Genetic risk factors for ME/CFS identified using combinatorial analysis

Why does malaise/fatigue occur? Underlying mechanisms and potential relevance to treatments in rheumatoid arthritis

INTRODUCTION

Fatigue and malaise are commonly associated with a wide range of medical conditions, including rheumatoid arthritis (RA). Evidence suggests that fatigue and malaise can be overwhelming for patients, yet these symptoms remain inadequately-managed, largely due to an incomplete elucidation of the underlying causes.

AREAS COVERED

In this assessment of the published literature relating to the pathogenesis of fatigue or malaise in chronic conditions, four key mechanistic themes were identified. Each theme (inflammation, hypothalamic-pituitary-adrenal axis, dysautonomia, and monoamines) is discussed, as well as the complex network of interconnections between themes which suggests a key role for inflammatory cytokines in the development and persistence of fatigue.

EXPERT OPINION

Fatigue is multifaceted, poorly defined, and imperfectly comprehended. Moreover, the cause and severity of fatigue may change over time, as a consequence of the natural disease course or pharmacologic treatment. This detailed synthesis of available evidence permits us to identify avenues for current treatment optimization and future research, to improve the management of fatigue and malaise in RA. Within the development pipeline, several new anti-inflammatory therapies are currently under investigation, and we anticipate that the next five years will herald much-needed progress to reduce the debilitating nature of fatigue in patients with RA.

Longitudinal cytokine and multi-modal health data of an extremely severe ME/CFS patient with HSD reveals insights into immunopathology, and disease severity

Introduction

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) presents substantial challenges in patient care due to its intricate multisystem nature, comorbidities, and global prevalence. The heterogeneity among patient populations, coupled with the absence of FDA-approved diagnostics and therapeutics, further complicates research into disease etiology and patient managment. Integrating longitudinal multi-omics data with clinical, health,textual, pharmaceutical, and nutraceutical data offers a promising avenue to address these complexities, aiding in the identification of underlying causes and providing insights into effective therapeutics and diagnostic strategies.

Methods

This study focused on an exceptionally severe ME/CFS patient with hypermobility spectrum disorder (HSD) during a period of marginal symptom improvements. Longitudinal cytokine profiling was conducted alongside the collection of extensive multi-modal health data to explore the dynamic nature of symptoms, severity, triggers, and modifying factors. Additionally, an updated severity assessment platform and two applications, ME-CFSTrackerApp and LexiTime, were introduced to facilitate real-time symptom tracking and enhance patient-physician/researcher communication, and evaluate response to medical intervention.

Results

Longitudinal cytokine profiling revealed the significance of Th2-type cytokines and highlighted synergistic activities between mast cells and eosinophils, skewing Th1 toward Th2 immune responses in ME/CFS pathogenesis, particularly in cognitive impairment and sensorial intolerance. This suggests a potentially shared underlying mechanism with major ME/CFS comorbidities such as HSD, Mast cell activation syndrome, postural orthostatic tachycardia syndrome (POTS), and small fiber neuropathy. Additionally, the data identified potential roles of BCL6 and TP53 pathways in ME/CFS etiology and emphasized the importance of investigating adverse reactions to medication and supplements and drug interactions in ME/CFS severity and progression.

Discussion

Our study advocates for the integration of longitudinal multi-omics with multi-modal health data and artificial intelligence (AI) techniques to better understand ME/CFS and its major comorbidities. These findings highlight the significance of dysregulated Th2-type cytokines in patient stratification and precision medicine strategies. Additionally, our results suggest exploring the use of low-dose drugs with partial agonist activity as a potential avenue for ME/CFS treatment. This comprehensive approach emphasizes the importance of adopting a patient-centered care approach to improve ME/CFS healthcare management, disease severity assessment, and personalized medicine. Overall, these findings contribute to our understanding of ME/CFS and offer avenues for future research and clinical practice.

IgG Antibody Responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their Effective Potential for Disease Diagnosis and Pathological Antigenic Mimicry

Background and Objectives: The diagnosis and pathology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections. Materials and Methods: In this work, we analyzed a large public dataset on the IgG antibodies to 3054 EBV peptides to understand whether these immune responses could help diagnose patients and trigger pathological autoimmunity; we used healthy controls (HCs) as a comparator cohort. Subsequently, we aimed at predicting the disease status of the study participants using a super learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets. Results: When we compared the data of all ME/CFS patients or the data of a subgroup of those patients with non-infectious or unknown disease triggers to the data of the HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset. However, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from the HCs with 100% and 90% accuracies in the train and test sets, respectively. We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies. We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies. Conclusions: In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis in a subset of patients. However, the peptides associated with these antibodies are less likely to induce autoimmune B-cell responses that could explain the pathogenesis of ME/CFS.

Mitochondrial Dysfunction and Coenzyme Q10 Supplementation in Post-Viral Fatigue Syndrome: An Overview

Post-viral fatigue syndrome (PVFS) encompasses a wide range of complex neuroimmune disorders of unknown causes characterised by disabling post-exertional fatigue, myalgia and joint pain, cognitive impairments, unrefreshing sleep, autonomic dysfunction, and neuropsychiatric symptoms. It includes myalgic encephalomyelitis, also known as chronic fatigue syndrome (ME/CFS); fibromyalgia (FM); and more recently post-COVID-19 condition (long COVID). To date, there are no definitive clinical case criteria and no FDA-approved pharmacological therapies for PVFS. Given the current lack of effective treatments, there is a need to develop novel therapeutic strategies for these disorders. Mitochondria, the cellular organelles responsible for tissue energy production, have recently garnered attention in research into PVFS due to their crucial role in cellular bioenergetic metabolism in these conditions. The accumulating literature has identified a link between mitochondrial dysfunction and low-grade systemic inflammation in ME/CFS, FM, and long COVID. To address this issue, this article aims to critically review the evidence relating to mitochondrial dysfunction in the pathogenesis of these disorders; in particular, it aims to evaluate the effectiveness of coenzyme Q10 supplementation on chronic fatigue and pain symptoms as a novel therapeutic strategy for the treatment of PVFS.

