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Peer-reviewedCC BYNature Communications

Identification of methylation-sensitive human transcription factors using meSMiLE-seq

  • Antoni J. Gralak1,2
  • Kateřina Faltejsková3,4,5,6
  • Ally W. H. Yang7,8
  • Clemence Steiner1
  • Julie Russeil1,2
  • Nadia Grenningloh1
  • Sachi Inukai9,1
  • Mustafa Demir10,1
  • Riccardo Dainese10,1
  • C. C. Owen1
  • Eugenia V. Pankevich1
  • Philipp Bücher2,8
47 more authors
  • Oriol Fornés11,12,13
  • Jan Grau14,1,2
  • Ivo Große14,15,1,2
  • Arttu Jolma7,1,2
  • Fedor A. Kolpakov16,17
  • Vsevolod J. Makeev18,13
  • Mihai Albu7
  • Marjan Barazandeh7
  • Alexander V. Brechalov7
  • Zhenfeng Deng7
  • Ali Fathi7
  • Chun Hu7
  • Samuel A. Lambert7
  • Kaitlin U. Laverty7
  • Zain M. Patel7
  • Sara E. Pour7
  • Rozita Razavi7
  • Mikhail Salnikov7
  • Isaac Yellan7
  • Hong Zheng7
  • Georgy Meshcheryakov12
  • Giovanna Ambrosini19,20
  • Marie-Luise Plescher14
  • Semyon K. Kolmykov17
  • Ivan Yevshin21
  • Nikita Gryzunov12,22
  • Ivan Kozin23,12
  • Mikhail Nikonov23
  • Vladimir Nozdrin23
  • Arsenii O. Zinkevich23
  • Pavel Kravchenko24
  • Sergey Abramov25,13
  • Alexandr Boytsov25,13
  • Vasilii Kamenets18,13
  • Dmitry Penzar13
  • Anton Vlasov26
  • Ilya E. Vorontsov13
  • Aldo Hernández-Corchado27
  • Hamed Shateri Najafabadi27
  • Quaid Morris28
  • Xiaoting Chen29
  • Matthew T. Weirauch29
  • Timothy R. Hughes7
  • Ivan V. Kulakovskiy30,12,13
  • Judith F. Kribelbauer-Swietek31,1,2
  • Guido van Mierlo32,1,2
  • Bart Deplancke33,34
34 affiliations
  1. 1Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  2. 2Swiss Institute of Bioinformatics, Lausanne, Switzerland
  3. 3Computer Science Institute, Faculty of Mathematics and Physics, Charles University, Prague, Czechia
  4. 4Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Prague, Czechia
  5. 5Computer Science Institute, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
  6. 6Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Prague, Czech Republic
  7. 7Donnelly Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
  8. 8University of Toronto, Toronto, Ontario, Canada
  9. 9Chugai Pharmaceutical Co. Ltd, Tokyo, Japan
  10. 10Alithea Genomics SA, Epalinges, Switzerland
  11. 11Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
  12. 12Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia
  13. 13Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
  14. 14Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany
  15. 15Department of Medical BioSciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
  16. 16Bioinformatics Laboratory, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
  17. 17Department of Computational Biology, Sirius University of Science and Technology, Sirius, Russia
  18. 18Moscow Center for Advanced Studies, Moscow, Russia
  19. 19Bioinformatics Competence Center, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
  20. 20Bioinformatics Competence Center, Université de Lausanne, Lausanne, Switzerland
  21. 21Biosoft.Ru LLC, Novosibirsk, Russia
  22. 22Life Improvement by Future Technologies (LIFT) Center, Moscow, Russia
  23. 23Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
  24. 24Max Planck Institute of Biochemistry, Planegg, Germany
  25. 25Altius Institute for Biomedical Sciences, Seattle, WA, USA
  26. 26TUD Dresden University of Technology, Center for Molecular and Cellular Bioengineering (CMCB), Biotechnologisches Zentrum (BIOTEC), Dresden, Germany
  27. 27McGill University and Génome Québec Innovation Centre, Montréal, QC, Canada
  28. 28Memorial Sloan Kettering Cancer Center, Rockefeller Research Laboratories, New York, NY, USA
  29. 29Cincinnati Children’s Hospital, Cincinnati, OH, USA
  30. 30Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, Ufa, Russia
  31. 31Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
  32. 32Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
  33. 33Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. [email protected]
  34. 34Swiss Institute of Bioinformatics, Lausanne, Switzerland. [email protected]
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Abstract

