Transcriptomics reveals the potential mechanism of ellagic acid extract from raspberry on wound healing

ABSTRACT

The aim of this study was to reveal the mechanism of its wound healing function at the molecular level using transcriptome sequencing technology. The results showed that EAE mainly upregulated Cyclin A gene through AMPK signalling pathway, activated cell cycle pathway, promoted DNA synthesis, accelerated mitosis, and thus promoted the proliferation of Human immortalized keratinocytes (HaCaT) cells. In addition, analysis of downregulated gene enrichment revealed that these genes were significantly or very significantly enriched not only in the cytokine-cytokine receptor interaction pathway but also in the TNF and the NF-κB signalling inflammatory classical pathway. It was mainly through downregulation of genes such as Fn14, CCL2, CCL5, CARD14 and LTB that prevented inflammatory responses while delaying apoptosis. The results of this study provided important information for the molecular mechanism of wound healing of EAE and provided a basis for further development of functional products of raspberry.

KEYWORDS:

• Raspberry
• ellagic acid
• cell proliferation
• transcriptome
• wound healing

1. Introduction

Raspberry, also known as raspberry, is a medicinal food plant of the rosaceae family (Youwei et al., 2023), rich in a variety of functional active ingredients, such as amino acids, vitamins, mineral elements, polyphenols, flavonoids, etc. (Fotschki et al., 2023; Tan et al., 2023). The raspberry fruit is delicious and has antioxidant, anticancer, cholesterol-lowering and other nutritional values (Ash et al., 2011; Cetojevic-Simin et al., 2015; Skrovankova et al., 2015). It has great development potential in the application of fresh food and the development of functional food and has been widely concerned by researchers. Raspberry is rich in ellagic acid, which is an ideal raw material for ellagic acid extraction. And ellagic acid is an important active ingredient in raspberry, so the content of ellagic acid is an important parameter for raspberry quality evaluation. Ellagic acid has a wide range of effects, but at present, the functional activities of ellagic acid are mainly focused on antioxidant, anti-inflammatory, antibacterial, anticancer (Jing et al., 2020; Khanduja et al., 1999; J. Li et al., 2019; Zhao et al., 2020) and the development of new functions is a novel pointer for the development of its functional products. It has been found that ellagic acid inhibits the proliferation of cancer cells without affecting the growth and proliferation of normal cells (Shiow et al., 2009), and it has been found to have a protective effect on cell proliferation in mammals (Mottola et al., 2020). It has also been found that pomegranate seed ellagic acid extract made into ointment promotes cell proliferation and has a positive effect on the healing of scalded skin (Yang et al., 2019). This will provide a favourable theoretical basis for exploring the wound healing efficacy of EAE.

As the body's largest organ system, the skin protects the body from external stimuli. However, loss of skin integrity caused by injury or disease is common (Y. Z. Zhang et al., 2022). The massive release of oxygen radicals around wounds can cause inflammation, cellular damage, necrosis and dysfunction (Rippon et al., 2022; P. H. Wang et al., 2018). Wound healing is an important and common physiological process that involves dynamic homeostasis, inflammation, proliferation, migration and tissue recovery of the organism. HaCat are the main cell type that constitutes the epidermis of the skin (C. C. Wang et al., 2014) and has functions such as epidermal composition, wound repair, cellular keratinization, cytokine secretion and immune surveillance, and is often used as a cell line for vitro skin studies (Shibata et al., 2012). The healing of epidermal wounds is a complex physiological process in which the body restores the integrity of damaged tissues (Yariswamy et al., 2013). Cell proliferation and migration is an important condition in the recovery process of damaged epidermis, and also an important factor affecting the speed and quality of trauma recovery, and cell migration is mainly dependent on cell polarization and directional cell movement (Augustine et al., 2021). The Wound Healing method is mainly studied on living cells without the influence of apoptotic cells, and can approximate the in vivo cell growth and epidermal trauma recovery process, and is the most visual representation of cell proliferation and cell migration ability. The scratch test essentially reflects the regenerative ability of epidermal cells after epidermal trauma and their healing ability to migrate to the wound spacing with time, and the reduction of the epidermal cell scratch area reflects the enhancement of cell proliferation and cell migration ability. Therefore, the study of cell proliferation and migration ability by wound healing method is an important way to explore the wound healing process.

