Morphological, cellular, and molecular basis of brain infection in COVID-19 patients

  1. Fernanda Crunfli
  2. Victor C. Carregari
  3. Flavio P. Veras
  4. Lucas S. Silva
  5. Mateus Henrique Nogueira
  6. André Saraiva Leão Marcelo Antunes
  7. Pedro Henrique Vendramini
  8. Aline Gazzola Fragnani Valença
  9. Caroline Brandão-Teles
  10. Giuliana da Silva Zuccoli
  11. Guilherme Reis-de-Oliveira
  12. Lícia C. Silva-Costa
  13. Verônica Monteiro Saia-Cereda
  14. Bradley J. Smith
  15. Ana Campos Codo
  16. Gabriela F de Souza
  17. Stéfanie P. Muraro
  18. Pierina Lorencini Parise
  19. Daniel A. Toledo-Teixeira
  20. Ícaro Maia Santos de Castro
  21. Bruno Marcel Melo
  22. Glaucia M. Almeida
  23. Egidi Mayara Silva Firmino
  24. Isadora Marques Paiva
  25. Bruna Manuella Souza Silva
  26. Rafaela Mano Guimarães
  27. Niele D. Mendes
  28. Raíssa L. Ludwig
  29. Gabriel P. Ruiz
  30. Thiago L. Knittel
  31. Gustavo G. Davanzo
  32. Jaqueline Aline Gerhardt
  33. Patrícia Brito Rodrigues
  34. Julia Forato
  35. Mariene Ribeiro Amorim
  36. Natália S. Brunetti
  37. Matheus Cavalheiro Martini
  38. Maíra Nilson Benatti
  39. Sabrina S. Batah
  40. Li Siyuan
  41. Rafael B. João
  42. Ítalo K. Aventurato
  43. Mariana Rabelo de Brito
  44. Maria J. Mendes
  45. Beatriz A. da Costa
  46. Marina K. M. Alvim
  47. José Roberto da Silva Júnior
  48. Lívia L. Damião
  49. Iêda Maria P. de Sousa
  50. Elessandra D. da Rocha
  51. Solange M. Gonçalves
  52. Luiz H. Lopes da Silva
  53. Vanessa Bettini
  54. Brunno M. Campos
  55. Guilherme Ludwig
  56. Lucas Alves Tavares
  57. Marjorie Cornejo Pontelli
  58. Rosa Maria Mendes Viana
  59. Ronaldo B. Martins
  60. Andre Schwambach Vieira
  61. José Carlos Alves-Filho
  62. Eurico Arruda
  63. Guilherme Gozzoli Podolsky-Gondim
  64. Marcelo Volpon Santos
  65. Luciano Neder
  66. André Damasio
  67. Stevens Rehen
  68. Marco Aurélio Ramirez Vinolo
  69. Carolina Demarchi Munhoz
  70. Paulo Louzada-Junior
  71. Renê Donizeti Oliveira
  72. Fernando Q. Cunha
  73. Helder I. Nakaya
  74. Thais Mauad
  75. Amaro Nunes Duarte-Neto
  76. Luiz Fernando Ferraz da Silva
  77. Marisa Dolhnikoff
  78. Paulo Hilario Nascimento Saldiva
  79. Alessandro S. Farias
  80. Fernando Cendes
  81. Pedro Manoel M. Moraes-Vieira
  82. Alexandre T. Fabro
  83. Adriano Sebollela
  84. José L. Proença-Modena
  85. Clarissa L. Yasuda
  86. Marcelo A. Mori
  87. Thiago M. Cunha
  88. Daniel Martins-de-Souza

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Abstract

Although increasing evidence confirms neuropsychiatric manifestations associated mainly with severe COVID-19 infection, long-term neuropsychiatric dysfunction (recently characterized as part of “long COVID-19” syndrome) has been frequently observed after mild infection. We show the spectrum of cerebral impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, ranging from long-term alterations in mildly infected individuals (orbitofrontal cortical atrophy, neurocognitive impairment, excessive fatigue and anxiety symptoms) to severe acute damage confirmed in brain tissue samples extracted from the orbitofrontal region (via endonasal transethmoidal access) from individuals who died of COVID-19. In an independent cohort of 26 individuals who died of COVID-19, we used histopathological signs of brain damage as a guide for possible SARS-CoV-2 brain infection and found that among the 5 individuals who exhibited those signs, all of them had genetic material of the virus in the brain. Brain tissue samples from these five patients also exhibited foci of SARS-CoV-2 infection and replication, particularly in astrocytes. Supporting the hypothesis of astrocyte infection, neural stem cell–derived human astrocytes in vitro are susceptible to SARS-CoV-2 infection through a noncanonical mechanism that involves spike–NRP1 interaction. SARS-CoV-2–infected astrocytes manifested changes in energy metabolism and in key proteins and metabolites used to fuel neurons, as well as in the biogenesis of neurotransmitters. Moreover, human astrocyte infection elicits a secretory phenotype that reduces neuronal viability. Our data support the model in which SARS-CoV-2 reaches the brain, infects astrocytes, and consequently, leads to neuronal death or dysfunction. These deregulated processes could contribute to the structural and functional alterations seen in the brains of COVID-19 patients.

