The utility of RNA and transcriptomic analysis in improving diagnostic and understanding disease mechanism in respiratory disorders; exemplars Primary Ciliary Dyskinesia and COVID-19

Field: Medicine (Health Sciences)

Primary ciliary dyskinesia (PCD) is a primarily autosomal recessive genetically heterogenous disease with a worldwide prevalence of around 1 in 7,500 live births. Patients with PCD have an impaired mucociliary clearance due to abnormal ciliary movement. Currently no single diagnostic test can be used to diagnose PCD and multiple diagnostic tests are implemented with one of these being genetic testing. Despite genetic testing contributing to the confirmation of PCD, in 30% of the cases a complete genetic diagnosis remains unsolved due to the presence of variants of unknown significance or failure of identifying biallelic pathogenic variants. This work aims to uplift the PCD diagnostic rate through implementing transcriptome analysis focusing on deleterious alternative splicing events. Alternative splicing is a mechanism whereby the non-coding introns, located in between protein-coding exons, are removed and the coding exons ligated together, resulting in a mature mRNA molecule. A global transcriptome comparison between ex vivo and in vitro Air-Liquid-Interface (ALI) cultured nasal epithelial cells revealed transcriptome similarity in cilia related gene expression from ALI-culture day 14 onwards. Subsequent analysis revealed that the majority of 49 known PCD causative genes had peak gene expression on ALI-culture day 21 indicating that this would be an ideal time-point for RNA isolation. Analysis of protein-coding transcript abundances revealed that, although for the majority of the 49 PCD causative genes the highest abundance was found for the default clinical MANE transcript, for some genes other protein-coding transcript were more abundant. A proof-of-concept study with four known PCD patients revealed that the impact of previously identified causative variants on splicing patterns of the relevant gene was most prominently detected on the optimal ALI-culture time-point for RNA isolation. Furthermore, it demonstrated that the bioinformatic tools used could successfully identify causative aberrant splicing events in patients with nucleotide variants predicted to impact RNA splicing, while no causative aberrant splicing events were detected in the patients with nucleotide variants not predicted to impact RNA splicing. After these initial promising results an additional 23 patients were assessed for deleterious aberrant splicing events bringing the total to 27 patients assessed. Overall, through transcriptome analysis a diagnostic uplift of 22% was achieved through identifying both deleterious aberrant splicing events and downregulation of expression of the PCD causative gene. These deleterious aberrant splicing events included exon skipping, usage of a novel splice site, inclusion of a pseudoexon, and mutually exclusive exon usage. Furthermore, for 26% of cases the known PCD causative gene was found to be downregulated, with no deleterious aberrant splicing event detected. In conclusion, the transcriptome of in vitro ALI cultured nasal epithelial cells from day 14 onwards are a good representation, compared to ex vivo nasal epithelium, of cilia related gene expression and protein-coding gene transcript abundances. By using predetermined optimal ALI-culture time-points for RNA isolation, transcriptome analysis of ALI cultured nasal epithelial cells was able to uplift the diagnostic rate in PCD patients with an incomplete genetic diagnosis. The uplift in the diagnostic rate obtained through the bioinformatic pipeline implemented consisted out of additional diagnostic information involving several different deleterious aberrant splicing events and/or decreased expression of PCD causative genes. During this PhD candidature the COVID-19 pandemic, caused by SARS-CoV-2, spread around the world. This led to incorporation of a cross-disciplinary and cross-institutional whole blood RNA-seq study whereby the immune responses were assessed between patients with COVID-19 and patients with influenza. It was found that an increased innate immune response was more associated with influenza and COVID-19 non-survivors, while an increased adaptive immune response was associated with COVID-19 survivors. These distinct gene expression differences led to the identification of a distinct immune signature gene expression profile which could be used to classify, upon admission to hospital, patients whom are likely to survive or not.