Open Access

Comprehensive analysis of mRNAs, lncRNAs and circRNAs in the early phase of microglial activation

  • Authors:
    • Jiagen Wen
    • Yujie Liu
    • Zhen Zhan
    • Shiqing Chen
    • Bingfeng Hu
    • Jinfang Ge
    • Qiliang Xie
  • View Affiliations

  • Published online on: October 20, 2021
  • Article Number: 1460
  • Copyright: © Wen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Sepsis‑associated encephalopathy (SAE) is a common complication of sepsis that may seriously affect the prognosis and quality of life of patients with sepsis. Microglial activation is vital to the neuroinflammation and the pathology of SAE. In the present study, in vitro cultured BV‑2 microglial cells stimulated with lipopolysaccharide (LPS) were employed as a model of microglia activation. The altered profiles of long noncoding (lnc)RNAs, circular (circ)RNAs and mRNAs in BV‑2 cells after 4 h of LPS exposure were arrayed by using the Agilent competing endogenous (ce)RNA Microarray Chip. Using fold change >2 and P<0.05 as the cutoff criteria, 1,135 mRNAs and 2,488 lncRNAs were determined to be upregulated and 630 mRNAs and 744 lncRNAs to be downregulated. The number of differentially expressed circRNAs was lower, with 140 upregulated and 123 downregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of DE mRNAs suggested that inflammatory responses, as well as lipid metabolism, were involved in microglial activation. Furthermore, analyses of ceRNA networks of the lncRNA‑miRNA‑mRNA or circRNA‑miRNA‑mRNA interrelations were performed. The present study revealed a multitude of novel candidate mRNAs, lncRNAs and circRNAs involved in microglial activation, which may improve the current knowledge on neuroinflammation and provide potential therapeutic targets for SAE.


Sepsis-associated encephalopathy (SAE) is a common complication of sepsis that is prevalent in hospitalized patients (sepsis happens in ~2% of all hospitalized patients) (1). Furthermore, it has been estimated that SAE affects 9-71% of those patients with severe sepsis (2). The major manifestations of SAE include emotional abnormalities, impaired memory and cognitive impairment. Even after the acute phase of sepsis, these mental manifestations persist, severely affecting the prognosis and quality of life of patients (3). To date, numerous biological processes have been reported to be involved in the pathogenesis of SAE, including inflammatory response, collapse of the blood-brain barrier (BBB), cerebral ischemia and alterations in neurotransmitters. Although the specific mechanism has remained to be established, uncontrolled neuroinflammation is probably the leading cause of SAE (4).

During the early phase of sepsis, brain microglial cells that function as peripheral macrophages are activated and transformed to the M1 cell type (5,6). Thereafter, A great number of inflammatory cytokines, including interleukin-1 (IL-1), IL-6 and tumor necrosis factor-α (TNF-α), are secreted into the brain and lead to uncontrolled neuroinflammation (7). In addition, the activated astrocytes are also capable of releasing multiple types of inflammatory mediators, further facilitating the development of neuroinflammation (8). Overactivation of microglia and other immune cells may cause damage to neuron and endothelial cells, and furthermore deteriorate the BBB and enhance the release of reactive oxygen species, which seriously disturb brain function (9). Recent studies have verified that the suppression of microglial activation and central inflammatory responses may partially reverse the cognitive impairment caused by SAE (7,10). However, there is still a lack of effective treatment for SAE in the clinic, probably due to the detailed pathology of SAE having remained to be fully elucidated.

Non-coding (nc) RNAs, including microRNA (miRNA/miR), long ncRNA (lncRNA) and circular RNA (circRNA), have pivotal roles in the regulation of cell proliferation (11), autophagy (12) and apoptosis (13), as well as inflammatory responses (14), ischemia-reperfusion injury (15) and tumorigenesis (16). Recent studies have indicated that lncRNAs and circRNAs participate in the process of microglial activation (17-19). However, the orchestra of lncRNAs and circRNAs in microglial activation has remained to be fully determined, particularly during the early phase of SAE. In the present study, high-throughput microarray techniques were used to profile the expression of mRNAs, lncRNAs and circRNAs in BV-2 microglial cells after a short stimulation with lipopolysaccharide (LPS). Through the analysis of differentially expressed (DE) RNAs, promising novel signals involving non-coding RNAs were provided in the present study to provide clues for future investigation of microglial activation.

Materials and methods

Cell lines

Murine BV-2 microglial cells were obtained from the National Infrastructure of Cell Line Resource. The cells were cultured in high-glucose Dulbecco's modified Eagle's medium (DMEM; Thermo Fisher Scientific, Inc.) supplemented with 10% fetal bovine serum (FBS; Biological Industries, Inc.), 1 mM pyruvate, 100 U/ml penicillin and 100 µg/ml streptomycin in a humidified atmosphere with 5% CO2 at 37˚C.

