445 1686 667 1044 747 969 43 776 287 86 231 42 44 309 69 195 37 445 33 104 99 82 93 428 449 122 72 54 37 266 568 15 13 324 363 15 305 8.3.E-06 5.9.E-06 7.0.E-05 3.1.E-04 5.4.E-04 1.9.E-03 3.9.E-02 6.0.E-03 7.3.E-03 4.0.E-02 1.9.E-02 4.5.E-02 2.5.E-02 2.3.E-02 4.4.E-02 2.4.E-02 4.7.E-02 2.7.E-02 4.5.E-02 3.2.E-02 3.4.E-02 3.2.E-02 3.2.E-02 3.4.E-02 3.9.E-02 4.4.E-02 4.9.E-02 1.2.E-16 1.1.E-09 1.8.E-07 4.2.E-03 4.7.E-03 1.7.E-02 3.8.E-02 3.0.E-02 3.4.E-02 4.7.E-02 Table 3. Biological processes enriched in the wildtype downregulated collective mode. Biological processes (Panther) and pathways (KEGG) # genes p-value Oxidative phosphorylation Ribosome Protein biosynthesis mRNA splicing Histidine metabolism Proteasome Other metabolism Pre-mRNA processing Valine, leucine and isoleucine degradation Protein metabolism and modification doi:10.1371/journal.pone.0004905.t003 Table 2. Biological processes enriched in the wildtype upregulated collective mode. Biological processes (Panther) and pathways (KEGG) # genes p-value Developmental processes mRNA transcription regulation Cell structure mRNA transcription Signal transduction Proteolysis Melanogenesis Cation transport Oxidative phosphorylation Cytokine-cytokine receptor interaction Neurogenesis Toll-like receptor signaling pathway Blood circulation and gas exchange Cell structure and motility Focal adhesion Cell communication Prostate cancer Ion transport ErbB signaling pathway B-cell- and antibody-mediated immunity Metabolism of cyclic nucleotides Neuronal activities Complement-mediated immunity Cell surface receptor mediated signal transduction Protein phosphorylation Ectoderm development Exocytosis doi:10.1371/journal.pone.0004905.t002 Macrophage Genome-Wide Control sources cannot be performed in a similar manner [32,33]. Nevertheless, the strong invariance between different conditions of the same cell-type, can be considered as a sort of dynamical attractor encompassing the entire transcriptome, reflecting hidden genome-wide differential regulations [27]. Understanding the link between the ordered behaviors observed for i) single gene expression when intrinsic noise is low [33] and ii) genome-wide conditions, promises to be a very fruitful future direction. The discovery of two modes of response has also been shown recently for protein dynamics to a drug perturbation where a rapid translocation of specific proteins and a slower, wide-ranging temporal wave of protein degradation and accumulation occurred [34]. Our work points to the presence of a highly-ordered, coordinated, genome-wide expression dynamics of LPS stimula- tion, thereby requesting the need to consider global phenomena when interpreting immune response. In general, the consideration of the general rearrangement of the entire expression network after a specific stimulus, with the consequent activation of functions not directly linked to the original perturbation, could be the basis for rationalizing the onset of unexpected side-effects after drug treatments. Materials and Methods Biological datasets We re-analyzed microarray data obtained from time-series experiments (0, 1, and 4 hours) performed on peritoneal macrophages from wildtype, MyD88 KO, TRIF KO, and MyD88/TRIF DKO mice treated with 100 ng/ml of LPS (Salmonella Minnesota Re595, Sigma) [10]. Affymetrix mouse expression array A430 microarray chips were used for gene expression detection. The microarray dataset obtained from these experiments contains expression levels for 22690 Affymetrix probe set IDs. We reprocessed our Affymetrix microarray chip data using Robust Multichip Average (RMA) for further background adjustment and to reduce false positives of our Affymetrix microarray chip [35–37]. The complete experimental details can be found in Hirotani et al [10]. Statistical Analysis Auto- and cross-correlation analysis for interpreting LPS response. To investigate the correlation between any two expression vectors, x and y with n ( = 22690) dimension and mean values of expressions x and y, we calculate their mutual Pearson, r~ðx,yÞ by n n P ðxi{xÞðyi{yÞ P XiYi rðx,yÞ~ sﬃﬃﬃniﬃ~ﬃﬃﬃﬃ1ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃnﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ~ sﬃﬃﬃnﬃﬃiﬃ~ﬃﬃﬃﬃ1ﬃﬃﬃsﬃﬃﬃnﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ P ðxi{xÞ2 P ðyi{yÞ2 P Xi2 P Yi2 i~1 i~1 i~1 i~1 ~ X:Y jX jjYj ~cos h, 1 ð Þ where X ~ðx1{x,x2{x,::,xn{x , Y~ðy1{y,y2{y,::,yn{yÞ Þ and h is angle between two expression vectors. Geometrically, Eq. 1 shows the correlation coefficient, r(x,y), can be viewed as the cosine of the angle (h) on n-dimensional space between the two vectors of data representing a measure of response. However, when h = 0 (i.e., r = 1), generally X = aY (a.0). In the case when a = 1 i.e., X = Y, this implies X and Y has the same response, otherwise, X and Y have different but globally proportional response. We extend the Pearson correlation analysis to measure global temporal gene expression response to a given stimulation, PLoS ONE | www.plosone.org 11 March 2009 | Volume 4 | Issue 3 | e4905