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Multi-omics blood atlas reveals unique features of immune and platelet responses to SARS-CoV-2 Omicron breakthrough infection. | LitMetric

Multi-omics blood atlas reveals unique features of immune and platelet responses to SARS-CoV-2 Omicron breakthrough infection.

Immunity

Haihe Laboratory of Cell Ecosystem, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China. Electronic address:

Published: June 2023

AI Article Synopsis

  • The study analyzed over 1,000 blood samples from patients infected with the Omicron variant of SARS-CoV-2 to understand the host's immune response using various advanced techniques, known as multi-omics.
  • Researchers found that Omicron infections led to increased antiviral activity in platelets and changes in how these cells interacted with other immune cells, highlighting a unique immune response.
  • The study also identified a decrease in B cell activity and antibody production in patients who tested positive again for the virus, while a machine learning model was developed to predict the likelihood of re-positivity in these patients.

Article Abstract

Although host responses to the ancestral SARS-CoV-2 strain are well described, those to the new Omicron variants are less resolved. We profiled the clinical phenomes, transcriptomes, proteomes, metabolomes, and immune repertoires of >1,000 blood cell or plasma specimens from SARS-CoV-2 Omicron patients. Using in-depth integrated multi-omics, we dissected the host response dynamics during multiple disease phases to reveal the molecular and cellular landscapes in the blood. Specifically, we detected enhanced interferon-mediated antiviral signatures of platelets in Omicron-infected patients, and platelets preferentially formed widespread aggregates with leukocytes to modulate immune cell functions. In addition, patients who were re-tested positive for viral RNA showed marked reductions in B cell receptor clones, antibody generation, and neutralizing capacity against Omicron. Finally, we developed a machine learning model that accurately predicted the probability of re-positivity in Omicron patients. Our study may inspire a paradigm shift in studying systemic diseases and emerging public health concerns.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186977PMC
http://dx.doi.org/10.1016/j.immuni.2023.05.007DOI Listing

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