Multi-omics analysis of attenuated variant reveals potential evaluation marker of host damaging for SARS-CoV-2 variants

SARS-CoV-2 continues to threaten human society by generating novel variants via mutation and recombination.The high number of mutations that appeared in emerging variants not only enhanced their immune-escaping ability but also made it difficult to predict the pathogenicity and virulence based on viral nucleotide sequences.Molecular markers for evaluating the pathogenicity of new variants are therefore needed.By comparing host responses to wild-type and variants with attenuated pathogenicity at proteome and metabolome levels,six key molecules on the polyamine biosynthesis pathway including putrescine,SAM,dc-SAM,ODC1,SAMS,and SAMDC were found to be differentially upregulated and associated with pathogenicity of variants.To validate our discovery,human airway organoids were subsequently used which recapitulates SARS-CoV-2 replication in the airway epithelial cells of COVID-19 patients.Using ODC1 as a proof-ofconcept,differential activation of polyamine biosynthesis was found to be modulated by the renin-angiotensin system(RAS)and positively associated with ACE2 activity.Further experiments demonstrated that ODC1 expression could be differentially activated upon a panel of SARS-CoV-2 variants of concern(VOCs)and was found to be correlated with each VOCs’pathogenic properties.Particularly,the presented study revealed the discriminative ability of key molecules on polyamine biosynthesis as a predictive marker for virulence evaluation and assessment of SARS-CoV-2 variants in cell or organoid models.Our work,therefore,presented a practical strategy that could be potentially applied as an evaluation tool for the pathogenicity of current and emerging SARS-CoV-2 variants.

Data-Driven Prediction of Foodborne Disease Pathogens

In recent years,foodborne diseases have become one of the most Data analysis technology has been widely used in the field of public health,and greatly facilitates the preliminary judgment of medical staff.Foodborne pathogens,as the main factor of foodborne diseases,play an important role in the treatment and prevention of foodborne diseases.However,foodborne diseases caused by different pathogens lack specificity in clinical features,and the actual clinical pathogen detection ratio is very low in reality.This paper proposes a data-driven foodborne disease pathogen prediction model,which paves the way for early and effective patient identification and treatment.Data analysis was implemented to model the foodborne disease case data.The best model achieves good classification accuracy for Salmonella,Norovirus,Vibrio parahaemolyticus,Staphylococcus aureus,Shigella and Escherichia coli.With the patient data input,the model can conduct rapid risk assessment.The experimental results show that the data-driven approach reduces manual intervention and the difficulty of testing.