Elena Potokina

Differential gene expression during seed germination in barley (Hordeum vulgare L.)

A barley cDNA macroarray comprising 1,440 unique genes was used to analyze the spatial and temporal patterns of gene expression in embryo, scutellum and endosperm tissue during different stages of germination. Among the set of expressed genes, 69 displayed the highest mRNA level in endosperm tissue, 58 were up-regulated in both embryo and scutellum, 11 were specifically expressed in the embryo and 16 in scutellum tissue. Based on Blast X analyses, 70% of the differentially expressed genes could be assigned a putative function. One set of genes, expressed in both embryo and scutellum tissue, included functions in cell division, protein translation, nucleotide metabolism, carbohydrate metabolism and some transporters. The other set of genes expressed in endosperm encodes several metabolic pathways including carbohydrate and amino acid metabolism as well as protease inhibitors and storage proteins. As shown for a storage protein and a trypsin inhibitor, the endosperm of the germinating barley grain contains a considerable amount of residual mRNA which was produced during seed development and which is degraded during early stages of germination. Based on similar expression patterns in the endosperm tissue, we identified 29 genes which may undergo the same degradation process.

SFP Genotyping From Affymetrix Arrays Is Robust But Largely Detects Cis -acting Expression Regulators

The recent development of Affymetrix chips designed from assembled EST sequences has spawned considerable interest in identifying single-feature polymorphisms (SFPs) from transcriptome data. SFPs are valuable genetic markers that potentially offer a physical link to the structural genes themselves. However, most current SFP prediction methodologies were developed for sequenced species although SFPs are particularly valuable for species with complex and unsequenced genomes. To establish the sensitivity and specificity of prediction, we explored four methods for identifying SFPs from experiments involving two tissues in two commercial barleys and their doubled-haploid progeny. The methods were compared in terms of numbers of SFPs predicted and their ability to identify known sequence polymorphisms in the features, to confirm existing SNP genotypes and to match existing maps and individual haplotypes. We identified >4000 separate SFPs that accurately predicted the SNP genotype of >98% of the doubled-haploid (DH) lines. They were highly enriched for features containing sequence polymorphisms but all methods uniformly identified a majority of SFPs (∼64%) in features for which there was no sequence polymorphism while 5% mapped to different locations, indicating that “SFPs” mainly represent polymorphism in cis-acting regulators. All methods are efficient and robust at predicting markers for gene mapping.

Expression quantitative trait loci analysis in plants

An expression Quantitative Trait Locus or eQTL is a chromosomal region that accounts for a proportion of the variation in abundance of a mRNA transcript observed between individuals in a genetic mapping population. A single gene can have one or multiple eQTLs. Large scale mRNA profiling technologies advanced genome-wide eQTL mapping in a diverse range of organisms allowing thousands of eQTLs to be detected in a single experiment. When combined with classical or trait QTLs, correlation analyses can directly suggest candidates for genes underlying these traits. Furthermore, eQTL mapping data enables genetic regulatory networks to be modelled and potentially provide a better understanding of the underlying phenotypic variation. The mRNA profiling data sets can also be used to infer the chromosomal positions of thousands of genes, an outcome that is particularly valuable for species with unsequenced genomes where the chromosomal location of the majority of genes remains unknown. In this review we focus on eQTL studies in plants, addressing conceptual and technical aspects that include experimental design, genetic polymorphism prediction and candidate gene identification.

Functional association between malting quality trait components and cDNA array based expression patterns in barley (Hordeum vulgare L.)

We developed an approach for relating differences in gene expression to the phenotypic variation of a trait of interest. This allows the identification of candidate genes for traits that display quantitative variation. To validate the principle, gene expression was monitored on a cDNA array with 1400 ESTs to identify genes involved in the variation of the complex trait ‘malting quality’ in barley. RNA profiles were monitored during grain germination in a set of 10 barley genotypes that had been characterized for 6 quality-associated trait components. The selection of the candidate genes was achieved via a correlation of dissimilarity matrices that were based on (i) trait variation and (ii) gene expression data. As expected, a comparison based on the complete set of differentially-expressed genes did not reveal any correlation between the matrices, because not all genes that show differential expression between the 10 cultivars are responsible for the observed differences in malting quality. However, by iteratively taking out one gene (with replacement) and re-computing the correlation, those genes that are positively contributing to the correlation could be identified. Using this procedure between 17 and 30 candidate genes were identified for each of the six malting parameters analysed. In addition to genes of unknown function, the list of candidates contains well-known malting-related genes. Five out of eight mapped candidate genes display linkage to known QTLs for malting quality traits. The described functional association strategy may provide an efficient link between functional genomics and plant breeding.

