Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence

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   ScreenIT

   May 3, 2022

   SciScore for 10.1101/2022.04.29.22274267: (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

   Software and Algorithms
   Sentences	Resources
   Total RNA quality and concentration was analysed using Agilent Tapestation	Agilent Tapestation suggested: (Agilent TapeStation Laptop, RRID:SCR_019547)
   Poly-A RNA was purified using poly-T oligo-attached magnetic beads followed by hemoglobin mRNA depletion using QIAseq	QIAseq suggested: (QIAGEN GeneGlobe Data Analysis Center, RRID:SCR_021211)
   FastQC [59] was used to evaluate and merge paired reads prior to adapter trimming using Trimgalore [60].	FastQC suggested: None
   We used STAR [61] to align reads to GRCh38 and htseq-count [62] to generate a counts matrix.	STAR suggested: None
   We primarily used …

   SciScore for 10.1101/2022.04.29.22274267: (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

   Software and Algorithms
   Sentences	Resources
   Total RNA quality and concentration was analysed using Agilent Tapestation	Agilent Tapestation suggested: (Agilent TapeStation Laptop, RRID:SCR_019547)
   Poly-A RNA was purified using poly-T oligo-attached magnetic beads followed by hemoglobin mRNA depletion using QIAseq	QIAseq suggested: (QIAGEN GeneGlobe Data Analysis Center, RRID:SCR_021211)
   FastQC [59] was used to evaluate and merge paired reads prior to adapter trimming using Trimgalore [60].	FastQC suggested: None
   We used STAR [61] to align reads to GRCh38 and htseq-count [62] to generate a counts matrix.	STAR suggested: None
   We primarily used ENSEMBL identifiers [65], however for plots we report the HGNC gene ID [66] where available.	ENSEMBL suggested: (Ensembl, RRID:SCR_002344)
   The SomaScan v4.1 assay contains 7,288 modified-aptamers (Somamers) that target human proteins.	SomaScan suggested: None
   For differential expression of proteins, we applied LMM using the lmerTest package [70].	lmerTest suggested: (R package: lmerTest, RRID:SCR_015656)
   We used WGCNA’s pickSoftThreshold.fromSimilarity function to pick the minimum soft-thresholding power that satisfied the minimum scale free topology fitting index (R2>0.85) and maximum mean connectivity (100).	WGCNA’s suggested: None
   The regression model used is displayed using Wilkinson-style notation below: Latent_factor ∼ clinical_course * time + sex + age + ethnicity + wave + (1 | individual) Longitudinal modelling of cytokines and cytokine receptors: We modelled the temporal profiles of 232 plasma proteins that fell within the KEGG pathway “Cytokine-cytokine receptor interaction”.	KEGG suggested: None
   84]; caret uses the randomForest package to fit random forest models and glmnet [85] to fit lasso models.	randomForest suggested: (RandomForest Package in R, RRID:SCR_015718)
   Aurora Spectral Flow Cytometry (Cytek®) and FlowJo software, version 10 (Tree Star Inc. Ashland, OR, USA) were used for analysis of all samples.	FlowJo suggested: (FlowJo, RRID:SCR_008520)

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   Results from OddPub: Thank you for sharing your code and data.

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

   Our study has several limitations. ESKD patients have considerable multi-morbidity and deranged physiology, and our findings may not all be generalisable to other patient populations. We lacked a comparator group of ESKD patients with another viral infection to delineate COVID-19 specific features. We studied peripheral blood; while this can provide valuable information, it does not always reflect processes at the site of tissue injury. We performed bulk RNA-seq on PBMCs. Thus, transcriptomic signatures may reflect both changes in gene expression and also alteration in the distribution of cell subtypes within PBMCs. We mitigated this issue through use of deconvolution methods and flow cytometry, but future studies using single cell RNA-seq and CITE-seq will provide further granularity. We did not have measurements of viral load which would have aided interpretation of the magnitude of host responses (e.g. interferon signaling). Finally, the convalescent samples were taken relatively soon after clinical recovery: it will be important for future studies to establish how long molecular abnormalities persist. In summary, we demonstrate dynamic transcriptomic, proteomic and cellular signatures that vary both with time and COVID-19 severity. We show that in patients with a severe clinical course there is increased type 1 interferon signaling early in the illness, with increases in pro- inflammatory cytokines later in disease. We identify plasma levels of the proposed alternative SA...

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   Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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   Version published to 10.1101/2022.04.29.22274267v1 on medRxiv

   May 1, 2022