Use Of Metabolomics Research Articles

Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID

The COVID-19 outbreak caused saturations of hospitals, highlighting the importance of early patient triage to optimize resource prioritization. Herein, our objective was to test if high definition metabolomics, combined with ML, can improve prognostication and triage performance over standard clinical parameters using COVID infection as an example. Using high resolution mass spectrometry, we obtained metabolomics profiles of patients and combined them with clinical parameters to design machine learning (ML) algorithms predicting severity (herein determined as the need for mechanical ventilation during patient care). A total of 64 PCR-positive COVID patients at the Poitiers CHU were recruited. Clinical and metabolomics investigations were conducted 8 days after the onset of symptoms. We show that standard clinical parameters could predict severity with good performance (AUC of the ROC curve: 0.85), using SpO2, first respiratory rate, Horowitz quotient and age as the most important variables. However, the performance of the prediction was substantially improved by the use of metabolomics (AUC = 0.92). Our small-scale study demonstrates that metabolomics can improve the performance of diagnosis and prognosis algorithms, and thus be a key player in the future discovery of new biological signals. This technique is easily deployable in the clinic, and combined with machine learning, it can help design the mathematical models needed to advance towards personalized medicine.

The use of metabolomics and machine learning algorithms to predict post-transplant diabetes mellitus in renal transplant patients on Tacrolimus therapy.

Tacrolimus (TAC) has significantly improved kidney graft survival following transplantation, though it is associated with adverse side effects. The most prevalent complication resulting from excessive TAC exposure is the onset of de novo diabetes mellitus (DM), a condition that can negatively impact both renal graft function and patient outcomes. De novo DM is linked to an increased risk of chronic transplant dysfunction, as well as cardiovascular morbidity and mortality. Although the underlying mechanisms remain unclear, emerging research in the field of omics shows promise. The aim of this study was to investigate the metabolomic profile of kidney transplant patients who developed de novo DM, in comparison to those who did not, following TAC exposure, using untargeted metabolomic analysis through ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) and machine learning algorithms. A cohort of 34 kidney transplant patients on a Tacrolimus regimen for at least 6 months was enrolled in the study, with serum samples collected from each patient. Comprehensive profiling of serum metabolites was performed, enabling the classification of patients into de novo diabetes mellitus and non diabetes groups. The metabolomic analysis of serum was conducted using UHPLC-MS. Of the 34 patients, 16 were diagnosed with TAC-induced diabetes. A total of 334 metabolites were identified in the serum samples, of which 10 demonstrated a significant correlation with the de novo diabetes mellitus group. Most of these metabolites were linked to alterations in lipid metabolism. The application of metabolomics in kidney transplant patients undergoing a Tacrolimus regimen is both feasible and effective in identifying metabolites associated with de novo diabetes mellitus. This approach may provide valuable insights into the metabolic alterations underlying TAC-induced diabetes.

The Application of Metabolomics in Hyperlipidemia: Insights into Biomarker Discovery and Treatment Efficacy Assessment.

Hyperlipidemia is a lipid metabolism disorder that refers to increased levels of total triglycerides (TGs), cholesterol (TC), and low-density lipoprotein-cholesterol (LDL-C) and decreased levels of high-density lipoprotein-cholesterol (HDL-C). It is a major public health issue with increased prevalence and incidence worldwide. The ability to identify individuals at risk of this disorder before symptoms manifest will facilitate timely intervention and management to avert potential complications. This can be achieved by employing metabolomics as an early detection method for the diagnostic biomarkers of hyperlipidemia. Metabolomics is an analytical approach used to detect and quantify metabolites. This provides the ability to explain the metabolic processes involved in the development and progression of certain diseases. In recent years, interest in the use of metabolomics to identify disease biomarkers has increased, and several biomarkers have been discovered, such as docosahexaenoic acid, glycocholic acid, citric acid, betaine, and carnitine. This review discusses the primary metabolic alterations in the context of hyperlipidemia. Furthermore, we provide an overview of recent studies on the application of metabolomics to the assessment of the efficacy of traditional herbal products and common lipid-lowering medications.

