Haocheng Zhang1, Jing-Wen Ai1, Wenjiao Yang2, Xian Zhou1, Fusheng He2, Shumei Xie2, Weiqi Zeng2,3, Yang Li1, Yiqi Yu1, Xuejing Gou2, Yongjun Li2, Xiaorui Wang2, Hang Su2, Zhaoqin Zhu4, Teng Xu2,3, Wenhong Zhang1. 1. Department of Infection Diseases, Huashan Hospital Affiliated to Fudan University, Shanghai, China. 2. Vision Medicals Center for Infectious Diseases, Guangzhou, Guangdong, China. 3. Key Laboratory of Animal Gene Editing and Animal Cloning in Yunnan Province and College of Veterinary Medicine, Yunnan Agricultural University, Kunming, China. 4. Department of Laboratory Medicine, Shanghai Public Health Clinical Center, Shanghai, China.
Abstract
BACKGROUND: The recent identification of a novel coronavirus, also known as severe acute respiratory syndrome coronavirus 2, has caused a global outbreak of respiratory illnesses. The rapidly developing pandemic has posed great challenges to diagnosis of this novel infection. However, little is known about the metatranscriptomic characteristics of patients with coronavirus disease 2019 (COVID-19). METHODS: We analyzed metatranscriptomics in 187 patients (62 cases with COVID-19 and 125 with non-COVID-19 pneumonia). Transcriptional aspects of 3 core elements, pathogens, the microbiome, and host responses, were evaluated. Based on the host transcriptional signature, we built a host gene classifier and examined its potential for diagnosing COVID-19 and indicating disease severity. RESULTS: The airway microbiome in COVID-19 patients had reduced alpha diversity, with 18 taxa of differential abundance. Potentially pathogenic microbes were also detected in 47% of the COVID-19 cases, 58% of which were respiratory viruses. Host gene analysis revealed a transcriptional signature of 36 differentially expressed genes significantly associated with immune pathways, such as cytokine signaling. The host gene classifier built on such a signature exhibited the potential for diagnosing COVID-19 (area under the curve of 0.75-0.89) and indicating disease severity. CONCLUSIONS: Compared with those with non-COVID-19 pneumonias, COVID-19 patients appeared to have a more disrupted airway microbiome with frequent potential concurrent infections and a special trigger host immune response in certain pathways, such as interferon-gamma signaling. The immune-associated host transcriptional signatures of COVID-19 hold promise as a tool for improving COVID-19 diagnosis and indicating disease severity.
BACKGROUND: The recent identification of a novel coronavirus, also known as severe acute respiratory syndrome coronavirus 2, has caused a global outbreak of respiratory illnesses. The rapidly developing pandemic has posed great challenges to diagnosis of this novel infection. However, little is known about the metatranscriptomic characteristics of patients with coronavirus disease 2019 (COVID-19). METHODS: We analyzed metatranscriptomics in 187 patients (62 cases with COVID-19 and 125 with non-COVID-19 pneumonia). Transcriptional aspects of 3 core elements, pathogens, the microbiome, and host responses, were evaluated. Based on the host transcriptional signature, we built a host gene classifier and examined its potential for diagnosing COVID-19 and indicating disease severity. RESULTS: The airway microbiome in COVID-19patients had reduced alpha diversity, with 18 taxa of differential abundance. Potentially pathogenic microbes were also detected in 47% of the COVID-19 cases, 58% of which were respiratory viruses. Host gene analysis revealed a transcriptional signature of 36 differentially expressed genes significantly associated with immune pathways, such as cytokine signaling. The host gene classifier built on such a signature exhibited the potential for diagnosing COVID-19 (area under the curve of 0.75-0.89) and indicating disease severity. CONCLUSIONS: Compared with those with non-COVID-19 pneumonias, COVID-19patients appeared to have a more disrupted airway microbiome with frequent potential concurrent infections and a special trigger host immune response in certain pathways, such as interferon-gamma signaling. The immune-associated host transcriptional signatures of COVID-19 hold promise as a tool for improving COVID-19 diagnosis and indicating disease severity.
Authors: Hong Zhou; Cixiu Li; Tao Hu; Ti Liu; Nan Ni; Weijun Chen; Huailong Zhao; Shiman Ruan; Juan Li; Honglong Wu; Sarah François; Oliver G Pybus; Edward C Holmes; Dianmin Kang; Peiqiang Hou; Weifeng Shi Journal: J Infect Date: 2020-12-08 Impact factor: 6.072
Authors: Brendan J Keating; Eyas H Mukhtar; Eric D Elftmann; Feyisope R Eweje; Hui Gao; Lina I Ibrahim; Ranganath G Kathawate; Alexander C Lee; Eric H Li; Krista A Moore; Nikhil Nair; Venkata Chaluvadi; Janaiya Reason; Francesca Zanoni; Alexander T Honkala; Amein K Al-Ali; Fatima Abdullah Alrubaish; Maha Ahmad Al-Mozaini; Fahad A Al-Muhanna; Khaldoun Al-Romaih; Samuel B Goldfarb; Ryan Kellogg; Krzysztof Kiryluk; Sarah J Kizilbash; Taisa J Kohut; Juhi Kumar; Matthew J O'Connor; Elizabeth B Rand; Robert R Redfield; Benjamin Rolnik; Joseph Rossano; Pablo G Sanchez; Arash Alavi; Amir Bahmani; Gireesh K Bogu; Andrew W Brooks; Ahmed A Metwally; Tejas Mishra; Stephen D Marks; Robert A Montgomery; Jay A Fishman; Sandra Amaral; Pamala A Jacobson; Meng Wang; Michael P Snyder Journal: Transpl Int Date: 2021-05-05 Impact factor: 3.782