Targeted screening of an anti-inf lammatory polypeptide from Rhopilema esculentum Kishinouye cnidoblasts and elucidation of its mechanism in alleviating ulcerative colitis based on an analysis of the gut microbiota and metabolites

Ziyan Wang, Qiuyu Shi, Ying Fng, Jiaojiao Han, Chnyang LuJun Zhou Zhonghua Wang, Xiurong Su

a State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Ningbo University, Ningbo 315000, China

b School of Marine Science, Ningbo University, Ningbo 315000, China

c Key Laboratory of Aquaculture Biotechnology (Ningbo University), Ministry of Education, Ningbo University, Ningbo 315000, China

d College of Life Sciences, Tonghua Normal University, Tonghua 134000, China

e Shandong Beiyou Biotechnology Co., Ltd., Weifang 261000, China

Keywords: Ulcerative colitis Rhopilema esculentum Kishinouye Cnidoblasts Marine bioactive polypeptides Metagenome Serum metabolome

ABSTRACT Ulcerative colitis (UC) is a recurrent inf lammatory bowel disease that imposes a severe burden on families and society. In recent years, exploiting the potential of marine bioactive peptides for the treatment of diseases has become a topic of intense research interest. This study revealed the mechanism underlying the protective effect of the dominant polypeptide PKKVV (Pro-Lys-Lys-Val-Val) of Rhopilema esculentum cnidoblasts against DSS-induced UC through a combined analysis of the metagenome and serum metabolome. Specif ically, the polypeptide composition of R. esculentum cnidoblasts was determined by matrix-assisted laser desorption ionization time-of-flight mass spectrometry ( MALDI-TOF/TOF-MS). Molecular docking showed that the dominant peptide PKKVV could bind better with tumor necrosis factor-α (TNF-α) than the original ligand.Subsequent animal experiments suggested that PKKVV could modulate disorganized gut microorganisms in mice with UC; affect serum metabolites through the arachidonic acid, glycerophospholipid and linoleic acid metabolism pathways; and further alleviate UC symptoms. This study provides a reference for the comprehensive development of marine bioactive substances and nonpharmaceutical treatments for UC.

1. Introduction

RhopilemaesculentumKishinouye is a well-liked seafood in Asian countries and occupies an important position in the economic marine f ishery resources of China.R.esculentumhas become an ideal resource for obtaining bioactive peptides and developing nutritious and functional foods because it is rich in protein and inorganic salts and has a low fat content[1]. A previous study confirmed thatR.esculentumcollagen and collagen hydrolysate protect the skin from UV radiation damage[2]. Mouse experiments have confirmed that jellyfish collagen hydrolysate has antifatigue and antioxidant functions[3]and has the potential to regulate blood pressure by inhibiting angiotensin I-converting enzyme (ACE) activity[4].Moreover,R.esculentumpolysaccharide increases the expression of mucins and tight junction proteins, modulates inflammation-related signaling pathways and the composition of the gut microbiota, and restores the production of short-chain fatty acids, thereby ameliorating ulcerative colitis (UC) in mice[5]. We thus wondered whetherR.esculentumpeptides, as a rich source of protein, would have the same anti-inflammatory properties as jellyfish skin polysaccharides.Therefore,R.esculentumwas subjected to enzymatic hydrolysis to explore the biological activity of the peptides.

Because the enzymatic hydrolysate is a mixture of various peptides, individualinvivotests were conducted to identify the active function of the hydrolysate. However, the traditional method cannot selectively analyze the key active components and is time-consuming and laborious[6]. At present, molecular docking is widely used to study the interaction between peptides and proteins based on geometric matching and energy matching[7]and can be used for high-throughput and accurate functional peptide screening. Two dominant peptides in tuna roe have been predicted by molecular docking to have antioxidant activity, and this activity was confirmed via cell experiments and animal experiments[8]. Molecular docking predicted that the crucian carp swim bladder hydrolytic peptide GLpGER forms stable complexes with pockets exhibiting ethanol dehydrogenase activity through hydrogen bonding and electrostatic and hydrophobic interactions, and its function in reducing acute alcohol-induced liver damage has been verified through experiments in mice[9].

UC is a chronic nonspecific intestinal disease characterized by abdominal pain, diarrhea, rectal bleeding and weight reduction[10]. The pathological manifestations of UC include damage to the intestinal mucosal barrier, disruption of the crypt architecture, an increase in basal plasma cells, lymphocyte aggregation and inherent layer fibrosis[11]. It is widely confirmed that chronically active UC poses a substantial colitis-associated colon cancer risk[12]. At present, various conventional drugs, such as 5-aminosalicylate, glucocorticoids, and sulfasalazine, are available for UC treatment, but these treatments are often accompanied by serious side effects, such as hypertension and gastric ulcers[13]. Therefore, the development of a natural, safe and effective treatment for UC is essential.

