scholarly journals The Gut Microbiome in Myalgic Encephalomyelitis (ME)/Chronic Fatigue Syndrome (CFS)

2022 ◽  
Vol 12 ◽  
Author(s):  
Rahel S. König ◽  
Werner C. Albrich ◽  
Christian R. Kahlert ◽  
Lina Samira Bahr ◽  
Ulrike Löber ◽  
...  
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Lactic Acidosis ◽  
Bacterial Translocation ◽  
Gut Microbiome ◽  
Antibiotic Use ◽  
Chronic Fatigue ◽  
Fecal Microbiota ◽  
Gut Permeability ◽  
Dietary Interventions ◽  
Fatigue Syndrome

Myalgic encephalomyelitis (ME) or Chronic Fatigue Syndrome (CFS) is a neglected, debilitating multi-systemic disease without diagnostic marker or therapy. Despite evidence for neurological, immunological, infectious, muscular and endocrine pathophysiological abnormalities, the etiology and a clear pathophysiology remains unclear. The gut microbiome gained much attention in the last decade with manifold implications in health and disease. Here we review the current state of knowledge on the interplay between ME/CFS and the microbiome, to identify potential diagnostic or interventional approaches, and propose areas where further research is needed. We iteratively selected and elaborated on key theories about a correlation between microbiome state and ME/CFS pathology, developing further hypotheses. Based on the literature we hypothesize that antibiotic use throughout life favours an intestinal microbiota composition which might be a risk factor for ME/CFS. Main proposed pathomechanisms include gut dysbiosis, altered gut-brain axis activity, increased gut permeability with concomitant bacterial translocation and reduced levels of short-chain-fatty acids, D-lactic acidosis, an abnormal tryptophan metabolism and low activity of the kynurenine pathway. We review options for microbiome manipulation in ME/CFS patients including probiotic and dietary interventions as well as fecal microbiota transplantations. Beyond increasing gut permeability and bacterial translocation, specific dysbiosis may modify fermentation products, affecting peripheral mitochondria. Considering the gut-brain axis we strongly suspect that the microbiome may contribute to neurocognitive impairments of ME/CFS patients. Further larger studies are needed, above all to clarify whether D-lactic acidosis and early-life antibiotic use may be part of ME/CFS etiology and what role changes in the tryptophan metabolism might play. An association between the gut microbiome and the disease ME/CFS is plausible. As causality remains unclear, we recommend longitudinal studies. Activity levels, bedridden hours and disease progression should be compared to antibiotic exposure, drug intakes and alterations in the composition of the microbiota. The therapeutic potential of fecal microbiota transfer and of targeted dietary interventions should be systematically evaluated.

scholarly journals Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome

2020 ◽  
Author(s):  
Ruud Raijmakers ◽  
Megan E. Roerink ◽  
Anne F.M. Jansen ◽  
Stephan P. Keijmel ◽  
Ranko Gacesa ◽  
...  
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Gut Microbiome ◽  
Inflammatory Markers ◽  
Molecular Mechanisms ◽  
Q Fever ◽  
Chronic Fatigue ◽  
Metagenomic Sequencing ◽  
Illumina Hiseq ◽  
Fatigue Syndrome ◽  
Q Fever Fatigue Syndrome

Abstract Background: Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). Methods: The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. Results: Inflammatory markers, including 4E-BP1 (P = 9.60-16 and 1.41-7) and MMP-1 (P = 7.09-9 and 3.51-9), are significantly more expressed in both QFS and CFS patients compared to HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobacteria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found.Conclusions: We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.

The reification of the clinical diagnosis of myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) as an immune and oxidative stress disorder: construction of a data-driven nomothethic network and exposure of ME/CFS subgroups.

2021 ◽  
Vol 21 ◽  
Author(s):  
Michael Maes ◽  
Marta Kubera ◽  
Kristina Stoyanova ◽  
Jean-Claude Leunis
Keyword(s):  
Chronic Fatigue Syndrome ◽  
T Cell Activation ◽  
Bacterial Translocation ◽  
Coenzyme Q10 ◽  
Cell Activation ◽  
Chronic Fatigue ◽  
Myalgic Encephalomyelitis ◽  
Data Driven ◽  
Fatigue Syndrome ◽  
Fierce Competition

