Open Access

Expression profile of lncRNAs and mRNAs in intestinal macrophages

  • Authors:
    • Qais Ahmad Naseer
    • Lulu Liu
    • Xiaofei Xue
    • Siche Chen
    • Jixiang Chen
    • Jianguo Qu
    • Lei Cui
    • Xiaogang Wang
    • Shengchun Dang
  • View Affiliations

  • Published online on: August 28, 2020
  • Pages: 3735-3746
  • Copyright: © Naseer et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Non-coding RNAs (ncRNAs) have been previously reported to serve an important role in transcription. In addition, several studies have revealed that long ncRNAs (lncRNAs) have a crucial role in human diseases. However, the association between lncRNAs and inflammation‑induced intestinal macrophages in the intestinal mucosal barrier has remained elusive. In the present study, intestinal macrophages from healthy Sprague Dawley rats were divided into two groups: The experimental group, consisting of intestinal macrophages treated with 1 mg/l lipopolysaccharide (LPS) and the control group, composed of untreated cells. Differentially expressed (DE) lncRNAs and mRNAs between the control and experimental groups were identified using microarray profiling. The levels of DE mRNAs and lncRNAs were measured by reverse transcription‑quantitative PCR (RT‑qPCR). Furthermore, Gene Ontology (GO) and pathway enrichment analyses of DE mRNAs and lncRNAs were performed. To identify core regulatory factors among DE lncRNAs and mRNAs, a lncRNA‑mRNA network was constructed. A total of 357 DE lncRNAs and 542 DE mRNAs between the LPS‑treated and untreated groups were identified (fold-change >1.5; P<0.05). In addition, selected microarray data were confirmed by RT‑qPCR. GO analysis of the DE mRNAs indicated that the biological functions of the upregulated mRNAs included inflammatory response, immune response, metabolic process and signal transduction, whereas those of the downregulated mRNAs were metabolic process, cell cycle, apoptosis and inflammatory response. In addition, pathway enrichment analysis of the upregulated mRNAs revealed that the most enriched pathways were the NF‑κB signaling pathway, B‑cell receptor signaling pathway and apoptosis, while the downregulated mRNAs were significantly involved in metabolic pathways, the phosphatidylinositol signaling system, cytokine‑cytokine receptor interaction and the Toll‑like receptor signaling pathway. The lncRNA‑mRNA co‑expression network suggested that lncRNAs NONMMUT024673 and NONMMUT062258 may have an important role in LPS‑induced intestinal macrophages. The present study identified the DE profiles between LPS‑ and non‑LPS‑treated intestinal macrophages. These DE lncRNAs and mRNAs may be used as potential targets for attenuating excessive inflammatory response in intestinal mucosal barrier dysfunction.


Human intestinal barriers are important in facilitating the absorption of nutrients and the prevention of bacterial invasion (1). However, numerous pathological conditions, including severe acute pancreatitis (2), inflammatory bowel disease (3) and infectious diarrheal syndromes, may lead to intestinal barrier dysfunction (IBD). Furthermore, macrophages have an important role in IBD. One of the major pathophysiological changes in IBD is the overactivation of the inflammatory response, with intestinal macrophages acting as the most important effector cells in this response. Macrophages are the core cells that initiate and regulate the inflammatory reaction, and therefore, their activation and dysfunction are associated with IBD development (4).

Protein-coding genes only represent a small portion of the human genome (<2%), while the majority of the genome is transcribed into non-coding RNAs (ncRNAs) (57). Long ncRNA (lncRNA) transcripts lack significant open reading frames and are composed of molecules >200 nucleotides in length (8). Initially, lncRNAs were considered as transcriptional artifacts; however, they were then revealed to have important regulatory roles that remain to be fully elucidated in detail. With the development of lncRNA microarrays, high-throughput sequencing and bioinformatics, an increasing number of lncRNAs have been identified. LncRNAs have attracted considerable attention in the research fields of molecular biology and biomedical science. Functionally, lncRNAs regulate gene expression at the epigenetic, transcriptional and post-transcriptional levels and are involved in several biological processes, including genomic imprinting, chromosome dosage compensation, X chromosome silencing, chromosome modification, intra-nuclear transport and transcriptional activation (4,9). Recent studies have suggested that lncRNAs participate in various complex human diseases, including aging, hematopoiesis, neurobiology, cancer, muscle biology and immunology (1016). Several studies have indicated that lncRNA H19 may regulate the intestinal epithelial barrier (17,18), whereas the expression of lncRNA cyclin-dependent kinase inhibitor 2B antisense 1 has been associated with inflammatory bowel disease and intestinal barrier formation (19).

