NMR-based Metabolomics approach assessed phytochemical profile in seed and husk of three cardamom species: Elettaria cardamomum (green), Amomum subulatum (black), and Aframomum corrorima (white). NMR Spectroscopy identified 20 metabolites belonging to sugars, amino-, organic-, fatty acids, terpenes, and phenolics. Multivariate data analyses revealed distinct metabolic profiles among the 3 species, and further in seed versus husk. A. subulatum seed showed the highest sugar and amino acid levels, while E. cardamomum seed was richer in ω-3 fatty acids. Husk, especially from A. subulatum and E. cardamomum, contained high levels of phenolic acids. Compared to other cardamom taxa, A. corrorima exhibited lower levels of most chemicals. This study highlights the potential value of cardamom husk, particularly from A. subulatum and E. cardamomum species enriched in phenolic acids and terpenes known for their antioxidant and antimicrobial properties, for use as a food preservative. The antimicrobial and antioxidant activities were assessed through in vitro assays, revealing their potential for value-added applications in food preservation and therapeutic uses.

Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for in vitro diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naïve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.

Chronic periodontitis is a condition impacting approximately 50% of the world's population. As chronic periodontitis progresses, the bacteria in the oral cavity change resulting in new microbial interactions which in turn influence metabolite production. Chronic periodontitis manifests with inflammation of the periodontal tissues, which is progressively developed due to bacterial infection and prolonged bacterial interaction with the host immune response. The bi-directional relationship between periodontitis and systemic diseases has been reported in many previous studies. Traditional diagnostic methods for chronic periodontitis and systemic diseases such as chronic kidney diseases (CKD) have limitations due to their invasiveness, requiring practised individuals for sample collection, frequent blood collection, and long waiting times for the results. More rapid methods are required to detect such systemic diseases, however, the metabolic profiles of the oral cavity first need to be determined.

In this review, we explored metabolomics studies that have investigated salivary metabolic profiles associated with chronic periodontitis and systemic illnesses including CKD, oral cancer, Alzheimer's disease, Parkinsons's disease, and diabetes to highlight the most recent methodologies that have been applied in this field.

Of the rapid, high throughput techniques for metabolite profiling, Nuclear magnetic resonance (NMR) spectroscopy was the most applied technique, followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Furthermore, Raman spectroscopy was the most used vibrational spectroscopic technique for comparison of the saliva from periodontitis patients to healthy individuals, whilst Fourier Transform Infra-Red Spectroscopy (FT-IR) was not utilised as much in this field. A recommendation for cultivating periodontal bacteria in a synthetic medium designed to replicate the conditions and composition of saliva in the oral environment is suggested to facilitate the identification of their metabolites. This approach is instrumental in assessing the potential of these metabolites as biomarkers for systemic illnesses.

In this work, 37 enological tannins of different classes were studied to investigate whether linear sweep voltammetry (LSV) could be a method to determine the family of a sample and its antioxidant capacity. A "wholistic" approach was used, combining LSV data with nuclear magnetic resonance (NMR), polyphenol quantification (Folin-Ciocalteu method and gravimetric analysis), antiradical activity (DPPH assay), and reducing capacity (FRAP assay). Voltammetric data were processed with statistical techniques and the results show the clustering of tannins in three different classes: ellagitannins, gallotannins, and condensed tannins. These findings were confirmed by NMR data treated with the same procedure. Finally, ellagitannins showed a high reducing capacity and gallotannins showed a high antiradical capacity. Importantly, LSV indices were shown to be significantly correlated with DPPH and FRAP parameters. Therefore, the hypothesis of LSV as a potentially useful technique to choose the most suitable tannin for a determined antioxidant purpose was successfully proved.

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.

Early detection of colorectal cancer (CRC) is crucial to enhance the disease treatment and prognosis of patients. Colonoscopy remains the gold standard for CRC detection; however, it requires trained personnel with expensive tools. Currently, serum metabolites have been discovered to be used to discriminate patients with polyps and CRC. This study aimed to identify the most commonly detected predictive serum metabolites for polyps and CRC.

A systematic search of the Web of Science, PubMed, and Cochrane Library databases was conducted using PRISMA guidelines. Ten studies investigating serum metabolite biomarkers of CRC and polyps using different analytical platforms and study populations were included. QUADOMICS tool was used to analyse the quality of the included studies. All reported metabolites were then enriched into the pathways using MetaboAnalyst 5.0.

We found that several potential signature metabolites overlapped between studies, including tyrosine, lysine, cystine, arabinose, and lactate for CRC and lactate and glutamate for polyps. The most affected pathways related to CRC were the urea cycle, glutathione metabolism, purine metabolism, glutamate metabolism, and ammonia recycling. In contrast, those affected in the polyps were the urea cycle, glutamate metabolism, glutathione metabolism, arginine and proline metabolism, and carnitine synthesis.

This review has found commonly detected serum metabolites for polyps and CRC with huge potential to be used in clinical settings. However, the differences between altered pathways in polyps and CRC, other external factors, and their effects on the regulation level, sensitivity, and specificity of each identified metabolite remained unclear, which could benefit from a further extensive cohort study and well-defined analysis equipment.

