Predicting network activity from high throughput metabolomics

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

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.

Improving insights from metabolomic functional analysis combining multivariate tools

BACKGROUND

Metabolomics is a scientific field that relies on the comprehensive analysis of metabolites to provide direct insights into functional processes in biological systems. Metabolomic data provides valuable insights into the functional processes of biological systems, often analyzed through univariate and multivariate approaches, and well as with functional or pathway analysis using different methods such as mummichog. Yet, the integration of results from these sources to aid the interpretation of their biological significance remains challenging. This represents a significant bottleneck limiting the applicability of multivariate analysis of metabolomic data, despite its potential for providing deep biological insights.

RESULTS

In this work we propose two straightforward methods to facilitate the interpretation of results from multivariate analysis and functional metabolic analysis using: i) p-values from multivariate tests as input in functional analysis, and ii) cluster-CV to assess the impact on the predictive performance of a multivariate model at the pathway level. Four simulated data sets were analyzed including a data set with no class separation, and three data sets with a statistically significant discrimination between classes by including either univariate, multivariate, or both types of discriminant effects. The data sets were analyzed using univariate tests and OPLS-DA. Furthermore, p-values for each feature estimated by univariate analysis and OPLS-DA were used as input for functional analysis in mummichog. Cluster-CV was then used to assess the effect of detected metabolic pathways on the class separation observed by OPLS-DA.

SIGNIFICANCE

Through simulated data, we show how these approaches enhance the interpretation of biological effects driving multivariate models and support the identification of altered pathways not detected by univariate analysis. By providing a deeper understanding of metabolic phenotypes, these methods might improve the biological insights derived from statistical and functional analysis of future or previous studies.

Metabolic Perturbations Associated with both PFAS Exposure and Perinatal/Antenatal Depression in Pregnant Individuals: A Meet-in-the-Middle Scoping Review

PURPOSE OF REVIEW

Depression during the perinatal or antenatal period affects at least 1 in 10 women worldwide, with long term health implications for the mother and child. Concurrently, there is increasing evidence associating maternal exposure to per- and poly-fluoroalkyl substances (PFAS) to adverse pregnancy outcomes. We reviewed the body of evidence examining both the associations between PFAS exposure and perturbations in the maternal metabolome, and the associations between the maternal metabolome and perinatal/antenatal depression. Through this, we sought to explore existing evidence of the perinatal metabolome as a potential mediation pathway linking PFAS exposure and perinatal/antenatal depression.

RECENT FINDINGS

There are few studies examining the metabolomics of PFAS exposure-specifically in pregnant women-and the metabolomics of perinatal/antenatal depression, let alone studies examining both simultaneously. Of the studies reviewed (N = 11), the majority were cross sectional, based outside of the US, and conducted on largely homogenous populations. Our review identified 23 metabolic pathways in the perinatal metabolome common to both PFAS exposure and perinatal/antenatal depression. Future studies may consider findings from our review to conduct literature-derived hypothesis testing focusing on fatty acid metabolism, alanine metabolism, glutamate metabolism, and tyrosine metabolism when exploring the biochemical mechanisms conferring the risk of perinatal/antenatal depression due to PFAS exposure. We recommend that researchers also utilize heterogenous populations, longitudinal study designs, and mediation approaches to elucidate key pathways linking PFAS exposures to perinatal/antenatal depression.

A Direct Link Implicating Loss of SLC26A6 to Gut Microbial Dysbiosis, Compromised Barrier Integrity, and Inflammation

BACKGROUND & AIMS

Putative anion transporter-1 (PAT1, SLC26A6) plays a key role in intestinal oxalate and bicarbonate secretion. PAT1 knockout (PKO) mice exhibit hyperoxaluria and nephrolithiasis. Notably, diseases such as inflammatory bowel disease are also associated with higher risk of hyperoxaluria and nephrolithiasis. However, the potential role of PAT1 deficiency in gut-barrier integrity and susceptibility to colitis is currently elusive.

METHODS

Age-matched PKO and wild-type littermates were administered 3.5% dextran sulfate sodium in drinking water for 6 days. Ileum and colon of control and treated mice were harvested. Messenger RNA and protein expression of tight junction proteins were determined by reverse transcription polymerase chain reaction and western blotting. Severity of inflammation was assessed by measuring diarrheal phenotype, cytokine expression, and hematoxylin and eosin staining. Gut microbiome and associated metabolome were analyzed by 16S ribosomal RNA sequencing and mass spectrometry, respectively.

RESULTS

PKO mice exhibited significantly higher loss of body weight, gut permeability, colonic inflammation, and diarrhea in response to dextran sulfate sodium treatment. In addition, PKO mice showed microbial dysbiosis and significantly reduced levels of butyrate and butyrate-producing microbes compared with controls. Co-housing wild-type and PKO mice for 4 weeks resulted in PKO-like signatures on the expression of tight junction proteins in the colons of wild-type mice.

CONCLUSIONS

Our data demonstrate that loss of PAT1 disrupts gut microbiome and related metabolites, decreases gut-barrier integrity, and increases host susceptibility to intestinal inflammation. These findings, thus, highlight a novel role of the oxalate transporter PAT1 in promoting gut-barrier integrity, and its deficiency appears to contribute to the pathogenesis of inflammatory bowel diseases.

Multi-omics correlates of insulin resistance and circadian parameters mapped directly from human serum

While it is generally known that metabolic disorders and circadian dysfunction are intertwined, how the two systems affect each other is not well understood, nor are the genetic factors that might exacerbate this pathological interaction. Blood chemistry is profoundly changed in metabolic disorders, and we have previously shown that serum factors change cellular clock properties. To investigate if circulating factors altered in metabolic disorders have circadian modifying effects, and whether these effects are of genetic origin, we measured circadian rhythms in U2OS cell in the presence of serum collected from diabetic, obese or control subjects. We observed that circadian period lengthening in U2OS cells was associated with serum chemistry that is characteristic of insulin resistance. Characterizing the genetic variants that altered circadian period length by genome-wide association analysis, we found that one of the top variants mapped to the E3 ubiquitin ligase MARCH1 involved in insulin sensitivity. Confirming our data, the serum circadian modifying variants were also enriched in type 2 diabetes and chronotype variants identified in the UK Biobank cohort. Finally, to identify serum factors that might be involved in period lengthening, we performed detailed metabolomics and found that the circadian modifying variants are particularly associated with branched chain amino acids, whose levels are known to correlate with diabetes and insulin resistance. Overall, our multi-omics data showed comprehensively that systemic factors serve as a path through which metabolic disorders influence circadian system, and these can be examined in human populations directly by simple cellular assays in common cultured cells.

Alzheimer's disease CSF biomarkers correlate with early pathology and alterations in neuronal and glial gene expression

INTRODUCTION

Normal pressure hydrocephalus (NPH) patients undergoing cortical shunting frequently show early Alzheimer's disease (AD) pathology on cortical biopsy, which is predictive of progression to clinical AD. The objective of this study was to use samples from this cohort to identify cerebrospinal fluid  (CSF) biomarkers for AD-related central nervous system (CNS) pathophysiologic changes using tissue and fluids with early pathology, free of post mortem artifact.

METHODS

We analyzed Simoa, proteomic, and metabolomic CSF data from 81 patients with previously documented pathologic and transcriptomic changes.

RESULTS

AD pathology on biopsy correlates with CSF β-amyloid-42/40, neurofilament light chain (NfL), and phospho-tau-181(p-tau181)/β-amyloid-42, while several gene expression modules correlate with NfL. Proteomic analysis highlights seven core proteins that correlate with pathology and gene expression changes on biopsy, and metabolomic analysis of CSF identifies disease-relevant groups that correlate with biopsy data.

DISCUSSION

As additional biomarkers are added to AD diagnostic panels, our work provides insight into the CNS pathophysiology these markers are tracking.

HIGHLIGHTS

AD CSF biomarkers correlate with CNS pathology and transcriptomic changes. Seven proteins correlate with CNS pathology and gene expression changes. Inflammatory and neuronal gene expression changes correlate with YKL-40 and NPTXR, respectively. CSF metabolomic analysis identifies pathways that correlate with biopsy data. Fatty acid metabolic pathways correlate with β-amyloid pathology.

