Datasets

Accessing MCAM Data for Research Complete the Data Use Agreement (DUA) , including: A brief description of the proposed research. Principal Investigator (PI) information. An institutional signature from a Signing Official. Submit the completed DUA to mapMECFS@rti.org for review. Proposal Review Process Initial Review The mapMECFS staff will review submissions for completeness. NINDS Data Access Committee Review The NINDS Data Access Committee will evaluate the proposed research to ensure it is appropriate for the MCAM data. Timeline The review process typically takes 1-4 weeks . Notification Approved investigators will receive an email with instructions for accessing the data. Once a proposal is approved, the following information will be viewable by other mapMECFS users: Project title. Abstract (<500 words). Principal Investigator details. Additional Data Requests Additional variables (e.g., medication data) may be available upon request to qualified researchers. For more information, please email cfs@cdc.gov .

This dataset includes all necessary documentation for approved investigators to understand the MCAM pediatric longitudinal clinical dataset, including relevant codebooks and a list of publications that document the MCAM study methodology.

Single-cell gene expression data from PBMC samples before and 24h after exercise were analyzed to generate composite (pseudobulk) profiles for each sample and cell type. The dataset contains 60 ME-CFS cases and 56 controls, at 2 timepoints (D1 = pre-exercise, D2 = 24h post-exercise), over 29 clusters. Demographic and clinical information can be found with the respective journal publication and in the phenotype file below. Additional demographic and clinical data is held in an accompanying dataset . Use the 'cor_id' column to link participant across the datasets.

This dataset includes all necessary documentation for approved investigators to understand the MCAM adult longitudinal clinical dataset, including relevant codebooks and a list of publications that document the MCAM study methodology.

This dataset includes all necessary documentation for approved investigators to understand the MCAM Cognition and Exercise Testing sub-study clinical dataset, including relevant codebooks and a list of publications that document the MCAM study methodology.

Adapted from Hoel, F., Hoel, A., Pettersen, I. K., Rekeland, I. G., Risa, K., Alme, K., Sørland, K., Fosså, A., Lien, K., Herder, I., Thürmer, H. L., Gotaas, M. E., Schäfer, C., Berge, R. K., Sommerfelt, K., Marti, H. P., Dahl, O., Mella, O., Fluge, Ø., & Tronstad, K. J. (2021). A map of metabolic phenotypes in patients with myalgic encephalomyelitis/chronic fatigue syndrome. JCI insight, 6(16), e149217. https://doi.org/10.1172/jci.insight.149217 Available at https://insight.jci.org/articles/view/149217 under Creative Commons Attribution 4.0 (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/ This study compares metabolomics, lipidomics, and hormone measurements from 83 ME/CFS cases to 35 healthy controls. Investigation of serum metabolites and lipidomics was performed using the HD4 and CLP platforms, respectively, from Metabolon, Inc. Metabolic hormone assays were performed using Luminex multiplex bead immunoassay technology (Luminex 100 instrument, Luminex Corp.) to detect FABP4, insulin, HMW adiponectin, and leptin (catalog LXSAMH, R&D Systems). ELISA was used to measure FGF21 (catalog DF2100, R&D Systems) and C-peptide (catalog DICP00, R&D Systems).

Adapted from Nepotchatykh, E., Elremaly, W., Caraus, I. et al. Profile of circulating microRNAs in myalgic encephalomyelitis and their relation to symptom severity, and disease pathophysiology. Sci Rep 10, 19620 (2020). https://doi.org/10.1038/s41598-020-76438-y Available at https://www.nature.com/articles/s41598-020-76438-y#Abs1 under Creative Commons Attribution 4.0 International License (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/ This study investigates circulating microRNA expression in severely ill ME/CFS patients and healthy controls before and after an innovative stress challenge that stimulates Post Exertional Malaise (PEM). Peripheral blood samples of participants were collected at two time-points (baseline, T0 and after the stress-test, T90). miRNAs were extracted from plasma samples obtained from ME/CFS patients and matched healthy controls using microRNA array.

