# Comprehensive Profiling of N 6‐methyladnosine (m6A) Readouts Reveals Novel m6A Readers That Regulate Human Embryonic Stem Cell Differentiation

## TL;DR

This study explores how m6A modifications affect RNA in different cells and identifies new proteins that influence stem cell differentiation.

## Contribution

The study identifies novel m6A reader proteins (FUBP3, FXR2, L1TD1, and DDX6) that regulate human embryonic stem cell differentiation.

## Key findings

- m6A readouts show high cell type specificity and cannot be predicted by m6A levels alone.
- Machine learning models using RNA-binding protein context can predict m6A readouts effectively.
- FUBP3 and L1TD1 regulate mRNA stability and influence hESC differentiation.

## Abstract

N
6‐methyladenosine (m6A) 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 m6A 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 m6A methylome data, reveals high cell type specificity in m6A readouts where m6A level alone is insufficient to predict m6A readouts. Nonetheless, machine learning models focusing on RNA‐binding protein (RBP) binding context can effectively predict the readouts and prioritize four novel m6A‐associated proteins (FUBP3, FXR2, L1TD1, and DDX6). Their m6A‐binding ability is validated by m6A RNA pull‐down, transcriptome‐wide binding site mapping, and electrophoretic mobility shift assay, while FUBP3 and L1TD1 are further suggested as m6A 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 m6A functional readouts and lays the groundwork for future research on m6A‐mediated stem cell fate decisions.

This research deciphers the m6A transcriptome by profiling its sites and functional readout effects: from mRNA stability, translation to alternative splicing, across five different cell types. Machine learning model identifies novel m6A‐binding proteins DDX6 and FXR2 and novel m6A reader proteins FUBP3 and L1TD1. Further experimental investigation reveals their new regulatory role in human embryonic stem cell differentiation.