Modulation of Metabolomic Profile in Sepsis According to the State of Immune Activation

OBJECTIVE

To investigate the metabolomic profiles associated with different immune activation states in sepsis patients.

DESIGN

Subgroup analysis of the PROVIDE (a Personalized Randomized trial of Validation and restoration of Immune Dysfunction in severe infections and Sepsis) prospective clinical study.

SETTING

Results of the PROVIDE study showed that patients with sepsis may be classified into three states of immune activation: 1) macrophage-activation-like syndrome (MALS) characterized by hyperinflammation, sepsis-induced immunoparalysis, and 3) unclassified or intermediate patients without severe immune dysregulation.

PATIENTS OR SUBJECTS

Two hundred ten patients from 14 clinical sites in Greece meeting the Sepsis-3 definitions with lung infection, acute cholangitis, or primary bacteremia.

INTERVENTIONS

During our comparison, we did not perform any intervention.

MEASUREMENTS AND MAIN RESULTS

Untargeted metabolomics analysis was performed on plasma samples from 210 patients (a total of 1394 products). Differential abundance analysis identified 221 significantly different metabolites across the immune states. Metabolites were enriched in pathways related to ubiquinone biosynthesis, tyrosine metabolism, and tryptophan metabolism when comparing MALS to immunoparalysis and unclassified patients. When comparing MALS to unclassified, 312 significantly different metabolites were found, and pathway analysis indicated enrichment in multiple pathways. Comparing immunoparalysis to unclassified patients revealed only two differentially regulated metabolites.

CONCLUSIONS

Findings suggest distinct metabolic dysregulation patterns associated with different immune dysfunctions in sepsis: the strongest metabolic dysregulation is associated with MALS.

Biomarker patterns and mechanistic insights into hypothermia from a postmortem metabolomics investigation

Postmortem metabolomics holds promise for identifying crucial biological markers relevant to death investigations and clinical scenarios. We aimed to assess its applicability in diagnosing hypothermia, a condition lacking definitive biomarkers. Our retrospective analysis involved 1095 postmortem femoral blood samples, including 150 hypothermia cases, 278 matched controls, and 667 randomly selected test cases, analyzed using UHPLC-QTOF mass spectrometry. The model demonstrated robustness with an R2 and Q2 value of 0.73 and 0.68, achieving 94% classification accuracy, 92% sensitivity, and 96% specificity. Discriminative metabolite patterns, including acylcarnitines, stress hormones, and NAD metabolites, along with identified pathways, suggest that metabolomics analysis can be helpful to diagnose fatal hypothermia. Exposure to cold seems to trigger a stress response in the body, increasing cortisol production to maintain core temperature, possibly explaining the observed upregulation of cortisol levels and alterations in metabolic markers related to renal function. In addition, thermogenesis seems to increase metabolism in brown adipose tissue, contributing to changes in nicotinamide metabolism and elevated levels of ketone bodies and acylcarnitines, these findings highlight the effectiveness of UHPLC-QTOF mass spectrometry, multivariate analysis, and pathway identification of postmortem samples in identifying metabolite markers with forensic and clinical significance. The discovered patterns may offer valuable clinical insights and diagnostic markers, emphasizing the broader potential of postmortem metabolomics in understanding critical states or diseases.

The role of puff volume in vaping emissions, inhalation risks, and metabolic perturbations: a pilot study

Secondhand vaping exposure is an emerging public health concern that remains understudied. In this study, saliva and exhaled emissions from ENDS users (secondhand) and non-ENDS users (baseline) were collected, firsthand emissions were generated using an automated ENDS aerosol generation system programmed to simulate puffing topography profiles collected from ENDS users. Particulate concentrations and sizes along with volatile organic compounds were characterized. We revealed puffing topography metrics as potential mediators of firsthand and secondhand particle and chemical exposures, as well as metabolic and respiratory health outcomes. Particle deposition modeling revealed that while secondhand emissions displayed smaller deposited mass, total and pulmonary particle deposition fractions were higher than firsthand deposition levels, possibly due to smaller secondhand emission particle diameters. Lastly, untargeted metabolomic profiling of salivary biomarkers of lung injury due to firsthand ENDS exposures revealed potential early indicators of respiratory distress that may also be relevant in bystanders exposed to secondhand vaping scenarios. By leveraging system toxicology, we identified 10 metabolites, including leukotriene D4, that could potentially serve as biomarkers for ENDS use, exposure estimation, and the prediction of vaping-related disease. This study highlights characterization of vaping behavior is an important exposure component in advancing our understanding of potential health effects in ENDS users and bystanders.

An isotopically-labelled temporal mass spectrometry imaging data analysis workflow to reveal glucose spatial metabolism patterns in bovine lens tissue

Application of stable isotopically labelled (SIL) molecules in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) over a series of time points allows the temporal and spatial dynamics of biochemical reactions to be tracked in a biological system. However, these large kinetic MSI datasets and the inherent variability of biological replicates presents significant challenges to the rapid analysis of the data. In addition, manual annotation of downstream SIL metabolites involves human input to carefully analyse the data based on prior knowledge and personal expertise. To overcome these challenges to the analysis of spatiotemporal MALDI-MSI data and improve the efficiency of SIL metabolite identification, a bioinformatics pipeline has been developed and demonstrated by analysing normal bovine lens glucose metabolism as a model system. The pipeline consists of spatial alignment to mitigate the impact of sample variability and ensure spatial comparability of the temporal data, dimensionality reduction to rapidly map regional metabolic distinctions within the tissue, and metabolite annotation coupled with pathway enrichment modules to summarise and display the metabolic pathways induced by the treatment. This pipeline will be valuable for the spatial metabolomics community to analyse kinetic MALDI-MSI datasets, enabling rapid characterisation of spatio-temporal metabolic patterns from tissues of interest.

Metabolic pathways altered by air pollutant exposure in association with coagulation function among the rural elderly

Air pollution exposure has been linked with coagulation function. However, evidence is limited for the relationships between air pollution, coagulation function and metabolomics in humans. We recruited a panel of 130 rural elderly from the Chayashan township in China, all of whom were free of pre-existing cardiovascular diseases and had provided residential address information. We conducted clinical examinations and collected blood samples from these rural elderly for the detection of coagulation biomarkers (e.g, activated partial thromboplastin time, fibrinogen, thrombin time, and prothrombin time) and untargeted metabolites in both December 2021 and August 2022. We used mini ambient air quality monitor to measure the mean levels of five air pollutants (e.g., PM2.5, SO2, NO2, CO and O3) during 1 to 2 weeks before blood sample collection. The Mummichog pathway analysis was used to identified potential metabolic features and pathways. In this study, we identified 5 pathways associated with both air pollution and coagulation function, and further pinpointed eight metabolic features within these pathways. The majority of these features were lipids, including arachidonic acid and linoleic acid. Overall, the findings of this study offer insights into potential mechanisms, particularly lipid metabolism, that may underlie the association between air pollution and coagulation function.

A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study

The etiology of lung cancer in never-smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never-smokers using untargeted metabolomics. This nested case-control study included 395 never-smoking women who developed lung cancer and 395 matched never-smoking cancer-free women from the prospective Shanghai Women's Health Study with 15,353 metabolic features quantified in pre-diagnostic plasma using liquid chromatography high-resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso-glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic-related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never-smokers.

