SPF Male C57BL/6 J mice (7–8 weeks, 20–23 g), were purchased from Guangdong Zhiyuan Biopharmaceutical Technology Co., Ltd. [Guangdong, China, license no. SCXK(YUE)2021–0057]. All mice were housed under GLP laboratory conditions and Ad libitum feeding for 7 days. Shixiangru essential oil (SEO) were provided by Hunan Phyto-way Plant Resources Co., Ltd. (Hunan, China).

At 8th day, the mice were randomly divided into five groups (n = 8), including the control group (oral administered 10 mL/kg sterile water every day for 7 days), the model group (administered 0. 9% saline every day for 7 days), the SASP group (administered 200 mg/10 mL/kg SASP for 7 days), low dose of SEO (SEO-L) group (oral administered 100 mg/10 mL/kg SEO for 7 days), and high dose of SEO (SEO-H) group (oral administered 200 mg/10 mL/kg SEO for 7 days). The doses of SEO were selected based on the effective dose ranges of congeneric plant essential oils and preliminary safety tests in our laboratory, where SEO at ≤300 mg/kg did not induce overt toxicity in mice. UC was induced in mice by administering 3.0% (w/v) DSS (Dalian Meilun Biotech Co., Ltd) in sterile drinking water for 7 days. Control mice received normal sterile drinking water throughout the experiment.

At the end of the treatment, orbital enucleation for blood collection was performed under isoflurane anesthesia (2–3% in oxygen, inhalational) to ensure animal welfare. After blood collection, mice were euthanized via cervical dislocation while deeply anesthetized, followed by rapid dissection of colons. And blood samples were collected from each mouse using 2 mL precooled vacuum tubes. The mice were euthanized via cervical dislocation, followed by the rapid dissection of their colons. Following a previous study (35), plasma was isolated via centrifugation at 3,000 rpm for 10 min at 4 °C and subsequently stored at −80 °C for future analysis. Assessment of DAI.

The disease activity index (DAI) was assessed daily throughout the experiment, based on weight loss, stool consistency, and rectal bleeding. As outlined in Table 1, weight loss is evaluated on a scale based on the percentage reduction from baseline, with scores ranging from 0 points (indicating no weight loss) to 4 points (indicating more than 20% weight loss). Stool consistency is evaluated with scores of 0 for normal, 2 for loose stool, and 4 for diarrhea, while rectal bleeding is assessed with scores of 0 for no bleeding, 2 for a positive hemoccult test, and 4 for gross bleeding. DAI scores ranging from 0 to 12 indicate the severity of colitis, from healthy to severe (36).

After evacuating the colonic content of mice, the colon tissue was rinsed with PBS and fixed in 10% buffered formalin. The samples underwent dehydration in ethanol followed by hematoxylin and eosin (HE) staining. Upon dyeing, the sections were dehydrated and mounted with neutral mounting medium. Analyzing all sections with an optical microscope. Pathological scores were evaluated following the previous study (37). As showed in Table 2, The pathological score was evaluated by summing up the scores for four histological alteration (All alteration was scored 0 to 4), including intestinal mucosal inflammation, intestinal inflammation, crypt glands, and goblet cells (38).

Blood was collected from each mouse and centrifuged at 3000 g for 10 min at 4 °C. ZO-1 (BY-EM220420), Occludin (BY-EM220419), TNF-α (BY-EM220852), IL-1β (BY-EM220174), IL-6 (BY-EM220188), IL-10 (BY-EM220162) levels were determined by using ELISA kits (BOYAN, Nanjing, China) with serum and colon tissue homogenate as samples. Colon homogenates were prepared by homogenizing 50 mg of colon tissue in 500 μL RIPA lysis buffer (supplemented with 1% protease inhibitor) at 4 °C, followed by centrifugation at 12,000 × g for 15 min to collect the supernatant.

The levels of CAT and NO were evaluated by commercial kits (Nanjing Jiancheng Bioengineering Institute Co., Ltd. Nanjing, China) following by manufacturer’s instructions. The antioxidant activity was assessed using T-AOC kits (Nanjing Jiancheng Bioengineering Institute Co., Ltd. Nanjing, China) according to the manufacturer’s instructions.

