Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

Molecular networking: An efficient tool for discovering and identifying natural products

Natural products (NPs), play a crucial role in drug development. However, the discovery of NPs is accidental, and conventional identification methods lack accuracy. To overcome these challenges, an increasing number of researchers are directing their attention to Molecular networking (MN). MN based on secondary mass spectrometry has become an important tool for the separation, purification and structural identification of NPs. However, most new tools are not well known. This review started with the most basic MN tool and explains it from the principle, workflow, and application. Then introduce the principles and workflows of the remaining eight new types of MN tools. The reliability of various MNs is mainly verified based on the application of phytochemistry and metabolomics. Subsequently, the principles and applications of 12 structural annotation tools are introduced. For the first time, the scope of 9 kinds of MN tools is compared horizontally, and 12 kinds of structured annotation tools are classified from the type of compound structure suitable for identification. The advantages and disadvantages of various tools are summarized, and make suggestions for future application directions and the development of computing tools in this review. MN tools are expected to enhance the efficiency of the discovery and identification in NPs.

High throughput recurrent pregnancy loss screening: urine metabolic fingerprints via LDI-MS and machine learning

Infertility is a significant challenge faced by many families worldwide, with recurrent pregnancy loss (RPL) being a prevalent cause of infertility among women. This condition causes immense emotional and physical distress for affected individuals and their families. In this study, we present a rapid, efficient, and high-throughput analytical method using PS@Fe3O4-NH2 magnetic beads as a matrix for the detection of urinary metabolite fingerprints in RPL patients via laser desorption/ionization mass spectrometry (LDI-MS) combined with machine learning (ML). This approach offers rich metabolic information from urine samples, through subsequent analysis we identify 17 metabolites that significantly differ between RPL patients and healthy controls (HC). The application of mass spectrometry features in conjunction with ML enabled effective screening of RPL patients and the identification of dysregulated metabolic pathways. This method presents a promising, non-invasive, and rapid screening approach for early detection of RPL, facilitating timely intervention and contributing to women's health.

The International Space Station has a unique and extreme microbial and chemical environment driven by use patterns

Space habitation provides unique challenges in built environments isolated from Earth. We produced a 3D map of the microbes and metabolites throughout the United States Orbital Segment (USOS) of the International Space Station (ISS) with 803 samples collected during space flight, including controls. We find that the use of each of the nine sampled modules within the ISS strongly drives the microbiology and chemistry of the habitat. Relating the microbiology to other Earth habitats, we find that, as with human microbiota, built environment microbiota also align naturally along an axis of industrialization, with the ISS providing an extreme example of an industrialized environment. We demonstrate the utility of culture-independent sequencing for microbial risk monitoring, especially as the location of sequencing moves to space. The resulting resource of chemistry and microbiology in the space-built environment will guide long-term efforts to maintain human health in space for longer durations.

Testing the role of biotic interactions in shaping elevational diversity gradients: An ecological metabolomics approach

Seminal hypotheses in ecology and evolution postulate that stronger and more specialized biotic interactions contribute to higher species diversity at lower elevations and latitudes. Plant-chemical defenses mediate biotic interactions between plants and their natural enemies and provide a highly dimensional trait space in which chemically mediated niches may facilitate plant species coexistence. However, the role of chemically mediated biotic interactions in shaping plant communities remains largely untested across large-scale ecological gradients. Here, we used ecological metabolomics to quantify the chemical dissimilarity of foliar metabolomes among 473 tree species in 16 tropical tree communities along an elevational gradient in the Bolivian Andes. We predicted that tree species diversity would be higher in communities and climates where co-occurring tree species are more chemically dissimilar and exhibit faster evolution of secondary metabolites (lower chemical phylogenetic signal). Further, we predicted that these relationships should be especially pronounced for secondary metabolites known to include antiherbivore and antimicrobial defenses relative to primary metabolites. Using structural equation models, we quantified the direct effects of rarefied median chemical dissimilarity and chemical phylogenetic signal on tree species diversity, as well as the indirect effects of climate. We found that chemical dissimilarity among tree species with respect to all metabolites and secondary metabolites had positive direct effects on tree species diversity, and that climate (higher temperature and precipitation, and lower temperature seasonality) had positive indirect effects on species diversity by increasing chemical dissimilarity. In contrast, chemical dissimilarity of primary metabolites was unrelated to species diversity and climate. Chemical phylogenetic signal of all metabolite classes had negative direct effects on tree species diversity, indicating faster evolution of metabolites in more diverse communities. Climate had a direct effect on species diversity but did not indirectly affect diversity through chemical phylogenetic signal. Our results support the hypothesis that chemically mediated biotic interactions shape elevational diversity gradients by imposing stronger selection for chemical divergence in more diverse communities and maintaining higher chemical dissimilarity among species in warmer, wetter, and more stable climates. Our study also illustrates the promise of ecological metabolomics in the study of biogeography, community ecology, and complex species interactions in high-diversity ecosystems.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

