HL-60 cells were obtained from the Bioresource Collection and Research Center. The HL-60 cells were maintained in RPMI 1640 medium with L-glutamine (GeneDireX, Inc.) supplemented with 20% fetal bovine serum (Gibco; Thermo Fisher Scientific, Inc.) and antibiotics, including 100 U/ml penicillin, 100 µg/ml streptomycin and 0.25 µg/ml amphotericin B, at 37˚C in a humidified atmosphere with 5% CO₂. The cell density was maintained between 1x10⁵ and 1x10⁶ cells/ml. To differentiate the HL-60 cells into a neutrophil-like phenotype, the cells were cultured at a density of 1x10⁶ cells/ml in RPMI 1640 medium containing L-glutamine supplemented with 10% fetal bovine serum, 10 mM HEPES (Gibco; Thermo Fisher Scientific, Inc.), the aforementioned antibiotics and 1.25% DMSO for 6 days.

To observe the induction of NETs in dHL-60 cells, dHL-60 cells were collected 6 days after DMSO stimulation. Extracellular DNA of NET were then visualized using SYTOX Green staining. Briefly, dHL-60 cells were seeded into 24-well plates (2x10⁵ cells per well) in serum-free RPMI 1640 medium containing 1% bovine serum albumin and 1 mM calcium chloride, then treated with vehicle (DMSO) or 20 nM phorbol myristate acetate (PMA; MedChemExpress) at 37˚C. After 4 h, dHL-60 cells were stained with 5 µM SYTOX Green (Invitrogen; Thermo Fisher Scientific, Inc.) for 10 min in the dark at room temperature. Images were then obtained using a Nikon Eclipse TE2000-S inverted fluorescence microscope equipped with x10 magnification objectives. The experiment was repeated 3 times.

NET formation in dHL-60 cells was evaluated using a NETosis Assay Kit (Cayman Chemical Company). Briefly, 2x10⁵ dHL-60 cells were treated with either vehicle or PMA and then incubated at 37˚C for 4 h to induce NET formation, according to the manufacturer's instructions. After 4 h, the culture supernatant was collected and NET-associated neutrophil elastase activity was detected at 405 nm. The experiment was repeated 3 times.

ROS production in dHL-60 cells was detected by staining with a DCFDA/H2DCFDA-Cellular ROS Assay Kit (Abcam). After a 4-h stimulation with vehicle, 2 or 20 nM PMA, the dHL-60 cells were stained with 10 µM DCFDA for 30 min at 37˚C and then analyzed on a BD Accuri C6 Flow Cytometer (BD Biosciences), and the data were analyzed using FCSalyzer ver. 0.9.22-alpha (https://sourceforge.net/projects/fcsalyzer/). The experiment was repeated 2 times.

For RNA sequencing, 2x10⁶ uHL-60 cells were treated with 1 µg/ml S. aureus LTA (cat.no. L2515; Sigma-Aldrich; Merck KGaA) or vehicle (ddH2O) for 4 and 24 h (n=1), and 2x10⁶ dHL-60 cells were treated with 1 µg/ml or 10 µg/ml S. aureus LTA or vehicle (ddH2O) for 4 and 24 h (n=2) in 37˚C incubator with 5% CO₂. Total RNA from 2x10⁶ uHL-60 and dHL-60 cells was extracted using a Total RNA Purification Kit (Norgen Biotek Corp.). The purified RNA was used for the preparation of the sequencing library by TruSeq Stranded mRNA Library Prep Kit (Illumina, Inc.) following the manufacturer's recommendations. After the generation of double-strand cDNA and adenylation on 3' ends of DNA fragments, the adaptors were ligated and purified with AMPure XP system (Beckman Coulter, Inc.). The quality of the libraries was assessed on the Agilent Bioanalyzer 2100 system and a Real-Time PCR system. RNA sequencing was performed using the Illumina NovaSeq 6000 platform with 150 bp paired end read lengths and 20 million clean reads per sample by a commercial vendor (Genomics BioSci & Tech Co. Ltd.). The bases with low quality and sequences from adapters in raw data were removed using program fastp (version 0.20.0; https://github.com/OpenGene/fastp). The filtered reads were aligned to the reference genomes using HISAT2 (version 2.1.0; https://daehwankimlab.github.io/hisat2/). The software FeatureCounts (v2.0.1; https://subread.sourceforge.net) in Subread package was applied for the quantification of the gene abundance. For identification of differentially expressed genes (DEGs), DESeq2 (version 1.28.0; https://bioconductor.org/packages/release/bioc/html/DESeq2.html) and EdgeR (version 3.36.0; https://bioconductor.org/packages/release/bioc/html/edgeR.html) were used to analyze samples with and without biological replicates respectively. The criteria for DEGs were set at fold change ≥2.0 and P<0.05 in the dHL-60 cell group. Compared with the dHL-60 cells, the gene expression of uHL-60 was less affected by LTA treatment. Therefore, the criterion of DEGs for the uHL-60 cells was set at P<0.05, owing to the small number of DEGs. Raw and processed RNA sequencing data were uploaded to the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE239859).

