Identification of the potential cytokines involved in LPS induced acute lung injury in mouse

doi:10.21203/rs.3.rs-2003335/v1

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Identification of the potential cytokines involved in LPS induced acute lung injury in mouse

https://doi.org/10.21203/rs.3.rs-2003335/v1

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To identify the differentially expressed genes encoding promising inflammatory cytokines in murine lungs in the context of acute lung injury (ALI). Through searching the Gene Expression Omnibus (GEO) databases, three GEO datasets were identified based on the LPS induced animal model within 24hr, differentially expressed genes (DEGs) were extracted and analyzed with R studio, the properties of those DEGs were further analyzed by Protein-Protein-Interaction (PPI), Cytoscape, GO and KEGG bioinformatics techniques as well. The potential hub genes in inflammatory related cluster were retrieved and examined furthermore by basal experiments. By searching, GSE1871, GSE104214, and GSE130936 were screened with a priority of animal model formed in less than 24hr. With a criterion of |log₂ fold change (FC)| ≥ 2 and an adjusted P-value < 0.01, 923 DEGs were found altogether in three datasets, eventually 87 overlapped genes were achieved commonly. Most of those DEGs come from anchored component of basal membrane and modulate the cytokine-receptors activity, participating in the regulation of responses to cellular stimuli or cytokine-mediated signaling pathways. Through bioinformatics techniques, 2 clusters were separated by K- means algorithm in 87 DEGs, 20 ranked hub genes were finally concluded. The GO and KEEG enrichment analysis of these 20 genes were highly consistent with previous results except that these hub genes are involved in both virus-related and LPS-related biological processes. For LPS induced in vivo experiment, 7 out of 20 genes exhibited mRNA overexpression under cytokine-cytokine receptors interaction pathway, having a more probability to function in inflammation-related cytokine signaling judging from the predicted transcription factor. Altogether, these findings find out some potential DEGs involved in virus and inflammation related biological process, identified 7 potential hub genes could be the therapeutic target on the cytokine-receptor interaction pathway ground in LPS induced ALI.

Cytokines

bioinformatical analysis

differentially expressed genes

acute lung injury

During the discovery of the underlying mechanism of ALI, the innate immunity and cytokines response syndrome (CRS) are found the prominent therapeutic targets for therapy. However, the improvement doesn’t seem to be satisfactory in spite of huge progress has been made, such as the COVID-19 pandemic event. So, it’s still urgent to find out those promising cytokines or uncover the exact function of immune cells during this procedure.

ALI and Acute respiratory distress syndrome (ARDS), the leading cause of death in respiratory system, are an acute respiratory illness. After so many years research, it’ s well established that the innate immune cells, such as macrophages and monocytes, undertake the first impact when stimuli encounter, subsequently a great number of cytokines, such as chemokines, coagulation factors, complement and inflammatory factors, are released into blood or infectious sites. As for the outcome of infection, it heavily depends on the strength balance between the innate immunity and the inflammation at an early stage. To achieve lung homeostasis, the state of body’s immune system and inflammatory response should be kept moderately. A “cytokine storm” is inevitable if patient had a dysfunctional immunity to eliminate the invasion. For instance, Firoz, et al¹ once stated the importance of inflammatory cytokines and immunity in treating COVID-19-related ALI. These secreted cytokines on one hand could enlarge the inflammatory response in a positive manner through enhancing the intracellular signaling to trigger downstream inflammatory response, on another hand could orchestrate the recruitment of neutrophils into the infectious sites. Consequently, severe complications, such as ARDS and multiple organ dysfunction (MOF), are about “cytokine storm”². So, the regulation of cytokines contributes to the resolve of infections induced by LPS or virus.

Microarray analysis is an effective method to screen DEGs under specific circumstance including ALI and ARDS. For recent years, several studies have examined a bunch of DEGs in the lung tissues of sepsis-induced ALI^3-5. However, those DEGs screened in these studies were based on the overall gene expression, so that the specific spatial-time characteristic, or even the actual experimental outcome has a likelihood to change due to different condition applied and the unknown immune state. Sometimes controversial results could be reach possibly. In this way, the present study aimed to identify some candidate DEGs involving in the cytokine-cytokine receptor signaling pathway specifically in LPS-induced ALI model to cater for forthcoming molecular mechanism study.

