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

Abstract

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.

Back to the future: implications of genetic complexity for the structure of hybrid breeding programs

“…, Fang et al 2017 ), and wheat ( e.g. , Harrington et al 2020 ). The following section will illustrate this motivation using our first-hand experience with maize as an example.…”

Annotation and Molecular Characterisation of the TaIRO3 and TaHRZ Iron Homeostasis Genes in Bread Wheat (Triticum aestivum L.)

“…We propose the TaHRZ proteins in wheat are pivotal for managing Fe homeostasis, and that downregulation of the TaHRZ genes after 24 h of Fe deficiency allows for upregulation of Fe deficiency response genes (including TaIRO3 and NA/DMA biosynthesis genes), to ensure wheat plants can absorb sufficient Fe for growth [ 29 , 30 , 31 ]. Our KnetMiner/Genie 3 network confirms that all TaHRZ and TaIRO3 genes are distinct homoeologs present in the bread wheat genome and identifies regulatory associations predicted from gene expression data between the TaHRZ and TaIRO3 genes, with five genes belonging to the subgroup Ib bHLH TF family (including a putative OsIRO2 ortholog) and three genes belonging to the ABI3/VP1 TF family in bread wheat ( Figure 7 ) [ 35 , 49 ]. These ABI3/VP1 genes could be related to the master Fe sensor OsIDEF1 in rice, and future analysis will aim to characterise these novel TFs in bread wheat and determine whether the TaABI3/VP1 genes share similar Fe sensing roles to OsIDEF1 [ 64 ].…”

“…A TaIRO3 , TaHRZ1 and TaHRZ2 gene network was generated using the bread wheat database of KnetMiner software ( (accessed on 4 September 2020)), which compiles a network of bread wheat genes and related orthologs from Arabidopsis and rice. Regulatory associations between bread wheat transcription factors and our queries were predicted by Genie3 software, which was recently integrated into KnetMiner [ 49 ]. A complete network containing all the phenotypic traits and molecular functions associated with the TaIRO3 homoeologs and TaHRZ gene family is available at , and all bread wheat gene identification numbers are provided in Supplementary Table S1 .…”

KnetMiner: a comprehensive approach for supporting evidence‐based gene discovery and complex trait analysis across species

“…The presented case studies have shown practical applications of KnetMiner to the understanding of challenging and complex traits in wheat and Arabidopsis. KnetMiner was used in 2014 to investigate traits such as height of biomass willows (Hanley and Karp, 2014 ) and has more recently become part of a wider roadmap for gene function characterization in crops (Adamski et al, 2020 ; Harrington et al, 2020 ). Public KnetMiner resources (e.g.…”

“…KnetMiner is being used extensively to drive gene-trait discovery research in the publicly funded 'Designing Future Wheat' programme (https://designingfuturewheat.org.uk/), see for example (Adamski et al, 2020;Alabdullah et al, 2019;Harrington et al, 2020). Wheat (Triticum aestivum) is the third most-grown cereal crop in the world after maize and rice, and has a hexaploid 15 Gb genome which is 5 times the size of the human genome (The International Wheat Genome Sequencing Consortium Figure 1 Diverse and heterogenous FAIR data sets are harmonized into a knowledge graph using the KnetBuilder software.…”