Susan Levine

Distinct plasma immune signatures in ME/CFS are present early in the course of illness

Cytokine alterations are more strongly correlated with illness duration than with measures of illness severity. Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is an unexplained incapacitating illness that may affect up to 4 million people in the United States alone. There are no validated laboratory tests for diagnosis or management despite global efforts to find biomarkers of disease. We considered the possibility that inability to identify such biomarkers reflected variations in diagnostic criteria and laboratory methods as well as the timing of sample collection during the course of the illness. Accordingly, we leveraged two large, multicenter cohort studies of ME/CFS to assess the relationship of immune signatures with diagnosis, illness duration, and other clinical variables. Controls were frequency-matched on key variables known to affect immune status, including season of sampling and geographic site, in addition to age and sex. We report here distinct alterations in plasma immune signatures early in the course of ME/CFS (n = 52) relative to healthy controls (n = 348) that are not present in subjects with longer duration of illness (n = 246). Analyses based on disease duration revealed that early ME/CFS cases had a prominent activation of both pro- and anti-inflammatory cytokines as well as dissociation of intercytokine regulatory networks. We found a stronger correlation of cytokine alterations with illness duration than with measures of illness severity, suggesting that the immunopathology of ME/CFS is not static. These findings have critical implications for discovery of interventional strategies and early diagnosis of ME/CFS.

Neuroendocrine Aspects of Chronic Fatigue Syndrome

Chronic fatigue syndrome (CFS) is a serious health concern affecting over 800,000 Americans of all ages, races, socioeconomic groups and genders. The etiology and pathophysiology of CFS are unknown, yet studies have suggested an involvement of the neuroendocrine system. A symposium was organized in March 2001 to explore the possibility of an association between neuroendocrine dysfunction and CFS, with special emphasis on the interactions between neuroendocrine dysfunction and other abnormalities noted in the immune and autonomic nervous systems of individuals with CFS. This paper represents the consensus of the panel of experts who participated in this meeting.

A Multicenter Blinded Analysis Indicates No Association between Chronic Fatigue Syndrome/Myalgic Encephalomyelitis and either Xenotropic Murine Leukemia Virus-Related Virus or Polytropic Murine Leukemia Virus

ABSTRACT The disabling disorder known as chronic fatigue syndrome or myalgic encephalomyelitis (CFS/ME) has been linked in two independent studies to infection with xenotropic murine leukemia virus-related virus (XMRV) and polytropic murine leukemia virus (pMLV). Although the associations were not confirmed in subsequent studies by other investigators, patients continue to question the consensus of the scientific community in rejecting the validity of the association. Here we report blinded analysis of peripheral blood from a rigorously characterized, geographically diverse population of 147 patients with CFS/ME and 146 healthy subjects by the investigators describing the original association. This analysis reveals no evidence of either XMRV or pMLV infection. IMPORTANCE Chronic fatigue syndrome/myalgic encephalomyelitis has an estimated prevalence of 42/10,000 in the United States, with annual direct medical costs of

Immunologic Aspects of Chronic Fatigue Syndrome

7 billion. Here, the original investigators who found XMRV and pMLV (polytropic murine leukemia virus) in blood of subjects with this disorder report that this association is not confirmed in a blinded analysis of samples from rigorously characterized subjects. The increasing frequency with which molecular methods are used for pathogen discovery poses new challenges to public health and support of science. It is imperative that strategies be developed to rapidly and coherently address discoveries so that they can be carried forward for translation to clinical medicine or abandoned to focus resource investment more productively. Our study provides a paradigm for pathogen dediscovery that may be helpful to others working in this field. Chronic fatigue syndrome/myalgic encephalomyelitis has an estimated prevalence of 42/10,000 in the United States, with annual direct medical costs of

Chronic Fatigue Syndrome: What Role Does the Autonomic Nervous System Play in the Pathophysiology of This Complex Illness?

7 billion. Here, the original investigators who found XMRV and pMLV (polytropic murine leukemia virus) in blood of subjects with this disorder report that this association is not confirmed in a blinded analysis of samples from rigorously characterized subjects. The increasing frequency with which molecular methods are used for pathogen discovery poses new challenges to public health and support of science. It is imperative that strategies be developed to rapidly and coherently address discoveries so that they can be carried forward for translation to clinical medicine or abandoned to focus resource investment more productively. Our study provides a paradigm for pathogen dediscovery that may be helpful to others working in this field.

Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome

Chronic fatigue syndrome (CFS) is a serious health concern affecting over 800,000 Americans of all ages, races, socioeconomic groups and genders. The etiology and pathophysiology of CFS are unknown, yet studies have suggested an involvement of the immune system. A symposium was organized in October 2001 to explore the possibility of an association between immune dysfunction and CFS, with special emphasis on the interactions between immune dysfunction and other abnormalities noted in the neuroendocrine and autonomic nervous systems of individuals with CFS. This paper represents the consensus of the panel of experts who participated in this meeting. Data suggest that persons with CFS manifest changes in immune responses that fall outside normative ranges, but current research does not provide definitive evidence on whether these immune abnormalities are a cause or result of the illness. It has become clear that CFS cannot be understood based on single measurements of immune, endocrine, cardiovascular, or autonomic nervous system dysfunction. This panel encourages a new emphasis on multidisciplinary research into CFS.

Findings from a clinical and laboratory database developed for discovery of pathogenic mechanisms in myalgic encephalomyelitis/chronic fatigue syndrome

Chronic fatigue syndrome (CFS) is a serious health concern affecting over 800,000 Americans of all ages, races and socioeconomic groups and both genders. The etiology and pathophysiology of CFS are unknown, yet studies have suggested an involvement of the autonomic nervous system (ANS). A symposium was organized in December 2000 to explore the possibility of an association between ANS dysfunction and CFS, with special emphasis on the interactions between ANS dysfunction and other abnormalities noted in the immune and endocrine systems of individuals with CFS. This paper represents the consensus of the panel of experts who participated in this meeting.

Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated Data

BackgroundMyalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by unexplained persistent fatigue, commonly accompanied by cognitive dysfunction, sleeping disturbances, orthostatic intolerance, fever, lymphadenopathy, and irritable bowel syndrome (IBS). The extent to which the gastrointestinal microbiome and peripheral inflammation are associated with ME/CFS remains unclear. We pursued rigorous clinical characterization, fecal bacterial metagenomics, and plasma immune molecule analyses in 50 ME/CFS patients and 50 healthy controls frequency-matched for age, sex, race/ethnicity, geographic site, and season of sampling.ResultsTopological analysis revealed associations between IBS co-morbidity, body mass index, fecal bacterial composition, and bacterial metabolic pathways but not plasma immune molecules. IBS co-morbidity was the strongest driving factor in the separation of topological networks based on bacterial profiles and metabolic pathways. Predictive selection models based on bacterial profiles supported findings from topological analyses indicating that ME/CFS subgroups, defined by IBS status, could be distinguished from control subjects with high predictive accuracy. Bacterial taxa predictive of ME/CFS patients with IBS were distinct from taxa associated with ME/CFS patients without IBS. Increased abundance of unclassified Alistipes and decreased Faecalibacterium emerged as the top biomarkers of ME/CFS with IBS; while increased unclassified Bacteroides abundance and decreased Bacteroides vulgatus were the top biomarkers of ME/CFS without IBS. Despite findings of differences in bacterial taxa and metabolic pathways defining ME/CFS subgroups, decreased metabolic pathways associated with unsaturated fatty acid biosynthesis and increased atrazine degradation pathways were independent of IBS co-morbidity. Increased vitamin B6 biosynthesis/salvage and pyrimidine ribonucleoside degradation were the top metabolic pathways in ME/CFS without IBS as well as in the total ME/CFS cohort. In ME/CFS subgroups, symptom severity measures including pain, fatigue, and reduced motivation were correlated with the abundance of distinct bacterial taxa and metabolic pathways.ConclusionsIndependent of IBS, ME/CFS is associated with dysbiosis and distinct bacterial metabolic disturbances that may influence disease severity. However, our findings indicate that dysbiotic features that are uniquely ME/CFS-associated may be masked by disturbances arising from the high prevalence of IBS co-morbidity in ME/CFS. These insights may enable more accurate diagnosis and lead to insights that inform the development of specific therapeutic strategies in ME/CFS subgroups.

Sensitivity of PCR assays for murine gammaretroviruses and mouse contamination in human blood samples.

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, chronic illness that is often disabling. This paper introduces the Chronic Fatigue Initiative, which conducted a large multi-center study to more fully characterize ME/CFS and ultimately to describe and understand the underlying mechanisms and pathogenesis of this illness. Methods: A total of 203 patients with ME/CFS (cases) and 202 matched healthy controls (HCs) were enrolled from 5 geographically different expert clinical sites to create a well-characterized population linked to a national biorepository. ME/CFS subjects were compared to a one-to-one matched HC population for analyses of symptoms and illness severity. Cases were further evaluated for frequency and severity of symptoms and symptom clusters, and the effects of illness duration and acute vs. gradual onset. Results: This study collected more than 4000 pieces of data from each subject in the study. Marked impairment was demonstrated for cases vs. controls. Symptoms of fatigue were identified, but also, nearly as frequent and severe, were symptoms of cognitive dysfunction, inflammation, pain and autonomic dysfunction. Potential subgrouping strategies were suggested by these identified symptom clusters: sleep, neurocognitive, autonomic, inflammatory, neuroinflammatory, gastrointestinal and endocrine symptoms. Conclusions: Clearly, ME/CFS is not simply a state of chronic fatigue. These data indicate that fatigue severity is matched by cognitive, autonomic, pain, inflammatory and neuroinflammatory symptoms as the predominant clinical features. These findings may assist in the clarification and validation of case definitions. In addition, the data can aid clinicians in recognizing and understanding the overall illness presentation. Framing ME/CFS as a multisystem disorder may assist in developing therapies targeting the multifaceted domains of illness.

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

A number of approaches have been developed to call somatic variation in high-throughput sequencing data. Here, we present an adaptive approach to calling somatic variations. Our approach trains a deep feed-forward neural network with semi-simulated data. Semi-simulated datasets are constructed by planting somatic mutations in real datasets where no mutations are expected. Using semi-simulated data makes it possible to train the models with millions of training examples, a usual requirement for successfully training deep learning models. We initially focus on calling variations in RNA-Seq data. We derive semi-simulated datasets from real RNA-Seq data, which offer a good representation of the data the models will be applied to. We test the models on independent semi-simulated data as well as pure simulations. On independent semi-simulated data, models achieve an AUC of 0.973. When tested on semi-simulated exome DNA datasets, we find that the models trained on RNA-Seq data remain predictive (sens 0.4 & spec 0.9 at cutoff of P > = 0.9), albeit with lower overall performance (AUC=0.737). Interestingly, while the models generalize across assay, training on RNA-Seq data lowers the confidence for a group of mutations. Haloplex exome specific training was also performed, demonstrating that the approach can produce probabilistic models tuned for specific assays and protocols. We found that the method adapts to the characteristics of experimental protocol. We further illustrate these points by training a model for a trio somatic experimental design when germline DNA of both parents is available in addition to data about the individual. These models are distributed with Goby (http://goby.campagnelab.org).