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Probing the functional impact of sequence variation on p53-DNA interactions using a novel microsphere assay for protein-DNA binding with human cell extracts.

Noureddine MA, Menendez D, Campbell MR, Bandele OJ, Horvath MM, Wang X, Pittman GS, Chorley BN, Resnick MA, Bell DA - PLoS Genet. (2009)

Bottom Line: Using MAPD we measured sequence-specific p53 binding of doxorubicin-activated or transiently expressed p53 to REs from established p53 target genes and p53 consensus REs.A group of eight single nucleotide polymorphisms (SNPs) was examined and binding profiles closely matched transactivation capability tested in luciferase constructs.Using a set of 26 bona fide REs, we observed distinct binding patterns characteristic of transiently expressed wild type and mutant p53s.

View Article: PubMed Central - PubMed

Affiliation: Environmental Genomics Group, Laboratory of Molecular Genetics, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA.

ABSTRACT
The p53 tumor suppressor regulates its target genes through sequence-specific binding to DNA response elements (REs). Although numerous p53 REs are established, the thousands more identified by bioinformatics are not easily subjected to comparative functional evaluation. To examine the relationship between RE sequence variation -- including polymorphisms -- and p53 binding, we have developed a multiplex format microsphere assay of protein-DNA binding (MAPD) for p53 in nuclear extracts. Using MAPD we measured sequence-specific p53 binding of doxorubicin-activated or transiently expressed p53 to REs from established p53 target genes and p53 consensus REs. To assess the sensitivity and scalability of the assay, we tested 16 variants of the p21 target sequence and a 62-multiplex set of single nucleotide (nt) variants of the p53 consensus sequence and found many changes in p53 binding that are not captured by current computational binding models. A group of eight single nucleotide polymorphisms (SNPs) was examined and binding profiles closely matched transactivation capability tested in luciferase constructs. The in vitro binding characteristics of p53 in nuclear extracts recapitulated the cellular in vivo transactivation capabilities for eight well-established human REs measured by luciferase assay. Using a set of 26 bona fide REs, we observed distinct binding patterns characteristic of transiently expressed wild type and mutant p53s. This microsphere assay system utilizes biologically meaningful cell extracts in a multiplexed, quantitative, in vitro format that provides a powerful experimental tool for elucidating the functional impact of sequence polymorphism and protein variation on protein/DNA binding in transcriptional networks.

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Sequence logos for p53 consensus created from two datasets.The height of the letter indicates the frequency or binding preference at that nucleotide position in the RE. A) Traditional position weight matrix with p53 RE sequences from Table S2. B) Sequence logo developed from the binding data (Figure 3B) using a modified approach of Veprintsev and Fersht [30]. Binding values were put into a binding position weight matrix (BPWM) and the letter height represents experimentally determined binding preference. C) Binding values for bona fide p53 RE sequences (as in Figure 6 and Table S1) plotted against traditional PWM model prediction (4C) and binding based model prediction (4D). The correlation between PWM calculation and measured binding improved from r2 = 0.40 with the traditional PWM model (Figure 4C) to r2 = 0.64 with binding-based model (4D).
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pgen-1000462-g004: Sequence logos for p53 consensus created from two datasets.The height of the letter indicates the frequency or binding preference at that nucleotide position in the RE. A) Traditional position weight matrix with p53 RE sequences from Table S2. B) Sequence logo developed from the binding data (Figure 3B) using a modified approach of Veprintsev and Fersht [30]. Binding values were put into a binding position weight matrix (BPWM) and the letter height represents experimentally determined binding preference. C) Binding values for bona fide p53 RE sequences (as in Figure 6 and Table S1) plotted against traditional PWM model prediction (4C) and binding based model prediction (4D). The correlation between PWM calculation and measured binding improved from r2 = 0.40 with the traditional PWM model (Figure 4C) to r2 = 0.64 with binding-based model (4D).

