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Identification and characterization of Long COVID-19 patients by whole blood transcriptomics

Identification and characterization of Long COVID-19 patients by whole blood transcriptomics
通过全血转录组学鉴定和表征 Long COVID-19 患者
批准号:
466168626
负责人:
Dr. Anna Aschenbrenner
金额:
$0.0万
依托单位:
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2021
资助国家:
德国
项目状态:
已结题
起止时间:
2020-12-31 至 2022-12-31

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中文摘要
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英文摘要
Increasing evidence indicates a rising prevalence for Long COVID-19, a recently coined term for non-recovering patients with persisting clinical symptoms for more than six months after acute COVID-19. Although a derailed immune system has been postulated as a possible cause, there are currently no means of identifying Long COVID-19 patients early post acute COVID-19. We have recently demonstrated the use of whole blood transcriptomics to build disease classifiers for acute COVID-19 as well as dissect disease heterogeneity by molecular phenotypes. The DeCOI (German COVID-19 Omics) network has established patient cohorts at multiple sites in Germany, which have already proven tremendously valuable in the analysis and understanding of acute COVID-19. Bringing together clinical scientists, DFG-funded sequencing centers, and computational experts, we will combine the knowledge and expertise in clinical infectiology, genomics and machine learning to shed light on Long COVID-19. We will use the samples from these cohorts to establish whole blood transcriptome signatures and classifiers to identify Long COVID-19 patients and determine their molecular phenotypes to elucidate disease heterogeneity. Further, we will identify possible genetic susceptibility for Long COVID-19 by aberrant expression analysis as well as assessing blood-derived microbial profiles as potential biomarker. Collectively, these analyses will enable the identification of molecular mechanisms of Long-COVID-19-related biology and potential therapeutic targets for further exploitation in clinical trials.
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Determination of immunological deviations in post-COVID-19 syndromes by single cell omics
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