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

Urine organic acid metabolomic profiling by gas chromatography mass spectrometry: Assessment of solvent extract evaporation parameters on the recovery of key diagnostic metabolites

BACKGROUND

Analysis of urinary organic acids (UOAs) by gas chromatography mass-spectrometry (GC-MS) is widely used in metabolomic studies. It is a complex test with many limitations and pitfalls yet there is limited evidence in the literature to support best practice. This study investigated the impact of drying down time and temperature on the recovery of 16 key analytes from solvent extracts.

METHODS

Pooled urine specimens were enriched with organic acids. Urine aliquots (n = 3) were acidified and extracted into diethyl ether and ethyl acetate. Extracts were dried under nitrogen at ambient temperature (25 °C); 40 °C; 60 °C then left for 0; +5; +15 min. Dried extracts were derivatised with N,O,-bis-(trimethylsilyl)trifluoroacetamide prior to analysis by GC-MS. Urine specimens from individuals with biotinidase deficiency, maple syrup urine disease (MSUD) and ketotic hypoglycemia were analysed to demonstrate the potential clinical impact.

RESULTS

Recovery of shorter chain hydroxycarboxylic acids decreased significantly when extracts were dried above 25 °C (mean recovery 89 % at 60 °C, p < 0.01) or left under nitrogen post-drying (mean recovery at ambient + 15 min, 40 °C + 15mins and 60 °C + 15mins was 56 %, 12 % and 2 %, respectively, p < 0.01). Whilst dicarboxylic acids/medium chain fatty acids were unaffected by temperature (mean recovery 100 %), prolonged drying reduced recovery (mean recovery 85 % at 60 °C + 15mins, p < 0.01).

CONCLUSIONS

Evaporation of solvent extracts with heat and/or prolonged drying under nitrogen results in significant losses of the shorter chain hydroxycarboxylic acids. The evaporation protocol must be carefully controlled to ensure accurate and reproducible results, preventing misdiagnoses and/or misinterpretation of results.

Caenorhabditis nematodes influence microbiome and metabolome characteristics of their natural apple substrates over time

The microbiomes of host organisms and their direct source environments are closely linked and key for shaping microbial community dynamics. The relationship between these linked dynamics is largely unexplored because source substrates are usually unavailable. To address this current knowledge gap, we employed bacteriovorous Caenorhabditis nematodes as a unique model system, for which source substrates like rotting apples can be easily collected. We compared single host microbiomes with their corresponding apple source substrates, as well as nematode-free substrates, over a 2-year sampling period in the botanical garden in Kiel, Germany. We found that single worms have unique microbiomes, which overlap most strongly with nematodes from the same source apple. A comparison to previous, related work revealed that variation in microbiome composition of natural Caenorhabditis isolates is significantly influenced by the substrate type, from which worms were obtained (e.g., fruits or compost). Our current sampling further showed that microbiome assembly is mostly driven by dispersal limitation. Importantly, two independent analysis approaches consistently suggest that worm microbiomes significantly influence characteristics of the apple microbiomes, possibly indicating niche construction by nematodes. Moreover, combining apple microbiome and metabolome data, we identified individual microbes and specific compounds indicative of fruit ripening that are significantly associated with nematode presence. In conclusion, our study elucidates the complex relationship between host microbiomes and their directly connected substrate microbiomes. Our analyses underscore the significant influence of nematode microbiomes on shaping the apple microbiome and, consequently, the fruit's metabolic capacity, thereby enhancing our general understanding of host-microbiome interactions in their natural habitat.IMPORTANCEAlmost all complex organisms are host to a microbial community, the microbiome. This microbiome can influence diverse host functions, such as food processing, protection against parasites, or development. The relationship between host and microbiome critically depends on the assembly of the microbial community, which may be shaped by microbes in the directly linked environment, the source microbiome. This assembly process is often not well understood because of the unavailability of source substrates. Here, we used Caenorhabditis nematodes as a model system that facilitates a direct comparison of host and source microbiomes. Based on a 2-year sampling period, we identified (i) a clear link between assembly dynamics of host and source microbiomes, (ii) a significant influence of nematode microbiomes on apple microbiomes, and (iii) specific microbes and compounds that are associated with the presence of nematodes in the sampled substrates. Overall, our study enhances our understanding of microbiome assembly dynamics and resulting functions.

