Predicting network activity from high throughput metabolomics

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.

Metabolomic signatures of ideal cardiovascular health in black adults

Plasma metabolomics profiling is an emerging methodology to identify metabolic pathways underlying cardiovascular health (CVH). The objective of this study was to define metabolomic profiles underlying CVH in a cohort of Black adults, a population that is understudied but suffers from disparate levels of CVD risk factors. The Morehouse-Emory Cardiovascular (MECA) Center for Health Equity study cohort consisted of 375 Black adults (age 53 ± 10, 39% male) without known CVD. CVH was determined by the AHA Life's Simple 7 (LS7) score, calculated from measured blood pressure, body mass index (BMI), fasting blood glucose and total cholesterol, and self-reported physical activity, diet, and smoking. Plasma metabolites were assessed using untargeted high-resolution metabolomics profiling. A metabolome wide association study (MWAS) identified metabolites associated with LS7 score after adjusting for age and sex. Using Mummichog software, metabolic pathways that were significantly enriched in metabolites associated with LS7 score were identified. Metabolites representative of these pathways were compared across clinical domains of LS7 score and then developed into a metabolomics risk score for prediction of CVH. We identified novel metabolomic signatures and pathways associated with CVH in a cohort of Black adults without known CVD. Representative and highly prevalent metabolites from these pathways included glutamine, glutamate, urate, tyrosine and alanine, the concentrations of which varied with BMI, fasting glucose, and blood pressure levels. When assessed in conjunction, these metabolites were independent predictors of CVH. One SD increase in the novel metabolomics risk score was associated with a 0.88 higher LS7 score, which translates to a 10.4% lower incident CVD risk. We identified novel metabolomic signatures of ideal CVH in a cohort of Black Americans, showing that a core group of metabolites central to nitrogen balance, bioenergetics, gluconeogenesis, and nucleotide synthesis were associated with CVH in this population.

Real-Time Exposure to 3D-Printing Emissions Elicits Metabolic and Pro-Inflammatory Responses in Human Airway Epithelial Cells

Three-dimensional (3D) printer usage in household and school settings has raised health concerns regarding chemical and particle emission exposures during operation. Although the composition of 3D printer emissions varies depending on printer settings and materials, little is known about the impact that emissions from different filament types may have on respiratory health and underlying cellular mechanisms. In this study, we used an in vitro exposure chamber system to deliver emissions from two popular 3D-printing filament types, acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA), directly to human small airway epithelial cells (SAEC) cultured in an air-liquid interface during 3D printer operation. Using a scanning mobility particle sizer (SMPS) and an optical particle sizer (OPS), we monitored 3D printer particulate matter (PM) emissions in terms of their particle size distribution, concentrations, and calculated deposited doses. Elemental composition of ABS and PLA emissions was assessed using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX). Finally, we compared the effects of emission exposure on cell viability, inflammation, and metabolism in SAEC. Our results reveal that, although ABS filaments emitted a higher total concentration of particles and PLA filaments emitted a higher concentration of smaller particles, SAEC were exposed to similar deposited doses of particles for each filament type. Conversely, ABS and PLA emissions had distinct elemental compositions, which were likely responsible for differential effects on SAEC viability, oxidative stress, release of inflammatory mediators, and changes in cellular metabolism. Specifically, while ABS- and PLA-emitted particles both reduced cellular viability and total glutathione levels in SAEC, ABS emissions had a significantly greater effect on glutathione relative to PLA emissions. Additionally, pro-inflammatory cytokines including IL-1β, MMP-9, and RANTES were significantly increased due to ABS emissions exposure. While IL-6 and IL-8 were stimulated in both exposure scenarios, VEGF was exclusively increased due to PLA emissions exposures. Notably, ABS emissions induced metabolic perturbation on amino acids and energy metabolism, as well as redox-regulated pathways including arginine, methionine, cysteine, and vitamin B3 metabolism, whereas PLA emissions exposures caused fatty acid and carnitine dysregulation. Taken together, these results advance our mechanistic understanding of 3D-printer-emissions-induced respiratory toxicity and highlight the role that filament emission properties may play in mediating different respiratory outcomes.

Dioxin(-like)-Related Biological Effects through Integrated Chemical-wide and Metabolome-wide Analyses

Dioxin(-like) exposures are linked to adverse health effects, including cancer. However, metabolic alterations induced by these chemicals remain largely unknown. Beyond known dioxin(-like) compounds, we leveraged a chemical-wide approach to assess chlorinated co-exposures and parent compound products [termed dioxin(-like)-related compounds] among 137 occupational workers. Endogenous metabolites were profiled by untargeted metabolomics, namely, reversed-phase chromatography with negative electrospray ionization (C18-negative) and hydrophilic interaction liquid chromatography with positive electrospray ionization (HILIC-positive). We performed a metabolome-wide association study to select dioxin(-like) associated metabolic features using a 20% false discovery rate threshold. Metabolic features were then characterized by pathway enrichment analyses. There are no significant features associated with polychlorinated dibenzo-p-dioxins (PCDDs), a subgroup of known dioxin(-like) compounds. However, 3,110 C18-negative and 2,894 HILIC-positive features were associated with at least one of the PCDD-related compounds. Abundant metabolic changes were also observed for polychlorinated dibenzofuran-related and polychlorinated biphenyl-related compounds. These metabolic features were primarily enriched in pathways of amino acids, lipid and fatty acids, carbohydrates, cofactors, and nucleotides. Our study highlights the potential of chemical-wide analysis for comprehensive exposure assessment beyond targeted chemicals. Coupled with advanced endogenous metabolomics, this approach allows for an in-depth exploration of metabolic alterations induced by environmental chemicals.

Lysophosphatidylcholines are associated with P-tau181 levels in early stages of Alzheimer's Disease

Background

We profiled circulating plasma metabolites to identify systemic biochemical changes in clinical and biomarker-assisted diagnosis of Alzheimer's disease (AD).

Methods

We used an untargeted approach with liquid chromatography coupled to high-resolution mass spectrometry to measure small molecule plasma metabolites from 150 clinically diagnosed AD patients and 567 age-matched healthy elderly of Caribbean Hispanic ancestry. Plasma biomarkers of AD were measured including P-tau181, Aβ40, Aβ42, total-tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). Association of individual and co-abundant modules of metabolites were tested with clinical diagnosis of AD, as well as biologically-defined AD pathological process based on P-tau181 and other biomarker levels.

