome sequence (Schnable et al., 2009), maize genomics research has been greatly expedited.RNA-sequencing (RNA-Seq) has become the favored technique for detecting genomewide expression patterns. RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al., 2009;Han et al., 2015;Conesa et al., 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al., 2013;Li et al., 2013a;Lonsdale et al., 2013;Fadista et al., 2014). Because of its widespread adaptability, greater than 5000 Illumina platform Maize RNA-Seq libraries (Fig. 1A) are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Leinonen et al., 2010), adding to the body of data that can be used to study the maize genome.The maize genome is large and heterogeneous, and the genome annotation is still far from complete (Cigan et al., 2005;Ficklin and Feltus, 2011). Although recent work has...

show abstract

Identification of genes involved in cell wall biogenesis in grasses by differential gene expression profiling of elongating and non-elongating maize internodes

Cited by 117 publications

References 102 publications

Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population

Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population

Transcriptional dynamics during cell wall removal and regeneration reveals key genes involved in cell wall development in rice

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

Contact Info

Product

Resources

About