The Resistant Depression Response to Esketamine Assessing Metabolomics (ReDREAM) Project-Untargeted Metabolomics to Identify Biomarkers of Treatment Response to Intranasal Esketamine in Individuals with Treatment-Resistant Depression: A Study Protocol.

Treatment-resistant depression (TRD) affects around 20-30% of people with major depressive disorder. In 2019, esketamine nasal spray was approved for TRD by both the US Food and Drug Administration and the European Medicines Agency. While its clinical efficacy and safety are proven, the mechanisms underlying its antidepressant effect remain unclear. The use of metabolomics may allow understanding the metabolic effects of esketamine and predicting biological features associated with clinical response in TRD. Nonetheless, there is a lack of studies exploring the predictive value of metabolomics. The Resistant Depression Response to Esketamine Assessing Metabolomics (ReDREAM) project aims at identifying metabolic biosignatures that may represent novel correlates of response to esketamine treatment. This is the protocol of an observational, prospective study. We plan to select 60 people with TRD from 3 clinical sites in Italy. The participants will be administered with esketamine nasal spray, following standard clinical practice, twice a week for 4 weeks ("induction phase"), then once a week for 4 additional weeks ("maintenance phase"). We will test the correlations between baseline metabolic profile and depressive symptom improvement at study endpoints (weeks 4 and 8) and we will explore the likelihood of different metabolic phenotypes between responders and non-responders. An involvement of energy metabolism, amino acid metabolism, urea cycle, and nitric oxide synthesis in response to treatment with esketamine nasal spray is hypothesized. Unbiased data from untargeted metabolomics associated with clinical changes after esketamine treatment may contribute to define new paradigms for precision psychiatry-oriented, personalized care of TRD.

Host microbiome depletion attenuates biofluid metabolite responses following radiation exposure.

Development of novel biodosimetry assays and medical countermeasures is needed to obtain a level of radiation preparedness in the event of malicious or accidental mass exposures to ionizing radiation (IR). For biodosimetry, metabolic profiling with mass spectrometry (MS) platforms has identified several small molecules in easily accessible biofluids that are promising for dose reconstruction. As our microbiome has profound effects on biofluid metabolite composition, it is of interest how variation in the host microbiome may affect metabolomics based biodosimetry. Here, we 'knocked out' the microbiome of male and female C57BL/6 mice (Abx mice) using antibiotics and then irradiated (0, 3, or 8 Gy) them to determine the role of the host microbiome on biofluid radiation signatures (1 and 3 d urine, 3 d serum). Biofluid metabolite levels were compared to a sham and irradiated group of mice with a normal microbiome (Abx-con mice). To compare post-irradiation effects in urine, we calculated the Spearman's correlation coefficients of metabolite levels with radiation dose. For selected metabolites of interest, we performed more detailed analyses using linear mixed effect models to determine the effects of radiation dose, time, and microbiome depletion. Serum metabolite levels were compared using an ANOVA. Several metabolites were affected after antibiotic administration in the tryptophan and amino acid pathways, sterol hormone, xenobiotic and bile acid pathways (urine) and lipid metabolism (serum), with a post-irradiation attenuative effect observed for Abx mice. In urine, dose×time interactions were supported for a defined radiation metabolite panel (carnitine, hexosamine-valine-isoleucine [Hex-V-I], creatine, citric acid, and Nε,Nε,Nε-trimethyllysine [TML]) and dose for N1-acetylspermidine, which also provided excellent (AUROC ≥ 0.90) to good (AUROC ≥ 0.80) sensitivity and specificity according to the area under the receiver operator characteristic curve (AUROC) analysis. In serum, a panel consisting of carnitine, citric acid, lysophosphatidylcholine (LysoPC) (14:0), LysoPC (20:3), and LysoPC (22:5) also gave excellent to good sensitivity and specificity for identifying post-irradiated individuals at 3 d. Although the microbiome affected the basal levels and/or post-irradiation levels of these metabolites, their utility in dose reconstruction irrespective of microbiome status is encouraging for the use of metabolomics as a novel biodosimetry assay.

