In-depth investigation of the mechanism in rats with alcoholic acute pancreatitis via DNA methylation, intestinal flora, and fecal metabolomics

Abstract

Background

Alcohol consumption is the main cause of acute pancreatitis (AP). Alcohol combined with cerulein (CI) exerts direct damage to pancreas, with its mechanism undefined.

Methods

Rats were randomly divided into three subgroups, with subgroup I including Control and Alcohol groups at weeks 6, 8, and 10, subgroup II including Control and Alcohol groups administered CI (12.5, 25, and 50 µg/kg), and subgroup III including Control and Alcohol groups administered CI (50 µg/kg). Following measurement of liver and kidney function indicators, pathological damage of liver, kidney, lung, and intestine was detected using HE staining. Methylation-Specific PCR, Sequenom MassARRAY methylation, ELISA, Western blot, and correlation analysis of 16S rRNA sequencing and metabolomics were performed.

Results

Compared to Control group, Alcohol + CI group increased pathological damage, serum aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, blood urea nitrogen, alkaline phosphatase, amylase, lipase, TNF-α, IL-6, and IL-1β levels (P < 0.01). Meanwhile, p-mTOR/mTOR, hypoxia-inducible factor 1 alpha (HIF-1α), and vascular endothelial growth factor were enhanced, with lower creatinine and HIF-1α DNA methylation (P < 0.01). Alcohol at week 8 caused higher HIF-1α-25_CpG_35 methylation (P = 0.0222), which were reduced by further CI (50 µg/kg, P = 0.0034). Moreover, strong correlation was reported between key flora (Methanobrevibacter_A, Ruminococcus_C_59129, Lactobacillus, Eubacterium_Q, and Phascolarctobacterium_A) and metabolites (Homocysteine, Aspartic acid, Agmatine, 6-Ketoprostaglandin e1, Stearic acid, and Palmitic acid).

Conclusion

HIF-1α-25_CpG_35 methylation, intestinal flora, and metabolites involved in regulating AP pathogenesis. Methanobrevibacter_A, Ruminococcus_C_59129, Lactobacillus, Eubacterium_Q, Phascolarctobacterium_A, Homocysteine, Aspartic acid, Agmatine, 6-Ketoprostaglandin e1, Stearic acid, and Palmitic acid were the potential biomarkers, offering new insights into the pathogenesis of AP.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12950-025-00463-9.

Introduction

Pancreatitis is a common inflammatory pancreatic disease associated with high mortality and healthcare burden worldwide [1]. In particular, acute pancreatitis (AP) has a rapid onset, with key features including sudden abdominal pain, elevated serum levels of pancreatic enzymes, and pancreatic inflammation [2]. Notably, moderate and severe cases can progress to necrotizing pancreatitis, with a mortality rate of 20%−40% [3]. Despite advancements in therapeutic strategies for AP, the associated mortality rates remain high, and the quality of life for affected individuals continues to deteriorate [4]. Therefore, it is important to identify potential targets for AP. Importantly, as a global emerging healthcare issue, excessive alcohol consumption is the second leading cause of AP after gallstones [5]. Furthermore, it is also a notable risk factor for the recurrence of AP [6]. It has been found that excessive alcohol consumption reduces the secretion of pancreatic digestive enzymes, leading to the accumulation of zymogen granules in the gland, and, meanwhile, heightens the susceptibility of zymogen granules to accelerate the alveolar cell death [7]. Excessive alcohol consumption also leads to the misfolding or unfolding of proteins, which can trigger the premature activation of zymogens by alveolar cells, further intensifying the pro-inflammatory responses and cell death pathways [7, 8]. Additionally, the rising mortality of AP has been demonstrated to be driven by alcoholic AP [9]. The rate of readmission for patients with alcoholic AP within an 11-month period reached as high as 43.1% [10]. Therefore, it is crucial to deeply investigate the pathogenesis of alcoholic AP and to explore novel therapeutic targets to advance the development of therapeutic strategies for alcoholic AP.

During the onset of AP, the activation of hypoxia-inducible factor-1α (HIF-1α) occurs, leading to its translocation into the nucleus where it subsequently enhances the expression of vascular endothelial growth factor (VEGF), which contributes to elevated microcirculatory resistance and microthrombus formation, leading to microcirculatory disorders, pancreatic damage, and inflammatory responses [11, 12]. The level of VEGF indicates the severity of microcirculatory disturbances in patients with pancreatitis [13]. The inhibition of the HIF-1α pathway has been shown to mediate the amelioration of AP by Diosgenin derivative D [14]. Notably, HIF has been demonstrated to modulate the progression of tumor by epigenetic modifications, including DNA methylation, demethylation, and acetylation, and thus plays an important role in the regulation of the tumor microenvironment and metastasis, as well as in the development of targeted therapeutic strategies [15]. Furthermore, the occurrence of alcoholic pancreatitis is closely linked to specific gene mutations, DNA methylation, and histone modifications [16]. Zhao et al. [17] demonstrated that chronic alcohol exposure over an eight-week period resulted in lowered levels of IL-1α DNA methylation and an increase in its protein expression in acinar cells, which in turn damaged pancreatic tissues. Researchers conducting whole-genome DNA methylation sequencing on the cardiac tissues of Tibetan pigs from high altitudes and Yorkshire pigs from plains found that most methylation-differential genes were concentrated in the HIF signaling pathway [14]. Therefore, we hypothesised that the methylation level of HIF-1α may be a potential indicator of alcoholic AP.

Intestinal flora has been demonstrated to play an important regulatory role in AP [18]. Lactobacillus was identified as the predominant genus in normal rats, however severe acute pancreatitis (SAP) rats showed almost none of Lactobacillus, with increased Clostridiaceae 1, Clostridium sensu stricto 1, and Bacteroidales S24-7 [19]. A Mendelian randomization analysis identified five bacterial genera strongly associated with AP, including Coprococcus 3, Eubacterium fissicatena, Erysipelotrichaceae UCG-003, Fusicatenibacter, and Ruminiclostridium 6 [20]. Moreover, metabolomics is extensively employed to identify the predictive biomarkers linked to disease staging, severity, and etiology, serving as a robust instrument for the diagnosis of AP [21]. Notably, significant alterations in amino acid metabolism have been observed in both the pancreas and serum of individuals with AP [22]. Huang et al. [23] identified several metabolites associated with AP through gas chromatography-mass spectrometry (GC-MS) metabolomics, including L-Lactic acid, (R)−3-Hydroxybutyric acid, Phosphoric acid, Glycine, Erythronic acid, L-Phenylalanine, d-Galactose, and L-Tyrosine. Therefore, revealing the changes and association of intestinal flora and metabolomics in AP may further elucidate the potential mechanism of AP and explore the new therapeutic targets.

In summary, this study investigated the role of HIF-1α DNA methylation in alcoholic AP, while also identifying the potential biomarkers of AP through the analysis of intestinal flora and metabolomics. This study provided new insights into the mechanism of alcoholic AP and promoted the development of the therapeutic strategies for AP.

Materials and methods

Experimental materials

Seventy-two male Sprague-Dawley (SD) rats, each weighing 200 g, 8 weeks old, were purchased from Shanghai SLAC Laboratory Animal Co., Ltd.(animal production license number SCXK (Hu) 2022-0004). During the breeding period, the animals were kept behavioral adaptive training and fed a standard rodent pellet diet, and provided with ad libitum water access, under a 12-hour light/dark cycle. In addition, this study was conducted in accordance with the principles of ethical research practice: Animal Experimentation Ethics Committee of Zhejiang Eyong Pharmaceutical Research and Development Center, No. ZJEY-20221024-06.

