Tag: metabolomics

Integrated ‘omics analysis for the gut microbiota response to moxibustion in a rat model of chronic fatigue syndrome

Abstract:

Objective: To observe the efficacy of moxibustion in the treatment of chronic fatigue syndrome (CFS) and explore the effects on gut microbiota and metabolic profiles.

Methods: Forty-eight male Sprague-Dawley rats were randomly assigned to control group (Con), CFS model group (Mod, established by multiple chronic stress for 35 d), MoxA group (CFS model with moxibustion Shenque (CV8) and Guanyuan (CV4), 10 min/d, 28 d) and MoxB group (CFS model with moxibustion Zusanli (ST36), 10 min/d, 28 d).

Open-field test (OFT) and Morris-water-maze test (MWMT) were determined for assessment the CFS model and the therapeutic effects of moxibustion.16S rRNA gene sequencing analysis based gut microbiota integrated untargeted liquid chromatograph-mass spectrometer (LC-MS) based fecal metabolomics were executed, as well as Spearman correlation analysis, was utilized to uncover the functional relevance between the potential metabolites and gut microbiota.

Results: The results of our behavioral tests showed that moxibustion improved the performance of CFS rats in the OFT and the MWMT. Microbiome profiling analysis revealed that the gut microbiomes of CFS rats were less diverse with altered composition, including increases in pro-inflammatory species (such as Proteobacteria) and decreases in anti-inflammatory species (such as Bacteroides, Lactobacillus, Ruminococcus, and Prevotella). Moxibustion partially normalized these changes in the gut microbiota.

Furthermore, CFS was associated with metabolic disorders, which were effectively ameliorated by moxibustion. This was demonstrated by the normalization of 33 microbiota-related metabolites, including mannose (P = 0.001), aspartic acid (P = 0.009), alanine (P = 0.007), serine (P = 0.000), threonine (P = 0.027), methionine (P = 0.023), 5-hydroxytryptamine (P = 0.008), alpha-linolenic acid (P = 0.003), eicosapentaenoic acid (P = 0.006), hypoxanthine (P = 0.000), vitamin B6 (P = 0.000), cholic acid (P = 0.013), and taurocholate (P = 0.002).

Correlation analysis showed a significant association between the perturbed fecal microbiota and metabolite levels, with a notable negative relationship between LCA and Bacteroides.

Conclusions: In this study, we demonstrated that moxibustion has an antifatigue-like effect. The results from the 16S rRNA gene sequencing and metabolomics analysis suggest that the therapeutic effects of moxibustion on CFS are related to the regulation of gut microorganisms and their metabolites. The increase in Bacteroides and decrease in LCA may be key targets for the moxibustion treatment of CFS.

Source: Chaoran LI, Yan Y, Chuwen F, Heng LI, Yuanyuan QU, Yulin W, Delong W, Qingyong W, Jing G, Tianyu S, Xiaowei S, Xue W, Yunlong H, Zhongren S, Tiansong Y. Integrated ‘omics analysis for the gut microbiota response to moxibustion in a rat model of chronic fatigue syndrome. J Tradit Chin Med. 2023 Oct;43(6):1176-1189. doi: 10.19852/j.cnki.jtcm.20231018.004. PMID: 37946480; PMCID: PMC10623263. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623263/ (Full text)

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Abstract:

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power.

We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as “prior information” into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels.

Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients.

Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Source: Brydges C, Che X, Lipkin WI, Fiehn O. Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts. Metabolites. 2023 Aug 31;13(9):984. doi: 10.3390/metabo13090984. PMID: 37755264; PMCID: PMC10535181. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535181/ (Full text)

A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating disease with a significant global prevalence of over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. A multidisciplinary approach is essential for diagnosing, treating, and managing this complex disease.

The current study aims at employing explainable artificial intelligence (XAI) and machine learning (ML) techniques to identify discriminative metabolites for ME/CFS.

Material and Methods: The present study used a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites, classified into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics.