The triterpenoid curcumene mediates the relative hydrophilicity of Bacillus subtilis spores

Spores of Bacillus subtilis are surrounded and protected by the coat and the crust, multi-layered structures mainly made of proteins and polysaccharides. These polysaccharides are covalently linked to some of the coat and crust proteins and influence some spore properties, such as surface adhesion and hydrophilicity. This study reports that a mutant strain lacking the spsA-L operon, encoding 11 enzymes involved in the synthesis of spore surface polysaccharides, produced spores exposing on their surface hydrophobic molecules that were responsible for the drastic reduction of hydrophilicity of the mutant spores. Biochemical and genetic data support the identification of the C35-terpenoid curcumene, a precursor of the spore-associated lipid sporulene, as the highly hydrophobic molecule present on the surface of mutant spores.IMPORTANCEBacterial spores are the most resistant cell forms on Earth. The metabolically quiescent spores withstand conditions that would be lethal for other cells, maintaining the capacity to sense the environment and respond to the presence of favorable conditions by germinating. Such remarkable resistance is also due to the complex layers that surround the spore cytoplasm and protect it against damaging factors. Altogether, the spore surface layers form a complex cell structure composed of proteins, polysaccharides, and, as highlighted by this study, also of lipids. Understanding the complexity of the spore surface and the specific molecules involved in its structure is an essential step for unraveling the mechanisms underlying the spore's resistance to environmental assaults.

Optimized high-throughput protocols for comprehensive metabolomic and lipidomic profiling of brain sample

Establishing direct causal and functional links between genotype and phenotype requires thoroughly analyzing metabolites and lipids in systems biology. Tissue samples, which provide localized and direct information and contain unique compounds, play a significant role in objectively classifying diseases, predicting prognosis, and deciding personalized therapeutic strategies. Comprehensive metabolomic and lipidomic analyses in tissue samples need efficient sample preparation steps, optimized analysis conditions, and the integration of orthogonal analytical platforms because of the physicochemical diversities of biomolecules. Here, we propose simple, rapid, and robust high-throughput analytical protocols based on the design of experiment (DoE) strategies, with the various parameters systematically tested for comprehensively analyzing the heterogeneous brain samples. The suggested protocols present a systematically DoE-based strategy for performing the most comprehensive analysis for integrated GC-MS and LC-qTOF-MS from brain samples. The five different DoE models, including D-optimal, full factorial, fractional, and Box-Behnken, were applied to increase extraction efficiency for metabolites and lipids and optimize instrumental parameters, including sample preparation and chromatographic parameters. The superior simultaneous extraction of metabolites and lipids from brain samples was achieved by the methanol-water-dichloromethane (2:1:3, v/v/v) mixture. For GC-MS based metabolomics analysis, incubation time, temperature, and methoxyamine concentration (10 mg/mL) affected metabolite coverage significantly. For LC-qTOF-MS based metabolomics analysis, the extraction solvent (methanol-water; 2:1, v/v) and the reconstitution solvent (%0.1 FA in acetonitrile) were superior on the metabolite coverage. On the other hand, the ionic strength and column temperature were critical and significant parameters for high throughput metabolomics and lipidomics studies using LC-qTOF-MS. In conclusion, DoE-based optimization strategies for a three-in-one single-step extraction enabled rapid, comprehensive, high-throughput, and simultaneous analysis of metabolites, lipids, and even proteins from a 10 mg brain sample. Under optimized conditions, 475 lipids and 158 metabolites were identified in brain samples.

