Introduction
Contrary to initial concerns raised during the early phase of the severe coronavirus disease 2019 (COVID-19) pandemic, accumulating evidence has shown that compared to the general population, patients with asthma do not seem to have an increased risk of developing severe COVID-19 [1-3].
However, asthma and COVID-19 have substantially affected the natural course of each disease. Recent studies showed that COVID-19 is associated with increased risk of new-onset asthma [4,5], and that some asthmatic patients who are infected with COVID-19 progress to severe disease, which is highly fatal, and could lead to mortality [6-10]. Furthermore, following severe COVID-19, patients are more likely to experience worse events, such as exacerbations and mortality [11,12].
Previous studies have shown that the disease status of asthma (e.g., systemic steroid dependence, hospitalized patients, uncontrolled disease status) and phenotypes (non-allergic phenotypes) are significantly related to the progression to severe COVID-19 [6-8,10]. However, the underlying mechanism predisposing progression to severe COVID-19 in asthma remains unclear. Regarding immune mechanism, studies have suggested eosinophilia is negatively associated with severe COVID-19 [13], while non-allergic phenotypes and lymphopenia are positively associated with severe COVID-19 in patients with asthma [9,14]. However, no in-depth single-cell RNA sequencing (scRNA-seq) studies have evaluated key immune signaling pathways leading to severe COVID-19 in asthma.
In the general population, peripheral immune paralysis is characterized by a substantial decrease in peripheral T-cells and natural killer (NK) cells (especially for innate-like T-cells and CD8+ T-cells), while showing increased frequencies of various CD4+ T-cell subsets (e.g., among T-cell subsets, Th1, Th2, and Th17 were shown to play key roles in the progression to severe COVID-19) [15]. Similar mechanisms are thought to also be involved in patients with asthma. However, considering that patients with asthma have altered immunity, the immune signaling pathway that drives severe COVID-19 in asthma might be considerably different from that of the general population. Accordingly, we use scRNA-seq to evaluate the association between immune dysfunction and progression to severe COVID-19 in asthma.
Materials and Methods
1. Study populations
The patient recruitment period was from October 29, 2020, to August 25, 2021. Asthma patients were defined as individuals who had been diagnosed, and were under follow-up observation, based on the clinical judgment of the participating physicians. Four patients with asthma and eight patients without asthma from three medical centers in South Korea were used for analysis. COVID-19 severity was classified into the mild group for asymptomatic, mild, and moderate illnesses, and the severe group for severe and critical illnesses, according to the National Institutes of Health treatment guidelines [16]. The COVID-19 severe group was defined as individuals who had an pulse oximetry <94 % on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen <300 mm Hg, a respiratory rate >30 breaths/min, or lung infiltrates involving >50% of the lung fields, as well as individuals who experienced respiratory failure, septic shock, or multiple organ dysfunction [16]. Patients were classified into four categories according to the presence or absence of asthma and COVID-19 severity (mild vs. severe): non-asthma/mild COVID-19 (n=5), non-asthma/severe COVID-19 (n=3), asthma/mild COVID-19 (n=3), and asthma/severe COVID-19 (n=1) groups.
The study protocol was approved by the Institutional Review Board of Konkuk University Hospital (No. KUMC 2022-09-028). All participants provided written informed consent prior to enrolment in the study.
2. Sample collection
For each patient, blood samples were collected over three-time points. First time point sampling was performed on the same day that the patients were confirmed to have COVID-19, second time point sampling was performed 7 days after contracting COVID-19, and third time point sampling was performed 14 days after contracting COVID-19. Supplementary Table S1 summarizes the sampling by patient and time point.
Blood samples were collected using different types of collection tubes: a 10 mL ethylenediaminetetraacetic acid (EDTA) tube (BD Vacutainer K2EDTA-367525, BD, Franklin Lakes, NJ, USA), an 8.5 mL serum separate tube (SST) (BD Vacutainer SST II- 367953), and two 8 mL cell preparation (CPT) tubes (BD Vacutainer CPT-36276). After collection, the EDTA blood sample was gently inverted 8−10 times to prevent micro-clotting, and stored at 2°C−8°C. SST blood sample was inverted 8−10 times, and kept at room temperature (RT) for 60 minutes. After centrifugation at 1,100 to 1,300 ×g for 10 minutes, the serum layer was separated and stored at 2°C−8°C. CPT blood sample was inverted 8−10 times, centrifuged at 1,100 to 1,300 ×g for 10 minutes, and stored at RT. After collection, the surface of each tube was wiped with 70% ethanol.
