Elevated phospholipids and acylcarnitines C4 and C5 in cerebrospinal fluid distinguish viral CNS infections from autoimmune neuroinflammation.

BACKGROUND: Viral and autoimmune encephalitis may present with similar symptoms, but require different treatments. Thus, there is a need for biomarkers to improve diagnosis and understanding of pathogenesis. We hypothesized that virus-host cell interactions lead to different changes in central nervous system (CNS) metabolism than autoimmune processes and searched for metabolite biomarkers in cerebrospinal fluid (CSF) to distinguish between the two conditions. METHODS: We applied a targeted metabolomic/lipidomic analysis to CSF samples from patients with viral CNS infections (n = 34; due to herpes simplex virus [n = 9], varicella zoster virus [n = 15], enteroviruses [n = 10]), autoimmune neuroinflammation (n = 25; autoimmune anti-NMDA-receptor encephalitis [n = 8], multiple sclerosis [n = 17), and non-inflamed controls (n = 31; Gilles de la Tourette syndrome [n = 20], Bell's palsy with normal CSF cell count [n = 11]). 85 metabolites passed quality screening and were evaluated as biomarkers. Standard diagnostic CSF parameters were assessed for comparison. RESULTS: Of the standard CSF parameters, the best biomarkers were: CSF cell count for viral infections vs. controls (area under the ROC curve, AUC = 0.93), Q-albumin for viral infections vs. autoimmune neuroinflammation (AUC = 0.86), and IgG index for autoimmune neuroinflammation vs. controls (AUC = 0.90). Concentrations of 2 metabolites differed significantly (p < 0.05) between autoimmune neuroinflammation and controls, with proline being the best biomarker (AUC = 0.77). In contrast, concentrations of 67 metabolites were significantly higher in viral infections than controls, with SM.C16.0 being the best biomarker (AUC = 0.94). Concentrations of 68 metabolites were significantly higher in viral infections than in autoimmune neuroinflammation, and the 10 most accurate metabolite biomarkers (AUC = 0.89-0.93) were substantially better than Q-albumin (AUC = 0.86). These biomarkers comprised six phosphatidylcholines (AUC = 0.89-0.92), two sphingomyelins (AUC = 0.89, 0.91), and acylcarnitines isobutyrylcarnitine (C4, AUC = 0.92) and isovalerylcarnitine (C5, AUC = 0.93). Elevated C4 and C5 concentrations suggested dysfunctional mitochondrial ß-oxidation and correlated only moderately with CSF cell count (Spearman ρ = 0.41 and 0.44), indicating that their increase is not primarily driven by inflammation. CONCLUSIONS: Changes in CNS metabolism differ substantially between viral CNS infections and autoimmune neuroinflammation and reveal CSF metabolites as pathophysiologically relevant diagnostic biomarkers for the differentiation between the two conditions. In viral CNS infections, the observed higher concentrations of free phospholipids are consistent with disruption of host cell membranes, whereas the elevated short-chain acylcarnitines likely reflect compromised mitochondrial homeostasis and energy generation.

HiPerMAb: a tool for judging the potential of small sample size biomarker pilot studies.

Common statistical approaches are not designed to deal with so-called "short fat data" in biomarker pilot studies, where the number of biomarker candidates exceeds the sample size by magnitudes. High-throughput technologies for omics data enable the measurement of ten thousands and more biomarker candidates for specific diseases or states of a disease. Due to the limited availability of study participants, ethical reasons and high costs for sample processing and analysis researchers often prefer to start with a small sample size pilot study in order to judge the potential of finding biomarkers that enable - usually in combination - a sufficiently reliable classification of the disease state under consideration. We developed a user-friendly tool, called HiPerMAb that allows to evaluate pilot studies based on performance measures like multiclass AUC, entropy, area above the cost curve, hypervolume under manifold, and misclassification rate using Monte-Carlo simulations to compute the p-values and confidence intervals. The number of "good" biomarker candidates is compared to the expected number of "good" biomarker candidates in a data set with no association to the considered disease states. This allows judging the potential in the pilot study even if statistical tests with correction for multiple testing fail to provide any hint of significance.

Elevated Free Phosphatidylcholine Levels in Cerebrospinal Fluid Distinguish Bacterial from Viral CNS Infections.

The identification of CSF biomarkers for bacterial meningitis can potentially improve diagnosis and understanding of pathogenesis, and the differentiation from viral CNS infections is of particular clinical importance. Considering that substantial changes in CSF metabolites in CNS infections have recently been demonstrated, we compared concentrations of 188 metabolites in CSF samples from patients with bacterial meningitis (n = 32), viral meningitis/encephalitis (n = 34), and noninflamed controls (n = 66). Metabolite reprogramming in bacterial meningitis was greatest among phosphatidylcholines, and concentrations of all 54 phosphatidylcholines were significantly (p = 1.2 × 10-25-1.5 × 10-4) higher than in controls. Indeed, all biomarkers for bacterial meningitis vs. viral meningitis/encephalitis with an AUC ≥ 0.86 (ROC curve analysis) were phosphatidylcholines. Four of the five most accurate (AUC ≥ 0.9) phosphatidylcholine biomarkers had higher sensitivity and negative predictive values than CSF lactate or cell count. Concentrations of the 10 most accurate phosphatidylcholine biomarkers were lower in meningitis due to opportunistic pathogens than in meningitis due to typical meningitis pathogens, and they correlated most strongly with parameters reflecting blood-CSF barrier dysfunction and CSF lactate (r = 0.73-0.82), less so with CSF cell count, and not with blood CRP. In contrast to the elevated phosphatidylcholine concentrations in CSF, serum concentrations remained relatively unchanged. Taken together, these results suggest that increased free CSF phosphatidylcholines are sensitive biomarkers for bacterial meningitis and do not merely reflect inflammation but are associated with local disease and a shift in CNS metabolism.

Targeted metabolomic profiling of cerebrospinal fluid from patients with progressive multifocal leukoencephalopathy.

Progressive multifocal leukoencephalopathy (PML), caused by JC polyomavirus, is a demyelinating disease of the central nervous system that primarily affects oligodendrocytes. It can cause significant morbidity and mortality. An early diagnosis is of high relevance as timely immune reconstitution is essential. However, diagnosis can be challenging if virus detection via cerebrospinal fluid (CSF) PCR remains negative. Hence, identifying CSF biomarkers for this disease is of crucial importance. We applied a targeted metabolomic screen to CSF from 23 PML patients and eight normal pressure hydrocephalus (NPH) patients as controls. Out of 188 potentially detectable metabolites, 48 (13 amino acids, 4 biogenic amines, 1 acylcarnitine, 21 phosphatidylcholines, 8 sphingolipids, and the sum of hexoses) passed the quality screen and were included in the analyses. Even though there was a tendency towards lower concentrations in PML (mostly of phosphatidylcholines and sphingomyelins), none of the differences between PML and controls in individual metabolite concentrations reached statistical significance (lowest p = 0.104) and there were no potential diagnostic biomarkers (highest area under the ROC curve 0.68). Thus, CSF metabolite changes in PML are likely subtle and possibly larger group sizes and broader metabolite screens are needed to identify potential CSF metabolite biomarkers for PML.