Olfactory Bulb and Amygdala Gene Expression Changes in Subjects Dying with COVID-19

  1. Ignazio S. Piras
  2. Matthew J. Huentelman
  3. Jessica E. Walker
  4. Richard Arce
  5. Michael J. Glass
  6. Daisy Vargas
  7. Lucia I. Sue
  8. Anthony J. Intorcia
  9. Courtney M. Nelson
  10. Katsuko E. Suszczewicz
  11. Claryssa L. Borja
  12. Marc Desforges
  13. Michael Deture
  14. Dennis W. Dickson
  15. Thomas G. Beach
  16. Geidy E. Serrano

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Abstract

In this study we conducted RNA sequencing on two brain regions (olfactory bulb and amygdala) from subjects who died from COVID-19 or who died of other causes. We found several-fold more transcriptional changes in the olfactory bulb than in the amygdala, consistent with our own work and that of others indicating that the olfactory bulb may be the initial and most common brain region infected. To some extent our results converge with pseudotime analysis towards common processes shared between the brain regions, possibly induced by the systemic immune reaction following SARS-CoV-2 infection. Changes in amygdala emphasized upregulation of interferon-related neuroinflammation genes, as well as downregulation of synaptic and other neuronal genes, and may represent the substrate of reported acute and subacute COVID-19 neurological effects. Additionally, and only in olfactory bulb, we observed an increase in angiogenesis and platelet activation genes, possibly associated with microvascular damages induced by neuroinflammation. Through coexpression analysis we identified two key genes ( CAMK2B for the synaptic neuronal network and COL1A2 for the angiogenesis/platelet network) that might be interesting potential targets to reverse the effects induced by SARS-CoV-2 infection. Finally, in olfactory bulb we detected an upregulation of olfactory and taste genes, possibly as a compensatory response to functional deafferentation caused by viral entry into primary olfactory sensory neurons. In conclusion, we were able to identify transcriptional profiles and key genes involved in neuroinflammation, neuronal reaction and olfaction induced by direct CNS infection and/or the systemic immune response to SARS-CoV-2 infection.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.12.21263291: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableThe mean ages of the COVID-19 and non-Covid-19 control groups are 77.5 (SD 12.9) and 83.9 (SD 8.9) respectively (ns); each group has 9 females and 11 males.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisWe computed soft-thresholding power (β), using the pickSoftThreshold function.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Sequencing libraries were prepared with 100 ng of total RNA using Illumina’s Stranded Total RNA Prep Ligation with Ribo-Zero Plus (Illumina, Inc. cat # 20040529) following the manufacturer’s protocol.
    Ribo-Zero Plus
    suggested: None
    After sequencing, FASTQs files were aligned to the Human Reference Genome HG38 using STAR 105, and summarized at the gene level with HTSeq.
    STAR
    suggested: (STAR, RRID:SCR_004463)
    HTSeq
    suggested: (HTSeq, RRID:SCR_005514)
    Quality controls were conducted using MultiQC software 106 and Principal Component Analysis (PCA).
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    Differential gene expression was calculated between the COVID-19 samples and the controls using DEseq2, adjusting for age at death, sex, brain tissue source (Banner or Mayo Clinic) and neuropathologically-determined presence or absence of a diagnostic level of a major neurodegenerative disease.
    DEseq2
    suggested: (DESeq2, RRID:SCR_015687)
    Pathway analysis was conducted on the DEGs using a hypergeometric statistic referencing the REACTOME database as implemented in the clusterProfiler R package.
    REACTOME
    suggested: (Reactome, RRID:SCR_003485)
    The eigengenes were compared by module between COVID-19 positive and controls using a linear model as implemented in limma, adjusting the p-values for multiple testing by accounting for the number of modules using the FDR method.
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    Finally, the top hub genes in the coexpression modules were identified using the function chooseTopHubInEachModule as implemented in the WGCNA package.
    WGCNA
    suggested: (Weighted Gene Co-expression Network Analysis, RRID:SCR_003302)
    Pathway analysis was conducted using the genes significantly correlated with pseudotime by means of the clusterProfiler R package.
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • No protocol registration statement was detected.

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

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.09.12.21263291v1 on medRxiv