Mass Spectrometry-based Molecular Networking Research Articles

Putative Identification of New Phragmaline-Type Limonoids from the Leaves of Swietenia macrophylla King: A Case Study Using Mass Spectrometry-Based Molecular Networking.

Swietenia macrophylla King is a plant commonly known as Brazilian mahogany. The wood from its stem is highly prized for its exceptional quality, while its leaves are valued for their high content of phragmalin-type limonoids, a subclass of compounds known for their significant biological activities, including antimalarial, antitumor, antiviral, and anti-inflammatory properties. In this context, twelve isolated limonoids from S. macrophylla leaves were employed as standards in mass spectrometry-based molecular networking to unveil new potential mass spectrometry signatures for phragmalin-type limonoids. Consequently, ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was utilized for data acquisition. Subsequently, the obtained data were analyzed using the Global Natural Products Social Molecular Networking platform based on spectral similarity. In summary, this study identified 24 new putative phragmalin-type limonoids for the first time in S. macrophylla. These compounds may prove valuable in guiding future drug development efforts, leveraging the already established biological activities associated with limonoids.

HPLC-LTQ orbitrap mass spectrometry-based molecular networking for identifying anthelmintic molecules in Morinda lucida Benth

Morinda lucida Benth (Rubiaceae) is a medicinal plant traditionally used to treat malaria and digestive parasitosis. In vitro pharmacological studies have shown that extracts of the plant were very active against nematodes. The aim of our work was to identify the compounds responsible for the anthelmintic activity of M. lucida. To achieve this, the leaf MeOH extract was fractionated and the anthelmintic activity of the fractions was evaluated on Haemonchus contortus and Caenorhabditis elegans and their cytotoxicity was evaluated on WI38 cells. The fractions were analyzed by HPLC-HRMS/MS and manual dereplication was coupled to molecular networking to identify the major compounds in the most active fractions. The results showed that three fractions, F8, F11 and F13 were the most active on both nematode models and less cytotoxic. In total, sixteen different compounds were putatively identified in these three fractions, 9 in F8, 12 in F11 and 12 in F13, as some are present in different fractions. Among these compounds, ten were identified for the first time in M. lucida. These are mainly iridoids including loganic acid (2) and isomer (4), methoxygaertneroside or isomer (14), dehydromethoxygaertneroside or isomer (18), borreriagenin (3) and isomer (5). We also identified flavonoids including rutin pentoside (10), kaempferol-3-O-rutinoside pentoside (12), and kaempferol-3-O-rutinoside (15). The Bioactivity-Based approach identified loganic acid or isomer I (2), asperulosidic acid or isomer (6), methoxygaertneroside or isomer (14), and kaempferol-3-O-rutinoside or isomer (15) as potential compounds responsible for the anthelmintic activity of the three fractions. However, it seems necessary to quantify them and evaluate the anthelmintic activity of each of these compounds in order to confirm the results of the Bioactivity-Based approach and to identify potential new anthelmintic molecules to prevent resistance development. Furthermore, possible synergy between them has also to be analyzed.

Polypharmacology-Labeled Molecular Networking: An Analytical Technology Workflow for Accelerated Identification of Multiple Bioactive Constituents in Complex Extracts.

Discovery of sustainable and benign-by-design drugs to combat emerging health pandemics calls for new analytical technologies to explore the chemical and pharmacological properties of Nature's unique chemical space. Here, we present a new analytical technology workflow, polypharmacology-labeled molecular networking (PLMN), where merged positive and negative ionization tandem mass spectrometry-based molecular networking is linked with data from polypharmacological high-resolution inhibition profiling for easy and fast identification of individual bioactive constituents in complex extracts. The crude extract of Eremophila rugosa was subjected to PLMN analysis for the identification of antihyperglycemic and antibacterial constituents. Visually easy-interpretable polypharmacology scores and polypharmacology pie charts as well as microfractionation variation scores of each node in the molecular network provided direct information about each constituent's activity in the seven assays included in this proof-of-concept study. A total of 27 new non-canonical nerylneryl diphosphate-derived diterpenoids were identified. Serrulatane ferulate esters were shown to be associated with antihyperglycemic and antibacterial activities, including some showing synergistic activity with oxacillin in clinically relevant (epidemic) methicillin-resistant Staphylococcus aureus strains and some showing saddle-shaped binding to the active site of protein-tyrosine phosphatase 1B. PLMN is scalable in the number and types of assays included and thus holds potential for a paradigm shift toward polypharmacological natural-products-based drug discovery.