Gene expression profile

Through searching the website (http://www.ncbi.nlm.nih.gov/geo) using key words ((inflamma*) AND (mice or mouse)) AND （lung or pulmonary）, 396 microarray datasets were found. The perfectly match dataset should met the following criteria: (i) data were derived from C57BL/6J mice, (ii) samples was from lung tissues, and (iii) ALI was induced within 24hr by LPS. At last, GSE1871, GSE104214, and GSE130936 were found suitable to arrange the extraction, the gene expression profile data were subsequently downloaded. The dataset profile was listed as following：GSE1871 were produced on the GPL1261 platform (Mouse430_2, Affymetrix Mouse Genome 430 2.0 Array), GSE104214 on the GPL10787platform（Agilent-028005 SurePrint G3 Mouse GE 8x60K Microarray (Probe Name version ), and GSE130936 on the GPL339 platform (Affymetrix Mouse Expression 430A Array). Extracted data was divided into control and treatment group, respectively.

Data processing and enrichment analysis

With the help of R-package (version 4.1.2), the gene expression data of each GSE was pooled and analyzed. After normalization, a two-dimensional (2D) principal components analysis (PCA) was conducted to reveal the primary source of variability in the data. A gene expression matrix was then constructed with corresponding DEGs defined as the combination of p- value < 0.01 and logFC >2. Several online websites and analytical tools, such as DAVID (https://david.ncifcrf.gov/), GO (http://geneontology.org/), Kyoto Encyclopedia of Genomes and Genes (KEGG), were used to categorize based on their cellular function, cellular components, and biological processes. Moreover, for the prediction of possible signaling pathways consisting of those candidate DEGs, the KEGG website was used, and the output plot was depicted by R studio to visualize the DEGs between the treatment and control groups.

PPI network analysis and determination of hub genes

The Search Tool for the Retrieval of Interacting (STRING, https://cn.string-db.org/,version 11.5) database is usually applied for analyzing PPI ⁶. At first, the DEGs list was uploaded onto the STRING database and a confidence score of ≥0.7 was used as the cutoff criterion. An SVC format file containing all calculated genes was imported into Cytoscape. A network module in a PPI network may contain specific biological functions. Then, the Cytoscape Molecular Complex Detection (MCODE) plug-in was used to identify modules in the PPI network. The advanced options were set as node score cutoff = 0.2, degree cutoff = 2, K-core = 2, and max depth = 100 ⁷. Finally, the Cytoscape plugin Cyto-Hubba was used to select hub genes in the PPI network. The top 20 nodes ranked by Maximal Clique Centrality (MCC) were selected and validated.

The definition of the potential genes in cytokine-related pathway

As described before, the GO and KEGG enrichment analysis were performed for the 20 hub genes to characterize the properties of these genes. Those GO terms used also were cellular function, cellular components, and biological processes. Moreover, the KEGG website was used as well, and the output bubble plot was depicted by R studio to visualize the first 20 important signaling pathways. Among them, the genes in cytokine-cytokine receptor pathway were chosen to overlap the 20 hub genes, in this way, the potential target genes could be concluded and determined.

Animal model and mRNA expression

To verify the prediction derived from the above bioinformatics analysis, a LPS induced in vivo inflammatory injury model was constructed in mice. All animals were raised in the animal center of zhejiang university (SYXK-2020-0007) under standard conditions. All procedures were in accordance with the "Consensus of author guidelines on animal ethics and welfare" and approved by the Laboratory Animal Ethics Committee of JXMC (PUMC2021-124). LPS was administered intraperitoneally for 24 h with doses ranging from 0 to 30 ug/g. The concentrations of tumor necrosis alpha (TNF⍺) and IL-6 in blood were measured using ELISA kit to confirm the state of inflammation in body. Based on the experimental parameters, mice were randomly divided into control and LPS groups (n=5). Hematoxylin and eosin (H&E) staining, the wet/dry ratio, neutrophil number and Immunohistochemistry of murine lungs at 24hr were measured as well as the injury score.