Integration of metabolomics and transcriptomics provides insights into the molecular mechanism of temporomandibular joint osteoarthritis.

The deficiency of clinically specific biomarkers has made it difficult to achieve an accurate diagnosis of temporomandibular joint osteoarthritis (TMJ-OA) and the insufficient comprehension of the pathogenesis of the pathogenesis of TMJ-OA has posed challenges in advancing therapeutic measures. The combined use of metabolomics and transcriptomics technologies presents a highly effective method for identifying vital metabolic pathways and key genes in TMJ-OA patients. In this study, an analysis of synovial fluid untargeted metabolomics of 6 TMJ-OA groups and 6 temporomandibular joint reducible anterior disc displacement (TMJ-DD) groups was conducted using liquid and gas chromatography mass spectrometry (LC/GC-MS). The differential metabolites (DMs) between TMJ-OA and TMJ-DD groups were analyzed through multivariate analysis. Meanwhile, a transcriptomic dataset (GSE205389) was obtained from the GEO database to analyze the differential metabolism-related genes (DE-MTGs) between TMJ-OA and TMJ-DD groups. Finally, an integrated analysis of DMs and DE-MTGs was carried out to investigate the molecular mechanisms associated with TMJ-OA. The analysis revealed significant differences in the levels of 46 DMs between TMJ-OA and TMJ-DD groups, of which 3 metabolites (L-carnitine, taurine, and adenosine) were identified as potential biomarkers for TMJ-OA. Collectively, differential expression analysis identified 20 DE-MTGs. Furthermore, the integration of metabolomics and transcriptomics analysis revealed that the tricarboxylic acid (TCA) cycle, alanine, aspartate and glutamate metabolism, ferroptosis were significantly enriched. This study provides valuable insights into the metabolic abnormalities and associated pathogenic mechanisms, improving our understanding of TMJOA etiopathogenesis and facilitating potential target screening for therapeutic intervention.

Insight into Metabolomic Profiling of Tinospora cordifolia: Recent Advances and Future Perspectives of Quality Control

Abstract The objective of this study was to offer a review of previous works on Tinospora cordifolia (TC) metabolomics with a highlight on the use of metabolomics in comparative evaluations between varieties and selection of optimum cultivation, collection, and extraction procedures, providing insights into pharmaceutical and food product quality monitoring. Scholarly databases were searched to gather relevant information on TC’s therapeutic usefulness, phytoconstituent studies, metabolomics, and applications. PubChem was used to learn the structures of several of the metabolites. The various medicinal properties of TC were extensively researched. The most essential portions of the plant in Ayurvedic medicines are the stems. Bioactive phytochemicals such as steroidal lactones, alkaloids, diterpenoids, and phenolic compounds are present in the roots and leaves. Metabolomic analysis revealed that, in addition to the season and geographical region of the sample collected, the metabolite profiles of TC also depend on the parts of the plant taken as a sample and also the plant’s stage of growth. The amounts of secondary metabolites differed between morpho/chemotypes within the TC species. Despite the fact that research on TC began several decades ago, the diversity of phytoconstituents was not appreciated because of a lack of reliable techniques for phytochemical fingerprinting. The uncovering of several novel metabolites was aided by advances in chromatography combined with mass spectrometry. To produce a full metabolomic profile, researchers used mutually complementary techniques. Following that, data analysis and searches against spectrum databases allowed for signal annotation and interpretation of metabolites in large quantities without separating them separately. The current review includes a critical assessment of metabolomic data and an overview of the technique’s various applications. It is vital to establish the metrics of quality control of herbal medicines in order to accomplish formulation purity assessments. Such information would assist us in determining the best geolocation for the plants and the best time to harvest them. Metabolomic investigation of TC-based herbal products is recommended for quality standards and the identification of novel bioactive components.

