Comprehensive analysis of gene-environmental interactions with temporal gene expression profiles in Pseudomonas aeruginosa

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Abstract

To explore gene-environment interactions, based on temporal gene expression information, we analyzed gene and treatment information intensively and inferred interaction networks accordingly. The main idea is that gene expression reflects the response of genes to environmental factors, assuming that variations of gene expression occur under different conditions. Then we classified experimental conditions into several subgroups based on the similarity of temporal gene expression profiles. This procedure is useful because it allows us to combine diverse gene expression data as they become available, and, especially, allowing us to lay the regulatory relationships on a concrete biological basis. By estimating the activation points, we can visualize the gene behavior, and obtain a consensus gene activation order, and hence describe conditional regulatory relationships. The estimation of activation points and building of synthetic genetic networks may result in important new insights in the ongoing endeavor to understand the complex network of gene regulation. © 2012 Song et al.

Figures

  • Figure 1. Expression Profiles of the 31 Genes in One Condition.
  • Figure 2. The aprA gene expression profiles in 72 conditions and 60 time points. doi:10.1371/journal.pone.0035993.g002
  • Figure 3. The fluctuation of standard deviation of aprA gene in different conditions and time series.
  • Figure 4. The networks of the five subgroups. The thickness and color of line indicate the popularity in each comprehensive genetic network. The direction of transit is clockwise. doi:10.1371/journal.pone.0035993.g004
  • Figure 5. Pattern matching of temporal data. A. This is an original similarity matrix from PCA similarity analysis, the deep red on diagonal is similarity of itself, the similarity is 1. B. This is the reorganized similarity matrix based on clustering analysis. doi:10.1371/journal.pone.0035993.g005
  • Figure 6. The mapping of unknown condition based on pattern matching of expression. For example, the expression pattern in the complex media of sputum extracts looks most like minimal media growth conditions. The clustering analysis of the expression data for the 72 conditions can yield groups of conditions with similar expression profiles, which can be used for pattern mapping of unknown condition based on expression pattern mapping. doi:10.1371/journal.pone.0035993.g006
  • Figure 7. The expression profile of the rpoS gene. The turn point is the half position from lift maximum, the turn off point is the half position from the right maximum. doi:10.1371/journal.pone.0035993.g007
  • Figure 8. The visualization tool with Visual Basic. The red bar indicates the gene turning off point, the green bar indicates the gene turning on point. The gene order by genes and conditions can obtained via sorting the data with turn on and turn off options. doi:10.1371/journal.pone.0035993.g008

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APA

Duan, K., McCullough, W. M., Surette, M. G., Ware, T., & Song, J. (2012). Comprehensive analysis of gene-environmental interactions with temporal gene expression profiles in Pseudomonas aeruginosa. PLoS ONE, 7(4). https://doi.org/10.1371/journal.pone.0035993

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