To test the mRNA expression of those key genes under inflammation, RT-PCR was performed as follows: the lungs of mouse were taken down after several times flush by PBS under anesthesia, then cut into small pieces and immersed in trizol at -80°. The tissue homogenate was made on tissue grinder for 30min at 4°, then transferred to the centrifuge at 12000g for 10min. The supernatant, which was the overall RNA, was gathered and the RNA concentration was determined using a nanodrop machine (ThermoFisher Scientific,USA). Using commercial RNA Reverse transcriptional Kit (PrimeScript RT Master Mix (for Real Time, RR036A) and qPCR kit (TB Green Premix DimerEraser [Perfect Real Time], RR091A) provided by Takara Corporation (Takara Biomedical Technology [Beijing] Co., Ltd), quantitative real-time experiments were completed following the required protocols by manufacturers. Briefly, up to 1 ug total RNA was reverse-transcribed to cDNA. The genetic assays were conducted in a 20ul reactive system containing Master Mix and 1ul of cDNA. The reaction parameters consisted of several set conditions: (1) stage 1 at 95 ºC for 1 min, (2) stage 2 at 95 ºC for 5 s and (3) 60 ºC for 1 min for 40 cycles. Subsequently, the relative gene expression levels of each gene were calculated compared to housekeeping gene glyceraldehyde-phosphate dehydrogenase (GAPDH) accordingly. Primers used in this procedure could been found in supplementary file 1.

Prediction of the TF of the 7 potential genes

To further explore the functional roles of the 7 genes in present experimental context, the GSEABase and clusterProfiler packages of R studio were used to calculate the possible transcription factors (TF) to indirectly clarify their perspective involvement in inflammatory reaction. For them, the prediction was based on all mouse TFs in subunit C3 of the GSEA database (http://www.gsea-msigdb.org/gsea/msigdb/ collections.jspC3), which incorporates all mouse and rat TF and miRNA data. The summarized results were illustrated in supplementary file 2.

Statistical analysis

Values are presented as the mean ± standard error (SE) if the dataset is normally distributed. Statistical analyses were carried out using GraphPad Prism version 7 soft-ware (GraphPad Software, San Diego, USA; www.graphpad.com). Independent t-test was used to detect the statistical significance between the treatment and control group, P<0.05 were considered statistical significance.

Identification of DEGs in GEO database

Of the three datasets, six samples (three normal samples and three LPS samples) were selected from GSE1871 and GSE130936, eleven samples (six normal samples and five LPS samples) from GSE104214. The data expression feature of GSE1871 was representative in figure 1, the rest two were deposited in supplementary file 3 and 4, respectively. The expression profile of each dataset was clustered and presented in 2D PCA plots, which showed an obvious biological variability between control and LPS treatment samples (Figure 1a). The control sample and treatment sample were compared in each dataset after normalization. The box plot shows the range of gene expression values of each lung tissue sample, with the middle black lines indicating that median gene expression values (Figure 1b). Volcano plots showed that 12998genes (260 upregulated and 101 downregulated) were identified as DEGs in the GSE1871 dataset, 22886 genes (393 upregulated and 81downregulated) in the GSE104214 dataset, 12604 genes (227 upregulated and 167 downregulated) in the GSE130936 dataset (Figure 1c). To visualize those DEGs between different groups, the top 300 DEGs in each set were displayed in the heatmap (Figure 1d).

GO and KEGG pathway enrichment analysis

In Venn diagrams, it showed the overlap result of DEGs from the three datasets which included 87 genes (85 upregulated and 2 downregulated, Figure 2a). For further gene functional analysis, GO enrichment (available online website:http://geneont ology.org/) and KEGG signaling pathway were conducted with 87 DEGs. The analyzed terms include biological process (BP), cellular components (CC), and molecular functions (MF). The top eight significant GO terms for each enrichment were listed according to the gene count and p-value as shown in Figure 2b. It can be seen from the chart that most genes anchored at the component of membrane showed differential expression under inflammatory stimulation. They mainly exerted the molecular function of cytokine activity, cytokine receptor binding and receptor ligand activity to be involved in a series of signaling pathways including cytokine−mediated signaling pathway, positive regulation of response to external stimulus and response to lipopolysaccharide, et al. From the online KEGG tool (available online: http://www.genome.jp/kegg), functional signaling pathway enrichment of the top 20 pathways were pooled and depicted by R-studio in Figure 2c. It is clear that the prior important signaling pathways used by those DEGs was cytokine-cytokine receptor interaction pathways, which counted above 16 genes with statistically significant p-value (P<0.0001).