3. Separation of peripheral blood mononuclear cells from CPT blood collection tubes
After separating the plasma from the gel layer in the CPT tube, we added 10 mL of Roswell Park Memorial Institute (RPMI) 1640 to the remaining peripheral blood mononuclear cell (PBMC), and performed an inverted mix. The mixture was then centrifuged at 1,500 to 1,800 ×g for 10 minutes. After discarding the supernatant, we added 10 mL of RPMI 1640 media to the cell pellet, and used pipette to count the number of cells and measure the viability. After centrifugation at 1,500 to 1,800 ×g for 10 minutes, we removed the supernatant, leaving only the PBMC. We mixed an appropriate amount of freezing medium based on the measured cell count, and transferred the PBMC into 1.8 mL cryotubes with barcode labels. Each cryotube was filled with 1 mL of PBMC suspension, containing 2×106 cells/mL, resulting in the production of nine or 10 cryotubes. The cryotubes were next placed in a freezing container, and frozen in a mechanical freezer at −75°C for 1 day. Next, each cryotube was wiped with 70%, ethanol and divided into a 100-hole cryobox. The cryoboxes were then stored in a liquid nitrogen freezer at temperatures below −175°C. The final yield of CPT PBMC was 2×106 cells/mL multiplied by nine or 10 cryotubes. For analysis, two cryotubes were used, while seven or eight cryotubes were designated for bio-banking purposes.
4. Cytokine profiling assay
We conducted cytokine profiling analysis on the plasma obtained from CPT tubes that met the quality criteria using the Luminex MAGPIX protocol (customized panel; Luminex, Austin, TX, USA). Standard curves for each cytokine were generated based on the median fluorescence intensity values obtained from the Luminex MAGPIX. Using the standard curve as a reference, we calculated the cytokine concentrations (pg/μL) in the samples
5. Building scRNA-seq libraries and sequencing
We prepared scRNA-seq libraries using the Chromium Next gel bead-in-emulsion (GEM) Single-Cell 3' Kit v3.1 (PN-1000268, 10× Genomics, Pleasanton, CA, USA). First, we combined gel beads with cell barcodes, a master mix containing 10,000 cells, and partitioning oil on a Chromium Chip B to create GEM. Each GEM contained one cell and a set of barcoded gel beads. Reverse transcription was then performed inside each GEM using the enzymes provided in the kit, and cDNA was generated. We next performed cDNA amplification and constructed the resulting library with Illumina adapters and sample indices. Then we loaded the library into the Illumina Novaseq 6000 sequencing platform (Illumina, San Diego, CA, USA), using the S2 Reagent Kit 200-cycle paired-end mode to perform sequencing.
6. scRNA-seq data processing, cell clustering, and dimension reduction
Sequenced reads were aligned to the hg19 human reference genome by Cell Ranger version 5.0 (10×Genomics). The quality of the cells was set according to the following criteria: (1) the number of sequenced genes was greater than 3,000; (2) the average number of reads per cell was greater than 50,000; (3) the median value of genes per cell was greater than 1,000; and (4) the proportion of mitochondrial RNA was less than 10% per cell.
Preprocessing, including data integration, cell clustering, and dimensionality reduction, was performed by Seurat version 4.1.3. First, we performed normalization using the NormalizeData function of Seurat. Using the FindVariableFeatures function, we identified 3,000 highly variable genes (HVGs) to be used in the following analysis. Next, different samples were integrated using the data integration function to remove batch effects via harmony. Using these HVGs, a principal component analysis (PCA) matrix was then calculated with the top 30 components via the RunPCA function. After that, we used the FindNeighbors function to establish the nearest neighbor graph and the FindClusters function to cluster the cells. Finally, we used RunUMAP, a non-linear dimensionality reduction method in the Seurat package, to visualize the clustered cells by projecting them into a two-dimensional space.
7. Cell type annotation
We used a combination of the R package ScType and previously reported genetic markers for cell type annotation, and the FindAllMarkers function to analyze the top genes in each cell subpopulation (min.pct=0.25, logfc.threshold=0.25). A total of seven cell types were identified in this way. For T-cells, we used a similar procedure mentioned earlier to further divide them into nine subtypes. To obtain high-resolution cell clusters for each subset, we set the parameter resolution to 1.0.
