Machine learning of RNA code artifacts in inflammatory diseases towards precision medicine

炎症性疾病中 RNA 编码伪影的机器学习走向精准医学

• 批准号: 2601923
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601923
• 关键词: Machine; learning; RNA; code; artifacts

Background: Endogenous modifications of RNA nucleotides ("epitranscriptome") have recently emerged as critical post/co-transcriptional determinants of RNA fate affecting almost every biological process and disease. Which are the common molecular determinants/changes driving multiple inflammatory diseases, like atherosclerotic heart disease and autoimmune disorders, are currently unclear. We have previously established a striking regulation of enzymes catalyzing the most prevalent RNA modifications in patients with chronic inflammatory disorders. We now want to harness this knowledge using innovative epitranscriptomics (methods studying epitranscriptome across genome) and machine learning approaches to apply our bench findings in personalized medicine. Objectives: Herein, we aim to: 1) map the two most prevalent RNA modifications (adenosine deamination and m6A RNA methylation) on the transcriptome (=epitranscriptome) of healthy individuals and patients with chronic inflammatory disorders, 2) define the differential regulation of epitranscriptome and its effect on gene expression and biological processes during disease, 3) integrate the differential epitranscriptome dynamics (sites and gene expression changes) combined with clinical data in machine learning approaches in order to determine the prognostic and anti-inflammatory drug-response predictive value of RNA modifications in chronic inflammatory diseases. Novelty and timeliness: With the recent advancement of next-generation sequencing and development of antibody-based methods that recognize RNA modifications, an in-depth study of adenosine RNA modifications is now accessible. However, combination of 'omic and clinical data bears restricted utility without interpretation methods. Machine learning, emerging the last couple of years, has been proven to reliably identify clinically relevant patterns. By employing these cutting-edge methods, we will synthesize for the first time the epitranscriptional profile of inflammatory diseases and will provide novel molecular determinants (e.g modification of specific sites) functionally relevant to stratification of patients, drug-responses and prediction of clinical outcomes. Experimental approach: In Objective 1 and 2, we will map and differentially quantify the adenosine RNA modifications in own and available bulk and methylated RNA-seq data from peripheral blood and other relevant tissues (cardiac, synovial) from highly phenotyped patients (for disease activity and anti-inflammatory drug-response where applicable) with rheumatoid arthritis, lupus, systemic sclerosis, atherosclerotic heart disease and in healthy individuals through established pipelines at the lab of Main-supervisor and Third-supervisor. In Objective 3, together with Second-supervisor, we will follow a supervised machine learning approach, by training algorithms using our data, to test whether these epitranscriptomic changes predict disease extent, progression and response to current therapeutic options in large available datasets with significant net reclassification index (>20%) over current clinical-based algorithms.

背景资料：RNA核苷酸的内源性修饰（“表转录组”）最近已经成为影响几乎所有生物过程和疾病的RNA命运的关键转录后/共转录决定簇。目前尚不清楚哪些是驱动多种炎症性疾病（如动脉粥样硬化性心脏病和自身免疫性疾病）的常见分子决定因素/变化。我们以前已经建立了一个惊人的调节酶催化最普遍的RNA修饰的慢性炎症性疾病患者。我们现在希望利用创新的epitranscriptomics（跨基因组研究epitranscriptome的方法）和机器学习方法来利用这些知识，将我们的实验室发现应用于个性化医疗。目的：在此，我们的目标是：1）绘制两种最普遍的RNA修饰（腺苷脱氨和m6 A RNA甲基化）（=表转录组），2）定义表转录组的差异调节及其对疾病期间基因表达和生物过程的影响，3）整合差异表转录组动力学（位点和基因表达变化）结合机器学习方法中的临床数据，以确定预后和抗炎药物-RNA修饰在慢性炎症性疾病中的反应预测价值。新奇和及时性：随着新一代测序技术的进步和基于抗体的识别RNA修饰方法的发展，现在可以对腺苷RNA修饰进行深入研究。然而，没有解释方法，组学和临床数据的组合具有有限的效用。近几年出现的机器学习已被证明可以可靠地识别临床相关模式。通过采用这些尖端方法，我们将首次合成炎性疾病的表观特征，并将提供与患者分层、药物反应和临床结果预测功能相关的新分子决定因素（例如特定位点的修饰）。实验方法：在目标1和2中，我们将绘制和差异定量来自外周血和其他相关组织的自身和可用的批量和甲基化RNA-seq数据中的腺苷RNA修饰（心脏，滑膜）来自高表型患者（用于疾病活动和抗炎药物反应，如适用）类风湿性关节炎、狼疮、系统性硬化症，动脉粥样硬化性心脏病和健康个体通过建立的管道在实验室的主要主管和第三主管。在目标3中，我们将与Second-supervisor一起，通过使用我们的数据训练算法，遵循监督机器学习方法，以测试这些表观变化是否预测疾病程度，进展和对当前治疗方案的反应，在大型可用数据集中具有显著的净重新分类指数（>20%），超过当前基于临床的算法。