PPI network and significant modules

As for the PPI enrichment analysis, the 87 DEGs was enrolled into the STRING website and visualized in Cytoscape (Figure 3). In total, 87 DEGs constructed a network containing 87 nodes and 498 edges under a high confidence (0.7), making the average node degree goes to 11.4 and the p-value reaches less than 1e-16 (Figure 3a), eventually, the connected PPI network included 51 genes with tight connection. Subsequently, the PPI net-work was analyzed using the Cytoscape MCODE plugin to identify significant modules. The most significant two modules were found. The key Module with high degree contains genes such as Rsad2, cxcl10, Irf6, Ifit1, Ifit3, Cd274, Ifit2, Ifi204, Gbp2, Gbp6, Cxcl9, Oasl2, Zbp1 and Ifi44. The other module included Il6, Cd14, Cxcl1, Cxcl2, Csf3 and Ccl2(Figure 3b). The details members of these clusters are shown in supplementary file 5.

prediction and enrichment analysis of the hub genes

After the establishment of PPI network, the Cytoscape plugin CytoHubba was used to select the top 20 hub genes ranked by MCC. These 20 genes were Cxcl10, Cd274, Irf7, Ifit2, Ifit1, Rsad2, Ifit3, Zbp1, Gbp2, Oasl2 according to the scores (Figure 3c), and Cxcl10 was the first significant gene among them. The GO enrichment analysis of these genes revealed that they mainly execute the biological processes of response to virus and LPS with their interactional molecular functions with cytokines or cytokine receptors (generatio>0.5, counted genes>10, and Pvalue<0.0025), (Figure 4a). For KEGG enrichment (Figure 4b), these genes were primarily involved in the signaling pathways such as NOD-like signaling pathway and cytokine-cytokine receptor interaction, screened with counted genes>7 and Pvalue<0.0002. With an overlap between the 20 screened genes and those subunit genes in cytokine-cytokine receptor pathway, 7 genes were eventually found to be highly connected to ALI and cytokine including Csf3, Il6, Cxcl9, Ccl2, Cxcl2, Cxcl1, Cxcl10.

The construct of experimental in vivo model

As for inflammatory indicators, TNF-a and IL-6 were found in blood to be very valuable molecules as the concentration of each one showed a significant increase in a dose-dependent manner after stimulation by LPS with especially at 20 and 30 ug/g LPS. Similar results were also found in the BAL analysis (Figure 5A-C). so, the next doses in the experiment would be 20 ug/g. Compared to the control group, LPS induced an obvious increase in terms of number of neutrophils in murine lungs examined by immunohistology (Figure 5D, Figure 5Ec)). In addition, the injury score, the wet/dry ratio and average weight of Evans infiltration in LPS group traumatically elevated when compared to control treatment. (Figure 5Ea-d). All in all, these results tested the successful construct of the in vivo inflammatory model.

Experimental verification of seven potential genes

With the constructed in vivo model, the mRNA relative expression of those seven potential genes was performed accordingly. Compared to the control group, LPS induced significant overexpression of all seven genes by several to hundreds of folds (P<0.05). From figure 6a-g, it could be seen that CCL2 and CXCL10 were the utmost increased molecules under the stimulation of LPS at 20 ug/g dose. The rest had less expansion relatively, but statistically importance (P<0.05). For the prediction of transcription factors (TF) of those seven molecules, the ranked results of TF were listed in figure 6h, and it’s clear that the most preferable TFs were the top three, such as Nfkb1, Ikbkb and JUN, which had high probability to transcript above half of those seven genes (Figure 6h).

During immuno-inflammatory response, imbalanced cytokines regulation might provide systemic inflammatory responses or immunosuppression which is also called cytokine dysregulation, leading to multiple organ dysfunction or infectious disorders^{8, 9}, even worsening the outcomes of patients resulting from cytokine storm and collateral organ damage, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)¹⁰. For the treatment process, drugs targeting cytokines signaling are extensively studied, including IL-6 IL-1β inhibitors¹¹. So, it’s very clear that the potential cytokines are of more importance for treatment of these type of diseases. In this study, 87 DEGs were obtained from specific GEO database representing the classical in vivo ALI induced by LPS administration. With the bioinformatics analysis, 20 hub genes were screened out of the dataset and of crucial importance to the immuno-inflammatory reaction. Among them, 7 out of 20 genes were discovered to be closely related to the cytokine-cytokine receptor pathways, and were further verified overexpressed in basal experiments in terms of mRNA expression, and might be new biomarkers to deal with LPS-induced ALI.