Metabolic and phenotypic changes induced by PFAS exposure in two human hepatocyte cell models

PFAS are ubiquitous industrial chemicals with known adverse health effects, particularly on the liver. The liver, being a vital metabolic organ, is susceptible to PFAS-induced metabolic dysregulation, leading to conditions such as hepatotoxicity and metabolic disturbances. In this study, we investigated the phenotypic and metabolic responses of PFAS exposure using two hepatocyte models, HepG2 (male cell line) and HepaRG (female cell line), aiming to define phenotypic alterations, and metabolic disturbances at the metabolite and pathway levels. The PFAS mixture composition was selected based on epidemiological data, covering a broad concentration spectrum observed in diverse human populations. Phenotypic profiling by Cell Painting assay disclosed predominant effects of PFAS exposure on mitochondrial structure and function in both cell models as well as effects on F-actin, Golgi apparatus, and plasma membrane-associated measures. We employed comprehensive metabolic characterization using liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS). We observed dose-dependent changes in the metabolic profiles, particularly in lipid, steroid, amino acid and sugar and carbohydrate metabolism in both cells as well as in cell media, with HepaRG cell line showing a stronger metabolic response. In cells, most of the bile acids, acylcarnitines and free fatty acids showed downregulation, while medium-chain fatty acids and carnosine were upregulated, while the cell media showed different response especially in relation to the bile acids in HepaRG cell media. Importantly, we observed also nonmonotonic response for several phenotypic features and metabolites. On the pathway level, PFAS exposure was also associated with pathways indicating oxidative stress and inflammatory responses. Taken together, our findings on PFAS-induced phenotypic and metabolic disruptions in hepatocytes shed light on potential mechanisms contributing to the broader comprehension of PFAS-related health risks.

Glutathione-mediated redox regulation in Cryptococcus neoformans impacts virulence

The fungal pathogen Cryptococcus neoformans is well adapted to its host environment. It has several defence mechanisms to evade oxidative and nitrosative agents released by phagocytic host cells during infection. Among them, melanin production is linked to both fungal virulence and defence against harmful free radicals that facilitate host innate immunity. How C. neoformans manipulates its redox environment to facilitate melanin formation and virulence is unclear. Here we show that the antioxidant glutathione is inextricably linked to redox-active processes that facilitate melanin and titan cell production, as well as survival in macrophages and virulence in a murine model of cryptococcosis. Comparative metabolomics revealed that disruption of glutathione biosynthesis leads to accumulation of reducing and acidic compounds in the extracellular environment of mutant cells. Overall, these findings highlight the importance of redox homeostasis and metabolic compensation in pathogen adaptation to the host environment and suggest new avenues for antifungal drug development.

Dual Transcriptomic and Metabolomic Analysis of Elicited Flax Sheds Light on the Kinetics of Immune Defense Activation Against the Biotrophic Pathogen Oidium lini

Flax (Linum usitatissimum) grown under controlled conditions displayed genotype-dependent resistance to powdery mildew (Oidium lini) following COS-OGA (comprising chitosan- and pectin-derived oligomers) elicitor application. The present study reveals a two-step immune response in plants preventively challenged with the elicitor: an initial, rapid response characterized by the transcription of defense genes whose protein products act in contact with or within the cell wall, where biotrophic pathogens initially thrive, followed by a prolonged activation of cell wall peroxidases and accumulation of secondary metabolites. Thus, dozens of genes encoding membrane receptors, pathogenesis-related proteins, and wall peroxidases were initially overexpressed. Repeated COS-OGA treatments had a transient effect on the transcriptome response while cumulatively remodeling the metabolome over time, with a minimum of two applications required for maximal metabolomic shifts. Secondary metabolites, in particular terpenoids and phenylpropanoids, emerged as major components of this secondary defense response alongside pathogenesis-related proteins and wall peroxidases. The sustained accumulation of secondary metabolites, even after cessation of elicitation, contrasted with the short-lived transcriptomic response. Wall peroxidase enzyme activity also exhibited cumulative effects, increasing strongly for weeks after a third elicitor treatment. This underscores the plasticity of the plant immune response in the face of a potential infection, and the need for repeated preventive applications to achieve the full protective potential of the elicitor.

High-resolution plasma metabolomics and thiamine status in critically Ill adult patients

INTRODUCTION

Thiamine (Vitamin B1) is an essential micronutrient and is classically considered a co-factor in energy metabolism. The association between thiamine status and whole-body metabolism in critical illness has not been studied.

OBJECTIVES

To determine association between whole blood thiamine pyrophosphate (TPP) concentrations and plasma metabolites and connected metabolic pathways using high resolution metabolomics (HRM) in critically ill patients.

METHODS

Cross-sectional study performed at Erciyes University Hospital, Kayseri, Turkey and Emory University, Atlanta, GA, USA. Participants were critically ill adults with an expected length of intensive care unit stay longer than 48 h and receiving chronic furosemide therapy. A total of 76 participants were included. Mean age was 69 years (range 33-92 years); 65% were female. Blood for TPP and metabolomics was obtained on the day of ICU admission. Whole blood TPP was measured by HPLC and plasma HRM was performed using liquid chromatography/mass spectrometry. Data was analyzed using regression analysis of TPP levels against all plasma metabolomic features in metabolome-wide association studies (MWAS). MWAS using the highest and lowest TPP concentration tertiles was performed as a secondary analysis.

RESULTS

Specific metabolic pathways associated with whole blood TPP levels in regression and tertile analysis included pentose phosphate, fructose and mannose, branched chain amino acid, arginine and proline, linoleate, and butanoate pathways.

CONCLUSIONS

Plasma HRM revealed that thiamine status, determined by whole blood TPP concentrations, was significantly associated with metabolites and metabolic pathways related to metabolism of energy, carbohydrates, amino acids, lipids, and the gut microbiome in adult critically ill patients.

An intense 60-day weight-loss course leads to an 18 kg body weight reduction and metabolic reprogramming of soldiers with obesity

Soldiers of the Mexican Army with obesity were subjected to an intense 60-day weight-loss course consisting of a controlled diet, daily physical training, and psychological sessions. The nutritional treatment followed the European Society of Cardiology (ESC) recommendations, incorporating elements of the traditional milpa diet in the nutritional intervention. The total energy intake was reduced by 200 kcal every 20 days, starting with 1,800 kcal and ending with 1,400 kcal daily. On average, the participants reduced their body weight by 18 kg. We employed an innovative approach to monitor the progress of the twelve soldiers who completed the entire program. We compared the untargeted metabolomics profiles of their urine samples, taken before and after the course. The data obtained through liquid chromatography and high-resolution mass spectrometry (LC-MS) provided insightful results. Classification models perfectly separated the profiles pre and post-course, indicating a significant reprogramming of the participants' metabolism. The changes were observed in the C1-, vitamin, amino acid, and energy metabolism pathways, primarily affecting the liver, biliary system, and mitochondria. This study not only demonstrates the potential of rapid weight loss and metabolic pathway modification but also introduces a non-invasive method for monitoring the metabolic state of individuals through urine mass spectrometry data.

Maternal Serum Metabolomics in Mid-Pregnancy Identifies Lipid Pathways as a Key Link to Offspring Obesity in Early Childhood

Maternal metabolism during pregnancy shapes offspring health via in utero programming. In the Healthy Start study, we identified five subgroups of pregnant women based on conventional metabolic biomarkers: Reference (n = 360); High HDL-C (n = 289); Dyslipidemic-High TG (n = 149); Dyslipidemic-High FFA (n = 180); Insulin Resistant (IR)-Hyperglycemic (n = 87). These subgroups not only captured metabolic heterogeneity among pregnant participants but were also associated with offspring obesity in early childhood, even among women without obesity or diabetes. Here, we utilize metabolomics data to enrich characterization of the metabolic subgroups and identify key compounds driving between-group differences. We analyzed fasting blood samples from 1065 pregnant women at 18 gestational weeks using untargeted metabolomics. We used weighted gene correlation network analysis (WGCNA) to derive a global network based on the Reference subgroup and characterized distinct metabolite modules representative of the different metabolomic profiles. We used the mummichog algorithm for pathway enrichment and identified key compounds that differed across the subgroups. Eight metabolite modules representing pathways such as the carnitine-acylcarnitine translocase system, fatty acid biosynthesis and activation, and glycerophospholipid metabolism were identified. A module that included 189 compounds related to DHA peroxidation, oxidative stress, and sex hormone biosynthesis was elevated in the Insulin Resistant-Hyperglycemic vs. the Reference subgroup. This module was positively correlated with total cholesterol (R:0.10; p-value < 0.0001) and free fatty acids (R:0.07; p-value < 0.05). Oxidative stress and inflammatory pathways may underlie insulin resistance during pregnancy, even below clinical diabetes thresholds. These findings highlight potential therapeutic targets and strategies for pregnancy risk stratification and reveal mechanisms underlying the developmental origins of metabolic disease risk.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

The associations between pre-conception urinary phthalate concentrations, the serum metabolome, and live birth among women undergoing assisted reproduction

BACKGROUND

Phthalates are ubiquitous endocrine disruptors. Past studies have shown an association between higher preconception urinary concentrations of phthalate metabolites and lower fertility in women; however, the biological mechanisms remain unclear. Our exploratory study aimed to understand the metabolites and pathways associated with maternal preconception phthalate exposure and examine if any may underline the association between phthalate exposure and live birth using untargeted metabolomics.

