Dynamic activity in cis-regulatory elements of leukocytes identifies transcription factor activation and stratifies COVID-19 severity in ICU patients
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
No abstract available
Article activity feed
-
-
SciScore for 10.1101/2021.08.24.457187: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: After informed consent, blood was drawn on hospitalization days 1, 3, 5, 7, 9, 11 and discharge/death for analysis.
IRB: Study Approval: The study was approved by the Institutional Review Board at the University of California, San Diego (UCSD IRB#190699).Sex as a biological variable not detected. Randomization Due to the size of the dataset, 10,000 random TSRs were first selected for hierarchical clustering. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources (REDCap) electronic data capture tool hosted at the University of California, San Diego. REDCapsuggested: (REDCap, RRID:SCR_003445)Transcription Start Regions (TSRs), … SciScore for 10.1101/2021.08.24.457187: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: After informed consent, blood was drawn on hospitalization days 1, 3, 5, 7, 9, 11 and discharge/death for analysis.
IRB: Study Approval: The study was approved by the Institutional Review Board at the University of California, San Diego (UCSD IRB#190699).Sex as a biological variable not detected. Randomization Due to the size of the dataset, 10,000 random TSRs were first selected for hierarchical clustering. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources (REDCap) electronic data capture tool hosted at the University of California, San Diego. REDCapsuggested: (REDCap, RRID:SCR_003445)Transcription Start Regions (TSRs), representing loci with significant transcription initiation activity (i.e. ‘peaks’ in csRNA-seq), were defined using HOMER’s findcsRNATSS.pl tool, which uses short input RNA-seq, traditional RNA-seq, and annotated gene locations to eliminate loci with csRNA-seq signal arising from non-initiating, high abundance RNAs that nonetheless are captured and sequenced by the method (full description is available in Duttke et al.(11). HOMER’ssuggested: NoneSTAR was also used to quantify read counts per gene using transcripts defined by GENCODE (version 34). STARsuggested: (STAR, RRID:SCR_004463)GENCODEsuggested: (GENCODE, RRID:SCR_014966)For total RNA-seq from Overmyer et al (47), sequencing reads were downloaded from GSE157103 and were processed in the same fashion (i.e. mapped with STAR, rlog normalized with DESeq2). DESeq2suggested: (DESeq, RRID:SCR_000154)H3K27ac ChIP-seq data for 21 different peripheral blood cell types were downloaded from the Blueprint Epigenome project (https://www.blueprint-epigenome.eu/) (18) to assess regions with active chromatin modifications. https://www.blueprint-epigenome.eu/suggested: (Blueprint Epigenome, RRID:SCR_003844)Uniquely aligned reads (MAPQ>10) were then analyzed using HOMER to find peaks using “-style atac” and “-style factor” for ATAC-seq and TF ChIP-seq experiments, respectively. HOMERsuggested: (HOMER, RRID:SCR_010881)The same approach was used to score ChIP-seq specific enrichment (i.e. Supp. Fig. 3, 5c) by quantifying each ChIP-seq experiment across TSRs (+/-200 bp for TF ChIP-seq, +/- 500 bp for H3K27ac ChIP-seq). ChIP-seqsuggested: (ChIP-seq, RRID:SCR_001237)Cell type enrichment patterns were further hierarchically clustered using Cluster 3.0 (51 Clustersuggested: (Cluster, RRID:SCR_013505)These target genes were submitted for Gene Ontology and Pathway Analysis using Metascape (55). Metascapesuggested: (Metascape, RRID:SCR_016620)LEGENDplex Data Analysis Software (BioLegend) was used for analysis: Drug repurposing & connectivity mapping: CMap (https://clue.io/cmap) provides expression similarity scores for a specific expression profile with other drug-induced transcriptional profiles, including consensus transcriptional signatures of 2,837 drugs grouped into 83 drug classes (29). CMapsuggested: (CMAP, RRID:SCR_009034)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:Our work has several limitations. First, the study design maximizes temporal resolution, which limits patient numbers. A dedicated study with a larger cohort for csRNA-seq would be ideal for confirmation. Nonetheless, the validation analysis using a large external COVID-19 cohort confirmed the cistrome association with poor disease outcomes. It also demonstrates the feasibility of identifying target genes as a proxy for TF network activity. Secondly, we profiled the cistrome of all peripheral leukocytes, a heterogeneous cellular population with different proportions. The imbalance in cellular proportion influences clustering resolution, which is more sensitive to cells making up the majority of the heterogeneous population. Cell sorting prior to cistrome analysis would address this issue but presents technical and feasibility challenges requiring larger blood volumes from clinically unstable patients. To address cell-type identity, we cross-examined publicly available cistrome databases and successfully identified major inflammatory pathways in smaller subsets of circulatory immune cells, including the lymphocytic NFkB and the monocytic ARE/SMAD/AP1 programs. We also identified the combined STAT/BCL6 and E2F/MYB signature from immature neutrophils, which usually represent < 10- 20% of total leukocytes even in critical illnesses. In summary, we used a novel unbiased technique to examine active genomic regulatory elements by profiling levels of initiating transcripts directly f...
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.
-
