Drosophila melanogaster
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA384195 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Organismal sensitivity to environmental agents induced toxicity: A comparative ecotoxicogenomics approach
| PRJNA384181 | ENA
Project description:Organismal sensitivity to environmental agents induced toxicity: A comparative ecotoxicogenomics approach [Caenorhabditis elegans]
| PRJNA384194 | ENA
Project description:The National Institute on Alcohol Abuse and Alcoholism has estimated that approximately 14 million people in the United States suffer from alcoholism. Alcohol sensitivity, the development of tolerance to alcohol and susceptibility to addiction vary in the population. Whereas environmental factors, such as stress and social experience, contribute to individual variation in sensitivity to chronic alcohol consumption, genetic factors have also been implicated. However, genetic polymorphisms that predispose to alcoholism remain largely unknown due to extensive genetic and environmental variation in human populations. Drosophila, however, allows studies on genetically identical individuals in controlled environments. Although addiction to alcohol has not been demonstrated in Drosophila, flies show responses to alcohol exposure that resemble human intoxication, including hyperactivity, loss of postural control, sedation, and exposure-dependent development of tolerance. We assessed whole-genome transcriptional responses following alcohol exposure and demonstrate immediate down-regulation of olfactory sensitivity and, concomitant with development of tolerance, altered transcription of enzymes associated with fatty acid biosynthesis. Our results identify key enzymes in conserved metabolic pathways that may contribute to human alcohol sensitivity. Keywords: Drosophila, model system, alcohol sensitivity, tolerance
2006-07-27 | GSE5382 | GEO
Project description:Alcoholism is a complex disorder determined by interactions between genetic and environmental risk factors. Drosophila represents a powerful model system to dissect the genetic architecture of alcohol sensitivity, as large numbers of flies can readily be reared in defined genetic backgrounds and under controlled environmental conditions. Furthermore, flies exposed to ethanol undergo physiological and behavioral changes that resemble human alcohol intoxication, including loss of postural control, sedation, and development of tolerance. We performed artificial selection for alcohol sensitivity for 25 generations and created duplicate selection lines that are either highly sensitive or resistant to ethanol exposure along with unselected control lines. We used whole genome expression analysis to identify 1,678 probe sets with different expression levels between the divergent lines, pooled across replicates, at a false discovery rate of q < 0.001. We assessed to what extent genes with altered transcriptional regulation might be causally associated with ethanol sensitivity by measuring alcohol sensitivity of 37 co-isogenic P-element insertional mutations in 35 candidate genes, and found that 32 of these mutants differed in sensitivity to ethanol exposure from their co-isogenic controls. Furthermore, 23 of these novel genes have human orthologues. Combining whole genome expression profiling with selection for genetically divergent lines is an effective approach for identifying candidate genes that affect complex traits, such as alcohol sensitivity. Because of evolutionary conservation of function, it is likely that human orthologues of genes affecting alcohol sensitivity in Drosophila may contribute to alcohol-associated phenotypes in humans. Keywords: artificial selection, whole genome expression profiling
2007-11-20 | GSE7614 | GEO
Project description:Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.
2021-08-05 | MTBLS2060 | MetaboLights
Project description:Alcoholism is a complex disorder determined by interactions between genetic and environmental risk factors. Drosophila represents a powerful model system to dissect the genetic architecture of alcohol sensitivity, as large numbers of flies can readily be reared in defined genetic backgrounds and under controlled environmental conditions. Furthermore, flies exposed to ethanol undergo physiological and behavioral changes that resemble human alcohol intoxication, including loss of postural control, sedation, and development of tolerance. We performed artificial selection for alcohol sensitivity for 25 generations and created duplicate selection lines that are either highly sensitive or resistant to ethanol exposure along with unselected control lines. We used whole genome expression analysis to identify 1,678 probe sets with different expression levels between the divergent lines, pooled across replicates, at a false discovery rate of q < 0.001. We assessed to what extent genes with altered transcriptional regulation might be causally associated with ethanol sensitivity by measuring alcohol sensitivity of 37 co-isogenic P-element insertional mutations in 35 candidate genes, and found that 32 of these mutants differed in sensitivity to ethanol exposure from their co-isogenic controls. Furthermore, 23 of these novel genes have human orthologues. Combining whole genome expression profiling with selection for genetically divergent lines is an effective approach for identifying candidate genes that affect complex traits, such as alcohol sensitivity. Because of evolutionary conservation of function, it is likely that human orthologues of genes affecting alcohol sensitivity in Drosophila may contribute to alcohol-associated phenotypes in humans. Experiment Overall Design: Starting with flies from Raleigh natural population (see material and methods) we performed artificial selection for alcohol sensitivity for 35 generation. In each generations we scored 60 males and females, separately, from each line (resistant, sensitive, and control)using inebriometer, and the 20 highest-scoring flies from the resistant lines and the 20 lowest-scoring flies from the sensitive lines were selected as parents for the next generation. Control line flies were scored each generation and 20 random flies were used as parents. Experiment Overall Design: At generation 25, two replicates of 15 three-five day old virgin males and females were collected from each selection line. Total RNA was extracted from the 24 samples . Biotinylated cRNA probes were hybridized to high density oligonucleotide microarrays (Affymetrix, Inc. Drosophila GeneChip 2.0) and visualized with a streptavidin-phycoerythrin conjugate, as described in the Affymetrix GeneChip Expression Analysis Technical Manual (2000), using internal references for quantification. The quantitative estimate of expression of each probe set is the Signal (Sig) metric, as described in the Affymetrix Microarray Suite, Version 5.0.
2008-03-13 | E-GEOD-7614 | biostudies-arrayexpress
Project description:<p>Chronic sleep loss profoundly impacts metabolic health and shortens lifespan, but studies of the mechanisms involved have focused largely on acute sleep deprivation. To identify metabolic consequences of chronically reduced sleep, we conducted unbiased metabolomics on heads of three adult Drosophila short-sleeping mutants with very different mechanisms of sleep loss: fumin (fmn), redeye (rye), and sleepless (sss). Common features included elevated ornithine and polyamines, with lipid, acyl-carnitine, and TCA cycle changes suggesting mitochondrial dysfunction. Studies of excretion demonstrate inefficient nitrogen elimination in adult sleep mutants, likely contributing to their polyamine accumulation. Increasing levels of polyamines, particularly putrescine, promote sleep in control flies but poison sleep mutants. This parallels the broadly enhanced toxicity of high dietary nitrogen load from protein in chronically sleep-restricted Drosophila, including both sleep mutants and flies with hyper-activated wake-promoting neurons. Together, our results implicate nitrogen stress as a novel mechanism linking chronic sleep loss to adverse health outcomes-and perhaps for linking food and sleep homeostasis at the cellular level in healthy organisms.</p>
2023-03-18 | MTBLS3318 | MetaboLights
Project description:Gene and pathways affected by CAG-repeat RNA-based toxicity in Drosophila
| PRJNA145239 | ENA
Project description:Phenotypic and transcriptional response to selection for alcohol sensitivity in Drosophila melanogaster
| PRJNA100153 | ENA
Project description:PeptideRank is an approach that uses a rank-based algorithm for peptide detectability prediction from shotgun proteomics data. The best performance is achieved when it is trained on organism-specific shotgun proteomics datasets. The Drosophila shotgun proteomics data presented here have been used amongst others to train and validate PeptideRank.
2019-06-04 | PXD000726 | Pride