Cell treatments

BV-2 microglial cells were seeded in a 96-well plate at a density of 50,000 cells or in a 6-well plate at a density of 500,000 cells in each well 24 h prior to the experiment. During the experiment, the culture medium was changed to high-glucose DMEM supplemented with 0.5% FBS, and then LPS was added into each well (final concentration of 1.00 µg/ml) for incubation for 2, 4, 8, 16 or 24 h. In the control wells, the same volume of PBS was added and the cells were cultured for 24 h. A Cell Counting Kit-8 (CCK-8; BestBio) was used to detect the cell viability. CCK-8 stain (10 µl) was added into the 100 µl culture medium and the cells were further cultured for 4 h in the 37˚C incubator prior to the detection of the optical density at the wavelength of 450 nm on a SpectraMax multimode microplate (SpectraMax iD3; Molecular Devices).

The morphology of BV-2 cells with the different incubation times (2, 4, 8, 16 or 24 h) of LPS or incubated with PBS for 24 h in the 6-well plate was examined under an inverted microscope (DMi8; Leica Microsystems) in an independent experiment and the same cells in the 6-well plate were finally collected for RNA extraction and the detection of the expression levels of TNF-α, IL-6 and IL-1β. Furthermore, cells seeded in the 6-well plate were treated with different concentrations of LPS (0, 0.01, 0.10 or 1.00 µg/ml) for 4 h. After washing with PBS, the total RNA in each well was extracted by RNAiso Plus (Takara Bio, Inc.) for detecting the mRNA levels of TNF-α, IL-6 and IL-1β.

For the microarray analysis, cells were seeded in a 35-mm dish at a density of 500,000 cells 24 h prior to the experiment. BV-2 cells were stimulated with 1.00 µg/ml LPS or PBS for 4 h. Finally, the cells were washed with PBS and collected for the subsequent treatment.

RNA extraction and microarray assay

RNAiso Plus (Takara Bio, Inc.) was used to extract total RNA following the manufacturer's protocol. Total RNA was quantified by a NanoDrop® 2000 (Thermo Fisher Scientific, Inc.) and the RNA integrity was assessed using an Agilent Bioanalyzer 2100 (Agilent Technologies, Inc.). The sample labeling, microarray hybridization, washing and scanning were performed based on the manufacturer's standard protocols (Agilent Technologies, Inc.) by OE Biotech. In brief, total RNA was reversely transcribed to double-stranded cDNA (The first strand of cDNA complementary to the RNA was synthesized first and the second strand of cDNA complementary to the first strand was subsequently synthesized). Next, cRNA was synthesized using the double-stranded cDNA as the template and labeled with Cyanine-3-cytidine triphosphate (CTP). The labeled cRNAs were hybridized onto the microarray. After washing, the arrays were scanned by the Agilent Scanner G2505C (Agilent Technologies, Inc). Feature Extraction software (version; Agilent Technologies, Inc.) was used to analyze array images to obtain raw data. The raw data were submitted to the Gene Expression Omnibus (GEO) database (dataset no. GSE171696).

Bioinformatics analysis

DE genes were then identified by performing Student's t-test to calculate the fold change (FC) and P-value. The threshold set for upregulated and downregulated genes was FC >2.0 and P<0.05.

Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis

GO analysis of target genes was performed in the categories of biological process (BP), cellular component (CC) and molecular function (MF) ( KEGG pathway analysis ( was also applied to analyze the key regulatory pathways involved in the activation of BV-2 cells. The upregulated or downregulated RNAs were individually analyzed via an online tool (

CeRNA network analysis

DE lncRNAs/circRNAs that had a significant positive correlation (correlation coefficient >0.9) with DE mRNAs were selected as the targets for the competing endogenous (ce)RNA analysis. Target prediction of miRNAs to the aforementioned DE lncRNAs, circRNAs and mRNAs was performed by using all known mouse miRNAs in the database Mibase22. The ternary regulatory networks of lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA were established using the predicted miRNAs as a bridge and the top 100 ceRNA network, which contained the top 100 links (ranked by the P-value of each link) of lncRNA-miRNA-mRNA or circRNA-miRNA-mRNA, was drawn by using the R (version 3.5.1) software (20).

Reverse transcription-quantitative PCR (RT-qPCR)

Total RNA was extracted from BV-2 cells using RNAiso Plus. For each sample, 1 µg RNA was reverse-transcribed into cDNA using the Vazyme HiScript III 1st Strand cDNA Synthesis Kit with random hexamer (Vazyme Biotech Co. Ltd.) according to the manufacturer's protocols. For qPCR, 5 µl reaction mixture composed of 5 µl Vazyme SYBR qPCR Master Mix (Vazyme Biotech Co. Ltd.), 3 µl 1:9 diluted cDNA and 2 µl of 10 nmol/l primers were incubated in a Bio-Rad 96-well PCR plate. The thermocycling conditions in the RT-qPCR System (CFX Connect; Bio-Rad Laboratories, Inc.) was set to 95˚C for 5 min, followed by 40 cycles of 95˚C for 10 sec and 60˚C for 30 sec. Data analysis was further performed using Bio-Rad CFX Maestro software (Bio-Rad Laboratories, Inc.). The relative expression levels of RNAs were calculated using the 2-ΔΔCq method with β-actin as a reference (21). The primer sequences of lncRNAs, circRNAs and mRNAs were displayed in Tables SI and SII.

