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Functional genomic delineation of TLR-induced transcriptional networks.

Elkon R, Linhart C, Halperin Y, Shiloh Y, Shamir R - BMC Genomics (2007)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: The David and Inez Myers Laboratory for Genetic Research, Department of Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel. ranel@post.tau.ac.il

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). 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. 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.

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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)).
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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)).

Mentions: Expression profiles in response to LPS stimulation were recorded at denser time points (20 mins, 40 mins, 1 hr, 80 mins, 2 hrs, 8 hrs and 24 hrs in the MmBMM dataset, and 1 hr, 2 hrs, 4 hrs, 8 hrs and 24 hrs in the MmRAW dataset), which permitted detailed analysis of the kinetics of the transcriptional program induced by this agent. We partitioned the genes that were induced by LPS (1,719 and 1,239 genes in MmBMM and MmRAW, respectively) into three sets according to the kinetics of their induction, as follows: For each gene we recorded the first time at which it exceeded the 1.8-fold induction threshold, as well as the time at which its expression was highest; we defined three kinetic patterns: 1) Early induction and early peak ('EE' set), containing the genes that peaked (and, obviously, were first induced) before 2 hrs; 2) Early induction and delayed peak ('ED' set) – the genes that were first induced before 2 hrs and peaked at 2 hrs or later; and 3) Delayed induction and delayed peak ('DD' set) – the genes that were first induced (and thus also peaked) at 2 hrs or later (Figure 3A). In both datasets, the 'DD' set was considerably larger than the two other sets, reflecting the fact that the main transcriptional response to LPS exposure was at 2 hrs or later.


Functional genomic delineation of TLR-induced transcriptional networks.

Elkon R, Linhart C, Halperin Y, Shiloh Y, Shamir R - BMC Genomics (2007)

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)).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2175519&req=5

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)).
Mentions: Expression profiles in response to LPS stimulation were recorded at denser time points (20 mins, 40 mins, 1 hr, 80 mins, 2 hrs, 8 hrs and 24 hrs in the MmBMM dataset, and 1 hr, 2 hrs, 4 hrs, 8 hrs and 24 hrs in the MmRAW dataset), which permitted detailed analysis of the kinetics of the transcriptional program induced by this agent. We partitioned the genes that were induced by LPS (1,719 and 1,239 genes in MmBMM and MmRAW, respectively) into three sets according to the kinetics of their induction, as follows: For each gene we recorded the first time at which it exceeded the 1.8-fold induction threshold, as well as the time at which its expression was highest; we defined three kinetic patterns: 1) Early induction and early peak ('EE' set), containing the genes that peaked (and, obviously, were first induced) before 2 hrs; 2) Early induction and delayed peak ('ED' set) – the genes that were first induced before 2 hrs and peaked at 2 hrs or later; and 3) Delayed induction and delayed peak ('DD' set) – the genes that were first induced (and thus also peaked) at 2 hrs or later (Figure 3A). In both datasets, the 'DD' set was considerably larger than the two other sets, reflecting the fact that the main transcriptional response to LPS exposure was at 2 hrs or later.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: The David and Inez Myers Laboratory for Genetic Research, Department of Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel. ranel@post.tau.ac.il

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). 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. 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.

Show MeSH
Related in: MedlinePlus