SARS‐CoV ‐2 infection impacts carbon metabolism and depends on glutamine for replication in Syrian hamster astrocytes

  1. Lilian Gomes de Oliveira
  2. Yan de Souza Angelo
  3. Pedro Yamamoto
  4. Victor Corasolla Carregari
  5. Fernanda Crunfli
  6. Guilherme Reis‐de‐Oliveira
  7. Lícia Costa
  8. Pedro Henrique Vendramini
  9. Érica Almeida Duque
  10. Nilton Barreto dos Santos
  11. Egidi Mayara Firmino
  12. Isadora Marques Paiva
  13. Glaucia Maria Almeida
  14. Adriano Sebollela
  15. Carolina Manganeli Polonio
  16. Nagela Ghabdan Zanluqui
  17. Marília Garcia de Oliveira
  18. Patrick da Silva
  19. Gustavo Gastão Davanzo
  20. Marina Caçador Ayupe
  21. Caio Loureiro Salgado
  22. Antônio Francisco de Souza Filho
  23. Marcelo Valdemir de Araújo
  24. Taiana Tainá Silva‐Pereira
  25. Angélica Cristine de Almeida Campos
  26. Luiz Gustavo Bentim Góes
  27. Marielton dos Passos Cunha
  28. Elia Garcia Caldini
  29. Maria Regina D'Império Lima
  30. Denise Morais Fonseca
  31. Ana Márcia de Sá Guimarães
  32. Paola Camargo Minoprio
  33. Carolina Demarchi Munhoz
  34. Cláudia Madalena Cabrera Mori
  35. Pedro Manoel Moraes‐Vieira
  36. Thiago Mattar Cunha
  37. Daniel Martins‐de‐Souza
  38. Jean Pierre Schatzmann Peron

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

COVID‐19 causes more than million deaths worldwide. Although much is understood about the immunopathogenesis of the lung disease, a lot remains to be known on the neurological impact of COVID‐19. Here, we evaluated immunometabolic changes using astrocytes in vitro and dissected brain areas of SARS‐CoV‐2 infected Syrian hamsters. We show that SARS‐CoV‐2 alters proteins of carbon metabolism, glycolysis, and synaptic transmission, many of which are altered in neurological diseases. Real‐time respirometry evidenced hyperactivation of glycolysis, further confirmed by metabolomics, with intense consumption of glucose, pyruvate, glutamine, and alpha ketoglutarate. Consistent with glutamine reduction, the blockade of glutaminolysis impaired viral replication and inflammatory response in vitro. SARS‐CoV‐2 was detected in vivo in hippocampus, cortex, and olfactory bulb of intranasally infected animals. Our data evidence an imbalance in important metabolic molecules and neurotransmitters in infected astrocytes. We suggest this may correlate with the neurological impairment observed during COVID‐19, as memory loss, confusion, and cognitive impairment. image

Article activity feed

  1. Version published to 10.1111/jnc.15679
  2. Rapid Reviews Infectious Diseases

    Alberto Ramos

    Review 2: "SARS-CoV-2 Infection Impacts Carbon Metabolism and Depends on Glutamine for Replication in Syrian Hamster Astrocytes"

    This preprint examines the effects of SARS-CoV-2 infection on astrocytes and finds that SARS-CoV-2 targets astroglial metabolism for the viral replication/assembly. Reviewers thought the results presented support the study's conclusions.

  3. Rapid Reviews Infectious Diseases

    Juan de la Torre

    Review 1: "SARS-CoV-2 Infection Impacts Carbon Metabolism and Depends on Glutamine for Replication in Syrian Hamster Astrocytes"

    This preprint examines the effects of SARS-CoV-2 infection on astrocytes and finds that SARS-CoV-2 targets astroglial metabolism for the viral replication/assembly. Reviewers thought the results presented support the study's conclusions.

  5. ScreenIT

    SciScore for 10.1101/2021.10.23.465567: (What is this?)