Fecal samples from different groups of mice first stored in liquid nitrogen and subsequently kept at −80 °C. To detect structural differences in gut microbiota, we delivered samples to Beijing Biomarker Technologies Co., Ltd. for 16S rRNA sequencing. Bacterial DNA was extracted and quantified using the QuantiT™ ds DNA HS Reagent. All 16S rRNA sequencing procedures were performed by Beijing Biomarker Technologies Co., Ltd.

Mouse colonic tissue total RNA was extracted using the TRIzol method, and mRNA was enriched through Oligo (dT) magnetic bead selection. Libraries were prepared and sequenced using paired-end reads on the DNBSEQ-T7 platform. Post-processing identified differentially co-expressed genes, which underwent functional enrichment analysis of GO terms and KEGG pathways. All procedures were performed by Beijing Biomarker Technologies Co., Ltd. Differential expression analysis was performed using the DESeq2 package (v1.38.3) in R (v4.3.1) with thresholds of |log2 (fold change) | > 1 and adjusted p < 0.05. GO and KEGG pathway enrichment analyses were conducted using the clusterProfiler package (v4.6.2) in R, with adjusted p < 0.05 as the significance cutoff.

Colon tissue total RNA was extracted using TRIZOL reagent (Solarbio Science and Technology Co., Ltd., Beijing, China). Reverse transcription was conducted utilizing the PCR kit from Accurate Biotechnology Co., Ltd., Hunan, China. cDNA was amplified using specific primers to analyze the mRNA. We utilized the Analytikjena qTOWER3G Real-Time PCR EasyTM-SYBR Green system (Germany) to evaluate target gene expression. The protocol included an initial pre-cycling step at 95 °C for 30 s, followed by 40 cycles of 5 s at 95 °C and 30 s at 60 °C. The mRNA expression levels of PI3K, AKT, Itga7, Myb, Lama3, and Ngfr in colon tissue were quantified by qPCR using SYBR Premix Ex Taq reagents (Accurate Biotechnology Co., Ltd., Hunan, China). Primer sequences are listed in Table 3, and mRNA levels were quantified using the 2−ΔΔCT method with β-actin as the control.

Data were expressed as mean ± SEM and analyzed with Graphpad Prism software version 10. A t-test assessed the significance of differences between two groups, whereas a one-way ANOVA with LSD multiple comparison test evaluated differences among more than two groups. Significance levels are indicated as follows: * for p < 0. 05, ** for p < 0. 01, *** for p < 0. 001, and **** for p < 0. 0001, compared to the Model.

A 3% DSS-induced colitis model was developed to evaluate SEO’s protective effects on UC (Figure 1A). During the DSS treatment, all the mice experienced weight loss; however, we found that SEO greatly alleviated this loss (Figure 1D). Compared with the DSS group, mice in the SEO plus DSS group exhibited significantly lower DAI scores and longer colons after repeated cycles of the DSS treatment that in the DSS group did, suggesting less inflammation (Figures 1B–E). H&E staining and histological analysis indicated that SEO treatment mitigated DSS-induced tissue damage and inflammatory cell infiltration in the mucosa and submucosa, resulting in a reduced microscopic histological score compared to the DSS group (Figures 2A,B). The data suggested that SEO may have a protective effect against DSS-induced colitis.

To assess the effect of SEO on inflammatory symptoms, we detected the levels of several key molecules. The findings showed that SEO mitigated the DSS-induced increase in TNF-α, IL-1β, IL-6, and IL-10 expression (Figures 3A–D). Meanwhile, high dose SEO showed more effective inhibitory impact. We also found that SEO increased the levels of T-AOC and CAT, while decreasing the level of NO (Figures 3G–I). The findings indicate that SEO has potent anti-inflammatory effects by modulating the expression levels of molecules involved in pro-inflammatory cytokine production.

A hallmark change in UC is the compromised integrity of the intestinal barrier. Tight junction (TJ) proteins are crucial for bridging intercellular gaps and regulating intestinal mucosa permeability. Previous studies have shown that TJ protein levels are inversely related to the severity of UC. ZO and claudin family proteins collectively form tight junction (TJ) proteins. Inflammatory factors such as IL-6, IL-1β, and TNF-α decrease TJ protein levels, resulting in increased intestinal permeability and impaired barrier function. To confirm this, we assessed the levels of ZO-1 and Occludin (Figures 3E,F). These results suggested that SEO can maintain intestinal barrier integrity by modulating the levels of ZO-1 and Occludin to preserve TJ homeostasis.

