Deep-sea-derived viridicatol relieves allergic response by suppressing MAPK and JAK-STAT signalling pathways of RBL-2H3 cells

ABSTRACT

Our previous studies reported that viridicatol isolated from the deep-sea-derived fungus Penicillium griseofulvum could regulate the stabilisation of mast cells to relieve food allergy. To understand the molecular role of viridicatol in stabilising mast cells, transcriptomes of viridicatol-treated RBL-2H3 cells were analysed by RNA-sequencing. There were 128 differentially expressed genes in activated RBL-2H3 cells with or without viridicatol treatment. The mast cell activation-related genes were significantly reduced by treatment with viridicatol through RT-qPCR analysis. Moreover, Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that viridicatol was important in mast cell stabilisation by affecting MAPK and JAK-STAT signalling pathways. Additionally, molecular docking and western blot analysis revealed that the phosphorylated JNK, ERK, P38, and STAT6 proteins were inhibited by viridicatol. Taken together, viridicatol has the potential to be used as a new type of anti-food allergic functional material via controlling MAPK and JAK-STAT signalling pathways of mast cells.

KEYWORDS:

• Anti-food allergy
• MAPK
• mast cells
• RNA sequencing
• viridicatol

1. Introduction

Allergic disease is one of the most common chronic diseases in the world, and is listed as an important disease for prevention and treatment in the twenty-first century (Marshall, 2019). The allergic reactions can occur in any age group and affect over 25% of the population in industrialised countries and are increasing in prevalence in developing countries (Barker et al., 2021; Dribin et al., 2022). Allergic diseases are a large group of complex systemic diseases caused by an abnormal immune response, including allergic asthma, allergic rhinitis, atopic dermatitis, food allergy, and drug allergy (Ogulur et al., 2021). A large fraction of allergic diseases is characterised by Immunoglobulin E (IgE)-mediated type 2 immune response involving Th2 cells, type 2 innate lymphoid cells, eosinophils, basophil, and mast cells (Breiteneder et al., 2020; Ogulur et al., 2021).

At present, the therapeutic drugs for allergic diseases mainly include antihistamines (loratadine (AlMasoud et al., 2022), cetirizine (Sharma et al., 2014)), glucocorticoids (Shimba & Ikuta, 2020), leukotriene receptor antagonist (montelukast (Zhang et al., 2014)), anti-IgE monoclonal antibody (omalizumab (Okayama et al., 2020)), anti-IL-4/13 monoclonal antibody (dupilumab (Harb & Chatila, 2020)), and mast cell stabilisers (cromolyn sodium (Netzer et al., 2012; Zhang et al., 2016)). The activation of mast cell and basophil was deemed to be the important effective stage of IgE-mediated anaphylaxis (Jensen et al., 2020). Mast cell mediators are implicated in many different conditions including allergic rhinitis, conjunctivitis, asthma, psoriasis, and mastocytosis (Varricchi et al., 2019). There is intense interest in the development of agents which prevent or inhibit mast cell mediator release. The targets of existing mast cell stabilisers are often not unique to mast cell (Zhang et al., 2016). As a consequence, more compounds targeting mast cells are needed for the development of therapeutic drugs for allergic diseases.

The molecular regulation of mast cell activation by FcϵRI-mediated signalling via immunoreceptor tyrosine-based activation motifs has been investigated in depth. Mast cells are activated by cross-linking of specific IgE bound to FcϵRI on their membrane, and result in phosphorylation of tyrosine kinase, mobilisation of internal calcium ions, and activation of protein kinase C, mitogen-activated protein kinases (MAPK), Janus kinase-signal transducer and activator of transcription (JAK-STAT), and nuclear factor-κB (NF-κB) followed by secretion of a variety of mediators (Kalesnikoff & Galli, 2008). The production of proinflammatory cytokines such as IL-4 and TNF-α is influenced by the activation of MAPK including JNK, ERK, and P38, as well as their upstream kinases. The MAPK and JAK-STAT pathways are established as critical intracellular mechanisms directing mast cell-induced inflammation (Yeung et al., 2018). There are several natural products were reported to inhibit anaphylaxis inflammation by targeting MAPK or NF-κB pathways of mast cells (Fu et al., 2019). Vitamin D could relieve chronic spontaneous urticaria by inhibiting Akt/P38 MAPK pathway of mast cell (Zhao et al., 2020). The suppression activity of berberine on mast cell-mediated allergic responses via regulating FcϵRI-mediated and MAPK signalling was verified (Fu et al., 2019). Our previous studies have shown several deep-sea derived natural compounds could inhibit food allergy by reducing mast cell activation (Liu et al., 2018; Shu et al., 2020; Xing et al., 2019, 2021). As the mechanism of their regulation on mast cells activation is poorly understood, it is necessary to comprehensively analyse the effects of deep-sea derives compounds on signal pathways.