Statistical analysis

Values are expressed as the mean ± standard error of the mean. An unpaired Student's t-test for parametric data and the Mann-Whitney U-test for nonparametric data were utilized for comparisons between two groups. GraphPad Prism 5 (GraphPad Software, Inc.) was used for all statistical analyses. Pearson correlation analysis was used to confirm the co-expression of lncRNAs/circRNAs and mRNA with an online tool ( P<0.05 was considered to indicate statistical significance.


Differential expression of mRNAs, lncRNAs and circRNAs in LPS-treated BV-2 cells

Treatment with LPS (1 µg/ml) for 2-24 h had no impact on the viability of BV-2 cells (Fig. S1A and B) and 4 h of treatment with LPS concentration-dependently (0.01, 0.1 and 1 µg/ml) induced the transcription of TNF-α, IL-6 and IL-1β (Fig. S1C). The expression levels of TNF-α, IL-6 and IL-1β mRNAs changed after treatment with LPS for different durations (Fig. S1D). However, the transcription of these inflammatory factor genes was already induced after 4 h of treatment. Therefore, the LPS concentration of 1 µg/ml and the incubation time of 4 h was used in the subsequent experiments.

The expression profiles of mRNAs, lncRNA and circRNAs in BV-2 cells were analyzed using microarray technology. A total of 1,135 mRNAs were identified to be upregulated and 630 mRNAs were downregulated. There were 2,488 DE lncRNAs, including 1,744 upregulated and 744 downregulated lncRNAs. Furthermore, 140 circRNAs were upregulated and 123 circRNAs were downregulated. The top 30 DE mRNAs ranked by FC, including both upregulated and downregulated, were present as a heatmap (Fig. 1). Volcano plot analysis was used to assess the variations in mRNA (Fig. 2A), lncRNA (Fig. 2B) and circRNA (Fig. 2C) expression profiles between the two groups.

GO and KEGG analysis of the DE mRNAs

The results of the GO functional enrichment analysis indicated that the upregulated DE mRNAs were involved in 585 BP, 54 CC and 112 MF terms. Furthermore, the downregulated mRNAs were involved in 182 BP, 19 CC and 58 MF terms. The top 10 results with the most significant P-values of the upregulated DE mRNAs in BP and MF were presented in the bar blot (Fig. 3A). GO term analysis of upregulated DE mRNAs revealed that the most enriched GO terms were cytokine activity (MF) and inflammatory response (BP). The upregulated mRNAs in BV-2 cells were most significantly enriched in the TNF signaling pathway, Herpes simplex infection, NOD-like receptor signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction (Fig. 3B). For the downregulated mRNAs, the most enriched GO terms were GTPase activator activity (MF) and negative regulation of transcription by RNA polymerase II (BP), which were shown in Fig. 4A. KEGG pathway analysis of DE mRNAs determined 79 pathways for upregulated mRNAs and 33 pathways for downregulated mRNAs. Furthermore, the pathways most significantly enriched by the downregulated mRNAs were ABC transporters, transcriptional misregulation in cancers, endocrine resistance, osteoclast differentiation and T-cell receptor signaling pathway (Fig. 4B). In addition, pathways associated with lipid metabolism, including bile secretion, regulation of lipolysis in adipocyte and insulin resistance, were among the top 30 pathways enriched by downregulated mRNAs (Fig. 4B).

CeRNA network analysis

Co-expressed lncRNAs/circRNAs and mRNAs were selected to predict the association with miRNAs. In total, >300,000 lncRNA-miRNA-mRNA links were predicted. The top 100 links were chosen according to the P-value of their correlation and were present in the top 100 ceRNA network (Fig. 5). Furthermore, 4,053 circRNA-miRNA-mRNA links were predicted, with the top 100 ceRNA network presented in Fig. 6.

Validation of DE mRNAs, lncRNAs and circRNA

DE mRNAs, lncRNAs and circRNAs from the expression profile with a high FC or in the hub of the ceRNA networks were selected for validation (Fig. 7). RT-qPCR validated that seven selected mRNAs, including TNF receptor (TNFR)-associated factor 1 (Traf1), formyl peptide receptor 2 (Fpr2), POU class 2 homeobox 2 (Pou2f2), suppressor of cytokine signaling 1 (Socs1), agrin (Agrn), laccase domain containing 1 (Lacc1) and Rap guanine nucleotide exchange factor 2 (Rapgef2), were increased after LPS stimulation, and that the mRNA expression of α/β-hydrolase domain containing protein 15 (Abhd15) was decreased. Furthermore, four lncRNAs (ENSMUST00000155949.1, NONMMUT144861.1, ENSMUST00000181460.2 and NONMMUT141017.1) and three circRNAs (CIRCpedia_38964, CIRCpedia_37520 and CIRCpedia_216004) were demonstrated to be upregulated and one lncRNA (ENSMUST00000203956.1) was proven to be downregulated.