Bioactive polypeptides have the characteristics of broad-spectrum health-promoting benefits, high efficiency, good selectivity, and low toxicity, and the potential value of peptides in the treatment of UC has been described[14]. For instance, animal experiments have indicated that vasoactive intestinal peptide could treat UC by maintaining the integrity of the colonic mucosa through its free radical scavenging function[15]. The precise etiology of UC remains unclear, but it is generally believed that gut microorganisms are involved in the onset of UC[16]. A previous study showed that a reduced gut microbial diversity and dysregulation of homeostasis, which are mainly represented by a decrease in anti-inflammatory microorganisms and an increase in proinflammatory microorganisms, are characteristics of patients with UC[17]. However, mouse experiments have demonstrated that small-molecule active peptides isolated from tuna can alleviate DSS-induced UC by improving gut microbiota disorders and metabolites[18]. The dietary bioactive peptide alanyl-glutamine attenuates UC in mice by modulating the gut microbiota[19].

In this study, the peptide composition ofR.esculentumcnidoblast hydrolysate was determined by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/TOF-MS),and molecular docking indicated that the dominant polypeptide PKKVV (Pro-Lys-Lys-Val-Val) has anti-inflammatory activity. The performance of PKKVV was then investigated in a DSS-induced UC mouse model. The mechanism through which this peptide relieves UC was analyzed mainly based on the transcription levels of inflammatory cytokines and the changes in the gut microbiota and metabolic pathways in mice with diet-induced UC.

2. Materials and methods

2.1 Preparation and functional identification of polypeptides

Our previous study determined the optimal enzymatic hydrolysis conditions of tuna dark muscle via single factor and response surface experiments, and the enzymatic hydrolysis conditions of cnidoblasts ofR.esculentum(Zhoushan Daishan Port, Zhoushan, Zhejiang,China) were adjusted on this basis[6]. Trypsin (2%, 4 000 U/g) and alkaline protease (2.0 × 105U/g) (Novozymes Biotechnology Co.,Ltd., Tianjin, China) were mixed at a ratio of 3:2 and then incubated with the samples at 40 °C for 3.5 h. After inactivation by boiling water for 10 min, the mixture was cooled to room temperature, and the solution was centrifuged at 4 000 r/min at 4 °C to obtain the supernatant. Then, polypeptides less than 1 kDa were obtained by ultrafiltration. The amino acid sequences of the peptides (≤ 1 kDa)were determined by MALDI-TOF/TOF-MS (Applied Biosystems,CA, USA) as previously described[8].

The obtained dominant peptide sequences were assayed for their binding ability with tumor necrosis factor-α (TNF-α) as the target protein (PDB ID: 2AZ5) by molecular docking (DS 2018, Beijing Tronden Technology Co., Ltd., Beijing, China) for the targeted screening of polypeptides with inhibitory effects on inflammation.The binding ability of the screened peptide to TNF-αin vitrowas detected by Microscale Thermophoresis (MST) on Monolith NT.115(NanoTemper Technologies, München, Germany). TNF-α (Sangon Biotech Co., Ltd., Shanghai, China) was diluted with sterile distilled water to obtain 90 μL of protein solution with a concentration of about 6.4 μmol/L, and incubated with 10 μL of dye solution (RED-NHS)with a concentration of 300 μmol/L for 30 min at room temperature in the dark. The excess dye was separated from the labeled protein via a gravity flow column. Binding check was performed with appropriate concentrations of the screened peptide and labeled protein to test the binding ability. The signal-to-noise ratio greater than 5 proves that the peptide can bind to the protein.

2.2 Animal experiments

C57BL/6 male mice were purchased from Hangzhou Ziyuan Laboratory Animal Technology Co., Ltd. (Hangzhou, Zhejiang,China). The certificate number for the mice is SCXK ZHE 2019-0004,No. 1911070205. All experimental and animal care procedures were performed according to the Guide for the Care and Use of Laboratory Animals developed by the Ningbo Customs Technology Center, and all of the animal protocols were approved by the Ningbo Customs Technology Center Animal Center under permit number SYXK (ZHE 2018-0003). All necessary measures were taken to alleviate pain throughout the experiment.

The mice were reared at a constant temperature of (23 ± 1) °C and a humidity of (55 ± 5)%. Additionally, ambient lighting was supplied from 8 AM to 8 PM, and food and water were providedad libitum. After 1 week of acclimatization, 24 of 8-week-old male mice ((22 ± 2) g) were randomly allocated into 3 groups (8 mice per group): a control group (control), a DSS model group (model) and anR.esculentumcnidoblast dominant peptide PKKVV-treated group(PKKVV). All mice were given a standard diet. The mice in the control group were administered distilled water, whereas the mice in the other groups were given 2.5% DSS for 9 days. At the same time,the mice in the PKKVV group were administered the peptide PKKVV(10 mg/(kg∙day), MuJin BioTech Co., Ltd., Shanghai, China) by daily gavage[20]as previously described. This dose was equivalent to 48.65 mg/day for a 60-kg human[21]. Moreover, the mice in the control and model groups were given equal amounts of saline (Fig. 1). At the end of the experiment, blood samples, colon tissue, and the cecal contents of the mice were collected and stored at −80 °C.

