Open Access

Next-generation sequencing predicts interaction network between miRNA and target genes in lipoteichoic acid-stimulated human neutrophils

  • Authors:
    • Meng‑Chi Yen
    • I‑Jeng Yeh
    • Kuan‑Ting Liu
    • Shu‑Fang Jian
    • Chia‑Jung Lin
    • Ming‑Ju Tsai
    • Po‑Lin Kuo
  • View Affiliations

  • Published online on: July 31, 2019     https://doi.org/10.3892/ijmm.2019.4295
  • Pages: 1436-1446
  • Copyright: © Yen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Toll‑like receptors (TLRs), which are a class of pattern‑recognition receptors, can sense specific molecules of pathogens and then activate immune cells, such as neutrophils. The regulation of TLR signaling in immune cells has been investigated by various studies. However, the interaction of TLR signaling‑activated microRNAs (miRNAs) and genes has not been well investigated in a specific type of immune cells. In the present study, neutrophils were isolated from peripheral blood of a healthy donor, and then treated for 16 h with Staphylococcus aureus lipoteichoic acid (LTA), which is an agonist of TLR2. The miRNA and mRNA expression profiles were analyzed via next‑generation sequencing and bioinformatics approaches. A total of 290 differentially expressed genes between LTA‑treated and vehicle‑treated neutrophils were identified. Gene ontology analysis revealed that various biological processes and pathways, including inflammatory responses, defense response, positive regulation of cell migration, motility, and locomotion, and cell surface receptor signaling pathway, were significantly enriched. In addition, 38 differentially expressed miRNAs were identified and predicted to be involved in regulating signal transduction and cell communication. The interaction of 4 miRNAs (hsa‑miR‑34a‑5p, hsa‑miR‑34c‑5p, hsa‑miR‑708‑5p, and hsa‑miR‑1271‑5p) and 5 genes (MET, CACNB3, TNS3, TTYH3, and HBEGF) was proposed to participate in the LTA‑induced signaling network. The present findings may provide novel information for understanding the detailed expression profiles and potential networks between miRNAs and their target genes in LTA‑stimulated healthy neutrophils.

Introduction

The innate immune system can detect the presence of pathogens, such as viruses and bacteria, and activate immune responses to eliminate the infections. These pathogens can be recognized by pattern-recognition receptors (PRRs) and trigger activation of innate immunity (1,2). The family of Toll-like receptors (TLRs) is a class of PRRs in mammals (3). TLR4 is an important receptor recognizing lipopolysaccharide (LPS), which is a component of the outer membrane of Gram-negative bacteria (4,5). By contrast, the wall components of Gram-positive bacteria, such as peptidoglycan (PGN) and lipoteichoic acid (LTA), are recognized by TLR2 (6-8). PGN and LTA can induce septic shock and multiple organ failure (9).

TLR2 expression can be detected in various types of human immune cells, including monocytes, macrophages, dendritic cells and polymorphonuclear leukocytes (also termed granulocytes and include neutrophils, basophils and eosinophils) (10). In peripheral blood, neutrophils are the most abundant type of granulocytes and the first r immune cells to respond to infections. When human neutrophils are exposed to LTA, cell migration, degranulation, secretion of pro-inflammatory factors [including interleukin (IL)-8, tumor necrosis factor-α (TNF-α) and granulocyte colony-stimulating factor (G-CSF)], increased production of reactive oxygen species (ROS) and antimicrobial activity, and activation of TLR2 and NF-κB-mediated signaling pathways have been reported (11-14).

MicroRNA (miRNA) is a group of small non-coding RNAs with ~22 nucleotides. Emerging evidence suggests that miRNAs are involved in regulation of gene expression and immune responses (15,16). For example, miR-155, miR-146a, miR-UL112-3p and miR-344b-1-3p have been demonstrated to interact with TLR2 in pathological conditions (17-21). However, the interaction of miRNA and LTA-mediated immune activation has not been extensively investigated in a specific type of immune cells. Thus, the present study aimed to investigate the expression mRNA and miRNA in Staphylococcus aureus LTA-stimulated human neutrophils via next-generation sequencing. To understand the LTA-mediated effect in healthy immune cells, neutrophils were obtained from the peripheral blood of a healthy donor.

Materials and methods

Neutrophil isolation and LTA treatment

The present study was approved by the Institutional Review Board of Kaohsiung Medical University Hospital (IRB no. KMUH-IRB-20120287). A total of 10 ml venous blood was obtained from a healthy donor. The participant agreed to the use of their sample in research and signed informed consent during the period Jan 2013 to Jan 2014. Human neutrophils were separated from whole blood using CD66abce microbeads (Miltenyi Biotec GbmH), according to manufacturer's instruction. Subsequently, 3×107 isolated neutrophils were cultured in RPMI1640 medium containing 10% fetal bovine serum, 100 U/ml penicillin G, 100 µg/ml streptomycin and 0.25 µg/ml amphotericin B (Thermo Fisher Scientific, Inc.), and 1 µg/ml of LTA (from S. aureus; LTA group; cat. no. L2515; Sigma-Aldrich; Merck KgaA) or double distilled water (vehicle group) in 5% CO2 air atmosphere at 37°C for 16 h. Neutrophils were collected for RNA isolation. The purity of isolated CD66abce+ cells was evaluated via flow cytometry. Cells were stained with Alexa Fluor 647-conjugated anti-human CD66b (1:20; cat. no. 561645; BD Pharmingen), according to manufacturer's instructions. The cells were then washed and analyzed using a BD Accuri C6 flow cytometer with BD Accuri C6 software version 1.0.264.21 (BD Biosciences).

RNA isolation

Total RNA was extracted using TRIzol® reagent (Thermo Fisher Scientific, Inc.) according to the supplier's protocol. Purified RNA was quantified using a ND-1000 spectrophotometer (NanoDrop Technologies; Thermo Fisher Scientific, Inc.) and the quality was confirmed using an Agilent 2100 Bioanalyzer and an RNA 6000 Pico LabChip RNA (Agilent Technologies, Inc.). The RNA integrity number (RIN) resulting from the Agilent Bioanalyzer was 8.3 for the LTA-stimulated cells and 7.6 for the vehicle-stimulated cells. The quality report is shown in Fig. S1.

