Mass Spectral Molecular Networking to Profile the Metabolome of Biostimulant Bacillus Strains

Components analysis of San-Bai decoction, and its pharmacodynamics and mechanism on preventing and treating melasma

ETHNOPHARMACOLOGICAL RELEVANCE

San-Bai Decoction (SBD) is a classic whitening prescription originally recorded in the 'Introduction to Medicine' of the Ming Dynasty. SBD has been known for invigorating Qi and blood, promoting spleen and stomach, whitening skin, and fading melasma. However, its pharmacodynamic material basis and specific mechanism remain unclear.

AIM OF THE STUDY

The aim of this study is to clarify the pharmacodynamic material basis of SBD and its mechanism of removing melasma.

MATERIALS AND METHODS

The positive and negative ion mass spectrum data of SBD extract were collected by UHPLC-Q-Exactive Orbitrap MS/MS, imported into Compound Discoverer (CD) 3.1 software, matched through the online database, and manually checked. Finally, the in vitro chemical components of SBD were classified. Similarly, the mass spectrum data of SBD in the serum of normal rats and melasma model rats were also analyzed by CD 3.1 software. The in vitro identified Compound file of SBD was imported into the Expected Compounds and the Generate Expected Compounds project was selected. The SBD compounds were then chosen under the Compound Section. All phase I and II reaction types related to SBD components were selected, and the metabolic platform of CD 3.1 software was utilized to process the results and obtain possible metabolites. The metabolites were scored and products with high scores were subsequently screened. According to literature comparison, the final metabolites of SBD in both normal rats and melasma model rats were determined and comprehensively analyzed. The Melasma model rats were constructed through intramuscular injection of progesterone and ultraviolet radiation B (UVB) irradiation. The preventing and treating effect of SBD on melasma were evaluated by regulating inflammation, epidermal collagen content, and oxidative stress. Additionally, the effect of SBD on the Phosphatidylinositol 3-kinase (PI3K)/Protein kinase B (Akt)/Glycogen synthase kinase 3β (GSK3β) pathway was investigated through Western blot (WB) to explore its underlying mechanism on whitening and removing melasma efficacy.

RESULTS

Ultimately, 94 components were identified in SBD, including 41 flavonoids, 27 organic acids, and 9 glycosides, 3 terpenoids, 2 amides, 2 aldehydes, 1 phenylpropanoid and 9 other compounds. In the blood of normal rat group, a total of 24 prototype components and 61 metabolites were identified. Similarly, there were19 prototype components and 44 metabolites identified from the blood of melasma model rats. Pharmacodynamic experiment results indicated that SBD effectively reduced the incidence of melasma, prevent the loss of epidermal collagen, and elevate the activity of superoxide dismutase and decrease the malondialdehyde content in both liver and skin. Interestingly, the WB results demonstrated that SBD effectively activated PI3K/Akt/GSK3β pathway, and down-regulated the expression of melanin-related proteins.

CONCLUSIONS

For the first time, the components of SBD extracts, and its prototype components and metabolites in the blood of normal rats and melasma model rats were successfully identified by high-resolution liquid chromatography-mass spectrometry with CD software. Additionally, the differences of in vivo components of SBD between normal rats and melasma model rats were analyzed. The preventive and therapeutic effect of SBD on melasma was verified in the melasma model rats induced by progesterone and UVB irradiation, and its mechanism was related to activating PI3K/Akt/GSK3β pathway and downregulating the expression of melanin-related proteins. These results provide an experimental foundation for further research on the pharmacodynamic substance basis and pharmacodynamic mechanism of SBD, as well as developing new anti-melasma formula with SBD.

Comparative metabolites profiling of different solvent extracts of Asparagus species cladodes using liquid chromatography-mass spectrometry-based metabolomics and molecular networking

INTRODUCTION

Asparagus species are naturally distributed worldwide and are known for their pharmacological properties that offer cures for various ailments. However, the metabolic choreography of these Asparagus species is not well characterized, and the compounds contributing to their bioactivities remain unknown.

OBJECTIVE

This study aimed to profile and compare the metabolomes of three Asparagus species cladodes using different solvent extractions.

