Journal: bioRxiv
Article Title: Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic network
doi: 10.1101/2022.06.02.494523
Figure Lengend Snippet: ( a ) The generations of unknown metabolites from 46 metabolite mixtures (46std_mix) with in-silico reactions or enzymatic biotransformation via human liver S9 fraction incubation. ( b-c ) The annotated peaks in positive ( b ) and negative modes ( c ); the left cyclic bars represent the annotated peaks in different networks. The right bar represents the involved biotransformation in unknown annotation. ( d - f ) The unknown annotations from seed metabolite inosine 5’-monophosphate (IMP, L0019): ( d ) IMP generates 8 unknowns through 4 transformations in knowledge-based metabolic reaction network; ( e ) knowledge-guided MS2 similarity network annotates 2 unknowns from the seed; ( f ) 29 abiotic peaks were annotated from 3 metabolites in global peak correlation network. ( g-h ) Validation of two annotated unknowns: inosine ( f , labeled as Exd000182) and inosine 5’-sulfate ( g , labeled as Exd006673). ( i ) The validation of annotated unknowns with different strategies.
Article Snippet: Pooled human liver S9 fraction (H0610.S9) and NADPH regenerating system (K5100-5) were purchased from Sekisui Xenotech (Kansas City, KS, USA).
Techniques: In Silico, Incubation, Labeling