Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling

Insecticide-Induced Metabolic Dysregulation in Model Microbe E. coli Discovered by Comprehensive Metabolic Profiling

Fipronil, malathion, and permethrin are widely used insecticides in agriculture, public areas, and residential spaces. The globally abused application of these chemicals results in residues surpassing established maximum residue levels, giving rise to potential toxicity in unintended organisms. Long-term exposure and the persistent accumulation of these insecticides in animals and humans pose threats such as neurotoxicity, liver and kidney damage, and microbiota dysbiosis. Despite the known risks, the specific impact of these insecticides on gut microbiota and their metabolic processes, as well as the subsequent effects on host health, remain largely unknown. This study aimed to address this gap by utilizing nonpathogenic Escherichia coli as a representative of human gut bacteria and examining its growth and metabolic perturbations induced by exposure to fipronil, malathion, and permethrin. Our research showed that exposure of E. coli to fipronil, malathion, and permethrin at physiologically relevant concentrations resulted in significant growth inhibition. Furthermore, we have observed the biodegradation of fipronil and permethrin by E. coli, while no biodegradation was found for malathion. Thus, E. coli is capable of degrading fipronil and permethrin, thereby enabling the removal of those substances. Next, we studied how insecticides affect bacterial metabolism to understand their influence on the functions of the microbes. Our metabolomics analysis revealed chemical-dependent alterations in metabolic profiles and metabolite compositions following insecticide exposure. These changes encompassed shifts in carboxylic acids and derivatives, organooxygen compounds, as well as indoles and their derivatives. To gain a deeper insight into the systematic changes induced by these insecticides, we conducted a metabolic pathway analysis. Our data indicated that fipronil, compared with malathion and permethrin, exhibited opposite regulation in glycine, serine, and threonine metabolism and valine, leucine, and isoleucine biosynthesis. In summary, our study demonstrates the capability of E. coli to degrade fipronil and permethrin, leading to their removal, while malathion remains unaffected. Additionally, we reveal chemical-dependent alterations in bacterial metabolism induced by insecticide exposure, with specific impacts on metabolic pathways, particularly in pathways related to amino acid metabolism.

GC-MS metabolomics of French lettuce (Lactuca Sativa L. var capitata) leaves exposed to bisphenol A via the hydroponic media

INTRODUCTION

Bisphenol A (BPA), an organic compound used to produce polycarbonate plastics and epoxy resins, has become a ubiquitous contaminant due to its high-volume production and constant release to the environment. Plant metabolomics can trace the stress effects induced by environmental contaminants to the variation of specific metabolites, making it an alternative way to study pollutants toxicity to plants. Nevertheless, there is an important knowledge gap in metabolomics applications in this area.

OBJECTIVE

Evaluate the influence of BPA in French lettuce (Lactuca Sativa L. var capitata) leaves metabolic profile by gas chromatography coupled to mass spectrometry (GC-MS) using a hydroponic system.

METHODS

Lettuces were cultivated in the laboratory to minimize biological variation and were analyzed 55 days after sowing (considered the plant's adult stage). Hexanoic and methanolic extracts with and without derivatization were prepared for each sample and analyzed by GC-MS.

RESULTS

The highest number of metabolites was obtained from the hexanoic extract, followed by the derivatized methanolic extract. Although no physical differences were observed between control and contaminated lettuce leaves, the multivariate analysis determined a statistically significant difference between their metabolic profiles. Pathway analysis of the most affected metabolites showed that galactose metabolism, starch and fructose metabolism and steroid biosynthesis were significantly affected by BPA exposure.

CONCLUSIONS

The preparation of different extracts from the same sample permitted the determination of metabolites with different physicochemical properties. BPA alters the leaves energy and membrane metabolism, plant growth could be affected at higher concentrations and exposition times.

Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

Metabolomic Applications in Gut Microbiota-Host Interactions in Human Diseases

The human gut microbiota, consisting of trillions of microorganisms, encodes diverse metabolic pathways that impact numerous aspects of host physiology. One key way in which gut bacteria interact with the host is through the production of small metabolites. Several of these microbiota-dependent metabolites, such as short-chain fatty acids, have been shown to modulate host diseases. In this review, we examine how disease-associated metabolic signatures are identified using metabolomic platforms, and where metabolomics is applied in gut microbiota-disease interactions. We further explore how integration of metagenomic and metabolomic data in human studies can facilitate biomarkers discoveries in precision medicine.

Integrating histology and phytohormone/metabolite profiling to understand rooting in yellow camellia cuttings

Vegetative propagation through cutting is a widely used clonal approach for maintaining desired genotypes. However, some woody species have difficulty forming adventitious roots (ARs) with this approach, including yellow camellia (YC) C. nitidissima. Yellow camellias, prized for their ornamental value and potential health benefits in tea, remain difficult to propagate clonally due to this rooting recalcitrance. As part of the efforts to understand YC cuttings' recalcitrance, we conducted a detailed investigation into AR formation in yellow camellia cuttings via histology and endogenous phytohormone dynamics during this process. We also compared YC endogenous phytohormone and metabolite phytohormone profiles with those of easy-to-root poplar and willow cuttings. Our results indicate that the induction of ARs in YC cuttings is achievable through auxin treatment, and YC ARs are initiated from cambial derivatives and develop a vascular system connected with that of the stem. During AR induction, endogenous hormones showed a dynamic profile, with IAA continuing to increase starting 9 days after auxin induction. JA, JA-Ile, and OPDA showed a similar trend as IAA but decreased by the 45th day. Cytokinin first decreased to its lowest level by the 18th day and then increased. SA largely exhibited an increasing trend with a drop on the 36th day, while ABA first increased to its peak level by the 18th day and then decreased. Compared to poplar, YC cuttings had a low level of IAA, IAA-Asp, and OPDA, and a high level of cytokinin and SA. Metabolite profiling highlighted significant down-accumulation of compounds associated with AR formation in yellow camellias, such as citric and ascorbic acid, fructose, sucrose, flavonoids, and phenolic acid derivatives. Our study reveals the unfavorable endogenous hormone and metabolite profiles underlying the rooting recalcitrance of YC cuttings, providing valuable knowledge for addressing this challenge in clonal propagation.

Metabolomics Uncovers the Mechanisms of Nitrogen Response to Anthocyanins Synthesis and Grain Quality of Colored Grain Wheat (Triticum aestivum L.)

Nitrogen (N) is a key factor for plant growth and affects anthocyanin synthesis. This study aimed to clarify the potential mechanisms of N levels (LN, 0 kg·ha-1; MN, 150 kg·ha-1; HN, 225 kg·ha-1) in anthocyanin synthesis and grain quality of colored grain wheat. HN increased the yield component traits and grain morphology traits in colored grain wheat while decreasing the processing and nutrient quality traits. Most quality traits were significantly negatively correlated with the yield composition and morphological traits. Anthocyanin was more accumulated under LN conditions, but other related yield and morphological traits of colored grain wheat declined. The anthocyanin content was the highest in blue wheat, followed by that in purple wheat. Cyanidin-3-O-(6-O-malonyl-β-d-glucoside) and cyanidin-3-O-rutinoside were the predominant anthocyanins in blue and purple wheat. The identified anthocyanin-related metabolites were associated with flavonoid biosynthesis, anthocyanin biosynthesis, and secondary metabolite biosynthesis. Therefore, the study provided information for optimizing nitrogen fertilizer management in producing high quality colored wheat and verified the close relationship between anthocyanin and low N condition.

