TRIF double (DKO)) focused on 148 highly expressed genes out of 22690 ORFs based on 3-fold expression increase and 100 expression unit cut-off from 0 to 4 h after LPS stimulation [10]. Although the study showed novel local insights of immune- related genes in different KOs, it did not show the capacity of LPS to induce pleiotropic biological processes not directly linked to immunity [11,12]. To infer system-level emergent complexity, we re-investigated the same data without any biased expressions cut-off. Building upon the accuracy and reliability of a correlation metrics based upon thousands of statistical units (genes, ORFs), we investigated temporal whole genome response to LPS stimulation in the above-mentioned 4 different genotypes of macrophages. In contrast to individually analyzing each microarray elements (ORFs), where weakly expressed ORFs usually has been considered to incur high noise-to-signal ratio, we analyzed the temporal expression changes of entire ORFs set considered as a whole. We confirmed LPS induces pleiotropic biological response and, additionally, found two characteristic response modes: acute- stochastic and collective. Acute-stochastic mode is largely immune- related response, while collective mode participates in diverse regulatory processes normally unrelated to immunity that transform the initial local response to the antigenic stimulus into a general state change of the cell. Overall, we show genome-wide differential response mainly occur through lowly expressed genes. This global LPS response has been previously neglected by biased gene-expression cut-offs. Our work can be used to understand both specific and global responses of biological systems. Results and Discussion Genome-wide invariance between wildtype, single and double KOs LPS stimulates the MyD88- and TRIF-dependent pathways to activate the innate immune response (Figure 1). We evaluated the correlation structure of Affymetrix mouse expression data for wildtype, MyD88 KO, TRIF KO, DKO at 0, 1 and 4 hours (Methods and [10]). The analyses of only highly up- or down- regulated gene expressions ignoring lowly-expressed genes may not be sufficient to unravel genome-wide organizational principles [13–15]. To investigate the existence of such principles, we analyzed the whole genome cDNA microarray expression without any threshold cut-off. First, we performed Pearson correlation analysis on the entire genome expression vector. This ensemble property of the population of genes is a robust measure that is not biased by noise at the level of individual gene measurement [16,17]. The whole genome correlations of the same cell-type between different genotypes are extremely high (Pearson r above 0.98, Figure 2A–B), indicating a strong common order parameter influencing the expression level of the entire genome, correspon- dent to the cell-type characterization [17]. High correlation between genotypes may suggest that technical noise of our microarray dataset is rather low [18,19]. Furthermore, our results are coincident with the results obtained on erythroid cell lineages using the same metrics [20]. The presence of such invariant order spanning more than twenty-thousand elements (genes, ORFs) and around four orders of magnitude of expression levels is a signature of general order parameters organizing the entire cell regulation network. This organization is, in our opinion, a ‘fact of nature’ that, for its dimensions and invariance, asks for a deep thinking in analogy of what happened for other collective phenomena in physics (magnetism, laser coherence, super-fluid helium, hydro- dynamic instabilities). Such strong invariance is imposed by the presence of a common attractor correspondent to the cell kind Macrophage Genome-Wide Control [16,20], between all genotypes especially when we already know that MyD88 KO and DKO show significantly impaired proin- flammatory responses [21]. Hence, to investigate specific proin- flammatory and global LPS response, we compared the between genotypes Pearson similarities as computed both on the whole genome and on different extractions of gene subsets (random and immune-related). Temporal Pearson correlation reveals genotype differences We adopted two measures to compare different genotypes by means of a Pearson correlation metrics: i) auto-correlation: Pearson r between 0 h (t0) and other time points (t1, t4) of the same genotype and ii) cross-correlation: Pearson r between wildtype and other genotypes at same time point (Methods). The auto-correlation analysis measures progressive response from t0 for the same genotype, while the cross-correlation analysis measures the temporal difference from wildtype response. The whole genome auto-correlation of all genotypes shows progressive response, correspondent to a progressive displacement of correlation from unity, to LPS stimulation using RMA normalized data (Figure 3A and Methods). We further checked the consistency of this result using another normalization process, MAS5 data (Figure S1A). Wildtype response follows distinctly different trend from MyD88 KO, TRIF KO and DKO that in turn are remarkably similar to each other. These results show i) 1% of auto-correlations variability is sufficient to discriminate different genotypes, and ii) the similar global gene expression behavior between single KOs and DKO suggests DKO possesses LPS response, too (see also section ‘Emergence of regulatory signature from scattered expressions in all genotypes’ for further proof of DKO response). To find the source for genome-wide response similarity between DKO and single KOs, we calculated the cross-correlation for all genotypes. Cross-correlation shows the response of TRIF KO is the most similar to wildtype, followed by MyD88 KO and DKO (Figure 3B and Figure S1B). Although the auto-correlations are similar, the different cross-correlations of other genotypes, with wildtype as a reference, show that the gene expression responses between genotypes are distinct from one another. We find these differences originate from differential activation machinery of gene expressions (see section ‘Deciphering gene regulatory mechanisms from emergent signature’). Since LPS is a well known inducer of immune response, we next, specifically focused on the auto-correlations of 157 immune- related genes (according to GenMAPP [22], Table S1). By this, we shift the focus from the ‘whole-genome’ response to the ‘local immune-related’ response of the system. The result shows that DKO has a flat profile (as expected, almost perfect correlation between different times is observed due to the lack of any classical immune response to LPS), followed by TRIF KO and MyD88 KO displaying a linear displacement in time from unity correlation but to a lesser extent than wildtype, pointing to a diminished immune response of single KO with respect to wildtype. (Figure 3C and Figure S1C). These results are consistent with current literature which suggests that MyD88 is key for LPS stimulation and consequently, for DKO, no activation of immune response is expected to occur [23,24]. The cross-correlations of immune-related genes show TRIF KO was closest to wildtype response, followed by MyD88 KO, while DKO was the farthest (Figure 3D and Figure S1D). Based on these results, we concur that the i) 157 immune-related genes are dependent on both MyD88 and TRIF, and that they are mainly activated by the MyD88-dependent pathway, ii) MyD88 and PLoS ONE | www.plosone.org 2 March 2009 | Volume 4 | Issue 3 | e4905