Neuroinflammation is proposed to have a central role in the pathogenesis of SAE, which increases the burden on the health system and may cause major damage to patients' quality of life (3,22). Microglial cells, which act as residential macrophages, are activated during the pathological process of neuroinflammation (5,6). The modulation of microglial cell activation has become a promising therapeutic direction in the treatment of SAE, as well as neuropathic pain (10,23). The murine microglial cell line BV-2 is commonly used for study of neuroinflammation and microglial activation (24,25). Henn et al (26) indicated that the cell line has a high transcriptome homology with primary microglial cells and a similar response to inflammatory challenges. Therefore, in the present study, BV-2 cells stimulated with LPS were employed as the model of microglial activation. Microarrays of mRNAs, lncRNAs and circRNAs of stimulated or vehicle-treated BV-2 cells were performed and the data were analyzed.

The results of the present study indicated that the expression profiles of mRNAs and lncRNAs were acutely altered after 4 h of LPS exposure. A total of 1,135 mRNAs were determined to be significantly upregulated and 630 mRNAs were downregulated. The top upregulated mRNAs included IL-6, IL-1β, IL-1α, neutrophil gelatinase-associated lipocalin (Lcn2), C-X-C motif chemokine 2 (Cxcl2), Fpr2, Traf1 and CD69 molecule. Compared with the study from Li et al (17), who prolonged the exposure to LPS to 24 h, there were slight variations in the top changed mRNAs. Traf1, Fpr2 and CD69 were among the top 30 upregulated mRNAs in the early phase of LPS exposure, while IL-6, IL-1β, Lcn2, serum amyloid A-3 protein and Cxcl2 were the top upregulated genes in both the early and late phase. Furthermore, the probes contained in the chip used in the present study were designed to discern the different transcripts of genes. For instance, two transcripts of the IL-6 gene (202 and 203), recorded in the Ensemble Database, were among the top 30 upregulated mRNAs, while IL6-201 was upregulated to a lesser degree (FC=71.15). The different expression of the alternative transcripts of the same gene requires further exploration to elucidate their potential function.

Recently, Sun et al (27) also employed transcriptome sequencing approaches to characterize the effects of LPS on lncRNA and mRNA expression patterns in brain tissue isolated from Sprague Dawley rats. In this study, Sprague Dawley rats were exposed to LPS for 6 or 24 h and a large number of lncRNAs and mRNAs were indicated to be changed. However, no behavioral observation of these rats was made, which is required to diagnose SAE. The upregulated gene regulator of G-protein signaling 14 identified by Sun et al (27) was also verified in the BV-2 cells of the present study. Furthermore, the difference in the lncRNA expression signature between the present study and the study by Sun et al (27) was huge, possibly due to the gap between different species and different cell types.

GO analysis of these early upregulated DE mRNAs in the category BP included inflammatory response, immune system process, response to LPS and response to cytokine, which were all related to inflammation and with a log10 (P-value) >10. This differed from downregulated DE mRNAs, which mainly involved the regulation of transcription, with log10 (P-value) <5. Among the top downregulated coding RNAs, several had the function of gene transcription or cooperation with transcription factor. For instance, MAF bZIP transcription factor B (Mafb), an important transcription factor gene strongly expressed in macrophages, is a member of the Maf transcription factor family and regulates gene transcription via the binding to the Maf recognition element (28). A study demonstrated that Mafb determined the anti-inflammatory properties of macrophages (29). Consistently, in the present study of BV-2 cells, short-time exposure to LPS markedly decreased the expression of Mafb.

In the KEGG analysis of the downregulated mRNAs, it was noted that pathways associated with lipid metabolism were prevalent. Bile secretion, regulation of lipolysis in adipocyte and insulin resistance were among the top 30 terms enriched by downregulated mRNAs. Accumulating evidence has indicated that different metabolic pathways or energy homeostasis are required for programming M1 and M2 macrophages (30). Furthermore, it has been indicated that LPS treatment may induce lipid droplet accumulation and increased cellular triglyceride content in microglia. Lipid metabolism was linked with inflammatory signaling and microglial activation (31). Wang et al (32) indicated that exogenous recombinant human fibroblast growth factor 21, which may suppress lipid accumulation through the activation of adenosine 5'-monophosphate-activated protein kinase (AMPK) (33), ameliorated LPS-induced depressive-like behavior by inhibiting microglial expression of proinflammatory cytokines. In addition, it has been indicated that the activation of AMPK by ENERGI-F704 (a chemical component identified from bamboo shoot extract) ameliorated the LPS-induced inflammation in BV-2 cells (34). Of note, in the present study, Abhd15, which is associated with glucolipid metabolism commonly expressed in adipose tissue, was significantly downregulated in LPS-stimulated BV-2 cells. Furthermore, the expression of Lpin1 that modulates lipid metabolism and glucose homeostasis was reduced by 14-fold after LPS stimulation. The present results further substantiated the association between lipid metabolism and microglial activation. How these lipid-related genes function in the microglial activation and whether the modulation of lipid metabolism is able to impact neuroinflammation remains elusive and further investigation is required.

