Plasma Metabolomics Research Articles

Targeted plasma metabolomics reveals potential biomarkers of the elderly with mild cognitive impairment in Qingdao rural area

IntroductionPrevious research has suggested a link between the onset of Alzheimer’s disease (AD) and metabolic disorder; however, the findings have been inconsistent. To date, the majority of metabolomics studies have focused on AD, resulting in a relative paucity of research on early-stage conditions such as mild cognitive impairment (MCI) underexplored. In this study, we employed a comprehensive platform for the early screening of individuals with MCI using high-throughput targeted metabolomics.MethodWe included 171 participants including 124 individuals with MCI and 47 healthy subjects. Univariate statistical analysis was conducted using t-tests or Wilcoxon rank-sum tests, with p-values corrected by the Benjamini-Hochberg method. The screening criteria were set at FDR < 0.05 and fold change (FC) > 1.5 or < 0.67. Multivariate analysis was performed using orthogonal partial least squares discriminant analysis (OPLS-DA), where differential metabolites were identified based on variable influence on projection (VIP) scores (VIP > 1 and FDR < 0.05). Random forest analysis was used to further evaluate the ability of the metabolic data to distinguish effectively between the two groups.ResultsA total of 14 differential metabolites were identified, leading to the discovery of a biomarker panel consisting of three plasma metabolites including uric acid, pyruvic acid and isolithocholic acid that effectively distinguished MCI patients from healthy subjects.DiscussionThese findings have provided a comprehensive metabolic profile, offering valuable insights into the early prediction and understanding of the pathogenic processes underlying MCI. This study holds the potential for advancing early detection and intervention strategies for MCI.

Plasma metabolomics profiling of EGFR-mutant NSCLC patients treated with third-generation EGFR-TKI

Third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the latest and a vital treatment option for non-small cell lung cancer (NSCLC) patients. Although EGFR-sensitive mutations are an indication for third-generation EGFR-TKI therapy, 30% of NSCLC patients lack response and all patients inevitably progress. There is a lack of biomarkers to predict the efficacy of EGFR-TKI therapy. In this report, we performed comprehensive plasma metabolomic profiling on 186 baseline and 20 post-treatment samples, analyzing 1,019 metabolites using four ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. The dataset contains detailed clinical and metabolic information for 186 patients. Rigorous quality control measures were implemented. No significant differences in body mass index and biochemical metabolic parameters were observed between responders and non-responders. The datasets were utilized to characterize the responsive metabolic traits of third-generation EGFR-TKI therapy. All datasets are available for download on the OMIX website. We anticipate that these datasets will serve as valuable resources for future studies investigating NSCLC metabolism and for the development of personalized therapeutic strategies.

Altered amino and fatty acids metabolism in Sudanese prostate cancer patients: insights from metabolic analysis.

Prostate cancer (PCa) management presents a multifaceted clinical challenge, intricately linking oncological considerations with cardiovascular health. Despite the recognized importance of lipid metabolism and hypertension in this interwoven relationship, their involvement in PCa development remains partially understood. This study aimed to explore variations in plasma metabolome among Sudanese PCa patients and their associated comorbidities. Plasma samples were collected from 50 patients across four hospitals in Sudan and profiled by nuclear magnetic resonance (NMR) spectroscopy. One-dimensional proton NMR spectra were acquired for each sample using standard nuclear Overhauser effect spectroscopy pulse sequence presat on a 500 MHz Bruker Avance III HD NMR spectrometer. Metabolite concentrations were quantified using R scripts developed in-house. Univariate and multivariate analyses were generated in the R software. Patients were categorized into four distinct metabotypes based on their metabolic profiles, and statistical analyses were conducted to evaluate the significance of observed differences. Our findings revealed high levels of fatty acids, phospholipids, cholesterol, valine, leucine, and isoleucine associated with non-hypertensive patients. In contrast, hypertensive patients were associated with high GlycA and GlycB levels and altered amino acid metabolism. These findings underscore the intricate interplay between metabolic dysregulation and hypertension in PCa patients. Further research is warranted to elucidate the precise molecular pathways underlying lipid metabolism in PCa and to explore the therapeutic potential of targeting these pathways. In conclusion, our study contributes to a deeper understanding of the metabolic landscape of PCa in Sudanese patients, emphasizing the importance of personalized approaches in cancer management.