UHPLC Q-Orbitrap Mass Spectrometry-Based Molecular Networking for Identification of Chemical Constituents in the Multi-Herb Formula Runyan Mixture.

Traditional Chinese medicine (TCM) in-hospital preparations are approved for use only in the hospital where they are prepared. They are widely used in China because of their efficacy and affordable price. However, few researchers focused on their quality controls and treatment mechanisms, for which a key consideration is the elucidation of their chemical composition. Runyan mixture (RY) is a typical in-hospital TCM preparation comprising a formula of eight herbal drugs used for adjuvant therapy of upper respiratory tract infections. The chemical constituents of formulated RY have not yet been elucidated. In the present work, RY was analyzed by a ultrahigh-performance liquid chromatography system equipped with high-resolution orbitrap mass spectrometry (MS). The acquired MS data were processed by MZmine and a feature-based molecular networking was constructed to identify the metabolites of RY. 165 compounds including 41 flavonoid O-glycosides, 11 flavonoid C-glycosides, 18 quinic acids, 54 coumaric acids, 11 iridoids, and 30 others were identified. This study demonstrates an efficient method to identify compounds in complex herbal drug mixtures using high-resolution MS and molecular networking tools which will support future research into quality controls and treatment mechanisms of in-hospital TCM preparations.

The Potential Biocontrol Agent Paenibacillus polymyxa TP3 Produces Fusaricidin-Type Compounds Involved in the Antagonism Against Gray Mold Pathogen Botrytis cinerea.

Paenibacillus polymyxa is a beneficial bacterium for plant health. P.polymyxa TP3 exhibits antagonistic activity toward Botrytis cinerea and alleviates gray mold symptoms on the leaves of strawberry plants. Moreover, suppression of gray mold on the flowers and fruits of strawberry plants in field trials, including vegetative cells and endospores, was demonstrated, indicating the potential of strain TP3 as a biological control agent. To examine the anti-B. cinerea compounds produced by P.polymyxa TP3, we performed matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and fusaricidin-corresponding mass spectra were detected. Moreover, fusaricidin-related signals appeared in imaging mass spectrometry of TP3 when confronted with B. cinerea. By using liquid chromatography mass spectrometry-based molecular networking approach, we identified several fusaricidins including a new variant of mass/charge ratio 917.5455 with serine in the first position of the hexapeptide. Via advanced mass spectrometry and network analysis, fusaricidin-type compounds produced by P. polymyxa TP3 were efficiently disclosed and were presumed to play roles in the antagonism against gray mold pathogen B. cinerea.

Comprehensive profiling of Lingzhihuang capsule by liquid chromatography coupled with mass spectrometry-based molecular networking and target prediction

Abstract Objective: Lingzhihuang capsule (LZHC) is a natural product that consists of 10 commonly used medicinal plants, and it is used in traditional Chinese medicine to promote people's overall health. Previously, LZHC was successfully used as adjuvant therapy for treating patients with cancer. However, the chemical constituents of LZHC and their potential biological functions remain unclear. The aim of this study is to investigate the major bioactive compounds in LZHC and predict their pharmacological targets. Methods: The LZHC constituents were putatively identified by ultra-high performance liquid chromatography coupled with time-of-flight mass spectrometry combined with mass spectrometry-based molecular networking. The targets were predicted using SwissTargetPrediction software, and the associated gene ontology and Kyoto encyclopedia of genes and genomes pathways were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The mass spectrometry-based molecular network and compound-target-pathway network were constructed using Cytoscape 3.8.0 software. Results: We putatively identified 94 compounds of LZHC by mass spectrometry-based molecular networking, including triterpene (the main structural type) and other clusters (ie, flavonoids and organic acids). Our results suggested that multiple pivotal targets were regulated by LZHC, including tumor necrosis factor, nitric oxide synthase 2, glucocorticoid receptor, estrogen receptor, 3-oxo-5-alpha-steroid 4-dehydrogenase 2, prostaglandin e2 receptor ep4 subtype, estrogen receptor beta, phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform, mitogen-activated protein kinase 3, and rac-alpha serine, which are related to signal transduction, positive regulation of transcription from RNA polymerase II promoters, oxidation-reduction processes, inflammatory responses, and other biological processes. Functional annotation of those targets suggested that several signaling pathways may be regulated by LZHC, such as cancer-related proteoglycans, the PI3K-Akt-signaling pathway, and the cAMP-signaling pathway. Conclusions: Our findings reveal the chemical constituents of LZHC and provided scientific support for the efficacy of LZHC in terms of immune regulation, anti-aging, and tumor suppression.

