microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data.

microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms' role in ecology and human health.

Chemical and microbial diversity of a tropical intertidal ascidian holobiont.

The tropical ascidian Eudistoma vannamei, endemic to the northeastern coast of Brazil, is considered a prolific source of secondary metabolites and hosts Actinomycetota that produce bioactive compounds. Herein, we used an omics approach to study the ascidian as a holobiont, including the microbial diversity through 16S rRNA gene sequencing and metabolite production using mass spectrometry-based metabolomics. Gene sequencing analysis revealed all samples of E. vannamei shared about 50% of the observed ASVs, and Pseudomonadota (50.7%), Planctomycetota (9.58%), Actinomycetota (10.34%), Bacteroidota (12.05%) were the most abundant bacterial phyla. Analysis of tandem mass spectrometry (MS/MS) data allowed annotation of compounds, including phospholipids, amino acids, and pyrimidine alkaloids, such as staurosporine, a member of a well-known chemical class recognized as a microbial metabolite. Isolated bacteria, mainly belonging to Streptomyces and Micromonospora genera, were cultivated and extracted with ethyl acetate. MS/MS analysis of bacterial extracts allowed annotation of compounds not detected in the ascidian tissue, including marineosin and dihydroergotamine, yielding about 30% overlapped ions between host and isolated bacteria. This study reveals E. vannamei as a rich source of microbial and chemical diversity and, furthermore, highlights the importance of omic tools for a comprehensive investigation of holobiont systems.

Host macrocyclic acylcarnitines mediate symbiotic interactions between frogs and their skin microbiome.

The host-microbiome associations occurring on the skin of vertebrates significantly influence hosts' health. However, the factors mediating their interactions remain largely unknown. Herein, we used integrated technical and ecological frameworks to investigate the skin metabolites sustaining a beneficial symbiosis between tree frogs and bacteria. We characterize macrocyclic acylcarnitines as the major metabolites secreted by the frogs' skin and trace their origin to an enzymatic unbalance of carnitine palmitoyltransferases. We found that these compounds colocalize with bacteria on the skin surface and are mostly represented by members of the Pseudomonas community. We showed that Pseudomonas sp. MPFS isolated from frogs' skin can exploit acylcarnitines as its sole carbon and nitrogen source, and this metabolic capability is widespread in Pseudomonas. We summarize frogs' multiple mechanisms to filter environmental bacteria and highlight that acylcarnitines likely evolved for another function but were co-opted to provide nutritional benefits to the symbionts.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics.

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Ex vivo study of molecular changes of stained teeth following hydrogen peroxide and peroxymonosulfate treatments.

White teeth can give confidence and tend to be associated with a healthier lifestyle in modern society. Therefore, tooth-bleaching strategies have been developed, including the use of hydrogen peroxide. Recently, peroxymonosulfate has been introduced as an alternative bleaching method to hydrogen peroxide. Although both chemicals are oxidizing agents, their effects on the molecular composition of the stained teeth are yet unknown. In this study, the molecular profiles of teeth bleached with hydrogen peroxide and peroxymonosulfate were compared using Liquid Chromatography-Tandem Mass Spectrometry. Statistical analyses were used to assess the samples. In addition, reference spectral libraries and in silico tools were used to perform metabolite annotation. Overall, principal component analysis showed a strong separation between control and hydrogen peroxide and peroxymonosulfate samples (p < 0.001). The analysis of molecular changes revealed amino acids and dipeptides in stained teeth samples after hydrogen peroxide and peroxymonosulfate treatments. Noteworthy, the two bleaching methods led to distinct molecular profiles. For example, diterpenoids were more prevalent after peroxymonosulfate treatment, while a greater abundance of alkaloids was detected after hydrogen peroxide treatment. Whereas non-bleached samples (controls) showed mainly lipids. Therefore, this study shows how two different tooth-whitening peroxides could affect the molecular profiles of human teeth.

A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data.

