Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression

Evolution of enzyme levels in metabolic pathways: A theoretical approach. Part 2

Metabolism is essential for cell function and adaptation. Because of their central role in metabolism, kinetic parameters and enzyme concentrations are under constant selective pressure to adapt the fluxes of the metabolic networks to the needs of the organism. In line with various studies dealing with enzyme evolution, we recently developed a model of the evolution of enzyme concentrations under selection for increased flux, considered as a proxy for fitness (Coton et al., 2022). With this model, taking into account two realistic cellular constraints, competition for resources and co-regulation, we determined the evolutionary equilibria and range of neutral variations of enzyme concentrations. In this article, we expanded this model by considering that the enzymes in a pathway can belong to different co-regulation groups. We determined the equilibria and showed that the constraints modify the adaptive landscape by limiting the number of independent dimensions. We also showed that any trade-off between enzyme concentrations is sufficient to limit the flux and relax selection for increasing the concentration of other enzymes. Even though this model is based on simplifying assumptions, the complexity of the relationship between enzyme concentrations prevents the formal analysis of the range of neutral variation of enzyme concentrations. However, we could show that selection for maximizing the flux results in selective neutrality for all enzymes regardless the constraints applied, giving generality to the prediction of Hartl et al. (1985).

Evolution of enzyme levels in metabolic pathways: A theoretical approach

The central role of metabolism in cell functioning and adaptation has given rise to countless studies on the evolution of enzyme-coding genes and network topology. However, very few studies have addressed the question of how enzyme concentrations change in response to positive selective pressure on the flux, considered a proxy of fitness. In particular, the way cellular constraints, such as resource limitations and co-regulation, affect the adaptive landscape of a pathway under selection has never been analyzed theoretically. To fill this gap, we developed a model of the evolution of enzyme concentrations that combines metabolic control theory and an adaptive dynamics approach, and integrates possible dependencies between enzyme concentrations. We determined the evolutionary equilibria of enzyme concentrations and their range of neutral variation, and showed that they differ with the properties of the enzymes, the constraints applied to the system and the initial enzyme concentrations. Simulations of long-term evolution confirmed all analytical and numerical predictions, even though we relaxed the simplifying assumptions used in the analytical treatment.

What is a phenotype? History and new developments of the concept

Even though the word "phenotype", as well as the expression "genotype-phenotype relationship", are a part of the everyday language of biologists, they remain abstract notions that are sometimes misunderstood or misused. In this article, I begin with a review of  the genesis of the concept of phenotype and of the meaning of the genotype-phenotype "relationship" from a historical perspective. I then illustrate how the development of new approaches for exploring the living world has enabled us to phenotype organisms at multiple levels, with traits that can either be measures or parameters of functions, leading to a virtually unlimited amount of phenotypic data. Thus, pleiotropy becomes a central issue in the study of the genotype-phenotype relationship. Finally, I provide a few examples showing that important genetic and evolutionary features clearly differ with the phenotypic level considered. The way genotypic variation propagates across the phenotypic levels to shape fitness variation is an essential research program in biology.

QTL Analysis of Stem Elongation and Flowering Time in Lettuce Using Genotyping-by-Sequencing

Lettuce plants tend to undergo floral initiation by elongation of flower stalks (bolting) under high-temperature and long-day conditions, which is a serious problem for summer lettuce production. Our objective was to generate a high-density genetic map using SNPs obtained from genotyping-by-sequencing (GBS) analysis of F5 recombinant inbred lines (RILs) and to map QTLs involved in stem growth and flowering time in lettuce. A set of 127 intra-specific RIL mapping populations derived from a cross between two varieties, green and red leaf lettuce, were used to identify QTLs related to the number of days from sowing to bolting (DTB), to flowering of the first flower (DTF), to seed-setting of the first flower (DTS), and the total number of leaves (LN), plant height (PH), and total number of branches of main inflorescence (BN) for two consecutive years. Of the 15 QTLs detected, one that controls DTB, DTF, DTS, LN, and PH detected on LG 7, and another QTL that controls DTF, DTS, and PH detected on LG 1. Analysis of the genomic sequence corresponding to the QTL detected on LG 7 led to the identification of 22 putative candidate genes. A consistent QTL related to bolting and flowering time, and corresponding candidate genes has been reported. This study will be valuable in revealing the genetic basis of stem growth and flowering time in lettuce.

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation

Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.

QTL-BSA: A Bulked Segregant Analysis and Visualization Pipeline for QTL-seq

In recent years, the application of Whole Genome Sequencing (WGS) on plants has generated sufficient data for the identification of trait-associated genomic loci or genes. A high-throughput genome-assisted QTL-seq strategy, combined with bulked-segregant analysis and WGS of two bulked populations from a segregating progeny with opposite phenotypic trait values, has gained increasing popularities in research community. However, there is no publicly available user friendly software for the identification and visualization. Hence, we developed a tool named QTL-BSA (QTL-bulked segregant analysis and visualization pipeline), which could facilitate the rapid identification and visualization of candidate QTLs from QTL-seq. As a proof-of-concept study, we have applied the tool for the rapid discovery and the identification of genes related with the partial blast resistance in rice. Genomic region of the major QTL identified on chromosome 6, is located between 1.52 and 4.32 Mb, which is consistent with previous studies (2.39-4.39 Mb). We also derived the gene and QTLs functional annotation of this region. QTL-BSA offers a comprehensive solution to facilitate a wide range of programming and visualization tasks in QTL-seq analysis, is expected to be used widely by the research community.

Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations

Heterosis, the superiority of hybrids over their parents for quantitative traits, represents a crucial issue in plant and animal breeding as well as evolutionary biology. Heterosis has given rise to countless genetic, genomic and molecular studies, but has rarely been investigated from the point of view of systems biology. We hypothesized that heterosis is an emergent property of living systems resulting from frequent concave relationships between genotypic variables and phenotypes, or between different phenotypic levels. We chose the enzyme-flux relationship as a model of the concave genotype-phenotype (GP) relationship, and showed that heterosis can be easily created in the laboratory. First, we reconstituted in vitro the upper part of glycolysis. We simulated genetic variability of enzyme activity by varying enzyme concentrations in test tubes. Mixing the content of "parental" tubes resulted in "hybrids," whose fluxes were compared to the parental fluxes. Frequent heterotic fluxes were observed, under conditions that were determined analytically and confirmed by computer simulation. Second, to test this model in a more realistic situation, we modeled the glycolysis/fermentation network in yeast by considering one input flux, glucose, and two output fluxes, glycerol and acetaldehyde. We simulated genetic variability by randomly drawing parental enzyme concentrations under various conditions, and computed the parental and hybrid fluxes using a system of differential equations. Again we found that a majority of hybrids exhibited positive heterosis for metabolic fluxes. Cases of negative heterosis were due to local convexity between certain enzyme concentrations and fluxes. In both approaches, heterosis was maximized when the parents were phenotypically close and when the distributions of parental enzyme concentrations were contrasted and constrained. These conclusions are not restricted to metabolic systems: they only depend on the concavity of the GP relationship, which is commonly observed at various levels of the phenotypic hierarchy, and could account for the pervasiveness of heterosis.

The phosphatidylinositide 3-kinase (PI3K) signaling pathway is a determinant of zileuton response in adults with asthma

Variable responsiveness to zileuton, a leukotriene antagonist used to treat asthma, may be due in part to genetic variation. While individual SNPs were previously associated with zileuton-related lung function changes, specific quantitative trait loci (QTLs) and biological pathways that may contribute have not been identified. In this study, we investigated the hypothesis that genetic variation within biological pathways is associated with zileuton response. We performed an integrative QTL mapping and pathway enrichment study to investigate data from a GWAS of zileuton response, in addition to mRNA expression profiles and leukotriene production data from lymphoblastoid cell lines (LCLs) (derived from asthmatics) that were treated with zileuton or ethanol (control). We identified 1060 QTLs jointly associated with zileuton-related differential LTB₄ production in LCLs and lung function change in patients taking zileuton, of which eight QTLs were also significantly associated with persistent LTB₄ production in LCLs following zileuton treatment (i.e., ‘poor’ responders). Four nominally significant trans-eQTLs were predicted to regulate three candidate genes (SELL, MTF2, and GAL), the expression of which was significantly reduced in LCLs following zileuton treatment. Gene and pathway enrichment analyses of QTL associations identified multiple genes and pathways, predominantly related to phosphatidyl inositol signaling via PI3K. We validated the PI3K pathway activation status in a subset of LCLs demonstrating variable zileuton-related LTB₄ production, and show that in contrast to LCLs that responded to zileuton, the PI3K pathway was activated in poor responder LCLs. Collectively, these findings demonstrate a role for the PIK3 pathway and its targets as important determinants of differential responsiveness to zileuton.

Deciphering mechanisms underlying the genetic variation of general production and liver quality traits in the overfed mule duck by pQTL analyses

Background

The aim of this study was to analyse the mechanisms that underlie phenotypic quantitative trait loci (QTL) in overfed mule ducks by identifying co-localized proteomic QTL (pQTL). The QTL design consisted of three families of common ducks that were progeny-tested by using 294 male mule ducks. This population of common ducks was genotyped using a genetic map that included 334 genetic markers located across 28 APL chromosomes (APL for Anas platyrhynchos). Mule ducks were phenotyped for 49 traits related to growth, metabolism, overfeeding ability and meat and fatty liver quality, and 326 soluble fatty liver proteins were quantified.

Results

One hundred and seventy-six pQTL and 80 phenotypic QTL were detected at the 5% chromosome-wide significance threshold. The great majority of the identified pQTL were trans-acting and localized on a chromosome other than that carrying the coding gene. The most significant pQTL (1% genome-wide significance) were found for alpha-enolase on APL18 and fatty acid synthase on APL24. Some proteins were associated with numerous pQTL (for example, 17 and 14 pQTL were detected for alpha-enolase and apolipoprotein A1, respectively) and pQTL hotspots were observed on some chromosomes (APL18, 24, 25 and 29). We detected 66 co-localized phenotypic QTL and pQTL for which the significance of the two-trait QTL (2t-QTL) analysis was higher than that of the strongest QTL using a single-trait approach. Among these, 16 2t-QTL were pleiotropic. For example, on APL15, melting rate and abundance of two alpha-enolase spots appeared to be impacted by a single locus that is involved in the glycolytic process. On APLZ, we identified a pleiotropic QTL that modified both the blood level of glucose at the beginning of the force-feeding period and the concentration of glutamate dehydrogenase, which, in humans, is involved in increased glucose absorption by the liver when the glutamate dehydrogenase 1 gene is mutated.

Conclusions

We identified pleiotropic loci that affect metabolic pathways linked to glycolysis or lipogenesis, and in the end to fatty liver quality. Further investigation, via transcriptomics and metabolomics approaches, is required to confirm the biomarkers that were found to impact the genetic variability of these phenotypic traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0313-6) contains supplementary material, which is available to authorized users.

Resources for Systems Genetics

A key characteristic of systems genetics is its reliance on populations that vary to a greater or lesser degree in genetic complexity-from highly admixed populations such as the Collaborative Cross and Diversity Outcross to relatively simple crosses such as sets of consomic strains and reduced complexity crosses. This protocol is intended to help investigators make more informed decisions about choices of resources given different types of questions. We consider factors such as costs, availability, and ease of breeding for common scenarios. In general, we recommend using complementary resources and minimizing depth of resampling of any given genome or strain.

Defining the consequences of genetic variation on a proteome-wide scale

Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we identify 2,866 protein quantitative trait loci (pQTL) with twice as many local as distant genetic variants. These data support distinct transcriptional and post-transcriptional models underlying the observed pQTL effects. Using a sensitive approach to mediation analysis, we often identified a second protein or transcript as the causal mediator of distant pQTL. Our analysis reveals an extensive network of direct protein-protein interactions. Finally, we show that local genotype can provide accurate predictions of protein abundance in an independent cohort of collaborative cross mice.

