Functional genomic delineation of TLR-induced transcriptional networks
© Elkon et al; licensee BioMed Central Ltd. 2007
Received: 04 July 2007
Accepted: 29 October 2007
Published: 29 October 2007
The innate immune system is the first line of defense mechanisms protecting the host from invading pathogens such as bacteria and viruses. The innate immunity responses are triggered by recognition of prototypical pathogen components by cellular receptors. Prominent among these pathogen sensors are Toll-like receptors (TLRs). We sought global delineation of transcriptional networks induced by TLRs, analyzing four genome-wide expression datasets in mouse and human macrophages stimulated with pathogen-mimetic agents that engage various TLRs.
Combining computational analysis of expression profiles and cis-regulatory promoter sequences, we dissected the TLR-induced transcriptional program into two major components: the first is universally activated by all examined TLRs, and the second is specific to activated TLR3 and TLR4. Our results point to NF-κB and ISRE-binding transcription factors as the key regulators of the universal and the TLR3/4-specific responses, respectively, and identify novel putative positive and negative feedback loops in these transcriptional programs. Analysis of the kinetics of the induced network showed that while NF-κB regulates mainly an early-induced and sustained response, the ISRE element functions primarily in the induction of a delayed wave. We further demonstrate that co-occurrence of the NF-κB and ISRE elements in the same promoter endows its targets with enhanced responsiveness.
Our results enhance system-level understanding of the networks induced by TLRs and demonstrate the power of genomics approaches to delineate intricate transcriptional webs in mammalian systems. Such systems-level knowledge of the TLR network can be useful for designing ways to pharmacologically manipulate the activity of the innate immunity in pathological conditions in which either enhancement or repression of this branch of the immune system is desired.
Immune systems in vertebrates have two basic arms: innate and adaptive immunity. The innate immune system is the first line of defense protecting the host from invading pathogens such as bacteria and viruses. It consists of various types of leukocytes (e.g., blood monocytes, neutrophils, tissue macrophages, dendritic cells) that specialize in phagocytosis (ingesting and digesting pathogens) and in evoking a complex response at the site of infection, collectively known as inflammation. The adaptive immunity arm is capable of specifically recognizing and selectively eliminating foreign microorganisms and molecules. It relies on T and B lymphocytes that express antigen-specific receptors. Upon encountering their specific antigens, these lymphocytes undergo extensive proliferation (clone expansion), maturation and activation. There are multiple cross-talks between the innate and adaptive immunity arms. For example, the phagocytic cells are intimately involved in the activation of the adaptive arm by functioning as antigen presenting cells (APCs) required for the activation of T lymphocytes, and TH lymphocytes secrete stimulatory cytokines that enhance phagocytosis by the specialized phagocytic cells.
Innate immune responses to pathogens are triggered by recognition of prototypical pathogen components, called pathogen-associated molecular patterns (PAMPs), through cellular pattern recognition receptors (PRRs). Prominent among these pathogen sensors is the family of Toll-like receptors (TLRs). To date, ten and thirteen TLR genes have been cloned in human and mouse, respectively; each of the TLRs appears to recognize a unique set of PAMPs [1, 2]. TLR1, 2, 4, 5 and 6 are expressed on the cell surface membrane and recognize bacterial and fungal products, while TLR3, 7, 8 and 9 reside in intracellular endosomes and specialize in detection of pathogens' nucleic acids . For example, lipopolysaccharide (LPS), which is a common structure of the cell wall of Gram-negative bacteria, is recognized by the extracellular TLR4, whereas double-stranded RNA (dsRNA), which is a viral PAMP, triggers the intracellular TLR3 signaling. The function of the other TLRs is less characterized.
