Circulating in China and 158 other countries and areas, the ongoing COVID-19 outbreak has caused devastating mortality and posed a great threat to public health. However, efforts to identify effectively supportive therapeutic drugs and treatments has been hampered by our limited understanding of host immune response for this fatal disease. To characterize the transcriptional signatures of host inflammatory response to SARS-CoV-2 (HCoV-19) infection, we carried out transcriptome sequencing of the RNAs isolated from the bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) specimens of COVID-19 patients. Our results reveal distinct host inflammatory cytokine profiles to SARS-CoV-2 infection in patients, and highlight the association between COVID-19 pathogenesis and excessive cytokine release such as CCL2/MCP-1, CXCL10/IP-10, CCL3/MIP-1A, and CCL4/MIP1B. Furthermore, SARS-CoV-2 induced activation of apoptosis and P53 signalling pathway in lymphocytes may be the cause of patients’ lymphopenia. The transcriptome dataset of COVID-19 patients would be a valuable resource for clinical guidance on anti-inflammatory medication and understanding the molecular mechansims of host response.

A small number of cases of coronavirus disease 2019 (Covid-19) have been described in children,^1,2 and our understanding of the spectrum of illness is limited.³ We conducted a retrospective analysis involving hospitalized children in Wuhan, China.

From December 2019, an outbreak of unusual pneumonia was reported in Wuhan with many cases linked to Huanan Seafood Market that sells seafood as well as live exotic animals. We investigated two patients who developed acute respiratory syndromes after independent contact history with this market. The two patients shared common clinical features including fever, cough, and multiple ground-glass opacities in the bilateral lung field with patchy infiltration. Here, we highlight the use of a low-input metagenomic next-generation sequencing (mNGS) approach on RNA extracted from bronchoalveolar lavage fluid (BALF). It rapidly identified a novel coronavirus (named 2019-nCoV according to World Health Organization announcement) which was the sole pathogens in the sample with very high abundance level (1.5% and 0.62% of total RNA sequenced). The entire viral genome is 29,881 nt in length (GenBank MN988668 and MN988669, Sequence Read Archive database Bioproject accession PRJNA601736) and is classified into β-coronavirus genus. Phylogenetic analysis indicates that 2019-nCoV is close to coronaviruses (CoVs) circulating in Rhinolophus (Horseshoe bats), such as 98.7% nucleotide identity to partial RdRp gene of bat coronavirus strain BtCoV/4991 (GenBank KP876546, 370 nt sequence of RdRp and lack of other genome sequence) and 87.9% nucleotide identity to bat coronavirus strain bat-SL-CoVZC45 and bat-SL-CoVZXC21. Evolutionary analysis based on ORF1a/1b, S, and N genes also suggests 2019-nCoV is more likely a novel CoV independently introduced from animals to humans.

Quantitative real time PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, early treat, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 nucleic acid from 77 clinical throat swabs, and compared with RT-PCR in terms of the diagnostic accuracy based on the results of follow-up survey. According to our study, 26 samples from COVID-19 patients with negative RT-PCR reports were detected as positive by ddPCR. The sensitivity, specificity, PPV, NPV, negative likelihood ratio (NLR) and accuracy were improved from 40% (95% CI: 27-55%), 100% (95% CI: 54-100%), 100%, 16% (95% CI: 13-19%), 0.6 (95% CI: 0.48-0.75) and 47% (95% CI: 33-60%) for RT-PCR to 94% (95% CI: 83-99%), 100% (95% CI: 48-100%), 100%, 63% (95% CI: 36-83%), 0.06 (95% CI: 0.02-0.18) and 95% (95% CI: 84-99%) for ddPCR, respectively. Moreover, 6/14 (42.9%) convalescents were detected as positive by ddPCR, which still carry detectable SARS-CoV-2 at 5-12 days post discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the current standard RT-PCR.