Group division

Alcohol exposure

After one-week of acclimatization, the rats were randomly divided. The rats in the Control group were fed with AIN93M liquid control diet (TP 4020 C, Trophic, Jiangsu, China), while the Alcohol group was fed with AIN93M liquid alcohol diet (5% of alcohol, occupying 36% calories, TP 4020B, Trophic, Jiangsu, China). The alcohol diet composition was 36% alcohol calories, 10% fat, 14% protein, and 40% carbohydrates, while the control diet contained 10% fat, 14% protein, and 76% carbohydrates. To acclimate the Alcohol group animals to the alcohol diet, a transition feeding protocol was implemented. During the transition period, the Control group continued to receive the control liquid diet daily, whereas the Alcohol group gradually increased the proportion of alcohol liquid diet according to a set schedule, simultaneously decreasing the amount of control liquid diet. Specifically, from day 1 to day 2, the Alcohol group received no alcohol liquid diet; from day 3 to day 4, the alcohol liquid diet constituted 40%, and the control liquid diet 60%; and so on, until the evening of day 8, when the Alcohol group was completely transitioned to the alcohol liquid diet. After the transition period, to ensure isocaloric feeding, the diet amounts for the Control and Alcohol groups were adjusted based on the previous day’s leftovers. Feeding was scheduled between 3 PM and 5 PM daily, during which no additional water was provided. The liquid diets were freshly prepared daily, and any unconsumed diet was discarded, with water bottles cleaned and disinfected daily.

Group1

Thirty-six SD rats were divided into the Control group and Alcohol group. After 6, 8, and 10 weeks of feeding, respective, 6 rats from each group were anesthesia with isoflurane to collect peripheral blood and pancreatic tissues.

Group2

Then the rats were divided into eight groups: Control, Alcohol, Control + CI-1, Control + CI-2, Control + CI-3, Alcohol + CI-1, Alcohol + CI-2, and Alcohol + CI-3 groups. Following feeding in the Control and Alcohol groups for 8 weeks, it was established by intraperitoneal injection of CI (17397-89-6, Sigma) doses of 12.5 µg/kg, 25 µg/kg, and 50 µg/kg for CI-1, CI-2, and CI-3, respectively, with 6 rats in each subgroup via intraperitoneal injection, with injections given every 1 h for a total of 9 injections [24–26]. The rats were fed with standard laboratory pellet feed. Fasting was enforced for 12 h before modeling in all groups, but water was not restricted. After that, 6 rats from each group were anesthesia with isoflurane to collect peripheral blood and liver, kidney, intestine and pancreatic tissue after 6 h of the last injection of CI.

Group 3

The rats were chosen from the group 2 (at 8 weeks) as Control, Alcohol, Control + CI (50 µg/kg of CI), and Alcohol + CI groups (n = 6), with collection of peripheral blood and pancreatic tissues.

HE staining

Tissues were collected from the pancreas, lung, liver, kidney, and intestine, and fixed in a suitable fixative (10% formalin). Following dehydrating the tissues in a graded series of ethanol (Sinopharm Chemical Reagent Co., Ltd., 100092683) and clearing in xylene (Sinopharm Chemical Reagent Co., Ltd., 10023418), the tissues were infiltrated and embed in neutral balsam (Sinopharm Chemical Reagent Co., Ltd., 10004160). The embedded tissues were cut into thin sections using a microtome. Then the sections were rehydrated through a graded series of ethanol to water and stained with hematoxylin (Sigma, H3136) to highlight the nuclei. After washing using the running tap water, the counterstain with eosin (Sigma, E4009) to stain the cytoplasm and other tissue elements was performed. The sections were dehydrated through a graded series of ethanol and cleared in xylene. Then the stained sections were mounted onto slides using a mounting medium, and the Nikon DS-Fi2 imaging system was used for capturing high-resolution images of the stained sections. We further utilized the KF-PRO-120 full-scan panoramic scanner (Ningbo Jiangfeng Biotechnology) for comprehensive imaging of the tissue sections, and the stained sections for pathological changes were analyzed.

Western blot analysis

The pancreas tissues were obtained, and homogenized in RIPA Lysis Buffer (Beyotime, P0013B) to extract proteins. The PMSF (Beyotime, ST506) and protease inhibitor (Kangwei Century, CW2200S) were added to the lysis buffer to prevent protein degradation. Then the protein concentration was quantified using the BCA Protein Assay Kit (Beyotime, pc0020). The separating gel was prepared using 30% acrylamide (Sangon Biotech, B546017-0500) and 1.5 M Tris pH 8.8 (Biosharp, BL516A), with the stacking gel prepared using 1.0 M Tris pH 6.8 (Biosharp, BL514A) and 10% APS (BBI Life Sciences, A600072-0025). Then the protein samples and a pre-stained protein marker (Solarbio, PR1910) were loaded onto the gel. The gel were run in an electrophoresis system until the proteins were adequately separated and the proteins from the gel were transferred to a PVDF membrane (GE Healthcare Life, 10600023) using a wet or semi-dry transfer system. Following blocking the membrane with a solution containing BSA (BioFRoxx, 4240GR100) to prevent non-specific binding, the membrane was incubated with primary antibodies in Table 1. After the incubation with appropriate HRP-conjugated secondary antibodies (Table 1), the blot was developed using a chemiluminescent substrate and the signal was captured using a suitable imaging system. The bands were analyzed to assess the expression levels of the proteins.

Table 1.

Antibody information of Western blot

Antibody	Company	Dilution ratio	Cat#
TNF-α Antibody	Affinity	1:1000	AF7014
IL-6 Antibody	CST	1:1000	12,912 S
IL-1β Antibody	Affinity	1:1000	AF5103
mTOR Antibody	Affinity	1:1000	AF6308
p-mTOR Antibody	Affinity	1:1000	AF3308
HIF-1α Antibody	Affinity	1:1000	BF8002
VEGF Antibody	Affinity	1:1000	AF5131
Anti-rabbit IgG, HRP-linked Antibody	CST	1:6000	7074
Anti-mouse IgG, HRP-linked Antibody	CST	1:6000	7076
GAPDH Antibody	Proteintech	1:10000	10494-1-AP

Biochemical analysis

After the blood collection, blood was centrifuged for 3500 r/min for 15 min to get the serum, and the contents of amylase, lipase, alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), creatinine, and blood urea nitrogen (BUN) were detected by an automatic biochemical detector (Hitachi 3110).

ELISA assay

The blood samples were centrifuged for 20 min at 2000 rpm to collect the supernatant to detect the HIF-1α and VEGF levels using Rat HIF-1α ELISA Kit (Jiangsu Enzyme Immune Industrial Co., Ltd., MM-0390R1) and Rat VEGF ELISA Kit (Jiangsu Enzyme Immune Industrial Co., Ltd., MM-0179R1). The absorbance (OD value) was measured at 450 nm for each well within 15 min of adding the stop solution.

Methylation-specific PCR (MSP)

Extraction of DNA was performed using a standard DNA extraction protocol. Then 1 µg of extracted DNA was modified using the EZ DNA Methylation-Gold™ Kit (Zymo Research, D5005) following the manufacturer’s instructions. The bisulfite-treated DNA was stored at −80 °C for no more than one month. Then the methylated and unmethylated regions of the HIF-1α promoter were amplified using specific primers:

Methylated HIF-1α: Forward - TAGTCGGAGGAGTAATTAGGAATTC, Reverse - AAATCCAAAAACGAAATATAAAACG.

Unmethylated HIF-1α: Forward - TTGGAGGAGTAATTAGGAATTTGA, Reverse - ATCCAAAAACAAAATATAAAACAAA.

The detailed procedure was shown below: Pre-denaturation at 95 °C for 10 min, followed by 35 cycles of denaturation at 95 °C for 45 s, annealing at 58 °C (for methylated)/57°C (for unmethylated) for 45 s, and extension at 72 °C for 45 s, with a final extension at 72 °C for 10 min. The PCR products were resolved on an agarose gel and a gel imaging system was used to visualize and photograph the bands.