It's well known that ALI is associated with the function of immune system, inflammatory response and host defense response, any part of which could be disabled during the onset of ALI/ARDS. The imbalanced immunity could directly lead to the activation of different kinds of immune cells and secret amounts of cytokines in body. The overexpression of cytokines and chemokines triggers the severe CRS, which increases the severity of the disease even more¹². Meanwhile, cytokine networks are necessary for endothelial activation and neutrophil migration during pulmonary inflammation which is pivotal to the immuno-inflammatory responses leading to ALI. Therefore, remarkable treatments have focused on the elimination of lethal pathogens and mitigation of the oversecreted cytokines in recent years, and even the management of the cytokine storm¹⁰. In our study, 85 out of 87 DEGs (most of them) were upregulated through screening by R studio, GO term and KEGG pathway analyses showed that these DEGs were mainly enriched in inflammation, chemokine-mediated signaling pathways, and cytokine-cytokine receptor interaction. This result was partly consistent with Wang’s report¹³ that the up-regulated mRNAs in LPS induced ALI were mainly involved in immune response and defense response, the related signal pathways were cytokines and inflammatory response. Likewise, Lu’s study¹⁴ also concluded that DEGs were specifically enriched in regulation of inflammatory response, immune system process and innate immune response, and their KEGG analysis showed the prior pathway was cytokine–cytokine receptor interaction as well. Altogether, there are still more studies than we can cite by Microarrays to illustrate that the LPS induced ALI could directly lead to the overexpression of molecules involved in inflammatory response and immune system process.

In recent years, a considerable number of studies have reported the modulation of ALI by control of cytokines^15-17, but conclusive reviews about the candidate genes in this field is missed. So presently, with the help of PPI network and cytoscape software, the DEGs were divided into two modules base on the MCODE plug-in. The first Module with high degree contains genes such as Rsad2, cxcl10, Irf6, Ifit1, Ifit3, Cd274, Ifit2, Ifi204, Gbp2, Gbp6, Cxcl9, Oasl2, Zbp1 and Ifi44, which were mostly interferons (IFN-γ) signaling pathway related and downstream proteins. Identical with Lu’s study¹⁴, there were ten genes, such as Cd14, Gbp6, Ifit1, Ifit2, Cxcl10, Cxcl1, Cxcl2, Ifit3, Gbp2 and Rsad2, overlapped with ours. The other module in our study included Il6, Cd14, Cxcl1, Cxcl2, Csf3 and Ccl2, which were primarily LPS induced signaling pathway related inflammatory proteins. Just like statement of Hsu’ study¹⁰, the inflammatory mediator and viral infectious proteins cold coexist in the cytokine storm as IFN is also a type of cytokine that plays a central role in protecting against viral infections through antiviral or immunomodulatory functions.

According to them, several proinflammatory cytokines and chemokines are produced at the onset of inflammation, including TNF, IL-6, IFN-γ, CCL2, CCL3, CCL4, and CXCL10, which lead to the recruitment of monocytes, macrophages, and T cells to the infection sites. So, it’s crucial to decide which cytokine is involved in this process. Subsequently, the top 20 hub genes were identified and their associated GO, KEGG enrichment analysis were conducted ae well. The results of this analysis were so well compatible to those previous enrichment that those hub genes could anticipate in the similar biological processes with almost same molecular functions, and also enrolled mostly in the chemokine signaling pathway and cytokine-cytokine receptor interaction.

As well all know, sepsis-associated lung injury may have higher association with chemokine signaling pathway and cytokine-cytokine receptor interaction^{18, 19}, so it’s necessary to evaluate the cytokines embedded in DEGs to facilitate the management of ALI. The seven key genes screened by overlapping those hub genes with genes listed in cytokine-cytokine receptor interaction pathway were screened, including Cxcl10, Csf3, IL6, Cxcl9, Ccl2, Cxcl2 and Cxcl1.

