Project description:Changes in the performance of genotypes in different environments are defined as genotype 3 environment (G3E) interactions. In grapevine (Vitis vinifera), complex interactions between different genotypes and climate, soil and farming practices yield unique berry qualities. However, the molecular basis of this phenomenon remains unclear. To dissect the basis of grapevine G3E interactions we characterized berry transcriptome plasticity, the genome methylation landscape and within-genotype allelic diversity in two genotypes cultivated in three different environments over two vintages. We identified, through a novel data-mining pipeline, genes with expression profiles that were: unaffected by genotype or environment, genotype-dependent but unaffected by the environment, environmentally-dependent regardless of genotype, and G3E-related. The G3E-related genes showed different degrees of within-cultivar allelic diversity in the two genotypes and were enriched for stress responses, signal transduction and secondary metabolism categories. Our study unraveled the mutual relationships between genotypic and environmental variables during G3E interaction in a woody perennial species, providing a reference model to explore how cultivated fruit crops respond to diverse environments. Also, the pivotal role of vineyard location in determining the performance of different varieties, by enhancing berry quality traits, was unraveled.
Project description:Changes in the performance of genotypes in different environments are defined as genotype 3 environment (G3E) interactions. In grapevine (Vitis vinifera), complex interactions between different genotypes and climate, soil and farming practices yield unique berry qualities. However, the molecular basis of this phenomenon remains unclear. To dissect the basis of grapevine G3E interactions we characterized berry transcriptome plasticity, the genome methylation landscape and within-genotype allelic diversity in two genotypes cultivated in three different environments over two vintages. We identified, through a novel data-mining pipeline, genes with expression profiles that were: unaffected by genotype or environment, genotype-dependent but unaffected by the environment, environmentally-dependent regardless of genotype, and G3E-related. The G3E-related genes showed different degrees of within-cultivar allelic diversity in the two genotypes and were enriched for stress responses, signal transduction and secondary metabolism categories. Our study unraveled the mutual relationships between genotypic and environmental variables during G3E interaction in a woody perennial species, providing a reference model to explore how cultivated fruit crops respond to diverse environments. Also, the pivotal role of vineyard location in determining the performance of different varieties, by enhancing berry quality traits, was unraveled.
Project description:Coevolutionary change requires reciprocal selection between interacting species, i.e., that the partner genotypes that are favored in one species depend on the genetic composition of the interacting species. Coevolutionary genetic variation is manifested as genotype ´ genotype (G ´ G) interactions for fitness from interspecific interactions. Although quantitative genetic approaches have revealed abundant evidence for G ´ G interactions in symbioses, the molecular basis of this variation remains unclear. Here we study the molecular basis of G ´ G interactions in a model legume-rhizobium mutualism using gene expression microarrays. We find that, like quantitative traits such as fitness, variation in the symbiotic transcriptome may be partitioned into additive and interactive genetic components. Our results suggest that plant genetic variation is the largest influence on nodule gene expression, and that plant genotype and the plant genotype ´ rhizobium genotype interaction determine global shifts in rhizobium gene expression that in turn feedback to influence plant fitness benefits. Moreover, the transcriptomic variation we uncover implicates regulatory changes in both species as drivers of symbiotic gene expression variation. Our study is the first to partition genetic variation in a symbiotic transcriptome, and illuminates potential molecular routes of coevolutionary change. We assayed gene expression using three biological replicates for each plant genotype × rhizobium genotype combination (4 combinations) for a total of 12 chips.
Project description:A dissection of Genotype x Environment interactions in grapevine berry using a multi-layered -omics approach Grapevine, the most widely-cultivated perennial fruit crop, is also considered a very environmentally sensitive crop. It is characterized by remarkable phenotypic plasticity which in turn is believed to effectively buffer environmental extremes especially through transcriptomic and epigenomic reprogramming. Thus, the final phenotype (P) of a given vine is the result of the close interaction between its genetic composition (G) and the environment (E). Here we analyzed Genotype x Environment (GxE) interactions in two grapevine varieties by characterizing their transcriptome plasticity when cultivated in different environments. Specifically, two genotypes (Sangiovese and Cabernet Sauvignon) were cultivated in three different locations in Italy (Bolgheri -littoral Tuscany-, Montalcino – Appennine Tuscany- and Romagna -foothill area-), trained in an almost identical manner, and sampled at four developmental stages over two grapevine growing seasons, 2011 and 2012, for a total of 144 samples that were analyzed by hybridization to a whole-genome microarray and by Reduced Representation Bisulfite Sequencing (RRBS-seq). In order to study the relationships among differential gene expression profiles and environmental cues, we have developed a new statistical data mining tool based on data reduction approaches which allowed a dissection of the transcriptomic data into stage-specific, cultivar-related and GxE important clusters of gene expression. This deep inspection of inner relationships between the different dataset variables allowed the identification of several candidate genes that could represent putative markers of berry quality traits in grapevine GxE interactions. Moreover, the methods used to establish our model provide a framework for the analysis of transcriptome plasticity in other crops as they respond to diverse environments.