Sequenom MassARRAY methylation analysis of HIF-1α promoter methylation status

As described previously [27], on the basis of base-specific cleavage and the time of flight mass spectrometry (MALDI-TOF MS), quantitative methylation detection of the HIF-1α gene was performed using Sequenom MassARRAY platform (Agena Bioscience) according to the manufacturer’s protocol. The EpiDesigner software (www.epidesigner.com) was used to design primers, and the primer sequences were 5’-aggaagagag GTTGGTTTGTTAAGGTTTGGTTAGA-3′ (forward primer) and 5′-cagtaatacgactcactatagggagaaggct ACTTAACATCCCACTTACTCCAACA-3′ (reverse primer). Subsequently, the T7 promoter sequence was added to the PCR product for in vitro RNA transcription, and small RNA fragments with CpG sites were obtained by base-specific cleavage. Detection of molecular weight of each fragment was carried out by MALDI-TOF MS. Measurement of methylation level was conducted by quantitative methylation analysis (Sequenom).

16S rRNA gene sequence analysis

Mice were placed in metabolic cages and faeces were collected after 12 h of fasting, quickly frozen in liquid nitrogen and stored at −80 °C. The soil DNA Kit (T09-096, Major Bio) was used to extract the DNA. The highly variable region V3-V4 of the bacterial 16S rRNA was amplified using the broad-range bacterial primers, including the 338F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), and then separation was performed by 2% agarose gel electrophoresis. Purification was carried out using Zymoclean Gel DNA Recovery Kit (R1011, Zymo Research). Subsequently, sequencing was performed on an Illumina HiSeq 2500 platform (PE250). The whole analysis and screening of differential flora was performed using the Wekemo Bioincloud (https://www.bioincloud.tech) [28]. Alpha diversity was determined by Chao 1 index, Faith’s phylogenetic diversity (Faith_PD), observed_features, Shannon index, and Simpson index. Beta diversity was assessed and visualized by principal component analysis (PCA), orthogonal partial least square discriminant analysis (OPLS-DA), and Non-metric multidimensional scaling (NMDS). The differential species were detected by linear discriminant analysis (LDA) Effect Size (LEfSe) analysis (LDA > 4.0, P < 0.05).

Fecal non-targeted metabolomics

Sample pre-treatment [29, 30]: The 50 mg of faecal samples were collected and added with 500 µL of ddH₂O to homogenise for 5 min. Centrifugation at 13,000 r/min for 10 min at 4 °C was conducted, with 400 µL of supernatant taken to add 500 µL of pre-cooled methanol for homogenising for 5 min. Following the centrifugation under the same conditions for 10 min, 400 µL of supernatant was collected and the above two supernatants were mixed. The 10 µL of pre-cooled methanol (containing 300 µg/mL of internal standard 1,2-¹³C₂ myristic acid) was added, vortexed for 5 min and evaporated to dryness for 2 h. Then the samples were added with 60 µL of methoxyaminopyridine solution (10 g/L), vortex for 30 s, and shook at 450 r/min for 1.5 h at 30 °C. Following addition of 60 µL of N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA) and vortex for 30 s, shaking at 450 r/min for 1 h at 37 °C was performed. After centrifugation at 18,000 r/min for 10 min, 100 µL of supernatant was taken for test on the Trace1310/TSQ8000 GC-MS (Thermo).

GC-MS analysis conditions [29, 31]: The chromatographic separation was performed by a G-5MS capillary column (30 m × 0.25 mm × 0.25 μm film thickness). The sample was injected in a volume of 1 µL with a split ratio of 20:1. Helium was used as the carrier gas at a flow rate of 1.2 mL/min. The injector and ion source temperatures were set to 250 °C and 280 °C, respectively. The warming programme consisted of 60 °C for 1 min, followed by a 20 °C/min temperature increase to 320 °C which maintained for 5 min. Data acquisition was performed in full scan mode using electron impact ionization (70 eV) with the MS scan range set to m/z 50–500.

Correlation analysis between differential flora and differential metabolites (DEMs)

Correlation of top 20 genus-level gut flora and top 50 DEMs between the Alcohol_8w and Control_8w groups and between the Alcohol_CI3 and Control_8w groups was performed using Spearman analysis. Furthermore, O2PLS analysis was used to better explore the association between top 20 genus-level gut flora and top 50 DEMs.

Statistical analysis

Data analysis was conducted using SPSS 20.0 statistical software. All experiments were repeated independently no less than three times. For the comparison between two independent groups, the Student’s t-test was applied. For multiple groups’ quantitative data, if the data were normally distributed and passed the homogeneity of variance test, One-way ANOVA (Analysis of Variance) was used for analysis. Post-hoc pairwise comparisons between groups was conducted using the Tukey test.

Results

Alcohol caused liver and kidney injury and pancreas histopathological damage in rats at weeks 8 and 10

Figure 1A was the animal experiment design chart. The AST and ALT levels was measured for the assessment of liver injury, as well as GGT, BUN, ALP, and CREA levels for the assessment of liver and kidney function in rats at weeks 6, 8, and 10 using the biochemical analysis (Fig. 1B). Compared to the Control group, the serum levels of AST, ALT, GGT, BUN, ALP, and CREA in rats with alcohol administration were enhanced, with reduced CREA levels at week 10 (P < 0.05 or P < 0.01). Also, at week 8, alcohol caused higher GGT and BUN levels (P < 0.05 or P < 0.01). Additionally, in Fig. 1C, HE staining was employed to examine the histopathological damage to the pancreas. The pancreatic tissues of the Control group were essentially normal with no significant damage at weeks 6, 8, and 10. It was observed that alcohol led to serious histopathological damage to the pancreas in rats at weeks 6, 8, and 10. In the Alcohol group, there were widened interlobular spaces, with the presence of inflammatory cells, necrotic cells, hemorrhagic spots, and acinar cysts in the pancreatic tissues at weeks 6, 8, and 10. ELISA assay was performed to detect the HIF-1α and VEGF levels (Fig. 1D). The HIF-1α levels of the alcohol-treated rats were lower than those of the Control group, with higher VEGF levels at weeks 8 and 10 (P < 0.05 or P < 0.01). The above results indicated that alcohol led to severe liver and kidney injury and pancreas histopathological damage in rats at weeks 8 and 10.

Fig. 1.

Alcohol caused liver and kidney injury and pancreas histopathological damage in rats at weeks 8 and 10. A Animal experiment design chart; B Biochemical analysis was conducted to measure levels of AST, ALT, GGT, BUN, ALP, and CREA of the serum in rats at weeks 6, 8, and 10. n = 6. ^&P < 0.05 and ^&&P < 0.01 vs. Control group. C HE staining was employed to evaluate pancreatic pathology injuries (magnification 200×, scale bar: 100 μm). D ELISA kits were utilized to assess the serum expression of HIF-1α and VEGF, n = 6. ^▲P < 0.05 and ^▲▲P < 0.01 vs. Control group. Note: AP: Acute Pancreatitis; AST: Aspartate Aminotransferase; ALT: Alanine Aminotransferase; GGT: Gamma-Glutamyl Transferase; BUN: Blood Urea Nitrogen; ALP: Alkaline Phosphatase; CREA: Creatinine; HE: Hematoxylin and Eosin; ELISA: Enzyme-Linked Immunosorbent Assay; HIF-1α: Hypoxia-inducible factor 1 alpha; VEGF: Vascular Endothelial Growth Factor

Alcohol administration enhanced HIF-1α−25 promoter methylation in rats at weeks 8 and 10

Sequenom MassARRAY methylation analysis was conducted to detect the HIF-1α−25 promoter methylation status in the Control and Alcohol groups at weeks 6, 8, and 10 (Table 2 and Figure S1). The HIF-1α−25 promoter test fragment contained 20 amplified fragments and 45 CpG sites, and sites 4, 7, 9, 23, 30, 33, and 40 were not detected. In comparison to the Control group, the methylation levels of CpG_5, CpG_6, CpG_11, CpG_24, CpG_25, CpG_26, CpG_35, CpG_36, and CpG_39 were higher in the Alcohol group at weeks 6, 8, and 10, whereas the methylation levels of CpG_8 and CpG_34 were reduced. Notably, there was a significant difference in the changes in CpG_35 site of the Alcohol group at week 8 (P = 0.0222) and week 10 (P = 0.0346). It was obvious that alcohol led to the elevated levels of HIF-1α−25 promoter methylation, with suppression of its expression. Therefore, we finally selected an 8-week alcohol exposure time in the follow-up experiments.