Random forest-based feature selection and Bayesian Approach based-hyperparameter optimization were implemented on the target data. Four different ML algorithms [Gaussian Naive Bayes (GNB), Gradient Boosting Classifier (GBC), Logistic regression (LR) and Random Forest Classifier (RFC)] were used to classify individuals as ME/CFS patients and healthy individuals. XAI approaches were applied to clinically explain the prediction decisions of the optimum model. Performance evaluation was performed using the indices of accuracy, precision, recall, F1 score, Brier score, and AUC.

Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis.

The RFC learning model outperformed GNB, GBC, and LR in ME/CFS prediction using the 1000 iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC.

Conclusion: RFC model proposed in this study correctly classified and evaluated ME/CFS patients through the selected biomarker candidate metabolites. The methodology combining ML and XAI can provide a clear interpretation of risk estimation for ME/CFS, helping physicians intuitively understand the impact of key metabolomics features in the model.

Source: Yagin, F.H., Alkhateeb, A., Raza, A., Samee, N.A., Mahmoud, N.F., Colak, C., & Yagin, B. (2023). A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Preprints. https://doi.org/10.20944/preprints202307.1585.v1 https://www.preprints.org/manuscript/202307.1585/v1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706650/ (Full text of completed study)

Altered Lipid, Energy Metabolism and Oxidative Stress Are Common Features in a Range of Chronic Conditions

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Gulf War Syndrome (GWS) and Fibromyalgia are chronic illnesses that, despite their prevalence in society, are still of unknown aetiology. All three conditions present similar clinical symptoms and are difficult to diagnose due to a lack of appropriate biomarkers. Currently, diagnosis consists of satisfying clinical criteria and eliminating other conditions, a lengthy and often costly process for patients. The discovery of biomarkers would significantly speed up patient diagnosis and allow the development of pharmacological therapies that target the underlying metabolic causes of these diseases.

Metabolomics is an emerging research area used to characterise the metabolites present within biological specimens. Developments within this field now allow the analysis of thousands of metabolites within different samples and model systems, and have the potential to aid in unravelling the metabolic phenotypes that underpin complex metabolic diseases. ME/CFS, GWS and Fibromyalgia are three conditions that could benefit from a plasma/tissue metabolomics analysis, allowing a greater understanding of their aetiology and identify common pathways. An analysis of the literature in these conditions reveals alterations within pathways associated with energy and lipid metabolism with alterations in key metabolites associated with elevated oxidative stress. Understanding what might drive the elevated oxidative stress within all three illnesses will not only be important in future research but could also be a potential therapeutic target for antioxidant medications which could be implemented to reduce the symptom burden in these illnesses.

Source: MORTEN, Karl Jonathan and Davis, Leah and Lodge, Tiffany A. and Strong, James and Espejo-Oltra, José Andrés and Zalewski, Pawel and Pretorius, Etheresia, Altered Lipid, Energy Metabolism and Oxidative Stress Are Common Features in a Range of Chronic Conditions. Available at SSRN: https://ssrn.com/abstract=4455366 or http://dx.doi.org/10.2139/ssrn.4455366 (Full text available as PDF file)

The plasma metabolome of long COVID-19 patients two years after infection

Abstract:

Background One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as “long COVID”) which has emerged as a consequence of the SARS-CoV-2 epidemic. The World Health Organization (WHO) recognized long COVID as a distinct clinical entity in 2021. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. This has put a tremendous strain on still-overstretched healthcare systems around the world.

Methods In this study, our goal was to assess the plasma metabolome in a total of 108 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC-MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin concentrations were measured in long COVID patients by immunoenzymatic assay.

Results The comparison of paired COVID-19/post-COVID-19 samples revealed 53 metabolites that were statistically different (FDR < 0.05). Compared to controls, 29 metabolites remained dysregulated even after two years. Notably, glucose, kynurenine, and certain acylcarnitines continued to exhibit altered concentrations similar to the COVID-19 phase, while sphingomyelins and long saturated and monounsaturated LysoPCs, phenylalanine, butyric acid, and propionic acid levels normalized. Post-COVID-19 patients displayed a heterogeneous metabolic profile, with some showing no symptoms while others exhibiting a variable number of symptoms. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, sarcosine, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients.

