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Dynamic zebrafish interactome reveals transcriptional mechanisms of dioxin toxicity.

Alexeyenko A, Wassenberg DM, Lobenhofer EK, Yen J, Linney E, Sonnhammer EL, Meyer JN - PLoS ONE (2010)

Bottom Line: The most notable processes altered at later developmental stages were calcium and iron metabolism, embryonic morphogenesis including neuronal and retinal development, a variety of mitochondria-related functions, and generalized stress response (not including induction of antioxidant genes).Within the interactome, many of these responses were connected to cytochrome P4501A (cyp1a) as well as other genes that were dioxin-regulated one day after exposure.This suggests that cyp1a may play a key role initiating the toxic dysregulation of those processes, rather than serving simply as a passive marker of dioxin exposure, as suggested by earlier research.

View Article: PubMed Central - PubMed

Affiliation: Stockholm Bioinformatics Centre, Stockholm University, Stockholm, Sweden.

ABSTRACT

Background: In order to generate hypotheses regarding the mechanisms by which 2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin) causes toxicity, we analyzed global gene expression changes in developing zebrafish embryos exposed to this potent toxicant in the context of a dynamic gene network. For this purpose, we also computationally inferred a zebrafish (Danio rerio) interactome based on orthologs and interaction data from other eukaryotes.

Methodology/principal findings: Using novel computational tools to analyze this interactome, we distinguished between dioxin-dependent and dioxin-independent interactions between proteins, and tracked the temporal propagation of dioxin-dependent transcriptional changes from a few genes that were altered initially, to large groups of biologically coherent genes at later times. The most notable processes altered at later developmental stages were calcium and iron metabolism, embryonic morphogenesis including neuronal and retinal development, a variety of mitochondria-related functions, and generalized stress response (not including induction of antioxidant genes). Within the interactome, many of these responses were connected to cytochrome P4501A (cyp1a) as well as other genes that were dioxin-regulated one day after exposure. This suggests that cyp1a may play a key role initiating the toxic dysregulation of those processes, rather than serving simply as a passive marker of dioxin exposure, as suggested by earlier research.

Conclusions/significance: Thus, a powerful microarray experiment coupled with a flexible interactome and multi-pronged interactome tools (which are now made publicly available for microarray analysis and related work) suggest the hypothesis that dioxin, best known in fish as a potent cardioteratogen, has many other targets. Many of these types of toxicity have been observed in mammalian species and are potentially caused by alterations to cyp1a.

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Related in: MedlinePlus

Network of biological processes based on genes dioxin-altered on sequential days.Nodes of the network are defined as GO “biological process” categories that include one or more differentially expressed genes in the course of the experiment. Thus if a GO category is enriched on day 1 in differentially expressed genes, and linked by an unexpectedly high number of FunCoup interactions to a second GO category that on day 2 is enriched in differentially expressed genes, we infer that this temporal relationship is indicative of causality. Network interactions (edges) represent the sum of interactions between the gene members of the two connected GO categories. Node color represents the fraction of the genes in that node that are regulated by dioxin on any day (green is low, red is high). Edge thickness and opacity represent the number of gene-gene links between two categories and χ2 enrichment score for the likelihood that this pair of categories is enriched in links, respectively. Edge color and arrows show timing of differential expression between gene-gene pairs in respective GO categories.
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pone-0010465-g004: Network of biological processes based on genes dioxin-altered on sequential days.Nodes of the network are defined as GO “biological process” categories that include one or more differentially expressed genes in the course of the experiment. Thus if a GO category is enriched on day 1 in differentially expressed genes, and linked by an unexpectedly high number of FunCoup interactions to a second GO category that on day 2 is enriched in differentially expressed genes, we infer that this temporal relationship is indicative of causality. Network interactions (edges) represent the sum of interactions between the gene members of the two connected GO categories. Node color represents the fraction of the genes in that node that are regulated by dioxin on any day (green is low, red is high). Edge thickness and opacity represent the number of gene-gene links between two categories and χ2 enrichment score for the likelihood that this pair of categories is enriched in links, respectively. Edge color and arrows show timing of differential expression between gene-gene pairs in respective GO categories.

Mentions: Dioxin-altered genes in individual gene-gene interactions were labeled with days when the participating genes were first detected as differentially expressed. The inferred GO-GO interactions then fell into 2 classes: ones where both genes were first altered on the same day, and those where expression of one of the genes changed earlier than the other. By only using the latter class, we could augment the analysis with a temporal dimension. Thus, if there were a significant number of genes of GO category X first altered on day d interacting with genes in GO category Y first altered on day (d+1), then we could hypothesize a causative relation (meta-flow) X→Y. Limiting the output to only enriched GO-GO connections, i.e. ones with significantly more gene-gene interactions (given that both genes were dioxin-regulated) than expected by chance, allowed us to focus on the major tendencies of propagation of toxicity and organismal response to it. Compared to the individual category enrichment, this approach yielded a much richer analysis for interpretation. The dynamic picture of day-by-day meta-flow is presented in Figure 4. The combined map of all flows, as well as same-day networks, are offered as Figure S6 and Data File S8 (a manipulable Cytoscape session file).


