Christine Dillmann

High Genetic Variability of Herbivore-Induced Volatile Emission within a Broad Range of Maize Inbred Lines

Maize plants (Zea mays) attacked by caterpillars release a mixture of odorous compounds that attract parasitic wasps, natural enemies of the herbivores. We assessed the genetic variability of these induced volatile emissions among 31 maize inbred lines representing a broad range of genetic diversity used by breeders in Europe and North America. Odors were collected from young plants that had been induced by injecting them with caterpillar regurgitant. Significant variation among lines was found for all 23 volatile compounds included in the analysis: the lines differed enormously in the total amount of volatiles emitted and showed highly variable odor profiles distinctive of each genotype. Principal component analysis performed on the relative quantities of particular compounds within the blend revealed clusters of highly correlated volatiles, which may share common metabolic pathways. European and American lines belonging to established heterotic groups were loosely separated from each other, with the most clear-cut difference in the typical release of (E)-β-caryophyllene by European lines. There was no correlation between the distances among the lines based on their odor profiles and their respective genetic distances previously assessed by neutral RFLP markers. This most comprehensive study to date on intraspecific variation in induced odor emission by maize plants provides a further example of the remarkably high genetic diversity conserved within this important crop plant. A better understanding of the genetic control of induced odor emissions may help in the development of maize varieties particularly attractive to parasitoids and other biological control agents and perhaps more repellent for herbivores.

A comparative study of molecular and morphological methods of describing relationships between perennial ryegrass (Lolium perenne L.) varieties

Abstract A sample set of registered perennial ryegrass varieties was used to compare how morphological characterisation and AFLP® (AFLP® is a registered trademark of Keygene N.V.) and STS molecular markers described variety relationships. All the varieties were confirmed as morphologically distinct, and both the STS and AFLP markers exposed sufficient genetic diversity to differentiate these registered ryegrass varieties. Distances obtained by each of the approaches were compared, with special attention given to the coincidences and divergences between the methods. When correlations between morphological, AFLP and STS distances were calculated and the corresponding scatter-plots constructed, the variety relationships appeared to be rather inconsistent across the methods, especially between morphology and the molecular markers. However, some consistencies were found for closely related material. An implication could be that these molecular-marker techniques, while not yet suited to certain operations in the traditional registration of new varieties, could be suitable methods for investigating disputable distinctness situations or possible EDV (EDV= essentially derived variety. An EDV is a variety being clearly distinct from, but conforming in the expression of the essential characteristics of, an ’initial variety’ (IV) from which it is found to have been predominantly derived) relationships, subject to establishing standardised protocols and statistical techniques. Some suggestions for such a protocol, including a statistical test for distinctness, are given.

Genetic variability of proteome expression and metabolic control

Quantitative proteomics, the technology for high-throughput measurement of protein concentrations from 2-D electrophoresis, often reveals high levels of genetic variability of proteome expression in the species studied. A majority of proteins, including enzymes, display quantitative variation, the extent of which may exceed an order of magnitude. As can be attested in various studies, and most notably in maize, the concentration of individual proteins appears to be a polygenic trait, whose loci may be distributed throughout the genome, with possibly large effects and frequent epistatic interactions. In order to analyse the genetic consequences of these variations, we used the metabolic control theory, developing equations that explicitly incorporate the enzyme concentration as the relevant variable. The metabolic fluxes were modelled as quantitative traits affected by genes modulating enzyme quantities. We showed that, in addition to the classical positive dominance of a large concentration over a small concentration, heterosis for the flux is observed in cases of complementary dominance at different loci. Constraints on the total quantity of enzymes may produce overdominance, reinforcing heterosis. Beyond these genetic aspects, biochemical modelling appears as an important component of genomic and post-genomic approaches, allowing the integration of data generated in those high-throughput programs.

