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| Content Provider | frontiers |
|---|---|
| Author | Liu, Yang Wu, Yankang Liu, Bing Zhang, Youpeng San, Dan Chen, Yu Zhou, Yu Yu, Long Zeng, Haihong Zhou, Yun Zhou, Fuxiang Yang, Heng Yin, Lei Huang, Yafei |
| Abstract | The COVID-19 pandemic caused by SARS-CoV-2 infection is a global crisis, however, our current understanding of the host immune response to SARS-CoV-2 infection remains limited. Herein, we performed RNA sequencing using peripheral blood from acute and convalescent patients, and interrogated the dynamic changes of adaptive immune response to SARS-CoV-2 infection over time. Our results revealed numerous alterations in these cohorts in terms of gene expression profiles and the features of immune repertoire. Moreover, a machine learning method was developed and resulted in the identification of 5 independent biomarkers and a collection of biomarkers that could accurately differentiate and predict the development of COVID-19. Interestingly, the increased expression of one of these biomarkers, UCHL1, a molecule related to nervous system damage, was associated with the clustering of severe symptom. Importantly, analyses on immune repertoire metrics revealed the distinct kinetics of T cell and B cell response to SARS-CoV-2 infection, with B cell response plateaued in the acute phase and declined thereafter, whereas T cell response can be maintained for up to 6 months post infection onset and T cell clonality was positively correlated with the serum level of anti-SARS-CoV-2 IgG. Together, the significantly altered genes or biomarkers, as well as the abnormally high levels of B cell response in acute infection may contribute to the pathogenesis of COVID-19 through mediating inflammation and immune responses, whereas prolonged T cell response in the convalescents might help these patients in preventing reinfection. Thus, our findings could provide insight into the underlying molecular mechanism of host immune response to COVID-19 and facilitate the development of novel therapeutic strategies and effective vaccines. |
| ISSN | 16643224 |
| DOI | 10.3389/fimmu.2021.677025 |
| Volume Number | 12 |
| Journal | Frontiers in Immunology |
| Language | English |
| Publisher Date | 2021-08-24 |
| Access Restriction | Open |
| Subject Keyword | Machine learning SARS-CoV-2 Transcriptomic characteristics IgG Biomarker |
| Content Type | Text |
| Resource Type | Article |
| Subject | Immunology and Allergy Immunology |
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