Comparing the Extraction Performance in Mouse Plasma of Different Biphasic Methods for Polar and Nonpolar Compounds

Many metabolomic studies are interested in both polar and nonpolar analyses. However, the available sample volume often precludes multiple separate extractions. Therefore, there are major advantages in performing a biphasic extraction and retaining both phases for subsequent separate analyses. To be successful, such approaches require the method to be robust and repeatable for both phases. Hence, we determined the performance of three extraction protocols, plus two variant versions, using 25 μL of commercially available mouse plasma. The preferred option for nonpolar lipids was a modified diluted version of a method employing methyl tert-butyl ether (MTBE) suggested by Matyash and colleagues due to its high repeatability for nonpolar compounds. For polar compounds, the Bligh-Dyer method performs best for sensitivity but with consequentially poorer lipid performance. Overall, the scaled-down version of the MTBE method gave the best overall performance, with high sensitivity for both polar and nonpolar compounds and good repeatability for polar compounds in particular.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

GC-MS-based untargeted plasma metabolomics identifies a 2-biomarker panel for possible diagnosis of precancerous cervical intraepithelial neoplasia stages from cervical cancer

Cervical cancer (CC) remains a major health concern globally, much of the brunt of which is experienced by the low- and middle-income countries where screening in terms of cytology and DNA genotyping for the high-risk oncogenic subtypes of the human papilloma virus (hr-HPV) is either inadequate or performed rather late. In this study, we aimed to determine biomarkers or panels of biomarkers that are capable of diagnosing the precancerous cervical intraepithelial neoplasia (CIN) stages from healthy and CC patients via untargeted gas chromatography-mass spectrometry-based metabolomics. Various cross-comparisons were conducted from which differential metabolites were identified. The underlying metabolic pathways based on the differential metabolites identified from the various cross-comparisons mainly related to amino acids biosynthesis and metabolism and steroid hormone biosynthesis. From all cross-comparisons, two common metabolites namely, 2-methyl-1-propylamine (also known as isobutylamine) and estrone were found to possess excellent to good diagnostic abilities, especially in distinguishing the early stages of CIN (CIN I, CIN II) from healthy women and CC patients. These findings have clinical significance in the sense that, once validated the 2-biomarker panel could be adopted in clinical practice for early diagnosis of CIN and invasive carcinoma. This would therefore inform the choice of treatment to be initiated by the clinician.

Type 2 Diabetes Mellitus in Low- and Middle-Income Countries: The Significant Impact of Short-Chain Fatty Acids and Their Quantification

Globally, type 2 diabetes mellitus (T2DM) is a major threat to the public's health, particularly in low- and middle-income countries (LMICs). The production of short-chain fatty acids (SCFAs) by the gut microbiota has been reported to have the potential to reduce the prevalence of T2DM, particularly in LMICs where the disease is becoming more common. Dietary fibers are the primary source of SCFAs; they can be categorized as soluble (such as pectin and inulin) or insoluble (such as resistant starches). Increased consumption of processed carbohydrates, in conjunction with insufficient consumption of dietary fiber, has been identified as a significant risk factor for type 2 diabetes (T2DM). However, there are still controversies over the therapeutic advantages of SCFAs on human glucose homeostasis, due to a lack of studies in this area. Hence, a few questions need to be addressed to gain a better understanding of the beneficial link between SCFAs and glucose metabolism. These include the following: What are the biochemistry and biosynthesis of SCFAs? What role do SCFAs play in the pathology of T2DM? What is the most cost-effective strategy that can be employed by LMICs with limited laboratory resources to enhance their understanding of the beneficial function of SCFAs in patients with T2DM? To address the aforementioned questions, this paper aims to review the existing literature on the protective roles that SCFAs have in patients with T2DM. This paper further discusses possible cost-effective and accurate strategies to quantify SCFAs, which may be recommended for implementation by LMICs as preventive measures to lower the risk of T2DM.

Integrated Multi-Omics Analysis of Cerebrospinal Fluid in Postoperative Delirium

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case-no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual and combined omics datasets. Probabilistic graph models were used to identify an integrated multi-omics interaction network, which included clusters of heterogeneous omics interactions among lipids, metabolites, and proteins. The combined multi-omics signature of 25 molecules attained an AUC of 0.96 [95% CI: 0.85-1.00], showing improvement over individual omics-based classification. We conclude that multi-omics integration of preoperative CSF identifies potential risk markers for delirium and generates new insights into the complex pathways associated with delirium. With future validation, this hypotheses-generating study may serve to build robust biomarkers for delirium and improve our understanding of its pathophysiology.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Transcriptomic and metabolic changes in Trichoderma reesei caused by mutation in xylanase regulator 1 (xyr1)

BACKGROUND

Trichoderma reesei is known for its ability to produce large amounts of extracellular proteins and is one of the most important industrially used filamentous fungus. Xylanase regulator 1 (XYR1) is the master regulator responsible for the activation of cellulase and hemicellulase gene expression under inducing conditions. It has been reported that strains with point mutations in certain areas of xyr1 bypass the need for inducing carbon source, allowing high (hemi)cellulase production even in the presence of glucose. These mutations also change the profile of produced proteins, shifting it more towards xylanase production, and increase the overall protein production in inducing conditions. However, how these mutations alter the metabolism and other cellular processes to cause these changes remains unclear.

RESULTS

In this study, we aimed to explore changes caused by a point mutation in xyr1 on transcriptomic and metabolic level to better understand the reasons behind the increased protein production in both repressing glucose and inducing lactose conditions. As expected, the expression of many carbohydrate-active enzyme (CAZy) genes was increased in the xyr1 mutant in both conditions. However, their induction was higher under inducing conditions. The xyr1 mutant strain built more biomass and produced more extracellular proteins during growth on lactose compared to the wild type xyr1 strain. Genes involved in oxidoreductive D-galactose catabolism pathway were upregulated in the xyr1 mutant strain, potentially contributing to the more efficient utilization of lactose. In addition to CAZy genes, clustering and enrichment analysis showed over-representation of mitochondria-related Gene Ontology terms in clusters where gene expression was higher in the xyr1 mutant, indicating that mitochondria play a role in the altered metabolic state associated with the xyr1 mutation. Metabolomics revealed that free tyrosine was more abundant in the xyr1 mutant strain in all measured timepoints, whereas multiple fatty acids were less abundant in the mutant strain on glucose.

CONCLUSIONS

The results contribute to more in-depth knowledge on T. reesei physiology growing under inducing and repressing carbon sources and gives new insights on the function of the master regulator XYR1. The vast data generated serve as a source for new targets for improved protein production.

Altered neuronal lactate dehydrogenase A expression affects cognition in a sex- and age-dependent manner

The astrocyte-neuron lactate shuttle (ANLS) model posits that astrocyte-generated lactate is transported to neurons to fuel memory processes. However, neurons express high levels of lactate dehydrogenase A (LDHA), the rate-limiting enzyme of lactate production, suggesting a cognitive role for neuronally generated lactate. It was hypothesized that lactate metabolism in neurons is critical for learning and memory. Here transgenic mice were generated to conditionally induce or knockout (KO) the Ldha gene in CNS neurons of adult mice. High pattern separation memory was enhanced by neuronal Ldha induction in young females, and by neuronal Ldha KO in aged females. In older mice, Ldha induction caused cognitive deficits whereas Ldha KO caused cognitive improvements. Genotype-associated cognitive changes were often only observed in one sex or oppositely in males and females. Thus, neuronal-generated lactate has sex-specific cognitive effects, is largely indispensable at young age, and may be detrimental to learning and memory with aging.

