Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

Investigation of the oxidation rules and oxidative stability of seabuckthorn fruit oil during storage based on lipidomics and metabolomics

Seabuckthorn fruit oil (SBFO) is recognized for its high nutritional value, yet it remains highly prone to oxidation during storage. The changes in its primary components and micronutrient molecules during storage have not been thoroughly investigated. This study employed untargeted lipidomics and metabolomics to dynamically monitor alterations in lipid composition and metabolites of SBFO over 30 days of accelerated storage. Lipidomics analysis revealed an increase in TGs and oxidized fatty acids, while sphingolipids, glycerophospholipids, and total lipid content showed significant reductions (p < 0.05). After 30 days, metabolomics combined with bioinformatics analysis identified 13 critical pathways, with linoleic acid metabolism consistently associated with SBFO oxidation. Key oxidation products included 9(S)-HpODE, 9,10,13-TriHOME, and 9,10-DHOME. This study provides potential targets for developing endogenous antioxidants in SBFO and offers new perspectives on the oxidation mechanisms of edible oils.

Xenometabolomics in Ecotoxicology: Concepts and Applications

Nontargeted high-resolution mass spectrometry (HRMS) allows for the characterization of a large fraction of the exposome, i.e., the entirety of chemicals an organism is exposed to, and helps detect important exogenous chemical compounds that could be key drivers of toxicological impact. Along with these chemical compounds occur endogenous metabolites that are essential for the health of the host organism. Chemical compounds derived from the biotransformation of xenobiotics present in the exposome are referred to as the xenometabolome, while endogenous metabolites derived from the host organism are referred to as the endometabolome. Recent advancements in HRMS technology allow for the detection of chemical features of biological and ecological importance in the context of chemical safety assessments with unprecedented sensitivity and resolution. In this perspective, we highlight the application of HRMS-based metabolomics of organisms in the context of ecotoxicology, the complexity of comprehensively characterizing the endometabolome, and distinguishing chemical compounds of the xenometabolome.

Mass spectrometry-based characterisation of the cardiac microtissue metabolome and lipidome

INTRODUCTION

The use of large, non-sample specific metabolite reference libraries often results in high proportions of false positive annotations in untargeted metabolomics.

OBJECTIVE

This study aimed to measure and curate a library of polar metabolites and lipids present in cardiac microtissues.

RESULTS

Untargeted ultra-high performance liquid chromatography-coupled mass spectrometry measurements of cardiac microtissue intracellular extracts were annotated by comparison against four spectral databases and a retention time library. The annotations were combined to create a library of 313 polar metabolites and 1004 lipids.

CONCLUSIONS

The curated library will facilitate higher confidence metabolite annotation in mass spectrometry-based untargeted metabolomics of cardiac microtissues.

Phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis

BACKGROUND

Metabolomics describes the metabolic profile of an organism at a given time by the concentrations of its constituent metabolites. When studied over time, metabolite concentrations can help understand the dynamical evolution of a biological process. However, metabolites are involved into sequences of chemical reactions, called metabolic pathways, related to a given biological function. Accounting for these pathways into statistical methods for metabolomic data is thus a relevant way to directly express results in terms of biological functions and to increase their interpretability.

METHODS

We propose a new method, phoenics, to perform differential analysis for longitudinal metabolomic data at the pathway level. In short, phoenics proceeds in two steps: First, the matrix of metabolite quantifications is transformed by a dimension reduction approach accounting for pathway information. Then, a mixed linear model is fitted on the transformed data.

RESULTS

This method was applied to semi-synthetic NMR data and two real NMR datasets assessing the effects of antibiotics and irritable bowel syndrome on feces. Results showed that phoenics properly controls the Type I error rate and has a better ability to detect differential metabolic pathways and to extract new impacted biological functions than alternative methods. The method is implemented in the R package phoenics available on CRAN.

Advances in bioinformatic methods for the acceleration of the drug discovery from nature

BACKGROUND

Drug discovery from nature has a long, ethnopharmacologically-based background. Today, natural resources are undeniably vital reservoirs of active molecules or drug leads. Advances in (bio)informatics and computational biology emphasized the role of herbal medicines in the drug discovery pipeline.

PURPOSE

This review summarizes bioinformatic approaches applied in recent drug discovery from nature.

STUDY DESIGN

It examines advancements in molecular networking, pathway analysis, network pharmacology within a systems biology framework and AI for assessing the therapeutic potential of herbal preparations.

METHODS

A comprehensive literature search was conducted using Pubmed, SciFinder, and Google Database. Obtained data was analyzed and organized in subsections: AI, systems biology integrative approach, network pharmacology, pathway analysis, molecular networking, structure-based virtual screening.

RESULTS

Bioinformatic approaches is now essential for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug repurposing and side-effects prediction. Large datasets obtained from "omics" approaches require bioinformatic calculations to unveil interactions, and patterns in disease-relevant conditions. These tools enable databases annotations, pattern-matching, connections discovery, molecular relationship exploration, and data visualisation.

CONCLUSION

Despite the complexity of plant metabolites, bioinformatic approaches assist in characterization of herbal preparations and selection of bioactive molecule. It is perceived as powerful tool for uncovering multi-target effects and potential molecular mechanisms of compounds. By integrating multiple networks that connect gene-disease, drug-target and gene-drug-target, drug discovery from natural sources is experiencing a remarkable comeback.