Library preparation, sequencing, alignment and differential expression analysis

Sequencing for mRNA and miRNA was commercially performed by Welgene Biotech Co., Ltd. All RNA sample preparation procedures were carried out according to the official Illumina protocol (Illumina, Inc.). For mRNA sequencing, Agilent's SureSelect Strand-Specific RNA Library Preparation kit (Agilent Technologies, Inc.) was used for library construction, followed by AMPure XP Beads (Agilent Technologies, Inc.) size selection. The sequence was directly determined via Illumina's sequencing-by-synthesis technology. Sequencing data were generated by Welgene's pipeline based on Illumina's base-calling program bcl2fastq v2.2.0. For miRNA sequencing, samples were prepared using the TruSeq™ miRNA Library kit (Illumina, Inc.), following the supplier's guide. Libraries were sequenced on an Illumina instrument (75-cycle single-end read; 75SE) and miRNA sequencing data was processed using the Illumina software BCL2FASTQ v2.20. Sequence Quality Trimming, performed by Trimmomatic version 0.36 (22). HISAT2 was used for mRNA alignment (23) and miRDeep2 was used for miRNA alignment (24). The expression levels were normalized by calculating fragments per kilobase of transcript per million mapped reads (FPKM). Differential expression analysis was performed via Cuffdiff (Cufflinks 2.2.1) (25). P-value was calculated by Cuffdiff with non-grouped sample using the 'blind' method, in which all samples are treated as replicates of a single global 'condition' and used to build one model (25).

Reverse transcription-quantitative PCR (RT-qPCR)

Isolated cells (5×105) were seeded into several wells of a 24-well plate and treated with vehicle or 1 µg/ml LTA for 16 h. Total RNA was isolated using TRIzol reagent (Thermo Fisher Scientific, Inc.). Equal amount of total RNA was reverse transcribed via the PrimeScript RT reagent kit (Clontech Laboratories, Inc.). qPCR was performed with SYBR-Green Master Mix (Applied Biosystems; Thermo Fisher Scientific, Inc.) on a Real-Time PCR system (QuantStudio 3D Digital PCR System; Thermo Fisher Scientific, Inc.). The thermocycling conditions were: 20 sec at 95°C, followed by 40 amplification cycles of 95°C for 3 sec and 60°C for 30 sec. The primers were as follows: Human chemokine (C-C motif) ligand (CCL) 2, forward 5′-TCTGTGCCTGCTGCTCATAG-3′ and reverse 5′-TGGAATCCTGAACCCACTTC-3′; human CCL7, forward 5′-ACCACCAGTAGCCACTGTCC-3′ and reverse 5′-TTGGGTTTTCTTGTCCAGGT-3′; human C-X-C motif chemokine ligand 5 (CXCL5), forward 5′-TGTTTACAGACCACGCAAGG-3′ and reverse 5′-GGGGCTTCTGGATCAAGAC-3′; and human GAPDH, forward 5′-GAGTCAACGGATTTGGTCGT-3′ and reverse 5′-TTGATTTTGGAGGGATCTCG-3′. The relative mRNA expression levels were normalized to the GAPDH expression and calculated using the 2−∆∆Cq method (26).

Gene ontology (GO) analysis of genes and miRNAs

The criteria of differential mRNA expression were set at fold change ≥2.0, FPKM >0.8 and P-value <0.05. For determining the function of LTA-affected genes, the biological process of GO (GOTERM_BP_ALL) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed via DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov/home.jsp) (27,28). In addition, gene set enrichment analysis (GSEA; http://www.broad.mit.edu/gsea/) (29,30) was performed using the GO biological processes database c5.bp.v6.2. The criteria of differential miRNA expression were set at fold change ≥2.0 and reads per million (RPM) >1. GO analysis of miRNA was performed via the GSEA method of miRNA Enrichment Analysis and Annotation Tool (miEAA; https://ccb-compute2.cs.uni-saar-land.de/mieaa_tool/) (31).

Interaction between miRNA and mRNA

To predict the miRNA-targeted mRNAs, the Funrich software version 3.1.3 (32) and miRDB 6.0 (miRNAs with Target Score >90 were selected) were used (33). The miRNA target genes were determined using two databases: TargetScan 7.2 (http://www.targetscan.org/vert_72/) (34) and miRTarBase 7.0 (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) (35). The network was drawn by using stringApp 1.4.1 plugin in Cytoscape software 3.7.1 (36,37).

Statistical analysis

The Venn diagram was drawn via the website http://bioinformatics.psb.ugent.be/webtools/Venn/, accessed on 22 January 2019. The statistical analysis associated with the Venn diagram was performed via the website http://nemates.org/MA/progs/overlap_stats.html. The number of genes in the genome was set to 1,917 miRNAs according to the latest information of miRBase. For GSEA, P-values <0.01 and false discovery rate (FDR) <25% were considered significant. For GSEA of miRNAs P-values <0.05 were considered significant. All other graphs were produced in GraphPad Prism 8 software (GraphPad Software, Inc.). The Student's t-test was used for analysis of differences between the vehicle and LTA-treated groups, using GraphPad Prism 8. P<0.05 was considered to indicate a statistically significant difference.

