<b>Targeted metabolomics identifies accurate CSF metabolite biomarkers for the differentiation between COVID-19 with neurological involvement and CNS infections with neurotropic viral pathogens</b>

Background. COVID-19 is primarily considered a respiratory tract infection, but it can also affect the central nervous system (CNS), which can result in long-term sequelae. In contrast to CNS infections by classic neurotropic viruses, SARS-CoV-2 is usually not detected in cerebrospinal fluid (CSF) from patients with COVID-19 with neurological involvement (neuro-COVID), suggesting fundamental differences in pathogenesis.Methods. To assess differences in CNS metabolism in neuro-COVID compared to CNS infections with classic neurotropic viruses, we applied a targeted metabolomic analysis of 630 metabolites to CSF from patients with (i) COVID-19 with neurological involvement (n=16, comprising acute (n=13) and post-COVID-19 (n=3)), (ii) viral meningitis, encephalitis, or myelitis (n=10) due to herpes simplex virus (n=2), varicella zoster virus (n=6), enterovirus (n=1) and tick-borne encephalitis virus (n=1), and (iii) aseptic neuroinflammation (meningitis, encephalitis, or myelitis) of unknown etiology (n=21) as additional disease controls.Results. Standard CSF parameters indicated absent or low neuroinflammation in neuro-COVID. Indeed, CSF cell count was low in neuro-COVID (median 1 cell/µL, range 0-12) and discriminated it accurately from viral CNS infections (AUC=0.99) and aseptic neuroinflammation (AUC=0.98). 32 CSF metabolites passed quality assessment and were included in the analysis. Concentrations of differentially abundant (fold change ≥|1.5|, FDR ≤0.05) metabolites were both higher (9 and 5 metabolites) and lower (2 metabolites) in neuro-COVID than in the other two groups. Concentrations of citrulline, ceramide (d18:1/18:0), and methionine were most significantly elevated in neuro-COVID. Remarkably, triglyceride TG(20:1_32:3) was much lower (mean fold change = 0.09 and 0.11) in neuro-COVID than in all viral CNS infections and most aseptic neuroinflammation samples, identifying it as highly accurate biomarker with AUC=1 and 0.93, respectively. Across all samples, TG(20:1_32:3) concentration correlated only moderately with CSF cell count (r=0.65), protein concentration (r=0.64), and Q-albumin (r=0.48), suggesting that its low levels in neuro-COVID CSF are only partially explained by less pronounced neuroinflammation.Conclusions. The results suggest that CNS metabolite responses in neuro-COVID differ fundamentally from viral CNS infections and aseptic neuroinflammation and may be used to discover accurate diagnostic biomarkers in CSF and to gain insights into differences in pathophysiology between neuro-COVID, viral CNS infections and aseptic neuroinflammation.</p

Influence of DMF and MMF on NO production of LPS stimulated microglia.

<p>After stimulation for 24 h with 10 ng/ml LPS a dose dependent decrease of NO production in cells incubated with DMF (5, 10, 50µM/ml) could be observed. All data represent three independent experiments compared to a daily standard curve using known concentrations of sodium nitrite in culture medium. Results are shown as means ± SEM. Significant post hoc effects versus controls (treated with LPS) are indicated by asterisks (*p<0.05, **p<0.01, ***p<0.001). CO: medium control without LPS.</p

Induction of apoptosis in mouse PBMC by DMF or MMF for 48h as determined with PI (A) and Annexin (B) staining by flow cytometry.

<p>For controls PBMC were treated with 1.0% methanol or with medium alone (CO). Results are shown as means ± SEM. Significant post hoc effects versus controls (treated with methanol) are indicated by asterisks (**p<0.01, ***p<0.001).</p

Influence of DMF and MMF on de- and remyelination in the cortex.

<p>Cortical myelin was demonstrated by scoring of MBP (A) and PLP (B). In the cortex score of 0 represents complete myelin protein loss, score of 4 represents normal myelin protein amount. Results are shown as means ± SEM.</p

Influence of DMF and MMF on glial reactions during de- and remyelination.

<p>Graphs represent cell numbers of Nogo-A (A), Olig-2 (B), and Mac-3 (D) positive cells in the corpus callosum. In C results of the APP staining to demonstrate axonal damage during cuprizone treatment are shown. Cell numbers are given as cells/mm². Results are shown as means ± SEM.</p

Marker IDs, molecular mass, amino acids sequence, parental protein, mean abundance (expression) in cases and controls and mean fold change of ALS patients vs. controls (columns in sequence from left to right) in differentially regulated peptides in ALS CSF.

Marker IDs, molecular mass, amino acids sequence, parental protein, mean abundance (expression) in cases and controls and mean fold change of ALS patients vs. controls (columns in sequence from left to right) in differentially regulated peptides in ALS CSF.</p

Matrix associations between sequences peptides and ALS severity scale.

For multiple nVGF and OPN peptides, no association differences were detected and only one peptide is displayed.</p

Anthropometric characteristics, CSF phosphorylated neurofilament heavy chain (pNfH) and serum light chain (s-NfL) concentrations, both pg/mL), disease severity (ALSFRS-r) and disease progression rate (ALS-PR) clustered by neurological region of ALS onset.

Anthropometric characteristics, CSF phosphorylated neurofilament heavy chain (pNfH) and serum light chain (s-NfL) concentrations, both pg/mL), disease severity (ALSFRS-r) and disease progression rate (ALS-PR) clustered by neurological region of ALS onset.</p

Peptides with parental proteins, fold change vs. non-ALS controls, and differential regulation data from the literature.

Take notice of opposite regulation of different VGF and OPN peptides.</p

Additional file 1: Figure S1. of Mass-spectrometric profiling of cerebrospinal fluid reveals metabolite biomarkers for CNS involvement in varicella zoster virus reactivation

Biclustering analysis based on differentially abundant metabolites. Fold changes (ratio of mean concentrations in each group relative to control) of metabolites with significant across-group differences (uncorrected Kruskal-Wallis P value < 0.05, n = 39) were used as input using the R function gplots::heatmap.2 ( www.r-project.org , Authors: Andy Liaw; revised by R. Gentleman, M. Maechler, W. Huber, G. Warnes). Between-group relationships support those identified in the nonmetric MDS analysis (Fig. 2). The greatest concentration changes are evident in Z. meningoencephalitis. Some co-regulation of metabolites is evident in the dendrogram, in particular clustering of five almost exclusively downregulated metabolites (including Arg, Trp and Ser, and the sum of hexoses (H1), all of which correlated negatively with CSF leukocyte count, see Fig. 5). The apparent upregulation of C5 in the Z. facial group was due to four patients with high concentrations of unknown significance. (TIFF 337 kb