Sequence Variation and p53 DNA Binding Figure 3. Sequence variation affects p53 binding. A) Evaluating the impact of single nucleotide variations in p21 RE on p53 binding using nuclear extracts from untreated and DOXO-treated cells. A set of 19 oligonucleotides, each conjugated to a unique bead, were multiplexed and treated with 1.75 mg of non-treated nuclear extracts (NT: checkered bar) or activated nuclear extracts (black bar: DOXO-treated lymphoblastoid cells). Relative binding intensity is shown on the horizontal axis (note the break in the scale to accommodate the signal from all oligonucleotides). Relative binding values were obtained for each oligonucleotide as discussed in Materials and Methods. The oligonucleotide bearing the p53 consensus sequence (ConA) is first on the list (top left Y-axis) followed by wild type p21RE (p21LWT). p21GL3 bears the same 20-nt RE as p21LWT but with a synthetic backbone sequence from pGL3 vector. Fifteen single or double variants of p21 RE are shown. The p21 sequence is shown as inset. The number denotes the numerical position of the base within the p21 RE. Arrowheads point to the nts that have been chosen for alteration. WRNC is a negative control oligonucleotide and carries a nonbinding sequence in the same backbone as the other oligonucleotides shown with the exception of P21GL3. Values shown are the mean+/2SD (n = 3). B) Multiplex binding measurements of 60 variants of a p53 consensus sequence. Sixty oligonucleotides, each bearing a single nucleotide variation of the p53 consensus binding site (ConA = GGGCATGTCCGGGCATGTCC) were generated. ConA sequence is shown in large letters under arrow. CATG core of each half-site is shown in a right-facing bracket. Systematic base substitutions at all positions within ConA are shown in small letters. For instance, 1G.C is an RE with the first G converted to a C. In addition, a positive control bead (ConA RE) and a negative control bead (WRNC: lacks a p53 binding RE) were included. Equal amounts of each microsphere type were mixed to generate a multiplex of 62 bead types. Following treatment with activated nuclear extracts (DOXO-treated or untreated lymphoblastoid cells, Figure S3), the relative binding intensity (value shown on upper horizontal axis) was obtained for each oligonucleotide (white bar) as discussed in Materials and Methods. Bar values are means for each bead type+/2SD (n = 3). The black bar graph in the background (e.g. black bars) shows the calculated PWM value for each variant oligonucleotide tested (scale depicted on the lower horizontal axis). A list of PWM values for REs used can be found in Supporting Information. doi:10.1371/journal.pgen.1000462.g003 sequence variation has the potential to increase transcription factor binding. Among mammalian p53 REs, the most conserved positions are C and G nucleotides 4, 7, 14, and 17, and many studies have observed that changes at these positions strongly impact p53 transactivation [3,4,5,6,27,28,29]. The present data demonstrate that these effects are due to decreased p53 binding. Experimental Support for Computational Binding Models TFBS predictions using position weight matrix (PWM) calculations are ubiquitous in the genomics literature. Binding sequences gleaned from publications (or a database such as TRANSFAC) are used to create a PWM to evaluate and score new sequences. Such statistical models determine the nucleotide frequency in a sequence motif database under the simplifying assumption that the sampled sequences are bona fide functional REs with similar binding properties and all nts act independently. Experimental quantification of TF binding strength has been laborious. Importantly, while TF binding strength is expected to greatly influence function of the p53-regulated network, or other networks, this information is rarely incorporated into binding models or RE prediction. To demonstrate the impact of SNPs on p53 binding and to show how quantitative binding data may be useful for refining RE prediction models, we generated a set of 60 oligonucleotides, each bearing a single nt variant of ConA along with positive and negative controls to yield a 62-plex experiment. 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. Evaluation of SNPs in p53 Response Elements SNPs in 8 putative p53 REs were identified using the bioinformatics approach described in Tomso et al [5]. Sequences are shown in Table S1. Oligonucleotides containing the predicted weak and strong alleles for these p53 REs were placed on microspheres and evaluated for binding to activated p53 in nuclear extracts (Figure 5). Negative and positive control oligonucleotides were hybridized in parallel in the multiplexed reaction and nuclear extract from untreated cells was also tested as a negative control (mean binding to p21 probe for untreated cell extracts was 1.6%+/20.19%, n = 6 of the Doxo treated value); binding to p21 was consistent with p53 content assessed by western blot and TransAm ELISA. Among these putative p53 REs, the predicted strong alleles for REs in ADARB1, ARHGEF7, and PLoS Genetics | www.plosgenetics.org 5 May 2009 | Volume 5 | Issue 5 | e1000462