Metabolomics of Chinese Hamster Ovary Cells

Increasing demand of protein biotherapeutics produced using Chinese hamster ovary (CHO) cell lines necessitates improvement in the production yield of the bioprocess. Various cell engineering, improved media formulation and process-design based approaches utilizing the power of OMICS technologies, specifically, genomics and proteomics, have been employed; however, the potential of metabolomics largely remains unexplored. Metabolomics enables the detection, identification, and/or quantitation of small molecules, commonly known as metabolites, in and around the cells and may help to unlock the cellular molecular mechanism(s) that regulates cell growth and productivity in the bioprocess and improves cellular performance during the bioprocess. Currently, liquid chromatography (LC)/gas chromatography (CG)- coupled with mass-spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the most commonly used approaches for metabolomics. Therefore, in this chapter, we have discussed the standard procedures of investigating CHO metabolites using LC/GC-MS and/or NMR-based approaches.

Metabolic Reprogramming in Cancer: Implications for Immunosuppressive Microenvironment

Cancer is a complex and heterogeneous disease characterised by uncontrolled cell growth and proliferation. One hallmark of cancer cells is their ability to undergo metabolic reprogramming, which allows them to sustain their rapid growth and survival. This metabolic reprogramming creates an immunosuppressive microenvironment that facilitates tumour progression and evasion of the immune system. In this article, we review the mechanisms underlying metabolic reprogramming in cancer cells and discuss how these metabolic alterations contribute to the establishment of an immunosuppressive microenvironment. We also explore potential therapeutic strategies targeting metabolic vulnerabilities in cancer cells to enhance immune-mediated anti-tumour responses. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02044861, NCT03163667, NCT04265534, NCT02071927, NCT02903914, NCT03314935, NCT03361228, NCT03048500, NCT03311308, NCT03800602, NCT04414540, NCT02771626, NCT03994744, NCT03229278, NCT04899921.

(Pre)Clinical Metabolomics Analysis

Metabolomics is the scientific field with the eager goal to comprehensively analyze the entirety of all small molecules of a biological system, i.e., the metabolome. Over the last few years, metabolomics has matured to become an analytical cornerstone of life science research across diverse fields, from fundamental biochemical applications to preclinical studies, including biomarker discovery and drug development. In this chapter, we provide an introduction to (pre)clinical metabolomics. We define key metabolomics aspects and provide the basis to thoroughly understand the relevance of this field in a biological and clinical context. We present and explain state-of-the-art analytical technologies devoted to metabolomic analysis as well as emerging technologies, discussing both strengths and weaknesses. Given the ever-increasing demand for handling complex datasets, the role of bioinformatics approaches in the context of metabolomic analysis is also illustrated.

Detecting N-Phenyl-2-Naphthylamine, L-Arabinose, D-Mannose, L-Phenylalanine, L-Methionine, and D-Trehalose via Photocurrent Measurement

The concentration of small molecules reflects the normality of physiological processes in the human body, making the development of simple and efficient detection equipment essential. In this work, inspired by a facile strategy in point-of-care detection, two devices were fabricated to detect small molecules via photocurrent measurement. A linear response of the photocurrent against the concentration of the small molecules was found. In the first device, metal ions were introduced into gel substrates made by xanthan gum to enhance photocurrent response. N-phenyl-2-naphthylamine was measured when iron or manganese ions were used. L-Phenylalanine was measured when the gel was modified by samarium, iron, cerium, or ytterbium ions. L-(+)-Arabinose was detected via the gels modified by iron or holmium ions. D-(+)-Mannose was detected when the gel was modified by ytterbium, manganese, chromium, or sodium ions. L-Methionine was detected in the gels modified by samarium, zinc, or chromium ions. The second device was based on a paper sheet. A sugar-like molecule was first synthesized, which was then used to modify the paper. The detection was possible since the photocurrent showed a linear trend against the concentration of D-Trehalose. A linear fit was conducted to derive the sensitivity, whose value was found to be 5542.4. This work offers a novel, simple, and environmentally sustainable platform that is potentially useful in remote areas lacking medical devices.