Macrophages have been considered to be important effectors of the inflammatory response, which in turn may be regulated by lncRNAs (20,21). However, the potential association between lncRNAs and macrophages/monocytes in post-IBD has remained to be fully investigated. Therefore, in the present study, an in vitro cell model was established to investigate the expression profiles of lncRNAs and mRNAs in macrophages. The aim of the present study was to determine the expression profiles of lncRNAs and mRNAs in lipopolysaccharide (LPS)-induced rat macrophages and to provide a direction for further studies.

Materials and methods


A total of six Sprague-Dawley (SD) rats (female and male, 6–8 weeks, weight, 200–250 g) were provided by the Yangzhou University Experimental Animal Center. RPMI-1640 medium, fetal bovine serum (FBS), penicillin and streptomycin were purchased from Hyclone (Cytiva). Isotonic cell separation solution (Percoll solution) and collagenase IV were obtained from Worthington Biochemical Corp. The primary rabbit polyclonal antibody against CD14 (cat. no. AF2128; Beyotime Institute of Biotechnology) was diluted at 1:300 prior to use, whereas the secondary FITC-conjugated goat anti-rabbit IgG (H+L) (Beyotime Institute of Biotechnology) was diluted at 1:50.

Furthermore, total RNA was extracted with a TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. RNA quantity and quality were measured using the NanoDrop ND-1000 spectrophotometer (PeqLab). RNA integrity was evaluated by agarose gel electrophoresis on the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.). In the present study, the following materials were also used: Primers/probes; TRIzol reagent (cat. no. 15596-026); diethyl pyrocarbonate-treated H2O (cat. no. 750024), SuperScriptIII reverse transcriptase (cat. no. R250-01); SYBR-Green I (cat. no. CS7561); oligo Dt/random primers/specific primers; Platinum Taq DNA polymerase (cat. no. 10966034); 100 mM deoxyribonucleoside triphosphates (cat. no. 18427013; all from Invitrogen; Thermo Fisher Scientific, Inc.); miRNAeasy Mini kit (cat. no. 217184; Qiagen); avantin; and RNase inhibitor (cat. no. E00381; Fermentas; Thermo Fisher Scientific, Inc.).

Isolation of intestinal macrophages

SD rats were housed in individual cages and maintained at 21–23°C and 60±10% humidity on a 12 h light/dark schedule. The rats were acclimatized for one week prior to any experimental procedure and had free access to standard rat chow and water. All animal experiments were approved by the Ethics Committee of the Affiliated Hospital of Jiangsu University (Zhenjiang, China) and were performed in compliance with the guidelines specified by the Institutional Ethics Board. All animal experiments were also in compliance with the Animal Research: Reporting of In Vivo Experiments guidelines (22,23). All efforts were made to minimize the number of animals used and their suffering. Rats were anesthetized with an intraperitoneal injection of 40 mg/kg sodium pentobarbital and were subsequently dissected to remove the whole intestine. Following the isolation of the entire small intestine from a rat by a 5-min experimental procedure, the SD rats were sacrificed by decapitation. The experimental time for isolation of intestinal macrophages from the six rats was 1 week. Each rat was processed individually for sampling. Intestines were opened longitudinally, washed with cold PBS (pH 7.4), placed in Hank's balanced salt solution (HBSS) containing 1 g/l EDTA and incubated in a 37°C water bath with gentle agitation for 60 min. The supernatant was subsequently discarded, collagenase IV (5 g/l) was added and the tissue was incubated for an additional 2 h. The mixture was poured through a 400-mesh screen filter and subsequently, the resulting cells were washed with HBSS, suspended in 500 g/l cell separation isotonic solution (Beyotime Institute of Biotechnology) and centrifuged at 733 × g at 4°C for 15 min. The resulting pellet containing the intestinal macrophages was washed three times with HBSS without calcium and magnesium and cells were stained with trypan blue. The viability of macrophages following treatment was ~85%. Intestinal macrophages were cultured in RPMI-1640 medium supplemented with 10 g/l FBS, 100 µg/ml penicillin and 100 U/ml streptomycin. Following dilution to a density of 5×l05 cells/ml, 2 ml and 200 µl of macrophage suspension were seeded into 6 and 96-well culture plates, respectively. The plates were then incubated at 37°C in a humidified incubator with 5% CO2. Macrophages were divided into two groups, namely the control group (without LPS) and the experimental group (treated with LPS at a concentration of 1 mg/l). A total of three experimental and three control groups were used for repeated experiments, so that that one experimental and one control group with three samples each were used. Following the addition of LPS, cells were cultured for 6 h and total RNA was extracted. The LPS concentration was selected according to a previous study (24).