Transcriptomics has been recognised as a comprehensive and efficient approach to elucidate the effects on the signal pathways of natural compounds and to identify new biomarkers (de Jong & Bosco, 2021). For example, Lertnimitphun et al reported that safranal alleviated ovalbumin (OVA)-induced asthma and inhibited mast cell activation, and uncovered the regulating effects on MAPK or NF-κB pathways by RNA-sequencing (RNA-seq) (Lertnimitphun et al., 2021). To characterise the heterogeneity with precise stratification, Tian et al performed high-throughput RNA-seq on skin samples from atopic dermatitis patients and showed a marked Th17 activation in extrinsic atopic dermatitis rather than intrinsic atopic dermatitis (Tian et al., 2021). Meanwhile, using RNA-seq technique, Zhang et al. not only clarified the regulatory signalling pathways of trigonelline on mast cell activation but also revealed the HIF-1α to be a potential target for allergic reaction (Zhang et al., 2021). In our previous report, viridicatol which was a quinoline alkaloid isolated from the deep-sea-derived fungus Penicillium griseofulvum could alleviate food allergic symptoms by inhibiting the activation of mast cells (Figure 1) (Shu et al., 2020). As a natural compound from deep sea, viridicatol has great potential as a therapeutic drug for food allergy. Nevertheless, how viridicatol regulate mast cell activation in food allergy is poorly understood.

Figure 1. The structure of viridicatol. (a) Chemical structure of viridicatol. (b) The X-ray crystallography of viridicatol.

In the present study, the IgE-mediated RBL-2H3 cell model was established for comprehensive analysis of altered transcript levels before and after mast cell activation using RNA-seq. The effects of viridicatol on transcriptome from activated RBL-2H3 cells were explored by differentially expressed genes (DEGs), Protein–protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. By focusing on the significant signalling pathways, the pivotal regulated genes of viridicatol were verified by molecular docking and western blot. The regulation mechanism on IgE-mediated mast cells of viridicatol was unambiguously investigated for its mast cell stabilisers in allusion to food allergy.

2. Materials and methods

2.1. Materials

Eagle’s minimum essential medium (EMEM), trypsin solution, and penicillin–streptomycin solution were obtained from Hyclone (Logan, UT, USA). Fetal bovine serum (FBS) was purchased from Gemini (Woodland, CA, USA). Anti-dinitro-phenyl (DNP)-IgE was purchased from Sigma-Aldrich (St Louis, MO, USA). DNP-BSA was purchased from Biosearch (Petaluma, CA, USA). β-actin Rabbit monoclonal antibody (mAb), JNK Rabbit mAb, P38 Rabbit mAb, ERK1/2 Rabbit mAb, Phospho-JNK (Thr183/Tyr185) Rabbit Antibody, Phospho-P38 (Thr180/Tyr182) Rabbit mAb, Phospho-P44/42 MAPK (ERK1/2) (Thr202/Tyr204) Rabbit mAb were obtained from Cell Signalling (Danvers, MA, USA), and STAT6 mAb, Phospho-STAT6 (Tyr641) mAb were from Affinity Biosciences (Cincinnati, OH, USA). Goat anti-rabbit IgG was obtained from Sigma Aldrich (St Louis, Mo, USA).

2.2. Cell line

The RBL-2H3 cell line (passage 5) was purchased from the Fuxiang Biotechology (Shanghai, China). Cells were cultured in EMEM supplemented with 10% FBS at 37 °C in an atmosphere containing 5% CO₂.

2.3. Viridicatol extraction, isolation, and purification

The defatted crude extract was obtained from the fermentation broth by column chromatography with petroleum ether and CH₂Cl₂ on silica gel, followed by column chromatography on Sephadex LH-20 (MeOH), and the viridicatol was obtained. In our previous report, no relatively miscellaneous peaks were found in the results of the ¹³C NMR spectrum and ¹H NMR spectrum, indicating that the sample was pure (Shu et al., 2020). And the purity of viridicatol is over 99% (Figure S1).

2.4. Library construction and sequencing

The IgE-mediated RBL-2H3 cell model as a usual mast cells model was established (Huang et al., 2018). According to previous studies, viridicatol had no effect on cell culture at 0–10 μg/mL, so the concentration of 10 μg/mL was used in subsequent experiments (Shu et al., 2020). RBL-2H3 cells, adjusted to 1.5 × 10⁶ cells/mL, were inoculated into 6-well plates and pre-treated with anti-DNP-IgE (100 ng/mL) for 16 h in 10% FBS-EMEM. The supernatant was discarded and Tyrode’s Solution was added. The viridicatol group was added with viridicatol (10 μg/mL), and the negative group and positive group were added with PBS (pH 7.4). The cells were incubated at 37 °C in 5% CO₂ for 1 h. The viridicatol group and the positive group were stimulated with DNP-BSA (500 ng/mL) for 1 h (the negative group was added with PBS). They were divided into three groups: a PBS group (not stimulated by DNP-BSA), a positive group (stimulated by DNP-BSA), a viridicatol group (10 μg/mL viridicatol pre-treated and stimulated by DNP-BSA).