The iPhylo suite: an interactive platform for building and annotating biological and chemical taxonomic trees

Accurate and rapid taxonomic classifications are essential for systematically exploring organisms and metabolites in diverse environments. Many tools have been developed for biological taxonomic trees, but limitations apply, and a streamlined method for constructing chemical taxonomic trees is notably absent. We present the iPhylo suite (https://www.iphylo.net/), a comprehensive, automated, and interactive platform for biological and chemical taxonomic analysis. The iPhylo suite features web-based modules for the interactive construction and annotation of taxonomic trees and a stand-alone command-line interface (CLI) for local operation or deployment on high-performance computing (HPC) clusters. iPhylo supports National Center for Biotechnology Information (NCBI) taxonomy for biologicals and ChemOnt and NPClassifier for chemical classifications. The iPhylo visualization module, fully implemented in R, allows users to save progress locally and customize the underlying R code. Finally, the CLI module facilitates analysis across all hierarchical relational databases. We showcase the iPhylo suite's capabilities for visualizing environmental microbiomes, analyzing gut microbial metabolite synthesis preferences, and discovering novel correlations between microbiome and metabolome in humans and environment. Overall, the iPhylo suite is distinguished by its unified and interactive framework for in-depth taxonomic and integrative analyses of biological and chemical features and beyond.

Current and emerging tools and strategies for the identification of bioactive natural products in complex mixtures

Covering: up to 2024The prompt identification of (bio)active natural products (NPs) from complex mixtures poses a significant challenge due to the presence of numerous compounds with diverse structures and (bio)activities. Thus, this review provides an overview of current and emerging tools and strategies for the identification of (bio)active NPs in complex mixtures. Traditional approaches of bioassay-guided fractionation (BGF), followed by nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis for compound structure elucidation, continue to play an important role in the identification of active NPs. However, recent advances (2018-2024) have led to the development of novel techniques such as (bio)chemometric analysis, dereplication and combined approaches, which allow efficient prioritization for the elucidation of (bio)active compounds. For researchers involved in the search for bioactive NPs and who want to speed up their discoveries while maintaining accurate identifications, this review highlights the strengths and limitations of each technique and provides up-to-date insights into their combined use to achieve the highest level of confidence in the identification of (bio)active natural products from complex matrices.

Leaf Endophytes Relationship with Host Metabolome Expression in Tropical Gymnosperms

Plant-microbe interactions play a pivotal role in shaping host fitness, especially concerning chemical defense mechanisms. In cycads, establishing direct correlations between specific endophytic microbes and the synthesis of highly toxic defensive phytochemicals has been challenging. Our research delves into the intricate relationship between plant-microbe associations and the variation of secondary metabolite production in two closely related Zamia species that grow in distinct habitats; terrestrial and epiphytic. Employing an integrated approach, we combined microbial metabarcoding, which characterize the leaf endophytic bacterial and fungal communities, with untargeted metabolomics to test if the relative abundances of specific microbial taxa in these two Zamia species were associated with different metabolome profiles. The two species studied shared approximately 90% of the metabolites spanning diverse biosynthetic pathways: alkaloids, amino acids, carbohydrates, fatty acids, polyketides, shikimates, phenylpropanoids, and terpenoids. Co-occurrence networks revealed positive associations among metabolites from different pathways, underscoring the complexity of their interactions. Our integrated analysis demonstrated to some degree that the intraspecific variation in metabolome profiles of the two host species was associated with the abundance of bacterial orders Acidobacteriales and Frankiales, as well as the fungal endophytes belonging to the orders Chaetothyriales, Glomerellales, Heliotiales, Hypocreales, and Sordariales. We further associate individual metabolic similarity with four specific fungal endophyte members of the core microbiota, but no specific bacterial taxa associations were identified. This study represents a pioneering investigation to characterize leaf endophytes and their association with metabolomes in tropical gymnosperms, laying the groundwork for deeper inquiries into this complex domain.

