Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment

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1. Version published to 10.1186/s13195-021-00850-3

   Jun 9, 2021
2. 

   ScreenIT

   Mar 24, 2021

   SciScore for 10.1101/2021.03.15.435423: (What is this?)

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

   Table 1: Rigor

   NIH rigor criteria are not applicable to paper type.

   Table 2: Resources

   Experimental Models: Cell Lines
   Sentences	Resources
   Specifically, six of these datasets were based on CRISPR-Cas9 assay results, including (1-2) CRISPR_A549-H and CRISPR_A549-L, based on high (-H) and low (-L) multiplicity of infection of SARS-CoV-2 in A549 cells [21]; (3-5) CRISPR_HuH7-SARS2, CRISPR_HuH7-229E, CRISPR_HuH7-OC43, based on HuH7 cells infected by SARS-CoV-2, HCoV-229E, and HCoV-OC43, respectively [22]; and (6) CRISPR_VeroE6, based on SARS-CoV-2-infected VeroE6 cells [23].	A549 suggested: None HuH7 suggested: None VeroE6 suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
   Software and Algorithms
   Sentences	Resources
   Functional …

   SciScore for 10.1101/2021.03.15.435423: (What is this?)

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

   Table 1: Rigor

   NIH rigor criteria are not applicable to paper type.

   Table 2: Resources

   Experimental Models: Cell Lines
   Sentences	Resources
   Specifically, six of these datasets were based on CRISPR-Cas9 assay results, including (1-2) CRISPR_A549-H and CRISPR_A549-L, based on high (-H) and low (-L) multiplicity of infection of SARS-CoV-2 in A549 cells [21]; (3-5) CRISPR_HuH7-SARS2, CRISPR_HuH7-229E, CRISPR_HuH7-OC43, based on HuH7 cells infected by SARS-CoV-2, HCoV-229E, and HCoV-OC43, respectively [22]; and (6) CRISPR_VeroE6, based on SARS-CoV-2-infected VeroE6 cells [23].	A549 suggested: None HuH7 suggested: None VeroE6 suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
   Software and Algorithms
   Sentences	Resources
   Functional enrichment analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) biological process enrichment analyses, were performed using Enrichr [30] for the CRISPR datasets.	KEGG suggested: (KEGG, RRID:SCR_012773) Enrichr suggested: (Enrichr, RRID:SCR_001575)
   Neurological disease gene profiles: We extracted neurologic disease-associated genes/proteins from the Human Gene Mutation Database (HGMD) [31], and defined a gene to be disease-associated, if it had at least one disease-associated mutation from HGMD reported in the literature.	Human Gene Mutation Database suggested: (Human Gene Mutation Database, RRID:SCR_001621)
   All single-cell analyses were performed using Seurat v3.1.5 [39] following the processing steps from the original publication of each dataset.	Seurat suggested: (SEURAT, RRID:SCR_007322)
   For the bulk RNA-sequencing dataset, differential expression analysis was performed using edgeR v3.12 [40]	edgeR suggested: (edgeR, RRID:SCR_012802)
   Each PPI edge has one or more source information of five categories of evidence from publicly available databases and datasets: protein complexes identified by robust affinity purification-mass spectrometry from BioPlex V2.016 [49]; binary PPIs discovered by high-throughput yeast two-hybrid systems in three datasets [24, 50, 51]; signaling networks revealed by low-throughput experiments from SignaLink2.0 [52]; low-throughput or high-throughput experiments uncovered kinase-substrate interactions from KinomeNetworkX [53], Human Protein Resource Database (HPRD) [54], PhosphoNetworks [55], PhosphositePlus [56], DbPTM 3.0 [57], and Phospho.	BioPlex suggested: (BioPlex, RRID:SCR_016144) HPRD suggested: None DbPTM suggested: (dbPTM: An informational repository of proteins and post-translational modifications, RRID:SCR_007619)
   ELM [58]; and PPIs curated from literatures identified by yeast two-hybrid studies, affinity purification-mass spectrometry, low-throughput experiments, or protein three-dimensional structures from BioGRID [59]	BioGRID suggested: (BioGrid Australia, RRID:SCR_006334)
   PINA [60], Instruct [61], MINT [62], IntAct [63], and InnateDB [64].	MINT suggested: (MINT, RRID:SCR_001523) IntAct suggested: (IntAct, RRID:SCR_006944) InnateDB suggested: (InnateDB, RRID:SCR_006714)
   Eigenvector centrality [66] of the nodes in the networks were computed using Gephi 0.9.2 [67] to evaluate the influence of the nodes considering the importance of their neighbors.	Gephi suggested: (Gephi, RRID:SCR_004293)
   Statistical analysis and network visualization: Python package SciPy v1.3.0 [69] was used for the statistical tests unless specified otherwise.	Python suggested: (IPython, RRID:SCR_001658) SciPy suggested: (SciPy, RRID:SCR_008058)
   Networks were visualized with Gephi 0.9.2 [67] and Cytoscape 3.8.0 [70].	Cytoscape suggested: (Cytoscape, RRID:SCR_003032)
   The transcriptomic datasets used in this study (GSE147528, GSE157827, GSE138852, GSE157103, GSE149689, and GSE163005) were downloaded from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/).	NCBI GEO suggested: None https://www.ncbi.nlm.nih.gov/geo/ suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)

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   Results from OddPub: Thank you for sharing your data.

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   Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

   Limitations: We acknowledge several limitations. First, our human protein-protein interactome was built using high-quality data from multiple sources; yet it is still incomplete. The PPIs in our interactome is undirected. However, it has been shown that incorporating directionality of the human PPI does not change network proximity results [114]. Therefore, the network associations could be either positive or negative, and require further investigation. In addition, as our network proximity analysis relies on disease-associated genes, literature bias could affect the results because more highly-studied genes are more likely to appear in the dataset. Second, we analyzed expression levels of the key SARS-CoV-2 entry factors and found low expression levels for ACE2 and TMPRSS2. However, we cannot rule out the possibility of SARS-CoV-2 directly targeting the brain via as-yet unidentified mechanisms. Third, possible pathways of neuroinflammation and microvascular injury were tested using data of either individuals with AD or COVID-19, but not both. Future studies using genetics and multi-omics data from individuals with both AD and COVID-19 will be needed to confirm and extend these network-based findings. The significance of our findings in the context of the general population of COVID-19 frequently suffering from “brain fog” without a formal diagnosis of AD needs further investigation.

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   Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
   

   Identifier	Status	Title
   NCT04320277	Not yet recruiting	Baricitinib in Symptomatic Patients Infected by COVID-19: an…
   NCT04321993	Recruiting	Treatment of Moderate to Severe Coronavirus Disease (COVID-1…

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

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

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   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.

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3. 

   Version published to 10.1101/2021.03.15.435423v1 on bioRxiv

   Mar 22, 2021