To screen crucial genes involved in ARDS and M1-polarized macrophages for further research, we conducted interrelated bioinformatic analysis of a mRNA matrix from ARDS patients and gene expression profiles of macrophages. The mRNA matrix from ARDS patients was built from the biological specimen bank of the Critical Care, Zhongda Hospital. These gene expression profiles of macrophages were screened from the public GEO database.

The current research was performed in accordance with the amended Declaration of Helsinki. The human sample collection was reviewed and approved by the Institutional Ethics Committee of Zhongda Hospital. The ethical documents for human studies can be downloaded in attach files.

Similarly, to further validate whether the hub genes are involved in ARDS, the mRNA expression of the hub genes was also measured using RT-PCR in lung tissue homogenates from LPS-induced ARDS mice (n=6) and control mice (n=6). A t test was performed to confirm whether hub gene expression was significantly upregulated or downregulated in ARDS, with a cut-off value of P <0.05.

The Committee of Animal Care and Use of Southeast University reviewed and approved the animal experiments in the current study. All animal experiments were carried out in accordance with the Institutional Animal Use and Care Committee. The Laboratory Animal Center (Shanghai, China) provided all C57BL/6 mice (male, aged 6–8 weeks) used in experiments. The mice were maintained on a 12/12-h light/dark cycle at 22–26°C with sterile pellet food and water provided ad libitum. After mice were anaesthetized with an intraperitoneal injection of 5.0% (w/v) pentobarbital sodium (4.0 ml/kg), they were subjected to intratracheal administration of LPS (E. coli 0111:B4; Sigma-Aldrich, St. Louis, MO). The sham operations were conducted in similar manners with the same volume of normal saline.

To screen candidates involved in ARDS and M1-polarized macrophages for further research, 5 steps were implemented in this study.

1) To screen candidate genes associated with the severity of ARDS, weighted correlation network analysis (WGCNA) was performed on all gene expression profiles from the 26 ARDS patients. Briefly, we utilized the WGCNA R package to classify all expressed genes in microarrays into several module eigengenes (MEs) based on coexpression relationships (38). Then, the MEs were compared with the severity of ARDS using Spearman’s correlation corrected for clusters. The genes from modules that were highly associated with the severity of ARDS (the maximum correlation coefficient and P < 0.05) were selected for further analysis.

The WGCNA algorithm is a systems biology method for describing the correlation patterns among genes across microarray samples. WGCNA can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures (38).

The process of WGCNA included three steps: construction of gene-correlation networks, scale-free topology and exploration of links between gene networks and clinical characteristics. Construction of gene-correlation networks was based on the biweight midcorrelation or the Spearman correlation, with the formula S_ij =|cor(x_i, x_j)|. The aim of scale-free topology is to simplify and optimize the gene-correlation networks. A weighed network adjacency can be defined by raising the coexpression similarity to a power, with the formula a_ij = s ij β . The exploration of links between gene networks and clinical characteristics was based on Pearce correlation or Spearman correlation analysis.

2) To investigate the genes potentially involved in M1 macrophage polarization, we need to screen significantly differentially expressed genes between M1 macrophages and M0 and M2 macrophages (2-fold difference, FDR<0.05). Therefore, we searched for all high-throughput experiments that matched terms of macrophage M1 polarization in the GEO database. The exclusion criteria were as follows: a) lack of at least one macrophage polarization phenotype, b) incomplete transcriptome data, and c) inability to obtain macrophage polarization phenotype information for each sample. d) Furthermore, if the macrophages were treated with drugs, siRNA, shRNA, plasmid, or CRISPR, these databases were removed because such high-throughput results were deeply influenced by these interventions. e) In addition, studies that lack robust experiments to validate macrophage polarization will be removed. The robust experiments included western blot, flow cytometry, immunofluorescence, and other experiments for investigating macrophage polarization markers. The flow process of this search is shown in Figure S1.

The GSE46903 dataset was used as the macrophage training dataset since this dataset had the largest sample size. The other datasets were used as macrophage validation datasets to further observe the results of analysis on the training dataset.

