Abstract
Transcription factors (TFs) regulate gene expression by interacting with DNA in a sequence-specific manner. High-throughput in vitro technologies, such as protein-binding microarrays1,2,3,4,5,6 and HT-SELEX (high-throughput systematic evolution of ligands by exponential enrichment)7,8, have revealed the DNA-binding specificities of hundreds of TFs. However, they have limited ability to reliably identify lower-affinity DNA binding sites, which are increasingly recognized as important for precise spatiotemporal control of gene expression9,10,11,12,13,14,15,16,17,18,19. Here, to address this limitation, we developed protein affinity to DNA by in vitro transcription and RNA sequencing (PADIT-seq), with which we comprehensively assayed the binding preferences of six TFs to all possible ten-base-pair DNA sequences, detecting hundreds of novel, lower-affinity binding sites. The expanded repertoire of lower-affinity binding sites revealed that nucleotides flanking high-affinity DNA binding sites create overlapping lower-affinity sites that together modulate TF genomic occupancy in vivo. We propose a model in which TF binding is not determined by individual binding sites, but rather by the sum of multiple, overlapping binding sites. The overlapping binding model explains how competition between paralogous TFs for shared high-affinity binding sites is determined by flanking nucleotides that create differential numbers of overlapping, lower-affinity binding sites. Critically, the model transforms our understanding of noncoding-variant effects, revealing how single nucleotide changes simultaneously alter multiple overlapping sites to additively influence gene expression and human traits, including diseases.
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Data availability
PADIT-seq data have been deposited in the Gene Expression Omnibus (GEO) database under accession number GSE250601. MITOMI Kd data for EGR1 was downloaded from the supporting information of ref. 41. The UniPROBE database (http://thebrain.bwh.harvard.edu/pbms/UniPROBE_staging/browse.php) was used to access uPBM data74,75. HT-SELEX data were downloaded using the ENA accession code ERP001826. ChIP–seq and ChIP-nexus data analysed in the paper were downloaded using accession codes GSE81356, GSE89457, GSE85628, GSE67482 and GSE207001. BET-seq data were downloaded using the accession code GSE111936. Competition gcPBM data were downloaded from the supplementary materials of ref. 64. Processed SNP-SELEX data were downloaded from the supplementary data of ref. 65. Allele-specific EGR1 ChIP–seq data were downloaded from the ADASTRA database: https://adastra.autosome.org/mabel. Processed MPRA data were downloaded from supplementary tables of refs. 70,71,72,73.
Code availability
Code and processed data for generating the figures are available at https://github.com/BulykLab/PADIT-seq.
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Acknowledgements
The authors thank S. Gisselbrecht for help with calculating PBM 9-mer E-scores for EGR1; K. Struhl, L. Mariani and X. K. Liu for critical reading of the manuscript, and members of the Bulyk laboratory for helpful discussions. This work was supported by grants from the National Institutes of Health (R21 HG010200 and R01 HG010501 to M.L.B.; K99 HG013675 to S.K.) and the American Heart Association (24POST1192017 to S.K.).
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M.L.B. and S.K. conceived and designed the research project. S.K. performed PADIT-seq experiments, and S.K. and B.S.C. performed custom PBM experiments. S.K. conducted the analyses and prepared the figures. M.L.B. supervised the research. M.L.B. and S.K. wrote the manuscript. All authors approved the final version of the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 PADIT-seq demonstrates high reproducibility and correlates with orthogonal binding assays.
(a-b) First experiment with HOXD13, EGR1, and ‘NoDBD’ controls (R1-3). (a) Heatmap showing pairwise Pearson correlations between replicates with unsupervised row and column clustering. (b) PCA plot explaining ~93% of variation. (c) Second experiment with Pho4, Cbf1, TBX5, NKX2.5, and additional NoDBD controls (R4-7). Previous ‘NoDBD’ controls (R1-3) included for comparison. PCA plot explaining ~71% of variation. (d-e) PADIT-seq activities for HOXD13 (d) and EGR1 (e) from the all-10mers library and the small-scale library are compared. Red TFBS are active in both libraries. Black TFBS are not active in either libraries. Blue TFBS are active only in the small-scale library. (f) PADIT-seq activity from the small-scale library and MITOMI-derived dissociation constants (Kd) for EGR1 are compared. Red TFBS are active, whereas black TFBS are inactive. (g) Comparison of binding preferences measured by uPBM Z-scores (x-axis) and PADIT-seq (y-axis) for 4 human TFs, HOXD13, NKX2.5, TBX5, and EGR1, and 2 S. cerevisiae yeast TFs, Pho4 and Cbf1. Active k-mers are colored red, and inactive k-mers are colored black. (inset) AUROC comparing the ability of uPBM E-scores (orange) and Z-scores (purple) to discriminate between PADIT-seq active and inactive k-mers.