Cxcl10 is a member of the chemokine CxC family, involving in a variety of biological processes such as differentiation and chemotaxis, the corresponding protein is IP10 which regulate the cell growth, apoptosis and modulation of angiostatic effects.²⁰ Mechanistically, the binding of CXCL10 and Cxcl9 to the CXCR3 receptor could activate G protein-mediated signaling and results in the increase in intracellular actin reorganization and calcium production²¹. CXCL1 and CXCL2 belong to another chemokine CXCL family, acting in a sequential manner to guide neutrophils through venular walls during the process of neutrophil activation²², so, they have chemotactic activity for neutrophils. Csf3 belongs to the IL-6 superfamily, inducing the production and mature of the granulocytes and the monocytes-macrophages. The potential role of granulocyte colony-stimulating factor (G-CSF) in the development of acute lung injury has been debated for the last many years. Several lines of studies suggest that a bimodal association between baseline plasma G-CSF levels and subsequent morbidity and mortality from this disease in patients with sepsis related ALI. C-C motif ligand 2 (CCL2) was originally reported as a chemical mediator attracting mononuclear cells to inflammatory tissue. Many studies have reported that CCL2 can directly activate cancer cells through a variety of mechanisms.²² so, these seven genes are closely connected with immunity and inflammation, and might serve as potential curable target for immune-inflammatory diseases.

Apart from genes above mentioned, IL-6, a traditional proinflammatory mediator, was ubiquitously adopted as an in vitro inflammatory biomarker because it is a downstream molecule of TLR4/NF-kappaB induced by LPS. IL-6 is overexpressed in ALI through the interaction between LPS and TLR4, and followed by activation and phosphorylation of NF-kappaB, eventually leading to the secretion of IL-6 in supernatant, the whole process is TLR4 dependent²³. In this way, IL-6 was adopted as the indicator of inflammation in in vivo experiment as well as TNF-a. As indicated, inflammatory response showed significant harsh in a dose-dependent manner after stimulation by LPS with especially at 20 and 30 ug/g LPS. Similar results were also found in the BAL analysis. In addition, the injury score, the wet/dry ratio and average weight of Evans infiltration in LPS group traumatically elevated when compared to control treatment. These results, consistent with other’s report, showed the in vivo model to be successful.

As predicted, the mRNA relative expression of those seven potential genes was significant increased by several to hundreds of folds (P<0.05), especially the CCL2 and CXCL10 were the utmost increased molecules under the stimulation of LPS at 20 ug/g dose. This result was consistent with Otto²⁴ and Sasaki's²⁵ research that CCL2 and CXCL10 were elevated in TNF-a induced inflammation, moreover, the blockade of CXCL10 signaling could ameliorates inflammation caused by immunoproteasome dysfunction. For the prediction of transcription factors (TF) of those seven molecules, the most possible TFs were Nfkb1, Ikbkb and JUN, which are all inflammatory pathway related TFs and had high probability to transcript above half of those seven genes, especially CXCL10 that accounted for 5/9 of those TFs. Once again, these facts above at least illustrated directly or indirectly that these seven molecules are extremely potential in ALI.

There’s some limitation needs to address in this study. Firstly, the source of cytokine could not be defined exactly whether they were secreted by immune cells or not due to the in vivo experiment, they could be secreted by immune cells or endothelial cells in lung. Secondly, the mRNA level of the seven potential genes was the only examine technique, protein expression was absent. Finally, it will be better if the corresponding mechanism research was supplied too.

Through searching the three different GEO databases under the condition of LPS induced ALI, 87 DEGs were achieved with a criterion of |log₂fold change (FC)| ≥ 2 and an adjusted P-value <0.01. Moreover, most of them are involved in both virus-related and LPS-related biological processes, and 7 out of 20 hub genes was discovered as potential key genes involving the cytokine-cytokine receptors interaction pathway and having a more probability to function in inflammation-related cytokine signaling judging from the predicted transcription factor. Last but not least, the 7 potential genes were experimentally tested again, especially CXCL10, to ensure a forthcoming research in ALI.

Acknowledgments

We thank the invaluable assistance of professor Lina Yu (Department of Anesthesiology, the Second Affiliated Hospital of Medical College of Zhejiang University) and Dr Gaojian Wang (Department of Anesthesiology, the Run Run Shaw Hospital of Medical College of Zhejiang University) during the whole experiment.

Funding

This research was supported by the Project of the Health Commission of Zhejiang Province (grant no. 2019ZD053).

Disclosure

The author reports no conflicts of interest in this work.

