Artificial Intelligence Boosted Evolution and Detection of Genetically Encoded Reporters for In Vivo Imaging

人工智能促进体内成像基因编码报告基因的进化和检测

• 批准号: 10379290
• 负责人: CHRISTIAN T FARRAR
• 金额: $ 68.27万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10379290
• 关键词: Algorithms; Amides; Artificial Intelligence; Biological Response Modifier Therapy; Cardiac; Cells; Chemicals; Clinic; Clinical; Clinical assessments; Collaborations; Detection; Development; Diagnosis; Discrimination; Disease; Drug Kinetics; Evolution; Fingerprint; Gene Transfer; Genes; Genetic Programming; Glioblastoma; Goals; Histologic; Image; Infection; Longitudinal Studies; Lysine; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Measurement; Medical Imaging; Metabolic; Methods; Modeling; Monitor; Mus; Oncolytic; Oncolytic viruses; Pathology; Performance; Physiologic pulse; Problem Solving; Protein Engineering; Proteins; Protons; Reporter; Reporter Genes; Resistance; Resolution; Schedule; Sensitivity and Specificity; Signal Transduction; Specificity; Technology; Testing; Therapeutic; Therapeutic Agents; Time; Tissues; Tumor Cell Line; Viral; Virus Diseases; Virus Replication; Water; Work; base; cancer imaging; clinical translation; deep learning; deep neural network; detection method; detection sensitivity; gene therapy; imaging modality; improved; in vivo imaging; neoplastic cell; neural network architecture; novel; oncolytic virotherapy; pre-clinical; radio frequency; rapid technique; response; therapeutic gene; tool; trafficking; transgene expression; tumor; viral gene delivery

PROJECT SUMMARY Magnetic resonance (MR) reporter genes have the potential to monitor transgene expression non-invasively in real time at high resolution. These genes can be applied to interrogate the efficacy of gene therapy, to monitor viral therapeutics and viral gene delivery, to assess cellular differentiation, cell trafficking, and specific metabolic activity, and also assess changes in the microenvironment. Efforts toward the development of MR reporter genes have been made for over a decade, but, despite these efforts, the field is still in its early developmental stage. This reflects the fact that there are numerous complications, caused by the low sensitivity of detection, the need for substrates with their associated undesirable pharmacokinetics, and/or the difficult and, in some cases, delayed interpretation of signal changes. We have previously demonstrated that many of these challenges can be overcome with the use of a lysine rich protein (LRP) reporter gene, that is detectable by chemical exchange saturation transfer (CEST) MRI. However, to mature the CEST reporter gene technology and bring it towards clinical translation, its sensitivity and specificity need to be improved. In particular, the LRP reporter gene specificity is limited by the fact that the lysine amide exchangeable protons of LRP have the same chemical shift as amide protons from endogenous proteins. It is therefore difficult to distinguish the reporter CEST contrast from the background CEST contrast, both of which may be changing with time. The specificity is further limited by the sensitivity of the CEST contrast to intracellular pH where the qualitative CEST contrast cannot distinguish between exchange rate and concentration effects. Finally, a decrease in cytosolic pH, observed in many disease pathologies, reduces the amide proton exchange rate and hence the CEST reporter sensitivity. We therefore propose to develop improved MRI reporter genes and quantitative MRI detection methods that will facilitate the clinical translation of these methods for imaging biological therapeutics, such as oncolytic virotherapy. We hypothesize that CEST reporter genes with improved sensitivity and specificity along with improved quantitative CEST methods will enable viral infection and replication to be monitored longitudinally throughout OV tumor therapy. To test this hypothesis and establish the clinical potential of MRI reporter genes we will capitalize on two transformative technologies developed in our labs; (Aim 1) an artificial intelligence based genetic programming algorithm will be used for optimizing the sensitivity and specificity of the CEST reporter gene and (Aim 2) a CEST magnetic resonance fingerprinting (MRF) method will be used for the rapid quantification of both the reporter protein concentration and chemical exchange rate. (Aim 3) These methods will be validated for imaging oncolytic viral infection and replication in mouse glioblastoma tumor models.

项目摘要 磁共振（MR）报告基因有潜力监测转基因表达的非侵入性， 高分辨率的真实的时间。这些基因可用于询问基因治疗的疗效， 病毒治疗和病毒基因递送，以评估细胞分化、细胞运输和特异性 代谢活动，并评估微环境的变化。努力发展MR 报道基因已经被制造了十多年，但是，尽管有这些努力，该领域仍处于早期阶段。 发育阶段这反映了一个事实，即有许多并发症，造成的低 检测的灵敏度，对底物及其相关的不期望的药代动力学的需要，和/或 信号变化的解释困难，并且在某些情况下延迟。 我们以前已经证明，这些挑战中的许多可以通过使用富含赖氨酸的 蛋白（LRP）报告基因，可通过化学交换饱和转移（CEST）MRI检测。 然而，要使CEST报告基因技术成熟并将其推向临床翻译，其敏感性 特异性有待提高。特别地，LRP报告基因特异性受到以下事实的限制： LRP的赖氨酸酰胺可交换质子具有与LRP的酰胺质子相同的化学位移， 内源性蛋白质因此很难将报告者CEST对比与背景区分开来 CEST对比度，两者都可能随时间而变化。特异性进一步受到以下灵敏度的限制： CEST与细胞内pH值对比，其中定性CEST对比无法区分 汇率和集中效应。最后，在许多疾病中观察到的细胞溶质pH值降低， 病理学，降低了酰胺质子交换率，从而降低了CEST报告基因的敏感性。 因此，我们建议开发改进的MRI报告基因和定量MRI检测方法， 将促进这些方法的临床转化，用于成像生物治疗剂，例如溶瘤剂， 病毒疗法我们假设，敏感性和特异性沿着提高的CEST报告基因， 改进的定量CEST方法将能够纵向监测病毒感染和复制 整个OV肿瘤治疗过程中。为了验证这一假设，并建立MRI报告基因的临床潜力， 我们将利用我们实验室开发的两项变革性技术;（目标1）人工智能 将基于遗传规划算法的CEST用于优化敏感性和特异性 报告基因和（目的2）CEST磁共振指纹（MRF）方法将用于快速 报告蛋白浓度和化学交换速率的定量。(Aim（3）这些方法 将被验证用于在小鼠胶质母细胞瘤肿瘤模型中成像溶瘤病毒感染和复制。