In recent years, high-throughput sequencing technology has become an important research method in the fields of food, biology and medicine (Chambers et al., 2019; Y. L. Liu et al., 2023). Transcriptomics is a scientific tool for the holistic study of all genes and transcripts in a cell in terms of number, type, function and molecular regulation (Kester & van Oudenaarden, 2018). Using transcriptomic sequencing technology, it is possible to identify all the gene transcripts in a cell in terms of number, function and the signalling pathway networks involved (Jun-Young et al., 2019).

In our previous work, EAE has been found to have good wound healing properties, but the exact mechanism of action remains unclear (W. J. Lu et al., 2021). In this work, the cytogenetic differences between the three treated and untreated groups of EAE, ellagic acid standard (EAS) and positive control human recombinant epidermal growth factor (rh-EGF) were compared using transcriptome sequencing to reveal the potential mechanism of action of EAE on wound healing efficacy. This will provide a new research direction for the development of raspberry functional foods and also has some guiding significance for human health.

2. Materials and methods

2.1. Chemicals and reagents

Ellagic acid (high-performance liquid chromatography grade) was ordered from Sigma-Aldrich (St. Louis, MO, USA). Raspberry ellagic acid extract was prepared and characterized in our laboratory. Dulbecco’s Modified Eagle Medium (DMEM) was obtained from Gibco (Grand Island, NY, USA). Recombinant human epidermal growth factor (rh-EGF) was procured from PeproTech (Rocky Hill, NJ, USA). Dimethyl sulfoxide (DMSO) and phosphate-buffered saline (PBS) were obtained from Solarbio (Beijing, China). TRIzol® Reagent and SuperScript double-stranded cDNA synthesis Kit were procured from Invitrogen (Carlsbad, CA, USA). Q411-02/03 ChamQ SYBR COLOR qPCR Master Mix (2×) and HiScript Q RT SuperMix for qPCR (+gDNA wiper) were procured from Vazyme Biotech Co., Ltd (Nanjing, China). All other chemicals were analytical grade unless otherwise stated.

2.2. Cell culture

Human immortalized epidermal HaCaT cells were obtained from Shengbo Biomedical Technology Co., Ltd. (Shanghai, China). The HaCaT cells were cultured in DMEM supplemented with 10% newborn calf serum and 1% penicillin−streptomycin. Cell culture was conducted in an incubator with a humidified atmosphere of 5% CO₂ at 37°C.

2.3. RNA extraction and quality evaluation

HaCaT cells (6 × 10⁹ cells/mL) were inoculated onto cell culture plates and incubated for 24 h to cover 90% of the well area. Then, draw lines along the ruler with a 200 μL tip. Cells were washed 2-3 times with PBS and then cultured with medium containing 5 μg/mL raspberry ellagic acid extract or ellagic acid, the blank group was not treated, and the positive control group was cultured with medium containing 3 ng/L rh-EGF, which was selected as the suitable effector concentration in previous experiments. Four groups of cells with different treatments were quickly transferred to a sterile operating table after 48 h of culture to end the experiment, and the floating cells were washed with PBS 2∼3 times, and the cells were lysed and RNA was extracted according to the TRIzol reagent instructions. The concentration and purity of RNA were determined by ND-2000 (NanoDrop Technologies) and agarose gel electrophoresis (OD260/OD280) and Agilent 2100 to determine the RNA integrity value (RNA integrity number, RIN) to ensure the quality of RNA in the samples.

2.4. Transcriptome sequencing and quantification

The gene library construction and Illumina Hiseq xten/NovaSeq 6000 sequencing were constructed by Shanghai Meiji Biomedical Technology Co. To ensure the accuracy and reliability of the experimental data, the raw data were first quality-controlled, and the software was used to filter the raw data and keep the high-quality clean reads. Clean reads (Mapped Reads) on the comparison with Homo sapiens reference genome were then spliced to assess the quality of the transcript comparison results. The clean reads were analyzed for expression levels of genes and transcripts using software to obtain the expression amounts (reads counts) of relevant genes and transcripts, and the quantitative results of this study were all in TPM, thus homogenizing the metrics for each sample in subsequent experiments.