Results

Over 6000 metabolomic features were measured with high accuracy. First principal component (PC) of lysophosphatidylcholines (lysoPC) that bind to or interact with docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (AHA) was associated with decreased risk of AD (OR = 0.91 [0.89-0.96], p = 2e-04). Association was restricted to individuals without an APOE ε4 allele (OR = 0.89 [0.84-0.94], p = 8.7e-05). Among individuals carrying at least one APOE ε4 allele, PC4 of lysoPCs moderately increased risk of AD (OR = 1.37 [1.16-1.6], p = 1e-04). Essential amino acids including tyrosine metabolism pathways were enriched among metabolites associated with P-tau181 levels and heparan and keratan sulfate degradation pathways were associated with Aβ42/Aβ40 ratio.

Conclusions

Unbiased metabolic profiling can identify critical metabolites and pathways associated with β-amyloid and phosphotau pathology. We also observed an APOE-ε4 dependent association of lysoPCs with AD and biologically based diagnostic criteria may aid in the identification of unique pathogenic mechanisms.

Enhanced phenylpropanoid metabolism underlies resistance to Fusarium oxysporum f. sp. vasinfectum race 4 infection in the cotton cultivar Pima-S6 (Gossypium barbadense L.)

Introduction: Fusarium oxysporum f. sp. vasinfectum (FOV) race 4 (FOV4) is a highly pathogenic soil-borne fungus responsible for Fusarium wilt in cotton (Gossypium spp.) and represents a continuing threat to cotton production in the southwest states of the United States, including California, New Mexico, and Texas. Pima (G. barbadense L.) cotton, which is highly valued for its fiber quality, has been shown to be more susceptible to this pathogen than Upland (G. hirsutum L.) cotton. Still, some Pima cultivars present resistance to FOV4 infection. Methods: To gain insights into the FOV4-resistance mechanism, we performed comparative transcriptional and metabolomic analyses between FOV4-susceptible and FOV4-resistant Pima cotton entries. FOV4-resistant Pima-S6 and FOV4-susceptible Pima S-7 and Pima 3-79 cotton plants were infected with FOV4 in the greenhouse, and the roots harvested 11 days post-infection for further analysis. Results: We found that an enhanced root phenylpropanoid metabolism in the resistant Pima-S6 cultivar determines FOV4-resistance. Gene-ontology enrichment of phenylpropanoid biosynthesis and metabolism categories correlated with the accumulation of secondary metabolites in Pima-S6 roots. Specifically, we found esculetin, a coumarin, an inhibitor of Fusarium's growth, accumulated in the roots of Pima-S6 even under non-infected conditions. Genes related to the phenylpropanoid biosynthesis and metabolism, including phenylalanine ammonia-lyase 2 (PAL2) and pleiotropic drug resistance 12 (PDR12) transporter, were found to be upregulated in Pima-S6 roots. Discussion: Our results highlight an essential role for the phenylpropanoid synthesis pathway in FOV4 resistance in Pima-S6 cotton. These genes represent attractive research prospects for FOV4-disease resistance and breeding approaches of other cotton cultivars of economic relevance.

ALTERATIONS IN HISTIDINE METABOLISM IS A FEATURE OF EARLY AUTOSOMAL DOMINANT POLYCYSTIC KIDNEY DISEASE (ADPKD)

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by epithelial proliferation and progressive cyst enlargement. Using a non-targeted high-resolution metabolomics approach, we analyzed biofluids from 36 ADPKD and 18 healthy controls with estimated glomerular filtration rate (eGFR) > 60 ml/min to identify features specific to ADPKD or that associate with disease severity [eGFR or height-corrected total kidney volume (htTKV)]. Multiple pathways differed between ADPKD subjects and controls, with the histidine pathway being the most highly represented. Plasma histidine, urinary N-methylhistamine, methylimidazole-acetaldehyde, and imidazole-acetaldehyde, as well as 3-methylhistidine and anserine were increased, while plasma N-acetylhistamine and urinary imidazole-acetic acid were decreased in ADPKD compared to controls. In ADPKD, urinary histidine and a histidine derivative, urocanate (a precursor of glutamate), were significantly associated. HtTKV and eGFR were inversely associated with urinary glutamine and plasma 4-imidazolone-5-propionic acid, respectively. Supernatant from cultured human ADPKD renal cystic epithelia demonstrated increased aspartate and glutamate levels at 8 and 24 hours compared to primary tubular epithelia (p < 0.001). Following exposure over 48 hours to α-fluromethylhistidine, an inhibitor of histamine production, primary human PKD1 cyst epithelia proliferation increased significantly from baseline (p < 0.01) and greater than non-cystic epithelia (p < 0.05). The histidine ammonia lyase inhibitor nitromethane reversed α-fluromethylhistidine-induced cyst epithelia proliferation indicating a role for glutamate in cyst growth. In conclusion, histidine metabolism is altered preferentially leading to glutamate production and epithelial proliferation in ADPKD and associates with disease severity.

Combined cerebrospinal fluid metabolomic and cytokine profiling in tuberculosis meningitis reveals robust and prolonged changes in immunometabolic networks

Much of the high mortality in tuberculosis meningitis (TBM) is attributable to excessive inflammation, making it imperative to identify targets for host-directed therapies that reduce pathologic inflammation and mortality. In this study, we investigate how cytokines and metabolites in the cerebral spinal fluid (CSF) associate with TBM at diagnosis and during TBM treatment. At diagnosis, TBM patients (n = 17) demonstrate significant increases of cytokines and chemokines that promote inflammation and cell migration including IL-17A, IL-2, TNFα, IFNγ, and IL-1β versus asymptomatic controls without known central nervous system pathology (n = 20). Inflammatory immune signaling had a strong positive correlation with immunomodulatory metabolites including kynurenine, lactic acid, and carnitine and strong negative correlations with tryptophan and itaconate. Inflammatory immunometabolic networks were only partially reversed with two months of effective TBM treatment and remained significantly different compared to CSF from controls. Together, these data highlight a critical role for host metabolism in regulating the inflammatory response to TBM and indicate the timeline for restoration of immune homeostasis in the CSF is prolonged.

