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Multi-Omics integration analysis of respiratory specimen characterizes baseline molecular determinants associated with COVID-19 diagnosis.
Jaswinder Singh Maras; Shvetank Sharma; Adil Rafiq Bhat; Reshu Aggarwal; Ekta Gupta; Shiv K Sarin.
Afiliación
  • Jaswinder Singh Maras; Institute of Liver and Biliary Sciences
  • Shvetank Sharma; Institute of Liver and Biliary Sciences
  • Adil Rafiq Bhat; Institute of Liver and Biliary Sciences
  • Reshu Aggarwal; Institute of Liver and Biliary Sciences
  • Ekta Gupta; Institute of Liver and Biliary Sciences
  • Shiv K Sarin; Institute of Liver and Biliary Sciences (ILBS)
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20147082
ABSTRACT
Rapid diagnosis and precise prognostication of SARS-CoV-2 infection remains a major challenge. A multi-omic approach was adopted, and in the discovery phase, global proteome/metaproteome/metabolome were analysed in the respiratory specimens of SARS-CoV-2 positive [n=20], negative [n=20], and H1N1 positive [n=5] cases. We identified MX1 (MX Dynamin Like GTPase 1) and WARS (Tryptophan--tRNA ligase) as clues to viral diagnosis and validated in 200 SARS-CoV-2 suspects. MX1 >30pg/ml and WARS >25ng/ml segregated virus positives patients [(AUC=94%CI(0.91-0.97)]. Distinct increase in SARS-CoV-2 induced immune activation, metabolic reprograming and a decrease in oxygen transport, wound healing, fluid regulation, vitamin and steroid metabolism was seen (p<0.05). Multi-omics profiling correlated with viraemia and segregated asymptomatic COVID-19 patients. Additionally, the multiomics approach identified increased respiratory pathogens [Burkholderiales, Klebsiella pneumonia] and decreased lactobacillus salivarius (FDR<0.05, p<0.05) in COVID-19 specimens. ConclusionNovel proteins [MX1 and WARS] can rapidly and reliably diagnose SARS-CoV-2 infection and identify asymptomatic and mild disease.
Licencia
cc_by_nc_nd
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint