Proteomic biomarkers of Kleine-Levin syndrome

Abstract

Study objectives: Kleine-Levin syndrome (KLS) is characterized by relapsing-remitting episodes of hypersomnia, cognitive impairment, and behavioral disturbances. We quantified cerebrospinal fluid (CSF) and serum proteins in KLS cases and controls.

Methods: SomaScan was used to profile 1133 CSF proteins in 30 KLS cases and 134 controls, while 1109 serum proteins were profiled in serum from 26 cases and 65 controls. CSF and serum proteins were both measured in seven cases. Univariate and multivariate analyses were used to find differentially expressed proteins (DEPs). Pathway and tissue enrichment analyses (TEAs) were performed on DEPs.

Results: Univariate analyses found 28 and 141 proteins differentially expressed in CSF and serum, respectively (false discovery rate <0.1%). Upregulated CSF proteins included IL-34, IL-27, TGF-b, IGF-1, and osteonectin, while DKK4 and vWF were downregulated. Pathway analyses revealed microglial alterations and disrupted blood-brain barrier permeability. Serum profiles show upregulation of Src-family kinases (SFKs), proteins implicated in cellular growth, motility, and activation. TEA analysis of up- and downregulated proteins revealed changes in brain proteins (p < 6 × 10-5), notably from the pons, medulla, and midbrain. A multivariate machine-learning classifier performed robustly, achieving a receiver operating curve area under the curve of 0.90 (95% confidence interval [CI] = 0.78-1.0, p = 0.0006) in CSF and 1.0 (95% CI = 1.0-1.0, p = 0.0002) in serum in validation cohorts, with some commonality across tissues, as the model trained on serum sample also discriminated CSF samples of controls versus KLS cases.

Conclusions: Our study identifies proteomic KLS biomarkers with diagnostic potential and provides insight into biological mechanisms that will guide future research in KLS.

Keywords: CSF; Kleine–Levine syndrome; aptamers; brain immunity; hypersomnia; microglia; proteomics; serum.

Figure 1.

Volcano plots presenting the univariate analysis for the KLS protein signature in CSF and serum assays (A) for CSF, with 23 KLS samples and 80 controls, y-axis represents FDR and x-axis represents the log-2 fold change for which a positive value indicates upregulation for KLS samples and a negative value downregulation for KLS. Red points indicate |logFC| > 0.5 and FDR < 10⁻⁴. Blue points only pass FDR < 10⁻⁴. Green points only pass |logFC| > 1. Gray points pass neither threshold. (B) for serum, 20 KLS cases and 54 controls. Red points indicate |logFC| > 1 and FDR < 10⁻⁴. Blue points only pass FDR < 10⁻⁴. Green points only pass |logFC| > 1. Gray points pass neither threshold.

Figure 2.

Receiver-operator curves for the classifier cross-validated training and independent validations with CIs at 95% obtained with bootstrapping. The lasso models are trained on the intersection of the available protein in each dataset. Data are normalized in each dataset before performing the training and validation task. (A) For CSF training set (n = 103 [23 KLS cases, 80 controls], number of features = 504), the cross-validated training shows an AUC of 0.98 [0.96–1] in blue and significantly validates on the independent cohort (n = 61) with an AUC of 0.90 [0.78–1] in gray. (B) For serum (n = 74 [20 KLS cases, 54 controls], number of features = 602), AUC of 0.96 [0.91–0.99] on training in blue and significantly validates on the independent cohort (n = 17) with and AUC of 1 [1–1] in gray (in this case, because of the AUC of 1, a proper CI cannot be obtained). (C) For the models across tissue, the first model in gray is trained on CSF (n = 103, number of features = 505) and tested on serum samples and significantly discriminates between KLS and controls with an AUC of 0.80 [0.69–0.89]. Similarly, the model trained on serum in blue discriminates CSF samples with an AUC of 0.71 [0.59–0.82].