METHODS

An ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based metabolomics and molecular networking approach was used to study the effects of different solvents (ethyl acetate, methanol, and chloroform) with varying polarity on metabolites extraction and identification of bioactive compounds from three Asparagus species cladodes (Asparagus falcatus, Asparagus plumosus, and Asparagus densiflorus 'Meyersii').

RESULTS

Multivariate statistical analyses (mainly principal component analysis) revealed a significant separation between the three solvents and the three species, indicating notable metabolic differences. A total of 118 metabolites were identified in the three species extracted with the different solvents, with methanolic and chloroform extracts containing more metabolites compared with ethyl acetate extracts. These metabolites were identified as belonging to the flavonoids, cinnamic acids, organooxygen compounds, steroids, fatty acids, benzenes, and glycerophospholipids compound classes. Furthermore, these compounds classes were differentially distributed among the three species, indicating chemical/chemotaxis differences between the compared species. Chloroform and methanol are recommended as the optimal solvents to obtain a high content of phytochemical compounds from Asparagus species cladodes.

Bioprospecting of soil-borne microorganisms and chemical dereplication of their anti-microbial constituents with the aid of UPLC-QTOF-MS and molecular networking approach

Due to the emergence of drug-resistant microorganisms, the search for broad-spectrum antimicrobial compounds has become extremely crucial. Natural sources like plants and soils have been explored for diverse metabolites with antimicrobial properties. This study aimed to identify microorganisms from agricultural soils exhibiting antimicrobial effects against known human pathogens, and to highlight the chemical space of the responsible compounds through the computational metabolomics-based bioprospecting approach. Herein, bacteria were extracted from soil samples and their antimicrobial potential was measured via the agar well diffusion method. Methanolic extracts from the active bacteria were analyzed using the liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) technique, and the subsequent data was further analyzed through molecular networking approach which aided in identification of potential anti-microbial compounds. Furthermore, 16S rRNA gene sequencing enabled identification of the active bacterial isolates, where isolate 1 and 2 were identified as strains of Bacillus pumilus, whilst isolate 3 was found to be Bacillus subtilis. Interestingly, isolate 3 (Bacillus subtilis) displayed wide-ranging antimicrobial activity against the tested human pathogens. Molecular networking revealed the presence of Diketopiperazine compounds such as cyclo (D-Pro-D-Leu), cyclo (L-Tyr-L-Pro), cyclo (L-Pro-D-Phe), and cyclo (L-Pro-L-Val), alongside Surfactin C, Surfactin B, Pumilacidin E, and Isarrin D in the Bacillus strains as the main anti-microbial compounds. The application of the molecular networking approach represents an innovation in the field of bio-guided bioprospection of microorganisms and has proved to be an effective and feasible towards unearthing potent antimicrobial compounds. Additionally, the (computational metabolomics-based) approach accelerates the discovery of bioactive compounds and isolation of strains which offer a promising avenue for discovering new clinical antimicrobials. Finally, soil microbial flora could serve an alternative source of anti-microbial compounds which can assist in the fight against emergence of multi-drug resistance bacterial pathogens.

Charting the Cannabis plant chemical space with computational metabolomics

INTRODUCTION

The chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant.

OBJECTIVES

Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.

METHODS

Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).

RESULTS

The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.

CONCLUSION

These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data

Liquid chromatography coupled with high-resolution mass spectrometry data-independent acquisition (LC-HRMS/DIA), including MSE, enable comprehensive metabolomics analyses though they pose challenges for data processing with automatic annotation and molecular networking (MN) implementation. This motivated the present proposal, in which we introduce DIA-IntOpenStream, a new integrated workflow combining open-source software to streamline MSE data handling. It provides 'in-house' custom database construction, allows the conversion of raw MSE data to a universal format (.mzML) and leverages open software (MZmine 3 and MS-DIAL) all advantages for confident annotation and effective MN data interpretation. This pipeline significantly enhances the accessibility, reliability and reproducibility of complex MSE/DIA studies, overcoming previous limitations of proprietary software and non-universal MS data formats that restricted integrative analysis. We demonstrate the utility of DIA-IntOpenStream with two independent datasets: dataset 1 consists of new data from 60 plant extracts from the Ocotea genus; dataset 2 is a publicly available actinobacterial extract spiked with authentic standard for detailed comparative analysis with existing methods. This user-friendly pipeline enables broader adoption of cutting-edge MS tools and provides value to the scientific community. Overall, it holds promise for speeding up metabolite discoveries toward a more collaborative and open environment for research.

