Pimephales promelas
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ABSTRACT:
PROVIDER: PRJNA289972 | ENA |
REPOSITORIES: ENA
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Project description:Fish Connectivity Mapping: Linking Chemical Stressors by Their MOA-Driven Transcriptomic Profiles
| PRJNA289593 | ENA
Project description:Fathead minnow and zebrafish are among the most intensively studied fish species in environmental toxicogenomics. To aid the assessment and interpretation of subtle transcriptomic effects from treatment conditions of interest, there needs to be a better characterization and understanding of the natural variation in gene expression among fish individuals within populations. Little effort, however, has been made in this area. Leveraging the transcriptomics data from a number of our toxicogenomics studies conducted over the years, we conducted a meta-analysis of nearly 600 microarrays generated from the ovary tissue of untreated, reproductively mature fathead minnow and zebrafish samples. As expected, there was considerable batch-to-batch transcriptomic variation; this “batch-effect” appeared to impact the fish transcriptomes randomly. The overall level of variation within-batch was quite low in fish ovary tissue, making it a suitable system for studying chemical stressors with subtle biological effects. The within-batch variation, however, differed considerably among individual genes and molecular pathways. This difference in variability is probably both technical and biological, thus suggesting a need to take into account both the expression levels and variance in evaluating and interpreting the transcriptional impact on genes and pathways by experimental conditions. There was significant conservation of both the genomes and transcriptomes between fathead minnow and zebrafish. The conservation to such a degree would enable not only a comparative biology approach in studying the mechanisms of action underlying environmental stressors, but also effective sharing of a large amount of existing public transcriptomics data for future development of toxicogenomics applications. total RNA from the ovary tissue of treated or control fish labeled in single color was hybridized to Agilent fathead minnow microarray (design 019597)
2015-08-01 | E-GEOD-60126 | biostudies-arrayexpress
Project description:Fathead minnow and zebrafish are among the most intensively studied fish species in environmental toxicogenomics. To aid the assessment and interpretation of subtle transcriptomic effects from treatment conditions of interest, there needs to be a better characterization and understanding of the natural variation in gene expression among fish individuals within populations. Little effort, however, has been made in this area. Leveraging the transcriptomics data from a number of our toxicogenomics studies conducted over the years, we conducted a meta-analysis of nearly 600 microarrays generated from the ovary tissue of untreated, reproductively mature fathead minnow and zebrafish samples. As expected, there was considerable batch-to-batch transcriptomic variation; this “batch-effect” appeared to impact the fish transcriptomes randomly. The overall level of variation within-batch was quite low in fish ovary tissue, making it a suitable system for studying chemical stressors with subtle biological effects. The within-batch variation, however, differed considerably among individual genes and molecular pathways. This difference in variability is probably both technical and biological, thus suggesting a need to take into account both the expression levels and variance in evaluating and interpreting the transcriptional impact on genes and pathways by experimental conditions. There was significant conservation of both the genomes and transcriptomes between fathead minnow and zebrafish. The conservation to such a degree would enable not only a comparative biology approach in studying the mechanisms of action underlying environmental stressors, but also effective sharing of a large amount of existing public transcriptomics data for future development of toxicogenomics applications.
2015-08-01 | GSE60126 | GEO
Project description:Effects of DHT on the ovary of fathead minnow
| PRJNA167623 | ENA
Project description:Small fish comp tox FHM haloperidol 96 h ovary
| PRJNA123105 | ENA
Project description:Influence of Gross Ovarian Stage on Transcript Profiles in Fathead Minnow (Pimephales promelas) Ovary Tissue
| PRJNA119595 | ENA
Project description:Transcriptional signature of progesterone-induced germinal vesicle breakdown in the fathead minnow ovary (Pimephales promelas)
| PRJNA186798 | ENA
Project description:The ureic-based herbicide linuron and the model anti-androgen flutamide regulate common gene networks in the fathead minnow (Pimephales promelas) ovary
| PRJNA167621 | ENA
Project description:Background: Complex biological networks control fundamental processes such as reproduction. The relationship of network structure to biological function was investigated in a global gene interaction network developed from ovary tissues of the model fish, fathead minnow (Pimephales promelas). Results: A global ovary gene interaction network was inferred from gene expression in ovaries of fathead minnow representing 288 different exposure conditions and 1,472 microarrays. More than 74 percent of the interactions in two subnetworks and 37% of the connections in a third subnetwork were also present as known connections found in curated databases and the literature. Subnetworks within the network were enriched in specific pathways and key ovarian functions. The network location of differentially expressed genes from different ovary stages was consistent with known functional changes at those stages. The global network provide additional insight into gene function when gene directly linked to differentially expressed genes are considered in enrichment analysis. Analysis of the impact of bisphenol A on ovarian gene expression demonstrated that the network provides an informative frame work in which to investigate mechanisms by which chemical effects occur. Conclusions: Our results suggest that the ovary transcriptional network is composed of several connected subnetworks where each is enriched in specific functions. Construction of a global gene regulatory network from multiple experiments provides valuable insight into the function of genes and the impact of chemicals on biological systems. total RNA from the ovary tissue of treated or control fish labeled in single color was hybridized to Agilent fathead minnow microarray (design 019597)
2016-07-06 | E-GEOD-70936 | biostudies-arrayexpress
Project description:Background: Complex biological networks control fundamental processes such as reproduction. The relationship of network structure to biological function was investigated in a global gene interaction network developed from ovary tissues of the model fish, fathead minnow (Pimephales promelas). Results: A global ovary gene interaction network was inferred from gene expression in ovaries of fathead minnow representing 288 different exposure conditions and 1,472 microarrays. More than 74 percent of the interactions in two subnetworks and 37% of the connections in a third subnetwork were also present as known connections found in curated databases and the literature. Subnetworks within the network were enriched in specific pathways and key ovarian functions. The network location of differentially expressed genes from different ovary stages was consistent with known functional changes at those stages. The global network provide additional insight into gene function when gene directly linked to differentially expressed genes are considered in enrichment analysis. Analysis of the impact of bisphenol A on ovarian gene expression demonstrated that the network provides an informative frame work in which to investigate mechanisms by which chemical effects occur. Conclusions: Our results suggest that the ovary transcriptional network is composed of several connected subnetworks where each is enriched in specific functions. Construction of a global gene regulatory network from multiple experiments provides valuable insight into the function of genes and the impact of chemicals on biological systems.
2016-07-06 | GSE70936 | GEO