Molecular Approaches to Understand Nutritional Potential of Coarse Cereals

Coarse grains are important group of crops that constitutes staple food for large population residing primarily in the arid and semi-arid regions of the world. Coarse grains are designated as nutri-cereals as they are rich in essential amino acids, minerals and vitamins. In spite of having several nutritional virtues in coarse grain as mentioned above, there is still scope for improvement in quality parameters such as cooking qualities, modulation of nutritional constituents and reduction or elimination of anti-nutritional factors. Besides its use in traditional cooking, coarse grains have been used mainly in the weaning food preparation and other malted food production. Improvement in quality parameters will certainly increase consumer's preference for coarse grains and increase their demand. The overall genetic gain in quality traits of economic importance in the cultivated varieties will enhance their industrial value and simultaneously increase income of farmers growing these varieties. The urgent step for improvement of quality traits in coarse grains requires a detailed understanding of molecular mechanisms responsible for varied level of different nutritional contents in different genotypes of these crops. In this review we have discussed the progresses made in understanding of coarse grain biology with various omics tool coupled with modern breeding approaches and the current status with regard to our effort towards dissecting traits related to improvement of quality and nutritional constituents of grains.

Targeted Proteomics Approach for Precision Plant Breeding

Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that enables precise quantitation of hundreds of peptides in a single run. This technique provides new opportunities for multiplexed protein biomarker measurements. For precision plant breeding, DNA-based markers have been used extensively, but the potential of protein biomarkers has not been exploited. In this work, we developed an SRM marker panel with assays for 104 potato (Solanum tuberosum) peptides selected using univariate and multivariate statistics. Thereafter, using random forest classification, the prediction markers were identified for Phytopthora infestans resistance in leaves, P. infestans resistance in tubers, and plant yield in potato leaf secretome samples. The results suggest that the marker panel has the predictive potential for three traits, two of which have no commercial DNA markers so far. Furthermore, the marker panel was also tested and found to be applicable to potato clones not used during the marker development. The proposed workflow is thus a proof-of-concept for targeted proteomics as an efficient readout in accelerated breeding for complex and agronomically important traits.

The quest for tolerant varieties: the importance of integrating "omics" techniques to phenotyping

The primary objective of crop breeding is to improve yield and/or harvest quality while minimizing inputs. Global climate change and the increase in world population are significant challenges for agriculture and call for further improvements to crops and the development of new tools for research. Significant progress has been made in the molecular and genetic analysis of model plants. However, is science generating false expectations? Are 'omic techniques generating valuable information that can be translated into the field? The exploration of crop biodiversity and the correlation of cellular responses to stress tolerance at the plant level is currently a challenge. This viewpoint reviews concisely the problems one encounters when working on a crop and provides an outline of possible workflows when initiating cellular phenotyping via "-omic" techniques (transcriptomics, proteomics, metabolomics).

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.

Interactions between genotype and environment drive the metabolic phenotype within Escherichia coli isolates

To gain insights into the adaptation of the Escherichia coli species to different environments, we monitored protein abundances using quantitative proteomics and measurements of enzymatic activities of central metabolism in a set of five representative strains grown in four contrasted culture media including human urine. Two hundred and thirty seven proteins representative of the genome-scale metabolic network were identified and classified into pathway categories. We found that nutrient resources shape the general orientation of metabolism through coordinated changes in the average abundances of proteins and in enzymatic activities that all belong to the same pathway category. For example, each culture medium induces a specific oxidative response whatever the strain. On the contrary, differences between strains concern isolated proteins and enzymes within pathway categories in single environments. Our study confirms the predominance of genotype by environment interactions at the proteomic and enzyme activity levels. The buffering of genetic variation when considering life-history traits suggests a multiplicity of evolutionary strategies. For instance, the uropathogenic isolate CFT073 shows a deregulation of iron demand and increased oxidative stress response.

Genetic influences on translation in yeast

Heritable differences in gene expression between individuals are an important source of phenotypic variation. The question of how closely the effects of genetic variation on protein levels mirror those on mRNA levels remains open. Here, we addressed this question by using ribosome profiling to examine how genetic differences between two strains of the yeast S. cerevisiae affect translation. Strain differences in translation were observed for hundreds of genes. Allele specific measurements in the diploid hybrid between the two strains revealed roughly half as many cis-acting effects on translation as were observed for mRNA levels. In both the parents and the hybrid, most effects on translation were of small magnitude, such that the direction of an mRNA difference was typically reflected in a concordant footprint difference. The relative importance of cis and trans acting variation on footprint levels was similar to that for mRNA levels. There was a tendency for translation to cause larger footprint differences than expected given the respective mRNA differences. This is in contrast to translational differences between yeast species that have been reported to more often oppose than reinforce mRNA differences. Finally, we catalogued instances of premature translation termination in the two yeast strains and also found several instances where erroneous reference gene annotations lead to apparent nonsense mutations that in fact reside outside of the translated gene body. Overall, genetic influences on translation subtly modulate gene expression differences, and translation does not create strong discrepancies between genetic influences on mRNA and protein levels.

Individuals in a species differ from each other in many ways. For many traits, a fraction of this variation is genetic—it is caused by DNA sequence variants in the genome of each individual. Some of these variants influence traits by altering how much certain genes are expressed, i.e. how many mRNA and protein molecules are made in different individuals. Surprisingly, earlier work has found that the effects of genetic variants on mRNA and protein levels for the same genes appear to be very different. Many variants appeared to influence only mRNA (but not protein) levels, and vice versa. In this paper, we studied this question by using a technique called “ribosome profiling” to measure translation (the cellular process of reading mRNA molecules and synthesizing protein molecules) in two yeast strains. We found that the genetic differences between these two strains influence translation for hundreds of genes. Because most of these effects were small in magnitude, they explain at most a small fraction of the discrepancies between the effects of genetic variants on mRNA and protein levels.

