Skip to content
#protein folding Open access

Screening a thermotolerant Komagataella phaffii strain with methanol cross-tolerance and the associated physiological and transcriptional responses

Oct 2026 · Synthetic and Systems Biotechnology · Vol 19, pp. 57 - 68 · 0 citations · 37 references
Medicine

Abstract

Komagataella phaffii is a prominent methylotrophic yeast widely used in industrial biotechnology for heterologous protein production, chemical synthesis, and C1 feedstock utilization. However, its industrial application is limited by thermal stress and methanol toxicity, while the relationship between heat adaptation and methanol tolerance remains poorly understood. Here, we obtained a thermotolerant mutant ATH through ARTP mutagenesis followed by adaptive laboratory evolution (ALE) under heat stress (37 °C). ATH tolerated heat and methanol robustly under individual stress conditions, achieving 3-fold and 8-fold higher cell densities than GS115 at 37 °C or in 5% (v/v) methanol, respectively, yet failed under the combined stress of 37 °C and 5% methanol. Using three proteins (GFP, OPH, and ChiA) as reporters, we found that ATH showed higher heterologous expression than GS115 at 33 °C. Physiologically, ATH displayed enhanced cell wall/membrane integrity, reduced ROS accumulation and lower lipid peroxidation (>30% reduction in MDA), and accelerated formaldehyde clearance. Transcriptomics identified 561 shared differentially expressed genes, with coordinated upregulation of heat shock proteins (HSP70/40/90 families; Hsp104 and Hsp70-SSA2), antioxidant enzymes (catalase, peroxiredoxin), and cell wall/membrane remodeling genes, alongside downregulation of amino acid and secondary metabolite biosynthesis. Whole-genome resequencing further distinguished ATH from GS115 by only 19 variants (9 SNPs and 10 InDels), highlighting a compact genetic basis for the evolved phenotype. This work provides a K. phaffii chassis with improved individual heat and methanol tolerance and elucidates the physiological, transcriptional, and genomic mechanism underlying heat–methanol cross-adaptation.

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.