After recognition of their ligands, TLRs trigger intricate cellular signaling pathways that endow the cells with antiviral and antibacterial states, which are acquired by the induction of protein effectors that impede viral replication and bacteria growth, and of inflammatory cytokines, chemokines and co-stimulatory molecules that enhance the activation of the adaptive immune response [2, 4]. The activation of this broad response is mediated by a signaling cascade that leads to stimulation of several transcription factors (TFs), primarily NF-κB, IRF3/7, and AP-1. Important among the induced cytokines are the interferons (IFNs), whose secretion results in the induction of a set of IFN-stimulated genes (ISGs), which are vital components in the development of antiviral and antimicrobial cellular states . The transactivation of the ISGs is controlled via the JAK/STAT signaling pathway either by an IFNα/β-activated TF complex termed ISGF3 (composed of STAT1, STAT2 and IRF9), which binds to a regulatory element denoted as ISRE (IFN-stimulated response element) [5, 6], or by an IFNγ-activated STAT1 homodimer complex, which binds primarily to the GAS regulatory element .
The transcriptional program spanned by activated TLRs encompasses hundreds of genes. The advent of gene expression microarrays and the availability of complete sequences of the mouse and human genomes enable study of these networks on the system level. Here, we analyzed four publicly available genome-wide datasets that recorded expression profiles in mouse and human macrophages stimulated with various pathogen-mimetic agents, with the goal of obtaining global delineation of the transcriptional network activated by TLRs. Combining computational analyses of gene expression profiles and cis-regulatory promoter sequences, we dissected the TLR-induced transcriptional program into two major components: the first is universally activated by all examined TLRs, and the second is specific to TLR3 and TLR4. Our results identify NF-κB as the key regulator of the universal TLR response and the ISRE element as the key control site of the TLR3/4 specific component, and reveal, on a genomic scale, known and novel target genes regulated by these elements. We also identify novel putative positive and negative feedback loops in these transcriptional programs, further increasing the complexity of the known tightly regulated network induced in response to pathogen invasion. Analysis of the kinetics of the induced network showed that while NF-κB regulates mainly an early-induced and sustained response, the ISRE element functions primarily in the induction of a delayed wave. In addition, we demonstrate that the pair of NF-κB and ISRE elements constitutes a cis-regulatory module that endows its targets with enhanced responsiveness to TLR3/4 activation. By combining expression and promoter analyses, we substantially reduced the high level of noise inherent in genome-wide analysis of such data, and obtained highly reliable results supported by independent datasets from both human and mouse.
Summary of datasets analyzed in this study
Gilchrist et al. (2006) 
Nau et al. (2002) 
Jeffrey et al. (2006) 
LPS, CpG, PAM2, PAM3, PIC, R848
LPS, CpG, PAM2, PAM3, PIC, R848
0 h, 20 m, 40 m, 1 h, 80 m, 2 h, 8 h*, 24 h*
0 h, 1 h*, 2 h*, 4 h, 8 h*, 24 h*
0 h, 1 h, 2 h, 6 h, 12 h, 24 h
0 h, 4 h
Affymetrix MG430 2.0
Two-channel oligonucleotide chip (Operon)
# distinct annotated genes
One (two at time 0 h)
Characterization of TLR-induced transcriptional networks
Stimulators used in the mouse MmBMM and MmRAW datasets
Lipopolysaccharide is a component of the bacterial cell wall (gram-negative bacteria)
Synthetic diacylated lipopeptide (mimics bacterial lipoproteins)
Synthetic triacylated lipopeptide (mimics bacterial lipoproteins)
Polyinosine-polycytidylic acid (Poly I:C) is a synthetic mimic of viral double-stranded RNA
Synthetic molecule of the imidazoquinoline family (mimics a viral product)
Mimics bacterial and viral CpG DNA motifs
Functional characterization utilizing the standard GO ontology  revealed that the universal and TLR3/4-specific responder sets were highly enriched for functions related to the innate immune response, including inflammation, and chemokine and cytokine activities (Figure 2B). Interestingly, no enrichment for any functional category was detected for the agent-specific sets. One explanation could be that these sets contain more false positives, as detection of genes induced only in a single condition is more prone to noise. In addition, it is possible that genes specifically induced by a single stimulator are less functionally characterized.
