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Transcription factor binding divergence drives transcriptional and phenotypic variation in maize

Nature Plants volume 11, pages 1205–1219 (2025)Cite this article

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Abstract

Regulatory elements are essential components of plant genomes that have shaped the domestication and improvement of modern crops. However, their identity, function and diversity remain poorly characterized, limiting our ability to harness their full power for agricultural advances using induced or natural variation. Here we mapped transcription factor (TF) binding for 200 TFs from 30 families in two distinct maize inbred lines historically used in maize breeding. TF binding comparison revealed widespread differences between inbreds, driven largely by structural variation, that correlated with gene expression changes and explained complex quantitative trait loci such as Vgt1, an important determinant of flowering time, and DICE, an herbivore resistance enhancer. CRISPR–Cas9 editing of TF binding regions validated the function and structure of regulatory regions at various loci controlling plant architecture and biotic resistance. Our maize TF binding catalogue identifies functional regulatory regions and enables collective and comparative analysis, highlighting its value for agricultural improvement.

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Fig. 1: Large-scale DAP-seq profiling of maize TFs provides high-quality genome-wide binding site data for B73v5 and Mo17.
Fig. 2: Maize TF diversity panel reveals functional cis-regulatory modules.
Fig. 3: Genotype-specific peaks are prevalent in B73 and Mo17 and explain genetically defined QTLs.
Fig. 4: DoubleDAP-seq analysis of Gln-Ala-Arg-type bHLHs.
Fig. 5: CRISPR-induced cis-regulatory variation drives expression and phenotypic differences.

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Data availability

The raw and processed data for the DAP-seq datasets generated in this study are available from NCBI GEO under accession number GSE275897. The processed data are available via Zenodo at https://doi.org/10.5281/zenodo.14991915 (ref. 97). Comparative peak data tracks from the different inbred lines can be viewed on JBrowse2 at http://hlab.bio.nyu.edu/projects/zm_crm/.

Code availability

The analysis pipelines are available via GitHub at https://github.com/hlab1/Maize_B73_Mo17_DAP.

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Acknowledgements

We thank R. Macrae for helpful comments and suggestions on the manuscript and maizeGDB for data curation of informatics resources. We acknowledge the Office of Advanced Research Computing at Rutgers, the State University of New Jersey, for providing access to the Amarel cluster. This work was supported in part through the NYU IT High Performance Computing resources, services and staff expertise. This work was supported by NSF Plant Genome Research Project grant no. IOS-1916804 to A.G. and S.-s.C.H. and NIH award no. R35GM138143 to S.-s.C.H. A.C. is a Fulbright Scholar and recipient of a Meta Prevorsek Turner Fellowship. This material was also in part based on work supported by the Center for Bioenergy Innovation, US Department of Energy, Office of Science, Biological and Environmental Research Program under award no. ERKP886 and the Oak Ridge National Laboratory Director’s R&D Program (M.L.). Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the Office of Science of the US Department of Energy under contract no. DE-AC05-00OR22725.

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Authors and Affiliations

  1. Waksman Institute of Microbiology, Rutgers University, Piscataway, NJ, USA

    Mary Galli, Zongliang Chen, Amina Chaudhry, Jason Gregory, Fan Feng & Andrea Gallavotti

  2. Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA

    Tara Ghandour, Miaomiao Li & Shao-shan Carol Huang

  3. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Miaomiao Li

  4. Department of Agronomy, University of Wisconsin, Madison, WI, USA

    Nathaniel Schleif & Heidi F. Kaeppler

  5. Department of Genetics, University of Georgia, Athens, GA, USA

    Xuan Zhang & Yinxin Dong

  6. Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, USA

    Gaoyuan Song & Justin W. Walley

  7. University of California, Berkeley, Berkeley, CA, USA

    George Chuck

  8. Department of Biology, Brigham Young University, Provo, UT, USA

    Clinton Whipple

  9. Wisconsin Crop Innovation Center, University of Wisconsin, Middleton, WI, USA

    Heidi F. Kaeppler

  10. Department of Plant Biology, Rutgers University, New Brunswick, NJ, USA

    Andrea Gallavotti

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Contributions

M.G., S.-s.C.H. and A.G. conceptualized the project, analysed the data and prepared the paper. M.G., Z.C., T.G., M.L. and S.-s.C.H. conducted the TF binding analysis and visualization. S.-s.C.H. conducted the GWAS analysis. M.G., Z.C., A.C., J.G., F.F. and A.G. conducted the CRISPR experiments and analysis. M.G., N.S., X.Z., Y.D., G.S., J.W.W., G.C., C.W. and H.F.K. provided reagents.

