Nicotiana spp. for the Expression and Purification of Functional IgG3 Antibodies Directed Against the Staphylococcus aureus Alpha Toxin

Abstract

Immunoglobulin subclass IgG1 is bound and neutralized effectively by Staphylococcus aureus protein A, allowing the bacterium to evade the host’s adaptive immune response. In contrast, the IgG3 subclass is not bound by protein A and can be used to treat S. aureus infections, including drug-resistant strains such as methicillin-resistant Staphylococcus aureus (MRSA). However, the yields of recombinant IgG3 are generally low because this subclass is prone to degradation, and recovery is hindered by the inability to use protein A as an affinity ligand for antibody purification. Here, we investigated plants (Nicotiana spp.) as an alternative to microbes and mammalian cell cultures for the production of an IgG3 antibody specific for the S. aureus alpha toxin. We targeted recombinant IgG3 to different subcellular compartments and tested different chromatography conditions to improve recovery and purification. Finally, we tested the antigen-binding capacity of the purified antibodies. The highest IgG3 levels in planta (>130 mg kg−1 wet biomass) were achieved by targeting the endoplasmic reticulum or apoplast. Although the purity of IgG3 exceeded 95% following protein G chromatography, product recovery requires further improvement. Importantly, the binding affinity of the purified antibodies was in the nanomolar range and thus comparable to previous studies using murine hybridoma cells as the production system.

The transient expression of recombinant proteins in plant cell packs facilitates stable isotope labelling for NMR spectroscopy

“…Furthermore, using this carbon source instead of sucrose boosted GB1 expression by a factor of two (Figure S1). Similar results have been reported for the expression of IgG1 and IgG3 antibodies in PCPs derived from BY‐2 cells (Opdensteinen et al., 2021).…”

“…First, we tested the expression of different GB1 variants (Table 4) in PCPs to identify the subcellular compartment/localization achieving the highest protein accumulation, which is a routine procedure in transient protein production (Gengenbach et al., 2020). We included the carbon source during BY‐2 cell cultivation as an additional variable because using glucose instead of the standard sucrose had more than doubled transient expression of proteins in PCPs in the past (Opdensteinen et al., 2021). Similarly, we found that glucose increased GB1 accumulation in PCPs up to twofold compared with sucrose and that overall plastid‐targeting in combination with a CHS 5′UTR resulted in the highest product levels of ~100 mg/kg biomass when using glucose as carbon source (Figure S1).…”

“… c Average of last two cell passages before harvest; the growth rate of the same cells in medium H was 0.54 (Opdensteinen et al., 2021). …”

Reducing water uptake into BY‐2 cells by systematically optimizing the cultivation parameters increases product yields achieved by transient expression in plant cell packs

“…We also tested the expression of GB1, [ 18 ] DsRed, [ 13 ] and our previously described IgG1 and IgG3 antibodies. [ 24 ]…”

“…[ 28 ] IgG1 and IgG3 antibodies were quantified by surface plasmon resonance (SPR) spectroscopy, using sensors coated with protein A (IgG1) or protein G (IgG3) and 2G12 as a standard, as previously described. [ 24 ] DsRed was quantified using eight standards in the 0 to 225 mg L ‐1 range as previously described [ 29 ] and the concentration of total soluble protein (TSP) was determined using eight dilutions of bovine serum albumin in the range 0 to 2000 mg L ‐1 as previously described. [ 30 ] Osmolality‐dependent DsRed expression was approximated using a log‐normal model (Equation 1): $$\begin{equation}y = {y_0} + \frac{A}{{\sqrt {2\pi \cdot w\cdot x} }}{e^{\frac{{ - {{\left[ {\ln \frac{x}{{{x_c}}}} \right]}^2}}}{{2{w^2}}}}}\end{equation}$$where y 0 is the minimal value ( y ‐offset) of the distribution (here, the minimal DsRed expression), x is the independent variable (here, the osmolality), x c is the center of the distribution, w is the standard deviation (width) of the distribution, and A is the area under the curve.…”