Abstract P408: Plasma Metabolomic Profile of Dyslipidemia in Childhood: Genetics of Glucose Regulation in Gestation and Growth (Gen3G) Study

Objective: Dyslipidemia is a major risk factor for atherosclerosis development. Evidence suggests atherosclerotic plaque accumulation can begin in childhood and lead to adverse cardiovascular events into adulthood. The use of metabolomics to identify metabolites specific to dyslipidemia may provide insights into the pathogenesis of cardiovascular-related outcomes. Thus, our aim was to identify metabolite networks associated with dyslipidemia in childhood. Methods: We used cross-sectional data from 326 children (median age of 5.2 years) from Gen3G, a prospective pre-birth cohort. We evaluated 1,038 plasma metabolites (798 annotated and 240 unannotated). Lipid measures included total cholesterol, non-HDL, LDL, triglyceride, and HDL. Using the AHA and the American Academy of Pediatrics guidelines, we identified normal and borderline/high classifications for each lipid. We applied weighted-correlation network analysis to find highly correlated metabolite networks. Spearman’s partial correlations were applied to assess the associations of lipids with metabolite networks, adjusting for age and sex, with false discovery rate correction. Results: We identified a green module of 120 metabolites, mainly comprised of lipids, that showed positive correlations with total cholesterol, non-HDL, and LDL classified as borderline/high vs normal (ρ adjusted = 0.28 to 0.40) and as continuous measures (ρ adjusted = 0.32 to 0.63), while HDL (borderline/high vs normal) was inversely associated ( Figure ). In this module, sphingomyelin (d18:2/16:0, d18:1/16:1)* and sphingomyelin (d18:2/23:0, d18:1/23:1, d17:1/24:1)* appeared to be driving the associations. Also, the blue and yellow modules showed inverse correlations (mainly driven by 10-nonadecenoate (19:1n9) and 3-hydroxyhexanoate, respectively), while the brown module showed positive correlations with triglyceride (driven by 1-palmitoyl-GPC (16:0)). Conclusions: A unique network of metabolites was associated with dyslipidemia in childhood.

Soil metabolomics - current challenges and future perspectives

Soil is an extremely complex and dynamic matrix, in part, due to the wide diversity of organisms living within it. Soil organic matter (SOM) is the fundamental substrate on which the delivery of ecosystem services depends, providing the metabolic fuel to drive soil function. As such, studying the soil metabolome (the diversity and concentration of low molecular weight metabolites), as a subset of SOM, holds the potential to greatly expand our understanding of the behaviour, fate, interaction and functional significance of small organic molecules in soil. Encompassing a wide range of chemical classes (including amino acids, peptides, lipids and carbohydrates) and a large number of individual molecules (ca. n = 105 to 106), the metabolome is a resultant (indirect) output of several layers of a biological hierarchy, namely the metagenome, metatranscriptome and metaproteome. As such, it may also provide support and validation for these “multi-omics” datasets. We present a case for the increased use of untargeted metabolomics in soil biochemistry, particularly for furthering our fundamental understanding of the functions driving SOM composition and biogeochemical cycling. Further, we discuss the scale of the challenge in terms of metabolite extraction, analysis and interpretation in complex plant-soil-microbial systems. Lastly, we highlight key knowledge gaps which currently limit our use of metabolomic approaches to better understand soil processes, including: (i) interpretation of large untargeted metabolomic datasets; (ii) the source, emission and fate of soil-derived volatile organic compounds (VOCs), (iii) assessing temporal fluxes of metabolites, and (iv) monitoring ecological interactions in the rhizosphere. While the application of metabolomics in ecosystem science is still in its relative infancy, its importance in understanding the biochemical system in relation to regulation, management and underpinning the delivery of ecosystem services is key to further elucidating the complex links between organisms, as well as the fundamental ability of the biological community to process and cycle key nutrients.

MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data.

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.

Comparative antioxidant and metabolomic analysis for the identification of differential response of mussel (Mytilus coruscus) to four succinate dehydrogenase inhibitor fungicides.

Succinate dehydrogenase inhibitor fungicides (SDHIs) are frequently detected in the marine environment. However, studies on the toxicity of SDHIs to marine organisms, Mytilus coruscus (M. coruscus), are poorly reported. Therefore, the antioxidant activities and metabolomic response of four SDHIs, namely, boscalid (BC), thifluzamide (TF), fluopyram (FO), and bixafen (BIX), to (M. coruscus), were comprehensively investigated. The antioxidant activity of BC and TF was significantly increased (p<0.05), whereas those of FO and BIX were significantly decreased. Furthermore, metabolite discriminations among M. coruscus to four SDHIs were illustrated by an untargeted metabolomics approach. A total of 52, 50, 93, and 129 differential metabolites were obtained for BC, TF, FO, and BIX. KEGG of the different metabolites show that the four SDHIs had differential effects on the metabolic pathways of M. coruscus. The current study demonstrated four SDHIs triggered glucose metabolism, lipid metabolism, tricarboxylic acid cycle, and oxidative phosphorylation processes and caused the disruption of nutrient and energy conversion processes in mussels. Finally, five biomarkers were screened by analyzing common differential metabolites that emerged from the four SDHI exposures, which could be used for risk assessment of marine ecosystem exposure to SDHIs. Our results demonstrated the use of metabolomics to understand the potential mechanisms of toxicity of four SDHIs to mussels and to identify potential targets for future targeted risk assessment.

Evidence of interregional similarity in crayfish metabolomes at reference sites: Progress towards the metabolome as a biomonitoring tool

It has been proposed that biomonitoring may benefit from the use of metabolomics (the study of all small molecules in an organism) to detect sub-lethal organism stress through changes in the metabolite profile (i.e., the metabolome). However, to integrate the metabolome into biomonitoring programs the amount of natural variability among and within populations of indicator taxa must be established prior to generating a reference condition. This study determined variation in the metabolome among ecoregion and stream of origin in the northern crayfish (Faxonius virilis) and if that variation inhibited detection of stressor effects at sites exposed to human activities. We collected crayfish from seven minimally disturbed streams (i.e., reference streams), distributed across three level II ecoregions in central Canada and compared their metabolomes. We found ecoregion and stream origin were poor predictors of crayfish metabolomes. This result suggests crayfish metabolomes were similar, despite differing environmental conditions. Metabolomes of crayfish collected from three stream sites exposed to agricultural activity and municipal wastewater (i.e., test sites) were then compared to the crayfish metabolomes from the seven reference streams. Findings showed that crayfish metabolomes from test sites were strongly differentiated from those at all reference sites. The consistency in the northern crayfish metabolome at the studied reference streams indicates that a single reference condition may effectively detect impacts of human activities across the sampled ecoregions.

Serum Metabolomics Analysis Revealed Metabolic Pathways Related to AECOPD Complicated with Anxiety and Depression.

