journal article·2026-03Open access

Comprehensive Profiling of N 6 ‐methyladnosine (m 6 A) Readouts Reveals Novel m 6 A Readers That Regulate Human Embryonic Stem Cell Differentiation

Zhou Huang, Rucong Liu, Zibaguli Wubulikasimu, Wanqing Zhao, Jiaqi Huang, Jiaxuan Wang, Tianyuan Zhang, Rui Fan, Wei Kong, Qinghua Cui, Yang Li, Yuan Zhou

Advanced Science· Volume 13· Issue 18

Abstract

ABSTRACT N 6 ‐methyladenosine (m 6 A) modification constitutes a crucial layer of post‐transcriptional regulations, but the landscape of its downstream readout effects remains less comprehensively understood. Therefore, we systematically assess the readout effects of m 6 A on mRNA half‐life, translation efficiency, and alternative splicing across five cell lines (A549, HEK293T, HUVEC, JURKAT, and human embryonic stem cells (hESCs)) using actinomycin D‐disrupted temporal transcriptome, ribosome sequencing, and ultra‐high‐depth transcriptome sequencing, respectively. Our analysis, coupled with the integration of public and newly profiled m 6 A methylome data, reveals high cell type specificity in m 6 A readouts where m 6 A level alone is insufficient to predict m 6 A readouts. Nonetheless, machine learning models focusing on RNA‐binding protein (RBP) binding context can effectively predict the readouts and prioritize four novel m 6 A‐associated proteins (FUBP3, FXR2, L1TD1, and DDX6). Their m 6 A‐binding ability is validated by m 6 A RNA pull‐down, transcriptome‐wide binding site mapping, and electrophoretic mobility shift assay, while FUBP3 and L1TD1 are further suggested as m 6 A readers regulating mRNA stability based on half‐life profiling of knockout cells. Finally, FUBP3, FXR2, and L1TD1 are demonstrated to regulate hESC differentiation without affecting self‐renewal. Together, this study bridges the gap in understanding m 6 A functional readouts and lays the groundwork for future research on m 6 A‐mediated stem cell fate decisions.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: RNA modifications and cancer · RNA and protein synthesis mechanisms · Cancer-related gene regulation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Biomedical Informatics School of Basic Medical Sciences Peking University Beijing China

State Key Laboratory of Vascular Homeostasis and Remodeling Peking University Beijing China

Department of Cell Biology School of Basic Medical Sciences Peking University Stem Cell Research Center Peking University Beijing China

Cancer Center Daping Hospital Army Medical University Chongqing China

National Institute of Geriatrics National Health Commission Beijing Hospital Beijing China

State Key Laboratory of Female Fertility Promotion Center for Reproductive Medicine Department of Obstetrics and Gynecology Peking University Third Hospital Beijing China

Shenzhen Key Laboratory of Cardiovascular Disease Fuwai Hospital Chinese Academy of Medical Sciences Shenzhen China

Department of Physiology and Pathophysiology School of Basic Medical Sciences Peking University Beijing China

Department of Integrated Traditional Chinese and Western Medicine School of Basic Medical Sciences Peking University Beijing China

Funding

National Natural Science Foundation of China · Michigan Medicine-PKUHSC Joint Institute for Translational and Clinical Research

References (74)

