Multifactorial analysis of terminator performance on heterologous gene expression in Physcomitrella

Abstract

Characterization of Physcomitrella 3'UTRs across different promoters yields endogenous single and double terminators for usage in molecular pharming. 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 hosts to produce eukaryotic proteins. In particular, the good manufacturing practice (GMP)-compliant moss Physcomitrella (Physcomitrium patens) has outstanding features, such as excellent genetic amenability, reproducible bioreactor cultivation, and humanized protein glycosylation patterns. In this study, we selected and characterized novel terminators for their effects on heterologous gene expression. The Physcomitrella genome contains 53,346 unique 3'UTRs (untranslated regions) of which 7964 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 1000-fold between promoters PpActin5 and NOS, whereas terminators increased reporter levels by less than tenfold, demonstrating the stronger effect promoters play as compared to terminators. Among selected terminator attributes, the number of polyadenylation sites as well as polyadenylation signals were found to influence terminator performance the most. Our results improve the biotechnology platform Physcomitrella and further our understanding of how terminators influence gene expression in plants in general.

Keywords: Molecular pharming; Moss; Physcomitrium; Plant biotechnology; UTR.

Fig. 1

Analysis of 3′UTRs in Physcomitrella. A Length distribution of all 53,346 unique 3′UTRs. B Length distribution of unique 3′UTRs with intron(s). C Length distribution of unique 3′UTRs without intron(s). In A, B, & C sequences longer than 5500 bp were excluded. D Number of poly(A) sites predicted via PASS 2.0 software (Ji et al. 2007, 2015). For poly(A) site prediction, only sequences longer than 50 bp were applicable, reducing the dataset to 52,947 sequences. The red and black dashed lines and numbers indicate the median and average, respectively

Fig. 2

Terminator candidate selection workflow. The terminator candidate list was created by selecting transcripts either via abundance or gene ontology and yielded terminators 1–14 as compiled in Table 1

Fig. 3

Characterization of single terminator candidates. Terminator candidates (Table 1) were cloned into a vector carrying the firefly luciferase coding sequence and the 35S promoter. Terminators were characterized via Dual-Luciferase assay and obtained firefly-luc/Renilla-luc reporter values of single candidates normalized to the 35S terminator. Blue dots represent means of three technical replicates of a biological replicate. For each terminator n ≥ 3 biological replicates were analysed. Bars represent means of all biological replicates with standard deviations. Significance levels are based on a one-way ANOVA (p = 7.91E−11, α = 0.05) with subsequent Tukey HSD post hoc test. Indicated differences are between 35St and the respective terminator (*p < 0.05; **p < 0.005; ***p < 0.0005)

Fig. 4

Characterization of double terminators. Single terminators were fused in homologous and heterologous pairs in vectors bearing the firefly luciferase coding sequence and the 35S promoter. Double terminators and their respective single terminators were characterized via Dual-Luciferase assays and obtained firefly-luc/Renilla-luc reporter values normalized to the 35S terminator. Blue dots represent means of three technical replicates of a biological replicate. Bars represent means of all biological replicates (n ≥ 3) with standard deviations. No significant difference in performance were observed for combinations of the 35St and NOSt (A one-way ANOVA, p = 0.11, α = 0.05). Significant differences in performance were observed in combinations of PpCab2t and PppetEt (B one-way ANOVA, p = 4.16E−7, α = 0.05) and in combinations of AtHSP90t and PpRPS6et (C one-way ANOVA, p = 8.42E−05, α = 0.05). Significance levels and compact letter display are based on a one-way ANOVA with subsequent Tukey HSD post hoc test

Fig. 5

Characterization of terminators in combination with the NOS or the PpActin5 promoter. A selection of single terminators was cloned in vectors bearing the firefly luciferase coding sequence and either the A NOS promoter or the B PpActin5 promoter. The promoter–terminator combinations were characterized via Dual-Luciferase assays and obtained firefly-luc/Renilla-luc reporter values normalized to a testing vector bearing the 35S promoter and 35S terminator. Blue dots represent means of three technical replicates of a biological replicate. Bars represent means of all biological replicates (n ≥ 3) with standard deviations. In combination with the NOS promoter, only terminator PpnoIUK2t shows significant difference in comparison to 35St (A, one-way ANOVA, p = 0.0012, α = 0.05). For the PpActin5 promoter, no tested terminator significantly differed in performance from 35St (B, one-way ANOVA, p = 0.046, α = 0.05). Significance levels are based on a one-way ANOVA with subsequent Tukey HSD post hoc test. Indicated differences are between 35St and the respective terminator (*p < 0.05)

Fig. 6

Principal component analysis of reporter levels and terminator attributes. A PCA was calculated for the reporter levels obtained, the number of PAS, the number of poly(A) sites, and terminator lengths. Reporter levels of the single promoter series (PpActin5p, 35Sp, NOSp) were normalized to the respective promoter combined with the 35S terminator to remove promoter effects from the dataset. The biplot in A shows the different loading vectors of reporter levels and terminator attributes. The PCA plot in B shows the result of the model-based clustering using Mclust (Fraley and Raferty 2003). Reporter levels correlate with the number of PAS whereas the number of poly(A) sites and terminator length determine cluster formation. Promoter–terminator combinations are sorted into three clusters in black, green, and red. The scores for each datapoint can be found in Supplementary table S8