MicrobeMASST, a taxonomically-informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbial-derived metabolites and relative producers, without a priori knowledge, will vastly enhance the understanding of microorganisms' role in ecology and human health.

Antibacterial Activity from Momordica charantia L. Leaves and Flavones Enriched Phase.

Momordica charantia L. (Cucurbitaceae) is a plant known in Brazil as "melão de São Caetano", which has been related to many therapeutic applications in folk medicine. Herein, we describe antibacterial activities and related metabolites for an extract and fractions obtained from the leaves of that species. An ethanolic extract and its three fractions were used to perform in vitro antibacterial assays. In addition, liquid chromatography coupled to mass spectrometry and the molecular networking approach were used for the metabolite annotation process. Overall, 25 compounds were annotated in the ethanolic extract from M. charantia leaves, including flavones, terpenes, organic acids, and inositol pyrophosphate derivatives. The ethanolic extract exhibited low activity against Proteus mirabilis (MIC 312.5 µg·mL-1) and Klebsiella pneumoniae (MIC 625 µg·mL-1). The ethyl acetate phase showed interesting antibacterial activity (MIC 156.2 µg·mL-1) against Klebsiella pneumoniae, and it was well justified by the high content of glycosylated flavones. Therefore, based on the ethyl acetate phase antibacterial result, we suggest that M. charantia leaves could be considered as an alternative antibacterial source against K. pneumoniae and can serve as a pillar for future studies as well as pharmacological application against the bacteria.

Untargeted Metabolomics Sheds Light on the Diversity of Major Classes of Secondary Metabolites in the Malpighiaceae Botanical Family.

Natural products produced by plants are one of the most investigated natural sources, which substantially contributed to the development of the natural products field. Even though these compounds are widely explored, the literature still lacks comprehensive investigations aiming to explore the evolution of secondary metabolites produced by plants, especially if classical methodologies are employed. The development of sensitive hyphenated techniques and computational tools for data processing has enabled the study of large datasets, being valuable assets for chemosystematic studies. Here, we describe a strategy for chemotaxonomic investigations using the Malpighiaceae botanical family as a model. Our workflow was based on MS/MS untargeted metabolomics, spectral searches, and recently described in silico classification tools, which were mapped into the latest molecular phylogeny accepted for this family. The metabolomic analysis revealed that different ionization modes and extraction protocols significantly impacted the chemical profiles, influencing the chemotaxonomic results. Spectral searches within public databases revealed several clades or genera-specific molecular families, being potential chemical markers for these taxa, while the in silico classification tools were able to expand the Malpighiaceae chemical space. The classes putatively annotated were used for ancestral character reconstructions, which recovered several classes of metabolites as homoplasies (i.e., non-exclusive) or synapomorphies (i.e., exclusive) for all sampled clades and genera. Our workflow combines several approaches to perform a comprehensive evolutionary chemical study. We expect it to be used on further chemotaxonomic investigations to expand chemical knowledge and reveal biological insights for compounds classes in different biological groups.

Mass spectrometry-based metabolomics in microbiome investigations.

Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters.

Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment.

Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.

The Diversity, Metabolomics Profiling, and the Pharmacological Potential of Actinomycetes Isolated from the Estremadura Spur Pockmarks (Portugal).

The Estremadura Spur pockmarks are a unique and unexplored ecosystem located in the North Atlantic, off the coast of Portugal. A total of 85 marine-derived actinomycetes were isolated and cultured from sediments collected from this ecosystem at a depth of 200 to 350 m. Nine genera, Streptomyces, Micromonospora, Saccharopolyspora, Actinomadura, Actinopolymorpha, Nocardiopsis, Saccharomonospora, Stackebrandtia, and Verrucosispora were identified by 16S rRNA gene sequencing analyses, from which the first two were the most predominant. Non-targeted LC-MS/MS, in combination with molecular networking, revealed high metabolite diversity, including several known metabolites, such as surugamide, antimycin, etamycin, physostigmine, desferrioxamine, ikarugamycin, piericidine, and rakicidin derivatives, as well as numerous unidentified metabolites. Taxonomy was the strongest parameter influencing the metabolite production, highlighting the different biosynthetic potentials of phylogenetically related actinomycetes; the majority of the chemical classes can be used as chemotaxonomic markers, as the metabolite distribution was mostly genera-specific. The EtOAc extracts of the actinomycete isolates demonstrated antimicrobial and antioxidant activity. Altogether, this study demonstrates that the Estremadura Spur is a source of actinomycetes with potential applications for biotechnology. It highlights the importance of investigating actinomycetes from unique ecosystems, such as pockmarks, as the metabolite production reflects their adaptation to this habitat.