1. Ahmed, F., A Network-Based Analysis Reveals the Mechanism Underlying Vitamin D in Suppressing Cytokine Storm and Virus in SARS-CoV-2 Infection. Frontiers in immunology 2020, 11, 590459.

2. Lingeswaran, M.; Goyal, T.; Ghosh, R.; Suri, S.; Mitra, P.; Misra, S.; Sharma, P., Inflammation, Immunity and Immunogenetics in COVID-19: A Narrative Review. Indian journal of clinical biochemistry : IJCB 2020, 35 (3), 260-273.

3. Mohamed, G. A.; Ibrahim, S. R. M.; El-Agamy, D. S.; Elsaed, W. M.; Sirwi, A.; Asfour, H. Z.; Koshak, A. E.; Elhady, S. S., Terretonin as a New Protective Agent against Sepsis-Induced Acute Lung Injury: Impact on SIRT1/Nrf2/NF-kappaBp65/NLRP3 Signaling. Biology (Basel) 2021, 10 (11).

4. Nie, Y.; Nirujogi, T. S.; Ranjan, R.; Reader, B. F.; Chung, S.; Ballinger, M. N.; Englert, J. A.; Christman, J. W.; Karpurapu, M., PolyADP-Ribosylation of NFATc3 and NF-kappaB Transcription Factors Modulate Macrophage Inflammatory Gene Expression in LPS-Induced Acute Lung Injury. J Innate Immun 2021, 13 (2), 83-93.

5. Liang, Y.; Yang, N.; Pan, G.; Jin, B.; Wang, S.; Ji, W., Elevated IL-33 promotes expression of MMP2 and MMP9 via activating STAT3 in alveolar macrophages during LPS-induced acute lung injury. Cell Mol Biol Lett 2018, 23, 52.

6. Szklarczyk, D.; Gable, A. L.; Nastou, K. C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N. T.; Legeay, M.; Fang, T.; Bork, P.; Jensen, L. J.; von Mering, C., The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021, 49 (D1), D605-D612.

7. Korbecki, J.; Kupnicka, P.; Chlubek, M.; Gorący, J.; Gutowska, I.; Baranowska-Bosiacka, I., CXCR2 Receptor: Regulation of Expression, Signal Transduction, and Involvement in Cancer. International Journal of Molecular Sciences 2022, 23 (4).

8. Hsing, C. H.; Wang, J. J., Clinical implication of perioperative inflammatory cytokine alteration. Acta Anaesthesiol Taiwan 2015, 53 (1), 23-8.

9. Gierlikowska, B.; Stachura, A.; Gierlikowski, W.; Demkow, U., The Impact of Cytokines on Neutrophils' Phagocytosis and NET Formation during Sepsis-A Review. Int J Mol Sci 2022, 23 (9).

10. Hsu, R. J.; Yu, W. C.; Peng, G. R.; Ye, C. H.; Hu, S.; Chong, P. C. T.; Yap, K. Y.; Lee, J. Y. C.; Lin, W. C.; Yu, S. H., The Role of Cytokines and Chemokines in Severe Acute Respiratory Syndrome Coronavirus 2 Infections. Front Immunol 2022, 13, 832394.

11. Wang, Y.; Zhu, K.; Dai, R.; Li, R.; Li, M.; Lv, X.; Yu, Q., Specific Interleukin-1 Inhibitors, Specific Interleukin-6 Inhibitors, and GM-CSF Blockades for COVID-19 (at the Edge of Sepsis): A Systematic Review. Frontiers in pharmacology 2021, 12, 804250.

12. Jafarzadeh, A.; Chauhan, P.; Saha, B.; Jafarzadeh, S.; Nemati, M., Contribution of monocytes and macrophages to the local tissue inflammation and cytokine storm in COVID-19: Lessons from SARS and MERS, and potential therapeutic interventions. Life sciences 2020, 257, 118102.

13. Wang, J.; Shen, Y. C.; Chen, Z. N.; Yuan, Z. C.; Wang, H.; Li, D. J.; Liu, K.; Wen, F. Q., Microarray profiling of lung long non-coding RNAs and mRNAs in lipopolysaccharide-induced acute lung injury mouse model. Biosci Rep 2019, 39 (4).

14. Lu, W.; Ji, R., Identification of significant alteration genes, pathways and TFs induced by LPS in ARDS via bioinformatical analysis. BMC Infect Dis 2021, 21 (1), 852.