Although the validated expression levels of Fpr2 and TNF receptor-associated factor 1 (Tarf1) after LPS exposure were not as high as those detected by microarray in the present study, their functions in inflammation signaling were striking (35). Fpr2, a G-protein-coupled receptor, has an important role in host defense and inflammation. Fpr2 inhibition is able to markedly reduce the activation of transforming growth factor beta-activated kinase 1 induced by LPS and decrease inflammation and oxidative stress in LPS-induced lung injury (36). Mounting evidence has indicated that Fpr2 participates in the pathogenesis of sepsis (37,38). Tarf1 is a signaling adaptor first identified as part of the TNFR2 signaling complex (39). Tarf1 has a key role in pro-survival signaling downstream of TNFR superfamily members, such as TNFR2 and CD40. Moreover, it has been demonstrated that Tarf1 negatively regulates Toll-like receptor and Nod-like receptor signaling, through sequestering the linear ubiquitin assembly complex (40). In a study of acute lung injury, the interferon of Tarf1 markedly attenuated LPS-induced mortality, alleviated lung injury and markedly reduced inflammation (41).

In 2011, Salmena et al (42) proposed the ceRNA hypothesis, which aimed to explain how RNAs ‘talk’ to each other to establish interactions to modify functional genetic information, and function in pathological conditions. Through miRNA response elements, miRNAs recognize and bind to regions with partial complementary sequences on target RNA transcripts, resulting in the repression of the target gene. Thereby, miRNAs mediate regulatory crosstalk between various components of the transcriptome. On the contrary, lncRNAs or circRNAs competitively bind and absorb miRNAs via specific nucleotide elements, acting like a ‘sponge’, and release the coding mRNAs from repression. Based on the expression profiles and the ceRNA analysis, several lncRNAs, circRNAs and mRNAs were selected for validation in the present study. For mRNAs, Traf1, Fpr2, Pou2f2, Socs1, Agrn, Lacc1, Rapgef and Birc3 were demonstrated to be upregulated and Abhd15 was downregulated. LncRNA ENSMUST00000155949.1, also named 6530402F18Rik, was demonstrated to have high transcript levels. The gene of 6530402F18Rik is located on chromosome 2 and does not encode any protein. In the ceRNA network analysis, it was predicted that 6530402F18Rik is able to combine with miR-762 and miR-7648-3p, with the predicted scores of 750 and 735, respectively. Both miR-762 and miR-7648-3p were predicted to target Pou2f2, with a predicted score >600. Socs1 was also predicted to be associated with 6530402F18Rik through miR-762, as well as miR-7648-3p, with high scores. Unlike 6530402F18Rik, ENSMUST00000203956.1 (2010008C14Rik) was demonstrated to be reduced in expression. In the prediction, 2010008C14Rik was able to ‘sponge’ miR-149-3p. Thus, it is probable that the downregulated 2010008C14Rik released more free miR-149-3p that had a predicted domain for Abhd15, leading to the decreased expression of Abhd15. However, functional research on 6530402F18Rik and 2010008C14Rik is scarce. LncRNAs functioning in the activation of BV-2 cells and modulating BV-2 cell activation via ‘sponging’ are worth further investigation.

The present study detected thousands of DE lncRNAs and mRNAs, while the number of significant DE circRNAs was lower, with only 140 circRNAs determined to be upregulated and 123 circRNAs were downregulated. The circRNAs with a high FC were not all included in the circRNA-miRNA-mRNA network. For instance, CIRCpedia_216004, the top increased circRNA, was determined to be highly expressed, but was not predicted in the ceRNA network. For CIRCpedia_37520, there was only one predicted association with Rapgef2, which was also validated in the present study, likely via miR-5620-3p. Furthermore, it was predicted that CIRCpedia_38964 was able to sequester miRNA and lead to the increased translation of Agrn and Lacc1 that were indicated to moderately increase. It remains elusive whether these circRNAs are only biomarkers of inflammation or have other potential functions.

Despite the results obtained, the present study has certain limitations. First, the pathogenesis of SAE involves not only microglial cells, but also neurons, endothelial cells and other cell types. Furthermore, although LPS is the primary inflammatory substance in sepsis, other components of microorganisms and the substances of damaged host cells are capable of inducing inflammatory responses. The in vitro microglial cells stimulated by LPS cannot completely imitate the pathogenesis of SAE. Furthermore, the genes and links validated in the present study require further validation in human microglial cells and in vivo SAE models and their functions in inflammation should also be explored in the future.