Effects of individual traits vs. trait syndromes on assemblages of various herbivore guilds associated with central European Salix

Plants employ diverse anti-herbivore defences that can covary to form syndromes consisting of multiple traits. Such syndromes are hypothesized to impact herbivores more than individual defences. We studied 16 species of lowland willows occurring in central Europe and explored if their chemical and physical traits form detectable syndromes. We tested for phylogenetic trends in the syndromes and explored whether three herbivore guilds (i.e., generalist leaf-chewers, specialist leaf-chewers, and gallers) are affected more by the detected syndromes or individual traits. The recovered syndromes showed low phylogenetic signal and were mainly defined by investment in concentration, richness, or uniqueness of structurally related phenolic metabolites. Resource acquisition traits or inducible volatile organic compounds exhibited a limited correlation with the syndromes. Individual traits composing the syndromes showed various correlations to the assemblages of herbivores from the three studied guilds. In turn, we found some support for the hypothesis that defence syndromes are composed of traits that provide defence against various herbivores. However, individual traits rather than trait syndromes explained more variation for all studied herbivore assemblages. The detected negative correlations between various phenolics suggest that investment trade-offs may occur primarily among plant metabolites with shared metabolic pathways that may compete for their precursors. Moreover, several traits characterizing the recovered syndromes play additional roles in willows other than defence from herbivory. Taken together, our findings suggest that the detected syndromes did not solely evolve as an anti-herbivore defence.

Machine learning of cellular metabolic rewiring

Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.

Rapid screening of infertility-associated gynecological conditions via ambient glow discharge mass spectrometry utilizing urine metabolic fingerprints

Infertility presents a widespread challenge for many families worldwide, often arising from various gynecological diseases (GDs) that hinder successful pregnancies. Current diagnostic methods for GDs have disadvantages such as low efficiency, high cost, misdiagnose, invasive injury and etc. This paper introduces a rapid, non-invasive, efficient, and straightforward analytical method that utilizes desorption, separation, and ionization mass spectrometry (DSI-MS) platform in conjunction with machine learning (ML) to detect urine metabolite fingerprints in patients with different GDs. We analyzed 257 samples from patients diagnosed with polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), endometriosis (EMS), recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), and 87 samples from healthy control (HC) individuals. We identified metabolite differences and dysregulated pathways through dimensionality reduction methods, with the result of the discovery of 7 potential biomarkers for GDs diagnosis. The ML method effectively distinguished subtle differences in urine metabolite fingerprints. We anticipate that this innovative approach will offer a patient-friendly, rapid screening, and differentiation method for infertility-related GDs patients.

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.

A comprehensive metabolomics and lipidomics atlas for the legumes common bean, chickpea, lentil and lupin

Legumes represent an important component of human and livestock diets; they are rich in macro- and micronutrients such as proteins, dietary fibers and polyunsaturated fatty acids. Whilst several health-promoting and anti-nutritional properties have been associated with grain content, in-depth metabolomics characterization of major legume species remains elusive. In this article, we used both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to assess the metabolic diversity in the five legume species commonly grown in Europe, including common bean (Phaseolus vulgaris), chickpea (Cicer arietinum), lentil (Lens culinaris), white lupin (Lupinus albus) and pearl lupin (Lupinus mutabilis), at the tissue level. We were able to detect and quantify over 3400 metabolites covering major nutritional and anti-nutritional compounds. Specifically, the metabolomics atlas includes 224 derivatized metabolites, 2283 specialized metabolites and 923 lipids. The data generated here will serve the community as a basis for future integration to metabolomics-assisted crop breeding and facilitate metabolite-based genome-wide association studies to dissect the genetic and biochemical bases of metabolism in legume species.