Mentions: Figure 3B displays p53 binding to the 62 oligonucleotides (Figure S6 shows NT binding) and clearly reveals differences in the impact of both the position and type of base-change (e.g., purine to purine, purine to pyrimidine) and also differences from the PWM model prediction. Comparing PWM data (black bars) to experimental data (open bars) for the core motif (4C, 7G, 14C, 17G, brackets), there is a general similarity in pattern, but for many base-changes, the correlation of PWM with binding is poor. For example, in the PWM model, changes at positions 3G>C, 8T>G, 12G>C, or 18T>G have little effect (<20%), but our results show a >75% binding reduction at these positions. It is clear that PWM values, which vary by only 2-fold cannot provide a robust prediction of binding, which varies as much as 13-fold just among these sequences. Since sequences used as input to PWM models typically have been evaluated in qualitative binding assays such as EMSA, it is not surprising that PWM models often have limited predictive ability. Using the technique of Veprintsev and Fersht [30] we have generated a sequence logo from the binding data that can be compared with the traditional consensus sequence (Figure 4). The sequence logo derived from binding data differs from the traditional PWM model consensus, yet the pattern for the natively DOXO-activated p53 in nuclear extracts is generally consistent with the recent in vitro half-site binding analysis using a purified, ultra-stable mutant p53 and fluorescent anisotropy [30]. The binding-based PWM supports a strong role for C and G nucleotides at positions 4, 7, 14, 17 and also for G at position 3. The A at position 5 is characteristic of very strong p53 binding REs. In addition, these data were generated using 20mers and appear to support binding strength asymmetry within the half-site (nt 1–5 versus nt 6–10) and also asymmetry across the two half-sites, as suggested by the p53 ChIP data of Wei et al [31] and the model of Riley et al [28]. Incorporating detailed experimental binding data will allow for more accurate computational binding models. Preliminary evidence for improved model building is demonstrated in Figure 4. The binding-based PWM model was tested on the panel of bona fide RE sequences listed in Table S1. Experimental binding values for these REs are plotted versus predicted binding values using the traditional PWM (4C) and binding-based PWM (4D) models. The correlation coefficient is considerably improved (from r2 = 0.40 to r2 = 0.64) by the use of binding data in the position weight matrix model. While this represents a limited experimental data set and a simple linear model, it suggests that binding information may be useful for informing the identification of binding sites. We suggest that experiments examining a large set of strong to weak p53 REs (with various quarter-site orientations, insertions/deletions and spacer lengths) will provide data that can be incorporated into more sophisticated computational models, such as those proposed by Veprintsev and Fersht [30], and Riley, et al [28], and this should greatly improve p53 binding site prediction.


Probing the functional impact of sequence variation on p53-DNA interactions using a novel microsphere assay for protein-DNA binding with human cell extracts.

Noureddine MA, Menendez D, Campbell MR, Bandele OJ, Horvath MM, Wang X, Pittman GS, Chorley BN, Resnick MA, Bell DA - PLoS Genet. (2009)

Sequence logos for p53 consensus created from two datasets.The height of the letter indicates the frequency or binding preference at that nucleotide position in the RE. A) Traditional position weight matrix with p53 RE sequences from Table S2. B) Sequence logo developed from the binding data (Figure 3B) using a modified approach of Veprintsev and Fersht [30]. Binding values were put into a binding position weight matrix (BPWM) and the letter height represents experimentally determined binding preference. C) Binding values for bona fide p53 RE sequences (as in Figure 6 and Table S1) plotted against traditional PWM model prediction (4C) and binding based model prediction (4D). The correlation between PWM calculation and measured binding improved from r2 = 0.40 with the traditional PWM model (Figure 4C) to r2 = 0.64 with binding-based model (4D).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2667269&req=5