Corresponding authors

Correspondence to Shao-shan Carol Huang or Andrea Gallavotti.

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The authors declare no competing interests.

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Nature Plants thanks James Schnable, Feng Tian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 DAP-seq data samples broadly across maize TF families.

a, Stacked bar graph showing 36 TF families in which at least one family member was tested in DAP-seq. The number of members tested for each is shown with a colored bar; the number of members for which high quality datasets were obtained is shown in parentheses next to the family name. b, Scatterplot showing the number of B73 and Mo17 peaks for each TF. c, Boxplots showing the distribution of the number of target genes obtained for each TF, n = 200 d, Heatmap showing Pearson correlation of TF binding profiles genome wide (10-bp bins). Side annotation colors correspond to TF structural families. TFs in the diversity panel are indicated with an asterisk. e, Average number of reads in peaks and number of peaks with GEM motif are indicated. Each datapoint corresponds to a DAP-seq dataset from either B73 or Mo17. Whiskers in boxplots in panels c and e indicate the minimum and maximum values; central lines correspond to medians and box boundaries denote the upper (25th percentile) and lower (75th percentile) quartiles with n = 200 TFs.

Extended Data Fig. 2 Enrichment analysis of selected TFs and GWAS traits.

a, GO enrichment analysis of putative target genes assigned to peaks of selected TFs. b, Bubble plot showing enrichment for NAM panel GWAS SNPs associated with branch zone, cob length, and leaf angle that overlapped SBP30/UB3 DAP-seq peaks (right panel; red boxed areas) and bubble plot showing MADS73 DAP-seq peaks were significantly enriched for SNPs associated with several flowering time-related traits (left panel; black boxed areas). c, Bubble plot showing enrichment for Wisconsin Diversity panel GWAS SNPs associated with branching phenotypes that overlapped SBP30/UB3 DAP-seq peaks (right panel; red boxed areas) and bubble plot showing MADS73 DAP-seq peaks were significantly enriched for SNPs associated with flowering time-related traits (left panel; black boxed areas).

Extended Data Fig. 3 B73 TF diversity panel and DAP-CRM analysis.

a, Heatmap showing Pearson correlation of genome-wide binding profiles for 66 TFs in the B73 TF diversity panel. b, Phylogeny, tissue-specific RNA-expression (TPM; transcripts per million), and binding motifs of selected SBP TFs tested in maize DAP-seq. Expression patterns are often different for family members with similar motifs. c, Genome browser screenshot of SBP TFs shown in b at the TSH1 locus. d, Histogram showing the size distribution of DAP-CRMs. The dotted line indicates the average size of all DAP-CRMs. e, Histogram of the number of TFs per DAP-CRM. The black dotted line indicates the average number of TFs per DAP-CRM. The inset shows a close-up on the number of TFs from 20-63 TFs per DAP-CRM. f, Frequency of DAP-CRMs versus randomly shuffled CRMs of the same size within a 3 kb window surrounding the TSS.

Extended Data Fig. 4 Genome browser views of composite DAP-seq peaks.

a, B73v5 IGV genome browser view of the Vgt1 and RAP2.7 locus showing binding by DAP-seq TFs. Lower panel shows close-up of DAP-CRMs with binding by TFs not displayed as main tracks due to space constraints. b, Genome browser view of the UNBRANCHED2 (UB2) locus showing binding by DAP-seq TFs. c, Genome browser view of the INDETERMINATE GAMETOPHYTE1 (IG1) locus showing binding site location of DAP-seq TFs. In each panel the number below the DAP-CRM track indicates the number of TF peaks present in the DAP-CRM. Please visit website genome browser for a display of all TF tracks http://hlab.bio.nyu.edu/projects/zm_crm/.