Results

Distribution of mRNA expression in human neutrophils following LTA stimulation

Previous publications have reported that stimulation with 0.1-10 µg/ml of S. aureus LTA induced the release of cytokines, such as IL-8 and TNF-α, in human monocytes within 1-6 h (38,39). In addition, the gene expression profile of the human monocyte cell line THP-1 following stimulation with 25 µg/ml LTA for 6 h was detected via microarray analysis (40). The results indicated that genes involved in inflammatory responses, cell adhesion, cytokines and chemokines were upregulated following S. aureus LTA stimulation (40). In the present study, to investigate the mRNA and miRNA expression changes in human neutrophils, human CD66abce+ cells were enriched from peripheral blood obtained from a healthy donor. In peripheral blood, the majority of the enriched CD66abce+ cells are considered neutrophils (41,42). The purity of the CD66acbe+ cells following enrichment was >98%, as evidenced by flow cytometry analysis (Fig. 1A). Because 1 µg/ml of LTA stimulation has been shown to be sufficient to activate signaling downstream of TLR2 in immune cells (43), and the gene expression profile of LTA-stimulated neutrophils has not been investigated, the isolated human CD66abce+ cells were stimulated with 1 µg/ml LTA from S. aureus (LTA group) or vehicle control (ddH2O; Veh group) for 16 h and then the total RNA was extracted. Quality assessment of the RNA sequencing analysis is shown in Figs. S2 and S3, reporting high scores in per-base sequence quality and per-sequence quality in both groups. The mapped reads for both RNA and small RNA sequencing are listed in Table SI. The expression was normalized in FPKM mapped reads. The distribution of the FPKM values of the two samples was presented in a density plot (Fig. 1B). The results suggested that the FPKM distribution was similar in the two samples. To further investigate the differential gene expression in response to LTA stimulation, the distribution of differentially expressed genes between the two samples was plotted in a volcano plot (Fig. 1C). Genes with fold change ≥2.0 (log2 fold change >1 or <-1), FPKM >0.8 and P-value <0.05 (-log10 P-value >1.3) were considered as significant. According to these criteria, 290 significant differentially expressed genes were selected for subsequent analysis (the full gene list is presented in Table SII). Furthermore, the mRNA expression changes of CCL2, CCL7 and CXCL5 were validated by RT-qPCR (Fig. 1D), confirming that these genes were demonstrate to be significantly upregulated following LTA stimulation by both the RT-qPCR and the RNA sequencing analyses.

Evaluating the function of LTA-affected genes

Previous studies have suggested that LTA stimulation results in induction of inflammatory cytokines and chemokines, cell migration and antimicrobial responses in immune cells. To further investigate the function of the differential gene expression in response to LTA stimulation, the 290 selected genes were subjected to GO analysis for biological processes and KEGG pathway analysis via the DAVID gene functional classification tool. Results with P-values <0.001 and FDR <25% were considered as significantly enriched biological processes and KEGG pathways. The results revealed that >200 biological processes and 4 KEGG pathways were enriched. The gene list in each enriched biological process and KEGG pathway is present in Tables SIII and SIV. The top 20 most significant enriched biological processes are presented in Table I. These biological processes, including positive regulation of cell migration and motility, positive regulation of cellular component movement, response to external stimulus, defense responses, inflammatory responses and cell surface receptor signaling pathway, were similar with the LTA-induced responses of neutrophils in previous studies (11-14). The results of KEGG pathway analysis are presented Table II.

Table I

GO analysis for biological processes via DAVID gene functional classification tool.

Table I

GO analysis for biological processes via DAVID gene functional classification tool.

GO IDGO termEnrichmentP-valueFDR
GO:0030335Positive regulation of cell migration6.237193 6.81×10−19 1.29×10−15
GO:0051272Positive regulation of cellular component movement6.020298 7.20×10−19 1.36×10−15
GO:2000147Positive regulation of cell motility6.023695 2.17×10−18 4.11×10−15
GO:0030334Regulation of cell migration4.543111 2.72×10−18 5.16×10−15
GO:0040017Positive regulation of locomotion5.838131 6.11×10−18 1.16×10−14
GO:2000145Regulation of cell motility4.316392 8.70×10−18 1.65×10−14
GO:0006954Inflammatory response4.580108 2.92×10−17 5.54×10−14
GO:0040012Regulation of locomotion4.136543 4.70×10−17 8.90×10−14
GO:0006952Defense response2.989709 4.85×10−17 9.19×10−14
GO:0051270Regulation of cellular component movement4.042019 5.25×10−17 9.93×10−14
GO:0016477Cell migration3.285506 3.73×10−16 6.33×10−13
GO:0070887Cellular response to chemical stimulus2.301508 9.52×10−16 1.89×10−12
GO:0006950Response to stress2.010083 2.23×10−15 4.21×10−12
GO:0009605Response to external stimulus2.502035 4.33×10−15 8.19×10−12
GO:0051674Localization of cell3.022336 4.56×10−15 8.62×10−12
GO:0048870Cell motility3.022336 4.56×10−15 8.62×10−12
GO:0007166Cell surface receptor signaling pathway2.234817 1.38×10−14 2.61×10−11
GO:0032879Regulation of localization2.280856 3.45×10−14 6.54×10−11
GO:0010033Response to organic substance2.159601 4.98×10−14 9.42×10−11
GO:0048583Regulation of response to stimulus1.961424 6.01×10−14 1.14×10−10

[i] GO, Gene Ontology; FDR, false discovery rate.

Table II

Kyoto encyclopedia of genes and genomes pathway analysis via DAVID gene functional classification tool.

Table II

Kyoto encyclopedia of genes and genomes pathway analysis via DAVID gene functional classification tool.

Path IDPath nameEnrichmentP-valueFDR
hsa04060Cytokine-cytokine receptor interaction4.2657 2.88×10−8 3.62×10−5
hsa05205Proteoglycans in cancer3.5337 7.46×10−50.0938
hsa04015Rap1 signaling pathway3.3655 1.26×10−40.1585
hsa04062Chemokine signaling pathway3.5464 1.38×10−40.1738

[i] FDR, false discovery rate.

The functions of the 290 genes were also analyzed by GSEA. The results revealed that 53 gene sets were significant at FDR <25% and 16 gene sets were significant at nominal P-value <1%. According to the FDR value, the top 10 most significant gene sets are presented in Fig. 2. The gene sets identified by GSEA analysis were similar with those from GO analysis, including cell motility, locomotion, cellular component movement, G protein-coupled receptor signaling and positive regulation of ERK1 and ERK2 cascade, were shown. Upregulation of cell migration and granule degranulation in LTA-stimulated neutrophils has been reported in previous studies (11-14). By contrast, upregulation of cytokines, such as IL-8, IL-6, TNF-α and G-CSF, was not observed in the present results; the expression changes of those four genes were <2-fold in the present RNA sequencing results (data not shown).