Adapted from Montoya JG, Holmes TH, Anderson JN, Maecker HT, Rosenberg-Hasson Y, Valencia IJ, Chu L, Younger JW, Tato CM, Davis MM. Cytokine signature associated with disease severity in chronic fatigue syndrome patients. Proc Natl Acad Sci U S A. 2017 Aug 22;114(34):E7150-E7158. doi: 10.1073/pnas.1710519114. Epub 2017 Jul 31. PMID: 28760971; PMCID: PMC5576836. Available at https://www.pnas.org/content/114/34/E7150 under Creative Commons Attribution 4.0 International License (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/ This study profiles and compares serum cytokine profiles of of 186 ME/CFS patients and 388 controls using a 51-multiplex array on a Luminex system.

This data is adapted from the publication: Hornig M, Montoya JG, Klimas NG, Levine S, Felsenstein D, Bateman L, Peterson DL, Gottschalk CG, Schultz AF, Che X, Eddy ML, Komaroff AL, Lipkin WI. Distinct plasma immune signatures in ME/CFS are present early in the course of illness. Sci Adv 2015 Feb;1(1):e1400121. doi: 10.1126/sciadv.1400121. PMID: 26079000; PMCID: PMC4465185. Study compares plasma immune signatures using a multiplex cytokine panel in ME/CFS cases to healthy controls. ME/CFS cases were separated into short (n=52) and long (n=246) duration of illness at 3 years. The model was adjusted for age and sex. Cytokines from the panel were included in the model if it was selected by either the LASSO procedure or was selected by both principal components analysis (PCA) and partial least squares (PLS) dimensionality reduction procedures, described in the supplementary methods.

This data has been adapted from the publication: Mandarano AH, Giloteaux L, Keller BA, Levine SM, Hanson MR. Eukaryotes in the gut microbiota in myalgic encephalomyelitis/chronic fatigue syndrome. PeerJ. 2018 Jan 22;6:e4282. doi: 10.7717/peerj.4282. PMID: 29375937; PMCID: PMC5784577. Investigate dysbiosis in the ME/CFS gut microbiome, the authors profile the microbiome of 17 healthy individuals and 17 ME/CFS patients.

Adapted from Kitami T, Fukuda S, Kato T, Yamaguti K, Nakatomi Y, Yamano E, Kataoka Y, Mizuno K, Tsuboi Y, Kogo Y, Suzuki H, Itoh M, Morioka MS, Kawaji H, Koseki H, Kikuchi J, Hayashizaki Y, Ohno H, Kuratsune H, Watanabe Y. Deep phenotyping of myalgic encephalomyelitis/chronic fatigue syndrome in Japanese population. Sci Rep. 2020 Nov 16;10(1):19933. doi: 10.1038/s41598-020-77105-y. PMID: 33199820; PMCID: PMC7669873. Available at https://www.nature.com/articles/s41598-020-77105-y under Creative Commons Attribution 4.0 International License (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/ This study profiles multiple molecular markers of ME/CFS in 48 patients and 52 controls. Reported here are baseline characteristics of cases and controls (Table S1), metabolite profiles of cases and controls (Table S2), and lipoprotein profiles of cases and controls (Table S3). Please see the original publication for additional data and figures.

This data was adapted from the publication: Billing-Ross, P., Germain, A., Ye, K. et al. Mitochondrial DNA variants correlate with symptoms in myalgic encephalomyelitis/chronic fatigue syndrome. J Transl Med 14, 19 (2016). https://doi.org/10.1186/s12967-016-0771-6 Illumina sequencing of mtDNA was conducted to identify heteroplasmy of 193 ME/CFS cases and 196 age-matched controls. Results are presented for haplogroups ('HG' molecule columns) and the Single Nucleotide Polymorphism (SNP) level ('mt:position' molecule columns).

Adapted from Milivojevic M, Che X, Bateman L, Cheng A, Garcia BA, Hornig M, et al. (2020) Plasma proteomic profiling suggests an association between antigen driven clonal B cell expansion and ME/CFS. PLoS ONE 15(7): e0236148. https://doi.org/10.1371/journal.pone.0236148 Available at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236148 under Creative Commons Attribution 4.0 (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/ This study compares plasma proteomes from 39 ME/CFS cases to 41 healthy controls. Untargeted ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to conduct plasma proteomic profiling.