Extended Data Fig. 2 Custom PBM confirms PADIT-seq active k-mers represent genuine TF binding sites.
(a) Schematic of custom PBM design showing PADIT-seq active k-mers (8-mers for HOXD13 and 9-mers for EGR1) embedded within constant flanking sequences. FLAG-tagged HOXD13 or EGR1 binding was detected using Alexa Fluor 488 conjugated anti-FLAG antibodies. (b) Scatter plots comparing PADIT-seq activity (y-axis) and custom PBM signal (x-axis) for HOXD13 at three protein concentrations (300 nM, 500 nM, and 800 nM). Points are colored by PADIT-seq false discovery rate (FDR): red (FDR < 0.01), cyan (0.01 ≤ FDR < 0.05), and orange (0.05 ≤ FDR < 0.10). (c) Corresponding analysis for EGR1 at the same three protein concentrations. (d) ROC curves comparing the predictive performance of uPBM E-scores (orange) and PWM scores from FIMO (blue) in distinguishing between PADIT-seq active and inactive k-mers. In cases where PWM models were longer than PADIT-seq active k-mers, we scanned all possible relative positions (registers) of the PWM model against each k-mer and retained the highest affinity score for our analysis. (e) ROC curves comparing the predictive performance of uPBM E-scores (orange) and ProBound-predicted affinities (blue) in distinguishing between PADIT-seq active and inactive k-mers. (insets) Due to the large imbalance between active and inactive k-mers, even seemingly small false positive rates translate to substantial numbers of false predictions. For example, a 5% FPR corresponds to 1,556 false positives for HOXD13 and even a 1% FPR corresponds to 1,305 false positives for EGR1.
Extended Data Fig. 3 Lower affinity binding sites increase TF genomic occupancy at ChIP-seq and ChIP-nexus peaks.
(a) ROC curves comparing the performance of PADIT-seq, PWM FIMO, and ProBound in distinguishing ChIP-seq peaks from background genomic regions for HOXD13, NKX2.5, TBX5 and EGR1. For HOXD13, in addition to random, length-matched background genomic intervals, false positives were also determined with the background sequences defined to be embryonic forelimb bud ATAC-seq peaks not overlapping HOXD13 ChIP-seq peaks. For Pho4 and Cbf1, ROC curves compare the performance of PADIT-seq and PWM FIMO in distinguishing ChIP-nexus peaks from background genomic regions (ProBound motifs were not available). The number of foreground and background genomic intervals were equal for all 6 TFs. (b) The sum of PADIT-seq activities of all the active k-mers in ChIP-seq and ChIP-nexus peaks is plotted against the corresponding read counts normalized to peak length for each TF. Pearson correlation coefficients and significance values are shown. (c) Pearson correlation coefficient between normalized ChIP-seq and ChIP-nexus read counts and PADIT-seq predictions when varying the number of top active k-mers included. Red horizontal line indicates the maximum correlation achieved using PADIT-seq. Blue horizontal line shows the correlation achieved using PWM FIMO log-likelihood scores summed across peaks, which yielded higher correlation coefficients than using maximum PWM scores alone.
Extended Data Fig. 4 ChIP-seq and ChIP-nexus peaks have significantly more consecutive active k-mers, irrespective of how background genomic sequences were defined.
(a) Histograms showing the distribution of consecutive active k-mers in peaks (red) for six TFs: HOXD13, NKX2.5, TBX5, and EGR1 (ChIP-seq), and Pho4 and Cbf1 (ChIP-nexus). Background regions were generated by selecting genomic sequences flanking each ChIP peak. Statistical significance was determined by two-sided Wilcoxon rank sum tests. For EGR1, the large effect size resulted in a Pvalue below computational precision. (b) The vast majority of binding sites are detected with high statistical confidence. Across all six TFs, 56–76% of active k-mers are found at FDR < 0.01.