Comparative analysis of the plasma metabolome of migrating passerines: novel insights into stopover metabolism

During long‐distance migration, many birds experience periods of either prolonged fasting (during endurance flights) or extensive feeding (during stopovers). Despite decades of research on avian metabolism during migration, many questions have remained unanswered, as such research mainly focused on targeted metabolites and fat metabolism. Here, we examined the plasma‐metabolome of two migrating passerine species before they crossed the Sahara Desert. Birds were sampled at two sites populated by Pistacia trees bearing fat‐rich fruits and at an additional site dominated by blooming Eucalyptus trees. The blood samples were analyzed using both GC‐MS and LC‐MS, using an untargeted approach. Examination of metabolic pathways activated during stopovers indicated a crucial role for cycling glucose through the Cori and Cahill cycles in resting and recovery processes. This novel perspective, conducted on free‐ranging birds, suggests the evolution of avian insulin resistance due to factors such as endurance exercise, fasting, and a preference for fatty acid oxidation during migration, akin to cell trauma recovery. We detected significant inter‐site variations in birds' polar and lipophilic metabolic profiles. We interpret the differences in the polar metabolites to be associated with the physiological state of the birds, with birds that are considered to have landed during the night prior to capture showing different metabolic profiles compared to birds that have spent more time at the stopover site. In contrast, distinctions in the lipophilic profiles of birds were associated with variations in the primary food source that was available to them in the different sites. This study underscores the challenge of interpreting commonly used indicators for assessing migrating birds' physiological state, which was predominantly derived from lipid metabolism in complex ecological systems.

Untargeted metabolomics and lipidomics in COVID-19 patient plasma reveals disease severity biomarkers.

Coronavirus disease 2019 (COVID-19) has widely varying clinical severity. Currently, no single marker or panel of markers is considered standard of care for prediction of COVID-19 disease progression. The goal of this study is to gain mechanistic insights at the molecular level and to discover predictive biomarkers of severity of infection and outcomes among COVID-19 patients. This cohort study (n = 76) included participants aged 16-78years who tested positive for SARS-CoV-2 and enrolled in Memphis, TN between August 2020 to July 2022. Clinical outcomes were classified as Non-severe (n = 39) or Severe (n = 37). LC/HRMS-based untargeted metabolomics/lipidomics was conducted to examine the difference in plasma metabolome and lipidome between the two groups. Metabolomics data indicated that the kynurenine pathway was activated in Severe participants. Significant increases in short chain acylcarnitines, and short and medium chain acylcarnitines containing OH-FA chain in Severe vs. Non-severe group, which indicates that (1) the energy pathway switched to FA β-oxidation to maintain the host energy homeostasis and to provide energy for virus proliferation; (2) ROS status was aggravated in Severe vs. Non-severe group. Based on PLS-DA and correlation analysis to severity score, IL-6, and creatine, a biomarker panel containing glucose (pro-inflammation), ceramide and S1P (inflammation related), 4-hydroxybutyric acid (oxidative stress related), testosterone sulfate (immune related), and creatine (kidney function), was discovered. This novel biomarker panel plus IL-6 with an AUC of 0.945 provides a better indication of COVID-19 clinical outcomes than that of IL-6 alone or the three clinical biomarker panel (IL-6, glucose and creatine) with AUCs of 0.875 or 0.892.

Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers.

Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived biomarkers. We analyzed a multicenter public dataset comprising 61 OSCC patients and 61 healthy controls. Plasma metabolomics data were processed to extract 29 numerical and 47 ratio features. The Extra Trees (ET) algorithm was applied for feature selection, and the TabPFN model was used for classification and prediction. The model achieved an area under the curve (AUC) of 93% and an overall accuracy of 76.6% when using top-ranked individual biomarkers. Key metabolic features significantly differentiated OSCC patients from healthy controls, providing a detailed metabolic fingerprint of the disease. Our findings demonstrate the utility of integrating omics data with advanced machine learning techniques to develop accurate, non-invasive diagnostic tools for OSCC. The study highlights actionable metabolic signatures that have potential applications in personalized therapeutics and early intervention strategies.

