Host and microbiome features of secondary infections in lethal covid-19

  1. Martin Zacharias
  2. Karl Kashofer
  3. Philipp Wurm
  4. Peter Regitnig
  5. Moritz Schütte
  6. Margit Neger
  7. Sandra Ehmann
  8. Leigh M. Marsh
  9. Grazyna Kwapiszewska
  10. Martina Loibner
  11. Anna Birnhuber
  12. Eva Leitner
  13. Andrea Thüringer
  14. Elke Winter
  15. Stefan Sauer
  16. Marion J. Pollheimer
  17. Fotini R. Vagena
  18. Carolin Lackner
  19. Barbara Jelusic
  20. Lesley Ogilvie
  21. Marija Durdevic
  22. Bernd Timmermann
  23. Hans Lehrach
  24. Kurt Zatloukal
  25. Gregor Gorkiewicz

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Abstract

No abstract available

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  1. Version published to 10.1016/j.isci.2022.104926
  2. ScreenIT

    SciScore for 10.1101/2022.02.18.22270995: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The following antibodies were used: Anti-SARS-CoV-2 nucleoprotein (NP) antibody (clone ID: 019, dilution 1:100, rabbit IgG; Sino Biological, Beijing; detection-system: Dako REAL TM EnVision TM HRP rabbit/mouse Dako K5007); CD68 (Ventana anti-CD68 (KP-1) monoclonal mouse 790-2931; detection-system: Ventana Ultra View DAB); TTF1 (Cell marque 343M-96 Clone 8G7G3/1 monoclonal mouse 1:200; detection-system: Dako K5007); TGFß1 (Santacruz polyclonal rabbit AB; clone SC-146 1:50; detection-system: Ventana Ultra View DAB); LAG3 (Abcam polyclonal rabbit; clone ab180187 1:5000; detection-system: Dako K5007); C1q (Dako polyclonal rabbit, clone A0136 1:5000; detection-system: Dako K5007); CD163 (Ventana monoclonal mouse, clone MRQ-26 1:50; detection-system: Ventana Ultra View DAB).
    Anti-SARS-CoV-2 nucleoprotein (NP
    suggested: None
    anti-CD68
    suggested: (LSBio (LifeSpan Cat# LS-C88159-200, RRID:AB_1792428)
    KP-1
    suggested: None
    AB
    suggested: (Abcam Cat# ab180187, RRID:AB_2888645)
    LAG3
    suggested: (Abcam Cat# ab180187, RRID:AB_2888645)
    Subsequently, the membranes were incubated with antibodies against C1q (Dako Denmark A/S 1:5000), TGFß1 (Cell Signaling Technology, 1:1000), and GAPDH (Cell Signaling Technology, 1:1000) overnight at 4°C.
    C1q
    suggested: (LSBio (LifeSpan Cat# LS-C20978-5000, RRID:AB_10638452)
    GAPDH
    suggested: None
    Thereafter, membranes were washed and incubated with the appropriate HRP-conjugated secondary antibody (Amersham, ECL Anti-Rabbit IgG, 1:5000).
    Anti-Rabbit IgG
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    1500 rcf) the supernatants were filtered through a 0.45µm membrane filter (Millipore) and inoculated on Vero CCL-81 cells with OptiPro SFM medium with 4mM L-Glutamine and 1% penicillin-streptomycin in T25 flasks (ThermoFisher).
    Vero CCL-81
    suggested: None
    RNAs from VeroE6 cell cultures were isolated by using the QIAamp Viral RNA Mini Kit (Qiagen) without addition of carrier RNA and transcribed into cDNA with the High-Capacity cDNA Reverse Transcription Kit with RNase Inhibitor (Applied Biosystems) according to manufacturer’s instructions.
    VeroE6
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    Software and Algorithms
    SentencesResources
    Amplification data was downloaded and processed using the qpcR package of the R project (https://www.r-project.org/).
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    Sequences were aligned to the SARS-CoV-2 reference genome (acc. no.: NC_045512.2) using TMAP (v5.10.11) and variants were called with the Torrent Variant Caller (v5.10-12).
    TMAP
    suggested: (TMAP, RRID:SCR_000687)
    Libraries were pooled in two pools of 13 samples each by concentration measured with Qubit (ThermoFisher), followed by a bead-cleanup step and an additional QC with Qubit (ThermoFisher) and BioAnalyzer (Agilent).
    ThermoFisher
    suggested: (ThermoFisher; SL 8; Centrifuge, RRID:SCR_020809)
    BioAnalyzer
    suggested: (BioAnalyzer 2100, RRID:SCR_019715)
    Read counts on plus-/minus-strand were counted using custom python scripts.
    python
    suggested: (IPython, RRID:SCR_001658)
    Exact positioning of the reads on plus-/minus-strand was done splitting the bam files aligned to NC_045512.2 using samtools -f 0×10 and samtools -F 0×10 (v0.1.19-44428cd) and bedtools genomecov -ibam BAM NC_045512.2 -d (bedtools v2.17.0).
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    RNA profiling: Gene counts were determined using HTSeq (v0.12.4) and normalized as fragments per kilobase per million (FPKM) after TMM correction.
    HTSeq
