The modern evolutionary synthesis suggests that both environmental variation and genetic diversity are critical determinants of pathogen success. However, the relative contribution of these two sources of variation is not routinely measured. To estimate the relative contribution of plasticity and genetic diversity for virulence-associated phenotypes in a generalist plant pathogen, we grew a population of 15 isolates of Botrytis cinerea from throughout the world, under a variety of in vitro and in planta conditions. Under in planta conditions, phenotypic differences between the isolates were determined by the combination of genotypic variation within the pathogen and environmental variation. In contrast, phenotypic differences between the isolates under in vitro conditions were predominantly determined by genetic variation in the pathogen. Using a correlation network approach, we link the phenotypic variation under in vitro experimental conditions to phenotypic variation during plant infection. This study indicates that there is a high level of phenotypic variation within B. cinerea that is controlled by a mixture of genetic variation, environment, and genotype × environment. This argues that future experiments into the pathogenicity of B. cinerea must account for the genetic and environmental variation within the pathogen to better sample the potential phenotypic space of the pathogen.

Recent studies are starting to show that genetic control over stochastic variation is a key evolutionary solution of single celled organisms in the face of unpredictable environments. This has been expanded to show that genetic variation can alter stochastic variation in transcriptional processes within multi-cellular eukaryotes. However, little is known about how genetic diversity can control stochastic variation within more non-cell autonomous phenotypes. Using an Arabidopsis reciprocal RIL population, we showed that there is significant genetic diversity influencing stochastic variation in the plant metabolome, defense chemistry, and growth. This genetic diversity included loci specific for the stochastic variation of each phenotypic class that did not affect the other phenotypic classes or the average phenotype. This suggests that the organism's networks are established so that noise can exist in one phenotypic level like metabolism and not permeate up or down to different phenotypic levels. Further, the genomic variation within the plastid and mitochondria also had significant effects on the stochastic variation of all phenotypic classes. The genetic influence over stochastic variation within the metabolome was highly metabolite specific, with neighboring metabolites in the same metabolic pathway frequently showing different levels of noise. As expected from bet-hedging theory, there was more genetic diversity and a wider range of stochastic variation for defense chemistry than found for primary metabolism. Thus, it is possible to begin dissecting the stochastic variation of whole organismal phenotypes in multi-cellular organisms. Further, there are loci that modulate stochastic variation at different phenotypic levels. Finding the identity of these genes will be key to developing complete models linking genotype to phenotype.

Growth is a complex trait that adapts to the prevailing conditions by integrating many internal and external signals. Understanding the molecular origin of this variation remains a challenging issue. In this study, natural variation of shoot growth under mannitol-induced stress was analyzed by standard quantitative trait locus mapping methods in a recombinant inbred line population derived from a cross between the Col-0 and Cvi-0 Arabidopsis thaliana accessions. Cloning of a major QTL specific to mannitol-induced stress condition led to identification of EGM1 and EGM2, a pair of tandem-duplicated genes encoding receptor-like kinases that are potentially involved in signaling of mannitol-associated stress responses. Using various genetic approaches, we identified two non-synonymous mutations in the EGM2[Cvi] allele that are shared by at least ten accessions from various origins and are probably responsible for a specific tolerance to mannitol. We have shown that the enhanced shoot growth phenotype contributed by the Cvi allele is not linked to generic osmotic properties but instead to a specific chemical property of mannitol itself. This result raises the question of the function of such a gene in A. thaliana, a species that does not synthesize mannitol. Our findings suggest that the receptor-like kinases encoded by EGM genes may be activated by mannitol produced by pathogens such as fungi, and may contribute to plant defense responses whenever mannitol is present.

A central goal of systems biology is to develop models that are both predictive and accurately describe the biological system. One complexity to this endeavor is that it is possible to develop models that appear predictive even if they use far fewer components than the biological system itself uses for the same process. This problem also occurs in quantitative genetics where it is often possible to describe the variation in a system using fewer genes than are actually variable due to the complications of linkage between causal polymorphisms and population structure. Thus, there is a crucial need to begin an empirical investigation into the true number of components that are used by biological systems to determine a phenotypic outcome. In this study, we use a meta-analysis of directly comparable metabolomics quantitative studies using quantitative trait locus mapping and genome wide association mapping to show that it is currently not possible to estimate how many genetic loci are truly polymorphic within Arabidopsis thaliana. Our analysis shows that it would require the analysis of at least a 1000 line bi-parental population to begin to estimate how many polymorphic loci control metabolic variation within Arabidopsis. Understanding the base number of loci that are actually involved in determining variation in metabolic systems is fundamental to developing systems models that are truly reflective of how metabolism is modulated within a living organism.