We utilized the Limma R package to identify differentially expressed genes based on mRNA microarray data and the edgeR R package for screening differentially expressed molecules based on RNA sequencing data. The algorithm of differentially expressed analysis implements an empirical Bayesian approach to estimate gene expression changes using moderated t-tests.

3) Furthermore, to identify the candidate genes simultaneously associated with ARDS and M1 macrophages, a Venn diagram was plotted to capture the overlap of the genes found in (1) and (2).

4) To determine the crucial genes, we performed hub gene analysis through connectivity degree analysis (number of neighbors). First, we plotted the protein-protein interaction (PPI) network based on the STRING website (http://string-db.org/cgi/input.pl). Second, the total connectivity degree of each node in the network was calculated using R to find the genes with the highest connectivity degrees. Genes above the first inflection point of connectivity degree were identified as hub genes (39).

5) The gene with the strongest ARDS-related correlation was identified as a primary candidate for further investigation in vitro. To select the gene with the strongest ARDS-related correlation, we utilized the RobustRankAggreg algorithm (RobustRankAggreg R package) to calculate interrelated P values based on the P values from human and animal experiments. The hub genes were ranked according to interrelated P values from minimum to maximum. The gene with the minimum interrelated P value and maximum Spearman correlation coefficient was selected as the crucial gene for further research (40).

As the outputs of individual experiments can be rather noisy, it is essential to look for findings that are supported by several pieces of evidence to increase the signal and lessen the fraction of false positive findings. The RobustRankAggreg algorithm aims to obtain the best out of all these alternatives according to integrating their results in an unbiased manner. The RobustRankAggreg algorithm calculated the interrelated P value through the formula P(r)=min_k=1…n β_k,n(r) (40).

As the expression of IFIH1 is known to be up-regulated in M1 polarized macrophage based on the results of bioinformatics analysis, we asked whether IFIH1 can affect M1 macrophage polarization. To confirm this possibility, shRNA sequence and plasmid expression vectors for IFIH1 were introduced to construct stably transfected RAW264.7 cell lines with IFIH1 overexpression or knockdown, respectively, followed by LPS or Poly(I:C) induction for 24 h. Furthermore, we investigated markers of M1 macrophage polarization and pro-inflammatory cytokines in each group via western blotting, flow cytometry, and ELISA. These markers of M1 macrophage polarization included iNOS and CD86. The pro-inflammatory cytokines included IL-1β and CCL2.

Considering that RAW264.7 cells intensively proliferate, mouse primary macrophages, bone marrow-derived macrophages (BMDMs), were used as supplementary evidence. We re-conducted all experiments on RAW264.7 cells and BMDMs simultaneously.

C57BL/6 mice (male, aged 8 weeks) were killed by cervical dislocation. Then, femurs and tibias were separated from tissue and sterilized with 75% ethanol for 30 min. The bones were cut from both ends and flushed with medium using a 10-ml syringe. Furthermore, these cell media were centrifuged at 1000 rpm and resuspended in new medium. The isolated cells were cultured in DMEM containing 10% FBS and 20 ng/ml M-CSF for 7 days to obtain adherent cells (BMDMs). Flow cytometry was used to detect F4/80 (macrophage marker) for the identification of BMDMs.

The Cell Resource Center of the Shanghai Institutes for Biological Sciences (Chinese Academy of Science) provided us with the murine-derived macrophage cell line RAW264.7. RAW264.7 cells and BMDMs were cultured in Dulbecco’s modified medium (DMEM; Wisent Biotechnology, Nanjing, China) containing 10% fetal bovine serum (FBS, Coring, Australia), 100 IU/ml penicillin, and 100 μg/ml streptomycin at 37°C in a humidified atmosphere with 5% CO2.

It has been robustly demonstrated that IFIH1 and the RIG-I pathway has robustly demonstrated that Poly(I:C) is the activator of IFIH1 and RIG-I. In addition, LPS has been repeatedly confirmed as the classical activator for M1 macrophage polarization. Bacterial lipopolysaccharides have been robustly identified as crucial pathogenic factors of ARDS and are widely used to construct ARDS animal models inflammatory cell models. In consideration of these factors, Poly(I:C) and LPS were used as stimulators for M1 macrophage polarization in the current study.

