Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Firefly toxin lucibufagins evolved after the origin of bioluminescence

Fireflies were believed to originally evolve their novel bioluminescence as warning signals to advertise their toxicity to predators, which was later adopted in adult mating. Although the evolution of bioluminescence has been investigated extensively, the warning signal hypothesis of its origin has not been tested. In this study, we test this hypothesis by systematically determining the presence or absence of firefly toxin lucibufagins (LBGs) across firefly species and inferring the time of origin of LBGs. We confirm the presence of LBGs in the subfamily Lampyrinae, but more importantly, we reveal the absence of LBGs in other lineages, including the subfamilies of Luciolinae, Ototretinae, and Psilocladinae, two incertae sedis lineages, and the Rhagophthalmidae family. Ancestral state reconstructions for LBGs based on firefly phylogeny constructed using genomic data suggest that the presence of LBGs in the common ancestor of the Lampyrinae subfamily is highly supported but unsupported in more ancient nodes, including firefly common ancestors. Our results suggest that firefly LBGs probably evolved much later than the evolution of bioluminescence. We thus conclude that firefly bioluminescence did not originally evolve as direct warning signals for toxic LBGs and advise that future studies should focus on other hypotheses. Moreover, LBG toxins are known to directly target and inhibit the α subunit of Na+, K+-ATPase (ATPα). We further examine the effects of amino acid substitutions in firefly ATPα on its interactions with LBGs. We find that ATPα in LBG-containing fireflies is relatively insensitive to LBGs, which suggests that target-site insensitivity contributes to LBG-containing fireflies' ability to deal with their own toxins.

Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm, PeakCET, designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram (GC × GC). This method innovatively combines contour edge tracking with affinity propagation (AP) clustering for peak detection in GC × GC fingerprints, the first in this field. Contour edge tracking significantly reduces false positives caused by "burr" signals, while AP clustering enhances detection accuracy in the face of false negatives. The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin. PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples. Furthermore, this algorithm compares the GC × GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins. The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues. Each sample exhibits unique characteristic components alongside common ones, and variations in content may influence their therapeutic effectiveness. This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional (2D) fingerprint analysis of GC × GC data.

A Relook into the Flavonoid Chemical Space of Moringa oleifera Lam. Leaves through a Combination of LC-MS and Molecular Networking

Moringa oleifera Lam. is a functional tree that is known to produce a variety of metabolites with purported pharmacological activities. It is frequently called the "miracle tree" due to its utilization in numerous nutraceutical and pharmacological contexts. This study was aimed at studying the chemical space of M. oleifera leaf extracts through molecular networking (MN), a tool that identifies metabolites by classifying them based on their MS-based fragmentation pattern similarities and signals. In this case, a special emphasis was placed on the flavonoid composition. The MN unraveled different molecular families such as flavonoids, carboxylic acids and derivatives, lignin glycosides, fatty acyls, and macrolactams that are found within the plant. In silico annotation tools such as network annotation propagation (NAP) and DEREPLICATOR, an unsupervised substructure identification tool (MS2LDA), and MolNet enhancer were also explored to further compliment the classic molecular networking output within the Global Natural Product Social (GNPS) site. In this study, common flavonoids found within Moringa oleifera were further annotated using MS2LDA. Utilizing computational tools allowed for the discovery of a wide range of structurally diverse flavonoid molecules within M. oleifera leaf extracts. The expansion of the flavonoid chemical repertoire in this plant arises from intricate glycosylation modifications, leading to the creation of structural isomers that manifest as isobaric ions during mass spectrometry (MS) analyses.

