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. 2023 Aug 25;9(9):e19437. doi: 10.1016/j.heliyon.2023.e19437

HUB genes transcriptionally regulate lipid metabolism in alveolar type II cells under LPS stimulation

Xianjun Chen 1,1, Chuan Xiao 1,1, Ying Liu 1, Qing Li 1, Yumei Cheng 1, Shuwen Li 1, Wei Li 1, Jia Yuan 1, Ying Wang 1, Feng Shen 1,∗
PMCID: PMC10472236  PMID: 37662799

Abstract

Objective

Alveolar type II (ATII) cells produce pulmonary surfactant (PS) essential for maintaining lung function. The aberration or depletion of PS can cause alveolar collapse, a hallmark of acute respiratory distress syndrome (ARDS). However, the intricacies underlying these changes remain unclear. This study aimed to elucidate the mechanisms underlying PS perturbations in ATII cells using transcriptional RNA-seq, offering insights into the pathogenesis of ARDS.

Methods

ATII cells were identified using immunofluorescence targeting surface-active protein C. We used 24-h lipopolysaccharide (LPS)-induced ATII cells as an ARDS cell model. The efficacy of the injury model was gauged by detecting the presence of tumour necrosis factor-α and interleukin-6. RNA-seq analysis was performed to investigate the dynamics of PS deviation in unaltered and LPS-exposed ATII cells.

Results

Whole-transcriptome sequencing revealed that LPS-stimulated ATII cells showed significantly increased transcription of genes, including Lss, Nsdhl, Hmgcs1, Mvd, Cyp51, Idi1, Acss2, Insig1, and Hsd17b7, which play key roles in regulating cholesterol biosynthesis. We further verified gene levels using real-time quantitative PCR, and the results showed that the mRNA expression of these genes increased, which was consistent with the RNA-seq results.

Conclusion

Our study revealed pivotal transcriptional shifts in ATII cells after LPS exposure, particularly in nine key lipid and cholesterol metabolism genes. This altered expression might disrupt the lipid balance, ultimately affecting PS function. This finding deepens our understanding of the aetiology of ARDS and may lead to new therapeutic directions.

Keywords: ATII cells, Pulmonary surfactant, Lipid metabolism, RNA-Seq, Hub gene, ARDS

1. Introduction

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening pulmonary disease caused by multiple factors [1], and intractable hypoxaemia is a critical characteristic associated with high morbidity and mortality in ARDS [2]. In ARDS, extensive lung tissue destruction and alveolar collapse are important pathological bases responsible for intractable hypoxaemia and reduced lung volumes [3]. Hereditary diseases of surfactant dysfunction may lead to significant morbidity and mortality in infants, children, and adults [4]. Studies have shown decreased synthesis or dysfunction of pulmonary surfactants (PS) is the main cause of alveolar collapse in ARDS [5]. Under physiological conditions, PS is synthesised in the endoplasmic reticulum (ER) of alveolar type II (ATII) cells, stored and transported within the cell in the lamellar body, and finally secreted into the alveolar lumen by cytosolic exocytosis, forming a monomolecular layer at the air-fluid interface that minimises surface tension, thereby preventing alveolar collapse and maintaining alveolar stability [6].

ATII cells are assumed to play a critical role in maintaining the structural integrity of the alveoli, preventing invasion by foreign pathogens, and healing lung injury, although they comprise just 3–5% of the alveolar area [7]. ATII cells play an innate immune role by producing cytokines and chemokines during lung injury. In addition, ATII cells produce, secrete, and circulate surface-active substances to maintain dynamic homeostasis in the distal lung and reduce surface tension at the pulmonary air-fluid interface, thereby preventing pulmonary atelectasis. ATII cells act as precursors of alveolar type I (ATI) cells and contribute to alveolar epithelial repairment and regeneration [8].