Quantitative genetic analysis has long been used to study how natural variation of genotype can influence an organism's phenotype. While most studies have focused on genetic determinants of phenotypic average, it is rapidly becoming understood that stochastic noise is genetically determined. However, it is not known how many traits display genetic control of stochastic noise nor how broadly these stochastic loci are distributed within the genome. Understanding these questions is critical to our understanding of quantitative traits and how they relate to the underlying causal loci, especially since stochastic noise may be directly influenced by underlying changes in the wiring of regulatory networks. We identified QTLs controlling natural variation in stochastic noise of glucosinolates, plant defense metabolites, as well as QTLs for stochastic noise of related transcripts. These loci included stochastic noise QTLs unique for either transcript or metabolite variation. Validation of these loci showed that genetic polymorphism within the regulatory network alters stochastic noise independent of effects on corresponding average levels. We examined this phenomenon more globally, using transcriptomic datasets, and found that the Arabidopsis transcriptome exhibits significant, heritable differences in stochastic noise. Further analysis allowed us to identify QTLs that control genomic stochastic noise. Some genomic QTL were in common with those altering average transcript abundance, while others were unique to stochastic noise. Using a single isogenic population, we confirmed that natural variation at ELF3 alters stochastic noise in the circadian clock and metabolism. Since polymorphisms controlling stochastic noise in genomic phenotypes exist within wild germplasm for naturally selected phenotypes, this suggests that analysis of Arabidopsis evolution should account for genetic control of stochastic variance and average phenotypes. It remains to be determined if natural genetic variation controlling stochasticity is equally distributed across the genomes of other multi-cellular eukaryotes.

Background

Genome-wide association (GWA) is gaining popularity as a means to study the architecture of complex quantitative traits, partially due to the improvement of high-throughput low-cost genotyping and phenotyping technologies. Glucosinolate (GSL) secondary metabolites within Arabidopsis spp. can serve as a model system to understand the genomic architecture of adaptive quantitative traits. GSL are key anti-herbivory defenses that impart adaptive advantages within field trials. While little is known about how variation in the external or internal environment of an organism may influence the efficiency of GWA, GSL variation is known to be highly dependent upon the external stresses and developmental processes of the plant lending it to be an excellent model for studying conditional GWA.

Methodology/principal findings

To understand how development and environment can influence GWA, we conducted a study using 96 Arabidopsis thaliana accessions, >40 GSL phenotypes across three conditions (one developmental comparison and one environmental comparison) and ∼230,000 SNPs. Developmental stage had dramatic effects on the outcome of GWA, with each stage identifying different loci associated with GSL traits. Further, while the molecular bases of numerous quantitative trait loci (QTL) controlling GSL traits have been identified, there is currently no estimate of how many additional genes may control natural variation in these traits. We developed a novel co-expression network approach to prioritize the thousands of GWA candidates and successfully validated a large number of these genes as influencing GSL accumulation within A. thaliana using single gene isogenic lines.

Conclusions/significance

Together, these results suggest that complex traits imparting environmentally contingent adaptive advantages are likely influenced by up to thousands of loci that are sensitive to fluctuations in the environment or developmental state of the organism. Additionally, while GWA is highly conditional upon genetics, the use of additional genomic information can rapidly identify causal loci en masse.

Understanding genome to phenotype linkages has been greatly enabled by genomic sequencing. However, most genome analysis is typically confined to the nuclear genome. We conducted a metabolomic QTL analysis on a reciprocal RIL population structured to examine how variation in the organelle genomes affects phenotypic variation. This showed that the cytoplasmic variation had effects similar to, if not larger than, the largest individual nuclear locus. Inclusion of cytoplasmic variation into the genetic model greatly increased the explained phenotypic variation. Cytoplasmic genetic variation was a central hub in the epistatic network controlling the plant metabolome. This epistatic influence manifested such that the cytoplasmic background could alter or hide pairwise epistasis between nuclear loci. Thus, cytoplasmic genetic variation plays a central role in controlling natural variation in metabolomic networks. This suggests that cytoplasmic genomes must be included in any future analysis of natural variation. DOI: http://dx.doi.org/10.7554/eLife.00776.001.