Associations of a Prenatal Serum Per- and Polyfluoroalkyl Substance Mixture with the Cord Serum Metabolome in the HOME Study

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous and persistent chemicals associated with multiple adverse health outcomes; however, the biological pathways affected by these chemicals are unknown. To address this knowledge gap, we used data from 264 mother-infant dyads in the Health Outcomes and Measures of the Environment (HOME) Study and employed quantile-based g-computation to estimate covariate-adjusted associations between a prenatal (∼16 weeks' gestation) serum PFAS mixture [perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexanesulfonic acid (PFHxS), and perfluorononanoic acid (PFNA)] and 14,402 features measured in cord serum. The PFAS mixture was associated with four features: PFOS, PFHxS, a putatively identified metabolite (3-monoiodo-l-thyronine 4-O-sulfate), and an unidentified feature (590.0020 m/z and 441.4 s retention time; false discovery rate <0.20). Using pathway enrichment analysis coupled with quantile-based g-computation, the PFAS mixture was associated with 49 metabolic pathways, most notably amino acid, carbohydrate, lipid and cofactor and vitamin metabolism, as well as glycan biosynthesis and metabolism (P(Gamma) <0.05). Future studies should assess if these pathways mediate associations of prenatal PFAS exposure with infant or child health outcomes, such as birthweight or vaccine response.

Methylprednisolone therapy induces differential metabolic trajectories in severe COVID-19 patients

IMPORTANCE

The SARS-CoV-2 virus infection in humans induces significant inflammatory and systemic reactions and complications of which corticosteroids like methylprednisolone have been recommended as treatment. Our understanding of the metabolic and metabolomic pathway dysregulations while using intravenous corticosteroids in COVID-19 is limited. This study will help enlighten the metabolic and metabolomic pathway dysregulations underlying high daily doses of intravenous methylprednisolone in COVID-19 patients compared to those receiving placebo. The information on key metabolites and pathways identified in this study together with the crosstalk with the inflammation and biochemistry components may be used, in the future, to leverage the use of methylprednisolone in any future pandemics from the coronavirus family.

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.

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

INTRODUCTION

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

METHOD

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

RESULTS

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

CONCLUSION

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

Quantitative Dynamic Analysis of de novo Sphingolipid Biosynthesis in Arabidopsis thaliana

Sphingolipids are pivotal for plant development and stress responses. Growing interest has been directed towards fully comprehending the regulatory mechanisms of the sphingolipid pathway. We explore its de novo biosynthesis and homeostasis in Arabidopsis thaliana cell cultures, shedding light on fundamental metabolic mechanisms. Employing 15N isotope labeling and quantitative dynamic modeling approach, we developed a regularized and constraint-based Dynamic Metabolic Flux Analysis (r-DMFA) framework to predict metabolic shifts due to enzymatic changes. Our analysis revealed key enzymes such as sphingoid-base hydroxylase (SBH) and long-chain-base kinase (LCBK) to be critical for maintaining sphingolipid homeostasis. Disruptions in these enzymes were found to affect cellular viability and increase the potential for programmed cell death (PCD). Thus, this work enhances our understanding of sphingolipid metabolism and demonstrates the utility of dynamic modeling in analyzing complex metabolic pathways.

Secondary-Electrospray Ionization Mass Spectrometry-Based Online Analyses of Mouse Volatilome Uncover Gut Microbiome-Dictated Metabolic Changes in the Host

The symbiotic relationship between the gut microbial population is capable of regulating numerous aspects of host physiology, including metabolism. Bacteria can modulate the metabolic processes of the host by feeding on nutritional components within the lumen and releasing bioactive components into circulation. Endogenous volatile organic compound (VOC) synthesis is dependent on the availability of precursors found in mammalian metabolism. Herein, we report that microbial-mediated metabolic influences can alter the host volatilome and the prominent volatile changes can be uncovered by a novel volatile analysis technique named secondary electrospray ionization mass spectrometry. Mice were subjected to an antibiotic cocktail to deplete the microbiome and then inoculated with either single strain bacteria or fecal matter transplantation (FMT) to replete the microbial population in the gut. VOC sampling was achieved by using an advanced secondary electrospray ionization (SESI) source that directly mounted onto a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). A principal component analysis summarizing the volatile profiles of the mice revealed independent clustering of each strain of the FMT-inoculated groups, suggesting unique volatile profiles. The Mummichog algorithm uncovered phenylalanine metabolism as a significantly altered metabolic profile in the volatilome of the microbiome-repleted mice. Our results indicated that the systemic metabolic changes incurred by the host are translated to unique volatile profiles correlated to the diversity of the microbial population colonized within the host. It is thus possible to take advantage of SESI-HRMS-based platforms for noninvasive screening of VOCs to determine the contribution of various microbial colonization within human gut that may impact host health.

Metabolomic Signatures of Exposure to Nitrate and Trihalomethanes in Drinking Water and Colorectal Cancer Risk in a Spanish Multicentric Study (MCC-Spain)

We investigated the metabolomic profile associated with exposure to trihalomethanes (THMs) and nitrate in drinking water and with colorectal cancer risk in 296 cases and 295 controls from the Multi Case-Control Spain project. Untargeted metabolomic analysis was conducted in blood samples using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses were conducted after data quality control, normalization, and imputation. Linear regression and partial least-squares analyses were conducted for chloroform, brominated THMs, total THMs, and nitrate among controls and for case-control status, together with a N-integration model discriminating colorectal cancer cases from controls through interrogation of correlations between the exposure variables and the metabolomic features. Results revealed a total of 568 metabolomic features associated with at least one water contaminant or colorectal cancer. Annotated metabolites and pathway analysis suggest a number of pathways as potentially involved in the link between exposure to these water contaminants and colorectal cancer, including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provide insights into the underlying biological mechanisms and potential biomarkers associated with water contaminant exposure and colorectal cancer risk. Further research in this area is needed to better understand the causal relationship and the public health implications.

