Bindu Joseph

Combining Genome-Wide Association Mapping and Transcriptional Networks to Identify Novel Genes Controlling Glucosinolates in Arabidopsis thaliana

Genome-wide association mapping is highly sensitive to environmental changes, but network analysis allows rapid causal gene identification.

Using knockout mutants to reveal the growth costs of defensive traits

We used a selection of Arabidopsis thaliana mutants with knockouts in defence genes to demonstrate growth costs of trichome development and glucosinolate production. Four of the seven defence mutants had significantly higher size-standardized growth rates (SGRs) than the wild-type in early life, although this benefit declined as plants grew larger. SGR is known to be a good predictor of success under high-density conditions, and we confirmed that mutants with higher growth rates had a large advantage when grown in competition. Despite the lack of differences in flowering-time genes, the mutants differed in flowering time, a trait that strongly correlated with early growth rate. Aphid herbivory decreased plant growth rate and increased flowering time, and aphid population growth rate was closely coupled to the growth rate of the host plant. Small differences in early SGR thus had cascading effects on both flowering time and herbivore populations.

Genomic analysis of QTLs and genes altering natural variation in stochastic noise.

Quantitative genetic analysis has long been used to study how natural variation of genotype can influence an organisms 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.

Natural genetic variation in Arabidopsis thaliana defense metabolism genes modulates field fitness.

Natural populations persist in complex environments, where biotic stressors, such as pathogen and insect communities, fluctuate temporally and spatially. These shifting biotic pressures generate heterogeneous selective forces that can maintain standing natural variation within a species. To directly test if genes containing causal variation for the Arabidopsis thaliana defensive compounds, glucosinolates (GSL) control field fitness and are therefore subject to natural selection, we conducted a multi-year field trial using lines that vary in only specific causal genes. Interestingly, we found that variation in these naturally polymorphic GSL genes affected fitness in each of our environments but the pattern fluctuated such that highly fit genotypes in one trial displayed lower fitness in another and that no GSL genotype or genotypes consistently out-performed the others. This was true both across locations and within the same location across years. These results indicate that environmental heterogeneity may contribute to the maintenance of GSL variation observed within Arabidopsis thaliana. DOI: http://dx.doi.org/10.7554/eLife.05604.001

Cytoplasmic genetic variation and extensive cytonuclear interactions influence natural variation in the metabolome

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

Hierarchical Nuclear and Cytoplasmic Genetic Architectures for Plant Growth and Defense within Arabidopsis

A lack of concordance between quantitative trait loci (QTL) regulating defense and those regulating growth was initially detected in a recombinant inbred line population containing nuclear and cytoplasmic genetic variation, probably due to the small population size examined. After accounting for allelic differences in growth QTLs, this study uncovers a significant effect of glucosinolates on growth. To understand how genetic architecture translates between phenotypic levels, we mapped the genetic architecture of growth and defense within the Arabidopsis thaliana Kas × Tsu recombinant inbred line population. We measured plant growth using traditional size measurements and size-corrected growth rates. This population contains genetic variation in both the nuclear and cytoplasmic genomes, allowing us to separate their contributions. The cytoplasmic genome regulated a significant variance in growth but not defense, which was due to cytonuclear epistasis. Furthermore, growth adhered to an infinitesimal model of genetic architecture, while defense metabolism was more of a moderate-effect model. We found a lack of concordance between quantitative trait loci (QTL) regulating defense and those regulating growth. Given the published evidence proving the link between glucosinolates and growth, this is likely a false negative result caused by the limited population size. This size limitation creates an inability to test the entire potential genetic landscape possible between these two parents. We uncovered a significant effect of glucosinolates on growth once we accounted for allelic differences in growth QTLs. Therefore, other growth QTLs can mask the effects of defense upon growth. Investigating direct links across phenotypic hierarchies is fraught with difficulty; we identify issues complicating this analysis.

Genetic Variation in the Nuclear and Organellar Genomes Modulates Stochastic Variation in the Metabolome, Growth, and Defense

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 organisms 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.

Meta-analysis of metabolome QTLs in Arabidopsis: trying to estimate the network size controlling genetic variation of the metabolome.

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.

Genome Wide Association Mapping in Arabidopsis thaliana Identifies Novel Genes Involved in Linking Allyl Glucosinolate to Altered Biomass and Defense.

A key limitation in modern biology is the ability to rapidly identify genes underlying newly identified complex phenotypes. Genome wide association studies (GWAS) have become an increasingly important approach for dissecting natural variation by associating phenotypes with genotypes at a genome wide level. Recent work is showing that the Arabidopsis thaliana defense metabolite, allyl glucosinolate (GSL), may provide direct feedback regulation, linking defense metabolism outputs to the growth, and defense responses of the plant. However, there is still a need to identify genes that underlie this process. To start developing a deeper understanding of the mechanism(s) that modulate the ability of exogenous allyl GSL to alter growth and defense, we measured changes in plant biomass and defense metabolites in a collection of natural 96 A. thaliana accessions fed with 50 μM of allyl GSL. Exogenous allyl GSL was introduced exclusively to the roots and the compound transported to the leaf leading to a wide range of heritable effects upon plant biomass and endogenous GSL accumulation. Using natural variation we conducted GWAS to identify a number of new genes which potentially control allyl responses in various plant processes. This is one of the first instances in which this approach has been successfully utilized to begin dissecting a novel phenotype to the underlying molecular/polygenic basis.

Quantitative Variation in Responses to Root Spatial Constraint within Arabidopsis thaliana

Rosette growth in Arabidopsis is sensitive to the placement of a small physical constraint around the root and this requires a number of naturally variable loci. Among the myriad of environmental stimuli that plants utilize to regulate growth and development to optimize fitness are signals obtained from various sources in the rhizosphere that give an indication of the nutrient status and volume of media available. These signals include chemical signals from other plants, nutrient signals, and thigmotropic interactions that reveal the presence of obstacles to growth. Little is known about the genetics underlying the response of plants to physical constraints present within the rhizosphere. In this study, we show that there is natural variation among Arabidopsis thaliana accessions in their growth response to physical rhizosphere constraints and competition. We mapped growth quantitative trait loci that regulate a positive response of foliar growth to short physical constraints surrounding the root. This is a highly polygenic trait and, using quantitative validation studies, we showed that natural variation in EARLY FLOWERING3 (ELF3) controls the link between root constraint and altered shoot growth. This provides an entry point to study how root and shoot growth are integrated to respond to environmental stimuli.