METHODS

Participants (n = 183) were part of the Environment and Reproductive Health (EARTH) study, a prospective cohort that followed women undergoing in vitro fertilization (IVF) at the Massachusetts General Hospital Fertility Center (2005-2016). On the same day, women provided a serum sample during controlled ovarian stimulation, which was analyzed for metabolomics using liquid chromatography coupled with high-resolution mass spectrometry and two chromatography columns, and a urine sample, which was analyzed for 11 phthalate metabolites using targeted approaches. We used multivariable generalized linear models to identified metabolic features associated with urinary phthalate metabolite concentrations and live birth, followed by enriched pathway analysis. We then used a meet-in-the-middle approach to identify overlapping pathways and features.

RESULTS

Metabolic pathway enrichment analysis revealed 43 pathways in the C18 negative and 32 pathways in the HILIC positive columns that were significantly associated (p < 0.05) with at least one of the 11 urinary phthalate metabolites or molar sum of di-2-ethylhexyl phthalate metabolites. Lipid, amino acid, and carbohydrate metabolism were the most common pathways associated with phthalate exposure. Five pathways, tryptophan metabolism, tyrosine metabolism, biopterin metabolism, carnitine shuttle, and vitamin B6 metabolism, were also identified as being associated with at least one phthalate metabolite and live birth following IVF.

CONCLUSION

Our study provides further insight into the metabolites and metabolomics pathways, including amino acid, lipid, and vitamin metabolism that may underlie the observed associations between phthalate exposures and lower fertility in women.

Alzheimer's disease CSF biomarkers correlate with early pathology and alterations in neuronal and glial gene expression

INTRODUCTION

Normal pressure hydrocephalus (NPH) patients undergoing cortical shunting frequently show early AD pathology on cortical biopsy, which is predictive of progression to clinical AD. The objective of this study was to use samples from this cohort to identify CSF biomarkers for AD-related CNS pathophysiologic changes using tissue and fluids with early pathology, free of post-mortem artifact.

METHODS

We analyzed Simoa, proteomic, and metabolomic CSF data from 81 patients with previously documented pathologic and transcriptomic changes.

RESULTS

AD pathology on biopsy correlates with CSF β-amyloid-40/42, neurofilament light chain (NfL), and phospho-tau-181(p-tau181)/β-amyloid-42, while several gene expression modules correlate with NfL. Proteomic analysis highlights 7 core proteins that correlate with pathology and gene expression changes on biopsy, and metabolomic analysis of CSF identifies disease-relevant groups that correlate with biopsy data..

DISCUSSION

As additional biomarkers are added to AD diagnostic panels, our work provides insight into the CNS pathophysiology these markers are tracking.

Untargeted metabolomics reveal signatures of a healthy lifestyle

This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, β-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.

Metabolic Changes in Pseudomonas oleovorans Isolated from Contaminated Construction Material Exposed to Varied Biocide Treatments

Biocide resistance poses a significant challenge in industrial processes, with bacteria like Pseudomonas oleovorans exhibiting intrinsic resistance to traditional antimicrobial agents. In this study, the impact of biocide exposure on the metabolome of two P. oleovorans strains, namely, P. oleovorans P4A, isolated from contaminated coating material, and P. oleovorans 1045 reference strain, were investigated. The strains were exposed to 2-Methylisothiazol-3(2H)-one (MI) MIT, 1,2-Benzisothiazol-3(2H)-one (BIT), and 5-chloro-2-methyl-isothiazol-3-one (CMIT) at two different sub-inhibitory concentrations and the lipids and polar and semipolar metabolites were analyzed by ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry UPLC-Q-TOF/MS. Exposure to the BIT biocide induced significant metabolic modifications in P. oleovorans. Notable changes were observed in lipid and metabolite profiles, particularly in phospholipids, amino acid metabolism, and pathways related to stress response and adaptation. The 1045 strain showed more pronounced metabolic alterations than the P4A strain, suggesting potential implications for lipid, amino acid metabolism, energy metabolism, and stress adaptation. Improving our understanding of how different substances interact with bacteria is crucial for making antimicrobial chemicals more effective and addressing the challenges of resistance. We observed that different biocides trigged significantly different metabolic responses in these strains. Our study shows that metabolomics can be used as a tool for the investigation of metabolic mechanisms underlying biocide resistance, and thus in the development of targeted biocides. This in turn can have implications in combating biocide resistance in bacteria such as P. oleovorans.

Development of a Novel Non-invasive Metabolomics Assay to Predict Implantation Potential of Human Embryos

Can a set of metabolites present in embryo culture media correlate with embryo implantation? Case-control study in two phases: discovery phase (101 samples) and validation phase (169 samples), collected between 2018 and 2022, with a total of 218 participants. Culture media samples with known implantation outcomes were collected after blastocyst embryo transfer (including both PGT and non-PGT cycles) and were analyzed using chromatography followed by mass spectrometry. The spectra were processed and analyzed using statistical and machine learning techniques to identify biomarkers associated with embryo implantation, and to develop a predictive model. In the discovery phase, 148 embryo implantation biomarkers were identified using high resolution equipment, and 47 of them were characterized. Our results indicate a significant enrichment of tryptophan metabolism, arginine and proline metabolism, and lysine degradation biochemical pathways. After transferring the method to a lower resolution equipment, a model able to assign a Metabolite Pregnancy Index (MPI) to each embryo culture media was developed, taking the concentration of 36 biomarkers as input. Applying this model to 20% of the validation samples (N=34) used as the test set, an accuracy of 85.29% was achieved, with a PPV (Positive Predictive Value) of 88% and a NPV (Negative Predictive Value) of 77.78%. Additionally, informative results were obtained for all the analyzed samples. Metabolite concentration in the media after in vitro culture shows correlation with embryo implantation potential. Furthermore, the mathematical combination of biomarker concentrations using Artificial Intelligence techniques can be used to predict embryo implantation outcome with an accuracy of around 85%.

Metabolomic changes following GenX and PFBS exposure in developing zebrafish

Short chain per- and polyfluoroalkyl substances (PFAS), including hexafluoropropylene oxide dimer acid (GenX) and perfluorobutane sulfonate (PFBS), are replacement chemicals for environmentally persistent, long-chain PFAS. Although GenX and PFBS have been detected in surface and ground water worldwide, few studies provide information on the metabolic alterations or risks associated with their exposures. In this study, larval zebrafish were used to investigate the toxicity of early-life exposure to GenX or PFBS. Zebrafish were chronically exposed from 4 h post-fertilization (hpf) to 6 days post-fertilization (dpf) to 150 µM GenX or 95.0 µM PFBS. Ultra-high-performance liquid chromatography paired with high-resolution mass spectrometry was used to quantify uptake of GenX and PFBS into zebrafish larvae and perform targeted and untargeted metabolomics. Our results indicate that PFBS was 20.4 % more readily absorbed into the zebrafish larvae compared to GenX. Additionally, PFBS exposure significantly altered 13 targeted metabolites and 21 metabolic pathways, while GenX exposure significantly altered 1 targeted metabolite and 17 metabolic pathways. Exposure to GenX, and to an even greater extent PFBS, resulted in a number of altered metabolic pathways in the amino acid metabolism, with other significant alterations in the carbohydrate, lipid, cofactors and vitamins, nucleotide, and xenobiotics metabolisms. Our results indicate that GenX and PFBS impact the zebrafish metabolome, with implications of global metabolic dysregulation, particularly in metabolic pathways relating to growth and development.