15. Wu, Y. H.; Wei, C. Y.; Hong, W. C.; Pang, J. S., Berberine Suppresses Leukocyte Adherence by Downregulating CX3CL1 Expression and Shedding and ADAM10 in Lipopolysaccharide-Stimulated Vascular Endothelial Cells. Int J Mol Sci 2022, 23 (9).

16. Wang, Y.; Pan, L., Knockdown of CXCL3-inhibited apoptosis and inflammation in lipopolysaccharide-treated BEAS-2B and HPAEC through inactivating MAPKs pathway. Allergologia et immunopathologia 2022, 50 (4), 10-16.

17. Lou, Y.; Huang, Z.; Wu, H.; Zhou, Y., Tranilast attenuates lipopolysaccharide‑induced lung injury via the CXCR4/JAK2/STAT3 signaling pathway. Molecular medicine reports 2022, 26 (1).

18. Root-Bernstein, R., Innate Receptor Activation Patterns Involving TLR and NLR Synergisms in COVID-19, ALI/ARDS and Sepsis Cytokine Storms: A Review and Model Making Novel Predictions and Therapeutic Suggestions. International Journal of Molecular Sciences 2021, 22 (4).

19. Ding, X.; Tong, Y.; Jin, S.; Chen, Z.; Li, T.; Billiar, T. R.; Pitt, B. R.; Li, Q.; Zhang, L.-M., Mechanical ventilation enhances extrapulmonary sepsis-induced lung injury: role of WISP1-αvβ5 integrin pathway in TLR4-mediated inflammation and injury. Critical Care (London, England) 2018, 22 (1), 302.

20. Gao, N.; Liu, X.; Wu, J.; Li, J.; Dong, C.; Wu, X.; Xiao, X.; Yu, F.-S. X., CXCL10 suppression of hem- and lymph-angiogenesis in inflamed corneas through MMP13. Angiogenesis 2017, 20 (4), 505-518.

21. Rappert, A.; Bechmann, I.; Pivneva, T.; Mahlo, J.; Biber, K.; Nolte, C.; Kovac, A. D.; Gerard, C.; Boddeke, H. W. G. M.; Nitsch, R.; Kettenmann, H., CXCR3-dependent microglial recruitment is essential for dendrite loss after brain lesion. J Neurosci 2004, 24 (39), 8500-8509.

22. Iwamoto, H.; Izumi, K.; Mizokami, A., Is the C-C Motif Ligand 2-C-C Chemokine Receptor 2 Axis a Promising Target for Cancer Therapy and Diagnosis? International Journal of Molecular Sciences 2020, 21 (23).

23. Bianchi, M. G.; Chiu, M.; Taurino, G.; Bergamaschi, E.; Cubadda, F.; Macaluso, G. M.; Bussolati, O., The TLR4/NFκB-Dependent Inflammatory Response Activated by LPS Is Inhibited in Human Macrophages Pre-Exposed to Amorphous Silica Nanoparticles. Nanomaterials (Basel, Switzerland) 2022, 12 (13).

24. Otto, M.; Dorn, B.; Grasmik, T.; Doll, M.; Meissner, M.; Jakob, T.; Hrgovic, I., Apremilast effectively inhibits TNFalpha-induced vascular inflammation in human endothelial cells. J Eur Acad Dermatol Venereol 2022, 36 (2), 237-246.

25. Sasaki, Y.; Arimochi, H.; Otsuka, K.; Kondo, H.; Tsukumo, S. I.; Yasutomo, K., Blockade of the CXCR3/CXCL10 axis ameliorates inflammation caused by immunoproteasome dysfunction. JCI Insight 2022, 7 (7).

No competing interests reported.

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Identification of the potential cytokines involved in LPS induced acute lung injury in mouse

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Abstract

Figures

Introduction

Materials And Methods

Data processing and enrichment analysis

PPI network analysis and determination of hub genes

The definition of the potential genes in cytokine-related pathway

Animal model and mRNA expression

Prediction of the TF of the 7 potential genes

Statistical analysis

Results

Identification of DEGs in GEO database

GO and KEGG pathway enrichment analysis

PPI network and significant modules

prediction and enrichment analysis of the hub genes

The construct of experimental in vivo model

Experimental verification of seven potential genes

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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