Expression genetics and haplotype analysis reveal cis regulation of serine carboxypeptidase I (Cxp1), a candidate gene for malting quality in barley (Hordeum vulgare L.)

Using a cDNA array-based functional genomics approach in barley, several candidate genes for malting quality including serine carboxypeptidase I (Cxp1) were previously identified (Potokina et al. in Mol Breed 14:153, 2004). The gene was mapped as a single nucleotide polymorphism (SNP) marker on chromosome 3H using the Steptoe (feeding grade) × Morex (malting grade) mapping population. Subsequently, the relative level of Cxp1 expression was determined by real-time RT-PCR for each of the 134 progeny lines and mapped as a quantitative trait. Only one quantitative trait locus (QTL) could be identified that significantly influenced the level of the Cxp1 expression. The expressed QTL maps to the same region on chromosome 3H as does the structural gene and corresponds to a QTL for “diastatic power,” one among several traits measured to assess malting quality. An analysis of 90 barley cultivars sampled from a worldwide collection revealed six SNPs at the Cxp1 locus, three of which display complete linkage disequilibrium and define two haplotypes. The Cxp1 expression level in a set of barley accessions showing haplotype I was significantly higher than that of accessions displaying haplotype II. The data provide evidence that (1) the expression of Cxp1 is regulated in cis and that (2) the level of diastatic power in the barley seed is influenced by the level of Cxp1 expression.

Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork

BackgroundA typical genetical genomics experiment results in four separate data sets; genotype, gene expression, higher-order phenotypic data and metadata that describe the protocols, processing and the array platform. Used in concert, these data sets provide the opportunity to perform genetic analysis at a systems level. Their predictive power is largely determined by the gene expression dataset where tens of millions of data points can be generated using currently available mRNA profiling technologies. Such large, multidimensional data sets often have value beyond that extracted during their initial analysis and interpretation, particularly if conducted on widely distributed reference genetic materials. Besides quality and scale, access to the data is of primary importance as accessibility potentially allows the extraction of considerable added value from the same primary dataset by the wider research community. Although the number of genetical genomics experiments in different plant species is rapidly increasing, none to date has been presented in a form that allows quick and efficient on-line testing for possible associations between genes, loci and traits of interest by an entire research community.DescriptionUsing a reference population of 150 recombinant doubled haploid barley lines we generated novel phenotypic, mRNA abundance and SNP-based genotyping data sets, added them to a considerable volume of legacy trait data and entered them into the GeneNetwork http://www.genenetwork.org. GeneNetwork is a unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance, may interact to condition more complex biological phenotypes (higher-order traits). Here we describe these barley data sets and demonstrate some of the functionalities GeneNetwork provides as an easily accessible and integrated analytical environment for exploring them.ConclusionBy integrating barley genotypic, phenotypic and mRNA abundance data sets directly within GeneNetworks analytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (barley) in a database that has been designed for an animal model species (mouse) with a well established genome sequence, we prove the importance of the concept and practice of modular development and interoperability of software engineering for biological data sets.

Methods for evaluating gene expression from Affymetrix microarray datasets

BackgroundAffymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.ResultsThe present study reports a comprehensive survey of the performance of all seven commonly used methods in evaluating genome-wide gene expression from a well-designed experiment using Affymetrix microarrays. The experiment profiled eight genetically divergent barley cultivars each with three biological replicates. The dataset so obtained confers a balanced and idealized structure for the present analysis. The methods were evaluated on their sensitivity for detecting differentially expressed genes, reproducibility of expression values across replicates, and consistency in calling differentially expressed genes. The number of genes detected as differentially expressed among methods differed by a factor of two or more at a given false discovery rate (FDR) level. Moreover, we propose the use of genes containing single feature polymorphisms (SFPs) as an empirical test for comparison among methods for the ability to detect true differential gene expression on the basis that SFPs largely correspond to cis-acting expression regulators. The PDNN method demonstrated superiority over all other methods in every comparison, whilst the default Affymetrix MAS5.0 method was clearly inferior.ConclusionA comprehensive assessment of seven commonly used data extraction methods based on an extensive barley Affymetrix gene expression dataset has shown that the PDNN method has superior performance for the detection of differentially expressed genes.