Fig. 1 Schematic diagram of the modeling and treatment cycle.

2.3 Physiological index assessment

The body weight and stool occult blood degree of the mice were recorded every day. At the end of the experiment, the length of the colon was measured, and the severity of UC was assessed through disease activity index (DAI) scores[22].

2.4 Histopathological and histochemical assessments

Some colon tissues were fixed in 4% paraformaldehyde solution.After dehydration, the sections were embedded in paraffin, sectioned to 4-μm thickness, and stained with hematoxylin and eosin (H＆E).An Olympus BX51 microscope was then used to observe and photograph the sections. The criteria for scoring were as follows:0, no inflammation, no loss of goblet cells; 1, little inflammatory cell infiltration, a small degree of goblet cell destruction; 2, scattered inflammatory cell infiltration, part of the goblet cells missing;3, diffuse inflammatory cell infiltration, most of the goblet cells missing; and 4, extensive inflammatory cell infiltration, absence of goblet cells or crypts.

Immunohistochemistry (IHC) and immunofluorescence (IF)were performed to assess intestinal barrier function. Dewaxing,hydration, blocking of endogenous peroxidase activity, antigen repair, and closure of paraffin sections were performed as previously described[23]. The treated colon tissue sections were stained with CY3-labeled zonula occludens-1 (ZO-1)antibody or FITC-labeled occludin antibody, and 4,6-diamidino-2-phenylindole (DAPI) was then added for nuclear staining. After autofluorescence quenching, the sealed sections were observed by laser-scanning confocal microscopy(Bio-Rad Microscience, Cambridge, MA, USA), and fluorescence images were obtained.

2.5 Quantification of gene transcription

Total RNA from colon tissue was extracted using the TransZol Up Plus RNA kit (TransGen Biotech Co., Ltd., Beijing, China)in accordance with the manufacturer’s instructions. cDNA was synthesized using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Life Technologies, Carlsbad, CA, USA).Quantitative real-time PCR was performed with SYBR Green Master Mix and Quant Studio 6 Flex following the manufacturer’s protocol.Relative quantification was calculated by the 2−ΔΔCtmethod[24]. The detailed primer sequences are listed in Table S1.

2.6 Fecal metagenome

The feces of each group were collected separately on the 9thday for metagenomic sequencing. Following the manufacturer’s protocol,DNA was extracted from different samples using the E.Z.N.A.®Stool DNA Kit (D4015-02, Omega, Inc., USA). The total DNA was measured by PCR by LC Biotech Co., Ltd. (Hangzhou, Zhejiang,China). DNA library construction, raw sequencing read processing,metagenomic construction, and unigene annotation were performed as previously described[25]. The abundance of unigenes in the control and PKKVV groups was compared with that in the model group, and the top 1 000 different unigenes were screened according to the fold change (FC) in abundance (FC ≥ 2 or FC ≤ 1/2).

2.7 Serum metabolome

Serum metabolites were extracted with 50% precooled methanol solution and chromatographically separated using an ultraperformance liquid chromatography (UPLC) system (SCIEX, UK). Reversed-phase separation was performed using an ACQUITY UPLC T3 column(100 mm × 2.1 mm, 1.8 μm, Waters, USA). The columneluted metabolites were detected using a TripleTOF 5600 Plus(SCIEX, UK) high-resolution tandem mass spectrometer in the positive and negative ion modes[26]. Peak extraction and quality control were performed with XCMS software, and CAMERA was responsible for ion annotation. The identification, quantification and screening of metabolites were carried out by metaX software(the secondary mass spectrometry information was matched with the in-house standard database). The identified substances were functionally annotated using the kyoto encyclopaedia of genes and genomes (KEGG) database. The screening conditions for differential metabolites were FC ≥ 2 or FC ≤ 1/2,qvalue ≤ 0.05, and VIP ≥ 1(partial least squares discriminant analysis (PLS-DA)).

2.8 Statistical analyses

All the data are expressed as the mean ± standard deviation(mean ± SD). Unpaired student’sttests were performed to evaluate the statistical significance of basic physical biomarkers between two groups.P＜ 0.05 represents a statistically significant difference.Spearman’s method was used to analyze the relationship between the screened unigenes and routine indicators, as well as the relationship between microorganisms and metabolites.

3. Results

3.1 Compositional analysis and molecular docking

A total of 878 peaks were determined in the range ofm/z0−2 000.PKKVV (MH+:m/z569.378 3; relative content, 10.1%) was the dominant peptide and was found at a level higher than 10% inR.esculentumcnidoblasts (Table S2).