Then, the supernatants were discarded, washed twice with PBS, and total RNA of cultured cells was extracted with Trizol reagent (Roche, Basel, Switzerland) following the instructions. The RNA samples were tested by Majorbio (Shanghai, China). The RNA integrity was tested by 1% agarose gel electrophoresis, and RNA quality and quantity were evaluated using Nanodrop 2000 (Waltham, MA, USA) (Chen et al., 2021). The RNA integrity number was further assessed with Agilent 2100 (Palo Alto, CA, USA). Samples with qualified purity (RNA quantity ≥1 μg, concentrations ≥35 ng/μL, OD₂₆₀/OD₂₈₀ ≥ 1.8, and OD₂₆₀/OD₂₃₀ ≥ 1.0) were used for subsequent library construction preparation. Messenger RNA (mRNA) with Poly-(A)- were enriched using oligo (dT) beads and were broken into 300 bp fragments using fragmentation buffer. Using mRNA as a template, the first strand of complementary DNA was synthesised by reverse transcriptase and random hexamer primer. Then, the second-strand complementary DNA was synthesised, complemented by the flat end using End Repair Mix, and connected to the adapter ligation. Finally, the enriched library was obtained by PCR amplification. The library preparation was sequenced using Illumina high-throughput sequencing platform Novaseq 6000 (San Diego, CA, USA), and paired-end reads of 150-bp length were generated (Ungar et al., 2021). As shown in Table 1, the sample concentrations were more than 98 ng/μL, OD₂₆₀/OD₂₈₀ ≥ 2.0, and OD₂₆₀/OD₂₃₀ ≥ 1.7, indicating that the extracted RNA from RBL-2H3 cell were of good quality and could be used for subsequent detection and analysis. RNA sequence data can be available from the NCBI Sequence Read Archive (BioProject: PRJNA911003).

Table 1. The RNA sample detection information.

Sample	Concentration (ng/μL)	Total quantity (μg)	OD260/OD280	OD260/OD230
negative 1	102.5	4.10	2.10	1.90
negative 2	141.8	5.67	1.99	1.73
negative 3	158.5	6.34	2.01	1.76
positive 1	98.5	3.94	2.07	2.00
positive 2	167.4	6.70	2.03	1.76
positive 3	170.9	6.84	2.02	1.77
viridicatol 1	110.6	4.42	2.08	2.04
viridicatol 2	155.1	6.20	2.01	1.80
viridicatol 3	131.2	5.25	2.01	1.72

2.5. Identification of DEGs

High-quality clean reads were obtained for subsequent analysis by filtering the sequencing data, and base distribution statistics and quality assessment were conducted for the data after quality control (Fastres et al., 2020). The expression of genes was calculated and normalised to fragments per kilobases per million reads using RSEM (http://deweylab.github.io/RSEM/). DEGs were identified using DESeq2 (http://bioconductor.org/). The P-value was adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate (Glickman et al., 2021). Genes with an adjusted P < 0.05 and fold change (FC) ≥1 or≤−1 were assigned as DEGs.

2.6. PPI analysis

PPI data were collected from STRING database (https://string-db.org/). PPI network was constructed by Cytoscape 3.9.0 software (Seattle, WA, USA). Key nodes in gene interaction network can be obtained by statistics of various topological properties of gene interaction network, such as Degree, Betweeness, Closeness, and Cluster Coefficient of all nodes in network.

2.7. Quantitative real-time polymerase chain reaction

For quantitative Real-Time Polymerase Chain Reaction (RT-qPCR), cells were adjusted to 1.5 × 10⁶ cells/mL and seeded in 6-well plates and pre-treated with anti-DNP-IgE (100 ng/mL) in EMEM for 16 h before performing assays. Viridicatol (10 μg/mL) was added for 1 h, and cells were stimulated with DNP-BSA (500 ng/mL) for 1 h at 37°C and 5% CO₂ in an incubator. RNA was extracted using Trizol followed by the synthesis of cDNA via reverse transcriptase. RT-qPCR analysis was run on an ABI7300 real-time PCR system (Austin, TX, USA) using the SuperReal Premix SYBR Green kit (Beijing, China) according to the manufacturer’s protocol, and the results were analysed using the 2^−ΔΔCt method and normalised to β-actin expression. The main primers were shown in Table 2.

Table 2. Sequences of primers for RT-qPCR.

Genes	Forward primer	Reverse primer
Tnfα	5′-TCAGTTCCATGGCCCAGAC-3′	5′-GTTGTCTTTGAGATCCATGCCATT-3′
Ccl2	5′-CTATGCAGGTCTCTGTCACGC-3′	5′-CAGCCGACTCATTGGGATCA-3′
Il4	5′-ATGTAACGACAGCCCTCTG-3′	5′-CTCCGTGGTGTTCCTTGT-3′
Jun	5′-GCACATCACCACTACACCGA-3′	5′-TATGCAGTTCAGCTAGGGCG-3′
Fos	5′-TCGGCAGAAGGGGCAAAGTA-3′	5′-TCTCCGCTTGGAGCGTATCT-3′
Il13	5′-ACAGGACCCAGAGGATATTGAAT-3′	5′-GCGGAAAAGTTGCTTGGAGTA-3′
Ccl7	5′-AAGGGGTAGGAACGGTCTGT-3′	5′-AACAAAGTCAATGGGGCGGG-3′
Socs1	5′-TGGGTGTGGAGGGTGAG-3′	5′-GGAGAGGTAGGCGTGGAG-3′
βactin	5′-GCAGGAGTACGATGAGTCCG-3′	5′-ACGCAGCTCAGTAACAGTCC-3′

2.8. GO and KEGG analysis of DEGs

GO enrichment was performed using Goatools (https://github.com/tanghaibao/GOatools) to decipher functionally grouped gene ontology and pathway annotation networks. KEGG pathway enrichment analysis was performed. DAVID was used to carry out Pathway annotation and enrichment analysis of differentially expressed genes and link their information and functions, forming the connection of information between genomes reflected by the network (http://www.genome.jp/kegg/) (Wu et al., 2021).

