The YTHDF proteins modulate Alzheimer's disease-associated brain gene signatures.
Shinya Tasaki, Denis R Avey, Nicola A Kearns, Chunjiang Yu, Sashini De Tissera, Himanshu Vyas, Lin Cheng, Jishu Xu, Artemis Iatrou, Daniel J Flood, Wenlong Li, Lisa L Barnes, Katherine Rothamel, Aliza P Wingo, Thomas S Wingo, Nicholas T Seyfried, Chuan He, Philip L De Jager, Gene W Yeo, Chris Gaiteri, David A Bennett, Yanling Wang
Molecular neurodegeneration · 2026 · Vol. 21 (1)
Abstract
Gene signatures of Alzheimer's disease (AD) brains reflect the output of a complex interplay of genetic, epigenetic, epi-transcriptomic, and post-transcriptional regulations. To nominate candidate factors modulating these signatures, we developed a machine learning model to integrate cellular and molecular features explaining differential gene expression in AD. Among the features tested, YTHDF proteins, the canonical readers of N6-methyladenosine (m6A) RNA modification, are among the most influential predictors of AD gene signatures. Protein modules containing YTHDFs were downregulated in human AD brains, and knockdown or pharmacological inhibition of YTHDFs in iPSC-derived 2D and 3D neuronal models recapitulated key AD-associated gene signatures. Furthermore, eCLIP-seq revealed altered YTHDF binding to transcripts in AD brains, at both m6A-dependent and m6A-independent sites. Together, these results support an important role for YTHDF proteins in modulating AD-associated gene signatures in the human brain.
Linked datasets 1
Investigating m6A-relevant gene function in neural progenitor cells using CRISPR-Cas9 genome editing and RNA-seq
Homo sapiens
66 data files
| File | Type | Size |
|---|---|---|
| SRR31926192_GSM8717703 | RNA-Seq | 2.4 GB |
| SRR31926193_GSM8717702 | RNA-Seq | 2.6 GB |
| SRR31926194_GSM8717701 | RNA-Seq | 3.7 GB |
| SRR31926195_GSM8717700 | RNA-Seq | 2.0 GB |
| SRR31926196_GSM8717699 | RNA-Seq | 2.1 GB |
| SRR31926197_GSM8717698 | RNA-Seq | 2.3 GB |
| SRR31926198_GSM8717697 | RNA-Seq | 1.7 GB |
| SRR31926199_GSM8717696 | RNA-Seq | 3.4 GB |
| SRR31926200_GSM8717695 | RNA-Seq | 2.8 GB |
| SRR31926201_GSM8717694 | RNA-Seq | 3.1 GB |
| SRR31926202_GSM8717693 | RNA-Seq | 2.1 GB |
| SRR31926203_GSM8717692 | RNA-Seq | 1.9 GB |
| SRR31926204_GSM8717691 | RNA-Seq | 1.9 GB |
| SRR31926205_GSM8717690 | RNA-Seq | 2.9 GB |
| SRR31926206_GSM8717689 | RNA-Seq | 2.6 GB |
| SRR31926207_GSM8717688 | RNA-Seq | 3.3 GB |
| SRR31926208_GSM8717687 | RNA-Seq | 2.5 GB |
| SRR31926209_GSM8717686 | RNA-Seq | 1.8 GB |
| SRR31926210_GSM8717685 | RNA-Seq | 2.1 GB |
| SRR31926211_GSM8717684 | RNA-Seq | 2.0 GB |
| SRR31926212_GSM8717683 | RNA-Seq | 1.6 GB |
| SRR31926213_GSM8717682 | RNA-Seq | 2.4 GB |
| SRR31926214_GSM8717681 | RNA-Seq | 3.4 GB |
| SRR31926215_GSM8717680 | RNA-Seq | 3.1 GB |
| SRR31926216_GSM8717679 | RNA-Seq | 2.7 GB |
| SRR31926217_GSM8717678 | RNA-Seq | 2.1 GB |
| SRR31926218_GSM8717677 | RNA-Seq | 2.3 GB |
| SRR31926219_GSM8717676 | RNA-Seq | 2.5 GB |
| SRR31926220_GSM8717675 | RNA-Seq | 2.6 GB |
| SRR31926221_GSM8717674 | RNA-Seq | 2.1 GB |