Mulching has been demonstrated to improve the soil environment and promote plant growth. However, the effects of mulching and mulch-derived microplastics (MPs) on nitrogen fixation by root nodules remain unclear. In this study, we investigated the effects of polyethylene (PE) and polylactic acid-polybutylene adipate-co-terephthalate (PLA-PBAT) film mulching on nitrogen fixation by root nodules after 4 years of continuous mulching using 15N tracer technology. Additionally, we examined the relationship between nitrogen fixation and MPs. We found a reduction in the proportion of nitrogen fixation by nodules (54.3 %-58.7 %) due to mulching. This decrease may be attributed to reduced dinitrogenase activity and flavonoid content at the seedling stage caused by mulching, and mulching with PLA-PBAT films significantly decreased the abundance of Bradyrhizobium at maturity. Furthermore, combined analysis of nitrogen-fixing bacteria (nifH) and metabolomes indicated that N-lauroylethanolamine may act as a regulatory signal influencing the root nodule nitrogen fixation process and that mulching resulted in significant changes in its content. The mantel test and PLS-PM suggest that microplastic from mulching may harm root nodule nitrogen fixation. This study reveals the influence of mulching on plant nitrogen uptake and the potential threat of mulch-derived microplastics, with a special focus on root nodule nitrogen fixation.

Bioenergetics plays a crucial role in sperm functions, including motility, capacitation-related protein modifications, oocyte recognition and interaction, all of which are essential for fertilization. Sperm metabolism is recognized as flexible, responding to environmental cues and energetic demands during ejaculation, the journey along the female tract, and until fertilization. Recent studies suggest that sperm metabolic functions are relevant beyond fertilization and may influence zygote and embryo development, impacting paternal-derived effects on offspring development and health. In recent years, sperm metabolic functions and homeostasis have gained increasing interest in male reproduction research. Given the crucial implications of sperm metabolism on fertility-related processes, this field is of interest not only in human male fertility but also in livestock research, semen conservation, and assisted reproductive techniques. Newly developed assessment tools are allowing a better understanding of sperm metabolism under different conditions and identifying species-specific peculiarities. This review aims to discuss the current knowledge of mammalian sperm metabolism, focusing on species-specific features, changes during the sperm journey, and potential contributions to translational research and reproductive biotechnologies. Furthermore, we propose future perspectives on sperm bioenergetics research.

With the use of advanced technology, metabolomics allows for a thorough examination of metabolites and other small molecules found in biological specimens, blood, and tissues. In recent years, metabolomics has been recognized that is closely related to the development of malignancies in the hematological system. Alterations in metabolomic pathways and networks are important in the pathogenesis of hematologic malignancies and can also provide a theoretical basis for early diagnosis, efficacy evaluation, accurate staging, and individualized targeted therapy. In this review, we summarize the progress of metabolomics, including glucose metabolism, amino acid metabolism, and lipid metabolism in lymphoma, myeloma, and leukemia through specific mechanisms and pathways. The research of metabolomics gives a new insight and provides therapeutic targets for the treatment of patients with hematologic malignancies.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

The existing diagnostic methods of epilepsy have great limitations, and more reliable and less difficult diagnostic methods are needed. We collected serum samples of adult patients with first-diagnosed epilepsy (EPs) and seizure control patients (EPRs) for non-targeted metabolomics detection and found that they were both significantly altered, with increased expression of nicotine addiction, linoleic acid metabolism, purine metabolism, and other metabolic pathways. The diagnostic model based on 4 differential metabolites achieved a diagnostic efficiency of 99.4% in the training cohort and 100% in the validation cohort. In addition, the association analysis of oral flora, serum metabolism, and clinical indicators also provided a new angle to analyze the mechanism of epilepsy. In conclusion, this study characterized the serum metabolic characteristics of EPs and EPRs and the changes before and after epilepsy control based on a large cohort, demonstrating the potential of metabolites as non-invasive diagnostic tools for epilepsy.

The field of exercise physiology has undergone significant technological advancements since the pioneering works of exercise physiologists in the early to mid-20th century. Historically, the ability to detect metabolites in biofluids from exercising participants was limited to single-metabolite analyses. However, the rise of metabolomics, a discipline focused on the comprehensive analysis of metabolites within a biological system, has facilitated a more intricate understanding of metabolic pathways and networks in exercise. This review explores some of the pivotal technological and bioinformatic advancements that have propelled metabolomics to the forefront of exercise physiology research. Metabolomics offers a unique 'fingerprint' of cellular activity, offering a broader spectrum than traditional single-metabolite assays. Techniques, including mass spectrometry and nuclear magnetic resonance spectroscopy, have significantly improved the speed and sensitivity of metabolite analysis. Nonetheless, challenges persist, including study design and data interpretation issues. This review aims to serve as a guide for exercise physiologists to facilitate better research design, data analysis and interpretation within metabolomics. The potential of metabolomics in bridging the gap between genotype and phenotype is emphasised, underscoring the critical importance of careful study design and the selection of appropriate metabolomics techniques. Furthermore, the paper highlights the need to deeply understand the broader scientific context to discern meaningful metabolic changes. The emerging field of fluxomics, which seeks to quantify metabolic reaction rates, is also introduced as a promising avenue for future research.

Understanding growth regulatory pathways is important in aquaculture, fisheries, and vertebrate physiology generally. Machine learning pattern recognition and sensitivity analysis were employed to examine metabolomic small molecule profiles and transcriptomic gene expression data generated from liver and white skeletal muscle of hybrid striped bass (white bass Morone chrysops x striped bass M. saxatilis) representative of the top and bottom 10 % by body size of a production cohort.

