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

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.