Machine learning of cellular metabolic rewiring

Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry (GC/MS) to predict abundance changes in metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. The model learned captures shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting potential organ-tailored cellular adaptations. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Contrasting levels of β-diversity and underlying phylogenetic trends indicate different paths to chemical diversity in highland and lowland willow species

Diverse specialised metabolites contributed to the success of vascular plants in colonising most terrestrial habitats. Understanding how distinct aspects of chemical diversity arise through heterogeneous environmental pressures can help us understand the effects of abiotic and biotic stress on plant evolution and community assembly. We examined highland and lowland willow species within a phylogenetic framework to test for trends in their chemical α-diversity (richness) and β-diversity (variation among species sympatric in elevation). We show that differences in chemistry among willows growing at different elevations occur mainly through shifts in chemical β-diversity and due to convergence or divergence among species sharing their elevation level. We also detect contrasting phylogenetic trends in concentration and α-diversity of metabolites in highland and lowland willow species. The resulting elevational patterns contribute to the chemical diversity of willows and suggest that variable selective pressure across ecological gradients may, more generally, underpin complex changes in plant chemistry.

Microbiomes and metabolomes of dominant coral reef primary producers illustrate a potential role for immunolipids in marine symbioses

The dominant benthic primary producers in coral reef ecosystems are complex holobionts with diverse microbiomes and metabolomes. In this study, we characterize the tissue metabolomes and microbiomes of corals, macroalgae, and crustose coralline algae via an intensive, replicated synoptic survey of a single coral reef system (Waimea Bay, O'ahu, Hawaii) and use these results to define associations between microbial taxa and metabolites specific to different hosts. Our results quantify and constrain the degree of host specificity of tissue metabolomes and microbiomes at both phylum and genus level. Both microbiome and metabolomes were distinct between calcifiers (corals and CCA) and erect macroalgae. Moreover, our multi-omics investigations highlight common lipid-based immune response pathways across host organisms. In addition, we observed strong covariation among several specific microbial taxa and metabolite classes, suggesting new metabolic roles of symbiosis to further explore.

Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression

Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

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.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Quantifying chemodiversity considering biochemical and structural properties of compounds with the R package chemodiv

Plants produce large numbers of phytochemical compounds affecting plant physiology and interactions with their biotic and abiotic environment. Recently, chemodiversity has attracted considerable attention as an ecologically and evolutionary meaningful way to characterize the phenotype of a mixture of phytochemical compounds. Currently used measures of phytochemical diversity, and related measures of phytochemical dissimilarity, generally do not take structural or biosynthetic properties of compounds into account. Such properties can be indicative of the compounds' function and inform about their biosynthetic (in)dependence, and should therefore be included in calculations of these measures. We introduce the R package chemodiv, which retrieves biochemical and structural properties of compounds from databases and provides functions for calculating and visualizing chemical diversity and dissimilarity for phytochemicals and other types of compounds. Our package enables calculations of diversity that takes the richness, relative abundance and - most importantly - structural and/or biosynthetic dissimilarity of compounds into account. We illustrate the use of the package with examples on simulated and real datasets. By providing the R package chemodiv for quantifying multiple aspects of chemodiversity, we hope to facilitate investigations of how chemodiversity varies across levels of biological organization, and its importance for the ecology and evolution of plants and other organisms.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

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.

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools

BACKGROUND

Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

AIM OF REVIEW

We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.

Enhancing untargeted metabolomics using metadata-based source annotation

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Linking research of biomedical datasets

Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.