Larger fish (good-growth) had significantly greater weight, total length, hepatosomatic index, and specific growth rate compared to smaller fish (poor-growth) and also had significantly more muscle fibers of smaller diameter (≤ 20 µm diameter), indicating active hyperplasia. Differences in metabolomic pathways included enhanced energetics (glycolysis, citric acid cycle) and amino acid metabolism in good-growth fish, and enhanced stress, muscle inflammation (cortisol, eicosanoids) and dysfunctional liver cholesterol metabolism in poor-growth fish. The majority of gene transcripts identified as differentially expressed between groups were down-regulated in good-growth fish. Several molecules associated with important growth-regulatory pathways were up-regulated in muscle of fish that grew poorly: growth factors including agt and agtr2 (angiotensins), nicotinic acid (which stimulates growth hormone production), gadd45b, rgl1, zfp36, cebpb, and hmgb1; insulin-like growth factor signaling (igfbp1 and igf1); cytokine signaling (socs3, cxcr4); cell signaling (rgs13, rundc3a), and differentiation (rhou, mmp17, cd22, msi1); mitochondrial uncoupling proteins (ucp3, ucp2); and regulators of lipid metabolism (apoa1, ldlr). Growth factors pttg1, egfr, myc, notch1, and sirt1 were notably up-regulated in muscle of good-growing fish.

A combinatorial pathway analysis using metabolomic and transcriptomic data collectively suggested promotion of cell signaling, proliferation, and differentiation in muscle of good-growth fish, whereas muscle inflammation and apoptosis was observed in poor-growth fish, along with elevated cortisol (an anti-inflammatory hormone), perhaps related to muscle wasting, hypertrophy, and inferior growth. These findings provide important biomarkers and mechanisms by which growth is regulated in fishes and other vertebrates as well.

Biochar and organic compost are widely used in agricultural soil remediation as soil immobilization agents. However, the effects of biochar and compost on microbial community assembly processes in polluted soil under freezingthawing need to be further clarified. Therefore, a freezethaw cycle experiment was conducted with glyphosate (herbicide), imidacloprid (insecticide) and pyraclostrobin (fungicide) polluted to understand the effect of biochar and compost on microbial community assembly and metabolic behavior. We found that biochar and compost could significantly promote the degradation of glyphosate, imidacloprid and pyraclostrobin in freezethaw soil decrease the half-life of the three pesticides. The addition of immobilization agents improved soil bacterial and fungal communities and promoted the transformation from homogeneous dispersal to homogeneous selection. For soil metabolism, the combined addition of biochar and compost alleviated the pollution of glyphosate, imidacloprid and imidacloprid to soil through up-regulation of metabolites (DEMs) in amino acid metabolism pathway and down-regulation of DEMs in fatty acid metabolism pathway. The structural equation modeling (SEM) results showed that soil pH and DOC were the main driving factors affecting microbial community assembly and metabolites. In summary, the combined addition of biochar and compost reduced the adverse effects of pesticides residues.

Phase separation and percolation contribute to phase transitions of multivalent macromolecules. Contributions of percolation are evident through the viscoelasticity of condensates and through the formation of heterogeneous distributions of nano- and mesoscale pre-percolation clusters in sub-saturated solutions. Here, we show that clusters formed in sub-saturated solutions of FET (FUS-EWSR1-TAF15) proteins are affected differently by glutamate versus chloride. These differences on the nanoscale, gleaned using a suite of methods deployed across a wide range of protein concentrations, are prevalent and can be unmasked even though the driving forces for phase separation remain unchanged in glutamate versus chloride. Strikingly, differences in anion-mediated interactions that drive clustering saturate on the micron-scale. Beyond this length scale the system separates into coexisting phases. Overall, we find that sequence-encoded interactions, mediated by solution components, make synergistic and distinct contributions to the formation of pre-percolation clusters in sub-saturated solutions, and to the driving forces for phase separation.

Traditional Chinese medicine has been utilized in China for approximately thousands of years in clinical settings to prevent Alzheimer's disease (AD) and enhance memory, despite the lack of a systematic exploration of its biological underpinnings. Exciting research has corroborated the beneficial effects of tetrahydroxy stilbene glycoside (TSG), an extract derived from Polygonum multiflorum, in delaying learning and memory impairment in a model that mimics AD. Therefore, the primary objective of this study is to investigate the major function of TSG upon protein regulation in AD. Herein, a novel approach, encompassing data independent acquisition (DIA), DIA phosphorylated proteomics, and parallel reaction monitoring (PRM), was utilized to integrate quantitative proteomic data collected from APP/PS1 mouse model exhibiting toxic intracellular aggregation of Aβ. Initially, we deliberated upon both single and multi-dimensional data pertaining to AD model mice. Furthermore, we authenticated disparities in protein phosphorylation quantity and expression, phosphorylation function, and ultimately phosphorylation kinase analysis. In order to validate the results, we utilized PRM ion monitoring technology to identify potential protein or peptide biomarkers. In the mixed samples, targeted detection of 50 target proteins revealed that 26 to 33 target proteins were stably detected by PRM. In summary, our findings provide new candidates for AD biomarker, which have been identified and validated through protein researches conducted on mouse brains. This offers a wealth of potential resources for extensive biomarker validation in neurodegenerative diseases. SIGNIFICANCE: DIA phosphorylated proteomics technique was used to detect and analyze phosphorylated proteins in brain tissues of mice with AD. Data were analyzed by various bioinformatics tools to explore the phosphorylation events and characterize them related to TSG. The results of DIA were further verified by PRM. Besides, we mapped the major metabolite classes emerging from the analyses to key biological pathways implicated in AD to understand the potential roles of the molecules and the interactions in triggering symptom onset and progression of AD. Meanwhile, we clarified that in the context of AD onset and TSG intervention, the changes in proteins, protein phosphorylation, phosphorylation kinases, and the internal connections.

Trauma is a common cause of morbidity and mortality in humans and companion animals. Recent efforts in procedural development, training, quality systems, data collection, and research have positively impacted patient outcomes; however, significant unmet need still exists. Coordinated efforts by collaborative, translational, multidisciplinary teams to advance trauma care and improve outcomes have the potential to benefit both human and veterinary patient populations. Strategic use of veterinary clinical trials informed by expertise along the research spectrum (i.e., benchtop discovery, applied science and engineering, large laboratory animal models, clinical veterinary studies, and human randomized trials) can lead to increased therapeutic options for animals while accelerating and enhancing translation by providing early data to reduce the cost and the risk of failed human clinical trials. Active topics of collaboration across the translational continuum include advancements in resuscitation (including austere environments), acute traumatic coagulopathy, trauma-induced coagulopathy, traumatic brain injury, systems biology, and trauma immunology. Mechanisms to improve funding and support innovative team science approaches to current problems in trauma care can accelerate needed, sustainable, and impactful progress in the field. This review article summarizes our current understanding of veterinary and human trauma, thereby identifying knowledge gaps and opportunities for collaborative, translational research to improve multispecies outcomes. This translational trauma group of MDs, PhDs, and DVMs posit that a common understanding of injury patterns and resulting cellular dysregulation in humans and companion animals has the potential to accelerate translation of research findings into clinical solutions.

Chronic kidney disease (CKD) presents a persistent global health challenge, characterized by complex pathophysiology and diverse progression patterns. Metabolomics has emerged as a valuable tool in unraveling the intricate molecular mechanisms driving CKD progression.

This comprehensive review provides a summary of recent progress in the field of metabolomics in kidney disease with a focus on spatial metabolomics to shed important insights to enhancing our understanding of CKD progression, emphasizing its transformative potential in early disease detection, refined risk assessment, and the development of targeted interventions to improve patient outcomes.

Through an extensive analysis of metabolic pathways and small-molecule fluctuations, bulk and spatial metabolomics offers unique insights spanning the entire spectrum of CKD, from early stages to advanced disease states. Recent advances in metabolomics technology have enabled spatial identification of biomarkers to provide breakthrough discoveries in predicting CKD trajectory and enabling personalized risk assessment. Furthermore, metabolomics can help decipher the complex molecular intricacies associated with kidney diseases for exciting novel therapeutic approaches. A recent example is the identification of adenine as a key marker of kidney fibrosis for diabetic kidney disease using both untargeted and targeted bulk and spatial metabolomics. The metabolomics studies were critical to identify a new biomarker for kidney failure and to guide new therapeutics for diabetic kidney disease. Similar approaches are being pursued for acute kidney injury and other kidney diseases to enhance precision medicine decision-making.

Tumor cells can acquire a large amount of energy and structural components by reprogramming energy metabolism; moreover, metabolic profiles slightly differ according to cancer type. This study compared and assessed the metabolic profile of head and neck squamous cell carcinoma (HNSCC) and normal tissues, which were collected from patients without cancer.

Overall, 23 patients with HNSCC and 6 patients without cancer were included in the analysis. Metabolomic profiles were analyzed using capillary electrophoresis-mass spectrometry. Gene expression was evaluated using real-time reverse transcription-polymerase chain reaction.

Glycolysis, the pentose phosphate pathway, tricarboxylic acid cycle, and glutamine metabolism were upregulated in HNSCC tissues based on gene expression analysis. HNSCC could then have enhanced energy production and structural component. The levels of lactate, succinate, glutathione, 2-hydroxyglutarate, and S-adenosylmethionine, considered as oncometabolites, increased and these had accumulated in HNSCC tissues.

The level of metabolites and the expression of enzymes differ between HNSCC and normal tissues. Reprogramming metabolism in HNSCC provides an energy source as well as structural components, creating a system that offers rapid proliferation, progression, and is less likely to be eliminated.

Previous metabolic profiling of liver cancer has mostly used untargeted metabolomic approaches and was unable to quantitate the absolute concentrations of metabolites. In this study, we examined the association between the concentrations of 186 targeted metabolites and liver cancer risk using prediagnostic plasma samples collected up to 14 years prior to the clinical diagnosis of liver cancer.

We conducted a nested case-control study (n = 322 liver cancer cases, n = 322 matched controls) within the Shanghai Men's Health Study. Conditional logistic regression models adjusted for demographics, lifestyle factors, dietary habits, and related medical histories were used to estimate the odds ratios. Restricted cubic spline functions were used to characterise the dose-response relationships between metabolite concentrations and liver cancer risk.