Anticancer Potential of Compounds from the Brazilian Blue Amazon.

"Blue Amazon" is used to designate the Brazilian Economic Exclusive Zone, which covers an area comparable in size to that of its green counterpart. Indeed, Brazil flaunts a coastline spanning 8000 km through tropical and temperate regions and hosting part of the organisms accredited for the country's megadiversity status. Still, biodiversity may be expressed at different scales of organization; besides species inventory, genetic characteristics of living beings and metabolic expression of their genes meet some of these other layers. These metabolites produced by terrestrial creatures traditionally and lately added to by those from marine organisms are recognized for their pharmaceutical value, since over 50% of small molecule-based medicines are related to natural products. Nonetheless, Brazil gives a modest contribution to the field of pharmacology and even less when considering marine pharmacology, which still lacks comprehensive in-depth assessments toward the bioactivity of marine compounds so far. Therefore, this review examined the last 40 years of Brazilian natural products research, focusing on molecules that evidenced anticancer potential-which represents ~ 15% of marine natural products isolated from Brazilian species. This review discusses the most promising compounds isolated from sponges, cnidarians, ascidians, and microbes in terms of their molecular targets and mechanisms of action. Wrapping up, the review delivers an outlook on the challenges that stand against developing groundbreaking natural products research in Brazil and on a means of surpassing these matters.

Metabolomic study of marine Streptomyces sp.: Secondary metabolites and the production of potential anticancer compounds.

Resorting to a One Strain Many Compounds (OSMAC) approach, the marine Streptomyces sp. BRB081 strain was grown in six different media settings over 1, 2, 3 or 7 days. Extractions of mycelium and broth were conducted separately for each media and cultivation period by sonication using methanol/acetone 1:1 and agitation with ethyl acetate, respectively. All methanol/acetone and ethyl acetate crude extracts were analysed by HPLC-MS/MS and data treatment was performed through GNPS platform using MZmine 2 software. In parallel, the genome was sequenced, assembled and mined to search for biosynthetic gene clusters (BGC) of secondary metabolites using the AntiSMASH 5.0 software. Spectral library search tool allowed the annotation of desferrioxamines, fatty acid amides, diketopiperazines, xanthurenic acid and, remarkably, the cyclic octapeptides surugamides. Molecular network analysis allowed the observation of the surugamides cluster, where surugamide A and the protonated molecule corresponding to the B-E isomers, as well as two potentially new analogues, were detected. Data treatment through MZmine 2 software allowed to distinguish that the largest amount of surugamides was obtained by cultivating BRB081 in SCB medium during 7 days and extraction of culture broth. Using the same data treatment, a chemical barcode was created for easy visualization and comparison of the metabolites produced overtime in all media. By genome mining of BRB081 four regions of biosynthetic gene clusters of secondary metabolites were detected supporting the metabolic data. Cytotoxic evaluation of all crude extracts using MTT assay revealed the highest bioactivity was also observed for extracts obtained in the optimal conditions as those for surugamides production, suggesting these to be the main active compounds herein. This method allowed the identification of compounds in the crude extracts and guided the selection of best conditions for production of bioactive compounds.

Can Statistical Evaluation Tools for Chromatographic Method Development Assist in the Natural Products Workflow? A Case Study on Selected Species of the Plant Family Malpighiaceae.