Evaluating the function of LTA-affected miRNAs

The miRNA expression was also determined via miRNA sequencing in the present study. miRNA expression was considered significantly changed based on fold change ≥2.0 and reads per million (RPM) >1. Compared to vehicle-stimulated neutrophils, 38 miRNAs, including 36 downregulated miRNAs and 2 upregulated miRNAs, were identified as significantly differentially expressed in LTA-stimulated neutrophils. The list of the 38 significant differentially expressed miRNAs is presented in Table SV. The miRNA enrichment analysis was determined via Funrich software according to biological process. The Funrich analysis suggested that these miRNAs were signifi-cantly involved in signal transduction and cell communication (P<0.05; Fig. 3A). In addition, the GSEA-like method of gene ontology analysis was performed via the miEAA website, which is a miRNA enrichment analysis and annotation web-based application. Pathways such as nucleotide binding, signal transduction, cell cortex, protein autophosphorylation, transcription corepressor and energy reserve metabolic process were enriched in the LTA-stimulated neutrophils (Fig. 3B and Table III). Pathways such as microtubule-based process, cytoskeleton-dependent intracellular transport, protein polymerization and ubiquitin binding were suppressed (Fig. 3B and Table III). Signal transduction was the only enriched biological process observed with both the analysis methods.

Table III

GO analysis of miRNA via miEAA website.

Table III

GO analysis of miRNA via miEAA website.

GO IDPath nameEnrichmentP-valuemiRNA
GO0000166Nucleotide bindingEnriched0.0057hsa-miR-10a-3p; hsa-miR-193a-3p; hsa-miR-22-5p; hsa-miR-331-5p; hsa-miR-34a-5p; hsa-miR-34c-5p; hsa-miR-362-5p; hsa-miR-378a-5p; hsa-miR-940
GO0007017Microtubule based processDepleted0.0085hsa-miR-708-5p; hsa-miR-940
GO0030705Cytoskeleton dependentDepleted0.0085hsa-miR-708-5p; hsa-miR-940
intracellular transport
GO0051258Protein polymerizationDepleted0.0085hsa-miR-708-5p; hsa-miR-940
GO0043130Ubiquitin bindingDepleted0.0110hsa-miR-34a-5p; hsa-miR-708-5p; hsa-miR-708-3p; hsa-miR-940
GO0007165Signal transductionEnriched0.0132hsa-miR-10a-3p; hsa-miR-1271-5p; hsa-miR-22-5p; hsa-miR-31-3p; hsa-miR-331-5p; hsa-miR-337-3p; hsa-miR-34a-5p; hsa-miR-34c-5p; hsa-miR-378a-5p; hsa-miR-3928-3p; hsa-miR-625-5p; hsa-miR-708-5p
GO0005938Cell cortexEnriched0.0149hsa-miR-193a-3p; hsa-miR-31-3p; hsa-miR-34a-5p; hsa-miR-34c-5p
GO0046777Protein autophosphorylationEnriched0.0149hsa-miR-193a-3p; hsa-miR-31-3p; hsa-miR-34a-5p; hsa-miR-34c-5p
GO0003714Transcription corepressor activityEnriched0.0176hsa-miR-193a-3p; hsa-miR-34a-5p; hsa-miR-34c-5p; hsa-miR-362-3p; hsa-miR-378a-5p
GO0006112Energy reserve metabolic processEnriched0.0176hsa-miR-10a-3p; hsa-miR-337-3p; hsa-miR-34a-5p; hsa-miR-34c-5p; hsa-miR-378a-5p

[i] GO, Gene Ontology; miRNA, microRNA.

Evaluating the potential interaction between LTA-affected miRNA and genes

To further identify whether the 38 miRNAs may interact with the 290 LTA-affected genes, the Funrich software and the miRDB website were used. The analysis results of the Funrich software and the miRDB website, respectively, indicated 264 miRNAs and 350 miRNAs might target to 342 genes. Seven and 15 shared miRNAs were respectively identified between the 38 miRNAs and 264 Funrich-predicted miRNAs (Fig. 4A), and 38 miRNAs and 350 miRDB-predicted miRNAs (Fig. 4B). The results further revealed that 5 miRNAs, hsa-miR-1271-5p, hsa-miR-708-5p, hsa-miR-362-3p, hsa-miR-34c-5p and hsa-miR-34a-5p, were observed in both analyses. The potential interaction between 5 miRNAs and 290 genes was further validated by Targetscan and miRTarBase database analyses. The interaction between the 4 miRNAs and the 5 genes is presented in Table IV. These genes were involved in various biological processes. For example, MET proto-oncogene (MET) and heparin binding EGF like growth factor (HBEGF) were involved in positive regulation of cell migration and cellular component movement, calcium voltage-gated channel auxiliary subunit β3 (CACNB3) was involved in immune system process and immune response, tensin 3 (TNS3) was involved in cell migration and motility, and tweety family member 3 (TTYH3) involved in localization (Table SII). The interactions between hsa-miR-34a-5p, hsa-miR-34c-5p and MET, hsa-miR-34a-5p and CACNB3, and their biological function have been validated in other publications (44-52).

Table IV

Target genes of miRNAs.

Table IV

Target genes of miRNAs.

miRNAFold change of miRNAaGene symbolFold change of mRNAaTargetScanmiRTarBase(Refs.)
hsa-miR-34a-5p−2.18MET2.23YesYes(40,46-48)
hsa-miR-34a-5p−2.18CACNB30.49YesYes(45)
hsa-miR-34c-5p−2.11MET2.23YesYes(40-44)
hsa-miR-708-5p−2.07TNS36.41YesNo
hsa-miR-1271-5p−2.98TTYH32.70YesNo
hsa-miR-1271-5p−2.98TNS36.41YesNo
hsa-miR-1271-5p−2.98HBEGF3.78YesNo

a Fold change in lipoteichoic acid-treated group vs. vehicle group. MET, MET proto-oncogene; CACNB3, calcium voltage-gated channel auxiliary subunit β3; TNS3, tensin 3; TTYH3, tweety family member 3; HBEGF, heparin binding EGF like growth factor.