Table 2.

Methylation levels of each CpG site between the control and alcohol (at weeks 6, 8, and 10) groups

CpG site (mean ± SD)	Control	Alcohol at week 6	Alcohol at week 8	Alcohol at week 10
HIF1α−25_CpG_1.2	0.0411 ± 0.0283	0.1600 ± 0.0424	0.1075 ± 0.0943	0.0500 ± 0.0668
HIF1α−25_CpG_3	NA	0.5280 ± 0.5039	0.2400 ± 0.3667	0.1450 ± 0.2051
HIF1α−25_CpG_5.6	0.0461 ± 0.0212	0.0600 ± 0.0200	0.0567 ± 0.0242	0.0667 ± 0.0288
HIF1α−25_CpG_8	0.0988 ± 0.1231	0.0867 ± 0.1344	0.0450 ± 0.1054	0.0250 ± 0.0612
HIF1α−25_CpG_10	0.0124 ± 0.0268	0.0517 ± 0.0574	0.0567 ± 0.0459	0.0283 ± 0.0694
HIF1α−25_CpG_11	0.0947 ± 0.1322	0.0850 ± 0.1330	0.1100 ± 0.1607	0.2675 ± 0.0556
HIF1α−25_CpG_12.13.14.15.16.17.18	0.0917 ± 0.0416	0.1267 ± 0.0468	0.1000 ± 0.0261	0.1000 ± 0.0322
HIF1α−25_CpG_19.20.21.22	0.2250 ± 0.0791	0.2683 ± 0.0624	0.2117 ± 0.1026	0.1750 ± 0.0339
HIF1α−25_CpG_24.25.26	0.0600 ± 0.0576	0.0567 ± 0.0115	0.1180 ± 0.0733	0.1567 ± 0.0115
HIF1α−25_CpG_27.28.29	0.0400 ± 0.0343	0.0483 ± 0.0436	0.0233 ± 0.0103	0.0383 ± 0.0117
HIF1α−25_CpG_31.32	0.0261 ± 0.0194	0.0300 ± 0.0167	0.0483 ± 0.0204	0.0217 ± 0.0147
HIF1α−25_CpG_34	0.0988 ± 0.1231	0.0867 ± 0.1344	0.0450 ± 0.1054	0.0250 ± 0.0612
HIF1α−25_CpG_35	0.0631 ± 0.0417	0.0680 ± 0.0661	0.1100 ± 0.0082	0.1150 ± 0.0311
HIF1α−25_CpG_36	0.0235 ± 0.0497	0.0283 ± 0.0646	0.0867 ± 0.0543	0.1200 ± 0.1455
HIF1α−25_CpG_37.38	0.0372 ± 0.0237	0.0317 ± 0.0248	0.0333 ± 0.0266	0.0400 ± 0.0141
HIF1α−25_CpG_39	0.0947 ± 0.1322	0.0850 ± 0.1330	0.1100 ± 0.1607	0.2675 ± 0.0556
HIF1α−25_CpG_41.42	0.0050 ± 0.0099	0.0100 ± 0.0126	0.0083 ± 0.0075	0.0200 ± 0.0219
HIF1α−25_CpG_43	0.0200 ± 0.0194	0.0033 ± 0.0052	0.0233 ± 0.0288	0.0217 ± 0.0117
HIF1α−25_CpG_44	0.0506 ± 0.0106	0.0517 ± 0.0147	0.0467 ± 0.0207	0.0533 ± 0.0234
HIF1α−25_CpG_45	0.0378 ± 0.0421	0.0133 ± 0.0327	0.0367 ± 0.0344	0.0183 ± 0.0248

Intestinal flora dysbiosis increased in the alcohol-treated rats

Subsequently, the detection of intestinal flora at weeks 6, 8, and 10 was conducted by 16S rRNA sequencing technology. The OTUs of the six groups were statistically analyzed using petal plots (n > 5, Figure S2A), with a total of 281 common OTUs among the six groups. The α-diversity analysis (Figure S2B-F) showed that Chao1, Observed species, Faith_PD, Shannon, and Simpson indexes of the Alcohol groups were reduced than those of the Control groups at weeks 6, 8, and 10, respectively (P < 0.05 or P < 0.01), further suggesting that alcohol led to reduced species richness and diversity of intestinal flora in rats. In Figure S2G-H, β-diversity was calculated to reveal that the compositional structure of the Alcohol and Control groups was not similar at weeks 6, 8, and 10.

Additionally, the top 10 flora at the phylum level (Fig. 2A) included Firmicutes_A, Bacteroidota, Methanobacteriota_A_1229, Firmicutes_D, Proteobacteria, Spirochaetota, Firmicutes_C, Desulfobacterota_I, Actinobacteriota, and Patescibacteria. The relative abundance of Spirochaetota of the Alcohol_8w group was reduced than that of the Control_8w group. At the genus level (Fig. 2B), Methanobrevibacter_A, CAG_485, Lactobacillus, CAG_632, Faecousia, Eubacterium_Q, CAG_269, UBA7173, Turicibacter, Duncaniella, and UMGS1994 were the top 10 genera. Additionally, the heatmap of gut flora at top 20 genus level of each samples was shown in Fig. 2C. In comparison to the Control_8w group, the Alcohol_8w group had higher Methanobrevibacter_A, Faecousia, and Eubacterium_Q, as well as reduced Lactobacillus, Turicibacter, Treponema_D, and CAG_973. The screening for differential species was carried out using LEfSe analysis (Fig. 2D-E). It was found that g_Eubacterium_Q, g_Faecousia, o_Eubacteriales_45227, g_Ructibacterium, f_Oscillospiraceae_45227, and g_Bacteroides_H were enriched in the Alcohol_8w group. Species significantly enriched in the Control_8w group mainly included p_Firmicutes_D, p_Spirochaetota, o_Clostridiales, g_Lactobacillus, g_Turicibacter, and g_Paramuribaculum. Finally, in Fig. 2F, the PICRUSt2 function prediction showed that in the Level 3 KEGG pathway, the Alcohol groups were predominantly enriched in Benzoate degradation, Butanoate metabolism, Citrate cycle (TCA cycle), Nitrogen metabolism, Propanoate metabolism, and Pyruvate metabolism. According to the above findings, alcohol administration led to an increased imbalance of intestinal flora in rats.

Fig. 2.

Intestinal flora dysbiosis increased in the alcohol-treated rats. A Histogram of the top 20 flora composition at phylum level; B Histogram of the top 20 flora composition at genus level; C Heatmap of intestinal flora at genus level; D-E LEfSe analysis for differentially intestinal flora; F Level 3 KEGG pathway of differentially flora. n = 6. Note: AP: Acute Pancreatitis

Alcohol resulted in fecal metabolism disturbance of rats

The fecal metabolites in rats was assessed by untargeted metabolomics. As presented in Fig. 3A-B, PCA and OPLS-DA results revealed significant differences in metabolites between the Alcohol and Control groups at weeks 6, 8, and 10. According to OPLS-DA, variable importance in projection (VIP) > 1 and P < 0.05 were the screening criterion for DEMs. The heatmap (Fig. 3C) showed the DEMs among the six groups. Among them, the peptides and lipids of the Alcohol groups were enhanced than those of the Control groups at weeks 6, 8, and 10, with reduced carbohydrates and organic acid. In addition, the Alcohol groups had elevated 4-Hydroxybenzyl alcohol, L-Leucine, L-Valine, Stearic acid, L-Isoleucine, and L-5-Oxoproline, as well as decreased Lactic acid, D-Xylose, Lyxose, and D-Lyxose when comparing to the Control groups at weeks 6, 8, and 10. The significant DEMs among the six groups were shown in Figure S3. The Sulphuric acid, N-Acetyl-L-glutamate, 6-Ketoprostaglandin e1, N-acetyl-aspartic acid, and Alanine were increased in the Alcohol groups than in the Control groups, with lower L-Arginine and 2-Deoxy-D-galactose. In Fig. 3D and Table S1, 265 up-regulated and 171 down-regulated DEMs were determined between the Alcohol_8w and Control_8w groups, with 250 up-regulated and 186 down-regulated DEMs between the Alcohol_10w and Control_10w groups in Fig. 3E and Table S2. According to the KEGG analysis among the six groups (Figure S4), DEMs were mainly enriched in Biosynthesis of amino acids, 2-Oxocarboxylicacidmetabolism, Arginine biosynthesis, and Valine, leucine and isoleucine biosynthesis. Between Alcohol_8w and Control_8w groups (Fig. 3F), DEMs were also mainly enriched in Valine, leucine and isoleucine biosynthesis, 2-Oxocarboxylicacidmetabolism, Arginine biosynthesis, and Biosynthesis of amino acids. Therefore, the fecal metabolism disturbance was exacerbated in the alcohol-treated rats.