Extended Data Fig. 5 ChIP-seq and ChIP-nexus peaks are significantly enriched for consecutive active k-mers irrespective of FDR threshold.
Distribution of consecutive active k-mers within ChIP-seq and ChIP-nexus peaks (red) compared to random, length-matched genomic regions (black) for each TF at three different FDR thresholds, 1% (left panels), 5% (middle panels), and 10% (right panels). Statistical significance was determined by two-sided Wilcoxon rank sum tests.
Extended Data Fig. 6 Minor groove width (MGW) and Propeller twist (ProT) at the extended recognition sequences bound by TFs is distinct from flanking genomic regions.
(a-b) Predicted MGW (a) and ProT (b) are shown for genomic regions containing consecutive active k-mers within ChIP-seq peaks (red) and their 4-bp flanking regions (blue): HOXD13 (13–15 bp with consecutive active 8-mers), NKX2.5 (11–13 bp with consecutive active 8-mers), TBX5 (10–12 bp with consecutive active 8-mers), and EGR1 (11–13 bp with consecutive active 9-mers). Adjusted Pvalues < 0.05 from paired two-sided Wilcoxon rank sum tests are indicated by *.
Extended Data Fig. 7 ChIP-nexus footprint expansion persists after controlling for core motif positioning.
(a) For Pho4, the increased number of significant cuts and expanded footprint size observed for 5 overlapping binding sites is not because CACGTG occurs at more variable registers. Strand-specific distribution of ChIP-nexus 5’ends (red: positive strand; black: negative strand) averaged across peaks containing exactly 5 consecutive overlapping active 8-mers for Pho4. (b) For Cbf1, an expanded footprint size was observed even after constraining the position of CACGTG at the 3rd register. Strand-specific distribution of ChIP-nexus 5’ends (red: positive strand; black: negative strand) averaged across peaks containing exactly 3 (top) or 5 (bottom) consecutive overlapping active 8-mers with CACGTG constrained to be exclusively at the 3rd register. Peaks with 4 consecutive overlapping binding sites are not included because no significant cuts above background were observed due to low statistical power, which makes it difficult to objectively determine the size of footprints. (a-b) Dashed blue lines indicate the boundaries of the consecutive overlapping binding sites. Letters indicate cuts occurring significantly above background in the flanking 15-bp genomic regions (permutation test adjusted P < 0.05). Sequence logos above each plot show the relative frequency at which the 4 nucleotides occur at each position in the genomic sequences containing the indicated category of ChIP-nexus peaks. For these sequence logos, the y-axis represents information content ranging from a minimum of 0 to a maximum of 2.
Extended Data Fig. 8 Overlapping binding sites additively increase TF occupancy in vitro.
(a) The fraction of HT-SELEX reads (y-axis) with consecutive overlapping PADIT-seq active k-mers after 0–4 rounds of selection (x-axis). (b) Box plots showing uPBM signal intensities for 60-bp probes (n ≈ 42,000) categorized by the number of consecutive overlapping active k-mers. The bounds of the box plots define the 25th, 50th and 75th percentiles, and whiskers extend to the furthest data points within 1.5× the interquartile range. Two-sided Wilcoxon tests for all pairwise comparisons have an adjusted P < 0.05 (not indicated). (c) HOXD13, NKX2.5, TBX5, Pho4, and Cbf1 10-mer PADIT-seq activity versus mean uPBM E-scores of constituent 8-mers, centered around a fixed 8-mer in the center (green). EGR1 PADIT-seq activity of 10-mers containing ‘GCGTGGGTG’ (green) versus uPBM E-scores of constituent 9-mers. (d) For all the PADIT-seq active HOXD13, NKX2.5, TBX5, Pho4, and Cbf1 8-mers: distribution of Pearson correlation coefficients between 10-mer PADIT-seq activities and mean uPBM E-scores of constituent 8-mers. For all the PADIT-seq active EGR1 9-mers: correlations between 10-mer PADIT-seq activity and uPBM E-scores of constituent 9-mers.
Extended Data Fig. 9 PADIT-seq outperforms MotifBreakR in predicting variant effects on TF binding and gene expression.