Identification of intestinal macrophages

In general, intestinal macrophages lack the expression of the innate immune receptor CD14 and do not secrete pro-inflammatory cytokines in response to commensal bacterial infection. In a previous study, a unique macrophage subset in the human intestine was identified. This macrophage subset secreted larger amounts of CD14-associated pro-inflammatory cytokines compared with typical intestinal resident CD14 CD33+ macrophages (25).

For the identification of intestinal macrophages, cells were seeded into 24-well plates and allowed to attach. Once optimal cell growth was achieved (the density was moderate, the cells were connected in a spindle shape, the adhesion was good, and the confluence had reached ~95%), the culture medium was removed, cells were rinsed twice with PBS for 10 min and fixed with 1 ml of 4°C pre-cooled methanol. Subsequently, cells were incubated with CD14 rabbit polyclonal antibody (dilution, 1:300) overnight at 4°C. Cells were then incubated with FITC-conjugated goat anti-rabbit IgG (H+L; dilution, 1:50) at 37°C for 1 h, washed three times with PBS and images were captured under a fluorescence microscope. Green fluorescence indicated a positive signal.

Total RNA extraction

Cells were cultured in the presence of LPS (1 mg/ml) for 6 h and total RNA was extracted using a TRIzol reagent according to the manufacturer's protocol. RNA quality was determined using a Nanodrop 2000 Spectrometer (Thermo Fisher Scientific, Inc.) and RNA integrity was assessed by visualization of the 28S and 18S ribosomal (r)RNA bands on an agarose gel. Following removal of rRNA, the purified RNA was amplified and reverse transcribed into fluorescent complementary (c)DNA using a Quick Amp Labeling kit (Agilent Technologies, Inc.) according to the manufacturer's protocol.

Microarray analysis

GeneChip Mouse Transcriptome Array 1.0 (Affymetrix; Thermo Fisher Scientific, Inc.) is considered as the most powerful and flexible tool for measuring a broad range of expression profiles across the whole mouse transcriptome. Therefore, this tool was used in the present study.

Differentially expressed (DE) lncRNAs and mRNAs demonstrating statistical significances were identified by volcano plot filtering and hierarchical clustering. Subsequently, the purified RNA was reverse-transcribed into cDNA according to the requirements set by GMINIX BioTech. The cDNA was then fragmented, labeled with fluorescent dyes and hybridization was performed in a GeneChip Hybridization Oven 645 (Affymetrix; Thermo Fisher Scientific, Inc.). Following chip washing, arrays were scanned with the GeneChip Scanner (Affymetrix; Thermo Fisher Scientific, Inc.) to determine fluorescence intensity. Imaging data were saved and probe summarization was performed using the Expression Console software (v.; Affymetrix; Thermo Fisher Scientific, Inc.). The significance analysis of the microarray data was used to identify DE lncRNAs and mRNAs. Following significant and false-discovery rate analyses, the significance threshold for the upregulated and downregulated genes was set to a fold change of >1.5 and P≤0.05. In the present study, variance was relatively low; therefore, cut-off values were set to ≥1.5-fold and ≤0.66-fold change for upregulated and downregulated genes, respectively (26,27). Fold modification provided more significant insight, with 1.5 considered as a good eliminator of background noise. As the fold change level increased to ≥2, the number of genes significantly decreased.