After adjusting for potential confounders and correcting for multiple testing, 28 metabolites were associated with liver cancer risk. Significant non-linear relationships were observed for 22 metabolites. The primary bile acid biosynthesis and phenylalanine, tyrosine and tryptophan biosynthesis were found to be important pathways involved in the aetiology of liver cancer. A metabolic score consisting of 10 metabolites significantly improved the predictive ability of traditional epidemiological risk factors for liver cancer, with an optimism-corrected AUC increased from 0.84 (95% CI: 0.81-0.87) to 0.89 (95% CI: 0.86-0.91).

This study characterised the dose-response relationships between metabolites and liver cancer risk, providing insights into the complex metabolic perturbations prior to the clinical diagnosis of liver cancer. The metabolic score may serve as a candidate risk predictor for liver cancer.

National Key Project of Research and Development Program of China [2021YFC2500404, 2021YFC2500405]; US National Institutes of Health [subcontract of UM1 CA173640].

Given its wide distribution in the environment and latent toxic effects, 1,3,6,8-tetrabromo-9H-carbazole (1368-BCZ) is an emerging concern that has gained increasing attention globally. 1368-BCZ exposure is reported to have potential cardiovascular toxicity. Although atherosclerosis is a cardiovascular disease and remains a primary cause of mortality worldwide, no evidence has been found regarding the impact of 1368-BCZ on atherosclerosis. Therefore, we aimed to explore the deleterious effects of 1368-BCZ on atherosclerosis and the underlying mechanisms. Serum samples from 1368-BCZ-treated atherosclerotic model mice were subjected to metabolomic profiling to investigate the adverse influence of the pollutant. Subsequently, the molecular mechanism associated with the metabolic pathway of atherosclerotic mice that was identified following 1368-BCZ exposure was validated in vitro. Serum metabolomics analysis revealed that 1368-BCZ significantly altered the tricarboxylic acid cycle, causing a disturbance in energy metabolism. In vitro, we further validated general markers of energy metabolism based on metabolome data: 1368-BCZ dampened adenosine triphosphate (ATP) synthesis and increased reactive oxygen species (ROS) production. Furthermore, blocking the aryl hydrocarbon receptor (AhR) reversed the high production of ROS induced by 1368-BCZ. It is concluded that 1368-BCZ decreased the ATP synthesis by disturbing the energy metabolism, thereby stimulating the AhR-mediated ROS production and presumably causing aggravated atherosclerosis. This is the first comprehensive study on the cardiovascular toxicity and mechanism of 1368-BCZ based on rodent models of atherosclerosis and integrated with in vitro models.

Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This integrative approach has established a robust theoretical foundation, enabling a deeper understanding of sheep genetics and fostering improvements in breeding strategies. The comprehensive insights obtained through this approach shed light on diverse facets of sheep development, including growth, reproduction, disease resistance, and the quality of livestock products. This review primarily focuses on the application of principal omics analysis technologies in sheep, emphasizing correlation studies between multiomics data and specific traits such as meat quality, wool characteristics, and reproductive features. Additionally, this paper anticipates forthcoming trends and potential developments in this field.

Changing the intrinsic rate of metabolic heat production is the main adaptive strategy for small birds to cope with different ambient temperatures. In this study, we tested the hypothesis that the small passerine the white-shouldered starling (Sturnus sinensis) can modulate basal metabolism under temperature acclimation by changing the morphological, physiological and biochemical state of its tissues and organs. We measured the effects of temperature on body mass, basal metabolic rate (BMR), wet mass of various internal organs, state 4 respiration (S4R) and cytochrome c oxidase (CCO) activity in the pectoral muscle and organs, metabolites in the pectoral muscle, energy intake, histological dynamics and the activity of duodenal digestive enzymes. Warm acclimation decreased BMR to a greater extent than cold acclimation. At the organ level, birds in the cold-acclimated group had significantly heavier intestines but significantly lighter pectoral muscles. At the cellular level, birds in the cold-acclimated group showed significantly higher S4R in the liver and heart and CCO activity in the liver and kidney at both the mass-specific and whole-organ levels. A metabolomic analysis of the pectoral tissue revealed significantly higher lipid decomposition, amino acid degradation, ATP hydrolysis, and GTP and biotin synthesis in cold-acclimated birds. Acclimation to cold significantly increased the gross energy intake (GEI), feces energy (FE) and digestive energy intake (DEI) but significantly decreased the digestive efficiency of these birds. Furthermore, cold-acclimated birds had a higher maltase activity and longer villi in the duodenum. Taken together, these data show that white-shouldered starlings exhibit high phenotypic flexibility in metabolic adjustments and digestive function under temperature acclimation, consistent with the notion that small birds cope with the energy challenges presented by a cold environment by modulating tissue function in a way that would affect BMR.

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

This work investigated the mechanisms of action of conventional drugs, cisplatin and oxaliplatin, and the potentially less deleterious drug Pd2Spermine (Spm) and its Pt(II) analog, against osteosarcoma MG-63 cells, using nuclear-magnetic-resonance metabolomics of the cellular lipidome. The Pt(II) chelates induced different responses, namely regarding polyunsaturated-fatty-acids (increased upon cisplatin), suggesting that cisplatin-treated cells have higher membrane fluidity/permeability, thus facilitating cell entry and justifying higher cytotoxicity. Both conventional drugs significantly increased triglyceride levels, while Pt2Spm maintained control levels; this may reflect enhanced apoptotic behavior for conventional drugs, but not for Pt2Spm. Compared to Pt2Spm, the more cytotoxic Pd2Spm (IC50 comparable to cisplatin) induced a distinct phospholipids profile, possibly reflecting enhanced de novo biosynthesis to modulate membrane fluidity and drug-accessibility to cells, similarly to cisplatin. However, Pd2Spm differed from cisplatin in that cells had equivalent (low) levels of triglycerides as Pt2Spm, suggesting the absence/low extent of apoptosis. Our results suggest that Pd2Spm acts on MG-63 cells mainly through adaptation of cell membrane fluidity, whereas cisplatin seems to couple a similar effect with typical signs of apoptosis. These results were discussed in articulation with reported polar metabolome adaptations, building on the insight of these drugs' mechanisms, and particularly of Pd2Spm as a possible cisplatin substitute.