The neonatal blood spot metabolome in retinoblastoma

Background

Retinoblastoma is rare but nevertheless the most common pediatric eye cancer that occurs in children under age 5. High-resolution metabolomics (HRM) is a powerful analytical approach to profile metabolic features and pathways or identify metabolite biomarkers. To date, no studies have used pre-diagnosis blood samples from retinoblastoma cases and compared them to healthy controls to elucidate early perturbations in tumor pathways.

Objectives

Here, we report on metabolic profiles of neonatal blood comparing cases later in childhood diagnosed with retinoblastoma and controls.

Methods

We employed untargeted metabolomics analysis using neonatal dried blood spots for 1327 children (474 retinoblastoma cases and 853 healthy controls) born in California from 1983 to 2011. Cases were selected from the California Cancer Registry and controls, frequency matched to cases by birth year, from California birth rolls. We performed high-resolution metabolomics to extract metabolic features, partial least squares discriminant analysis (PLS-DA) and logistic regression to identify features associated with disease, and Mummichog pathway analysis to characterize enriched biological pathways.

Results

PLS-DA identified 1917 discriminative features associated with retinoblastoma and Mummichog identified 14 retinoblastoma-related enriched pathways including linoleate metabolism, pentose phosphate pathway, pyrimidine metabolism, fructose and mannose metabolism, vitamin A metabolism, as well as fatty acid and lipid metabolism.

Interpretation

Our findings linked a retinoblastoma diagnosis in early life to newborn blood metabolome perturbations indicating alterations in inflammatory pathways and energy metabolism. Neonatal blood spots may provide a venue for early detection for this or potentially other childhood cancers.

Multi-omic responses of fish exposed to complex chemical mixtures in the Shenandoah River watershed

To evaluate relationships between different anthropogenic impacts, contaminant occurrence, and fish health, we conducted in situ fish exposures across the Shenandoah River watershed at five sites with different land use. Exposure water was analyzed for over 500 chemical constituents, and organismal, metabolomic, and transcriptomic endpoints were measured in fathead minnows. Adverse reproductive outcomes were observed in fish exposed in the upper watershed at both wastewater treatment plant (WWTP) effluent- and agriculture-impacted sites, including decreased gonadosomatic index and altered secondary sex characteristics. This was accompanied with increased mortality at the site most impacted by agricultural activities. Molecular biomarkers of estrogen exposure were unchanged and consistent with low or non-detectable concentrations of common estrogens, indicating that alternative mechanisms were involved in organismal adverse outcomes. Hepatic metabolomic and transcriptomic profiles were altered in a site-specific manner, consistent with variation in land use and contaminant profiles. Integrated biomarker response data were useful for evaluating mechanistic linkages between contaminants and adverse outcomes, suggesting that reproductive endocrine disruption, altered lipid processes, and immunosuppression may have been involved in these organismal impacts. This study demonstrated linkages between human-impact, contaminant occurrence, and exposure effects in the Shenandoah River watershed and showed increased risk of adverse outcomes in fathead minnows exposed to complex mixtures at sites impacted by municipal wastewater discharges and agricultural practices.

Evaluating changes in firefighter urinary metabolomes after structural fires: an untargeted, high resolution approach

Firefighters have elevated rates of urinary tract cancers and other adverse health outcomes, which may be attributable to environmental occupational exposures. Untargeted metabolomics was applied to characterize this suite of environmental exposures and biological changes in response to occupational firefighting. 200 urine samples from 100 firefighters collected at baseline and two to four hours post-fire were analyzed using untargeted liquid-chromatography and high-resolution mass spectrometry. Changes in metabolite abundance after a fire were estimated with fixed effects linear regression, with false discovery rate (FDR) adjustment. Partial least squares discriminant analysis (PLS-DA) was also used, and variable important projection (VIP) scores were extracted. Systemic changes were evaluated using pathway enrichment for highly discriminating metabolites. Metabolome-wide-association-study (MWAS) identified 268 metabolites associated with firefighting activity at FDR q < 0.05. Of these, 20 were annotated with high confidence, including the amino acids taurine, proline, and betaine; the indoles kynurenic acid and indole-3-acetic acid; the known uremic toxins trimethylamine n-oxide and hippuric acid; and the hormone 7a-hydroxytestosterone. Partial least squares discriminant analysis (PLS-DA) additionally implicated choline, cortisol, and other hormones. Significant pathways included metabolism of urea cycle/amino group, alanine and aspartate, aspartate and asparagine, vitamin b3 (nicotinate and nicotinamide), and arginine and proline. Firefighters show a broad metabolic response to fires, including altered excretion of indole compounds and uremic toxins. Implicated pathways and features, particularly uremic toxins, may be important regulators of firefighter's increased risk for urinary tract cancers.

Robust knowledge-guided biclustering for multi-omics data

Biclustering is a useful method for simultaneously grouping samples and features and has been applied across various biomedical data types. However, most existing biclustering methods lack the ability to integratively analyze multi-modal data such as multi-omics data such as genome, transcriptome and epigenome. Moreover, the potential of leveraging biological knowledge represented by graphs, which has been demonstrated to be beneficial in various statistical tasks such as variable selection and prediction, remains largely untapped in the context of biclustering. To address both, we propose a novel Bayesian biclustering method called Bayesian graph-guided biclustering (BGB). Specifically, we introduce a new hierarchical sparsity-inducing prior to effectively incorporate biological graph information and establish a unified framework to model multi-view data. We develop an efficient Markov chain Monte Carlo algorithm to conduct posterior sampling and inference. Extensive simulations and real data analysis show that BGB outperforms other popular biclustering methods. Notably, BGB is robust in terms of utilizing biological knowledge and has the capability to reveal biologically meaningful information from heterogeneous multi-modal data.

