Claes Gustafsson

The contemporary shift toward multispecific antibodies, antibody-drug conjugates (ADCs), and bespoke glycoengineered therapeutics have exposed the limitations of standard genomic engineering tools. This paper presents a novel iterative engineering paradigm utilizing the Leap-In Transposase ^® platform. By leveraging a suite of three mutually orthogonal transposase-transposon systems, we demonstrate the sequential modification of the Chinese Hamster Ovary (CHO) genome to achieve three distinct functional outcomes: (i) First, the creation of a glutamine synthetase (GS)-deficient host (CHO-K1-GS) via targeted knockdown, (ii) Second, the integration of multiple copies of a model therapeutic IgG1 for expression, and (iii) Third, the subsequent knockdown of the fucosylation pathway to modulate the glycan profile of the expressed IgG1. Genetic stability (copy number & sequence) of each integration event was confirmed using Targeted Locus Amplification (TLA) and Next-Generation Sequencing (NGS). Functional stability (expression levels, metabolic phenotype, and glycan phenotypes) was confirmed using standard cell culture and analytical techniques. Crucially, the truly orthogonal nature of the transposase-transposon pairs prevents cross-mobilization and ensures the structural and functional integrity of previously integrated cargo. This study establishes a “What You See Is What You Get” (WYSIWYG) methodology that provides a robust, scalable, and predictable framework for developing next-generation complex biopharmaceutical manufacturing cell lines.

Mammalian cell lines are the preferred hosts for producing commercially relevant therapeutic proteins such as antibodies, antibody derivatives, and cytokine fusion proteins. Even though significant investment is made to optimize upstream and downstream processes, the optimal gene design parameters for heterologous recombinant protein expression remain poorly understood. We describe a generic approach to gene optimization in which design features are systematically sampled and iteratively modulated using machine learning (ML). Synthetic gene variants encoding the Dasher fluorescent protein, differing only in synonymous codons, were used to interrogate the gene-sequence preferences of transient antibody-expressing HEK293 cells. Synonymous codon variations influenced expression by more than 2 orders of magnitude. This variation in protein yield was used to build ML models relating gene design features, which were then employed to design further-improved genes. The ML models were shown to be expression system-specific. Messenger RNA levels and ribosome occupancy were highly correlated with protein levels, suggesting that mRNA lifetime is influenced by coding bias. Our results illustrate a novel, generally applicable method to improve gene expression via synonymous re-coding for any protein target or host cell.

SUMMARY: The recently emerged SARS-CoV-2 Omicron variant harbors 37 amino acid substitutions in the spike (S) protein, 15 of which are in the receptor-binding domain (RBD), thereby raising concerns about the effectiveness of available vaccines and antibody therapeutics. Here, we show that the Omicron RBD binds to human ACE2 with enhanced affinity relative to the Wuhan-Hu-1 RBD and acquires binding to mouse ACE2. Severe reductions of plasma neutralizing activity were observed against Omicron compared to the ancestral pseudovirus for vaccinated and convalescent individuals. Most (26 out of 29) receptor-binding motif (RBM)-directed monoclonal antibodies (mAbs) lost in vitro neutralizing activity against Omicron, with only three mAbs, including the ACE2-mimicking S2K146 mAb ¹ , retaining unaltered potency. Furthermore, a fraction of broadly neutralizing sarbecovirus mAbs recognizing antigenic sites outside the RBM, including sotrovimab ² , S2X259 ³ and S2H97 ⁴ , neutralized Omicron. The magnitude of Omicron-mediated immune evasion and the acquisition of binding to mouse ACE2 mark a major SARS-CoV-2 mutational shift. Broadly neutralizing sarbecovirus mAbs recognizing epitopes conserved among SARS-CoV-2 variants and other sarbecoviruses may prove key to controlling the ongoing pandemic and future zoonotic spillovers.

We have used design of experiments (DOE) and systematic variance to efficiently explore glutathione transferase substrate specificities caused by amino acid substitutions. Amino acid substitutions selected using phylogenetic analysis were synthetically combined using a DOE design to create an information-rich set of gene variants, termed infologs. We used machine learning to identify and quantify protein sequence–function relationships against 14 different substrates. The resulting models were quantitative and predictive, serving as a guide for engineering of glutathione transferase activity toward a diverse set of herbicides. Predictive quantitative models like those presented here have broad applicability for bioengineering.