pgen-1000462-g004: Sequence logos for p53 consensus created from two datasets.The height of the letter indicates the frequency or binding preference at that nucleotide position in the RE. A) Traditional position weight matrix with p53 RE sequences from Table S2. B) Sequence logo developed from the binding data (Figure 3B) using a modified approach of Veprintsev and Fersht [30]. Binding values were put into a binding position weight matrix (BPWM) and the letter height represents experimentally determined binding preference. C) Binding values for bona fide p53 RE sequences (as in Figure 6 and Table S1) plotted against traditional PWM model prediction (4C) and binding based model prediction (4D). The correlation between PWM calculation and measured binding improved from r2 = 0.40 with the traditional PWM model (Figure 4C) to r2 = 0.64 with binding-based model (4D).
Mentions: Figure 3B displays p53 binding to the 62 oligonucleotides (Figure S6 shows NT binding) and clearly reveals differences in the impact of both the position and type of base-change (e.g., purine to purine, purine to pyrimidine) and also differences from the PWM model prediction. Comparing PWM data (black bars) to experimental data (open bars) for the core motif (4C, 7G, 14C, 17G, brackets), there is a general similarity in pattern, but for many base-changes, the correlation of PWM with binding is poor. For example, in the PWM model, changes at positions 3G>C, 8T>G, 12G>C, or 18T>G have little effect (<20%), but our results show a >75% binding reduction at these positions. It is clear that PWM values, which vary by only 2-fold cannot provide a robust prediction of binding, which varies as much as 13-fold just among these sequences. Since sequences used as input to PWM models typically have been evaluated in qualitative binding assays such as EMSA, it is not surprising that PWM models often have limited predictive ability. Using the technique of Veprintsev and Fersht [30] we have generated a sequence logo from the binding data that can be compared with the traditional consensus sequence (Figure 4). The sequence logo derived from binding data differs from the traditional PWM model consensus, yet the pattern for the natively DOXO-activated p53 in nuclear extracts is generally consistent with the recent in vitro half-site binding analysis using a purified, ultra-stable mutant p53 and fluorescent anisotropy [30]. The binding-based PWM supports a strong role for C and G nucleotides at positions 4, 7, 14, 17 and also for G at position 3. The A at position 5 is characteristic of very strong p53 binding REs. In addition, these data were generated using 20mers and appear to support binding strength asymmetry within the half-site (nt 1–5 versus nt 6–10) and also asymmetry across the two half-sites, as suggested by the p53 ChIP data of Wei et al [31] and the model of Riley et al [28]. Incorporating detailed experimental binding data will allow for more accurate computational binding models. Preliminary evidence for improved model building is demonstrated in Figure 4. The binding-based PWM model was tested on the panel of bona fide RE sequences listed in Table S1. Experimental binding values for these REs are plotted versus predicted binding values using the traditional PWM (4C) and binding-based PWM (4D) models. The correlation coefficient is considerably improved (from r2 = 0.40 to r2 = 0.64) by the use of binding data in the position weight matrix model. While this represents a limited experimental data set and a simple linear model, it suggests that binding information may be useful for informing the identification of binding sites. We suggest that experiments examining a large set of strong to weak p53 REs (with various quarter-site orientations, insertions/deletions and spacer lengths) will provide data that can be incorporated into more sophisticated computational models, such as those proposed by Veprintsev and Fersht [30], and Riley, et al [28], and this should greatly improve p53 binding site prediction.

Bottom Line: Using MAPD we measured sequence-specific p53 binding of doxorubicin-activated or transiently expressed p53 to REs from established p53 target genes and p53 consensus REs.A group of eight single nucleotide polymorphisms (SNPs) was examined and binding profiles closely matched transactivation capability tested in luciferase constructs.Using a set of 26 bona fide REs, we observed distinct binding patterns characteristic of transiently expressed wild type and mutant p53s.

View Article: PubMed Central - PubMed

Affiliation: Environmental Genomics Group, Laboratory of Molecular Genetics, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA.

ABSTRACT
The p53 tumor suppressor regulates its target genes through sequence-specific binding to DNA response elements (REs). Although numerous p53 REs are established, the thousands more identified by bioinformatics are not easily subjected to comparative functional evaluation. To examine the relationship between RE sequence variation -- including polymorphisms -- and p53 binding, we have developed a multiplex format microsphere assay of protein-DNA binding (MAPD) for p53 in nuclear extracts. Using MAPD we measured sequence-specific p53 binding of doxorubicin-activated or transiently expressed p53 to REs from established p53 target genes and p53 consensus REs. To assess the sensitivity and scalability of the assay, we tested 16 variants of the p21 target sequence and a 62-multiplex set of single nucleotide (nt) variants of the p53 consensus sequence and found many changes in p53 binding that are not captured by current computational binding models. A group of eight single nucleotide polymorphisms (SNPs) was examined and binding profiles closely matched transactivation capability tested in luciferase constructs. The in vitro binding characteristics of p53 in nuclear extracts recapitulated the cellular in vivo transactivation capabilities for eight well-established human REs measured by luciferase assay. Using a set of 26 bona fide REs, we observed distinct binding patterns characteristic of transiently expressed wild type and mutant p53s. This microsphere assay system utilizes biologically meaningful cell extracts in a multiplexed, quantitative, in vitro format that provides a powerful experimental tool for elucidating the functional impact of sequence polymorphism and protein variation on protein/DNA binding in transcriptional networks.

Show MeSH
Related in: MedlinePlus