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I-IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

Metabolic Profiling Changes Induced by Fermented Blackberries in High-Fat-Diet-Fed Mice Utilizing Gas Chromatography-Mass Spectrometry Analysis

The aim of this study was to investigate the metabolic changes associated with the anti-obesity effects of fermented blackberry extracts in the liver tissues of high-fat-diet-fed mice using mass spectrometry-based metabolomics analysis. C57BL/6J mice were divided into eight groups: normal-diet-fed mice, high-fat-diet-fed mice, high-fat diet treated with blackberry extract, high-fat-diet mice treated with blackberry fermented by L. plantarum, and high-fat diet with blackberry fermented by L. brevis. After 12 weeks, the high-fat-diet group exhibited a greater increase in liver weight compared to the control group, and among the groups, the group administered with blackberry fermented with L. plantarum showed the most pronounced reduction in liver weight. As the primary organ responsible for amino acid metabolism, the liver is crucial for maintaining amino acid homeostasis. In our study, we observed that the levels of several essential amino acids, including isoleucine and valine, were decreased by the high-fat diet, and were recovered by administration of blackberry extract fermented with L. plantarum. Our results demonstrated the potential of blackberry extract fermented with L. plantarum as a functional material for metabolic disorders by restoring some of the amino acid metabolism disturbances induced by a high-fat diet.

Sensitive Detection and Quantification of Oxygenated Compounds in Complex Samples Using GC-Combustion-MS

This work introduces a new element-selective gas chromatography detector for the accurate quantification of traces of volatile oxygen-containing compounds in complex samples without the need for specific standards. The key to this approach is the use of oxygen highly enriched in 18O as the oxidizing gas in a combustion unit (800 °C) that allows us to directly and unambiguously detect the natural oxygen present in the GC-separated compounds through its incorporation into the volatile species formed after their combustion and their subsequent degradation to 16O in the ion source. The unspecific signal due to the low 16O abundance in the oxidizing gas could be compensated by measuring the m/z 12 that comes as well from the CO2 degradation. Equimolarity was proved with several O-containing compounds with different sizes and functionalities. A detection limit of 28 pg of injected O was achieved, which is the lowest ever reported for any GC detector, which barely worsened to 55 and 214 pg of O when the oxygenate partially or completely coeluted with a very abundant matrix compound. Validation was attained by the analysis of a SRM to obtain accurate (99-103%) and precise (1-4% RSD) results. Robustness was tested after spiking a hydrotreated diesel with 10 O-compounds at the ppm level, which could be discriminated from the matrix crowd and quantified (mean recovery of 102 ± 9%) with a single generic standard. Finally, it was also successfully applied to easily spot and quantify the 33 oxygenates naturally present in a complex wood bio-oil sample.

High-throughput identification of gut microbiome-dependent metabolites

A significant hurdle that has limited progress in microbiome science has been identifying and studying the diverse set of metabolites produced by gut microbes. Gut microbial metabolism produces thousands of difficult-to-identify metabolites, which present a challenge to study their roles in host biology. In recent years, mass spectrometry-based metabolomics has become one of the core technologies for identifying small metabolites. However, metabolomics expertise, ranging from sample preparation to instrument use and data analysis, is often lacking in academic labs. Most targeted metabolomics methods provide high levels of sensitivity and quantification, while they are limited to a panel of predefined molecules that may not be informative to microbiome-focused studies. Here we have developed a gut microbe-focused and wide-spectrum metabolomic protocol using liquid chromatography-mass spectrometry and bioinformatic analysis. This protocol enables users to carry out experiments from sample collection to data analysis, only requiring access to a liquid chromatography-mass spectrometry instrument, which is often available at local core facilities. By applying this protocol to samples containing human gut microbial metabolites, spanning from culture supernatant to human biospecimens, our approach enables high-confidence identification of >800 metabolites that can serve as candidate mediators of microbe-host interactions. We expect this protocol will lower the barrier to tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling hypothesis-driven mechanistic studies and accelerating our understanding of the gut microbiome at the chemical level.

Integrated untargeted and targeted testicular metabolomics to reveal the regulated mechanism of Gushudan on the hypothalamic-pituitary-gonadal axis of kidney-yang-deficiency-syndrome rats

Modern studies have shown that neuroendocrine disorders caused by the dysfunction of the hypothalamic-pituitary-gonadal (HPG) axis are one of the important pathogenetic mechanisms of kidney-yang-deficiency-syndrome (KYDS). The preventive effect of Gushudan on KYDS has been reported, but its regulatory mechanisms on the HPG axis have not been elucidated. In this study, we developed an integrated untargeted and targeted metabolomics analysis strategy to investigate the regulatory mechanism of Gushudan on the HPG axis in rats with KYDS. In untargeted metabolomics, we screened 14 potential biomarkers such as glycine, lysine, and glycerol that were significantly associated with the HPG axis. To explore the effect of changes in the levels of potential biomarkers on KYDS, all of them were quantified in targeted metabolomics. With the quantitative results, correlations between potential biomarkers and testosterone, a functional indicator of the HPG axis, were explored. The results showed that oxidative stress, inflammatory response, and energy depletion, induced by metabolic disorders in rats, were responsible for the decrease in testosterone levels. Gushudan improves metabolic disorders and restores testosterone levels, thus restoring HPG axis dysfunction. This finding elucidates the special metabolic characteristics of KYDS and the therapeutic mechanism of Gushudan from a new perspective.

Ectomycorrhizal fungi alter soil food webs and the functional potential of bacterial communities

Most of Earth's trees rely on critical soil nutrients that ectomycorrhizal fungi (EcMF) liberate and provide, and all of Earth's land plants associate with bacteria that help them survive in nature. Yet, our understanding of how the presence of EcMF modifies soil bacterial communities, soil food webs, and root chemistry requires direct experimental evidence to comprehend the effects that EcMF may generate in the belowground plant microbiome. To this end, we grew Pinus muricata plants in soils that were either inoculated with EcMF and native forest bacterial communities or only native bacterial communities. We then profiled the soil bacterial communities, applied metabolomics and lipidomics, and linked omics data sets to understand how the presence of EcMF modifies belowground biogeochemistry, bacterial community structure, and their functional potential. We found that the presence of EcMF (i) enriches soil bacteria linked to enhanced plant growth in nature, (ii) alters the quantity and composition of lipid and non-lipid soil metabolites, and (iii) modifies plant root chemistry toward pathogen suppression, enzymatic conservation, and reactive oxygen species scavenging. Using this multi-omic approach, we therefore show that this widespread fungal symbiosis may be a common factor for structuring soil food webs.IMPORTANCEUnderstanding how soil microbes interact with one another and their host plant will help us combat the negative effects that climate change has on terrestrial ecosystems. Unfortunately, we lack a clear understanding of how the presence of ectomycorrhizal fungi (EcMF)-one of the most dominant soil microbial groups on Earth-shapes belowground organic resources and the composition of bacterial communities. To address this knowledge gap, we profiled lipid and non-lipid metabolites in soils and plant roots, characterized soil bacterial communities, and compared soils amended either with or without EcMF. Our results show that the presence of EcMF changes soil organic resource availability, impacts the proliferation of different bacterial communities (in terms of both type and potential function), and primes plant root chemistry for pathogen suppression and energy conservation. Our findings therefore provide much-needed insight into how two of the most dominant soil microbial groups interact with one another and with their host plant.

Sputum microbe community alterations induced by long-term inhaled corticosteroid use are associated with airway function in chronic obstructive pulmonary disease patients based on metagenomic next-generation sequencing (mNGS)

Objective: Inhaled corticosteroids (ICS) are widely used in chronic obstructive pulmonary disease (COPD) patients as a treatment option. However, ICS may also increase the risk of pneumonia and alter the composition of airway microbiota. In clinical application, the overuse of ICS exists pervasively and may potentially lead to adverse effects. Whether the long-term use of ICS confers enough benefit to COPD patients to justify its use so far remains unknown. Therefore, this study employed a single-center retrospective cohort study to compare alterations in airway function and the sputum microbial community structure between COPD patients who had undergone either long-term or short-term treatment with ICS. Methods: Sixty stable COPD patients who had used ICS were recruited and classified into the long-term use group (more than 3 months) and short-term use group (less than 3 months). The demographic features and clinical information of the subjects were investigated and their sputum samples were collected and subjected to metagenomic next-generation sequencing (mNGS). Results: The study found that compared with short-term ICS use, long-term ICS use did not further improve the clinical airway function, decrease the number of acute exacerbations, or decrease hospital readmission. In terms of sputum microbiota, the long-term use of ICS significantly altered the beta diversity of the microbial community structure (p < 0.05) and the top three phyla differed between the two groups. At the genus level, long-term ICS induced higher relative abundances of Abiotrophia, Schaalia, Granulicatella, Mogibacterium, Sphingobium, and Paraeggerthella compared to short-term ICS use. Additionally, alpha diversity was positively associated with clinical airway indicators (pre-bronchodilatory FEV1 and pre-bronchodilatory FVC) in the long-term ICS group. The relative abundances of Rothia, Granulicatella, Schaalia, and Mogibacterium genera had positive correlations with the eosinophil % (of all white blood cells). Conclusion: This study reveals the effect of long-term and short-term ICS use on sputum microbiota among COPD patients and provides a reference for the appropriate application of clinical ICS treatment in COPD patients.