Current Role and Potential of Triple Quadrupole Mass Spectrometry in Biomedical Research and Clinical Applications

Mass-spectrometry-based assays nowadays play an essential role in biomedical research and clinical applications. There are different types of commercial mass spectrometers on the market today, and triple quadrupole (QqQ) is one of the time-honored systems. Here, we overview the main areas of QqQ applications in biomedicine and assess the current level, evolution, and trends in the use of QqQ in these areas. Relevant data were extracted from the Scopus database using the specified terms and Boolean operators defined for each field of the QqQ application. We also discuss the recent advances in QqQ and QqQ-based analytical platforms, which promote the clinical application of these systems, and explain the indicated substantial increase in triple quadrupole use in biomedicine. The number of biomedical studies utilizing QqQ increased 2-3 times this decade. Triple quadrupole is most intensively used in the field of endocrine research and testing. On the contrary, the relative rate of immunoassay utilization-a major competitor of chromatography-mass spectrometry-decreased in this area as well as its use within Therapeutic drug monitoring (TDM) and forensic toxicology. Nowadays, the applications of high-resolution accurate mass (HRAM) mass spectrometers in the investigated areas represent only a small fraction of the total amount of research using mass spectrometry; however, their application substantially increased during the last decade in the untargeted search for new biomarkers.

From Division to Death: Metabolomic Analysis of Nicotiana tabacum BY-2 Cells Reveals the Complexity of Life in Batch Culture

Tobacco BY-2 cell culture is one of the most widely used models in plant biology. The main advantage of BY-2 suspension cultures is the synchronization of cell development and the appearance of polar elongation. In batch culture, BY-2 cells passed through the lag, proliferation, elongation, and stationary phases. During this process, the composition of the growth medium changed dramatically. Sucrose was rapidly eliminated; hexose first accumulated and then depleted. The medium's pH initially decreased and then rose with aging. As a result of the crosstalk between the internal and external stimuli, cells pass through complicated systemic rearrangements, which cause metabolomic alterations. The early stages were characterized by high levels of amino acids and sterols, which could be interpreted as the result of synthetic activity. The most intense rearrangements occurred between the proliferation and active elongation stages, including repression of amino acid accumulation and up-regulation of sugar metabolism. Later stages were distinguished by higher levels of secondary metabolites, which may be a non-specific response to deteriorating conditions. Senescence was followed by some increase in fatty acids and sterols as well as amino acids, and probably led to self-destructive processes. A correlation analysis revealed relationships between metabolites' covariation, their biochemical ratio, and the growth phase.

Metabolomics-based study on the significance of differential metabolite binding IgG isoforms in Hemolytic disease of newborn

BACKGROUND

Hemolytic disease of the newborn (HDN) is a common condition that can have a severe impact on the health of newborns due to the hemolytic reactions it triggers. Although numerous studies have focused on understanding the pathogenesis of HDN, there are still many unanswered questions.

METHODS

In this retrospective study, serum samples were collected from 15 healthy newborns and 8 infants diagnosed with hemolytic disease. The relationship between different metabolites and various IgG subtypes in Healthy, HDN and BLI groups was studied by biochemical technique and enzyme-linked immunosorbent assay (ELISA). Metabolomics analysis was conducted to identify the differential metabolites associated with HDN. Subsequently, Pearson's correlation analysis was used to determine the relation of these differential metabolites with IgG isoforms. The relationship between the metabolites and IgG subtypes was observed after treatment.

RESULTS

The study results revealed that infants with hemolytic disease exhibited abnormal elevations in TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4 levels when compared to healthy newborns. Additionally, differences in metabolite contents were also observed. N, N-DIMETHYLARGININE showed negative correlations with TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4, while 2-HYDROXYBUTYRATE, AMINOISOBUTANOATE, Inosine, and ALLYL ISOTHIOCYANATE exhibited positive correlations with TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4. Through metabolomics-based research, we have discovered associations between differential metabolites and different IgG isoforms during the onset of HDN.

CONCLUSION

These findings suggest that changes in metabolite and IgG isoform levels are linked to HDN. Understanding the involvement of IgG isoforms and metabolites can provide valuable guidance for the diagnosis and treatment of HDN.

