Optimization of LC-MS2 Data Acquisition Parameters for Molecular Networking Applied to Marine Natural Products

Identifying sesterterpenoids via feature-based molecular networking and small-scale fermentation

Terpenoids are known for their diverse structures and broad bioactivities with significant potential in pharmaceutical applications. However, natural products with low yields are usually ignored in traditional chemical analysis. Feature-based molecular networking (FBMN) was developed recently to cluster compounds with similar skeletons, which can highlight trace amounts of unknown compounds. Fusoxypene A is a sesterterpene synthesized by Fusarium oxysporum fusoxypene synthase (FoFS) with a unique 5/6/7/3/5 ring system. In this study, the FoFS-containing biosynthetic gene cluster was identified from F. oxysporum FO14005, and an efficient FBMN-based strategy was established to characterize four new sesterterpenoids, fusoxyordienoid A-D (1-4), based on a small-scale fermentation strategy. A cytochrome P450 monooxygenase, FusB, was found to be involved in the functionalization of fusoxypene A at C-17 and C-24 and responsible for the hydroxylation of fusoxyordienoid A at C-1 and C-8. This study highlights the potential of FBMN as a powerful tool for the discovery and characterization of natural compounds with low abundance. KEY POINTS: Combined small-scale fermentation and FBMN for rapid discovery of fusoxyordienoids Characterization of four new fusoxyordienoids with 5/6/7/3/5 ring system Biosynthetic pathway elucidation via tandem expression and substrate feeding.

Myrtucommulones and Related Acylphloroglucinols from Myrtaceae as a Promising Source of Multitarget SARS-CoV-2 Cycle Inhibitors

The LABEXTRACT plant extract bank, featuring diverse members of the Myrtaceae family from Brazilian hot spot regions, provides a promising avenue for bioprospection. Given the pivotal roles of the Spike protein and 3CLpro and PLpro proteases in SARS-CoV-2 infection, this study delves into the correlations between the Myrtaceae species from the Atlantic Forest and these targets, as well as an antiviral activity through both in vitro and in silico analyses. The results uncovered notable inhibitory effects, with Eugenia prasina and E. mosenii standing out, while E. mosenii proved to be multitarget, presenting inhibition values above 72% in the three targets analyzed. All extracts inhibited viral replication in Calu-3 cells (EC50 was lower than 8.3 µg·mL-1). Chemometric analyses, through LC-MS/MS, encompassing prediction models and molecular networking, identified potential active compounds, such as myrtucommulones, described in the literature for their antiviral activity. Docking analyses showed that one undescribed myrtucommulone (m/z 841 [M - H]-) had a higher fitness score when interacting with the targets of this study, including ACE2, Spike, PLpro and 3CLpro of SARS-CoV-2. Also, the study concludes that Myrtaceae extracts, particularly from E. mosenii and E. prasina, exhibit promising inhibitory effects against crucial stages in SARS-CoV-2 infection. Compounds like myrtucommulones emerge as potential anti-SARS-CoV-2 agents, warranting further exploration.

Modulation of cecal microbiota and fecal metabolism in mice by walnut protein

Walnut meal is a by-product of walnut oil pressing, in which the protein content is more than 40%, which is an excellent food raw material, but at present, it is basically used as animal feed or discarded, which results in a great waste of resources, and its modulating effect on the intestinal microbiota is not clear. In this study, we used supercritically extracted walnut meal as a raw material, prepared walnut meal isolate protein (WP) by alkaline extraction and acid precipitation, and systematically analyzed its structure by Fourier infrared spectroscopy (FTIR), Raman spectroscopy (Raman), and scanning electron microscopy (SEM); meanwhile, we explored the effects of WP on the cecal bacterial flora and fecal metabolites of mice by microbiological and metabolomic techniques. The results showed that the protein content of WP prepared using alkaline extraction and acid precipitation was as high as 83.7%, in which arginine and glutamic acid were abundant, and it has the potential to be used as a raw material for weight-loss meal replacement food; FTIR and Raman analyses showed that the absorption peaks of WP's characteristic functional groups were obvious, and that the content of the α-helix and β-fold in the secondary structure was greater than 30%, which indicated that it was structurally stable; differential scanning calorimetry (DSC) and SEM analyses showed that WP is a typical spherical particle, its denaturation temperature is 73.6 °C, and it has good thermal stability. Supplementation of WP significantly altered the composition of the intestinal flora in mice, with an increase in beneficial bacteria and a decrease in harmful bacteria; the strongest modulation of the intestinal flora was achieved by altering the composition of the intestinal flora and by increasing the number of Akkermansia (p < 0.01), which consequently affects the function of the microbiota. Based on LC-MS metabolomic results, we identified a total of 87 WP-regulated metabolites, mainly enriched in the bile secretion pathway, which had the highest relevance, followed by benzoxazine biosynthesis. In summary, walnut protein is an important plant protein and has a positive impact on intestinal health, which may provide new ideas for the development of functional foods.

