The viral origin of myalgic encephalomyelitis/chronic fatigue syndrome

Comparison of the symptom networks of long-COVID and chronic fatigue syndrome: From modularity to connectionism

The objective was to compare the symptom networks of long-COVID and chronic fatigue syndrome (CFS) in conjunction with other theoretically relevant diagnoses in order to provide insight into the etiology of medically unexplained symptoms (MUS). This was a cross-sectional comparison of questionnaire items between six groups identified by clinical diagnosis. All participants completed a 65-item psychological and somatic symptom questionnaire (GSQ065). Diagnostically labelled groups were long-COVID (N = 107), CFS (N = 254), irritable bowel syndrome (IBS, N = 369), fibromyalgia (N = 1,127), severe asthma (N = 100) and healthy group (N = 207). The 22 symptoms that best discriminated between the six groups were selected for network analysis. Connectivity, fragmentation and number of symptom clusters (statistically related symptoms) were assessed. Compared to long-COVID, the symptom networks of CFS, IBS and fibromyalgia had significantly lower connectivity, greater fragmentation and more symptom clusters. The number of clusters varied between 9 for CFS and 3 for severe asthma, and the content of clusters varied across all groups. Of the 33 symptom clusters identified over the six groups 30 clusters were unique. Although the symptom networks of long-COVID and CFS differ, the variation of cluster content across the six groups is inconsistent with a modular causal structure but consistent with a connectionist (network, parallel distributed processing) biological basis of MUS. A connectionist structure would explain why symptoms overlap and merge between different functional somatic syndromes, the failure to discover a biological diagnostic test and how psychological and behavioral interventions are therapeutic.

Epigenetic reprograming in myalgic encephalomyelitis/chronic fatigue syndrome: A narrative of latent viruses

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic disease presenting with severe fatigue, post-exertional malaise, and cognitive disturbances-among a spectrum of symptoms-that collectively render the patient housebound or bedbound. Epigenetic studies in ME/CFS collectively confirm alterations and/or malfunctions in cellular and organismal physiology associated with immune responses, cellular metabolism, cell death and proliferation, and neuronal and endothelial cell function. The sudden onset of ME/CFS follows a major stress factor that, in approximately 70% of cases, involves viral infection, and ME/CFS symptoms overlap with those of long COVID. Viruses primarily linked to ME/CFS pathology are the symbiotic herpesviruses, which follow a bivalent latent-lytic lifecycle. The complex interaction between viruses and hosts involves strategies from both sides: immune evasion and persistence by the viruses, and immune activation and viral clearance by the host. This dynamic interaction is imperative for herpesviruses that facilitate their persistence through epigenetic regulation of their own and the host genome. In the current article, we provide an overview of the epigenetic signatures demonstrated in ME/CFS and focus on the potential strategies that latent viruses-particularly Epstein-Barr virus-may employ in long-term epigenetic reprograming in ME/CFS. Epigenetic studies could aid in elucidating relevant biological pathways impacted in ME/CFS and reflect the physiological variations among the patients that stem from environmental triggers, including exogenous viruses and/or altered viral activity.

BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome

Chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-'omics dataset for ME/CFS to date. This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-'omics to asymptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification. We also created the first connectivity map of these 'omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS. Thus, we proposed several innovative mechanistic hypotheses for ME/CFS: Disrupted microbial functions - SCFA (butyrate), BCAA (amino acid), tryptophan, benzoate - lost connection with plasma lipids and bile acids, and activated inflammatory and mucosal immune cells (MAIT, γδT cells) with INFγ and GzA secretion. These abnormal dynamics are linked to key disease symptoms, including gastrointestinal issues, fatigue, and sleep problems.

Exploring the Pathophysiology of Long COVID: The Central Role of Low-Grade Inflammation and Multisystem Involvement

Long COVID (LC), also referred to as Post COVID-19 Condition, Post-Acute Sequelae of SARS-CoV-2 Infection (PASC), and other terms, represents a complex multisystem disease persisting after the acute phase of COVID-19. Characterized by a myriad of symptoms across different organ systems, LC presents significant diagnostic and management challenges. Central to the disorder is the role of low-grade inflammation, a non-classical inflammatory response that contributes to the chronicity and diversity of symptoms observed. This review explores the pathophysiological underpinnings of LC, emphasizing the importance of low-grade inflammation as a core component. By delineating the pathogenetic relationships and clinical manifestations of LC, this article highlights the necessity for an integrated approach that employs both personalized medicine and standardized protocols aimed at mitigating long-term consequences. The insights gained not only enhance our understanding of LC but also inform the development of therapeutic strategies that could be applicable to other chronic conditions with similar pathophysiological features.

