Generating predictive multi-omics networks of Northern Corn Leaf Blight resistance
Creators: name: Clark, Natalie M
name: Song, Gaoyuan
name: Lopez, Miriam
name: Kabahuma, Mercy
name: Christensen, Shawn
name: Lauter, Nick
name: Walley, Justin W
Publisher: name: Zenodo
Year: 2021

description: <strong>Introduction</strong> Northern Corn Leaf Blight (NLB) is one of the most significant corn diseases, causing the largest crop loss of any disease in the Northern United States from 2012-2015. Understanding how gene expression correlates with specific disease phenotypes is critical for developing disease-resistant crops that enhance global food security and limit economic losses. However, the underlying regulatory mechanisms mediating resistance to NLB are not well understood. Thus, the goal of this study is to integrate transcriptome, (phospho)proteome, and metabolome measurements with disease phenotyping and computational modeling using a systems-biology approach to build predictive regulatory networks of NLB resistance. <strong>Methods</strong> One hundred thirty-four (134) Intermated B73xMo17 Doubled Haploid Lines (IBMDHLs) were planted in a field plot using a randomized complete block design. Plants were treated with NLB inoculum at the sixth and seventh vegetative stages. Seven days after inoculation, leaf material was collected from 3 infected plants for each of the lines. Transcriptomic profiling on the infected plant samples was performed using 3’ QuantSeq on an Illumina HiSeq 3000, and mass spectrometry was performed using 11-plex Tandem Mass Tag (TMT) peptide labeling on a Thermo Q-Exactive Plus high-resolution quadruple Orbitrap mass spectrometer. Metabolomics was performed on a Thermo Q-Exactive mass spectrometer. Quantitative Trait Loci (QTL) mapping was performed on each molecular dataset as well as disease measurements using R/qtl2. <strong>Preliminary Data</strong> Using these methods, we were able to detect over 44,000 transcripts, 11,000 proteins, 42,000 phosphosites, and 36,000 metabolic features. We additionally identified over 34,000 transcript, 5,000 protein, 4,500 phosphosite, and 11,000 metabolite QTL (eQTL, pQTL, phosQTL, and mQTL, respectively), of which approximately 20% co-localize with disease QTL. In addition, only approximately 20% of pQTL and 10% of phosQTL overlap with eQTL, illustrating that the incorporation of proteomic data identifies complimentary regulatory events. While these QTL provide information about the putative causal NLB resistance transcripts, (phospho)proteins, and metabolites, they do not provide information about the regulatory mechanisms. Thus, we leveraged our integrative omics workflow named Spatiotemporal Clustering for Integrative Omics Networks (SC-ION: https://github.com/nmclark2/SCION) to generate a predictive, integrative regulatory network. SC-ION uses the inferred QTL to group causal genes with their predicted traits. Then, SC-ION can integrate different levels of data into the regulatory prediction, such as using the protein abundance of transcription factors to predict the transcript abundance of genes; using the phosphosite intensity of kinases to predict potential substrates; or, using the protein abundance of genes to predict metabolite levels. This allows us to predict the signaling cascade of NLB resistance, beginning with kinase phosphorylation, continuing with transcription factor-target regulation, and ending with differential metabolite levels. Our predictive networks along with our QTL analysis has allowed us to identify candidate genes for further study. We have identified ten genes of interest from our integrative omics analysis for which we have obtained Uniform Mu insertion lines for disease phenotyping. We have additionally identified ten kinases of interest which we will purify for <em>in vitro </em>kinase assays to predict downstream substrates. Together, this systems biology approach involving integrated, multi-omics data provides a comprehensive view of the gene regulatory landscape of corn leaves in response to NLB. <strong>Novel Aspect</strong> This is the largest phosQTL study to date, and SC-ION uniquely integrates multi-omics data to identify putative NLB resistance genes.