Genetics of gene expression in CNS

Transcriptome studies have revealed a surprisingly high level of variation among individuals in expression of key genes in the CNS under both normal and experimental conditions. Ten-fold variation is common, yet the specific causes and consequences of this variation are largely unknown. By combining classic gene mapping methods-family linkage studies and genomewide association-with high-throughput genomics, it is now possible to define quantitative trait loci (QTLs), single-gene variants, and even single SNPs and indels that control gene expression in different brain regions and cells. This review considers some of the major technical and conceptual challenges in analyzing variation in expression in the CNS with a focus on mRNAs, rather than noncoding RNAs or proteins. At one level of analysis, this work has been highly successful, and we finally have techniques that can be used to track down small numbers of loci that control expression in the CNS. But at a higher level of analysis, we still do not understand the genetic architecture of gene expression in brain, the consequences of expression QTLs on protein levels or on cell function, or the combined impact of expression differences on behavior and disease risk. These important gaps are likely to be bridged over the next several decades using (1) much larger sample sizes, (2) more powerful RNA sequencing and proteomic methods, and (3) novel statistical and computational models to predict genome-to-phenome relations.

Systems genetics of hepatocellular damage in vivo and in vitro: identification of a critical network on chromosome 11 in mouse

Quantitative trait locus (QTL) mapping is a powerful method to find modifier loci that influence disease risk and progression without prior knowledge of underlying genetic mechanisms. The aim of this study is to identify gene loci that contribute to individual differences in liver fibrosis following chronic liver damage. For this purpose, we carried out a mapping study across a panel of 21 BXD recombinant inbred strains using primary hepatocytes challenged with transforming growth factor (TGF)-β for 48 h. We identified a 6 Mb interval on chromosome 11 that is a major modifier of TGF-β-induced hepatocyte injury. Corresponding in vivo genetic analysis of fibrosis after chronic hepatotoxic injury by carbon tetrachloride (CCl4 ip for 6 wk) highlighted the same locus. Expression QTL (eQTL) analysis in liver tissues in the BXD family identified six polymorphisms in this region that are associated with strong cis eQTLs and that correlate well with gene expression in liver after both 6 wk CCl4 treatment and acute ethanol damage of the liver. Within this interval we rank two genes containing coding sequence variants as strong candidates that may modulate the severity of liver fibrosis: 1) the extracellular proteinase inhibitor gene Expi (also known as Wdnm1 or Wfdc18) and 2) musashi RNA-binding protein 2 (Msi2). The powerful combination of experimental, genetics, and bioinformatics methods, as well as combined in vitro and in vivo approaches can be used to define QTLs, genes, and even candidate sequence variants linked to hepatotoxicity and fibrosis.

Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults

BACKGROUND

Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is part of a complex system of genetic and environmental influences.

METHODS

We conducted an expression quantitative trait locus (eQTL) study on two independent human brain gene expression datasets assessing G × E for selected psychiatric genes and smoking status. We employed linear regression to model the significance of the Gene × Smoking interaction term, followed by meta-analysis across datasets.

RESULTS

Overall, we observed that the effect of DNA variation on gene expression is moderated by smoking status. Expression of 16 genes was significantly associated with single nucleotide polymorphisms that demonstrated G × E effects. The strongest finding (p = 1.9 × 10⁻¹¹) was neurexin 3-alpha (NRXN3), a synaptic cell-cell adhesion molecule involved in maintenance of neural connections (such as the maintenance of smoking behavior). Other significant G × E associations include four glutamate genes.

CONCLUSIONS

This is one of the first studies to demonstrate G × E effects within the human brain. In particular, this study implicated NRXN3 in the maintenance of smoking. The effect of smoking on NRXN3 expression and downstream behavior is different based upon SNP genotype, indicating that DNA profiles based on SNPs could be useful in understanding the effects of smoking behaviors. These results suggest that better measurement of psychiatric conditions, and the environment in post-mortem brain studies may yield an important avenue for understanding the biological mechanisms of G × E interactions in psychiatry.

Proteomics: a biotechnology tool for crop improvement

A sharp decline in the availability of arable land and sufficient supply of irrigation water along with a continuous steep increase in food demands have exerted a pressure on farmers to produce more with fewer resources. A viable solution to release this pressure is to speed up the plant breeding process by employing biotechnology in breeding programs. The majority of biotechnological applications rely on information generated from various -omic technologies. The latest outstanding improvements in proteomic platforms and many other but related advances in plant biotechnology techniques offer various new ways to encourage the usage of these technologies by plant scientists for crop improvement programs. A combinatorial approach of accelerated gene discovery through genomics, proteomics, and other associated -omic branches of biotechnology, as an applied approach, is proving to be an effective way to speed up the crop improvement programs worldwide. In the near future, swift improvements in -omic databases are becoming critical and demand immediate attention for the effective utilization of these techniques to produce next-generation crops for the progressive farmers. Here, we have reviewed the recent advances in proteomics, as tools of biotechnology, which are offering great promise and leading the path toward crop improvement for sustainable agriculture.

Mendelian and non-Mendelian regulation of gene expression in maize

Transcriptome variation plays an important role in affecting the phenotype of an organism. However, an understanding of the underlying mechanisms regulating transcriptome variation in segregating populations is still largely unknown. We sought to assess and map variation in transcript abundance in maize shoot apices in the intermated B73×Mo17 recombinant inbred line population. RNA–based sequencing (RNA–seq) allowed for the detection and quantification of the transcript abundance derived from 28,603 genes. For a majority of these genes, the population mean, coefficient of variation, and segregation patterns could be predicted by the parental expression levels. Expression quantitative trait loci (eQTL) mapping identified 30,774 eQTL including 96 trans-eQTL “hotspots,” each of which regulates the expression of a large number of genes. Interestingly, genes regulated by a trans-eQTL hotspot tend to be enriched for a specific function or act in the same genetic pathway. Also, genomic structural variation appeared to contribute to cis-regulation of gene expression. Besides genes showing Mendelian inheritance in the RIL population, we also found genes whose expression level and variation in the progeny could not be predicted based on parental difference, indicating that non-Mendelian factors also contribute to expression variation. Specifically, we found 145 genes that show patterns of expression reminiscent of paramutation such that all the progeny had expression levels similar to one of the two parents. Furthermore, we identified another 210 genes that exhibited unexpected patterns of transcript presence/absence. Many of these genes are likely to be gene fragments resulting from transposition, and the presence/absence of their transcripts could influence expression levels of their ancestral syntenic genes. Overall, our results contribute to the identification of novel expression patterns and broaden the understanding of transcriptional variation in plants.