In conclusion, the present study revealed the ceRNA networks involved the interrelation of lncRNA-miRNA-mRNA or circRNA-miRNA-mRNA in the early phase of the microglial activation. Furthermore, most of the DE genes were mainly involved in inflammatory responses, immune responses and lipid metabolism. Although more in vivo and in vitro functional studies are required, the present study may improve the present knowledge on neuroinflammation and provide potential therapeutic targets for SAE.

Supplementary Material

Stimulation of LPS on BV-2 cells. (A) The morphology of BV-2 cells after 0, 2, 4, 8, 16, 24 incubation of 1 μg/ml LPS (magnification, x200). (B) Viability of BV-2 cells after 0, 2, 4, 8, 16 and 24 h of incubation with 1.00 μg/ml LPS determined using the Cell Counting Kit-8. The ordinate value is the OD at 450 nm in each well. (C) mRNA expression levels of TNF-α, IL-6 and IL-1β after 4 h of incubation with 0.01, 0.10 or 1.00 μg/ml LPS. (D) Changes in mRNA expression levels of TNF-α, IL-6 and IL-1β after incubation with LPS for different durations (0, 2, 4, 8, 16, 24 h). **P<0.010, compared with the group at 0 h (two-tailed Student's t-test); ##P<0.05, compared with the group of 0.10 μg/ml (two-tailed Student's t-test). LPS, lipopolysac-charide; OD, optical density; RQ, relative quantity; (relative quantity were calculated according to the standard 2-ΔΔCq method using the Cq of β-actin to normalize).
Primers used for the validation of mRNAs.
Primers used for the validation of lncRNAs and circRNAs.


The authors acknowledge the support of Shanghai OE Biomedical Technology Co., Ltd. for the microarray of RNAs.


This work was supported by the Natural Science Foundation of Anhui Province (grant no. 1808085MH308) and the School Research Fund Project of Anhui Medical University (grant no. 2019xkj178).

Availability of data and materials

The datasets of the microarray used in the present study are available from the GEO database under accession no. GSE171696 ( Other datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Authors' contributions

JW and QX conceived and designed the experiments. YL and SC contributed to the cell culture and treatments. JW, JG, ZZ and BH analyzed the data and drafted the manuscript. JG and QX contributed to manuscript editing. JW, JG and QX confirm the authenticity of the raw data. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.



Huang M, Cai SL and Su JQ: The pathogenesis of sepsis and potential therapeutic targets. Int J Mol Sci. 20(5376)2019.PubMed/NCBI View Article : Google Scholar


Sprung CL, Peduzzi PN, Shatney CH, Schein RM, Wilson MF, Sheagren JN and Hinshaw LB: Impact of encephalopathy on mortality in the sepsis syndrome. The veterans administration systemic sepsis cooperative study group. Crit Care Med. 18:801–806. 1990.PubMed/NCBI View Article : Google Scholar


Iwashyna TJ, Ely EW, Smith DM and Langa KM: Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 304:1787–1794. 2010.PubMed/NCBI View Article : Google Scholar


Ren C, Yao RQ, Zhang H, Feng YW and Yao YM: Sepsis-associated encephalopathy: A vicious cycle of immunosuppression. J Neuroinflammation. 17(14)2020.PubMed/NCBI View Article : Google Scholar


Block ML, Zecca L and Hong JS: Microglia-mediated neurotoxicity: Uncovering the molecular mechanisms. Nat Rev Neurosci. 8:57–69. 2007.PubMed/NCBI View Article : Google Scholar


Lee E, Eo JC, Lee C and Yu JW: Distinct features of brain-resident macrophages: Microglia and non-parenchymal brain macrophages. Mol Cells. 44:281–291. 2021.PubMed/NCBI View Article : Google Scholar


Li Y, Yin L, Fan Z, Su B, Chen Y, Ma Y, Zhong Y, Hou W, Fang Z and Zhang X: Microglia: A potential therapeutic target for sepsis-associated encephalopathy and sepsis-associated chronic pain. Front Pharmacol. 11(600421)2020.PubMed/NCBI View Article : Google Scholar


Shulyatnikova T and Verkhratsky A: Astroglia in sepsis associated encephalopathy. Neurochem Res. 45:83–99. 2020.PubMed/NCBI View Article : Google Scholar


Catarina AV, Branchini G, Bettoni L, De Oliveira JR and Nunes FB: Sepsis-associated encephalopathy: From pathophysiology to progress in experimental studies. Mol Neurobiol. 58:2770–2779. 2021.PubMed/NCBI View Article : Google Scholar


Ye B, Tao T, Zhao A, Wen L, He X, Liu Y, Fu Q, Mi W and Lou J: Blockade of IL-17A/IL-17R pathway protected mice from sepsis-associated encephalopathy by inhibition of microglia activation. Mediators Inflamm. 2019(8461725)2019.PubMed/NCBI View Article : Google Scholar