Metabolome Mining of Curcuma longa L. Using HPLC-MS/MS and Molecular Networking

Turmeric, Curcuma longa L., is a type of medicinal plant characterized by its perennial nature and rhizomatous growth. It is a member of the Zingiberaceae family and is distributed across the world's tropical and subtropical climates, especially in South Asia. Its rhizomes have been highly valued for food supplements, spices, flavoring agents, and yellow dye in South Asia since ancient times. It exhibits a diverse array of therapeutic qualities that encompass its ability to combat diabetes, reduce inflammation, act as an antioxidant, exhibit anticancer properties, and promote anti-aging effects. In this study, organic extracts of C. longa rhizomes were subjected to HPLC separation followed by ESI-MS and low-energy tandem mass spectrometry analyses. The Global Natural Product Social Molecular Networking (GNPS) approach was utilized for the first time in this ethnobotanically important species to conduct an in-depth analysis of its metabolomes based on their fragments. To sum it up, a total of 30 metabolites including 16 diarylheptanoids, 1 diarylpentanoid, 3 bisabolocurcumin ethers, 4 sesquiterpenoids, 4 cinnamic acid derivatives, and 2 fatty acid derivatives were identified. Among the 16 diarylheptanoids identified in this study, 5 of them are reported for the first time in this species.

Foodomics Reveals Anti-Obesity Properties of Cannabinoids from Hemp Oil

SCOPE

Molecular networking (MN) analysis intends to provide chemical insight of untargeted mass spectrometry (MS) data to the user's underlying biological questions. Foodomics is the study of chemical compounds in food using advanced omics methods. In this study, an MS-MN-based foodomics approach is developed to investigate the composition and anti-obesity activity of cannabinoids in hemp oil.

METHODS AND RESULTS

A total of 16 cannabinoids are determined in optimized microwave pretreatment of hemp oil using the developed approach. Untargeted metabolomics analysis reveals that cannabinoid extract (CE) and its major constituent (cannabidiol, CBD), can alleviate high glucose-induced increases in lipids and carbohydrates, and decreases in amino acid and nucleic acid. Moreover, CE and CBD are also found to suppress the expression levels of mdt-15, sbp-1, fat-5, fat-6, fat-7, daf-2, and elevate the expression level of daf-1, daf-7, daf-16, sod-3, gst-4, lipl-4, resulting in the decrease of lipid synthesis and the enhance of kinetism. Canonical correspondence analysis (CCA) uncovers strong associations between specific metabolic alterations and gene expression levels.

CONCLUSION

These findings from this exploratory study offer a new insight into the roles of cannabinoids in the treatment of obesity and related complications.

Bacillus for Plant Growth Promotion and Stress Resilience: What Have We Learned?

The rhizosphere is a thin film of soil that surrounds plant roots and the primary location of nutrient uptake, and is where important physiological, chemical, and biological activities are occurring. Many microbes invade the rhizosphere and have the capacity to promote plant growth and health. Bacillus spp. is the most prominent plant growth promoting rhizobacteria due to its ability to form long-lived, stress-tolerant spores. Bacillus-plant interactions are driven by chemical languages constructed by a wide spectrum of metabolites and lead to enhanced plant growth and defenses. Thus, this review is a synthesis and a critical assessment of the current literature on the application of Bacillus spp. in agriculture, highlighting gaps that remain to be explored to improve and expand on the Bacillus-based biostimulants. Furthermore, we suggest that omics sciences, with a focus on metabolomics, offer unique opportunities to illuminate the chemical intercommunications between Bacillus and plants, to elucidate biochemical and molecular details on modes of action of Bacillus-based formulations, to generate more actionable insights on cellular and molecular events that explain the Bacillus-induced growth promotion and stress resilience in plants.