Abstract
BackgroundCOVID-19, caused by SARS-CoV-2 virus, is a global pandemic with high mortality and morbidity. Limited diagnostic methods hampered the infection control. Since the direct detection of virus mainly by RT-PCR may cause false-negative outcome, host response-dependent testing may serve as a complementary approach for improving COVID-19 diagnosis.ObjectiveOur study discovered a highly-preserved transcriptional profile of Type I interferon (IFN-I)-dependent genes for COVID-19 complementary diagnosis.MethodsComputational language R-dependent machine learning was adopted for mining highly-conserved transcriptional profile (RNA-sequencing) across heterogeneous samples infected by SARS-CoV-2 and other respiratory infections. The transcriptomics/high-throughput sequencing data were retrieved from NCBI-GEO datasets (GSE32155, GSE147507, GSE150316, GSE162835, GSE163151, GSE171668, GSE182569). Mathematical approaches for homological analysis were as follows: adjusted rand index-related similarity analysis, geometric and multi-dimensional data interpretation, UpsetR, t-distributed Stochastic Neighbor Embedding (t-SNE), and Weighted Gene Co-expression Network Analysis (WGCNA). Besides, Interferome Database was used for predicting the transcriptional factors possessing IFN-I promoter-binding sites to the key IFN-I genes for COVID-19 diagnosis.ResultsIn this study, we identified a highly-preserved gene module between SARS-CoV-2 infected nasal swab and postmortem lung tissue regulating IFN-I signaling for COVID-19 complementary diagnosis, in which the following 14 IFN-I-stimulated genes are highly-conserved, including BST2, IFIT1, IFIT2, IFIT3, IFITM1, ISG15, MX1, MX2, OAS1, OAS2, OAS3, OASL, RSAD2, and STAT1. The stratified severity of COVID-19 may also be identified by the transcriptional level of these 14 IFN-I genes.ConclusionUsing transcriptional and computational analysis on RNA-seq data retrieved from NCBI-GEO, we identified a highly-preserved 14-gene transcriptional profile regulating IFN-I signaling in nasal swab and postmortem lung tissue infected by SARS-CoV-2. Such a conserved biosignature involved in IFN-I-related host response may be leveraged for COVID-19 diagnosis.
Highlights
The novel coronavirus disease 2019 (COVID-19) induced by SARS-CoV-2 infection has resulted in a sustained threat to human life and economic growth
COVID-19 progression is driven by the populations of myeloid-lineage cells with distinct inflammatory transcriptional features in blood, lung, and airway [7]. These findings suggested that identification of a specific genomic profile of host response may be served as a supplementary method for COVID-19 diagnosis
Our study aims to identify a common diagnostic host characteristic from nasal swabs and lung tissues that can supplement the diagnostic strategy of COVID-19
Summary
The novel coronavirus disease 2019 (COVID-19) induced by SARS-CoV-2 infection has resulted in a sustained threat to human life and economic growth. Clinical manifestations of SARS-CoV-2 infection vary from asymptomatic to severe symptoms [2] Such a wide range of clinical features make it difficult to establish a highly-conserved diagnostic profile of COVID-19. Using RT-PCR alone may yield false-negative results due to fluctuated viral loads and evolution [4]. This adverse situation is detrimental for hampering COVID-19 outbreak. Apart from the direct recognition for SARSCoV-2, deciphering the host response, especially the virusrelated fluctuated genomic profile, may be pivotal for serving as a supplementary approach for COVID-19 diagnosis. Since the direct detection of virus mainly by RT-PCR may cause false-negative outcome, host response-dependent testing may serve as a complementary approach for improving COVID-19 diagnosis
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