The Brain Reacting to COVID-19: Analysis of the Cerebrospinal Fluid and Serum Proteome,Transcriptome and Inflammatory Proteins

  1. Dirk Reinhold
  2. Vadim Farztdinov
  3. Yan Yan
  4. Christian Meisel
  5. Henrik Sadlowski
  6. Joachim Kühn
  7. Frank H. Perschel
  8. Mathias Endres
  9. Emrah Düzel
  10. Stefan Vielhaber
  11. Karina Guttek
  12. Alexander Goihl
  13. Morten Venø
  14. Bianca Teegen
  15. Winfried Stöcker
  16. Paula Stubbemann
  17. Florian Kurth
  18. Leif E. Sander
  19. Markus Ralser
  20. Carolin Otto
  21. Simon Streit
  22. Sven Jarius
  23. Klemens Ruprecht
  24. Helena Radbruch
  25. Jørgen Kjems
  26. Michael Mülleder
  27. Frank Heppner
  28. Peter Körtvelyessy

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Abstract

Patients with COVID-19 can have a variety of neurological symptoms, but the pathomechanism of CNS involvement in COVD-19 remains unclear. While routine cerebrospinal fluid (CSF) analyses in patients with neurological manifestations of COVID-19 generally show no or only mild inflammation, more detailed data on inflammatory mediators in the CSF of patients with COVID-19 are scarce.

Here, we used mass spectrometry to study the proteome, Enzym-linkend immunoassays, semiquantitative cytokine arrays, autoantibody screening, and RNA profiling to study the neuroinflammation. We study the inflammatory response in paired CSF and serum samples of patients with COVID-19 (n=38). Patients with herpes simplex virus encephalitis (HSVE, n=10) and patients with non-inflammatory, non-neurodegenerative neurological diseases (n=28) served as controls. Proteomics on single protein level and subsequent pathway analysis showed similar yet strongly attenuated inflammatory changes in the CSF of COVID-19 patients compared to HSVE patients. CSF/serum indices of interleukin-6, interleukin-16 and CXCL10 together point at an origin from these inflammatory proteins from outside the central nervous system. When stratifying COVID-19 patients into those with and without bacterial superinfection as indicated by elevated procalcitonin levels, inflammatory markers were significantly higher in those with concomitant bacterial superinfection. RNA sequencing in the CSF revealed 101 linear RNAs comprising messenger RNAs, micro RNAs and t-RNA fragments being significantly differentially expressed in COVID-19 than in HSVE or controls.

Our findings may explain the absence of signs of intrathecal inflammation upon routine CSF testing despite the presence of SARS-CoV2 infection-associated neurological symptoms. The relevance of blood-derived mediators of inflammation in the CSF for neurological post-COVID-19 symptoms deserves further investigation.

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    SciScore for 10.1101/2022.04.10.22273673: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: Demographic data and clinical features: This study has been approved by the ethics committee of the Charité Universitätsmedzin Berlin (EA176_20).
    Consent: Every patient gave written and informed consent.
    Sex as a biological variable28 were male and 10 female.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Autoantibody measurements: Thirty-two CSF and 32 serum samples from the same time from 32 COVID-19 patients were tested for anti-neuronal and anti-glial autoantibodies.
    anti-glial
    suggested: None
    At Labor Berlin antibody screening in CSF/serum included testing for antibodies against NMDAR, LGI-1, Caspr2, DPPX, AMPAR, GABAbR, aquaporin-4, myelin, glycine receptor, dopamin-2R, mGluR5, GAD-65 using cell-based assays (CBA) and 12 paraneoplastic antibodies (anti-CV2, - Hu, -Ri, -Yo, -Ma2/Ta, -Zic4, -titin, -SOX1, -amphiphysin, -GAD65, -recoverin, Tr/DNER using immunblots (Euroimmun, Germany).
    antibodies against NMDAR
    suggested: (Aviva Systems Biology Cat# ARP48989_P050, RRID:AB_2047175)
    Caspr2
    suggested: None
    aquaporin-4
    suggested: None
    myelin , glycine receptor , dopamin-2R , mGluR5
    suggested: None
    anti-CV2
    suggested: None
    -titin
    suggested: None
    -GAD65
    suggested: None
    Stöcker the same panel of 24 antibodies tested by Labor Berlin plus 12 additional antibodies (anti-CARPVIII, mGluR1, - GABAaR, -ARHGAP26/anti-Ca, ITPR1/anti-Sj, -Homer3, -Neurexin-3 alpha, -MOG, -
    anti-CARPVIII
    suggested: None
    mGluR1
    suggested: None
    ITPR1/anti-Sj , -Homer3 , -Neurexin-3 alpha , -MOG , -
    suggested: None
    Software and Algorithms
    SentencesResources
    Functional analysis of proteomics data: Functional analysis (GSEA) was carried out using R package clusterProfiler (Yu 2012).
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    Total RNA sequencing data analysis: Sequencing data were pre-processed by removing adapter sequence and trimming away low quality bases with a Phred score below 20 using Trim Galore (v0.4.1).
    Trim Galore
    suggested: (Trim Galore, RRID:SCR_011847)
    Quality control was performed using FastQC to ensure high quality data.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Quantification of gene expression was performed by mapping the filtered reads to the human genome (hg19) using Tophat2.
    Tophat2
    suggested: None
    The software featureCounts was used to quantify the number of reads mapping to each gene using gene annotation from the Gencode release 29.
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Using bedtools detected circRNAs were checked against identical circRNAs annotated in circBase (PMID: 25234927), CIRCpedia (PMID: 30172046) and circAtlas (PMID: 32345360).
    bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    Differential expression analysis was performed using DESeq2 in R on the combined gene and circRNA expression levels.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    The enrichment analysis was done using Enrichr https://maayanlab.cloud/Enrichr/)(47
    Enrichr
    suggested: (Enrichr, RRID:SCR_001575)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2022.04.10.22273673 on medRxiv