Phenotypes are determined by the expression of genes, the environment, and the interaction of gene expression and the environment. However, a complete understanding of the inheritance of and genome-wide regulation of gene expression is lacking. One approach, called expression quantitative trait locus (eQTL) mapping provides the opportunity to examine the genome-wide inheritance and regulation of gene expression. In this paper, we conducted high-throughput sequencing of gene transcripts to examine gene expression in the shoot apex of a maize biparental mapping population. We quantified expression levels from 28,603 genes in the population and showed that the vast majority of genes exhibited the expected pattern of Mendelian inheritance. We genetically mapped the expression patterns and identified genomic regions associated with gene expression. Notably, we detected gene expression patterns that exhibited non-Mendelian inheritance. These included 145 genes that exhibited expression patterns in the progeny that were similar to only one of the parents and 210 genes with unexpected presence/absence expression patterns. The findings of non-Mendelian inheritance underscore the complexity of gene expression and provide a framework for understanding these complexities.

Lens culinaris Medik. seed proteome: analysis to identify landrace markers

Unlike modern cultivars selected for their growth performances in specific environmental conditions, local landraces have a high genetic variability that is an important resource for plant breeding. Consequent to their high adaptation to different environmental conditions, these landraces may have evolved adaptive gene complexes To promote the survival of endangered lentil landraces, we previously investigated the genetic relationship between two ancient landraces cultivated in the Molise region (Capracotta and Conca Casale, south-central Italy) and widely spread commercial varieties using an integrated approach consisting of morphological, DNA and protein characterization. In the present study, we used a proteomic approach to compare the mature seed proteomes of the Capracotta and Conca Casale lentil landraces. Multivariate analysis of 145 differentially expressed protein spots demonstrated that 52 proteins are required to discriminate among the two landraces. Therefore, these 52 proteins can be considered "landrace markers". The results of this study show that the combination of proteomics and multivariate analysis can be used to identify physiological and/or environmental markers, and is thus a powerful tool that complements the analysis of biodiversity in plant ecotypes.

A composite map of expressed sequences in maize

A maize genetic map based mainly on expressed sequences has been constructed. The map incorporates data from four segregating populations. Three recombinant inbred line populations were derived from the nonreciprocal crosses between three inbred lines. A map derived from an independent F2 progeny from one of the crosses was also used. With a total of 521 genotyped individuals, accuracy in gene order is expected. Five sources of markers were used: (i) 109 loci corresponding to 69 genes of known function, (ii) 39 loci controlling protein position shifts revealed by two-dimensional electrophoresis, (iii) 8 isozyme loci, (iv) 17 loci corresponding to 14 sequenced cDNAs for which no homology was found in gene banks, and (v) 102 loci corresponding to 81 anonymous probes. As many loci were common to all maps, we tested heterogeneity between recombination fractions. The comparison of recombination fractions revealed: (i) a good correspondence between the maps derived from the same cross, (ii) few significant differences in interval distances, and (iii) global differences, which can reach 20% of the total map length. A composite map of 275 loci covering 1765 cM has been constructed. Key words : Zea mays L., RFLP, genetic map, molecular markers, proteins.

Genotyping-by-Sequencing in Plants

The advent of next-generation DNA sequencing (NGS) technologies has led to the development of rapid genome-wide Single Nucleotide Polymorphism (SNP) detection applications in various plant species. Recent improvements in sequencing throughput combined with an overall decrease in costs per gigabase of sequence is allowing NGS to be applied to not only the evaluation of small subsets of parental inbred lines, but also the mapping and characterization of traits of interest in much larger populations. Such an approach, where sequences are used simultaneously to detect and score SNPs, therefore bypassing the entire marker assay development stage, is known as genotyping-by-sequencing (GBS). This review will summarize the current state of GBS in plants and the promises it holds as a genome-wide genotyping application.

Computational tools for discovery and interpretation of expression quantitative trait loci

Expression quantitative trait locus (eQTL) analysis is rapidly moving from a cutting-edge concept in genomics to a mature area of investigation, with important connections to genome-wide association studies for human disease, pharmacogenomics and toxicogenomics. Despite the importance of the topic, many investigators must develop their own code or use tools not specifically suited for eQTL analysis. Convenient computational tools are becoming available, but they are not widely publicized, and investigators who are interested in discovery or eQTL, or in using them to interpret genome-wide association study results may have difficulty navigating the available resources. The purpose of this review is to help investigators find appropriate programs for eQTL analysis and interpretation.

Complex control of GABA(A) receptor subunit mRNA expression: variation, covariation, and genetic regulation

GABA type-A receptors are essential for fast inhibitory neurotransmission and are critical in brain function. Surprisingly, expression of receptor subunits is highly variable among individuals, but the cause and impact of this fluctuation remains unknown. We have studied sources of variation for all 19 receptor subunits using massive expression data sets collected across multiple brain regions and platforms in mice and humans. Expression of Gabra1, Gabra2, Gabrb2, Gabrb3, and Gabrg2 is highly variable and heritable among the large cohort of BXD strains derived from crosses of fully sequenced parents—C57BL/6J and DBA/2J. Genetic control of these subunits is complex and highly dependent on tissue and mRNA region. Remarkably, this high variation is generally not linked to phenotypic differences. The single exception is Gabrb3, a locus that is linked to anxiety. We identified upstream genetic loci that influence subunit expression, including three unlinked regions of chromosome 5 that modulate the expression of nine subunits in hippocampus, and that are also associated with multiple phenotypes. Candidate genes within these loci include, Naaa, Nos1, and Zkscan1. We confirmed a high level of coexpression for subunits comprising the major channel—Gabra1, Gabrb2, and Gabrg2—and identified conserved members of this expression network in mice and humans. Gucy1a3, Gucy1b3, and Lis1 are novel and conserved associates of multiple subunits that are involved in inhibitory signaling. Finally, proximal and distal regions of the 3′ UTRs of single subunits have remarkably independent expression patterns in both species. However, corresponding regions of different subunits often show congruent genetic control and coexpression (proximal-to-proximal or distal-to-distal), even in the absence of sequence homology. Our findings identify novel sources of variation that modulate subunit expression and highlight the extraordinary capacity of biological networks to buffer 4–100 fold differences in mRNA levels.