Our next goal was to identify the regulators that underlie the induction of the TLR-mediated transcriptional programs. We and others have demonstrated that combining computational analysis of cis-regulatory promoter elements with gene expression measurements can identify major transcription factors (TFs) that regulate transcriptional networks, even in complex mammalian systems [13–16]. We applied the promoter analysis algorithm PRIMA  implemented in the EXPANDER package . Given a target set and a background set of genes, PRIMA performs statistical tests to identify TFs whose binding site (BS) signatures are significantly more prevalent in the promoters of the target set than in the background set. Here, each of the eight gene sets was considered a target set and the entire set of 10,113 genes present on both arrays used in the MmBMM and MmRAW datasets served as the background set (see Methods). PRIMA identified significant over-representation of the NF-κB binding site signature in the group of genes that were induced by all TLRs (p = 2·10-12), and of the ISRE element in the set of genes that were induced only by LPS and PIC (p = 10-12) (Figure 2C). As in the functional analysis, no over-represented promoter signals were detected for the agent-specific clusters. PRIMA tests are confined to TFs with characterized binding site signatures. Search for novel elements using the MEME motif discovery tool  did not find any additional motif, except for the ubiquitous Sp1 signature in several sets. Taken together, the analysis suggests that while NF-κB is universally activated by all TLRs, the TFs that act via the ISRE element (namely, IRF3/7 and the STAT1:STAT2:IRF9 (ISGF3) complex) are activated specifically by the TLR4- and TLR3-mediated signaling pathways. Indeed, many key targets of NF-κB and the ISRE element are in the universal and TLR3/4 sets, respectively, as shown in Figure 2A. Notably, in support of this model, the Nf-κb1, Nf-κb2, Rel and Relb subunits of NF-κB are themselves included in the universal set (that is, they were induced in response to all agents), while Irf7, Stat1 and Stat2, which bind the ISRE, were specifically induced by the LPS and PIC treatments. (Irf9, the third component of the ISGF3 complex, was up-regulated in response to LPS and PIC as well, but only at late time-points – 8 h, 24 h for LPS, 4 h for PIC. As noted above, here we analyzed only time-points 0–4 h, which are common to all the examined TLR-inducing agents.)
Carrying out a similar analysis on the sets of down-regulated genes (using the minimum expression value over time-points 0–4 h in all six agents) did not yield any significant results. However, taking into account the later time-points of 8 h and 24 h (measured only for LPS) identified enrichment of cell-cycle related GO categories and TFs (namely, E2F, NF-Y; data not shown), reflecting proliferation arrest upon pathogen recognition.
Kinetics of the LPS-induced transcriptional response
TFBS over-represented in kinetic waves induced by LPS
Enriched TFBS motifs
# of genes
An additive effect of the pair of NF-κB and ISRE elements
Corroboration of the findings on independent human macrophage datasets
The results presented hitherto were inferred from analysis of responses of mouse macrophages to various TLR stimuli. Seeking corroboration of our findings in human cells, we analyzed two publicly available datasets that profiled transcriptional responses in immunologically challenged human macrophages. The first study, by Nau et al. , examined expression profiles in human monocyte-macrophages at several time points (1 hr, 2 hrs, 6 hrs, 12 hrs and 24 hrs) after stimulation by various agents; among them LPS and PIC are common to the stimuli examined by the mouse datasets we analyzed (this dataset is hereafter called HsM1). The second study, by Jeffery et al.  (hereafter called HsM2), profiled transcriptional responses in several human leukocytes challenged with various stimuli, among which monocyte-macrophages treated with LPS for 4 hrs were relevant to our analysis (see Table 1). These two studies provided us with independent data that profiled the transcriptional network induced by activated human macrophages, and allowed us to examine whether our findings on the major roles of NF-κB and ISRE elements in the activation of the transcriptional networks induced by activated TLR4 (LPS) and TLR3 (PIC) are valid also in humans.
