Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19
Xinge Wang, … , Yang Dai, Jalees Rehman
Xinge Wang, … , Yang Dai, Jalees Rehman
Published February 17, 2022
Citation Information: JCI Insight. 2022;7(7):e157255. https://doi.org/10.1172/jci.insight.157255.
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Resource and Technical Advance COVID-19 Cell biology Immunology

Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19

Abstract

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there are not enough methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic data sets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy, to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal data sets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk and single-cell RNA-Seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways, as well as impaired type I IFN (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.

Authors

Xinge Wang, Mark A. Sanborn, Yang Dai, Jalees Rehman

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Figure 4

Temporal analysis of whole-blood RNA-Seq data in patients grouped according to disease severity.

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Temporal analysis of whole-blood RNA-Seq data in patients grouped accord...
(A) Venn diagram of DDEGs identified from 3 COVID-19 severity groups, including mild, moderate, and severe. (B) Top GO enrichment from shared DDEGs across 3 groups compared with top GO enrichment from DDEGs only identified in severe group. The x axis represents comparison groups with the number of DDEGs shown in the brackets; the y axis represents the enriched GO terms; p.adjust represents adjusted P values using Holm-Bonferroni methods; and P values were generated by Fisher’s exact test. Dot size represents gene ratio. (C) TimeHeatmap of the top dynamic pathways from the severe group. Each column represents a time window. “0W-1W” represents week 0 (healthy control) to week 1. The “%GO” column represents the percentage of DDEGs found in the corresponding pathway. The “nDDEG” column represents number of DDEGs found in the corresponding pathway. The number in each grid represents the Avg_log2FC of gene expressions compared with the previous time window. Color represents the Avg_log2FC of the DDEGs within each time window for the corresponding pathway. (D–G) LOESS curve fitting of DDEGs identified in the severe COVID-19 group of the neutrophil activation pathway, humoral immune response pathway, blood coagulation pathway, and respiratory burst pathway. Red curves represent the severe group, blue curves represent the moderate group, and green curves represent the mild group. The x axis represents time in weeks; the y axis represents the Avg_log2FC of gene expressions compared with the baseline.