The concentration of LPS was determined according to our previous work (500 ng/ml) (37). A concentration gradient of Poly(I:C) was used to explore the optimum concentration of Poly(I:C) to induce M1- macrophage polarization. Western blot analysis indicated that 50 ng/ml was the optimum concentration for Poly(I:C)-derived M1 macrophages (Figure S2).

We initially designed three different sequences specifically targeting mouse IFIH1 according to GeneChem Co., Ltd. (http://www.genechem.com.cn; Table S1). The sequences of the shRNAs are indicated in Table S2. In brief, constructs expressing the shRNAs targeting endogenous IFIH1 were encoded separately into the lentiviral vector GV248 (pLV3ltr-GFP-Puro-U6-Linear), which also encoded puromycin and green fluorescent protein (GFP). To check whether the targeted sequences were encoded in shRNAs, the lentiviral vectors were evaluated through sequencing.

RAW264.7 cells and BMDMs were transfected with lentivirus supernatant (multiplicity of infection = 50). To select the optimal shRNA, we detected the efficiency of IFIH1 knockdown by RT-PCR and western blot (measuring IFIH1 mRNA and protein expression) 3 days after transfection. The sequence of the most efficient ShRNA-IFIH1 was finally identified as GCAGAAGCTGAGAAACAATGA. The control sequence was TTCTCCGAACGTGTCACGTT.

We first amplified the full-length coding sequence of IFIH1 (NM_001164477.1, 2931 bp) from RAW264.7 and BMDM cRNA through PCR. The primers for IFIH1 were as follows:

The PCR products were purified and cloned into the pCMV3-GFP-Puro vector and then sequenced. The IFIH1 overexpression vector and an empty vector were transfected into separate RAW264.7 cells and BMDMs. To evaluate the knockdown and overexpression efficiencies of transfection, RT-PCR and western blotting were used to measure the mRNA and protein expression of IFIH1, respectively, in every group of transfected RAW264.7 cells and BMDMs.

Total RNA was extracted from lung tissue samples or cells with TRIzol. Prime ScriptTM Trimester Mix (Takara, Japan) was utilized for reverse transcription of RNA. SYBR Premix Ex TaqTM11 (Takara, Japan) and a Step One Plus RT-PCR system (Life Technologies, USA) were utilized to perform RT-PCR based on the manufacturer’s instructions. The results were normalized to those for β-actin, and quantification was performed with the 2^{-ΔΔCt of each sample}/2^{-ΔΔCt of Ctrl} method. The primers are shown in Table S3.

We extracted total protein with RIPA lysis buffer. Then, we measured the protein concentrations of the cell lysates with a BCA protein assay (Beyotime, China) and equalized them. The proteins were separated by 8%–12% SDS-PAGE, blotted onto PVDF membranes, and subsequently incubated with primary antibodies, including antibodies against iNOS, IFIH1, IRF3, p-IRF3, and β-tubulin (Cell Signaling Technology, USA, 1:1000), followed by incubation with HRP-labeled secondary antibodies. ECL detection kits (Beyotime, China) were used to detect proteins and visualize the results on autoradiography film. ImageJ software (win64) was utilized to quantify the western blot band intensities.

RAW264.7 cells and BMDMs were collected with a scraper, blocked with a blocking agent (Miltenyi Biotech, Germany) for 10 min, and then incubated with a PE-conjugated anti-mouse CD86 (1:200) or FITC-conjugated anti-mouse F4/80 (1:200) antibody based on the manufacturers’ instructions. F4/80 was utilized to identify macrophages, and CD86 was utilized as the marker of M1 macrophages. The expression of CD86 was calculated from the fluorescence intensity. All data were collected by flow cytometry (ACEA NovoCyte, China) using Novo Express (ACEA NovoCyte, China) and calculated using FlowJo software version X (Tree Star, USA).

Supernatants were collected from each culture condition. In these supernatants, IL-1β and CCL2 were detected using enzyme-linked immunosorbent assay (ELISA) based on the manufacturer’s instructions (R&D Systems, USA).