2.9. Molecular docking

Auto Dock Tools 1.5.6 (San Diego, CA, USA) was used to realise the docking between viridicatol and JNK, ERK, P38 or STAT6, and PyMol (New York, NY, USA) was used to analyse the docking results. Viridicatol was constructed using Chem3D software (Cambridge, Co, USA). JNK (PDB ID: 6G54), ERK (PDB ID: 3ERK), P38 (PDB ID: 5UOJ), and STAT6 (PDB ID:2J7Y) protein 3D structures were first downloaded from PDB database, and dehydrated and hydrogenated by Auto Dock Tools 1.5.6. JNK, ERK, P38, or STAT6, and viridicatol were docked using the flexible docking tool Auto Dock Tools 1.5.6. After the calculation, the configuration of JNK, ERK, P38, and STAT6 with viridicatol complex was sorted according to the binding energy, and the docking diagram was drawn with PyMol software.

2.10. Western blot

RBL-2H3 were adjusted to 1.5 × 10⁶ cells/mL and seeded in 6-well plates and pre-treated with anti-DNP-IgE (100 ng/mL) in EMEM for at 16 h before performing assays. Viridicatol (2.5, 5, and 10 μg/mL) was added for 1 h, and cells were stimulated with DNP-BSA (500 ng/mL) for 15 min at 37°C and 5% CO₂ in an incubator. Briefly, the supernatant was discarded and cells were lysed in the Cell lysis buffer for Western blot (Shanghai, China) including phenylmethylsulfonyl fluorid (Beijing, China), and then total proteins were extracted from cells. Protein concentration was determined using Nanodrop 1000 spectrophotometer (Waltham, MA, USA). The protein sample was separated and transferred to the PVDF. After blocking in 5% skimmed (non-fat) milk (1.5 h), The blots were washed three times with Tween 20/Tris-buffered saline (TBST), subsequently primary antibodies (β-actin, JNK, ERK1/2, P38, Phospho-JNK, Phospho-ERK1/2, Phospho-P38, STAT6, and Phospho-STAT6 mAb) were added and incubated at 4°C overnight. The membranes were washed seven times with TBST, and incubated with Goat anti-rabbit IgG in TBST (25°C, 1 h). Azure Biosystems C280 (Dublin, CA, USA) was used for image acquisition.

2.11. Statistical analysis

The experimental data are presented as the mean ± SD and were analysed using one-way ANOVA followed by a multiple comparison test with Tukey’s test using GraphPad Prism 8.0 (GraphPad, La Jolla, CA, USA). Statistical significance is displayed as *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

3. Results

3.1. Viridicatol affects gene expression in activated RBL-2H3 cells

As reported in our previous study, viridicatol regulates mast cell stability and alleviates food allergies (Shu et al., 2020). To investigate the effect of viridicatol on transcriptome genes of activated RBL-2H3 cells, we performed RNA sequencing on negative, positive, and viridicatol group. IgE-sensitised RBL-2H3 cells were pre-treated with viridicatol for 60 min, followed by DNP-BSA crosslinking for 1 h. The untreated group was negative group, the positive group was activated by DNP-BSA, and the viridicatol group was pre-treated by viridicatol. The principal component analysis (PCA) analysis of these data demonstrated that each group was distinct from the other (Figure 2a). Transcripts of RBL-2H3 DEGs were characterised using RNA-seq analyses between the viridicatol group and positive group. There were 10,881 common genes between the viridicatol group and positive group, 198 specific genes in the positive group and 176 specific genes in the viridicatol group (Figure 2b). In the volcano map, “up” represents significantly up-regulated gene expression, “down” represents significantly down-regulated gene expression, and “nosing” represents no significant change in gene expression There were 232 DEGs between the Positive group and Negative group, including 18 down-regulated genes and 214 up-regulated genes (Figure 2c) (Table S1). And compared with the positive group, there are 128 DEGs in the viridicatol group including 87 down-regulated genes and 41 up-regulated genes (Figure 2d) (Table S2). Heatmap analyses of these commonly changed genes in the negative, and the positive versus viridicatol group comparison is shown in Figure 2e. Furthermore, most of these genes are inflammatory factors and chemokines. As Figure 2e depicted, the expression of two different groups is distinguished clearly, consistent with the PCA analysis. Thus, DEGs analysis revealed that 128 genes were differentially expressed significantly between the viridicatol and positive groups.

Figure 2. Transcriptomic analysis revealed the effects of viridicatol on activated RBL-2H3. (a) The PCA analysis of negative, positive, and viridicatol groups. (b) Venn diagram of shared referenced genes between viridicatol and positive groups. (c) Volcano plot of DEGs between negative and positive groups. (d) Volcano plot of DEGs between viridicatol and positive groups. X-axis represented the fold change of expression of DEGs, and Y-axis represented statistical significance of fold change. Each point represented a DEG. Red dots represented significantly up-regulated DEGs, green dots represented significantly down-regulated genes, and grey dots represented insignificantly DEGs. (e) Cluster heatmap of DEGs. Rows and columns represent genes and samples. The legend represents Log2FC of gene abundance. The red and blue columns indicate high and low expression of DEGs, respectively.

