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Metatranscriptomic analysis of COVID-19 patients show disrupted airway microbiome

A team of researchers have conducted a metatranscriptomic characterisation study of patients with COVID-19 and found specific immune-associated host transcriptional signatures that could be used to improve diagnosis and indicate disease severity.

The study was published last week in Clinical Infectious Diseases, where the team analysed the metatranscriptomic profiles of 187 patients, 62 of whom had tested positive for COVID-19 and 125 of whom had been diagnosed with non-COVID-19 pneumonia. They analysed the transcriptional aspects of the pathogens, the microbiome and host responses to help build a host gene classifier based on the host transcriptional signature.

They found that the airway microbiomes of the COVID-19 patients had a reduced alpha diversity, with 18 taxa of differential abundance. They also found potentially pathogenic microbes in 47% of the patients with COVID-19, 58% of which were respiratory viruses. The analysis of host genes showed a transcriptional signature of 36 differentially expressed genes associated with immune pathways.

The authors wrote “compared to those with non-COVID-19 pneumonias, COVID-19 patients appeared to have a more disrupted airway microbiome with frequent potential concurrent infections and a special trigger host immune response in certain pathways such as interferon gamma signalling”.

The researchers constructed a database of 18,556 species of bacteria, viruses, fungi and parasites to characterise the microbial profiles of COVID-19 patients. They then analysed the unbiased metatranscriptomic sequencing data from their patient cohort, using the database for classification. They found 31 species in nasopharyngeal samples and 178 species in sputum samples with different abundance between the COVID-19 and non-COVID-19 patients. They then examined possible concurrent infections, where they identified 24 microbes with potential pathogenicity in some of the COVID-19 patients – which included 16 microbial species and 8 viral pathogens. They also found more potential co-infections with viruses than bacteria or fungi, implying that people should take caution when ruling out COVID-19 in patients diagnosed with other infections such as influenza.

The researchers then compared gene expression between COVID-19 and non-COVID-19 subjects to help understand the host transcriptional response to SARS-CoV-2, where they identified 279 differentially expressed genes (DEGs) in the nasopharyngeal samples.  Of the DEGs, 68.8% of them had reduced expression and 31.2% had increased expression in the COVID-19 cases. They then performed gene enrichment analysis with common DEGs using the KEGG and Reactome databases and found 16 differential pathways, 8 of which were related to immune signalling. 12 genes were associated with the immune pathway, and of which, the cytokine signalling pathway was the most deregulated, followed by innate immune system and neutrophil degranulation pathways, indicated critical roles of the immune system in COVID-19.

Finally, the researchers used 36 common genes to build a predictive classifier based on host transcriptional signature to help COVID-19 diagnosis. They found that the host signature could identify 10.5% of COVID-19 cases that would have been missed by a metagenomic assay if they were only analysing for the presence of SARS-CoV-2 sequences. To test the potential of the host gene classifier, they categorised the COVID019 subjects and found a clear clustering of severe and mild COVID-10 cases when they categorised the COVID-19 subjects.

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