Feature-Based Molecular Networking Combined with Multivariate Analysis for the Characterization of Glutathione Adducts as a Smoking Gun of Bioactivation

Feature-based molecular networking (FBMN) is a powerful analytical tool for mass spectrometry (MS)-based untargeted metabolomics data analysis. FBMN plays an important role in drug metabolism studies, enabling the visualization of complex metabolomics data to achieve metabolite characterization. In this study, we propose a strategy for the characterization of glutathione (GSH) adducts formed via in vitro metabolic activation using FBMN assisted by multivariate analysis (MVA). Acetaminophen was used as a model substrate for method development, and the practical potential of the method was investigated by its application to 2-aminophenol (2-AP) and 2,4-dinitrochlorobenzene (DNCB). Two 2-AP GSH adducts and one DNCB GSH adduct were successfully characterized by forming networks with GSH even though the mass spectral information obtained for the parent compound was deficient. False positives were effectively filtered out by the variable influence on projection cutoff criteria obtained from orthogonal partial least-squares-discriminant analysis. The GSH adducts formed by enzymatic or nonenzymatic reactions were intuitively distinguished by the pie chart of FBMN results. In summary, our approach effectively characterizes GSH adducts, which serve as compelling evidence of bioactivation. It can be widely utilized to enhance risk assessment in the context of drug metabolism.

Application of effect-directed analysis using TLC-bioautography for rapid isolation and identification of antidiabetic compounds from the leaves of Annona cherimola Mill

INTRODUCTION

Type 2 diabetes mellitus is a globally prevalent chronic disease characterised by hyperglycaemia and oxidative stress. The search for new natural bioactive compounds that contribute to controlling this condition and the application of analytical methodologies that facilitate rapid detection and identification are important challenges for science. Annona cherimola Mill. is an important source of aporphine alkaloids with many bioactivities.

OBJECTIVE

The aim of this study is to isolate and identify antidiabetic compounds from alkaloid extracts with α-glucosidase and α-amylase inhibitory activity from A. cherimola Mill. leaves using an effect-directed analysis by thin-layer chromatography (TLC)-bioautography.

METHODOLOGY

Guided fractionation for α-glucosidase and α-amylase inhibitors in leaf extracts was done using TLC-bioassays. The micro-preparative TLC was used to isolate the active compounds, and the identification was performed by mass spectrometry associated with web-based molecular networks. Additionally, in vitro estimation of the inhibitory activity and antioxidant capacity was performed in the isolated compounds.

RESULTS

Five alkaloids (liriodenine, dicentrinone, N-methylnuciferine, anonaine, and moupinamide) and two non-alkaloid compounds (3-methoxybenzenepropanoic acid and methylferulate) with inhibitory activity were isolated and identified using a combination of simple methodologies. Anonaine, moupinamide, and methylferulate showed promising results with an outstanding inhibitory activity against both enzymes and antioxidant capacity that could contribute to controlling redox imbalance.

CONCLUSIONS

These high-throughput methodologies enabled a rapid isolation and identification of seven compounds with potential antidiabetic activity. To our knowledge, the estimated inhibitory activity of dicentrinone, N-methylnuciferine, and anonaine against α-glucosidase and α-amylase is reported here for the first time.

Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform

Non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Mass spectrometry DDA parameters and global coverage of the metabolome: Spectral molecular networks of momordica cardiospermoides plants

INTRODUCTION

Molecular networking (MN) has emerged as a key strategy to organize and annotate untargeted tandem mass spectrometry (MS/MS) data generated using either data independent- or dependent acquisition (DIA or DDA). The latter presents a time-efficient approach where full scan (MS¹) and MS² spectra are obtained with shorter cycle times. However, there are limitations related to DDA parameters, some of which are (i) intensity threshold and (ii) collision energy. The former determines ion prioritization for fragmentation, and the latter defines the fragmentation of selected ions. These DDA parameters inevitably determine the coverage and quality of spectral data, which would affect the outputs of MN methods.

OBJECTIVES

This study assessed the extent to which the quality of the tandem spectral data relates to MN topology and subsequent implications in the annotation of metabolites and chemical classification relative to the different DDA parameters employed.

METHODS

Herein, characterising the metabolome of Momordica cardiospermoides plants, we employ classical MN performance indicators to investigate the effects of collision energies and intensity thresholds on the topology of generated MN and propagated annotations.

RESULTS

We demonstrated that the lowest predefined intensity thresholds and collision energies result in comprehensive molecular networks. Comparatively, higher intensity thresholds and collision energies resulted in fewer MS² spectra acquisition, subsequently fewer nodes, and a limited exploration of the metabolome through MN.

