Integrated Methylation/isomiR/gene Expression (MIG) bio-profiles for prediction of treatment response in rheumatoid arthritis
Integrated miRNA/isomiR/Gene Expression (MIG) bio-profiles for prediction of treatment response in rheumatoid arthritis
We are developing analytical tools to investigate next generation sequencing (NGS) data collected from rheumatoid arthritis patients in Norway. We have created a tool for investigating variation in short RNAs, which are responsible for gene regulation and tested it against these NGS data. This is the first major milestone for our project.
Rheumatoid arthritis (RA) is a chronic inflammatory disorder of the joints occurring in ~1% of the Norwegian population. Treatments are available but commonly associated with major side effects and effectiveness varies among subjects. Genetic disposition and environmental factors are thought to be associated with both the onset of RA and response to treatment and we are trying to establish and clarify these roles. In this way it may be possible to predict severity of onset and optimal treatment for a patient. Our project is an extension of existing HSØ funded RA studies involving Benedicte Lie, also in the department of medical genetics at OUS, and Diakonhjemmet and Martina Hanssen Hospitals who are enrolling new patients to the study. Full details of the patient enrollment and study design are provided in the eReport “MicroRNAs as biomarkers for treatment response in rheumatoid arthritis”. In brief, there are two study groups comprising (i) newly diagnosed patients, and at three months treatment with methotrexate and (ii) patients receiving methotrexate treatment who have been in remission for more than a year. In Lie’s project, they are investigating variation in microRNAs (miRNAs) levels. MiRNAs are short (~22nt) RNAs that influence gene regulation; a change in the level of a miRNA can effect a change in downstream expression of its target genes. By looking for significant changes in miRNA levels between conditions and examining/predicting their targets it may be possible to identify associations with disease state. However, the reality is more complex - many miRNAs actually exist as a population of modified forms, or isomiRs, manifested as additions or deletions at either end of the sequence, or as mutations within the sequence. A major milestone is to characterize these isomiR populations, understand how they vary among conditions and interpret the significance of these changes. For example, a major change in isomiR population may impact the target genes that will be regulated. As a first step we have defined a system to define the types of isomiRs that can be present in a miRNA population. From this, we can characterize a miRNA, in terms of its isomiRs, and investigate how the population varies among conditions. In addition, we have been developing the necessary software tools for analysing small RNA NGS datasets to extract isomiR data, identify significant isomiR population changes (IPCs) amongst conditions, and estimate the impact of these changes. A preliminary set of RA NGS data is available from Lie’s project, and we have also been working with other publicly available data to test the software and learn what kind of IPCs we might expect. We have also been investigating changes in IPCs associated with different experimental protocols. This is crucial, as we need to distinguish biological IPCs from experimental artefacts. Our initial results are interesting. We have identified significant changes in isomiR populations between cancer and healthy subjects in two different datasets. Specifically, we consistently found that cancer subjects appear to have much narrower isomiR populations compared to healthy subjects. The likely impact of these differences is that cancer patients will have a much less noisy gene regulation network. As more RA small RNA data is collected, we hope to have sufficient power to resolve similar differences in patient data. This will form the primary focus of our work in 2017.