INTRODUCTION
Inflammation is a part of the complex biological response of body tissues to detrimental stimuli, such as microbial infection, damaged cells, and irritants (1–3) (2, 4–6) (2–4, 7)
Macrophages are vital to the development of inflammation and regulation of immune responses (2–4, 7) (8, 9) (10, 11) (11) (12) (13) (14)
lncRNAs are most commonly defined as nonprotein coding transcripts of more than 200 nucleotides that structurally resemble mRNAs and were defined as “transcriptional noise” and nonfunctional for a long time (15) (16) (17) (18) (19) (20) (21) (22) (17, 23) (24)
Although the role of lncRNAs in inflammation and immune responses is attracting more attention, there is still a lack of systematic studies on the regulation of mRNAs by lncRNAs in LPS-induced inflammatory models. In this study, we performed whole transcriptome analysis (RNA-seq ) to characterize the expression profiles of mRNAs and lncRNAs in LPS-induced macrophage inflammatory response. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to predict the biological functions and potential signaling pathways enriched by the differentially expressed genes, followed by coexpression network analysis of mRNAs and lncRNAs, to explore the potential roles of novel lncRNAs in inflammation and immune responses.
MATERIALS AND METHODS
Cell culture and LPS treatment
RAW264.7 cells were obtained from American Type Culture Collection (ATCC, Manassas, Va) cultured in Dulbecco's modified Eagle's medium (DMEM) (Cat# C11995500BT; Gibco) containing 10% fetal bovine serum (FBS) (Cat# 1795588; Gibco). For all experiments, the cells were grown to 80% to 90% confluence and were subjected to no more than 10 passages. The cells were maintained in a humidified incubator with 95% air and 5% CO2 at 37°C. Medium containing appropriate agents was replaced every other day. After serum starvation for 2 h, the cells were treated with or without 100 ng/mL LPS (Cat# L2630; Sigma) for 6 h.
Cytokine assay
Quantification of TNF-α and IL-6 was performed with the LiquiChip system (Qiagen, Hilden, Germany) (5, 25, 26)
Total RNA extraction
Total RNA was extracted using TriPure Isolation Reagent (Cat# A244914; Roche) according to the manufacturer's instructions. Briefly, 200 μL of chloroform was added to the lysis mixture, and the tubes were inverted gently for 5 min. The mixture was centrifuged at 12,000 × g for 15 min at 4°C, and the clear upper solution was transferred to a new tube, and an equal volume isopropanol was added to the tube. The tubes were inverted before incubation on ice for 5 min. The lysis mixture was centrifuged at 12,000 × g for 10 min at 4°C, and the supernatant was discarded. Then, 75% precooled ethanol was added to the RNA pellet for gentle washing. After centrifuging as above for 5 min, the ethanol was removed. The RNA pellets were dried at room temperature for 5 to 10 min before reconstitution in 20 μL RNase-free pure water. Total RNA concentration and purity of samples were assessed using NanoDrop spectrophotometer (Thermo Fisher Scientific, Wilmington, Del).
cDNA library preparation for RNA-seq
For RNA-seq , RNA libraries were made from each sample using the NEBNext Ultra II RNA Library Prep Kit for Illumina (Cat# E7770; New England Biolabs). The first step in the workflow involved the removal of ribosomal RNA using the Ribo-Zero rRNA Removal Kit (Cat# MRZG12324; Illumina). After purification, total RNA was fragmented into small pieces using fragmentation buffer. The cleaved RNA fragments were copied into first-strand cDNA using reverse transcriptase and 6 bp random hexamers, followed by second-strand cDNA synthesis using dNTPs, DNA polymerase I, and RNase H. The synthetic cDNA fragments were then processed through an end-repair reaction by purification, terminal repair, and addition of a single “A” base, followed by ligation of the adapters. The products of these reactions were then purified and enriched by PCR to generate the final cDNA library. Then, the cDNA fragments were sequenced at Huayin Medical Laboratory Center, Guangzhou, China, by using the Illumina 2000 system (Illumina Inc., San Diego, Calif). The quality of sequencing data was shown in Table S1 (see https://links.lww.com/SHK/A754
Quantitative real-time PCR
Pure RNA was treated with RNase-free DNase (Cat# M6101; Promega) and reverse transcribed using a ReverTra Ace qPCR RT kit (Cat# FSQ-101; Toyobo, Japan) in a 10 μL-reaction volume according to manufacturer's instructions. Then, quantification of transcripts was performed by quantitative real-time PCR (qRT-PCR) analysis using SYBR qPCR Mix (Cat# QPK-201; Toyobo, Japan) on a 7,500 Fast Real-time PCR System (Applied Biosystems, Foster City, Calif). Briefly, 50 μL qPCR volume contained 25 μL SYBR Green PCR Master Mix, 1 μmol/L primers and 12.5 μL of cDNA template. The qPCR assay was performed with the following amplification program: 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. Primers for the amplification of the interested lncRNAs (MSTRG.12080.2, MSTRG.4418.1, MSTRG.3904.14, and MSTRG.38272.1) and mRNAs (Cd40, Traf1, Slc43a2, and Ccnd1) were purchased from Beijing Genomics Institute (BGI) (Table S2, https://links.lww.com/SHK/A754 −△△CT method. The experiments were performed in biological triplicate.