Integrated Multi-Omics Analyses Reveal Lipid Metabolic Signature in Osteoarthritis

Osteoarthritis (OA) is the most common degenerative joint disease and the second leading cause of disability worldwide. Single-omics analyses are far from elucidating the complex mechanisms of lipid metabolic dysfunction in OA. This study identified a shared lipid metabolic signature of OA by integrating metabolomics, single-cell and bulk RNA-seq, as well as metagenomics. Compared to the normal counterparts, cartilagesin OA patients exhibited significant depletion of homeostatic chondrocytes (HomCs) (P=0.03) and showed lipid metabolic disorders in linoleic acid metabolism and glycerophospholipid metabolism which was consistent with our findings obtained from plasma metabolomics. Through high-dimensional weighted gene co-expression network analysis (hdWGCNA), weidentified PLA2G2A as a hub gene associated with lipid metabolic disorders in HomCs. And an OA-associated subtype of HomCs, namely HomC1 (marked by PLA2G2A, MT-CO1, MT-CO2, and MT-CO3) was identified, which also exhibited abnormal activation of lipid metabolic pathways. This suggests the involvement of HomC1 in OA progression through the shared lipid metabolism aberrancies, which were further validated via bulk RNA-Seq analysis. Metagenomic profiling identified specific gut microbial species significantly associated with the key lipid metabolism disorders, including Bacteroides uniformis (P<0.001, R=-0.52), Klebsiella pneumonia (P=0.003, R=0.42), Intestinibacter_bartlettii (P=0.009, R=0.38), and Streptococcus anginosus (P=0.009, R=0.38). By integrating the multi-omics features, a random forest diagnostic model with outstanding performance was developed (AUC=0.97). In summary, this study deciphered the crucial role of a integrated lipid metabolic signature in OA pathogenesis, and established a regulatory axis of gut microbiota-metabolites-cell-gene, providing new insights into the gut-joint axis and precision therapy for OA.

Comparison of the effects of taurine and methionine supplementation on the nitrogen metabolism of beef steers elucidated through plasma metabolome profiling

The objectives of the experiment were to compare the effects of rumen-protected taurine (RPT) and rumen-protected methionine (RPM) on the nitrogen (N) metabolism, plasma biochemical parameters, and metabolomics in beef steers and to clarify whether taurine plays similar roles as methionine (Met) in improving the regulation of N metabolism in beef steers. Six Simmental steers aged 12 months (liveweight 325 ± 7 kg) were used as experimental animals. The experimental treatments included a basal diet, the basal diet + 70.0 g/d RPT and the basal diet + 74.2 g/d RPM. The treatments were assigned in a replicated 3 × 3 Latin square design. Each experimental period included 15 d of adaptation and 5 d of sampling. The results showed that supplementing the diet with RPT or RPM did not affect the apparent nutrient digestibility (P > 0.05). Supplementing the diet with RPT or RPM increased the N retention (P < 0.05) and the N utilization efficiency (NUE) (P < 0.05) and decreased the urinary excretion of 3-Methylhistidine (P < 0.05) and the estimated skeletal protein degradation rate (P < 0.05). Supplementing the diet with RPT increased the plasma concentrations of taurine (P < 0.001), cysteine (P = 0.010), valine (P = 0.013) and total non-essential amino acids (NEAA) (P = 0.047) and tended to increase the plasma concentrations of essential amino acids (EAA) + NEAA (P = 0.087), but it did not affect the plasma concentrations of total EAA (P > 0.05). Supplementing the diet with RPM increased the plasma concentrations of methionine (P = 0.033), lysine (P = 0.047), cysteine (P = 0.007), leucine (P = 0.046), isoleucine (P = 0.046), valine (P = 0.034), total EAA (P = 0.028), total NEAA (P = 0.004) and EAA + NEAA (P = 0.004). The plasma metabolomics profiling revealed that supplementing the diet with RPT upregulated the plasma concentrations of taurine (P < 0.001), L-Cysteine (P = 0.004) and some amino acid (AA) analogues (P < 0.05) and RPM upregulated the plasma concentrations of Met (P = 0.021), L-Isoleucine (P = 0.036), L-Tryptophan (P = 0.006) and some AA analogues (P < 0.05). In conclusion, taurine has similar impacts to Met in improving the N retention and the NUE in beef steers. Taurine deficiency is one of the main reasons that the NUE of beef steers is low. Supplementation of the diet with taurine is beneficial to the N utilization in beef steers.

