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. 2007 Oct 29:8:394.
doi: 10.1186/1471-2164-8-394.

Functional genomic delineation of TLR-induced transcriptional networks

Ran Elkon  1 , Chaim Linhart, Yonit Halperin, Yosef Shiloh, Ron Shamir
Affiliations

Functional genomic delineation of TLR-induced transcriptional networks

VSports注册入口 - Ran Elkon et al. BMC Genomics. .

Abstract

Background: 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) VSports手机版. 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. .

Results: 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-kappaB 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 V体育安卓版. Analysis of the kinetics of the induced network showed that while NF-kappaB 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-kappaB and ISRE elements in the same promoter endows its targets with enhanced responsiveness. .

Conclusion: 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. V体育ios版.

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Figures

Figure 1
Figure 1
Analysis Flow. A schematic sketch of the major steps in our analysis. Using two comprehensive mouse gene expression datasets, we partitioned the genes into distinct groups according to the subset of TLR stimulators to which they were responsive (A), and identified the TFs that control each response group by computational analysis of cis-regulatory promoter elements. We then characterized three kinetic patterns of the transcriptional network induced by LPS treatment (B), and again discovered the TFs that regulate each pattern. A similar analysis of two independent human datasets confirmed our main findings. Integrating the various sources of information points to novel putative targets of the studied TFs, adding new regulatory links to the transcriptional network of the innate immune system.
Figure 2
Figure 2
TLR-induced transcriptional programs. (A) Genes that were induced by at least one of the six examined TLR stimulators (induction of at least 1.8-fold at any time point) were partitioned into distinct sets according to their agent-induction pattern. Taking into account sets that contained at least 40 genes, only two complex induction patterns were identified in addition to the six agent-specific patterns: universal and LPS-PIC patterns. Selected genes are shown in the heat-map for each set (a complete list of genes is provided in Additional File 1). The maximum induction of the gene over the examined time points per stimulator is depicted in the heat-map. (B) Enriched GO functional categories were identified in the universal and LPS-PIC sets (p-values in parentheses are corrected for multiple testing using a bootstrap procedure on 1,000 randomly chosen gene sets of the same size as the true sets). (C) Highly significant over-represented cis-regulatory elements were identified in the promoters of the universal and LPS-PIC sets, pointing to a pivotal role for NF-κB and ISRE in the induction of these two components of the TLR-induced transcriptional program.
Figure 3
Figure 3
Kinetics of the LPS-induced transcriptional response. (A) Genes that were induced by LPS (by at least 1.8-fold) were divided into three kinetic sets according to the time their expression was first induced and the time it peaked. The 'EE' set contains the early induction, early peak genes; the 'ED' set contains early induction, delayed peak genes; and the 'DD' set contains delayed induction, delayed peak genes. The figure displays the mean expression patterns of the genes assigned to the three kinetic sets in the MmBMM dataset (y-axis is log2 of induction fold). (B) Mean expression of induced genes that encode for TFs: ATF/CREB (Atf3, Fos, Jun, Junb), NF-κB (Nfkb1, Nfkb2, Rel, Relb), and ISRE (Irf1, Irf2, Irf7, Stat1, Stat2, Stat3, Stat5a). The expression pattern of each TF is highly correlated with that of the kinetic wave, in which the computational promoter analysis found an over-representation of its BSs (compare the kinetic expression of the TF genes (B) and the induced waves (A)).
Figure 4
Figure 4
Identification of the NFkB+ISRE cis-regulatory module. Mean expression patterns after exposure to LPS (MmBMM dataset) were computed for three disjoint sets of genes – putative targets of each single element separately (604 NF-κB targets, 838 ISRE targets), and targets of both elements (55 genes), obtained by scanning the promoters of all the genes in the MmBMM dataset. Y-axis is average log2 of induction fold relative to time 0. Genes whose promoters contain hits for both NF-κB and ISRE elements were more strongly induced by LPS than genes whose promoters contain a hit for only one of these two elements.
Figure 5
Figure 5
TLR-induced signaling pathways and transcriptional programs. The map, constructed using our SPIKE knowledge-base of signaling pathways [40], presents current knowledge on signaling cascades emanating from activated TLRs and culminating in activation of several key TFs and their respective target genes to achieve robust antiviral and antimicrobial responses. SPIKE maps contain nodes representing three biological entities: gene/proteins (violet nodes); protein complexes (green nodes, e.g., the ISGF3 complex); and gene families (yellow nodes, e.g., the IκB family of NF-κB inhibitors). The map contains two types of edges: Blue edges represent regulations between genes/proteins. Arrowheads (→) correspond to activation, and T-shaped edges (---|) represent inhibition. Green edges represent containment relations between nodes (e.g., the relationships between a complex and its components). Red and green dots within a node indicate that not all the regulation and containment relations stored in SPIKE's DB for that node are displayed on the map. Genes that were universally induced by all examined TLRs are marked by a red bar to the left of the node; genes that were specifically induced by LPS and PIC (which activate TLR4 and TLR3, respectively) are marked by a yellow bar. Novel regulatory links identified in this study that close feedback loops within the TLR-induced network are emphasized in the map by a dashed arrow.
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