Show source-provided references
1. 10.1038/nature1111210.1038/nature11112 ↗
2. 10.1016/j.cell.2012.05.00310.1016/j.cell.2012.05.003 ↗
3. 10.1093/nar/gkad107010.1093/nar/gkad1070 ↗
4. 10.1038/s41392‐024‐01777‐510.1038/s41392‐024‐01777‐5 ↗
5. 10.1038/nrg372410.1038/nrg3724 ↗
6. 10.1093/nar/gkad69110.1093/nar/gkad691 ↗
7. 10.1038/s41556‐019‐0315‐410.1038/s41556‐019‐0315‐4 ↗
8. 10.1016/j.molcel.2019.09.03210.1016/j.molcel.2019.09.032 ↗
9. 10.1101/gad.349983.12210.1101/gad.349983.122 ↗
10. 10.1016/j.cell.2020.05.01210.1016/j.cell.2020.05.012 ↗
11. 10.1038/nature1273010.1038/nature12730 ↗
12. 10.1016/j.cell.2015.05.01410.1016/j.cell.2015.05.014 ↗
13. 10.1016/j.molcel.2016.01.01210.1016/j.molcel.2016.01.012 ↗
14. 10.1038/s41556‐018‐0045‐z10.1038/s41556‐018‐0045‐z ↗
15. 10.1038/onc.2017.35110.1038/onc.2017.351 ↗
16. 10.1016/j.molcel.2022.12.01910.1016/j.molcel.2022.12.019 ↗
17. 10.1038/nsmb.346210.1038/nsmb.3462 ↗
18. 10.1038/ncb290210.1038/ncb2902 ↗
19. 10.1016/j.stem.2015.01.01610.1016/j.stem.2015.01.016 ↗
20. 10.1126/science.126141710.1126/science.1261417 ↗
21. 10.1101/gad.340695.12010.1101/gad.340695.120 ↗
22. 10.1038/s41586‐021‐03313‐910.1038/s41586‐021‐03313‐9 ↗
23. 10.1186/s13059‐018‐1436‐y10.1186/s13059‐018‐1436‐y ↗
24. 10.1038/s41587‐022‐01487‐910.1038/s41587‐022‐01487‐9 ↗
25. 10.1038/nature2467810.1038/nature24678 ↗
26. 10.1038/s41556‐022‐00968‐y10.1038/s41556‐022‐00968‐y ↗
27. 10.1016/j.cell.2017.06.01010.1016/j.cell.2017.06.010 ↗
28. 10.1371/journal.pgen.100741210.1371/journal.pgen.1007412 ↗
29. 10.1101/gad.301036.11710.1101/gad.301036.117 ↗
30. 10.1016/j.cell.2016.03.00710.1016/j.cell.2016.03.007 ↗
31. 10.1261/rna.064691.11710.1261/rna.064691.117 ↗
32. 10.1038/s41596‐018‐0020‐y10.1038/s41596‐018‐0020‐y ↗
33. 10.1016/j.stem.2014.09.01910.1016/j.stem.2014.09.019 ↗
34. 10.1016/j.stem.2019.08.01810.1016/j.stem.2019.08.018 ↗
35. 10.1038/nature1497810.1038/nature14978 ↗
36. 10.1177/1947603522114474710.1177/19476035221144747 ↗
37. 10.1242/jcs.15486410.1242/jcs.154864 ↗
38. 10.1016/j.bbrc.2019.03.08110.1016/j.bbrc.2019.03.081 ↗
39. 10.1038/s41467‐018‐05856‐410.1038/s41467‐018‐05856‐4 ↗
40. 10.1016/j.ydbio.2014.11.01210.1016/j.ydbio.2014.11.012 ↗
41. 10.1242/dev.20251610.1242/dev.202516 ↗
42. 10.1016/s0092‐8674(00)81769‐910.1016/s0092‐8674(00)81769‐9 ↗
43. 10.1016/s0092‐8674(03)00392‐110.1016/s0092‐8674(03)00392‐1 ↗
44. 10.1038/s41571‐023‐00774‐x10.1038/s41571‐023‐00774‐x ↗
45. 10.1038/s41422‐018‐0040‐810.1038/s41422‐018‐0040‐8 ↗
46. 10.1093/nar/gkm49110.1093/nar/gkm491 ↗
47. 10.1016/j.cell.2005.07.01210.1016/j.cell.2005.07.012 ↗
48. 10.1371/journal.pbio.004021010.1371/journal.pbio.0040210 ↗
49. 10.1038/s41388‐020‐1266‐810.1038/s41388‐020‐1266‐8 ↗
50. 10.1111/jcmm.1758410.1111/jcmm.17584 ↗
51. 10.1038/s41423‐022‐00878‐x10.1038/s41423‐022‐00878‐x ↗
52. 10.1016/j.neuron.2011.03.02710.1016/j.neuron.2011.03.027 ↗
53. 10.1371/journal.pone.002046110.1371/journal.pone.0020461 ↗
54. 10.1016/j.stemcr.2015.01.01410.1016/j.stemcr.2015.01.014 ↗
55. 10.1016/j.xgen.2025.10087210.1016/j.xgen.2025.100872 ↗
56. 10.1038/s41592‐025‐02623‐410.1038/s41592‐025‐02623‐4 ↗
57. 10.1016/j.gde.2024.10227010.1016/j.gde.2024.102270 ↗
58. 10.1038/nprot.2007.5410.1038/nprot.2007.54 ↗
59. 10.1038/s41467‐019‐09903‐610.1038/s41467‐019‐09903‐6 ↗
60. 10.1038/nprot.2012.08610.1038/nprot.2012.086 ↗
61. 10.1038/ncomms1119410.1038/ncomms11194 ↗
62. 10.1038/s41596‐023‐00944‐210.1038/s41596‐023‐00944‐2 ↗
63. 10.1016/j.xinn.2021.10014110.1016/j.xinn.2021.100141 ↗
64. 10.1016/j.ymeth.2014.06.00810.1016/j.ymeth.2014.06.008 ↗
65. 10.1093/nar/gky83010.1093/nar/gky830 ↗
66. 10.1093/bioinformatics/bts56910.1093/bioinformatics/bts569 ↗
67. 10.1038/s41586‐020‐2077‐310.1038/s41586‐020‐2077‐3 ↗
68. 10.1126/science.aaz177610.1126/science.aaz1776 ↗
69. T.ChenandC.Guestrin “XGBoost: a Scalable Tree Boosting System ” inProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining San Francisco California USA (2016) https://doi.org/10.1145/2939672.2939785.10.1145/2939672.2939785 ↗
70. 10.1080/15476286.2021.187518010.1080/15476286.2021.1875180 ↗
71. J.Zhou D.You J.Bai et al. “Machine Learning Methods in Real‐World Studies of Cardiovascular Disease ”Cardiovasc Innovation Applied7 no.1(2023): CVIA.2023.0011.10.15212/CVIA.2023.0011 ↗
72. 10.1016/j.xpro.2021.10063410.1016/j.xpro.2021.100634 ↗
73. 10.1093/nar/gku36510.1093/nar/gku365 ↗
74. 10.1007/978‐1‐4939‐9728‐2_1210.1007/978‐1‐4939‐9728‐2_12 ↗