Multiscale Synergy Networks Offer Insights into Disease and Comorbidity Mechanisms

Complex diseases involve extensive metabolic interactions within intricate biological networks. Consequently, it is advantageous to analyze metabolic phenotype data through metabolite interactions rather than focus on individual metabolites in isolation. In this article, we propose a novel analysis strategy called SynNet, which constructs multiscale synergy networks associated with specific metabolic phenotypes, offering new perspectives on the metabolic response mechanisms of diseases, including the mechanisms underlying disease comorbidity. The SynNet strategy begins with the construction of a metabolite-level synergy network (m-SynNet). This network is based on the definition and identification of significant metabolite pair interactions that distinguish disease phenotypes. Subsequently, a pathway synergy effect is defined by mapping these synergistic metabolite pairs onto the predefined metabolic pathways and performing a hypergeometric test to assess the probability of these pairs affecting a given pathway pair. The resulting significant pathway pairs identified form a pathway-level synergy network (p-SynNet). Both m-SynNet and p-SynNet offer complementary insights into disease mechanisms that go beyond conventional metabolomics analysis. For example, nodes with high connectivity in m-/p-SynNet suggest a strong correlation with the phenotype, while shared pathways across different phenotypes offer clues about the mechanisms of disease comorbidity. We applied the SynNet strategy to two real-world metabolomic data sets of disease comorbidity and identified key pathways associated with disease comorbidity from the p-SynNet. The candidate pathways are supported by the existing literature. Thus, the SynNet strategy may represent an alternative approach for metabolomic data analysis, providing novel insights into disease mechanisms and comorbidity.

Effects of patent foramen ovale in migraine: a metabolomics-based study

Patent foramen ovale (PFO), a cardiac anatomical anomaly inducing abnormal haemodynamics, leads to a paradoxical bypass of the pulmonary circulation. PFO closure might alleviate migraines; however, clinical evidence and basic experiments for the relationship are lacking. To explore the effect of PFO on migraine, 371 migraineurs finishing blood tests and contrast transthoracic echocardiography for the detection of PFO were prospectively included. Multivariate regression analysis revealed that PFO was independently associated with aura, and lower cystatin-C (cys-C) and calcium levels. Among them, patients with PFO who underwent percutaneous PFO closure were continuously followed up 1 year after the operation. The intensity of migraine was significantly relieved and the levels of cys-C and calcium increased after PFO closure. Untargeted and targeted metabolomics of plasma from migraineurs before and after PFO closure revealed that 5-HT and glutathione (GSH) metabolites were differentially expressed after PFO closure. The differential metabolites were then validated in the plasma and brain tissues of PFO mouse models by LC-MS/MS analysis. Desorption electrospray ionization mass imaging demonstrated that these metabolic alterations occurred mainly in the posterior cerebral cortex. Collectively, aura, cys-C and calcium could be biomarkers of migraineurs with PFO. PFO might have an impact on the posterior head associated with the regulation of 5-HT and GSH. PFO closure might relieve migraine by improving 5-HT clearance metabolism and ameliorating redox reactions. Our results may provide evidence for an indication of PFO closure in migraine and support the related potential mechanism. KEY POINTS: Aura, and levels of cystatin-C and calcium are biomarkers of migraineurs with a patent foramen ovale (PFO). The clearance of pulmonary metabolism of 5-HT and deoxygenated blood might be the reason for the improvement of migraine symptoms in patients with PFO. The posterior region of the brain is the main area responsible for PFO-induced migraine.

Integrative multi-omics analysis of autism spectrum disorder reveals unique microbial macromolecules interactions

INTRODUCTION

Gut microbiota alterations have been implicated in Autism Spectrum Disorder (ASD), yet the mechanisms linking these changes to ASD pathophysiology remain unclear.

OBJECTIVES

This study utilized a multi-omics approach to uncover mechanisms linking gut microbiota to ASD by examining microbial diversity, bacterial metaproteins, associated metabolic pathways and host proteome.

METHODS

The gut microbiota of 30 children with severe ASD and 30 healthy controls was analyzed. Microbial diversity was assessed using 16S rRNA V3 and V4 sequencing. A novel metaproteomics pipeline identified bacterial proteins, while untargeted metabolomics explored altered metabolic pathways. Finally, multi-omics integration was employed to connect macromolecular changes to neurodevelopmental deficits.

RESULTS

Children with ASD exhibited significant alterations in gut microbiota, including lower diversity and richness compared to controls. Tyzzerella was uniquely associated with the ASD group. Microbial network analysis revealed rewiring and reduced stability in ASD. Major metaproteins identified were produced by Bifidobacterium and Klebsiella (e.g., xylose isomerase and NADH peroxidase). Metabolomics profiling identified neurotransmitters (e.g., glutamate, DOPAC), lipids, and amino acids capable of crossing the blood-brain barrier, potentially contributing to neurodevelopmental and immune dysregulation. Host proteome analysis revealed altered proteins, including kallikrein (KLK1) and transthyretin (TTR), involved in neuroinflammation and immune regulation. Finally, multi-omics integration supported single-omics findings and reinforced the hypothesis that gut microbiota and their macromolecular products may contribute to ASD-associated symptoms.

CONCLUSIONS

The integration of multi-omics data provided critical evidence that alteration in gut microbiota and associated macromolecule production may play a role in ASD-related symptoms and co-morbidities. Key bacterial metaproteins and metabolites were identified as potential contributors to neurological and immune dysregulation in ASD, underscoring possible novel targets for therapeutic intervention.

Glutathione metabolism contributes to citric acid tolerance and antioxidant capacity in Acetobacter tropicalis

Acetobacter is one of the main species producing fruit vinegar and its tolerance mechanism to citric acid has not been fully studied. This limits fruit vinegar production from high-citric-acid fruits, which are excellent materials for fruit vinegar production. This study analyzed the metabolic differences between two strains of A. tropicalis with different citric acid tolerances using non-targeted metabolomics. Differential metabolites and metabolic pathways analysis showed that the enhanced amino acid metabolism significantly improved the citric acid tolerance of A. tropicalis and the deamination of amino acids may also play a role. In addition, the up-regulated phosphatidylcholine (PC) and N-heptanoylhonoserine lactone indicated decreased membrane permeability and enhanced quorum sensing (QS), respectively. The analysis of the interaction between pathways and metabolites indicated that Gln, Cys, and Tyr contribute to improving citric acid tolerance, which was also confirmed by the exogenous addition. After adding the amino acids, the down-regulated qdh, up-regulated ggt, and improved glutathione reductase (GR) activity in J-2736 indicated that glutathione metabolism played an important role in resisting citric acid, and cellular antioxidant capacity was increased. This study provides a theoretical basis for efficient fruit vinegar production from citric-acid-type fruits.

