CHO cell engineering directed by genomescale modelling

与传统的小分子疗法相比，生物制药(由(糖蛋白)蛋白质和/或核酸组成的药物产品)彻底改变了许多使人衰弱和威胁生命的疾病的治疗方法，部分原因是它们具有高度的特异性和较低的毒性。因此，生物制药的市场增长高于市场增长，促使制药行业的全球领导者更加关注生物制药。这扩大了对强化和改进制造工艺的策略的研究，旨在降低成本，同时提高效率和产品质量。生物制药产品主要是通过将重组DNA导入宿主细胞系来制造的，由此表达、收获和纯化产物。中国仓鼠卵巢(CHO)细胞是这种表达的最广泛的哺乳动物宿主，占市场上所有单抗的80%。这在很大程度上是由于它们的长期研究、人类兼容的糖形式、对生物反应器的适应性以及遗传操作的简便性。然而，尽管它们的长期和广泛的使用，仍然有很大的空间来改进CHO表达系统，通过定向细胞系工程，允许强化和改进制造过程。基因组规模建模(GEM)是一种计算方法，代表了细胞内发生的所有已知代谢过程的结构化数据库。通过集成每个代谢过程中涉及的代谢物、酶和基因，这些模型可以应用于计算细胞内的代谢通量、基因表达调控和蛋白质分泌。当与优化算法相结合时，GEMS允许识别潜在的基因工程策略。例如，通过识别非必要的代谢途径，可以确定候选基因进行敲除/敲除，潜在地释放细胞资源并减少宿主细胞蛋白质，提高产品质量和生产率。已经投入了大量的精力来打造一个Cho宝石。值得注意的是，2016年发布了一个通用的CHO GEM模型iCHO1766，其中包含了大多数已知的CHO基因、酶和代谢物。这一模型最近由Gutierrez等人扩展。包括分泌途径(ICHO2048)，从而能够计算每种分泌蛋白质的能量成本和机械需求。这些建模工作显然为定向CHO细胞工程的研究铺平了道路。最近，Kol et al.利用这种iCHO2048 GEM为宿主细胞蛋白质生成敲除克隆，报告了特定克隆的更高生产率和改善的生长特性，同时减少了下游加工的压力。然而，在这项工作之外，利用这些模型来指导CHO细胞工程策略的例子仍然很少，这使得它成为一个有吸引力的研究领域。因此，本博士项目旨在探索这一研究差距。首先，通过扩展iCHO2048 GEM，例如通过包括翻译后修饰和蛋白质降解途径，以提高生物相关性并允许卓越的建模能力。关键是，为了有效的建模，从而有效的定向细胞工程，用实验数据约束模型是至关重要的。因此，在湿实验室验证选定的目标之前，应使用代谢进度来限制和减少该模型，以便将优化算法应用于模型，以确定基因工程的目标，主要目的是优化细胞比生产率。

Biopharmaceuticals (pharmaceutical products consisting of (glyco)proteins and/or nucleic acids) have revolutionised the treatment of many debilitating and life threating diseases, thanks in part to their high specificity and reduced toxicity compared to traditional small-molecule therapeutics. As a result, biopharmaceuticals boast above market growth, stimulating increased focus from global leaders within the pharmaceutical industry. This has amplified research into strategies for the intensification and improvement of manufacturing processes, aiming to reduce cost while increasing efficiency and product quality.Biopharmaceutical products are predominantly manufactured by transfecting recombinant DNA into a host cell line, whereby the product is expressed, harvested, and purified. Chinese Hamster Ovary (CHO) cells are the most widely used mammalian host for such expression, accounting for the production of 80% of all monoclonal antibodies in the market. This is largely due to their long-term study, human compatible glycoforms, adaptability to bioreactors and ease of genetic manipulation. Despite their long-term and widespread use however, there remains significant scope to improve CHO expression systems through directed cell line engineering, allowing for the intensification and improvement of manufacturing processes.Genome-scale modelling (GeM) is a computational methodology representing a structured database of all known metabolic processes that take place within a cell. By integrating the metabolites, enzymes and genes involved within each metabolic process, these models can be applied to compute intracellular metabolic fluxes, gene expression regulation and protein secretion. When coupled with optimization algorithms, GeMs allows for the identification of potential genetic engineering strategies. For instance, by identifying non-essential metabolic pathways, candidate genes may be identified for knockout/knockdown, potentially freeing up cellular resources and reducing host cell proteins, improving product quality and productivity. Substantial effort has already been invested into building a CHO GeM. Significantly, a generic CHO GeM model, iCHO1766, containing most known CHO genes, enzymes and metabolites was published in 2016. This model was recently expanded by Gutierrez et al. to include the secretory pathway(iCHO2048), enabling the computation of energetic costs and machinery demands of each secreted protein.These modelling efforts clearly pave the way for research into directed CHO cell engineering. Recently, Kol et al. utilised this iCHO2048 GeM to generate knockout clones for host cell proteins, reporting higher productivity and improved growth characteristics in specific clones, while reducing pressure in downstream processing. Outside of this work however, there remains few examples utilising these models to directed CHO cell engineering strategies, making it an attractive area for investigation.This PhD project therefore aims to explore this research gap. Firstly, by expanding the iCHO2048 GeM, for instance by including post translational modification and protein degradation pathways, to improve biological relevance and allow superior modelling capabilities. Critically, for effective modelling, and therefore effective directed cell engineering, it is vital to constrain models with experimental data. This model shall therefore be constrained and reduced using metabolic, progress to apply optimisation algorithms to models to identify targets for genetic engineering, primarily aiming to optimise cell specific productivity, before selected targets are validated in the wet lab.