Fig. 3.

Alcohol resulted in fecal metabolism disturbance of rats. A PCA analysis; B OPLS-DA analysis; C Heatmap of DEMs in rats with alcohol at weeks 6, 8, and 10; D Volcano map of the DEMs between the Alcohol_8w and Control_8w groups; E Volcano map of the DEMs between the Alcohol_10w and Control_10w groups: F KEGG pathway analysis between the Alcohol_8w and Control_8w group. n = 6. Note: AP: Acute Pancreatitis; PCoA: Principal Coordinates Analysis; OPLS-DA: orthogonal partial least squares discriminant analysis; DEMs: differential metabolites

Alcohol + CI administration aggravated pancreatic histopathological damage and reduced HIF-1α methylation in alcoholic AP rats

HE staining was performed to detect the pancreatic histopathological damage of rats administrated with alcohol combined with different dosage of CI (12.5, 25, and 50 µg/kg) at week 8 (Fig. 4A). The rat pancreatic tissues of the Control group were essentially normal with no significant damage. In the Alcohol, Control + CI-1, Control + CI-2, and Control + CI-3 groups, the pancreatic tissues exhibited widened acinar space, with the presence of inflammatory cells, necrotic cells, hemorrhagic spots, and acinar cysts. More importantly, there was more severe pancreatic tissue damage in the Alcohol + CI-1, Alcohol + CI-2, and Alcohol + CI-3 groups, with enlarged acinar space and an increased presence of inflammatory cells, necrotic cells, and hemorrhagic areas. Furthermore, in Figure S5, the histopathological damage of lungs, liver, kidneys, and intestine was detected using HE staining. In contrast to the essentially normal tissues of the Control group, the Alcohol, Control + CI-1, Control + CI-2, and Control + CI-3 groups exhibited varying degrees of damage, including inflammatory cell infiltration, widened alveolar septa in the lungs, vacuolar degeneration in the liver and kidneys, and mild epithelial damage in the colon. Meanwhile, the above damage was more pronounced in the Alcohol + CI-1, Alcohol + CI-2, and Alcohol + CI-3 groups. These results revealed that the Alcohol + CI administration aggravated the severe histopathological damage of pancreas, lungs, liver, kidneys, and intestine in alcoholic AP rats.

Fig. 4.

Alcohol + CI administration aggravated pancreatic histopathological damage and reduced HIF-1α methylation in alcoholic AP rats. A HE staining was applied to assess pancreatic pathology injuries at various dosages of CI in rats (magnification 200×, scale bar: 100 μm), n = 3. B ELISA assay was performed for serum expression of HIF-1α and VEGF, n = 6. ^▲P < 0.05 and ^▲▲P < 0.01 vs. Control group; ^★P < 0.05 and ^★★P < 0.01 vs. Alcohol group. Note: AP: Acute Pancreatitis; CI: Cerulein; HE: Hematoxylin and Eosin; ELISA: Enzyme-Linked Immunosorbent Assay; HIF-1α: Hypoxia-inducible factor 1 alpha; VEGF: Vascular Endothelial Growth Factor

In Fig. 4B, the serum HIF-1α and VEGF levels were tested by ELISA. Alcohol led to reduced HIF-1α and increased VEGF levels in comparison to the Control group (P < 0.01). The Control + CI-3 group exhibited increased HIF-1α and VEGF levels than those of the Control group (P < 0.01). Moreover, compared to the Alcohol group, the HIF-1α levels of Alcohol + CI-1, Alcohol + CI-2, and Alcohol + CI-3 groups were higher (P < 0.01), and VEGF levels were enhanced in the Alcohol + CI-3 group (P < 0.05). Sequenom MassARRAY methylation analysis was used to detect the HIF-1α−25 promoter methylation level (Figure S1 and Tables 3 and 4). The changes of methylation level of HIF-1α−25_CpG_35 was consistent with the trend of HIF-1α levels. Alcohol combined with CI treatment reduced the methylation level of HIF-1α−25_CpG_35 in comparison to the Alcohol group (P < 0.05 or P < 0.01). In addition, the effects of alcohol and different concentrations of CI on the HIF-1α−25_CpG_35 were further assessed by analytical factor analysis (Table 5). The main effect of alcohol exposure alone on HIF-1α−25_CpG_35 was not significant (F = 1.345, P = 0.253), and the main effect of CI on HIF-1α−25_CpG_35 was significant (F = 3.064, P = 0.039). Meanwhile, there was significant interaction effect of alcohol exposure with CI (F = 3.52, P = 0.023). Finally, the concentration of CI in subsequent experiments was clarified to be 50 µg/kg based on the above results. To sum up, Alcohol + CI administration reduced HIF-1α methylation in alcoholic AP rats, and HIF-1α−25_CpG_35 methylation was involved in regulating the development of AP.

Table 3.

Methylation levels of each CpG site in the Control group with three different dosage of CI (12.5, 25, and 50 µg/kg)

CpG site (mean ± SD)	Control (at week 8)	Control + CI-1	Control + CI-2	Control + CI-3
HIF1α−25_CpG_1.2	0.0411 ± 0.0283	0.0350 ± 0.0432	0.0183 ± 0.0183	0.0433 ± 0.0383
HIF1α−25_CpG_3	0	0.1050 ± 0.2100	0.2100 ± 0.2935	0.1175 ± 0.2350
HIF1α−25_CpG_5.6	0.0461 ± 0.0212	0.0500 ± 0.0379	0.0800 ± 0.0379	0.0133 ± 0.0163
HIF1α−25_CpG_8	0.0988 ± 0.1231	0.0300 ± 0.0687	0.0283 ± 0.0440	0.0083 ± 0.0133
HIF1α−25_CpG_10	0.0124 ± 0.0268	0.0417 ± 0.0926	0.0133 ± 0.0121	0.0017 ± 0.0041
HIF1α−25_CpG_11	0.0947 ± 0.1322	0.0100 ± 0.0245	0.0233 ± 0.0572	0.1467 ± 0.1579
HIF1α−25_CpG_12.13.14.15.16.17.18	0.0917 ± 0.0416	0.1233 ± 0.0388	0.2383 ± 0.0755	0.1650 ± 0.0459
HIF1α−25_CpG_19.20.21.22	0.2250 ± 0.0791	0.2717 ± 0.1144	0.3333 ± 0.0715	0.3233 ± 0.1965
HIF1α−25_CpG_24.25.26	0.0600 ± 0.0576	0.0217 ± 0.0172	0.0267 ± 0.0280	0.0233 ± 0.0186
HIF1α−25_CpG_27.28.29	0.0400 ± 0.0343	0.0267 ± 0.0121	0.0367 ± 0.0242	0.0533 ± 0.0327
HIF1α−25_CpG_31.32	0.0261 ± 0.0194	0.0350 ± 0.0197	0.0400 ± 0.0237	0.0517 ± 0.0223
HIF1α−25_CpG_34	0.0988 ± 0.1231	0.0300 ± 0.0687	0.0283 ± 0.0440	0.0083 ± 0.0133
HIF1α−25_CpG_35	0.0631 ± 0.0417	0.0783 ± 0.0194	0.0533 ± 0.0472	0.0633 ± 0.0635
HIF1α−25_CpG_36	0.0235 ± 0.0497	0.0450 ± 0.0704	0.0117 ± 0.0204	0.0583 ± 0.1052
HIF1α−25_CpG_37.38	0.0372 ± 0.0237	0.0350 ± 0.0345	0.0800 ± 0.0390	0.0950 ± 0.0740
HIF1α−25_CpG_39	0.0947 ± 0.1322	0.0100 ± 0.0245	0.0233 ± 0.0572	0.1467 ± 0.1579
HIF1α−25_CpG_41.42	0.0050 ± 0.0099	0.0067 ± 0.0121	0.0133 ± 0.0137	0.0050 ± 0.0122
HIF1α−25_CpG_43	0.0200 ± 0.0194	0.0167 ± 0.0121	0.0450 ± 0.0302	0.0450 ± 0.0740
HIF1α−25_CpG_44	0.0506 ± 0.0106	0.0450 ± 0.0084	0.0533 ± 0.0280	0.0383 ± 0.0240
HIF1α−25_CpG_45	0.0378 ± 0.0421	0.0117 ± 0.0160	0.0033 ± 0.0082	0.0083 ± 0.0133

