COVID-19 - Coronavirus disease 2019

Real-time blood transcriptomic of COVID-19 patient – COVIDOMICs

Submission summary

The COVID-19 epidemic is spreading massively. This virus being new, the questions of diagnosis, prognosis, choice of treatment and pathophysiology are open.
The aim is to generate standardized, real-time transcriptome data from whole blood of COVID patients, looking for diagnostic, prognostic and predictive response molecular markers. These data may also help for better understanding of COVID infection.
Methods :
-A pilot study of 500 patients hospitalized for suspected COVID (450 COVID positive and 50 COVID negative controls). Power estimation: 150 expected events; main end-point: ARDS / criteria for intensive care admission in normal condition.
- Legal framework: the APHP Covid cohort (ethics committee (CPP) submitted ; CNIL fast-track on-going ); this project has been presented to the COVID scientific committee of APHP, and the specific BCT RNA tubes required for blood transcriptome have been included in this framework.
-Extraction of whole blood RNA; RNAseq (Illumina) transcriptome generation
-Bioinformatics: transcripts alignments and counts (Star), unsupervised classifications (NMF), group comparison (DESeq).
- link with basic clinical data (survival, treatments), selection of prognostic and predictive markers (regression +/- penalized models, non-linear models).

Expected results :
The comparison of positive patients and negative controls will provide a transcriptomic signature of COVID infection, and will be compared with current PCR-based virus detection techniques.
Using unsupervised classification, patients clusters may be identified, associated with specific profiles, potentially opening the way to novel mechanisms.
Genes which expression is associated with pejorative evolution will be identified. For these genes, expression will then be assayed by quantitative RT-PCR. Such markers could allow better anticipation of individual care.
If it turns out that certain treatments are effective in certain patients, specific transcriptome profiles will be searched, in order to identify early potential responders.
As the expression data are not identifying patients, we plan to release publicly this data, as soon as they are produced, associated with anonymized clinical characteristics. These data will allow multiple analysis by the bioinformatics community.
Conclusion
We offer a large-scale transcriptome characterization of whole blood from COVID patients, with data being made available to the community as it is produced.

Project coordination

Guillaume Assié (Institut Cochin)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

IC Institut Cochin

Help of the ANR 199,999 euros
Beginning and duration of the scientific project: April 2020 - 12 Months

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