Applications and Restrictions of Integrated Genomic and Metabolomic Screening: An Accelerator for Drug Discovery from Actinomycetes?

Since the golden age of antibiotics in the 1950s and 1960s actinomycetes have been the most prolific source for bioactive natural products. However, the number of discoveries of new bioactive compounds decreases since decades. New procedures (e.g., activating strategies or innovative fermentation techniques) were developed to enhance the productivity of actinomycetes. Nevertheless, compound identification remains challenging among others due to high rediscovery rates. Rapid and cheap genome sequencing as well as the advent of bioinformatical analysis tools for biosynthetic gene cluster identification in combination with mass spectrometry-based molecular networking facilitated the tedious process of dereplication. In recent years several studies have been dedicated to accessing the biosynthetic potential of Actinomyces species, especially streptomycetes, by using integrated genomic and metabolomic screening in order to boost the discovery rate of new antibiotics. This review aims to present the various possible applications of this approach as well as the newly discovered molecules, covering studies between 2014 and 2021. Finally, the effectiveness of this approach with regard to find new bioactive agents from actinomycetes will be evaluated.

Metabolite Profiling of Christia vespertilionis Leaf Metabolome via Molecular Network Approach

Christia vespertilionis (L.f.) Bakh. f. is an ornamental plant with unique butterfly-shaped leaves, hence its vernacular name “butterfly wing” or “rerama” in Malay. In Malaysia, the green-leafed variety of this plant has gained popularity in recent years due to testimonial reports by local consumers of its medicinal uses, which include treatment for cancer. Despite these popular uses, there is very limited information on the phytochemistry of the leaf of this plant, presenting a significant gap in the cheminformatics of the plant species. Herein, we report a substantially detailed phytochemical profile of the leaf metabolome of the green-leafed variety of C. vespertilionis, obtained by deploying an untargeted tandem mass spectrometry-based molecular networking approach. The detailed inspection of the molecular network map generated for the leaf metabolome enabled the putative identification of 60 metabolites, comprising 13 phenolic acids, 20 flavonoids, 2 benzyltetrahydroisoquinoline-type alkaloids, 4 hydroxyjasmonic acid derivatives, 2 phenethyl derivatives, 3 monoacylglycerols, 4 fatty acid amides, 2 chlorophyll derivatives, 4 carotenoids, 2 organic acids, 1 nucleoside, and 3 amino acids. Flavonoids are the major class of metabolites that characterize the plant leaves. Employing a mass-targeted isolation approach, two new derivatives of apigenin-6-C-β-glucoside, the major constituents of the plant leaf, were successfully purified and spectroscopically characterized as apigenin-6-C-β-glucoside 4′-O-α-apiofuranoside (28) and apigenin-6-C-β-[(4″,6″-O-dimalonyl)-glucoside] 4′-O-α-apiofuranoside (47). This work provides further information on the chemical space of the plant leaf, which is a prerequisite to further research towards its valorization as a potential phytopharmaceutical product.

A Metabolomics and Molecular Networking Approach to Elucidate the Structures of Secondary Metabolites Produced by Serratia marcescens Strains.

An integrated approach that combines reverse-phase high-performance liquid chromatography (RP-HPLC), electrospray ionization mass spectrometry, untargeted ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MSE) and molecular networking (using the Global Natural Products Social molecular network platform) was used to elucidate the metabolic profiles and chemical structures of the secondary metabolites produced by pigmented (P1) and non-pigmented (NP1) Serratia marcescens (S. marcescens) strains. Tandem mass spectrometry-based molecular networking guided the structural elucidation of 18 compounds for the P1 strain (including 6 serratamolides, 10 glucosamine derivatives, prodigiosin and serratiochelin A) and 15 compounds for the NP1 strain (including 8 serratamolides, 6 glucosamine derivatives and serratiochelin A) using the MSE fragmentation profiles. The serratamolide homologues were comprised of a peptide moiety of two L-serine residues (cyclic or open-ring) linked to two fatty acid chains (lengths of C10, C12, or C12:1). Moreover, the putative structure of a novel open-ring serratamolide homologue was described. The glucosamine derivative homologues (i.e., N-butylglucosamine ester derivatives) consisted of four residues, including glucose/hexose, valine, a fatty acid chain (lengths of C13 – C17 and varying from saturated to unsaturated) and butyric acid. The putative structures of seven novel glucosamine derivative homologues and one glucosamine derivative congener (containing an oxo-hexanoic acid residue instead of a butyric acid residue) were described. Moreover, seven fractions collected during RP-HPLC, with major molecular ions corresponding to prodigiosin, serratamolides (A, B, and C), and glucosamine derivatives (A, C, and E), displayed antimicrobial activity against a clinical Enterococcus faecalis S1 strain using the disc diffusion assay. The minimum inhibitory and bactericidal concentration assays however, revealed that prodigiosin exhibited the greatest antimicrobial potency, followed by glucosamine derivative A and then the serratamolides (A, B, and C). These results provide crucial insight into the secondary metabolic profiles of pigmented and non-pigmented S. marcescens strains and confirms that S. marcescens strains are a promising natural source of novel antimicrobial metabolites.