3.2. Viridicatol down-regulated gene expression associated with mast cell activation

To further analyse the effect of viridicatol on the transcriptome of RBL-2H3 cells, PPI was used to analyse the proteins with more obvious regulation. As shown in Figure 3a, we found that TNF-α, CCL2, JUN, FOS, IL-4, CCL7, IL-13, and SOCS1 were highly related among the differentially expressed genes. In order to verify the reliability of transcriptomic data, these genes were verified by RT-qPCR based on the obtained screening results of significantly different genes. Representative Tnfα, Ccl2, Jun, Fos, Il4, Ccl7, Il13, and Socs1 with significant differences were selected for the following RT-qPCR experiments to further determine the effect of viridicatol on genes related to mast cell function. Compared to the positive group, the addition of viridicatol could significantly down-regulate the mRNA expression levels of Tnfα, Ccl2, Jun, Fos, Il4, Ccl7, Il13, and Socs1, and these effects were consistent with the RNA-seq results (Figure 3b). In a word, viridicatol could significantly inhibit the expression of genes related to mast cell activation.

Figure 3. The effects of viridicatol on mast cell activation-related proteins and genes. (a) PPI of gene expression of 128 referenced DEGs between viridicatol and positive groups providing a rationale for functional significance of “core DEGs”. (b) The expression of “core DEGs” (Tnfα, Ccl2, Jun, Fos, Il4, Ccl7, Il13 and Socs1) related to mast cell activation were confirmed by RT-qPCR. All values are expressed as the mean ± SD (n = 3). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 versus positive group

3.3. Viridicatol-regulated biological process in activated RBL-2H3 cells

To learn the biological effects of viridicatol on the activated RBL-2H3 cells, GO analysis was performed in the present study. GO analysis revealed that the DEGs between viridicatol group and positive group belong to biological processes, molecular functions, and cellular components (Figure 4a). To further understand the biological effects of viridicatol on differential gene regulation in activated RBL-2H3 cells, we performed GO enrichment analysis in different treatment groups. The largest number of DEGs were associated with the cellular process, followed by biological regulation, single-organism process, and regulation of the biological process. In terms of cellular components, the cell part was involved in the largest number of DEGs, followed by cell, and organelle, When RBL-2H3 cells were activated, the inflammatory factors stored in the cells were released, which involved the work of the cell parts and organelles and the production of mediators. In the aspect of molecular function, the significantly over-represented items were binding, catalytic activity, nucleic acid binding transcription factor activity, and signal transducer activity. “binding” represented the binding of RBL-2H3 cells to IgE. This suggested that mast cells cross-link with allergen-specific-IgE in allergic reactions, and when re-exposed to the allergen, mast cells were activated to release intracellular inflammatory mediators. The GO enrichment analysis was performed at the level of adjusted P < 0.05. The top 20 ranked GO terms of DEGs are shown in Figure 4b. Regulation of phosphate metabolic process occupied the strongest enrichment degrees, which was consistent with the activation of RBL-2H3 cells, which leaded to protein phosphorylation in the FcϵRI signalling pathway. The results of GO functional significance enrichment analysis were visualised as shown in Figure 4c. The regulation of phosphate metabolic process (23 genes) contained the most DEGs, followed by the regulation of phosphorus metabolic process (22 genes) and regulation of phosphorylation (22 genes). Taken together, viridicatol was able to inhibit mast cell activation through the phosphate metabolic process of biological process.

Figure 4. GO annotation and enrichment analysis of DEGs. (a) GO functional annotation of DEGs. (b) GO enrichment analysis of DEGs. Enriched bubble chart showing enrichment of GO functions. X-axis represented enrichment ratio, and Y-axis represented top 20 GO terms. Number: Bubble size represented number of genes annotated to a GO Pathway. Padjust: colour indicated enriched adjusted P-value. (c) GO chord plot of top 10 ranked GO terms. Chords indicated a detailed relationship between expression levels of DEGs (left semicircle perimeter) and their enriched GO terms (right semicircle perimeter). Genes are linked to their annotated terms via coloured ribbons.

3.4. Viridicatol influenced MAPK, JAK-STAT, TNF, and toll-like receptor signalling pathways

In order to explore the main signal transduction and biochemical metabolism pathways involved in the enrichment of DEGs, the KEGG database was used for pathway annotation analysis. Pathway enriched in the database was significantly different between viridicatol group and positive group with 16 up-regulated and 24 down-regulated second KEGG categories. The KEGG pathways enriched in the down-regulated DEGs were mainly specific to signal transduction (17 genes) and immune system (13 genes) (Figure 5a). Signal transduction including Map3k8, Dusp5, Myc, etc., and immune system including Il4, Fos, Jun, etc. It was shown that some pathways were changed significantly by viridicatol including “cytokine-cytokine receptor interaction”, “MAPK signaling pathway”, “JAK-STAT signaling pathway”, “TNF signaling pathway”, “C-type lectin receptor signaling pathway”, and “Toll-like receptor signaling pathway”, etc. (Figure 5b,c). Thus, viridicatol treatment primarily affected MAPK, JAK-STAT, TNF, and Toll-like receptor signalling pathways to inhibit mast cell activation.

