Skip to content
#protein folding Open access

Fusion Partner and ER-Retention Signal Are Associated with Distinct Host Proteomic and Metabolic Responses During Interleukin-15 Production in Nicotiana benthamiana

Sep 2026 · Plants · Vol 15 · 0 citations · 60 references
Medicine

Abstract

Plant molecular farming has emerged as a promising platform to produce recombinant biopharmaceuticals. The expression of foreign proteins changes the host proteome and metabolome, and these changes might have an effect on the accumulation of recombinant proteins. In this study, we investigated how fusion partners and ER-retention strategies influence host responses during transient production of recombinant human interleukin-15 (IL-15) in Nicotiana benthamiana. Four IL-15 constructs carrying either an 8 × His tag or an IgG1 Fc fusion, with or without an N-terminal signal peptide and C-terminal SEKDEL motif (KD), were compared at 4 days post-infiltration. Western blot analysis detected IL-15 only in the Fc-fusion constructs. Plants expressing IL15-Fc accumulated more antioxidant enzymes and phenylpropanoid-derived phenolics. Expression of IL15-Fc with the SP/KD construct showed increased abundance of proteins involved in protein folding and translation, together with reduced representation of secondary metabolic pathways. Overall, the results demonstrate that fusion partner and SP/KD targeting strategy jointly shape the host response to recombinant IL-15 expression and should therefore be evaluated together during construct design for plant molecular farming.

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.