2.5. DEG screening and analysis

After obtaining the expression of the corresponding genes and transcripts, the blank control (Control, CTL) and raspberry EAE-treated (CTL-VS-EAE), EAS-treated (CTL-VS-EAS) and rh-EGF-treated (CTL-VS-rh- EGF) samples were analyzed for differences using the software DESeq2 (DESeq2 V1.24.0). The screened differentially expressed genes (DEG) were subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis using the GO V 2019.7.1 and KEGG V 2017.08, with P-adjust <0.05 as the screening condition, and GO Term or metabolic pathways meeting this condition were indicated to be significantly enriched.

2.6. Quantitative real-time PCR

RNA was extracted from the transcriptome sequencing samples according to method 2.3, and the samples were used as RT-qPCR samples after passing quality control. GAPDH was selected as the internal reference gene, and the primers were designed by Primer 3.0 with reference to the internal reference primer design of previous studies, and the primers were synthesized by Biotech Biological Engineering Co. The specific information of the synthesized primers is shown in Table 1. After first removing DNA from the genome, and then adding 4 μL of 5×qRT SuperMix II at 50°C for 15 min and 80°C for 2 min to reverse transcribe RNA into cDNA, and then performing PCR amplification, the results of relative mRNA expression were used in the following formula:

Table 1. The sequences of the primers for real-time PCR.

Gene	Sequence (5'-3’)
GAPDH	F: TCCACTGGCGTCTTCACC R: GGCAGAGATGATGACCCTTTT
SCD-1	F: GTCAGTGCCCTGGGCATAAC R: GTGTTGGCAATGATCAGAAA
FASN	F: ATTCACTCCGAGGAACACAC R: GCTGTAGCCCACGAGTGTCT
SREBP-1c	F: CAGCAGCTACTGACAGTCAC R: GAGTCTGCCTTGATGAAGTG
PTTG	F: TGTTCCTGCCTCAGATGATG R: CAATCTGGTGCTCTTCAGGC
CDC20	F: GCCTGAAAACAGCCAGACGC R: CACTGAGCCGAAGGATCTTG
Cyclin A	F: AGTACCAGACTACCATGAGG R: CGAGGATAGCTCTCATACTG
NGFR	F: GAACACCGTGTGCGAGGAGT R: GGGGGTGTGGACCGTGTAAT
Fn14	F: GGCTTTTTGGTCTGGAGACG R: TCTCCGCCGGTCTCCTCTAT
CCL2	F: GAATCACCAGCAGCAAGTGT R: GGAATCCTGAACCCACTTCT

(1) $\begin{array}{rl}{2}^{-\mathrm{\Delta }\mathrm{\Delta }\mathrm{\Delta }Ct},\mathrm{\Delta }Ct& =Ctvalue\left(targetgene\right)-Ctvalue\left(GAPDH\right)\\ \mathrm{\Delta }\mathrm{\Delta }\mathrm{C}\mathrm{t}& =\mathrm{\Delta }\mathrm{C}\mathrm{t}\left(\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\right)-\mathrm{\Delta }\mathrm{C}\mathrm{t}\left(\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}\right)\end{array}$(1)

2.7. Statistical analysis

The transcriptome analysis was tested in three independent experiments with three biological replicates. Data are expressed as the mean ± SD of three independent experiments. All statistical analyses were performed using SPSS version 17.0 software (IBM). The values were compared with a one-way ANOVA followed by Duncan’s test. p < 0.05 was considered statistically significant.

3. Results and discussion

3.1. RNA extraction and quality evaluation

The results of total RNA detection and analysis of four groups of HaCaT cells cultured under different conditions are shown in Table 2, from which it is known that the OD260nm/OD280nm values are greater than 2, which are in accordance with the test conditions; the results of agarose gel electrophoresis are shown in Figure 1, and the three bands are 5SrRNA, 18SrRNA and 28SrRNA (from top to bottom), each band is bright and clear, and does not contain pigment, protein The results of Agilent 2100 showed that the RIN values of 12 samples were ≥9.30, OD260/280 ≥ 2.02, OD260/230 ≥ 1.80, and the total amount of RNA met the standard requirements.