To gain insight into the mechanisms underlying the regulatory role of the crucial gene, gene set enrichment analysis (GSEA) was conducted with mRNA datasets of macrophages, with FDR<0.05 as the cut-off value. GSEA is an algorithm designed to assess the concerted behavior of functionally related genes forming a set between two well-defined groups of samples. Because it does not rely on a “gene list” of interest but on the entire ranking of genes, GSEA has been shown to provide greater sensitivity to find gene expression changes of small magnitude that operate coordinately in specific sets of functionally related genes. GSEA calculates separate enrichment scores (ES) for each pairing of a sample based on the GSEABase R package (41, 42). Each ES stands for the degree to which the genes in the specific gene set (mechanisms) are coordinately up or down expressed within a sample. The GSEA database and algorithm introduction are clearly indicated in https://www.gsea-msigdb.org/gsea/index.jsp.

GSEA predicted that IFIH1 regulates M1 macrophage polarization via the RIG-I pathway. To confirm our bioinformatic prediction, phosphorylated and nucleoprotein western blotting were implemented to investigate the crucial characteristics of RIG-I pathway activation (IRF3 phosphorylation and IRF3 translocation into the nucleus) on macrophages, transfected macrophages and controls.

To investigate whether LPS could also activate IRF3 via IFIH1, similar to Poly(I:C), these two stimulation agents were both utilized to construct M1-polarized macrophage models simultaneously. In addition, ST 2825 (a specific MyD88 dimerization inhibitor) was utilized to further explore whether LPS is involved in MyD88-dependent or independent mechanism activation of IRF3.

R x64 3.6.1 was used to process and analyze data and plot diagrams.

To screen genes associated with the severity of ARDS, we utilized the weighted gene coexpression network analysis (WGCNA) algorithm on all gene expression profiles from 26 ARDS patients. The cluster heatmap in Figure 1A illuminated that all gene expression profiles from the 26 ARDS were classified as 20 phenotypes (models) via the WGCNA algorithm. A correlation heatmap of the WGCNA results indicated that the MEsienna3 model (including 234 genes) was significantly associated with the severity of ARDS (P= 6×10⁵, Spearman correlation coefficient = 0.71), as shown in Figure 1B. These 26 ARDS patients comprised nine mild ARDS patients, seven moderate ARDS patients and 10 severe ARDS patients graded by the Berlin definition of ARDS. The detailed demographic and clinical characteristics of the study sample population grouped according to ARDS diagnosis are listed in Table S4.

To investigate the genes potentially involved in M1 macrophage polarization, we identified significantly differentially expressed genes between M1 macrophages and M0 and M2 macrophages [2-fold difference, false discovery rate (FDR<0.05)]. After the search strategy and inclusion criteria were executed, one mRNA dataset of human alveolar macrophages, one mRNA dataset of human microglia, nine mRNA datasets of human peripheral blood monocyte-derived macrophages and two mRNA datasets of mouse bone marrow-derived macrophages were screened (Table S5). The GSE46903 dataset was enrolled as the macrophage training dataset since this dataset had the largest sample size (human alveolar macrophages from 125 volunteers). Simultaneously, other datasets were utilized as macrophage validation datasets. One hundred M1-distinct genes were identified via differential expression gene analysis of the macrophage training dataset (human alveolar macrophages from 125 volunteers).

Furthermore, to find candidate genes simultaneously associated with ARDS and M1 macrophages, a Venn diagram was plotted to capture the overlap between 234 genes in the MEsienna3 model (significantly associated with the severity of ARDS) and 100 genes specifically expressed in M1 macrophages (Figure 1C). The Venn diagram identified 36 candidate genes simultaneously associated with ARDS and M1 macrophages. The expression profiles of the 36 candidate genes in different subsets of macrophages and different severities of ARDS are described in Figures 1D, E, respectively.

To determine these crucial genes (hub genes) among the 36 candidates, we plotted a PPI network diagram via STRING, as shown in Figure 2A. Then, the total connectivity degree of each node in the network was calculated by connectivity degree analysis (number of neighbors), as shown in Figure 2B. There was an inflection point between IRF1 and CXCL10, which indicated that the genes above this inflection point had a markedly higher degree of connectivity (more number of interactions) than the genes below (Figure 2B). Therefore, STAT1, IFIH1, GBP1, IFIT3, and IRF1 were identified as hub genes.