: The approach towards myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) remains in a permanent state of crisis with fierce competition between the psychosocial school, which attributes ME/CFS to the perception of effort, and the medical approach (Maes and Twisk, BMC Med, 2010,8,35). The aim of this paper is to review how to construct a nomothetic model of ME/CFS using Partial Least Squares (PLS) path analysis and ensembling causome (bacterial translocation as assessed with IgM/IgA responses to LPS), protectome (lowered coenzyme Q10), adverse outcome pathways (AOP) including increased lysozyme, CD38+ T cell activation, cell-mediated immune activation (CMI), and IgM responses to oxidative specific epitopes and NO-adducts (IgM OSENO). Using PLS, we trained, tested and validated this knowledge- and data-driven causal ME/CFS model, which showed adequate convergence, construct and replicability validity. This bottom-up explicit data model of ME/CFS objectivates the descriptive narratives of the ME/CFS phenome, using causome-protectome-AOP data, whereby the abstract concept ME/CFS is translated into pathways, thereby securing the reification of the ME/CFS phenome. We found that 31.6% of the variance in the physiosomatic symptom dimension of ME/CFS was explained by the cumulative effects of CMI and CD38+ activation, IgM OSENO, IgA LPS, lysozyme (all positive) and coenzyme Q10 (inversely). Cluster analysis performed on the PLS-generated latent vector scores of all feature sets exposed three distinct immune groups of ME/CFS, namely one with increased lysozyme, one with increased CMI + CD38 activation + depressive symptoms, and another with increased bacterial translocation + autoimmune responses to OSENO.

scholarly journals Reduced diversity and altered composition of the gut microbiome in individuals with myalgic encephalomyelitis/chronic fatigue syndrome

Microbiome ◽  
2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Ludovic Giloteaux ◽  
Julia K. Goodrich ◽  
William A. Walters ◽  
Susan M. Levine ◽  
Ruth E. Ley ◽  
...  
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Gut Microbiome ◽  
Chronic Fatigue ◽  
Myalgic Encephalomyelitis ◽  
Fatigue Syndrome

scholarly journals Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Ruud P. H. Raijmakers ◽  
Megan E. Roerink ◽  
Anne F. M. Jansen ◽  
Stephan P. Keijmel ◽  
Ranko Gacesa ◽  
...  
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Gut Microbiome ◽  
Inflammatory Markers ◽  
Molecular Mechanisms ◽  
Q Fever ◽  
Chronic Fatigue ◽  
Metagenomic Sequencing ◽  
Illumina Hiseq ◽  
Fatigue Syndrome ◽  
Q Fever Fatigue Syndrome

Abstract Background Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). Methods The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. Results Inflammatory markers, including 4E-BP1 (P = 9.60–16 and 1.41–7) and MMP-1 (P = 7.09–9 and 3.51–9), are significantly more expressed in both QFS and CFS patients compared to HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobacteria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found. Conclusions We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.

scholarly journals Deficient Butyrate-Producing Capacity in the Gut Microbiome of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Patients is Associated with Fatigue Symptoms

2021 ◽  
Author(s):  
Cheng Guo ◽  
Xiaoyu Che ◽  
Thomas Briese ◽  
Orchid Allicock ◽  
Rachel A. Yates ◽  
...  
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Gut Microbiome ◽  
Symptom Severity ◽  
Chronic Fatigue ◽  
The United States ◽  
Small Sample ◽  
Myalgic Encephalomyelitis ◽  
Healthy Controls ◽  
Shotgun Metagenomics ◽  
Fatigue Syndrome

Abstract Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, debilitating disease of unknown cause for which there is no specific therapy. Patients suffering from ME/CFS commonly experience persistent fatigue, post-exertional malaise, cognitive dysfunction, sleep disturbances, orthostatic intolerance, fever and irritable bowel syndrome (IBS). Recent evidence implicates gut microbiome dysbiosis in ME/CFS. However, most prior studies are limited by small sample size, differences in clinical criteria used to define cases, limited geographic sampling, reliance on bacterial culture or 16S rRNA gene sequencing, or insufficient consideration of confounding factors that may influence microbiome composition. In the present study, we evaluated the fecal microbiome in the largest prospective, case-control study to date (n=106 cases, n=91 healthy controls), involving subjects from geographically diverse communities across the United States. Results: Using shotgun metagenomics and qPCR and rigorous statistical analyses that controlled for important covariates, we identified decreased relative abundance and quantity of Faecalibacterium , Roseburia , and Eubacterium species and increased bacterial load in feces of subjects with ME/CFS. These bacterial taxa play an important role in the production of butyrate, a multifunctional bacterial metabolite that promotes human health by regulating energy metabolism, inflammation, and intestinal barrier function. Functional metagenomic and qPCR analyses were consistent with a deficient microbial capacity to produce butyrate along the acetyl-CoA pathway in ME/CFS. Metabolomic analyses of short-chain fatty acids (SCFAs) confirmed that fecal butyrate concentration was significantly reduced in ME/CFS. Further, we found that the degree of deficiency in butyrate-producing bacteria correlated with fatigue symptom severity among ME/CFS subjects. Finally, we provide evidence that IBS comorbidity is an important covariate to consider in studies investigating the microbiome of ME/CFS subjects, as differences in microbiota alpha diversity, some bacterial taxa, and propionate were uniquely associated with self-reported IBS diagnosis. Conclusions: Our findings indicate that there is a core deficit in the butyrate-producing capacity of the gut microbiome in ME/CFS subjects compared to healthy controls. The relationships we observed among symptom severity and these gut microbiome disturbances may be suggestive of a pathomechanistic linkage, however, additional research is warranted to establish any causal relationship. These findings provide support for clinical trials that explore the utility of dietary, probiotic and prebiotic interventions to boost colonic butyrate production in ME/CFS.