The docking results indicated that PKKVV exhibited a better binding ability with TNF-α than the original ligand (formula:C32H32F3N3O2), as indicated by a lower CDOCKER interaction energy(CIE, −68.678 1 kJ/mol) (Table S3). As shown in Fig. 2, 8 amino acid residues were involved in the interaction between PKKVV and TNF-α. Moreover, 2 amino acid residues in PKKVV formed conventional hydrogen bonds with TNF-α residues, and 1 amino acid residue in PKKVV formed a salt bridge with an amino acid residue of TNF-α. In addition, MST analysis was used to detect the binding ability of PKKVV to TNF-α. The results showed that the binding signal-to-noise ratio was 6.53 ± 1.91 under the condition of fluorescence values of 200−400, which confirmed that PKKVV could bind to TNF-αin vitro.

Fig. 2 Functional prediction of the major polypeptide of R. esculentum by molecular docking. (A) PKKVV binding to the active site of TNF-α. (B) Interaction of PKKVV with TNF-α. (C) 2D diagram of the interaction between PKKVV and TNF-α amino acid residues.

3.2 General indicators

During the experiment, the mice in the control group were in good condition and showed an increase of (5.71 ± 0.65)% from the original body weight. After being allowed to drink DSS solution freely for 6 days, the weight of the mice in the model group decreased from (100.46 ± 1.32)% (5thday) to (97.37 ± 1.57)% (6thday),and the weight of the mice in the PKKVV group decreased from(103.51 ± 1.52)% (5thday) to (101.88 ± 1.66)% (6thday). The mice in the DSS-drinking groups exhibited lethargy, inactivity, reduced water consumption, diarrhea and hematochezia, and these phenomena gradually worsened with prolonged modeling time. At the end of the experiment, a significant difference in body weight was found among the control and model groups (P＜ 0.01), and the body weight of the mice in the PKKVV group ((90.10 ± 1.24)%) had significantly recovered with respect to that of the model group ((81.68 ± 1.65)%)(P＜ 0.01) (Fig. 3A, Table S4). The DAI score of the PKKVV group (2.20 ± 0.56) was significantly lower than that of the model group (3.56 ± 0.27) (P＜ 0.01) (Fig. 3B). The colon length of the mice in the model group ((3.13 ± 0.39) cm) was significantly shorter (P＜ 0.01) than that of the mice in the control group((6.68 ± 0.28) cm). However, the colon shortening induced by DSS was somewhat ameliorated in the PKKVV group ((4.43 ± 0.29) cm)(P＜ 0.01) (Figs. 3C, D).

Fig. 3 PKKVV supplementation alleviated the manifestation of DSS-induced UC in mice. (A) Rate of weight changes. (B) DAI score. (C-D) Length of the colon.(E) Histopathological scores. (F) Histopathological sections of colon tissues with H＆E staining at 400× magnification. Gc, goblet cell; Ic, inflammatory cell;Cr, crypt. The results are shown as the mean ± SD, n = 8. *P ＜ 0.05; **P ＜ 0.01. The same below.

According to the histological scores, the colon histopathological score (5.67 ± 0.58) of the model group was significantly higher than that of the control group (0.00 ± 0.00) (P＜ 0.01), and the pathological score of the PKKVV group was significantly lower than that of the model group (2.67 ± 0.58) (P＜ 0.01) (Fig. 3E). The H＆E-stained sections showed that the colon structure in the control group was normal. In the model group, the intestinal mucosa was discontinuous, with large areas of goblet cells missing and extensive inflammatory cell infiltration in the submucosa. Nevertheless, a few crypts were absent in the PKKVV group, and scattered inflammatory cell infiltration was observed in the mucosa and submucosa (Fig. 3F),which indicated that PKKVV exerted a mitigating effect on DSS-induced UC.

3.3 Expression of colonic intestinal barrier proteins

According to the IHC and IF results, occludin and ZO-1 were more widely distributed in the control group than in the model group,whereas the optical density values of occludin and ZO-1 proteins in the PKKVV group were close to those in the control group (Fig. 4A and Table S5). In addition, Western blot assays showed that the claudin-1 expression in the model group was significantly lower than that in the control group (P＜ 0.01) and that the claudin-1 expression in the PKKVV group was significantly higher than that in the model group (P＜ 0.01) (Fig. S1).

Fig. 4 Effects of PKKVV on intestinal barrier proteins and colon inflammatory cytokines in mice. (A) Distribution of intestinal barrier proteins. (B) Relative expression of intestinal barrier proteins. (C) Relative transcription of the proinflammatory cytokines TNF-α, IL-1β and IL-6. (D) Relative transcription of TGF-β and IL-10.

The expression of ZO-1, occludin and claudin-1 in the model group was significantly lower than that in the control group (P＜ 0.01).However, the expression of these proteins in the PKKVV group was significantly higher than that in the model group (P＜ 0.01) (Fig. 4B).

3.4 Expression of inflammation-related cytokines

Compared with their expression in the control group, the expression of the proinflammatory cytokines TNF-α, interleukin-1β(IL-1β) and interleukin-6 (IL-6) in the model group was significantly upregulated (P＜ 0.01), and the gene expression levels ofTGF-βand the anti-inflammatory cytokine interleukin-10(IL-10) were significantly downregulated (P＜ 0.01). However,PKKVV completely reversed the expression trend of these genes compared with the model group (P＜ 0.01) (Figs. 4C, D).

