Global transcriptome analysis uncovers the gene co-expression regulation network and key genes involved in grain development of wheat (Triticum aestivum L.)

Chi, Qing; Guo, Lijian; Ma, Meng; Zhang, Lijian; Mao, Hude; Wu, Baowei; Liu, Xiangli; Ramírez-González, Ricardo H.; Uauy, Cristóbal; Appels, R.; Zhao, Huixian

doi:10.1007/s10142-019-00678-z

Cited by 23 publications

(27 citation statements)

References 44 publications

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“…These new resources are powerful not only for association mapping but also for identifying potential functional changes in genes and regions subjected to domestication and/or selection. Fourth, transcriptomic resources of wheat are increasingly accumulating [154][155][156]. Other multi-omic resources are emerging, including epigenomics [157][158][159].…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat

Tu¹,

Li²

2020

Plants

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Kernel hardness is one of the most important single traits of wheat seed. It classifies wheat cultivars, determines milling quality and affects many end-use qualities. Starch granule surfaces, polar lipids, storage protein matrices and Puroindolines potentially form a four-way interaction that controls wheat kernel hardness. As a genetic factor, Puroindoline polymorphism explains over 60% of the variation in kernel hardness. However, genetic factors other than Puroindolines remain to be exploited. Over the past two decades, efforts using population genetics have been increasing, and numerous kernel hardness-associated quantitative trait loci (QTLs) have been identified on almost every chromosome in wheat. Here, we summarize the state of the art for mapping kernel hardness. We emphasize that these steps in progress have benefitted from (1) the standardized methods for measuring kernel hardness, (2) the use of the appropriate germplasm and mapping population, and (3) the improvements in genotyping methods. Recently, abundant genomic resources have become available in wheat and related Triticeae species, including the high-quality reference genomes and advanced genotyping technologies. Finally, we provide perspectives on future research directions that will enhance our understanding of kernel hardness through the identification of multiple QTLs and will address challenges involved in fine-tuning kernel hardness and, consequently, food properties.

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Section: Conclusion and Future Perspectivementioning

confidence: 99%

Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat

Tu¹,

Li²

2020

Plants

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“…To check the expression patterns of selected genes in grafted watermelon at 0, 3, and 15 DAG, quantitative reverse transcription PCR (RTq-PCR) was performed. The entire data were analyzed using the 2-ΔΔCt method [61]. Three independent biological replicates were used for gene expression analysis [62].…”

Section: Rtq-pcr Expression Analysis Of Genes Involved In Compatibilimentioning

confidence: 99%

Comparative Physiological and Biochemical Mechanisms in Di, Tri, and Tetraploid Watermelon (<em>Citrullus lanatus</em> L.) Grafted by Branches

Kaseb¹,

Umer²,

Gebremeskel³

et al. 2020

Preprint

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Polyploid seeds production is laborious, complicated, and costly work. Tetraploid and triploid plants produce a fewer number of seeds/fruit, and triploid embryos are fairly weak, covered with a more hardened seed coat as compared to diploid seeds. Here we investigated the interactive effect of new grafting technique of polyploid watermelon scion onto rootstock on plants' survival rate, biochemical, and hormones contents. In this study, three different branches, apical meristem (AM), branch with 1 node (1N), and 2 nodes (2N) from di, Tri, and tetraploid watermelon plants, were used as scion and grafted onto squash rootstock. The results showed highly significant differences between polypoid watermelon when 1N using as a scion, tetraploid showed maximum survival rates, higher contents of hormones, and antioxidants (AOX) activities, compared to diploid, these may be the possible reasons for high compatibility in tetraploid and degrading the grafting zone in diploid. RTq-PCR results confirm that the expression of genes linked to compatibility is consistent with the hormonal and AOX activities. This study provides an alternative and economical approach to produce more tetraploid and triploid plants for breeding or seeds production by using branches as scions.

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“…More recently, a high-quality genome and gene annotation has facilitated transcriptomics work in wheat ( Appels et al 2018 ; Ramírez-González et al 2018 ). This has allowed the use of substantial RNA-Seq datasets to build gene regulatory networks and predict transcription factors involved in complex processes such as senescence ( Borrill et al 2019 ) and grain development ( Pfeifer et al 2014 ; Chi et al 2019 ). However, these studies typically use bespoke RNA-Seq datasets to generate the regulatory networks, rather than exploiting publicly-available data.…”

mentioning

confidence: 99%

The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

Harrington

Backhaus

Singh

et al. 2020

G3 Genes|Genomes|Genetics

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Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analysed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homoeologous transcription factors are likely to share predicted targets in common. However, homoeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.

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Global transcriptome analysis uncovers the gene co-expression regulation network and key genes involved in grain development of wheat (Triticum aestivum L.)

Cited by 23 publications

References 44 publications

Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat

Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat

Comparative Physiological and Biochemical Mechanisms in Di, Tri, and Tetraploid Watermelon (<em>Citrullus lanatus</em> L.) Grafted by Branches

The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

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