Transcription factors (TFs) are key players in eukaryotic gene regulation, but the DNA binding specificity of many TFs remains unknown. Here, we assayed 284 mostly poorly characterized, putative human TFs using selective microfluidics-based ligand enrichment followed by sequencing (SMiLE-seq), revealing 72 new DNA binding motifs. To investigate whether some of the 158 TFs for which we did not find motifs preferably bind epigenetically modified DNA (i.e. methylated CG dinucleotides), we developed methylation-sensitive SMiLE-seq (meSMiLE-seq). This microfluidic assay simultaneously probes the affinity of a protein to methylated and unmethylated DNA, augmenting the capabilities of the original method to infer methylation-aware binding sites. We assayed 114 TFs with meSMiLE-seq and identified DNA-binding models for 48 proteins, including the known methylation-sensitive binding modes for POU5F1 and RFX5. For 11 TFs, binding to methylated DNA was preferred or resulted in the discovery of alternative, methylation-dependent motifs (e.g. PRDM13), while aversion towards methylated sequences was found for 13 TFs (e.g. USF3). Finally, we uncovered a potential role for ZHX2 as a putative binder of Z-DNA, a left-handed helical DNA structure which is adopted more frequently upon CpG methylation. Altogether, our study significantly expands the human TF codebook by identifying DNA binding motifs for 98 TFs, while providing a versatile platform to quantitatively assay the impact of DNA modifications on TF binding.

