Endoplasmic reticulum-directed recombinant mRNA displays subcellular localization equal to endogenous mRNA during transient expression in CHO cells

Recombinant Human CD19 in CHO-K1 Cells: Glycosylation Patterns as a Quality Attribute of High Yield Processes

CD19 is an essential protein in personalized CD19-targeting chimeric antigen receptor (CAR)-T cell-based cancer immunotherapies and CAR-T cell functionality evaluation. However, the recombinant expression of this "difficult to-express" (DTE) protein is challenging, and therefore, commercial access to the protein is limited. We have previously described the successful stable expression of our soluble CD19-AD2 fusion protein of the CD19 extracellular part fused with human serum albumin domain 2 (AD2) in CHO-K1 cells. The function, stability, and secretion rate of DTE proteins can be improved by culture conditions, such as reduced temperature and a shorter residence time. Moreover, glycosylation, as one of the most important post-translational modifications, represents a critical quality attribute potentially affecting CAR-T cell effector function and thus impacting therapy's success. In this study, we increased the production rate of CD19-AD2 by 3.5-fold through applying hypothermic culture conditions. We efficiently improved the purification of our his-tagged CD19-AD2 fusion protein via a Ni-NTA-based affinity column using a stepwise increase in the imidazole concentration. The binding affinity to commercially available anti-CD19 antibodies was evaluated via Bio-Layer Interferometry (BLI). Furthermore, we revealed glycosylation patterns via Electrospray Ionization Mass Spectrometry (ESI-MS), and five highly sialylated and multi-antennary N-glycosylation sites were identified. In summary, we optimized the CD19-AD2 production and purification process and were the first to characterize five highly complex N-glycosylation sites.

EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network "EL-RMLocNet", predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds_genetic_analysis.opendfki.de/subcellular_loc/).

Secretion analysis of intracellular "difficult-to-express" immunoglobulin G (IgG) in Chinese hamster ovary (CHO) cells

The Chinese hamster ovary (CHO) cell line is the most widely used host cell for therapeutic antibody production. Although its productivity has been improved by various strategies to satisfy the growing global demand, some difficult-to-express (DTE) antibodies remain at low secretion levels. To improve the production of various therapeutic antibodies, it is necessary to determine possible rate-limiting steps in DTE antibody secretion in comparison with other high IgG producers. Here, we analyzed the protein secretion process in CHO cells producing the DTE immunoglobulin G (IgG) infliximab. The results from chase assays using a translation inhibitor revealed that infliximab secretion could be nearly completed within 2 h, at which time the cells still retained about 40% of heavy chains and 65% of light chains. Using fluorescent microscopy, we observed that these IgG chains remained in the endoplasmic reticulum and Golgi apparatus. The cells inefficiently form fully assembled heterodimer IgG by making LC aggregates, which may be the most serious bottleneck in the production of DTE infliximab compared with other IgG high producers. Our study could contribute to establish the common strategy for constructing DTE high-producer cells on the basis of rate-limiting step analysis.

Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion

Recombinant protein production coopts the host cell machinery to provide high protein yields of industrial enzymes or biotherapeutics. However, since protein translation is energetically expensive and tightly controlled, it is unclear if highly expressed recombinant genes are translated as efficiently as host genes. Furthermore, it is unclear how the high expression impacts global translation. Here, we present the first genome-wide view of protein translation in an IgG-producing CHO cell line, measured with ribosome profiling. Through this we found that our recombinant mRNAs were translated as efficiently as the host cell transcriptome, and sequestered up to 15% of the total ribosome occupancy. During cell culture, changes in recombinant mRNA translation were consistent with changes in transcription, demonstrating that transcript levels influence specific productivity. Using this information, we identified the unnecessary resistance marker NeoR to be a highly transcribed and translated gene. Through siRNA knock-down of NeoR, we improved the production- and growth capacity of the host cell. Thus, ribosomal profiling provides valuable insights into translation in CHO cells and can guide efforts to enhance protein production.