Metabolomics based early warning model for acute kidney injury risk in patients exposed to diquat.

Acute kidney injury (AKI) is one of the most important indications of severe clinical symptoms in patients with diquat poisoning and is closely related to poor prognosis. However, current studies have rarely focused on early warnings of diquat-related AKI, which is not conducive to the treatment of patients with early clinical diquat poisoning. In this study, untargeted plasma metabolomics was employed to reveal the differences between diquat-poisoned patients with and without AKI, as well as between patients and healthy volunteers. The results showed that 48 metabolites were significantly changed in the patients, among which 3-hydroxybutyrylcarnitine, SAICAR, dodecanoic acid, and tetrahydrofolyl-[Glu](2) could be used to effectively differentiate the above three groups. Based on the ratios of the first two metabolites and the ratios of the last two metabolites, a decision tree model for the early warning of diquat-induced AKI was established with an accuracy rate of 88.7 %. This model provides great support for accurate clinical diagnosis and intervention regarding the AKI risk of diquat-exposed patients.

Blood metabolomic profiling reveals new targets in the management of psychological symptoms associated with severe alcohol use disorder.

Alcohol use disorder (AUD) is a global health problem with limited therapeutic options. The biochemical mechanisms that lead to this disorder are not yet fully understood, and in this respect, metabolomics represents a promising approach to decipher metabolic events related to AUD. The plasma metabolome contains a plethora of bioactive molecules that reflects the functional changes in host metabolism but also the impact of the gut microbiome and nutritional habits. In this study, we investigated the impact of severe AUD (sAUD), and of a 3-week period of alcohol abstinence, on the blood metabolome (non-targeted LC-MS metabolomics analysis) in 96 sAUD patients hospitalized for alcohol withdrawal. We found that the plasma levels of different lipids ((lyso)phosphatidylcholines, long-chain fatty acids), short-chain fatty acids (i.e. 3-hydroxyvaleric acid) and bile acids were altered in sAUD patients. In addition, several microbial metabolites, including indole-3-propionic acid, p-cresol sulfate, hippuric acid, pyrocatechol sulfate, and metabolites belonging to xanthine class (paraxanthine, theobromine and theophylline) were sensitive to alcohol exposure and alcohol withdrawal. 3-Hydroxyvaleric acid, caffeine metabolites (theobromine, paraxanthine, and theophylline) and microbial metabolites (hippuric acid and pyrocatechol sulfate) were correlated with anxiety, depression and alcohol craving. Metabolomics analysis in postmortem samples of frontal cortex and cerebrospinal fluid of those consuming a high level of alcohol revealed that those metabolites can be found also in brain tissue. Our data allow the identification of neuroactive metabolites, from interactions between food components and microbiota, which may represent new targets arising in the management of neuropsychiatric diseases such as sAUD. Gut2Behave project was initiated from ERA-NET NEURON network (Joint Transnational Call 2019) and was financed by Academy of Finland, French National Research Agency (ANR-19-NEUR-0003-03) and the Fonds de la Recherche Scientifique (FRS-FNRS; PINT-MULTI R.8013.19, Belgium). Metabolomics analysis of the TSDS samples was supported by grant from the Finnish Foundation for Alcohol Studies.

Multiomics of yaks reveals significant contribution of microbiome into host metabolism

An intensive feeding system might improve the production cycle of yaks. However, how intensive feeding system contributes to yak growth is unclear. Here, multi-omics, including rumen metagenomics, rumen and plasma metabolomics, were performed to classify the regulatory mechanisms of intensive feeding system on yaks. Increased growth performance were observed. Rumen metagenomics revealed that Clostridium, Methanobrevibacter, Piromyces and Anaeromyces increased in the intensively fed yaks, contributing to amino acid and carbohydrate metabolism. The grazing yaks had more cellulolytic microbes. These microbiomes were correlated with the pathways of “Alanine aspartate and glutamate metabolism” and “Pyruvate metabolism”. Intensive feeding increased methane degradation functions, while grazing yaks had higher methyl metabolites associated with methane production. These rumen microbiomes and their metabolites resulted in changes in plasma metabolome, finally influencing yaks’ growth. Thus, an intensive feeding system altered the rumen microbiome and metabolism as well as host metabolism, resulting in improvements of yak growth.

