Application of long-read RNA sequencing to characterise and distinguish between infections: a pathway to novel diagnostics
Irina Chelysheva, a member of the Oxford Vaccine Group (University of Oxford, UK) began her talk by describing Salmonella enterica serovar Typhi, the pathogen that causes typhoid fever. This disease causes over 150,000 deaths annually, with the highest disease burden in low- and middle-income countries. The symptoms of typhoid fever (fever, tiredness, dry cough) are non-specific, making it difficult to distinguish from other diseases with similar symptoms. This can cause misdiagnosis and overuse of antibiotics, contributing to antimicrobial resistance (AMR).
Irina then introduced how she and her team studied the transcriptomics of S. Typhi host infection with nanopore sequencing. The group performed a typhoid human challenge, in which healthy volunteers ingested S. Typhi bacteria; approximately 70% were then diagnosed with typhoid fever. Blood samples were taken both before the typhoid challenge and upon diagnosis. RNA was extracted from these samples, and prepared for sequencing using the PCR-cDNA Sequencing Kit (SQK-PCS110) from Oxford Nanopore. The cDNA libraries were sequenced on the PromethION platform, with two paired samples sequenced per flow cell. The bioinformatics pipeline used featured alignment of the transcriptome data with minimap2, quantification with Salmon, differential gene expression (DGE) analysis, and gene ontology enrichment analysis.
In total, the team generated >50 million reads, with a median transcript read length of ~700-900 bases. >13,000 genes were taken forward for downstream analysis. Principal component analysis (PCA) revealed two distinct clusters – one for the baseline samples and one for those taken after the onset of typhoid fever, indicating similar expression within the groups and differential expression between them. Moving on to DGE analysis, Irina highlighted the significant number of genes up- or down-regulated at the point of diagnosis. General pathways corresponding to the immune response to the infection, were observed to be upregulated, as well as some more specific pathways, such as the response to the bacterium in cells, demonstrating that it was possible to see from the transcriptomic data that the pathogen was bacterial. Going even further, they detected the cellular response to the lipopolysaccharide component of the outer membrane of S. Typhi.
Next, Irina and her team moved on to studying SARS-CoV-2, the cause of COVID-19. As of April 2021, the virus had caused over 3.1 million deaths. Again, COVID-19 has mainly non-specific symptoms (fever, tiredness, dry cough), and though reliable diagnostic tests have been developed, they are not always available. Multiple vaccines have been developed and approved, with one developed and tested by the Oxford Vaccine Group, produced in collaboration with AstraZeneca. For this RNA sequencing experiment, they took blood samples from volunteers in the Oxford vaccine trial – first, before they were given the vaccine or control, then upon testing positive or negative for COVID-19. Paired samples for those who received the vaccine control were taken forward for sequencing, using the same pipeline as for the typhoid study. This time, PCA showed clustering together of the baseline samples with those of the COVID-negative volunteers, with no differentially expressed genes identified between the groups. Comparing the baseline samples with the COVID-positive samples showed the two groups to separate into clearly distinguished clusters, with a ‘sizeable’ set of genes differentially expressed between them, here indicating the immune response to infection. In gene ontology analysis of the COVID-positive samples, they again observed some general immune response pathways, plus some specific to the response to the virus.
Irina then compared the typhoid fever and COVID-19 datasets to assess whether it was possible to distinguish between them on a transcriptomic level. Plotting the PCA analysis results for both datasets, she highlighted that not only were the baseline samples different to the point-of-diagnosis samples, but the typhoid-positive samples were different to the COVID-positive samples. Comparing the two disease-positive groups revealed a set of differentially expressed genes between typhoid fever and COVID-19; Irina described how these genes could potentially be used as biomarkers for each disease. Some of these represented genes that were previously characterised as being upregulated in Typhoid fever, but strikingly, they also identified novel Typhoid biomarkers, such as upregulation of NRN1 and P2RY14, previously described as cancer biomarkers. They also identified potential biomarkers for COVID-19 infection which were not upregulated in Typhoid fever. Some of these had been previously shown to be upregulated in COVID-19 or other viral infections, and some were specific to COVID-19 - such as LY6E, a potential restriction factor.
Concluding her talk, Irina noted how ‘Oxford Nanopore RNA-sequencing was able to clearly distinguish between typhoid fever and COVID-19 indicating the potential of this readily deployable technique, if it is similarly distinctive for other pathogens, to be a major step forward in rapid clinical diagnostics.’ Finally, she stressed the importance of rapid turnaround times for future diagnostics in tackling AMR.