A Markov chain model for N-linked protein glycosylation--towards a low-parameter tool for model-driven glycoengineering.

Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production.

Targeted delivery of alcohol-containing payloads with antibody-drug conjugates.

We herein describe the cell-specific release of alcohol-containing payloads via a sulfatase-sensitive linker in antibody-drug conjugates (ADCs). The linker shows efficient sulfatase-mediated release and high stability in human and mouse plasma. In vitro evaluation demonstrates potent antigen dependent toxicity towards breast cancer cell lines.

Sympathetic cooling in an optically trapped mixture of alkali and spin-singlet atoms.

We report on the realization of a stable mixture of ultracold lithium and ytterbium atoms confined in a far-off-resonance optical dipole trap. We observe sympathetic cooling of 6Li by 174Yb and extract the s-wave scattering length magnitude |a(6Li-174Yb)|=(13±3)a0 from the rate of interspecies thermalization. Using forced evaporative cooling of 174Yb, we achieve reduction of the 6Li temperature to below the Fermi temperature, purely through interspecies sympathetic cooling.

A Chinese hamster transcription start site atlas that enables targeted editing of CHO cells.

Chinese hamster ovary (CHO) cells are widely used for producing biopharmaceuticals, and engineering gene expression in CHO is key to improving drug quality and affordability. However, engineering gene expression or activating silent genes requires accurate annotation of the underlying regulatory elements and transcription start sites (TSSs). Unfortunately, most TSSs in the published Chinese hamster genome sequence were computationally predicted and are frequently inaccurate. Here, we use nascent transcription start site sequencing methods to revise TSS annotations for 15 308 Chinese hamster genes and 3034 non-coding RNAs based on experimental data from CHO-K1 cells and 10 hamster tissues. We further capture tens of thousands of putative transcribed enhancer regions with this method. Our revised TSSs improves upon the RefSeq annotation by revealing core sequence features of gene regulation such as the TATA box and the Initiator and, as exemplified by targeting the glycosyltransferase gene Mgat3, facilitate activating silent genes by CRISPRa. Together, we envision our revised annotation and data will provide a rich resource for the CHO community, improve genome engineering efforts and aid comparative and evolutionary studies.

A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering.

Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modelling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation.

BiP Inducer X: An ER Stress Inhibitor for Enhancing Recombinant Antibody Production in CHO Cell Culture.

Prolonged endoplasmic reticulum (ER) stress reduces protein synthesis and induces apoptosis in mammalian cells. When dimethyl sulfoxide (DMSO), a specific monoclonal antibody productivity (qmAb )-enhancing reagent, is added to recombinant Chinese hamster ovary (rCHO) cell cultures (GSR cell line), it induces ER stress and apoptosis in a dose-dependent manner. To determine an effective ER stress inhibitor, three ER stress inhibitors (BiP inducer X [BIX], tauroursodeoxycholic acid, and carbazole) are examined and BIX shows the best production performance. Coaddition of BIX (50 µm) with DMSO extends the culture longevity and enhances qmAb . As a result, the maximum mAb concentration is significantly increased with improved galactosylation. Coaddition of BIX significantly increases the expression level of binding immunoglobulin protein (BiP) followed by increased expression of chaperones (calnexin and GRP94) and galactosyltransferase. Furthermore, the expression levels of CHOP, a well-known ER stress marker, and cleaved caspase-3 are significantly reduced, suggesting that BIX addition reduces ER stress-induced cell death by relieving ER stress. The beneficial effect of BIX on mAb production is also demonstrated with another qmAb -enhancing reagent (sodium butyrate) and a different rCHO cell line (CS13-1.00). Taken together, BIX is an effective ER stress inhibitor that can be used to increase mAb production in rCHO cells.