Figure 5. KEGG annotation and enrichment analysis of DEGs. (a) KEGG pathway annotation of DEGs. (b) KEGG enrichment analysis of DEGs. Viridicatol is compared with negative group and positive group, respectively. Enriched bubble chart showing enrichment of KEGG pathways. X-axis represented enrichment ratio, and Y-axis represented top 20 KEGG pathways. Number: Bubble size represented number of genes annotated to a KEGG Pathway. Padjust: colour indicated enriched adjusted P-value. (c) KEGG chord plot of top 10 ranked KEGG terms. Chords indicated a detailed relationship between expression levels of DEGs (left semicircle perimeter) and their enriched GO terms (right semicircle perimeter). Genes are linked to their annotated terms via coloured ribbons.

3.5. The effects of viridicatol on the genes expression from influenced signalling pathways

According to KEGG pathway analysis of differential genes, major pathways related to allergy were selected for further analysis and discussion, including MAPK, JAK-STAT, TNF, and Toll-like signalling pathway. The heat map under MAPK, JAK-STAT, TNF, and Toll-like receptor signalling pathways were explored to identify the genes with significant influence. The expressions of genes with significant differences from these pathways in the negative group, positive group, and viridicatol group were compared in the heat map. Some DEGs, such as Myc, Jun, and Map3k8, were involved in the regulation of MAPK and JAK-STAT signal pathways. More DEGs associated with MAPK and JAK-STAT signal pathways were reduced by viridicatol (Figure 6a–d). Therefore, viridicatol might affect the expression of Myc, Jun, and Map3k8 genes, thereby mainly inhibiting the MAPK and JAK-STAT signalling pathways.

Figure 6. Heatmap of main DEGs for MAPK, JAK-STAT, TNF and Toll-like receptor signalling pathways. (a) Heatmap of MAPK signal pathway between viridicatol and positive groups. (b) Heatmap of JAK-STAT signal pathway between viridicatol and positive groups. (c) Heatmap of TNF signal pathway between viridicatol and positive groups. (d) Heatmap of Toll-like receptor signalling pathway between viridicatol and positive groups. The colour scale shown in the figure illustrates the relative mRNA expression levels in all samples.

3.6. The suppression of viridicatol on the protein phosphorylation from MAPK and JAK-STAT signalling pathways

JNK, ERK, P38, and STAT6 were selected as representative signal molecules of MAPK and JAK-STAT signalling pathways in the present study. To predict the molecular interaction between viridicatol and JNK, ERK, P38, or STAT6, Auto Dock Tools was used for molecular docking to simulation the possible clathrate. As shown in Figure 7, viridicatol was successfully simulated with JNK, ERK, P38, or STAT6 proteins, and the best returned pose between viridicatol and JNK, ERK, P38, or STAT6 were −7.74, −6.94, −6.15 and −4.95 kcal/mol, respectively. After docking with the JNK, ERK, P38, or STAT6 proteins, Mean Square Deviation (RMSD) of viridicatol (ligand) was calculated by PyMol software, and the results showed that RMSD values were all less than 2 Å, indicating that the docking software was more accurate and the docking results were reliable. Viridicatol and JNK were linked by forming hydrogen bonds at Asp247 and Ser233 (Figure 7a). As shown in Figure 7b, the docking result predicted that two hydrogens of ERK (Met244 and Ser287) could be formed by viridicatol. The ribbon model showed that two hydrogen bonds of P38 were formed with amino acids Lys6, Asp89, and Thr92, respectively (Figure 7c). Meanwhile, STAT6 mainly forms a complex with viridicatol in the form of hydrogen bonds through Leu4 and Ser128 (Figure 7d). Molecular docking studies showed that viridicatol interacts effectively with JNK, ERK, and P38 space structures, resulting in better ligand activity of viridicatol.

Figure 7. The interaction between viridicatol and JNK, ERK, P38, or STAT6. (a) The molecular docking results for viridicatol with JNK protein model. (b) The molecular docking results for viridicatol with ERK protein model. (c) The molecular docking results for viridicatol with P38 protein model. (d) The molecular docking results for viridicatol with STAT6 protein model. After the calculation, the configurations of the complex of viridicatol and JNK, ERK, P38, or STAT6 were ordered according to the binding energy, and the docking diagram was drawn with PyMol software. The coordinates of the docking operation are X: 126, Y: 126 and Z: 126. The grid spacing is 0.5 A. After calculation with the program, the configuration of JNK, ERK, P38, STAT6 complex with viridicatol was sorted according to the binding energy, and the docking diagram was drawn with PyMol software.

