Dataset for suppressors of amyloid-beta toxicity and their functions in recombinant protein production in yeast
Journal article, 2022

The production of recombinant proteins at high levels often induces stress-related phenotypes by protein misfolding or aggregation. These are similar to those of the yeast Alzheimer's disease (AD) model in which amyloid-beta peptides (A beta 42) were accumulated [1,2] . We have previously identified suppressors of A beta 42 cytotoxicity via the genome-wide synthetic genetic array (SGA) [3] and here we use them as metabolic engineering targets to evaluate their potentiality on recombinant protein production in yeast Saccharomyces cerevisiae. In order to investigate the mechanisms linking the genetic modifications to the improved recombinant protein production, we perform systems biology approaches (transcriptomics and proteomics) on the resulting strain and intermediate strains. The RNAseq data are preprocessed by the nf-core/RNAseq pipeline and analyzed using the Platform for Integrative Analysis of Omics (PIANO) package [4] . The quantitative proteome is analyzed on an Orbitrap Fusion Lumos mass spectrometer interfaced with an Easy-nLC1200 liquid chromatography (LC) system. LC-MS data files are processed by Proteome Discoverer version 2.4 with Mascot 2.5.1 as a database search engine. The original data presented in this work can be found in the research paper titled "Suppressors of Amyloid-beta Toxicity Improve Recombinant Protein Produc-tion in yeast by Reducing Oxidative Stress and Tuning Cellu-lar Metabolism", by Chen et al. [5] .

Gene engineering

Proteome

Amyloid-beta

Transcriptome

Yeast Saccharomyces cerevisiae

Author

Xin Chen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Xiaowei Li

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Boyang Ji

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Yanyan Wang

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Olena Ishchuk

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Egor Vorontsov

University of Gothenburg

Dina Petranovic Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Verena Siewers

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Martin Engqvist

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Data in Brief

23523409 (eISSN)

Vol. 42 108322

Subject Categories

Biochemistry and Molecular Biology

Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)

Bioinformatics and Systems Biology

DOI

10.1016/j.dib.2022.108322

PubMed

35677454

More information

Latest update

6/28/2022