Gene Ontology (GO) analysis

GO analysis ( was applied to analyze the function of DE genes (DEGs). GO is the key functional classification tool associated with the National Center for Biotechnology Information. It organizes genes into hierarchical categories to determine the gene regulatory networks in the categories biological process, molecular function and cellular component.

Pathway enrichment analysis

Pathway enrichment analysis was used to determine the significant pathways associated with DEGs according to Kyoto Encyclopedia of Genes and Genomes (KEGG) (, Biocarta ( and Reactome databases (

lncRNA-mRNA network

A lncRNA-mRNA network was constructed to identify the interactions between genes and lncRNAs. Pearson's correlation coefficient (PCC) was calculated and the R-value was used to compare the PCC of the correlation between lncRNAs and coding genes. Network structure analysis was further performed to identify the core regulatory factors (genes). Finally, a coding-non-coding gene co-expression network was constructed by selecting the significantly co-expressed lncRNAs and mRNAs.

Reverse transcription-quantitative PCR (RT-qPCR) analysis

Total RNA from treated cells was isolated using TRIzol reagent and reverse-transcribed into cDNA with SuperScript III reverse transcriptase (both from Invitrogen; Thermo Fisher Scientific, Inc.). The expression levels of lncRNAs or mRNAs were detected using RT-qPCR. The specific primers are listed in Table I. lncRNA expression levels were normalized to the expression of 18S rRNA according to the double-standard curves method (8). The primers for β-actin amplification are mentioned in Table I. Calculations were performed in Excel 15.0.5249.1001 (Microsoft Corp.) using equations provided by Applied Biosystems (Thermo Fisher Scientific, Inc.). The SYBR-Green (GMINIX BioTech) dye detection method and a CFX96™ Real-Time System (Bio-Rad Laboratories, Inc.) were used to perform qPCR analysis. The qPCR conditions were as follows: 95°C for 2 min and 40 cycles of 10 sec at 95°C, 60°C for 30 sec and 70°C for 45 sec. Melting curve analysis was performed at 65–95°C for 10 sec, with a 0.5°C increment per read and a hold time of 5 sec. Three samples in the experimental group and three samples in the control group were analyzed. All samples were normalized to β-actin and each experiment was repeated three times.

Table I.

Primer sequences used to detect the expression of long non-coding RNA and mRNA by quantitative PCR.

Table I.

Primer sequences used to detect the expression of long non-coding RNA and mRNA by quantitative PCR.

Primer nameSequences (5′→3′)

[i] F, forward; R, reverse; IL, interleukin; TNF, tumor necrosis factor; TLR, Toll-like receptor; MALT1, MALT1 paracaspase.

Statistical analysis

The random variance model (RVM)-modified t-test, combining a two-samples t-test with RVM, was performed to analyze DE lncRNAs and mRNAs using the BRB-arrayTools package (v.4.3.2; Biometrics Research Branch National Cancer Institute). DE lncRNAs were further analyzed by hierarchical clustering using the cluster 3.0 ( and TreeView tools ( Furthermore, GO and pathway enrichment analyses were performed and data were analyzed by Fisher's exact test. A paired-samples t-test was used to analyze the RT-qPCR results (GraphPad Prism 5.00; GraphPad Software, Inc.). P<0.05 was considered to indicate a statistically significant difference.


Isolation and identification of intestinal macrophages

Intestinal macrophages were identified by their cellular morphology under a microscope (Fig. 1A). A positive signal for intestinal macrophages was indicated by green fluorescence of FITC-labeled CD14 under a fluorescence microscope (Fig. 1B).

Microarray profiling of lncRNAs

Microarray analysis was used to examine differences in the expression profiles of lncRNAs and mRNAs between the control macrophage and LPS-treated macrophage groups for the identification of lncRNAs with biological roles during IBD progression. A total of 357 DE lncRNAs were identified with a fold-change of at least 1.5 (Fig. 2). Among the samples from the control group, inter-individual differences were detected by chip testing. Although there were more significant inter-individual differences across samples from the same group, overall, the differences in the control group were more significant than those within the experimental group; therefore, the trend of the overall differences reflected by them was significant. Among these lncRNAs, 245 were upregulated and 112 were downregulated in the LPS-treated macrophage group compared with the control group, with a fold-change threshold of 1.5 (P<0.05; Tables II and III).