8. Analyzing differentially expressed genes
To analyze differentially expressed genes (DEGs), we used the FindMarkers function in Seurat (version 4.1.2). Genes were defined as significantly up-regulated if they had a log fold change (logFC) greater than 0.25 and an adjusted p-value less than 0.01. Genes were defined as significantly down-regulated if the logFC was less than −0.25 and the adjusted p-value was less than 0.01. Tests for differential expression between individual groups were also performed, and the DEGs (based on mean logFC) unique to each cluster were visualized as heatmaps.
9. Single-cell trajectory analysis
In our analysis, we used the Monocle2 R package to sort T-cell subtype cells into a pseudo-chronological order. The Monocle2 package is a popular tool for single-cell trajectory analysis, and provides several useful functions to analyze and visualize single-cell data. We used DDRTree to reduce the dimensional space to better understand the relationships between different T-cell subtypes. We also used the plot_cell_trajectory function to visualize the minimum spanning tree of cells, and derive insights into the relationships between different cell types.
Results
1. Baseline characteristics
In this study, the samples were collected from COVID-19 patients consisting of 10 females and two males, with ages ranging 28 to 74 years old. Of this population, four patients had asthma, and two patients had hypertension. All individuals were never smokers. Supplementary Table S2 summarizes other baseline characteristics, symptoms at diagnosis, radiologic findings, and laboratory results. Supplementary Tables S3, S4 summarize the cytokine information for each time point in the study population, while Supplementary Table S5 summarizes the cytokines that were significantly different between groups divided by asthma status and severity. In the asthma/severe group, granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin1 alpha (IL-1 alpha)/IL1-1F1, serine protease inhibitor B3/squamous cell carcinoma antigen (Serpin B3/SCCA), chemokine (C-X3-C motif) ligand 1 (CX-3CL1)/Fractalkine, chemokine (C-X-C motif) ligand 12 (CXCL12), chemokine (C-X-C motif) ligand 13/B lymphocyte chemoattractant/B cell-attracting chemokine 1 (CXCL13/BLC/BCA-1), follistatin-related gene protein/follistatin-related gene protein (FLRG), human epididymis protein 4/WAP four-disulfide core domain protein 2 (HE4/WFDC2), interferon gamma (IFN-gamma), IL-1 beta/IL-1F2, IL-5, IL-12 p70, IL-13, IL-31, IL-33, IL-7, macrophage migration inhibitory factor (MIF), matrix metalloproteinase (MMP)-13, MMP-2, neuregulin-1 beta 1/neuregulin-1 beta 1 (NRG1) beta 1, transforming growth factor (TGF)-beta 2, thymic stromal lymphopoietin (TSLP), and vascular adhesion protein-1/amine oxidase copper-containing 3 (VAP-1/AOC3) were common cytokines that showed significant differences, when compared to the other groups (Supplementary Tables S3-S5).
2. Overview of the peripheral immune landscape
A high-quality scRNA-seq dataset composed of 155,565 cells was generated that characterized peripheral immune cells. We annotated cell clusters based on known cell markers (Figure 1A, B show the uniform manifold approximation and projection [UMAP] of the combined scRNA-seq dataset at all time points and the second time point, respectively). The clustering analysis showed 21 clusters and seven major cell types annotated by marker genes, while Supplementary Figure S1 shows the comparison dataset before and after applying the batch correction via harmony. The characteristics of each group showed no significant difference when all time points were included (integrated analysis of time points 1−3) (Figure 1C).
Combination of the sample results at all time points revealed no significant differences in the proportion of cell type among the groups (Figure 2A). When the proportion of cell type was analyzed by each time point, a decrease in the proportion of T-cells was shown at the second time point in the asthma/severe COVID-19 group, compared to other groups (Figure 2C). However, at the first and third time points, there was no difference in cell type proportion among the groups (Figure 2B, D).
Supplementary Figure S2 presents further analyses on the proportion for each cell type across the groups. When the analyses were performed across all time points, the proportion of NK cells was significantly higher than those in the asthma/severe COVID-19 group, compared to the asthma/mild group (p<0.05) (Supplementary Figure S2A). At the second time point, the proportion of T-cells was lower in the asthma/severe COVID-19 group than in other groups; however, these differences were not statistically significant. (Supplementary Figure S2B).
When looking at the change of the cell proportion plot according to the time order for each group, the T-cell ratio in the asthma/severe COVID-19 group rapidly decreased at the second time point, and then recovered at the third time point (Supplementary Figure S3).
Gene expressions were projected to UMAP (Supplementary Figure S4). We confirmed the gene expression pattern characteristic of severe asthma through this UMAP. DEG analysis showed relatively higher levels of granzyme B (GZMB), interleukin 1 receptor type 2 (IL1R2), cluster of differentiation 163 (CD163), cyclin dependent kinase inhibitor 1A (CDKN1A), MAF BZIP transcription factor B (MAFB), aryl hydrocarbon receptor (AHR), C-type lectin domain family 12 member A (CLEC12A), jumonji and at-rich interaction domain containing 2 (JARID2), small integral membrane protein 25 (SMIM25), annexin A5 (ANXA5), integrin subunit alpha 5 (ITGA5), tyrosylprotein sulfotransferase 1 (TPST1), sprouty RTK signaling antagonist 2 (SPRY2), ADAM metallopeptidase with thrombospondin type 1 motif 2 (ADAMTS2), thrombospondin 1 (THBS1), interferon gamma receptor 2 (IFNGR2), jun dimerization protein 2 (JDP2), Kruppel-like factor 10 (KLF10), thrombomodulin (THBD), Zinc finger and BTB domain containing 16 (ZBTB16), mitogen-activated protein kinase kinase kinase 8 (MAP3K8), FK506 binding protein 5 (FKBP5), Sin3A associated protein 30 (SAP30), metallothionein 2A (MT2A), and amphiregulin (AREG) in the asthma/ severe COVID-19 group, compared to the other groups (Figure 3A). In contrast, relatively lower levels of genes, such as interleukin 1 beta (IL1B), C-C motif chemokine ligand 4 (CCL4), superoxide dismutase 2 (SOD2), heat shock protein family A member 1A (HSPA1A), heat shock protein family A member 1B (HSPA1B), C-C motif chemokine ligand 3 like 1 (CCL3L1), and DnaJ heat shock protein family member B1 (DNAJB1), were observed in the asthma/severe COVID-19 group, compared to the other groups. CCL3 expression was decreased in the asthma/severe group, compared to the normal/severe group (Figure 3B).
In addition, Supplementary Table S6 presents the DEG analysis results for the asthma/mild group, compared to the non-asthma/mild group. Genes identified as highly expressed in the asthma/mild group relative to the non-asthma/mild group included hemoglobin subunit beta (HBB), ribosomal protein S10 (RPS10), cathepsin W (CTSW), protein tyrosine phosphatase receptor type C associated protein (PTPRCAP), killer cell lectin like receptor subfamily B member 1 (KLRB1), fibroblast growth factor binding protein 2 (FGFBP2), mitochondrially encoded ATP synthase membrane subunit 8 (MT-ATP8), natural killer cell granule protein 7 (NKG7), and granzyme H (GZMH).
3. Peripheral T-cell compartments in patients
We re-divided T-cells into detailed nine clusters (naïve, CD4, CD8, CD4 naïve, CD8 naïve, gamma delta, proliferating, regulatory, and exhausted T-cells), and confirmed the distribution type for each group (Figure 4A for all time points, and Figure 4B for the second time point). When confirmed by clustering in detail, the CD8+ T-cell ratio increased within T-cells in the asthma/severe COVID-19 group, compared to the other groups (Figure 4C). When the sample results at all time points were combined, there were no significant differences in the T-cell subtype proportions among the groups (Figure 5A). When the proportion of T-cell subtype was analyzed by each time point, a relative increase in the proportion of CD8+ T-cells was shown at the second time point in the asthma/severe COVID-19 group, compared to the other groups (Figure 5C). There were no significant differences in the T-cell subtype proportions at the first and third time points among the groups (Figure 5B, D).
Supplementary Figure S5 presents further analyses on the proportion of each T-cell subtype across the groups. When the analyses were performed across all time points, the CD8+ T-cell proportion in the asthma/severe COVID-19 group was significantly higher compared to the other groups, except the asthma/mild COVID-19 group (p<0.05). At the second time point, the CD8+ T-cell proportion in the asthma/severe COVID-19 group tended to be higher; but compared to the other groups, the difference was not statistically significant (Supplementary Figure S5B).
The T-cell proportion plot according to the time order for each group showed that in the asthma/severe COVID-19 group, the CD8+ T-cell ratio increased at the second time point, then decreased at the third time point (Supplementary Figure S6).
To investigate the transcriptional changes in T-cells under varying clinical conditions, we performed differential gene expression analysis, and used a heatmap to visualize the results (Figure 5). Notably, the asthma/severe group exhibited distinct upregulation of several immune-related genes, compared to the other groups. Hierarchical clustering further revealed that the gene expression patterns were more closely aligned between the severe conditions (normal/severe and asthma/severe groups) (Supplementary Figure S7). At the second time point, the cytotoxicity score was higher in the asthma/severe COVID-19 group, compared to the other groups (Supplementary Figure S8B). In contrast, the exhaustion score was lower in the asthma/severe COVID-19 group, compared to the other groups (Supplementary Figure S8A).
Pseudo-time trajectory analysis was performed to infer lineage relationships among the CD4+ and CD8+ T-cell subsets (Figure 6). Trajectory-based cell density analysis by T-cell subtypes confirmed a differentiation pathway from Naïve T-cells to CD8+ T-cells, and subsequently to exhausted T-cells (Figure 6A). Cells from the asthma/severe COVID-19 group showed a pattern of significant clustering at the CD8 T-cell end of the UMAP (Figure 6B).
Pathway analysis revealed that compared to the other groups, the asthma/severe COVID-19 group had relatively down-regulated gene expressions associated with regulation to topologically incorrect protein and regulation of DNA-templated transcription in response to stress, and up-regulated gene expressions associated with T-cell activation, regulation of leukocytes, cell-to-cell adhesion, and neutrophilic degranulation (Supplementary Figure S9).
Discussion
The current study investigated through scRNA-seq analysis the mechanism by which COVID-19 infection causes severe COVID-19 in asthma patients. We found that remarkably decreased T-cell proportion, while showing a higher proportion of CD8+ T-cells, was associated with severe COVID-19 in asthma. Gene expression analyses showed high expression of genes related to T-cells, severe COVID-19, and asthma pathogenesis, and low expression of proinflammatory genes related to severe COVID-19 in asthma.
Previously published studies reported that compared to COVID-19 patients with mild symptoms, COVID-19 patients with severe symptoms have progressive lymphopenia [17-19]. Beyond COVID-19 infection, transient lymphopenia is also a common feature of many respiratory viral infections, such as with H1N1 influenza A virus, human rhinovirus, and respiratory syncytial virus [20,21]. However, lymphopenia associated with COVID-19 may be more intense or long-lasting than in other viral infections, and appears to specifically target T-cell lineages to a greater degree [22]. Studies have shown that decreased T-cells characterize severe COVID-19 [23], suggesting that in-depth investigation of changes in T-cell features are key elements to reveal the underlying mechanism of severe COVID-19 progression. Contrary to the early concerns that COVID-19 might cause severe diseases in patients with asthma, epidemiologic studies revealed that the risk of severe disease is only increased in those with uncontrolled asthma, those who recently received systemic steroids, or were hospitalized; considering that those features may affect T-cell signatures, in-depth analyses of T-cell response by different time points during COVID-19 infection in patients with asthma might be helpful to understand the pathognomonic mechanism for the progression to severe COVID-19 in patients with asthma.
To the best of our knowledge, our study is the first to address the change in immunity during the acute phase (1 to 2 weeks after infection) of COVID-19 that causes differences in the severity of infection in patients with asthma. Using the time-series analyses, our study revealed two important changes in T-cell immunity associated with severe COVID-19 in patients with asthma. First, during the first week following COVID-19 infection, the proportion of T-cells decreased in patients with asthma who had severe COVID-19, compared to the other groups. Second, while the proportion of T-cells decreased, the proportion of CD8+ T-cells expressing cytotoxicity enriched genes disproportionally increased.
The role of CD8+ T-cells in the pathogenesis of severe COVID-19, as well as severe or aggravated asthma status, has been relatively well elucidated; regarding the association between CD8+ T-cells and severe COVID-19, Bergamaschi et al. [24] reported that the activated CD8+ T-cells over time differ according to disease severity, showing the increase in activated CD8+ T-cells occurs later in severe COVID-19, compared to mild disease. Zeng et al. [25] also reported that severe disease is associated with high numbers of overactivated CD8+ T-cells in severe COVID-19 patients with hypertension. Compared with the surviving patients, the patients with fatal outcomes exhibited high and prolonged expression of CD38+, human leukocyte antigen-DR isotype (HLA-DR)+, and CD38+, programmed cell death protein 1 (PD-1)+ on CD8+ T-cells. Li et al. [26] also identified elevated levels of identified elevated levels of Killer-cell immunoglobulin- like receptor (KIR)+CD8+ T-cells, but not of CD4+ regulatory T-cells, in COVID-19 patients were associated with disease severity.