Table 4.

Methylation levels of each CpG site in the Alcohol group with three different dosage of CI (12.5, 25, and 50 µg/kg)

CpG site (mean ± SD)	Alcohol (at week 8)	Alcohol + CI-1	Alcohol + CI-2	Alcohol + CI-3
HIF1α−25_CpG_1.2	0.1075 ± 0.0943	0.0233 ± 0.0288	0.0283 ± 0.0264	0.0133 ± 0.0234
HIF1α−25_CpG_3	0.2400 ± 0.3667	0	0.1200 ± 0.1860	0.3875 ± 0.4837
HIF1α−25_CpG_5.6	0.0567 ± 0.0242	0.0600 ± 0.0155	0.0400 ± 0.0237	0.1060 ± 0.0966
HIF1α−25_CpG_8	0.0450 ± 0.1054	0.0233 ± 0.0383	0.0317 ± 0.0371	0.0450 ± 0.0715
HIF1α−25_CpG_10	0.0567 ± 0.0459	0.0083 ± 0.0160	0.0267 ± 0.0413	0.0133 ± 0.0216
HIF1α−25_CpG_11	0.1100 ± 0.1607	0.0683 ± 0.0886	0.0840 ± 0.0832	0.1150 ± 0.1456
HIF1α−25_CpG_12.13.14.15.16.17.18	0.1000 ± 0.0261	0.1200 ± 0.0283	0.1367 ± 0.0463	0.2260 ± 0.0934
HIF1α−25_CpG_19.20.21.22	0.2117 ± 0.1026	0.3583 ± 0.0958	0.2733 ± 0.1511	0.3667 ± 0.0781
HIF1α−25_CpG_24.25.26	0.1180 ± 0.0733	0.0233 ± 0.0350	0.0183 ± 0.0147	0.0200 ± 0.0216
HIF1α−25_CpG_27.28.29	0.0233 ± 0.0103	0.0450 ± 0.0288	0.0217 ± 0.0279	0.0817 ± 0.0920
HIF1α−25_CpG_31.32	0.0483 ± 0.0204	0.0350 ± 0.0152	0.0217 ± 0.0232	0.0683 ± 0.0804
HIF1α−25_CpG_34	0.0450 ± 0.1054	0.0233 ± 0.0383	0.0317 ± 0.0371	0.0450 ± 0.0715
HIF1α−25_CpG_35	0.1100 ± 0.0082	0.0333 ± 0.0186	0.0433 ± 0.0175	0.0183 ± 0.0299
HIF1α−25_CpG_36	0.0867 ± 0.0543	0.0133 ± 0.0280	0.0200 ± 0.0490	0.0767 ± 0.1097
HIF1α−25_CpG_37.38	0.0333 ± 0.0266	0.0567 ± 0.0493	0.0633 ± 0.0398	0.0750 ± 0.0464
HIF1α−25_CpG_39	0.1100 ± 0.1607	0.0683 ± 0.0886	0.0840 ± 0.0832	0.1150 ± 0.1456
HIF1α−25_CpG_41.42	0.0083 ± 0.0075	0.0050 ± 0.0084	0.0050 ± 0.0122	0.0133 ± 0.0197
HIF1α−25_CpG_43	0.0233 ± 0.0288	0.0217 ± 0.0214	0.0200 ± 0.0363	0.0217 ± 0.0286
HIF1α−25_CpG_44	0.0467 ± 0.0207	0.0383 ± 0.0293	0.0567 ± 0.0103	0.0467 ± 0.0301
HIF1α−25_CpG_45	0.0367 ± 0.0344	0.0067 ± 0.0103	0.0350 ± 0.0644	0.0533 ± 0.0686

Table 5.

Effect of alcohol combined with CI at different concentrations (12.5, 25, 50 µg/kg) on HIF1α−25_CpG_35 site

Factor A (Alcohol)	Factor B (CI)
	B1 (12.5 µg/kg)	B2 (25 µg/kg)	B3 (50 µg/kg)
A0 (Control)	0.0783 ± 0.0194	0.0533 ± 0.0472	0.0633 ± 0.0635
A1 (Alcohol)	0.0333 ± 0.0186	0.0433 ± 0.0175	0.0183 ± 0.0299

Alcohol + CI exacerbated hepatic and pancreatic injury and inflammatory response through HIF-1α methylation in alcoholic AP rats

The serum content of lipase and amylase was examined by ELISA assay (Fig. 5A-B), and the biochemical indicators of liver function were measured in Fig. 5C-H. Both of the Alcohol and Control + CI groups showed higher lipase, amylase, AST, ALT, GGT, ALP, and BUN levels than those of the Control group (P < 0.05 or P < 0.01), with lower CREA levels (P < 0.01). Likewise, the lipase, amylase, AST, ALT, GGT, ALP, and BUN levels of the Alcohol + CI group were increased in comparison to the Alcohol group (P < 0.05 or P < 0.01), with reduced CREA levels (P < 0.05). Therefore, Alcohol + CI led to the impaired liver and pancreas function in alcoholic AP rats.

Fig. 5.

Alcohol + CI exacerbated hepatic and pancreatic injury and inflammatory response through HIF-1α methylation in alcoholic AP rats. A, B The serum content of lipase and amylase was examined by ELISA assay, n = 6; C-H Biochemical analysis was performed to measure the serum levels of AST, ALT, GGT, BUN, ALP, and CREA, n = 6; I Western blot analysis for TNF-α, IL-6, and IL-1β expression in pancreas of rats was made, n = 3; J MSP was used to evaluate HIF-1α methylation in pancreas of rats, n = 3; K Western blot analysis was employed to assess the expression of mTOR, p-mTOR, HIF-1α, and VEGF in pancreas of rats, n = 3. ^▲P < 0.05 and ^▲▲P < 0.01 vs. Control group; ^★P < 0.05 and ^★★P < 0.01 vs. Alcohol group. Note: AP: Acute Pancreatitis; CI: Cerulein; AST: Aspartate Aminotransferase; ALT: Alanine Aminotransferase; GGT: Gamma-Glutamyl Transferase; BUN: Blood Urea Nitrogen; ALP: Alkaline Phosphatase; CREA: Creatinine; TNF-α: Tumor Necrosis Factor-alpha; IL-6: Interleukin 6; IL-1β: Interleukin 1 beta; MSP: Methylation-Specific PCR (Polymerase Chain Reaction); mTOR: Mammalian Target of Rapamycin; p-Mtor: Phosphorylated Mammalian Target of Rapamycin; HIF-1α: Hypoxia-Inducible Factor 1 Alpha, VEGF: Vascular Endothelial Growth Factor

Moreover, Western blot was carried out to examine the expression of TNF-α, IL-6, and IL-1β in pancreas of rats in Fig. 5I. All of the Alcohol, Control + CI, and Alcohol + CI groups showed higher TNF-α, IL-6, and IL-1β levels compared to the Control group (P < 0.05 or P < 0.01). Meanwhile, compared to the Alcohol group, the levels of the above inflammatory factors increased in the Alcohol + CI group (P < 0.05 or P < 0.01). Consequently, Alcohol + CI enhanced the inflammatory response in alcoholic AP rats.