Figure 1. Electrophoresis of total RNA detection (M, Marker; 1∼3, Control group; 4∼6, EAE group; 7∼9, EAS group; 10∼12, rh-EGF Group).

Table 2. RNA quality assessment.

Number	Samples	Concentration (ng/µL)	Content (µg)	OD260/280	OD260/230	RIN
1	EA_standard_1	200.30	7.01	2.02	2.18	9.90
2	EA_standard_2	197.30	6.91	2.03	2.20	9.90
3	EA_standard_3	197.40	6.91	2.02	2.18	9.80
4	EA_extract_1	173.60	6.08	2.04	2.18	9.70
5	EA_extract_2	206.30	7.22	2.03	2.20	9.70
6	EA_extract_3	181.90	6.37	2.03	2.18	9.80
7	Control_1	136.80	4.79	2.05	2.16	9.30
8	Control_2	137.60	4.82	2.05	2.11	9.40
9	Control_3	132.60	4.64	2.04	1.80	9.30
10	rh-EGF_1	276.40	9.67	2.03	2.16	10.00
11	rh-EGF_2	269.90	9.45	2.02	2.20	9.90
12	rh-EGF_3	271.50	9.50	2.02	2.14	9.90

3.2. RNA sequencing data assessment

Transcriptome analysis of 12 samples yielded a total of 89.7 Gb of Clean data, with each sample clean data reaching more than 6.61 Gb and the percentage of Q30 bases at more than 94.21%. The samples in the blank CTL group (CTL_1, CTL_2, CTL_3) obtained 55895638, 48889532, 44486364 high-quality clean reads; the EAE group (EAE_1, EAE_2, EAE_3) and EAS group (EAS_1, EAS_2, EAS_3) each sample obtained 55106260, 50530186, 49356994, 45273352, 52960132, 49784486 high-quality clean. The average error rate of sequenced bases for clean data was below 0.05%. The average error rate of sequenced bases corresponding to clean data was less than 0.05%; the GC content of total bases of clean data of each sample reached more than 49%; the Q20 of all four groups of samples was more than 98% and Q30 was more than 94%, thus indicating the high reliability of sequencing data (Table 3). High-quality clean data were obtained by screening RNA-Seq sequencing results for subsequent analysis.

Table 3. Statistics and quality estimation of RNA-seq reads.

Sample	Clean Reads	Clean Bases	Base mismatch rate (%)	GC content (%)	Q20 (%)	Q30 (%)
Control_1	55895638	8334975951	0.0237	51.71	98.56	95.5
Control_2	48889532	7269444476	0.0237	52.24	98.53	95.44
Control_3	44486364	6614227520	0.0237	52.01	98.56	95.53
EA_extract_1	55106260	8194479110	0.0237	52.29	98.54	95.44
EA_extract_2	50530186	7535916864	0.0241	52.35	98.40	95.07
EA_extract_3	49356994	7326367721	0.0235	52.39	98.60	95.63
EA_standard_1	45273352	6736256799	0.0249	52.43	98.11	94.21
EA_standard_2	52960132	7887177415	0.0237	52.82	98.52	95.42
EA_standard_3	49784486	7397541743	0.0237	52.66	98.53	95.45
rh-EGF_1	53512534	7953546721	0.0238	52.66	98.51	95.38
rh-EGF_2	48751860	7256319101	0.0235	52.57	98.61	95.65
rh-EGF_3	50276716	7194109583	0.0232	49.8	98.75	96