Fatica A and Bozzoni I: Long non-coding RNAs: New players in cell differentiation and development. Nat Rev Genet. 15:7–21. 2014.PubMed/NCBI View Article : Google Scholar


Yao H, Han B, Zhang Y, Shen L and Huang R: Non-coding RNAs and autophagy. Adv Exp Med Biol. 1206:199–220. 2019.PubMed/NCBI View Article : Google Scholar


Su Z, Yang Z, Xu Y, Chen Y and Yu Q: MicroRNAs in apoptosis, autophagy and necroptosis. Oncotarget. 6:8474–8490. 2015.PubMed/NCBI View Article : Google Scholar


Wang J, Yan S, Yang J, Lu H, Xu D and Wang Z: Non-coding RNAs in rheumatoid arthritis: From bench to bedside. Front Immunol. 10(3129)2020.PubMed/NCBI View Article : Google Scholar


Ghafouri-Fard S, Shoorei H and Taheri M: Non-coding RNAs participate in the ischemia-reperfusion injury. Biomed Pharmacother. 129(110419)2020.PubMed/NCBI View Article : Google Scholar


Kondo Y, Shinjo K and Katsushima K: Long non-coding RNAs as an epigenetic regulator in human cancers. Cancer Sci. 108:1927–1933. 2017.PubMed/NCBI View Article : Google Scholar


Li Y, Li Q, Wang C, Li S and Yu L: Long noncoding RNA expression profile in BV2 microglial cells exposed to lipopolysaccharide. Biomed Res Int. 2019(5387407)2019.PubMed/NCBI View Article : Google Scholar


Xiaoying G, Guo M, Jie L, Yanmei Z, Ying C, Shengjie S, Haiyan G, Feixiang S, Sihua Q and Jiahang S: CircHivep2 contributes to microglia activation and inflammation via miR-181a-5p/SOCS2 signalling in mice with kainic acid-induced epileptic seizures. J Cell Mol Med. 24:12980–12993. 2020.PubMed/NCBI View Article : Google Scholar


Wu T, Li Y, Liang X, Liu X and Tang M: Identification of potential circRNA-miRNA-mRNA regulatory networks in response to graphene quantum dots in microglia by microarray analysis. Ecotoxicol Environ Saf. 208(111672)2021.PubMed/NCBI View Article : Google Scholar


R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria, 2018. URL


Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001.PubMed/NCBI View Article : Google Scholar


Widmann CN and Heneka MT: Long-term cerebral consequences of sepsis. Lancet Neurol. 13:630–636. 2014.PubMed/NCBI View Article : Google Scholar


Huang CT, Lue JH, Cheng TH and Tsai YJ: Glycemic control with insulin attenuates sepsis-associated encephalopathy by inhibiting glial activation via the suppression of the nuclear factor kappa B and mitogen-activated protein kinase signaling pathways in septic rats. Brain Res. 1738(146822)2020.PubMed/NCBI View Article : Google Scholar


Kimura T, Toriuchi K, Kakita H, Tamura T, Takeshita S, Yamada Y and Aoyama M: Hypothermia attenuates neuronal damage via inhibition of microglial activation, including suppression of microglial cytokine production and phagocytosis. Cell Mol Neurobiol. 41:459–468. 2021.PubMed/NCBI View Article : Google Scholar


Weis GCC, Assmann CE, Mostardeiro VB, Alves AO, da Rosa JR, Pillat MM, de Andrade CM, Schetinger MRC, Morsch VMM, da Cruz IBM and Costabeber IH: Chlorpyrifos pesticide promotes oxidative stress and increases inflammatory states in BV-2 microglial cells: A role in neuroinflammation. Chemosphere. 278(130417)2021.PubMed/NCBI View Article : Google Scholar


Henn A, Lund S, Hedtjärn M, Schrattenholz A, Pörzgen P and Leist M: The suitability of BV2 cells as alternative model system for primary microglia cultures or for animal experiments examining brain inflammation. ALTEX. 26:83–94. 2009.PubMed/NCBI View Article : Google Scholar


Sun W, Pei L and Liang Z: mRNA and long non-coding RNA expression profiles in rats reveal inflammatory features in sepsis-associated encephalopathy. Neurochem Res. 42:3199–3219. 2017.PubMed/NCBI View Article : Google Scholar


Gautier EL, Shay T, Miller J, Greter M, Jakubzick C, Ivanov S, Helft J, Chow A, Elpek KG, Gordonov S, et al: Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat Immunol. 13:1118–1128. 2012.PubMed/NCBI View Article : Google Scholar


Cuevas VD, Anta L, Samaniego R, Orta-Zavalza E, Vladimir de la Rosa J, Baujat G, Dominguez-Soto Á, Sanchez-Mateos P, Escribese MM, Castrillo A, et al: MAFB determines human macrophage anti-inflammatory polarization: Relevance for the pathogenic mechanisms operating in multicentric carpotarsal osteolysis. J Immunol. 198:2070–2081. 2017.PubMed/NCBI View Article : Google Scholar