PS, synthesised and secreted by ATII cells, is composed of 90% of lipids and 10% of proteins, and the lipids consist of dipalmitoylphosphatidylcholine, unsaturated phosphatidylcholine, phosphatidylglycerol, cholesterol, and other neutral lipids. Surface-active proteins include plasma and surfactant proteins A, B, C, and D [9]. When the cholesterol content of PS is greater than 20%, its surface tension activity is impaired [10]. Elevated cholesterol levels in PS are thought to be a mechanism of ventilator-induced lung injury [11]. Elevated cholesterol crystals have been found in the bronchoalveolar lavage fluid of patients with idiopathic pulmonary fibrosis [12]. Cholesterol and neutral lipid levels are also increased in the bronchoalveolar lavage fluid of patients with ARDS. Neutral lipid supplementation with clinically used or natural surfactants reduces surface tension properties, and monoglycerides and cholesterol exhibit significant inhibition of surface substance activity [13]. However, the molecular mechanisms that cause altered lipid metabolism of PS substances in ARDS are not known. Considering the importance of lipids in PS, we hypothesised that injured alveolar epithelial cells impair normal PS production and secretion by affecting the expression of genes associated with lipid and cholesterol metabolism during ARDS development. To identify specifically related genes, we used lipopolysaccharide (LPS)-induced ATII cells as an ARDS cell model to detect and analyse the expression of genes related to lipid metabolism after cell injury.

In this study, we analysed the transcriptome data of LPS-induced ATII cells and identified key HUB genes that affect lipid metabolism synthesis in ATII cells, including Lss, Nsdhl, Hmgcs1, Mvd, Cyp51, Idi1, Acss2, Insig1, and Hsd17b7, which may play key roles in the cholesterol biosynthesis pathway and lipid metabolism synthesis and were further validated using real-time quantitative PCR (RT-PCR). Our findings imply that LPS-induced ATII cells influence lipid metabolism via these HUB genes, affecting PS biosynthesis. This study provides new insights into the potential mechanisms underlying abnormal lipid metabolism in ATII cells during ARDS.

2. Materials and methods

2.1. Cell cultures

Rat ATII cells (RLE-6TN; No. BNCC337708), purchased from BeNa Culture Collection (Henan, China), were confirmed to be rat ATII cells by morphology and surface-active protein C (SP–C) immunofluorescence expression, with a purity of more than 90%, and the cell culture method and conditions were performed as previously described [14], which were cultured in DMEM containing 10% foetal bovine serum and 100 IU/mL penicillin-streptomycin in a humidified atmosphere of 5% CO2 and 95% air at 37 °C. Then ATII cells were cultured either with LPS (5 μg/mL, LPS group) or normal saline (Control group) for 24 h. All the cell experiments were completed in less than five generations.

2.2. Immunofluorescence

As progenitor cells, ATII easily differentiate into AT I. SP-C expression was detected using immunofluorescence to identify cell types, as reported in the literature [8,15]. AT II cells were plated onto coverslips in 6-well plates, and the growth was stopped. The cells were rinsed thrice in phosphate-buffered saline before being fixed with 4% paraformaldehyde for 30 min, permeabilised with 0.3% Triton ×100 for 15 min, and then sealed with 5% bovine serum albumin for 1 h. Rabit anti-SP-C (affinity biosciences, USA, 1:100) primary antibody was incubated overnight at 4 °C, and FITC Goat Anti-Rabbit IgG(H + L) antibody (APExBIO, USA, 1:300) was incubated for 1 h at room temperature in the dark. Images were acquired using a Zeiss LSM880 fluorescent microscope (Zeiss).

2.3. Enzyme-linked immunosorbent assay (ELISA)

To confirm the successful LPS-injured cell model, we measured tumour necrosis factor (TNF)-α and interleukin (IL)-6 concentrations in ATII cells according to the manufacturer's instructions (Elabscience Biotechnology Co., Ltd., China). The optical density (OD) at 450 nm was determined using a Power Wave microplate reader (BioTek Synergy H4, USA).

2.4. RNA extraction and quality control

The RNA concentration in each sample was measured using a NanoDrop ND-1000 instrument (Thermo Fisher Scientific, Waltham, MA, USA). The OD260/OD280 ratio was used as the RNA purity index. If the value of OD260/OD280 ranges from 1.8 to 2.1, the RNA purity is qualified, and QC Results are marked as “Pass”, i.e., qualified. RNA integrity and gDNA contamination were measured using denatured agarose gel electrophoresis. Finally, the library quality was measured using an Agilent 2100 Bioanalyzer.