3.3. Gene expression overview

The expression levels of genes and transcripts were quantitatively analyzed using the software, and the results showed that the log TPM values of the 12 samples ranged from −2 to 5, the distribution of TPM values ranged from 10⁻² to 10⁵ (Figure 2(A)), and the expression levels of genes and transcripts of each sample showed a normal distribution condition. The PCA analysis of correlation among the samples in the four different treatment groups is shown in Figure 2(B). In the three groups of CTL, EAE and EAS, the three samples in the same group were more concentrated, indicating higher correlation, better replicate trials and no outlier samples. Among them, the six samples of EAE and EAS were more concentrated and the distinction was not obvious, which was consistent with the results of the preliminary cell scratching experiment (W. J. Lu et al., 2021), indicating that the gap between the effects of EAE and EAS on HaCaT cells was not large. However, in the rh-EGF group, rh-EGF_3 was slightly more discrete from rh-EGF_1 and rh-EGF_2, which might be due to the error caused by the manipulation during sample preparation. DESeq2 is an analysis of differentially expressed genes between groups of assay samples with biological replicates, which in turn leads to the set of differentially expressed genes between different treatment groups (Love et al., 2014). The expression results of differential genes that meet the conditions of EAE, EAS, rh-EGF and CTL are shown in Figures 1(C) and 2. From the figures, it could be seen that there are 344 differential genes in CTL_VS_EAE, including 261 up-regulated genes and 83 down-regulated genes; 398 differential genes in CTL_VS_EAS, including 285 up-regulated genes and 113 down-regulated genes; 1075 differential genes in CTL_VS_rh-EGF, including 620 up-regulated genes and 455 down-regulated genes. The difference between CTL_VS_EAE and CTL_VS_EAS differential genes was not large, but both were less than 50% of CTL_VS_rh-EGF differential genes, which showed that rh-EGF transcribed more gene fragments during the proliferation and migration of HaCaT cells. We also found three differential groups containing 207 shared genes. This suggested that EAE, EAS and rh-EGF have the same mode of action in the proliferation and migration of HaCaT cells (Figure 3).

Figure 2. (A) TPM number distribution violin illustration. (B) Different treatment group expression between sample quantity PCA analysis. (C) Venn diagram of differentially expressed genes.

Figure 3. Volcano map of differentially expressed genes between different groups. (A) CTL_VS_EAE, (B) CTL_VS_EAS, (C) CTL_VS_rh-EGF.

3.4. GO enrichment analysis

The GO database is an international standard classification system constructed according to gene function, which is widely adapted to almost all organisms, and is also being improved and standardized with the continuous development of high-throughput technologies (Kwok et al., 2020; Y. H. Zhang et al., 2021). The GO database consists of three main components, namely molecular function, and biological process (Y. C. Li et al., 2022). Figure 4 showed the GO term results for the top 20 differentially expressed genes in GO classification order differentially expressed genes in three different treatment groups. In terms of molecular function, the differentially expressed genes of EAE, EAS and rh-EGF in different treatment groups accounted for a relatively high proportion (Figure 4(A)): binding, catalytic activity and molecular function regulator; the high proportion in the cellular component was: cell part, organelle, membrane, organelle part and membrane part (Figure 4(B)); the three aspects in the biological process were cellular process, biological regulation and metabolic process (Figure 4(C)).

Figure 4. GO classification statistics. (A) CTL_VS_EAE, (B) CTL_VS_EAS, (C) CTL_VS_rh-EGF.

Among the GO enrichment data of CTL_VS_EAE, the subunits that were significantly enriched and had a high number of genes or transcripts with GO term on the comparison were microtubule protein binding (GO:0015631), microtubule cytoskeleton organization (GO:0000226), extracellular matrix organization (GO:0030198), spindle formation (GO:0005819), regulation of mitotic nuclear division (GO:0007088), midbody (GO:0030496) and chromosomal mitotic region (GO:0000775), regulation of metaphase/anaphase cell cycle transition (GO:1902099) (Figure 5(A)); GO enrichment data of CTL_VS_EAS, genes or transcripts that were significantly enriched and had a high number of GO term on the comparison, the subunits with high number of GO terms are biological regulation (GO:0030198), membrane (GO:0016020), response to external stimulus (GO:0050896), integral component of plasma membrane (GO:0005886), extracellular region part (GO:0044421), regulation of biological quality (GO:0065008) (Figure 5(B)); in the GO enrichment data of CTL_VS_rh-EGF, the subunits that were significantly enriched and had a high number of genes or transcripts with GO term on the ratio were cell cycle (GO:0007049), extracellular matrix (GO: 0031012), microtubules (GO: 0005874), chromosome region (GO:0098687), spindle formation (GO:0005819), chromosome trophoblast region (GO:0000775), mitotic spindle organization (GO:0007052), and more significantly enriched in chromosome segregation (GO:0007059) (Figure 5(C)).

