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A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood.

ABSTRACT: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease-19 (COVID-19), has emerged as the cause of a global pandemic. We used RNA sequencing to analyze 286 nasopharyngeal (NP) swab and 53 whole-blood (WB) samples from 333 patients with COVID-19 and controls. Overall, a muted immune response was observed in COVID-19 relative to other infections (influenza, other seasonal coronaviruses, and bacterial sepsis), with paradoxical down-regulation of several key differentially expressed genes. Hospitalized patients and outpatients exhibited up-regulation of interferon-associated pathways, although heightened and more robust inflammatory responses were observed in hospitalized patients with more clinically severe illness. Two-layer machine learning-based host classifiers consisting of complete (>1000 genes), medium (<100), and small (<20) gene biomarker panels identified COVID-19 disease with 85.1-86.5% accuracy when benchmarked using an independent test set. SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for COVID-19 diagnosis.

SUBMITTER: Ng DL

PROVIDER: S-EPMC7857687 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood.

Ng Dianna L DL Granados Andrea C AC Santos Yale A YA Servellita Venice V Goldgof Gregory M GM Meydan Cem C Sotomayor-Gonzalez Alicia A Levine Andrew G AG Balcerek Joanna J Han Lucy M LM Akagi Naomi N Truong Kent K Neumann Neil M NM Nguyen David N DN Bapat Sagar P SP Cheng Jing J Martin Claudia Sanchez-San CS Federman Scot S Foox Jonathan J Gopez Allan A Li Tony T Chan Ray R Chu Cynthia S CS Wabl Chiara A CA Gliwa Amelia S AS Reyes Kevin K Pan Chao-Yang CY Guevara Hugo H Wadford Debra D Miller Steve S Mason Christopher E CE Chiu Charles Y CY

Science advances 20210203 6

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease-19 (COVID-19), has emerged as the cause of a global pandemic. We used RNA sequencing to analyze 286 nasopharyngeal (NP) swab and 53 whole-blood (WB) samples from 333 patients with COVID-19 and controls. Overall, a muted immune response was observed in COVID-19 relative to other infections (influenza, other seasonal coronaviruses, and bacterial sepsis), with paradoxical down-regulation of several key di ...[more]

PMID: 33536218

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Project description: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.

Dataset Information

A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood.

Publications

A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood.

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