In Fig. 5J, the MSP method was used for detection of methylation levels of HIF-1α in pancreatic tissues. Although alcohol treatment led to an increase in methylation levels (P < 0.01), the Alcohol + CI group had reduced HIF-1α methylation in relative to the Control and Alcohol groups (P < 0.05 or P < 0.01). In addition, the expression of p-mTOR/mTOR, HIF-1α, and VEGF was detected using Western blot (Fig. 5K). Among them, the changes of p-mTOR/mTOR and VEGF expression were in the same trend as the above inflammatory factors (P < 0.05 or P < 0.01). Moreover, the HIF-1α expression was reduced in the Alcohol group and increased in the Control + CI group than that of the Control group (P < 0.01). According to the above findings, Alcohol + CI administration exacerbated hepatic and pancreatic injury and enhanced the inflammatory response through HIF-1α methylation in alcoholic AP rats.

Alcohol + CI treatment enhanced intestinal flora dysbiosis in alcoholic AP rats

The changes in intestinal flora of alcoholic AP rats with different concentrations of CI (12.5, 25, and 50 µg/kg) was examined using 16S rRNA sequencing. The OTUs of the eight groups (Figure S6A) were statistically analyzed using petal plots (n > 5), and there were 231 common OTUs. In Figure S6B-F, the α-diversity results indicated that there were lower Chao1, Observed species, Faith_PD, Shannon, and Simpson indexes in the Alcohol_8w, Alcohol_CI1, Alcohol_CI2, and Alcohol_CI3 groups than in the Control_8w group, and similarly these parameters were reduced in the Alcohol_CI3 group than in the Control_CI3 group. The β-diversity was calculated by PCoA and NMDS (Figure S6G-H), indicating that the structure of the Control_8w group differed from that of the Control_CI1, Control_CI2, and Control_CI3 groups, as well as that of the Alcohol_CI3 group from that of the Control_CI3 group. These results further suggested that alcohol combined with CI decreased species richness and diversity of intestinal flora in rats.

In Fig. 6A, Firmicutes_A, Bacteroidota, Methanobacteriota_A_1229, Firmicutes_D, Proteobacteria, Firmicutes_C, Desulfobacterota_I, Actinobacteriota, Spirochaetota, and Verrucomicrobiota were the top 10 phyla. Intestinal flora composition at genus level was demonstrated by histograms (Fig. 6B). Methanobrevibacter_A, CAG_485, Faecousia, Lactobacillus, Ructibacterium, Eubacterium_Q, CAG_269, Escherichia_710834, Phascolarctobacterium_A, and Fimenecus were the top 10 genera. Additionally, the heatmap of gut flora at top 20 genus level was shown in Fig. 6C. It was found that the relative abundance of Phascolarctobacterium_A, Dubosiella, and Methanobrevibacter_A in the Alcohol_CI3 group was higher than that of the Control_8w group (P < 0.01). Moreover, based on the LEfSe analysis (Fig. 6D-E), g__Ruminococcus_C_59129, k__Archaea, o__Methanobacteriales, c__Methanobacteria, f__Methanobacteriaceae, g__Methanobrevibacter_A, p__Methanobacteriota_A_1229, and f__Ruminococcaceae were enriched in the Alcohol_CI3 group. In Table S3, the Level 3 KEGG pathway showed that the relative abundance of Benzoate degradation, Butanoate metabolism, Citrate cycle (TCA cycle), Histidine metabolism, Metabolic pathways, and Phenylalanine metabolism in the Alcohol_CI3 group was higher than that of the Control_8w group. Therefore, Alcohol + CI aggravated the progression of alcoholic AP by modulating intestinal flora in alcoholic AP rats.

Fig. 6.

Alcohol + CI treatment enhanced intestinal flora dysbiosis in alcoholic AP rats. A Histogram of the top 20 flora composition at phylum level; B Histogram of the top 20 flora composition at genus level; C Heatmap of intestinal flora at genus level; D-E LEfSe analysis for differentially intestinal flora. n = 6. Note: AP: Acute Pancreatitis; CI: Cerulein

Alcohol + CI induced fecal metabolism disturbance in alcoholic AP rats

Non-targeted metabolomics was carried out to measure the fecal metabolism in rats (Fig. 7). The PCA and OPLS-DA analysis revealed the significant differences in metabolites between the Control and Alcohol groups with CI (12.5, 25, and 50 µg/kg) treatment (Fig. 7A-B). Among the eight groups, heatmap of DEMs was presented in Fig. 7C and showed that all the Alcohol_CI groups had higher peptides and reduced organic acid compared to the Control_CI groups. When compared to the Control_8w group, the Control_CI groups showed enhanced peptides and lipids, including 4-Hydroxybenzyl alcohol, L-5-Oxoproline, Stearic acid, as well as reduced carbohydrates and organic acid, including Pentofuranose, Lactic acid, and L-(+)-Lactic acid. As shown in Table S4 and the volcano plot analysis of Fig. 7D, there were 182 down-regulated and 254 up-regulated DEMs between the Control_CI3 and Control_8w groups. Additionally, there were 213 up-regulated and 223 down-regulated DEMs between the Alcohol_CI3 and Alcohol_8w groups (Fig. 7E and Table S5), as well as 246 up-regulated and 190 down-regulated DEMs between the Alcohol_CI3 and Control_8w groups (Fig. 7F and Table S6). The common increased DEMs included 6-Ketoprostaglandin e1, L-5-Oxoproline, Homocysteine, Inosine, Stearic acid, Adenosine, Aspartic acid, Palmitic acid, and Glyceryl palmitate (Table S6), as well as reduced alpha-D-Quinovopyranose, Phytosphingosine, 2-Deoxy-D-galactose, Agmatine, and 3-HydroxyBenzoate (Fig. 7F). Ultimately, in the comparison of the Alcohol_CI3 and Control_8w groups (Fig. 7G), DEMs were mainly enriched in Valine, leucine and isoleucine biosynthesis, Biosynthesis of amino acids, 2-Oxocarboxylicacidmetabolism, and Arginine biosynthesis. Therefore, based on these findings, Alcohol + CI induced fecal metabolism disturbance in alcoholic AP rats to exacerbate the severity of alcoholic AP.

Fig. 7.

Alcohol + CI induced fecal metabolism disturbance in alcoholic AP rats. A PCA analysis; B OPLS-DA analysis; C Heatmap of DEMs in rats with different concentrations of CI; D Volcano map of the DEMs between the Control_8w and Control_CI3 groups; E Volcano map of the DEMs between the Alcohol_8w and Alcohol_CI3 groups: F Volcano map of the DEMs between the Control_8w and Alcohol_CI3 groups; G Level 3 KEGG pathway analysis between the Control_8w and Alcohol_CI3 groups. n = 6. Note: AP: Acute Pancreatitis; CI: Cerulein; PCoA: Principal Coordinates Analysis; OPLS-DA: orthogonal partial least squares discriminant analysis; DEMs: differential metabolites

Correlation analysis between intestinal flora and DEMs

Between the Alcohol_8w and Control_8w groups (Figure S7A), Paramuribaculum was positively correlated with 2-Deoxy-D-galactose, alpha-D-Quinovopyranose, and Agmatine, as well as negatively correlated with N-acetylserine, 6-Ketoprostaglandin e1, and N-Acetyl-L-glutamate. Notably, the correlation between Methanobrevibacter_A and Eubacterium_Q and these DEMs was completely opposite. In Figure S7B, the correlation analysis between the Alcohol_CI3 and Control_8w groups showed that Lactobacillus was negatively correlated with 6-Ketoprostaglandin e1, N-acetylserine, and N-Acetyl-L-glutamate, whereas positively correlated with Agmatine, 2-Deoxy-D-galactose, Pyruvate, and alpha-D-Quinovopyranose. And the correlation between these DEMs and Eubacterium_Q, Methanobrevibacter_A, Ructibacterium, and Phascolarctobacterium_A was opposite.