Figure 5. GO function annotation and function enrichment analysis. (A) CTL_VS_EAE, (B) CTL_VS_EAS, (C) CTL_VS_rh-EGF.

From the above GO annotation and enrichment analysis of differentially expressed genes, it was clear that both EAE and rh-EGF-treated groups have spindle formation (GO:0005819), chromosomal mitotic region (GO:0000775) along with similar extracellular matrix organization (GO:0030198) and extracellular matrix (GO:0031012). The common feature of these functions was that they would provide certain conditions for cell mitosis, which may also be the reason why EAE promotes cell proliferation.

3.5. KEGG pathway analysis

KEGG enrichment analysis can elucidate the function of differentially expressed genes at the level of metabolic pathways (Kumar et al., 2021). In KEGG, a gene can function in a single or multiple metabolic pathways simultaneously; at the same time, there can be crossover between multiple metabolic pathways due to one or several genes (Hu et al., 2020), therefore, KEGG enrichment analysis of differentially expressed genes is needed (Y. Liu et al., 2022). The KEGG metabolic pathway enrichment analysis of differentially expressed genes was done with P-adjust < 0.05 as the screening condition.

The KEGG metabolic pathway functional enrichment analysis of differentially expressed genes in the EAE, EAS and rh-EGF treatment groups and the CTL group revealed that CTL_VS_EAE was annotated with 220 genes enriched in 225 metabolic pathways; CTL_VS_EAS was annotated with 248 genes enriched in 239 metabolic pathways; CTL_VS_rh-EGF was annotated with 693 genes enriched in 295 metabolic pathways. Figure 6 showed the bubble diagram of the top 20 metabolic pathways ranked by enrichment level. The vertical coordinates represent the different KEGG pathways and the horizontal coordinates are the enrichment factors. The colour of the bubbles from cool to warm indicates the P-adjustment value is closer to 0, and the size of the bubbles represents the number of genes or transcripts on the pathway; the larger the bubbles and the colour is closer to red indicates the more significant enrichment in that pathway.

Figure 6. KEGG enrichment analysis bubble chart of different treatment groups. (A) CTL_VS_EAE (B) CTL_VS_EAS, (C) CTL_VS_rh-EGF.

The results showed that CTL_VS_EAE is most significantly enriched in the cytokine-cytokine receptor interaction and phenylalanine metabolism pathways in the KEGG enrichment analysis of the three differentially expressed genes (Figure 6(A)). The most significant enrichment was consistent with the results of CTL_VS_rh-EGF, where 22 and 12 differentially expressed genes were found in the metabolic pathway of cytokine-cytokine receptor interaction in the two treatment groups, respectively (Figure 6(C)). The most significant effect of phenylalanine metabolism enrichment was observed in CTL_VS_EAS, followed by histidine metabolism and regulation of lipolysis in adipocytes, and only after that by the cytokine-cytokine receptor interaction pathway, but it was evident that the highest number of differential genes was enriched in the cytokine-cytokine receptor interaction pathway (Figure 6(B)). It indicated that the promotion of cell proliferation by EAE and the interaction between cytokines and cytokine receptors are closely related (Rens & Merks, 2020).