2.5. RNA-seq library preparation and sequencing

We used the GenSeq® rRNA Removal Kit (GenSeq, Inc.) to deplete rRNAs from total RNA. Subsequently, rRNA-depleted samples were processed into libraries using the GenSeq® Low Input RNA Library Prep Kit (GenSeq, Inc.) in strict accordance with the manufacturer's protocol. Quality assessment and library quantification were conducted using an Agilent BioAnalyzer 2100 system (Agilent Technologies, Inc., USA). The libraries were then sequenced on an Illumina NovaSeq platform, generating 150 bp paired-end reads. Quality checks of the raw RNA-sequencing data ensured their suitability for advanced analyses. After alignment with the reference genome and filtering low-quality reads, these data served as the basis for subsequent evaluations. Illumina NovaSeq 6000 provided raw reads for post-image analysis and base-calling stages. A Q30 threshold was maintained with scores above 80%, indicating superior sequencing quality. The entire RNA-seq library was prepared and sequenced by CloudSeq Biotech (Shanghai, China).

2.6. Bioinformatic analysis process

Raw paired-end sequencing data were obtained using the Illumina NovaSeq 6000 system. The sequence quality was verified against the Q30 benchmark. The cutadapt tool (v1.9.3) was employed to trim 3′ adaptors and discard subpar reads. The refined sequences were aligned to the reference rat genome (RN5) using the hisat2 tool (v2.0.4). HTSeq software (v0.9.1) was used to quantify the reads. Data were normalised using EdgeR. Significant mRNA changes were identified based on the set criteria for p-values and fold changes. Further analyses focused on gene ontology (GO) and pathway enrichment relative to the observed mRNA changes.

2.7. Protein-protein interaction network analysis

To further explore the mechanism of differential genes involved in the pathogenesis of ARDS and to explore the interaction between differential genes, a protein-protein interaction (PPI) network based on upregulated genes was established on a string database for functional association analysis. The analysis process is as follows: click on the official website of string (https://cn.string-db.org/), select “Multiple proteins”, enter the genes to be analysed, select “Rattus norvegicus” for species, click on “SEARCH” for analysis, and download the analysis data. Cytoscape software (version 3.9.1) was used for sequential visualisation. The plug-in “cytohubba” was used to find the top 10 hub genes and the differential genes directly related to them, according to the association grade difference between different genes.

2.8. Validation of gene expression data using RT-PCR

RNA was isolated according to standard protocols using the TRIzol reagent (Invitrogen, USA). The integrity and concentration of RNA were ascertained through spectrophotometry, particularly the A260 absorption and the A230/260 and A260/280 ratios. After isolation, genomic DNA was removed using DNase I (Takara, Japan). A mixture containing Prime Script RT Enzyme Mix I, RT Primer Mix, 5 × Prime Script Buffer, and RNase-free dH2O was prepared. Subsequent heat treatment was conducted at 37 °C for 15 min and 85 °C for 5 s, leading to cDNA synthesis. RT-PCR was performed using TB Green Premix Ex TaqII, cDNA, primers, and RNase-free ddH2O. Employing the 2−ΔΔCt technique and referencing GAPDH, relative transcription levels were determined. The primers used for RT-qPCR are listed in Supplementary Table S1.

2.9. Statistical analysis

Data are presented as mean ± SEM in accordance with conventional protocols. For mechanical threshold assessments, two-way ANOVA was employed, complemented by Sidak's post-hoc tests. The RT-qPCR results were analysed using an unpaired two-tailed Student's t-test. Statistical significance was set at p < 0.05.

3. Results

3.1. Expression of SP-C in cells

SP-C staining was performed on the cytoplasm of ATII cells in the basal state to identify the cell types. The results revealed that more than 90% of the cells expressed SP-C in the cytoplasm (Fig. 1), indicating that the purity of AT II cells was greater than 90%.

Fig. 1.

Fig. 1

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Immunofluorescence was used to visualise SP-C expression in ATII cells (scale bar = 50 μm). (A) Nucleus was stained in DAPI (blue), (B) SP-C proteins are depicted in red, and (C) merge.

3.2. Increased expression of TNF-α and IL-6 in ATII cells after LPS stimulation

We examined the levels of TNF-a and IL-6 in ATII cells. LPS stimulation boosted TNF-a and IL-6 production in ATII cells, indicating that the ATII cells injury model was successfully constructed (Fig. 2).

Fig. 2.

Fig. 2

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Increased expression of TNF-α (A) and IL-6 (B) in ATII after LPS stimulation. ***p < 0.001 (vs. NC group).

