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. 2020 Jul 21;15(7):e0236148.
doi: 10.1371/journal.pone.0236148. eCollection 2020.

Plasma proteomic profiling suggests an association between antigen driven clonal B cell expansion and ME/CFS

Affiliations

Plasma proteomic profiling suggests an association between antigen driven clonal B cell expansion and ME/CFS

Milica Milivojevic et al. PLoS One. .

Abstract

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is an unexplained chronic, debilitating illness characterized by fatigue, sleep disturbances, cognitive dysfunction, orthostatic intolerance and gastrointestinal problems. Using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), we analyzed the plasma proteomes of 39 ME/CFS patients and 41 healthy controls. Logistic regression models, with both linear and quadratic terms of the protein levels as independent variables, revealed a significant association between ME/CFS and the immunoglobulin heavy variable (IGHV) region 3-23/30. Stratifying the ME/CFS group based on self-reported irritable bowel syndrome (sr-IBS) status revealed a significant quadratic effect of immunoglobulin lambda constant region 7 on its association with ME/CFS with sr-IBS whilst IGHV3-23/30 and immunoglobulin kappa variable region 3-11 were significantly associated with ME/CFS without sr-IBS. In addition, we were able to predict ME/CFS status with a high degree of accuracy (AUC = 0.774-0.838) using a panel of proteins selected by 3 different machine learning algorithms: Lasso, Random Forests, and XGBoost. These algorithms also identified proteomic profiles that predicted the status of ME/CFS patients with sr-IBS (AUC = 0.806-0.846) and ME/CFS without sr-IBS (AUC = 0.754-0.780). Our findings are consistent with a significant association of ME/CFS with immune dysregulation and highlight the potential use of the plasma proteome as a source of biomarkers for disease.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Quadratic effect of immunoglobulin proteins with ME/CFS and ME/CFS subgroups.
Two separate models were fitted: one with only the linear term of the protein levels, and one with both linear and quadratic terms of the protein levels as independent variables. In both models we adjusted for BMI, sr-IBS, antidepressant medication use, age, sex, race/ethnicity, geographic/clinical site and season of sampling. Likelihood-ratio tests were used to compare the goodness-of-fit between the two nested models. The Hochberg step-up procedure was applied to correct for the multiple tests over the annotated proteins, controlling the family-wise error rate (FWER) at the level of 0.05. For the protein analytes associated with ME/CFS with significant quadratic effect, adjusted odds ratios (aORs), together with their 95% confidence intervals (95% CI), were calculated comparing ME/CFS risk of various protein levels to that of the reference level at which the ME/CFS risk was at the lowest. (A) All ME/CFS cases versus controls, (B) ME/CFS cases with sr-IBS versus controls, (C & D) ME/CFS cases without sr-IBS versus controls. ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome, a.u.: arbitrary units, sr-IBS: self-reported irritable bowel syndrome, IGHV: immunoglobulin heavy variable, IGLC: immunoglobulin lambda constant, IGKV: immunoglobulin kappa variable.
Fig 2
Fig 2. Diagnostic performance (AUROC) of ME/CFS and ME/CFS subgroup plasma proteomes.
Three machine learning algorithms were used to examine the utility of the proteomics assay as a biomarker tool for ME/CFS: Lasso (least absolute shrinkage and selection operator), Random Forests, and XGboost. We fitted all protein analytes, excluding the ones with more than 50% undetectable/filtered values, as predictors in the three classifiers and measured the importance for each predictor in the classifiers. The protein analytes that were ranked in the top 20 in all three importance measurements were fitted in the classifiers again (Trimmed set), except that here we used the logistic regression model instead of Lasso. The predictive performance was evaluated in random resampling cross-validation (CV) with 1,000 iterations from which we calculated the Area under the Receiver Operating Characteristic curve (AUROC) values and generated Receiver Operating Characteristic (ROC) curves for (A) all ME/CFS cases, (B) ME/CFS cases with sr-IBS and (C) ME/CFS cases without sr-IBS. ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome, sr-IBS: self-reported irritable bowel syndrome.

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