Category: Biomarker

Immunosignature Analysis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Abstract:

A random-sequence peptide microarray can interrogate serum antibodies in a broad, unbiased fashion to generate disease-specific immunosignatures. This approach has been applied to cancer detection, diagnosis of infections, and interrogation of vaccine response. We hypothesized that there is an immunosignature specific to ME/CFS and that this could aid in the diagnosis.

We studied two subject groups meeting the Canadian Consensus Definition of ME/CFS. ME/CFS (n = 25) and matched control (n = 25) sera were obtained from a Canadian study. ME/CFS (n = 25) sera were obtained from phase 1/2 Norwegian trials (NCT01156909). Sera from six healthy controls from the USA were included in the analysis. Canadian cases and controls were tested for a disease immunosignature.

By combining results from unsupervised and supervised analyses, a candidate immunosignature with 654 peptides was able to differentiate ME/CFS from controls. The immunosignature was tested and further refined using the Norwegian and USA samples. This resulted in a 256-peptide immunosignature with the ability to separate ME/CFS cases from controls in the international data sets.

We were able to identify a 256-peptide signature that separates ME/CFS samples from healthy controls, suggesting that the hit-and-run hypothesis of immune dysfunction merits further investigation. By extending testing of both our signature and one previously reported in the literature to larger cohorts, and further interrogating the specific peptides we and others have identified, we may deepen our understanding of the origins of ME/CFS and work towards a clinically meaningful diagnostic biomarker.

Source: Günther, O.P., Gardy, J.L., Stafford, P. et al. Immunosignature Analysis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) Mol Neurobiol (2018). https://doi.org/10.1007/s12035-018-1354-8  https://link.springer.com/article/10.1007%2Fs12035-018-1354-8 (Full article)

Cardiopulmonary Exercise Test Methodology for Assessing Exertion Intolerance in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Background: Concise methodological directions for administration of serial cardiopulmonary exercise testing (CPET) are needed for testing of patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Maximal CPET is used to evaluate the coordinated metabolic, muscular, respiratory and cardiac contributions to energy production in patients with ME/CFS. In this patient population, CPET also elicits a robust post-exertional symptom flare (termed, post-exertional malaise); a cardinal symptom of the disease. CPET measures are highly reliable and reproducible in both healthy and diseased populations. However, evidence to date indicates that ME/CFS patients are uniquely unable to reproduce CPET measures during a second test, despite giving maximal effort during both tests, due to the effects of PEM on energy production.

Methodology: To document and assess functional impairment due to the effects of post-exertional malaise in ME/CFS, a 2-day CPET procedure (2-day CPET) has been used to first measure baseline functional capacity (CPET1) and provoke post-exertional malaise, then assess changes in CPET variables 24 h later with a second CPET to assess the effects of post-exertional malaise on functional capacity. The second CPET measures changes in energy production and physiological function, objectively documenting the effects of post-exertional malaise. Use of CPET as a standardized stressor to induce post-exertional malaise and quantify impairment associated with post-exertional malaise has been employed to examine ME/CFS pathology in several studies. This article discusses the results of those studies, as well as the standardized techniques and procedures for use of the 2-day CPET in ME/CFS patients, and potentially other fatiguing illnesses.

Conclusions: Basic concepts of CPET are summarized, and special considerations for performing CPET on ME/CFS patients are detailed to ensure a valid outcome. The 2-day CPET methodology is outlined, and the utility of the procedure is discussed for assessment of functional capacity and exertion intolerance in ME/CFS.

Source: Staci Stevens, Chris Snell, Jared Stevens, Betsy Keller and J. Mark VanNess.  Cardiopulmonary Exercise Test Methodology for Assessing Exertion Intolerance in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Front. Pediatr., 04 September 2018 | https://doi.org/10.3389/fped.2018.00242 https://www.frontiersin.org/articles/10.3389/fped.2018.00242/full  (Full article)

A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy

Abstract:

Chronic fatigue syndrome (CFS), also called myalgic encephalomyelitis (ME), is a debilitating disorder characterized by physical and mental exhaustion. Mitochondrial and energetic dysfunction has been investigated in CFS patients due to a hallmark relationship with fatigue, however, no consistent conclusion has yet been achieved.

