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Natural selection canalizes expression variation of environmentally induced plasticity-enabling genes.

Shaw JR, Hampton TH, King BL, Whitehead A, Galvez F, Gross RH, Keith N, Notch E, Jung D, Glaholt SP, Chen CY, Colbourne JK, Stanton BA - Mol. Biol. Evol. (2014)

Bottom Line: We observe that natural selection acts to preserve canalized gene expression in populations of killifish that are most tolerant to abrupt salinity change and that these populations show the least variability in their transcription of genes enabling plasticity of the gill.Collectively these findings, which are drawn from the relationships between environmental challenge, plasticity, and canalization among populations, suggest that the selective processes that facilitate phenotypic plasticity do so by targeting the regulatory networks that gives rise to the response.These findings also provide a generalized, conceptual framework of how genes might interact with the environment and evolve toward the development of plastic traits.

View Article: PubMed Central - PubMed

Affiliation: The School of Public and Environmental Affairs, Indiana University, Bloomington The Center for Genomics and Bioinformatics, Indiana University, Bloomington The Mount Desert Island Biological Laboratory, Salisbury Cove, ME Environmental Genomics Group, School of Biosciences, University of Birmingham, Birmingham, United Kingdom joeshaw@indiana.edu.

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Phenotypic plasticity-enabling genes display reduced transregulatory complexity. Gene regulatory networks constructed using IPA software (Ingenuity Systems) between the uniquely interaction gene sets (A) and the uniquely main effect gene sets (B) are highlighted in orange, and their known, direct and indirect upstream transregulating molecules are highlighted in blue. Visual inspection of the networks suggests that the interaction gene sets form less complex networks (A) than noninteraction gene sets (B). Density distribution of these relationships (C) is significantly reduced in the unique interaction gene set. These networks are scale free and the probability that a vertex in the network interacts with k other vertices decays as a power law: P(k) ∼ k−g. Analysis of covariance comparing log values of P(k) to log values of k determines that the slope (g) for the interaction gene sets is significantly different (n = 13 levels of k for interaction genes, 26 levels of k for main effects genes; P < 0.05) indicating less connectivity among the genes.
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msu241-F4: Phenotypic plasticity-enabling genes display reduced transregulatory complexity. Gene regulatory networks constructed using IPA software (Ingenuity Systems) between the uniquely interaction gene sets (A) and the uniquely main effect gene sets (B) are highlighted in orange, and their known, direct and indirect upstream transregulating molecules are highlighted in blue. Visual inspection of the networks suggests that the interaction gene sets form less complex networks (A) than noninteraction gene sets (B). Density distribution of these relationships (C) is significantly reduced in the unique interaction gene set. These networks are scale free and the probability that a vertex in the network interacts with k other vertices decays as a power law: P(k) ∼ k−g. Analysis of covariance comparing log values of P(k) to log values of k determines that the slope (g) for the interaction gene sets is significantly different (n = 13 levels of k for interaction genes, 26 levels of k for main effects genes; P < 0.05) indicating less connectivity among the genes.

Mentions: Gene regulatory networks were constructed using IPA from the interaction versus main effects gene sets and their known transcriptional regulators (fig. 4A and B and supplementary fig. S2, Supplementary Material online). Network analysis comparing interaction and main effects gene sets revealed a significant reduction in the complexity of gene regulatory networks formed from interaction genes and their known upstream regulators (fig. 4A) compared with main effects genes and their known upstream regulators (fig. 4B and C; P < 0.05). To validate these findings, we also constructed negative-control reference networks between these two sets of genes, and downstream molecules they are reported to regulate (supplementary fig. S3B and C, Supplementary Material online). No differences in network complexity were observed between the two gene sets and their regulatory targets.Fig. 4.


Natural selection canalizes expression variation of environmentally induced plasticity-enabling genes.

Shaw JR, Hampton TH, King BL, Whitehead A, Galvez F, Gross RH, Keith N, Notch E, Jung D, Glaholt SP, Chen CY, Colbourne JK, Stanton BA - Mol. Biol. Evol. (2014)

Phenotypic plasticity-enabling genes display reduced transregulatory complexity. Gene regulatory networks constructed using IPA software (Ingenuity Systems) between the uniquely interaction gene sets (A) and the uniquely main effect gene sets (B) are highlighted in orange, and their known, direct and indirect upstream transregulating molecules are highlighted in blue. Visual inspection of the networks suggests that the interaction gene sets form less complex networks (A) than noninteraction gene sets (B). Density distribution of these relationships (C) is significantly reduced in the unique interaction gene set. These networks are scale free and the probability that a vertex in the network interacts with k other vertices decays as a power law: P(k) ∼ k−g. Analysis of covariance comparing log values of P(k) to log values of k determines that the slope (g) for the interaction gene sets is significantly different (n = 13 levels of k for interaction genes, 26 levels of k for main effects genes; P < 0.05) indicating less connectivity among the genes.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4209136&req=5

msu241-F4: Phenotypic plasticity-enabling genes display reduced transregulatory complexity. Gene regulatory networks constructed using IPA software (Ingenuity Systems) between the uniquely interaction gene sets (A) and the uniquely main effect gene sets (B) are highlighted in orange, and their known, direct and indirect upstream transregulating molecules are highlighted in blue. Visual inspection of the networks suggests that the interaction gene sets form less complex networks (A) than noninteraction gene sets (B). Density distribution of these relationships (C) is significantly reduced in the unique interaction gene set. These networks are scale free and the probability that a vertex in the network interacts with k other vertices decays as a power law: P(k) ∼ k−g. Analysis of covariance comparing log values of P(k) to log values of k determines that the slope (g) for the interaction gene sets is significantly different (n = 13 levels of k for interaction genes, 26 levels of k for main effects genes; P < 0.05) indicating less connectivity among the genes.
Mentions: Gene regulatory networks were constructed using IPA from the interaction versus main effects gene sets and their known transcriptional regulators (fig. 4A and B and supplementary fig. S2, Supplementary Material online). Network analysis comparing interaction and main effects gene sets revealed a significant reduction in the complexity of gene regulatory networks formed from interaction genes and their known upstream regulators (fig. 4A) compared with main effects genes and their known upstream regulators (fig. 4B and C; P < 0.05). To validate these findings, we also constructed negative-control reference networks between these two sets of genes, and downstream molecules they are reported to regulate (supplementary fig. S3B and C, Supplementary Material online). No differences in network complexity were observed between the two gene sets and their regulatory targets.Fig. 4.

Bottom Line: We observe that natural selection acts to preserve canalized gene expression in populations of killifish that are most tolerant to abrupt salinity change and that these populations show the least variability in their transcription of genes enabling plasticity of the gill.Collectively these findings, which are drawn from the relationships between environmental challenge, plasticity, and canalization among populations, suggest that the selective processes that facilitate phenotypic plasticity do so by targeting the regulatory networks that gives rise to the response.These findings also provide a generalized, conceptual framework of how genes might interact with the environment and evolve toward the development of plastic traits.

View Article: PubMed Central - PubMed

Affiliation: The School of Public and Environmental Affairs, Indiana University, Bloomington The Center for Genomics and Bioinformatics, Indiana University, Bloomington The Mount Desert Island Biological Laboratory, Salisbury Cove, ME Environmental Genomics Group, School of Biosciences, University of Birmingham, Birmingham, United Kingdom joeshaw@indiana.edu.

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Related in: MedlinePlus