Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data

The Hormetic Adaptative Capacity and Resilience to Oxidative Stress Is Strengthened by Exposome Enrichment with Air Cold Atmospheric Plasma: A Metabolome Targeted Follow-Up Approach

Background/Objectives: The exposome, encompassing all environmental influences on health, plays a pivotal role in oxidative stress-related diseases. Negative air ions (NAIs), generated via cold atmospheric plasma (CAP), have been proposed as potential modulators of oxidative resilience. This study aims to investigate the metabolic adaptations induced by prolonged exposure to an NAI-enriched environment in mice, focusing on its effects in oxidative stress markers and energy metabolism in liver and blood. Methods: Twenty male C57BL/6J mice were divided into four groups: two experimental groups exposed to NAI-enriched air generated by an Air Cold Atmospheric Plasma-Nanoparticle Removal (aCAP-NR) device for either 18 days (short-term, ST) or 28 days (long-term, LT), and two control groups without exposure. Targeted metabolomics was performed in whole blood and liver using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). Statistical and pathway analyses were conducted to assess metabolic alterations. Results: Metabolic profiling revealed significant shifts in oxidative stress-related pathways, including enhanced glutathione metabolism, reduced lipid peroxidation, and modulation of purine metabolism. Short-term exposure led to increased mitochondrial efficiency and energy homeostasis, while long-term exposure induced adaptive metabolic reprogramming, with higher inosine levels suggesting enhanced antioxidant and anti-inflammatory responses. No adverse effects on systemic or hepatic health markers were observed. Conclusions: NAI exposure via aCAP-NR elicits a hormetic response, enhancing metabolic efficiency and resilience to oxidative stress. These findings suggest that controlled environmental enrichment with NAIs may serve as a novel non-invasive strategy for mitigating oxidative damage and improving metabolic health, as hormetic adaptative capacity and resilience to oxidative stress, warranting further translational research.

The need for a cancer exposome atlas: a scoping review

BACKGROUND

Despite advances in understanding genetic susceptibility to cancer, much of cancer heritability remains unidentified. At the same time, the makeup of industrial chemicals in our environment only grows more complex. This gap in knowledge on cancer risk has prompted calls to expand cancer research to the comprehensive, discovery-based study of nongenetic environmental influences, conceptualized as the "exposome."

METHODS

Our scoping review aimed to describe the exposome and its application to cancer epidemiology and to study design limitations, challenges in analytical methods, and major unmet opportunities in advanced exposome profiling methods that allow the quantification of complex chemical exposure profiles in biological matrices. To evaluate progress on incorporating measurements of the exposome into cancer research, we performed a review of such "cancer exposome" studies published through August 2023.

RESULTS

We found that only 1 study leveraged untargeted chemical profiling of the exposome as a method to measure tens of thousands of environmental chemicals and identify prospective associations with future cancer risk. The other 13 studies used hypothesis-driven exposome approaches that targeted a set of preselected lifestyle, occupational, air quality, social determinant, or other external risk factors. Many of the included studies could only leverage sample sizes with less than 400 cancer cases (67% of nonecologic studies) and exposures experienced after diagnosis (29% of studies). Six cancer types were covered, most commonly blood (43%), lung (21%), or breast (14%) cancer.

CONCLUSION

The exposome is underutilized in cancer research, despite its potential to unravel complex relationships between environmental exposures and cancer and to inform primary prevention.

DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data

BACKGROUND

Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data. This approach is limited to endogenous metabolites that can be readily mapped to metabolic pathways. An alternative to pathway analysis that can be used for any classes of metabolites, including unknown compounds that are ubiquitous in untargeted metabolomics data, involves defining metabolite-metabolite interactions using experimental data. Our group has developed several network-based methods that use partial correlations of experimentally determined metabolite measurements. These were implemented in CorrelationCalculator and Filigree, two software tools for the analysis of metabolomics data we developed previously. The latter tool implements the Differential Network Enrichment Analysis (DNEA) algorithm. This analysis is useful for building differential networks from metabolomics data containing two experimental groups and identifying differentially enriched metabolic modules. While Filigree is a user-friendly tool, it has certain limitations when used for the analysis of large-scale metabolomics datasets.