scholarly journals Multi-omics Examination of Q Fever Fatigue Syndrome Identifies Similarities with Chronic Fatigue Syndrome

2020 ◽  
Author(s):  
Ruud Raijmakers ◽  
Megan E. Roerink ◽  
Anne F.M. Jansen ◽  
Stephan P. Keijmel ◽  
Ranko Gacesa ◽  
...  
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Gut Microbiome ◽  
Inflammatory Markers ◽  
Molecular Mechanisms ◽  
Q Fever ◽  
Chronic Fatigue ◽  
Metagenomic Sequencing ◽  
Illumina Hiseq ◽  
Fatigue Syndrome ◽  
Q Fever Fatigue Syndrome

Abstract Background: Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). Methods: The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. Results: Inflammatory markers, including 4E-BP1 (P = 9.60-16 and 1.41-7) and MMP-1 (P = 7.09-9 and 3.51-9), are significantly more expressed in both QFS and CFS patients compared to HC, and QFS patients show more of an inflammatory profile than CFS patients and HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobateria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found. Conclusions: We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.

scholarly journals The Reification of the Clinical Diagnosis of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) as an Immune and Oxidative Stress Disorder: Construction of a Data-Driven Nomothethic Network and Exposure of ME/CFS Subgroups

2021 ◽  
Author(s):  
Michael Maes ◽  
Marta Kubera ◽  
Kristina Stoyanova ◽  
Jean–Claude Leunis
Keyword(s):  
Chronic Fatigue Syndrome ◽  
T Cell Activation ◽  
Bacterial Translocation ◽  
Coenzyme Q10 ◽  
Cell Activation ◽  
Chronic Fatigue ◽  
Myalgic Encephalomyelitis ◽  
Data Driven ◽  
Fatigue Syndrome ◽  
Fierce Competition

The approach towards myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) remains in a permanent state of crisis with fierce competition between the psychosocial school, which attributes ME/CFS to the perception of effort, and the medical approach (Maes and Twisk, BMC Med, 2010,8,35). The aim of this paper is to review how to construct a nomothetic model of ME/CFS using Partial Least Squares (PLS) path analysis and ensembling causome (bacterial translocation as assessed with IgM/IgA responses to LPS), protectome (lowered coenzyme Q10), adverse outcome pathways (AOP) including increased lysozyme, CD38+ T cell activation, cell-mediated immune activation (CMI), and IgM responses to oxidative specific epitopes and NO-adducts (IgM OSENO). Using PLS, we trained, tested and validated this knowledge- and data-driven causal ME/CFS model, which showed adequate convergence, construct and replicability validity. This bottom-up explicit data model of ME/CFS objectivates the descriptive narratives of the ME/CFS phenome, using causome-protectome-AOP data, whereby the abstract concept ME/CFS is translated into pathways, thereby securing the reification of the ME/CFS phenome. We found that 31.6% of the variance in the physiosomatic symptom dimension of ME/CFS was explained by the cumulative effects of CMI and CD38+ activation, IgM OSENO, IgA LPS, lysozyme (all positive) and coenzyme Q10 (inversely). Cluster analysis performed on the PLS-generated latent vector scores of all feature sets exposed three distinct immune groups of ME/CFS, namely one with increased lysozyme, one with increased CMI + CD38 activation + depressive symptoms, and another with increased bacterial translocation + autoimmune responses to OSENO.

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