High-Resolution Plasma Metabolomics and Thiamine Status in Critically Ill Adult Patients

BACKGROUND AND AIM

Thiamine (Vitamin B1) is an essential micronutrient and a co-factor for metabolic functions related to energy metabolism. We determined the association between whole blood thiamine pyrophosphate (TPP) concentrations and plasma metabolites using high resolution metabolomics in critically ill patients. Methods Cross-sectional study performed in Erciyes University Hospital, Kayseri, Turkey and Emory University, Atlanta, GA, USA. Participants were ≥ 18 years of age, with an expected length of ICU stay longer than 48 hours, receiving furosemide therapy for at least 6 months before ICU admission. Results Blood for TPP and metabolomics was obtained on the day of ICU admission. Whole blood TPP concentrations were measured using high-performance liquid chromatography (HPLC). Liquid chromatography/mass spectrometry was used for plasma high-resolution metabolomics. Data was analyzed using regression analysis of TPP levels against all plasma metabolomic features in metabolome-wide association studies. We also compared metabolomic features from patients in the highest TPP concentration tertile to patients in the lowest TPP tertile as a secondary analysis. We enrolled 76 participants with a median age of 69 (range, 62.5-79.5) years. Specific metabolic pathways associated with whole blood TPP levels, using both regression and tertile analysis, included pentose phosphate, fructose and mannose, branched chain amino acid, arginine and proline, linoleate, and butanoate pathways. Conclusions Plasma high-resolution metabolomics analysis showed that whole blood TPP concentrations are significantly associated with metabolites and metabolic pathways linked to the metabolism of energy, amino acids, lipids, and the gut microbiome in adult critically ill patients.

Metabolic reprograming and increased inflammation by cadmium exposure following early-life respiratory syncytial virus infection-the involvement of protein S-palmitoylation

Early-life respiratory syncytial virus (RSV) infection (eRSV) is one of the leading causes of serious pulmonary disease in children. eRSV is associated with higher risk of developing asthma and compromised lung function later in life. Cadmium (Cd) is a toxic metal, widely present in the environment and in food. We recently showed that eRSV re-programs metabolism and potentiates Cd toxicity in the lung, and our transcriptome-metabolome-wide study showed strong associations between S-palmitoyl transferase expression and Cd-stimulated lung inflammation and fibrosis signaling. Limited information is available on the mechanism by which eRSV re-programs metabolism and potentiates Cd toxicity in the lung. In the current study, we used a mouse model to examine the role of protein S-palmitoylation (Pr-S-Pal) in low dose Cd-elevated lung metabolic disruption and inflammation following eRSV. Mice exposed to eRSV were later treated with Cd (3.3 mg CdCl2/L) in drinking water for 6 weeks (RSV+Cd). The role of Pr-S-Pal was studied using a palmitoyl transferase inhibitor, 2-bromopalmitate (BP, 10 µM). Inflammatory marker analysis showed that cytokines, chemokines and inflammatory cells were highest in the RSV+Cd group, and BP decreased inflammatory markers. Lung metabolomics analysis showed that pathways including phenylalanine, tyrosine and tryptophan, phosphatidylinositol and sphingolipid were altered across treatments. BP antagonized metabolic disruption of sphingolipid and glycosaminoglycan metabolism by RSV+Cd, consistent with BP effect on inflammatory markers. This study shows that Cd exposure following eRSV has a significant impact on subsequent inflammatory response and lung metabolism, which is mediated by Pr-S-Pal, and warrants future research for a therapeutic target.

Database-assisted, globally optimized targeted secondary electrospray ionization high resolution mass spectrometry (dGOT-SESI-HRMS) and spectral stitching enhanced volatilomics analysis of bacterial metabolites

Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an innovative analytical technique for the rapid and non-invasive analysis of volatile organic compounds (VOCs). However, compound annotation and ion suppression in the SESI source has hindered feature detection, stability and reproducibility of SESI-HRMS in untargeted volatilomics. To address this, we have developed and optimized a novel pseudo-targeted approach, database-assisted globally optimized targeted (dGOT)-SESI-HRMS using the microbial-VOC (mVOC) database, and spectral stitching methods to enhance metabolite detection in headspace of anaerobic bacterial cultures. Headspace volatiles from representative bacteria strains were assessed using full scan with data dependent acquisition (DDA), conventional globally optimized targeted (GOT) method, and spectral stitching supported dGOT experiments based on a MS peaks list derived from mVOC. Our results indicate that spectral stitching supported dGOT-SESI-HRMS can proportionally fragment peaks with respect to different analysis windows, with a total of 109 VOCs fragmented from 306 targeted compounds. Of the collected spectra, 88 features were confirmed as culture derived volatiles with respect to media blanks. Annotation was also achieved with a total of 25 unique volatiles referenced to standard databases allowing for biological interpretation. Principal component analysis (PCA) summarizing the headspace volatile demonstrated improved separation of clusters when data was acquired using the dGOT method. Collectively, our dGOT-SESI-HRMS method afforded robust capability of capturing unique VOC profiles from different bacterial strains and culture conditions when compared to conventional GOT and DDA modes, suggesting the newly developed approach can serve as a more reliable analytical method for the sensitive monitoring of gut microbial metabolism.

Serum and follicular fluid metabolome and markers of ovarian stimulation

STUDY QUESTION

What metabolic pathways and metabolites in the serum and follicular fluid are associated with peak estradiol levels and the number of mature oocytes?

SUMMARY ANSWER

In the serum metabolome, mostly fatty acid and amino acid pathways were associated with estradiol levels and mature oocytes while in the follicular fluid metabolome, mostly lipid, vitamin, and hormone pathways were associated with peak estradiol levels and mature oocytes.

WHAT IS KNOWN ALREADY

Metabolomics has identified several metabolic pathways and metabolites associated with infertility but limited data are available for ovarian stimulation outcomes.

STUDY DESIGN, SIZE, DURATION

A prospective cohort study of women undergoing IVF from 2009 to 2015.

PARTICIPANTS/MATERIALS, SETTING, METHODS

A total of 125 women undergoing a fresh IVF cycle at a fertility clinic in the Northeast United States who provided a serum and follicular fluid sample. Untargeted metabolomics profiling was conducted using liquid chromatography with high-resolution mass spectrometry in two chromatography columns (C18 and hydrophilic interaction chromatography (HILIC)). The main ovarian stimulation outcomes were peak serum estradiol levels and number of mature oocytes. We utilized adjusted generalized linear regression models to identify significant metabolic features. Models were adjusted for age,BMI, initial infertility diagnosis, and ovarian stimulation protocol. We then conducted pathway analysis using mummichog and metabolite annotation using level-1 evidence.