Although interactions were predicted between the 4 miRNAs and the 5 genes, potential interactions between most of genes and miRNAs identified in the present study were not identified. Because LTA stimulation induces defense responses, further analysis focused on the genes and miRNAs involved in the biological processes of positive regulation of cell migration and motility and cellular component movement (Fig. 5). The results revealed that various genes with >2-fold changes may also interact with miRNAs with <2-fold changes. Further experimental evidence will be necessary to confirm whether miRNA-mRNA interactions may be important for regulation of LTA-induced signaling pathways.

Discussion

LTA is a cell wall polymer in Gram-positive bacteria and a risk factor for sepsis. Based on their chemical structures, LTAs can be grouped into different types. Type I LTA is present in bacteria including S. aureus, Listeria monocytogenes and Bacillus subtilis (53). Prior publications have reported that LTAs from S. aureus and Streptococcus pneumoniae (type IV) can induce secretion of IL-8, IL-6, IL-1β and TNF-α in monocytes and macrophages (54,55). Although the half-life of circulating neutrophils is only 6-8 h (56), a previous study demonstrated that the production of IL-1β, IL-8 and TNF-α was significantly induced when human neutrophils were incubated with 10 µg/ml S. aureus LTA for 16 and 24 h (14). In addition, Hattar et al (14) demonstrated that the protein levels of IL-8 were induced by stimulation with 1, 5 and 10 µg/ml LTA after 16 h of incubation. In the present study, the results of RNA sequencing provided a whole molecular picture of LTA-induced gene expression, including a trend for increased expression of IL-8, IL-6, TNF-α and G-CSF (although <2-fold), and 290 genes with significantly differential expression in human neutrophils following 16 h stimulation with 1 µg/ml LTA.

LTA-affected genes have been reported in several types of cells in previous studies. Two previously published datasets in the Gene Expression Omnibus (GEO) database, GSE15512 and GSE21188, include results from microarray analysis determining the gene expression of an LTA-treated monocyte cell line (25 µg/ml LTA was used to stimulate THP-1 cells for 6 h) and peripheral blood mononuclear cells (PBMCs; 10 µg/ml LTA was used to stimulate PBMCs for 7 h), respectively. Although the dose and duration of the LTA stimulations were not identical, the expression of inflammatory genes in the present study was compared with that in both datasets. In general, the upregulated expression of genes including IL-1β, IL-6, CXCL8, CCL2 and CCL20 were similar in the present study and both datasets. Notably, LTA stimulation in THP-1 cells induced more significant changes in gene expression compared with LTA stimulation in PBMCs and in the present study. Based on 20 shared inflammatory-associated genes among the present study and the two public databases, the gene expression profile in the present study was moderately positively correlated with that in the public databases. These findings might suggest that genes can be affected differently by the various doses of LTA in a short (6-7 h) and long (16 h) incubation period.

The results of mRNA sequencing revealed various biological processes and signaling pathways that were enriched following LTA stimulation. In Table I, positively regulation of cell migration, cell motility and locomotion, as well as defense responses and inflammatory responses, were observed. Additionally, KEGG pathway analysis revealed that 4 pathways were enriched, including cytokine-cytokine receptor interaction and chemokine signaling pathway (Table II). A previous study demonstrated that B. subtilis LTA increases the secretion of CCL2 and CXCL10 in odontoblasts (57). To the best of our knowledge, the induction of chemokines is not fully elucidated in neutrophils. In odontoblasts, fibroblasts and pulpal cells, activation of TLR2, TLR3 and TLR4 pathways induces the production of several chemokines, such as CCL2, CCL7, IL-8 (CXCL8) and CXCL10 (58,59). In human lymphatic endothelium cells, LTA stimulation induces the expression of CCL2, CCL5, CXCL1, CXCL3, CXCL5, CXCL6 and IL-8 (CXCL8) through a TLR2-depedent mechanism (60). The present study revealed upregulation of CCL2, CCL7 and CXCL5 in LTA-stimulated neutrophils (Fig. 5). In addition, the expression of TLR2 was also upregulated (by 2.02-fold) following LTA stimulation. Therefore, it is supposed that TLR2 might be also essential for chemokine signaling pathway in human neutrophils. The role of TLR2 in the regulation of the chemokine signaling pathway, the function of proteoglycans, and the role of the Rap1 signaling pathway in neutrophils, will be further investigated in subsequent studies.

The effect of LTA stimulation on the miRNA expression remained unclear. In a mouse model, Staphylococcus epidermidis LTA induced the expression of miR-143 via TLR2 signaling (61). When mice were exposed to LTA from B. subtilis, S. faecalis and S. aureus, the expression of miR-451, miR-668, miR-1902 and miR-1904 was induced in whole blood and serum (62). The present study found 38 miRNAs with >2-fold change in expression following LTA stimulation; the majority of these 38 miRNAs were novel and not reported in previous publications. However, miR-143, miR-451, miR-668, miR-1902 and miR-1904 were not significantly altered in the human LTA-stimulated neutrophils in the present study. Because a miRNA can target many genes (63), GO analysis of the 38 miRNAs was performed (Table III). However, the function of these miRNAs and the regulatory mechanism of these enriched biological processes and LTA-mediated responses remained unknown. Therefore, the miRNA-target gene interactions were further investigated via multiple bioin-formatic tools. The results revealed potential novel interactions between hsa-miR-34a-5p, hsa-miR-34c-5p, hsa-miR-708-5p hsa-miR-1271-5p and MET, HBEGF, CACNB3, TNS3 and TTYH3, and that these interactions may regulate cell migration and motility, cellular component movement, immune system process and immune response. Although these findings were interesting, there are some limitations in the current study. Firstly, only one sample from one donor was analyzed in the present study. Furthermore, the interactions between miRNA and mRNA were not experimentally confirmed. The proposed interactions require further validation through functional experiments in the future. The summary of the present findings is presented in Fig. 6.