Differential regulation of brain microvessel transcriptome and brain metabolome by western and heart-healthy dietary patterns in Ossabaw pigs

Diet is a potentially modifiable neurodegenerative disease risk factor. We studied the effects of a typical Western diet (WD; high in refined carbohydrates, cholesterol and saturated fat), relative to a heart-healthy diet (HHD; high in unrefined carbohydrates, polyunsaturated fat and fiber, and low in cholesterol) on brain microvessel transcriptomics and brain metabolomics of the temporal region in Ossabaw minipigs. Thirty-two pigs (16 male and 16 females) were fed a WD or HHD starting at the age of 4 months for a period of 6 months. The WD and HHD were isocaloric and had a similar macronutrient content but differed in macronutrient quality. Within each dietary group, half of the pigs also received atorvastatin. Relative to HHD-fed pigs, WD-fed pigs had 175 genes differentially expressed (fold change > 1.3, FDR < 0.05) by diet, 46 upregulated and 129 downregulated. Gene Set Enrichment Analysis identified 22 gene sets enriched in WD-fed pigs, comprising pathways related to inflammation, angiogenesis, and apoptosis, and 53 gene sets enriched in the HHD-fed pigs, including cell energetics, neurotransmission, and inflammation resolution pathways. Metabolite analysis showed enrichment in arginine, tyrosine, and lysine in WD-fed pigs, and ergothioneine and S-adenosyl methionine in HHD-fed pigs. Atorvastatin treatment did not affect gene expression. These results suggest a likely contribution of diet to brain pathologies characterized by neuroinflammation and neurodegeneration.

Applying newly suggested simultaneous analysis of metabolomics and lipidomics into perfluorooctanesulfonate-derived neurotoxicity mechanism in zebrafish embryos

Developing methodologies for performing multi-omics with one sample has been challenging in zebrafish toxicology; however, related studies are lacking. A new strategy for the simultaneous analysis of metabolomics and lipidomics in zebrafish embryos was proposed and applied to explore the neurotoxicity mechanisms of perfluorooctanesulfonate (PFOS). Metabolite and lipid profiled simultaneously with methyl tert-butyl ether (MTBE) were compared with individual results from other extraction solvents. Behavioral alterations were measured after the zebrafish embryos were exposed to 0.1-20 μM PFOS for 5 days. The metabolite-lipid profiles of the MTBE-based strategy analyzed with optimized larval pooling size of 30 were comparable to those of other extraction solvents, indicating the feasibility and efficiency of MTBE-based multi-omics analysis. Many metabolites and lipids, which were enriched more than those previously reported, completed the toxicity pathways involved in energy metabolism and sphingolipids, improving our understanding of PFOS-induced neurotoxicity mechanism manifested by increased movement under dark conditions. Our novel MTBE-based strategy enabled the multi-omics analysis of one sample with minimal use of zebrafish embryos, thereby improving data reliability on changes in multi-layered biomolecules. This study will advance multi-omics technologies that are critical to elucidating the toxicity mechanisms of toxic chemicals including per- and polyfluoroalkyl substances.

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

Metabolomic Profiling and Machine Learning Models for Tumor Classification in Patients with Recurrent IDH-Wild-Type Glioblastoma: A Prospective Study

BACKGROUND/OBJECTIVES

The recurrence of glioblastoma is an inevitable event in this disease's course. In this study, we sought to identify the metabolomic signature in patients with recurrent glioblastomas undergoing surgery and radiation therapy.

METHODS

Blood samples collected prospectively from six patients with recurrent IDH-wildtype glioblastoma who underwent one surgery at diagnosis and a second surgery at relapse were analyzed using untargeted gas chromatography-time-of-flight mass spectrometry to measure metabolite abundance. The data analysis techniques included univariate analysis, correlation analysis, and a sample t-test. For predictive modeling, machine learning (ML) algorithms such as multinomial logistic regression, gradient boosting, and random forest were applied to predict the classification of samples in the correct treatment phase.

RESULTS

Comparing samples after the first surgery and after the relapse surgeries to the pre-operative samples showed a significant decrease in sorbitol and mannitol; there was a significant increase in urea, oxoproline, glucose, and alanine. After chemoradiation, two metabolites, erythritol and 6-deoxyglucitol, showed a decrease, with a cut-off of three and a significant reduction for 6-deoxyglucitol, while 2,4-difluorotoluene and 9-myristoleate showed an increase post radiation, with a fold-change cut-off of three. The gradient-boosting ML model achieved a high performance for the prediction of tumor conditions in patients with glioblastoma who had undergone relapse surgery.