Single-cell Raman spectra (SCRS) are label-free biochemical profiles, indicating phenotypic fingerprints of single cells. In this study, we applied a new approach using single-cell Raman microspectroscopy (SCRM) to examine 0 cells that lack mitochondrial DNA (mtDNA), and peripheral blood mononuclear cells (PBMCs) from CFS patients and healthy controls.

The experimental results show that Raman bands associated with phenylalanine in 0 cells and CFS patient PBMCs were significantly higher than wild type model and healthy controls. Remarkably, an increase in intensities of Raman phenylalanine bands were also observed in CFS patients. As similar changes were observed in the 0 cell model with a known deficiency in the mitochondrial respiratory chain as well as in CFS patients, our results suggest that the increase in cellular phenylalanine may relate to mitochondrial/energetic dysfunction in both systems.

Interestingly, phenylalanine can be used as a potential biomarker for diagnosis of CFS by SCRM. A machine learning classification model achieved an accuracy rate of 98% correctly assigning Raman spectra to either the CFS group or the control group. SCRM combined with machine learning algorithm therefore has the potential to become a diagnostic tool for CFS.

Source: Jiabao Xu, Michelle Potter, Cara Tomas, Jo Elson, Karl Morten, Joanna Poulton, Ning Wang, Hanqing Jin, Zhaoxu Hou and Wei Huang. A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy. Analyst, 22 Aug 2018.  http://pubs.rsc.org/en/Content/ArticleLanding/2018/AN/C8AN01437J#!divAbstract

Insights from metabolites get us closer to a test for chronic fatigue syndrome

Press Release: Columbia University’s Mailman School of Public Health, July 9, 2018. A study led by researchers at the Center for Infection and Immunity (CII) at Columbia University’s Mailman School of Public Health has identified a constellation of metabolites related to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Combining this data with data from an earlier microbiome study, the researchers now report they can predict whether or not someone has the disorder with a confidence of 84 percent.

The research team analyzed blood samples provided by 50 patients with ME/CFS and 50 controls matched for sex and age who were recruited at four clinical sites across the United States. Using mass spectrometry, a laboratory technique used to identify molecules by measuring their mass, the scientists found 562 metabolites — microscopic byproducts of human and microbial processes such as sugar, fat, and protein molecules. They excluded molecules related to antidepressants and other drugs patients might be taking.

Their metabolomics analysis, among the most detailed and meticulous to date, uncovered altered levels of metabolites, including choline, carnitine and several complex lipids present in patients with ME/CFS. The altered metabolites suggest dysfunction of the mitochrondria, the cellular powerplant, a finding in line with those reported by other research teams. Uniquely, the CII study also reports a second distinct pattern of metabolites in patients with ME/CFS and irritable bowel syndrome (IBS), matching earlier findings from their 2017 fecal microbiome study. Half of the patients with ME/CFS also had IBS.

When the researchers combined biomarkers from both the microbiome study and the new metabolome study, they reported a .836 predictive score, indicating an 84 percent certainty as to the presence of ME/CFS — better than with either study alone.

“This is a strong predictive model that suggests we’re getting close to the point where we’ll have lab tests that will allow us to say with a high level of certainty who has this disorder,” says first author Dorottya Nagy-Szakal, MD, PhD, a researcher at CII.

Continue reading “Insights from metabolites get us closer to a test for chronic fatigue syndrome” →

Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics

Abstract:

The pathogenesis of ME/CFS, a disease characterized by fatigue, cognitive dysfunction, sleep disturbances, orthostatic intolerance, fever, irritable bowel syndrome (IBS), and lymphadenopathy, is poorly understood.

We report biomarker discovery and topological analysis of plasma metabolomic, fecal bacterial metagenomic, and clinical data from 50 ME/CFS patients and 50 healthy controls. We confirm reports of altered plasma levels of choline, carnitine and complex lipid metabolites and demonstrate that patients with ME/CFS and IBS have increased plasma levels of ceramide.

Integration of fecal metagenomic and plasma metabolomic data resulted in a stronger predictive model of ME/CFS (cross-validated AUC = 0.836) than either metagenomic (cross-validated AUC = 0.745) or metabolomic (cross-validated AUC = 0.820) analysis alone. Our findings may provide insights into the pathogenesis of ME/CFS and its subtypes and suggest pathways for the development of diagnostic and therapeutic strategies.