Botrytis cinerea is a fungal pathogen that causes necrotic disease on more than a thousand known hosts widely spread across the plant kingdom. How B. cinerea interacts with such extensive host diversity remains largely unknown. To address this question, we generated an infectivity matrix of 98 strains of B. cinerea on 90 genotypes representing eight host plants. This experimental infectivity matrix revealed that the disease outcome is largely explained by variations in either the host resistance or pathogen virulence. However, the specific interactions between host and pathogen account for 16% of the disease outcome. Furthermore, the disease outcomes cluster among genotypes of a species but are independent of the relatedness between hosts. When analyzing the host specificity and virulence of B. cinerea, generalist strains are predominant. In this fungal necrotroph, specialization may happen by a loss in virulence on most hosts rather than an increase of virulence on a specific host. To uncover the genetic architecture of Botrytis host specificity and virulence, a genome-wide association study (GWAS) was performed and revealed up to 1492 genes of interest. The genetic architecture of these traits is widespread across the B. cinerea genome. The complexity of the disease outcome might be explained by hundreds of functionally diverse genes putatively involved in adjusting the infection to diverse hosts.

In plant-pathogen relations, disease symptoms arise from the interaction of the host and pathogen genomes. Host-pathogen functional gene interactions are well described, whereas little is known about how the pathogen genetic variation modulates both organisms' transcriptomes. To model and generate hypotheses on a generalist pathogen control of gene expression regulation, we used the Arabidopsis thaliana-Botrytis cinerea pathosystem and the genetic diversity of a collection of 96 B. cinerea isolates. We performed expression-based genome-wide association (eGWA) for each of 23,947 measurable transcripts in Arabidopsis (host), and 9267 measurable transcripts in B. cinerea (pathogen). Unlike other eGWA studies, we detected a relative absence of locally acting expression quantitative trait loci (cis-eQTL), partly caused by structural variants and allelic heterogeneity hindering their identification. This study identified several distantly acting trans-eQTL linked to eQTL hotspots dispersed across Botrytis genome that altered only Botrytis transcripts, only Arabidopsis transcripts, or transcripts from both species. Gene membership in the trans-eQTL hotspots suggests links between gene expression regulation and both known and novel virulence mechanisms in this pathosystem. Genes annotated to these hotspots provide potential targets for blocking manipulation of the host response by this ubiquitous generalist necrotrophic pathogen.

How standing genetic variation within a pathogen contributes to diversity in host/pathogen interactions is poorly understood, partly because most studied pathogens are host-specific, clonally reproducing organisms which complicates genetic analysis. In contrast, Botrytis cinerea is a sexually reproducing, true haploid ascomycete that can infect a wide range of diverse plant hosts. While previous work had shown significant genomic variation between two isolates, we proceeded to assess the level and frequency of standing variation in a population of B. cinerea. To begin measuring standing genetic variation in B. cinerea, we re-sequenced the genomes of 13 different isolates and aligned them to the previously sequenced T4 reference genome. In addition one of these isolates was resequenced from four independently repeated cultures. A high level of genetic diversity was found within the 13 isolates. Within this variation, we could identify clusters of genes with major effect polymorphisms, i.e., polymorphisms that lead to a predicted functional knockout, that surrounded genes involved in controlling vegetative incompatibility. The genotype at these loci was able to partially predict the interaction of these isolates in vegetative fusion assays showing that these loci control vegetative incompatibility. This suggests that the vegetative incompatibility loci within B. cinerea are associated with regions of increased genetic diversity. The genome re-sequencing of four clones from the one isolate (Grape) that had been independently propagated over 10 years showed no detectable spontaneous mutation. This suggests that B. cinerea does not display an elevated spontaneous mutation rate. Future work will allow us to test if, and how, this diversity may be contributing to the pathogen's broad host range.