Intestinal dysbiosis is one of the important pathogenic factors in the development of UC. We utilized 16S rRNA sequencing to explore the anti-inflammatory effects of SEO and its influence on gut microbiota composition. The rarefaction curves in the alpha diversity analysis plateaued, suggesting that the sequencing depth adequately captured the sample diversity (Figure 4A). The Wayne plot results revealed that the model group, DSS + SEO-H (200 mg/10 mL/kg) group, and control group identified 3,773, 4,358, and 3,778 independent OTUs, respectively (Figure 4B). We evaluated the alpha diversity index to determine the effect of SEO on the abundance and diversity of intestinal flora. The results indicated that the model group had higher Shannon and Simpson indices, while the SEO-H group showed a significant decrease in these indices. Furthermore, the SEO group increased in Chao 1. Consequently, SEO may enhance the richness and diversity of intestinal microbiota (Figures 4C–E). Beta diversity indices, including PCoA and NMDS, were employed to assess the similarities and differences among the flora. Both indices showed notable alterations in the intestinal flora of DSS-induced mice, which were alleviated by SEO (Figures 4F,G). The findings indicated that SEO influenced the composition of intestinal flora.

The impact of SEO administration on intestinal microbial composition was assessed using linear discriminant analysis (LDA) effect size (LEfSe), following an analysis of intestinal flora diversity (Figure 5A). The study found that Escherichia_Shigella, Parabacteroides, Mucispirillum, and Faecalibaculum were the dominant bacterial genera in the mouse model group. Bacteroides and Akkermansia_muciniphila predominated in the SEO-H group.

We analyzed changes in gut microbiota composition at the phylum, family, and genus levels, as depicted in Figures 5B–D, respectively. SEO significantly decreased the relative abundance of Proteobacteria, Deferribacterota, Enterobacteriaceae, and Clostridiaceae, while increasing Bacteroidota, Verrucomicrobiota, and Akkermansiaceae in DSS-treated mice. SEO decreased the relative abundance of the genera Escherichia_Shigella, Parabacteroides, and Faecalibaculum, while it increased the abundance of Akkermansia, Bacteroides, and Turicibacter (Figure 5E). Overall, SEO alleviated UC by regulating specific gut microbiota.

To elucidate how SEO mitigates ulcerative colitis, we utilized RNA-seq to investigate the biological functions and molecular mechanisms of differentially expressed genes (DEGs) through integrated analysis. Transcriptome sequencing revealed 1,666 differentially expressed genes (DEGs) in the volcano plot, comprising 515 up-regulated and 1,151 down-regulated DEGs (Figure 6A). Gene Ontology (GO) annotation of all differentially expressed genes (DEGs) identified several biological processes (BP), primarily encompassing cellular processes, biological regulation, metabolic processes, and response to stimuli. The differentially expressed genes (DEGs) showed enrichment in cellular components such as cellular anatomical entities, intracellular structures, and protein-containing complexes. The molecular functions (MC) related to DEGs were mainly binding and catalytic activities. (Figure 6B). Furthermore, KEGG pathway enrichment analysis identified signaling pathways associated with the DEGs. Consequently, several significant KEGG signaling pathways were identified, as illustrated in Figure 6C. KEGG pathway enrichment analysis and GSEA of RNA-seq-derived DEGs identified the PI3K-AKT pathway as notably prominent (Figure 6D). The PI3K-AKT pathway is linked to cell differentiation and proliferation, and it can modulate various classic inflammatory immune-related pathways, either activating or inhibiting them (39). qPCR assays were performed to determine if SEO protected colon tissues by affecting the PI3K-AKT pathway.

In DSS-induced mice, the expression levels of PI3K, AKT, Itga7, Lama3, and Ngfr genes were significantly elevated compared to the control group. However, these levels were notably reduced in the SEO-H group relative to the model group (Figure 6E). This could be due to the inhibition of the PI3K-AKT pathway. These findings further confirm that SEO protect the DSS-induced in mice via the PI3K-AKT pathway.