TFBS over-represented in the response induced by LPS and PIC in the HsM1 dataset
# of genes
Enriched TFBS motifs
Induced only by LPS
Induced only by PIC
Induced by both LPS and PIC
TFBS over-represented in kinetic waves induced by LPS and PIC in the HsM1 dataset
# of genes
Statistical significance of increased expression of NF-κB+ISRE module.
Targets of module vs. targets of NF-κB
Targets of module vs. targets of ISRE
In this study we systematically delineated the transcriptional program induced by stimulation of various TLRs in macrophages. We dissected two major components of this program: the first is a core response universally activated by all examined TLRs, and the second is specifically activated by TLR3 and TLR4. Our analysis identified NF-κB and IRF-like TFs binding ISRE as the key regulators of these two components and pointed to their respective target genes on a genomic scale. While the involvement of NF-κB and IRF-like TFs in response to TLR induction has been known before, our study makes novel contributions to several aspects of system-level understanding of the transcriptional networks induced by innate immunity: (a) the combined, focused reanalysis of four independent datasets identifying a clean, combinatorial response; (b) revealing the intricate kinetics of the transcriptional response; (c) pinpointing novel specific genes involved in each of the responses; (d) identification of NF-κB and ISRE binding site locations over target genes; and (e) the refinement of the understanding of the regulatory circuitry involved in innate immune response.
Predicted NF-κB target genes in the universal TLR response network.
NFkB BS (location)
LPS maximum induction (log2)
Predicted ISRE target genes in the specific response to LPS and PIC
ISRE BS (location)
LPS maximum induction (log2)
The repertoire of the TLR universal response includes pro-inflammatory cytokines and chemokines (e.g., Ccl2-4, Csf1-3 and Cxcl1, which orchestrate innate immunity fight against pathogens), as well as co-stimulatory molecules (e.g., Il23a) that promote the activation of the T-cell branch of the adaptive immunity. The universal response also contains many general stress-responsive genes (e.g., Jun, Fos, Atf3, Egr1-3, Myc) that control cell proliferation and survival. Prominent among the genes specifically induced by TLR3 and TLR4 are the interferon (IFN)-induced genes (Figure 2A). IFN-induced genes comprise potent antiviral molecules (e.g., Mx2, Isg20, Oas2-3, Prkr) and are therefore expected to be induced by TLR3, which is activated by virally derived dsRNA. However, IFNs also have an important role in linking innate and adaptive immunity by regulating the induction of genes that enhance T-cell activation and antigen-presentation capacity in response to pathogen infection (e.g., Il15, Tap1, Psmb8), which explains their induction by bacterial stimuli such as LPS [19, 20].
Superimposed on the TLR universal program, we detected a robust TLR3/4-specific response, and demonstrated by promoter analysis that its key regulator is the ISRE element. In addition, our results indicate that this ISRE-mediated response is kinetically delayed compared to the NF-κB-regulated program. These findings too are corroborated by current biological knowledge. The ISRE cis-element is bound by members of the IRF and STAT TF families. Several studies demonstrated the existence of two waves of activation of TFs that act via ISRE by TLR3 and TLR4 [1, 20–22]. The emerging model is that IRF3, which is post-translationally activated by TLR3 and TLR4 via a cascade that involves the TRIF (TICAM1) and TRAM (TICAM2) adaptor proteins and their downstream kinases IKKε (IKBKE) and TBK1, promotes an early wave of IFN-β gene induction (Figure 5) [1, 23]. Once IFN-β is produced and secreted, it engages the type-I IFN receptor in both paracrine and autocrine fashion, thereby triggering the JAK-STAT signaling cascade that culminates in the activation of the ISGF3 TF complex, which is comprised of STAT1, STAT2 and IRF9 (official symbol: ISGF3G) . ISGF3 induces the expression of IRF7, which in turn further activates the expression of type-I IFNs. In this way, a positive loop is established, which ensures persistent expression of IFN-stimulated genes that enhance the antiviral and antimicrobial cellular state . Strikingly, in full compliance with this model, we observed that IFN-β and the IRF7, STAT1 and STAT2 TFs were specifically induced by LPS and PIC in the datasets we analyzed (Figure 5).