Robust detection and genotyping of single feature polymorphisms from gene expression data.

It is well known that Affymetrix microarrays are widely used to predict genome-wide gene expression and genome-wide genetic polymorphisms from RNA and genomic DNA hybridization experiments, respectively. It has recently been proposed to integrate the two predictions by use of RNA microarray data only. Although the ability to detect single feature polymorphisms (SFPs) from RNA microarray data has many practical implications for genome study in both sequenced and unsequenced species, it raises enormous challenges for statistical modelling and analysis of microarray gene expression data for this objective. Several methods are proposed to predict SFPs from the gene expression profile. However, their performance is highly vulnerable to differential expression of genes. The SFPs thus predicted are eventually a reflection of differentially expressed genes rather than genuine sequence polymorphisms. To address the problem, we developed a novel statistical method to separate the binding affinity between a transcript and its targeting probe and the parameter measuring transcript abundance from perfect-match hybridization values of Affymetrix gene expression data. We implemented a Bayesian approach to detect SFPs and to genotype a segregating population at the detected SFPs. Based on analysis of three Affymetrix microarray datasets, we demonstrated that the present method confers a significantly improved robustness and accuracy in detecting the SFPs that carry genuine sequence polymorphisms when compared to its rivals in the literature. The method developed in this paper will provide experimental genomicists with advanced analytical tools for appropriate and efficient analysis of their microarray experiments and biostatisticians with insightful interpretation of Affymetrix microarray data.

Detection of genetic determinants that define the difference in photoperiod sensitivity of Triticum aestivum L. near-isogenic lines

Identification of genetic determinants that define different degrees of line sensitivity to the photoperiod was acomplished using near-isogenic lines of the soft hexaploid wheat Triticum aestivum L. using SSR markers and markers specific to the Vrn and Ppd genes. It was established that the Ppd-s line contains a dominant Ppd-D1a allele located on chromosome 2D. This allele is characterized by a large deletion in the gene promoter region. For two other lines (Ppd-m and Ppd-w), introgression of the Ppd-B1 gene on chromosome 2B was detected from the parental Sonora variety, which is insensitive to the day length; however, the previously described Ppd-B1a.1 allele was not found. Another polymorphism that can cause weak photoperiodic sensitivity, an increased copy number of the Ppd-B1 gene, was detected for these lines.

The transcriptome analysis of barley (Hordeum vulgare L.) using the Affymetrix Barley1 GeneChip

An alternative to complete genome sequencing is development and analysis of ESTs—fragments of transcribed coding DNA sequences. The EST collections also enhanced the development of cDNA microarray technologies, which make possible assessing the transcription levels of several thousand genes in a studied tissue of an organism in the same experiment. This paper provides an overview of the results of experiments with a barley microarray, Affymetrix Barley1 GeneChip. The variation in transcription levels of over 22000 genes in germinating barley grain of 150 barley double haploid lines produced by crossing cultivars Steptoe and Morex. Variation in gene expression of each gene is a quantitative trait, which can be mapped in population of double haploids as the genetic loci determining its variation (expressed QTL or eQTL). A regulatory locus (eQTL) can colocalize with the corresponding gene on genetic map (cis-eQTL) or be distant from it, frequently on another chromosome (trans-eQTL). Thus, it is possible to detect and analyze cis- and trans-regulatory loci for genes on a genome-wide scale. The design of the Affymetrix oligonucleotide arrays makes it possible not only to concurrently test the transcription level of several thousand genes, but also to simultaneously detect the polymorphic regions in cDNA sequences, thereby finding a considerable fraction of all nucleotide substitutions between the compared genotypes. Two types of data (the expression levels of several thousand genes and the presence of polymorphic sites in their sequences) can be obtained concurrently when processing the results of the same experiment. The details of both procedures are illustrated with explanatory examples.