CONCLUSIONS

We developed an ML predictor for tumor phase based on the plasma metabolomic profile. Our study suggests the potential of combining metabolomics with ML as a new tool to stratify the risk of tumor progression in patients with glioblastoma.

Glycogen Supplementation in Vitro Promotes pH Decline in Dark-Cutting Beef by Reverting Muscle's Metabolome toward a Normal Postmortem Muscle State

Dysregulated muscle glycogen metabolism preslaughter contributes to aberrant postmortem muscle pH (>5.8) in dark-cutting beef phenotypes. However, the underlying mechanisms have remained elusive. Herein, we examine the glycogen dependent regulation of postmortem muscle pH decline and darkening in beef. We show that supplementation of glycogen in vitro restores postmortem pH decline in dark-cutting beef by reverting the metabolome toward a typical postmortem muscle state characterized by increased activities of enzymes glycogen phosphorylase and lactate dehydrogenase (p < 0.05) coupled with a pronounced abundance of glycolytic metabolites and reduced abundance of tricarboxylic acid cycle and amino acid metabolites. Furthermore, concurrent inhibition of mitochondrial respiration at complexes I, IV, and V with glycogen supplementation stimulates greater pH decline. Together, our findings show that supplementing glycogen at low concentrations (10 mM) can reprogram the dark-cutting beef muscle's metabolome toward typical postmortem state and promote muscle acidification. Thus, enhancing glycogen levels could represent a promising strategy for mitigating dark-cutting beef phenotypes and improving meat quality.

Enhanced lipid accumulation in microalgae Scenedesmus sp. under nitrogen limitation

Microalgae-based biofuel production is cost-effective only in a biorefinery, where valuable co-products offset high costs. Fatty acids produced by photosynthetic microalgae can serve as raw materials for bioenergy and pharmaceuticals. This study aims to understand the metabolic imprints of Scenedesmus sp. CABeR52, to decipher the physiological mechanisms behind lipid accumulation under nitrogen deprivation. Metabolomics profiles were generated using gas chromatography-mass spectrometry (GC-MS) of Scenedesmus sp. CABeR52 subjected to nutrient deprivation. Our initial data sets indicate that deprived cells have an increased accumulation of lipids (278.31 mg.g-1 dcw), 2.0 times higher than the control. The metabolomic profiling unveils a metabolic reprogramming, highlighting the upregulation of key metabolites involved in fatty acid biosynthesis, such as citric acid, succinic acid, and 2-ketoglutaric acid. The accumulation of trehalose, a stress-responsive metabolite, further underscores the microalga's adaptability. Interestingly, we found that a new fatty acid, nervonic acid, was identified in the complex, which has a significant role in brain development. These findings provide valuable insights into the metabolic pathways governing lipid accumulation in Scenedesmus sp., paving the way for its exploitation as a sustainable biofuel feedstock.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors

INTRODUCTION

Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications.

OBJECTIVES

This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements.

METHODS

An interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR).

RESULTS

Different laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques.

CONCLUSION

This collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.

Changes in SWEET-mediated sugar partitioning affect photosynthesis performance and plant response to drought

Sugars, produced through photosynthesis, are at the core of all organic compounds synthesized and used for plant growth and their response to environmental changes. Their production, transport, and utilization are highly regulated and integrated throughout the plant life cycle. The maintenance of sugar partitioning between the different subcellular compartments and between cells is important in adjusting the photosynthesis performance and response to abiotic constraints. We investigated the consequences of the disruption of four genes coding for SWEET sugar transporters in Arabidopsis (SWEET11, SWEET12, SWEET16, and SWEET17) on plant photosynthesis and the response to drought. Our results show that mutations in both SWEET11 and SWEET12 genes lead to an increase of cytosolic sugars in mesophyll cells and phloem parenchyma cells, which impacts several photosynthesis-related parameters. Further, our results suggest that in the swt11swt12 double mutant, the sucrose-induced feedback mechanism on stomatal closure is poorly efficient. On the other hand, changes in fructose partitioning in mesophyll and vascular cells, measured in the swt16swt17 double mutant, positively impact gas exchanges, probably through an increased starch synthesis together with higher vacuolar sugar storage. Finally, we propose that the impaired sugar partitioning, rather than the total amount of sugars observed in the quadruple mutant, is responsible for the enhanced sensitivity upon drought. This work highlights the importance of considering SWEET-mediated sugar partitioning rather than global sugar content in photosynthesis performance and plant response to drought. Such knowledge will pave the way to design new strategies to maintain plant productivity in a challenging environment.