Table II.

Top 20 upregulated lncRNAs in the macrophage + lipopolysaccharide group compared with the macrophage group. Results were filtered using fold-change >1.5 as the threshold.

Table II.

Top 20 upregulated lncRNAs in the macrophage + lipopolysaccharide group compared with the macrophage group. Results were filtered using fold-change >1.5 as the threshold.

Accession no.P-valueFDRFold-changeChromosome
NONMMUT067603 1.43×10−20.00111.088
NONMMUT016061 2.26×10−20.2694.5212
NONMMUT016098 2.49×10−20.2823.9712
NONMMUT059063 1.00×10−70.0013.506
NONMMUT065205 1.00×10−70.0013.428
NONMMUT057311 9.90×10−40.0603.246
NONMMUT028346 9.10×10−60.0093.0417
NONMMUT054995 1.95×10−40.0322.875
NONMMUT028345 9.73×10−50.0342.8117
NONMMUT023770 2.26×10−40.0252.5815
NONMMUT053098 9.28×10−40.0012.565
NONMMUT003807 2.00×10−70.0252.501
NONMMUT047081 1.01×10−40.0202.434
NONMMUT031303 4.89×10−50.0142.3718
NONMMUT022922 2.22×10−50.0102.2115
NONMMUT044804 2.72×10−40.0162.213
NONMMUT017063 1.07×10−50.0442.2113
NONMMUT009063 2.96×10−50.0242.1811
NONMMUT028242 6.49×10−30.1482.1717
NONMMUT068342 7.31×10−50.2462.169

[i] All of these lncRNAs are currently unnamed. lncRNA, long non-coding RNA; FDR, false discovery rate; up, upregulation.

Table III.

Downregulated lncRNAs in the macrophage + lipopolysaccharide group compared with the macrophage group. Results were filtered using fold-change >2 as the threshold.

Table III.

Downregulated lncRNAs in the macrophage + lipopolysaccharide group compared with the macrophage group. Results were filtered using fold-change >2 as the threshold.

Accession no.P-valueFDRFold-changeLncRNA nameChromosome
NR_028499 1.52×10−30.07411.49Fxyd29
KnowTID_00004330 1.00×10−70.0025.072
ENSMUST00000117094 2.96×10−30.1024.55Gm119144
NONMMUT041835 1.35×10−30.2882.942
NONMMUT046238 3.15×10−20.3142.944
ENSMUST00000181818 2.82×10−40.0352.78Kansl2-ps7
NONMMUT065008 1.80×10−50.0132.638
NONMMUT055602 3.90×10−60.0062.566
uc029szy.1 3.44×10−30.1102.5617
NONMMUT022580 3.08×10−20.3112.5615
NONMMUT005859 3.28×10−40.0382.5010
NONMMUT000091 1.10×10−20.1932.221
ENSMUST00000122180 4.79×10−20.3812.22Gm1213811
NONMMUT052111 4.71×10−20.3782.085
ENSMUST00000172263 8.96×10−50.0252.01Gm1848X
NONMMUT024673 2.89×10−20.3022.0615

[i] FDR, false discovery rate; lncRNA, long non-coding RNA.

Gene expression profiling by microarray analysis

DE mRNAs were identified following comparison between the LPS-treated and control macrophage groups. A total of 187 upregulated and 355 downregulated mRNAs were obtained with a fold change of ≥1.5 (P<0.05; Tables IV and V). Hierarchical clustering and volcano plot filtering were utilized to analyze the data (Fig. 2). Thereby, DE mRNAs and lncRNAs were identified between the LPS-treated and control macrophage groups.

Table IV.

Top 20 upregulated mRNAs in the macrophage + lipopolysaccharide group compared with the macrophage group. The results were filtered using a fold-change of >1.5 as the threshold.

Table IV.

