Systems genetics reveals a transcriptional network associated with susceptibility in the maize–grey leaf spot pathosystem

Abstract

We used a systems genetics approach to elucidate the molecular mechanisms of the responses of maize to grey leaf spot (GLS) disease caused by Cercospora zeina, a threat to maize production globally. Expression analysis of earleaf samples in a subtropical maize recombinant inbred line population (CML444 × SC Malawi) subjected in the field to C. zeina infection allowed detection of 20 206 expression quantitative trait loci (eQTLs). Four trans-eQTL hotspots coincided with GLS disease QTLs mapped in the same field experiment. Co-expression network analysis identified three expression modules correlated with GLS disease scores. The module (GY-s) most highly correlated with susceptibility (r = 0.71; 179 genes) was enriched for the glyoxylate pathway, lipid metabolism, diterpenoid biosynthesis and responses to pathogen molecules such as chitin. The GY-s module was enriched for genes with trans-eQTLs in hotspots on chromosomes 9 and 10, which also coincided with phenotypic QTLs for susceptibility to GLS. This transcriptional network has significant overlap with the GLS susceptibility response of maize line B73, and may reflect pathogen manipulation for nutrient acquisition and/or unsuccessful defence responses, such as kauralexin production by the diterpenoid biosynthesis pathway. The co-expression module that correlated best with resistance (TQ-r; 1498 genes) was enriched for genes with trans-eQTLs in hotspots coinciding with GLS resistance QTLs on chromosome 9. Jasmonate responses were implicated in resistance to GLS through co-expression of COI1 and enrichment of genes with the Gene Ontology term 'cullin-RING ubiquitin ligase complex' in the TQ-r module. Consistent with this, JAZ repressor expression was highly correlated with the severity of GLS disease in the GY-s susceptibility network.

Discovery, Biosynthesis and Stress-Related Accumulation of Dolabradiene-Derived Defenses in Maize

“…ZmAN2 was first described in a differential display analysis of highly elicited transcripts following F. graminearum challenge of maize silks (Harris et al, 2005). Following the first description of ZmAN2, pathogen-elicited transcripts have been observed and reported in a large number of maize studies with diverse microbes Schmelz et al, 2011Schmelz et al, , 2014van der Linde et al, 2011;Vaughan et al, 2014;Christie et al, 2017). The nearly ubiquitous presence of ZmAN2 as a pathogen defense transcript marker is consistent with the essential biosynthetic role in the modular formation of multiple specialized diterpenoid metabolites, including not only A-and B-series kauralexins but also dolabralexins.…”

Quantitative Resistance: More Than Just Perception of a Pathogen

“…One approach to identify the potential causal genes and/or mechanisms underlying quantitative disease resistance is to combine genetic mapping with systems biology approaches that attempt to identify multiple causal genes and/or mechanisms in parallel (Chen et al, 2010). One example of this systems approach to understand quantitative disease resistance is the comparison of phenotypic resistance QTLs to gray leaf spot to expression QTLs (eQTLs) mapped simultaneously using a whole-transcriptome analysis of the maize recombinant inbred line population (Christie et al, 2017). eQTLs are QTLs that control the accumulation of specific transcripts within the mapping population and can be used to link cause and effect within QTL mapping populations for a variety of traits (Hansen et al, 2008;Keurentjes et al, 2008;Kliebenstein, 2009a).…”

A systems genetics approach reveals environment-dependent associations between SNPs, protein co-expression and drought-related traits in maize

“…The detection of such hotspots is highly dependent on the mapping resolution and on the method used to cluster eQTLs or pQTLs, which may explain the contrasted results reported in the literature. Indeed, while several studies report hotspots associated to hundreds of transcripts (Munkvold et al 2013; Christie et al 2017; Orozco et al 2012), others detected hotspots associated to only a few tens of transcripts or proteins (Foss et al 2011; Ghazalpour et al 2011; Albert et al 2014) or even no hotspot at all (Mähler et al 2017). In our study, false hotspot detection was limited by the high mapping resolution and by the use of a pQTL clustering method taking into account the variations of LD across the genome (Negro et al , in print).…”

“…It consists in comparing the position of quantitative trait loci (QTLs) underlying phenotypic traits variation to that of QTLs underlying the variation of upstream molecular phenotypes such as transcript expressions (eQTLs) or protein abundances (pQTLs). Until now, this approach has been mostly applied in human and animals (Johnson et al 2015; Williams et al 2016; Moreno-Moral and Petretto 2016) and to a lesser extent in plants (Moreno-Moral and Petretto 2016; Munkvold et al 2013; Ogura and Busch 2016; Basnet et al 2016; Christie et al 2017; Mizrachi et al 2017).…”