3.5 Gut microorganism analysis

Compared with the results found for the control group, the abundances of bacteria and archaea were decreased in the model group, and those of eukaryotes and viruses were increased. However,these trends were reversed and the abundances restored to the control levels by PKKVV treatment (Fig. S2). No significant difference in the Chao1 index was found among the control, model and PKKVV groups (P＞ 0.05) (Fig. 5A). Analysis of the Shannon index revealed no significant difference between the control and model groups, but PKKVV significantly increased the Shannon index compared with that of the model group (P＜ 0.05) (Fig. 5B). Principal co-ordinates analysis (PCoA) revealed certain distinctions in species of the gut microbiota among the control, model and PKKVV groups, and the gut microorganism composition of the PKKVV group was closer to that of the control group (Fig. 5C).

Fig. 5 Effects of PKKVV on gut microorganisms in mice with UC. (A) Chao1 index. (B) Shannon index. (C) PCoA. (D) Phylum-level composition of the gut microbiota. (E) Genus-level composition of the gut microbiota. (F) Species-level composition of the gut microbiota.

At the phylum level, the gut microbiota of the control group consisted mainly of Bacteroidetes ((29.95 ± 16.19)%), Firmicutes((16.68 ± 8.13)%), Proteobacteria ((1.46 ± 0.34)%) and Chlamydia((0.74 ± 0.22)%). The model group exhibited lower abundances of Bacteroidetes ((15.01 ± 2.95)%) and Firmicutes ((11.40 ± 1.30)%) and higher abundances of Proteobacteria ((3.75 ± 1.28)%) and Chlamydia((6.70 ± 0.12)%) than the control group. PKKVV treatment reversed the trends found for the abovementioned bacteria in the model group, with the exception of the abundance of Proteobacteria((4.47 ± 1.11)%) (Fig. 5D).

At the genus level, the gut microbiota of the control group mainly includedChlamydia((2.42 ± 0.75)%),Bacteroides((4.13 ±1.88)%),Allstipes((1.85 ± 0.92)%),Clostridium((2.86 ± 1.02)%),Muribaculum((4.76 ± 2.27)%),Prevotella((4.28 ± 2.68)%),Odoribacter((0.53 ± 0.16)%),Helicobacter((0.65 ± 0.23)%)andOscillibacter((1.16 ± 0.78)%). The model group exhibited decreased abundances ofClostridium((1.93 ± 0.30)%),Muribaculum((0.61 ± 0.15)%),Prevotella((0.95 ± 0.15)%) andOscillibacter((1.07 ± 0.06)%) and increased abundances ofChlamydia((21.87 ±0.66)%),Bacteroides((6.23 ± 1.79)%),Allstipes((2.47 ± 0.23)%),Odoribacter((1.97 ± 0.29)%) andHelicobacter((1.93 ± 0.29)%)compared with the control group. The abundances of all the abovementioned genera, with the exception ofChlamydia((5.96 ± 6.92)%), were higher in the PKKVV group than in the model group (Fig. 5E).

At the species level, the control group mainly includedChlamydia abortus((2.31 ± 0.72)%),Acetatifactor muris((1.41 ± 0.92)%),Helicobacter ganmani((0.53 ± 0.20)%),Odoribacter laneus((0.13 ± 0.04)%),Escherichia coli((0.12 ± 0.02)%),Bacteroides sartorii((0.22 ± 0.08)%),Clostridioides difficile((0.16 ± 0.03)%),Bacteroides acidifaciens((0.71 ± 0.42)%),Chlamydia trachomatis((0.21 ± 0.06)%),Culturomica massiliensis((0.15 ± 0.05)%),Odoribacter splanchnicus((0.22 ± 0.06)%),Muribaculum intestinale((1.18 ± 0.71)%),Paramuribaculum intestinale((0.90 ± 0.50)%)andEubacterium plexicaudatum((0.65 ± 0.61)%). The model group had higher abundances ofC. abortus((20.95 ± 0.80)%),H. ganmani((1.61 ± 0.26)%),O. laneus((0.56 ± 0.09)%),E. coli((0.93 ± 0.86)%),B. sartorii((0.31 ± 0.06)%),C. difficile((0.62 ± 0.01)%),B. acidifaciens((1.64 ± 0.33)%),C. trachomatis((1.93 ± 0.16)%),C. massiliensis((0.67 ± 0.11)%) andO. splanchnicus((0.69 ± 0.09)%) and lower abundances ofA. muris((0.81 ± 0.37)%),M. intestinale((0.18 ± 0.06)%),P. intestinale((0.25 ± 0.11)%) andE. plexicaudatum((0.15 ±0.04)%) than the control group. PKKVV partially reversed the changes in the abundances ofC. abortus((5.71 ± 6.61)%),A. muris((1.14 ± 0.23)%),E. coli((0.50 ± 0.16)%),C. difficile((0.27 ± 0.15)%),C. trachomatis((0.52 ± 0.63)%),M. intestinale((0.33 ± 0.05)%),P. intestinale((0.34 ± 0.11)%) andE. plexicaudatum((0.27 ± 0.07)%) compared with those found in the model group(Figs. 5F and S3).