This data is adapted from the publication: Hornig M, Gottschalk CG, Eddy ML, et al. Immune network analysis of cerebrospinal fluid in myalgic encephalomyelitis/chronic fatigue syndrome with atypical and classical presentations. Transl Psychiatry. 2017;7(4):e1080. Published 2017 Apr 4. doi:10.1038/tp.2017.44 Study compares cytokine levels found in cerebrospinal fluid of ME/CFS cases after classifying cases by duration of illness (short vs. long) and presentation (classical vs. atypical).

Adapted from Sweetman, E., Kleffmann, T., Edgar, C. et al. A SWATH-MS analysis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome peripheral blood mononuclear cell proteomes reveals mitochondrial dysfunction. J Transl Med 18, 365 (2020). https://doi.org/10.1186/s12967-020-02533-3 Available at https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02533-3#Sec17 under Creative Commons Attribution 4.0 International License (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/ This study compares plasma proteomes from 11 ME/CFS cases to 9 healthy controls. SWATH- MS analysis was used to conduct plasma proteomic profiling.

The following datasets contains all data for the Muscle RNA-sequencing related to Post-Infectious MECFS at NIH study. ABOUT In 2016, the National Institutes of Health (NIH) launched an initiative to study ME/CFS. The NIH Division of Intramural Research developed an exploratory clinical research program to perform deep phenotyping on a cohort of PI-ME/CFS volunteers and healthy volunteers (HV) as controls. Prior to the SARS-CoV-2 pandemic, this study recruited a cohort of well-characterized PI-ME/CFS patients and applied modern broad and deep scientific measures to describe their biophenotype compared to HVs. The aim was to identify relevant group differences that could generate new hypotheses about the pathogenesis of PI-ME/CFS and provide direction for future research. Over 75 scientists and clinicians across 15 of the 27 institutes that comprise the NIH contributed to this multi-disciplinary work. Importantly, we developed rigorous inclusion criteria which comprised detailed medical and psychological evaluations to minimize diagnostic misattribution. A relatively homogenous population was recruited in whom symptoms were initiated after infection. This study aimed to investigate the underlying pathophysiological mechanisms. The volunteers underwent a multi-dimensional evaluation that included a wide range of physiological measures, physical and cognitive performance testing, and biochemical, microbiological, and immunological assays of blood, cerebrospinal fluid, muscle, and stool. Novel measurement techniques were developed to query issues such as physical capacity, effort preference, and deconditioning that may confound the results. Multi-omic measurements of gene expression, proteins, metabolites, and lipids were performed in parallel on collected samples. RNA Sequencing Data Processing RNA sequence data obtained from the libraries using the bcl2fastq v.2.17; Illumina software. RNA sequences were subjected to quality control ( FastQC , a quality control tool for high throughput sequence data, and trimmomatic to remove adapters, followed by alignment to the human genome (GRCh38) using STAR124. Gene expression levels were quantified using featuresCounts125. Differential Expression (DE) analysis was performed on PBMC and muscle RNAseq data using limma126 and genes with nominal p-value ≤ 0.05 were considered DE. BMI was used as a covariate in sex separated and combined cohort. Pathway enrichment analysis was performed using the R package clusterProfiler127 which uses the fisher test to determine statistical significance. Additionally, prior known protein-protein interactions for the DE genes were extracted from the STRING resource . Protein-protein interactions (PPI) with a confidence score of >0.7 were reported. The fold change information of the genes node in the PPI network are highlighted as red (for upregulated genes) and blue (for downregulated gene) color nodes in Cytoscape129. Citation Please include the following citations when using these data: Walitt, B., et al. “Deep phenotyping of Post-infectious Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Nature Communications . February 21, 2024. DOI: 10.1038/s41467-024-45107-3 Mathur, R.* & Carnes, M.U.*, et al. mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites. J Transl Med 19, 461 (2021). https://doi.org/10.1186/s12967-021-03127-3 *contributed equally and are designated co-first authors Contact Information mapMECFS is hosted by RTI International and supports data sharing for all organizations in the ME/CFS Network and other ME/CFS researchers. If you have questions about how to use this dataset or have general questions about how to use the portal please email mapmecfs@rti.org .For questions related to this specific study, please contact Brian Walitt brian.walitt@nih.gov .

