Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Array CGH of Drosophila compound chromosomes on heterochromatin custom array.

ABSTRACT: Mapping the Drosophila melanogaster centromeric heterochromatin by CGH analysis of embryos lacking specific chromosomes or chromosome arms. Nine chromosome or chromosome arm deletions were tested: embryos lacking the entire second chromosome (2En-), 2L (2L-), 2R (2R-), the entire third chromosome (3En-), 3L (3L-), 3R (3R-), the entire fourth chromosome (4En-), the X chromosome (X-), or both X and Y chromosomes (XY-). Control: Blastoderm stage wild type Oregon R embryos. For each experiment 100-150 embryos of the appropriate genotype were collected. DNA from randomly staged 0-8 hr wild type Oregon R embryos was used as reference for all experiments. Embryos with no X chromosome were obtained by crossing attached-X/Y females (C(1)DX, y f) to X/Y males. Embryos with no X and Y chromosomes were obtained by crossing attached-X/Y females (C(1)RM, y2suwawa) to attached-XY males (YSX YL, In(1)EN, y B). The compound II chromosomes RM(2L); RM(2R)=C(2)v and the compound III chromosomes RM(3L); RM(3R)=C(3)se were used to generate 2L- and 2R-, and 3L- and 3R- embryos, respectively. The compound II C(2)EN and compound III C(3)EN st1 cu1es stocks were used to generate embryos deficient for the entire second and third chromosome, respectively. The compound IV C(4)RM, ci1eyR/0 were used to generate embryos deficient for the fourth chromosome. Embryos deficient for chromosome 4 were identified by their defects in denticle belt patterning during late embryogenesis, whereas embryos deficient for other chromosome/chromosome arm were recognized based on their specific phenotypic defects during early embryonic development. All embryos were collected at room temperature.

ORGANISM(S): Drosophila melanogaster

SUBMITTER: Bing He

PROVIDER: E-GEOD-36260 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:This study is a quantitative trait linkage mapping study targeted at gene-environment interaction. A family of yeast derived from BY4716 and RM11-1a was grown in two carbon sources (glucose and ethanol) and expression profiled. The analysis revealed how the influence of genetic factors changes in different conditions. Abstract: The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene–environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene–environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain–condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between RM and BY in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene–environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in cis-regulatory elements, tend to be more stable across conditions, such that they are more likely to show the same effect or the same direction of effect across conditions. Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth- related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene–environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits. Keywords: QTL This data set includes 24 parental arrays (6 of each parent in each condition, 218 segregant arrays (109 in both conditions), and 4 allele replacement arrays (IRA2 replacements in both backgrounds in both conditions). All arrays are from an unique biological sample, and many are randomized for dye (no dye-swaps on the same RNA sample). For the 24 parental arrays, there are three of each strain*condition*dye combination. The segregant arrays were randomized for dye. The replacement arrays are all of the Cy5 = experimental, Cy3 = reference type. All arrays are hybridized to a common reference consisting of an equal portions of RNA samples. from BY and RM grown in glucose and ethanol. Analysis was performed using default settings in Agilent Feature Extractor software versions 8.0-9.5. Spots were excluded if their intensity was below 350 or if they were flagged as non-uniformity outliers. Transcripts were considered good if they were present in all parental arrays (for the parental analysis) or 80% of all arrays in each condition for the segregant analysis.

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Array CGH of Drosophila compound chromosomes on heterochromatin custom array.

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