Source: Dorottya Nagy-Szakal, Dinesh K. Barupal, Bohyun Lee, Xiaoyu Che, Brent L. Williams, Ellie J. R. Kahn, Joy E. Ukaigwe, Lucinda Bateman, Nancy G. Klimas, Anthony L. Komaroff, Susan Levine, Jose G. Montoya, Daniel L. Peterson, Bruce Levin, Mady Hornig, Oliver Fiehn & W. Ian Lipkin . Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics. Scientific Reports, volume 8, Article number: 10056 (2018) https://www.nature.com/articles/s41598-018-28477-9 (Full article)

A Glimpse into Dr. Ron Davis’ Talk in London

From Open Medicine Foundation.
June 1, 2018

Dear Friends,

I prepared this statement for Ashley Haugen to read yesterday at the Western Massachusetts  Department of Public Health screening of Unrest. This is new information from the Severely ill Patient Study (SIPS) that I also presented in London:

“We have made considerable progress in analyzing the data from the severely ill patient study. This has taken some time because we have only had one bioinformatic scientist analyzing the massive amount of data.

We have found that there are a considerable number of mutations that are more common in ME/CFS patients than in healthy controls. This would suggest that these mutations make a patient more susceptible to having ME/CFS. It could also indicate that some of the mutations are responsible for the severity of the patients we studied. We also see a large number of metabolomic changes that have been previously seen in less severe patients. These metabolomic differences between healthy controls and our severely ill patients are often much bigger than in studies with less severe patients. A more detailed analysis of this data may aid us in developing treatments.

One area we are currently studying using the genetic and metabolomic data is the possibility there may be one or more metabolic traps. This is a metabolic state that a patient can develop, possibly caused by physical stress such as infection. Once a patient is in this state they cannot easily get out by rest.

We are conducting system biology and pathway analysis that shows that a metabolic trap is possible, and that some of the observed mutations make it more likely. If this is the case we should be able to push the patients out of this state by a specific metabolic intervention. We are very hopeful that this could be a one time treatment, take only a few days, and be relatively inexpensive.”

Sending greetings from London,

Ronald W. Davis, PhD
Director, OMF ME/CFS Scientific Advisory Board
Director, Stanford Genome Technology Center

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients

Abstract:

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction.

We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

Source: Sevel LS, Boissoneault J, Letzen JE, Robinson ME, Staud R. Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients. Exp Brain Res. 2018 May 30. doi: 10.1007/s00221-018-5301-8. [Epub ahead of print] https://www.ncbi.nlm.nih.gov/pubmed/29846797

Circulating extracellular vesicles as potential biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis: an exploratory pilot study

Abstract:

Chronic Fatigue Syndrome (CFS), also known as Myalgic Encephalomyelitis (ME) is an acquired, complex and multisystem condition of unknown etiology, no established diagnostic lab tests and no universally FDA-approved drugs for treatment. CFS/ME is characterised by unexplicable disabling fatigue and is often also associated with numerous core symptoms. A growing body of evidence suggests that extracellular vesicles (EVs) play a role in cell-to-cell communication, and are involved in both physiological and pathological processes. To date, no data on EV biology in CFS/ME are as yet available.

The aim of this study was to isolate and characterise blood-derived EVs in CFS/ME. Blood samples were collected from 10 Spanish CFS/ME patients and 5 matched healthy controls (HCs), and EVs were isolated from the serum using a polymer-based method. Their protein cargo, size distribution and concentration were measured by Western blot and nanoparticle tracking analysis. Furthermore, EVs were detected using a lateral flow immunoassay based on biomarkers CD9 and CD63.

We found that the amount of EV-enriched fraction was significantly higher in CFS/ME subjects than in HCs (p = 0.007) and that EVs were significantly smaller in CFS/ME patients (p = 0.014). Circulating EVs could be an emerging tool for biomedical research in CFS/ME. These findings provide preliminary evidence that blood-derived EVs may distinguish CFS/ME patients from HCs. This will allow offer new opportunities and also may open a new door to identifying novel potential biomarkers and therapeutic approaches for the condition.

