A Landscape of lncRNA Expression Profile and the Predictive Value of a Candidate lncRNA for Silica-Induced Pulmonary Fibrosis

Abstract

Silicosis is the most common type of pneumoconiosis with the fastest progress and the most serious harm. At present, there is still a lack of effective treatment for silicosis, and the molecular mechanism of silicosis is very complex, which is not completely clear. This study aimed to identify crucial long noncoding RNA (lncRNA)–mRNA networks for silica-induced pulmonary fibrosis using microarray data from Gene Expression Omnibus database, including human lung epithelial cells Beas-2B and continuously exposed to 5 μg/mL amorphous silica nanoparticles for 40 passages. Differently expressed genes were calculated by “DESeq2” R package. Then we selected the differently expressed mRNAs (DEmRNAs) and differently expressed long noncoding RNAs (DElncRNAs) data construct lncRNA–mRNA coexpression network using weighted gene coexpression network analysis (WCGNA). A total of 1140 DEmRNA and 1406 DElncRNAs were identified, including 20 upregulated DEmRNAs, 1120 downregulated DEmRNAs as well as 213 upregulated DElncRNAs and 1193 downregulated DElncRNAs. Furthermore, we demonstrate that lncRNA AK131029 was specifically overexpressed in silicosis. Loss-of-function assay indicated that silencing AK131029 of inhibited cell proliferation in human lung fibroblast cells. In conclusion, this study preliminarily indicates that lncRNA AK131029 may play a role in pulmonary fibrosis.

Introduction

The pathogenesis of silicosis is that free silica enters the body through the respiratory tract, alveolar macrophages phagocytize free silica particles, and stimulated macrophages secrete various cytokines, chemotactic fibroblasts, resulting in a large number of extracellular matrix (ECM) components accumulations, but the exact mechanism has not been clarified. In ECM, collagen accounted for 11%, especially type I and type III collagen (Leung et al., 2012; Blanc and Seaton, 2016; Riley and Urbine, 2019; Li et al., 2020). Pulmonary fibrosis is the core of the irreversible deterioration of silicosis (Dinh et al., 2020). At present, there is no specific therapy, so the key to prevention and treatment is still early screening and reasonable clinical prediction. High kilovolt chest film is widely used as a standard method for diagnosis of silicosis. However, due to the nonspecific and lagging imaging performance, the standard still has some limitations in the early screening of silicosis (Liu et al., 2017a; Li et al., 2019b; Mayeux et al., 2019). Therefore, if some biomarkers can be selected to help the clinical early screening and monitoring of silicosis changes, it will be conducive to disease prevention and treatment, so that patients can further benefit.

Pulmonary fibrosis is a kind of end-stage changes of lung diseases characterized by fibroblast proliferation and a large number of ECM depositions with inflammatory damage and tissue structure destruction (Haak et al., 2020; Hunninghake et al., 2020; Teoh et al., 2020). Asbestos, mineral dust, drugs, and radiation damage can lead to pulmonary fibrosis. In view of pulmonary fibrosis, the existing measures mainly focus on the prevention and improvement of pulmonary function, and there is no effective and specific drug use strategy in the treatment (Marudamuthu et al., 2019; Yang et al., 2019; Wu et al., 2020).

The long noncoding RNA (lncRNA) transcripts are >200nt long and do not encode proteins themselves, which can regulate gene expression at epigenetic, transcribed, and post-transcribed levels (Prensner and Chinnaiyan, 2011; Khorkova et al., 2015). At present, there are few reports about lncRNA regulating pulmonary fibrosis. Whether lncRNA also plays an important role in the development of silicosis-induced pulmonary fibrosis remains to be further studied (Huang et al., 2020; Li et al., 2019a; Zhou et al., 2019). Therefore, it is of great practical significance to explore the mechanism of silicosis to improve the current status of silicosis treatment. Recent studies demonstrate that aberrant expression of lncRNAs was involved in the initiation and progression of silicosis.

In this study, we aimed to identify differentially coexpression network between silica-induced fibroblast and normal control cells by comprehensive analysis of two microarray data-sets downloaded from the public Gene Expression Omnibus (GEO) database. Furthermore, we indicated that silencing of lncRNA AK131029 promoted cell proliferation and migration in human lung fibroblast. These findings may provide novel therapeutic targets for silicosis.

