CoChat
Sign Up
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

Open access
Xiong, Yong, Liu, Yuan, Cao, Liu, Wang, Dehe, Guo, Ming, Jiang, Ao, Guo, Dong, Hu, Wenjia, Yang, Jiayi, Tang, Zhidong, Wu, Honglong, Lin, Yongquan, Zhang, Meiyuan, Zhang, Qi, Shi, Mang, Liu, Yingle, Zhou, Yu, Lan, Ke, Chen, Yu·2020·Emerging Microbes & Infections
DOI 10.1080/22221751.2020.1747363PMC PMC7170362
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/22221751.2020.1747363

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 53 references

Cited by

Sorted by citations

Items 101–200 of 1,000

Related

See all

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
ORCID
Venue
Emerging Microbes & Infections
Publisher
Informa UK Limited
Published
2020-01
Volume
9
Issue
1
Pages
761-770
ISSN
2222-1751 (ISSN-L)
DOI
10.1080/22221751.2020.1747363
PMC
PMC7170362
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
Licence
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review