Macronutrient-differential dietary pattern impacts on body weight, hepatic inflammation, and metabolism

Introduction

Obesity is a multi-factorial disease frequently associated with poor nutritional habits and linked to many detrimental health outcomes. Individuals with obesity are more likely to have increased levels of persistent inflammatory and metabolic dysregulation. The goal of this study was to compare four dietary patterns differentiated by macronutrient content in a postmenopausal model. Dietary patterns were high carbohydrate (HC), high fat (HF), high carbohydrate plus high fat (HCHF), and high protein (HP) with higher fiber.

Methods

Changes in body weight and glucose levels were measured in female, ovariectomized C57BL/6 mice after 15 weeks of feeding. One group of five mice fed the HCHF diet was crossed over to the HP diet on day 84, modeling a 21-day intervention. In a follow-up study comparing the HCHF versus HP dietary patterns, systemic changes in inflammation, using an 80-cytokine array and metabolism, by untargeted liquid chromatography-mass spectrometry (LCMS)-based metabolomics were evaluated.

Results

Only the HF and HCHF diets resulted in obesity, shown by significant differences in body weights compared to the HP diet. Body weight gains during the two-diet follow-up study were consistent with the four-diet study. On Day 105 of the 4-diet study, glucose levels were significantly lower for mice fed the HP diet than for those fed the HC and HF diets. Mice switched from the HCHF to the HP diet lost an average of 3.7 grams by the end of the 21-day intervention, but this corresponded with decreased food consumption. The HCHF pattern resulted in dramatic inflammatory dysregulation, as all 80 cytokines were elevated significantly in the livers of these mice after 15 weeks of HCHF diet exposure. Comparatively, only 32 markers changed significantly on the HP diet (24 up, 8 down). Metabolic perturbations in several endogenous biological pathways were also observed based on macronutrient differences and revealed dysfunction in several nutritionally relevant biosynthetic pathways.

Conclusion

Overall, the HCHF diet promoted detrimental impacts and changes linked to several diseases, including arthritis or breast neoplasms. Identification of dietary pattern-specific impacts in this model provides a means to monitor the effects of disease risk and test interventions to prevent poor health outcomes through nutritional modification.

Repeated exposure to eucalyptus wood smoke alters pulmonary gene and metabolic profiles in male Long-Evans rats

Exposure to wildfire smoke is associated with both acute and chronic cardiopulmonary illnesses, which are of special concern for wildland firefighters who experience repeated exposure to wood smoke. It is necessary to better understand the underlying pathophysiology by which wood smoke exposure increases pulmonary disease burdens in this population. We hypothesize that wood smoke exposure produces pulmonary dysfunction, lung inflammation, and gene expression profiles associated with future pulmonary complications. Male Long-Evans rats were intermittently exposed to smoldering eucalyptus wood smoke at 2 concentrations, low (11.0 ± 1.89 mg/m3) and high (23.7 ± 0.077 mg/m3), over a 2-week period. Whole-body plethysmography was measured intermittently throughout. Lung tissue and lavage fluid were collected 24 h after the final exposure for transcriptomics and metabolomics. Increasing smoke exposure upregulated neutrophils and select cytokines in the bronchoalveolar lavage fluid. In total, 3446 genes were differentially expressed in the lungs of rats in the high smoke exposure and only 1 gene in the low smoke exposure (Cd151). Genes altered in the high smoke group reflected changes to the Eukaryotic Initiation Factor 2 stress and oxidative stress responses, which mirrored metabolomics analyses. xMWAS-integrated analysis revealed that smoke exposure significantly altered pathways associated with oxidative stress, lung morphogenesis, and tumor proliferation pathways. These results indicate that intermittent, 2-week exposure to eucalyptus wood smoke leads to transcriptomic and metabolic changes in the lung that may predict future lung disease development. Collectively, these findings provide insight into cellular signaling pathways that may contribute to the chronic pulmonary conditions observed in wildland firefighters.

Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Untargeted serum metabolic profiling of diabetes mellitus among Parkinson's disease patients

Type 2 diabetes mellitus (T2DM) is a common comorbidity among Parkinson's disease (PD) patients. Yet, little is known about dysregulated pathways that are unique in PD patients with T2DM. We applied high-resolution metabolomic profiling in serum samples of 636 PD and 253 non-PD participants recruited from Central California. We conducted an initial discovery metabolome-wide association and pathway enrichment analysis. After adjusting for multiple testing, in positive (or negative) ion mode, 30 (25) metabolic features were associated with T2DM in both PD and non-PD participants, 162 (108) only in PD participants, and 32 (7) only in non-PD participants. Pathway enrichment analysis identified 17 enriched pathways associated with T2DM in both the PD and non-PD participants, 26 pathways only in PD participants, and 5 pathways only in non-PD participants. Several amino acid, nucleic acids, and fatty acid metabolisms were associated with T2DM only in the PD patient group suggesting a possible link between PD and T2DM.

Human Milk Composition Is Associated with Maternal Body Mass Index in a Cross-Sectional, Untargeted Metabolomics Analysis of Human Milk from Guatemalan Mothers

Background

Maternal overweight and obesity has been associated with poor lactation performance including delayed lactogenesis and reduced duration. However, the effect on human milk composition is less well understood.

Objectives

We evaluated the relationship of maternal BMI on the human milk metabolome among Guatemalan mothers.

Methods

We used data from 75 Guatemalan mothers who participated in the Household Air Pollution Intervention Network trial. Maternal BMI was measured between 9 and <20 weeks of gestation. Milk samples were collected at a single time point using aseptic collection from one breast at 6 mo postpartum and analyzed using high-resolution mass spectrometry. A cross-sectional untargeted high-resolution metabolomics analysis was performed by coupling hydrophilic interaction liquid chromatography (HILIC) and reverse phase C18 chromatography with mass spectrometry. Metabolic features associated with maternal BMI were determined by a metabolome-wide association study (MWAS), adjusting for baseline maternal age, education, and dietary diversity, and perturbations in metabolic pathways were identified by pathway enrichment analysis.

Results

The mean age of participants at baseline was 23.62 ± 3.81 y, and mean BMI was 24.27 ± 4.22 kg/m2. Of the total metabolic features detected by HILIC column (19,199 features) and by C18 column (11,594 features), BMI was associated with 1026 HILIC and 500 C18 features. Enriched pathways represented amino acid metabolism, galactose metabolism, and xenobiotic metabolic metabolism. However, no significant features were identified after adjusting for multiple comparisons using the Benjamini-Hochberg false discovery rate procedure (FDRBH < 0.2).

Conclusions

Findings from this untargeted MWAS indicate that maternal BMI is associated with metabolic perturbations of galactose metabolism, xenobiotic metabolism, and xenobiotic metabolism by cytochrome p450 and biosynthesis of amino acid pathways. Significant metabolic pathway alterations detected in human milk were associated with energy metabolism-related pathways including carbohydrate and amino acid metabolism.This trial was registered at clinicaltrials.gov as NCT02944682.

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

Mechanistic insights into cardiovascular effects of ultrafine particle exposure: A longitudinal panel study

BACKGROUND

Ultrafine particle (UFP) has been linked with higher risks of cardiovascular diseases; however, the biological mechanisms remain to be fully elucidated.

OBJECTIVES

This study aims to investigate the cardiovascular responses to short-term UFP exposure and the biological pathways involved.

METHODS

A longitudinal panel study was conducted among 32 healthy, non-smoking young adults in Shanghai, China, who were engaged in five rounds of follow-ups between December 2020 and November 2021. Individual exposures were calculated based on the indoor and outdoor real-time measurements. Blood pressure, arterial stiffness, targeted biomarkers, and untargeted proteomics and metabolomics were examined during each follow-up. Linear mixed-effect models were applied to analyze the exposure and health data. The differential proteins and metabolites were used for pathway enrichment analyses.

RESULTS

Short-term UFP exposure was associated with significant increases in blood pressure and arterial stiffness. For example, systolic blood pressure increased by 2.10 % (95 % confidence interval: 0.63 %, 3.59 %) corresponding to each interquartile increase in UFP concentrations at lag 0-3 h, while pulse wave velocity increased by 2.26 % (95 % confidence interval: 0.52 %, 4.04 %) at lag 7-12 h. In addition, dozens of molecular biomarkers altered significantly. These effects were generally present within 24 h after UFP exposure, and were robust to the adjustment of co-pollutants. Molecular changes detected in proteomics and metabolomics analyses were mainly involved in systemic inflammation, oxidative stress, endothelial dysfunction, coagulation, and disturbance in lipid transport and metabolism.

