Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources

生产新一代重组DNA衍生的治疗性蛋白质（如赫赛汀和阿瓦斯汀等抗癌药物）的生物制药公司经常使用培养的哺乳动物细胞来制造蛋白质产品。从根本上说，所有的生产过程都是基于宿主哺乳动物细胞工厂使用合成DNA遗传“密码”来制造复杂蛋白质产品的能力。这是现代生物技术的基石。然而，由于蛋白质合成非常复杂，涉及许多细胞资源和机器，基因工程师很难设计出一种DNA密码，使哺乳动物细胞工厂最有效地运作。此外，由于单个哺乳动物细胞工厂可能变化很大，它们制造产品的相对能力可能存在很大差异。因此，在生物制药开发过程的初始阶段，公司必须花费大量的时间和金钱进行密集的筛选操作，以找到能够使用给定遗传密码的最佳细胞工厂（从大量人群中）。对于不同的蛋白质产品，有必要重新开始整个开发过程。在这个项目中，我们将利用最近可用的高信息量分子分析技术和计算工具来“解卷积”哺乳动物细胞工厂中蛋白质合成的复杂性。实际上，我们知道哺乳动物细胞工厂使用自己的遗传密码来制造数千种自己的蛋白质（机器），这些蛋白质（机器）一起执行各种功能，使细胞能够生长和分裂。这些蛋白质的合成速率变化很大，超过1000倍，因此细胞可以制造出适当数量的蛋白质来完成它的工作。我们将测量每个细胞蛋白质的制造效率，然后使用先进的生物信息分析（生物信息学）和数学，我们将确定细胞如何利用嵌入在每个基因中的信息片段来改变特定蛋白质的制造速度。这将使我们第一次创造出一套可用的“设计规则”（计算机程序），基因工程师和细胞工厂开发人员可以利用这些规则：(1)可靠地为任何给定的蛋白质产品设计最佳的遗传密码；(2)准确地预测哺乳动物细胞工厂可以生产多少蛋白质产品。这一点很重要，因为这意味着生物制药公司可以从零开始设计一个可预测的生产系统，从而能够更快地从漫长的细胞工厂开发过程过渡到（前）临床试验。

Biopharmaceutical companies producing the new generation of recombinant DNA derived therapeutic proteins (e.g. cancer medicines such as Herceptin and Avastin) often use mammalian cells grown in culture to make the protein product. All production processes are based, fundamentally, upon the ability of the host mammalian cell factory to use a synthetic DNA genetic "code" to manufacture the complex protein product. This is a cornerstone of modern biotechnology. However, because protein synthesis is so complex, involving many cellular resources and machines, it is extremely difficult for genetic engineers to design a DNA code that will best enable the mammalian cell factory to operate most efficiently. Moreover, as individual mammalian cell factories can be very variable, they may differ substantially in their relative ability to make the product. As a consequence, a lot of time and money has to be spent by companies on the initial phases of the biopharmaceutical development process conducting intensive screening operations to find the best cell factory (out of a large population) able to use the genetic code it has been given. For a different protein product it is necessary to start the whole development process again.In this project we will utilise recently available high information content molecular analysis technologies and computational tools to "de-convolute" the complexity of protein synthesis in mammalian cell factories. Effectively, we know that the mammalian cell factory uses its own genetic code to make thousands of its own proteins (machines) that together perform a variety of functions that enable the cell to grow and divide. The rate at which these proteins are made varies hugely, over 1000-fold, so that the cell can make each bit of protein machinery in the right quantity to do its job. We will measure how efficiently each cellular protein is made then using advanced biological information analysis (bioinformatics) and mathematics we will determine how the cell uses pieces of information embedded in each of its genes to vary the rate at which a specific protein is made.This will enable us to create, for the first time, a usable set of "design rules" (computer programmes) that genetic engineers and cell factory developers can employ to (i) reliably design the best genetic code for any given protein product and (ii) accurately predict how much of the protein product the mammalian cell factory can make. This is important as it means that biopharmaceutical companies can design a predictable production system from scratch, enabling a more rapid transition through lengthy cell factory development processes towards (pre-)clinical trials.