This data is adapted from the publication: Nagy-Szakal D, Barupal DK, Lee B, et al. Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics. Scientific reports. 2018 Jul 3;8(1):10056. doi: 10.1038/s41598-018-28477-9. PMID: 29968805; PMCID: PMC6030047. In addition, mass spectrometry metabolomic data used in this publication are deposited in the Metabolomics Workbench (http:// www.metabolomicsworkbench.org , project identifier PR000576). Study compares plasma metabolomics from ME/CFS cases to healthy controls using targeted and untargeted approaches. Participants were stratified by the presence or absense of comorbid irritable bowel syndrome (IBS). Tables describing the metabolomics results are adapted and presented here.

The following datasets contains all data for the PBMC RNA-sequencing related to Post-Infectious MECFS at NIH study. ABOUT In 2016, the National Institutes of Health (NIH) launched an initiative to study ME/CFS. The NIH Division of Intramural Research developed an exploratory clinical research program to perform deep phenotyping on a cohort of PI-ME/CFS volunteers and healthy volunteers (HV) as controls. Prior to the SARS-CoV-2 pandemic, this study recruited a cohort of well-characterized PI-ME/CFS patients and applied modern broad and deep scientific measures to describe their biophenotype compared to HVs. The aim was to identify relevant group differences that could generate new hypotheses about the pathogenesis of PI-ME/CFS and provide direction for future research. Over 75 scientists and clinicians across 15 of the 27 institutes that comprise the NIH contributed to this multi-disciplinary work. Importantly, we developed rigorous inclusion criteria which comprised detailed medical and psychological evaluations to minimize diagnostic misattribution. A relatively homogenous population was recruited in whom symptoms were initiated after infection. This study aimed to investigate the underlying pathophysiological mechanisms. The volunteers underwent a multi-dimensional evaluation that included a wide range of physiological measures, physical and cognitive performance testing, and biochemical, microbiological, and immunological assays of blood, cerebrospinal fluid, muscle, and stool. Novel measurement techniques were developed to query issues such as physical capacity, effort preference, and deconditioning that may confound the results. Multi-omic measurements of gene expression, proteins, metabolites, and lipids were performed in parallel on collected samples. RNA Sequencing Data Processing RNA sequence data obtained from the libraries using the bcl2fastq v.2.17; Illumina software. RNA sequences were subjected to quality control ( FastQC , a quality control tool for high throughput sequence data, and trimmomatic to remove adapters, followed by alignment to the human genome (GRCh38) using STAR124. Gene expression levels were quantified using featuresCounts125. Differential Expression (DE) analysis was performed on PBMC and muscle RNAseq data using limma126 and genes with nominal p-value ≤ 0.05 were considered DE. BMI was used as a covariate in sex separated and combined cohort. Pathway enrichment analysis was performed using the R package clusterProfiler127 which uses the fisher test to determine statistical significance. Additionally, prior known protein-protein interactions for the DE genes were extracted from the STRING resource . Protein-protein interactions (PPI) with a confidence score of >0.7 were reported. The fold change information of the genes node in the PPI network are highlighted as red (for upregulated genes) and blue (for downregulated gene) color nodes in Cytoscape129. Citation Please include the following citations when using these data: Walitt, B., et al. “Deep phenotyping of Post-infectious Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Nature Communications . February 21, 2024. DOI: 10.1038/s41467-024-45107-3 Mathur, R.* & Carnes, M.U.*, et al. mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites. J Transl Med 19, 461 (2021). https://doi.org/10.1186/s12967-021-03127-3 *contributed equally and are designated co-first authors Contact Information mapMECFS is hosted by RTI International and supports data sharing for all organizations in the ME/CFS Network and other ME/CFS researchers. If you have questions about how to use this dataset or have general questions about how to use the portal please email mapmecfs@rti.org .For questions related to this specific study, please contact Brian Walitt brian.walitt@nih.gov .

