Integrating transcriptomic and metabolomic data from people with rheumatoid arthritis to predict clinical response to drug treatment

摘要：类风湿性关节炎是一种以免疫介导病理为特征的慢性炎症性疾病。在英国大约有40万人不同程度地患有这种疾病。其潜在病理仍未得到充分了解。然而，很明显，不同的患者对传统的疾病改善抗风湿药物（cDMARDs）如甲氨蝶呤或生物bDMARDs如抗tnf单克隆抗体和JAK抑制剂（tsDMARDs）的治疗表现出不同的反应。苏格兰早期类风湿性关节炎队列（SERA n=1173），以及由格拉斯哥大学领导的一系列早期类风湿性关节炎患者的临床试验。“ORBIT”和“TASER”产生了一个独特的生物库，其中衍生了一系列转录组学和代谢组学数据集，但尚未在数据分析方面进行整合。具体来说，我们从SERA（早期未治疗的RA和未分化的关节炎）获得了RNAseq数据，从TASER（早期未治疗的RA）获得了RNAseq和质谱代谢组学数据，从ORBIT （RA首次cDMARD失败并随机接受利图昔单抗或TNF抑制剂）获得了RNAseq和短期代谢组学数据集（见McInnes et al. 2017）。到目前为止，使用药物基因组学方法或单独使用RNAseq的生物标志物方法在提供可靠的药物反应预测方面没有帮助。结合代谢组学和转录组学数据可能提供更多关于区分应答者和无应答者的因素的推断，但这些数据类型的组合仍然是一个开放的问题。因此，本项目旨在解决这一问题，以便从组合评分系统中识别新的生物标志物，这些生物标志物可用于预测可能的反应，并为每位患者选择正确的治疗方法。分析和比较来自几个RA患者队列的转录组数据集2。分析和比较几个RA患者队列的代谢组学数据集3。整合代谢组学和转录组学数据，寻求预测治疗反应途径的额外推断4。生成一组代谢物/转录物生物标志物，以便对RA治疗结果进行稳健的前瞻性预测。培训结果：学生将接受定向的生物信息学培训，以使用现有和新型软件分析转录组学（导师：Watson）和代谢组学（导师：Barrett）数据集。他们还将接受机器学习、贝叶斯统计和计算机编程方面的培训（导师：Rogers），以开发新的算法，为探索代谢组学和转录组学组合数据集提供新的方法。这项计算工作将有助于临床理解类风湿性关节炎（导师：McInnes）。参考文献：McInnes， i.b., Schett， G.（2017）类风湿关节炎治疗的发病机制。《柳叶刀》，389,2328-2337。creek, d.j., Jankevics， A., Burgess, k.e., Breitling， R., Barrett， M.P. (2012) IDEOM：用于分析基于液相色谱-质谱的代谢组学数据的Excel界面。生物信息学学报，28(2):448 - 449。范德胡特，王志强，王志强，等。RNA-Seq定量误差对人类疾病相关基因的影响[J] .基因组学杂志，2016,(17):26335491van der Hooft， J.J, Wandy， J, Barrett， M.P, Burgess， K.E, Rogers， S（2016）代谢组学非靶向亚结构探索的主题建模。科学通报，33(3):444 - 444。PMID: 27856765

Abstract: Rheumatoid arthritis is a chronic inflammatory disease characterized by immune-mediated pathology. In the order of 400,000 people in the UK suffer from the disease to varying degrees. The underlying pathology is still not sufficiently understood. However, it is clear that different patients display different responses to treatment with either conventional disease-modifying antirheumatic drugs (cDMARDs) e.g. methotrexate or biologic bDMARDs such as anti-TNF monoclonal antibodies and JAK inhibitors (tsDMARDs). The Scottish Early RA Cohort (SERA n=1173), together with a series of clinical trials that have been led by the University of Glasgow in people with early RA e.g. "ORBIT" and "TASER", have generated a unique biobank within which a range of transcriptomic and metabolomics datasets have been derived but as yet not integrated in terms of data analyses. Specifically, we have RNAseq data available from SERA (early untreated RA and undifferentiated arthritis), RNAseq and mass spec metabolomic data available from TASER (early untreated RA) and RNAseq and shortly metabolomics datasets available from ORBIT (RA failing first cDMARD and randomised to receive rituximab or TNF inhibitor(see McInnes et al. 2017). Biomarker approaches using pharmacogenomic approaches or using RNAseq alone have been unhelpful so far in RA in offering reliable drug response prediction. Combined metabolomics and trancriptomics data are likely to provide more inference about the factors that distinguish between responders and non-responders, but combination of these data types remains an open problem. This project therefore seeks to address this problem in order to identify new biomarkers arising from combinatorial scoring systems, that can be used to predict likely response and enable choice of the right treatment for each patient.Aims:1. Analyse and compare transcriptomic datasets from several RA patient cohorts2. Analyse and compare metabolomics datasets form several RA patient cohorts3. Integrate metabolomic and transcriptomic data to seek additional inference on pathways predictive of therapeutic response4. Generate a panel of metabolite/transcript biomarkers to enable robust prospective prediction of therapeutic outcomes in RA treatmentTraining outcomes:The student will receive directed Bioinformatics training to analyse transcriptomic (Supervisor: Watson) and metabolomics (Supervisor: Barrett) datasets using existing and novel software. They will also receive training in machine learning, Bayesian statistics and computer programming (Supervisor: Rogers) to develop novel algorithms that enable new ways to probe combined metabolomics and transcriptomics datasets. This computational work will feed into clinical understanding of RA (Supervisor: McInnes).References:McInnes, I.B., Schett, G. (2017) Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet. 389, 2328-2337. PMID: 28612747Creek, D.J., Jankevics, A., Burgess, K.E., Breitling, R., Barrett, M.P. (2012) IDEOM: an Excel interface for analysis of LC-MS-based metabolomics data. Bioinformatics. 28, 1048-9. PMID: 22308147Robert C, Watson M. Errors in RNA-Seq quantification affect genes of relevance to human disease (2015) Genome Biol. 16:177 PMID: 26335491van der Hooft, J.J., Wandy, J., Barrett, M.P., Burgess, K.E., Rogers, S (2016) Topic modeling for untargeted substructure exploration in metabolomics.. Proc Natl Acad Sci U S A. 113, 13738-13743. PMID: 27856765