Anxiety and depression are two of the most common comorbidities of COPD, which can directly lead to the number of acute exacerbations and hospitalizations of COPD patients and reduce their quality of life. At present, there are many studies on anxiety and depression in stable COPD, but few studies on anxiety and depression in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients. We aim to explore the changes of serum metabolomics in AECOPD complicated with anxiety and depression and to provide some clues for further understanding its pathogenesis. This is an observational high-throughput experimental study based on retrospective data extraction. Twenty-one AECOPD with anxiety and depressive patients and 17healthy controls (HCs) were retrospectively enrolled in the Second Affiliated Hospital of Anhui Medical University. Hamilton anxiety scale (HAMA) and Hamilton depression scale (HAMD) for anxiety and depression were used to assess the patients with AECOPD. Untargeted metabolomics analysis was carried out to investigate different molecules in the serum of all participants. General information of all participants, baseline data and clinical measurement data of AECOPD patients were collected. Statistical analysis and bioinformatics analysis were performed to reveal different metabolites and perturbed metabolic pathways. A total of 724 metabolites in positive ionization mode and 555 metabolites in negative ionization mode were different in AECOPD patients with anxiety and depression. The 1,279 serum metabolites could be divided into 77 categories. Based on multivariate and univariate analysis, 74 metabolites were detected in positive ionization mode, and 60 metabolites were detected in negative ionization as differential metabolites. The 134 metabolites were enriched in 18 pathways, including biosynthesis of unsaturated fatty acids, aldosterone synthesis and secretion, protein digestion and absorption, ovarian steroidogenesis, long-term depression, retrograde endocannabinoid signaling, and so on. This work highlights the key metabolites and metabolic pathways disturbed in AECOPD patients with anxiety and depression. These findings support the use of metabolomics to understand the pathogenic mechanisms involved in AECOPD patients with anxiety and depression.

A metabolomics perspective on root‐derived plant immunity and phytohormone interaction

AbstractA plant's root system is essential to its survival as it supports anchorage and absorbs water and minerals from the ground. Several microorganisms interact with roots in the rhizosphere, and they can be neutral, positive, or negative. Identifying beneficial and pathogenic microorganisms and limiting or promoting their colonization is essential for roots to survive. Signaling molecules and metabolites play an important regulatory role in determining root‐microbe associations. Phytohormones modulate plant‐microbe interactions by metabolic reprogramming and transmitting long‐range signals, resulting in local and systemic resistance. Mass spectrometry‐based metabolomics provides a powerful means to study root‐microbe interactions. It provides a qualitative and quantitative approach for determining the mechanisms of root‐microbe interactions and also helps to understand pathogen‐induced metabolic reprogramming. By using metabolomics, one can identify biomarker metabolites that indicate disease resistance or sensitivity in the roots. Compared to other omics methods, metabolomics is still underutilized in root‐pathogen interaction studies. It is, therefore, vital that metabolomics is popularized to gain a better understanding of disease resistance and susceptibility. Through untargeted metabolomics, we can gain insight into plant‐microbial interactions across a variety of tissues, which helps us develop more effective crop and plant protection measures. In this review, we describe the current status and progress in metabolomics applications for studying root‐microbe interactions with an emphasis on root‐derived phytohormones and specialized metabolites. In addition, we also proposed the use of metabolomics to identify novel biomarkers during root‐microbe interactions, to distinguish resistant genotypes from susceptible ones, or to predict nondestructively early disease progression.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research.

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Serum Metabolomic Profiling of Patients with Lipedema.

Lipedema is a chronic condition characterized by disproportionate and symmetrical enlargement of adipose tissue, predominantly affecting the lower limbs of women. This study investigated the use of metabolomics in lipedema research, with the objective of identifying complex metabolic disturbances and potential biomarkers for early detection, prognosis, and treatment strategies. The study group (n = 25) comprised women diagnosed with lipedema. The controls were 25 lean women and 25 obese females, both matched for age. In the patients with lipedema, there were notable changes in the metabolite parameters. Specifically, lower levels of histidine and phenylalanine were observed, whereas pyruvic acid was elevated compared with the weight controls. The receiver operating characteristic (ROC) curves for the diagnostic accuracy of histidine, phenylalanine, and pyruvic acid concentrations in distinguishing between patients with lipedema and those with obesity but without lipedema revealed good diagnostic ability for all parameters, with pyruvic acid being the most promising (area under the curve (AUC): 0.9992). Subgroup analysis within matched body mass index (BMI) ranges (30.0 to 39.9 kg/m2) further revealed that differences in pyruvic acid, phenylalanine, and histidine levels are likely linked to lipedema pathology rather than BMI variations. Changes in low-density lipoprotein (LDL)-6 TG levels and significant reductions in various LDL-2-carried lipids of patients with lipedema, compared with the lean controls, were observed. However, these lipids were similar between the lipedema patients and the obese controls, suggesting that these alterations are related to adiposity. Metabolomics is a valuable tool for investigating lipedema, offering a comprehensive view of metabolic changes and insights into lipedema's underlying mechanisms.