3.6 Correlation analysis

A total of 182 significantly different unigenes were identified and used for Spearman’s correlation analysis with routine indicators.Fifty-one differential unigenes exhibiting significant correlations with inflammatory cytokines or intestinal barrier indicators are presented in Fig. 6 along with their species annotation and KEGG classification information. Among these, unigenes annotated as belonging to Coriobacteriaceae bacterium andParasutterella excrementihominiswere positively correlated with the abundance of proinflammatory cytokines (IL-1β, IL-6, and TNF-α) and negatively correlated with anti-inflammatory cytokines (TGF-βandIL-10) and intestinal barrier indicators.Bacteroides intestinaliswas positively correlated with proinflammatory factors in general. Eighteen unigenes (annotated asClostridiumsp.,E. plexicaudatum,Prevotellasp., Muribaculaceae bacterium,Alistipes timonensis, Rikenellaceae bacterium,Acutalibacter muris, Lachnospiraceae bacterium, Clostridiales unclassified, and Bacteroidales bacterium), which were found to be mainly related to carbohydrate metabolism and nucleotide metabolism pathways, exhibited the opposite correlation.

Fig. 6 Correlations of important differentially expressed unigenes with inflammatory cytokines and intestinal barrier indicators. Correlation of 51 differentially expressed unigenes with inflammatory cytokines and intestinal barrier indices and their species annotation and KEGG classification information.

3.7 Serum metabolite analysis

Analysis of similarities (ANOSIM) of the metabolic ions identified in both the positive and negative ion modes revealed that the between-group differences among the control, model, and PKKVV groups were significantly greater than the within-group differences,which indicated that PKKVV modified the serum metabolites in mice(Figs. 7A, B).

Fig. 7 Differential analysis of serum metabolites in different groups. (A) ANOSIM in the positive ion mode. (B) ANOSIM in the negative ion mode.(C-D) PLS-DA of metabolites in the control, model and PKKVV groups in the positive ion mode. (E-F) PLS-DA of metabolites in the control, model and PKKVV groups in the negative ion mode.

PLS-DA was performed to screen the differential metabolites. In the positive and negative ion modes, the PLS-DA model parameters wereR2> 0.77 andQ2＜ −0.80, which indicated that the model was not overfitted. In both the positive and negative ion modes, the metabolic profiles of the model group were clearly separated from those of the control and PKKVV groups (Figs. 7C-F). In the positive ion mode, the model group had 1 033 upregulated differential metabolic ions and 418 downregulated differential metabolic ions compared with the control group, and in the negative ion mode,the model group had 1 120 upregulated differential metabolic ions and 715 downregulated differential metabolic ions compared with the control group. These metabolic ions were mainly involved in glycerophospholipid metabolism, linoleic acid metabolism, glycerol lipid metabolism, arachidonic acid metabolism,α-linolenic acid metabolism, tryptophan metabolism and steroid hormone biosynthesis(Fig. 8A). In the positive ion mode, 173 and 459 differential metabolic ions were upregulated and downregulated in the PKKVV group compared with the model group, respectively, and in the negative ion mode, 374 and 476 differential metabolic ions were upregulated and downregulated in the PKKVV group compared with the model group, respectively. These metabolic ions were mainly associated with glycerophospholipid metabolism, linoleic acid metabolism, and arachidonic acid metabolism (Fig. 8B). Additionally, in the positive and negative ion modes, 467 and 635 metabolites were shared among the differential metabolites identified from the comparisons of the control group and the PKKVV group with the model group, respectively.Some shared differential metabolites in the control and PKKVV groups compared with the model group were listed in Tables S6 and S7.

Fig. 8 KEGG enrichment pathway maps of differential serum metabolites.(A) Pathway diagram of differential metabolites enriched in the model group compared with the control group. (B) Pathway diagram of differential metabolites enriched in the PKKVV group compared with the model group. Significance is indicated by different colors: a darker color indicates significance at a stricter threshold.

