Additional file 1 of Elevated phospholipids and acylcarnitines C4 and C5 in cerebrospinal fluid distinguish viral CNS infections from autoimmune neuroinflammation
Creators: name: Al-Mekhlafi, Amani
name: Waqas, Fakhar H.
name: Krueger, Maike
name: Klawonn, Frank
name: Akmatov, Manas K.
name: Müller-Vahl, Kirsten
name: Trebst, Corinna
name: Skripuletz, Thomas
name: Stangel, Martin
name: Sühs, Kurt-Wolfram
name: Pessler, Frank
Publisher: name: figshare
Year: 2024
Subjects: subject: Immunology
subject: FOS: Clinical medicine

description: Additional file 1: Fig. S1. Quality screen used to identify analytes to be included in the analysis. Analytes were included that were detected ≥ LOD in ≥ 80% of all samples. The numbers on top of the bars state the number of analytes that passed this screen divided by the total number of analytes in the respective metabolite subgroup. Detection efficiency was highest for amino acids, but phosphatidylcholines constituted the largest group of included analytes. Abbreviations: AA, amino acids; AAM, amino acid metabolites; AC, acylcarnitines; lysoPC, lysophosphatidylcholines; PC, phosphatidylcholines; SM, sphingomyelins; Hex, sum of hexoses. Fig. S2. Phospholipids make the greatest contributions to variance in the CSF metabolite populations. A, C, E. Scree plots illustrating the contribution of each dimension to overall variance in 85 metabolites in the PCA shown in Fig. 1. A, Viral CNS infections vs. autoimmune neuroinflammation. C, Viral CNS infections vs. controls. E, Autoimmune neuroinflammation vs. controls. B, D, F. Contributions of each feature to variance in the 1st and 2nd dimension in the PCA. B, Viral CNS infections vs. autoimmune neuroinflammation. D, Viral CNS infections vs. controls. F, Autoimmune neuroinflammation vs. controls. Fig. S3. Correlations among concentrations of the 85 CSF metabolites across all samples. Correlations among all analytes were determined using Pearson correlation analysis. The resulting Pearson correlation coefficients (ρ) were then used as input into a hierarchical clustering analysis. The correlation coefficient values are indicated by the color scheme shown in the legend. The arrows point to short-chain acylcarnitines C4 and C5. Fig. S4. HAUCA curve analysis to assess the likelihood of false positive biomarker identification. The number of biomarkers exceeding a given AUC value in ROC analysis is plotted on the x-axis. The analysis compares the number of biomarkers identified in the real data set (blue curve) to those identified in a random data set of the same variance (black curve), indicating the number of biomarkers expected by chance alone. The red curve delineates the upper bound 95% CI of the random data set. An AUC of 0.8 was used as AUC cut-off to define accurate biomarkers in the ROC curve analysis shown in Fig. 3. A. Viral CNS infections vs. autoimmune neuroinflammation. No markers in the random data set are expected to exceed AUC = 0.8, but 38 did so in the real data set. B. Viral CNS infections vs. controls. No markers in the random data set are expected to exceed AUC = 0.8, but 37 did so in the real data set. C. Autoimmune neuroinflammation vs. controls. No markers in the random or the real data set exceed AUC = 0.8. Table S1. Diagnostic criteria and clinical information. Table S2. Demographic and clinical laboratory characteristics of the seven subgroups. Table S3: Differences among the metabolite classes in biomarker potential. Table S4. Comparison of standard parameters and the 10 most robust CSF metabolite biomarkers to differentiate between viral CNS infections and controls. Table S5. Comparison of standard parameters and the 10 most robust CSF metabolite biomarkers to differentiate between autoimmune neuroinflammation and controls. Table S6. Comparison of common blood and CSF standard diagnostic parameters and the top five CSF metabolite biomarkers: sensitivity, specificity, PPV, and NPV for viral CNS infections vs. controls. Table S7. Comparison of common blood and CSF standard diagnostic parameters and the top five CSF metabolite biomarkers: sensitivity, specificity, PPV, and NPV for autoimmune neuroinflammation vs. controls. Table S8. Sex-specific concentrations in Bell’s palsy CSF samples of the top 6 CSF metabolite biomarkers for the differentiation between viral CNS infections and autoimmune inflammation.