Conclusions Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

Source: Yamilé López-Hernández, Joel Monárrez Aquino, David Alejandro García López, Jiamin Zheng, Juan Carlos Borrego, Claudia Torres-Calzada, José Pedro Elizalde-Díaz, Rupasri Mandal, Mark Berjanskii, Eduardo Martínez-Martínez, Jesús Adrián López, David S. Wishart. The plasma metabolome of long COVID-19 patients two years after infection. doi: https://doi.org/10.1101/2023.05.03.23289456 (Full text)

Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients

Abstract:

Background: Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course.

Methods: This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). 1H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes.

Results: Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients.

Conclusion: The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

Source: Berezhnoy G, Bissinger R, Liu A, Cannet C, Schäfer H, Kienzle K, Bitzer M, Häberle H, Göpel S, Trautwein C, Singh Y. Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients. Front Immunol. 2023 May 9;14:1144224. doi: 10.3389/fimmu.2023.1144224. PMID: 37228606; PMCID: PMC10203989. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203989/ (Full text)

Ginsenoside Rg1 can reverse fatigue behavior in CFS rats by regulating EGFR and affecting Taurine and Mannose 6-phosphate metabolism

Abstract:

Background: Chronic fatigue syndrome (CFS) is characterized by significant and persistent fatigue. Ginseng is a traditional anti-fatigue Chinese medicine with a long history in Asia, as demonstrated by clinical and experimental studies. Ginsenoside Rg1 is mainly derived from ginseng, and its anti-fatigue metabolic mechanism has not been thoroughly explored.

Methods: We performed non-targeted metabolomics of rat serum using LC-MS and multivariate data analysis to identify potential biomarkers and metabolic pathways. In addition, we implemented network pharmacological analysis to reveal the potential target of ginsenoside Rg1 in CFS rats. The expression levels of target proteins were measured by PCR and Western blotting.

Results: Metabolomics analysis confirmed metabolic disorders in the serum of CFS rats. Ginsenoside Rg1 can regulate metabolic pathways to reverse metabolic biases in CFS rats. We found a total of 34 biomarkers, including key markers Taurine and Mannose 6-phosphate. AKT1, VEGFA and EGFR were identified as anti-fatigue targets of ginsenoside Rg1 using network pharmacological analysis. Finally, biological analysis showed that ginsenoside Rg1 was able to down-regulate the expression of EGFR.

Conclusion: Our results suggest ginsenoside Rg1 has an anti-fatigue effect, impacting the metabolism of Taurine and Mannose 6-phosphate through EGFR regulation. This demonstrates ginsenoside Rg1 is a promising alternative treatment for patients presenting with chronic fatigue syndrome.

Source: Lei C, Chen J, Huang Z, Men Y, Qian Y, Yu M, Xu X, Li L, Zhao X, Jiang Y, Liu Y. Ginsenoside Rg1 can reverse fatigue behavior in CFS rats by regulating EGFR and affecting Taurine and Mannose 6-phosphate metabolism. Front Pharmacol. 2023 Apr 10;14:1163638. doi: 10.3389/fphar.2023.1163638. PMID: 37101547; PMCID: PMC10123289. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123289/ (Full text)

Immunometabolic rewiring in long COVID patients with chronic headache

Abstract:

Almost 20% of patients with COVID-19 experience long-term effects, known as post-COVID condition or long COVID. Among many lingering neurologic symptoms, chronic headache is the most common. Despite this health concern, the etiology of long COVID headache is still not well characterized. Here, we present a longitudinal multi-omics analysis of blood leukocyte transcriptomics, plasma proteomics and metabolomics of long COVID patients with chronic headache. L

ong COVID patients experienced a state of hyper-inflammation prior to chronic headache onset and maintained persistent inflammatory activation throughout the progression of chronic headache. Metabolomic analysis also revealed augmented arginine and lipid metabolisms, skewing towards a nitric oxide-based pro-inflammation. Furthermore, metabolisms of neurotransmitters including serotonin, dopamine, glutamate, and GABA were markedly dysregulated during the progression of long COVID headache.

Overall, these findings illustrate the immuno-metabolomics landscape of long COVID patients with chronic headache, which may provide insights to potential therapeutic interventions.