To the best of our knowledge, the present study is the first to provide comprehensive information about transcriptome analysis of LTA-stimulated human neutrophils. A total of 290 mRNAs and 38 miRNAs which were significantly altered by 16 h-stimulation of S. aureus LTA in human neutrophils were identified. Furthermore, bioinformatic analysis proposed novel interactions between 4 miRNAs and 5 target genes. These findings may provide new insights of the LTA-mediated effect on peripheral neutrophils and the innate immune responses in a healthy person.

Supplementary Data

Acknowledgments

The authors thank the Center for Research Resources and Development of Kaohsiung Medical University.

Funding

This study was supported by grants from the Ministry of Science and Technology (grant nos. MOST 108-2314-B-037-097-MY3, 107-2320-B-037-011-MY3 and 106-2320-B-037-029-MY3), the Kaohsiung Medical University Hospital (grant nos. KMUH107-7M36, KMUH107-7R81, KMUHS10701 and KMUHS10712), the Kaohsiung Medical University Research Center Grant (grant no. KMU-TC108A04) and the Kaohsiung Medical University (grant nos. KMU-DK108003 and KMU-Q108005).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

MY and MT conceived and designed the experiments. IY, KL and SJ prepared the materials and performed the experiments. MY, IY, CL, MT and PK analyzed the data. MY wrote the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Kaohsiung Medical University Hospital (IRB no. KMUH-IRB-20120287). Signed informed consent was obtained.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Brubaker SW, Bonham KS, Zanoni I and Kagan JC: Innate immune pattern recognition: A cell biological perspective. Annu Rev Immunol. 33:257–290. 2015. View Article : Google Scholar : PubMed/NCBI

2 

Mogensen TH: Pathogen recognition and inflammatory signaling in innate immune defenses. Clin Microbiol Rev. 22:240–273, Table of Contents, 2009. PubMed/NCBI

3 

Kawasaki T and Kawai T: Toll-like receptor signaling pathways. Front Immunol. 5:4612014. View Article : Google Scholar : PubMed/NCBI

4 

Lu YC, Yeh WC and Ohashi PS: LPS/TLR4 signal transduction pathway. Cytokine. 42:145–151. 2008. View Article : Google Scholar : PubMed/NCBI

5 

Park BS and Lee JO: Recognition of lipopolysaccharide pattern by TLR4 complexes. Exp Mol Med. 45:e662013. View Article : Google Scholar : PubMed/NCBI

6 

Seo HS, Michalek SM and Nahm MH: Lipoteichoic acid is important in innate immune responses to gram-positive bacteria. Infect Immun. 76:206–213. 2008. View Article : Google Scholar :

7 

Schwandner R, Dziarski R, Wesche H, Rothe M and Kirschning CJ: Peptidoglycan- and lipoteichoic acid-induced cell activation is mediated by toll-like receptor 2. J Biol Chem. 274:17406–17409. 1999. View Article : Google Scholar : PubMed/NCBI

8 

Oliveira-Nascimento L, Massari P and Wetzler LM: The role of TLR2 in infection and immunity. Front Immunol. 3:792012. View Article : Google Scholar : PubMed/NCBI

9 

Kengatharan KM, De Kimpe S, Robson C, Foster SJ and Thiemermann C: Mechanism of gram-positive shock: Identification of peptidoglycan and lipoteichoic acid moieties essential in the induction of nitric oxide synthase, shock, and multiple organ failure. J Exp Med. 188:305–315. 1998. View Article : Google Scholar : PubMed/NCBI

10 

Kurt-Jones EA, Mandell L, Whitney C, Padgett A, Gosselin K, Newburger PE and Finberg RW: Role of toll-like receptor 2 (TLR2) in neutrophil activation: GM-CSF enhances TLR2 expression and TLR2-mediated interleukin 8 responses in neutrophils. Blood. 100:1860–1868. 2002.PubMed/NCBI

11 

Lotz S, Aga E, Wilde I, van Zandbergen G, Hartung T, Solbach W and Laskay T: Highly purified lipoteichoic acid activates neutrophil granulocytes and delays their spontaneous apoptosis via CD14 and TLR2. J Leukoc Biol. 75:467–477. 2004. View Article : Google Scholar

12 

Ginsburg I: Role of lipoteichoic acid in infection and inflammation. Lancet Infect Dis. 2:171–179. 2002. View Article : Google Scholar : PubMed/NCBI

13 

Nathan C: Neutrophils and immunity: Challenges and opportunities. Nat Rev Immunol. 6:173–182. 2006. View Article : Google Scholar : PubMed/NCBI

14 

Hattar K, Grandel U, Moeller A, Fink L, Iglhaut J, Hartung T, Morath S, Seeger W, Grimminger F and Sibelius U: Lipoteichoic acid (LTA) from Staphylococcus aureus stimulates human neutrophil cytokine release by a CD14-dependent, Toll-like-receptor-independent mechanism: Autocrine role of tumor necrosis factor-[alpha] in mediating LTA-induced interleukin-8 generation. Crit Care Med. 34:835–841. 2006. View Article : Google Scholar : PubMed/NCBI

15 

Drury RE, O'Connor D and Pollard AJ: The clinical application of MicroRNAs in infectious disease. Front Immunol. 8:11822017. View Article : Google Scholar : PubMed/NCBI

16 

Liu H, Lei C, He Q, Pan Z, Xiao D and Tao Y: Nuclear functions of mammalian MicroRNAs in gene regulation, immunity and cancer. Mol Cancer. 17:642018. View Article : Google Scholar : PubMed/NCBI

17 

Wen Z, Xu L, Chen X, Xu W, Yin Z, Gao X and Xiong S: Autoantibody induction by DNA-containing immune complexes requires HMGB1 with the TLR2/microRNA-155 pathway. J Immunol. 190:5411–5422. 2013. View Article : Google Scholar : PubMed/NCBI