MAIN RESULTS AND ROLE OF CHANCE

In the serum metabolome, 480 and 850 features were associated with peak estradiol levels in the C18 and HILIC columns, respectively. Additionally, 437 and 538 features were associated with mature oocytes in the C18 and HILIC columns, respectively. In the follicular fluid metabolome, 752 and 929 features were associated with peak estradiol levels in the C18 and HILIC columns, respectively, Additionally, 993 and 986 features were associated with mature oocytes in the C18 and HILIC columns, respectively. The most common pathways associated with peak estradiol included fatty acids (serum and follicular fluid), hormone (follicular fluid), and lipid pathways (follicular fluid). The most common pathways associated with the number of mature oocytes retrieved included amino acids (serum), fatty acids (serum and follicular fluid), hormone (follicular fluid), and vitamin pathways(follicular fluid). The vitamin D3 pathway had the strongest association with both ovarian stimulation outcomes in the follicularfluid. Four and nine metabolites were identified using level-1 evidence (validated identification) in the serum and follicular fluid metabolomes, respectively.

LIMITATIONS, REASONS FOR CAUTION

Our sample was majority White and highly educated and may not be generalizable to thewider population. Additionally, residual confounding is possible and the flushing medium used in the follicular fluid could have diluted our results.

WIDER IMPLICATIONS OF THE FINDINGS

The pathways and metabolites identified by our study provide novel insights into the biologicalmechanisms in the serum and follicular fluid that may underlie follicular and oocyte development, which could potentially be used to improve ovarian stimulation outcomes.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the following grants from the National Institute of Environmental Health Sciences (P30-ES019776, R01-ES009718, R01-ES022955, P30-ES000002, R00-ES026648, and T32-ES012870), and National Institute of Diabetes and Digestive and Kidney Diseases (P30DK046200). The authors have no competing interests to disclose.

TRIAL REGISTRATION NUMBER

N/A.

Immunological and metabolic characterization of environmental Mycobacterium chimaera infection in a murine model

Mycobacterium chimaera causes pulmonary disease, but little is known of gradations in isolate virulence. Previously, 17 M. chimaera isolates were screened for survival in THP1 macrophages. "M. chimaera 1" was categorized as "more virulent" because it showed the greatest survival in macrophages, whereas "M. chimaera 2" was categorized as "less virulent" with reduced survival. Herein, we infected C3HeB/FeJ mice to compare the in vivo immune responses to M. chimaera 1 and 2. Unlike macrophages, significantly lower M. chimaera 1 counts were recovered from mouse lung tissue and BAL cells with less lung histopathologic changes compared to M. chimaera 2. Compared to M. chimaera 2, significantly more IL-1β, IL-6, and TNFα was produced early after M. chimaera 1 infection. LC-MS metabolomics analyses of BAL fluid revealed divergence in sphingolipid, phospholipid metabolism between M. chimaera 1 versus M. chimaera 2 mice. From pan-GWAS analyses, virulence and organizing DNA/molecular structure genes were associated with more virulent M. chimaera isolates. Vigorous lung-specific immune responses to M. chimaera 1 may influence effective bacterial control, but for a different isolate M. chimaera 2, subvert immune control. Continued studies of the gradations in virulence among the same NTM species will advance our understanding of NTM pathogenesis.

The altered metabolic pathways of diffuse large B-cell lymphoma not otherwise specified

Altered metabolic fingerprints of Diffuse large B-cell lymphoma, not otherwise specified (DLBCL NOS) may offer novel opportunities to identify new biomarkers and improve the understanding of its pathogenesis. This study aimed to investigate the modified metabolic pathways in extranodal, germinal center B-cell (GCB) and non-GCB DLBCL NOS from the head and neck. Formalin-fixed paraffin-embedded (FFPE) tissues from eleven DLBCL NOS classified according to Hans' algorithm using immunohistochemistry, and five normal lymphoid tissues (LT) were analyzed by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Partial Least Squares Discriminant Analysis showed that GCB and non-GCB DLBCL NOS have a distinct metabolomics profile, being the former more similar to normal lymphoid tissues. Metabolite pathway enrichment analysis indicated the following altered pathways: arachidonic acid, tyrosine, xenobiotics, vitamin E metabolism, and vitamin A. Our findings support that GCB and non-GCB DLBCL NOS has a distinct metabolomic profile, in which GCB possibly shares more metabolic similarities with LT than non-GCB DLBCL NOS.

Metabolic Perturbations Associated with an Exposure Mixture of Per- and Polyfluoroalkyl Substances in the Atlanta African American Maternal-Child Cohort

Prenatal exposure to single chemicals belonging to the per- and polyfluoroalkyl substances (PFAS) family is associated with biological perturbations in the mother, fetus, and placenta, plus adverse health outcomes. Despite our knowledge that humans are exposed to multiple PFAS, the potential joint effects of PFAS on the metabolome remain largely unknown. Here, we leveraged high-resolution metabolomics to identify metabolites and metabolic pathways perturbed by exposure to a PFAS mixture during pregnancy. Targeted assessment of perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorooctanesulfonic acid (PFOS), and perfluorohexanesulfonic acid (PFHxS), along with untargeted metabolomics profiling, were conducted on nonfasting serum samples collected from pregnant African Americans at 6-17 weeks gestation. We estimated the overall mixture effect and partial effects using quantile g-computation and single-chemical effects using linear regression. All models were adjusted for maternal age, education, parity, early pregnancy body mass index, substance use, and gestational weeks at sample collection. Our analytic sample included 268 participants and was socioeconomically diverse, with the majority receiving public health insurance (78%). We observed 13.3% of the detected metabolic features were associated with the PFAS mixture (n = 1705, p < 0.05), which was more than any of the single PFAS chemicals. There was a consistent association with metabolic pathways indicative of systemic inflammation and oxidative stress (e.g., glutathione, histidine, leukotriene, linoleic acid, prostaglandins, and vitamins A, C, D, and E metabolism) across all metabolome-wide association studies. Twenty-six metabolites were validated against authenticated compounds and associated with the PFAS mixture (p < 0.05). Based on quantile g-computation weights, PFNA contributed the most to the overall mixture effect for γ-aminobutyric acid (GABA), tyrosine, and uracil. In one of the first studies of its kind, we demonstrate the feasibility and utility of using methods designed for exposure mixtures in conjunction with metabolomics to assess the potential joint effects of multiple PFAS chemicals on the human metabolome. We identified more pronounced metabolic perturbations associated with the PFAS mixture than for single PFAS chemicals. Taken together, our findings illustrate the potential for integrating environmental mixture analyses and high-throughput metabolomics to elucidate the molecular mechanisms underlying human health.