Quantitative measurement of allele-specific protein expression in a diploid yeast hybrid by LC-MS

Understanding the genetic basis of gene regulatory variation is a key goal of evolutionary and medical genetics. Regulatory variation can act in an allele-specific manner (cis-acting) or it can affect both alleles of a gene (trans-acting). Differential allele-specific expression (ASE), in which the expression of one allele differs from another in a diploid, implies the presence of cis-acting regulatory variation. While microarrays and high-throughput sequencing have enabled genome-wide measurements of transcriptional ASE, methods for measurement of protein ASE (pASE) have lagged far behind. We describe a flexible, accurate, and scalable strategy for measurement of pASE by liquid chromatography-coupled mass spectrometry (LC-MS). We apply this approach to a hybrid between the yeast species Saccharomyces cerevisiae and Saccharomyces bayanus. Our results provide the first analysis of the relative contribution of cis-acting and trans-acting regulatory differences to protein expression divergence between yeast species.

The ZmASR1 protein influences branched-chain amino acid biosynthesis and maintains kernel yield in maize under water-limited conditions

Abscisic acid-, stress-, and ripening-induced (ASR) proteins were first described about 15 years ago as accumulating to high levels during plant developmental processes and in response to diverse stresses. Currently, the effects of ASRs on water deficit tolerance and the ways in which their physiological and biochemical functions lead to this stress tolerance remain poorly understood. Here, we characterized the ASR gene family from maize (Zea mays), which contains nine paralogous genes, and showed that maize ASR1 (ZmASR1) was encoded by one of the most highly expressed paralogs. Ectopic expression of ZmASR1 had a large overall impact on maize yield that was maintained under water-limited stress conditions in the field. Comparative transcriptomic and proteomic analyses of wild-type and ZmASR1-overexpressing leaves led to the identification of three transcripts and 16 proteins up- or down-regulated by ZmASR1. The majority of them were involved in primary and/or cellular metabolic processes, including branched-chain amino acid (BCAA) biosynthesis. Metabolomic and transcript analyses further indicated that ZmASR1-overexpressing plants showed a decrease in BCAA compounds and changes in BCAA-related gene expression in comparison with wild-type plants. Interestingly, within-group correlation matrix analysis revealed a close link between 13 decreased metabolites in ZmASR1-overexpressing leaves, including two BCAAs. Among these 13 metabolites, six were previously shown to be negatively correlated to biomass, suggesting that ZmASR1-dependent regulation of these 13 metabolites might contribute to regulate leaf growth, resulting in improvement in kernel yield.

Brain expression quantitative trait locus mapping informs genetic studies of psychiatric diseases

Genome-wide association study (GWAS) can be used to identify genes that increase the risk of psychiatric diseases. However, much of the disease heritability is still unexplained, suggesting that there are genes to be discovered. Functional annotation of the genetic variants may increase the power of GWAS to identify disease genes, by providing prior information that can be used in Bayesian analysis or in reducing the number of tests. Expression quantitative trait loci (eQTLs) are genomic loci that regulate gene expression. Genetic mapping of eQTLs can help reveal novel functional effects of thousands of single nucleotide polymorphisms (SNPs). The present review mainly focused on the current knowledge on brain eQTL mapping, and discussed some major methodological issues and their possible solutions. The frequently ignored problems of batch effects, covariates, and multiple testing were emphasized, since they can lead to false positives and false negatives. The future application of eQTL data in GWAS analysis was also discussed.

Quantitative and qualitative stem rust resistance factors in barley are associated with transcriptional suppression of defense regulons

Stem rust (Puccinia graminis f. sp. tritici; Pgt) is a devastating fungal disease of wheat and barley. Pgt race TTKSK (isolate Ug99) is a serious threat to these Triticeae grain crops because resistance is rare. In barley, the complex Rpg-TTKSK locus on chromosome 5H is presently the only known source of qualitative resistance to this aggressive Pgt race. Segregation for resistance observed on seedlings of the Q21861 × SM89010 (QSM) doubled-haploid (DH) population was found to be predominantly qualitative, with little of the remaining variance explained by loci other than Rpg-TTKSK. In contrast, analysis of adult QSM DH plants infected by field inoculum of Pgt race TTKSK in Njoro, Kenya, revealed several additional quantitative trait loci that contribute to resistance. To molecularly characterize these loci, Barley1 GeneChips were used to measure the expression of 22,792 genes in the QSM population after inoculation with Pgt race TTKSK or mock-inoculation. Comparison of expression Quantitative Trait Loci (eQTL) between treatments revealed an inoculation-dependent expression polymorphism implicating Actin depolymerizing factor3 (within the Rpg-TTKSK locus) as a candidate susceptibility gene. In parallel, we identified a chromosome 2H trans-eQTL hotspot that co-segregates with an enhancer of Rpg-TTKSK-mediated, adult plant resistance discovered through the Njoro field trials. Our genome-wide eQTL studies demonstrate that transcript accumulation of 25% of barley genes is altered following challenge by Pgt race TTKSK, but that few of these genes are regulated by the qualitative Rpg-TTKSK on chromosome 5H. It is instead the chromosome 2H trans-eQTL hotspot that orchestrates the largest inoculation-specific responses, where enhanced resistance is associated with transcriptional suppression of hundreds of genes scattered throughout the genome. Hence, the present study associates the early suppression of genes expressed in this host-pathogen interaction with enhancement of R-gene mediated resistance.