To further validate the expression distribution of five hub genes in M0/M1/M2 macrophages, 11 external mRNA datasets (nine human macrophage and two mouse macrophage datasets) were defined as macrophage validation datasets. The results revealed that the expression of the five hub genes was markedly upregulated in M1-polarized macrophages (FDR<0.05), as shown in Figure S3.

To validate whether the hub genes are involved in ARDS, the mRNA levels of IFIH1, IRF1, IFIT3, GBP1, and STAT1 in BALF from patients with ARDS (n=6) and mechanically ventilated patients without ARDS (postoperative patients, n=6) were measured by real-time quantitative PCR (qRT-PCR). The detailed information of ARDS and control patients were shown in Table S6. A t test indicated that the levels of IFIH1, IRF1, GBP1, and STAT1 were significantly upregulated in the ARDS samples (P<0.05), as shown in Figure 2C. Additionally, animal experiments indicated that the mRNA levels of Ifih1, Irf1, Ifit3, Gbp1, and Stat1 were obviously upregulated in lung tissue homogenates from the ARDS model mice (n=6) compared with those from control mice (n=6), as shown in Figure 2D. In addition, Figure 2E shows the associations of the IFIH1, IRF1, IFIT3, GBP1, and STAT1 mRNA expression profiles with the severity of ARDS.

To identify the gene with the strongest ARDS-related correlation, we utilized the RobustRankAggreg algorithm to calculate interrelated P values based on the P values from the human and animal experiments. Then, the five hub genes were ranked according to interrelated P values from minimum to maximum, as shown in Figure 2F. IFIH1 was selected because it had the minimum interrelated P value and maximum Spearman correlation coefficient. Additionally, immunofluorescence indicated that IFIH1 protein expression was significantly upregulated in M1-polarized macrophages, as shown in Figure S4.

To validate whether IFIH1 could affect M1 macrophage polarization, shRNA) sequences and plasmid expression vectors for IFIH1 were introduced to construct transfected RAW264.7 cell lines and BMDMs with IFIH1 overexpression or knockdown, respectively, and these cell lines were treated with LPS induction for 24 h. As shown in Figure S5–S8, the shRNA sequence and plasmid expression vectors were able to silence and overexpress IFIH1, respectively, in the RAW264.7 cell line and BMDMs.

Western blot analysis indicated that both Poly(I:C)-induced and LPS-induced iNOS expression in RAW264.7 cells and BMDMs was markedly decreased after silencing IFIH1 compared with control treatment (control shRNA transfection; P<0.05, Figures 3A, B). In addition, the vector transfection of shIFIH1 and control shRNA did not influence iNOS expression in RAW264.7 cells and BMDMs.

Similarly, compared with that measured following control treatment (transfection with blank vectors), both Poly(I:C)-induced and LPS-induced iNOS expression measured in RAW264.7 cells and BMDMs after overexpression of IFIH1 was significantly increased (P<0.05, Figures 4A, B). In addition, only IFIH1 expression without Poly(I:C) or LPS stimulation could not influence iNOS expression in the RAW264.7 cell line and BMDMs.

To confirm our findings, we examined other M1 phenotypic markers. Flow cytometry results illustrated that the expression of CD86 in both Poly(I:C)-induced and LPS-induced M1-polarized cell models was markedly decreased after silencing IFIH1 in the RAW264.7 cell line and BMDMs (P<0.05, Figures 3C, D).

In contrast, CD86 expression was markedly increased in both Poly(I:C)-induced and LPS-induced M1-polarized cell models after overexpression of IFIH1 in the RAW264.7 cell line and BMDMs (P<0.05, Figures 4C, D). Additionally, only IFIH1 expression without Poly(I:C) or LPS stimulation could not influence CD86 expression in the RAW264.7 cell line and BMDMs.

The above results revealed that IFIH1 is the regulatory molecule involved in Poly(I:C)-induced and LPS-induced macrophage polarization from M0 to M1.