This dataset is a collection of links to commonly-used case definitions for ME/CFS. It is provided as a resource to investigators. mapMECFS takes no stance regarding the appropriateness of any given case definition.

The following datasets contains all data for the CSF SomaLogic Proteomics related to Post-Infectious MECFS at NIH study. ABOUT In 2016, the National Institutes of Health (NIH) launched an initiative to study ME/CFS. The NIH Division of Intramural Research developed an exploratory clinical research program to perform deep phenotyping on a cohort of PI-ME/CFS volunteers and healthy volunteers (HV) as controls. Prior to the SARS-CoV-2 pandemic, this study recruited a cohort of well-characterized PI-ME/CFS patients and applied modern broad and deep scientific measures to describe their biophenotype compared to HVs. The aim was to identify relevant group differences that could generate new hypotheses about the pathogenesis of PI-ME/CFS and provide direction for future research. Over 75 scientists and clinicians across 15 of the 27 institutes that comprise the NIH contributed to this multi-disciplinary work. Importantly, we developed rigorous inclusion criteria which comprised detailed medical and psychological evaluations to minimize diagnostic misattribution. A relatively homogenous population was recruited in whom symptoms were initiated after infection. This study aimed to investigate the underlying pathophysiological mechanisms. The volunteers underwent a multi-dimensional evaluation that included a wide range of physiological measures, physical and cognitive performance testing, and biochemical, microbiological, and immunological assays of blood, cerebrospinal fluid, muscle, and stool. Novel measurement techniques were developed to query issues such as physical capacity, effort preference, and deconditioning that may confound the results. Multi-omic measurements of gene expression, proteins, metabolites, and lipids were performed in parallel on collected samples. SomaLogic Data Processing Peripheral blood serum was isolated using SST tubes and cryopreserved according to Center for Human Immunology protocols and cryopreserved with corresponding cerebrospinal fluid samples. Proteomic analysis used the SOMAscan 1.3k Assay (SomaLogic). This is an aptamer-based assay able to detect 1305 protein analytes, optimized for analysis of human serum. Briefly, aptamers are short single-stranded DNA sequences modified to confer specific binding to target proteins and can be highly multiplexed for discovery of biomarker signatures. The proteins quantified include cytokines, hormones, growth factors, receptors, kinases, proteases, protease inhibitors, and structural proteins. A complete list of analytes measured can be found at http://somalogic.com/wp-content/uploads/2017/06/SSM-045-Rev-2-SOMAscan-Assay-1.3k-Content.pdf . The assay was performed according to manufacturer specifications for each of the serum and cerebrospinal fluid sample types. Briefly, serum samples were assayed at three dilutions (40%, 1%, and 0.005%) with each sample dilution added to a corresponding subset of the 1,305 SOMAmer detection reagents binned according to manufacturer’s predicted target abundance in serum. Cerebrospinal fluid was run at a single 15% concentration dilution with protease inhibitors and polyanionic competitor reagent added. Data then inspected using a web tool and subjected to quality control procedures as previously described. Citation Please include the following citations when using these data: Walitt, B., et al. “Deep phenotyping of Post-infectious Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Nature Communications . February 21, 2024. DOI: 10.1038/s41467-024-45107-3 Mathur, R.* & Carnes, M.U.*, et al. mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites. J Transl Med 19, 461 (2021). https://doi.org/10.1186/s12967-021-03127-3 *contributed equally and are designated co-first authors Contact Information mapMECFS is hosted by RTI International and supports data sharing for all organizations in the ME/CFS Network and other ME/CFS researchers. If you have questions about how to use this dataset or have general questions about how to use the portal please email mapmecfs@rti.org .For questions related to this specific study, please contact Brian Walitt brian.walitt@nih.gov .