Multifactorial analysis of terminator performance on heterologous gene expression in Physcomitrella

Abstract

The production of recombinant proteins for health applications accounts for a large share of the biopharmaceutical market. While many drugs are produced in microbial and mammalian systems, plants gain more attention as expression platforms to produce eukaryotic proteins. In particular the GMP-compliant moss Physcomitrella (Physcomitrium patens) has outstanding features such as genetic amenability, reproducible bioreactor cultivation, and humanized glycosylation patterns. In this study, we selected and characterized new terminators for their effects on heterologous gene expression. The Physcomitrella genome contains 53,346 unique 3’UTRs (untranslated regions) of which 7,964 transcripts contain at least one intron. Over 91% of 3’UTRs exhibit more than one polyadenylation site, indicating the prevalence of alternative polyadenylation in Physcomitrella. Out of all 3’UTRs, 14 terminator candidates were selected and characterized via transient Dual Luciferase assays, yielding a collection of endogenous terminators performing equally high as established heterologous terminators CaMV35S, AtHSP90, and NOS. High performing candidates were selected for testing as double terminators which impact reporter levels, dependent on terminator identity and positioning. Testing of 3’UTRs among the different promoters NOS, CaMV35S, and PpActin5 showed an increase of more than 1,000-fold between promoters PpActin5 and NOS, whereas terminators increased reporter levels by less than 10-fold, demonstrating the stronger effect promoters play as compared to terminators. The number of polyadenylation sites as well as polyadenylation signals were found to be major determinants of terminator performance. Our results improve the biotechnology platform Physcomitrella and further our understanding of how terminators influence gene expression in plants in general.

The supplemental files describe the selection process of terminator candidates using different datasets and selection criteria. The process is described in detail in the respective publication.