The Microbiome and Metabolome of the Gut of Children with Sepsis and Septic Shock

BACKGROUND

There is limited understanding of alteration of gut microbiota and metabolome in children with sepsis/septic shock.

METHODS

In this prospective observational study carried out in a pediatric intensive care unit of a tertiary care center from 2020 to 2022, patients aged <17 years with sepsis/septic shock and healthy children (HC) were enrolled. We characterized the gut bacterial compositions by metagenome sequencing and metabolomes by untargeted gas chromatography-mass spectrometry. The primary outcome was to compare the gut microbiota and metabolome of children with sepsis/septic shock with that of HC. The Firmicutes/Bacteroidetes (F/B) ratio was compared between children with sepsis/septic shock and HC. Key secondary outcomes were to evaluate association of factors associated with a low F/B ratio in children with sepsis/septic shock.

RESULTS

A total of 40 children (63% boys) (15 children with sepsis and septic shock and 10 healthy children) with a median (IQR) age of 5.5 (1.5, 10) years were enrolled. In the fecal microbiota, the α-diversity index including Shannon and Simpson indices of the sepsis/septic shock groups was significantly lower than that of the HC. The samples lacked beneficial Bifidobacterium spp. and were dominated by Bacteroides, Enterobacteriaceae, and Enterococcaceae. There was reduction in short-chain fatty acids (SCFAs) in patients with sepsis/septic shock as compared to healthy children. A lower F/B ratio (≤1.57) of the gut microbiota discriminated well between children with sepsis/septic shock and HC. Factors associated with lower F/B ratio were male gender, clinical GI dysfunction, elevated inflammatory markers, and higher organ failure scores.

CONCLUSION

There were significant alterations in the gut microbiota and metabolome in children with sepsis/septic shock as compared to healthy children. Larger study is needed to confirm these exploratory findings and develop potential therapeutic targets that will improve outcomes in children with sepsis/septic shock.

Current biomarkers and treatment strategies in Alzheimer disease: An overview and future perspectives

Alzheimer's disease (AD), a progressive degenerative disorder first identified by Alois Alzheimer in 1907, poses a significant public health challenge. Despite its prevalence and impact, there is currently no definitive ante mortem diagnosis for AD pathogenesis. By 2050, the United States may face a staggering 13.8 million AD patients. This review provides a concise summary of current AD biomarkers, available treatments, and potential future therapeutic approaches. The review begins by outlining existing drug targets and mechanisms in AD, along with a discussion of current treatment options. We explore various approaches targeting Amyloid β (Aβ), Tau Protein aggregation, Tau Kinases, Glycogen Synthase kinase-3β, CDK-5 inhibitors, Heat Shock Proteins (HSP), oxidative stress, inflammation, metals, Apolipoprotein E (ApoE) modulators, and Notch signaling. Additionally, we examine the historical use of Estradiol (E2) as an AD therapy, as well as the outcomes of Randomized Controlled Trials (RCTs) that evaluated antioxidants (e.g., vitamin E) and omega-3 polyunsaturated fatty acids as alternative treatment options. Notably, positive effects of docosahexaenoic acid nutriment in older adults with cognitive impairment or AD are highlighted. Furthermore, this review offers insights into ongoing clinical trials and potential therapies, shedding light on the dynamic research landscape in AD treatment.

Allele-specific dysregulation of lipid and energy metabolism in early-stage hypertrophic cardiomyopathy

Introduction

Hypertrophic cardiomyopathy (HCM) results from pathogenic variants in sarcomeric protein genes that increase myocyte energy demand and lead to cardiac hypertrophy. However, it is unknown whether a common metabolic trait underlies cardiac phenotype at the early disease stage. To address this question and define cardiac biochemical pathology in early-stage HCM, we studied two HCM mouse models that express pathogenic variants in cardiac troponin T (Tnt2) or myosin heavy chain (Myh6) genes, and have marked differences in cardiac imaging phenotype, mitochondrial function at early disease stage.

Methods

We used a combination of echocardiography, transcriptomics, mass spectrometry-based untargeted metabolomics (GC-TOF, HILIC, CSH-QTOF), and computational modeling (CardioNet) to examine cardiac structural and metabolic remodeling at early disease stage (5 weeks of age) in R92W-TnT+/- and R403Q-MyHC+/- mutant mice. Data from mutants was compared with respective littermate controls (WT).

Results

Allele-specific differences in cardiac phenotype, gene expression and metabolites were observed at early disease stage. LV diastolic dysfunction was prominent in TnT mutants. Differentially-expressed genes in TnT mutant hearts were predominantly enriched in the Krebs cycle, respiratory electron transport, and branched-chain amino acid metabolism, whereas MyHC mutants were enriched in mitochondrial biogenesis, calcium homeostasis, and liver-X-receptor signaling. Both mutant hearts demonstrated significant alterations in levels of purine nucleosides, trisaccharides, dicarboxylic acids, acylcarnitines, phosphatidylethanolamines, phosphatidylinositols, ceramides and triglycerides; 40.4 % of lipids and 24.7 % of metabolites were significantly different in TnT mutants, whereas 10.4 % of lipids and 5.8 % of metabolites were significantly different in MyHC mutants. Both mutant hearts had a lower abundance of unsaturated long-chain acyl-carnitines (18:1, 18:2, 20:1), but only TnT mutants showed enrichment of FA18:0 in ceramide and cardiolipin species. CardioNet predicted impaired energy substrate metabolism and greater phospholipid remodeling in TnT mutants than in MyHC mutants.

Conclusions

Our systems biology approach revealed marked differences in metabolic remodeling in R92W-TnT and R403Q-MyHC mutant hearts, with TnT mutants showing greater derangements than MyHC mutants, at early disease stage. Changes in cardiolipin composition in TnT mutants could contribute to impairment of energy metabolism and diastolic dysfunction observed in this study, and predispose to energetic stress, ventricular arrhythmias under high workloads such as exercise.

HSDL2 links nutritional cues to bile acid and cholesterol homeostasis

In response to energy and nutrient shortage, the liver triggers several catabolic processes to promote survival. Despite recent progress, the precise molecular mechanisms regulating the hepatic adaptation to fasting remain incompletely characterized. Here, we report the identification of hydroxysteroid dehydrogenase-like 2 (HSDL2) as a mitochondrial protein highly induced by fasting. We show that the activation of PGC1α-PPARα and the inhibition of the PI3K-mTORC1 axis stimulate HSDL2 expression in hepatocytes. We found that HSDL2 depletion decreases cholesterol conversion to bile acids (BAs) and impairs FXR activity. HSDL2 knockdown also reduces mitochondrial respiration, fatty acid oxidation, and TCA cycle activity. Bioinformatics analyses revealed that hepatic Hsdl2 expression positively associates with the postprandial excursion of various BA species in mice. We show that liver-specific HSDL2 depletion affects BA metabolism and decreases circulating cholesterol levels upon refeeding. Overall, our report identifies HSDL2 as a fasting-induced mitochondrial protein that links nutritional signals to BAs and cholesterol homeostasis.