Materials and Methods

Data sets download and screening of differentially expressed genes

We downloaded the publicly available microarray raw data from GEO database for identifying differently expressed genes (DEGs), including human lung epithelial cells Beas-2B and continuously exposed to 5 μg/mL amorphous silica nanoparticles for 40 passages. The counts normalization and differential expression analysis were performed using DeSeq2. The differently expressed long noncoding RNAs (DElncRNAs) and mRNAs were defined as the lncRNAs, or mRNAs with a Log2 (fold change [FC]) ≥1.0 and an adjusted p-value (Benjamini and Hochberg's BH multiple test correction) <0.05 heatmap plot were created to obtain an overview of the expression profile of lncRNA and mRNA using Factoextra and Pheatmap packages. The Fisher's exact test was used to assess statistical significance for DElncRNAs and differently expressed mRNAs (DEmRNAs) enrichment analysis, including GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes). The target genes of DElncRNAs were predicted by calculating the Pearson correlation coefficients and p-values among multiple genes, and the |correlation| ≥ 0.9 was used to filter the transcripts. Regulatory network of top 20 DElncRNAs including top 10 upregulated lncRNAs and top 10 downregulated lncRNAs by transtargets (mRNAs) was plotted using igraph package. No clinical trials were involved in this study.

Construction of differently expressed lncRNA–mRNAs interaction network

R package of “WGCNA” was employed to construct the coexpression network for the DEmRNAs and DElncRNAs. Obvious outlier samples or those with excessive number of missing entries were removed before network construction, followed by a step-by-step network construction and module detection to construct a matrix of similarity. The power of β = 6 was selected for raising the matrix of similarity to achieve a scale-free coexpression network, and soft-threshold power was used to calculate adjacencies, which was then transformed into Topological Overlap Matrix (TOM). At the same time, average linkage hierarchical clustering was analyzed by the TOM-based dissimilarity measure with a minimum size group of 30 genes for the genes dendrogram.

Cell culture and transfection

Human pulmonary fibroblasts (HPFs) were purchased from the ScienCell. Fibroblast medium consists of 500 mL of basal medium, 10 mL of FBS (Cat. No. 0010), 5 mL of fibroblast growth supplement (Cat. No. 2352), and 5 mL of penicillin/streptomycin solution (Cat. No. 0503). Human lung fibroblasts Hs888Lu cells were cultured in Dulbecco's modified Eagle's medium (DMEM, Gibco, Thermo Fisher Scientific, Inc., Waltham, MA) containing 10% FBS (Thermo Fisher Scientific, Waltham, MA), 100 U/mL of penicillin, and 100 mg/mL of streptomycin (Gibco, Thermo Fisher Scientific, Inc., Waltham, MA). Cells were maintained at 37°C with 5% CO₂. HPFs and Hs888Lu cells were transfected with AK131029-specific siRNAs and control siRNAs using the Lipofectamine 3000 reagent (Life Technologies, CA) according to the manufacturer's protocol.

RNA extraction and quantitative real-time PCR analysis

Total RNA extraction from cultured cells was acquired by TRIzol reagent (Invitrogen, Carlsbad CA), and cDNA was then synthesized as per the standard method of PrimeScript Reverse Transcriptase Kit (Takara, Shiga, Japan). The expression of each gene was determined by qRT-PCR using SYBR Green PCR Kit (Takara), relative expression was calculated by the 2^−ΔΔCt method and standardized to U6 snRNA or GAPDH mRNA.

EdU assay

Edu (Ribobio, Guangzhou, China) is a thymine nucleoside analogue, which can replace thymine (T) and infiltrate into the replicating DNA molecules during cell proliferation. The specific reaction between edu and Apollo fluorescent dyes can be used to quickly detect the DNA replication activity of cells. Forty-eight h after siRNA transfection, cells were collected and subjected to subsequent analysis. The cells transfected with siNC or siAK131029 were plated onto 96-well plates. When the cell density reached 70%, the culture medium was discarded and washed with PBS buffer three times every 5 min. The appropriate amount of reagent A was prepared by diluting reagent A with DMEM at 1000:1, adding 100 μL per well, reacting at 37°C for 2 h, and discarding edu. Each well was added with 100 μL of 4% paraformaldehyde, fixed at room temperature for 15 min, and then 100 μL glycine was added to each well for 5 min at room temperature. Add phosphate buffer and wash for three times. Each well was incubated with 100 μL permeating solution at room temperature for 10 min. In total, 100 μl of 1 × Hoechst 33342 reaction solution was added to each well, and incubated in a shaking table at room temperature for 15 min.

Cell counting kit-8 assay

Cell proliferation was detected using the cell count kit-8 (CCK-8, Dojindo, Japan).

In brief, cells were seeded into 96-well plates at ∼2000–3000 cells per well, and each group was replicated in quintuplicate. At the indicated time points, 10 μL CCK-8 solutions were added into each well. The absorption at 450 nm was measured using a microplate reader.