To further assess the pro-inflammatory role of IFIH1 in macrophages, ELISAs were performed to detect the interleukin (IL)-1β protein (an inflammatory cytokine secreted by M1 macrophages) and C-C motif chemokine ligand 2 (CCL2, a chemokine secreted by M1 macrophages) in culture medium from macrophages, transfected macrophages and controls.

The results revealed that the expression of IL-1β and CCL2 was markedly decreased in both Poly(I:C)-induced and LPS-induced M1-polarized cell models after silencing IFIH1 in the RAW264.7 cell line and BMDMs (P<0.05, Figure 5). Similar to the above results, the expression of IL-1β and CCL2 was significantly increased in both Poly(I:C)-induced and LPS-induced M1-polarized cell models after overexpression of IFIH1 in the RAW264.7 cell line and BMDMs (P<0.05, Figure 6). Additionally, only IFIH1 expression without Poly(I:C) or LPS stimulation could not influence IL-1β and CCL2 expression in the RAW264.7 cell line and BMDMs.

Collectively, these data indicate that IFIH1 regulates the inflammatory function of macrophages.

To gain insight into the mechanisms underlying the regulatory role of IFIH1, we conducted GSEA of the mRNA dataset of human macrophages (GSE46903). The GSEA results indicated that the RIG-I pathway was significantly activated and that the genes in the RIG-I pathway were obviously enriched when IFIH1 expression was upregulated (FDR<0.05, Figure S9).

It is well known that the characteristics of RIG-I pathway activation include IRF3 phosphorylation and IRF3 translocation into the nucleus. Therefore, phosphorylated and nucleoprotein western blotting was performed to validate whether IFIH1 could affect Poly(I:C)-induced or LPS-induced IRF3 activation.

Compared with the control group, the silenced group showed markedly decreased Poly(I:C)-induced and LPS-induced phosphorylation of IRF3 in RAW264.7 cells and BMDMs by western blot analysis (P<0.05, Figure 7). Additionally, compared with control treatments, Poly(I:C)–induced and LPS-induced IRF3 expression in the RAW264.7 and BMDMs cell nuclei were significantly downregulated after silencing IFIH1 (P<0.05, Figure 7).

In contrast, phosphorylation of IRF3 and IRF3 expression in the cell nucleus were markedly increased in both Poly(I:C)-induced and LPS-induced M1-polarized cell models after overexpression of IFIH1 in the RAW264.7 cell line and BMDMs (P<0.05, Figure 8). Additionally, only IFIH1 expression without Poly(I:C) or LPS stimulation could not influence the activation of IRF3 in the RAW264.7 cell line and BMDMs.

These results suggest that IFIH1 regulates macrophage polarization by activating IRF3.

Previous studies have robustly demonstrated that IFIH1 activation of IRF3 depends on dsRNA or RNA mimics. The current study first found that LPS could also activate IRF3 via IFIH1. It is well known that the classical pathway of LPS stimulation is MyD88-dependent. Therefore, ST 2825 (a specific MyD88 dimerization inhibitor) was utilized to further explore whether LPS is involved in MyD88-dependent or independent mechanism activation of IRF3.

Western blot analysis of phosphorylated IRF3 indicated that blocking the MyD88 pathway did not affect the Poly(I:C)-induced phosphorylation of IRF3, but the MyD88 inhibitor significantly decreased the LPS-induced phosphorylation of IRF3 compared with the control treatment in RAW264.7 cells and BMDMs (P<0.05) (Figure 9).

Similarly, the quantitative analysis of nuclear western blot indicated that blocking the MyD88 pathway did not affect the Poly(I:C)-induced expression of IRF3 in the RAW264.7 cell and BMDMs nucleus, but the MyD88 inhibitor significantly decreased the expression of IRF3 in RAW264.7 cells and BMDMs compared with the control treatment (P<0.05). (Figure 9).

These results indicates that LPS-induced activation of IFIH1-IRF3 depends on MyD88 pathway, and Poly(I:C)-induced activation of IFIH1-IRF3 is the independent mechanism.