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

Therapeutic implications of dapagliflozin on the metabolomics profile of diabetic rats: A GC-MS investigation coupled with multivariate analysis

BACKGROUND

Diabetes mellitus is a complex metabolic disorder with systemic implications, necessitating the search for reliable biomarkers and therapeutic strategies. This study investigates the metabolomics profile alterations in diabetic rats, with a focus on the therapeutic effects of Dapagliflozin, a drug known to inhibit renal glucose reabsorption, using Gas Chromatography-Mass Spectrometry analysis.

METHODS

A GC-MS based metabolomics approach combined with multivariate and univariate statistical analyses was utilized to study serum samples from a diabetic model of Wistar rats, treated with dapagliflozin. Metabolomics pathways analysis was also performed to identify the altered metabolic pathways associated with the disease and the intervention.

RESULTS

Dapagliflozin treatment in diabetic rats resulted in normalized levels of metabolites associated with insulin resistance, notably branched-chain and aromatic amino acids. Improvements in glycine metabolism were observed, suggesting a modulatory role of the drug. Additionally, reduced palmitic acid levels indicated an alleviation of lipotoxic effects. The metabolic changes indicate a restorative effect of dapagliflozin on diabetes-induced metabolic perturbations.

CONCLUSIONS

The comprehensive metabolomics analysis demonstrated the potential of GC-MS in revealing significant metabolic pathway alterations due to dapagliflozin treatment in diabetic model rats. The therapy induced normalization of key metabolic disturbances, providing insights that could advance personalized diabetes mellitus management and therapeutic monitoring, highlighting the utility of metabolomics in understanding drug mechanisms and effects.

Diagnostic performance of volatile organic compounds analysis and electronic noses for detecting colorectal cancer: a systematic review and meta-analysis

Introduction

The detection of Volatile Organic Compounds (VOCs) could provide a potential diagnostic modality for the early detection and surveillance of colorectal cancers. However, the overall diagnostic accuracy of the proposed tests remains uncertain.

Objective

This systematic review is to ascertain the diagnostic accuracy of using VOC analysis techniques and electronic noses (e-noses) as noninvasive diagnostic methods for colorectal cancer within the realm of clinical practice.

Methods

A systematic search was undertaken on PubMed, EMBASE, Web of Science, and the Cochrane Library to scrutinize pertinent studies published from their inception to September 1, 2023. Only studies conducted on human subjects were included. Meta-analysis was performed using a bivariate model to obtain summary estimates of sensitivity, specificity, and positive and negative likelihood ratios. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was deployed for quality assessment. The protocol for this systematic review was registered in PROSPERO, and PRISMA guidelines were used for the identification, screening, eligibility, and selection process.

Results

This review encompassed 32 studies, 22 studies for VOC analysis and 9 studies for e-nose, one for both, with a total of 4688 subjects in the analysis. The pooled sensitivity and specificity of VOC analysis for CRC detection were 0.88 (95% CI, 0.83-0.92) and 0.85 (95% CI, 0.78-0.90), respectively. In the case of e-nose, the pooled sensitivity was 0.87 (95% CI, 0.83-0.90), and the pooled specificity was 0.78 (95% CI, 0.62-0.88). The area under the receiver operating characteristic analysis (ROC) curve for VOC analysis and e-noses were 0.93 (95% CI, 0.90-0.95) and 0.90 (95% CI, 0.87-0.92), respectively.

Conclusion

The outcomes of this review substantiate the commendable accuracy of VOC analysis and e-nose technology in detecting CRC. VOC analysis has a higher specificity than e-nose for the diagnosis of CRC and a sensitivity comparable to that of e-nose. However, numerous limitations, including a modest sample size, absence of standardized collection methods, lack of external validation, and a notable risk of bias, were identified. Consequently, there exists an imperative need for expansive, multi-center clinical studies to elucidate the applicability and reproducibility of VOC analysis or e-nose in the noninvasive diagnosis of colorectal cancer.

Systematic review registration

https://www.crd.york.ac.uk/prospero/#recordDetails, identifier CRD42023398465.

Effect of phenolics on soil microbe distribution, plant growth, and gall formation

Phenolic compounds, abundant secondary metabolites in plants, profoundly influence soil ecosystems, plant growth, and interactions with herbivores. In this study, we explore the intricate relationships between phenolics, soil microbes, and gall formation in Ageratina adenophora (A. adenophora), an invasive plant species in China known for its allelopathic traits. Using metabolomic and microbial profiling, significant differences in soil microbial composition and metabolite profiles were observed between bulk and rhizosphere soil samples. Phenolics influenced bacterial communities, with distinct microbial populations enriched in each soil type. Additionally, phenolics impacted soil metabolic processes, with variations observed in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis between different soil treatments. Analysis of phenolic content in plant and soil samples revealed considerable variations, with higher concentrations observed in certain plant tissues and soil types. Bioactive phenols extracted from plant and soil samples were identified using gas chromatography/mass spectrometry (GC-MS), providing insights into the diverse chemical composition of these compounds. Furthermore, the effects of phenolics on plant growth and gall formation were investigated. Phenols exhibited both stimulatory and inhibitory effects on plant growth, with optimal concentrations promoting emergence but higher concentrations hindering growth. Gall formation was influenced by phenolic concentrations, leading to structural alterations in stem tissue and gall morphology. Histochemical analysis revealed starch and lipid accumulation in gall tissues, indicating metabolic changes induced by phenolics. The presence of phenolics disrupted tissue structures and influenced vascular bundle orientation in gall tissues. Overall, our study highlights the multifaceted roles of phenolic compounds in soil ecosystems, plant development, and gall formation, facilitating the utilization of secondary metabolites in agriculture.

Edaravone counteracts redox and metabolic disruptions in an emerging zebrafish model of sporadic ALS

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease in which the death of motor neurons leads to loss of muscle function. Additionally, cognitive and circadian disruptions are common in ALS patients, contributing to disease progression and burden. Most ALS cases are sporadic, and environmental exposures contribute to their aetiology. However, animal models of these sporadic ALS cases are scarce. The small vertebrate zebrafish is a leading organism to model neurodegenerative diseases; previous studies have proposed bisphenol A (BPA) or β-methylamino-l-alanine (BMAA) exposure to model sporadic ALS in zebrafish, damaging motor neurons and altering motor responses. Here we characterise the face and predictive validity of sporadic ALS models, showing their potential for the mechanistic study of ALS drugs. We phenotypically characterise the BPA and BMAA-induced models, going beyond motor activity and motor axon morphology, to include circadian, redox, proteostasis, and metabolomic phenotypes, and assessing their predictive validity for ALS modelling. BPA or BMAA exposure induced concentration-dependent activity impairments. Also, exposure to BPA but not BMAA induced motor axonopathy and circadian alterations in zebrafish larvae. Our further study of the BPA model revealed loss of habituation to repetitive startles, increased oxidative damage, endoplasmic reticulum (ER) stress, and metabolome abnormalities. The BPA-induced model shows predictive validity, since the approved ALS drug edaravone counteracted BPA-induced motor phenotypes, ER stress, and metabolic disruptions. Overall, BPA exposure is a promising model of ALS-related redox and ER imbalances, contributing to fulfil an unmet need for validated sporadic ALS models.