Top 20 upregulated mRNAs in the macrophage + lipopolysaccharide group compared with the macrophage group. The results were filtered using a fold-change of >1.5 as the threshold.

Accession no.P-valueFDRFold-changeGene nameChromosome
NM_022415 1.67×10−40.1702.47Ptges2
NM_019472 7.00×10−70.0302.63Myo1015
NM_001145799 4.26×10−40.0022.64Ctla2a13
NM_001276764 6.24×10−40.0402.72Dst1
NM_008311 1.00×10−70.0492.72Htr2b1
NM_011158 1.40×10−50.0012.73Prkar2b12
NM_009690 9.00×10−70.0122.87Cd5l3
NM_008491 4.34×10−50.0022.94Lcn22
NM_010554 2.75×10−20.0193.02Il1a2
NM_010329 2.00×10−70.2953.09Pdpn4
NM_008768 5.71×10−50.0013.10Orm14
NM_010766 4.04×10−20.0213.19Marco1
BC019760 7.54×10−40.3523.49Igk6
NM_018866 1.31×10−20.0533.52Cxcl135
NM_010016 1.66×10−30.2083.55Cd551
ENSMUST00000109711 4.81×10−20.0763.57Ighv1-212
NM_008903 9.65×10−40.3823.57Ppap2a13
ENSMUST00000103461 9.57×10−40.0584.59Ighv7-312
NM_138648 4.30×10−60.0635.86Olr16
BC055906 2.80×10−20.2987.55Igk6

[i] FDR, false discovery rate.

Table V.

Top 20 downregulated mRNAs in the macrophage + lipopolysaccharide group compared with the macrophage group. The results were filtered using a fold-change >1.5.

Table V.

Top 20 downregulated mRNAs in the macrophage + lipopolysaccharide group compared with the macrophage group. The results were filtered using a fold-change >1.5.

Accession no.P-valueFDRFold-changeGene nameChromosome
ENSMUST00000081924 1.71×10−50.00722.22Ifitm67
NM_133661 2.48×10−30.09310.87Slc6a126
NM_009801 5.58×10−40.0468.33Car23
NM_001145959 2.74×10−20.2944.76Ndrg214
NM_008969 2.20×10−60.0034.35Ptgs12
NM_011198 1.00×10−60.0024.21Ptgs21
NM_001204203 2.57×10−40.0354.61Spp15
NM_008361 6.87×10−30.1534.93Il1b2
NM_001038614 8.00×10−70.0023.85Olfm12
NM_001134475 2.59×10−30.0963.75Vcan13
NM_008035 4.42×10−50.0203.45Folr27
NM_016710 3.50×10−30.1113.45Hmgn5X
NM_008278 3.24×10−30.1073.33Hpgd8
NM_011311 1.33×10−20.2093.23S100a43
NM_017391 1.35×10−20.2103.23Slc5a316
NM_009250 1.56×10−20.2253.23Serpini13
NM_148933 1.23×10−20.2023.13Slco4a12
NM_009644 1.57×10−50.0132.94Ahrr13
NM_010330 1.20×10−20.0132.94Emb13
NM_178767 3.48×10−20.3302.94Agmo12

[i] FDR, false discovery rate.

GO analysis of DEGs

To determine the roles of DEGs in the categories biological process and molecular function, GO analysis was performed. A total of 303 GO terms were enriched by the upregulated genes (Fig. 3; P≤0.05), with the most significantly enriched terms being associated with inflammatory responses. Therefore, DE mRNAs were associated with IL-1α, NLR family pyrin domain-containing 3, lymphocyte antigen 96 and TNF. Furthermore, 366 GO terms were enriched by the downregulated genes, including lipid metabolic processes, inflammatory response, positive regulation of apoptotic processes and immune responses (Fig. 4).

