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. 2010 Sep;38(17):5718-34.
doi: 10.1093/nar/gkq212. Epub 2010 May 11.

Global mapping of binding sites for Nrf2 identifies novel targets in cell survival response through ChIP-Seq profiling and network analysis

Deepti Malhotra  1 , Elodie Portales-Casamar, Anju Singh, Siddhartha Srivastava, David Arenillas, Christine Happel, Casper Shyr, Nobunao Wakabayashi, Thomas W Kensler, Wyeth W Wasserman, Shyam Biswal
Affiliations

Global mapping of binding sites for Nrf2 identifies novel targets in cell survival response through ChIP-Seq profiling and network analysis

Deepti Malhotra et al. Nucleic Acids Res. 2010 Sep.

Abstract

The Nrf2 (nuclear factor E2 p45-related factor 2) transcription factor responds to diverse oxidative and electrophilic environmental stresses by circumventing repression by Keap1, translocating to the nucleus, and activating cytoprotective genes. Nrf2 responses provide protection against chemical carcinogenesis, chronic inflammation, neurodegeneration, emphysema, asthma and sepsis in murine models. Nrf2 regulates the expression of a plethora of genes that detoxify oxidants and electrophiles and repair or remove damaged macromolecules, such as through proteasomal processing. However, many direct targets of Nrf2 remain undefined. Here, mouse embryonic fibroblasts (MEF) with either constitutive nuclear accumulation (Keap1(-/-)) or depletion (Nrf2(-/-)) of Nrf2 were utilized to perform chromatin-immunoprecipitation with parallel sequencing (ChIP-Seq) and global transcription profiling. This unique Nrf2 ChIP-Seq dataset is highly enriched for Nrf2-binding motifs. Integrating ChIP-Seq and microarray analyses, we identified 645 basal and 654 inducible direct targets of Nrf2, with 244 genes at the intersection. Modulated pathways in stress response and cell proliferation distinguish the inducible and basal programs. Results were confirmed in an in vivo stress model of cigarette smoke-exposed mice. This study reveals global circuitry of the Nrf2 stress response emphasizing Nrf2 as a central node in cell survival response.