Our analysis points to novel feedback loops in the TLR-induced network, further increasing the known complexity of the regulatory circuits that modulate its induction and repression (see Figure 5 and Tables 7, 8): We identified IFIH1 (also known as MDA5) and LGP2 as novel putative targets regulated by the ISRE element. IFIH1 is a non-TLR cytoplasmic sensor that detects actively replicating viruses [2, 25], and triggers the induction of the NF-κB and IRF3 pathways via the activation of the adaptor protein VISA (also known as cardif or IPS-1) . Moreover, it has been recently demonstrated that IFIH1 detects cytoplasmic dsRNA generated during viral replication (while TLR3 detects viral dsRNA phagocytosed in endosomes), and that this sensor also binds to PIC and mediates type I IFN responses to this synthetic analog of viral dsRNA . Therefore, the transcriptional program induced by PIC stimulation probably reflects a combined outcome of the activation of TLR3-mediated and IFIH1-mediated pathways.
Interestingly, the second putative ISRE target we identified, LGP2, is a direct negative regulator of IFIH1 . The simultaneous activation of positive and negative regulators of the same pathway seems to be a recurrent theme in the logic of cellular signaling networks. Another novel putative positive loop in the ISRE-regulated network is mediated by NMI, which enhances the transcriptional activity of STAT-1 . In the NF-κB-regulated transcriptional response, which is universally activated by all examined TLRs, we identified MAP3K8 (also known as TPL-2 and COT) and RIPK2 as novel targets that form positive feedback loops which reinforce the persistent activation of this network [30, 31], and TNIP1 as a regulator that forms a negative feedback loop which inhibits the IκK complex, thereby contributing to the turning-off of this response .
The kinetic analysis of the response to LPS also suggests a role for the ATF/CREB cis-regulatory element. We identified a significant over-representation of this signature on promoters of genes whose expression peaked at very early time points (before 2 hrs). Two alternative interpretations of the role played by these elements are consistent with this rapid pattern of induction: According to the first, members of the ATF/CREB family activate this early and very short response; the second interpretation ascribes an inhibitory effect to these elements, implying that the TF(s) that act via them repress the expression of their target genes, and therefore the induction of these targets declines shortly after their activation. A recent study by Gilchrist et al.  demonstrating that ATF3 negatively regulates a subset of NF-κB target genes induced by TLR4 supports the second interpretation. Notably, the ATF3 gene itself is included in the TLR universal response, pointing to a negative loop that regulates a sub-network of TLR-induced transcriptional program.
The computational promoter analysis ferreted out the major regulators of the two components of the TLR-induced network. This complex transcriptional network is likely regulated by additional TFs, which were not detected by promoter analysis. Indeed, the TLR universal response contains several other TFs in addition to those discussed above (e.g., Egr1-3, c-Myc, Ets2, Fos). This could be explained by the fact that our statistical promoter analysis detects TFs with a relatively high number of direct targets, whose BSs are located within the scanned promoter region and which were responsive beyond a certain threshold in the studied conditions. It is therefore expected to miss TFs that: (a) have a small number of directly induced targets; (b) bind at large distances from the transcription start site; (c) regulate the TLR network by interacting with other TFs rather than directly binding to the DNA; or (d) have a very subtle (though, perhaps, biologically important) influence on the expression of their targets.
Our results suggest mainly distinct programs mediated by the NF-κB and ISRE cis-elements. However, when the two elements co-occured in the same target promoter, we detected an additive effect that boosts the induction of the target genes. This finding further defines the NF-κB+ISRE pair as a functional transcriptional module, and adds several novel candidates to the list of genes reported to be controlled by it [1, 33–35] (Tables 7, 8). Importantly, IFN-β is among the genes whose promoters were empirically demonstrated to be under the regulation of the NF-κB+ISRE pair .