Taenia solium cysticerci's extracellular vesicles Attenuate the AKT/mTORC1 pathway for Alleviating DSS-induced colitis in a murine model

The excretory-secretory proteome plays a pivotal role in both intercellular communication during disease progression and immune escape mechanisms of various pathogens including cestode parasites like Taenia solium. The cysticerci of T. solium causes infection in the central nervous system known as neurocysticercosis (NCC), which affects a significant population in developing countries. Extracellular vesicles (EVs) are 30-150-nm-sized particles and constitute a significant part of the secretome. However, the role of EV in NCC pathogenesis remains undetermined. Here, for the first time, we report that EV from T. solium larvae is abundant in metabolites that can negatively regulate PI3K/AKT pathway, efficiently internalized by macrophages to induce AKT and mTOR degradation through auto-lysosomal route with a prominent increase in the ubiquitination of both proteins. This results in less ROS production and diminished bacterial killing capability among EV-treated macrophages. Due to this, both macro-autophagy and caspase-linked apoptosis are upregulated, with a reduction of the autophagy substrate sequestome 1. In summary, we report that T. solium EV from viable cysts attenuates the AKT-mTOR pathway thereby promoting apoptosis in macrophages, and this may exert immunosuppression during an early viable stage of the parasite in NCC, which is primarily asymptomatic. Further investigation on EV-mediated immune suppression revealed that the EV can protect the mice from DSS-induced colitis and improve colon architecture. These findings shed light on the previously unknown role of T. solium EV and the therapeutic role of their immune suppression potential.

The Effect of Broccoli Glucoraphanin Supplementation on Ameliorating High-Fat-Diet-Induced Obesity through the Gut Microbiome and Metabolome Interface

SCOPE

Obesity and its metabolic comorbidities pose a major global challenge for public health. Glucoraphanin (GRN) is a natural bioactive compound enriched in broccoli that is known to have potential health benefits against various human chronic diseases.

METHODS AND RESULTS

This study investigats the effects of broccoli GRN supplementation on body weight, metabolic parameters, gut microbiome and metabolome associated with obesity. The study is conducted on an obese-related C57BL/6J mouse model through the treatment of normal control diet, high-fat diet (HFD)and GRN-supplemented HFD (HFD-GRN) to determine the metabolic protection of GRN. The results shows that GRN treatment alleviates obesity-related traits leading to improved glucose metabolism in HFD-fed animals. Mechanically, the study noticed that GRN significantly shifts the gut microbial diversity and composition to an eubiosis status. GRN supplement also significantly alters plasma metabolite profiles. Further integrated analysis reveal a complex interaction between the gut microbes and host metabolism that may contribute to GRN-induced beneficial effects against HFD.

CONCLUSION

These results indicate that beneficial effects of broccoli GRN on reversing HFD-induced adverse metabolic parameters may be attributed to its impacts on reprogramming microbial community and metabolites. Identification of the mechanistic functions of GRN further warrants it as a dietary candidate for obesity prevention.

Pharmacokinetics behavior of four cannabidiol preparations following single oral administration in dogs

Cannabidiol (CBD) is a natural phytochemical agent and one of the most abundant found in Cannabis sativa. It is known to exhibit pharmacological properties on various condition such as relieving-inflammation, pain, epilepsy, and anxiety effect. There has been an increasing trend globally in the use of CBD as a supplement in pets. Consequently, there are various CBD products being marketed that are specifically available for pets. Veterinarians and pet owners are concerned that following ingestion, different CBD formulations may result in a CBD level circulating in the blood that may affect the safe use and efficacy of CBD in pets. Several pharmacokinetics studies in animals have been mainly conducted with an oily form of CBD. To date, there is a lack of data regarding direct comparisons in animals among the CBD plasma kinetic profiles from an oral administration of the various preparation forms. Therefore, the current study evaluated and compared the plasma CBD levels from a single oral administration using four different CBD preparations-liquid (an oil-based form, a nanoemulsion form, or a water-soluble form) or a semi-solid form (as CBD mixed in a treat) in dogs. In total, 32 healthy, crossbreed dogs were randomly assigned into 4 groups and treated according to a 1-period, 4-treatment parallel-design. The three liquid forms were dosed at 5 mg/kg body weight, while the single semi-solid form was given at 50 mg/treat/dog. The results showed that the CBD plasma profile from the administration of a water-soluble form was comparable to that of the oil-based group. The nanoemulsion-based form tended to be rapidly absorbed and reached its peak sooner than the others. However, the CBD in all preparations reached the maximum plasma concentration within 3 h post-dose, with an average range of 92-314 μg/L. There were significant differences among certain parameters between the liquid and semi-solid forms. This was the first study to provide pharmacokinetics data regarding CBD in water soluble, nanoemulsion-based, and semi-solid forms for dogs as companion animals. The current data should facilitate the scrutiny of CBD plasma profiles based on different formulations via an oral route in dogs.

Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry as a Tool for the Untargeted Study of Hop and Their Metabolites

Since hop secondary metabolites have a direct correlation with the quality of beer and other hop-based beverages, and the volatile fraction of hop has a complex composition, requiring effective separation, here we explore the application of headspace solid-phase microextraction as a sample preparation method, coupled with comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) analysis. The methodology involved the use of a DVB/PDMS fibre with 500 mg of hop cone powder, extracted for 40 min at 50 °C, for both GC-MS and GC×GC-MS. The varieties Azacca, Cascade, Enigma, Loral, and Zappa were studied comprehensively. The results demonstrate that GC×GC-MS increases the number of peaks by over 300% compared to classical GC-MS. Overall, 137 compounds were identified or tentatively identified and categorised into 10 classes, representing between 87.6% and 96.9% of the total peak area. The composition revealed the highest concentration of sesquiterpene hydrocarbons for Enigma, whilst Zappa showed a relatively significant concentration of monoterpene hydrocarbons. Principal component analysis for all compounds and classes, along with hierarchical cluster analysis, indicated similarities between Zappa and Cascade, and Azacca and Loral. In conclusion, this method presents an optimistic advancement in hop metabolite studies with a simple and established sample preparation procedure in combination with an effective separation technique.

Multiomics Picture of Obesity in Young Adults

Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.

Critical Review of Selected Analytical Platforms for GC-MS Metabolomics Profiling-Case Study: HS-SPME/GC-MS Analysis of Blackberry's Aroma

Data processing and data extraction are the first, and most often crucial, steps in metabolomics and multivariate data analysis in general. There are several software solutions for these purposes in GC-MS metabolomics. It becomes unclear which platform offers what kind of data and how that information influences the analysis's conclusions. In this study, selected analytical platforms for GC-MS metabolomics profiling, SpectConnect and XCMS as well as MestReNova software, were used to process the results of the HS-SPME/GC-MS aroma analyses of several blackberry varieties. In addition, a detailed analysis of the identification of the individual components of the blackberry aroma club varieties was performed. In total, 72 components were detected in the XCMS platform, 119 in SpectConnect, and 87 and 167 in MestReNova, with automatic integral and manual correction, respectively, as well as 219 aroma components after manual analysis of GC-MS chromatograms. The obtained datasets were fed, for multivariate data analysis, to SIMCA software, and underwent the creation of PCA, OPLS, and OPLS-DA models. The results of the validation tests and VIP-pred. scores were analyzed in detail.

Comparative characterization of the infant gut microbiome and their maternal lineage by a multi-omics approach

The human gut microbiome establishes and matures during infancy, and dysregulation at this stage may lead to pathologies later in life. We conducted a multi-omics study comprising three generations of family members to investigate the early development of the gut microbiota. Fecal samples from 200 individuals, including infants (0-12 months old; 55% females, 45% males) and their respective mothers and grandmothers, were analyzed using two independent metabolomics platforms and metagenomics. For metabolomics, gas chromatography and capillary electrophoresis coupled to mass spectrometry were applied. For metagenomics, both 16S rRNA gene and shotgun sequencing were performed. Here we show that infants greatly vary from their elders in fecal microbiota populations, function, and metabolome. Infants have a less diverse microbiota than adults and present differences in several metabolite classes, such as short- and branched-chain fatty acids, which are associated with shifts in bacterial populations. These findings provide innovative biochemical insights into the shaping of the gut microbiome within the same generational line that could be beneficial in improving childhood health outcomes.