The exact underlying mechanism of developing diabetes-related cardiovascular disease (CVD) among patients with type 2 diabetes (T2D) is not clear. Metabolomics can provide a platform enabling the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The aim of this review is to summarize the available evidence on the relationship between metabolomics and cardiovascular diseases in patients with diabetes.

The literature was searched to find out studies that have investigated the relationship between the alteration of specific metabolites and cardiovascular diseases in patients with diabetes.

Evidence proposed that changes in the metabolism of certain amino acids, lipids, and carbohydrates, independent of traditional CVD risk factors, are associated with increased CVD risk.

Metabolomics can provide a platform to enable the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The association of the alteration in specific metabolites with CVD may be considered in the investigations for the development of new therapeutic targets for the prevention of CVD in patients with diabetes mellitus.

The purpose of this mini review is to describe metabolomics in cerebrospinal fluid (CSF) and its potential in aneurysmal subarachnoid hemorrhage (aSAH). In brain injury, patients' micro dialysis enables detecting biochemical change in brain tissue. Indicators for ischemia were detected such as lactate, pyruvate, glucose, and glutamate. In aSAH patients, the pathophysiology and the factor for poor outcome are not completely understood yet. Routine use of biomarkers in CSF, particularly in aSAH patients, is still lacking.

This mini review was performed on the role of metabolomics alterations after aneurysmal subarachnoid hemorrhage.

We identified five clinical studies that addressed metabolomics in patients with aneurysmal subarachnoid hemorrhage.

There is increasing evidence suggesting that biomarkers can give insight in the pathogenesis and can serve as an outcome predictor. In this mini review, we present a brief overview of metabolomics profiling in neuroscience and wish to discuss the predictive and therapeutic value in aSAH patients.

Previous research revealed that several seminal plasma (SP) metabolites are related to sperm functionality, fertility, and preservation. While it is understood that variations between species exist, whether the SP metabolome differs between donkeys and horses has not been previously investigated. The aim of this work, therefore, was to characterize and compare donkey and horse SP metabolites using nuclear magnetic resonance (NMR) spectroscopy, and relate them to sperm viability and motility. For this purpose, ejaculates from 18 different donkeys and 18 different horses were collected and separated into two aliquots: one for harvesting the SP by centrifugation and obtaining the metabolic profile through NMR, and the other for evaluating sperm viability and motility. Based on total motility and sperm viability, samples were classified as with good (GQ) or poor (PQ) quality. The metabolomic profile of donkey and horse SP revealed the presence of 28 metabolites, which coincided in the two species. Yet, differences between horses and donkeys were observed in the concentration of 18 of these 28 metabolites, as well as between ejaculates classified as GQ or PQ and in the relationship of metabolites with sperm motility and viability. These findings suggest that sperm from donkeys and horses differ in their metabolism and energetic requirements, and that the concentration of specific SP metabolites may be related to sperm functionality. Further research should shed light on the metabolic needs of donkey and horse sperm, and evaluate how the knowledge collected from the contribution of these metabolites can help improve semen preservation in the two species.

Lactic Acid Bacteria (LAB) are commonly used as starter cultures, probiotics, to produce lactic acid and other useful compounds, and even as natural preservatives. For use in any food product however, LAB need to survive the various stresses they encounter in the environment and during processing. Understanding these mechanisms may enable direction of LAB biochemistry with potential beneficial impact for the food industry.

To give an overview of the use of LAB in the food industry and then generate a deeper biochemical understanding of LAB stress response mechanisms via metabolomics, and methods of screening for robust strains of LAB.

Uses of LAB in food products were assessed and factors which contribute to survival and tolerance in LAB investigated. Changes in the metabolic profiles of LAB exposed to stress were found to be associated with carbohydrates, amino acids and fatty acid levels and these changes were proposed to be a result of the bacteria trying to maintain cellular homeostasis in response to external conditions and minimise cellular damage from reactive oxygen species. This correlates with morphological analysis which shows that LAB can undergo cell elongation and shortening, as well as thinning and thickening of cell membranes, when exposed to stress. It is proposed that these innate strategies can be utilised to minimise negative effects caused by stress through selection of intrinsically robust strains, genetic modification and/or prior exposure to sublethal stress. This work demonstrates the utility of metabolomics to the food industry.

Demonstrating biosimilarity entails comprehensive analytical assessment, clinical pharmacology profiling, and efficacy testing in patients for at least one medical indication, as required by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The efficacy testing can be waived if the drug has known pharmacodynamic (PD) markers, leaving most therapeutic proteins out of this concession. To overcome this, the FDA suggests that biosimilar developers discover PD biomarkers using omics technologies such as proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. This approach is redundant since the mode-action-action biomarkers of approved therapeutic proteins are already available, as compiled in this paper for the first time. Other potential biomarkers are receptor binding and pharmacokinetic profiling, which can be made more relevant to ensure biosimilarity without requiring biosimilar developers to conduct extensive research, for which they are rarely qualified.

Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.

Multivalent proteins undergo coupled segregative and associative phase transitions. Phase separation, a segregative transition, is driven by macromolecular solubility, and this leads to coexisting phases above system-specific saturation concentrations. Percolation is a continuous transition that is driven by multivalent associations among cohesive motifs. Contributions from percolation are highlighted by the formation of heterogeneous distributions of clusters in sub-saturated solutions, as was recently reported for Fused in sarcoma (FUS) and FET family proteins. Here, we show that clustering and phase separation are defined by a separation of length- and energy-scales. This is unmasked when glutamate is the primary solution anion. Glutamate is preferentially excluded from protein sites, and this enhances molecular associations. Differences between glutamate and chloride are manifest at ultra-low protein concentrations. These differences are amplified as concentrations increase, and they saturate as the micron-scale is approached. Therefore, condensate formation in supersaturated solutions and clustering in sub-saturated are governed by distinct energy and length scales. Glutamate, unlike chloride, is the dominant intracellular anion, and the separation of scales, which is masked in chloride, is unmasked in glutamate. Our work highlights how components of cellular milieus and sequence-encoded interactions contribute to amplifying distinct contributions from associative versus segregative phase transitions.

Currently, metabolic biomarkers with great practicability of gastric cancer (GC) and gastric precancerous lesions (GPL) are scarce. Thus, we are devoted to determining the plasma metabolic profiles of patients with GPL or GC and validate candidate biomarkers for disease diagnosis.

In this hospital-based case-control study, 68 plasma samples from 27 non-atrophic gastritis (NAG, control), 31 GPL, and 10 GC patients were collected for targeted metabolomics analysis. Univariate and multivariate analyses were used for selecting the differential metabolites. A receiver operating characteristic curve combined with binary logistic regression analysis was performed to test the diagnostic performance of the differential metabolites. Dietary data were obtained using a semiquantitative food frequency questionnaire.

Distinct metabolomic profiles were noted for NAG, GPL, and GC. Compared to the NAG patients, the levels of 5 metabolites in the GPL group and 4 metabolites in the GC group were found to significantly elevate. Compared with the model involving 9 traditional risk factors (AUC: 0.89, 95%CI: 0.78-1.00), Trimethylamine N-oxide, the most significant metabolite (P = 2.00 × 10-5, FDR = 0.003, FC > 2, VIP > 2), showed a good diagnostic performance for the patients with GC (AUC: 0.90, 95%CI: 0.78-1.00), and its diagnostic performance has been further improved with the integration of Rhamnose (AUC: 0.96, 95%CI: 0.89-1.00).

In our study, 9 defined metabolites might serve as meaningful biomarkers for identifying the high-risk population of GPL and GC, possibly enhancing the prevention and control of GPL and GC.

In recent years, high-throughput technologies have facilitated the widespread use of metabolomics to identify biomarkers and targets for oral squamous cell carcinoma (OSCC). As a result, the primary goal of this systematic review is to identify and evaluate metabolite biomarkers and their pathways for OSCC that featured consistently across studies despite methodological variations. Six electronic databases (Medline, Cochrane, Web of Science, CINAHL, ProQuest, and Embase) were reviewed for the longitudinal studies involving OSCC patients and metabolic marker analysis (in accordance with PRISMA 2020). The studies included ranged from the inception of metabolomics in OSCC (i.e., 1 January 2007) to 30 April 2023. The included studies were then assessed for their quality using the modified version of NIH quality assessment tool and QUADOMICS. Thirteen studies were included after screening 2285 studies. The majority of the studies were from South Asian regions, and metabolites were most frequently derived from saliva. Amino acids accounted for more than quarter of the detected metabolites, with glutamate and methionine being the most prominent. The top dysregulated metabolites indicated dysregulation of six significantly enriched pathways including aminoacyl-tRNA biosynthesis, glutathione metabolism and arginine biosynthesis with the false discovery rate (FDR) <0.05. Finally, this review highlights the potential of metabolomics for early diagnosis and therapeutic targeting of OSCC. However, larger studies and standardized protocols are needed to validate these findings and make them a clinical reality.

Milk thistle is one of the most popular hepatoprotectants, and is often sold in combination with other ingredients. Botanical supplements are known to be vulnerable to contamination and adulteration, and emerging technologies show promise to improve their quality control.

Untargeted and semi-targeted metabolomics based on UHPLC-QTOF-ESI⁺MS techniques, UV spectrometry, and DNA metabarcoding using Illumina MiSeq were used to authenticate eighteen milk thistle botanical formulations (teas, capsules, tablets, emulsion).

Untargeted metabolomics separated 217 molecules and by multivariate analysis the discrimination between the different preparations was established. The semi-targeted metabolomics focused on 63 phytochemicals, mainly silymarin flavonolignans and flavonoids, that may be considered as putative biomarkers of authenticity. All formulations contained molecules from silymarin complexes at different levels. The quantitative evaluation of silybins was done using in parallel UV spectrometry and UHPLC-QTOF-ESI⁺MS and their correlations were compared. DNA metabarcoding detected milk thistle in eleven out of sixteen retained preparations, whereas two others had incomplete evidence of milk thistle despite metabolomics validating specific metabolites, e.g., silymarin complex, identified and quantified in all samples. Meanwhile, the DNA metabarcoding provided insights into the total species composition allowing the interpretation of the results in a broad context.