After exploring the overall enrichment of differential genes in each group, further enrichment analysis was performed on the up- and down-regulated differential genes in each group. The enrichment analysis of the up-regulated and down-regulated genes of CTL_VS_rh-EGF revealed that a total of 430 genes were annotated in 257 metabolic pathways, and the enrichment of cell cycle metabolic pathways reached a highly significant level, with 16 differential genes CDKN2C, CDC20, PTTG1, CCNA2, ESPL1, E2F2, BUB1B, CCNB1, CCNB2, TTK, CDC45, PLBUB1B, CCNB1, CCNB2, TTK, CDC45, PLK1, CDC25C, CDK1, ORC1 and BUB1 were enriched, covering the whole process of cell division. 183 genes were annotated in KEGG for the upregulation of CTL_VS_EAE, enriched in 205 metabolic pathways, mainly through AMPK signalling. The pathway upregulates Cyclin A gene allowing cell cycle activation, thus upregulating the expression of some genes in the S-phase, G2-phase and M-phase of the cell cycle pathway and promoting DNA synthesis and mitosis (Ng et al., 2023), which was consistent with the transcriptomic analysis of Roy et al (Roy et al., 2008) studying the skin of mice 48-96 h after injury. Also, EAE upregulated SREBF1 (Li et al., 2014), which promoted the expression levels of FASN and SCD, providing favourable conditions for the neogenesis of traumatized skin (Lombardi et al., 2019; T. Lu et al., 2019; Zhou et al., 2021), however, it has also been shown that over expression of FASN will have the opposite effect on the proliferation of HaCaT cells; the CTL_VS_EAS group, consistent with raspberry ellagic acid extracts, activates the cell cycle by the AMPK signalling pathway, but upregulated genes are only expressed in mitotic G1 and M phases. Analysis of down-regulated gene enrichment revealed that the three treatment groups were significantly or very significantly enriched in TNF signalling pathway and NF-κB signalling inflammatory classical pathway, in addition to significant enrichment in cytokine-cytokine receptor interaction pathway, which prevented inflammatory response by down regulating Fn14, CCL2, CCL5, CARD14, LTB and other genes while delaying apoptosis of cells (Ridiandries et al., 2017; Wood et al., 2014; Zhu et al., 2018; Zotti et al., 2018). It could be seen that the effects of EAE and EAS on cell proliferation are inextricably linked to the anti-inflammatory effects of ellagic acid in addition to interfering with cell cycle signalling pathways.

3.6. Target gene screening and quantitative real-time PCR validation

RT-qPCR technique is a common method to validate the reliability of RNA-Seq sequencing results (Letsiou et al., 2021). 9 genes, including SCD-1, FASN, SREBP-1c, PTTG, CDC20, Cyclin A, NGFR, Fn14, and CCL2, which are differentially expressed genes with high expression in the common pathway in KEGG enrichment analysis, were selected as RT-qPCR for quantification, and GAPDH was chosen as the internal reference gene for validation of transcriptome sequencing results.

The results of RT-qPCR gene quantification validation were consistent with the results of RNA-Seq high-throughput sequencing. SCD-1, FASN, SREBP-1c, PTTG, CDC20, Cyclin A and NGFR genes were expressed in higher amounts in the EAE, EAS and rh-EGF groups than in the CTL group, and Fn14 and CCL2 genes were expressed in lower amounts in the EAE, EAS and rh-EGF group were lower than those in the CTL group (Figure 7). The results concluded that the RNA-Seq gene expression results were accurate and the results of high-throughput sequencing analysis were credible.

Figure 7. Comparison of RNA-Seq and RT-qPCR gene expression levels. (A) SCD-1 (B) FASN (C) SREBP-1c (D) PTTG (E) CDC20 (F) Cyclin A (G) NGFR (H) Fn14 (I) CCL2.

4. Conclusions

In the present study, transcriptomics was utilized to investigate the underlying mechanisms of EAE wound healing efficacy. EAE upregulated the Cyclin A gene through the AMPK pathway to activate the cell cycle, thereby upregulating the expression of some genes in the S, G2 and M-phases of the cell cycle pathway, promoting DNA synthesis and mitosis, and promoting cell proliferation. Moreover, it was significantly enriched in the cytokine-cytokine receptor interaction pathway. In addition, significant enrichment of transcription was also found in TNF signalling pathway and NF-κB signalling inflammatory classical pathway. It was mainly through downregulation of genes such as Fn14, CCL2, CCL5, CARD14 and LTB that prevented inflammatory responses while delaying apoptosis. The above findings indicated that EAE has certain intervention effects on the healing of wounds, which provided new pointers for the development of raspberry functional products and was of great significance for the transformation of raspberry products into high-value products and human health.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Hebei Provincial Major Science and Technology Achievement Transformation Project [grant number 21287101Z]; National Key Research and Development Program [grant number 2022YFD1600402]; Hebei Provincial Innovation and Entrepreneurship Team Project [grant number 215A7102D].