From metabolomics to energy balance physiology

Omics technologies are transforming our understanding of disease mechanisms and reshaping clinical practice. By enabling high-throughput, unbiased data collection at various molecular levels - including genes (genomics), mRNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics) - omics approaches offer a comprehensive view of biological states in both health and disease. Among these, metabolomics has emerged as a pivotal tool, rapidly evolving beyond diagnostics to become a cutting-edge technique for pinpointing metabolites that regulate key physiological processes. This chapter reviews the advances in metabolomics, its integration with other omics approaches, and its applications, particularly emphasizing energy homeostasis. By incorporating metabolomic insights into physiology, we move closer to an integrative understanding of biological systems, laying the groundwork for novel therapies to combat obesity and related metabolic disorders.

Effective data visualization strategies in untargeted metabolomics

Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.

Identify the key genes and pathways of melatonin in age-dependent mice hippocampus regulation by transcriptome analysis

CONTEXT

Dysregulation of energy metabolism is a fundamental contributor to all the hallmarks of brain aging. Melatonin, primarily secreted by the pineal gland, is closely associated with molecules and signaling pathways that sense and affect energy metabolism. However, the impact of melatonin on age-related mRNA expression in the hippocampus of mice at different ages remains poorly understood.

OBJECTIVE

The present study conducted transcriptome analysis of the hippocampus in melatonin-exposed mice at 9, 13, and 25 months of age. Differential gene analysis, GO and KEGG pathway enrichment analysis, GSEA analysis, as well as weighted gene co-expression network analysis (WGCNA), were performed on the transcriptome data.

RESULTS

Our study demonstrated that melatonin exerts a more pronounced regulatory effect on the transcriptome of 25-month old mice, and significantly enhances the expression level of TTR in the hippocampus of 13-month old mice. WGCNA analysis revealed that melatonin primarily modulates the energy metabolism of mouse hippocampus through the mTOR signaling pathway and AMPK signaling pathway.

CONCLUSIONS

In conclusion, our study provides new insights into the comprehensive understanding of the mechanism of melatonin's age-dependent regulation of the mice hippocampus.

Innovative omics strategies in fermented fruits and vegetables: Unveiling nutritional profiles, microbial diversity, and future prospects

Fermented fruits and vegetables (FFVs) are not only rich in essential nutrients but also contain distinctive flavors, prebiotics, and metabolites. Although omics techniques have gained widespread recognition as an analytical strategy for FFVs, its application still encounters several challenges due to the intricacies of biological systems. This review systematically summarizes the advances, obstacles and prospects of genomics, transcriptomics, proteomics, metabolomics, and multi-omics strategies in FFVs. It is evident that beyond traditional applications, such as the exploration of microbial diversity, protein expression, and metabolic pathways, omics techniques exhibit innovative potential in deciphering stress response mechanisms and uncovering spoilage microorganisms. The adoption of multi-omics strategies is paramount to acquire a multidimensional network fusion, thereby mitigating the limitations of single omics strategies. Although substantial progress has been made, this review underscores the necessity for a comprehensive repository of omics data and the establishment of universal databases to ensure precision in predictions. Furthermore, multidisciplinary integration with other physical or biochemical approaches is imperative, as it enriches our comprehension of this intricate process.

Analytical challenges and opportunities in the study of endocrine disrupting chemicals within an exposomics framework

Exposomics aims to measure human exposures throughout the lifespan and the changes they produce in the human body. Exposome-scale studies have significant potential to understand the interplay of environmental factors with complex multifactorial diseases widespread in our society and whose origin remain unclear. In this framework, the study of the chemical exposome aims to cover all chemical exposures and their effects in human health but, today, this goal still seems unfeasible or at least very challenging, which makes the exposome for now only a concept. Furthermore, the study of the chemical exposome faces several methodological challenges such as moving from specific targeted methodologies towards high-throughput multitargeted and non-targeted approaches, guaranteeing the availability and quality of biological samples to obtain quality analytical data, standardization of applied analytical methodologies, as well as the statistical assignment of increasingly complex datasets, or the identification of (un)known analytes. This review discusses the various steps involved in applying the exposome concept from an analytical perspective. It provides an overview of the wide variety of existing analytical methods and instruments, highlighting their complementarity to develop combined analytical strategies to advance towards the chemical exposome characterization. In addition, this review focuses on endocrine disrupting chemicals (EDCs) to show how studying even a minor part of the chemical exposome represents a great challenge. Analytical strategies applied in an exposomics context have shown great potential to elucidate the role of EDCs in health outcomes. However, translating innovative methods into etiological research and chemical risk assessment will require a multidisciplinary effort. Unlike other review articles focused on exposomics, this review offers a holistic view from the perspective of analytical chemistry and discuss the entire analytical workflow to finally obtain valuable results.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

CBFA2T3-GLIS2 mediates transcriptional regulation of developmental pathways through a gene regulatory network

CBFA2T3-GLIS2 is a fusion oncogene found in pediatric acute megakaryoblastic leukemia that is associated with a poor prognosis. We establish a model of CBFA2T3-GLIS2 driven acute megakaryoblastic leukemia that allows the distinction of fusion specific changes from those that reflect the megakaryoblast lineage of this leukemia. Using this model, we map fusion genome wide binding that in turn imparts the characteristic transcriptional signature. A network of transcription factor genes bound and upregulated by the fusion are found to have downstream effects that result in dysregulated signaling of developmental pathways including NOTCH, Hedgehog, TGFβ, and WNT. Transcriptional regulation is mediated by homo-dimerization and binding of the ETO transcription factor through the nervy homology region 2. Loss of nerve homology region 2 abrogated the development of leukemia, leading to downregulation of JAK/STAT, Hedgehog, and NOTCH transcriptional signatures. These data contribute to the understanding of CBFA2T3-GLIS2 mediated leukemogenesis and identify potential therapeutic vulnerabilities for future studies.

