## 3 Results and Discussion  
### Ion Identity Molecular Network Investigation of P. mauritianum Ethyl Acetate Extract  
Ethyl acetate extract obtained from the leaf of the voucher _P. mauritianum_ was analysed by UHPLC-HRMS/MS in a data-dependent acquisition mode and positive ionisation mode. The acquired MS1 and MS2 data were processed through the MZmine software pipeline [8], leading to the detection of 453 features. These features were structured and analysed through a feature-based ion identity molecular networking (IIMN) approach to gain a better understanding of their MS/MS spectral relationship and thus their chemical relatedness. MS/MS spectrum of features were compared and connected into a common cluster when exhibiting a cosine score over 0.7. Following the IIMN strategy, features having similar ion peak shapes, retention time, and characteristic ion mass differences (e.g., adducts) were clustered together. MS/MS spectra were also compared to the GNPS experimental mass spectra libraries, providing a spectral match for 61 features (13% of the set), which could be propagated within clusters. In parallel, MS1 and MS2 spectra of each feature were submitted to the SIRIUS pipeline [12] for an automated in silico annotation provided through the Sirius, Zodiac, CSI:FingerID, and Canopus modules. In this pipeline, each feature was attributed a molecular formula (based on its exact mass and isotopic pattern) by the Sirius and Zodiac modules, while a list of putative structure ranked according to a probability score was provided by the CSI:FingerID module. Finally, the Canopus module retrieved the ClassyFire chemical class for each candidate structure. The resulting dataset, visualized as an IIMN (Figure 1), was then manually curated by confronting the annotation provided by the GNPS and Sirius pipelines. The chemical annotations coming from Sirius and GNPS pipelines were compared and structured into a consensus annotation matching the metabolite annotation classification requirements [13], thus allowing to consolidate the confidence in the annotation level when the same annotation is provided by two different tools. This approach combined to the structural elucidation of compounds by NMR allowed the propogation of the annotation to poorly or unannotated features within each cluster, expanding the annotation coverage. Figures 2 and 3 are zoomed views of selected clusters of this IIMN with structural annotations. Figure 1: Ion identity molecular network from _P. mautianum_ ethyl acetate extract. Figure 2: Cluster 1 displaying features annotated as triterpenes. For readability, only one adduct per compound is displayed. The maximum number of edges per node is limited to five. (Edges with the lowest cosine are hidden.) Structures displayed in blue were isolated and characterized by NMR (level 1 annotation, 8 displayed out of 12), while structures in black are consensus provided by GNPS and Sirius (level 2 annotation, 5 displayed out of 11). Following [13] metabolite annotation classification, three annotation levels were proposed for IIMN features. A feature with no consensus molecular formula provided by the two annotation tools was labelled with a level 3 low-confidence metabolomic annotation. Meanwhile, a feature with similar molecular formula and compound class given by GNPS and Sirius was labelled as a level 2 annotation. For instance, feature 41 (\(m/z=425.3777\), circled in Figure 2, bottom corner) was identified by GNPS as 17(21)-hopen-6-one ([M+H]\({}^{+}\), C\({}_{30}\)H\({}_{48}\)O), while Sirius suggested lupeone ([M+H]\({}^{+}\), C\({}_{30}\)H\({}_{48}\)O). Since both sources proposed the same molecular formula and compound class, the annotation is of level 2. A feature with no GNPS match could be manually assigned a level 3 annotation based on the similarity of its annotation with neighbor nodes, which may have a higher annotation level. For instance, seven nodes with no GNPS annotation were located in the same cluster (Figure 3, bottom left cluster) next to three nodes of level 2 annotation.