Our analysis demonstrates the power of functional genomics approaches to delineate intricate transcriptional networks in mammalian systems. Microarray data are often noisy and do not distinguish between direct and secondary responses. Likewise, large-scale promoter scanning for putative TF targets produces many false positives due to the short and degenerate nature of BS signatures. Combining these two sources of information, and augmenting them by utilizing datasets and promoter sequences from both human and mouse, gave us an accurate, system-level delineation of the TLR-induced transcriptional program, and identified highly reliable putative direct targets of its key regulators. The findings reported in this study generalize, on a genomic scale, the current knowledge on the identity, function, kinetics and modular organization of the transcriptional regulators that mobilize the innate immune response, which is often based on studies of specific genes. Such knowledge can be useful for designing ways to pharmacologically manipulate the activity of the innate immunity in pathological conditions in which either enhancement or repression of this branch of the immune system is desired.
The four expression datasets analyzed in this study are summarized in Table 1. We used the original normalized probe expression values, as provided by the authors. In each dataset, we averaged measurements over replicate samples, and then, for each probe, we divided expression values in treated samples by the values in the corresponding control samples (time 0 hr). These fold-change ratios were log (base2)-transformed and averaged over probes that correspond to the same gene. Mapping probes in the MmBMM, HsM1 and HsM2 datasets to Ensembl gene ids was done using annotation files provided by Affymetrix. The MmRAW dataset included the Entrez-Gene id of each probe; we used Biomart  to map Entrez-Gene ids to Ensembl gene ids. The HsM1 experiment measured responses of macrophages cultured with LPS derived from E. coli (LPS_E) and Salmonella typhi (LPS_S). We regarded LPS_E and LPS_S as duplicates and averaged over these two conditions.
Definition of stimulator-induced genes
In all datasets except HsM1, a gene was considered to be induced by a given stimulator if its expression level in one or more of the time points was at least 1.8-fold higher than its expression at time 0. The results we report are not sensitive to the chosen cutoff and remained consistent for a wide range of values (from 1.5- to 2-fold). In HsM1 we used a more stringent threshold of 3.5-fold, since the expression values in this dataset showed a much larger variance, probably because no replicates were performed (except for time 0). This threshold was chosen so that a similar percentage of the genes will be considered induced in HsM1 as in the other datasets.
Groups of genes induced by subsets of stimulators
The two mouse datasets – MmBMM and MmRAW – share 10,113 genes. Using the maximum induction-fold of each of these genes, computed over six time-points (20 mins-2 hrs in MmBMM, and 4 hrs in MmRAW), for each of the six stimulators (LPS, PAM2, PAM3, PIC, R848 and CpG), we partitioned the genes into groups as follows. We enumerated all 63 (= 26-1) non-empty subsets of the six stimulators, and for each such subset we collected all the genes that were induced in those stimulators and not induced in the others. Ignoring sets with less than 40 genes, we obtained eight gene sets (Figure 2A): six agent-specific sets (i.e., genes that were induced only in one of the six stimulators), an LPS-PIC specific set, and a universal response set.
In humans, we repeated the above analysis for the LPS and PIC stimulators in the HsM1 dataset. Here, we used all five time-points (1 hr–24 hrs), and an induction threshold of 3.5-fold (see Table 4).
Functional categories analysis
Identification of enriched Gene Ontology (GO) biological processes categories was done using the TANGO algorithm implemented in the EXPANDER package . In brief, TANGO calculates the statistical significance of GO categories' over-representation within a given set of genes by computing the upper tail of the hypergeometric distribution. In order to account for multiple testing, a major challenge in such an analysis due to the strong dependencies among GO categories, TANGO estimates fixed p-values using an empirical distribution based on 1,000 randomly chosen gene sets. We report all GO categories with an enrichment p-value less than 10-5 (before correcting for multiple testing) (see Figure 2B). Association of mouse genes with GO categories was downloaded from the GO web-site  (Sep 2006).