Bifidobacterium longum and microbiome maturation modify a nutrient intervention for stunting in Zimbabwean infants

BACKGROUND

Small-quantity lipid-based nutrient supplements (SQ-LNS), which has been widely tested to reduce child stunting, has largely modest effects to date, but the mechanisms underlying these modest effects are unclear. Child stunting is a longstanding indicator of chronic undernutrition and it remains a prevalent public health problem. The infant gut microbiome may be a key contributor to stunting; and mother and infant fucosyltransferase (FUT) phenotypes are important determinants of infant microbiome composition.

METHODS

We investigated whether mother-infant FUT status (n = 792) and infant gut microbiome composition (n = 354 fecal specimens from 172 infants) modified the impact of an infant and young child feeding (IYCF) intervention, that included SQ-LNS, on stunting at age 18 months in secondary analysis of a randomized trial in rural Zimbabwe.

FINDINGS

We found that the impact of the IYCF intervention on stunting was modified by: (i) mother-infant FUT2+/FUT3- phenotype (difference-in-differences -32.6% [95% CI: -55.3%, -9.9%]); (ii) changes in species composition that reflected microbiome maturation (difference-in-differences -68.1% [95% CI: -99.0%, -28.5%); and (iii) greater relative abundance of B. longum (differences-in-differences 49.1% [95% CI: 26.6%, 73.6%]). The dominant strains of B. longum when the intervention started were most similar to the proficient milk oligosaccharide utilizer subspecies infantis, which decreased with infant age and differed by mother-infant FUT2+/FUT3- phenotypes.

INTERPRETATION

These findings indicate that a persistently "younger" microbiome at initiation of the intervention reduced its benefits on stunting in areas with a high prevalence of growth restriction.

FUNDING

Bill and Melinda Gates Foundation, UK DFID/Aid, Wellcome Trust, Swiss Agency for Development and Cooperation, US National Institutes of Health, UNICEF, and Nutricia Research Foundation.

Knowledge Graphs for drug repurposing: a review of databases and methods

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable.

Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

Navigating common pitfalls in metabolite identification and metabolomics bioinformatics

BACKGROUND

Metabolomics, the systematic analysis of small molecules in a given biological system, emerged as a powerful tool for different research questions. Newer, better, and faster methods have increased the coverage of metabolites that can be detected and identified in a shorter amount of time, generating highly dense datasets. While technology for metabolomics is still advancing, another rapidly growing field is metabolomics data analysis including metabolite identification. Within the next years, there will be a high demand for bioinformaticians and data scientists capable of analyzing metabolomics data as well as chemists capable of using in-silico tools for metabolite identification. However, metabolomics is often not included in bioinformatics curricula, nor does analytical chemistry address the challenges associated with advanced in-silico tools.

AIM OF REVIEW

In this educational review, we briefly summarize some key concepts and pitfalls we have encountered in a collaboration between a bioinformatician (originally not trained for metabolomics) and an analytical chemist. We identified that many misunderstandings arise from differences in knowledge about metabolite annotation and identification, and the proper use of bioinformatics approaches for these tasks. We hope that this article helps other bioinformaticians (as well as other scientists) entering the field of metabolomics bioinformatics, especially for metabolite identification, to quickly learn the necessary concepts for a successful collaboration with analytical chemists.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We summarize important concepts related to LC-MS/MS based non-targeted metabolomics and compare them with other data types bioinformaticians are potentially familiar with. Drawing these parallels will help foster the learning of key aspects of metabolomics.

Stool and blood metabolomics in the metabolic syndrome: a cross-sectional study

INTRODUCTION/OBJECTIVES

Changes in the stool metabolome have been poorly studied in the metabolic syndrome (MetS). Moreover, few studies have explored the relationship of stool metabolites with circulating metabolites. Here, we investigated the associations between stool and blood metabolites, the MetS and systemic inflammation.

METHODS

We analyzed data from 1,370 participants of the KORA FF4 study (Germany). Metabolites were measured by Metabolon, Inc. (untargeted) in stool, and using the AbsoluteIDQ® p180 kit (targeted) in blood. Multiple linear regression models, adjusted for dietary pattern, age, sex, physical activity, smoking status and alcohol intake, were used to estimate the associations of metabolites with the MetS, its components and high-sensitivity C-reactive protein (hsCRP) levels. Partial correlation and Multi-Omics Factor Analysis (MOFA) were used to investigate the relationship between stool and blood metabolites.

RESULTS

The MetS was significantly associated with 170 stool and 82 blood metabolites. The MetS components with the highest number of associations were triglyceride levels (stool) and HDL levels (blood). Additionally, 107 and 27 MetS-associated metabolites (in stool and blood, respectively) showed significant associations with hsCRP levels. We found low partial correlation coefficients between stool and blood metabolites. MOFA did not detect shared variation across the two datasets.

CONCLUSIONS

The MetS, particularly dyslipidemia, is associated with multiple stool and blood metabolites that are also associated with systemic inflammation. Further studies are necessary to validate our findings and to characterize metabolic alterations in the MetS. Although our analyses point to weak correlations between stool and blood metabolites, additional studies using integrative approaches are warranted.

Metabolomic and Lipidomic Analysis of Manganese-Associated Parkinsonism: a Case-Control Study in Brescia, Italy

Background and Objectives

Excessive Manganese (Mn) exposure is neurotoxic and can cause Mn-Induced Parkinsonism (MnIP), marked by cognitive and motor dysfunction. Although metabolomic and lipidomic research in Parkinsonism (PD) patients exists, it remains limited. This study hypothesizes distinct metabolomic and lipidomic profiles based on exposure status, disease diagnosis, and their interaction.