Thermostable chaperone-based polypeptide biosynthesis: Enfuvirtide model product quality and protocol-related impurities

Large peptide biosynthesis is a valuable alternative to conventional chemical synthesis. Enfuvirtide, the largest therapeutic peptide used in HIV infection treatment, was synthesized in our thermostable chaperone-based peptide biosynthesis system and evaluated for peptide quality as well as the profile of process-related impurities. Host cell proteins (HCPs) and BrCN cleavage-modified peptides were evaluated by LC-MS in intermediate. Cleavage modifications during the reaction were assessed after LC-MS maps were aligned by simple in-house algorithm and formylation/oxidation levels were estimated. Circular dichroism spectra of the obtained enfuvirtide were compared to the those of the chemically- synthesized standard product. Final-product endotoxin and HCPs content were assessed resulting 1.06 EU/mg and 5.58 ppm respectively. Peptide therapeutic activity was measured using the MT-4 cells HIV infection-inhibition model. The biosynthetic peptide IC50 was 0.0453 μM while the standard one had 0.0180 μM. Non-acylated C-terminus was proposed as a cause of IC50 and CD spectra difference. Otherwise, the peptide has met all the requirements of the original chemically synthesized enfuvirtide in the cell-culture and in vivo experiments.

Hierarchical clustering of liquid chromatography-tandem mass spectrometry data for screening of phosphodiesterase type 5 inhibitors and their analogues in adulterated dietary supplements

Sexual enhancement dietary supplements have often been adulterated with phosphodiesterase type 5 (PDE-5) inhibitors used for treatment of erectile dysfunction, and widely distributed through online markets. As the illegal adulterants, the original PDE-5 inhibitor drugs and a numerous number of synthetized analogues, more than 80, have already been found. Therefore, analytical methods that detect various PDE-5 inhibitors and uncover newly synthesized analogues are needed. In this study, we have developed a rapid and reliable screening method for PDE-5 inhibitors and their structural analogues by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by hierarchical clustering based on similarity of MS/MS spectra. Forty reference standards of PDE-5 inhibitors/analogues were measured using a quadrupole-orbitrap mass spectrometer in data-dependent mode. The 60 most intense fragment ions were extracted from each MS/MS spectra, and the ions observed within 1.5 mDa mass tolerance were considered to be the same ion. Based on fragment ion tables representing detected ions for each compound, hierarchical clustering was performed. The resulting dendrogram showed that the reference standards were separated into seven clusters according to their characteristic structures. Subsequently, two additional standards spiked into a herbal sample were analyzed. While herbal components were clearly separated from the clusters of the reference standards, the spiked standards were clustered closely with the structurally similar standards. Furthermore, application of our method to dietary supplements allowed for detection of sildenafil and tadalafil as adulterants. These results suggest that our screening method facilitates discovery of adulterant PDE-5 inhibitors/analogues by illustrating their structural similarity.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Genomic and Metabolomic Analysis of the Potato Common Scab Pathogen Streptomyces scabiei

Streptomyces scabiei is a key causative agent of common scab disease, which causes significant economic losses to potato growers worldwide. This organism produces several phytotoxins that are known or suspected to contribute to host-pathogen interactions and disease development; however, the full metabolic potential of S. scabiei has not been previously investigated. In this study, we used a combined metabolomic and genomic approach to investigate the metabolites that are produced by S. scabiei. The genome sequence was analyzed using antiSMASH and DeepBGC to identify specialized metabolite biosynthetic gene clusters. Using untargeted liquid chromatography-coupled tandem mass spectrometry (LC-MS2), the metabolic profile of S. scabiei was compared after cultivation on three different growth media. MS2 data were analyzed using Feature-Based Molecular Networking and hierarchical clustering in BioDendro. Metabolites were annotated by performing a Global Natural Products Social Molecular Networking (GNPS) spectral library search or using Network Annotation Propagation, SIRIUS, MetWork, or Competitive Fragmentation Modeling for Metabolite Identification. Using this approach, we were able to putatively identify new analogues of known metabolites as well as molecules that were not previously known to be produced by S. scabiei. To our knowledge, this study represents the first global analysis of specialized metabolites that are produced by this important plant pathogen.

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.