    suggested: (HTSeq, RRID:SCR_005514)
    Differential gene expression was conducted using edgeR (https://doi.org/doi:10.18129/B9.bioc.edgeR) [77].
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    Clustering of differentially expressed genes was performed using hclust hierachical clustering and subsequent cutting of the gene tree at R function cutree with h=0.25.
    hclust
    suggested: (HCLUST, RRID:SCR_009154)
    Microbiome analysis based on RNAseq: Microbiome analysis was performed with the following steps using all reads from STAR alignment not mapping to the human reference: quality filtering using fastx -q 30 -p 26 -Q33 (v0.0.13, http://hannonlab.cshl.edu/fastx_toolkit/), cleaning of the fasta file using seqclean-x86_64 -N -M -A (https://sourceforge.net/projects/seqclean/), realigning to the human reference using blastn against all databases and removal of all reads with 94% similarity.
    STAR
    suggested: (STAR, RRID:SCR_004463)
    Remaining reads were annotated using MetaPhlAn2 (v2.6.0) [80] and Pathseq (GATK v4.1.0.0) [81] with default settings.
    MetaPhlAn2
    suggested: None
    GATK
    suggested: (GATK, RRID:SCR_001876)
    Quality control and preprocessing of sequences was performed using FastQC (version 0.7), MultiQC (version 1.7) and trimmomatic (version 0.36.5) using following parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:200.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    16S-based analysis was performed with the latest SILVA 138 taxonomy and the Naive Bayes classifier trained on Silva 138 99% OTUs full-length sequences.
    SILVA
    suggested: (SILVA, RRID:SCR_006423)
    For ITS-based analysis a classifier was trained on the UNITE reference database (ver8-99-classifier; 04.02.2020) according to John Quensen (http://john-quensen.com/tutorials/training-the-qiime2-classifier-with-unite-its-reference-sequences/; assessed 20/08/2020).
    UNITE
    suggested: (UNITE, RRID:SCR_006518)
    For metagenomic biomarker discovery taxonomic feature-tables were introduced to LEfSe (linear discriminant analysis effect size) method (Galaxy version 1.0; p<0.05, LDA>2, All-against-all) [84].
    Galaxy
    suggested: (Galaxy, RRID:SCR_006281)
    Plots were generated with R (version 3.6.2)6 in RStudio (1.1.463)7 using following packages: tidyverse (1.3.0)8, qiime2r (0.99.6)9, ggplot2 (3.3.3)10, dplyr (1.0.6)11 and ggpubr (0.4.0.999)12 and GraphPad Prism.
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    The graphical abstract was created with BioRender (www.
    BioRender
    suggested: (Biorender, RRID:SCR_018361)
    GAPDH was used as loading control to determine protein abundance and band density was quantified and compared by using ImageJ.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)

    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: We detected the following sentences addressing limitations in the study:
    The limitations of our descriptive study are that causalities cannot be directly inferred and that the relatively small cohort cannot show the entire picture of severe covid-19 and associated secondary infections. Varying clinical courses and different comorbidities might also have influenced our findings. In addition, treatment of covid-19 has changed since the early pandemic, thus, current severe courses and developing sequels might also have changed. We also cannot be sure whether the two described forms of DAD might represent just a spectrum of pathophysiological states or are specific pathotypes. Moreover, post-mortem effects like RNA degradation might have introduced additional noise in our investigation. Nevertheless, we found autopsy complemented with microbiology and molecular measures as a powerful tool to gain relevant clues about covid-19 pathophysiology. Importantly, there exists an obvious knowledge gap in the understanding of the molecular mechanisms driving the development of secondary infections on top of in viral lung diseases. This should initiate further studies to understand the molecular pathways in more detail and to unravel chronological phases of immuno-suppression which could also lead to development of rational therapies counteracting this sequel not only in covid-19. For these investigations, autopsy specimens and associated molecular data might serve as a valuable resource.

    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.
    • 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.

  3. Version published to 10.1101/2022.02.18.22270995v1 on medRxiv