Methods

We used a case-control design with a 2×2 factorial framework to investigate the metabolomic and lipidomic alterations associated with Mn exposure and their link to PD. The study population of 97 individuals was divided into four groups: non-exposed controls (n=23), exposed controls (n=25), non-exposed with PD (n=26) and exposed with PD (n=23). Cases, defined by at least two cardinal PD features (excluding vascular, iatrogenic, and traumatic origins), were recruited from movement disorder clinics in four hospitals in Brescia, Northern Italy. Controls, free from neurological or psychiatric conditions, were selected from the same hospitals. Exposed subjects resided in metallurgic regions (Val Camonica and Bagnolo Mella) for at least 8 continuous years, while non-exposed subjects lived in low-exposure areas around Lake Garda and Brescia city. We conducted untargeted analyses of metabolites and lipids in whole blood samples using ultra-high-performance liquid chromatography (UHPLC) and mass spectrometry (MS), followed by statistical analyses including Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Two-Way Analysis of Covariance (ANCOVA).

Results

Metabolomic analysis revealed modulation of alanine, aspartate, and glutamate metabolism (Impact=0.05, p=0.001) associated with disease effect; butanoate metabolism (Impact=0.03, p=0.004) with the exposure effect; and vitamin B6 metabolism (Impact=0.08, p=0.03) with the interaction effect. Differential relative abundances in 3-sulfoxy-L-Tyrosine (β=1.12, FDR p<0.001), glycocholic acid (β=0.48, FDR p=0.03), and palmitelaidic acid (β=0.30, FDR p<0.001) were linked to disease, exposure, and interaction effects, respectively. In the lipidome, ferroptosis (Pathway Lipids=11, FDR p=0.03) associated with the disease effect and sphingolipid signaling (Pathway Lipids=9, FDR p=0.04) associated with the interaction effect were significantly altered. Lipid classes triacylglycerols, ceramides, and phosphatidylethanolamines showed differential relative abundances associated with disease, exposure, and interaction effects, respectively.

Discussion

These findings suggest that PD and Mn exposure induce unique metabolomic and lipidomic changes, potentially serving as biomarkers for MnIP and warranting further study.

Novel 3-substituted coumarins inspire a custom pharmacology prediction pipeline: an anticancer discovery adventure

Aim: This research aims to expand the established pharmacological space of tumor-associated carbonic anhydrases (TACAs) by exploring the synthetically accessible chemical space of 3-substituted coumarins, with the help of in silico pharmacology prediction.Materials & methods: 52 novel 3-substituted coumarins were sketched, prioritizing synthetic feasibility. Their pharmacological potentials were estimated using a custom machine-learning approach. 17 compounds were predicted as cytotoxic against HeLa cells by interfering with TACAs. Those compounds were synthesized and biologically tested against HeLa cells. The three most potent compounds were assayed against multiple carbonic anhydrases, and their enzyme binding mechanism(s) were studied using molecular docking.Results: Experimental results exhibited pronounced consensus with in silico pharmacology predictions.Conclusion: Novel 3-substituted coumarins are herein dispatched to the cancer therapeutics space.

Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review)

Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end‑stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in‑depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.

ginmappeR: an unified approach for integrating gene and protein identifiers across biological sequence databases

Summary

The proliferation of biological sequence data, due to developments in molecular biology techniques, has led to the creation of numerous open access databases on gene and protein sequencing. However, the lack of direct equivalence between identifiers across these databases difficults data integration. To address this challenge, we introduce ginmappeR, an integrated R package facilitating the translation of gene and protein identifiers between databases. By providing a unified interface, ginmappeR streamlines the integration of diverse data sources into biological workflows, so it enhances efficiency and user experience.

Availability and implementation

from Bioconductor: https://bioconductor.org/packages/ginmappeR.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

BACKGROUND

The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes.

RESULTS

To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA.

CONCLUSION

The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).

WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics

Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org.

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.

Workshop report - interdisciplinary metabolomic epidemiology: the pathway to clinical translation

Metabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.

Metabolomics captures the differential metabolites in the replication pathway of snakehead vesiculovirus regulated by glutamine

Snakehead vesiculovirus (SHVV) is a negative-sense single-stranded RNA virus that infects snakehead fish. This virus leads to illness and mortality, causing significant economic losses in the snakehead aquaculture industry. The replication and spread of SHVV in cells, which requires glutamine as a nitrogen source, is accompanied by alterations in intracellular metabolites. However, the metabolic mechanisms underlying the inhibition of viral replication by glutamine deficiency are poorly understood. This study utilized liquid chromatography-mass spectrometry to measure the differential metabolites between the channel catfish Parasilurus asotus ovary cell line infected with SHVV under glutamine-containing and glutamine-deprived conditions. Results showed that the absence of glutamine regulated 4 distinct metabolic pathways and influenced 9 differential metabolites. The differential metabolites PS(16:0/16:0), 5,10-methylene-THF, and PS(18:0/18:1(9Z)) were involved in amino acid metabolism. In the nuclear metabolism functional pathway, differential metabolites of guanosine were observed. In the carbohydrate metabolism pathway, differential metabolites of UDP-d-galacturonate were detected. In the signal transduction pathway, differential metabolites of SM(d18:1/20:0), SM(d18:1/22:1(13Z)), SM(d18:1/24:1(15 Z)), and sphinganine were found. Among them, PS(18:0/18:1(9Z)), PS(16:0/16:0), and UDP-d-galacturonate were involved in the synthesis of phosphatidylserine and glycoprotein. The compound 5,10-methylene-THF provided raw materials for virus replication, and guanosine and sphingosine are related to virus virulence. The differential metabolites may collectively participate in the replication, packaging, and proliferation of SHVV under glutamine deficiency. This study provides new insights and potential metabolic targets for combating SHVV infection in aquaculture through metabolomics approaches.

Metabolomics on depression: A comparison of clinical and animal research

BACKGROUND

Depression is a major cause of suicide and mortality worldwide. This study aims to conduct a systematic review to identify metabolic biomarkers and pathways for major depressive disorder (MDD), a prevalent subtype of clinical depression.