RESULTS

We developed the DNEA R package for the data-driven network analysis of metabolomics data. We present the DNEA workflow and functionality, algorithm enhancements implemented with respect to the package's predecessor, Filigree, and discuss best practices for analyses. We tested the performance of the DNEA R package and illustrated its features using publicly available metabolomics data from the environmental determinants of diabetes in the young. To our knowledge, this package is the only publicly available tool designed for the construction of biological networks and subsequent enrichment testing for datasets containing exogenous, secondary, and unknown compounds. This greatly expands the scope of traditional enrichment analysis tools that can be used to analyze a relatively small set of well-annotated metabolites.

CONCLUSIONS

The DNEA R package is a more flexible and powerful implementation of our previously published software tool, Filigree. The modular structure of the package, along with the parallel processing framework built into the most computationally extensive steps of the algorithm, make it a powerful tool for the analysis of large and complex metabolomics datasets.

Adverse impact of phthalate and polycyclic aromatic hydrocarbon mixtures on birth outcomes: A metabolome Exposome-Wide association study

It has been well-investigating that individual phthalates (PAEs) or polycyclic aromatic hydrocarbons (PAHs) affect public health. However, there is still a gap that the mixture of PAEs and PAHs impacts birth outcomes. Through innovative methods for mixtures in epidemiology, we used a metabolome Exposome-Wide Association Study (mExWAS) to evaluate and explain the association between exposure to PAEs and PAHs mixtures and birth outcomes. Exposure to a higher level of PAEs and PAHs mixture was associated with lower birth weight (maximum cumulative effect: 143.5 g) rather than gestational age. Mono(2-ethlyhexyl) phthalate (MEHP) (posterior inclusion probability, PIP = 0.51), 9-hydroxyphenanthrene (9-OHPHE) (PIP = 0.53), and 1-hydroxypyrene (1-OHPYR) (PIP = 0.28) were identified as the most important compounds in the mixture. In mExWAS, we successfully annotated four overlapping metabolites associated with both MEHP/9-OHPHE/1-OHPYR and birth weight, including arginine, stearamide, Arg-Gln, and valine. Moreover, several lipid-related metabolism pathways, including fatty acid biosynthesis and degradation, alpha-linolenic acid, and linoleic acid metabolism, were disturbed. In summary, these findings may provide new insights into the underlying mechanisms by which PAE and PAHs affect fetal growth.

Prenatal and Early Life Exposure to Ambient Air Pollutants Is Associated with the Fecal Metabolome in the First Two Years of Life

Prenatal and early life air pollution exposure has been linked with several adverse health outcomes. However, the mechanisms underlying these relationships are not yet fully understood. Therefore, this study utilizes fecal metabolomics to determine if pre- and postnatal exposure to ambient air pollutants (i.e., PM10, PM2.5, and NO2) is associated with the fecal metabolome in the first 2 years of life in a Latino cohort from Southern California. The aims of this analysis were to estimate associations between (1) prenatal air pollution exposure with fecal metabolic features at 1-month of age, (2) prior month postnatal air pollution exposure with fecal metabolites from 1-month to 2 years of age, and (3) how postnatal air pollution exposure impacts the change over time of fecal metabolites in the first 2 years of life. Prenatal exposure to air pollutants was associated with several Level-1 metabolites, including those involved in vitamin B6 and tyrosine metabolism. Prior month air pollution exposure in the postnatal period was associated with Level-1 metabolites involved in histidine metabolism. Lastly, we found that pre- and postnatal ambient air pollution exposure was associated with changes in metabolic features involved in metabolic pathways including amino acid metabolism, histidine metabolism, and fatty acid metabolism.

A new framework for exploratory network mediator analysis in omics data

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

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.

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.

Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson's disease

BACKGROUND

Untargeted high-resolution metabolomic profiling provides simultaneous measurement of thousands of metabolites. Metabolic networks based on these data can help uncover disease-related perturbations across interconnected pathways.

OBJECTIVE

Identify metabolic disturbances associated with Parkinson's disease (PD) in two population-based studies using untargeted metabolomics.

METHODS

We performed a metabolome-wide association study (MWAS) of PD using serum-based untargeted metabolomics data derived from liquid chromatography with high-resolution mass spectrometry (LC-HRMS) using two distinct population-based case-control populations. We also combined our results with a previous publication of 34 metabolites linked to PD in a large-scale, untargeted MWAS to assess external validation.