Plasma metabolite profiles related to dietary patterns: exploring the association with colorectal tumor risk.

Multiple diet patterns play a crucial role in the development of colorectal cancer and its precursor, colorectal adenoma, but mediating effect of plasma metabolite profiles is unclear. A total of 95,275 participants from UK Biobank with plasma metabolomics and dietary information were analyzed. Metabolite profile scores for 14 dietary patterns were estimated through elastic net regression. Cox regression analysis assessed the associations of dietary patterns and their metabolite profile scores with colorectal tumor risk. Mediating effects of identified metabolite profile scores were estimated in the associations. Fourteen metabolite profile scores, including a range of 28 to 205 signatures, were weak to moderate correlation with dietary patterns (all p < 0.001). Multivariable Cox regression analyses revealed that five dietary patterns were significantly correlated with a decreased risk of colorectal tumor after FDR correction and adjustment for covariates. HRs (95% CIs) per 1 SD for these diet patterns were as follows: WCRF (0.93, 0.90-0.96), CRC score (0.94, 0.92-0.97), AHEI-2010 (0.95, 0.92-0.97), DASH (0.94, 0.91-0.97), and hPDI (0.95, 0.93-0.98). Similarly, metabolite profile scores for these five dietary patterns were inversely associated with colorectal tumor risk, with HRs (95% CIs) per 1 SD as follows: WCRF (0.59, 0.49-0.70), CRC score (0.67, 0.58-0.77), AHEI-2010 (0.73, 0.65-0.80), DASH (0.75, 0.66-0.84), and hPDI (0.56, 0.47-0.67). The mediation proportions of five metabolite profile scores between dietary patterns and colorectal tumor risk ranged from 6.37 to 27.23% (all p < 0.001). Five dietary patterns and their metabolite profile scores, were inversely correlated with colorectal tumor risk. These findings highlight the potential of metabolite profiles as mediators in the association between dietary patterns and the risk of colorectal tumor, further contributing to the prevention of colorectal cancer or adenoma and providing new insights for future research.

Coffee consumption is associated with intestinal Lawsonibacter asaccharolyticus abundance and prevalence across multiple cohorts

Although diet is a substantial determinant of the human gut microbiome, the interplay between specific foods and microbial community structure remains poorly understood. Coffee is a habitually consumed beverage with established metabolic and health benefits. We previously found that coffee is, among >150 items, the food showing the highest correlation with microbiome components. Here we conducted a multi-cohort, multi-omic analysis of US and UK populations with detailed dietary information from a total of 22,867 participants, which we then integrated with public data from 211 cohorts (N = 54,198). The link between coffee consumption and microbiome was highly reproducible across different populations (area under the curve of 0.89), largely driven by the presence and abundance of the species Lawsonibacter asaccharolyticus. Using in vitro experiments, we show that coffee can stimulate growth of L. asaccharolyticus. Plasma metabolomics on 438 samples identified several metabolites enriched among coffee consumers, with quinic acid and its potential derivatives associated with coffee and L. asaccharolyticus. This study reveals a metabolic link between a specific gut microorganism and a specific food item, providing a framework for the understanding of microbial dietary responses at the biochemical level.

Plasma metabolomic characteristics of atrial fibrillation patients with spontaneous echo contrast

BackgroundThe spontaneous echo contrast (SEC) in patients with atrial fibrillation (AF) indicates a prethrombic state that ultimately progresses into thrombus formation. A comprehensive understanding of specific plasma metabolomics characteristics may protect AF patients from thrombus, particularly in the early stage.ObjectivesThrough the investigation of metabolic pathways, we endeavor to uncover the metabolomic characteristics associated with SEC states, and to examine the differential metabolites by which may exert their influence on thrombotic states.MethodsPatients with AF were enrolled, and the participants were divided into three groups based on the results of the echocardiogram: non-SEC, low-SEC and high-SEC group. Samples were collected and subjected to non-targeted metabolomics analysis. The analytical process included data quality control, metabolite difference analysis, component analysis, Kegg cluster analysis, etc.ResultsOur metabolic phenotype revealed a clear differential metabolic pattern between the SEC and non-SEC. Specifically, we identified 35 and 142 significantly differential metabolites in venous and atrial plasma, respectively, suggesting that SEC may be involved in pervasive metabolic dysregulation and that the degree of metabolic dysregulation in atrial plasma is more severe than that in venous blood.ConclusionPatients with SEC have a significantly different metabolic pattern compared to those without SEC. Our work promoted the understanding of mechanism of the occurrence and development of SEC, facilitated the screening of the target metabolites for its therapeutic intervention, and provided evidence for the prevention and treatment of SEC or thrombosis in AF. Our work also provided new directions for subsequent research in related fields. In conclusion, our study not only provides a theoretical basis for understanding the occurrence and development of SEC in AF, but also provides recommendations for the daily diet of AF patients with SEC, such as a balanced intake of essential amino acids, avoiding excessive intake of benzoic acid, and intake of appropriate inositol.Clinical trial numberNot applicable.

