The COVID-19 pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Herein, we profiled host responses to COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by ELISA using a larger cohort. These markedly altered proteins, including the biomarkers mediate pathophysiological pathways such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers, and shed light on the pathogenesis and potential therapeutic targets of COVID-19.

Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients’ demography, geographic locations and season of illness in China.

Mycoplasma pneumoniae is one of the major respiratory bacterial pathogens that cause pneumonia in humans. Multiple-locus variable-number tandem-repeat analysis (MLVA) is currently the most discriminative method for typing M. pneumoniae strains. To better understand the epidemic of M. pneumoniae-related pneumonia in pediatric patients in Beijing, China, we performed MLVA analysis on 118 specimens collected during an epidemic from 2010–2012. Eleven distinct MLVA types were identified, including four novel types. There was no obvious association of macrolide resistance with any of the genotypes. Considering the instability of VNTR locus Mpn1, we propose an amended MLVA nomenclature system based on the remaining four VNTR loci.

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