PHENOTYPIC AND GENOTYPIC CONVERGENCES ARE INFLUENCED BY HISTORICAL CONTINGENCY AND ENVIRONMENT IN YEAST

Different organisms have independently and recurrently evolved similar phenotypic traits at different points throughout history. This phenotypic convergence may be caused by genotypic convergence and in addition, constrained by historical contingency. To investigate how convergence may be driven by selection in a particular environment and constrained by history, we analyzed nine life‐history traits and four metabolic traits during an experimental evolution of six yeast strains in four different environments. In each of the environments, the population converged toward a different multivariate phenotype. However, the evolution of most traits, including fitness components, was constrained by history. Phenotypic convergence was partly associated with the selection of mutations in genes involved in the same pathway. By further investigating the convergence in one gene, BMH1, mutated in 20% of the evolved populations, we show that both the history and the environment influenced the types of mutations (missense/nonsense), their location within the gene itself, as well as their effects on multiple traits. However, these effects could not be easily predicted from ancestors’ phylogeny or past selection. Combined, our data highlight the role of pleiotropy and epistasis in shaping a rugged fitness landscape.

Systemic properties of metabolic networks lead to an epistasis-based model for heterosis

The genetic and molecular approaches to heterosis usually do not rely on any model of the genotype–phenotype relationship. From the generalization of Kacser and Burns’ biochemical model for dominance and epistasis to networks with several variable enzymes, we hypothesized that metabolic heterosis could be observed because the response of the flux towards enzyme activities and/or concentrations follows a multi-dimensional hyperbolic-like relationship. To corroborate this, we used the values of systemic parameters accounting for the kinetic behaviour of four enzymes of the upstream part of glycolysis, and simulated genetic variability by varying in silico enzyme concentrations. Then we “crossed” virtual parents to get 1,000 hybrids, and showed that best-parent heterosis was frequently observed. The decomposition of the flux value into genetic effects, with the help of a novel multilocus epistasis index, revealed that antagonistic additive-by-additive epistasis effects play the major role in this framework of the genotype–phenotype relationship. This result is consistent with various observations in quantitative and evolutionary genetics, and provides a model unifying the genetic effects underlying heterosis.

Flowering Time in Maize: Linkage and Epistasis at a Major Effect Locus

In a previous study, we identified a candidate fragment length polymorphism associated with flowering time variation after seven generations of selection for flowering time, starting from the maize inbred line F252. Here, we characterized the candidate region and identified underlying polymorphisms. Then, we combined QTL mapping, association mapping, and developmental characterization to dissect the genetic mechanisms responsible for the phenotypic variation. The candidate region contained the Eukaryotic Initiation Factor (eIF-4A) and revealed a high level of sequence and structural variation beyond the 3′-UTR of eIF-4A, including several insertions of truncated transposable elements. Using a biallelic single-nucleotide polymorphism (SNP) (C/T) in the candidate region, we confirmed its association with flowering time variation in a panel of 317 maize inbred lines. However, while the T allele was correlated with late flowering time within the F252 genetic background, it was correlated with early flowering time in the association panel with pervasive interactions between allelic variation and the genetic background, pointing to underlying epistasis. We also detected pleiotropic effects of the candidate polymorphism on various traits including flowering time, plant height, and leaf number. Finally, we were able to break down the correlation between flowering time and leaf number in the progeny of a heterozygote (C/T) within the F252 background consistent with causal loci in linkage disequilibrium. We therefore propose that both a cluster of tightly linked genes and epistasis contribute to the phenotypic variation for flowering time.