3.8 Omics conjoint analysis

Common differential metabolites with complete annotations were used for Spearman’s correlation analysis with key differential species, which revealed potential correlations between some microbe-metabolite pairs (P＜ 0.005). The results suggested that Lachnospiraceae bacterium, Muribaculaceae bacterium,Clostridiales unclassified, Bacteroidales bacterium,Prevotellasp.,Clostridiumsp., Rikenellaceae bacterium,E. plexicaudatum,A.timonensisandA. murishad potential correlations with various metabolites. The contents of multiple metabolites related to arachidonic acid metabolism (cis-5,8,11,14-eicosatetraenoic acid,5,6-dihydroxy-8Z,11Z,14Z-eicosatrienoic acid, phosphatidylcholine(PC) 20:3; PC (2:0/18:3), PC 22:5; and PC (4:0/18:5)) and glycerophospholipid metabolism (diacylglycerol (DG) 22:3;DG (6:0/16:3), lysophosphatidylglycerol (lysoPG) 22:6; lysoPG 22:6,PC 22:5; PC (4:0/18:5), lysophosphatidylinositol (lysoPI) 22:6; and lysoPI 22:6) were higher in the model group and negatively correlated with various probiotics (Lachnospiraceae bacterium, etc.), and the level of lysoPC 20:0 (involved in glycerophospholipid metabolism)was lower in the model group and positively correlated with Lachnospiraceae bacterium, Muribaculaceae bacterium, Clostridiales unclassified, andClostridiumsp., among others. However, the contents of lysophosphatidylethanolamine (lysoPE) 16:1, lysoPE 16:0,lysoPE 17:0, lysoPE 20:5 and PC 20:3; PC (2:0/18:3) (involved in glycerophospholipid metabolism) were negatively correlated only with the abundance of Muribaculaceae bacterium and the contents of lysoPE 22:6, PE (22:4(7Z,10Z,13Z,16Z)/20:5(5Z,8Z,11Z,14Z,17Z)) and lysoPG 20:4; in addition, lysoPG 20:4 (involved in glycerophospholipid metabolism) was negatively correlated only with the abundance ofA. timonensis(Tables S8, S9 and Fig. 9). All of the abovementioned metabolites exhibited increased abundance in the model group. Additionally, the levels of some metabolites involved in linoleic acid metabolism, such ascis-5,8,11,14-eicosatetraenoic acid, 9,10-epoxyoctadecenoic acid, 13-oxo-9Z,11E-octadecadienoic acid,α-dimorphecolic acid, PC 20:3; PC (2:0/18:3) and PC 22:5; PC(4:0/18:5), were increased in the model group and negatively correlated with one or more probiotics (Table S10 and Fig. 9).

Fig. 9 Correlation network diagram of important differential species and differential metabolites. P ＜ 0.05. Blue line, positive correlation; orange line,negative correlation.

Moreover, the contents of glycocholic acid, taurocholate and uric acid were positively correlated with the abundances of Lachnospiraceae bacterium, Muribaculaceae bacterium, Clostridiales unclassified,Clostridiumsp., Bacteroidales bacterium, Rikenellaceae bacterium andA. muris. Indolelactic acid was positively correlated with the abundances of Clostridiales unclassified, Lachnospiraceae bacterium and Bacteroidales bacterium. The contents of acylcarnitine 15:0, acylcarnitine 17:1, retinyl ester and 5-(8-pentadecenyl)-1,3-benzenediol were negatively correlated only with the abundance of Muribaculaceae bacterium. However, the contents of ricinoleic acid and acylcarnitine 14:0 were only negatively correlated with the abundance ofA. timonensis(Fig. 9).

4. Discussion

Small-molecule peptides exhibit a variety of functions and biological activities due to being absorbed and utilized in their complete form[27]. Nonetheless, the traditional peptide research method requires the separation and purification of tissue hydrolysate to obtain individual peptides, which is a tedious step, and peptides with similar molecular weights are not easily separated. MALDITOF/TOF-MS detection is simple and fast, is not strongly influenced by impurities, and resolves peptide sequences via a high-throughput approach. A previous study analyzed the peptide composition of tuna meat by MALDI-TOF/TOF-MS and screened the oligopeptide tuna meat oligopeptides (TMOP), which could alleviate hyperuricemia[20]. In this study, the peptide composition ofR.esculentumcnidoblasts was determined by MALDI-TOF/TOF-MS,and the function of the peptides was then explored, which revealed that the dominant peptide PKKVV alleviates UC in mice. Research has shown that the biological activity of a peptide depends on its amino acid composition[28]. A previous study identified a possible anti-inflammatory effect of proline (P) on lipopolysaccharideinduced brain cortex and cerebellum alterations in rats[29]. The antiinflammatory effect of valine (V) has been known for a long time[30].In addition, as a small peptide, PKKVV can be directly absorbed by the intestinal wall in large quantities. These factors might contribute to the anti-inflammatory effects of PKKVV. Furthermore, the use of molecular docking technology allows the rapid and accurate screening of functional peptides in peptide mixtures.

UC is typically characterized by inflammation in the intestine,which is closely related to the proinflammatory mediator TNF-α secreted by lamina cells[31], although the exact mechanism remains unclear. Anti-TNF-α agents can bind to TNF-α and block the transduction of downstream proinflammatory signals, and the development and application of these agents are important for the treatment of severe UC[32]. Therefore, the polypeptide PKKVV, which exhibits better binding ability to TNF-α than the original ligand, was screened in our study via molecular docking, and the results revealed that the polypeptide could occupy the active site of TNF-α and thus prevent an inflammatory response.