Remmerie A and Scott CL: Macrophages and lipid metabolism. Cell Immunol. 330:27–42. 2018.PubMed/NCBI View Article : Google Scholar


Chausse B, Kakimoto PA and Kann O: Microglia and lipids: How metabolism controls brain innate immunity. Semin Cell Dev Biol. 112:137–144. 2021.PubMed/NCBI View Article : Google Scholar


Wang X, Zhu L, Hu J, Guo R, Ye S, Liu F, Wang D, Zhao Y, Hu A, Wang X, et al: FGF21 attenuated LPS-induced depressive-like behavior via inhibiting the inflammatory pathway. Front Pharmacol. 11(154)2020.PubMed/NCBI View Article : Google Scholar


Salminen A, Kauppinen A and Kaarniranta K: FGF21 activates AMPK signaling: Impact on metabolic regulation and the aging process. J Mol Med (Berl). 95:123–131. 2017.PubMed/NCBI View Article : Google Scholar


Chen CC, Lin JT, Cheng YF, Kuo CY, Huang CF, Kao SH, Liang YJ, Cheng CY and Chen HM: Amelioration of LPS-induced inflammation response in microglia by AMPK activation. Biomed Res Int. 2014(692061)2014.PubMed/NCBI View Article : Google Scholar


Maciuszek M, Cacace A, Brennan E, Godson C and Chapman TM: Recent advances in the design and development of formyl peptide receptor 2 (FPR2/ALX) agonists as pro-resolving agents with diverse therapeutic potential. Eur J Med Chem. 213(113167)2021.PubMed/NCBI View Article : Google Scholar


Liu H, Lin Z and Ma Y: Suppression of Fpr2 expression protects against endotoxin-induced acute lung injury by interacting with Nrf2-regulated TAK1 activation. Biomed Pharmacother. 125(109943)2020.PubMed/NCBI View Article : Google Scholar


Zhou Y, Lei J, Xie Q, Wu L, Jin S, Guo B, Wang X, Yan G, Zhang Q, Zhao H, et al: Fibrinogen-like protein 2 controls sepsis catabasis by interacting with resolvin Dp5. Sci Adv. 5(eaax0629)2019.PubMed/NCBI View Article : Google Scholar


Gobbetti T, Coldewey SM, Chen J, McArthur S, le Faouder P, Cenac N, Flower RJ, Thiemermann C and Perretti M: Nonredundant protective properties of FPR2/ALX in polymicrobial murine sepsis. Proc Natl Acad Sci USA. 111:18685–18690. 2014.PubMed/NCBI View Article : Google Scholar


Abdul-Sater AA, Edilova MI, Clouthier DL, Mbanwi A, Kremmer E and Watts TH: The signaling adaptor TRAF1 negatively regulates toll-like receptor signaling and this underlies its role in rheumatic disease. Nat Immunol. 18:26–35. 2017.PubMed/NCBI View Article : Google Scholar


Edilova MI, Abdul-Sate AA and Watts TH: TRAF1 signaling in human health and disease. Front Immunol. 9(2969)2018.PubMed/NCBI View Article : Google Scholar


Bin W, Ming X and Wen-Xia C: TRAF1 meditates lipopolysaccharide-induced acute lung injury by up regulating JNK activation. Biochem Biophys Res Commun. 511:49–56. 2019.PubMed/NCBI View Article : Google Scholar


Salmena L, Poliseno L, Tay Y, Kats L and Pandolfi PPA: A ceRNA hypothesis: The rosetta stone of a hidden RNA language? Cell. 146:353–358. 2011.PubMed/NCBI View Article : Google Scholar

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Wen J, Liu Y, Zhan Z, Chen S, Hu B, Ge J and Xie Q: Comprehensive analysis of mRNAs, lncRNAs and circRNAs in the early phase of microglial activation. Exp Ther Med 22: 1460, 2021
Wen, J., Liu, Y., Zhan, Z., Chen, S., Hu, B., Ge, J., & Xie, Q. (2021). Comprehensive analysis of mRNAs, lncRNAs and circRNAs in the early phase of microglial activation. Experimental and Therapeutic Medicine, 22, 1460.
Wen, J., Liu, Y., Zhan, Z., Chen, S., Hu, B., Ge, J., Xie, Q."Comprehensive analysis of mRNAs, lncRNAs and circRNAs in the early phase of microglial activation". Experimental and Therapeutic Medicine 22.6 (2021): 1460.
Wen, J., Liu, Y., Zhan, Z., Chen, S., Hu, B., Ge, J., Xie, Q."Comprehensive analysis of mRNAs, lncRNAs and circRNAs in the early phase of microglial activation". Experimental and Therapeutic Medicine 22, no. 6 (2021): 1460.