## 3 Results and Discussion  
Metabolome Profiling of L. siceraria and C. sativus Fruit Extracts via HR-UPLC/PDA/ESI-MS Based Molecular Networking  
The comparative profiling of metabolites in bottle gourd and cucumber crude fruit methanol extracts was conducted via HR-UPLC/MS/MS in both negative and positive ionization modes (Section 2.4) for a comprehensive overview of metabolites that belong to different phytochemical classes. Gradient elution system of formic acid in water (0.1%): acetonitrile allowed for metabolites' elution in order of their decreasing polarity. The obtained base peak chromatograms (BPCs) of each fruit extract in both ionization modes are presented in Figure 1.  
Furthermore, molecular networking was applied herein for the in-depth exploration and discrimination of samples guided by the Global Natural Products Social (GNPS) networking software and its spectral library that aided in peaks' assignment and annotation based on analyzing the HR-tandem MS/MS data ([PERSON], 2010; [PERSON], 2010). Two molecular networks (MN) were constructed from both ionization mode analyses that provided visual discrimination of samples based on metabolites' abundance in each node, presented as a pie chart, allowing for rapid dereplication of known compounds (Figures 2 and S1). Samples were coded with orange and green colors for bottle gourd and cucumber, respectively. All nodes were labelled with parent mass and edges were labelled with neutral loss values. Figure 1: Base peak chromatograms of _Lagenaria sicararia_ (**A**,**B**) and _Cucumis sativus_ (**C**,**D**) crude fruit extracts as analyzed via HR-UPLC/MS/MS in both positive (**A**,**C**) and negative (**B**,**D**) ionization modes. Figure 2: Full molecular networking created using MS/MS data in negative ionization mode for _L. siceraria_ (bottle gourd) and _C. sativus_ (cucumber) crude fruit extracts showing 351 nodes and 449 edges. All nodes are labelled with parent mass and edges are labelled with neutral loss values. The network is displayed as a pie chart with orange and green colors representing distribution of the precursor ion intensity in the bottle gourd and cucumber extracts, respectively. **Clusters annotation**: (**A**): fatty acids/amides, (**B**): lysophospholipids, (**C**): flavonoids and ptercarpans, (**D**): alkyl glycosides, (**E**): saponins, (**F**): organic acids, (**G**): amino acids derivatives, (**H**): sesquiterpennes, (**I**): lysophosphatidic acid derivatives, and (**J**): cinnamates. Identification was based on determining retention time (Rt. min.) for each metabolite, its mass spectral data--including molecular ion, daughter ions, their respective formulae and fragmentation pattern--and comparing the collective data with the reported literature and databases such as HMDB, PubChem, FooDB, the Phytochemical dictionary of natural products, and others, combined with GNPS library annotations. The clustering of metabolites in MN (minimum two connected nodes) was based on shared fragments and their fragmentation pattern; this allowed us to extrapolate the identification of annotated compounds to the unknown peaks aided by the generated formulae [10]. Overall, 107 peaks were annotated in both modes (Table 1), belonging to amino acids, organic acids, cinnamates, alkaloids, flavonoids, pterocarpans, alkyl glycosides, sesquiterpenes, saponins, lignians, fatty acids/amides, and lysophospholipids, including several first-time reported metabolites and classes, as explained in detail in the following subsections. The constructed MN from the HR- negative ESI-MS/MS analysis was composed of 351 nodes and 449 edges, in which the clusters of interest included cluster A: fatty acids/amides, cluster B: lysophospholipids, cluster C: flavonoids and pterocarpans, cluster D: alkyl glycosides, cluster E: saponins, cluster F: organic acids, cluster G: amino acids derivatives, cluster H: sesquiterpenes, cluster I: lysophosphatidic acid derivatives, and cluster J: cinnamates (Figure 2).