GO annotations and KEGG pathways enrichment analysis
GO annotation and pathway analysis were used to explore the roles of the differentially expressed mRNAs as previously described (27) http://www.geneontology.org http://www.genome.jp/kegg/
Construction of coexpression network
To identify interactions among the differentially expressed lncRNAs and mRNAs, we constructed a coexpression network mainly based on a correlation analysis of the differentially expressed lncRNAs and mRNAs (28)
Statistical analysis
The data were represented as the mean ± standard error of the mean (SEM) and were analyzed using the statistical software SPSS (v22.0). Student t test was performed for comparison between two groups. Differences with P < 0.05 were considered statistically significant.
For RNA-seq results, fold change (FC) and false discovery rate (FDR) were used to analyze the statistical significance. For the expression of mRNAs and lncRNAs, FC >2 or <0.5 and FDR <0.05 were considered significant, whereas FC >4 or <0.25 and FDR <0.01 were considered very significant.
RESULTS
LPS-induced cytokine expression in macrophages
To evaluate the cell model of inflammatory response, we detected cytokine expression in RAW264.7 cells stimulated with LPS for 0, 3, 6, or 9 h. We found that LPS stimulation significantly increased the secretion of the proinflammatory cytokines TNF-α (Fig. 1 A) and IL-6 (Fig. 1 B). In this cell model, proinflammatory TNF-α reached peak levels at 6 h after LPS treatment (Fig. 1 A), whereas IL-6 reached a high level of expression at 6 h after LPS treatment, and the level increased even further after LPS treatment for 9 h (Fig. 1 B).
Fig. 1: Protein expression levels of cytokines in RAW264.7 cells stimulated with LPS.
mRNA and lncRNA expression profiles in macrophages treated with LPS
To gain a comprehensive understanding of the underlying mechanisms, we performed RNA-seq experiments to determine how LPS stimulation alters the transcriptome of RAW264.7 cells. Clustering analysis was used to assess the relationship among mRNAs in RAW264.7 cells treated with or without LPS (Fig. 2 A). We found that there were at least 8 to 9 mRNA expression patterns in LPS-treated RAW264.7 cells. Similarly, the heat map also revealed that there were more than 7 expression patterns of lncRNAs in RAW264.7 cells treated with LPS as shown by color saturation in Figure 2 B.
Fig. 2: Heat map and hierarchical clustering of the differentially expressed mRNAs (A) and lncRNAs (B) in RAW264.7 cells treated with or without LPS.
To further illustrate the difference in mRNA expression between RAW264.7 cells treated with LPS and control cells, we constructed a volcano plot. As shown in Figure 3 A, 1,187 mRNAs were significantly dysregulated in the LPS-treated RAW264.7 cells, in which 737 mRNAs were upregulated, whereas 450 mRNAs were downregulated, with the standard of FC greater than 4.0 or less than 0.25 and FDR less than 0.01. The top 50 of the most up- and downregulated mRNAs are shown in Table S3 and Table S4 (see https://links.lww.com/SHK/A754 2 (FC) of 13.6 and −8.2, respectively.
Fig. 3: Volcano plots for differentially expressed mRNAs and lncRNAs in RAW264.7 cells treated with or without LPS.
Similarly, we identified 325 lncRNAs that were significantly dysregulated in the LPS-treated RAW264.7 cells, among which 248 were upregulated, whereas 77 were downregulated, with the standard of FC greater than 4.0 or less than 0.25 and FDR less than 0.01. The top 50 of the most up- and downregulated lncRNAs are shown in Table S5 and Table S6 (see https://links.lww.com/SHK/A754 2 (FC) of 12.9, whereas MSTRG.3031.1 was the most downregulated, with a log2 (FC) of −5.9. The variation in lncRNA expression between the LPS-treated RAW264.7 cells and control cells is shown in the form of a volcano plot (Fig. 3 B).
