MICA:Synthetic untranslated regions for direct delivery of therapeutic mRNAs

注射蛋白质是疫苗接种和缺乏蛋白质的治疗的常用方法，例如用于糖尿病的胰岛素和用于治疗包括癌症在内的各种疾病的单克隆抗体。为患者提供蛋白质的另一种方法是注射mRNA，然后身体用它来制造蛋白质。这种方法在需要对新的传染原作出快速反应的情况下特别有用，例如在大流行期间。这是因为制造一种新的mRNA比制造一种蛋白质更快速，更可预测。mRNA在体内的效力受其稳定性和被翻译成蛋白质的程度的影响。这些因素在一定程度上是由编码蛋白质的片段侧面的序列所控制的，这些序列被称为非翻译区（utr）。一个稳定的、高度翻译的mRNA将在更长时间内产生更多的蛋白质，这有可能减少注射的频率和所需的mRNA剂量。这样做的优点是减少了对注射mRNA的免疫反应引起的副作用。此外，较小的剂量需求减少了在大流行中扩大潜在疫苗的制造负担。因此，迫切需要了解utr是如何工作的，并利用这些知识找到用于mRNA治疗的最佳utr。在这个项目中，我们将使用合成生物学来生成稳定且高度可翻译的新型utr。我们将使用编码与病毒大流行相关的蛋白质的mrna，在体外和体内测试这些合成utr （SUTRs）的功效。我们还将开发一种人工智能方法，以实现sutr的计算机设计。

Injection of proteins is a common approach for vaccination and therapies where a protein is deficient, such as insulin for diabetes and monoclonal antibodies to treat various diseases, including cancer. An alternative way to provide proteins in patients is to inject an mRNA, which the body then uses to make the protein. This approach has particular utility in situations where a rapid response to a new infectious agent is required, for example in a pandemic. This is because manufacturing a novel mRNA is rapid and more predictable than manufacturing a protein. The efficacy of an mRNA in vivo is influenced by its stability and how well it can be translated to make the protein. These factors are governed in part by the sequences which flank the segment encoding the protein, known as the untranslated regions (UTRs). A stable, highly translated mRNA will produce more protein for longer, which has the potential to reduce the frequency of injections and the dose of mRNA required. This has the advantage of reducing side effects caused by an immune response to the injected mRNA. Furthermore, a smaller dose requirement reduces the manufacturing burden for scaling up a potential vaccine in a pandemic. Thus, there is a critical need to understand how UTRs work and use this knowledge to find the best UTRs to use for mRNA therapeutics. In this project, we will use synthetic biology to generate novel UTRs which are stable and highly translatable. We will test the efficacy of these synthetic UTRs (SUTRs) in vitro and in vivo using mRNAs encoding proteins of relevance to viral pandemics. We will also develop an artificial intelligence approach to allow in silico design of SUTRs.