Project description:Coevolutionary change requires reciprocal selection between interacting species, i.e., that the partner genotypes that are favored in one species depend on the genetic composition of the interacting species. Coevolutionary genetic variation is manifested as genotype ´ genotype (G ´ G) interactions for fitness from interspecific interactions. Although quantitative genetic approaches have revealed abundant evidence for G ´ G interactions in symbioses, the molecular basis of this variation remains unclear. Here we study the molecular basis of G ´ G interactions in a model legume-rhizobium mutualism using gene expression microarrays. We find that, like quantitative traits such as fitness, variation in the symbiotic transcriptome may be partitioned into additive and interactive genetic components. Our results suggest that plant genetic variation is the largest influence on nodule gene expression, and that plant genotype and the plant genotype ´ rhizobium genotype interaction determine global shifts in rhizobium gene expression that in turn feedback to influence plant fitness benefits. Moreover, the transcriptomic variation we uncover implicates regulatory changes in both species as drivers of symbiotic gene expression variation. Our study is the first to partition genetic variation in a symbiotic transcriptome, and illuminates potential molecular routes of coevolutionary change. We assayed gene expression using three biological replicates for each plant genotype × rhizobium genotype combination (4 combinations) for a total of 12 chips. We compared gene expression in each of four combinations of Medicago truncatula families and Sinorhizobium meliloti strains using Affymetrix Medicago GeneChips to study how the entire transcriptome and individual genes responded to differences between plant families, between rhizobium strains, and due to the plant family × rhizobium strain (G × G) interaction.
Project description:Background: Cases where genotype-phenotype relationships depend on environmental factors have been quantified for many complex diseases. Such genotype-environment interactions (GEI or GxE) may also affect expression Quantitative Trait Loci (eQTL) present in tissues critical for the manifestation of disease. To assess this hypothesis, we performed an analysis of eQTL-GEI resulting from an individual's smoking environment in the lung small airway epithelium (SAE). While the SAE is challenging to sample, this is the cell population that shows the first signs of smoking related stress and gene expression in the SAE appears to play a role in mediating smoking effects on lung disease. Results: We used expression microarrays to assay the SAE transcriptome for a small sample of African-American individuals and we analyzed SNPs genotyped genome-wide to identify GEI affecting eQTL. While a genome-wide trans- analysis identified few instances of GEI after a multiple test correction, an analysis of cis-genotypes identified a small but significant number of GEI affecting lung SAE gene expression. We determined that significant cases of eQTL-GEI were not driven by outliers and we were also able to find corroborative evidence for a few of these eQTL-GEI in a small, independent sample of individuals of European ancestry. Conclusion: Given that the power of GEI tests is low compared to tests of genotype association and that the total sample size of our study was small, including only 61 African American individuals in our focal population, the identification of significant GEI in our study implies that there may be considerable genotype-specific effects on eQTL due to smoking environment. We discuss individual cases of GEI of interest for lung disease, such as SDC1 and ZAK, as well as the broader implications of our results for the analysis of eQTL and for genome-wide association analysis of complex diseases.
Project description:A fitness landscape (FL) describes the genotype-fitness relationship in a given environment. To explain and predict evolution, it is imperative to measure the FL in multiple environments because the natural environment changes frequently. Using a high-throughput method that combines precise gene replacement with next-generation sequencing, we determine the in vivo FL of a yeast tRNA gene comprising over 23,000 genotypes in four environments. Although genotype-by-environment interaction (G×E) is abundantly detected, its pattern is so simple that we can transform an existing FL to that in a new environment with fitness measures of only a few genotypes in the new environment. Under each environment, we observe prevalent, negatively biased epistasis between mutations (G×G). Epistasis-by-environment interaction (G×G×E) is also prevalent, but trends in epistasis difference between environments are predictable. Our study thus reveals simple rules underlying seemingly complex FLs, opening the door to understanding and predicting FLs in general.
Project description:Microarray time courses followed the response of 5 yeast strains to heat shock. Expression variation due to genetic, environmental, and genotype-by-environment interactions were identified. Keywords: Timecourse and single timepoint expression studies of stress response in yeast