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Figures

Figure 1.
Figure 1.
Nrf2 genome-wide ChIP-Seq dataset. (A) Keap1−/− MEFs have constitutive transcriptional activation of Nrf2 target genes (i) Nuclear Nrf2 protein levels in Keap1−/−, WT and Nrf2−/− MEFs using immunoblot analysis, (ii) ChIP–PCR and densitometry quantification analysis for known Nrf2-binding site in promoter of Nqo1 and Gsta3, (iii) qRT-PCR analysis of known Nrf2 targets, Nqo1 and Gsta3 and (iv) Immunoblot analysis for Nqo1 and Gsta3 in the MEFs. β-Actin was used as the loading control. (B) The distribution of Nrf2 ChIP-Seq peaks relative to the closest gene transcription start sites is similar to background. (C) Nrf2 ChIP-Seq peaks are enriched for high-scoring Nrf2 predicted motifs compared with background. (D) High-scoring predicted sites are preferentially located near the peak maximum height in the ChIP-Seq peaks whereas they are uniformly distributed in the background sequences. *P-value < 0.01, as analyzed by ANOVA analysis.
Figure 1.
Figure 1.
Nrf2 genome-wide ChIP-Seq dataset. (A) Keap1−/− MEFs have constitutive transcriptional activation of Nrf2 target genes (i) Nuclear Nrf2 protein levels in Keap1−/−, WT and Nrf2−/− MEFs using immunoblot analysis, (ii) ChIP–PCR and densitometry quantification analysis for known Nrf2-binding site in promoter of Nqo1 and Gsta3, (iii) qRT-PCR analysis of known Nrf2 targets, Nqo1 and Gsta3 and (iv) Immunoblot analysis for Nqo1 and Gsta3 in the MEFs. β-Actin was used as the loading control. (B) The distribution of Nrf2 ChIP-Seq peaks relative to the closest gene transcription start sites is similar to background. (C) Nrf2 ChIP-Seq peaks are enriched for high-scoring Nrf2 predicted motifs compared with background. (D) High-scoring predicted sites are preferentially located near the peak maximum height in the ChIP-Seq peaks whereas they are uniformly distributed in the background sequences. *P-value < 0.01, as analyzed by ANOVA analysis.
Figure 1.
Figure 1.
Nrf2 genome-wide ChIP-Seq dataset. (A) Keap1−/− MEFs have constitutive transcriptional activation of Nrf2 target genes (i) Nuclear Nrf2 protein levels in Keap1−/−, WT and Nrf2−/− MEFs using immunoblot analysis, (ii) ChIP–PCR and densitometry quantification analysis for known Nrf2-binding site in promoter of Nqo1 and Gsta3, (iii) qRT-PCR analysis of known Nrf2 targets, Nqo1 and Gsta3 and (iv) Immunoblot analysis for Nqo1 and Gsta3 in the MEFs. β-Actin was used as the loading control. (B) The distribution of Nrf2 ChIP-Seq peaks relative to the closest gene transcription start sites is similar to background. (C) Nrf2 ChIP-Seq peaks are enriched for high-scoring Nrf2 predicted motifs compared with background. (D) High-scoring predicted sites are preferentially located near the peak maximum height in the ChIP-Seq peaks whereas they are uniformly distributed in the background sequences. *P-value < 0.01, as analyzed by ANOVA analysis.
Figure 2.
Figure 2.
Nrf2-binding profiles. (A) The original Nrf2-binding profile was defined by the alignment of the 20 known binding sites described in Table 1. (B) The motif generated by the MEME motif discovery algorithm (50) on the Nrf2 ChIP-Seq dataset is highly similar to the original profile with a higher information content (IC; in bits). The position Frequency Matrix (PFM) captures the count of each nucleotide found at each position in the motif and the logo provides a visualization of the data. The information content is the sum of the information content of each position and is computed as described previously (61).
Figure 3.
Figure 3.
Global transcriptional profiling of MEF genetic models to decipher comprehensive Nrf2-dependent transcriptional targets. (A) Scheme depicts definitions for classification of Nrf2 target genes as inducible and/or basally regulated by Nrf2. Basal targets: differentially expressed genes between WT versus Nrf2−/− MEFs; Inducible targets: differentially expressed genes between Keap1−/− versus WT MEFs; and Common targets: genes satisfying both the former comparison criteria. (B) The panel represents the overlap of differentially expressed genes across experiments. The orange circle represents genes that were downregulated in Nrf2−/− MEFs compared with WT (basal targets) while the blue circle shows the genes upregulated in Keap1−/− MEFs compared with WT (inducible targets). (C) The panel represents the overlap between the microarray datasets and the ChIP-Seq dataset and intersections represent the genes common in the two datasets to identify Nrf2 direct (basal, inducible and common) transcriptional targets.
Figure 4.
Figure 4.
Antioxidant and xenobiotic detoxification genes are validated as Nrf2 direct targets. (A) and (B) The panels represent ChIP–PCR analysis and densitometry quantification for selected known Nrf2 target gene-binding sites—Txnrd1_P1 and its novel binding site Txnrd1_P2, novel binding sites in Gsta4, three Gstm1-binding sites (Gstm1_P1, Gstm1_P2, Gstm1_P3), Gstm3, Srxn1, Ephx1 and Als2. (C) qRT-PCR mRNA expression analysis in MEFs. (D) Immunoblot analysis for Txnrd1, Gsta4, Gstm1, Srxn1 and Als2 in MEFs. β-Actin was used as the loading control. (E) qRT-PCR for mRNA expression in lung lysates from WT and Nrf2−/− Air and CS exposed mice, corroborating with the in vitro Nrf2 targets. *P-value < 0.01, as analyzed by ANOVA analysis.
Figure 5.
Figure 5.
Cell proliferation genes and cell-cycle regulators are direct transcriptional targets of Nrf2. (A) and (B) ChIP–PCR and densitometry quantification analysis for cell proliferation genes and Cdkn genes—Cdkn1a, Cdkn2b-binding site (Cdkn2b_P1), comparing Keap1−/−, WT and Nrf2−/− MEFs. (C) qRT-PCR for mRNA expression analysis of proliferation genes and Cdkns respectively. (D) Immunoblot analysis for Bmpr1a, Igf1, Npnt, Pdgfc, Vegfc CDKN1A and CDKN2A in MEFs. β-Actin was used as the loading control. (E) QuantosTM Cell proliferation assay. (F) BrdU chemiluminescence assay. (G) Caspase activity (apoptosis) assay. (H) Cell senescence assay. (I) qRT-PCR for mRNA expression in lung lysates from WT and Nrf2−/− Air and CS exposed mice corroborating with the in vitro Nrf2 targets. *P-value < 0.01 as compared with control and †P-value < 0.01 as compared with WT at the same time point, as analyzed by ANOVA analysis.
Figure 5.
Figure 5.
Cell proliferation genes and cell-cycle regulators are direct transcriptional targets of Nrf2. (A) and (B) ChIP–PCR and densitometry quantification analysis for cell proliferation genes and Cdkn genes—Cdkn1a, Cdkn2b-binding site (Cdkn2b_P1), comparing Keap1−/−, WT and Nrf2−/− MEFs. (C) qRT-PCR for mRNA expression analysis of proliferation genes and Cdkns respectively. (D) Immunoblot analysis for Bmpr1a, Igf1, Npnt, Pdgfc, Vegfc CDKN1A and CDKN2A in MEFs. β-Actin was used as the loading control. (E) QuantosTM Cell proliferation assay. (F) BrdU chemiluminescence assay. (G) Caspase activity (apoptosis) assay. (H) Cell senescence assay. (I) qRT-PCR for mRNA expression in lung lysates from WT and Nrf2−/− Air and CS exposed mice corroborating with the in vitro Nrf2 targets. *P-value < 0.01 as compared with control and †P-value < 0.01 as compared with WT at the same time point, as analyzed by ANOVA analysis.
Figure 6.
Figure 6.
Summary of Nrf2-regulated global transcriptomics circuit. This Nrf2 interaction network was generated using the String interaction network analysis tool (http://string.embl.de). The genes represented are the basal (black), inducible (white) and common (grey) target genes associated with cell proliferation and/or glutathione metabolism as defined by the functional clusters obtained through the DAVID resource and described in Table 3.
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