Dataset Information

Transcriptional Regulation of Sorghum Stem Composition: Key Players Identified Through Co-expression Gene Network and Comparative Genomics Analyses.

ABSTRACT: Most sorghum biomass accumulates in stem secondary cell walls (SCW). As sorghum stems are used as raw materials for various purposes such as feed, energy and fiber reinforced polymers, identifying the genes responsible for SCW establishment is highly important. Taking advantage of studies performed in model species, most of the structural genes contributing at the molecular level to the SCW biosynthesis in sorghum have been proposed while their regulatory factors have mostly not been determined. Validation of the role of several MYB and NAC transcription factors in SCW regulation in Arabidopsis and a few other species has been provided. In this study, we contributed to the recent efforts made in grasses to uncover the mechanisms underlying SCW establishment. We reported updated phylogenies of NAC and MYB in 9 different species and exploited findings from other species to highlight candidate regulators of SCW in sorghum. We acquired expression data during sorghum internode development and used co-expression analyses to determine groups of co-expressed genes that are likely to be involved in SCW establishment. We were able to identify two groups of co-expressed genes presenting multiple evidences of involvement in SCW building. Gene enrichment analysis of MYB and NAC genes provided evidence that while NAC SECONDARY WALL THICKENING PROMOTING FACTOR NST genes and SECONDARY WALL-ASSOCIATED NAC DOMAIN PROTEIN gene functions appear to be conserved in sorghum, NAC master regulators of SCW in sorghum may not be as tissue compartmentalized as in Arabidopsis. We showed that for every homolog of the key SCW MYB in Arabidopsis, a similar role is expected for sorghum. In addition, we unveiled sorghum MYB and NAC that have not been identified to date as being involved in cell wall regulation. Although specific validation of the MYB and NAC genes uncovered in this study is needed, we provide a network of sorghum genes involved in SCW both at the structural and regulatory levels.

SUBMITTER: Hennet L

PROVIDER: S-EPMC7064007 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transcriptional Regulation of Sorghum Stem Composition: Key Players Identified Through Co-expression Gene Network and Comparative Genomics Analyses.

Hennet Lauriane L Berger Angélique A Trabanco Noemi N Ricciuti Emeline E Dufayard Jean-François JF Bocs Stéphanie S Bastianelli Denis D Bonnal Laurent L Roques Sandrine S Rossini Laura L Luquet Delphine D Terrier Nancy N Pot David D

Frontiers in plant science 20200303

Most sorghum biomass accumulates in stem secondary cell walls (SCW). As sorghum stems are used as raw materials for various purposes such as feed, energy and fiber reinforced polymers, identifying the genes responsible for SCW establishment is highly important. Taking advantage of studies performed in model species, most of the structural genes contributing at the molecular level to the SCW biosynthesis in sorghum have been proposed while their regulatory factors have mostly not been determined. ...[more]

PMID: 32194601

Similar Datasets

Project description:Disease response genes (DRGs) diverge under recurrent positive selection as a result of a molecular arms race between hosts and pathogens. Most of these studies were conducted in animals, and few defense genes have been shown to evolve adaptively in plants. To test for adaptation in the molecules mediating disease resistance in the cereals, we first combined information from the expression pattern of Sorghum bicolor genes and from divergence to the full genome of rice to identify candidate DRGs. We then used evolutionary analyses of orthologous gene sets from several grass species, to determine whether the DRGs show signals of positive selection and the residues targeted. We found 140 divergent genes upregulated under biotic stress in S. bicolor by evaluating the relative abundance of expressed sequence tags in different libraries and comparing them with rice genes. For 10 of these genes, we found sets of orthologs including sequences from rice and three other cereals; six genes showed a pattern of substitution that was consistent with positive selection. Three of these genes, a thaumatin, a peroxidase, and a barley mlo homolog, are known antifungal proteins. The other three genes with evidence of positive selection were a MCM-1 agamous deficiens SRF- (MADS) box transcription factor, an eIF5 translation initiation factor, and a gene of unknown function but with evidence of expression during stress. Permutation analyses, using different ortholog and paralog sequences, consistently identified five positively selected codons in the peroxidase, a member of a cluster of genes and a large gene family. We mapped the positively selected residues onto the structure of the peroxidase and thaumatin and found that all sites are on the surface of these proteins and several are close to biochemically determined active sites. Identifying new positively selected plant disease resistance genes and the critical amino acid sites provides a basis for functional studies that may increase our understanding of their underlying molecular mechanisms of action. Additionally, it may lead to the identification of individuals having variation at functionally important sites, as well as eventually using this information in the rational design and engineering of proteins involved in plant disease resistance.

Project description:BackgroundComparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed.ResultsHere we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota.ConclusionsOur results demonstrate the applicability of the CGB pipeline in multiple settings. CGB's ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks.

Project description:BACKGROUND:The microbial community of the built environment (BE) can impact the lives of people and has been studied for a variety of indoor, outdoor, underground, and extreme locations. Thus far, these microorganisms have mainly been investigated by culture-based methods or amplicon sequencing. However, both methods have limitations, complicating multi-study comparisons and limiting the knowledge gained regarding in-situ microbial lifestyles. A greater understanding of BE microorganisms can be achieved through basic information derived from the complete genome. Here, we investigate the level of diversity and genomic features (genome size, GC content, replication strand skew, and codon usage bias) from complete genomes of bacteria commonly identified in the BE, providing a first step towards understanding these bacterial lifestyles. RESULTS:Here, we selected bacterial genera commonly identified in the BE (or "Common BE genomes") and compared them against other prokaryotic genera ("Other genomes"). The "Common BE genomes" were identified in various climates and in indoor, outdoor, underground, or extreme built environments. The diversity level of the 16S rRNA varied greatly between genera. The genome size, GC content and GC skew strength of the "Common BE genomes" were statistically larger than those of the "Other genomes" but were not practically significant. In contrast, the strength of selected codon usage bias (S value) was statistically higher with a large effect size in the "Common BE genomes" compared to the "Other genomes." CONCLUSION:Of the four genomic features tested, the S value could play a more important role in understanding the lifestyles of bacteria living in the BE. This parameter could be indicative of bacterial growth rates, gene expression, and other factors, potentially affected by BE growth conditions (e.g., temperature, humidity, and nutrients). However, further experimental evidence, species-level BE studies, and classification by BE location is needed to define the relationship between genomic features and the lifestyles of BE bacteria more robustly.

Dataset Information

Transcriptional Regulation of Sorghum Stem Composition: Key Players Identified Through Co-expression Gene Network and Comparative Genomics Analyses.

Publications

Transcriptional Regulation of Sorghum Stem Composition: Key Players Identified Through Co-expression Gene Network and Comparative Genomics Analyses.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets