Cited by 1,076COVID-19 Clinical Research StudiesSARS-CoV-2 and COVID-19 ResearchLong-Term Effects of COVID-19

The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.

Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients

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Abstract

Circulating in China and 158 other countries and areas, the ongoing COVID-19 outbreak has caused devastating mortality and posed a great threat to public health. However, efforts to identify effectively supportive therapeutic drugs and treatments has been hampered by our limited understanding of host immune response for this fatal disease. To characterize the transcriptional signatures of host inflammatory response to SARS-CoV-2 (HCoV-19) infection, we carried out transcriptome sequencing of the RNAs isolated from the bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) specimens of COVID-19 patients. Our results reveal distinct host inflammatory cytokine profiles to SARS-CoV-2 infection in patients, and highlight the association between COVID-19 pathogenesis and excessive cytokine release such as CCL2/MCP-1, CXCL10/IP-10, CCL3/MIP-1A, and CCL4/MIP1B. Furthermore, SARS-CoV-2 induced activation of apoptosis and P53 signalling pathway in lymphocytes may be the cause of patients' lymphopenia. The transcriptome dataset of COVID-19 patients would be a valuable resource for clinical guidance on anti-inflammatory medication and understanding the molecular mechansims of host response.

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Crossref · 10.1080/22221751.2020.1747363

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Figures

Figure 1. Genome-wide profiling of gene expression in BALF and PBMC of COVID-19 patients. (A) Experimental design. PBMC and BALF were prepared from patients or control. Total RNA was extracted and analysed by RNA-seq to identify differentially expressed genes implicated in COVID-19 disease pathogenesis. (B, C) Heat map of genes significantly up-regulated and down-regulated (fold change > 2) in COVID-19 patients BALF (B, WHU01-2 vs. Ctrl1-3) and PBMC (C, P1-3 vs. N1-3) compared to controls, respectively. (D-F) RNA-seq signals in PBMC patients (P1, P2, P3) and healthy controls (N1, N2, N3) for 3 genes: IL6 (D), IL6R (E), and TP53 (F), respectively. The scale on the y axis indicates the read density per million of total normalized reads.
Figure 1. Genome-wide profiling of gene expression in BALF and PBMC of COVID-19 patients. (A) Experimental design. PBMC and BALF were prepared from patients or control. Total RNA was extracted and analysed by RNA-seq to identify differentially expressed genes implicated in COVID-19 disease pathogenesis. (B, C) Heat map of genes significantly up-regulated and down-regulated (fold change > 2) in COVID-19 patients BALF (B, WHU01-2 vs. Ctrl1-3) and PBMC (C, P1-3 vs. N1-3) compared to controls, respectively. (D-F) RNA-seq signals in PBMC patients (P1, P2, P3) and healthy controls (N1, N2, N3) for 3 genes: IL6 (D), IL6R (E), and TP53 (F), respectively. The scale on the y axis indicates the read density per million of total normalized reads.CC BY · PubMed Central
Figure 2. GO-term and KEGG pathway enrichment of up-regulated expressed genes in BALF and PBMC of COVID-19 patients. (A) GO-term functional enrichment by 3 categories (BP, MF, CC) and KEGG pathway analysis were performed for up-regulated genes in COVID-19 patients BALF. (B) Same as (A) for up-regulated genes in COVID-19 patients PBMC.
Figure 2. GO-term and KEGG pathway enrichment of up-regulated expressed genes in BALF and PBMC of COVID-19 patients. (A) GO-term functional enrichment by 3 categories (BP, MF, CC) and KEGG pathway analysis were performed for up-regulated genes in COVID-19 patients BALF. (B) Same as (A) for up-regulated genes in COVID-19 patients PBMC.CC BY · PubMed Central
Figure 3. GO-term and KEGG pathway enrichment of down-regulated expressed genes in BALF and PBMC of COVID-19 patients. (A) GO-term functional enrichment by 3 categories (BP, MF, CC) and KEGG pathway analysis were performed for down-regulated genes in COVID-19 patients BALF. (B) Same as (A) for down-regulated genes in COVID-19 patients PBMC.
Figure 3. GO-term and KEGG pathway enrichment of down-regulated expressed genes in BALF and PBMC of COVID-19 patients. (A) GO-term functional enrichment by 3 categories (BP, MF, CC) and KEGG pathway analysis were performed for down-regulated genes in COVID-19 patients BALF. (B) Same as (A) for down-regulated genes in COVID-19 patients PBMC.CC BY · PubMed Central
Figure 4. Inflammatory cytokines expression in COVID-19 patients. Heat map depicting inflammatory cytokine genes expression in COVID-19 patients BALF (A, WHU01-02 vs. Ctrl1-3) and PBMC (B, P1-3 vs. N1-3) compared with control. Genes significantly up-regulated and down-regulated are labelled with asterisks.
Figure 4. Inflammatory cytokines expression in COVID-19 patients. Heat map depicting inflammatory cytokine genes expression in COVID-19 patients BALF (A, WHU01-02 vs. Ctrl1-3) and PBMC (B, P1-3 vs. N1-3) compared with control. Genes significantly up-regulated and down-regulated are labelled with asterisks.CC BY · PubMed Central
Figure 5. Apoptosis-related pathway in PBMC. The heatmaps show the expression levels of differentially expressed genes in different signaling pathways, including (A) autophagy (- animal species) signal pathway, (B) apoptosis signal pathway, (C) p53 signaling pathway. Genes significantly up-regulated and down-regulated are labelled with asterisks.
Figure 5. Apoptosis-related pathway in PBMC. The heatmaps show the expression levels of differentially expressed genes in different signaling pathways, including (A) autophagy (- animal species) signal pathway, (B) apoptosis signal pathway, (C) p53 signaling pathway. Genes significantly up-regulated and down-regulated are labelled with asterisks.CC BY · PubMed Central
Figure 6. Comparison of differentially expressed genes in BALF and PBMC. (A) Venn diagram showing the number of changed genes with same or different trends between BALF and PBMC samples. (B) Heat map depicting the scaled gene expression changes with same or different trends between BALF and PBMC samples.
Figure 6. Comparison of differentially expressed genes in BALF and PBMC. (A) Venn diagram showing the number of changed genes with same or different trends between BALF and PBMC samples. (B) Heat map depicting the scaled gene expression changes with same or different trends between BALF and PBMC samples.CC BY · PubMed Central

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Metadata

Authors
Xiong, Y., Liu, Y., Cao, L., Wang, D., Guo, M., Jiang, A., Guo, D., Hu, W., Yang, J., Tang, Z., Wu, H., Lin, Y., Zhang, M., Zhang, Q., Shi, M., Liu, Y., Zhou, Y., Lan, K., & Chen, Y.
Affiliations
  • Wuhan University (CN)
  • Sun Yat-sen University (CN)
  • BGI Group (China) (CN)
  • State Key Laboratory of Virology, Modern Virology Research Center, College of Life Sciences, Wuhan University
  • The Centre for Infection and Immunity Studies, School of Medicine, Sun Yat-sen University
Corresponding author
  • Zhou, Yu
  • Lan, Ke
  • Chen, Yu
Venue
Emerging Microbes & Infections
Published
2020-01
Volume
9
Issue
1
Pages
761-770
ISSN
2222-1751 (ISSN-L)
OpenAlex
W3013843138
Cited by
1,076
References
53
Citations by year
2026: 12 · 2025: 27 · 2024: 68 · 2023: 121 · 2022: 206 · 2021: 370 · 2020: 272
FWCI
31.89
Type
journal article
Language
en
Open access
Locations
  • Emerging Microbes & Infections (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
Funders
  • National Natural Science Foundation of China (31871316, COVID-19)
  • Wuhan University
  • National Science and Technology Major Project (#2018ZX10733403)
Topics
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Health Sciences › Medicine › Clinical Neurology › Long-Term Effects of COVID-19
Keywords
COVID-19 pandemic, SARS-CoV-2, bronchoalveolar lavage fluid, peripheral blood mononuclear cells, host immune response, inflammatory cytokines, chemokines, lymphopenia, apoptosis, p53 pathway, lymphocyte activation, transcriptome
MeSH terms
Betacoronavirus, RNA-Seq, COVID-19, SARS-CoV-2, Bronchoalveolar Lavage Fluid, Humans, Leukocytes, Mononuclear, Lymphopenia, Pneumonia, Viral, Signal Transduction, Tumor Suppressor Protein p53, Cytokines, Apoptosis, Coronavirus Infections, Chemokines, Pandemics, Transcriptome

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