The processed RNA sequencing data deposited as GSE239859 in GEO database was further analyzed through the following methods. Principal Component Analysis (PCA), heatmaps and bubble plots were constructed using the online web tool, Srplot (http://www.bioinformatics.com.cn/srplot), and Venn diagrams were compiled using the website http://bioinformatics.psb.ugent.be/webtools/Venn/. To perform Gene Ontology (GO) analysis (biological processes) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, the DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID; v6.8; https://david.ncifcrf.gov/) (32). The identified DEGs of uHL-60 and dHL-60 were further processed as input data for the DAVID analyses. A significant difference was indicated by a P<0.001. The predicted gene-targeted microRNAs (miRNAs) were analyzed using the miRNet 2.0 website (https://www.mirnet.ca) (33). The interaction network was constructed using Cytoscape software 3.9.1 (https://cytoscape.org). Gene Set Enrichment Analysis (GSEA) was performed using the WeB-based GEne SeT Analysis Toolkit (http://www.webgestalt.org) (34). The gene set for the KEGG pathway analysis was obtained from the Molecular Signatures Database [curated gene sets, canonical pathways, KEGG analysis for human gene symbols; v2022.1; https://www.gsea-msigdb.org/gsea/index.jsp] (35), while a false discovery rate of <0.05 was set as the significance level. For GSEA analysis, all genes identified by RNA sequencing were as input data.

Total RNA of dHL-60 was extracted using a Total RNA Purification Kit (Norgen Biotek Corp.) and then reverse-transcribed to cDNA using an iScript^™ cDNA Synthesis Kit (Bio-Rad Laboratories, Inc.) according to the manufacturer's instructions. All qPCR experiments were run on a QuantStudio 5 Real-Time PCR system (Applied Biosystems; Thermo Fisher Scientific, Inc.) using the SYBR Green I-based TB Green Premix Ex Taq II (Takara Bio, Inc.) and the program, 95˚C for 30 sec, 40 cycles of 95˚C for 3 sec, and 60˚C for 30 sec. The relative mRNA expression was normalized to the expression of the internal control, b-actin, using by 2^-∆∆Cq method (36). The nucleotide sequences for the primers used are listed in Table I.

Bar plots and statistical analyses were performed using GraphPad Prism 8.4.3 software (Dotmatics). A two-tailed unpaired t-test was used to analyze the differences between two groups. One-way ANOVA and Tukey's multiple-comparison test were used to compare three groups. P<0.05 was considered to indicate a statistically significant difference.

A flowchart of the present study is presented in Fig. 1A. To obtain neutrophil-like phenotypes, uHL-60 cells were differentiated by DMSO treatment. The neutrophil-like phenotype of dHL-60 cells was then further evaluated. PMA is a known inducer of NET formation and ROS production (37,38). Fluorescence microscopy revealed the formation of NETs in PMA-stimulated dHL-60 cells (Fig. 1B). The NETosis assay also demonstrated that PMA stimulation significantly increased NET-associated elastase activity (Fig. 1C). In addition, ROS production was increased following PMA stimulation (Fig. 1D). These results indicated that DMSO-differentiated HL-60 cells had neutrophil-like phenotypes.

The doses of 0.1-10 µg/ml S. aureus LTA are used in several published studies (18,20), and treatment with LTA >1 µg/ml induces relatively significant immune responses in immune cells. However, the present pretest experiments showed that treatments with 1 or 10 µg/ml S. aureus did not induce the expression of inflammatory molecules in uHL-60 as in human primary neutrophils (data not shown). Therefore, the current study focused on LTA-treated dHL-60, while udHL-60 was tested only at a single dose of LTA using a single sample. For RNA sequencing analysis, uHL-60 cells were treated with 1 µg/ml S. aureus LTA or vehicle (ddH₂O) for 4 and 24 h (n=1), and dHL-60 cells were treated with 1 µg/ml or 10 µg/ml S. aureus LTA or vehicle (ddH₂O) for 4 and 24 h (n=2). Subsequently, the gene expression profiles of the harvested cells were analyzed by RNA sequencing, and PCA was performed to examine the distribution of DEGs in each group. Fig. 2A and B show the differences in gene expression profiles between the uHL-60 and dHL-60 cells. The PCA plot demonstrated a clear separation between the uHL-60 and dHL-60 cells, and LTA-treated dHL-60 and vehicle-treated dHL-60 were also distributed in two clusters. By contrast, there was no clear separation of LTA-treated uHL-60 and vehicle-treated uHL-60 cells at either time point. A heatmap plot revealed similar clusters among the uHL-60 and dHL-60 cell samples (Fig. S1).

The Venn diagram demonstrated that various shared DEGs were identified in the ‘LTA1 vs. Vehicle’ and ‘LTA10 vs. Vehicle’ groups for the dHL-60 cells at both time points (Fig. 2C and D). However, only 1 shared DEG [C-C motif chemokine ligand 1 (CCL1)] and 2 shared DEGs (hemoglobin subunit β and spermatogenesis and oogenesis specific basic helix-loop-helix 2 genes) were identified in the three groups comparison at 4 and 24 h, respectively. The results showed that the transcript levels of uHL-60 cells were completely different from those of dHL-60 cells after LTA treatment. The results suggested that uHL60 and dHL60 indeed had different characteristics, and their responses to LTA treatment were almost completely different. Therefore, the enriched pathways in uHL-60 and dHL-60 cells were investigated separately.