Source: Foo SS, Chen W, Jung KL, Azamor T, Choi UY, Zhang P, Comhair SA, Erzurum SC, Jehi L, Jung JU. Immunometabolic rewiring in long COVID patients with chronic headache. bioRxiv [Preprint]. 2023 Mar 6:2023.03.06.531302. doi: 10.1101/2023.03.06.531302. PMID: 36945569; PMCID: PMC10028820. https://www.biorxiv.org/content/10.1101/2023.03.06.531302v1.full (Full text)

Urine Metabolomics Exposes Anomalous Recovery after Maximal Exertion in Female ME/CFS Patients

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with unknown etiology or effective treatments. Post-exertional malaise (PEM) is a key symptom that distinguishes ME/CFS patients. Investigating changes in the urine metabolome between ME/CFS patients and healthy subjects following exertion may help us understand PEM.
The aim of this pilot study was to comprehensively characterize the urine metabolomes of eight female healthy sedentary control subjects and ten female ME/CFS patients in response to a maximal cardiopulmonary exercise test (CPET). Each subject provided urine samples at baseline and 24 h post-exercise. A total of 1403 metabolites were detected via LC-MS/MS by Metabolon® including amino acids, carbohydrates, lipids, nucleotides, cofactors and vitamins, xenobiotics, and unknown compounds.
Using a linear mixed effects model, pathway enrichment analysis, topology analysis, and correlations between urine and plasma metabolite levels, significant differences were discovered between controls and ME/CFS patients in many lipid (steroids, acyl carnitines and acyl glycines) and amino acid subpathways (cysteine, methionine, SAM, and taurine; leucine, isoleucine, and valine; polyamine; tryptophan; and urea cycle, arginine and proline).
Our most unanticipated discovery is the lack of changes in the urine metabolome of ME/CFS patients during recovery while significant changes are induced in controls after CPET, potentially demonstrating the lack of adaptation to a severe stress in ME/CFS patients.
Source: Glass KA, Germain A, Huang YV, Hanson MR. Urine Metabolomics Exposes Anomalous Recovery after Maximal Exertion in Female ME/CFS Patients. International Journal of Molecular Sciences. 2023; 24(4):3685. https://doi.org/10.3390/ijms24043685 https://www.mdpi.com/1422-0067/24/4/3685 (Full text available as PDF file)

Multi-‘omics of gut microbiome-host interactions in short- and long-term myalgic encephalomyelitis/chronic fatigue syndrome patients

Highlights

  • Multi-‘omics identified phenotypic, gut microbial, and metabolic biomarkers for ME/CFS.
  • Reduced gut microbial diversity and increased plasma sphingomyelins in ME/CFS.
  • Short-term patients had more severe gut microbial dysbiosis with decreased butyrate.
  • Long-term patients had more significant metabolic and clinical aberrations

Summary

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, debilitating disorder manifesting as severe fatigue and post-exertional malaise. The etiology of ME/CFS remains elusive.

Here, we present a deep metagenomic analysis of stool combined with plasma metabolomics and clinical phenotyping of two ME/CFS cohorts with short-term (<4 years, n = 75) or long-term disease (>10 years, n = 79) compared with healthy controls (n = 79).

First, we describe microbial and metabolomic dysbiosis in ME/CFS patients. Short-term patients showed significant microbial dysbiosis, while long-term patients had largely resolved microbial dysbiosis but had metabolic and clinical aberrations.

Second, we identified phenotypic, microbial, and metabolic biomarkers specific to patient cohorts. These revealed potential functional mechanisms underlying disease onset and duration, including reduced microbial butyrate biosynthesis and a reduction in plasma butyrate, bile acids, and benzoate.

In addition to the insights derived, our data represent an important resource to facilitate mechanistic hypotheses of host-microbiome interactions in ME/CFS.

Source: Ruoyun Xiong, Courtney Gunter, Elizabeth Fleming, Suzanne D. Vernon, Lucinda Bateman, Derya Unutmaz, Julia Oh. Multi-‘omics of gut microbiome-host interactions in short- and long-term myalgic encephalomyelitis/chronic fatigue syndrome patients. Cell Host & Microbe 31, 273–287. https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(23)00021-5 (Full text)