METHODS

We searched for metabolomics studies on depression published between January 2000 and January 2023 in the PubMed and Web of Science databases. The reported metabolic biomarkers were systematically evaluated and compared. Pathway analysis was implemented using MetaboAnalyst 5.0.

RESULTS

We included 26 clinical studies on MDD and 78 metabolomics studies on depressive-like animal models. A total of 55 and 77 high-frequency metabolites were reported consistently in two-thirds of clinical and murine studies, respectively. In the comparison between murine and clinical studies, we identified 9 consistently changed metabolites (tryptophan, tyrosine, phenylalanine, methionine, fumarate, valine, deoxycholic acid, pyruvate, kynurenic acid) in the blood, 1 consistently altered metabolite (indoxyl sulfate) in the urine and 14 disturbed metabolic pathways in both types of studies. These metabolic dysregulations and pathways are mainly implicated in enhanced inflammation, impaired neuroprotection, reduced energy metabolism, increased oxidative stress damage and disturbed apoptosis, laying solid molecular foundations for MDD.

LIMITATIONS

Due to unavailability of original data like effect-size results in many metabolomics studies, a meta-analysis cannot be conducted, and confounding factors cannot be fully ruled out.

CONCLUSIONS

This systematic review delineated metabolic biomarkers and pathways related to depression in the murine and clinical samples, providing opportunities for early diagnosis of MDD and the development of novel diagnostic targets.

Serum metabolite signatures of cardiac function and morphology in individuals from a population-based cohort

BACKGROUND

Changes in serum metabolites in individuals with altered cardiac function and morphology may exhibit information about cardiovascular disease (CVD) pathway dysregulations and potential CVD risk factors. We aimed to explore associations of cardiac function and morphology, evaluated using magnetic resonance imaging (MRI) with a large panel of serum metabolites.

METHODS

Cross-sectional data from CVD-free individuals from the population-based KORA cohort were analyzed. Associations between 3T-MRI-derived left ventricular (LV) function and morphology parameters (e.g., volumes, filling rates, wall thickness) and markers of carotid plaque with metabolite profile clusters and single metabolites as outcomes were assessed by adjusted multinomial logistic regression and linear regression models.

RESULTS

In 360 individuals (mean age 56.3 years; 41.9% female), 146 serum metabolites clustered into three distinct profiles that reflected high-, intermediate- and low-CVD risk. Higher stroke volume (relative risk ratio (RRR): 0.53, 95%-CI [0.37; 0.76], p-value < 0.001) and early diastolic filling rate (RRR: 0.51, 95%-CI [0.37; 0.71], p-value < 0.001) were most strongly protectively associated against the high-risk profile compared to the low-risk profile after adjusting for traditional CVD risk factors. Moreover, imaging markers were associated with 10 metabolites in linear regression. Notably, negative associations of stroke volume and early diastolic filling rate with acylcarnitine C5, and positive association of function parameters with lysophosphatidylcholines, diacylphosphatidylcholines, and acylalkylphosphatidylcholines were observed. Furthermore, there was a negative association of LV wall thickness with alanine, creatinine, and symmetric dimethylarginine. We found no significant associations with carotid plaque.

CONCLUSIONS

Serum metabolite signatures are associated with cardiac function and morphology even in individuals without a clinical indication of CVD.

Bifidobacterium longum modifies a nutritional intervention for stunting in Zimbabwean infants

Child stunting is an indicator of chronic undernutrition and reduced human capital. However, it remains a poorly understood public health problem. Small-quantity lipid-based nutrient supplements (SQ-LNS) have been widely tested to reduce stunting, but have modest effects. The infant intestinal microbiome may contribute to stunting, and is partly shaped by mother and infant histo-blood group antigens (HBGA). We investigated whether mother-infant fucosyltransferase status, which governs HBGA, and the infant gut microbiome modified the impact of SQ-LNS on stunting at age 18 months among Zimbabwean infants in the SHINE Trial ( NCT01824940 ). We found that mother-infant fucosyltransferase discordance and Bifidobacterium longum reduced SQ-LNS efficacy. Infant age-related microbiome shifts in B. longum subspecies dominance from infantis , a proficient human milk oligosaccharide utilizer, to suis or longum , proficient plant-polysaccharide utilizers, were partly influenced by discordance in mother-infant FUT2+/FUT3- phenotype, suggesting that a "younger" microbiome at initiation of SQ-LNS reduces its benefits on stunting.

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.

Impacts of neonicotinoid insecticides on bumble bee energy metabolism are revealed under nectar starvation

Bumble bees are an important group of insects that provide essential pollination services as a consequence of their foraging behaviors. These pollination services are driven, in part, by energetic exchanges between flowering plants and individual bees. Thus, it is important to examine bumble bee energy metabolism and explore how it might be influenced by external stressors contributing to declines in global pollinator populations. Two stressors that are commonly encountered by bees are insecticides, such as the neonicotinoids, and nutritional stress, resulting from deficits in pollen and nectar availability. Our study uses a metabolomic approach to examine the effects of neonicotinoid insecticide exposure on bumble bee metabolism, both alone and in combination with nutritional stress. We hypothesized that exposure to imidacloprid disrupts bumble bee energy metabolism, leading to changes in key metabolites involved in central carbon metabolism. We tested this by exposing Bombus impatiens workers to imidacloprid according to one of three exposure paradigms designed to explore how chronic versus more acute (early or late) imidacloprid exposure influences energy metabolite levels, then also subjecting them to artificial nectar starvation. The strongest effects of imidacloprid were observed when bees also experienced nectar starvation, suggesting a combinatorial effect of neonicotinoids and nutritional stress on bumble bee energy metabolism. Overall, this study provides important insights into the mechanisms underlying the impact of neonicotinoid insecticides on pollinators, and underscores the need for further investigation into the complex interactions between environmental stressors and energy metabolism.