Comprehensive GCMS and LC-MS/MS Metabolite Profiling of Chlorella vulgaris.

The commercial cultivation of microalgae began in the 1960s and Chlorella was one of the first target organisms. The species has long been considered a potential source of renewable energy, an alternative for phytoremediation, and more recently, as a growth and immune stimulant. However, Chlorella vulgaris, which is one of the most studied microalga, has never been comprehensively profiled chemically. In the present study, comprehensive profiling of the Chlorella vulgaris metabolome grown under normal culture conditions was carried out, employing tandem LC-MS/MS to profile the ethanolic extract and GC-MS for fatty acid analysis. The fatty acid profile of C. vulgaris was shown to be rich in omega-6, -7, -9, and -13 fatty acids, with omega-6 being the highest, representing more than sixty percent (>60%) of the total fatty acids. This is a clear indication that this species of Chlorella could serve as a good source of nutrition when incorporated in diets. The profile also showed that the main fatty acid composition was that of C16-C18 (>92%), suggesting that it might be a potential candidate for biodiesel production. LC-MS/MS analysis revealed carotenoid constituents comprising violaxanthin, neoxanthin, lutein, β-carotene, vulgaxanthin I, astaxanthin, and antheraxanthin, along with other pigments such as the chlorophylls. In addition to these, amino acids, vitamins, and simple sugars were also profiled, and through mass spectrometry-based molecular networking, 48 phospholipids were putatively identified.

Molecular networking aided metabolomic profiling of beet leaves using three extraction solvents and in relation to its anti-obesity effects

In the present study, the efficiency of three different solvents (H2O, acidified H2O, and 70% Methanol) for metabolites extraction from the leaves of sugar beet (Beta vulgaris subsp. vulgaris var. rubra) was investigated along with their inhibitory activity on pancreatic α-amylase and lipase for obesity management. The metabolic profile of the three extracts was analyzed by ultra-performance liquid chromatography (UPLC) coupled with electrospray ionization high-resolution mass spectrometric (ESI-HRMS-MS). Mass spectrometry-based molecular networking was employed to aid in metabolites annotation and for the visual investigation of the known metabolites and their analogues. The study led to the tentative identification of 45 metabolites including amino acids, purine derivatives, phenolic acids, flavonoids, fatty acids, and an alkaloid, articulating 24 compounds as a first time report from beet leaves along with 2 new putatively identified compounds: a flavone feruloyl conjugate (39) and a malonylated acacetin diglycoside (40). The three extracting systems exhibited comparable efficiency for pulling out the secondary metabolites from the beet leaves. The in vitro study supported this finding and demonstrated that the three extracts inhibited the activity of both pancreatic α-amylase and lipase enzymes with no significant difference observed regarding the percentage of the inhibition of the enzymes. Conclusively, the extraction protocol has a minimal effect on the anti-obesity properties of beet leaves.

Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease.

Annotation of lipids in untargeted lipidomic analysis remains challenging and a systematic approach needs to be developed to organize important datasets with the help of bioinformatic tools. For this purpose, we combined tandem mass spectrometry-based molecular networking with retention time (tR) prediction to annotate phospholipid and sphingolipid species. Sixty-five standard compounds were used to establish the fragmentation rules of each lipid class studied and to define the parameters governing their chromatographic behavior. Molecular networks (MNs) were generated through the GNPS platform using a lipid standards mixture and applied to lipidomic study of an in vitro model of dry eye disease, i.e., human corneal epithelial (HCE) cells exposed to hyperosmolarity (HO). These MNs led to the annotation of more than 150 unique phospholipid and sphingolipid species in the HCE cells. This annotation was reinforced by comparing theoretical to experimental tR values. This lipidomic study highlighted changes in 54 lipids following HO exposure of corneal cells, some of them being involved in inflammatory responses. The MN approach coupled to tR prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes.