Our study emphasizes that combining spectroscopic, chromatographic, and genetic techniques bring complementary information to guarantee the quality of the botanical formulations.

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

Metabolomics is a powerful research discovery tool with the potential to measure hundreds to low thousands of metabolites. In this review, we discuss the application of GC-MS and LC-MS in discovery-based metabolomics research, we define metabolomics workflows and we highlight considerations that need to be addressed in order to generate robust and reproducible data. We stress that metabolomics is now routinely applied across the biological sciences to study microbiomes from relatively simple microbial systems to their complex interactions within consortia in the host and the environment and highlight this in a range of biological species and mammalian systems including humans. However, challenges do still exist that need to be overcome to maximise the potential for metabolomics to help us understanding biological systems. To demonstrate the potential of the approach we discuss the application of metabolomics in two broad research areas: (1) synthetic biology to increase the production of high-value fine chemicals and reduction in secondary by-products and (2) gut microbial interaction with the human host. While burgeoning in importance, the latter is still in its infancy and will benefit from the development of tools to detangle host-gut-microbial interactions and their impact on human health and diseases.

The slow discovery of new antibiotics combined with the alarming emergence of antibiotic-resistant bacteria underscores the need for alternative treatments. In this regard, fish skin mucus has been demonstrated to contain a diverse array of bioactive molecules with antimicrobial properties, including peptides, proteins, and other metabolites. This review aims to provide an overview of the antimicrobial molecules found in fish skin mucus and its reported in vitro antimicrobial capacity against bacteria, fungi, and viruses. Additionally, the different methods of mucus extraction, which can be grouped as aqueous, organic, and acidic extractions, are presented. Finally, omic techniques (genomics, transcriptomics, proteomics, metabolomics, and multiomics) are described as key tools for the identification and isolation of new antimicrobial compounds. Overall, this study provides valuable insight into the potential of fish skin mucus as a promising source for the discovery of new antimicrobial agents.

Glaucoma is a progressive optic neuropathy and a leading cause of irreversible blindness worldwide. Primary open-angle glaucoma is the most common form, and yet the etiology of this multifactorial disease is poorly understood. We aimed to identify plasma metabolites associated with the risk of developing POAG in a case-control study (599 cases and 599 matched controls) nested within the Nurses' Health Studies, and Health Professionals' Follow-Up Study. Plasma metabolites were measured with LC-MS/MS at the Broad Institute (Cambridge, MA, USA); 369 metabolites from 18 metabolite classes passed quality control analyses. For comparison, in a cross-sectional study in the UK Biobank, 168 metabolites were measured in plasma samples from 2,238 prevalent glaucoma cases and 44,723 controls using NMR spectroscopy (Nightingale, Finland; version 2020). Here we show higher levels of diglycerides and triglycerides are adversely associated with glaucoma in all four cohorts, suggesting that they play an important role in glaucoma pathogenesis.

Follicular fluid has been found as a possible source of metabolic predictors for oocyte competence, and it is conveniently accessible during ovum pick-up (OPU). We used the OPU procedure to recover oocytes from 41 Holstein heifers for in vitro embryo production in this study. Follicular fluid was collected during OPU in order to establish a link between follicular amino acids and blastocyst formation. Each heifer's oocytes were collected, matured in vitro for 24 h and fertilized separately. The heifers were then divided into two groups based on blastocyst formation: those that produced at least one blastocyst (the blastocyst group, n = 29) and those that did not (the failed group, n = 12). The blastocyst group had higher follicular glutamine concentrations and lower aspartate levels than the failed group. Furthermore, network and Spearman correlation analyses revealed a link between blastocyst formation and aspartate (r = -0.37, p = 0.02) or glutamine (r = 0.38, p = 0.02). The receiver operator characteristic curve revealed that glutamine (AUC = 0.75) was the greatest predictor of blastocyst formation. These findings revealed that follicular amino acid levels in bovines can be used to predict blastocyst development.

The structural elucidation of metabolite molecules is important in many branches of the life sciences. However, the isomeric and isobaric complexity of metabolites makes their identification extremely challenging, and analytical standards are often required to confirm the presence of a particular compound in a sample. We present here an approach to overcome these challenges using high-resolution ion mobility spectrometry in combination with cryogenic vibrational spectroscopy for the rapid separation and identification of metabolite isomers and isobars. Ion mobility can separate isomeric metabolites in tens of milliseconds, and cryogenic IR spectroscopy provides highly structured IR fingerprints for unambiguous molecular identification. Moreover, our approach allows one to identify metabolite isomers automatically by comparing their IR fingerprints with those previously recorded in a database, obviating the need for a recurrent introduction of analytical standards. We demonstrate the principle of this approach by constructing a database composed of IR fingerprints of eight isomeric/isobaric metabolites and use it for the identification of these isomers present in mixtures. Moreover, we show how our fast IR fingerprinting technology allows to probe the IR fingerprints of molecules within just a few seconds as they elute from an LC column. This approach has the potential to greatly improve metabolomics workflows in terms of accuracy, speed, and cost.