4.8 Article

Enhanced Reliability of Avian Influenza Virus (AIV) and Newcastle Disease Virus (NDV) Identification Using Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry (MALDI-MS)

Journal

ANALYTICAL CHEMISTRY
Volume 83, Issue 5, Pages 1717-1725

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/ac102846q

Keywords

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Funding

  1. National Research Foundation of Korea (NRF) [2008-0061829]
  2. World Class University (WCU) [R32-10253]
  3. National Research Foundation of Korea [2008-0061829] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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In-solution enzymatic and nonenzymatic digestion methods have been successfully implemented in matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS)-based virus identification, extending to typing/subtyping of deadly influenza viruses. However, these methods are inefficient in obtaining more precise information on surface proteins of myxovirus particles, not only the hemagglutinin and neuraminidase of influenza virus but also the hemagglutinin-neuraminidase of Newcastle disease virus (NDV). Imbalances in viral protein composition cause ion suppression of tryptic fragments from low-abundant target proteins (surface proteins), adversely affecting reproducibility of mass spectra. Additionally, the coexistence of tryptic peptides from several proteins requires sophisticated statistical solutions for precise result interpretations. To circumvent these, we apply detergent-based (gel-free) partitioning of whole viruses into soluble surface proteins and insoluble virus materials, using differential centrifugation. MALDI-TOF or MALDI-TOF/TOF MS was applied to analyze tryptic peptides from separated viral proteins. In this study, we achieved type/subtype of avian influenza virus (AIV) within 5 h, based on 4 major proteins, by significantly reducing ion suppression and signal overlap from various protein sources. Hence, our approach can both yield dependable results and allow Web-based search engines to be directly employed, obviating the need for additional statistical strategy. Additionally, we demonstrate the utility of the method using NDV.

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