(a-b) PADIT-seq outperforms MotifBreakR in predicting SNP effects on TF binding. Representative variants, rs62523478 (A) and rs2914146 (B), whose effects on HOXD13 and EGR1 binding, respectively, cannot be explained by PWM models. (Right) Box plots show custom PBM signals from 16 probes for the reference and alternate alleles. (Left) PADIT-seq k-mers tiled across the reference and alternate alleles. Red points represent k-mers that are active in both alleles; green points represent k-mers that are uniquely active in the allele with higher TF binding. The bounds of the box plots define the 25th, 50th and 75th percentiles, and whiskers extend to the furthest data points within 1.5× the interquartile range. (c) Comparison of PADIT-seq and MotifBreakR predictions with ADASTRA EGR1 ChIP-seq allelic skew measurements. (d-f) Noncoding variants alter multiple overlapping EGR1 binding sites to influence gene expression. (d) Allelic skew in MPRA activity of 149 SNPs with differential EGR1 PADIT-seq activity. Shapes represent the different studies from which allelic skew in MPRA activity was obtained. Red shapes correspond to concordant directions of effect between differential EGR1 PADIT-seq activity and MPRA allelic skew. (e) Euler diagram of variants with MPRA allelic skew predicted by MotifBreakR to alter EGR1 binding (green) and with differential PADIT-seq activity (blue). (Bottom) Boxplots comparing MotifBreakR (left) and PADIT-seq (right) effect sizes. The bounds of the box plots define the 25th, 50th and 75th percentiles, and whiskers extend to the furthest data points within 1.5× the interquartile range. * indicates two-sided Wilcoxon rank sum test Pvalue < 0.05. (f) Number of active overlapping 9-mers altered by the 149 variants with differential PADIT-seq activity. Brown variants are predicted to alter EGR1 binding by MotifBreakR, while blue is not.
Extended Data Fig. 10 ‘Weavability’ of binding sites is an inherent property of TFs from different DBD classes.
(a) Schema to demonstrate the logic of network construction. All incoming and outgoing edges to and from the HOXD13 active 8-mer ‘ACTTTACT’ are shown. Active 8-mers are colored red, inactive 8-mers are colored black. Edges occur between active k-mers only. (b) (left) Network representation of HOXD13 active 8-mers and reverse complements (n = 3,536), connected by directed edges (arrows not shown). 3,446 out of 3,536 nodes (97.5%) form the largest, single connected component. (right) HOXD13 PADIT-seq activity of active 8-mers is plotted against the total number of incoming and outgoing edges per node. The bounds of the box plots define the 25th, 50th and 75th percentiles, and whiskers extend to the furthest data points within 1.5× the interquartile range. * Adjusted P < 0.05, two-sided Wilcoxon tests. (c) (left) Network representation of randomly selected 8-mers and reverse complements (n = 3,536) connected by directed edges (arrows not shown). Only 7 out of 3,536 nodes (0.2%) forms the largest, single connected component. (right) HOXD13 PADIT-seq activity of the 3,536 random 8-mers is plotted against the total number of incoming and outgoing edges per node. (d-h) Network representations and activity distributions for NKX2.5, TBX5, EGR1, Pho4, and Cbf1. For each TF, the fraction of active k-mers in the largest, single connected component is indicated. (i) Among the top 500 uPBM 8-mers and reverse complements, the fraction of nodes in the largest, single connected component is plotted for 200 TFs from 9 different families of DBDs. 1,000 random samplings of 500 8-mers and reverse complements are also shown, and were used to perform the permutation test.
Supplementary information
Supplementary Figure 1
Conservation analysis of ChIP–seq peaks containing varying numbers of overlapping, PADIT-seq active k-mers.
Supplementary Tables 1–6
Supplementary Table 1: List of primers used. Supplementary Table 2: List of gBlock and vector DNA sequences. Supplementary Table 3: Small-scale library design and sequence. Supplementary Table 4: PADIT-seq library sequencing statistics. Supplementary Table 5: PADIT-seq active and inactive k-mers. Supplementary Table 6: Processed custom PBM data.
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Khetan, S., Carroll, B.S. & Bulyk, M.L. Multiple overlapping binding sites determine transcription factor occupancy. Nature (2025). https://doi.org/10.1038/s41586-025-09472-3
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DOI: https://doi.org/10.1038/s41586-025-09472-3
