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Metatranscriptomic Study of COVID-19 Patients Shows More Disrupted Airway Microbiome

NEW YORK – A team of researchers in China conducted a metatranscriptomic characterization study of COVID-19 patients, and identified specific immune-associated host transcriptional signatures that could be used as a tool for improving COVID-19 diagnosis and indicating disease severity.

In a study published on Thursday in Clinical Infectious Diseases, the researchers said they analyzed the metatranscriptomic profiles of 187 patients, 62 of whom had COVID-19 and 125 of whom had been diagnosed with non-COVID-19 pneumonia. They analyzed the transcriptional aspects of the pathogens, the microbiome, and host responses, and then built a host gene classifier based on the host transcriptional signature.

The airway microbiomes of the COVID-19 patients exhibited reduced alpha diversity, with 18 taxa of differential abundance, the researchers noted. Potentially pathogenic microbes were also detected in 47 percent of the COVID-19 cases, 58 percent of which were respiratory viruses. Host gene analysis revealed a transcriptional signature of 36 differentially expressed genes significantly associated with immune pathways such as cytokine signaling.

"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 signaling," the authors wrote. "The immune-associated host transcriptional signatures of COVID-19 hold promise as a tool for improving COVID-19 diagnosis and indicating disease severity."

To fully characterize the microbial profiles in COVID-19, the researchers constructed a database consisting of 18,556 species of bacteria, viruses, fungi, and parasites. They then analyzed the unbiased metatranscriptomic sequencing data from their patient cohort, using this database for taxonomic classification.

The investigators identified 31 species in nasopharyngeal samples and 178 species in sputum samples with different abundance between COVID-19 and non-COVID-19 cases. Most were less abundant in COVID-19 cases, though 18 species were less abundant in both groups. No species common to both groups had increased abundance.

To examine possible concurrent infections, the researchers identified 24 microbes with potential pathogenicity in some of the COVID-19 patients. These included 16 different microbial species — with Candida albicans and Human alphaherpesvirus 1 being the most frequently detected opportunistic pathogens — and eight viral pathogens, such as human influenza and respiratory syncytial viruses. They also observed more potential co-infections with viruses than with bacteria or fungi, implying that caution should be taken when ruling out COVID-19 in patients already diagnosed with other infections such as influenza.

To better understand the host transcriptional response to SARS-CoV-2 infection, the investigators then compared gene expression between COVID-19 and non-COVID-19 subjects, and identified a total of 279 differentially expressed genes (DEGs) in the nasopharyngeal samples, 68.8 percent of which had reduced expression and 31.2 percent of which had increased expression in COVID-19 cases. The sputum group appeared more differentiated in COVID-19, with a total of 4,454 DEGs, 73.1 percent of which were under-expressed and 26.9 percent of which were over-expressed, the researchers said. They also found 36 common DEGs in both groups, among which 30 had lower expression in COVID-19 and six had higher expression.

"We further performed gene set enrichment analysis with the common DEGs using the KEGG and Reactome databases, and identified 16 differential pathways, half related to immune signaling," the authors wrote. "The immune system itself considered as a pathway was significantly overrepresented, with 12 differentially expressed genes. Among the subcategories of immune signaling pathways, cytokine signaling was the most significantly deregulated, followed by innate immune system and neutrophil degranulation pathways, indicating the critical roles of innate immune system and cytokine signaling in COVID-19."

The researchers then used these 36 common genes in order to build a predictive classifier based on the host transcriptional signature that could aid in COVID-19 diagnosis. They found that the host signature could identify 10.5 percent of COVID-19 cases that would had been missed by a metagenomic assay if only analyzing for the presence of the SARS-CoV-2 sequences. They also categorized the COVID-19 subjects to test the potential of the host gene classifier to discern disease severity and found a clear segregated clustering of severe and mild COVID-19 cases.