Proper chromatographic methods may reduce the challenges inherent in analyzing natural product extracts, especially when utilizing hyphenated detection techniques involving mass spectrometry. As there are many variations one can introduce during chromatographic method development, this can become a daunting and time-consuming task. To reduce the number of runs and time needed, the use of instrumental automatization and commercial software to apply Quality by Design and statistical analysis automatically can be a valuable approach to investigate complex matrices. To evaluate this strategy in the natural products workflow, a mixture of nine species from the family Malpighiaceae was investigated. By this approach, the entire data collection and method development procedure (comprising screening, optimization, and robustness simulation) was accomplished in only 4 days, resulting in very low limits of detection and quantification. The analysis of the individual extracts also proved the efficiency of the use of a mixture of extracts for this workflow. Molecular networking and library searches were used to annotate a total of 61 compounds, including O-glycosylated flavonoids, C-glycosylated flavonoids, quinic/shikimic acid derivatives, sterols, and other phenols, which were efficiently separated by the method developed. These results support the potential of statistical tools for chromatographic method optimization as an efficient approach to reduce time and maximize resources, such as solvents, to get proper chromatographic conditions.

ReDU: a framework to find and reanalyze public mass spectrometry data.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Linking genomics and metabolomics to chart specialized metabolic diversity.

Microbial and plant specialized metabolites constitute an immense chemical diversity, and play key roles in mediating ecological interactions between organisms. Also referred to as natural products, they have been widely applied in medicine, agriculture, cosmetic and food industries. Traditionally, the main discovery strategies have centered around the use of activity-guided fractionation of metabolite extracts. Increasingly, omics data is being used to complement this, as it has the potential to reduce rediscovery rates, guide experimental work towards the most promising metabolites, and identify enzymatic pathways that enable their biosynthetic production. In recent years, genomic and metabolomic analyses of specialized metabolic diversity have been scaled up to study thousands of samples simultaneously. Here, we survey data analysis technologies that facilitate the effective exploration of large genomic and metabolomic datasets, and discuss various emerging strategies to integrate these two types of omics data in order to further accelerate discovery.

Targeting the Oncogenic TBX2 Transcription Factor With Chromomycins.

The TBX2 transcription factor plays critical roles during embryonic development and it is overexpressed in several cancers, where it contributes to key oncogenic processes including the promotion of proliferation and bypass of senescence. Importantly, based on compelling biological evidences, TBX2 has been considered as a potential target for new anticancer therapies. There has therefore been a substantial interest to identify molecules with TBX2-modulatory activity, but no such substance has been found to date. Here, we adopt a targeted approach based on a reverse-affinity procedure to identify the ability of chromomycins A5 (CA5) and A6 (CA6) to interact with TBX2. Briefly, a TBX2-DNA-binding domain recombinant protein was N-terminally linked to a resin, which in turn, was incubated with either CA5 or CA6. After elution, bound material was analyzed by UPLC-MS and CA5 was recovered from TBX2-loaded resins. To confirm and quantify the affinity (KD) between the compounds and TBX2, microscale thermophoresis analysis was performed. CA5 and CA6 modified the thermophoretic behavior of TBX2, with a KD in micromolar range. To begin to understand whether these compounds exerted their anti-cancer activity through binding TBX2, we next analyzed their cytotoxicity in TBX2 expressing breast carcinoma, melanoma and rhabdomyosarcoma cells. The results show that CA5 was consistently more potent than CA6 in all tested cell lines with IC50 values in the nM range. Of the cancer cell types tested, the melanoma cells were most sensitive. The knockdown of TBX2 in 501mel melanoma cells increased their sensitivity to CA5 by up to 5 times. Furthermore, inducible expression of TBX2 in 501mel cells genetically engineered to express TBX2 in the presence of doxycycline, were less sensitive to CA5 than the control cells. Together, the data presented in this study suggest that, in addition to its already recognized DNA-binding properties, CA5 may be binding the transcription factor TBX2, and it can contribute to its cytotoxic activity.