Human Cerebrospinal Fluid Sample Preparation and Annotation for Integrated Lipidomics and Metabolomics Profiling Studies

Cerebrospinal fluid (CSF) is a metabolically diverse biofluid and a key specimen for exploring biochemical changes in neurodegenerative diseases. Detecting lipid species in CSF using mass spectrometry (MS)-based techniques remains challenging because lipids are highly complex in structure, and their concentrations span over a broad dynamic range. This work aimed to develop a robust lipidomics and metabolomics method based on commonly used two-phase extraction systems from human CSF samples. Prioritizing lipid detection, biphasic extraction methods, Folch, Bligh and Dyer (B&D), Matyash, and acidified Folch and B&D (aFolch and aB&D) were compared using 150 μL of human CSF samples for the simultaneous extraction of lipids and metabolites with a wide range of polarity. Multiple chromatographical separation approaches, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), and gas chromatography (GC), were utilized to characterize human CSF metabolome. The aB&D method was found as the most reproducible technique (RSD < 15%) for lipid extraction. The aB&D and B&D yielded the highest peak intensities for targeted lipid internal standards and displayed superior extracting power for major endogenous lipid classes. A total of 674 unique metabolites with a wide polarity range were annotated in CSF using, combining RPLC-MS/MS lipidomics (n = 219), HILIC-MS/MS (n = 304), and GC-quadrupole time of flight (QTOF) MS (n = 151). Overall, our findings show that the aB&D extraction method provided suitable lipid coverage, reproducibility, and extraction efficiency for global lipidomics profiling of human CSF samples. In combination with RPLC-MS/MS lipidomics, complementary screening approaches enabled a comprehensive metabolite signature that can be employed in an array of clinical studies.

Metabolomics for the diagnosis of bladder cancer: A systematic review

Objective

Metabolomics has been extensively utilized in bladder cancer (BCa) research, employing mass spectrometry and nuclear magnetic resonance spectroscopy to compare various variables (tissues, serum, blood, and urine). This study aimed to identify potential biomarkers for early BCa diagnosis.

Methods

A search strategy was designed to identify clinical trials, descriptive and analytical observational studies from databases such as Medline, Embase, Cochrane Central Register of Controlled Trials, and Latin American and Caribbean Literature in Health Sciences. Inclusion criteria comprised studies involving BCa tissue, serum, blood, or urine profiling using widely adopted metabolomics techniques like mass spectrometry and nuclear magnetic resonance. Primary outcomes included description of metabolites and metabolomics profiling in BCa patients and the association of metabolites and metabolomics profiling with BCa diagnosis compared to control patients. The risk of bias was assessed using the Quality Assessment of Studies of Diagnostic Accuracy.

Results

The search strategy yielded 2832 studies, of which 30 case-control studies were included. Urine was predominantly used as the primary sample for metabolite identification. Risk of bias was often unclear inpatient selection, blinding of the index test, and reference standard assessment, but no applicability concerns were observed. Metabolites and metabolomics profiles associated with BCa diagnosis were identified in glucose, amino acids, nucleotides, lipids, and aldehydes metabolism.

Conclusion

The identified metabolites in urine included citric acid, valine, tryptophan, taurine, aspartic acid, uridine, ribose, phosphocholine, and carnitine. Tissue samples exhibited elevated levels of lactic acid, amino acids, and lipids. Consistent findings across tissue, urine, and serum samples revealed downregulation of citric acid and upregulation of lactic acid, valine, tryptophan, taurine, glutamine, aspartic acid, uridine, ribose, and phosphocholine.

Tetraselmis chuii Edible Microalga as a New Source of Neuroprotective Compounds Obtained Using Fast Biosolvent Extraction

Tetraselmis chuii is an EFSA-approved novel food and dietary supplement with increasing use in nutraceutical production worldwide. This study investigated the neuroprotective potential of bioactive compounds extracted from T. chuii using green biobased solvents (ethyl acetate, AcOEt, and cyclopentyl methyl ether, CPME) under pressurized liquid extraction (PLE) conditions and supercritical fluid extraction (SFE). Response surface optimization was used to study the effect of temperature and solvent composition on the neuroprotective properties of the PLE extracts, including anticholinergic activity, reactive oxygen/nitrogen species (ROS/RNS) scavenging capacity, and anti-inflammatory activity. Optimized extraction conditions of 40 °C and 34.9% AcOEt in CPME resulted in extracts with high anticholinergic and ROS/RNS scavenging capacity, while operation at 180 °C and 54.1% AcOEt in CPME yielded extracts with potent anti-inflammatory properties using only 20 min. Chemical characterization revealed the presence of carotenoids (neoxanthin, violaxanthin, zeaxanthin, α- and β-carotene) known for their anti-cholinesterase, antioxidant, and anti-inflammatory potential. The extracts also exhibited high levels of omega-3 polyunsaturated fatty acids (PUFAs) with a favorable ω-3/ω-6 ratio (>7), contributing to their neuroprotective and anti-inflammatory effects. Furthermore, the extracts were found to be safe to use, as cytotoxicity assays showed no observed toxicity in HK-2 and THP-1 cell lines at or below a concentration of 40 μg mL-1. These results highlight the neuroprotective potential of Tetraselmis chuii extracts, making them valuable in the field of nutraceutical production and emphasize the interest of studying new green solvents as alternatives to conventional toxic solvents.

Unbiased serum metabolomic analysis in cats with naturally occurring chronic enteropathies before and after medical intervention

Chronic enteropathies (CE) are common disorders in cats and the differentiation between the two main underlying diseases, inflammatory bowel disease (IBD) and low-grade intestinal T-cell lymphoma (LGITL), can be challenging. Characterization of the serum metabolome could provide further information on alterations of disease-associated metabolic pathways and may identify diagnostic or therapeutic targets. Unbiased metabolomics analysis of serum from 28 cats with CE (14 cats with IBD, 14 cats with LGITL) and 14 healthy controls identified 1,007 named metabolites, of which 129 were significantly different in cats with CE compared to healthy controls at baseline. Random Forest analysis revealed a predictive accuracy of 90% for differentiating controls from cats with chronic enteropathy. Metabolic pathways found to be significantly altered included phospholipids, amino acids, thiamine, and tryptophan metabolism. Several metabolites were found to be significantly different between cats with IBD versus LGITL, including several sphingolipids, phosphatidylcholine 40:7, uridine, pinitol, 3,4-dihydroxybenzoic acid, and glucuronic acid. However, random forest analysis revealed a poor group predictive accuracy of 60% for the differentiation of IBD from LGITL. Of 129 compounds found to be significantly different between healthy cats and cats with CE at baseline, 58 remained different following treatment.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

The role of metabolomics in informing strategies for improving photosynthesis

Photosynthesis plays a vital role in acclimating to and mitigating climate change, providing food and energy security for a population that is constantly growing, and achieving an economy with zero carbon emissions. A thorough comprehension of the dynamics of photosynthesis, including its molecular regulatory network and limitations, is essential for utilizing it as a tool to boost plant growth, enhance crop yields, and support the production of plant biomass for carbon storage. Photorespiration constrains photosynthetic efficiency and contributes significantly to carbon loss. Therefore, modulating or circumventing photorespiration presents opportunities to enhance photosynthetic efficiency. Over the past eight decades, substantial progress has been made in elucidating the molecular basis of photosynthesis, photorespiration, and the key regulatory mechanisms involved, beginning with the discovery of the canonical Calvin-Benson-Bassham cycle. Advanced chromatographic and mass spectrometric technologies have allowed a comprehensive analysis of the metabolite patterns associated with photosynthesis, contributing to a deeper understanding of its regulation. In this review, we summarize the results of metabolomics studies that shed light on the molecular intricacies of photosynthetic metabolism. We also discuss the methodological requirements essential for effective analysis of photosynthetic metabolism, highlighting the value of this technology in supporting strategies aimed at enhancing photosynthesis.

