AbstractPrecision medicine is improving health outcomes by tailoring patient care to specific individuals. The improved patient stratification needed to support further implementation of precision medicine will require developments in molecular diagnostics and identification of novel biomarkers. Small non-coding RNAs, specifically miRNAs, have been shown to exist extracellularly and are stable in biofluids, including blood and urine. The expression profile of extracellular miRNAs is also altered in a number of diseases making them attractive candidates for novel, non-invasive biomarkers.Although RT-qPCR is considered the gold standard for detection of individual miRNAs, RNA-Seq is a next generation sequencing technology that provides greater potential to identify novel extracellular miRNA biomarkers because it provides a global profile of all small RNAs present. However, this approach can be challenging when working with biofluids, such as human plasma, due to low RNA content. Small RNA-Seq is also hampered by sequencing bias introduced during library preparation that can result in inaccurate quantification of miRNAs.The first aim of this thesis is to demonstrate the feasibility of routine miRNA profiling in blood plasma by identifying an optimal small RNA-Seq protocol. The second aim is to then evaluate the ability of global miRNA profiling to differentiate between disease states by implementing this workflow in several patient cohorts, including cardiovascular diseases and age-related macular degeneration (AMD).Methods – RNA was extracted from plasma using MagnaZol cfRNA isolation (Bioo Scientific) and miRNeasy serum/plasma (Qiagen) kits. RT-qPCR was used to quantify and validate expression of individual miRNAs. Small RNA libraries were prepared with CleanTag (TriLink), NEXTflex (Bioo Scientific) and QIAseq (Qiagen) library kits. Sequencing was performed on MiSeq or NextSeq Illumina systems. Data analysis was performed using both commercial (e.g. CLC Genomics Workbench, Qiagen) and publicly (e.g. STAR aligner and HTSeq) available tools for fastq processing, alignment to reference genome and quantification of miRNAs. A number of packages were used within R including EdgeR for differential expression analysis and heatmap3 for hierarchical clustering. Novel algorithms for the quantification of isomiRs were also implemented in R and machine learning models were applied to the outputs of these algorithms to identify key discriminators between disease groups.Results – The small RNA-Seq workflow was optimised by comparing a number of commercially available RNA extraction and library preparation methods. Small RNAs were consistently detected using all approaches, however differences in miRNA diversity and differing detection efficiency were apparent between alternative methods. MagnaZol RNA extraction was shown to increase the proportion of reads mapping to miRNAs and QIAseq library prep was shown to introduce the least sequencing bias during processing and correlate most closely to independent quantification using RT-qPCR. miRNAs were shown to be significantly differentially expressed between case and control groups within disease cohorts. miR-185-3p was significantly downregulated in individuals with atrial fibrillation (AF) compared to healthy controls and improved on the discriminatory power of current AF markers B-type natriuretic peptide (BNP) and collagen 3 synthesis marker PIIINP. miRs 144-3p, 142-5p and 660-5p were shown to be significantly downregulated in individuals with AMD compared to healthy controls. Hierarchical clustering analysis of isomiR data did not improve separation of samples into defined groups however did identify potential batch effects introduced during library preparation, suggesting that isomiR analysis is more sensitive to technical variation and could be applied as a quality control measure during analysis. From the machine learning model, isomiRs were shown to make up >60% of the key features distinguishing between disease groups.Conclusion – Small RNA-Seq has been shown to be a reliable method for quantifying circulating miRNAs and identifying differential expression between disease and control groups. However, global miRNA profiling is unlikely to become a routine diagnostic procedure at present due to variable reliability and consistency stemming from the lack of a clear set of recommended experimental guidelines. A comprehensive, multicentre centre comparison of all available methods is required to facilitate adoption of a consistent and universally accepted set of guidelines regarding RNA extraction and library preparation workflow for miRNA detection from plasma. If this can be achieved, small RNA-Seq would provide an excellent platform for the discovery of novel biomarkers of diagnostic value that can subsequently be measured clinically using RT-qPCR.
Thesis embargoed until 31 July 2024.
|Date of Award||Jul 2021|
|Supervisor||David Simpson (Supervisor), Caroline Meharg (Supervisor) & Chris Watson (Supervisor)|
- circulating biomarkers
- next generation sequencing