Integrating longitudinal clinical laboratory tests with targeted proteomic and transcriptomic analyses reveal the landscape of host responses in COVID-19

Tan, Yun; Zhang, Wei; Zhu, Zhaoqin; Qiao, Na; Ling, Yun; Guo, Mingquan; Yin, Tong; Fang, Hai; Xu, Xiaoguang; Lu, Gang; Zhang, Peipei; Yang, Shuangshuang; Fu, Ziyu; Liang, Dongguo; Xie, Yinyin; Zhang, Ruihong; Jiang, Lu; Yu, Shuting; Lu, Jing; Jiang, Fang-Ying; Chen, Jian; Xiao, Chenlu; Wang, Shengyue; Chen, Shuo; Bian, Xiu‐Wu; Lu, Hongzhou; Liu, Feng; Chen, Sai‐Juan

doi:10.1038/s41421-021-00274-1

Cited by 24 publications

(17 citation statements)

References 91 publications

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“…Additionally, in terms of individual genes in the turquoise module, we identified hub-high traffic genes such as UTP14A ( 398 ), RUVBL2 ( 139 ), PRKCD ( 280 , 399 ), KEAP1 ( 400 – 402 ), CEBPA (TF) ( 403 ), RPL26L1 ( 404 ), PTPN6 ( 255 ), PPARA (TF) ( 405 ), TNFRSF1A ( 406 – 408 ), IKBKB ( 275 , 409 ), TFEB (TF) ( 410 , 411 ), PSMD4 ( 412 ), and ARHGAP1 ( 413 ), important components in immunopathogenesis of SARS-CoV-2. For example, the TNF receptor superfamily member 1A ( TNFRSF1A ) hub-high traffic gene may be a biomarker of mortality in severe COVID-19 patients, as increased expression of this gene was significantly correlated with mortalities ( 414 ).…”

Section: Discussionmentioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

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BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

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Section: Discussionmentioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

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“…5 The mechanisms underlying angiogenesis in COVID-19 remain to be determined. Tan et al 29 reported on elevated angiogenic cytokines, eg, vascular endothelial growth factor (VEGF) and hepatocyte growth factor (HGF) in COVID-19 patients. Other factors, including PGRN, VCAM-1, and trace elements, such as copper, are also found to have a profound impact on angiogenesis.…”

Section: Discussionmentioning

confidence: 99%

Elevated Serum Levels of Progranulin and Soluble Vascular Cell Adhesion Molecule-1 in Patients with COVID-19

Yao¹,

Luo²,

Liu³

et al. 2021

JIR

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Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with the angiocentric inflammation and angiogenesis, yet the molecules involved in this process remain to be determined. Methods We did a cross-sectional study of a cohort of patients with COVID-19 in Zunyi, China between February 1 and March 30, 2020. Serum concentrations of PGRN were determined by enzyme-linked immunosorbent assay in patients with COVID-19 at hospital admission and at discharge. In parallel, the serum levels of soluble adhesion molecules, vascular cell adhesion molecule-1 (sVCAM-1), intercellular adhesion molecule-1 (sICAM-1), P-selectin (sP-selectin), and E-selectin (sE-selectin) were assayed by a human adhesion molecule multiplex kit. The association between serum PGRN levels and other laboratory test results was analyzed by Spearman correlation analysis. Results At baseline, the median serum PGRN levels in patients with COVID-19 were 94.8 ng/mL [interquartile range (IQR): 66.6–119.6 ng/mL], which was significantly elevated compared with those in healthy controls (46.3 ng/mL, IQR: 41.8–55.6 ng/mL). Moreover, the median serum sVCAM-1 levels were significantly higher in COVID-19 patients (1396.0 ng/mL, IQR: 1019.1–1774.8 ng/mL) than those in healthy controls (612.4 ng/mL, IQR: 466.4–689.3 ng/mL). However, the levels of sICAM-1, sP-selectin, and sE-selectin were not significantly elevated in patients with COVID-19 when compared to healthy controls. Further analysis showed that serum PGRN levels were significantly positively associated with sVCAM-1 (r= 0.675, P = 0.008) and inversely with sICAM-1 (r= −0.609, P = 0.021) and aspartate aminotransferase levels (r= −0.560, P = 0.037) in patients with COVID-19 at hospital admission. In COVID-19 patients, serum PGRN and sVCAM-1 levels fell significantly after successful treatment. Conclusion The present study demonstrates elevated serum PGRN and sVCAM-1 levels in patients with COVID-19, which may provide clues as to the mechanisms underlying the pathogenesis of COVID-19. Further studies are warranted to evaluate the potential of PGRN and sVCAM-1 as biomarkers and investigate their role in the pathogenesis of COVID-19.

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“…They studied myeloid impairment independently for each cell (243). Finally, Tang et al analyzed the origin of cytokine release on balf and pbmc samples (244). Moreover, all the previous studies had different perspectives correlating the airway and blood compartments, and centered their study in one cell-type.…”

Section: Discussionmentioning

confidence: 99%

On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers

et al. 2021

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COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis.

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Integrating longitudinal clinical laboratory tests with targeted proteomic and transcriptomic analyses reveal the landscape of host responses in COVID-19

Cited by 24 publications

References 91 publications

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Elevated Serum Levels of Progranulin and Soluble Vascular Cell Adhesion Molecule-1 in Patients with COVID-19

On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers

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