Altered Metabolism in Knockdown Lines of Two HXXXD/BAHD Acyltransferases During Wound Healing in Potato Tubers

Suberin biosynthesis involves the coordinated regulation of both phenolic and aliphatic metabolisms. HXXXD/BAHD acyltransferases occupy a unique place in suberization, as they function to crosslink phenolic and aliphatic monomers during suberin assembly. To date, only one suberin-associated HXXXD/BAHD acyltransferase, StFHT, has been described in potatoes, whereas, in Arabidopsis, at least two are implicated in suberin biosynthesis. RNAseq data from wound-induced potato tubers undergoing suberization indicate that transcripts for 28 HXXXD/BAHD acyltransferase genes accumulate in response to wounding. In the present study, we generated RNAi knockdown lines for StFHT and another highly wound-induced HXXXD/BAHD acyltransferase, designated StHCT, and characterized their wound-induced suberin phenotype. StFHT-RNAi and StHCT-RNAi knockdown lines share the same aliphatic suberin phenotype of reduced esterified ferulic acid and ferulates, which is similar to the previously described StFHT-RNAi knockdown suberin phenotype. However, the phenolic suberin phenotype differed between the two knockdown genotypes, with StHCT-RNAi knockdown lines having proportionately more p-hydroxyphenyl-derived moieties than either StFHT-RNAi knockdown or empty vector control lines. Analysis of soluble polar metabolites revealed that StHCT catalyzes a step upstream from StFHT. Overall, our data support the involvement of more than one HXXXD/BAHD acyltransferase in potato suberin biosynthesis.

Pharmacometabolomics in Drug Disposition, Toxicity, and Precision Medicine

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental, and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport, and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help describe factors mediating drug disposition, efficacy, and toxicity. While important advancements in this area have been made, there remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. SIGNIFICANCE STATEMENT: Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview of pharmacometabolomics including highlights of important examples.

Enhancing gas chromatography-mass spectrometry resolution and pure analyte discovery using intra-chromatogram elution profile matching

Chemometric decomposition methods like multivariate curve resolution-alternating least squares (MCR-ALS) are often employed in gas chromatography-mass spectrometry (GC-MS) to improve analyte identification and quantitation. However, these methods can perform poorly for analytes with a low chromatographic resolution (Rs) and a high degree of spectral contamination from noise and background interferences. Thus, we propose a novel computational algorithm, termed mzCompare, to improve analyte identification and quantitation when coupled to MCR-ALS. The mzCompare method utilizes an underlying requirement that the retention time and peak shape between mass channels (m/z) of the same analyte should be similar. By discovering the selective m/z for a given analyte in a chromatogram, a pure elution profile can be generated and used as an equality constraint in MCR-ALS. The performance of the mzCompare methodology is demonstrated with both experimental and simulated chromatograms. Experimentally, unresolved analytes with a Rs as low as 0.05 could be confidently identified with mzCompare assisted MCR-ALS. Furthermore, application of the mzCompare algorithm to a complex aerospace fuel resulted in the discovery of 335 analytes, a 44 % increase compared to conventional peak detection methods. GC-MS simulations of target-interferent analyte pairs demonstrated that the performance of MCR-ALS deteriorated below a Rs of ∼0.25. However, mzCompare assisted MCR-ALS showed excellent identification and acceptable quantitative accuracy at a Rs of ∼0.02. These results show that the mzCompare algorithm can help analysts overcome modeling ambiguities resulting from the chemometric multiplex disadvantage.

