Skip to content
#protein folding Open access

Short-term methionine starvation reshapes diurnal hepatic m6A RNA methylation.

Oct 2026 · Journal of Biological Chemistry · pp. 113645 · 0 citations
Medicine

Abstract

Dietary methionine restriction improves metabolic health and has therapeutic potential in multiple disease contexts. Methionine metabolism regulates transmethylation reactions, including N6-methyladenosine (m6A) RNA methylation, by modulating availability of S-adenosyl methionine (SAM). Both m6A methylation and methionine metabolism have separately been linked to circadian regulation, but whether dietary methionine influences circadian rhythms through m6A remains unknown. Here we show that short-term methionine deprivation induces diurnal variation in serum methionine and SAM and reprograms diurnal expression of m6A writers, erasers, and readers in mouse liver. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) at two timepoints (ZT9, ZT21) revealed that a methionine-deficient (MD) diet was associated with a roughly 10-fold increase in differentially methylated m6A peaks between timepoints relative to control, despite no change in methylation at core clock transcripts. These diet-induced oscillations arose at sites already methylated under the control diet, indicating that methionine restriction alters the rhythmicity of existing m6A marks rather than inducing new methylation, and were enriched in transcripts involved in RNA processing, translation, ubiquitination, and mTORC1 signaling. Integrating RNA-seq and proteomics, we found that a subset of these m6A changes co-occurred with altered mRNA and/or protein abundance, indicating gene-specific rather than global effects. Notably, reduced m6A methylation of Mat2a and Cbs, encoding enzymes central to methionine homeostasis, was associated with increased MAT2A and decreased CBS protein levels, respectively. These findings identify diet-induced, diurnally rhythmic m6A methylation as a candidate mechanism linking methionine metabolism to circadian biology and nominate specific m6A sites and pathways for future investigation.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.