Mesenchymal Stem Cell-Derived Mitochondria Enhance Extracellular Matrix-Derived Grafts for the Repair of Nerve Defect.

Peripheral nerve injuries (PNI) can lead to mitochondrial dysfunction and energy depletion within the affected microenvironment. The objective is to investigate the potential of transplanting mitochondria to reshape the neural regeneration microenvironment. High-purity functional mitochondria with an intact structure are extracted from human umbilical cord-derived mesenchymal stem cells (hUCMSCs) using the Dounce homogenization combined with ultracentrifugation. Results show that when hUCMSC-derived mitochondria (hUCMSC-Mitos) are cocultured with Schwann cells (SCs), they promote the proliferation, migration, and respiratory capacity of SCs. Acellular nerve allografts (ANAs) have shown promise in nerve regeneration, however, their therapeutic effect is not satisfactory enough. The incorporation of hUCMSC-Mitos within ANAs has the potential to remodel the regenerative microenvironment. This approach demonstrates satisfactory outcomes in terms of tissue regeneration and functional recovery. Particularly, the use of metabolomics and bioenergetic profiling is used for the first time to analyze the energy metabolism microenvironment after PNI. This remodeling occurs through the enhancement of the tricarboxylic acid cycle and the regulation of associated metabolites, resulting in increased energy synthesis. Overall, the hUCMSC-Mito-loaded ANAs exhibit high functionality to promote nerve regeneration, providing a novel regenerative strategy based on improving energy metabolism for neural repair.

Metabolomics analysis in saliva from periodontally healthy, gingivitis and periodontitis patients.

The aim of this study was to investigate metabolomics markers in the saliva of patients with periodontal health, gingivitis and periodontitis. The use of metabolomics for diagnosing and monitoring periodontitis is promising. Although several metabolites have been reported to be altered by inflammation, few studies have examined metabolomics in saliva collected from patients with different periodontal phenotypes. Saliva samples collected from a total of 63 patients were analysed by nuclear magnetic resonance (NMR) followed by ELISA for interleukin (IL)-1β. The patient sample, well-characterised clinically, included periodontal health (n = 8), gingivitis (n = 19) and periodontitis (n = 36) cases, all non-smokers and not diabetic. Periodontal diagnosis (healthy/gingivitis/periodontitis) was not associated with any salivary metabolites in this exploratory study. Periodontal staging showed nominal associations with acetoin (p = .030) and citrulline (p = .047). Among other investigated variables, the use of systemic antibiotics in the previous 3 months was associated with higher values of the amino acids taurine, glycine and ornithine (p = .002, p = .05 and p = .005, respectively, at linear regression adjusted for age, gender, ethnicity, body mass index and staging). While periodontal staging was marginally associated with some salivary metabolites, other factors such as systemic antibiotic use may have a much more profound effect on the microbial metabolites in saliva. Metabolomics in periodontal disease is still an underresearched area that requires further observational studies on large cohorts of patients, aiming to obtain data to be used for clinical translation.

Bushen Huoxue formula for the treatment of diminished ovarian reserve: A combined metabolomics and integrated network pharmacology analysis

