Validation and characterisation of a wheat GENIE3 network using an independent RNA-Seq dataset

Abstract

13Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate 14 gene discovery. However, the extent to which the network predictions are biologically relevant is 15 often unclear. Recently, as part of an analysis of the RefSeqv1.0 wheat transcriptome, a GENIE3 16network which predicted targets of wheat transcription factors was produced. Here we have used an 17 independent and publicly-available RNA-Seq dataset to validate the predictions of the wheat GENIE3 18 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-19analysed the RNA-Seq data against the RefSeqv1.0 genome and identified a de novo set of 20 differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated 21 the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-22predicted target genes of NAM-A1 overlap significantly with the de novo DEGs, more than would be 23 expected for a random transcription factor. Based on high levels of overlap between GENIE3-24 predicted target genes and the de novo DEGs, we also identified a set of candidate senescence 25 regulators. We then explored genome-wide trends in the network related to polyploidy and 26 homoeolog expression levels and found that only homoeologous transcription factors are likely to 27 share predicted targets in common. However, homoeologs in dynamic triads, i.e. with higher variation 28 in homoeolog expression levels across tissues, are less likely to share predicted targets than stable 29triads. This suggests that homoeologs in dynamic triads are more likely to act on distinct pathways. 30This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions 31 of transcription factor targets, which can be used for candidate gene prediction and for global analyses 32of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner 33 web application, facilitating its use in future studies. 34 35

Delaying or Delivering: Identification of novel NAM-1 alleles which delay senescence to extend grain fill duration of wheat

“…Paragon EMS mutants, with resource worth further exploration, including sequencing of NAM-1 and NAM-2 homoeologues of staygreen mutants to identify causative mutations due to their known role in senescence (Avni et al , 2014; Pearce et al , 2014; Borrill et al , 2019; Harrington et al , 2019a). Conversely, early senescing mutants may encode gain of function mutations affecting NAM-1 gene regulatory targets including C2C2-CO like transcription factors, RWD-RK or GRAS genes identified during transcriptional network modelling (Borrill et al , 2019; Harrington et al , 2019c).…”

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

“…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, 2019) . 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 (IWGSC) et al, 2018) .…”

Applying genomic resources to accelerate wheat biofortification

“…incorporated in Knetminer (Hassani-Pak et al 2016) (https://knetminer.rothamsted.ac.uk/KnetMiner/) for easy accessibility. Knetminer also provides access to a network of predicted transcription factor targets (IWGSC et al 2018), with the predictions validated using an independent RNA-seq dataset (Harrington et al 2019).…”