Engineering of Chinese hamster ovary cell lipid metabolism results in an expanded ER and enhanced recombinant biotherapeutic protein production

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Data for engineering lipid metabolism of Chinese hamster ovary (CHO) cells for enhanced recombinant protein production

The data presented in this article relates to the manuscript entitled ‘Engineering of Chinese hamster ovary cell lipid metabolism results in an expanded ER and enhanced recombinant biotherapeutic protein production’, published in the Journal Metabolic Engineering [1]. In the article here, we present data examining the overexpression of the lipid metabolism modifying genes SCD1 and SREBF1 in CHO cells by densitometry of western blots and by using mass spectrometry to investigate the impact on specific lipid species. We also present immunofluorescence data at the protein level upon SCD1 and SREBF1 overexpression. The growth profile data during batch culture of control CHO cells and CHO cells engineered to overexpress SCD1 and SREBF1 during batch culture are also reported. Finally, we report data on the yields of model secretory recombinant proteins produced from control, SCD1 or SREBF1 engineered cells using a transient expression systems

Engineering of Chinese hamster ovary cell lipid metabolism results in an expanded ER and enhanced recombinant biotherapeutic protein production

Chinese hamster ovary (CHO) cell expression systems have been exquisitely developed for the production of recombinant biotherapeutics (e.g. standard monoclonal antibodies, mAbs) and are able to generate efficacious, multi-domain proteins with human-like post translational modifications at high concentration with appropriate product quality attributes. However, there remains a need for development of new CHO cell expression systems able to produce more challenging secretory recombinant biotherapeutics at higher yield with improved product quality attributes. Amazingly, the engineering of lipid metabolism to enhance such properties has not been investigated even though the biosynthesis of recombinant proteins is at least partially controlled by cellular processes that are highly dependent on lipid metabolism. Here we show that the global transcriptional activator of genes involved in lipid biosynthesis, sterol regulatory element binding factor 1 (SREBF1), and stearoyl CoA desaturase 1 (SCD1), an enzyme which catalyzes the conversion of saturated fatty acids into monounsaturated fatty acids, can be overexpressed in CHO cells to different degrees. The amount of overexpression obtained of each of these lipid metabolism modifying (LMM) genes was related to the subsequent phenotypes observed. Expression of a number of model secretory biopharmaceuticals was enhanced between 1.5-9 fold in either SREBF1 or SCD1 engineered CHO host cells as assessed under batch and fed-batch culture. The SCD1 overexpressing polyclonal pool consistently showed increased concentration of a range of products. For the SREBF1 engineered cells, the level of SREBF1 expression that gave the greatest enhancement in yield was dependent upon the model protein tested. Overexpression of both SCD1 and SREBF1 modified the lipid profile of CHO cells and the cellular structure. Mechanistically, overexpression of SCD1 and SREBF1 resulted in an expanded endoplasmic reticulum (ER) that was dependent upon the level of LMM overexpression. We conclude that manipulation of lipid metabolism in CHO cells via genetic engineering is an exciting new approach to enhance the ability of CHO cells to produce a range of different types of secretory recombinant protein products via modulation of the cellular lipid profile and expansion of the ER

A proline metabolism selection system and its application to the engineering of lipid biosynthesis in Chinese hamster ovary cells

Chinese hamster ovary (CHO) cells are the leading mammalian cell host employed to produce complex secreted recombinant biotherapeutics such as monoclonal antibodies (mAbs). Metabolic selection marker technologies (e. g. glutamine synthetase (GS) or dihydrofolate reductase (DHFR)) are routinely employed to generate such re-combinant mammalian cell lines. Here we describe the development of a selection marker system based on the metabolic requirement of CHO cells to produce proline, and that uses pyrroline-5-carboxylase synthetase (P5CS) to complement this auxotrophy. Firstly, we showed the system can be used to generate cells that have growth kinetics in proline-free medium similar to those of the parent CHO cell line, CHOK1SV GS-KO™ grown in proline- containing medium. As we have previously described how engineering lipid metabolism can be harnessed to enhance recombinant protein productivity in CHO cells, we then used the P5CS selection system to re-engineer lipid metabolism by over-expression of either sterol regulatory element binding protein 1 (SREBF1) or stearoyl CoA desaturase 1 (SCD1). The cells with re-engineered proline and lipid metabolism showed consistent growth and P5CS, SCD1 and SREBF1 expression across 100 cell generations. Finally, we show that the P5CS and GS selection systems can be used together. A GS vector containing the light and heavy chains for a mAb was super- transfected into a CHOK1SV GS-KO™ host over-expressing SCD1 from a P5CS vector. The resulting stable transfectant pools achieved a higher concentration at harvest for a model difficult to express mAb than the CHOK1SV GS-KO™ host. This demonstrates that the P5CS and GS selection systems can be used concomitantly to enable CHO cell line genetic engineering and recombinant protein expression

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Localized Partial Evaluation of Belief Networks

Most algorithms for propagating evidence through belief networks have been exact and exhaustive: they produce an exact (pointvalued) marginal probability for every node in the network. Often, however, an application will not need information about every node in the network nor will it need exact probabilities. We present the localized partial evaluation (LPE) propagation algorithm, which computes interval bounds on the marginal probability of a specified query node by examining a subset of the nodes in the entire network. Conceptually, LPE ignores parts of the network that are "too far away" from the queried node to have much impact on its value. LPE has the "anytime" property of being able to produce better solutions (tighter intervals) given more time to consider more of the network. 1 Introduction Belief networks provide a way of encoding knowledge about the probabilistic dependencies and independencies of a set of variables in some domain. Variables are encoded as nodes in the ne..