Location-specific signatures of Crohn's disease at a multi-omics scale

BACKGROUND

Crohn's disease (CD), an inflammatory bowel disease (IBD) subtype, results from pathologic interactions between host cells and its resident gut microbes. CD manifests in both isolated disease locations (ileum or colon) or a combination of locations (ileocolonic). To date, a comprehensive understanding of how isolated CD subtypes influence molecular profiles remains outstanding. To address this, we sought to define CD location signatures by leveraging a large cross-sectional feature set captured from the stool of over 200 IBD patients and healthy controls using metaproteomics, shotgun metagenomics, 16S rRNA sequencing, metabolomic profiling, and host genetics paired with clinical endoscopic assessments.

RESULTS

Neither metagenomic nor host genetics alone distinguished CD location subtypes. In contrast, ileal and colonic CD were distinguished using mass spectrometry-based methods (metabolomics or metaproteomics) or a combined multi-omic feature set. This multi-omic feature set revealed colonic CD was strongly associated with neutrophil-related proteins. Additionally, colonic CD displayed a disease-severity-related association with Bacteroides vulgatus. Colonic CD and ulcerative colitis profiles harbored strikingly similar feature enrichments compared to ileal CD, including neutrophil-related protein enrichments. Compared to colonic CD, ileal CD profiles displayed increased primary and secondary bile acid levels and concomitant shifts in taxa with noted sensitivities such as Faecalibacterium prausnitzii or affinities for bile acid-rich environments, including Gammaproteobacteria and Blautia sp. Having shown robust molecular and microbial distinctions tied to CD locations, we leveraged these profiles to generate location-specific disease severity biomarkers that surpass the performance of Calprotectin.

CONCLUSIONS

When compared using multi-omics features, colonic- and ileal-isolated CD subtypes display striking differences that suggest separate location-specific pathologies. Colonic CD's strong similarity to ulcerative colitis, including neutrophil and Bacteroides vulgatus involvement, is also evidence of a shared pathology for colonic-isolated IBD subtypes, while ileal CD maintains a unique, bile acid-driven profile. More broadly, this study demonstrates the power of multi-omics approaches for IBD biomarker discovery and elucidating the underlying biology. Video Abstract.

Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning

Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Biofluids Metabolic Profiling Based on PS@Fe3O4-NH2 Magnetic Beads-Assisted LDI-MS for Liver Cancer Screening

Liver cancer (LC) is the third frequent cause of death worldwide, so early diagnosis of liver cancer patients is crucial for disease management. Herein, we applied NH₂-coated polystyrene@Fe₃O₄ magnetic beads (PS@Fe₃O₄-NH₂ MBs) as a matrix material in laser desorption/ionization mass spectrometry (LDI-MS). Rapid, sensitive, and selective metabolic profiling of the native biofluids was achieved without any inconvenient enrichment or purification. Then, based on the selected m/z features, LC patients were discriminated from healthy controls (HCs) by machine learning, with the high area under the curve (AUC) values for urine and serum assessments (0.962 and 0.935). Moreover, initial-diagnosed and subsequent-visited LC patients were also differentiated, which indicates potential applications of this method in early diagnosis. Furthermore, among these identified compounds by FT-ICR MS, the expression level of some metabolites changed from HCs to LCs, including 29 and 12 characteristic metabolites in human urine and serum samples, respectively. These results suggest that PS@Fe₃O₄-NH₂ MBs-assisted LDI-MS coupled with machine learning is feasible for LC clinical diagnosis.

Using interdisciplinary, phylogeny-guided approaches to understand the evolution of plant metabolism

To cope with relentless environmental pressures, plants produce an arsenal of structurally diverse chemicals, often called specialized metabolites. These lineage-specific compounds are derived from the simple building blocks made by ubiquitous core metabolic pathways. Although the structures of many specialized metabolites are known, the underlying metabolic pathways and the evolutionary events that have shaped the plant chemical diversity landscape are only beginning to be understood. However, with the advent of multi-omics data sets and the relative ease of studying pathways in previously intractable non-model species, plant specialized metabolic pathways are now being systematically identified. These large datasets also provide a foundation for comparative, phylogeny-guided studies of plant metabolism. Comparisons of metabolic traits and features like chemical abundances, enzyme activities, or gene sequences from phylogenetically diverse plants provide insights into how metabolic pathways evolved. This review highlights the power of studying evolution through the lens of comparative biochemistry, particularly how placing metabolism into a phylogenetic context can help a researcher identify the metabolic innovations enabling the evolution of structurally diverse plant metabolites.

