Dataset Information

Binding Model for the Interaction of Anticancer Arylsulfonamides with the p300 Transcription Cofactor.

ABSTRACT: Hypoxia inducible factors (HIFs) are transcription factors that activate expression of multiple gene products and promote tumor adaptation to a hypoxic environment. To become transcriptionally active, HIFs associate with cofactors p300 or CBP. Previously, we found that arylsulfonamides can antagonize HIF transcription in a bioassay, block the p300/HIF-1? interaction, and exert potent anticancer activity in several animal models. In the present work, KCN1-bead affinity pull down, (14)C-labeled KCN1 binding, and KCN1-surface plasmon resonance measurements provide initial support for a mechanism in which KCN1 can bind to the CH1 domain of p300 and likely prevent the p300/HIF-1? assembly. Using a previously reported NMR structure of the p300/HIF-1? complex, we have identified potential binding sites in the p300-CH1 domain. A two-site binding model coupled with IC50 values has allowed establishment of a modest ROC-based enrichment and creation of a guide for future analogue synthesis.

SUBMITTER: Shi Q

PROVIDER: S-EPMC4056939 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Binding Model for the Interaction of Anticancer Arylsulfonamides with the p300 Transcription Cofactor.

Shi Qi Q Yin Shaoman S Kaluz Stefan S Ni Nanting N Devi Narra Sarojini NS Mun Jiyoung J Wang Danzhu D Damera Krishna K Chen Weixuan W Burroughs Sarah S Mooring Suazette Reid SR Goodman Mark M MM Van Meir Erwin G EG Wang Binghe B Snyder James P JP

ACS medicinal chemistry letters 20120621 8

Hypoxia inducible factors (HIFs) are transcription factors that activate expression of multiple gene products and promote tumor adaptation to a hypoxic environment. To become transcriptionally active, HIFs associate with cofactors p300 or CBP. Previously, we found that arylsulfonamides can antagonize HIF transcription in a bioassay, block the p300/HIF-1α interaction, and exert potent anticancer activity in several animal models. In the present work, KCN1-bead affinity pull down, (14)C-labeled KC ...[more]

PMID: 24936238

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Project description:NF-κB plays a vital role in cellular immune and inflammatory response, survival, and proliferation by regulating the transcription of various genes involved in these processes. To activate transcription, RelA (a prominent NF-κB family member) interacts with transcriptional co-activators like CREB-binding protein (CBP) and its paralog p300 in addition to its cognate κB sites on the promoter/enhancer regions of DNA. The RelA:CBP/p300 complex is comprised of two components--first, DNA binding domain of RelA interacts with the KIX domain of CBP/p300, and second, the transcriptional activation domain (TAD) of RelA binds to the TAZ1 domain of CBP/p300. A phosphorylation event of a well-conserved RelA(Ser276) is prerequisite for the former interaction to occur and is considered a decisive factor for the overall RelA:CBP/p300 interaction. The role of the latter interaction in the transcription of RelA-activated genes remains unclear. Here we provide the solution structure of the latter component of the RelA:CBP complex by NMR spectroscopy. The structure reveals the folding of RelA-TA2 (a section of TAD) upon binding to TAZ1 through its well-conserved hydrophobic sites in a series of grooves on the TAZ1 surface. The structural analysis coupled with the mechanistic studies by mutational and isothermal calorimetric analyses allowed the design of RelA-mutants that selectively abrogated the two distinct components of the RelA:CBP/p300 interaction. Detailed studies of these RelA mutants using cell-based techniques, mathematical modeling, and genome-wide gene expression analysis showed that a major set of the RelA-activated genes, larger than previously believed, is affected by this interaction. We further show how the RelA:CBP/p300 interaction controls the nuclear response of NF-κB through the negative feedback loop of NF-κB pathway. Additionally, chromatin analyses of RelA target gene promoters showed constitutive recruitment of CBP/p300, thus indicating a possible role of CBP/p300 in recruitment of RelA to its target promoter sites.

Project description:BackgroundHow transcription factors (TFs) interact with cis-regulatory sequences and interact with each other is a fundamental, but not well understood, aspect of gene regulation.Methodology/principal findingsWe present a computational method to address this question, relying on the established biophysical principles. This method, STAP (sequence to affinity prediction), takes into account all combinations and configurations of strong and weak binding sites to analyze large scale transcription factor (TF)-DNA binding data to discover cooperative interactions among TFs, infer sequence rules of interaction and predict TF target genes in new conditions with no TF-DNA binding data. The distinctions between STAP and other statistical approaches for analyzing cis-regulatory sequences include the utility of physical principles and the treatment of the DNA binding data as quantitative representation of binding strengths. Applying this method to the ChIP-seq data of 12 TFs in mouse embryonic stem (ES) cells, we found that the strength of TF-DNA binding could be significantly modulated by cooperative interactions among TFs with adjacent binding sites. However, further analysis on five putatively interacting TF pairs suggests that such interactions may be relatively insensitive to the distance and orientation of binding sites. Testing a set of putative Nanog motifs, STAP showed that a novel Nanog motif could better explain the ChIP-seq data than previously published ones. We then experimentally tested and verified the new Nanog motif. A series of comparisons showed that STAP has more predictive power than several state-of-the-art methods for cis-regulatory sequence analysis. We took advantage of this power to study the evolution of TF-target relationship in Drosophila. By learning the TF-DNA interaction models from the ChIP-chip data of D. melanogaster (Mel) and applying them to the genome of D. pseudoobscura (Pse), we found that only about half of the sequences strongly bound by TFs in Mel have high binding affinities in Pse. We show that prediction of functional TF targets from ChIP-chip data can be improved by using the conservation of STAP predicted affinities as an additional filter.Conclusions/significanceSTAP is an effective method to analyze binding site arrangements, TF cooperativity, and TF target genes from genome-wide TF-DNA binding data.

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Dataset Information

Binding Model for the Interaction of Anticancer Arylsulfonamides with the p300 Transcription Cofactor.

Publications

Binding Model for the Interaction of Anticancer Arylsulfonamides with the p300 Transcription Cofactor.

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