Long read sequencing characterises a novel structural variant, revealing underactive AKR1C1 with overactive AKR1C2 as a possible cause of severe chronic fatigue

BACKGROUND

Causative genetic variants cannot yet be found for many disorders with a clear heritable component, including chronic fatigue disorders like myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). These conditions may involve genes in difficult-to-align genomic regions that are refractory to short read approaches. Structural variants in these regions can be particularly hard to detect or define with short reads, yet may account for a significant number of cases. Long read sequencing can overcome these difficulties but so far little data is available regarding the specific analytical challenges inherent in such regions, which need to be taken into account to ensure that variants are correctly identified. Research into chronic fatigue disorders faces the additional challenge that the heterogeneous patient populations likely encompass multiple aetiologies with overlapping symptoms, rather than a single disease entity, such that each individual abnormality may lack statistical significance within a larger sample. Better delineation of patient subgroups is needed to target research and treatment.

METHODS

We use nanopore sequencing in a case of unexplained severe fatigue to identify and fully characterise a large inversion in a highly homologous region spanning the AKR1C gene locus, which was indicated but could not be resolved by short-read sequencing. We then use GC-MS/MS serum steroid analysis to investigate the functional consequences.

RESULTS

Several commonly used bioinformatics tools are confounded by the homology but a combined approach including visual inspection allows the variant to be accurately resolved. The DNA inversion appears to increase the expression of AKR1C2 while limiting AKR1C1 activity, resulting in a relative increase of inhibitory GABAergic neurosteroids and impaired progesterone metabolism which could suppress neuronal activity and interfere with cellular function in a wide range of tissues.

CONCLUSIONS

This study provides an example of how long read sequencing can improve diagnostic yield in research and clinical care, and highlights some of the analytical challenges presented by regions containing tandem arrays of genes. It also proposes a novel gene associated with a novel disease aetiology that may be an underlying cause of complex chronic fatigue. It reveals biomarkers that could now be assessed in a larger cohort, potentially identifying a subset of patients who might respond to treatments suggested by the aetiology.

Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

BACKGROUND

Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that present with similar symptoms.

METHODS

We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics' Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms.

RESULTS

Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases. Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3. Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank. Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS.

CONCLUSION

This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.

REAC Neurobiological Modulation as a Precision Medicine Treatment for Fibromyalgia

Fibromyalgia syndrome (FS) is a disorder characterized by widespread musculoskeletal pain and psychopathological symptoms, often associated with central pain modulation failure and dysfunctional adaptive responses to environmental stress. The Radio Electric Asymmetric Conveyer (REAC) technology is a neuromodulation technology. The aim of this study was to evaluate the effects of some REAC treatments on psychomotor responses and quality of life in 37 patients with FS. Tests were conducted before and after a single session of Neuro Postural Optimization and after a cycle of 18 sessions of Neuro Psycho Physical Optimization (NPPO), using evaluation of the functional dysmetria (FD) phenomenon, Sitting and Standing (SS), Time Up and Go (TUG) tests for motor evaluation, Fibromyalgia Impact Questionnaire (FIQ) for quality of life. The data were statistically analyzed, and the results showed a statistically significant improvement in motor response and quality of life parameters, including pain, as well as reduced FD measures in all participants. The study concludes that the neurobiological balance established by the REAC therapeutic protocols NPO and NPPO improved the dysfunctional adaptive state caused by environmental and exposomal stress in FS patients, leading to an improvement in psychomotor responses and quality of life. The findings suggest that REAC treatments could be an effective approach for FS patients, reducing the excessive use of analgesic drugs and improving daily activities.

Towards a Better Understanding of the Complexities of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex condition arising in susceptible people, predominantly following viral infection, but also other stressful events. The susceptibility factors discussed here are both genetic and environmental although not well understood. While the dysfunctional physiology in ME/CFS is becoming clearer, understanding has been hampered by different combinations of symptoms in each affected person. A common core set of mainly neurological symptoms forms the modern clinical case definition, in the absence of an accessible molecular diagnostic test. This landscape has prompted interest in whether ME/CFS patients can be classified into a particular phenotype/subtype that might assist better management of their illness and suggest preferred therapeutic options. Currently, the same promising drugs, nutraceuticals, or behavioral therapies available can be beneficial, have no effect, or be detrimental to each individual patient. We have shown that individuals with the same disease profile exhibit unique molecular changes and physiological responses to stress, exercise and even vaccination. Key features of ME/CFS discussed here are the possible mechanisms determining the shift of an immune/inflammatory response from transient to chronic in ME/CFS, and how the brain and CNS manifests the neurological symptoms, likely with activation of its specific immune system and resulting neuroinflammation. The many cases of the post viral ME/CFS-like condition, Long COVID, following SARS-CoV-2 infection, and the intense research interest and investment in understanding this condition, provide exciting opportunities for the development of new therapeutics that will benefit ME/CFS patients.