Dynamic zebrafish interactome reveals transcriptional mechanisms of dioxin toxicity.

Alexeyenko A, Wassenberg DM, Lobenhofer EK, Yen J, Linney E, Sonnhammer EL, Meyer JN - PLoS ONE (2010)

Network of biological processes based on genes dioxin-altered on sequential days.Nodes of the network are defined as GO “biological process” categories that include one or more differentially expressed genes in the course of the experiment. Thus if a GO category is enriched on day 1 in differentially expressed genes, and linked by an unexpectedly high number of FunCoup interactions to a second GO category that on day 2 is enriched in differentially expressed genes, we infer that this temporal relationship is indicative of causality. Network interactions (edges) represent the sum of interactions between the gene members of the two connected GO categories. Node color represents the fraction of the genes in that node that are regulated by dioxin on any day (green is low, red is high). Edge thickness and opacity represent the number of gene-gene links between two categories and χ2 enrichment score for the likelihood that this pair of categories is enriched in links, respectively. Edge color and arrows show timing of differential expression between gene-gene pairs in respective GO categories.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2864754&req=5

pone-0010465-g004: Network of biological processes based on genes dioxin-altered on sequential days.Nodes of the network are defined as GO “biological process” categories that include one or more differentially expressed genes in the course of the experiment. Thus if a GO category is enriched on day 1 in differentially expressed genes, and linked by an unexpectedly high number of FunCoup interactions to a second GO category that on day 2 is enriched in differentially expressed genes, we infer that this temporal relationship is indicative of causality. Network interactions (edges) represent the sum of interactions between the gene members of the two connected GO categories. Node color represents the fraction of the genes in that node that are regulated by dioxin on any day (green is low, red is high). Edge thickness and opacity represent the number of gene-gene links between two categories and χ2 enrichment score for the likelihood that this pair of categories is enriched in links, respectively. Edge color and arrows show timing of differential expression between gene-gene pairs in respective GO categories.
Mentions: Dioxin-altered genes in individual gene-gene interactions were labeled with days when the participating genes were first detected as differentially expressed. The inferred GO-GO interactions then fell into 2 classes: ones where both genes were first altered on the same day, and those where expression of one of the genes changed earlier than the other. By only using the latter class, we could augment the analysis with a temporal dimension. Thus, if there were a significant number of genes of GO category X first altered on day d interacting with genes in GO category Y first altered on day (d+1), then we could hypothesize a causative relation (meta-flow) X→Y. Limiting the output to only enriched GO-GO connections, i.e. ones with significantly more gene-gene interactions (given that both genes were dioxin-regulated) than expected by chance, allowed us to focus on the major tendencies of propagation of toxicity and organismal response to it. Compared to the individual category enrichment, this approach yielded a much richer analysis for interpretation. The dynamic picture of day-by-day meta-flow is presented in Figure 4. The combined map of all flows, as well as same-day networks, are offered as Figure S6 and Data File S8 (a manipulable Cytoscape session file).

Bottom Line: The most notable processes altered at later developmental stages were calcium and iron metabolism, embryonic morphogenesis including neuronal and retinal development, a variety of mitochondria-related functions, and generalized stress response (not including induction of antioxidant genes).Within the interactome, many of these responses were connected to cytochrome P4501A (cyp1a) as well as other genes that were dioxin-regulated one day after exposure.This suggests that cyp1a may play a key role initiating the toxic dysregulation of those processes, rather than serving simply as a passive marker of dioxin exposure, as suggested by earlier research.

View Article: PubMed Central - PubMed

Affiliation: Stockholm Bioinformatics Centre, Stockholm University, Stockholm, Sweden.

ABSTRACT

Background: In order to generate hypotheses regarding the mechanisms by which 2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin) causes toxicity, we analyzed global gene expression changes in developing zebrafish embryos exposed to this potent toxicant in the context of a dynamic gene network. For this purpose, we also computationally inferred a zebrafish (Danio rerio) interactome based on orthologs and interaction data from other eukaryotes.

Methodology/principal findings: Using novel computational tools to analyze this interactome, we distinguished between dioxin-dependent and dioxin-independent interactions between proteins, and tracked the temporal propagation of dioxin-dependent transcriptional changes from a few genes that were altered initially, to large groups of biologically coherent genes at later times. The most notable processes altered at later developmental stages were calcium and iron metabolism, embryonic morphogenesis including neuronal and retinal development, a variety of mitochondria-related functions, and generalized stress response (not including induction of antioxidant genes). Within the interactome, many of these responses were connected to cytochrome P4501A (cyp1a) as well as other genes that were dioxin-regulated one day after exposure. This suggests that cyp1a may play a key role initiating the toxic dysregulation of those processes, rather than serving simply as a passive marker of dioxin exposure, as suggested by earlier research.

Conclusions/significance: Thus, a powerful microarray experiment coupled with a flexible interactome and multi-pronged interactome tools (which are now made publicly available for microarray analysis and related work) suggest the hypothesis that dioxin, best known in fish as a potent cardioteratogen, has many other targets. Many of these types of toxicity have been observed in mammalian species and are potentially caused by alterations to cyp1a.

Show MeSH
Related in: MedlinePlus