While CD8+ lymphocytes are important effectors of cell-mediated immunity, their exact role in the pathogenesis of asthma is unclear, and has rarely been investigated. One previous study showed that the CD8+ T-cell population, dominated by activated cytotoxic CD8+ lymphocytes, may contribute to asthma-related mortality [27]. Another study with 11 patients with asthma exacerbations and five healthy controls without asthma identified a significantly higher proportion of monocytes, CD8+ T-cells, and macrophages in the bronchoalveolar lavage (BAL) fluid of asthmatic patients [28]. CD8+ T-cells are the critical immune cell against viral infection, as they module virus clearance by directly killing virus-infected cells. Also, CD8+ T-cells are capable of producing either IL-5 or IL-13, suggesting their potential role in asthma exacerbation [29,30]. This evidence suggests that the expansion of CD8+ T-cell proportion could be a key mechanism causing severe COVID-19 in patients with asthma by aggravating the COVID-19 condition itself, as well as aggravating asthma status. The elevated levels of alarmins (TSLP, IL-33) and type 2 cytokines (IL-5, IL-13) in the asthma/severe group suggests this pattern may indeed reflect enhanced type 2 inflammatory responses triggered by viral infection, potentially leading to asthma exacerbation and progression to severe COVID-19. Cytokine profiles may also point to a prominent role of type 2 inflammation in this subgroup. Both mechanisms, type 2 inflammation and CD8+ T-cell responses, may contribute to disease severity, and their interplay warrants further research.
The results of the gene expression levels showed that macrophage-related genes (CD163, ZBTB16, and MAFB) [31-33], T-cell-related genes (MT2A and GZMB) [34-36], and monocyte-related genes (AREG, ADAMTS2, SMIM25, and CDKN1A) [37,38] showed a high expression level in the asthma/severe COVID-19 group. In addition, the levels of gene expression known to be up-regulated in patients with COVID-19, such as IL1R2 [39], AHR [40], CLEC12A [41], JARID2 [42], THBD [43], MAP3K8 [44], FKBP5 [45], THBS1 [46], JDP2 [47], ITGA5 [48], and TPST1 [49], as well as gene expression related asthma pathogenesis, such as such as annexin A5 (ANXA5) [50], SPRY2 [51], IFNGR2 [52], and SAP30 [53], were higher in the asthma/severe COVID-19 group compared to the others, suggesting that enhancing both COVID-19 and asthma-related gene expression might be crucial for the progression of severe COVID-19 in asthma. Interestingly, while proinflammatory cytokine genes and heat shock response genes were shown to be up-regulated in COVID-19 patients [35,54,55], in our study, lower expression of proinflammatory cytokine genes, such as IL1B, CCL3, CCL4, and CCL3L1 [55], and the heat shock response gene, DNAJB1 [54], were observed in the asthma/severe COVID-19 group. This result might suggest impaired immune response may hinder these proinflammatory cytokine-evoked transcriptional changes in immunocytes, which are important for the clearance of the virus.
Our study has several limitations. First, since the subjects of this study were all Asians and the study was only conducted in South Korea, it may be difficult to generalize the results of this study to other races or countries. Second, we lack detailed information on the past treatment history of the asthma patients and phenotype of asthma included in our study. However, none of them had severe asthma that required the use of biologics or systemic steroids. Third, another limitation is that the asthma/severe group included only one person, which may limit the ability to draw robust conclusions. Lastly, since analysis was not performed on BAL fluid or sputum specimen, it is unknown whether the T-cell responses of peripheral blood reflect events in the respiratory system.
In conclusion, our study provides new insights into the mechanisms underlying the progression of COVID-19 infection in patients with asthma. Notably when asthma and severe COVID-19 were accompanied, we observed a reduction in the percentage of T-cells, and a higher proportion of cytotoxic CD8+ T-cells. Further large-scale research is needed to confirm our findings, and explore potential interventions that may mitigate the risk of severe COVID-19 in this population.
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