Unveiling the impact of short-term polyethylene microplastics exposure on metabolomics and gut microbiota in earthworms (Eudrilus euganiae)

Microplastics (MPs) pose a significant environmental concern, particularly for terrestrial fauna. In this study, earthworms were used as a model organism to investigate the ecotoxicological effects of short-term exposure to virgin MPs on changes in metabolome and gut microbiota. High-throughput untargeted metabolomics showed significant internal reactions in the earthworms' metabolic processes due to MPs exposure, even when no visible stress signs, such as changes in growth or mortality rates, were present. Earthworms exposed to different concentrations of polyethylene (PE) MP exhibited significant disruption in 39 and 199 molecular features related to energy and lipid metabolism, anti-inflammatory, cell signaling, and membrane integrity. The activities of enzymes and transport proteins in earthworms were dysregulated when exposed to PE. Changes in the gut microbiota's community structure and complexity were observed in response to PE MPs exposure. Despite the relative stability in alpha-diversity and relative abundance, shifts in beta-diversity and network analysis in the PE-exposed group were indicative of an adaptive response to MPs. Earthworms exhibited resilience or adaptation in response to MPs exposure, potentially maintaining their functionality. This study provides preliminary insights into the impact of MPs on soil invertebrates like earthworms and highlights the need for further exploration of long-term effects and underlying molecular mechanisms.

Comparison of Phenolic Compounds and Evaluation of Antioxidant Properties of Porophyllum ruderale (Jacq.) Cass (Asteraceae) from Different Geographical Areas of Queretaro (Mexico)

Porophyllum ruderale (P. ruderale) is a well-known Mexican plant from the group of "Quelites", widely consumed plant species used for several food and medicinal purposes. As the production is very heterogeneous and the diverse agroclimatic conditions significantly impact the plant's phytochemical composition, this research aimed to compare the phenolic compound composition and the antioxidant capacity of the P. ruderale plant from three different collection sites (Queretaro, Landa de Matamoros, and Arroyo Seco) in the State of Queretaro (Mexico). Plants collected from Queretaro displayed the lowest total phenolic compounds, flavonoids, and condensed tannins, reflected in a lower antioxidant capacity (DPPH, FRAP, ABTS), compared to the other collection places. Flavones (epicatechin and epigallocatechin gallate) were the most abundant (36.1-195.2 μg equivalents/g) phenolics quantified by HPLC-DAD, while 31 compounds were identified by UHPLC-DAD-QToF/MS-ESI. Most compounds were linked to biological mechanisms related to the antioxidant properties of the leaves. A PCA analysis clustered Landa de Matamoros and Arroyo Seco into two groups based on flavones, hydroxybenzoic acids, the antioxidant capacity (ABTS and DPPH), and total phenolic compounds, the main contributors to its variation. The results indicated contrasting differences in the polyphenolic composition of collected P. ruderale in Queretaro, suggesting the need to standardize and select plants with favorable agroclimatic conditions to obtain desirable polyphenolic compositions while displaying potential health benefits.

Serum metabolomics of treatment response in myasthenia gravis

OBJECTIVE

High-dose prednisone use, lasting several months or longer, is the primary initial therapy for myasthenia gravis (MG). Upwards of a third of patients do not respond to treatment. Currently no biomarkers can predict clinical responsiveness to corticosteroid treatment. We conducted a discovery-based study to identify treatment responsive biomarkers in MG using sera obtained at study entry to the thymectomy clinical trial (MGTX), an NIH-sponsored randomized, controlled study of thymectomy plus prednisone versus prednisone alone.

METHODS

We applied ultra-performance liquid chromatography coupled with electro-spray quadrupole time of flight mass spectrometry to obtain comparative serum metabolomic and lipidomic profiles at study entry to correlate with treatment response at 6 months. Treatment response was assessed using validated outcome measures of minimal manifestation status (MMS), MG-Activities of Daily Living (MG-ADL), Quantitative MG (QMG) score, or a strictly defined composite measure of response.

RESULTS

Increased serum levels of phospholipids were associated with treatment response as assessed by QMG, MMS, and the Responders classification, but all measures showed limited overlap in metabolomic profiles, in particular the MG-ADL. A panel including histidine, free fatty acid (13:0), γ-cholestenol and guanosine was highly predictive of the strictly defined treatment response measure. The AUC in Responders' prediction for these markers was 0.90 irrespective of gender, age, thymectomy or baseline prednisone use. Pathway analysis suggests that xenobiotic metabolism could play a major role in treatment resistance. There was no association with outcome and gender, age, thymectomy or baseline prednisone use.

INTERPRETATION

We have defined a metabolomic and lipidomic profile that can now undergo validation as a treatment predictive marker for MG patients undergoing corticosteroid therapy. Metabolomic profiles of outcome measures had limited overlap consistent with their assessing distinct aspects of treatment response and supporting unique biological underpinning for each outcome measure. Interindividual variation in prednisone metabolism may be a determinate of how well patients respond to treatment.

Assessing Metabolic Differences Associated with Exposure to Polybrominated Biphenyl and Polychlorinated Biphenyls in the Michigan PBB Registry

BACKGROUND

Polybrominated biphenyls (PBB) and polychlorinated biphenyls (PCB) are persistent organic pollutants with potential endocrine-disrupting effects linked to adverse health outcomes.

OBJECTIVES

In this study, we utilize high-resolution metabolomics (HRM) to identify internal exposure and biological responses underlying PCB and multigenerational PBB exposure for participants enrolled in the Michigan PBB Registry.

METHODS

HRM profiling was conducted on plasma samples collected from 2013 to 2014 from a subset of participants enrolled in the Michigan PBB Registry, including 369 directly exposed individuals (F0) who were alive when PBB mixtures were accidentally introduced into the food chain and 129 participants exposed to PBB in utero or through breastfeeding, if applicable (F1). Metabolome-wide association studies were performed for PBB-153 separately for each generation and Σ PCB (PCB-118, PCB-138, PCB-153, and PCB-180) in the two generations combined, as both had direct PCB exposure. Metabolite and metabolic pathway alterations were evaluated following a well-established untargeted HRM workflow.