Pathway enrichment analysis of DEGs

Pathway enrichment analysis of DE mRNAs was performed to provide insight into the cellular pathways associated with the selected mRNAs. A total of 61 and 76 pathways were associated with the upregulated and downregulated genes, respectively, the most relevant pathway was ‘osteoclast differentiation’ (Fig. 5). The results were significant in the LPS-treated macrophage group, the most relevant was ‘metabolic pathways’ (Fig. 6). These findings suggested that these pathways were associated with the pathogenesis and biochemical characteristics of macrophages involved in IBD.

lncRNA-mRNA network

To identify the lncRNAs and mRNAs that are involved in intestinal macrophage-mediated inflammation, an lncRNA-mRNA interaction network was individually constructed for the LPS-treated and control groups, and their differences were then analyzed. For the coding-non-coding gene co-expression network, five DE lncRNAs and mRNAs were selected. Furthermore, six lncRNAs and mRNAs were selected by combining the co-expression network results with the microarray data. The results indicated that several lncRNAs, including NONMMUT028346, NONMMUT047081, XR_105830, NONMMUT024673, n-R5s54-F and NONMMUT046238 may serve an important role with the highest degree of interaction in the gene co-expression network (Fig. 7). The aforementioned coding genes were involved in several biological processes, including inflammatory response, apoptosis and cell death.

RT-qPCR verification

To further validate the microarray results, two DE lncRNAs and four DE mRNAs were selected from the lncRNA-mRNA network. Subsequently, RT-qPCR analysis was performed on two additional independent intestinal macrophage groups. The microarray results for the lncRNA and mRNA transcripts were consistent with those obtained using RT-qPCR analysis (Figs. 8 and 9).


Emerging evidence has revealed that thousands of lncRNAs are encoded by the human genome. lncRNAs function as transcriptional and post-transcriptional regulators, may directly act on chromatin-modifying complexes and affect several cellular and developmental pathways (28). The intestinal mucosal barrier is involved in the protection of the human body; however, several pathogenic factors may contribute to IBD (2932). Among the numerous types of inflammatory cells, macrophages may exert pro-inflammatory and anti-inflammatory properties. Intestinal macrophages in the lamina propria mucosa represent the largest pool of tissue macrophages in the human body. These macrophages have an important role in the regulation of the intestinal barrier function. Studies have indicated that intestinal macrophages inhibit overactivation of the inflammatory response to protect the intestinal mucosal barrier (33,34). Several immunological and non-immunological components contribute to intestinal barrier function. The epithelial barrier is one of the most important non-immunological components. A previous study by our group demonstrated that the expression of triggering receptor expressed on myeloid cells-1 (TREM-1) was downregulated by its selective inhibitor LP17 in intestinal macrophages, whereas TREM-1 expression inhibited the invasive ability of intestinal macrophages into the intestinal epithelium. Therefore, TREM-1 may be considered as a novel target for the treatment of IBD (28). In the present study, cells were analyzed from SD rats, but the microarray kit and the PCR primers used were specifically for mice. We have optimized our PCR primer with the aforementioned sequence, and it worked and amplified the target sequence efficiently, so therefore in our opinion, this primer sequence can be used for the amplification of genes from SD rat cells.

LncRNAs serve as transcriptional and post-transcriptional regulators and may bind to chromatin-modifying complexes to guide them to specific locations. In addition, lncRNAs have been reported to affect several cellular and developmental pathways. Therefore, it is not surprising that the dysregulation of lncRNAs appears to be a significant feature of several complex human diseases. Although rapid progress has been made in associated research fields, the role of lncRNAs in the regulation of inflammatory responses is only now becoming apparent. Carpenter et al (35) confirmed that murine lncRNA cytochrome C oxygenase II interacted with heterogeneous nuclear ribonucleoproteins to facilitate the activation and inhibition of immune response-associated genes. Cui et al (36) also demonstrated that lncRNA IL-7 receptor (IL7R), overlapping with the 3′-untranslated region of the human IL7R α-subunit gene, contributed to the regulation of inflammatory responses. Furthermore, downregulation of lncRNA nuclear paraspecle assembly transcript 1 attenuated the inflammatory response in IBD via modulating the intestinal epithelial barrier and exosome-mediated polarization of macrophages (37).