The functions of the identified DEGs in uHL-60 cells after 4 and 24 h of LTA treatment were annotated by GO analysis using DAVID. No significant pathways were enriched after 4 h of LTA treatment. The enriched pathways in uHL-60 cells after 24 h of LTA treatment are shown in Table II. Most of the identified pathways were associated with survival of motor neuron 1, telomeric (SMN1) and survival of motor neuron 2, centromeric (SMN2) genes. The results therefore suggested that treatment with 1 µg/ml LTA affected only a small proportion of genes in uHL-60 cells.

A large number of common DEGs were identified in the 1 µg/ml LTA (LTA1) and 10 µg/ml LTA (LTA10) treated dHL-60 cells at both time points (Fig. 2C and D). Compared with the vehicle group, increased expression levels of known LTA-induced inflammatory molecules were observed in the LTA1 and LTA10 groups, according to the RNA sequencing data (Fig. 3A and B). To further confirm the observed expression profiles, the mRNA expression levels of CCL2, CCL5, CXCL8, IL-1β and TNF were quantified using RT-qPCR. In general, the expression patterns of these molecules in the RT-qPCR analysis were similar to those observed in the RNA sequencing analysis (Fig. 3C-G).

To further investigate the enriched pathways in LTA-treated dHL-60 cells, GO (biological processes) and KEGG pathway analyses were performed using DAVID, according to the common upregulated 1,234 and 718 DEGs in the 4 and 24 h LTA-treated groups, respectively (Fig. 4A and B). Fig. 4C-F show the identified top 10 significantly enriched biological processes and KEGG pathways. The top 20 biological processes, KEGG pathways and detailed gene lists are presented in Table SI, Table SII, Table SIII and Table SIV. Numerous identical biological processes were observed including ‘inflammatory response’, ‘cellular response to lipopolysaccharide’, ‘immune response’, ‘cellular response to tumor necrosis factor’, ‘signal transduction’, ‘apoptotic process’, ‘chemokine-mediated signaling pathway’, ‘positive regulation of inflammatory response’, ‘neutrophil chemotaxis’, ‘positive regulation of cell migration’, ‘chemotaxis’ and ‘positive regulation of ERK1 and ERK2 cascade’ in the 4 or 24 h LTA-treated groups. Furthermore, identical KEGG pathways including ‘Cytokine-cytokine receptor interaction’, ‘TNF signaling pathway’, ‘Viral protein interaction with cytokine and cytokine receptor’, ‘Lipid and atherosclerosis’, ‘NF-kappa B signaling pathway’, ‘IL-17 signaling pathway’, ‘NOD-like receptor signaling pathway’, ‘Toll-like receptor signaling pathway’, ‘Chemokine signaling pathway’ and ‘Rheumatoid arthritis’ were observed in the top 20 enriched KEGG pathways. Therefore, the results demonstrated that the enriched pathways following 4 and 24 h LTA treatment of dHL-60 cells were similar.

Our previous study demonstrated that the expression levels of four miRNAs, including hsa-miR-34a-5p, hsa-miR-34c-5p, hsa-miR-708-5p and hsa-miR-1271-5p, are affected by LTA treatment in human primary neutrophils (39). Although the present study aimed to determine whether the same miRNA is regulated in LTA-treated dHL-60 cells, microRNA sequencing was not performed in this study. Therefore, the miRNAs that were predicted to interact with the commonly upregulated DEGs in the 4 or 24 h LTA-treated groups were analyzed using the miRnet website. The above four miRNAs were also included in the list of miRNAs predicted to interact with the upregulated DEGs. These results implied that similar miRNA-gene interactions can be found in both human primary neutrophils and dHL-60 cells treated with LTA. A possible interaction network between the shared upregulated DEGs in LTA-treated dHL-60 and the aforementioned four miRNAs is shown in Fig. 5.

The impact of high and low concentrations of LTA on dHL-60 cells was further investigated by comparing the enriched pathways between the two groups. A volcano plot presenting the DEGs between the LTA1 and LTA10-treated dHL-60 cells is shown in Fig. S2. The results revealed that less DEGs were found in the samples treated for 4 h compared with the samples treated for 24 h. GSEA was performed to further investigate the enriched pathways in both groups. The ‘Complement and coagulation cascades’ was the only pathway that reached statistical significance (false discovery rate <0.05) in the LTA1 cells treated for 4 h (Fig. 6A). By contrast, multiple significantly enriched KEGG pathways were observed in the LTA10 cells treated for 24 h (Fig. 6B-L). Several immune response-related pathways were enriched, including ‘Cytokine-cytokine receptors interaction,’ ‘Chemokine signaling pathways,’ ‘Toll-like receptor signaling pathway,’ ‘NOD like receptor signaling pathway’ and ‘Cytosolic DNA sensing pathway.’ These enriched pathways were associated with innate immune responses.