RESULTS

Mean levels were 1.75  ng / mL [standard deviation (SD): 13.9] for PBB-153 and 1.04  ng / mL (SD: 0.788) for Σ PCB . Sixty-two and 26 metabolic features were significantly associated with PBB-153 in F0 and F1 [false discovery rate (FDR) p < 0.2 ], respectively. There were 2,861 features associated with Σ PCB (FDR p < 0.2 ). Metabolic pathway enrichment analysis using a bioinformatics tool revealed perturbations associated with Σ PCB in numerous oxidative stress and inflammation pathways (e.g., carnitine shuttle, glycosphingolipid, and vitamin B9 metabolism). Metabolic perturbations associated with PBB-153 in F0 were related to oxidative stress (e.g., pentose phosphate and vitamin C metabolism) and in F1 were related to energy production (e.g., pyrimidine, amino sugars, and lysine metabolism). Using authentic chemical standards, we confirmed the chemical identity of 29 metabolites associated with Σ PCB levels (level 1 evidence).

CONCLUSIONS

Our results demonstrate that serum PBB-153 is associated with alterations in inflammation and oxidative stress-related pathways, which differed when stratified by generation. We also found that Σ PCB was associated with the downregulation of important neurotransmitters, serotonin, and 4-aminobutanoate. These findings provide novel insights for future investigations of molecular mechanisms underlying PBB and PCB exposure on health. https://doi.org/10.1289/EHP12657.

Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging

Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland.

Characterizing the metabolic response of the zebrafish kidney to overfeeding

Obesity is a global epidemic and risk factor for the development of chronic kidney disease. Obesity induces systemic changes in metabolism, but how it affects kidney metabolism specifically is not known. Zebrafish have previously been shown to develop obesity-related kidney pathology and dysfunction when fed hypercaloric diets. To understand the direct effects of obesity on kidney metabolic function, we treated zebrafish for 8 wk with a control and an overfeeding diet. At the end of treatment, we assessed changes in kidney and fish weights and used electron microscopy to evaluate cell ultrastructure. We then performed an untargeted metabolomic analysis on the kidney tissue of fish using ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry and used mummichog and gene set enrichment analysis to uncover differentially affected metabolic pathways. Kidney metabolomes differed significantly and consistently between the control and overfed diets. Among 9,593 features, we identified 235 that were significantly different (P < 0.05) between groups (125 upregulated in overfed diet, 110 downregulated). Pathway analysis demonstrated perturbations in glycolysis and fatty acid synthesis pathways, and analysis of specific metabolites points to perturbations in tryptophan metabolism. Our key findings show that diet-induced obesity leads to metabolic changes in the kidney tissue itself and implicates specific metabolic pathways, including glycolysis and tryptophan metabolism in the pathogenesis of obesity-related kidney disease, demonstrating the power of untargeted metabolomics to identify pathways of interest by directly interrogating kidney tissue.NEW & NOTEWORTHY Obesity causes systemic metabolic dysfunction, but how this affects kidney metabolism is less understood. This study used ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry to analyze the kidneys of overfed zebrafish. Metabolites in the kidneys of obese zebrafish revealed perturbations in metabolic pathways including glycolysis and tryptophan metabolism. These data suggest obesity alters metabolism within the kidney, which may play an important role in obesity-related kidney dysfunction.

Non-Targeted Metabolomic Analysis of Arabidopsis thaliana (L.) Heynh: Metabolic Adaptive Responses to Stress Caused by N Starvation

As sessile organisms, plants develop the ability to respond and survive in changing environments. Such adaptive responses maximize phenotypic and metabolic fitness, allowing plants to adjust their growth and development. In this study, we analyzed the metabolic plasticity of Arabidopsis thaliana in response to nitrate deprivation by untargeted metabolomic analysis and using wild-type (WT) genotypes and the loss-of-function nia1/nia2 double mutant. Secondary metabolites were identified using seedlings grown on a hydroponic system supplemented with optimal or limiting concentrations of N (4 or 0.2 mM, respectively) and harvested at 15 and 30 days of age. Then, spectral libraries generated from shoots and roots in both ionization modes (ESI +/-) were compared. Totals of 3407 and 4521 spectral signals (m/z_rt) were obtained in the ESI+ and ESI- modes, respectively. Of these, approximately 50 and 65% were identified as differentially synthetized/accumulated. This led to the presumptive identification of 735 KEGG codes (metabolites) belonging to 79 metabolic pathways. The metabolic responses in the shoots and roots of WT genotypes at 4 mM of N favor the synthesis/accumulation of metabolites strongly related to growth. In contrast, for the nia1/nia2 double mutant (similar as the WT genotype at 0.2 mM N), metabolites identified as differentially synthetized/accumulated help cope with stress, regulating oxidative stress and preventing programmed cell death, meaning that metabolic responses under N starvation compromise growth to prioritize a defensive response.

Early detection and metabolic pathway identification of T cell activation by in-process intracellular mass spectrometry

BACKGROUND AIMS

In-process monitoring and control of biomanufacturing workflows remains a significant challenge in the development, production, and application of cell therapies. New process analytical technologies must be developed to identify and control the critical process parameters that govern ex vivo cell growth and differentiation to ensure consistent and predictable safety, efficacy, and potency of clinical products.

METHODS

This study demonstrates a new platform for at-line intracellular analysis of T-cells. Untargeted mass spectrometry analyses via the platform are correlated to conventional methods of T-cell assessment.

RESULTS

Spectral markers and metabolic pathways correlated with T-cell activation and differentiation are detected at early time points via rapid, label-free metabolic measurements from a minimal number of cells as enabled by the platform. This is achieved while reducing the analytical time and resources as compared to conventional methods of T-cell assessment.

CONCLUSIONS

In addition to opportunities for fundamental insight into the dynamics of T-cell processes, this work highlights the potential of in-process monitoring and dynamic feedback control strategies via metabolic modulation to drive T-cell activation, proliferation, and differentiation throughout biomanufacturing.