DISCUSSION

This study provides novel and compelling evidence on the detrimental subclinical cardiovascular effects in response to short-term UFP exposure. The multi-omics profiling further offers holistic insights into the underlying biological pathways.

Metabolomic Profiles in Tissues of Nonhuman Primates Exposed to Either Total- or Partial-Body Radiation

A complex cascade of systemic and tissue-specific responses induced by exposure to ionizing radiation can lead to functional impairment over time in the surviving population. Current methods for management of survivors of unintentional radiation exposure episodes rely on monitoring individuals over time for the development of adverse clinical symptoms due to the lack of predictive biomarkers for tissue injury. In this study, we report on changes in metabolomic and lipidomic profiles in multiple tissues of nonhuman primates (NHPs) that received either 4.0 Gy or 5.8 Gy total-body irradiation (TBI) of 60Co gamma rays, and 4.0 or 5.8 Gy partial-body irradiation (PBI) from LINAC-derived photons and were treated with a promising radiation countermeasure, gamma-tocotrienol (GT3). These include small molecule alterations that correlate with radiation effects in the jejunum, lung, kidney, and spleen of animals that either survived or succumbed to radiation toxicities over a 30-day period. Radiation-induced metabolic changes in tissues were observed in animals exposed to both doses and types of radiation, but were partially alleviated in GT3-treated and irradiated animals, with lung and spleen being most responsive. The majority of the pathways protected by GT3 treatment in these tissues were related to glucose metabolism, inflammation, and aldarate metabolism, suggesting GT3 may exert radioprotective effects in part by sparing these pathways from radiation-induced dysregulation. Taken together, the results of our study demonstrate that the prophylactic administration of GT3 results in metabolic and lipidomic shifts that likely provide an overall advantage against radiation injury. This investigation is among the first to highlight the use of a molecular phenotyping approach in a highly translatable NHP model of partial- and total-body irradiation to determine the underlying physiological mechanisms involved in the radioprotective efficacy of GT3.

Web-based multi-omics integration using the Analyst software suite

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.

Functional regulation of microglia by vitamin B12 alleviates ischemic stroke-induced neuroinflammation in mice

Ischemic stroke is the second leading cause of death and disability worldwide, and efforts to prevent stroke, mitigate secondary neurological damage, and promote neurological recovery remain paramount. Recent findings highlight the critical importance of microbiome-related metabolites, including vitamin B12 (VB12), in alleviating toxic stroke-associated neuroinflammation. Here, we showed that VB12 tonically programmed genes supporting microglial cell division and activation and critically controlled cellular fatty acid metabolism in homeostasis. Intriguingly, VB12 promoted mitochondrial transcriptional and metabolic activities and significantly restricted stroke-associated gene alterations in microglia. Furthermore, VB12 differentially altered the functions of microglial subsets during the acute phase of ischemic stroke, resulting in reduced brain damage and improved neurological function. Pharmacological depletion of microglia before ischemic stroke abolished VB12-mediated neurological improvement. Thus, our preclinical studies highlight the relevance of VB12 in the functional programming of microglia to alleviate neuroinflammation, minimize ischemic injury, and improve host neurological recovery after ischemic stroke.

Neuroactive metabolites and bile acids are altered in extremely premature infants with brain injury

The gut microbiome is associated with pathological neurophysiological evolvement in extremely premature infants suffering from brain injury. The exact underlying mechanism and its associated metabolic signatures in infants are not fully understood. To decipher metabolite profiles linked to neonatal brain injury, we investigate the fecal and plasma metabolome of samples obtained from a cohort of 51 extremely premature infants at several time points, using liquid chromatography (LC)-high-resolution mass spectrometry (MS)-based untargeted metabolomics and LC-MS/MS-based targeted analysis for investigating bile acids and amidated bile acid conjugates. The data are integrated with 16S rRNA gene amplicon gut microbiome profiles as well as patient cytokine, growth factor, and T cell profiles. We find an early onset of differentiation in neuroactive metabolites between infants with and without brain injury. We detect several bacterially derived bile acid amino acid conjugates in plasma and feces. These results provide insights into the early-life metabolome of extremely premature infants.

High-Resolution Plasma Metabolomics Identifies Alterations in Fatty Acid, Energy, and Micronutrient Metabolism in Adults Across the Leprosy Spectrum

BACKGROUND

High-resolution metabolomics (HRM) is an innovative tool to study challenging infectious diseases like leprosy, where the pathogen cannot be grown with standard methods. Here, we use HRM to better understand associations between disease manifestations, nutrition, and host metabolism.

METHODS

From 2018 to 2019, adults with leprosy and controls were recruited in Minas Gerais, Brazil. Plasma metabolites were detected using an established HRM workflow and characterized by accurate mass, mass to charge ratio m/z and retention time. The mummichog informatics package compared metabolic pathways between cases and controls and between multibacillary (MB) and paucibacillary (PB) leprosy. Additionally, select individual metabolites were quantified and compared.

RESULTS

Thirty-nine cases (62% MB and 38% PB) and 25 controls were enrolled. We found differences (P < .05) in several metabolic pathways, including fatty acid metabolism, carnitine shuttle, retinol, vitamin D3, and C-21 steroid metabolism, between cases and controls with lower retinol and associated metabolites in cases. Between MB and PB, leukotrienes, prostaglandins, tryptophan, and cortisol were all found to be lower in MB (P < .05).

DISCUSSION

Metabolites associated with several nutrient-related metabolic pathways appeared differentially regulated in leprosy, especially MB versus PB. This pilot study demonstrates the metabolic interdependency of these pathways, which may play a role in the pathophysiology of disease.

An untargeted metabolomics approach to study changes of the medium during human cornea culture

INTRODUCTION

Two main approaches (organ culture and hypothermia) for the preservation and storage of human donor corneas are globally adopted for corneal preservation before the transplant. Hypothermia is a hypothermic storage which slows down cellular metabolism while organ culture, a corneal culture performed at 28-37 °C, maintains an active corneal metabolism. Researchers, till now, have just studied the impact of organ culture on human cornea after manipulating and disrupting tissues.

OBJECTIVES

The aim of the current work was to optimize an analytical procedure which can be useful for discovering biomarkers capable of predicting tissue health status. For the first time, this research proposed a preliminary metabolomics study on medium for organ culture without manipulating and disrupting the valuable human tissues which could be still used for transplantation.

METHODS

In particular, the present research proposed a method for investigating changes in the medium, over a storage period of 20 days, in presence and absence of a human donor cornea. An untargeted metabolomics approach using UHPLC-QTOF was developed to deeply investigate the differences on metabolites and metabolic pathways and the influence of the presence of the cornea inside the medium.

RESULTS

Differences in the expression of some compounds emerged from this preliminary metabolomics approach, in particular in medium maintained for 10 and 20 days in presence but also in the absence of cornea. A total of 173 metabolites have been annotated and 36 pathways were enriched by pathway analysis.

CONCLUSION

The results revealed a valuable untargeted metabolomics approach which can be applied in organ culture metabolomics.

Hippocampal metabolic profile during epileptogenesis in the pilocarpine model of epilepsy

Temporal lobe epilepsy (TLE) is a common form of refractory epilepsy in adulthood. The metabolic profile of epileptogenesis is still poorly investigated. Elucidation of such a metabolic profile using animal models of epilepsy could help identify new metabolites and pathways involved in the mechanisms of epileptogenesis process. In this study, we evaluated the metabolic profile during the epileptogenesis periods. Using a pilocarpine model of epilepsy, we analyzed the global metabolic profile of hippocampal extracts by untargeted metabolomics based on ultra-performance liquid chromatography-high-resolution mass spectrometry, at three time points (3 h, 1 week, and 2 weeks) after status epilepticus (SE) induction. We demonstrated that epileptogenesis periods presented different hippocampal metabolic profiles, including alterations of metabolic pathways of amino acids and lipid metabolism. Six putative metabolites (tryptophan, N-acetylornithine, N-acetyl-L-aspartate, glutamine, adenosine, and cholesterol) showed significant different levels during epileptogenesis compared to their respective controls. These putative metabolites could be associated with the imbalance of neurotransmitters, mitochondrial dysfunction, and cell loss observed during both epileptogenesis and epilepsy. With these findings, we provided an overview of hippocampal metabolic profiles during different stages of epileptogenesis that could help investigate pathways and respective metabolites as predictive tools in epilepsy.

