COVID-19 individuals have a number of immunological responses, which can result in asymptomatic SARS-CoV-2 infection or death.
After studying the blood samples from nearly 200 COVID-19 patients, the research team have revealed underlying metabolic changes that regulate how immune cells respond to the disease.
These changes are related to the severity of the disease and may be used to predict patient survival.
The researchers took 374 blood samples – two from each patient within one week of being diagnosed with SARS-CoV-2 infection – and studied their plasma and single immune cells. The investigation includes 1,387 genes and 1,050 plasma metabolites involved in metabolic pathways.
The study found that greater COVID-19 severity is connected with metabolite changes in plasma, implying enhanced immune-related activity. Additionally, researchers discovered that each major immune cell type has a specific metabolic profile by single-cell sequencing.
“We have found metabolic reprogramming that is highly specific to individual immune cell classes (e.g. “killer” CD8+ T cells, “helper” CD4+ T cells, antibody-secreting B cells, etc.) and even cell subtypes, and the complex metabolic reprogramming of the immune system is associated with the plasma global metabolome and are predictive of disease severity and even patient death,” said co-first and co-corresponding author Dr. Yapeng Su.
“Such deep and clinically relevant insights on sophisticated metabolic reprogramming within our heterogeneous immune systems are otherwise impossible to gain without advanced single-cell multi-omic analysis.”
“This work provides significant insights for developing more effective treatments against COVID-19. It also represents a major technological hurdle,” said Dr Jim Heath, president and professor of ISB and co-corresponding author on the paper.
“Many of the data sets that are collected from these patients tend to measure very different aspects of the disease, and are analyzed in isolation. Of course, one would like these different views to contribute to an overall picture of the patient. The approach described here allows for the sum of the different data sets to be much greater than the parts, and provides for a much richer interpretation of the disease.”
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