tetano
Editor, Senior Moderator
Cell Discov
. 2021 Mar 30;7(1):19.
doi: 10.1038/s41421-021-00248-3.
Integrated characterization of SARS-CoV-2 genome, microbiome, antibiotic resistance and host response from single throat swabs
Bo Lu[SUP] #[/SUP][SUP] 1 2 [/SUP], Yi Yan[SUP] #[/SUP][SUP] 3 4 5 6 [/SUP], Liting Dong[SUP] #[/SUP][SUP] 1 2 [/SUP], Lingling Han[SUP] #[/SUP][SUP] 7 [/SUP], Yawei Liu[SUP] #[/SUP][SUP] 8 [/SUP], Junping Yu[SUP] 3 [/SUP], Jianjun Chen[SUP] 3 4 5 [/SUP], Danyang Yi[SUP] 1 [/SUP], Meiling Zhang[SUP] 1 [/SUP], Xin Deng[SUP] 9 [/SUP], Chao Wang[SUP] 7 [/SUP], Runkun Wang[SUP] 7 [/SUP], Dengpeng Wang[SUP] 10 [/SUP], Hongping Wei[SUP] 11 [/SUP], Di Liu[SUP] 12 13 14 15 16 [/SUP], Chengqi Yi[SUP] 17 18 19 [/SUP]
Affiliations
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, poses a severe threat to humanity. Rapid and comprehensive analysis of both pathogen and host sequencing data is critical to track infection and inform therapies. In this study, we performed unbiased metatranscriptomic analysis of clinical samples from COVID-19 patients using a recently developed RNA-seq library construction method (TRACE-seq), which utilizes tagmentation activity of Tn5 on RNA/DNA hybrids. This approach avoids the laborious and time-consuming steps in traditional RNA-seq procedure, and hence is fast, sensitive, and convenient. We demonstrated that TRACE-seq allowed integrated characterization of full genome information of SARS-CoV-2, putative pathogens causing coinfection, antibiotic resistance, and host response from single throat swabs. We believe that the integrated information will deepen our understanding of pathogenesis and improve diagnostic accuracy for infectious diseases.
. 2021 Mar 30;7(1):19.
doi: 10.1038/s41421-021-00248-3.
Integrated characterization of SARS-CoV-2 genome, microbiome, antibiotic resistance and host response from single throat swabs
Bo Lu[SUP] #[/SUP][SUP] 1 2 [/SUP], Yi Yan[SUP] #[/SUP][SUP] 3 4 5 6 [/SUP], Liting Dong[SUP] #[/SUP][SUP] 1 2 [/SUP], Lingling Han[SUP] #[/SUP][SUP] 7 [/SUP], Yawei Liu[SUP] #[/SUP][SUP] 8 [/SUP], Junping Yu[SUP] 3 [/SUP], Jianjun Chen[SUP] 3 4 5 [/SUP], Danyang Yi[SUP] 1 [/SUP], Meiling Zhang[SUP] 1 [/SUP], Xin Deng[SUP] 9 [/SUP], Chao Wang[SUP] 7 [/SUP], Runkun Wang[SUP] 7 [/SUP], Dengpeng Wang[SUP] 10 [/SUP], Hongping Wei[SUP] 11 [/SUP], Di Liu[SUP] 12 13 14 15 16 [/SUP], Chengqi Yi[SUP] 17 18 19 [/SUP]
Affiliations
- PMID: 33785729
- DOI: 10.1038/s41421-021-00248-3
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, poses a severe threat to humanity. Rapid and comprehensive analysis of both pathogen and host sequencing data is critical to track infection and inform therapies. In this study, we performed unbiased metatranscriptomic analysis of clinical samples from COVID-19 patients using a recently developed RNA-seq library construction method (TRACE-seq), which utilizes tagmentation activity of Tn5 on RNA/DNA hybrids. This approach avoids the laborious and time-consuming steps in traditional RNA-seq procedure, and hence is fast, sensitive, and convenient. We demonstrated that TRACE-seq allowed integrated characterization of full genome information of SARS-CoV-2, putative pathogens causing coinfection, antibiotic resistance, and host response from single throat swabs. We believe that the integrated information will deepen our understanding of pathogenesis and improve diagnostic accuracy for infectious diseases.