18 

Yao H, Zhang H, Lan K, Wang H, Su Y, Li D, Song Z, Cui F, Yin Y and Zhang X: Purified Streptococcus pneumoniae endo-peptidase O (PepO) enhances particle uptake by macrophages in a toll-like receptor 2- and miR-155-dependent manner. Infect Immun. 85:e01012–e01016. 2017. View Article : Google Scholar :

19 

Xu H, Wu Y, Li L, Yuan W, Zhang D, Yan Q, Guo Z and Huang W: MiR-344b1-3p targets TLR2 and negatively regulates TLR2 signaling pathway. Int J Chron Obstruct Pulmon Dis. 12:627–638. 2017. View Article : Google Scholar :

20 

Landais I, Pelton C, Streblow D, DeFilippis V, McWeeney S and Nelson JA: Human cytomegalovirus miR-UL112-3p targets TLR2 and modulates the TLR2/IRAK1/NFκB signaling pathway. PLoS Pathog. 11:e10048812015. View Article : Google Scholar

21 

Quinn EM, Wang JH, O'Callaghan G and Redmond HP: MicroRNA-146a is upregulated by and negatively regulates TLR2 signaling. PLoS One. 8:e622322013. View Article : Google Scholar : PubMed/NCBI

22 

Bolger AM, Lohse M and Usadel B: Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 30:2114–2120. 2014. View Article : Google Scholar : PubMed/NCBI

23 

Kim D, Langmead B and Salzberg SL: HISAT: A fast spliced aligner with low memory requirements. Nat Methods. 12:357–360. 2015. View Article : Google Scholar : PubMed/NCBI

24 

Friedlander MR, Mackowiak SD, Li N, Chen W and Rajewsky N: miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 40:37–52. 2012. View Article : Google Scholar :

25 

Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL and Pachter L: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks. Nat Protoc. 7:562–578. 2012. View Article : Google Scholar : PubMed/NCBI

26 

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. View Article : Google Scholar

27 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

28 

Huang da W, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar

29 

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI

30 

Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, et al: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 34:267–273. 2003. View Article : Google Scholar : PubMed/NCBI

31 

Backes C, Khaleeq QT, Meese E and Keller A: miEAA: microRNA enrichment analysis and annotation. Nucleic Acids Res. 44:W110–W116. 2016. View Article : Google Scholar : PubMed/NCBI

32 

Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CY, Williamson NA, Mouradov D, Sieber OM, Simpson RJ, Salim A, et al: FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics. 15:2597–2601. 2015. View Article : Google Scholar : PubMed/NCBI

33 

Liu W and Wang X: Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol. 20:182019. View Article : Google Scholar : PubMed/NCBI

34 

Garcia DM, Baek D, Shin C, Bell GW, Grimson A and Bartel DP: Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol. 18:1139–1146. 2011. View Article : Google Scholar : PubMed/NCBI

35 

Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, et al: miRTarBase update 2018: A resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 46:D296–D302. 2018. View Article : Google Scholar :

36 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

37 

Doncheva NT, Morris JH, Gorodkin J and Jensen LJ: Cytoscape stringApp: Network analysis and visualization of proteomics data. J Proteome Res. 18:623–632. 2019. View Article : Google Scholar

38 

Finney SJ, Leaver SK, Evans TW and Burke-Gaffney A: Differences in lipopolysaccharide- and lipoteichoic acid-induced cytokine/chemokine expression. Intensive Care Med. 38:324–332. 2012. View Article : Google Scholar :

39 

Schröder NW, Morath S, Alexander C, Hamann L, Hartung T, Zähringer U, Göbel UB, Weber JR and Schumann RR: Lipoteichoic acid (LTA) of Streptococcus pneumoniae and Staphylococcus aureus activates immune cells via Toll-like receptor (TLR)-2, lipopolysaccharide-binding protein (LBP), and CD14, whereas TLR-4 and MD-2 are not involved. J Biol Chem. 278:15587–15594. 2003. View Article : Google Scholar : PubMed/NCBI

40 

Zeng RZ, Kim HG, Kim NR, Gim MG, Ko MY, Lee SY, Kim CM and Chung DK: Differential gene expression profiles in human THP-1 monocytes treated with Lactobacillus plantarum or Staphylococcus aureus lipoteichoic acid. J Korean Soc Appl Bi. 54:763–770. 2011. View Article : Google Scholar

41 

Sharma S, Davis RE, Srivastva S, Nylen S, Sundar S and Wilson ME: A subset of neutrophils expressing markers of antigen-presenting cells in human visceral leishmaniasis. J Infect Dis. 214:1531–1538. 2016. View Article : Google Scholar : PubMed/NCBI

42 

Chen X, Li SJ, Ojcius DM, Sun AH, Hu WL, Lin X and Yan J: Mononuclear-macrophages but not neutrophils act as major infiltrating anti-leptospiral phagocytes during leptospirosis. PLoS One. 12:e01810142017. View Article : Google Scholar : PubMed/NCBI

43 

Long EM, Millen B, Kubes P and Robbins SM: Lipoteichoic acid induces unique inflammatory responses when compared to other toll-like receptor 2 ligands. PLoS One. 4:e56012009. View Article : Google Scholar : PubMed/NCBI

44 

Hermeking H: The miR-34 family in cancer and apoptosis. Cell Death Differ. 17:193–199. 2010. View Article : Google Scholar

45 

Cai KM, Bao XL, Kong XH, Jinag W, Mao MR, Chu JS, Huang YJ and Zhao XJ: Hsa-miR-34c suppresses growth and invasion of human laryngeal carcinoma cells via targeting c-Met. Int J Mol Med. 25:565–571. 2010. View Article : Google Scholar : PubMed/NCBI

46 

Dong F and Lou D: MicroRNA-34b/c suppresses uveal melanoma cell proliferation and migration through multiple targets. Mol Vis. 18:537–546. 2012.PubMed/NCBI

47 

Hagman Z, Haflidadottir BS, Ansari M, Persson M, Bjartell A, Edsjö A and Ceder Y: The tumour suppressor miR-34c targets MET in prostate cancer cells. Br J Cancer. 109:1271–1278. 2013. View Article : Google Scholar : PubMed/NCBI

