Modulating carbohydrate–protein interactions through glycoengineering of monoclonal antibodies to impact cancer physiology

Diverse glycans on proteins help impact cell and organism physiology along with drug activity. Since many protein-based biotherapeutics are glycosylated and these glycans have biological activity, there is a desire to engineer glycosylation for recombinant protein-based biotherapeutics. Engineered glycosylation can impact the recombinant protein efficacy and also influence many cell pathways by first changing glycan-protein interactions and consequently modulating disease physiologies. However, its complexity is enormous. Due to recent advances in glycoengineering, modulating protein-glycan interactions become more amenable to therapeutic approaches. Here, we discuss how engineered glycans contribute to therapeutic monoclonal antibodies (mAbs) in the treatment of cancers, how these glycoengineered therapeutic mAbs affect the transformed phenotypes and downstream cell pathways, and how systems biology can help in the next generation mAb glycoengineering process by aiding in data analysis and guiding engineering efforts to tailor mAb glycan and ultimately drug efficacy, safety and affordability

What does a cell need for efficient protein secretion: Deciphering, modeling, and augmenting the CHO machinery

Please click Additional Files below to see the full abstract

A reference genome for the Chinese hamster based on a hybrid assembly strategy

Accurate and complete genome sequences are essential in biotechnology, especially to facilitate genome-based cell engineering efforts for the main production host of biotherapeutic proteins, Chinese hamster ovary (CHO) cells. While genome-enabled CHO cell engineering efforts promise to enhance drug production, the current genome assemblies for Cricetulus griseus, the Chinese hamster, are highly fragmented and replete with gap sequences and misassemblies, consistent with most short-read based assemblies. Here we have completely re-sequenced the Chinese hamster, C. griseus, using Single Molecule Real Time (SMRT) long-read technology and merged this assembly with Illumina-based assemblies. Please download the file below for full content

Revealing key determinants of clonal variation in transgene expression in recombinant CHO cells using targeted genome editing

Generation of recombinant Chinese hamster ovary (rCHO) cell lines is critical for the production of therapeutic proteins. However, the high degree of phenotypic heterogeneity among generated clones, referred to as clonal variation, makes the rCHO cell line development process inefficient and unpredictable. Here, we investigated the major genomic causes of clonal variation. We found the following: (1) consistent with previous studies, a strong variation in rCHO clones in response to hypothermia (33 vs 37 °C) after random transgene integration; (2) altered DNA sequence of randomly integrated cassettes, which occurred during the integration process, affecting the transgene expression level in response to hypothermia; (3) contrary to random integration, targeted integration of the same expression cassette, without any DNA alteration, into three identified integration sites showed the similar response of transgene expression in response to hypothermia, irrespective of integration site; (4) switching the promoter from CMV to EF1α eliminated the hypothermia response; and (5) deleting the enhancer part of the CMV promoter altered the hypothermia response. Thus, we have revealed the effects of integration methods and cassette design on transgene expression levels, implying that rCHO cell line generation can be standardized through detailed genomic understanding. Further elucidation of such understanding is likely to have a broad impact on diverse fields that use transgene integration, from gene therapy to generation of production cell lines

In situ detection of protein interactions for recombinant therapeutic enzymes

Despite their therapeutic potential, many protein drugs remain inaccessible to patients since they are difficult to secrete. Each recombinant protein has unique physicochemical properties and requires different machinery for proper folding, assembly, and posttranslational modifications (PTMs). Here we aimed to identify the machinery supporting recombinant protein secretion by measuring the protein-protein interaction (PPI) networks of four different recombinant proteins (SERPINA1, SERPINC1, SERPING1, and SeAP) with various PTMs and structural motifs using the proximity-dependent biotin identification (BioID) method. We identified PPIs associated with specific features of the secreted proteins using a Bayesian statistical model and found proteins involved in protein folding, disulfide bond formation, and N-glycosylation were positively correlated with the corresponding features of the four model proteins. Among others, oxidative folding enzymes showed the strongest association with disulfide bond formation, supporting their critical roles in proper folding and maintaining the ER stability. Knockdown of disulfide-isomerase PDIA4, a measured interactor with significance for SERPINC1 but not SERPINA1, led to the decreased secretion of SERPINC1, which relies on its extensive disulfide bonds, compared to SERPINA1, which has no disulfide bonds. Proximity-dependent labeling successfully identified the transient interactions supporting synthesis of secreted recombinant proteins and refined our understanding of key molecular mechanisms of the secretory pathway during recombinant protein production

In situ detection of protein interactions for recombinant therapeutic enzymes

Despite their therapeutic potential, many protein drugs remain inaccessible to patients since they are difficult to secrete. Each recombinant protein has unique physicochemical properties and requires different machinery for proper folding, assembly, and post-translational modifications (PTMs). Here we aimed to identify the machinery supporting recombinant protein secretion by measuring the protein-protein interaction (PPI) networks of four different recombinant proteins (SERPINA1, SERPINC1, SERPING1 and SeAP) with various PTMs and structural motifs using the proximity-dependent biotin identification (BioID) method. We identified PPIs associated with specific features of the secreted proteins using a Bayesian statistical model, and found proteins involved in protein folding, disulfide bond formation and N-glycosylation were positively correlated with the corresponding features of the four model proteins. Among others, oxidative folding enzymes showed the strongest association with disulfide bond formation, supporting their critical roles in proper folding and maintaining the ER stability. Knockdown of disulfide-isomerase PDIA4, a measured interactor with significance for SERPINC1 but not SERPINA1, led to the decreased secretion of SERPINC1, which relies on its extensive disulfide bonds, compared to SERPINA1, which has no disulfide bonds. Proximity-dependent labeling successfully identified the transient interactions supporting synthesis of secreted recombinant proteins and refined our understanding of key molecular mechanisms of the secretory pathway during recombinant protein production

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells

A reference genome of the Chinese hamster based on a hybrid assembly strategy.

Accurate and complete genome sequences are essential in biotechnology to facilitate genome-based cell engineering efforts. The current genome assemblies for Cricetulus griseus, the Chinese hamster, are fragmented and replete with gap sequences and misassemblies, consistent with most short-read-based assemblies. Here, we completely resequenced C. griseus using single molecule real time sequencing and merged this with Illumina-based assemblies. This generated a more contiguous and complete genome assembly than either technology alone, reducing the number of scaffolds by >28-fold, with 90% of the sequence in the 122 longest scaffolds. Most genes are now found in single scaffolds, including up- and downstream regulatory elements, enabling improved study of noncoding regions. With >95% of the gap sequence filled, important Chinese hamster ovary cell mutations have been detected in draft assembly gaps. This new assembly will be an invaluable resource for continued basic and pharmaceutical research