Proteomics research on forest trees, the most recalcitrant and orphan plant species

The contribution of proteomics to the knowledge of forest tree (the most recalcitrant and almost forgotten plant species) biology is being reviewed and discussed, based on the author's own research work and papers published up to November 2010. This review is organized in four introductory sections starting with the definition of forest trees (1), the description of the environmental and economic importance (2) and its derived current priorities and research lines for breeding and conservation (3) including forest tree genomics (4). These precede the main body of this review: a general overview to proteomics (5) for introducing the forest tree proteomics section (6). Proteomics, defined as scientific discipline or experimental approach, it will be discussed both from a conceptual and methodological point of view, commenting on realities, challenges and limitations. Proteomics research in woody plants is limited to a reduced number of genera, including Pinus, Picea, Populus, Eucalyptus, and Fagus, mainly using first-generation approaches, e.g., those based on two-dimensional electrophoresis coupled to mass spectrometry. This area joins the own limitations of the technique and the difficulty and recalcitrance of the plant species as an experimental system. Furthermore, it contributes to a deeper knowledge of some biological processes, namely growth, development, organogenesis, and responses to stresses, as it is also used in the characterization and cataloguing of natural populations and biodiversity (proteotyping) and in assisting breeding programmes.

A PQL (protein quantity loci) analysis of mature pea seed proteins identifies loci determining seed protein composition

Legume seeds are a major source of dietary proteins for humans and animals. Deciphering the genetic control of their accumulation is thus of primary significance towards their improvement. At first, we analysed the genetic variability of the pea seed proteome of three genotypes over 3 years of cultivation. This revealed that seed protein composition variability was under predominant genetic control, with as much as 60% of the spots varying quantitatively among the three genotypes. Then, by combining proteomic and quantitative trait loci (QTL) mapping approaches, we uncovered the genetic architecture of seed proteome variability. Protein quantity loci (PQL) were searched for 525 spots detected on 2-D gels obtained for 157 recombinant inbred lines. Most protein quantity loci mapped in clusters, suggesting that the accumulation of the major storage protein families was under the control of a limited number of loci. While convicilin accumulation was mainly under the control of cis-regulatory regions, vicilins and legumins were controlled by both cis- and trans-regulatory regions. Some loci controlled both seed protein composition and protein content and a locus on LGIIa appears to be a major regulator of protein composition and of protein in vitro digestibility.

The genetic basis of evolutionary change in gene expression levels

The regulation of gene expression is an important determinant of organismal phenotype and evolution. However, the widespread recognition of this fact occurred long after the synthesis of evolution and genetics. Here, we give a brief sketch of thoughts regarding gene regulation in the history of evolution and genetics. We then review the development of genome-wide studies of gene regulatory variation in the context of the location and mode of action of the causative genetic changes. In particular, we review mapping of the genetic basis of expression variation through expression quantitative trait locus studies and measuring the cis/trans component of expression variation in allele-specific expression studies. We conclude by proposing a systematic integration of ideas that combines global mapping studies, cis/trans tests and modern population genetics methodologies, in order to directly estimate the forces acting on regulatory variation within and between species.

Expression quantitative trait loci: replication, tissue- and sex-specificity in mice

By treating the transcript abundance as a quantitative trait, gene expression can be mapped to local or distant genomic regions relative to the gene encoding the transcript. Local expression quantitative trait loci (eQTL) generally act in cis (that is, control the expression of only the contiguous structural gene), whereas distal eQTL act in trans. Distal eQTL are more difficult to identify with certainty due to the fact that significant thresholds are very high since all regions of the genome must be tested, and confounding factors such as batch effects can produce false positives. Here, we compare findings from two large genetic crosses between mouse strains C3H/HeJ and C57BL/6J to evaluate the reliability of distal eQTL detection, including "hotspots" influencing the expression of multiple genes in trans. We found that >63% of local eQTL and >18% of distal eQTL were replicable at a threshold of LOD > 4.3 between crosses and 76% of local and >24% of distal eQTL at a threshold of LOD > 6. Additionally, at LOD > 4.3 four tissues studied (adipose, brain, liver, and muscle) exhibited >50% preservation of local eQTL and >17% preservation of distal eQTL. We observed replicated distal eQTL hotspots between the crosses on chromosomes 9 and 17. Finally, >69% of local eQTL and >10% of distal eQTL were preserved in most tissues between sexes. We conclude that most local eQTL are highly replicable between mouse crosses, tissues, and sex as compared to distal eQTL, which exhibited modest replicability.

Using expression genetics to study the neurobiology of ethanol and alcoholism

Recent simultaneous progress in human and animal model genetics and the advent of microarray whole genome expression profiling have produced prodigious data sets on genetic loci, potential candidate genes, and differential gene expression related to alcoholism and ethanol behaviors. Validated target genes or gene networks functioning in alcoholism are still of meager proportions. Genetical genomics, which combines genetic analysis of both traditional phenotypes and whole genome expression data, offers a potential methodology for characterizing brain gene networks functioning in alcoholism. This chapter will describe concepts, approaches, and recent findings in the field of genetical genomics as it applies to alcohol research.

Detection, validation, and downstream analysis of allelic variation in gene expression

Common sequence variants within a gene often generate important differences in expression of corresponding mRNAs. This high level of local (allelic) control-or cis modulation-rivals that produced by gene targeting, but expression is titrated finely over a range of levels. We are interested in exploiting this allelic variation to study gene function and downstream consequences of differences in expression dosage. We have used several bioinformatics and molecular approaches to estimate error rates in the discovery of cis modulation and to analyze some of the biological and technical confounds that contribute to the variation in gene expression profiling. Our analysis of SNPs and alternative transcripts, combined with eQTL maps and selective gene resequencing, revealed that between 17 and 25% of apparent cis modulation is caused by SNPs that overlap probes rather than by genuine quantitative differences in mRNA levels. This estimate climbs to 40-50% when qualitative differences between isoform variants are included. We have developed an analytical approach to filter differences in expression and improve the yield of genuine cis-modulated transcripts to approximately 80%. This improvement is important because the resulting variation can be successfully used to study downstream consequences of altered expression on higher-order phenotypes. Using a systems genetics approach we show that two validated cis-modulated genes, Stk25 and Rasd2, are likely to control expression of downstream targets and affect disease susceptibility.

