ABSTRACT
Influenza is a contagious respiratory illness caused by influenza viruses which possess the enormous threat to older people and young children. Rapid and precise discrimination of virus subtypes are quite crucial for the early therapy, prophylaxis and the prevention of epidemic outbreaks. Herein, a universal strategy, with influenza A virus (IAV) as a model, is proposed for the discrimination of virus subtypes based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Reference library based on nine IAVs subtypes (i.e., H1N1, H3N2, H4N8, H5N8, H6N6, H7N7, H9N2, H10N8, and H11N8) was set up for matching various IAVs subtypes. The simulative test spectra from IAVs showed that the corresponding IAVs subtypes could be distinguished in 90 min, accurately. Furthermore, the principal component analysis results also show that nine virus subtypes can be reliably distinguished. More importantly, this strategy provides an alternative method for identifying and distinguishing other viruses with high variability characteristics, such as SARS-CoV-2, which could be helpful for implementing public health strategies to counter pandemics. A MALDI-TOF MS based universal strategy for the discrimination of virus subtypes was developed, which possess the advantages of speed and high-accuracy. [Display omitted] • A home-made identification database of influenza A virus subtypes was set up. • The discrimination of influenza A virus subtypes could be finished within 90 min. • The influenza A virus subtypes could be distinguished with high accuracy. [ FROM AUTHOR]
ABSTRACT
Influenza is a contagious respiratory illness caused by influenza viruses which possess the enormous threat to older people and young children. Rapid and precise discrimination of virus subtypes are quite crucial for the early therapy, prophylaxis and the prevention of epidemic outbreaks. Herein, a universal strategy, with influenza A virus (IAV) as a model, is proposed for the discrimination of virus subtypes based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Reference library based on nine IAVs subtypes (i.e., H1N1, H3N2, H4N8, H5N8, H6N6, H7N7, H9N2, H10N8, and H11N8) was set up for matching various IAVs subtypes. The simulative test spectra from IAVs showed that the corresponding IAVs subtypes could be distinguished in 90 min, accurately. Furthermore, the principal component analysis results also show that nine virus subtypes can be reliably distinguished. More importantly, this strategy provides an alternative method for identifying and distinguishing other viruses with high variability characteristics, such as SARS-CoV-2, which could be helpful for implementing public health strategies to counter pandemics.
ABSTRACT
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for more than a year and has undergone several mutations and evolutions. Due to the lack of effective therapeutics and long-active vaccines, accurate and large-scale screening and early diagnosis of infected individuals are crucial to control the pandemic. Nevertheless, the current widely used RT-qPCR-based methods suffer from complicated temperature control, long processing time and the risk of false-negative results. Herein, we present a three-way junction induced exponential rolling circle amplification (3WJ-eRCA) combined MALDI-TOF MS assay for SARS-CoV-2 detection. The assay can detect simultaneously the target nucleocapsid (N) and open reading frame 1 ab (orf1ab) genes of SARS-CoV-2 in a single test within 30 min, with an isothermal process (55 °C). High specificity to discriminate SARS-CoV-2 from other coronaviruses, like SARS-CoV, MERS-CoV and bat SARS-like coronavirus (bat-SL-CoVZC45), was observed. We have further used the method to detect pseudovirus of SARS-CoV-2 in various matrices, e.g. water, saliva and urine. The results demonstrated a great potential of the method for large scale screening of COVID-19, which is an important part of the pandemic control.