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Cite

BibTeXRIS
BibTeX
@article{gralak2026identification,
  title = {{Identification of methylation-sensitive human transcription factors using meSMiLE-seq}},
  author = {Antoni J. Gralak and Kateřina Faltejsková and Ally W. H. Yang and Clemence Steiner and Julie Russeil and Nadia Grenningloh and Sachi Inukai and Mustafa Demir and Riccardo Dainese and C. C. Owen and Eugenia V. Pankevich and Philipp Bücher and Oriol Fornés and Jan Grau and Ivo Große and Arttu Jolma and Fedor A. Kolpakov and Vsevolod J. Makeev and Mihai Albu and Marjan Barazandeh and Alexander V. Brechalov and Zhenfeng Deng and Ali Fathi and Chun Hu and Samuel A. Lambert and Kaitlin U. Laverty and Zain M. Patel and Sara E. Pour and Rozita Razavi and Mikhail Salnikov and Isaac Yellan and Hong Zheng and Georgy Meshcheryakov and Giovanna Ambrosini and Marie-Luise Plescher and Semyon K. Kolmykov and Ivan Yevshin and Nikita Gryzunov and Ivan Kozin and Mikhail Nikonov and Vladimir Nozdrin and Arsenii O. Zinkevich and Pavel Kravchenko and Sergey Abramov and Alexandr Boytsov and Vasilii Kamenets and Dmitry Penzar and Anton Vlasov and Ilya E. Vorontsov and Aldo Hernández-Corchado and Hamed Shateri Najafabadi and Quaid Morris and Xiaoting Chen and Matthew T. Weirauch and Timothy R. Hughes and Ivan V. Kulakovskiy and Judith F. Kribelbauer-Swietek and Guido van Mierlo and Bart Deplancke},
  journal = {Nature Communications},
  year = {2026},
  month = aug,
  doi = {10.1038/s41467-026-71387-y},
  url = {https://doi.org/10.1038/s41467-026-71387-y},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Identification of methylation-sensitive human transcription factors using meSMiLE-seq
AU  - Antoni J. Gralak
AU  - Kateřina Faltejsková
AU  - Ally W. H. Yang
AU  - Clemence Steiner
AU  - Julie Russeil
AU  - Nadia Grenningloh
AU  - Sachi Inukai
AU  - Mustafa Demir
AU  - Riccardo Dainese
AU  - C. C. Owen
AU  - Eugenia V. Pankevich
AU  - Philipp Bücher
AU  - Oriol Fornés
AU  - Jan Grau
AU  - Ivo Große
AU  - Arttu Jolma
AU  - Fedor A. Kolpakov
AU  - Vsevolod J. Makeev
AU  - Mihai Albu
AU  - Marjan Barazandeh
AU  - Alexander V. Brechalov
AU  - Zhenfeng Deng
AU  - Ali Fathi
AU  - Chun Hu
AU  - Samuel A. Lambert
AU  - Kaitlin U. Laverty
AU  - Zain M. Patel
AU  - Sara E. Pour
AU  - Rozita Razavi
AU  - Mikhail Salnikov
AU  - Isaac Yellan
AU  - Hong Zheng
AU  - Georgy Meshcheryakov
AU  - Giovanna Ambrosini
AU  - Marie-Luise Plescher
AU  - Semyon K. Kolmykov
AU  - Ivan Yevshin
AU  - Nikita Gryzunov
AU  - Ivan Kozin
AU  - Mikhail Nikonov
AU  - Vladimir Nozdrin
AU  - Arsenii O. Zinkevich
AU  - Pavel Kravchenko
AU  - Sergey Abramov
AU  - Alexandr Boytsov
AU  - Vasilii Kamenets
AU  - Dmitry Penzar
AU  - Anton Vlasov
AU  - Ilya E. Vorontsov
AU  - Aldo Hernández-Corchado
AU  - Hamed Shateri Najafabadi
AU  - Quaid Morris
AU  - Xiaoting Chen
AU  - Matthew T. Weirauch
AU  - Timothy R. Hughes
AU  - Ivan V. Kulakovskiy
AU  - Judith F. Kribelbauer-Swietek
AU  - Guido van Mierlo
AU  - Bart Deplancke
PY  - 2026
DA  - 2026/08/05
JO  - Nature Communications
DO  - 10.1038/s41467-026-71387-y
UR  - https://doi.org/10.1038/s41467-026-71387-y
AB  - Transcription factors (TFs) are key players in eukaryotic gene regulation, but the DNA binding specificity of many TFs remains unknown. Here, we assayed 284 mostly poorly characterized, putative human TFs using selective microfluidics-based ligand enrichment followed by sequencing (SMiLE-seq), revealing 72 new DNA binding motifs. To investigate whether some of the 158 TFs for which we did not find motifs preferably bind epigenetically modified DNA (i.e. methylated CG dinucleotides), we developed methylation-sensitive SMiLE-seq (meSMiLE-seq). This microfluidic assay simultaneously probes the affinity of a protein to methylated and unmethylated DNA, augmenting the capabilities of the original method to infer methylation-aware binding sites. We assayed 114 TFs with meSMiLE-seq and identified DNA-binding models for 48 proteins, including the known methylation-sensitive binding modes for POU5F1 and RFX5. For 11 TFs, binding to methylated DNA was preferred or resulted in the discovery of alternative, methylation-dependent motifs (e.g. PRDM13), while aversion towards methylated sequences was found for 13 TFs (e.g. USF3). Finally, we uncovered a potential role for ZHX2 as a putative binder of Z-DNA, a left-handed helical DNA structure which is adopted more frequently upon CpG methylation. Altogether, our study significantly expands the human TF codebook by identifying DNA binding motifs for 98 TFs, while providing a versatile platform to quantitatively assay the impact of DNA modifications on TF binding.
SN  - 2041-1723
ER  -