Inherited variation in gene expression

Variation in gene expression constitutes an important source of biological variability within and between populations that is likely to contribute significantly to phenotypic diversity. Recent conceptual, technical, and methodological advances have enabled the genome-scale dissection of transcriptional variation. Here, we outline common approaches for detecting gene expression quantitative trait loci, and summarize the insights gleaned from these studies regarding the genetic architecture of transcriptional variation and the nature of regulatory alleles. Particular emphasis is placed on human studies, and we discuss experimental designs that ensure that increasingly large and complex studies continue to advance our understanding of gene expression variation. We conclude by discussing the evolution of gene expression levels, and we explore prospects for leveraging new technological developments to investigate inherited variation in gene expression in even greater depth.

Mapping and proteomic analysis of albumin and globulin proteins in hexaploid wheat kernels (Triticum aestivum L.)

Albumins and globulins of wheat endosperm represent 20% of total kernel protein. They are soluble proteins, mainly enzymes and proteins involved in cell functions. Two-dimensional gel immobiline electrophoresis (2DE) (pH 4-7) x SDS-Page revealed around 2,250 spots. Ninety percent of the spots were common between the very distantly related cultivars 'Opata 85' and 'Synthetic W7984', the two parents of the International Triticeae Mapping Initiative (ITMI) progeny. 'Opata' had 130 specific spots while 'Synthetic' had 96. 2DE and image analysis of the soluble proteins present in 112 recombinant inbred lines of the F9-mapped ITMI progeny enabled 120 unbiased segregating spots to be mapped on 21 wheat (Triticum aestivum L. em. Thell) chromosomes. After trypsic digestion, mapped spots were subjected to MALDI-Tof or tandem mass spectrometry for protein identification by database mining. Among the 'Opata' and 'Synthetic' spots identified, many enzymes have already been mapped in the barley and rice genomes. Multigene families of Heat Shock Proteins, beta-amylases, UDP-glucose pyrophosphorylases, peroxydases and thioredoxins were successfully identified. Although other proteins remain to be identified, some differences were found in the number of segregating proteins involved in response to stress: 11 proteins found in the modern selected cultivar 'Opata 85' as compared to 4 in the new hexaploid ;Synthetic W7984'. In addition, 'Opata' and 'Synthetic' differed in the number of proteins involved in protein folding (2 and 10, respectively). The usefulness of the mapped enzymes for future research on seed composition and characteristics is discussed.

Mapping and proteomic analysis of albumin and globulin proteins in hexaploid wheat kernels (Triticum aestivum L.)

Albumins and globulins of wheat endosperm represent 20% of total kernel protein. They are soluble proteins, mainly enzymes and proteins involved in cell functions. Two-dimensional gel immobiline electrophoresis (2DE) (pH 4-7) x SDS-Page revealed around 2,250 spots. Ninety percent of the spots were common between the very distantly related cultivars 'Opata 85' and 'Synthetic W7984', the two parents of the International Triticeae Mapping Initiative (ITMI) progeny. 'Opata' had 130 specific spots while 'Synthetic' had 96. 2DE and image analysis of the soluble proteins present in 112 recombinant inbred lines of the F9-mapped ITMI progeny enabled 120 unbiased segregating spots to be mapped on 21 wheat (Triticum aestivum L. em. Thell) chromosomes. After trypsic digestion, mapped spots were subjected to MALDI-Tof or tandem mass spectrometry for protein identification by database mining. Among the 'Opata' and 'Synthetic' spots identified, many enzymes have already been mapped in the barley and rice genomes. Multigene families of Heat Shock Proteins, beta-amylases, UDP-glucose pyrophosphorylases, peroxydases and thioredoxins were successfully identified. Although other proteins remain to be identified, some differences were found in the number of segregating proteins involved in response to stress: 11 proteins found in the modern selected cultivar 'Opata 85' as compared to 4 in the new hexaploid ;Synthetic W7984'. In addition, 'Opata' and 'Synthetic' differed in the number of proteins involved in protein folding (2 and 10, respectively). The usefulness of the mapped enzymes for future research on seed composition and characteristics is discussed.

Association of sugar content QTL and PQL with physiological traits relevant to frost damage resistance in pea under field and controlled conditions

To increase yield in pea (Pisum sativum L.), autumn sowing would be preferable. Hence, frost tolerance of pea became a major trait of interest for breeders. In order to better understand the cold acclimation in pea, Champagne a frost tolerant line and Terese, a frost sensitive line, and their recombinant inbred lines (RIL) were studied. RIL frost tolerance was evaluated by a frost damage scale under field as well as controlled conditions. A quantitative trait loci (QTL) approach was used to identify chromosomal regions linked to frost tolerance. The detected QTL explained from 6.5 to 46.5% of the phenotypic variance. Amongst them, those located on linkage groups 5 and 6 were consistent with over all experiments, in field as well as in controlled environments. In order to improve the understanding of the frost tolerance mechanisms, several cold acclimation key characters such as concentration of sugars, electrolyte leakage, osmotic pressure, and activity of RuBisCO were assessed. Some of these physiological QTL colocalised with QTL for frost damage, in particular two raffinose QTL on LG5 and LG6 and one RuBisCO activity QTL on LG6, explaining 8.8 to 27.0% of the phenotypic variance. In addition, protein quantitative loci were mapped; some of them colocalised with frost damage and physiological QTL on LG5 and LG6, explaining 16.0-43.6% of the phenotypic variance. Raffinose metabolism and RuBisCO activity and its effect on photosynthesis might play a major role in cold acclimation of pea.