Pyrimidines maintain mitochondrial pyruvate oxidation to support de novo lipogenesis

Cellular purines, particularly adenosine 5'-triphosphate (ATP), fuel many metabolic reactions, but less is known about the direct effects of pyrimidines on cellular metabolism. We found that pyrimidines, but not purines, maintain pyruvate oxidation and the tricarboxylic citric acid (TCA) cycle by regulating pyruvate dehydrogenase (PDH) activity. PDH activity requires sufficient substrates and cofactors, including thiamine pyrophosphate (TPP). Depletion of cellular pyrimidines decreased TPP synthesis, a reaction carried out by TPP kinase 1 (TPK1), which reportedly uses ATP to phosphorylate thiamine (vitamin B1). We found that uridine 5'-triphosphate (UTP) acts as the preferred substrate for TPK1, enabling cellular TPP synthesis, PDH activity, TCA-cycle activity, lipogenesis, and adipocyte differentiation. Thus, UTP is required for vitamin B1 utilization to maintain pyruvate oxidation and lipogenesis.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Multi-omics analysis of the gut microbiome and metabolites associated with the psychoneurological symptom cluster in children with cancer receiving chemotherapy

BACKGROUND

Children with cancer receiving chemotherapy commonly report a cluster of psychoneurological symptoms (PNS), including pain, fatigue, anxiety, depression, and cognitive dysfunction. The role of the gut microbiome and its functional metabolites in PNS is rarely studied among children with cancer. This study investigated the associations between the gut microbiome-metabolome pathways and PNS in children with cancer across chemotherapy as compared to healthy children.

METHODS

A case-control study was conducted. Cancer cases were recruited from Children's Healthcare of Atlanta and healthy controls were recruited via flyers. Participants reported PNS using the Pediatric Patient-Reported Outcomes Measurement Information System. Data for cases were collected pre-cycle two chemotherapy (T0) and post-chemotherapy (T1), whereas data for healthy controls were collected once. Gut microbiome and its metabolites were measured using fecal specimens. Gut microbiome profiling was performed using 16S rRNA V4 sequencing, and metabolome was performed using an untargeted liquid chromatography-mass spectrometry approach. A multi-omics network integration program analyzed microbiome-metabolome pathways of PNS.

RESULTS

Cases (n = 21) and controls (n = 14) had mean ages of 13.2 and 13.1 years. For cases at T0, PNS were significantly associated with microbial genera (e.g., Ruminococcus, Megasphaera, and Prevotella), which were linked with carnitine shuttle (p = 0.0003), fatty acid metabolism (p = 0.001) and activation (p = 0.001), and tryptophan metabolism (p = 0.008). Megasphaera, clustered with aspartate and asparagine metabolism (p = 0.034), carnitine shuttle (p = 0.002), and tryptophan (p = 0.019), was associated with PNS for cases at T1. Gut bacteria with potential probiotic functions, along with fatty acid metabolism, tryptophan, and carnitine shuttle, were more clustered in cancer cases than the control network and this linkage with PNS needs further studies.

CONCLUSIONS

Using multi-omics approaches, this study indicated specific microbiome-metabolome pathways linked with PNS in children with cancer across chemotherapy. Due to limitations such as antibiotic use in cancer cases, these findings need to be further confirmed in a larger cohort.

Pathogenicity of monokaryotic and dikaryotic mycelia of Ganoderma boninense revealed via LC-MS-based metabolomics

This study compared the pathogenicity of monokaryotic (monokaryon) and dikaryotic (dikaryon) mycelia of the oil palm pathogen Ganoderma boninense via metabolomics approach. Ethyl acetate crude extracts of monokaryon and dikaryon were analysed by liquid chromatography quadrupole/time-of-flight-mass spectrometry (LC-Q/TOF-MS) coupled with multivariate data analysis using MetaboAnalyst. The mummichog algorithm was also used to identify the functional activities of monokaryon and dikaryon without a priori identification of all their secondary metabolites. Results revealed that monokaryon produced lesser fungal metabolites than dikaryon, suggesting that monokaryon had a lower possibility of inducing plant infection. These findings were further supported by the identified functional activities. Monokaryon exhibits tyrosine, phenylalanine, and tryptophan metabolism, which are important for fungal growth and development and to produce toxin precursors. In contrast, dikaryon exhibits the metabolism of cysteine and methionine, arginine and proline, and phenylalanine, which are important for fungal growth, development, virulence, and pathogenicity. As such, monokaryon is rendered non-pathogenic as it produces growth metabolites and toxin precursors, whereas dikaryon is pathogenic as it produces metabolites that are involved in fungal growth and pathogenicity. The LC-MS-based metabolomics approach contributes significantly to our understanding of the pathogenesis of Ganoderma boninense, which is essential for disease management in oil palm plantations.

First comprehensive untargeted metabolomics study of suramin-treated Trypanosoma brucei: an integrated data analysis workflow from multifactor data modelling to functional analysis

INTRODUCTION

Human African trypanosomiasis, commonly known as sleeping sickness, is a vector-borne parasitic disease prevalent in sub-Saharan Africa and transmitted by the tsetse fly. Suramin, a medication with a long history of clinical use, has demonstrated varied modes of action against Trypanosoma brucei. This study employs a comprehensive workflow to investigate the metabolic effects of suramin on T. brucei, utilizing a multimodal metabolomics approach.

OBJECTIVES

The primary aim of this study is to comprehensively analyze the metabolic impact of suramin on T. brucei using a combined liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) approach. Statistical analyses, encompassing multivariate analysis and pathway enrichment analysis, are applied to elucidate significant variations and metabolic changes resulting from suramin treatment.

METHODS

A detailed methodology involving the integration of high-resolution data from LC-MS and NMR techniques is presented. The study conducts a thorough analysis of metabolite profiles in both suramin-treated and control T. brucei brucei samples. Statistical techniques, including ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA), ANOVA 2 analysis, and bootstrap tests, are employed to discern the effects of suramin treatment on the metabolomics outcomes.

RESULTS

Our investigation reveals substantial differences in metabolic profiles between the control and suramin-treated groups. ASCA and PCA analysis confirm distinct separation between these groups in both MS-negative and NMR analyses. Furthermore, ANOVA 2 analysis and bootstrap tests confirmed the significance of treatment, time, and interaction effects on the metabolomics outcomes. Functional analysis of the data from LC-MS highlighted the impact of treatment on amino-acid, and amino-sugar and nucleotide-sugar metabolism, while time effects were observed on carbon intermediary metabolism (notably glycolysis and di- and tricarboxylic acids of the succinate production pathway and tricarboxylic acid (TCA) cycle).

CONCLUSION

Through the integration of LC-MS and NMR techniques coupled with advanced statistical analyses, this study identifies distinctive metabolic signatures and pathways associated with suramin treatment in T. brucei. These findings contribute to a deeper understanding of the pharmacological impact of suramin and have the potential to inform the development of more efficacious therapeutic strategies against African trypanosomiasis.