Extended Data Fig. 5 Quality assessment of B73 and Mo17 DAP-seq datasets.

a, Pearson correlation of TF binding profiles for B73 and Mo17 genomes. Most B73 peak datasets (rows annotated in yellow) clustered with Mo17 peak datasets for the same TFs lifted to B73 (rows annotated in teal), as indicated by the magenta boxes. b, Percentage of non-crossmappable peaks (coordinates could not be lifted) that overlapped with “Non-Alignable” region (NotAligned), duplicated regions (DUP), or structural variants (SV, insertions or deletions greater than 50 bp). Each datapoint corresponds to the percentage of peaks from an individual TF overlapping the indicated category, n = 200. Whiskers in boxplots in panels b-d indicate the minimum and maximum values; central lines correspond to medians and box boundaries denote the upper (25th percentile) and lower (75th percentile) quartiles with n = 200 TFs. c, Upper: scatterplots showing no difference in average reads per peak or percentage of peaks with motif for shared and specific datasets. Each dot corresponds to a single TF. Lower: distribution of average reads per peak and percentage of peaks with motif for shared and specific datasets. n = 66 d, Percentage of peaks within each category that overlapped SVs and transposable elements (TEs), n = 200.

Extended Data Fig. 6 Shared and genotype-specific peaks.

a, Percentage of TF diversity panel peaks also called as peaks in Mo17 (shared, grey) and those called only in B73v5 but not Mo17 (B73-specific, blue). b, Percentage of TF diversity panel peaks also called as peaks in B73 (shared, grey) and those called only in Mo17 but not B73 (Mo17-specific, green). Mo17 datasets show a similar percentage of Mo17-specific and Mo17-B73 shared peaks relative to those seen for B73-specific and B73-Mo17 shared peaks (TF order is same as in a). c, JBrowse2 genome browser view showing similar binding intensity of MADS69 in the 5’UTR of CYCLIN13 (Zm00001eb193830) in both genotypes. d, scatterplot showing good correlation of MADS69 reads in B73v5 compared to Mo17 MADS69 reads converted to B73v5 coordinates. e, JBrowse2 view of the MATE18 locus showing B73-specific peaks residing near a GWAS hit. Note that the B73-specific sequence insertion is not annotated as a TE. f, B73-specific peaks more often contain more than four SNPs per peak (top plot) or more SNPs per peak center (20 bp region surrounding the peak summit) (bottom plot). Shared peaks more often contain less than four SNPs per peak (top plot) or per the 20 bp region surrounding the peak summit (bottom plot). Data points represent individual TFs, n = 65. For f and g, whiskers indicate the minimum and maximum values; central lines correspond to medians and box boundaries denote the upper (25th percentile) and lower (75th percentile) quartiles. g, Overlap of B73-specific peaks and B73-Mo17 shared peaks with orthogonal datasets or DAP-CRMs. Both B73-specific peaks and shared peaks show statistically significant enrichment (p value < 0.0001, two-tailed t-test) relative to randomly shuffled peaks, providing support that both B73-specific peaks and shared peaks could be functional. Data points represent individual TFs, n = 200.

Extended Data Fig. 7 Peak positional variation among genotypes.

a, Comparative JBrowse2 genome browser view showing shared peaks of B73 and Mo17 at the MADS67 locus. b, JBrowse2 genome browser view showing TF binding site positional variants at the ZmATL34 locus. Lower heatmap shows ZmATL34 RNA expression levels (transcripts per million) from several tissues40. A 13-fold increase in Mo17 RNA expression was observed in radicle tissue compared to B73. c, Top: percentage of shared TF diversity panel promoter peaks (top 20%) that correspond to positional variants (PosV) greater than 500 bp or less than 500 bp. Bottom: percentage of top (shared and specific) promoter peaks that corresponded to shared PosVs or B73-specific peaks. Each datapoint corresponds to an individual TF, n = 65. Whiskers indicate the minimum and maximum values; central lines correspond to medians and box boundaries denote the upper (25th percentile) and lower (75th percentile) quartiles. d, Plot showing percentage of B73-Mo17 shared promoter peaks (<-10kb) that were associated with differentially expressed genes in various tissues from40. Genes associated with PosV<500 bp peaks are shown in dark pink, while those associated with PosV>500 peaks are shown in light pink. Fisher’s exact tests were performed to assess the enrichment of DEGs in PosV>500 bp genes relative to PosV<500 bp genes for each TF and tissue combination, and the resulting p-values were adjusted for multiple testing. e, Heatmap of -log10 FDR-adjusted p-values from one-sided Fisher’s exact tests, evaluating whether root cell type marker genes determined by single cell RNA-seq data41 are enriched for putative target genes associated with DAP-seq peaks shared between B73 and Mo17, compared to putative targets of B73-specific peaks.