Validation of the RNA-seq data with qRT-PCR
A total of four dysregulated mRNAs and four lncRNAs were randomly selected for validation of the RNA-seq results using quantitative real-time PCR (qRT-PCR). Consistent with the sequencing data, the qRT-PCR results demonstrated that Cd40 and Traf1 mRNA levels were significantly increased with corresponding fold changes (Fig. 4 A), whereas Slc43a2 and Ccnd1 were confirmed to be prominently downregulated in RAW264.7 cells treated with LPS (Fig. 4 B). Similarly, the lncRNAs MSTRG.12080.2 and MSTRG.4418.1 were correspondingly upregulated (Fig. 4 C), whereas MSTRG.3904.14 and MSTRG.38272.1 were significantly downregulated in RAW264.7 cells treated with LPS (Fig. 4 D).
Fig. 4: Validation of the differential expression of mRNAs and lncRNAs by quantitative real-time PCR (qRT-PCR).
GO enrichment analysis
To predict the functions of the mRNAs induced by LPS, we used GO enrichment analysis as previously described (29) Fig. 5 A).
Fig. 5: Top 30 GO terms for differentially expressed mRNAs and dysregulated lncRNA-targeted genes in RAW264.7 cells treated with LPS.
Based on the same method, we predicted the functions of the lncRNAs induced by LPS. Similarly, we conducted a functional enrichment analysis of the mRNAs targeted by the differentially expressed lncRNAs. The enriched functional terms were used to predict the functions for each given lncRNA. As shown in Figure 5 B, GO analysis indicated that the most enriched GO terms associated with the mRNAs targeted by the lncRNAs were immune response (GO.0006955, biological process), extracellular region (GO.0005576, cellular component), and chemokine receptor binding (GO.0042379, molecular function).
KEGG pathway analysis
We further performed KEGG pathway analysis to investigate the significant pathways enriched by the differentially expressed mRNAs. The top 50 pathways enriched by the differential mRNAs in RAW264.7 cells are shown in Figure 6 A. The results demonstrated that the differential mRNAs were significantly enriched in immune- and inflammation-related signaling pathways, such as Herpes simplex infection, cytokine–cytokine receptor interaction, the TNF signaling pathway, the PI3K-Akt signaling pathway, the MAPK signaling pathway, the NF-κB signaling pathway, and the JAK-STAT signaling pathway.
Fig. 6: KEGG pathway analysis.
To elucidate the significant pathways enriched by the dysregulated lncRNA-targeted mRNAs in RAW264.7 cells, we also performed KEGG pathway analysis. The top 50 KEGG pathways enriched by the mRNAs targeted by these lncRNA are listed in Figure 6 B. Interestingly, we found that the lncRNA-targeted mRNAs were also involved in the regulation of the signaling pathways mainly related to immune and inflammation, such as cytokine–cytokine receptor interaction, Herpes simplex infection, the MAPK signaling pathway, the NF-κB signaling pathway, and the TNF signaling pathway.
lncRNA-mRNA network analysis
With the discovery that the most enriched GO terms for both LPS-induced mRNAs and lncRNA-associated mRNAs were in the same category of biological processes, we established a coexpression network based on a correlation analysis of the mRNAs and lncRNAs involved in the immune response.
As shown in Figure 7 , the overall coexpression network profile consisted of 93 network nodes and 145 connections among 70 differentially expressed mRNAs and 23 differential lncRNAs. There were 13 negative and 132 positive interactions within the network. Moreover, our data show that one lncRNA was correlated with 1 to 14 mRNAs, whereas 1 mRNA was correlated with 1 to 5 lncRNAs. In total, 10 subnetworks represented different regulatory models of mRNAs targeted by lncRNAs (Fig. 7 ).
Fig. 7: lncRNA-mRNA network analysis.
DISCUSSION
The expression profiles of mRNAs and lncRNAs in macrophages may provide new insights for our understanding of the development of inflammation. Previous studies have mainly focused on the study of LPS-regulated protein-coding genes while ignoring lncRNA functions. To explore the potential roles of lncRNAs in LPS-induced inflammation, we constructed a comprehensive bioinformatics pipeline to use RNA-seq data to analyze the relationship between dysregulated mRNAs and lncRNAs in RAW264.7 cells challenged with LPS.
In the present study, using high-throughput sequencing techniques, we identified 325 lncRNAs and 1,187 mRNAs that were significantly dysregulated in LPS-treated RAW264.7 cells compared with controls (FC >4.0 or <0.25, FDR <0.01). Interestingly, most of the 325 dysregulated lncRNAs were novel, and only 27 were identified in NONCODE databases (http://www.noncode.org/ RNA-seq findings were further confirmed by qRT-PCR and laid the foundation for further studies on the regulatory mechanisms between mRNAs by lncRNAs in macrophage inflammatory responses.