3.3. Analysis results of quality control and differentially expressed genes (DEGs)

Transcriptome data were obtained from cells in the NC and LPS groups. The average number of readings per sample was approximately 43.56 million; Q30 ranged from 92.55% to 93.15%, and 91.03%–92.93% of the samples were mapped to the reference genome, indicating that the sequencing results were reliable (Supplementary Table S2). Analytic results showed that clustering differential expression existed between LPS-stimulated and normal cells (Fig. 3A), among which 210 genes were upregulated, and 330 were downregulated in cells under LPS injury (Fig. 3B) (|log2FC| ≥ 2.0, p-value ≤ 0.05). The heat map depicts the expression of the top 50 upregulated genes (Fig. 3C).

Fig. 3.

Fig. 3

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Identification of differentially expressed genes. (A)The clustering of differentially expressed genes at NC and LPS groups. (B) Volcano plot of differential genes in NC and LPS groups, and the top 10 upregulated genes were labelled (The red represents DEGs upregulated, the green represents DEGs downregulated, and grey represents non-DEGs. Our data showed 210 genes upregulated and 330 genes downregulated in ATII after LPS injury (|log2FC| ≥ 2; p-value ≤ 0.05). (C) Heat map depicts the expression of the top 50 upregulated genes (Red indicates high expression levels, while purple represents low expression levels).

3.4. Analysis results of important Kyoto encyclopedia of genes and genomes (KEGG) pathways and GO analysis

We used the KEGG databases (https://www.genome.jp/kegg/) and the DAVID Bioinformatics Resources (https://david.ncifcrf.gov/tools.jsp) to analyse the KEGG pathway and functional annotation of different genes. Differential genes were significantly enriched in the classification of “Steroid biosynthesis” (Fig. 4A), suggesting that steroid biosynthesis occurs in ATII cells after LPS injury. We then analysed the GO of different genes from biological processes (BP) (Fig. 4B), cellular components (CC) (Fig. 4C), and molecular functions (MF) (Fig. 4D). From the GO analysis, we speculated that “cholesterol biosynthetic process”, “sterol biosynthetic process”, “cholesterol metabolic process”, “sterol metabolic process”, “cell surface”, and “protein binding” may be related to the role of ATII cells in the pathophysiologic process of ARDS.

Fig. 4.

Fig. 4

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Results of KEGG and GO analysis. (A) Pathway analysis. (B) Biological processes. (C) Cellular components. (D) Molecular functions.

3.5. Analysis results of PPI

To further explore the possible role of and the interaction between these differential genes in ARDS genesis, a PPI network established on a STRING database (Fig. 5A) and Cytoscape software were used to perform a sequential visualisation analysis of these differential genes, and the top 10 genes were identified, including Lss, Nsdhl, Hmgcs1, Mvd, Cyp51, Idi1, Acss2, Insig1, Hsd17b7, and RGD1562948 (Fig. 5B).

Fig. 5.

Fig. 5

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Identification of the TOP 10 HUB genes. (A) Results of the protein-protein interaction network. (B) Visualisation of the top 10 HUB genes.

3.6. Results of RT-PCR

To verify the consistency between the transcriptome sequencing results and the mRNA levels of these DEGs, we selected the top nine hub genes (Lss, Nsdhl, Hmgcs1, Mvd, Cyp51, Idi1, Acss2, Insig1, and Hsd17b7) and analysed their mRNA levels using RT-CR. The mRNA levels of these nine hub genes were consistent with the RNA-seq results, indicating that the RNA-sequencing results were reliable (Fig. 6).

Fig. 6.

Fig. 6

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RT-PCR relative expression results. **p < 0.01, ***p < 0.001 (vs. NC group).

4. Discussion

PS plays a vital role in maintaining normal respiratory function and preventing pulmonary atelectasis. PS is mainly synthesised and secreted by ATII cells and consists of 90% of lipids and 10% of proteins, which are synthesised by the ER of ATII cells, stored in the lamellar body, and finally secreted into the alveolar lumen via cytosolic exocytosis [3,16]. Recent studies have confirmed that the activation of protein kinase A, protein kinase C, and calcium/calmodulin-dependent protein kinase plays an important role in PS secretion [17]. However, PS secretion and recycling are still poorly understood. In addition, the mechanisms underlying the regulation of PS synthesis remain unclear. In this study, we selected ATII cells as the study cell line and explored the potential mechanisms affecting PS biosynthesis by constructing an ARDS cell model and analysing changes in gene expression under ATII cell injury conditions from a bioinformatics perspective.