The molecular impact of life in an indoor environment

The chemistry of indoor surfaces and the role of microbes in shaping and responding to that chemistry are largely unexplored. We found that, over 1 month, people's presence and activities profoundly reshaped the chemistry of a house. Molecules associated with eating/cooking, bathroom use, and personal care were found throughout the entire house, while molecules associated with medications, outdoor biocides, and microbially derived compounds were distributed in a location-dependent manner. The house and its microbial occupants, in turn, also introduced chemical transformations such as oxidation and transformations of foodborne molecules. The awareness of and the ability to observe the molecular changes introduced by people should influence future building designs.

Past accomplishments and future challenges of the multi-omics characterization of leaf growth

The advent of omics technologies has revolutionized biology and advanced our understanding of all biological processes, including major developmental transitions in plants and animals. Here, we review the vast knowledge accumulated concerning leaf growth in terms of transcriptional regulation before turning our attention to the historically less well-characterized alterations at the protein and metabolite level. We will then discuss how the advent of biochemical methods coupled with metabolomics and proteomics can provide insight into the protein-protein and protein-metabolite interactome of the growing leaves. We finally highlight the substantial challenges in detection, spatial resolution, integration, and functional validation of the omics results, focusing on metabolomics as a prerequisite for a comprehensive understanding of small-molecule regulation of plant growth.

Global Tracking of Transformation Products of Environmental Contaminants by 2H-Labeled Stable Isotope-Assisted Metabolomics

Stable isotope-assisted metabolomics (SIAM) enables global tracking of isotopic labels in nontargeted metabolomics in living organisms. However, its application in tracking transformation products (TPs, as metabolites of contaminants) of environmental contaminants is still a challenge due to limits in methodology, unmatured algorithms, and the high cost of ¹³C-labeled contaminants. Therefore, we developed a ²H-SIAM pipeline coupled with a highly flexible algorithm ²H-SIAM(1.0) (https://github.com/kechen1984/2H-SIAM), facilitating tracking TPs of contaminants in the environmental matrix. A detailed discussion illustrates the theory, behavior, and prospect of ²H-SIAM. We demonstrate that the proposed ²H-SIAM pipeline has unique advantages over ¹³C-SIAM, for example, cost-effective ²H-labeled contaminants, easy synthesis of ²H-labeled emerging contaminants, and providing more structural information. A pyrene soil degradation study further confirmed its high performance. It efficiently discarded 99% of noise signals and extracted 52 features from the nontargeted high resolution mass spectrometry (HRMS) data. Among them, 13 features were annotated as TPs of pyrene with identification confidence from Level 2a to Level 5, and 5 TPs were reported for the first time. In conclusion, the proposed ²H-SIAM pipeline is powerful in tracking potential TPs of environmental contaminants with unique advantages.

Mammalian gut metabolomes mirror microbiome composition and host phylogeny

In the past decade, studies on the mammalian gut microbiome have revealed that different animal species have distinct gut microbial compositions. The functional ramifications of this variation in microbial composition remain unclear: do these taxonomic differences indicate microbial adaptations to host-specific functionality, or are these diverse microbial communities essentially functionally redundant, as has been indicated by previous metagenomics studies? Here, we examine the metabolic content of mammalian gut microbiomes as a direct window into ecosystem function, using an untargeted metabolomics platform to analyze 101 fecal samples from a range of 25 exotic mammalian species in collaboration with a zoological center. We find that mammalian metabolomes are chemically diverse and strongly linked to microbiome composition, and that metabolome composition is further correlated to the phylogeny of the mammalian host. Specific metabolites enriched in different animal species included modified and degraded host and dietary compounds such as bile acids and triterpenoids, as well as fermentation products such as lactate and short-chain fatty acids. Our results suggest that differences in microbial taxonomic composition are indeed translated to host-specific metabolism, indicating that taxonomically distant microbiomes are more functionally diverse than redundant.