Statistical analysis

Data are exhibited as the means ± standard deviation of three separately conducted assays, and processed with GraphPad Prism 7 (La Jolla, CA). Group difference was studied using one-way analysis of variance or Student's t-test. Experimental results were collected when p < 0.05.

Results

Data processing and comprehensive analysis of the DEmRNAs

We downloaded the publicly available raw GSE82062 from GEO database, including human lung epithelial cells Beas-2B (Beas-P40 group), and continuously exposed to 5 μg/mL amorphous silica nanoparticles for 40 passages (BeasSiNPs-P40 group). After normalization (Fig. 1A, B), a total of 1140 DEmRNA and 1406 DElncRNAs were identified under the thresholds of |log2FC| > 1 and p < 0.05 (Fig. 1C, D).

FIG. 1.

Heatmap plot. (A, B) Data processing results after normalization of GSE82062. (C, D) The differently expressed lncRNA and mRNA expression profile by Heatmap plot. Red and green indicate the high and lower expression, respectively. lncRNA, long noncoding RNA. Color images are available online.

DElncRNAs and DEmRNAs assessed by volcano plot

Furthermore, the volcano plot showed that there were 213 upregulated DElncRNAs and 1193 downregulated DElncRNAs in the BeasSiNPs-P40 group compared with the Beas-P40 group (Fig. 2A). As for DEmRNAs, the volcano plot identified 20 upregulated DEmRNAs and 1120 downregulated DEmRNAs in the BeasSiNPs-P40 group compared with the Beas-P40 group (Fig. 2B).

FIG. 2.

Volcano plot. The upregulated/unchanged/downregulated lncRNAs (A), and the upregulated/unchanged/downregulated mRNAs by volcano plot (B). Color images are available online.

Identification of coexpression gene network and visualization by Cytoscape

Furthermore, All the DEGs, including DEmRNAs and DElncRNAs, were employed to construct a coexpression network according to the mentioned process. In this study, five modules in total were subsequently identified (Fig. 3).

FIG. 3.

All DElncRNAs and DEmRNAs coexpression network. DElncRNA, differently expressed long noncoding RNA. Color images are available online.

Enrichment analysis of DElncRNAs and DEmRNAs

Then the GO enrichment analysis displayed that the DElncRNAs were mainly enriched in Gene Ontology biological processes (GO-BP) including transport vesicle membrane, cell division, cytokine-mediated signaling pathway, and inflammatory response; molecular functions consisting of damaged DNA binding, protein binding, protein C-terminus binding, etc.; in addition, the DElncRNAs were mostly located at cytoplasm, extracellular vesicular exosome, mitochondrial matrix, etc. (Fig. 4A). And the KEGG enrichment analysis illuminated that the DElncRNAs were predominantly enriched in inflammatory, antigen processing and presentation, and regulation of actin cytoskeleton-related pathways such as the p53 signaling pathway, TGF-beta signaling pathway, small cell lung cancer, etc. In terms of DEmRNAs, the GO enrichment elucidated that they were significantly enriched in the BP of response to oxidative stress, inflammatory response, cytokine-mediated signaling pathway, etc.; the molecular functions of ATP binding, unfolded protein binding, protein heterodimerization activity, etc.; and they were mainly located in integral to membrane, extracellular space, endoplasmic reticulum membrane, etc. (Fig. 5A). Besides, the KEGG enrichment analysis showed that the DEmRNAs were markedly enriched in antigen processing and presentation, pathways in cancer, focal adhesion, etc.

FIG. 4.

Enrichment analysis of DE lncRNAs and DE mRNAs. GO enrichment (A) and KEGG enrichment (B) of DE lncRNAs. GO enrichment (C) and KEGG enrichment (D) of DE mRNAs. DE, differentially expressed; lncRNA, long non-coding RNA; GO, Gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological processes; CC, cell component; MF, molecular function. Color images are available online.

FIG. 5.

Regulatory network showing the interactions of the top 20 DElncRNAs with DEmRNAs. Color images are available online.

Functional annotation of the significant module and identification of hub genes

Since the cells perform their functions mainly by proteins that were translated by mRNAs, we evaluated the main function of the mRNAs in the module by functional enrichment analysis. The most overrepresented GO terms in BP were cell cycle checkpoint, cell division, and inflammatory response, and those terms in cellular component were integral to plasma membrane as well as intrinsic to plasma membrane. As shown in Figure 5, hub genes were identified, including EIF4G2, EXOSC5, LGALS1, C1QL1, TOMM5, DDX39B, ATP1A2, WT1, TNXB, TSPYL5, RBM24, IGF1, CNTN4, SALL1, IDH3G, and DCHS1. 3 lncRNAs were identified including n337310, BC017407, and N335614. n333710 (accn = BC134347 class = mRNAlike lncRNA) also named AK131029 in Genebank. The specific information of differential lncRNAs is shown in Supplementary Table S1.