The present study aimed to investigate the differential expression profiles of lncRNAs between non-LPS and LPS-treated intestinal macrophages. Several lncRNAs were identified in intestinal macrophages, whose expression was altered in response to LPS treatment. The RT-qPCR and microarray analyses were used to validate the expression profiles of lncRNAs. The results indicated that lncRNAs in intestinal macrophages were able to regulate the inflammatory response to LPS. Furthermore, GO analysis revealed that the upregulated coding genes were predominantly involved in inflammatory and innate immune responses, while the downregulated ones were involved in the positive regulation of the apoptotic process, and immune and inflammatory responses. In addition, pathway enrichment analysis suggested that inflammation-associated pathways, including the NF-κB and Toll-like receptor (TLR) signaling pathways, were enriched in the upregulated transcripts associated with rheumatoid arthritis. Ma et al (38) indicated that lncRNA TNF-α-induced protein 3, as a regulator of NF-κB, modulates inflammation-associated gene transcription in mouse macrophages.

Studies have also demonstrated that lncRNAs may serve a vital role in regulating the transcription of neighboring coding genes (39). The present study predicted that lncRNAs NONMMUT024673 and NONMMUT047081 are able to regulate the inflammatory response via modulating the expression of coding genes. Therefore, bioinformatics analysis may be useful in detecting target genes. Furthermore, the lncRNA-mRNA network revealed that lncRNA NONMMUT028346 interacted with the TLR4 gene. TLR4 is involved in several inflammatory pathways. Based on the co-expression network and microarray data, lncRNAs NONMMUT047081 and NONMMUT024673 were selected to further evaluate their predicted value in LPS-induced intestinal macrophages-mediated inflammatory responses. Therefore, RT-qPCR analysis was performed. The relative expression levels of lncRNAs NONMMUT024673 and NONMMUT047081 were significantly downregulated and upregulated, respectively, in the LPS-treated intestinal macrophage group compared with the control group.

In conclusion, in the present study, DE lncRNAs between LPS-treated and untreated rat intestinal macrophages were identified. The genomic data obtained in the present study may improve the understanding of the role of lncRNAs in the intestinal macrophage-mediated induction of inflammatory responses. However, further studies are required to elucidate the exact regulatory mechanisms of these DE lncRNAs. In addition, the microarray results provided a novel mechanism of macrophage-mediated regulation of IBD.


Not applicable.


This study was supported by a grant from the Six Talent Peaks Project in Jiangsu Province (grant no. 2016-WSN-007), the Jiangsu 333 Project Foundation (grant no. BRA2017560) and the Zhenjiang Science and Technology Committee (grant no. SH 2019061). The funding bodies had no role in the design of the study, the collection, analysis and interpretation of data or in writing the manuscript.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Authors' contributions

SD and LL conceived and supervised the study; LL, SC, QN, XX, XW and SD designed the experiments; QN, JC, JQ and LC performed the experiments and had a major role in the acquisition of data; LC and SD analyzed the data; SD, QN, LL and SC, QN, XX, XW revised the manuscript for intellectual content, designed the figures, contributed to interpreting the data and wrote the manuscript with input from all authors. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All protocols for animal experimentation were approved by the Institutional Animal Care and Use Committees of Jiangsu University (Zhenjiang, China) and experiments were performed according to the Regulation on Animal Experimentation at Jiangsu University (Zhenjiang, China).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.







long non-coding RNA


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Naseer QA, Liu L, Xue X, Chen S, Chen J, Qu J, Cui L, Wang X and Dang S: Expression profile of lncRNAs and mRNAs in intestinal macrophages. Mol Med Rep 22: 3735-3746, 2020
Naseer, Q.A., Liu, L., Xue, X., Chen, S., Chen, J., Qu, J. ... Dang, S. (2020). Expression profile of lncRNAs and mRNAs in intestinal macrophages. Molecular Medicine Reports, 22, 3735-3746.
Naseer, Q. A., Liu, L., Xue, X., Chen, S., Chen, J., Qu, J., Cui, L., Wang, X., Dang, S."Expression profile of lncRNAs and mRNAs in intestinal macrophages". Molecular Medicine Reports 22.5 (2020): 3735-3746.
Naseer, Q. A., Liu, L., Xue, X., Chen, S., Chen, J., Qu, J., Cui, L., Wang, X., Dang, S."Expression profile of lncRNAs and mRNAs in intestinal macrophages". Molecular Medicine Reports 22, no. 5 (2020): 3735-3746.