To simulate ARDS, we treated ATII cells with LPS (ARDS induced by bacterial pneumonia). Concentrations of IL-6 and TNF-α in cell supernatant significantly increased after 24 h of LPS stimulation, indicating the success of the damage model of ATII cells [15]. RNA-seq analysis demonstrated that 210 genes were upregulated and 330 genes were downregulated in the LPS-stimulated ATII cells. We further observed their possible association with the PS function using GO analysis. Our results showed that these DEGs were mainly enriched in BPs, including cholesterol biosynthesis, sterol biosynthesis, and cholesterol metabolism. Finally, we found these genes predominantly concentrated in the “steroid biosynthesis” signaling pathway using KEGG analysis, from which we screened the top 10 HUB genes, including Lss, Nsdhl, Hmgcs1, Mvd, Cyp51, Idi1, Acss2, Insig1, Hsd17b7, and RGD1562948. The expression of the top nine genes was confirmed to be increased using RT-PCR, which was consistent with the RNA-seq results.

Subsequently, we preliminarily analysed the roles of these hub genes. We found that some of these genes encode many enzymes, such as the Lss gene encoding lanolin synthase [18], the Hmgcs1 gene encoding 3-hydroxy-3-methylglutaryl coenzyme A synthase 1 [19], and the Mvd gene encoding mevalonate 5-diphosphate decarboxylase [20], all of which are key enzymes in the cholesterol biosynthesis pathway. This implies that the increased expression of these genes may cause an increase in cholesterol biosynthesis. Primary studies have indicated that superfluous cholesterol weakens the function of surfactants [11,21,22]. Other genes were associated with some proteins or enzymes, among which the Nsdhl gene encodes a steroid dehydrogenase-like protein that inhibits adipogenesis, and its function depends on its enzymatic activity in cholesterol biosynthesis [23]. CYP51 encode sterol 14α-demethylase, which is involved in the homeostatic regulation of most sterols, including cholesterol. Its deficiency leads to the loss of membrane structure and function [24,25]. As a key enzyme in biosterol synthesis, CYP51 is an important target of cholesterol-lowering drugs [24,26,27]. Idi encodes isopentenyl diphosphate isomerase, a cytoplasmic enzyme involved in isoprene biosynthesis, including cholesterol. Nakamura et al. [28] showed that isopentenyl diphosphate isomerase may be related to the production of cholesterol metabolites in neurons. Acss2 encodes ethyl coenzyme A synthase 2, a conserved nucleocytoplasmic enzyme that converts acetate to acetyl coenzyme A and promotes lipid storage and utilisation by selectively regulating the genes involved in lipid metabolism [29]. Insulin-inducible gene 1 (Insig1), an important regulator of lipid metabolism, affects lipid metabolism mainly by regulating the sterol regulatory element-binding protein (SREBP) and 3-hydroxy-3-methylglutaryl coenzyme A reductase. This gene anchors to the ER membrane in the form of the INSIG-SCAP-SREBP complex and regulates the activation of SREBP through the participation of intracellular sterols, thus affecting the uptake of exogenous cholesterol by cells through feedback regulation [30]. Some studies have suggested that Insig1 plays an important role in the development and progression of hypertriglyceridemia-associated obesity [31]. For the gene Hsd17b7, a member of the 17β-HSD family, there is a high consistency between the location of 17β-HSD 7 and that of HMG-CoA reductase, a rate-limiting enzyme in cholesterol synthesis [32,33]. In addition, some common transcription factor binding sites in the promoter region of cholesterol metabolising enzymes, such as hepatocyte nuclear factor 4, specificity protein 1, and SREBP, are also present in the promoter region of 17β-HSD7, and 17β- HSD7 transcription could also be affected by cholesterol [34]. Therefore, it could be thought that 17β-HSD7 is involved in cholesterol synthesis as a 3-steroid reductase.