GC-MS-based Metabolomics Unravels Metabolites across Larval Development and Diapause of a Specialist Insect

Plant-insect interactions are a driving force into ecosystem evolution and community dynamics. Many insect herbivores enter diapause, a developmental arrest stage in anticipation of adverse conditions, to survive and thrive through seasonal changes. Herein, we investigated the roles of medium- to non-polar metabolites during larval development and diapause in a specialist insect herbivore, Chlosyne lacinia, reared on Aldama robusta leaves. Varying metabolites were determined using gas chromatography-mass spectrometry (GC-MS)-based metabolomics. Sesquiterpenes and steroids were the main metabolites putatively identified in A. robusta leaves, whereas C. lacinia caterpillars were characterized by triterpenes, steroids, fatty acids, and long-chain alkanes. We found out that C. lacinia caterpillars biosynthesized most of the identified steroids and fatty acids from plant-derived ingested metabolites, as well as all triterpenes and long-chain alkanes. Steroids, fatty acids, and long-chain alkanes were detected across all C. lacinia instars and in diapausing caterpillars. Sesquiterpenes and triterpenes were also detected across larval development, yet they were not detected in diapausing caterpillars, which suggested that these metabolites were converted to other molecules prior to the diapause stage. Our findings shed light on the chemical content variation across C. lacinia development and diapause, providing insights into the roles of metabolites in plant-insect interactions.

Targeting Gut Microbiome With Prebiotic in Patients With CKD: The TarGut-CKD Study

Introduction

Disruption of gut microbiota underpins some of the metabolic alterations observed in chronic kidney disease (CKD).

Methods

In a nonrandomized, open-label, 3-phase pilot trial, with repeated measures within each phase, we examined the efficacy of oligofructose-enriched inulin (p-inulin) in changing the gut microbiome and their metabolic products in 15 patients with CKD. The stability of microbiome and metabolome was studied during the pretreatment phase (8 weeks), a p-inulin treatment phase (12 weeks), and a post treatment phase (8 weeks) of the study.

Results

Study participants completed 373 of the 420 expected study visits (88.8%). Adherence to p-inulin was 83.4%. 16S rRNA sequencing was performed in 368 stool samples. A total of 1085 stool, urine, and plasma samples were subjected to untargeted metabolomic studies. p-inulin administration altered the composition of the gut microbiota significantly, with an increase in abundance of Bifidobacterium and Anaerostipes. Intersubject variations in microbiome and metabolome were larger than intrasubject variation, indicating the stability of the gut microbiome within each phase of the study. Overall metabolite compositions assessed by beta diversity in urine and stool metabolic profiles were significantly different across study phases. Several specific metabolites in stool, urine, and plasma were significant at false discovery rate (FDR) ≤ 0.1 over phase. Specifically, there was significant enrichment in microbial metabolites derived from saccharolysis.

Conclusion

Results from our study highlight the stability of the gut microbiome and the expansive effect of p-inulin on microbiome and host cometabolism in patients with CKD. Findings from this study will enable rigorous design of microbiome-based intervention trials.

Rapid and Direct Detection of Trimethylamine N-oxide Using an Off-Chip Capacitance Biosensor with Readout SoC for Early-Stage Thrombosis and Cardiovascular Disease

A demonstration of an off-chip capacitance array sensor with a limit of detection of 1 μM trimethylamine N-oxide (TMAO) to diagnose a chronic metabolism disease in urine is presented. The improved Cole-Cole model is employed to determine the parameters of R_catalyzed, C_catalyzed, and Rp_catalyzed, enabling the prediction of the catalytic resistance of enzyme, reduction effects of the analyte, and characterize the small signal alternating current properties of ionic strength caused by catalysis. Based on the standard solutions, we investigate the effects of pixel geometry parameters, driving electrode width, and sensing electrode width on the electrical field change of the off-chip capacitance sensor; the proposed off-chip sensor with readout system-on-chip exhibits a high sensitivity of 21 analog-to-digital converter counts/μM TMAO (or 2.5 mV/μM TMAO), response time of 1 s, repetition of 98.9%, and drift over time of 0.5 mV. The proposed off-chip sensor effectively discriminates TMAO in a phosphate-buffered saline solution based on minute changes in capacitance induced by the TorA enzyme, resulting in a discernible 2.15% distinction. These measurements have been successfully corroborated using the conventional cyclic voltammetry method, demonstrating a mere 0.024% variance. The off-chip sensor is crafted with a specific focus on detecting TMAO, achieved by excluding any reduction reactions between the TMAO-specific enzyme TorA and the compounds creatine and creatinine present in urine. This deliberate omission ensures that the sensor's attention remains solely on TMAO, thereby enhancing its precision in achieving accurate and reliable TMAO detection.

Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer

Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or  ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.

Establishing a framework for best practices for quality assurance and quality control in untargeted metabolomics

BACKGROUND

Quality assurance (QA) and quality control (QC) practices are key tenets that facilitate study and data quality across all applications of untargeted metabolomics. These important practices will strengthen this field and accelerate its success. The Best Practices Working Group (WG) within the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) focuses on community use of QA/QC practices and protocols and aims to identify, catalogue, harmonize, and disseminate current best practices in untargeted metabolomics through community-driven activities.

AIM OF REVIEW

A present goal of the Best Practices WG is to develop a working strategy, or roadmap, that guides the actions of practitioners and progress in the field. The framework in which mQACC operates promotes the harmonization and dissemination of current best QA/QC practice guidance and encourages widespread adoption of these essential QA/QC activities for liquid chromatography-mass spectrometry.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Community engagement and QA/QC information gathering activities have been occurring through conference workshops, virtual and in-person interactive forum discussions, and community surveys. Seven principal QC stages prioritized by internal discussions of the Best Practices WG have received participant input, feedback and discussion. We outline these stages, each involving a multitude of activities, as the framework for identifying QA/QC best practices. The ultimate planned product of these endeavors is a "living guidance" document of current QA/QC best practices for untargeted metabolomics that will grow and change with the evolution of the field.

Evaluation of normalization strategies for GC-based metabolomics

INTRODUCTION

For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results.

OBJECTIVES

Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies.

METHODS

Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data.

RESULTS

Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes.

CONCLUSION

TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

Metabolomic Biomarkers of Dietary Approaches to Stop Hypertension (DASH) Dietary Patterns in Pregnant Women

Objective: the aim of this study was to identify plasma metabolomic markers of Dietary Approaches to Stop Hypertension (DASH) dietary patterns in pregnant women. Methods: This study included 186 women who had both dietary intake and metabolome measured from a nested case-control study within the NICHD Fetal Growth Studies-Singletons cohort (FGS). Dietary intakes were ascertained at 8-13 gestational weeks (GW) using the Food Frequency Questionnaire (FFQ) and DASH scores were calculated based on eight food and nutrient components. Fasting plasma samples were collected at 15-26 GW and untargeted metabolomic profiling was performed. Multivariable linear regression models were used to examine the association of individual metabolites with the DASH score. Least absolute shrinkage and selection operator (LASSO) regression was used to select a panel of metabolites jointly associated with the DASH score. Results: Of the total 460 known metabolites, 92 were individually associated with DASH score in linear regressions, 25 were selected as a panel by LASSO regressions, and 18 were identified by both methods. Among the top 18 metabolites, there were 11 lipids and lipid-like molecules (i.e., TG (49:1), TG (52:2), PC (31:0), PC (35:3), PC (36:4) C, PC (36:5) B, PC (38:4) B, PC (42:6), SM (d32:0), gamma-tocopherol, and dodecanoic acid), 5 organic acids and derivatives (i.e., asparagine, beta-alanine, glycine, taurine, and hydroxycarbamate), 1 organic oxygen compound (i.e., xylitol), and 1 organoheterocyclic compound (i.e., maleimide). Conclusions: our study identified plasma metabolomic markers for DASH dietary patterns in pregnant women, with most of being lipids and lipid-like molecules.