Specialized interferon ligand action in COVID19

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   ScreenIT

   Aug 4, 2021

   SciScore for 10.1101/2021.07.29.21261325: (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
   The Evosep One system (Evosep, Odense, Denmark) was used to separate peptides on a Pepsep column, (150 µm internal diameter, 15 cm) packed with ReproSil C18 1.9 µm, 120A resin.	ReproSil suggested: None
   Files were debarcoded using the Matlab DebarcoderTool (58).	Matlab suggested: (MATLAB, RRID:SCR_001622)
   Data were analyzed using Maven (Princeton University, Princeton, NJ, USA) in conjunction with the KEGG database and an in-house standard library.	KEGG suggested: (KEGG, RRID:SCR_012773)
   Biostatistics and bioinformatics analyses: Preprocessing, statistical analysis, and plot generation for all …

   SciScore for 10.1101/2021.07.29.21261325: (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
   The Evosep One system (Evosep, Odense, Denmark) was used to separate peptides on a Pepsep column, (150 µm internal diameter, 15 cm) packed with ReproSil C18 1.9 µm, 120A resin.	ReproSil suggested: None
   Files were debarcoded using the Matlab DebarcoderTool (58).	Matlab suggested: (MATLAB, RRID:SCR_001622)
   Data were analyzed using Maven (Princeton University, Princeton, NJ, USA) in conjunction with the KEGG database and an in-house standard library.	KEGG suggested: (KEGG, RRID:SCR_012773)
   Biostatistics and bioinformatics analyses: Preprocessing, statistical analysis, and plot generation for all datasets was carried out using R (R 4.0.1 / Rstudio	Biostatistics suggested: (BWH Biostatistics Center, RRID:SCR_009680)
   Bioconductor v 3.11) (61-63), as detailed below.	Bioconductor suggested: (Bioconductor, RRID:SCR_006442)
   Reads were demultiplexed and converted to fastq format using bcl2fastq (bcl2fastq v2.20.0.422).	bcl2fastq suggested: (bcl2fastq , RRID:SCR_015058)
   Data quality was assessed using FASTQC (v0.11.5) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and FastQ Screen (v0.11.0, https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/).	FASTQC suggested: (FastQC, RRID:SCR_014583) https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/ suggested: (FastQ Screen, RRID:SCR_000141)
   Trimming and filtering of low-quality reads was performed using bbduk from BBTools (v37.99)(64) and fastq-mcf from ea-utils (v1.05, https://expressionanalysis.github.io/ea-utils/).	ea-utils suggested: (ea-utils, RRID:SCR_005553)
   Alignment to the human reference genome (GRCh38) was carried out using HISAT2 (v2.1.0)(65) in paired, spliced-alignment mode with a GRCh38 index with a Gencode v33 annotation GTF, and alignments were sorted and filtered for mapping quality (MAPQ > 10) using Samtools (v1.5)(66)	HISAT2 suggested: (HISAT2, RRID:SCR_015530) Gencode suggested: (GENCODE, RRID:SCR_014966) Samtools suggested: (SAMTOOLS, RRID:SCR_002105)
   Gene-level count data were quantified using HTSeq-count (v0.6.1)(67) with the following options (--stranded=reverse –minaqual=10 –type=exon --mode=intersection-nonempty) using a Gencode v33 GTF annotation file.	HTSeq-count suggested: (htseq-count, RRID:SCR_011867)
   FCS() function from the flowCore package (v2.2.0) (71) and raw intensity values inverse hyperbolic sine transformed using the cytofAsinh() function with cofactor = 5 from the cytofkit package, and 1000 cells per FCS file sampled without replacement for downstream analysis.	flowCore suggested: (flowCore, RRID:SCR_002205)
   For visualization, dimensionality reduction was performed using the t-distributed stochastic neighbor embedding (t-SNE) method from the Rtsne package (v0.15) (72), using all markers.	Rtsne suggested: (Rtsne, RRID:SCR_016342)
   Unsupervised clustering, using all markers, was performed using the cytofkit implementation of the PhenoGraph algorithm (22)	PhenoGraph suggested: (Phenograph, RRID:SCR_016919)
   Differential abundance analysis: For RNAseq, gene-level differential expression in COVID+ versus COVID- was evaluated using DESeq2 (version 1.28.1)(68) in R (version 4.0.1), with q < 0.1 (FDR < 10%) as the threshold for differentially expressed genes, and considering only genes with ≥ 0.5 counts- per-million in at least two samples.	DESeq2 suggested: (DESeq, RRID:SCR_000154)
   Standardized fold-changes from each model were visualized by overlaying on t-SNE plots or as heatmaps using the ggplot2 (v3.3.1) (79) and ComplexHeatmap (v2.4.2) (78) packages.	ggplot2 suggested: (ggplot2, RRID:SCR_014601) ComplexHeatmap suggested: (ComplexHeatmap, RRID:SCR_017270)

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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:

   One limitation of our study is that all measurements were performed from peripheral blood, which can only inform about a subset of the pathophysiological processes modulated by the various ligands. Our study would be highly complemented by studies of IFN ligands in various tissues (e.g. (53)). It is also possible that the specialized biosignatures observed are driven in part by SARS-CoV-2 itself. Like other members of the coronavirus family, SARS-CoV-2 has evolved diverse strategies to evade the antiviral effects of IFN signaling, and it is possible that these escape mechanisms do not affect all IFN ligands equally (54). Despite these limitations, key observations produced by our study include the differential relationship between IFN ligands and the antiviral transcriptional program in circulating immune cells, the specialized relationship between seroconversion, immune cell type abundance and IFN ligand levels, and the distinct metabolic signatures associated with the ligands. Throughout the study, the contrast between IFNA2 and IFNA6 exemplifies these points. Both IFNA2 and IFNA6 are specifically recognized by the reagents employed and significantly upregulated in the COVID19 positive cohort. However, whereas IFNA2 is strongly associated with the IFN transcriptional program in immune cells, IFNA6 is not. IFNA2 proteomic signatures are enriched for cytokines and chemokines previously linked to IFN signaling, whereas IFNA6 proteomic signatures, similarly to those of IFNB1, a...

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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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2. 

   Version published to 10.1101/2021.07.29.21261325v1 on medRxiv

   Aug 1, 2021