Computational promoter analysis
Identification of enriched BS signature of known TFs was done using our PRIMA algorithm , which is implemented in the EXPANDER package. PRIMA identifies TFs whose BS signatures are significantly abundant in the promoters of a specified group of genes, given their distribution in the promoters of the entire background set (i.e., all the genes present on the chip). PRIMA uses position weight matrices (PWMs) as models for regulatory sites that are bound by TFs. 498 PWMs that represent human or mouse TFBSs were obtained from the TRANSFAC database (release 10.2, June 2006) . Promoter sequences corresponding to all known human and mouse genes were extracted from the Ensembl project (release 40, Sep 2006) . PRIMA scanned both strands of each promoter sequence in the region from 600 bps upstream to 100 bps downstream of the putative transcription start site (TSS). Repetitive elements were masked out. A detailed description of how PRIMA determines PWM cutoffs, identifies putative TFBSs, and computes enrichment scores is given in . We report TFs with an enrichment p-value less than 10-5. We used this stringent threshold due to the large number of PWMs examined. Note, however, that there is a very high level of redundancy in the TRANSFAC database. For example, there are seven different PWMs for NF-κB, which are naturally all very similar. Thus, the actual number of independent multiple tests performed by PRIMA is considerably less than the total number of PWMs. For each of the TFs reported in this study, we chose the PWM that gave the best overall results (in terms of enrichment): M00053 for NF-κB, M00258 for ISRE, and M00177 for ATF/CREB; other PWMs of these TFs often gave very similar p-values.
We also subjected each of the eight TLR-induced gene sets (Figure 2) to the MEME program (version 3.0.3) . MEME is a tool for discovering motifs de-novo in a group of related DNA sequences. MEME was run with a 4th-order Markov background model, which we constructed using all the mouse promoter sequences (from 600 bps upstream to 100 bps downstream the TSS). We searched for motifs of length 8 and 10, and used the following options: "-dna -revcomp -mod zoops -evt 0.001 -text -nostatus".
Statistical tests for the kinetics of TF targets
Using a similar statistical test, we showed that the peak time of putative targets of ATF/CREB is significantly earlier than that of all other induced genes. Denoting by t1 (t2) the number of LPS-induced genes that are (are not) putative targets of ATF/CREB, out of which s1 (s2) reached their maximal expression at or before 1 hr, we computed the hypergeometric probability as above.
Statistical evaluation of increased induction of targets of NF-κB+ISRE
To examine whether there is a significant additive effect between the NF-κB and ISRE elements, we performed the following test: Given the total number of genes whose promoter contains signatures of both NF-κB and ISRE, or only NF-κB, denoted t1 and t2, respectively, we checked whether there is an enrichment of NF-κB+ISRE joint targets within the 10% most highly induced NF-κB targets. Here, genes were ranked based on their maximum induction in response to LPS. Let s1 and s2 denote the number of NF-κB+ISRE and NF-κB (but not ISRE) targets, respectively, whose induction-fold is above the aforementioned 10% threshold (i.e., s1 + s2 = (t1 + t2)/10). Then, using the standard hypergeometric score (Equation 1), we computed the probability to observe at least s1 highly-induced NF-κB+ISRE targets, given t1, t2 and s2. For example, in the MmBMM dataset, we found an NF-κB signature in 659 genes, of which 55 also contained an ISRE element; among the 65 NF-κB targets with highest induction by LPS, 12 genes also had an ISRE element.
Thus, t1 = 55, t2 = 604, s1 = 12, and s2 = 53, which gives p = 0.004.
The above test evaluates the increased expression of putative targets of the pair NF-κB+ISRE with respect to all NF-κB targets. We performed a similar test to check the increased expression of NF-κB+ISRE relative to all ISRE targets.
We thank Richard Young, Richard Jenner and the Innate-Immunity System-Biology project for making their microarray data publicly available. We thank Ioannis Xenarios, Gideon Schreiber and Diego Jaitin for reading the manuscript and providing helpful advice. R. Elkon was supported by an Eshkol Fellowship from the Ministry of Science, Israel. R. Shamir was supported by the Wolfson Foundation.
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