Differential expression of candidate salt-tolerance genes in the halophyte Helianthus paradoxus and its glycophyte progenitors H. annuus and H. petiolaris (Asteraceae)

Adaptation to different habitats is considered to be a major force in the generation of organismal diversity. Understanding the genetic mechanisms that produce such adaptations will provide insights into long-standing questions in evolutionary biology and, at the same time, improve predictions of plant responses to changing environmental conditions. Here we used semiquantitative RT-PCR to study the expression of eight candidate salt-tolerance genes in leaves of the highly salt-tolerant diploid hybrid species Helianthus paradoxus and its salt-sensitive progenitor species H. annuus and H. petiolaris. Samples were collected after germination and growth under four different treatments: nonsaline (control), near-natural saline, saline with increased K(+), and saline with decreased Mg(2+) and Ca(2+). Three individuals from three populations per species were used. The hybrid species H. paradoxus constitutively under- or overexpressed genes related to potassium and calcium transport (homologues of KT1, KT2, ECA1), suggesting that these genes may contribute to the adaptation of H. paradoxus to salinity. In two other genes, variation between populations within species exceeded species level variation. Furthermore, homologues of the potassium transporter HAK8 and of a transcriptional regulator were generally overexpressed in saline treatments, suggesting that these genes are involved in sustained growth under saline conditions in Helianthus.

Linking Post-Translational Modifications and Variation of Phenotypic Traits

Enzymes can be post-translationally modified, leading to isoforms with different properties. The phenotypic consequences of the quantitative variability of isoforms have never been studied. We used quantitative proteomics to dissect the relationships between the abundances of the enzymes and isoforms of alcoholic fermentation, metabolic traits, and growth-related traits in Saccharomyces cerevisiae. Although the enzymatic pool allocated to the fermentation proteome was constant over the culture media and the strains considered, there was variation in abundance of individual enzymes and sometimes much more of their isoforms, which suggests the existence of selective constraints on total protein abundance and trade-offs between isoforms. Variations in abundance of some isoforms were significantly associated to metabolic traits and growth-related traits. In particular, cell size and maximum population size were highly correlated to the degree of N-terminal acetylation of the alcohol dehydrogenase. The fermentation proteome was found to be shaped by human selection, through the differential targeting of a few isoforms for each food-processing origin of strains. These results highlight the importance of post-translational modifications in the diversity of metabolic and life-history traits.

A Systems Approach to Elucidate Heterosis of Protein Abundances in Yeast

Heterosis is a universal phenomenon that has major implications in evolution and is of tremendous agro-economic value. To study the molecular manifestations of heterosis and to find factors that maximize its strength, we implemented a large-scale proteomic experiment in yeast. We analyzed the inheritance of 1,396 proteins in 55 inter- and intraspecific hybrids obtained from Saccharomyces cerevisiae and S. uvarum that were grown in grape juice at two temperatures. We showed that the proportion of heterotic proteins was highly variable depending on the parental strain and on the temperature considered. For intraspecific hybrids, this proportion was higher at nonoptimal temperature. Unexpectedly, heterosis for protein abundance was strongly biased toward positive values in interspecific hybrids but not in intraspecific hybrids. Computer modeling showed that this observation could be accounted for by assuming concave relationships between protein abundances and their controlling factors, in line with the metabolic model of heterosis. These results point to nonlinear processes that could play a central role in heterosis.

Hierarchical Bayesian Modelling for Saccharomyces cerevisiae population dynamics

Hierarchical Bayesian Modelling is powerful however under-used to model and evaluate the risks associated with the development of pathogens in food industry, to predict exotic invasions, species extinctions and development of emerging diseases, or to assess chemical risks. Modelling population dynamics of Saccharomyces cerevisiae considering its biodiversity and other sources of variability is crucial for selecting strains meeting industrial needs. Using this approach, we studied the population dynamics of S. cerevisiae, the domesticated yeast, widely encountered in food industry, notably in brewery, vinery, bakery and distillery. We relied on a logistic equation to estimate the key variables of population growth, but we took also into account factors able to affect them, namely environmental effects, genetic diversity and measurement errors. Our probabilistic approach allowed us: (i) to model the dynamical behaviour of strains in a given condition under some uncertainty, (ii) to measure environmental effects and (iii) to evaluate genetic variability of the growth key variables.