| SRR31926222_GSM8717673 | RNA-Seq | 3.8 GB |
| SRR31926223_GSM8717672 | RNA-Seq | 4.5 GB |
| SRR31926224_GSM8717671 | RNA-Seq | 3.5 GB |
| SRR31926225_GSM8717670 | RNA-Seq | 2.0 GB |
| SRR31926226_GSM8717669 | RNA-Seq | 2.5 GB |
| SRR31926227_GSM8717668 | RNA-Seq | 2.0 GB |
| SRR31926228_GSM8717667 | RNA-Seq | 2.1 GB |
| SRR31926229_GSM8717666 | RNA-Seq | 1.6 GB |
| SRR31926230_GSM8717665 | RNA-Seq | 2.2 GB |
| SRR31926231_GSM8717664 | RNA-Seq | 2.2 GB |
| SRR31926232_GSM8717663 | RNA-Seq | 3.8 GB |
| SRR31926233_GSM8717662 | RNA-Seq | 1.9 GB |
| SRR31926234_GSM8717661 | RNA-Seq | 2.1 GB |
| SRR31926235_GSM8717660 | RNA-Seq | 2.4 GB |
| SRR31926236_GSM8717659 | RNA-Seq | 2.1 GB |
| SRR31926237_GSM8717658 | RNA-Seq | 3.5 GB |
| SRR31926238_GSM8717657 | RNA-Seq | 3.0 GB |
| SRR31926239_GSM8717656 | RNA-Seq | 2.3 GB |
| SRR31926240_GSM8717655 | RNA-Seq | 2.4 GB |
| SRR31926241_GSM8717654 | RNA-Seq | 2.4 GB |
| SRR31926242_GSM8717653 | RNA-Seq | 2.2 GB |
| SRR31926243_GSM8717652 | RNA-Seq | 1.8 GB |
| SRR31926244_GSM8717651 | RNA-Seq | 1.3 GB |
| SRR31926245_GSM8717650 | RNA-Seq | 3.4 GB |
| SRR31926246_GSM8717649 | RNA-Seq | 2.7 GB |
| SRR31926247_GSM8717648 | RNA-Seq | 2.4 GB |
| SRR31926248_GSM8717647 | RNA-Seq | 2.7 GB |
| SRR31926249_GSM8717646 | RNA-Seq | 2.4 GB |
| SRR31926250_GSM8717645 | RNA-Seq | 3.5 GB |
| SRR31926251_GSM8717644 | RNA-Seq | 3.1 GB |
| SRR31926252_GSM8717643 | RNA-Seq | 2.5 GB |
| SRR31926253_GSM8717642 | RNA-Seq | 1.8 GB |
| SRR31926254_GSM8717641 | RNA-Seq | 1.9 GB |
| SRR31926255_GSM8717640 | RNA-Seq | 2.1 GB |
| SRR31926256_GSM8717639 | RNA-Seq | 2.1 GB |
| SRR31926257_GSM8717638 | RNA-Seq | 2.6 GB |
Potentially related datasets 1
These accessions were text-mined from the PMC full text. They may be referenced for comparison, cited from other studies, or otherwise mentioned without being primary data for this paper.
Funding 24 grants
| Grant | Funder |
|---|---|
| P30 AG072975 | NIA NIH HHS (United States) |
| R01 AG095602 | NIA NIH HHS (United States) |
| R01 AG074082 | NIA NIH HHS (United States) |
| R01 AG017917 | NIA NIH HHS (United States) |
| R01 AG057911 | NIA NIH HHS (United States) |
| U01 AG061357 | NIA NIH HHS (United States) |
| P30 AG010161 | NIA NIH HHS (United States) |
| R01 AG022018 | NIA NIH HHS (United States) |
| R01 AG034374 | NIA NIH HHS (United States) |
| U01 AG046161 | NIA NIH HHS (United States) |
| R01 AG061798 | NIA NIH HHS (United States) |
| RF1 AG036042 | NIA NIH HHS (United States) |
| U01 AG046152 | NIA NIH HHS (United States) |
| U01 AG079847 | NIA NIH HHS (United States) |
| R01 AG042210 | NIA NIH HHS (United States) |
| RF1 AG015819 | NIA NIH HHS (United States) |
| U01 AG061356 | NIA NIH HHS (United States) |
| RF1 AG074549 | NIA NIH HHS (United States) |
| U01 AG032984 | NIA NIH HHS (United States) |
| R01 AG072120 | NIA NIH HHS (United States) |
| R01 AG030146 | NIA NIH HHS (United States) |
| R01 AG079223 | NIA NIH HHS (United States) |
| R01 AG036836 | NIA NIH HHS (United States) |
| R01 AG015819 | NIA NIH HHS (United States) |