The KEGG results showed that viridicatol mainly affected MAPK and JAK-STAT signalling pathway, which might be because viridicatol could bind to JNK, ERK, P38, and STAT6 proteins, thereby inhibiting mast cells activation. Whereas the MAPK signalling pathways played a great role in inflammatory responses, DEGs between viridicatol and Positive groups were enriched into MAPK and JAK-STAT signalling pathways through KEGG. As shown in Figure 8a, the content of β-actin in each group was basically no difference, indicating that the total amount of protein in each group was the same. Compared with the Positive group, the Phospho-JNK, Phospho-ERK, Phospho-P38, and Phospho-STAT6 proteins in the viridicatol group showed a significant trend of weakening, and JNK, ERK, P38, or STAT6 had same protein content. Subsequently, the western blot results were quantitatively analysed by ImageJ software. The inhibition of viridicatol on the phosphorylation of JNK and ERK proteins was strongly concentration-dependent. While phosphorylation of P38 and STAT6 proteins was inhibited by viridicatol with low concentration-dependent (Figure 8b). As summarised in Figure 8c, the possible mechanism of the mast cell stabiliser function was that viridicatol could inhibit the phosphorylation of JNK, ERK, P38, and STAT proteins in the MAPK and JAK-STAT pathways.

Figure 8. Effects of viridicatol on phosphorylation levels of JNK, ERK, P38, and STAT6 from RBL-2H3 cells. (a) The levels of JNK, ERK, P38, and STAT6 were detected by western blot. (b) The amount of target protein calculated by grey scale analysis. (c) Effect of viridicatol on mast cell signalling pathways. Effects of viridicatol on MAPK and JAK-STAT signalling pathway protein expression in IgE-mediated RBL-2H3 cells after activation. After incubation with anti-DNP-IgE (100 ng/mL) for 16 h and pre-treatment with different concentrations of viridicatol (2.5, 5 and 10 μg/mL) for 1 h, DNP-BSA (500 ng/mL) was added for 15 min, and the levels of JNK, ERK, P38, and STAT6 were detected by western blot and grey scale analysis. The data are expressed as mean standard deviation. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

4. Discussion

Food allergy is urgent and has obvious symptoms, and its influence can be throughout the whole body, which is a hidden “killer” of human health. Food allergy was mainly a systemic IgE-mediated hypersensitivity response that involved inflammatory mediators of mast cells and basophils (Banafea et al., 2022). At present, most allergy drugs show adverse reactions and are expensive after long-term use, while natural compounds are attracting more and more attention for their potential medicinal value, safety, and high efficiency (Wang et al., 2022). Our previous study found that many secondary metabolites derived from deep-sea fungi can stabilise mast cells (Gao et al., 2017; Liu et al., 2018) Additionally, we previously found that viridicatol, which was a quinoline alkaloid isolated from the deep-sea-derived fungus Penicillium griseofulvum, could reduce degranulation and Ca²⁺ influx of mast cells, thus inhibiting the occurrence of allergic reactions (Shu et al., 2020). However, the molecular mechanism of viridicatol on stabilising mast cells was unclear. As a highly effective molecular biology tool, RNA-seq provides insights into transcriptional mechanisms in organisms, and it can reveal the relationship between genetic changes and complex biological processes (Wang et al., 2022). The data from RNA-seq demonstrated that environmental fine particulate matter affects MEKK4/JNK pathway mainly through up-regulated Gadd45b expression, which in turn affected reactive oxygen species production and promotes mast cells activation (Wang et al., 2021). In this study, IgE-mediated RBL-2H3 cells were used as a mast cell model, and RNA-seq analysis was applied to investigate the transcriptome changes of activated mast cells after viridicatol treatment. RNA-seq analysis revealed 128 DEGs, including 87 up-regulated genes and 41 down-regulated genes, between the viridicatol group and the Positive group. Based on DEGs, PPI network, GO, and KEGG analysis were adopted to study the effect of viridicatol on RBL-2H3 cells.

PPI was composed of proteins through their interactions with each other, which systematically analysed the interaction relationships of a large number of proteins in biological systems (Johnson et al., 2021). Lertnimitphun et al. found a potential target (CCL7 and CCXL10) to overcome allergic diseases through PPI (Lertnimitphun et al., 2021). It had been investigated the genes of activated mast cells treated with trigonelline, in which HIF-1α was associated with immune system diseases and showed significant RNA expression differences (Zhang et al., 2021). Qian et al had reported that scrodentoid significantly inhibited IgE-mediated mast cells activation by affecting mRNA and protein expression of TNF-α and IL-4 cytokines (Qian et al., 2019). Similarly, PPI was used to find that viridicatol could affect mast cells activation-related proteins including TNF-α, CCL2, JUN, FOS, IL-4, CCL7, IL-13, and SOCS1 in the current study. The gene level of these mast cell activation-related proteins was verified in our study, and the inhibitory effect of Viridicatol on Tnfα, Ccl2, Jun, Fos, Il4, Ccl7, Il13, and Socs1 was confirmed. Mast cell activation such as the release of cytokines IL-4 and TNF-α was associated with cascade activation of signalling pathways. In our previous study, we found that viridicatol could inhibit mast cell activation and reduce the expression of inflammatory mediators including histamine, mast cell protease-1, TNF-α, etc (Shu et al., 2020). Based on the results, we found that viridicatol significantly suppressed mast cells degranulation. Therefore, in this manuscript, we constructed an IgE-mediated RBL-2H3 cell activation model as a usual mast cell model. Through transcriptomic analysis, it was found that viridicatol can reduce the expression of Il4, Il13, Tnfα, and other inflammatory genes, which is consistent with our previous results. This suggests that viridicatol may be a potential therapeutic agent targeting mast cells.