Wekemo Bioincloud: A user-friendly platform for meta-omics data analyses

The increasing application of meta-omics approaches to investigate the structure, function, and intercellular interactions of microbial communities has led to a surge in available data. However, this abundance of human and environmental microbiome data has exposed new scalability challenges for existing bioinformatics tools. In response, we introduce Wekemo Bioincloud-a specialized platform for -omics studies. This platform offers a comprehensive analysis solution, specifically designed to alleviate the challenges of tool selection for users in the face of expanding data sets. As of now, Wekemo Bioincloud has been regularly equipped with 22 workflows and 65 visualization tools, establishing itself as a user-friendly and widely embraced platform for studying diverse data sets. Additionally, the platform enables the online modification of vector outputs, and the registration-independent personalized dashboard system ensures privacy and traceability. Wekemo Bioincloud is freely available at https://www.bioincloud.tech/.

Cluster-Partial Least Squares (c-PLS) regression analysis: Application to miRNA and metabolomic data

BACKGROUND

Biomedicine and biological research frequently involve analyzing large datasets generated by high-throughput technologies like genomics, transcriptomics, miRNomics, and metabolomics. Pathway analysis is a common computational approach used to understand the impact of experimental conditions, phenotypes, or interventions on biological pathways and networks. This involves statistical analysis of omic data to identify differentially expressed variables and mapping them onto predefined pathways. Analyzing such datasets often requires multivariate techniques to extract meaningful insights such as Partial Least Squares (PLS). Variable selection strategies like interval-PLS (iPLS) help improve understanding and predictive performance by identifying informative variables or intervals. However, iPLS is suboptimal to treat omic data such as metabolic or miRNA profiles, where features cannot be distributed along a continuous dimension describing their relationships as in e.g., vibrational or nuclear magnetic resonance spectroscopy.

RESULTS

This study introduces a novel variable selection approach called cluster PLS (c-PLS) that aims to assess the joint impact of variable groups selected based on biological characteristics (such as miRNA-regulated metabolic pathway or lipid classes) on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using miRNomic and metabolomic datasets obtained from the analysis of 24 liver tissue biopsies collected in the frame of a clinical study of steatosis.

SIGNIFICANCE AND NOVELTY

Results obtained show that c-PLS enables analyzing the effect of biologically relevant variable clusters, facilitating the identification of biological processes associated with the independent variable, and the prioritization of the biological factors influencing model performance, thereby improving the understanding of the biological factors driving model predictions. While the strategy is tested for the evaluation of PLS models, it could be extended to other linear and non-linear multivariate models.

IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ .

GINv2.0: a comprehensive topological network integrating molecular interactions from multiple knowledge bases

Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a "meta-pathway" structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate.

Red Blood Cell Membrane-Camouflaged Polydopamine and Bioactive Glass Composite Nanoformulation for Combined Chemo/Chemodynamic/Photothermal Therapy

Combinations of different therapeutic strategies, including chemotherapy (CT), chemodynamic therapy (CDT), and photothermal therapy (PTT), are needed to effectively address evolving drug resistance and the adverse effects of traditional cancer treatment. Herein, a camouflage composite nanoformulation (TCBG@PR), an antitumor agent (tubercidin, Tub) loaded into Cu-doped bioactive glasses (CBGs) and subsequently camouflaged by polydopamine (PDA), and red blood cell membranes (RBCm), was successfully constructed for targeted and synergetic antitumor therapies by combining CT of Tub, CDT of doped copper ions, and PTT of PDA. In addition, the TCBG@PRs composite nanoformulation was camouflaged with a red blood cell membrane (RBCm) to improve biocompatibility, longer blood retention times, and excellent cellular uptake properties. It integrated with long circulation and multimodal synergistic treatment (CT, CDT, and PTT) with the benefit of RBCms to avoid immune clearance for efficient targeted delivery to tumor locations, producing an "all-in-one" nanoplatform. In vivo results showed that the TCBG@PRs composite nanoformulation prolonged blood circulation and improved tumor accumulation. The combination of CT, CDT, and PTT therapies enhanced the antitumor therapeutic activity, and light-triggered drug release reduced systematic toxicity and increased synergistic antitumor effects.

PathBank 2.0-the pathway database for model organism metabolomics

PathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110 234 to 605 359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interface and general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.

Maternal and newborn metabolomic changes associated with urinary polycyclic aromatic hydrocarbon metabolite concentrations at delivery: an untargeted approach

INTRODUCTION

Prenatal exposure to polycyclic aromatic hydrocarbons (PAHs) has been associated with adverse human health outcomes. To explore the plausible associations between maternal PAH exposure and maternal/newborn metabolomic outcomes, we conducted a cross-sectional study among 75 pregnant people from Cincinnati, Ohio.

METHOD

We quantified 8 monohydroxylated PAH metabolites in maternal urine samples collected at delivery. We then used an untargeted high-resolution mass spectrometry approach to examine alterations in the maternal (n = 72) and newborn (n = 63) serum metabolome associated with PAH metabolites. Associations between individual maternal urinary PAH metabolites and maternal/newborn metabolome were assessed using linear regression adjusted for maternal and newborn factors while accounting for multiple testing with the Benjamini-Hochberg method. We then conducted functional analysis to identify potential biological pathways.

RESULTS

Our results from the metabolome-wide associations (MWAS) indicated that an average of 1% newborn metabolome features and 2% maternal metabolome features were associated with maternal urinary PAH metabolites. Individual PAH metabolite concentrations in maternal urine were associated with maternal/newborn metabolome related to metabolism of vitamins, amino acids, fatty acids, lipids, carbohydrates, nucleotides, energy, xenobiotics, glycan, and organic compounds.

CONCLUSION

In this cross-sectional study, we identified associations between urinary PAH concentrations during late pregnancy and metabolic features associated with several metabolic pathways among pregnant women and newborns. Further studies are needed to explore the mediating role of the metabolome in the relationship between PAHs and adverse pregnancy outcomes.