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  1. Version published to 10.1073/pnas.2200960119
  2. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/6098644.

    Main Claim & Relevance:

    This preprint brings forward several claims from different disciplines, all regarding potential long term effects of SARS-CoV-19 and their causes.

    Firstly, MRI scans revealed that mild COVID patients had reduced cortical thickness as well as signs of vasogenic edema in all lobes of the brain. Neuropsychological evaluation of 61 participants an average of 59 days after diagnosis revealed fatigue in approximately 70% of individuals and daytime sleepiness in 36%. Nearly 28% of participants performed abnormally on the logical memory cognitive function tests. Infection with COVID was also associated with higher instances of anxiety and depression compared to asymptomatic COVID positive patients.

    In brain tissue samples of 26 patients who died of COVID, a strong predominance of senile changes were found. Inflammatory processes were noted within the tissue, and in some cases areas of liquefactive necrosis were present. In all patients that showed degenerative changes in brain tissue, both SARS-CoV-2 genetic material and spike proteins were found within the tissue. The majority of cells infected with COVID were astrocytes.

    Further experimentation revealed that astrocytes readily allow for viral replication, and the primary route of entry is through NRP1 receptors. Infection of stem cell derived astrocytes with COVID impacts their metabolism which reduces their ability to support neurons. The presence of COVID-infected astrocytes increased the rate of neuron apoptosis in vitro significantly.

     

    Are the findings strong, reliable, potentially informative, not informative, or misleading?

    The findings of this preprint range from reliable to potentially informative. While the MRI evaluation of patients was controlled against healthy patients, the psychological evaluation of the participants of this study was not compared to an age matched group of healthy participants. This calls into question the strength of this evidence. Similarly, the analysis of brain tissue was only performed on COVID-infected patients and no analysis was performed on a control group of healthy patients. Some of the claims such as senile changes may be explained by the demographics of the patient population rather than possible effects of COVID. In contrast, the investigation involving the role of NRP1 receptors, metabolic changes, and neuronal viability is well powered and both reliable and reproducible.

     

    How might these ideas presented by the main claims further knowledge of the COVID-19 Pandemic?

    The long term cognitive effects of SARS-CoV-2 have been well documented over the course of the pandemic, however their causes are still largely unknown. The ideas presented by the authors provide a potential insight into the causes and severity of "long COVID" cases. This not only helps to further our knowledge of the virus and how it functions, but it may also impact future medical care of patients.