Source: Castro-Marrero J, Serrano-Pertierra E, Oliveira-Rodríguez M, Zaragozá MC, Martínez-Martínez A, Blanco-López MDC, Alegre J. Circulating extracellular vesicles as potential biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis: an exploratory pilot study. J Extracell Vesicles. 2018 Mar 22;7(1):1453730. doi: 10.1080/20013078.2018.1453730. eCollection 2018. https://www.ncbi.nlm.nih.gov/pubmed/29696075

Metabolic abnormalities in chronic fatigue syndrome/myalgic encephalomyelitis: a mini-review

Abstract:

Chronic fatigue syndrome (CFS), commonly known as myalgic encephalomyelitis (ME), is a debilitating disease of unknown etiology. CFS/ME is a heterogeneous disease associated with a myriad of symptoms but with severe, prolonged fatigue as the core symptom associated with the disease. There are currently no known biomarkers for the disease, largely due to the lack of knowledge surrounding the eitopathogenesis of CFS/ME. Numerous studies have been conducted in an attempt to identify potential biomarkers for the disease.

This mini-review offers a brief summary of current research into the identification of metabolic abnormalities in CFS/ME which may represent potential biomarkers for the disease. The progress of research into key areas including immune dysregulation, mitochondrial dysfunction, 5′-adenosine monophosphate-activated protein kinase activation, skeletal muscle cell acidosis, and metabolomics are presented here. Studies outlined in this mini-review show many potential causes for the pathogenesis of CFS/ME and identify many potential metabolic biomarkers for the disease from the aforementioned research areas.

The future of CFS/ME research should focus on building on the potential biomarkers for the disease using multi-disciplinary techniques at multiple research sites in order to produce robust data sets. Whether the metabolic changes identified in this mini-review occur as a cause or a consequence of the disease must also be established.

Source: Cara Tomas; Julia Newton. Metabolic abnormalities in chronic fatigue syndrome/myalgic encephalomyelitis: a mini-review. Biochemical Society Transactions Apr 17, 2018; DOI: https://doi.org/10.1042/BST20170503. http://www.biochemsoctrans.org/content/early/2018/04/16/BST20170503

Weighting of orthostatic intolerance time measurements with standing difficulty score stratifies ME/CFS symptom severity and analyte detection

Abstract:

BACKGROUND: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is clinically defined and characterised by persistent disabling tiredness and exertional malaise, leading to functional impairment.

METHODS: This study introduces the weighted standing time (WST) as a proxy for ME/CFS severity, and investigates its behaviour in an Australian cohort. WST was calculated from standing time and subjective standing difficulty data, collected via orthostatic intolerance assessments. The distribution of WST for healthy controls and ME/CFS patients was correlated with the clinical criteria, as well as pathology and cytokine markers. Included in the WST cytokine analyses were activins A and B, cytokines causally linked to inflammation, and previously demonstrated to separate ME/CFS from healthy controls. Forty-five ME/CFS patients were recruited from the CFS Discovery Clinic (Victoria) between 2011 and 2013. Seventeen healthy controls were recruited concurrently and identically assessed.

RESULTS: WST distribution was significantly different between ME/CFS participants and controls, with six diagnostic criteria, five analytes and one cytokine also significantly different when comparing severity via WST. On direct comparison of ME/CFS to study controls, only serum activin B was significantly elevated, with no significant variation observed for a broad range of serum and urine markers, or other serum cytokines.

CONCLUSIONS: The enhanced understanding of standing test behaviour to reflect orthostatic intolerance as a ME/CFS symptom, and the subsequent calculation of WST, will encourage the greater implementation of this simple test as a measure of ME/CFS diagnosis, and symptom severity, to the benefit of improved diagnosis and guidance for potential treatments.

Source: Richardson AM, Lewis DP, Kita B, Ludlow H, Groome NP, Hedger MP, de Kretser DM, Lidbury BA. Weighting of orthostatic intolerance time measurements with standing difficulty score stratifies ME/CFS symptom severity and analyte detection. J Transl Med. 2018 Apr 12;16(1):97. doi: 10.1186/s12967-018-1473-z. https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-018-1473-z (Full article)