RESULTS

LC-HRMS detected 4,762 metabolites for analysis (HILIC: 2716 metabolites; C18: 2046 metabolites). We identified 296 features associated with PD at FDR<0.05, 134 having a log2 fold change (FC) beyond ±0.5 (228 beyond ±0.25). Of these, 104 were independently associated with PD in both discovery and replication studies at p<0.05 (170 at p<0.10), while 27 were associated with levodopa-equivalent dose among the PD patients. Intriguingly, among the externally validated features were the microbial-related metabolites, p-cresol glucuronide (FC=2.52, 95% CI=1.67, 3.81, FDR=7.8e-04) and p-cresol sulfate. P-cresol glucuronide was also associated with motor symptoms among patients. Additional externally validated metabolites associated with PD include phenylacetyl-L-glutamine, trigonelline, kynurenine, biliverdin, and pantothenic acid. Novel associations include the anti-inflammatory metabolite itaconate (FC=0.79, 95% CI=0.73, 0.86; FDR=2.17E-06) and cysteine-S-sulfate (FC=1.56, 95% CI=1.39, 1.75; FDR=3.43E-11). Seventeen pathways were enriched, including several related to amino acid and lipid metabolism.

CONCLUSIONS

Our results revealed PD-associated metabolites, confirming several previous observations, including for p-cresol glucuronide, and newly implicating interesting metabolites, such as itaconate. Our data also suggests metabolic disturbances in amino acid and lipid metabolism and inflammatory processes in PD.

Longitudinal profiles of the fecal metabolome during the first 2 years of life

During the first 2 years of life, the infant gut microbiome is rapidly developing, and gut bacteria may impact host health through the production of metabolites that can have systemic effects. Thus, the fecal metabolome represents a functional readout of gut bacteria. Despite the important role that fecal metabolites may play in infant health, the development of the infant fecal metabolome has not yet been thoroughly characterized using frequent, repeated sampling during the first 2 years of life. Here, we described the development of the fecal metabolome in a cohort of 101 Latino infants with data collected at 1-, 6-, 12-, 18-, and 24-months of age. We showed that the fecal metabolome is highly conserved across time and highly personalized, with metabolic profiles being largely driven by intra-individual variability. Finally, we also identified several novel metabolites and metabolic pathways that changed significantly with infant age, such as valerobetaine and amino acid metabolism, among others.

Metabolome-wide association study of the relationship between chlorpyrifos exposure and first trimester serum metabolite levels in pregnant Thai farmworkers

INTRODUCTION

Organophosphate (OP) insecticides, including chlorpyrifos, have been linked with numerous harmful health effects on maternal and child health. Limited data are available on the biological mechanisms and endogenous pathways underlying the toxicity of chlorpyrifos exposures on pregnancy and birth outcomes. In this study, we measured a urinary chlorpyrifos metabolite and used high-resolution metabolomics (HRM) to identify biological perturbations associated with chlorpyrifos exposure among pregnant women in Thailand, who are disparately exposed to high levels of OP insecticides.

METHODS

This study included 50 participants from the Study of Asian Women and their Offspring's Development and Environmental Exposures (SAWASDEE). We used liquid chromatography-high resolution mass spectrometry to conduct metabolic profiling on first trimester serum samples collected from participants to evaluate metabolic perturbations in relation to chlorpyrifos exposures. We measured 3,5,6-trichloro-2-pyridinol (TCPy), a specific metabolite of chlorpyrifos and chlorpyrifos-methyl, in first trimester urine samples to assess the levels of exposures. Following an untargeted metabolome-wide association study workflow, we used generalized linear models, pathway enrichment analyses, and chemical annotation to identify significant metabolites and pathways associated with urinary TCPy levels.

RESULTS

In the 50 SAWASDEE participants, the median urinary TCPy level was 4.36 μg TCPy/g creatinine. In total, 691 unique metabolic features were found significantly associated with TCPy levels (p < 0.05) after controlling for confounding factors. Pathway analysis of metabolic features associated with TCPy indicated perturbations in 24 metabolic pathways, most closely linked to the production of reactive oxygen species and cellular damage. These pathways include tryptophan metabolism, fatty acid oxidation and peroxisome metabolism, cytochromes P450 metabolism, glutathione metabolism, and vitamin B3 metabolism. We confirmed the chemical identities of 25 metabolites associated with TCPy levels, including glutathione, cystine, arachidic acid, itaconate, and nicotinamide adenine dinucleotide.

DISCUSSION

The metabolic perturbations associated with TCPy levels were related to oxidative stress, cellular damage and repair, and systemic inflammation, which could ultimately contribute to health outcomes, including neurodevelopmental deficits in the child. These findings support the future development of sensitive biomarkers to investigate the metabolic underpinnings related to pesticide exposure during pregnancy and to understand its link to adverse outcomes in children.