HPLC-ESI- MS/MS analysis of beet (Beta vulgaris) leaves and its beneficial properties in type 1 diabetic rats

The phenolic profile of the leaves of Beta vulgaris subspecies vulgaris variety rubra was investigated by high-performance liquid chromatography (HPLC) coupled to electrospray ionization high resolution mass spectrometric (ESI-HRMS-MS) detection. Mass spectrometry-based molecular networking was employed to dereplicate the known compounds. Twelve known compounds, seven of which are previously undescribed as constituents in the B. vulgaris leaves were dereplicated and assigned with various levels of identification confidence. The ameliorative effects of the aqueous methanolic extract of the leaves were assessed against alloxan induced diabetic rats. It was found that the extract significantly decreased (p < 0.001) serum glucose, lipid profile, ALT, AST, TNF-α, IL-1β, IL-6, and hepatic MDA levels; and significantly increased (p < 0.001) hepatic TAO and GSH; and down-regulated the expression of hepatic NF-κB versus the untreated diabetic groups, in a dose-dependent manner. In molecular docking, all identified compounds exhibited good glide score against the PPAR-ɣ target, confirming the in vivo observed activities. In conclusion, B. vulgaris has immunomodulatory / antioxidant effects that could be helpful in slowing the progression of diabetic complications.

Integrated Genomic and Metabolomic Approach to the Discovery of Potential Anti-Quorum Sensing Natural Products from Microbes Associated with Marine Samples from Singapore.

With 70% of the Earth’s surface covered in water, the marine ecosystem offers immense opportunities for drug discovery and development. Due to the decreasing rate of novel natural product discovery from terrestrial sources in recent years, many researchers are beginning to look seaward for breakthroughs in new therapeutic agents. As part of an ongoing marine drug discovery programme in Singapore, an integrated approach of combining metabolomic and genomic techniques were initiated for uncovering novel anti-quorum sensing molecules from bacteria associated with subtidal samples collected in the Singapore Strait. Based on the culture-dependent method, a total of 102 marine bacteria strains were isolated and the identities of selected strains were established based on their 16S rRNA gene sequences. About 5% of the marine bacterial organic extracts showed quorum sensing inhibitory (QSI) activity in a dose-dependent manner based on the Pseudomonas aeruginosa QS reporter system. In addition, the extracts were subjected to mass spectrometry-based molecular networking and the genome of selected strains were analysed for known as well as new biosynthetic gene clusters. This study revealed that using integrated techniques, coupled with biological assays, can provide an effective and rapid prioritization of marine bacterial strains for downstream large-scale culturing for the purpose of isolation and structural elucidation of novel bioactive compounds.

A joint molecular networking study of a Smenospongia sponge and a cyanobacterial bloom revealed new antiproliferative chlorinated polyketides.

The bloom-forming cyanobacteria Trichodesmium sp. have been recently shown to produce some of the chlorinated peptides/polyketides previously isolated from the marine sponge Smenospongia aurea. A comparative analysis of extracts from S. aurea and Trichodesmium sp. was performed using tandem mass spectrometry-based molecular networking. The analysis, specifically targeted to chlorinated metabolites, showed that many of them are common to the two organisms, but also that some general differences exist between the two metabolomes. Following this analysis, six new chlorinated metabolites were isolated and their structures elucidated: four polyketides, smenolactones A-D (1-4) from S. aurea, and two new conulothiazole analogues, isoconulothiazole B (5) and conulothiazole C (6) from Trichodesmium sp. The absolute configuration of smenolactone C (3) was determined by taking advantage of the conformational rigidity of open 1,3-disubstituted alkyl chains. The antiproliferative activity of smenolactones was evaluated on three tumor cell lines, and they were active at low-micromolar or sub-micromolar concentrations.

The Tripod for Bacterial Natural Product Discovery: Genome Mining, Silent Pathway Induction, and Mass Spectrometry-Based Molecular Networking.

Natural products are the richest source of chemical compounds for drug discovery. Particularly, bacterial secondary metabolites are in the spotlight due to advances in genome sequencing and mining, as well as for the potential of biosynthetic pathway manipulation to awake silent (cryptic) gene clusters under laboratory cultivation. Further progress in compound detection, such as the development of the tandem mass spectrometry (MS/MS) molecular networking approach, has contributed to the discovery of novel bacterial natural products. The latter can be applied directly to bacterial crude extracts for identifying and dereplicating known compounds, therefore assisting the prioritization of extracts containing novel natural products, for example. In our opinion, these three approaches-genome mining, silent pathway induction, and MS-based molecular networking-compose the tripod for modern bacterial natural product discovery and will be discussed in this perspective.