Through GO analysis, it was found that viridicatol mainly affected the phosphorylation metabolism process. The MAPK and JAK-STAT signalling pathways were among the top 10 KEGG pathways, with the genes encoding them serving as treatment targets. The MAPK and JAK-STAT signalling pathways were chosen for experimental validation and key targets were identified through KEGG analysis. Cell signalling was manifested at the protein level. Western blot confirmed the effect of viridicatol on protein phosphorylation in MAPK and JAK-STAT signalling pathways. Our findings show that viridicatol can reduce the levels of phosphorylated JNK, ERK, P38, or STAT6 proteins in RBL-2H3 cells, indicating that it can play a role in the treatment of inflammatory by controlling relevant signalling pathways. MAPK and JAT-STAT signalling pathway were the classical signalling pathway of activated mast cells. Kim et al. found that increased phosphorylation of ERK and P38 proteins was able to promote the release of TNF-α (Kim et al., 2022). Ruxolitinib inhibited the production of IL-13 and TNF-α to suppress JAK-STAT signalling pathway, which controlled mast cell activation in turn (Hermans et al., 2018). Some studies had shown that the JAK-STAT signalling pathway could affect IL-4 release (Yang et al., 2020). Our study found that the gene level of Il-4 was inhibited in activated RBL-2H3 cells treated with viridicatol, suggesting that the JAK-STAT signal pathway could regulate IL-4 secretion and thus inhibit mast cell activation. Meanwhile, our study also found that MAPK and JAT-STAT pathways played a key role in mast cell activation. Some natural products targeted MAPK and JAK-STAT signal pathways were found and verified the application of mast cell stabilisers. Transcriptome enriched the key MAPK and JAK-STAT signalling pathways, and further compared with other targeted similar signalling pathways, western blot verified that these two signalling pathways were related to inflammation, indicating that our compound alleviated inflammation through MAPK and JAK-STAT signalling pathways. This means that our results are plausible. For example, polydatin significantly reduced the phosphorylation of ERK and P38, which are downstream effectors of FcϵRI, inhibiting the activation of mast cells (Ye et al., 2017). Besides, oleanolic acid isolated from Olea europaea and Syzygium aromaticum was proven to inhibit STAT1 activation to alleviate allergic reactions (Kang et al., 2021). As a deep-sea derived natural production, viridicatol could inhibit the expression of mast cell activation-related genes potentially via reducing the phosphorylated proteins in MAPK and JAK-STAT signalling pathway.

The physiological functions of cells vary with changes in protein levels caused by transcriptome changes. Therefore, changes in signalling pathways mined by transcriptome analysis could be verified by protein levels (Dong et al., 2020). Molecular docking was a fast, accurate, and easy to operate method to analyse the interaction between compound and protein (Cao et al., 2020). In order to preliminarily determine whether compounds affect certain protein functions, many studies have used molecular docking to make predictions. The RMSD values compared the structural differences of viridicatol before and after docking with proteins, and the results showed that the RMSD values were <2 Å, indicating that the docking software was more accurate and the docking results were reliable. Viridicatol could suppress the activation of mast cells by inhibiting protein phosphorylation in different signalling pathways. In this study, KEGG results were used to select key signalling pathways to demonstrate the stabilising effect of viridicatol on mast cells. In the present study, molecular docking was used to analyse the interaction between viridicatol and proteins in the MAPK and JAK-STAT signalling pathways. In the results of molecular docking, it could be seen that the binding energy of viridicatol and JNK, ERK, P38 resulting in better ligand activity. In addition, at the protein level, viridicatol actually down-regulated the levels of phosphorylated JNK, ERK, P38, and STAT6 proteins. Therefore, it was known that viridicatol was a good mast cell stabiliser mainly by targeting JNK, ERK, P38, and STAT6 proteins. As a common inhibitor, the SB203580 molecule can inhibit p38 MAPK. Currently, the inhibitor is modified to solve the toxicity and selectivity problems of the original compounds, while our compound is safe and effective. The viridicatol studied in this manuscript provides more possibilities for inhibitors (Shin et al., 2020). The activation of MAPK and JAK-STAT pathways was not only manifested in mast cell activation, but also correlated with inflammation, autoimmune, cancer, and other diseases. Therefore, it was speculated that viridicatol could be used not only to stabilise mast cells but also in other diseases related to MAPK and JAK-STAT activation.

5. Conclusion

In conclusion, a comprehensive transcriptome analysis of IgE-mediated RBL-2H3 cells was presented in this study. Viridicatol could regulate 128 DEGs in IgE-mediated RBL-2H3 cells. And viridicatol was able to regulate mast cell activation-related genes (Tnfα, Ccl2, Jun, Fos, Il4, Ccl7, Il13, and Socs1). Furthermore, the phosphorylation of JNK, ERK, P38, and STAT6 proteins in activated RBL-2H3 cells was mainly inhibited by viridicatol. In a word, the development of viridicatol targeting proteins in the MAPK and JAK-STAT signalling pathways is a promising method for the treatment of mast cell-derived anaphylaxis diseases.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the grants from the National Natural Science Foundation of China (32001695, 32072336, 31871720), the National Key Research and Development Program of China (2019YFD0901703), and Fujian Provincial Department of Science and Technology (2021L3013).