Transcriptomics analyses reveal the effects of Pentagamaboronon-0-ol on PI3K/Akt and cell cycle of HER2+ breast cancer cells

Introduction

Monoclonal antibodies and targeted therapies against HER2+ breast cancer has improved overall and disease-free survival in patients; however, encountering drug resistance causes recurrence, necessitating the development of newer HER2-targeted medications. A curcumin analog PGB-0-ol showed most cytotoxicity against HCC1954 HER2+ breast cancer cells than against other subtypes of breast cancer cells.

Objective

Here, we employed next-generation sequencing technology to elucidate the molecular mechanism underlying the effect of PGB-0-ol on HCC1954 HER2+ breast cancer cells.

Methods

The molecular mechanism underlying the action of PGB-0-ol on HCC1954 HER2+ breast cancer cells was determined using next-generation sequencing technologies. Additional bioinformatics studies were performed, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, disease-gene, and drug-gene associations, network topology analysis (NTA), and gene set enrichment analysis (GSEA).

Results

We detected 2,263 differentially expressed genes (DEGs) (1,459 upregulated and 804 downregulated) in the PGB-0-ol- and DMSO-treated HCC1954 cells. KEGG enrichment data revealed the control of phosphatidylinositol signaling system, and ErbB signaling following PGB-0-ol treatment. Gene ontology (GO) enrichment analysis demonstrated that these DEGs governed cell cycle, participated in the mitotic spindle and nuclear membrane, and controlled kinase activity at the molecular level. According to the NTA data for GO enrichment, GSEA data for KEGG, drug-gene and disease-gene, PGB-0-ol regulated PI3K/Akt signaling and cell cycle in breast cancer. Overall, our investigation revealed the transcriptomic profile of PGB-0-ol-treated HCC1954 breast cancer cells following PGB-0-ol therapy. Bioinformatics analyses showed that PI3K/Akt signaling and cell cycle was modulated. However, further studies are required to validate the findings of this study.

Longitudinal associations between metabolites and immediate, short- and medium-term exposure to ambient air pollution: Results from the KORA cohort study

BACKGROUND

Short-term exposure to air pollution has been reported to be associated with cardiopulmonary diseases, but the underlying mechanisms remain unclear. This study aimed to investigate changes in serum metabolites associated with immediate, short- and medium-term exposures to ambient air pollution.

METHODS

We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in this analysis. We collected daily averages of fine particles (PM2.5), coarse particles (PMcoarse), nitrogen dioxide (NO2), and ozone (O3) at urban background monitors located in Augsburg, Germany. Covariate-adjusted generalized additive mixed-effects models were used to examine the associations between immediate (2-day average of same day and previous day as individual's blood withdrawal), short- (2-week moving average), and medium-term exposures (8-week moving average) to air pollution and metabolites. We further performed pathway analysis for the metabolites significantly associated with air pollutants in each exposure window.

RESULTS

Of 9,620 observations from 4,261 study participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed quality control, multiple significant associations between metabolites and air pollutants with several exposure windows were identified at a Bonferroni corrected p-value threshold (p < 3.9 × 10-5). We found the highest number of associations for NO2, particularly at the medium-term exposure windows. Among the identified metabolic pathways based on the metabolites significantly associated with air pollutants, the glycerophospholipid metabolism was the most robust pathway in different air pollutants exposures.

CONCLUSIONS

Our study suggested that short- and medium-term exposure to air pollution might induce alterations of serum metabolites, particularly in metabolites involved in metabolic pathways related to inflammatory response and oxidative stress.

Targeted Amino Acids Profiling of Human Seminal Plasma from Teratozoospermia Patients Using LC-MS/MS

Identifying the metabolome of human seminal plasma (HSP) is a new research area to screen putative biomarkers of infertility. This case-control study was performed on HSP specimens of 15 infertile patients with teratozoospermia (defined as normal sperm morphology < 4%) and 12 confirmed fertile normozoospermic men as the control group to investigate the seminal metabolic signature and whether there are differences in the metabolome between two groups. HSPs were subjected to LC-MS-MS analysis. MetaboAnalyst5.0 software was utilized for statistical analysis. Different univariate and multivariate analyses were used, including T-tests, fold change analysis, random forest (RF), and metabolite set enrichment analysis (MSEA). Teratozoospermic samples contained seventeen significantly different amino acids. Upregulated metabolites include glutamine, asparagine, and glycylproline, whereas downregulated metabolites include cysteine, γ-aminobutyric acid, histidine, hydroxylysine, hydroxyproline, glycine, proline, methionine, ornithine, tryptophan, aspartic acid, argininosuccinic acid, α-aminoadipic acid, and β-aminoisobutyric acid. RF algorithm defined a set of 15 metabolites that constitute the significant features of teratozoospermia. In particular, increased glutamine, asparagine, and decreased cysteine, tryptophan, glycine, and valine were strong predictors of teratozoospemia. The most affected metabolic pathways in teratozoospermic men are the aminoacyl-tRNA, arginine, valine-leucine, and isoleucine biosynthesis. Altered metabolites detected in teratozoospermia were responsible for various roles in sperm functions that classified into four subgroups as follows: related metabolites to antioxidant function, energy production, sperm function, and spermatogenesis. The altered amino acid metabolome identified in this study may be related to the etiology of teratozoospermia, and may provide novel insight into potential biomarkers of male infertility for therapeutic targets.

Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations

The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.

Multiomics characterization of cell type repertoires for urine liquid biopsies

Urine is assayed alongside blood in medicine, yet current clinical diagnostic tests utilize only a small fraction of its total biomolecular repertoire, potentially foregoing high-resolution insights into human health and disease. In this work, we characterized the joint landscapes of transcriptomic and metabolomic signals in human urine. We also compared the urine transcriptome to plasma cell-free RNA, identifying a distinct cell type repertoire and enrichment for metabolic signal. Untargeted metabolomic measurements identified a complementary set of pathways to the transcriptomic analysis. Our findings suggest that urine is a promising biofluid yielding prognostic and detailed insights for hard-to-biopsy tissues with low representation in the blood, offering promise for a new generation of liquid biopsies.

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.