Dereplication of Flavonoid Glycoconjugates from Adenocalymma imperatoris-maximilianii by Untargeted Tandem Mass Spectrometry-Based Molecular Networking.

The interpretation of large datasets acquired using high performance liquid chromatography coupled with tandem mass spectrometry represents one of the major challenges in natural products research. Here we propose the use of molecular networking to rapid identify the known secondary metabolites from untargeted MS/MS analysis of Adenocalymma imperatoris-maximilianii plant extracts. The leaves, stems and roots of A.imperatoris-maximilianii were extracted using different solvents according to Snyder selectivity triangle. The samples were analyzed by HPLC coupled with ion trap mass spectrometer in a collision-induced dissociation MS/MS configuration in both positive and negative electrospray ionization modes. Molecular networking simultaneously organized the spectra by cosine similarity. The chemical identification was performed based on the systematic study of the main fragmentation pathways observed for the resulting network. The untargeted tandem mass spectrometry-based molecular networking allowed for the identification of 63 metabolites, mainly mono-, di- and tri-, C- and/or O-glycosyl flavones. Molecular networking was capable not only to dereplicate known flavonoids, but also to point out related prenyl derivatives, described for the first time in Adenocalymma species. The gas-phase reaction route to form the characteristic [M-H2O-(30/60/90)]+ fragments in C-glycosyl flavones was suggested as sequential sugar ring opening followed by retro-aldol elimination involving aldose-ketose isomerization. The use of molecular networking with LC-CID-MS/MS assisted the identification of various isomeric and isobaric flavonoid glycoconjugates by establishing clusters according to the fragmentation similarities. Additionally, the proposed cross-ring sugar cleavages can contribute to the identification of C-glycosides by MS/MS analysis.

Phenol Soluble Modulin (PSM) Variants of Community-Associated Methicillin-Resistant Staphylococcus aureus (MRSA) Captured Using Mass Spectrometry-Based Molecular Networking

Molecular genetic analysis indicates that the problematic human bacterial pathogen methicillin-resistant Staphylococcus aureus possesses more than 2000 open reading frames in its genome. This number of potential gene products, coupled with intrinsic mechanisms of posttranslational modification, endows methicillin-resistant Staphylococcus aureus with a highly complex biochemical repertoire. Recent proteomic and metabolomic advances have provided methodologies to better understand and characterize the biosynthetic factors released by microbial organisms. Here, the emerging tool of mass spectrometry-based molecular networking was used to visualize and map the repertoire of biosynthetic factors produced by a community-associated methicillin-resistant Staphylococcus aureus strain representative of the epidemic USA300 clone. In particular, the study focused on elucidating the complexity of the recently discovered phenol soluble modulin family of peptides when placed under various antibiotic treatment stresses. Novel PSM truncated variant peptides were captured, and the type of variants that were clustered by the molecular networks platform changed in response to the different antibiotic treatment conditions. After discovery, a group of the peptides were selected for functional analysis in vitro. The peptides displayed bioactive properties including the ability to induce proinflammatory responses in human THP-1 monocytes. Additionally, the tested peptides did not display antimicrobial activity as previously reported for other phenol soluble modulin truncated variants. Our findings reveal that the PSM family of peptides are quite structurally diverse, and suggest a single phenol soluble modulin parent peptide can functionally spawn differential bioactivities in response to various external stimuli.

Molecular Networking as a Dereplication Strategy

A major goal in natural product discovery programs is to rapidly dereplicate known entities from complex biological extracts. We demonstrate here that molecular networking, an approach that organizes MS/MS data based on chemical similarity, is a powerful complement to traditional dereplication strategies. Successful dereplication with molecular networks requires MS/MS spectra of the natural product mixture along with MS/MS spectra of known standards, synthetic compounds, or well-characterized organisms, preferably organized into robust databases. This approach can accommodate different ionization platforms, enabling cross correlations of MS/MS data from ambient ionization, direct infusion, and LC-based methods. Molecular networking not only dereplicates known molecules from complex mixtures, it also captures related analogues, a challenge for many other dereplication strategies. To illustrate its utility as a dereplication tool, we apply mass spectrometry-based molecular networking to a diverse array of marine and terrestrial microbial samples, illustrating the dereplication of 58 molecules including analogues.