F. prausnitzii potentially modulates the association between citrus intake and depression

BackgroundThe gut microbiome modulates the effects of diet on host health, but it remains unclear which specific foods and microbial features interact to influence risk of depression. To understand this interplay, we leveraged decades of dietary and depression data from a longitudinal cohort of women (n = 32,427), along with fecal metagenomics and plasma metabolomics from a substudy (n = 207) nested in this cohort, as well as an independent validation cohort of men (n = 307).ResultsWe report that citrus intake and its components are prospectively associated with a lower risk of depression and altered abundance of 15 gut microbial species, including enriched Faecalibacterium prausnitzii. In turn, we found a lower abundance of F. prausnitzii and its metabolic pathway, S-adenosyl-L-methionine (SAM) cycle I in participants with depression. To explore causality, we found that lower SAM production by F. prausnitzii may decrease intestinal monoamine oxidase A gene expression implicated in serotonin and dopamine synthesis.ConclusionsThese data underscore the role of diet in the prevention of depression and offer a plausible explanation for how the intestinal microbiome modulates the influence of citrus on mental health.ELPzvN5keGYhLtpEkepcjaVideo

Circulating metabolome in relation to cognitive impairment: a community-based cohort of older adults

The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors. In the Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired groups according to their performance in two objective cognitive tests. A two-step strategy of cross-sectional discovery followed by prospective validation was applied. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately-to-severely impaired) during the 2-year follow-up with 204 cognitively normal controls by age and sex at a 1:3 ratio, and conducted conditional logistic regression. We identified 28 out of 78 metabolites cross-sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were prospectively associated with 28%, 28%, 29%, and 33% lower risk of developing cognitive impairment, respectively. Incorporating 13 metabolite biomarkers selected through Lasso regression into the traditional risk factors-based prediction model substantially improved the ability to predict incident cognitive impairment (AUROC: 0.839 vs. 0.703, P < 0.001; AUPRC: 0.705 vs. 0.405, P < 0.001). This study identified specific plasma metabolites related to cognitive impairment. Incorporation of specific metabolites substantially improved the prediction performance for cognitive impairment.

Regulation of ROS metabolism in macrophage via xanthine oxidase is associated with disease progression in pulmonary tuberculosis.

Pulmonary tuberculosis (PTB) exacerbation can lead to respiratory failure, multi-organ failure, and symptoms related to central nervous system diseases. The purpose of this study is to screen biomarkers and metabolic pathways that can predict the progression of PTB, and to verify the role of the metabolic enzyme xanthine oxidase (XO) in the progression of PTB. To explore the biomarkers and mechanisms underlying the progression of PTB, plasma metabolomics sequencing was conducted on patients with severe PTB, non-severe PTB, and healthy individuals. Screening differential metabolites and metabolic pathways that can predict the progression of PTB, and verifying the function and mechanism of action of XO through experiments. The purine metabolism, sphingolipid metabolism, and amino acid metabolism between the three groups differ. In patients with severe PTB, the levels of xanthosine and hypoxanthine are increased, while the levels of D-tryptophan, dihydroceramide and uric acid are decreased. Inhibition of XO activity has been observed to reduce the levels of tumor necrosis factor (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6), as well as to suppress the production of reactive oxygen species (ROS) and the activation of the NF-κB pathway, while also promoting the growth of MTB within cells. D-tryptophan, xanthosine, and dihydroceramide can be utilized as biomarkers for progression of PTB, assisting in the evaluation of disease progression, and XO stands out as a potential therapeutic target for impeding the progression of PTB.

