# Metabolic Profiling of _Conyza sumatrensis_ ([PERSON].) [PERSON] from Lugazi, Uganda
## 3 Results and Discussion
### LC-MS/MS Phytochemical Profile of Ethanolic and Aqueous Extracts  
To obtain a more comprehensive phytochemical profile of _C. sumatrensis_, we applied LC-MS/MS analysis and generated feature-based mass spectral similarity networks. Generally, LC-MS is a proficient method to detect complex secondary metabolites, particularly non- and semi-volatile or thermally labile compounds [32]. In addition, tandem mass spectra provide valuable structural information on the detected metabolites. Hence, we analyzed ethanolic and aqueous extracts with untargeted tandem mass spectrometry in data-dependent acquisition (DDA) mode. The generated LC-MS/MS data were processed by MZmine [15] and visualized as a molecular network using the GNPS environment [14]. In molecular networking analysis, each signal (feature) is represented as a node with a specific \(m/z\) and retention time range. The metabolites are grouped into several clusters based on similarities of their MS/MS fragmentation patterns, indicating related chemical structures. With feature-based molecular networking (FBMN) [13], improved spectral annotation is possible compared to classical GNPS [11]. In our experiment, data processing revealed a total of 2565 features in all 12 samples, i.e., two cultivated plants, each extracted with two solvents, measured in triplicate. After filtering by the MZmine algorithm, 864 features could be identified that are present in at least three samples. Subsequently, we evaluated the distribution of the detected features across the different extraction methods (Figure 3A,B). A prediction of the corresponding compound class of several nodes was possible, and the nodes from the GNPS network were annotated (see Supplementary File Table S2). Figure 3: (**A**) Feature-based molecular network from _C. sumatrensis_ showing the comparison between ethanolic and aqueous extracts. Nodes are labeled with feature-ID. Orange nodes only occur in ethanolic extracts (217) and blue nodes only occur in aqueous extracts (53), the remaining 594 nodes occur in both kinds of extracts, depicted in gray. (**B**) Venn diagram of the number of features occurring in the ethanolic (orange) and aqueous (blue) extracts and overlapping features occurring in both kinds of extracts (gray). (**C**) Molecular network of _C. sumatrensis_ extracts analyzed by LC-MS/MS in positive mode with ClassyFire superclass prediction performed with SIRIUS highlighted in different colors. The singletons are not shown here (see Figure S4 with singletons). Six clusters are highlighted, and annotated structures of the representatives are shown. (Figure 3 is also added in the supplementary data). In ethanolic extracts, we detected 217, and in aqueous extracts, 53 exclusive features. Approximately two-thirds of the features (594) are present in both ethanolic and aqueous extracts. Next, the detected metabolites were subjected to automated chemical classification by SIRIUS [18] using the ClassyFire [20] and the Natural Product Classifier (NPC) algorithms [21]. Both algorithms rely on CANOPUS (class assignment and ontology prediction using mass spectrometry), a deep neural network, which is trained for chemical structure identification from MS/MS spectra [19]. CANOPUS uses a machine learning algorithm (CSI:FingerID) to compute fragmentation trees and predict molecular fingerprints, which can be compared to structure databases [39]. With the ClassyFire algorithm, 177 features were classified into four taxonomic chemical levels, from superclass, class, and subclass to the most specific class (Figure 4). With the NPC algorithm, 200 compounds were assigned to pathway, superclass, and class levels (see Figure S5). Figure 4: Hierarchical diagram of the classified compound classes from the processed LC-MS measurements from the aligned feature list of the ethanolic and aqueous extracts of _C. sumatrensis_. Predictions were performed with ClassyFire in SIRIUS and categorized into four taxonomic levels. The diagram is based on 177 features that could be predicted.