To understand the changes in transcriptional profiles, we performed GO and KEGG pathway enrichment analyses with the dysregulated mRNAs and found that all of the mRNAs are important for the development of inflammation and immune response. Specifically, for the category of biological processes, these mRNAs are mainly involved in the immune response, defense response, regulation of cytokine production, and innate immune response (Fig. 5 A), suggesting that all of the responses have the capability to enhance self-protection through initiating inflammatory and immune responses.
KEGG pathway analysis demonstrated that most of the differentially expressed mRNAs were enriched in signaling pathways associated with immune and inflammatory responses. For example, the NF-κB signaling pathway, which lies at the heart of the immune reaction as a key regulator of inflammatory gene transcription, is involved in attenuating the overproduction of inflammatory factors and inflammation resolution (30, 31)
To evaluate the potential roles of the dysregulated lncRNAs identified in this study, we performed GO and KEGG pathway analyses by using the lncRNA-associated mRNAs. Intriguingly, these mRNAs targeted by lncRNAs were also significantly enriched in inflammation in the category of biological processes, including immune response, regulation of natural killer cell chemotaxis, regulation of lymphocyte chemotaxis, monocyte chemotaxis, and inflammatory response. LPS-induced inflammation model represents the response to infection with gram-negative bacteria. Migration of immune cells to infection sites is pivotal for the human body to sense pathogen invasion. Our findings of GO enrichment in migration and chemotaxis suggest that lncRNAs serve as regulators in the recruitment of immune cells in response to LPS.
Consistent with the GO results, KEGG pathway analysis demonstrated that lncRNAs may make important contributions to inflammatory and immune responses through the regulation of gene expression by activating inflammatory signaling pathways. After sensing infection in tissues, the immune system initiates a biological process to kill and remove the invading pathogens. It is well established that phagocytosis plays an important role in pathogen clearance. Our KEGG pathway analysis results showing enrichment in the phagosome pathway of the lncRNA-associated genes indicate that lncRNAs participate in the defensive response of the immune system. These data provide new clues for further studies on the regulation of inflammatory and immune responses mediated by lncRNAs.
In the present study, a lncRNA-mRNA network for the immune response was constructed with the 106 dysregulated mRNAs and 24 dysregulated lncRNAs according to the principle of coexpression. The immune response network, in which 93 network nodes and 145 connections are included, is composed of 10 subnetworks that represent specific or unique mechanisms of mRNA regulation by lncRNAs with different network structures. The subnetworks of lncRNA-mRNA regulation may be very complicated, such as multiple lncRNAs targeting various mRNAs with either positive or negative interactions (Fig. 7 , A–F) or extremely simple, i.e., one mRNA is associated with only one lncRNA (Fig. 7 , I and J), and thus provide comprehensive clues for understanding various mechanisms of mRNA regulation by lncRNAs.
Many protein-coding genes perform their functions through protein–protein interactions that are highly conserved during the process of evolution. For example, we noted that the lncRNA MSTRG.52970.1 might target nine mRNAs that were characterized to participate in inflammatory and immune responses. Further analysis of these nine gene products with the STRING software revealed that nucleotide-binding oligomerization domain-containing 2 (Nod2), DEAD (Asp-Glu-Ala-Asp) box polypeptide 58 (Ddx58), DEXH (Asp-Glu-X-His) box polypeptide 58 (Dhx58), and interferon-induced protein with tetratricopeptide repeats 3 (Ifit3) form the core of the interaction network (Fig. S1, https://links.lww.com/SHK/A754 (32) (33) (34) (35)
Previous studies on the regulation of mRNAs by lncRNAs were mainly performed by gene microarray, which is limited by narrow dynamic range and inability to identify novel features such as splicing isoforms and fusion transcripts (27) RNA-seq technique to discover previously complexities in the transcriptome that were unaddressable with microarrays, such as allele-specific expression and novel promoters and isoforms. However, some limitations might exist in this study. First, the generation of all data was lack of multiple time points to reflect dynamic changes in a LPS-induced macrophage model. Second, the present study constructed the coexpression network between mRNAs and lncRNAs, but without experimental validation. It is significant for future studies to overcome these limitations.
In conclusion, our study used the RNA-seq technology to explore the expression profiles of mRNAs and lncRNAs and the regulatory mechanisms of gene expression by lncRNAs in LPS-induced macrophage inflammatory response. We identified 325 lncRNAs and 1,187 mRNAs with very significant changes in LPS-treated macrophages. GO and KEGG analyses revealed that the aberrantly expressed mRNAs and lncRNAs mainly participate in the inflammatory response and immune reaction. Importantly, the coexpression networks constructed with the differential mRNAs and lncRNAs shed new light on the mechanisms of mRNA regulation by lncRNAs, which provides the chance to elucidate the functions of novel lncRNAs.
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