Unknown,Transcriptomics,Genomics,Proteomics

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Gene expression data from isolated white winter wheat embryos at physiological maturity, environment 1 of 2.

ABSTRACT: Gene expression was measured in isolated wheat embryos at physiological maturity. The gene expression measurements were done across a doubled haploid white winter wheat population between the variety Cayuga and the variety Caledonia (CxC). Data can be used for mapping gene expression QTL (eQTL). These data are from the first (Helfer 2004) of two field environments. Different target labeling was used so the environments are contained in two separate series. The design was a two-channel dye-swap reference design with bulked total RNA from pre-anthesis wheat spikes serving as the reference. Normalization was done using the TIGR MIDAS suite using blockwise LOWESS smoothing and variance normalization. Between slide variance normalization was also done for all slides so direct comparisons could be made. Dye-swap was submitted unmerged to facilitate flexibility in downstream processing.

ORGANISM(S): Triticum aestivum

SUBMITTER: Jesse Munkvold

PROVIDER: E-GEOD-35300 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Wheat (Triticum aestivum ssp. aestivum) contributes to 20% of the human protein supply, delivers essential amino acids and is of fundamental importance for bread and pasta quality. Wheat proteins are also involved in adverse human reactions like celiac disease, wheat allergy and the non-celiac wheat sensitivity (NCWS). Using LC-MS-based LFQ proteomics of aqueous flour extracts we determined 756 proteins across 150 wheat varieties grown in three environments. However, only 303 proteins were stably expressed across all environments in at least one variety underlining the large influence of environmental conditions on the expression of many proteins. Moreover, only 89 proteins were comparably expressed by all 150 varieties, with high coefficients of variation for the other proteins. Heritability (h) ranged from 0-1 with 114 proteins having h² > 0.6. Therefore, the expression of the variable proteins should be amenable to targeted manipulation across the wheat supply chain by varietal choice and breeding. Our study provides a first approach towards a fast and high-throughput methodology for quantifying these proteins which is required to breed wheats with the desired properties. Amylase trypsin inhibitors (ATIs) appear as a potential trigger for NCWS inducing intestinal and extra-intestinal inflammation. Studies on the prevalence and genetic architecture of ATI proteins in wheat are lacking so far. Large differences in the content and composition of 8 ATIs in the different varieties were identified by QconCAT-assisted quantification. The ATI proteins had low coefficients of correlations with quality traits commonly analyzed in wheat breeding. However, heritability was quite low except for ATI 0.28 and ATI CM2. A genome wide association mapping revealed a complex genetic architecture built up on many small but few medium and two major quantitative trait loci (QTL). The latter were on chromosome 3B for ATI 0.19-like and 6B for ATI 0.28 explaining 70.6 and 68.7% of the genetic variance, respectively. Using the wheat reference genome sequence, seven potential candidate genes behind the medium and major QTL were described with only one showing polymorphism based on exome capture analysis. Consequently, wheat breeding could contribute to a reduction of ATI contents in wheat products if incidence of ATI on human health is further confirmed.

Project description:Microarray data analysis Intensity of 21,939 gene features per array was extracted from scanned microarray images using Feature Extraction 5.1.1 software (Agilent Technologies), which performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). The input file for NIA microarray analysis tool is available at http://lgsun.grc.nia.nih.gov/microarray/data.html

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Gene expression data from isolated white winter wheat embryos at physiological maturity, environment 1 of 2.

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