Extended Data Fig. 8 DoubleDAP-seq analysis.

a, Schematic showing the doubleDAP-seq assay in which putative heterodimeric TF-DNA complexes can be pulled down and compared to results from a single protein DAP-seq assay to assess binding site specificity differences. b, Neighbor-joining phylogeny based on amino acid similarity of group VII (blue) and group VIII (green) bHLHs from maize and Arabidopsis. Members that were tested in DAP-seq are shown in bold. Three members that were tested in DAP-seq that did not yield any peaks are shown in italics. c, AlphaFold prediction of BHLH85 homodimer. d, Normalized RNA-seq expression (TPM; transcripts per million) of group VIII and VII bHLHs showing heterodimer pairs are co-expressed in many tissues. Normalized expression data from Walley et al.98. e, Venn diagram showing low degree of overlap between Q-A-R (subclade VIII) homodimers and Q-A-R:ZmSPT (subclade VII) heterodimers as exemplified by HALO-BHLH113 (Q-A-R bHLH) in single DAP and HALO-BHLH113:SBPTag-BHLH125/ZmSPT1 in doubleDAP. Only 2% of HALO-BHLH113 peaks were captured in the doubleDAP dataset indicating that the heterodimer configuration is preferred relative to the homodimer.

Extended Data Fig. 9 CRISPR editing of maize cis-regulatory regions influences phenotype.

a, Genome browser screenshot of TSH1 locus showing binding in upstream promoter region by many TFs. Grey shaded areas upstream of and within CRM169681 indicate regions that were deleted in alleles shown in b. b, Schematic showing two independent CRISPR alleles with deletions and inversions that eliminate at least five TF binding sites (colored bars; colors match TFs shown in genome browser screenshot in a). Lightly shaded grey areas indicate regions deleted in both alleles. c, Images of mature tassels for WT, two tsh1 promoter CRISPR alleles, and tsh1-ref mutant (coding region mutation51). Both CRISPR promoter alleles show outgrowth of the tassel sheath leaf (white arrows) that is not present in the WT (black arrow). Tassel branching is reduced in the tsh1-ref, and the CRISPR promoter alleles (white brackets) relative to WT (black bracket). d, SEMs of immature tassels of the CRISPR promoter alleles showing bract outgrowth (white arrowheads). e, qRT-PCR analysis of CRISPR promoter alleles showing reduced expression in immature tassels relative to WT immature tassels. Data are presented as mean values. Error bars represent standard deviation of three biological replicates for each edited allele (two biological replicates for wildtype). Data points correspond to individual biological replicates. Statistical significance determined by two-sided t-test. f, CRISPR editing of BIF2 3’UTR ARF binding sites. A cis-regulatory module (CRM18765) is situated downstream of the BIF2 gene. Within this CRM region, there is a strong ARF peak containing five ARF binding motifs (three TGTCs and two GACAs)38. Three single guide RNAs (gRNAs) were designed for CRISPR-Cas9 editing that specifically targeted the ARF motifs. Three deletion alleles were obtained. Homozygous plants of these alleles exhibited a weak bif2 phenotype with various degrees of severity (BIF2-crm1cr1 and BIF2-crm1cr3 are more severe than BIF2-crm1cr2) during the early ear development stage, characterized by partial barren patches (white arrows) on the ear primordia as seen by SEM.

Extended Data Fig. 10 Sequence alignment of Mo17 tandem-duplicated CRMs.

Nucleotide alignment of B73v3_DICE (single copy), Mo17_CRM119798 (tandem copy 1), and Mo17_CRM119799 (tandem copy 2) showing conservation of individual TF binding motifs.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Tables 1, 2 and 4–8.

Reporting Summary

Supplementary Table 3

Combinations of three TFs that bound DAP-CRM regions.

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Galli, M., Chen, Z., Ghandour, T. et al. Transcription factor binding divergence drives transcriptional and phenotypic variation in maize. Nat. Plants 11, 1205–1219 (2025). https://doi.org/10.1038/s41477-025-02007-8

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