Our data showed that the expression of Lss, Nsdhl, Hmgcs1, Mvd, Cyp51, Idi1, Acss2, Insig1, and Hsd17b7 genes was significantly increased under LPS stimulation, and almost all of these genes were associated with cholesterol biosynthesis and metabolism. Surfactant lipids are precisely regulated during PS synthesis. Almost half of the surfactant lipids are recycled into ACE II cells for palingenesis or lysosomal degradation, whereas the remainder are internalised and degraded by alveolar macrophages [35]. Among surfactant lipids, cholesterol has been shown to improve the fluidity/spreading of tightly packed desaturated phospholipids. Studies have shown that excess cholesterol can impair surfactant function [11,21,22]. Therefore, precise regulation of cholesterol levels is crucial for the synthesis of PS. This suggests that the abnormal changes in cholesterol biosynthesis and metabolism caused by these genes may contribute to PS dysfunction or synthesis disorder in ARDS. However, further studies are needed to confirm whether and how these genes affect PS function and synthesis in ARDS. Our preliminary results provide new research directions for understanding the mechanisms of ARDS.

However, this study has some limitations. First, we did not observe any dynamic changes in the expression of these genes during LPS-induced ARDS [36]. Second, the potential mechanisms underlying changes in the expression of these genes have not been thoroughly explored. Finally, we did not observe the role of ATI in the synthesis of PS, which is another important alveolar epithelial cell type. It was found that AT I and AT II cells responded differently to LPS stimulation, with higher levels of cytokines TNF-a, IL-6 and IL-1b produced after endotoxin stimulation of primary rat ATI cells [37]. This suggests that ATI cells play an important role in the lung immune response, similar to ATII cells. In future studies, we plan to focus on these issues.

5. Conclusions

Our study provides pivotal insights into the underlying mechanism of ARDS, specifically pinpointing the pronounced upregulation of nine hub genes in ATII cells following LPS stimulation. These genes, namely Lss, Nsdhl, Hmgcs1, Mvd, Cyp51, Idi1, Acss2, Insig1, and Hsd17b7, predominantly play roles in lipid and cholesterol biosynthesis and metabolism. This alteration in gene expression highlights a potential pathway through which LPS-induced injury in ATII cells can lead to an imbalance in lipid metabolism, thereby compromising PS synthesis. Our research findings present an innovative perspective on the molecular intricacies of ARDS and set the stage for future investigations, reinforcing our understanding and potentially guiding therapeutic advancements in this domain.

Author contribution statement

Xianjun Chen: Conceived and designed the experiments; Performed the experiments; Analysed and interpreted the data; Wrote the paper.

Chuan Xiao: Performed the experiments; Analysed and interpreted the data; Wrote the paper.

Ying Liu, Qing Li: Performed the experiments; Analysed and interpreted the data.

Yumei Cheng, Shuwen Li, Wei Li, Jia Yuan, Ying Wang: Contributed reagents, materials, analysis tools or data.

Feng Shen: Conceived and designed the experiments; Wrote the paper.

Funding statement

This work was supported by In-Hospital Clinical Research Project of the Affiliated Hospital of Guizhou Medical University {2021-GMHCT-003}, Application and Industrialisation of Scientific and Technological Achievements in Guizhou Province, China (Clinical Special Program) {Qiankehe Results-LC[2023]031} and by National Natural Science Foundation of China {82160365}.

Data availability statement

Data associated with this study has been deposited at GEO under the accession number GSE226486.

Go to https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE226486.

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Declaration of competing interest

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank the members of our laboratory for the helpful discussions. We thank Cloudseq Biotech Ltd., Co. (Shanghai, China) for the mRNA-Seq service and subsequent bioinformatics analysis.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e19437.

Contributor Information

Xianjun Chen, Email: chenxj@gmc.edu.cn.

Chuan Xiao, Email: xc15973986196@163.com.

Ying Liu, Email: 464897058@qq.com.

Qing Li, Email: 2538853252@qq.com.

Yumei Cheng, Email: 64960877@qq.com.

Shuwen Li, Email: doctorlishuwen@163.com.

Wei Li, Email: 451391470@qq.com.

Jia Yuan, Email: 1006783013@qq.com.

Ying Wang, Email: 28917145@qq.com.

Feng Shen, Email: doctorshenfeng@gmc.edu.cn.

Appendix A. Supplementary data

The following is/are the supplementary data to this article:

Multimedia component 1
mmc1.docx (15KB, docx)
Multimedia component 2
mmc2.docx (16KB, docx)

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Associated Data

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Supplementary Materials

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Data Availability Statement

Data associated with this study has been deposited at GEO under the accession number GSE226486.

Go to https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE226486.

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