CONCLUSION

Contributing to ongoing efforts and conversations on improving DDA strategies, this study proposes a framework in which multiple DDA parameters are utilized to increase the coverage of ions acquired and improve the global coverage of MN, propagated annotations, and the chemical classification performed.

A sDOE (Simple Design-of-Experiment) Approach for Parameter Optimization in Mass Spectrometry. Part 1. Parameter Selection and Interference Effects in Top-Down ETD Fragmentation of Proteins in a UHR-QTOF Instrument

Design-of-experiment (DOE) approaches, originally conceived by Fischer, are widely applied in industry, particularly in the context of production for which they have been greatly expended. In a research and development context, DOE can be of great use for method development. Specifically, DOE can greatly speed up instrument parameter optimization by first identifying parameters that are critical to a given outcome, showing parameter interdependency where it occurs and accelerating optimization of said parameters using matrices of experimental conditions. While DOE approaches have been applied in mass spectrometry experiments, they have so far failed to gain widespread adoption. This could be attributed to the fact that DOE can get quite complex and daunting to the everyday user. Here we make the case that a subset of DOE tools, hereafter called SimpleDOE (sDOE), can make DOE accessible and useful to the Mass Spectrometry community at large. We illustrate the progressive gains from a purely manual approach to sDOE through a stepwise optimization of parameters affecting the efficiency of top-down ETD fragmentation of proteins on a high-resolution Q-TOF mass spectrometer, where the aim is to maximize sequence coverage of fragmentation events.

LC-MS Based Phytochemical Profiling towards the Identification of Antioxidant Markers in Some Endemic Aloe Species from Mascarene Islands

Aloe plant species have been used for centuries in traditional medicine and are reported to be an important source of natural products. However, despite the large number of species within the Aloe genus, only a few have been investigated chemotaxonomically. A Molecular Network approach was used to highlight the different chemical classes characterizing the leaves of five Aloe species: Aloe macra, Aloe vera, Aloe tormentorii, Aloe ferox, and Aloe purpurea. Aloe macra, A. tormentorii, and A. purpurea are endemic from the Mascarene Islands comprising Reunion, Mauritius, and Rodrigues. UHPLC-MS/MS analysis followed by a dereplication process allowed the characterization of 93 metabolites. The newly developed MolNotator algorithm was usedfor molecular networking and allowed a better exploration of the Aloe metabolome chemodiversity. The five species appeared rich in polyphenols (anthracene derivatives, flavonoids, phenolic acids). Therefore, the total phenolic content and antioxidant activity of the five species were evaluated, and a DPPH-On-Line-HPLC assay was used to determine the metabolites responsible for the radical scavenging activity. The use of computational tools allowed a better description of the comparative phytochemical profiling of five Aloe species, which showed differences in their metabolite composition, both qualitative and quantitative. Moreover, the molecular network approach combined with the On-Line-HPLC assay allowed the identification of 9 metabolites responsible for the antioxidant activity. Two of them, aloeresin A and coumaroylaloesin, could be the principal metabolites responsible for the activity. From 374 metabolites calculated by MolNator, 93 could be characterized. Therefore, the Aloe species can be a rich source of new chemical structures that need to be discovered.

Exploring Micromonospora as Phocoenamicins Producers

Over the past few years, new technological and scientific advances have reinforced the field of natural product discovery. The spirotetronate class of natural products has recently grown with the discovery of phocoenamicins, natural actinomycete derived compounds that possess different antibiotic activities. Exploring the MEDINA's strain collection, 27 actinomycete strains, including three marine-derived and 24 terrestrial strains, were identified as possible phocoenamicins producers and their taxonomic identification by 16S rDNA sequencing showed that they all belong to the Micromonospora genus. Using an OSMAC approach, all the strains were cultivated in 10 different media each, resulting in 270 fermentations, whose extracts were analyzed by LC-HRMS and subjected to High-throughput screening (HTS) against methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis H37Ra and Mycobacterium bovis. The combination of LC-UV-HRMS analyses, metabolomics analysis and molecular networking (GNPS) revealed that they produce several related spirotetronates not disclosed before. Variations in the culture media were identified as the most determining factor for phocoenamicin production and the best producer strains and media were established. Herein, we reported the chemically diverse production and metabolic profiling of Micromonospora sp. strains, including the known phocoenamicins and maklamicin, reported for the first time as being related to this family of compounds, as well as the bioactivity of their crude extracts. Although our findings do not confirm previous statements about phocoenamicins production only in unique marine environments, they have identified marine-derived Micromonospora species as the best producers of phocoenamicins in terms of both the abundance in their extracts of some major members of the structural class and the variety of molecular structures produced.