A terminal metabolite of niacin promotes vascular inflammation and contributes to cardiovascular disease risk

Despite intensive preventive cardiovascular disease (CVD) efforts, substantial residual CVD risk remains even for individuals receiving all guideline-recommended interventions. Niacin is an essential micronutrient fortified in food staples, but its role in CVD is not well understood. In this study, untargeted metabolomics analysis of fasting plasma from stable cardiac patients in a prospective discovery cohort (n = 1,162 total, n = 422 females) suggested that niacin metabolism was associated with incident major adverse cardiovascular events (MACE). Serum levels of the terminal metabolites of excess niacin, N1-methyl-2-pyridone-5-carboxamide (2PY) and N1-methyl-4-pyridone-3-carboxamide (4PY), were associated with increased 3-year MACE risk in two validation cohorts (US n = 2,331 total, n = 774 females; European n = 832 total, n = 249 females) (adjusted hazard ratio (HR) (95% confidence interval) for 2PY: 1.64 (1.10-2.42) and 2.02 (1.29-3.18), respectively; for 4PY: 1.89 (1.26-2.84) and 1.99 (1.26-3.14), respectively). Phenome-wide association analysis of the genetic variant rs10496731, which was significantly associated with both 2PY and 4PY levels, revealed an association of this variant with levels of soluble vascular adhesion molecule 1 (sVCAM-1). Further meta-analysis confirmed association of rs10496731 with sVCAM-1 (n = 106,000 total, n = 53,075 females, P = 3.6 × 10-18). Moreover, sVCAM-1 levels were significantly correlated with both 2PY and 4PY in a validation cohort (n = 974 total, n = 333 females) (2PY: rho = 0.13, P = 7.7 × 10-5; 4PY: rho = 0.18, P = 1.1 × 10-8). Lastly, treatment with physiological levels of 4PY, but not its structural isomer 2PY, induced expression of VCAM-1 and leukocyte adherence to vascular endothelium in mice. Collectively, these results indicate that the terminal breakdown products of excess niacin, 2PY and 4PY, are both associated with residual CVD risk. They also suggest an inflammation-dependent mechanism underlying the clinical association between 4PY and MACE.

Metabolomic Effects of Folic Acid Supplementation in Adults: Evidence from the FACT Trial

BACKGROUND

Folic acid (FA) is the oxidized form of folate found in supplements and FA-fortified foods. Most FA is reduced by dihydrofolate reductase to 5-methyltetrahydrofolate (5mTHF); the latter is the form of folate naturally found in foods. Ingestion of FA increases the plasma levels of both 5mTHF and unmetabolized FA (UMFA). Limited information is available on the downstream metabolic effects of FA supplementation, including potential effects associated with UMFA.

OBJECTIVE

We aimed to assess the metabolic effects of FA-supplementation, and the associations of plasma 5mTHF and UMFA with the metabolome in FA-naïve Bangladeshi adults.

METHODS

Sixty participants were selected from the Folic Acid and Creatine Trial; half received 800 μg FA/day for 12 weeks and half placebo. Plasma metabolome profiles were measured by high-resolution mass spectrometry, including 170 identified metabolites and 26,541 metabolic features. Penalized regression methods were used to assess the associations of targeted metabolites with FA-supplementation, plasma 5mTHF, and plasma UMFA. Pathway analyses were conducted using Mummichog.

RESULTS

In penalized models of identified metabolites, FA-supplementation was associated with higher choline. Changes in 5mTHF concentrations were positively associated with metabolites involved in amino acid metabolism (5-hydroxyindoleacetic acid, acetylmethionine, creatinine, guanidinoacetate, hydroxyproline/n-acetylalanine) and 2 fatty acids (docosahexaenoic acid and linoleic acid). Changes in 5mTHF concentrations were negatively associated with acetylglutamate, acetyllysine, carnitine, propionyl carnitine, cinnamic acid, homogentisate, arachidonic acid, and nicotine. UMFA concentrations were associated with lower levels of arachidonic acid. Together, metabolites selected across all models were related to lipids, aromatic amino acid metabolism, and the urea cycle. Analyses of nontargeted metabolic features identified additional pathways associated with FA supplementation.

CONCLUSION

In addition to the recapitulation of several expected metabolic changes associated with 5mTHF, we observed additional metabolites/pathways associated with FA-supplementation and UMFA. Further studies are needed to confirm these associations and assess their potential implications for human health.

TRIAL REGISTRATION NUMBER

This trial was registered at https://clinicaltrials.gov as NCT01050556.

Proteomic and Metabolomic Signatures of Diet Quality in Young Adults

The assessment of "omics" signatures may contribute to personalized medicine and precision nutrition. However, the existing literature is still limited in the homogeneity of participants' characteristics and in limited assessments of integrated omics layers. Our objective was to use post-prandial metabolomics and fasting proteomics to identify biological pathways and functions associated with diet quality in a population of primarily Hispanic young adults. We conducted protein and metabolite-wide association studies and functional pathway analyses to assess the relationships between a priori diet indices, Healthy Eating Index-2015 (HEI) and Dietary Approaches to Stop Hypertension (DASH) diets, and proteins (n = 346) and untargeted metabolites (n = 23,173), using data from the MetaAIR study (n = 154, 61% Hispanic). Analyses were performed for each diet quality index separately, adjusting for demographics and BMI. Five proteins (ACY1, ADH4, AGXT, GSTA1, F7) and six metabolites (undecylenic acid, betaine, hyodeoxycholic acid, stearidonic acid, iprovalicarb, pyracarbolid) were associated with both diets (p < 0.05), though none were significant after adjustment for multiple comparisons. Overlapping proteins are involved in lipid and amino acid metabolism and in hemostasis, while overlapping metabolites include amino acid derivatives, bile acids, fatty acids, and pesticides. Enriched biological pathways were involved in macronutrient metabolism, immune function, and oxidative stress. These findings in young Hispanic adults contribute to efforts to develop precision nutrition and medicine for diverse populations.

Multiple and Optimal Screening Subset: a method selecting global characteristic congeners for robust foodomics analysis

Metabolomics and foodomics shed light on the molecular processes within living organisms and the complex food composition by leveraging sophisticated analytical techniques to systematically analyze the vast array of molecular features. The traditional feature-picking method often results in arbitrary selections of the model, feature ranking, and cut-off, which may lead to suboptimal results. Thus, a Multiple and Optimal Screening Subset (MOSS) approach was developed in this study to achieve a balance between a minimal number of predictors and high predictive accuracy during statistical model setup. The MOSS approach compares five commonly used models in the context of food matrix analysis, specifically bourbons. These models include Student's t-test, receiver operating characteristic curve, partial least squares-discriminant analysis (PLS-DA), random forests, and support vector machines. The approach employs cross-validation to identify promising subset feature candidates that contribute to food characteristic classification. It then determines the optimal subset size by comparing it to the corresponding top-ranked features. Finally, it selects the optimal feature subset by traversing all possible feature candidate combinations. By utilizing MOSS approach to analyze 1406 mass spectral features from a collection of 122 bourbon samples, we were able to generate a subset of features for bourbon age prediction with 88% accuracy. Additionally, MOSS increased the area under the curve performance of sweetness prediction to 0.898 with only four predictors compared with the top-ranked four features at 0.681 based on the PLS-DA model. Overall, we demonstrated that MOSS provides an efficient and effective approach for selecting optimal features compared with other frequently utilized methods.

Exploring the interplay between running exercises, microbial diversity, and tryptophan metabolism along the microbiota-gut-brain axis

The emergent recognition of the gut-brain axis connection has shed light on the role of the microbiota in modulating the gut-brain axis's functions. Several microbial metabolites, such as serotonin, kynurenine, tryptamine, indole, and their derivatives originating from tryptophan metabolism have been implicated in influencing this axis. In our study, we aimed to investigate the impact of running exercises on microbial tryptophan metabolism using a mouse model. We conducted a multi-omics analysis to obtain a comprehensive insight into the changes in tryptophan metabolism along the microbiota-gut-brain axis induced by running exercises. The analyses integrated multiple components, such as tryptophan changes and metabolite levels in the gut, blood, hippocampus, and brainstem. Fecal microbiota analysis aimed to examine the composition and diversity of the gut microbiota, and taxon-function analysis explored the associations between specific microbial taxa and functional activities in tryptophan metabolism. Our findings revealed significant alterations in tryptophan metabolism across multiple sites, including the gut, blood, hippocampus, and brainstem. The outcomes indicate a shift in microbiota diversity and tryptophan metabolizing capabilities within the running group, linked to increased tryptophan transportation to the hippocampus and brainstem through circulation. Moreover, the symbiotic association between Romboutsia and A. muciniphila indicated their potential contribution to modifying the gut microenvironment and influencing tryptophan transport to the hippocampus and brainstem. These findings have potential applications for developing microbiota-based approaches in the context of exercise for neurological diseases, especially on mental health and overall well-being.