  3. Version published to 10.1101/2020.10.09.20207464v5 on medRxiv
  4. Version published to 10.1101/2020.10.09.20207464v4 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2020.10.09.20207464: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The autopsy studies were approved by the National Commission for Research Ethics (CAAE: 32475220.5.0000.5440 and CAAE: 38071420.0.1001.5404).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The experiments of SARS-CoV-2 entry were performed using SARS-CoV-2 (MOI 1.0) and VSV-eGFP-SARS-CoV-2 pseudotyped particles (MOI 1.0) in presence of NRP1 neutralizing antibody (BD Bioscience, Cat. 743129, Clone U21-1283).
    NRP1
    suggested: None
    We used anti-IgG2b as antibody control (Biolegend, Cat. 406703, Clone
    anti-IgG2b
    suggested: (BioLegend Cat# 406703, RRID:AB_315066)
    Subsequently, the samples were incubated with primary antibodies: mouse monoclonal anti-GFAP Alexa Fluor 488 (EMD Millipore, clone GA5, cat.
    anti-GFAP
    suggested: None
    ) containing rabbit anti-ACE2 polyclonal antibody (1:2000, Abcam, Cat. ab15348) or rabbit anti-NRP1 monoclonal antibody (1:1000, Abcam, clone EPR3113 Cat. ab81321) overnight.
    anti-ACE2
    suggested: (Abcam Cat# ab15348, RRID:AB_301861)
    Next, the membranes were incubated with anti-rabbit polyclonal antibody (1:5000, Invitrogen, Cat. 31460) for 1 h.
    anti-rabbit
    suggested: (Thermo Fisher Scientific Cat# 31460, RRID:AB_228341)
    Primary antibodies anti-GFAP (Abcam; cat. Ab7260); anti-Synaptophysin (D35E4)
    anti-Synaptophysin
    suggested: (Cell Signaling Technology Cat# 5461, RRID:AB_10698743)
    Secondary antibodies donkey anti-mouse IgG AlexaFluor 594 (Cell Signalling; cat. 8890S)
    anti-mouse IgG
    suggested: (Cell Signaling Technology Cat# 8890, RRID:AB_2714182)
    Experimental Models: Cell Lines
    SentencesResources
    Generation of human astrocytes (hES-derived): Differentiation of glial progenitor cells was performed from neural stem cells (NSC) derived from pluripotent human embryonic stem cells (hES, cell line BR-1) 67, according to the method published by Trindade, 2020 68.
    BR-1
    suggested: None
    Viral stock was propagated in Vero CCL-81 cells (ATCC) cultivated in DMEM supplemented with 10% heat-inactivated FBS and 1% of penicillin and streptomycin (Gibco, Walthmam, MA, USA), and incubated at 37°C with 5% CO2 atmosphere.
    Vero CCL-81
    suggested: None
    Antibodies for detecting SARS-CoV-2 in human brain tissue and human astrocytes in vitro were first validated on SARS-CoV-2-infected and non-infected Vero cells.
    Vero
    suggested: CLS Cat# 605372/p622_VERO, RRID:CVCL_0059)
    This protocol is one of the most commonly used for neuronal differentiation of SH-SY5Y cells 81–83.
    SH-SY5Y
    suggested: RRID:CVCL_RS81)
    Software and Algorithms
    SentencesResources
    The slides were washed twice with TBS-T (Tris-Buffered Saline with Tween 20) and incubated with secondary antibodies donkey anti-mouse IgG AlexaFluor 647 (Thermo Fisher Scientific; cat.
    Thermo Fisher Scientific
    suggested: (Thermo Fisher Scientific, RRID:SCR_008452)
    Colocalization analysis between GFAP and SARS-CoV-2 S1 or GFAP and dsRNA were quantified using the Fiji/ImageJ software.
    Fiji/ImageJ
    suggested: None
    The membranes were detected with an ECL system (Millipore, Cat. WBULS0500) and Chemidoc imaging systems (Bio-Rad Laboratories).
    Bio-Rad Laboratories
    suggested: (Bio-Rad Laboratories, RRID:SCR_008426)
    Bioinformatic Analysis: Proteomic data visualization was performed in house with Python programming language (v. 3.7.3)
    Bioinformatic
    suggested: (QFAB Bioinformatics, RRID:SCR_012513)
    Python
    suggested: (IPython, RRID:SCR_001658)
    Proteins differentially regulated (p < 0.05) were submitted to systems biology analysis in R (v. 4.0) and Cytoscape environments 72.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    While performing Over Representation Analysis (ORA), proteins were enriched using ClusterProfiler R package 73
    ClusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    CellMarker Database 74, and Kyoto Encyclopedia of Genes and Genomes (KEGG 75).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    The raw files were preprocessed by Progenesis QI software by Waters®, and identification was executed using multiple data banks.
    Progenesis QI
    suggested: (Progenesis QI, RRID:SCR_018923)
    All analyses were performed using GraphPad Prism 8.0 (San Diego, CA, USA) and a significance level of p ≤ 0.05 was adopted.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    All analyses were performed using GraphPad Prism 8.0 software (San Diego, CA, USA) and a significance level of p ≤ 0.05 was adopted.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Data were analyzed using FlowJo software (BD Biosciences).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Data availability: The mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE 87 partner repository with the dataset identifier PXD023781 and 10.6019/PXD023781.
    PRIDE
    suggested: (Pride-asap, RRID:SCR_012052)

    Results from OddPub: Thank you for sharing your data.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 64, 66, 45, 47, 49, 54 and 58. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

    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.

  6. Version published to 10.1101/2020.10.09.20207464v3 on medRxiv
  7. Version published to 10.1101/2020.10.09.20207464v2 on medRxiv
  8. Version published to 10.1101/2020.10.09.20207464v1 on medRxiv