Skip to content

A Non-Conventional Epigenetic Engineering Strategy: The ALKBH5/ITGB5 Axis Enhances Recombinant Protein Production in CHO Cells through FAK-Mediated Proliferation and Improved Redox Status

Sep 2026 · ACS Synthetic Biology · 0 citations · 57 references
Viral Infectious Diseases and Gene Expression in Insects

Abstract

Chinese hamster ovary (CHO) cells are the predominant host for producing complex recombinant therapeutic proteins. N6-methyladenosine (m6A) regulates recombinant protein production in CHO cells by influencing RNA stability and translation; however, the role of the m6A demethylase ALKBH5 remains poorly characterized in this context. Here, ALKBH5 overexpression increased the titers of three recombinant products by up to 2.87-fold, enhanced CHO cell proliferation, and reduced oxidative stress. Co-immunoprecipitation supported an association between ALKBH5 and ITGB5, and structure-guided mutagenesis identified Tyr205 of ITGB5 as functionally important for this association and ITGB5 protein stability. ALKBH5 overexpression did not measurably alter ITGB5 mRNA stability or m6A enrichment, supporting a mechanism that does not involve detectable m6A changes on the ITGB5 transcript, while not excluding m6A-dependent effects on other targets. FAK inhibition attenuated but did not abolish the production advantage, indicating that FAK signaling contributes to, but may not fully account for, the ALKBH5−ITGB5-associated phenotype. Metabolic and redox measurements further linked ALKBH5 overexpression to increased antioxidant capacity and ATP abundance, together with altered nutrient consumption and by-product formation. These findings identify ALKBH5 as a candidate host-cell engineering target for improving recombinant protein production and support further validation under industry-relevant fed-batch conditions.

View source

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 sequence constraints.

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

Related blog posts

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.

Google DeepMind Blog Nov 25, 2025

AlphaFold: Five years of impact

Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.

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