ObjectiveThis study aimed to explore the mechanism of the Bushen Huoxue Formula (BHF) in treating diminished ovarian reserve (DOR) through the use of metabolomics and integrated network pharmacology. MethodsThe study involved 24 non-pregnant female Sprague-Dawley rats, divided into four groups of six rats each: control, model, BHF, and DHEA (n = 6 per group). The model group was induced with DOR by administering Tripterygium glycosides orally [50 mg (kg·d)−1] for 14 days. Subsequently, BHF and Dehydroepiandrosterone (DHEA) treatments were given to the respective groups. Ovarian reserve function was assessed by measuring anti-Müllerian hormone (AMH), estradiol (E2), and follicle-stimulating hormone (FSH) levels and conducting hematoxylin-eosin staining. In addition, UHPLC-QTOF-MS analysis was performed to identify differential metabolites and pathways in DOR rats treated with BHF. In this study, LC-MS was utilized to identify the active ingredients of BHF, while network pharmacology was employed to investigate the correlations between BHF-related genes and DOR-related genes. An integrated analysis of metabonomics and network pharmacology was conducted to elucidate the mechanisms underlying the efficacy of BHF in treating DOR. ResultsThe model group exhibited a poor general condition and a significant decrease in the number of primordial, primary, and secondary follicles (P < 0.05) when compared to the control group. However, BHF intervention resulted in an increase in the number of primordial, primary, and secondary follicles (P < 0.05), along with elevated levels of AMH and E2 (P < 0.05), and a decrease in FSH levels (P < 0.05) in DOR rats. The modeling process identified eleven classes of metabolites, including cholesterol esters (CE), diacylglycerols (DAG), hexosylceramides (HCER), lysophosphatidylcholines (LPC), phosphatidylcholines (PC), phosphatidylethanolamines (PE), sphingomyelins (SM), ceramides (CER), free fatty acids (FFA), triacylglycerols (TAG), and lysophosphatidylethanolamines (LPE). The study found that PC, CE, DAG, and TAG are important metabolites in the treatment of DOR with BHF. LC-MS analysis showed that there were 183 active ingredients in ESI(+) mode and 51 in ESI(−) mode. Network pharmacology analysis identified 285 potential genes associated with BHF treatment for DOR in ESI(+) mode and 177 in ESI(−) mode. The combined analysis indicated that linoleic acid metabolism is the primary pathway in treating DOR with BHF. ConclusionBHF was found to improve ovarian function in rats with DOR induced by Tripterygium glycosides. The study identified key metabolites such as phosphatidylcholine (PC), cholesteryl ester (CE), diacylglycerol (DAG), triacylglycerol (TAG), and the linoleic acid metabolism pathway, which were crucial in improving ovarian function in DOR rats treated with BHF.

Effectiveness of Treatments That Alter Metabolomics in Cancer Patients-A Systematic Review.

Cancer is the leading cause of death worldwide, with the most frequent being breast cancer in women, prostate cancer in men and colon cancer in both sexes. The use of metabolomics to find new biomarkers can provide knowledge about possible interventions based on the presence of oncometabolites in different cancer types. The primary purpose of this review is to analyze the characteristic metabolome of three of the most frequent cancer types. We further want to identify the existence and success rate of metabolomics-based intervention in patients suffering from those cancer types. Our conclusions are based on the analysis of the methodological quality of the studies. We searched for studies that investigated the metabolomic characteristics in patients suffering from breast cancer, prostate cancer or colon cancer in clinical trials. The data were analyzed, as well as the effects of specific interventions based on identified metabolomics and one or more oncometabolites. The used databases were PubMed, Virtual Health Library, Web of Science, EBSCO and Cochrane Library. Only nine studies met the selection criteria. Study bias was analyzed using the Cochrane risk of bias tool. This systematic review protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023401474). Only nine studies about clinical trials were included in this review and show a moderate quality of evidence. Metabolomics-based interventions related with disease outcome were conflictive with no or small changes in the metabolic characteristics of the different cancer types. This systematic review shows some interesting results related with metabolomics-based interventions and their effects on changes in certain cancer oncometabolites. The small number of studies we identified which fulfilled our inclusion criteria in this systematic review does not allow us to draw definitive conclusions. Nevertheless, some results can be considered as promising although further research is needed. That research must focus not only on the presence of possible oncometabolites but also on possible metabolomics-based interventions and their influence on the outcome in patients suffering from breast cancer, prostate cancer or colon cancer.