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.

MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets

In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation—changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO—MS2 BasEd SaMple VectOrization—a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis.

Salivary bacterial signatures in depression-obesity comorbidity are associated with neurotransmitters and neuroactive dipeptides

BACKGROUND

Depression and obesity are highly prevalent, often co-occurring conditions marked by inflammation. Microbiome perturbations are implicated in obesity-inflammation-depression interrelationships, but how the microbiome mechanistically contributes to pathology remains unclear. Metabolomic investigations into microbial neuroactive metabolites may offer mechanistic insights into host-microbe interactions. Using 16S sequencing and untargeted mass spectrometry of saliva, and blood monocyte inflammation regulation assays, we identified key microbes, metabolites and host inflammation in association with depressive symptomatology, obesity, and depressive symptomatology-obesity comorbidity.

RESULTS

Gram-negative bacteria with inflammation potential were enriched relative to Gram-positive bacteria in comorbid obesity-depression, supporting the inflammation-oral microbiome link in obesity-depression interrelationships. Oral microbiome was more highly predictive of depressive symptomatology-obesity co-occurrences than of obesity or depressive symptomatology independently, suggesting specific microbial signatures associated with obesity-depression co-occurrences. Mass spectrometry analysis revealed significant changes in levels of signaling molecules of microbiota, microbial or dietary derived signaling peptides and aromatic amino acids among depressive symptomatology, obesity and comorbid obesity-depression. Furthermore, integration of the microbiome and metabolomics data revealed that key oral microbes, many previously shown to have neuroactive potential, co-occurred with potential neuropeptides and biosynthetic precursors of the neurotransmitters dopamine, epinephrine and serotonin.

CONCLUSIONS

Together, our findings offer novel insights into oral microbial-brain connection and potential neuroactive metabolites involved.

Structures and functions of the gut microbial lipidome

Microbial lipids provide signals that are responsible for maintaining host health and controlling disease. The differences in the structures of microbial lipids have been shown to alter receptor selectivity and agonist/antagonist activity. Advanced lipidomics is an emerging field that helps to elucidate the complex bacterial lipid diversity. The use of cutting-edge technologies is expected to lead to the discovery of new functional metabolites involved in host homeostasis. This review aims to describe recent updates on functional lipid metabolites derived from gut microbiota, their structure-activity relationships, and advanced lipidomics technologies.

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.

Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity

Ulcerative colitis (UC) is driven by disruptions in host-microbiota homoeostasis, but current treatments exclusively target host inflammatory pathways. To understand how host-microbiota interactions become disrupted in UC, we collected and analysed six faecal- or serum-based omic datasets (metaproteomic, metabolomic, metagenomic, metapeptidomic and amplicon sequencing profiles of faecal samples and proteomic profiles of serum samples) from 40 UC patients at a single inflammatory bowel disease centre, as well as various clinical, endoscopic and histologic measures of disease activity. A validation cohort of 210 samples (73 UC, 117 Crohn's disease, 20 healthy controls) was collected and analysed separately and independently. Data integration across both cohorts showed that a subset of the clinically active UC patients had an overabundance of proteases that originated from the bacterium Bacteroides vulgatus. To test whether B. vulgatus proteases contribute to UC disease activity, we first profiled B. vulgatus proteases found in patients and bacterial cultures. Use of a broad-spectrum protease inhibitor improved B. vulgatus-induced barrier dysfunction in vitro, and prevented colitis in B. vulgatus monocolonized, IL10-deficient mice. Furthermore, transplantation of faeces from UC patients with a high abundance of B. vulgatus proteases into germfree mice induced colitis dependent on protease activity. These results, stemming from a multi-omics approach, improve understanding of functional microbiota alterations that drive UC and provide a resource for identifying other pathways that could be inhibited as a strategy to treat this disease.