Silencing of lncRNA AK131029 inhibits cell proliferation and migration in lung fibroblast

The results of bioinformatics suggested that a gene plays an important role in silica-induced pulmonary fibrosis. To explore the biological role of AK131029 in human lung fibroblast cells, loss-of-function assays were performed. Before this, we transfected siRNAs targeting AK131029 to silence the expression of AK130129 in HPFs and Hs888Lu cells (Fig. 6A). Transwell migration assays showed that downregulation of AK131029 significantly decreased cell migration of human lung fibroblast cells (Fig. 6B).Then it was displayed in CCK-8 and EdU assays that silenced AK130129, effectively inhibiting cell proliferation in HPFs and Hs888Lu cells (Fig. 6C, D). These results suggest that AK131029 plays a key role in silica-induced pulmonary fibrosis by promoting cell proliferation and migration.

FIG. 6.

AK131029 silencing suppresses the migration and proliferation of lung fibroblast cells. (A) qRT-PCR analysis of the knockdown efficiency of si-AK131029 in HPFs and Hs888Lu cells. siNC represents transfected with negative control siRNA. (B) Transwell assay was conducted to indicate the migration capacity in HPFs and Hs888Lu cells transfected with siRNA. (C, D) CCK8 and EdU assays examined cell proliferation in HPFs and Hs888Lu cells transfected with siRNAs targeting AK131029. The percentage of positive cells in total cells detected by EdU was statistically analyzed. Error bars represent the means of three independent experiments (n = 3). **p < 0.01. HPFs, human pulmonary fibroblasts; qRT-PCR, quantitative real-time PCR. Color images are available online.

Discussion

Although lncRNA does not encode proteins, it can participate in pretranscriptional regulation with homologous DNA sequences or RNA sequences, such as the combination of DNA and DNA in some parts of lncRNA, and other parts of the genome are folded into higher structures, which combine with epigenetic factors to regulate downstream gene transcription (Mowel et al., 2017; Krause, 2018; Qian et al., 2018). lncRNA can also participate in the regulation of transcription process and post-transcriptional regulation, and play a variety of important biological functions. In the study of the mechanism of lncRNA and pulmonary fibrosis, lncRNA plays an important role in the occurrence and development of pulmonary fibrosis by regulating epithelial to mesenchymal transition (EMT), inflammation immune response, and apoptosis-related proteins or pathways (Krause, 2018; Li et al., 2018; Lu et al., 2018). The study of lncRNA in the mechanism of pulmonary fibrosis provides a new way to reveal the pathogenesis of pulmonary fibrosis, and also provides valuable biomarkers and therapeutic intervention targets for the diagnosis and treatment of pulmonary fibrosis in the future (Song et al., 2014; Liu et al., 2017b; Yan et al., 2017).

In this study, a total of 1140 DEmRNAs and 1406 DElncRNAs were identified in the progression of silica-induced pulmonary fibrosis. GO enrichment analysis displayed that the DElncRNAs were mainly enriched in GO-BP including transport vesicle membrane, cell division, cytokine-mediated signaling pathway, and inflammatory response; molecular functions consisting of damaged DNA binding, protein binding, protein C-terminus binding, etc.; in addition, the DElncRNAs were mostly located at cytoplasm, extracellular vesicular exosome, and mitochondrial matrix. Furthermore, loss-of-function assays demonstrate that knockdown of AK131029 significantly decreased cell proliferation and migration of human lung fibroblast cells. The proliferation of pulmonary fibroblasts and a large number of ECM depositions are the basis of the end stage of pulmonary fibrosis. It has been proved that lncRNA plays an important role in the process of inflammation by regulating the expression of inflammatory-related factors.

The study of lncRNA in pulmonary fibrosis is still in the initial stage, and many mechanisms are still unclear, such as how competitive endogenous binding mechanism plays a role and whether lncRNA can participate in the regulation of pulmonary fibrosis by endoplasmic reticulum pathway. How lncRNA regulates related pathways and genes before, during, and after transcription. These questions will guide us to continue the research of lncRNA and further explore its role in pulmonary fibrosis.

Availability of Data and Materials

All data generated and analyzed during this study are included in this published article and its additional files.

Footnotes

Disclosure Statement

No competing financial interests exist.

Funding Information

This study was supported by scientific research project of health and family planning in Sichuan Province (No. 18PJ588).

Supplementary Material

Supplementary Table S1

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