Chronic corticosterone exposure causes anxiety- and depression-related behaviors with altered gut microbial and brain metabolomic profiles in adult male C57BL/6J mice

Chronic exposure to glucocorticoids in response to long-term stress is thought to be a risk factor for major depression. Depression is associated with disturbances in the gut microbiota composition and peripheral and central energy metabolism. However, the relationship between chronic glucocorticoid exposure, the gut microbiota, and brain metabolism remains largely unknown. In this study, we first investigated the effects of chronic corticosterone exposure on various domains of behavior in adult male C57BL/6J mice treated with the glucocorticoid corticosterone to evaluate them as an animal model of depression. We then examined the gut microbial composition and brain and plasma metabolome in corticosterone-treated mice. Chronic corticosterone treatment resulted in reduced locomotor activity, increased anxiety-like and depression-related behaviors, decreased rotarod latency, reduced acoustic startle response, decreased social behavior, working memory deficits, impaired contextual fear memory, and enhanced cued fear memory. Chronic corticosterone treatment also altered the composition of gut microbiota, which has been reported to be associated with depression, such as increased abundance of Bifidobacterium, Turicibacter, and Corynebacterium and decreased abundance of Barnesiella. Metabolomic data revealed that long-term exposure to corticosterone led to a decrease in brain neurotransmitter metabolites, such as serotonin, 5-hydroxyindoleacetic acid, acetylcholine, and gamma-aminobutyric acid, as well as changes in betaine and methionine metabolism, as indicated by decreased levels of adenosine, dimethylglycine, choline, and methionine in the brain. These results indicate that mice treated with corticosterone have good face and construct validity as an animal model for studying anxiety and depression with altered gut microbial composition and brain metabolism, offering new insights into the neurobiological basis of depression arising from gut-brain axis dysfunction caused by prolonged exposure to excessive glucocorticoids.

Interleukin 6 blockage alters the plasma metabolome in out-of-hospital cardiac arrest

BackgroundComatose patients resuscitated from out-of-hospital cardiac arrest (OHCA) exhibit a systemic inflammatory response, as indicated by elevated interleukin-6 (IL-6) levels, which is associated with increased mortality. Tocilizumab, an IL-6 receptor antagonist that reduced C-reactive protein response and markers of myocardial injury in a phase II OHCA trial. AimTo describe the early effects of tocilizumab on circulating levels of metabolites in comatose patients resuscitated from OHCA. MethodPatients from the phase-II double-blinded randomized trial (NCT: 03863015) were included in this substudy. A total of 85 comatose patients resuscitated from OHCA were randomized at the time of arrival to the hospital to either tocilizumab 8 mg/kg or placebo, of which 80 received the intervention and did not later withdraw from the study. Plasma samples before randomization and 48 h later were analyzed by a targeted metabolomics approach quantifying 60 circulating metabolites. ResultsOf 80 enrolled patients (median age 62 years (IQR: 54–72), men 66 (83 %)), 39 were randomized to tocilizumab group and 41 to placebo. Comorbidities and cardiac arrest characteristics were overall well-balanced. At hospital arrival, levels of metabolites from the tricarboxylic acid (TCA) cycle were associated with time to return of spontaneous circulation and independently with early levels of IL-6 (all p < 0.05). The early levels of medium-chain acylcarnitines were associated with age, NT-proBNP, estimated glomerular filtration rate, and marker of neurological injury (neurofilament light chain) (all p < 0.01). At 48 h, tocilizumab increased the levels of plasma amino acids, especially threonine, glycine, and serine, by more than a factor of 1.5 (p < 0.01). Two eicosanoids 15(S)-HETE and 12(S)-HETE were 1.9 times higher (p < 0.01). ConclusionBlocking the IL-6 receptor with tocilizumab early after OHCA impacts circulating metabolites, particularly those within the glycine, serine, and threonine pathways, highlighting the connection between acute systemic inflammation and metabolism. Further, early levels of TCA metabolites are independently associated with early inflammatory response and early medium-chain acylcarnitine with later markers of neurological injury.

Examining the Clinical Relevance of the AML Plasma Metabolome

Examining the Clinical Relevance of the AML Plasma Metabolome