Together with TNF-α, the proinflammatory cytokines IL-1β and IL-6 synergistically mediate lamina propria inflammation[33], and their overexpression prevents occludin protein from accurately targeting tight junction proteins to function properly and thus further disrupts intestinal homeostasis[18]. Conversely, the typical anti-inflammatory cytokines IL-10 and TGF-β produced by a variety of cells (for instance, B and T lymphocytes and macrophages) are important in maintaining intestinal homeostasis and preventing UC[34-35]. An imbalance between pro- and anti-inflammatory factors induces the development of UC. In this study, the ingestion of PKKVV decreased the expression of the proinflammatory cytokines IL-1β, IL-6 and TNF-α and increased the expression of the anti-inflammatory cytokinesIL-10andTGF-βin the colon tissue of the host, which suggested that PKKVV inhibited the occurrence of colonic inflammation.

A previous study noted that the structure of the gut microbiota is directly related to the balance between proinflammatory and anti-inflammatory responses in the gut[36]. Dysregulation of gut microorganism homeostasis may induce an inflammatory response, which will lead to intestinal mucosal barrier dysfunction.Additionally, the weakening of the intestinal wall barrier could lead to translocation of the gut microbiota, which would further damage the intestinal mucosal barrier and aggravate the inflammatory response[37].A previous study confirmed that proinflammatory diets increase the abundance ofB. intestinalisin the intestine of patients with intestinal constipation, whereas the consumption of anti-inflammatory diets has the opposite result[38]. Consistent with this finding, supplementation with PKKVV in this study decreased the abundance ofB. intestinalis,which is generally positively associated with proinflammatory cytokines. Nevertheless, supplementation with PKKVV increased the abundance of the major butyrate-producing bacteriaClostridiumsp.,Lachnospiraceae bacterium,E.plexicaudatum[39]and Muribaculaceae bacterium[40], which were presumed to be associated with the suppression of inflammation according to the results from the correlation analysis. Butyrate exerts anti-inflammatory effects mainly by binding to free fatty acid receptors on the epithelial cell membrane of the colon, which induces the production of anti-inflammatory cytokines[41]. Therefore, it is hypothesized that PKKVV alleviates UC by modifying the structure of the gut microbiota.

Metabolites of the gut microbiota are the medium of communication between the microbiota and their hosts and play a major part in maintaining normal physiological status[42]. In our study,the differential metabolites shared by the control and treatment groups compared with the model group were mainly involved in arachidonic acid metabolism, glycerophospholipid metabolism and linoleic acid metabolism.

As a second messenger, arachidonic acid (cis-5,8,11,14-eicosatetraenoic acid) participates in various cell signal transduction processes in the body[43]; thus, to maintain normal physiological function, this metabolite should be maintained at normal levels.Arachidonic acid is also an inflammatory mediator that can be metabolized by cyclooxygenase and lipoxygenase to produce inflammatory mediators such as leukotriene B4 via the linoleic acid metabolism pathway[44]. Studies have shown that increased levels of arachidonic acid are associated with the development of UC, and its metabolism is one of the target metabolic pathways in the regulation of UC[45], which was consistent with the findings of this study:PKKVV reduced the levels of arachidonic acid and its metabolites and alleviated inflammation.

Arachidonic acid is also an intermediate product in the linoleic acid and glycerophospholipid metabolic pathways. Glycerophospholipids are the main lipid components of cell membranes and play a leading role in cell proliferation, differentiation and apoptosis. Changes in the cell membrane composition and permeability affect the changes in the concentration of glycerophospholipids[46]. PC is the main glycerophospholipid in cell membranes, and lysoPC can be produced after PC is hydrolyzed. Studies have shown that unsaturated lysoPC may be protective against inflammation, whereas saturated lysoPC can accelerate the release of proinflammatory factors and thereby aggravate inflammation, which explains the negative correlation between the content of glycerophospholipid-related metabolites and their correlation with anti-inflammatory-related probiotics. In addition, multiple glycerophospholipid metabolites were correlated only with Muribaculaceae bacterium orA. timonensis, which suggested that they might play important roles in the regulation of the glycerophospholipid metabolic pathway in our study.

The linoleic acid metabolic pathway is amplified in mice with UC[47], whereas inhibition of the linoleic acid pathway effectively reduces intestinal oxidative reactions and protects intestinal epithelial cells[4]. In this study, supplementation with PKKVV reduced the content of linoleic acid metabolites, which might result in a reduced inflammatory response.

Data availability

The sequences have been deposited in the NCBI Sequence Read Archive Database under the accession number PRJNA827933.

Competing interests

The authors declare no competing interest.

Acknowledgments

This work was sponsored by the National Key R＆D Program of China (2018YFD0901102), the Natural Science Foundation of Zhejiang Province (LQ22D060002), the Fund of State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products (ZS20190105), the Fundamental Research Funds for the Provincial Universities of Zhejiang (SJLY2021015) and the K.C. Wong Magna Fund of Ningbo University.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at http://doi.org/10.26599/FSHW.2022.9250112.