Advances in Microbiome-Derived Solutions and Methodologies Are Founding a New Era in Skin Health and Care

The microbiome, as a community of microorganisms and their structural elements, genomes, metabolites/signal molecules, has been shown to play an important role in human health, with significant beneficial applications for gut health. Skin microbiome has emerged as a new field with high potential to develop disruptive solutions to manage skin health and disease. Despite an incomplete toolbox for skin microbiome analyses, much progress has been made towards functional dissection of microbiomes and host-microbiome interactions. A standardized and robust investigation of the skin microbiome is necessary to provide accurate microbial information and set the base for a successful translation of innovations in the dermo-cosmetic field. This review provides an overview of how the landscape of skin microbiome research has evolved from method development (multi-omics/data-based analytical approaches) to the discovery and development of novel microbiome-derived ingredients. Moreover, it provides a summary of the latest findings on interactions between the microbiomes (gut and skin) and skin health/disease. Solutions derived from these two paths are used to develop novel microbiome-based ingredients or solutions acting on skin homeostasis are proposed. The most promising skin and gut-derived microbiome interventional strategies are presented, along with regulatory, safety, industrial, and technical challenges related to a successful translation of these microbiome-based concepts/technologies in the dermo-cosmetic industry.

Development of a widely targeted volatilomics method for profiling volatilomes in plants

Volatile organic compounds play essential roles in plant environment interactions as well as determining the fragrance of plants. Although gas chromatography-mass spectrometry-based untargeted metabolomics is commonly used to assess plant volatiles, it suffers from high spectral convolution, low detection sensitivity, a limited number of annotated metabolites, and relatively poor reproducibility. Here, we report a widely targeted volatilomics (WTV) method that involves using a "targeted spectra extraction" algorithm to address spectral convolution, constructing a high-coverage MS² spectral tag library to expand volatile annotation, adapting a multiple reaction monitoring mode to improve sensitivity, and using regression models to adjust for signal drift. The newly developed method was used to profile the volatilome of rice grains. Compared with the untargeted method, the newly developed WTV method shows higher sensitivity (for example, the signal-to-noise ratio of guaicol increased from 4.1 to 18.8), high annotation coverage (the number of annotated volatiles increased from 43 to 132), and better reproducibility (the number of volatiles in quality control samples with relative standard deviation value below 30.0% increased from 14 to 92 after normalization). Using the WTV method, we studied the metabolic responses of tomato to environmental stimuli and profiled the volatilomes of different rice accessions. The results identified benzothiazole as a potential airborne signal priming tomato plants for enhanced defense and 2-nonanone and 2-heptanone as novel aromatic compounds contributing to rice fragrance. These case studies suggest that the widely targeted volatilomics method is more efficient than those currently used and may considerably promote plant volatilomics studies.

Rapid screening for genitourinary cancers: mass spectrometry-based metabolic fingerprinting of urine

Genitourinary (GU) cancers are among the most common malignant diseases in men. Rapid screening is the key to GU cancers management for early diagnosis and treatment. Urine is a highly...

Multi-omics of human plasma reveals molecular features of dysregulated inflammation and accelerated aging in schizophrenia

Schizophrenia is a devastating psychiatric illness that detrimentally affects a significant portion of the worldwide population. Aging of schizophrenia patients is associated with reduced longevity, but the potential biological factors associated with aging in this population have not yet been investigated in a global manner. To address this gap in knowledge, the present study assesses proteomics and metabolomics profiles in the plasma of subjects afflicted with schizophrenia compared to non-psychiatric control patients over six decades of life. Global, unbiased analyses of circulating blood plasma can provide knowledge of prominently dysregulated molecular pathways and their association with schizophrenia, as well as features of aging and gender in this disease. The resulting data compiled in this study represent a compendium of molecular changes associated with schizophrenia over the human lifetime. Supporting the clinical finding of schizophrenia's association with more rapid aging, both schizophrenia diagnosis and age significantly influenced the plasma proteome in subjects assayed. Schizophrenia was broadly associated with prominent dysregulation of inflammatory and metabolic system components. Proteome changes demonstrated increased abundance of biomarkers for risk of physiologic comorbidities of schizophrenia, especially in younger individuals. These findings advance our understanding of the molecular etiology of schizophrenia and its associated comorbidities throughout the aging process.