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

Biosimilar drugs must closely resemble the pharmacological attributes of innovator products to ensure safety and efficacy to obtain regulatory approval. Glycosylation is one critical quality attribute that must be matched, but it is inherently difficult to control due to the complexity of its biogenesis. This usually implies that costly and time-consuming experimentation is required for clone identification and optimization of biosimilar glycosylation. Here, a computational method that utilizes a Markov model of glycosylation to predict optimal glycoengineering strategies to obtain a specific glycosylation profile with desired properties is described. The approach uses a genetic algorithm to find the required quantities to perturb glycosylation reaction rates that lead to the best possible match with a given glycosylation profile. Furthermore, the approach can be used to identify cell lines and clones that will require minimal intervention while achieving a glycoprofile that is most similar to the desired profile. Thus, this approach can facilitate biosimilar design by providing computational glycoengineering guidelines that can be generated with a minimal time and cost.

Enzymatic action of phospholipase A2 on liposomal drug delivery systems.

The overexpression of secretory phospholipase A2 (sPLA2) in tumors has opened new avenues for enzyme-triggered active unloading of liposomal antitumor drug carriers selectively at the target tumor. However, the effects of the liposome composition, drug encapsulation, and tumor microenvironment on the activity of sPLA2 are still not well understood. We carried out a physico-chemical study to characterize the sPLA2-assisted breakdown of liposomes using dye-release assays in the context of drug delivery and under physiologically relevant conditions. The influence of temperature, lipid concentration, enzyme concentration, and drug loading on the hydrolysis of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC, Tm=42°C) liposomes with snake venom sPLA2 was investigated. The sensitivity of human sPLA2 to the liposome composition was checked using binary lipid mixtures of phosphatidylcholine (PC) and phosphatidylglycerol (PG) phospholipids with C14 and C16 acyl chains. Increasing temperature (36-41°C) was found to mainly shorten the enzyme lag-time, whereas the effect on lipid hydrolysis rate was modest. The enzyme lag-time was also found to be inversely dependent on the lipid-to-enzyme ratio. Drug encapsulation can alter the hydrolysis profile of the carrier liposomes. The activity of human sPLA2 was highly sensitive to the phospholipid acyl-chain length and negative surface charge density of the liposomes. We believe our work will prove useful for the optimization of sPLA2-susceptible liposomal formulations as well as will provide a solid ground for predicting the hydrolysis profile of the liposomes in vivo at the target site.

Development of a cell-based bioassay for phospholipase A2-triggered liposomal drug release.

The feasibility of exploiting secretory phospholipase A2 (sPLA2) enzymes, which are overexpressed in tumors, to activate drug release from liposomes precisely at the tumor site has been demonstrated before. Although the efficacy of the developed formulations was evaluated using in vitro and in vivo models, the pattern of sPLA2-assisted drug release is unknown due to the lack of a suitable bio-relevant model. We report here on the development of a novel bioluminescence living-cell-based luciferase assay for the monitoring of sPLA2-triggered release of luciferin from liposomes. To this end, we engineered breast cancer cells to produce both luciferase and sPLA2 enzymes, where the latter is secreted to the extracellular medium. We report on setting up a robust and reproducible bioassay for testing sPLA2-sensitive, luciferin remote-loaded liposomal formulations, using 1,2-distearoyl-sn-glycero-3-phosphatidylcholine/1,2-distearoyl-sn-glycero-3-phosphatidylglycerol (DSPC/DSPG) 7:3 and DSPC/DSPG/cholesterol 4:3:3 as initial test systems. Upon their addition to the cells, the liposomes were degraded almost instantaneously by sPLA2 releasing the encapsulated luciferin, which provided readout from the luciferase-expressing cells. Cholesterol enhanced the integrity of the formulation without affecting its susceptibility to sPLA2. PEGylation of the liposomes only moderately broadened the release profile of luciferin. The provided bioassay represents a useful tool for monitoring active drug release in situ in real time as well as for testing and optimizing of sPLA2-sensitive lipid formulations. In addition, the bioassay will pave the way for future in-depth in vitro and in vivo studies.