Learning points about myalgic encephalomyelitis/chronic fatigue syndrome: Bridging the gap between research, clinical practice and awareness

This essay examines the complex landscape of myalgic encephalomyelitis, commonly known as chronic fatigue syndrome, highlighting its chronic and multisystemic nature with elusive causative factors. It discusses clinical challenges in diagnosis and management, emphasising the importance of increased education and awareness among healthcare professionals. The role of empathic, person-centred care in improving patient outcomes is underscored, urging for a paradigm shift towards understanding and addressing the profound impact of myalgic encephalitis/chronic fatigue syndrome on patients' lives.

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.

Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a serious and poorly understood disease. To understand immune dysregulation in ME/CFS, we use single-cell RNA sequencing (scRNA-seq) to examine immune cells in patient and control cohorts. Postexertional malaise (PEM), an exacerbation of symptoms following strenuous exercise, is a characteristic symptom of ME/CFS. To detect changes coincident with PEM, we applied scRNA-seq on the same cohorts following exercise. At baseline, ME/CFS patients display classical monocyte dysregulation suggestive of inappropriate differentiation and migration to tissue. We identify both diseased and more normal monocytes within patients, and the fraction of diseased cells correlates with disease severity. Comparing the transcriptome at baseline and postexercise challenge, we discover patterns indicative of improper platelet activation in patients, with minimal changes elsewhere in the immune system. Taken together, these data identify immunological defects present at baseline in patients and an additional layer of dysregulation in platelets.

A Characterization of Neurology Consults for Inpatients with SARS-CoV-2 Infection Compared to Other Respiratory Viruses

Introduction: Neurological consultation for patients infected with SARS-CoV-2 is common; it is currently unknown whether the neurologist's approach to inpatient consultation of patients with SARS-CoV-2 should differ from the paradigm used to evaluate hospitalized patients with similar respiratory viruses. The goal of the present study is to determine if the preponderance of new neurologic diagnoses differs between inpatients with SARS-CoV-2 and similar non-SARS-CoV-2 respiratory viruses for whom neurology is consulted. Methods: We performed a retrospective chart analysis of inpatient neurologic consultations at three major Philadelphia-based hospitals. We compared the final neurologic diagnosis of 152 patients infected with SARS-CoV-2 to 54 patients with a similar ubiquitous non-SARS-CoV-2 respiratory virus (influenza A, influenza B, respiratory syncytial virus, rhinovirus, or adenovirus, the most commonly tested respiratory viruses at our institution). Secondary metrics included age, sex, level of care, prior neurologic diagnoses, and mortality. A multinomial logistic regression model was utilized to evaluate the relative difference between diagnostic category groups on all metrics. Results: The proportion of patients with seizure who were infected with SARS-CoV-2 admitted to an intensive care unit (ICU) was significantly higher than those who were admitted to a medical-surgical floor. SARS-CoV-2 was also associated with increased risk for ICU admission compared to other common respiratory viruses. SARS-CoV-2 inpatients requiring neurologic consultation were also more likely to be older and female as compared to the non-SARS-CoV-2 cohort. In other domains, the proportion of neurologic diagnoses between SAR-CoV-2 and non-SARS-CoV-2 respiratory viruses showed no significant difference. Conclusion: Patients requiring inpatient neurologic consultation with a diagnosis of SARS-CoV-2 infection or another respiratory virus were found to be remarkably similar in terms of their ultimate neurologic diagnosis, with the exception of a larger preponderance of seizure in critical-care-level patients with SARS-CoV-2 infection. Our study suggests that the neurological approach to patients hospitalized with SARS-CoV-2 should be similar to that for patients with similar common respiratory infections, noting that seizure was seen more frequently in critically ill patients infected with SARS-CoV-2.