ABSTRACT
BACKGROUNDCoronavirus disease 2019 (COVID-19) triggers distinct patterns of pneumonia progression with multiorgan disease, calling for cell- and/or tissue-type specific host injury markers. METHODSAn integrated hypothesis-free single biomarker analysis framework was performed on nasal swabs (n = 484) from patients with COVID-19 in GSE152075. The origin of candidate biomarker was assessed in single-cell RNA data (GSE145926). The candidate biomarker was validated in a cross-sectional cohort (n = 564) at both nucleotide and protein levels. RESULTSPhospholipase A2 group VII (PLA2G7) was identified as a candidate biomarker in COVID-19. PLA2G7 was predominantly expressed by proinflammatory macrophages in lungs emerging with progression of COVID-19. In the validation stage, PLA2G7 was found in patients with COVID-19 and pneumonia, especially in severe pneumonia, rather than patients suffered mild H1N1 influenza infection. Up to 100% positive rates of PLA2G7 were positively correlated with not only viral loads in patients with COVID-19 but also severity of pneumonia in non-COVID-19 patients. Although Ct values of PLA2G7 in severe pneumonia was significantly lower than that in moderate pneumonia (P = 7.2e-11), no differences were observed in moderate pneumonia with COVID-19 between severe pneumonia without COVID-19 (P = 0.81). Serum protein levels of PLA2G7, also known as lipoprotein-associated phospholipase A2 (Lp-PLA2), were further found to be elevated and beyond the upper limit of normal in patients with COVID-19, especially among the re-positive patients. CONCLUSIONSWe firstly identified and validated PLA2G7, a biomarker for cardiovascular diseases (CVDs), was abnormally enhanced in COVID-19 patients at both nucleotide and protein aspects. These findings provided indications into the prevalence of cardiovascular involvements seen in COVID-19 patients. PLA2G7 could be a hallmark of COVID-19 for monitoring disease progress and therapeutic response. FUNDINGThis study was supported by grants from China Mega-Projects for Infectious Disease (2018ZX10711001), National Natural Science Foundation of China (82041023).
Subject(s)
COVID-19ABSTRACT
Background: In December 2019, an outbreak of new type of coronavirus named COVID-19 occurred in Wuhan, Hubei Province, China. In a very short time, this virus spread rapidly over China, greatly threatening public health and economic development. The Chinese government acted quickly and implemented a series of strategies to prevent diffusion of this disease. We therefore sought to evaluate the effects of these Chinese strategies for controlling the spread of COVID-19. Methods: From the data of cumulative confirmed cases from provincial Health Commission websites of China, we performed model fitting and calculated the growth speed of cumulative confirmed patients. We further analyzed the time when this growth speed, the rate of the number of new cases, reached its maximum (Speedmax). Comparing different times to Speedmax of different areas in China, we calculated the dates at which the growth speed began to decline in different areas. Also, The number of plateaus were analyzed. Results: The quartic model showed the best fit. For almost all areas in mainland China, the speed of infections reached Speedmax and began to decline within 14 days; exceptions were Hebei, Heilongjiang, Hainan, Guizhou, and Hubei. The number of plateaus was significantly correlated with the emigration index. However, the distance from other areas to Hubei and the number of plateaus had little influence on when a province or area arrived at Speedmax. Once strict intervention strategies were implemented, diffusion and deterioration of COVID-19 were inhibited quickly and effectively over China. Conclusion: Our study suggests that Chinese strategies are highly effective on controlling the diffusion and deterioration of the novel coronavirus-infected pneumonia. These strategies supply experience and guidelines for other countries to control the COVID-19 epidemic.