Simultaneous multi-targeted forensic toxicological screening in biological matrices by MRM-IDA-EPI mode

The toxicologist ascertains drug assumptions in case of paediatric intoxications and death for overdose. The analytical approach consists of initially screening and consequently confirming drug positivity. We developed a toxicological screening method and validated its use comparing the results with a LC-MS/MS analysis. The method identifies 751 drugs and metabolites (704 in positive and 47 in negative mode). Chromatographic separation was achieved eluting mobile phase A (10 mM ammonium formate) and B (0.05% formic acid in methanol) in gradient on Kinetex Phenyl-Hexyl (50 × 4.6 mm, 2.6 μm) with 0.7 mL/min flow rate for 11 min. Multiple Reaction Monitoring (MRM) was adopted as survey scan and, after an Information-Dependent Analysis (IDA) (threshold of 30,000 for positive and 1000 cps for negative mode), the Enhanced Product Ion (scan range: 50-700 amu) was triggered. The MS/MS spectrum generated was compared with one of the libraries for identification. Data processing was optimised through creation of rules. Sample preparation, mainly consisting of deproteinization and enzymatic hydrolysis, was set up for different matrices (blood, urine, vitreous humor, synovial fluid, cadaveric tissues and larvae). Cut-off for most analytes resulted in the lowest concentration tested. When the results from the screening and LC-MS/MS analysis were compared, an optimal percentage of agreement (100%) was assessed for all matrices. Method applicability was evaluated on real paediatric intoxications and forensic cases. In conclusion, we proposed a multi-targeted, fast, sensitive and specific MRM-IDA-EPI screening having an extensive use in different toxicological fields.

A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities' expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome.

Linoleic Acid Induces Metabolic Reprogramming and Inhibits Oxidative and Inflammatory Effects in Keratinocytes Exposed to UVB Radiation

Linoleic acid (LA), the primary ω-6 polyunsaturated fatty acid (PUFA) found in the epidermis, plays a crucial role in preserving the integrity of the skin's water permeability barrier. Additionally, vegetable oils rich in LA have been shown to notably mitigate ultraviolet (UV) radiation-induced effects, including the production of reactive oxygen species (ROS), cellular damage, and skin photoaging. These beneficial effects are primarily ascribed to the LA in these oils. Nonetheless, the precise mechanisms through which LA confers protection against damage induced by exposure to UVB radiation remain unclear. This study aimed to examine whether LA can restore redox and metabolic equilibria and to assess its influence on the inflammatory response triggered by UVB radiation in keratinocytes. Flow cytometry analysis unveiled the capacity of LA to diminish UVB-induced ROS levels in HaCaT cells. GC/MS-based metabolomics highlighted significant metabolic changes, especially in carbohydrate, amino acid, and glutathione (GSH) metabolism, with LA restoring depleted GSH levels post-UVB exposure. LA also upregulated PI3K/Akt-dependent GCLC and GSS expression while downregulating COX-2 expression. These results suggest that LA induces metabolic reprogramming, protecting against UVB-induced oxidative damage by enhancing GSH biosynthesis via PI3K/Akt signaling. Moreover, it suppresses UVB-induced COX-2 expression in HaCaT cells, making LA treatment a promising strategy against UVB-induced oxidative and inflammatory damage.

Automated Mixture Analysis via Structural Evaluation

The determination of chemical mixture components is vital to a multitude of scientific fields. Oftentimes spectroscopic methods are employed to decipher the composition of these mixtures. However, the sheer density of spectral features present in spectroscopic databases can make unambiguous assignment to individual species challenging. Yet, components of a mixture are commonly chemically related due to environmental processes or shared precursor molecules. Therefore, analysis of the chemical relevance of a molecule is important when determining which species are present in a mixture. In this paper, we combine machine-learning molecular embedding methods with a graph-based ranking system to determine the likelihood of a molecule being present in a mixture based on the other known species and/or chemical priors. By incorporating this metric in a rotational spectroscopy mixture analysis algorithm, we demonstrate that the mixture components can be identified with extremely high accuracy (≥97%) in an efficient manner.

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.