Systematic perturbation of cultures of K. phaffii by carbon co-feeding show gene signatures associated with production of recombinant proteins

Abstract

Background Demand for recombinant proteins is rapidly growing, driven by their use as biotherapeutics, vaccine components, industrial enzymes, and food ingredients. The growing market requires novel strategies for increasing protein production in cellular hosts. Systems-level frameworks have been used to improve production, but have had difficulty relating complex cellular pathways with protein expression. Here, we demonstrate a method for mapping relationships between gene expression signatures and carbon source-related phenotypes related to recombinant protein production.

Results Our approach induces systematic perturbations in cultures of K. phaffii using varied co-feeds of carbon sources. The different carbon sources significantly impacted cell growth, specific productivity, and transcriptional states. With these data, we identified metagenes for both immunoglobulin G1 monoclonal antibody (IgG1) and Variable domain on a heavy chain (VHH) antibody that explained significant transcriptomic variance. These metagenes strongly associated with two phenotypes: production of recombinant protein-to-biomass ratio, and response to methanol induction. We used these results to identify and knockout 31 novel gene targets for which expression inversely correlated with productivity. Nine of these genes improved productivity of IgG1 by up to 3x and ten genes increased productivity of VHH by up to 1.7x. Many of these genes are involved in the modulation and progression of the cell cycle but interestingly, disruption had little to no impact on cell growth.

Conclusion This study establishes a framework for relating gene signatures to complex cellular phenotypes, providing a robust methodology for assessing production processes and identifying new targets for cellular engineering. While the identified specific metagenes depend on the complexity and structure of the recombinant protein produced, this framework is extensible across diverse proteins and potentially other host organisms. These signatures may serve as scale-independent, cellular-level metrics for traits like efficiency of production of recombinant proteins, facilitating the translation of findings across different scales and cultivation modes. Furthermore, this framework enables the identification of novel targets for genomic modifications that can improve strain performance, offering a predictive tool for the rational design of high-performing microbial cell factories.