- Ng, Dianna L;
- Granados, Andrea C;
- Santos, Yale A;
- Servellita, Venice;
- Goldgof, Gregory M;
- Meydan, Cem;
- Sotomayor-Gonzalez, Alicia;
- Levine, Andrew G;
- Balcerek, Joanna;
- Han, Lucy M;
- Akagi, Naomi;
- Truong, Kent;
- Neumann, Neil M;
- Nguyen, David N;
- Bapat, Sagar P;
- Cheng, Jing;
- Martin, Claudia Sanchez-San;
- Federman, Scot;
- Foox, Jonathan;
- Gopez, Allan;
- Li, Tony;
- Chan, Ray;
- Chu, Cynthia S;
- Wabl, Chiara A;
- Gliwa, Amelia S;
- Reyes, Kevin;
- Pan, Chao-Yang;
- Guevara, Hugo;
- Wadford, Debra;
- Miller, Steve;
- Mason, Christopher E;
- Chiu, Charles Y
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.