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

    Table 1: Rigor

    EthicsIACUC: The protocols were approved by the Ethics Committee for Animal Research of University of Sao Paulo (CEUA n° 7971160320 / 3147240820) and all efforts were made to minimize animal suffering.
    Sex as a biological variableBrain samples from SARS-CoV-2-infected and uninfected hamsters from two unrelated experiments were used herein (one performed with 15–16-week-old females and another with 18-week-old males).
    RandomizationQuantification was made using ImageJ software (NIH), where 5 to 10 randomly chosen visual fields per coverslip from three independent cultures were analyzed.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    After antigen retrieval (Tris/EDTA-Tween-20 buffer -10mM Tris, 1mM EDTA, 0.05% Tween 20, pH 9 - for 30 minutes at 95°C, followed by 20 minutes at room temperature), cells were permeabilized with 0.5% Triton-X100 in PBS for 10 minutes, blocked with blocking solution (5% donkey serum + 0.05% Triton X-100 in PBS) (Sigma-Aldrich) for 2 hours at room temperature and incubated with primary antibodies: GFAP (1:1000, Sigma-Aldrich), IBA1 (1:500, Abcam), and MAP2 (1:500, Sigma-Aldrich) diluted in blocking solution, overnight at 4°C.
    GFAP
    suggested: None
    IBA1
    suggested: (ChromoTek Cat# smsG1Cys2-1, RRID:AB_2864263)
    MAP2
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    The inoculum was then added to the flask containing Vero cells and maintained for 1 hour at 37°C, following addition of fresh DMEM low glucose media supplemented with 2% FBS and 1% penicillin/streptomycin.
    Vero
    suggested: None
    This virus was obtained from nasopharyngeal swabs from the first patient (HIAE01) to be diagnosed with COVID-19 in Brazil, isolated in Vero ATCC CCL-81 cells and quantified by using the Median Tissue Culture Infectious Dose (TCID50) assay.
    Vero ATCC CCL-81
    suggested: None
    Plaque-forming unit assay: For virus titration, Vero CCL81 cells were seeded in 24 well plates one day before the infection for adhesion.
    Vero CCL81
    suggested: None
    Software and Algorithms
    SentencesResources
    Label free quantitative analysis was obtained using the relative abundance intensity integrated in Progenesis software, using all peptides identified for normalization.
    Progenesis
    suggested: (Progenesis QI, RRID:SCR_018923)
    Generated images were loaded into Fiji software for further analysis.
    Fiji
    suggested: (Fiji, RRID:SCR_002285)
    Mitochondrial analysis: For measurements of mitochondrial superoxide, the cells were stained with LIVE/DEAD™ Fixable Green (L34970; ThermoFisher®, 15 min) and MitoSOX™ Red mitochondrial superoxide indicator (M36008; ThermoFisher®; 2.5uM, 10 min).
    ThermoFisher®
    suggested: (ThermoFisher; SL 8; Centrifuge, RRID:SCR_020809)
    Next, cells were analyzed using a Accuri C6 Plus (BD Biosciences®) cytometer and data analyzed using FlowJo X software.
    BD Biosciences®
    suggested: (BD Biosciences, RRID:SCR_013311)
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Quantification was made using ImageJ software (NIH), where 5 to 10 randomly chosen visual fields per coverslip from three independent cultures were analyzed.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Single Nuclei Transcriptomic Profile Analysis: Raw snRNA-seq data were obtained from frozen medial frontal cortex tissue from six post- mortem control and seven COVID-19 patients assessed herein were obtained from the dataset GSE159812 on NCBI GeneExpression Omnibus public databank.
    NCBI GeneExpression Omnibus
    suggested: None
    (DEGs) corresponding to the Differentially Expressed Proteins (DEPs) from hamster brain proteomics and concordant fold-change were subjected to Gene Ontology analysis, performed through online database PANTHER using PANTHER Pathways dataset34.
    PANTHER
    suggested: (PANTHER, RRID:SCR_004869)
    Statistical analysis: Data was plotted and analyzed using the GraphPad Prism 8.0 software (GraphPad Software®, San Diego, CA).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 41. 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.

    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.

  6. Version published to 10.1101/2021.10.23.465567v1 on bioRxiv