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. 2024 Aug 5:15:1397629.
doi: 10.3389/fimmu.2024.1397629. eCollection 2024.

Longitudinal transcriptomic analysis reveals persistent enrichment of iron homeostasis and erythrocyte function pathways in severe COVID-19 ARDS

Affiliations

Longitudinal transcriptomic analysis reveals persistent enrichment of iron homeostasis and erythrocyte function pathways in severe COVID-19 ARDS

Moemen Eltobgy et al. Front Immunol. .

Abstract

Introduction: The acute respiratory distress syndrome (ARDS) is a common complication of severe COVID-19 and contributes to patient morbidity and mortality. ARDS is a heterogeneous syndrome caused by various insults, and results in acute hypoxemic respiratory failure. Patients with ARDS from COVID-19 may represent a subgroup of ARDS patients with distinct molecular profiles that drive disease outcomes. Here, we hypothesized that longitudinal transcriptomic analysis may identify distinct dynamic pathobiological pathways during COVID-19 ARDS.

Methods: We identified a patient cohort from an existing ICU biorepository and established three groups for comparison: 1) patients with COVID-19 ARDS that survived hospitalization (COVID survivors, n = 4), 2) patients with COVID-19 ARDS that did not survive hospitalization (COVID non-survivors, n = 5), and 3) patients with ARDS from other causes as a control group (ARDS controls, n = 4). RNA was isolated from peripheral blood mononuclear cells (PBMCs) at 4 time points (Days 1, 3, 7, and 10 following ICU admission) and analyzed by bulk RNA sequencing.

Results: We first compared transcriptomes between groups at individual timepoints and observed significant heterogeneity in differentially expressed genes (DEGs). Next, we utilized the likelihood ratio test to identify genes that exhibit different patterns of change over time between the 3 groups and identified 341 DEGs across time, including hemoglobin subunit alpha 2 (HBA1, HBA2), hemoglobin subunit beta (HBB), von Willebrand factor C and EGF domains (VWCE), and carbonic anhydrase 1 (CA1), which all demonstrated persistent upregulation in the COVID non-survivors compared to COVID survivors. Of the 341 DEGs, 314 demonstrated a similar pattern of persistent increased gene expression in COVID non-survivors compared to survivors, associated with canonical pathways of iron homeostasis signaling, erythrocyte interaction with oxygen and carbon dioxide, erythropoietin signaling, heme biosynthesis, metabolism of porphyrins, and iron uptake and transport.

Discussion: These findings describe significant differences in gene regulation during patient ICU course between survivors and non-survivors of COVID-19 ARDS. We identified multiple pathways that suggest heme and red blood cell metabolism contribute to disease outcomes. This approach is generalizable to larger cohorts and supports an approach of longitudinal sampling in ARDS molecular profiling studies, which may identify novel targetable pathways of injury and resolution.

Keywords: ARDS (acute respiratory disease syndrome); COVID - 19; RNA seq analysis; SARS-CoV-2; longitudinal analysis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Experimental design. Patients were identified from the Ohio State University Intensive Care Unit Registry (BuckICU). Subjects with available longitudinal biosamples with COVID-19 ARDS or non-COVID-19 ARDS controls were selected for inclusion in the study.
Figure 2
Figure 2
Differential expression analysis on individual days. (A) Venn diagram showing overlap of significant differentially expressed genes for each day. (B) Heatmap of the 150 significant DEGs (FDR < 0.05) identified at Day 1 with column clustering by gene expression pattern. (C) Ingenuity pathway analysis (IPA) of canonical pathways identified by DEGs at Day 1. The length of the bar indicates statistical significance of each pathway using -log10 BH multiple correction p-value.
Figure 3
Figure 3
Clustering diagram demonstrates differential dynamic gene expression across time. Using the likelihood ratio test, we compared differences in dynamic gene expression across 3 patient groups, COVID-19 ARDS survivors, COVID-19 ARDS non-survivors, and ARDS controls and 4 time points with Day 1 as reference. DEGs were clustered by similar patterns of gene expression. 314 of the 341 DEGs identified by the longitudinal analysis showed a similar pattern of dynamic change. Significance determined by the adjusted p-value from DESeq2 analysis.
Figure 4
Figure 4
The top 20 differentially expressed genes identified by longitudinal analysis. Following identification of significantly differentially expressed genes by longitudinal analysis, we plotted the 20 genes with most significant differences to observe individual patterns of change. Here, we plotted normalized gene expression by variance stabilizing transformation (y-axis) against time (days 1, 3, 7, and 10 on the x-axis). Colored boxes represent the 3 comparison groups.
Figure 5
Figure 5
Ingenuity pathway analysis. A bubble chart shows the top pathway categories of the IPA canonical pathways of significant genes (adjusted p < 0.05) detected across all days simultaneously. The size and color intensity of bubbles indicates the number of genes overlapping each pathway.

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