The research group determined, among other findings, 2 molecularly distinct subsets of long COVID signs with opposing gene expression patterns during acute COVID-19 in plasma cells, the immune systems antibody-producing cells. These opposing patterns observed in the very same cells, as well as extra unique patterns observed in other cell types, point to the presence of multiple independent processes leading to various long COVID signs; these procedures are currently present throughout the intense infection.
Utilizing the Mount Sinai COVID-19 Biobank, the researchers taken a look at gene expression data in blood samples from more than 500 patients hospitalized with COVID-19 between April and June 2020. The team evaluated each gene expressed in the blood for association with each long COVID symptom, accounting for ICU admission, COVID-19 severity during hospitalization, sex, age, and other variables.
Mount Sinai researchers have actually published one of the very first studies to associate modifications in blood gene expression throughout COVID-19 with the post-acute sequelae of SARS-CoV-2 infection, also called “long COVID,” in patients more than a year after they were hospitalized with severe COVID-19. Credit: Nature Medicine/Icahn School of Medicine at Mount Sinai
Mount Sinai researchers have actually published one of the very first research studies to associate changes in blood gene expression throughout COVID-19 with “long COVID” in patients more than a year after they were hospitalized with severe COVID-19. Long COVID is the typical name utilized for what is understood more technically as post-acute sequelae of SARS-CoV-2 infection.
The findings, released in the journal Nature Medicine on December 8, highlight the requirement for higher attention at the infection stage to much better comprehend how the processes that start then eventually cause long COVID, which could help improve both avoidance strategies and treatment choices for COVID-19 survivors experiencing persistent signs after infection.
The research study group determined, to name a few findings, two molecularly distinct subsets of long COVID signs with opposing gene expression patterns during severe COVID-19 in plasma cells, the body immune systems antibody-producing cells. In patients who went on to develop lung issues, antibody production genes were less abundant. For patients with other signs such as the loss of smell or taste and sleep disturbances, the very same antibody production genes were more abundant instead. These opposing patterns observed in the same cells, as well as additional special patterns observed in other cell types, indicate the presence of several independent procedures leading to various long COVID signs; these processes are already present throughout the intense infection.
” Our findings reveal that molecular processes leading to long COVID are already noticeable throughout COVID-19 infection,” said co-corresponding author Noam D. Beckmann, PhD, Assistant Professor of Medicine (Data Driven and Digital Medicine) and Associate Director of Data Science Strategy at The Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai. “Furthermore, we see the start of multiple molecularly unique paths resulting in long COVID, providing an unique viewpoint into distinctions between long-term symptoms.”
Using the Mount Sinai COVID-19 Biobank, the researchers analyzed gene expression data in blood samples from more than 500 clients hospitalized with COVID-19 between April and June 2020. The group evaluated each gene revealed in the blood for association with each long COVID sign, accounting for ICU admission, COVID-19 intensity throughout hospitalization, sex, age, and other variables.
” For long COVID signs, like odor or taste problems, connecting antibody gene expression in plasma cells with the actual levels of antibodies versus the SARS-CoV-2 spike protein demonstrates a direct link to the bodys action to the infection,” said lead author Ryan C. Thompson, PhD, Data Science Analyst at The Charles Bronfman Institute for Personalized Medicine. “On the other hand, the gene expression pattern for lung problems does not match up with SARS-CoV-2-specific antibody levels, highlighting the various immune procedures resulting in long COVID that are activated by COVID-19.”
The team stated long COVID still stays badly defined and future studies must take the initial phase of infection into account to more adequately characterize the molecular procedures of long COVID and determine biomarkers that can help predict, treat, and prevent extended signs.
” Our findings show there is the potential to use data from the infection stage to anticipate what might occur to the patient months later on,” said co-corresponding author Alexander W. Charney, MD, PhD, Associate Professor of Genetics and Genomic Sciences, and Co-Director of The Charles Bronfman Institute for Personalized Medicine. “We must not neglect the infection phase in research on long COVID– this is plainly a vital window of time where the bodys response to SARS-CoV-2 might be setting the stage for what is to come.”
Reference: “Molecular states throughout severe COVID-19 reveal unique etiologies of long-lasting sequelae” by Ryan C. Thompson, Nicole W. Simons, Lillian Wilkins, Esther Cheng, Diane Marie Del Valle, Gabriel E. Hoffman, Carlo Cervia, Brian Fennessy, Konstantinos Mouskas, Nancy J. Francoeur, Jessica S. Johnson, Lauren Lepow, Jessica Le Berichel, Christie Chang, Aviva G. Beckmann, Ying-chih Wang, Kai Nie, Nicholas Zaki, Kevin Tuballes, Vanessa Barcessat, Mario A. Cedillo, Dan Yuan, Laura Huckins, Panos Roussos, Thomas U. Marron, The Mount Sinai COVID-19 Biobank Team, Benjamin S. Glicksberg, Girish Nadkarni, James R. Heath, Edgar Gonzalez-Kozlova, Onur Boyman, Seunghee Kim-Schulze, Robert Sebra, Miriam Merad, Sacha Gnjatic, Eric E. Schadt, Alexander W. Charney and Noam D. Beckmann, 8 December 2022, Nature Medicine.DOI: 10.1038/ s41591-022-02107-4.
The University Hospital of Zurich, University of Zurich, University of Washington, and health intelligence business Sema4 contributed to this research study.