48 

Wang F, Lu J, Peng X, Wang J, Liu X, Chen X, Jiang Y, Li X and Zhang B: Integrated analysis of microRNA regulatory network in nasopharyngeal carcinoma with deep sequencing. J Exp Clin Cancer Res. 35:172016. View Article : Google Scholar : PubMed/NCBI

49 

Bavamian S, Mellios N, Lalonde J, Fass DM, Wang J, Sheridan SD, Madison JM, Zhou F, Rueckert EH, Barker D, et al: Dysregulation of miR-34a links neuronal development to genetic risk factors for bipolar disorder. Mol Psychiatry. 20:573–584. 2015. View Article : Google Scholar : PubMed/NCBI

50 

Yan D, Zhou X, Chen X, Hu DN, Dong XD, Wang J, Lu F, Tu L and Qu J: MicroRNA-34a inhibits uveal melanoma cell proliferation and migration through downregulation of c-Met. Invest Ophthalmol Vis Sci. 50:1559–1565. 2009. View Article : Google Scholar

51 

Guessous Li Y, Zhang F, Dipierro Y, Kefas C, Johnson B, Marcinkiewicz E, Jiang L, Yang J, Schmittgen YTD, et al: MicroRNA-34a inhibits glioblastoma growth by targeting multiple oncogenes. Cancer Res. 69:7569–7576. 2009. View Article : Google Scholar : PubMed/NCBI

52 

Yan K, Gao J, Yang T, Ma Q, Qiu X, Fan Q and Ma B: MicroRNA-34a inhibits the proliferation and metastasis of osteosarcoma cells both in vitro and in vivo. PLoS One. 7:e337782012. View Article : Google Scholar : PubMed/NCBI

53 

Percy MG and Grundling A: Lipoteichoic acid synthesis and function in gram-positive bacteria. Annu Rev Microbiol. 68:81–100. 2014. View Article : Google Scholar : PubMed/NCBI

54 

Standiford TJ, Arenberg DA, Danforth JM, Kunkel SL, VanOtteren GM and Strieter RM: Lipoteichoic acid induces secretion of interleukin-8 from human blood monocytes: A cellular and molecular analysis. Infect Immun. 62:119–125. 1994.PubMed/NCBI

55 

Mattsson E, Verhage L, Rollof J, Fleer A, Verhoef J and van Dijk H: Peptidoglycan and teichoic acid from Staphylococcus epidermidis stimulate human monocytes to release tumour necrosis factor-alpha, interleukin-1 beta and interleukin-6. FEMS Immunol Med Microbiol. 7:281–287. 1993.PubMed/NCBI

56 

Summers C, Rankin SM, Condliffe AM, Singh N, Peters AM and Chilvers ER: Neutrophil kinetics in health and disease. Trends Immunol. 31:318–324. 2010. View Article : Google Scholar : PubMed/NCBI

57 

Durand SH, Flacher V, Roméas A, Carrouel F, Colomb E, Vincent C, Magloire H, Couble ML, Bleicher F, Staquet MJ, et al: Lipoteichoic acid increases TLR and functional chemokine expression while reducing dentin formation in in vitro differentiated human odontoblasts. J Immunol. 176:2880–2887. 2006. View Article : Google Scholar : PubMed/NCBI

58 

Park C, Lee SY, Kim HJ, Park K, Kim JS and Lee SJ: Synergy of TLR2 and H1R on Cox-2 activation in pulpal cells. J Dent Res. 89:180–185. 2010. View Article : Google Scholar

59 

Staquet MJ, Durand SH, Colomb E, Roméas A, Vincent C, Bleicher F, Lebecque S and Farges JC: Different roles of odonto-blasts and fibroblasts in immunity. J Dent Res. 87:256–261. 2008. View Article : Google Scholar : PubMed/NCBI

60 

Sawa Y, Tsuruga E, Iwasawa K, Ishikawa H and Yoshida S: Leukocyte adhesion molecule and chemokine production through lipoteichoic acid recognition by toll-like receptor 2 in cultured human lymphatic endothelium. Cell Tissue Res. 333:237–252. 2008. View Article : Google Scholar : PubMed/NCBI

61 

Xia X, Li Z, Liu K, Wu Y, Jiang D and Lai Y: Staphylococcal LTA-induced miR-143 inhibits propionibacterium acnes-mediated inflammatory response in skin. J Invest Dermatol. 136:621–630. 2016. View Article : Google Scholar : PubMed/NCBI

62 

Hsieh CH, Yang JC, Jeng JC, Chen YC, Lu TH, Tzeng SL, Wu YC, Wu CJ and Rau CS: Circulating microRNA signatures in mice exposed to lipoteichoic acid. J Biomed Sci. 20:22013. View Article : Google Scholar : PubMed/NCBI

63 

Bartel DP: MicroRNAs: Target recognition and regulatory functions. Cell. 136:215–233. 2009. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

October 2019
Volume 44 Issue 4

Print ISSN: 1107-3756
Online ISSN:1791-244X

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
APA
Yen, M., Yeh, I., Liu, K., Jian, S., Lin, C., Tsai, M., & Kuo, P. (2019). Next-generation sequencing predicts interaction network between miRNA and target genes in lipoteichoic acid-stimulated human neutrophils. International Journal of Molecular Medicine, 44, 1436-1446. https://doi.org/10.3892/ijmm.2019.4295
MLA
Yen, M., Yeh, I., Liu, K., Jian, S., Lin, C., Tsai, M., Kuo, P."Next-generation sequencing predicts interaction network between miRNA and target genes in lipoteichoic acid-stimulated human neutrophils". International Journal of Molecular Medicine 44.4 (2019): 1436-1446.
Chicago
Yen, M., Yeh, I., Liu, K., Jian, S., Lin, C., Tsai, M., Kuo, P."Next-generation sequencing predicts interaction network between miRNA and target genes in lipoteichoic acid-stimulated human neutrophils". International Journal of Molecular Medicine 44, no. 4 (2019): 1436-1446. https://doi.org/10.3892/ijmm.2019.4295