To better explore the association between top 20 genus-level intestinal flora and top 50 DEMs in the Alcohol_8w and Control_8w groups, we further used the O2PLS model to better explain their association (R²X = 0.832, R²Y = 0.687, R²Xcorr = 0.677, R²Ycorr = 0.474) in Fig. 8A. The top five flora and metabolites, including Paramuribaculum, CAG_873, Treponema_D, UBA7173, Turicibacter, S-Adenosyl-L-methionine, Agmatine, Isomaltose, 3-HydroxyBenzoate, and Phytosphingosine. Subsequently, O2PLS analysis between the Alcohol_CI3 and Control_8w groups (Fig. 8B, R²X = 0.832, R2Y = 0.776, R²Xcorr = 0.647, R²Ycorr = 0.509) showed that Fimenecus, Bacteroides_H, Ruminococcus_C_59129, Faecousia, Eubacterium_Q, Lanster, b-Glutamic acid, 6-Ketoprostaglandin e1, Fucostanol, and 11beta-Hydroxyandrostenedione served as key factors in AP.

Fig. 8.

Correlation analysis between intestinal flora and DEMs using O2PLS. The O2PLS model was used to better explore the association between top 20 genus-level gut flora and top 50 DEMs in the Alcohol_8w and Control_8w groups (A), as well as in the Alcohol_CI3 and Control_8w groups (B). n = 6. Note: AP: Acute Pancreatitis; CI: Cerulein; DEMs: differential metabolites

Discussion

The characteristics of alcoholic AP mainly include high morbidity, high recurrence rate, high mortality rate, easy progression to chronic pancreatitis, and severe complications [10]. The incidence of recurrent alcoholic AP is approximately 25% [32]. More importantly, there remains an absence of a targeted pharmacological intervention up to date [10]. Therefore, elucidating the mechanism of alcoholic AP is of great significance for developing therapeutic strategies. The present study demonstrated that alcohol combined with CI enhanced liver and pancreas injury and inflammatory responses in alcoholic AP rats through down-regulation of HIF-1α promoter methylation, and participated in the progression of alcoholic AP by modulating the intestinal flora and metabolites, thereby exploring the potential biomarkers.

In the L-arginine-induced SAP mice, the HIF-1α inhibitor PX478 was found to attenuate the severity of SAP [11]. Meanwhile, the inhibition of HIF-1α and glutamate intervention alleviated the progression of AP by modulating energy stress [14]. More importantly, in our study, the mice of the Control + CI group showed elevated levels of HIF-1α compared to the Control group. It is well-known that alcohol exacerbates the progression of AP [33]. In fact, chronic ethanol exposure in rats led to reduced HIF-1α expression [34]. The prenatal alcohol exposure in rats caused a decrease of HIF-1α expression [35]. These findings aligned with our results, which demonstrated that alcohol intervention alone resulted in a down-regulation of HIF-1α expression. Notably, the HIF-1α expression in rats treated with alcohol combined with CI was lower than that in CI-treated mice, suggesting that HIF-1α expression was inhibited in alcoholic AP compared to non-alcoholic AP. DNA methylation serves as a reversible modification in epigenetic mechanisms that respond to both of the internal and external environmental stimuli, and it can be inherited through semi-conservative replication, thereby establishing an epigenetic framework for cellular memory [16]. Hypomethylation of promoter DNA is associated with the up-regulation of the corresponding genes [36]. Furthermore, HIF has been demonstrated to modulate the tumor disease progression through DNA methylation [15]. The results of the present study demonstrated that alcohol resulted in increased levels of HIF-1α methylation and its expression was suppressed, whereas further CI intervention decreased the levels of HIF-1α methylation and enhanced its expression. It was suggested that the levels of HIF-1α methylation were enhanced in alcoholic AP compared to non-alcoholic AP. The different concentrations of CI caused no significant difference in HIF-1α expression in either the Control or Alcohol groups, which may be due to the fact that changes in this fragment required a chronic process and its expression was not evident during the acute inflammation. In addition, interestingly, the level of HIF1α−25_CpG_35 was significantly lower in the Alcohol + CI-3 group than that of the Control + CI-3 group (Alcohol + CI-3 vs. Control + CI-3: 0.0183 ± 0.0299 vs. 0.0633 ± 0.0635). Therefore, the specific mechanisms involved need to be further explored in the subsequent studies.

The composition and functional characteristics of the intestinal flora provide the reference for assessing the therapeutic stage, severity, and etiology of AP [37]. Wang et al. [37] identified a significant association between specific bacterial species, including Parabacteroides johnsonii, Bacteroides stercoris, Methanobrevibacter smithii, Ruminococcus lactaris, and Coprococcus comes, and SAP. In addition, Erysipelatoclostridium and Prevotella9 had a negative correlation to AP, and Eubacteriumeligensgroup, Eubacteriumfissicatenagroup, and Coprococcus3 exhibited a positive correlation with AP [38]. As a well-known beneficial bacterium, Lactobacillus was found to be down-regulated in AP [18], which aligned with the enrichment of Lactobacillus in the Control_8w group of our study. Methanobrevibacter, an important methanogenic bacteria, is associated with various metabolic disorders and gastrointestinal diseases, and possesses the capability to evade the host immune response [39]. A high-fat/carbohydrate diet has been shown to shift the gut microbiota towards a predominance of Methanobrevibacter [40]. Additionally, Eubacterium hallii has been identified as a risk factor for the accumulation of subcutaneous adipose tissues [41]. Notably, alcohol has been investigated as a potential risk factor for obesity [42]. In the present study, Prevotella was enriched in the Control_10w group, and Eubacterium_Q and Eubacteriales_45227 were enriched in the Alcohol_8w group. Ruminococcaceae (UCG011) and Ruminiclostridium 9 have been considered as causative factors in pancreatic cancer [43], with pancreatic cancer patients exhibiting elevated levels of Phascolarctobacterium faecium [44]. Moreover, in our study, Methanobrevibacter_A and Ruminococcus_C_59129 were enriched in the Alcohol_CI3 group, while Phascolarctobacterium_A, Oscillospiraceae_45227, Erysipelotrichaceae, and Erysipelotrichales were enriched in the Control_CI3 group. Consequently, it is reasonable to infer that Methanobrevibacter_A, Ruminococcus_C_59129, Lactobacillus, Eubacterium_Q, and Phascolarctobacterium_A may serve as promising biomarkers for AP.

To date, the association between lipid metabolism and AP remains inadequately understood [45]. It has been demonstrated that free fatty acids are released via pancreatic lipase in AP and are thought to be the source of systemic lipotoxicity in AP [46]. At the same time, hypercholesterolaemia has been identified as a risk factor for SAP [47]. In comparison to healthy controls, AP patients exhibited significantly higher levels of palmitic, palmitoleic, oleic, and linoleic acid, with stearic acid levels also trending upwards, suggesting these fatty acids may be potential targets for SAP treatment [46]. Correspondingly, in our study, both the Alcohol_CI3 and Alcohol_8w groups showed increased levels of Cholesterol, Stearic acid, Palmitic acid, Glyceryl palmitate, Oleamide, Arachidonate, and gamma-Linolenic acid compared to the Control_8w group. The hydrolysis by lipase resulted in the release of unsaturated fatty acids, and the vesicular cytotoxicity of unsaturated linoleate, oleate, and linolenate esters ultimately contributed to pancreatic inflammatory response and necrosis [45]. Additionally, rats with CI-induced AP exhibited increased Sphinganine content [48]. Luo et al. [49] identified novel plasma biomarkers for the diagnosis of pancreatic cancer by metabolomics, including Sphinganine, Inosine, Creatine, and Glycocholic acid, which were highly consistent with our results.

The elevated levels of AST and ALT suggest hepatocellular injury, which are the hallmark of alcoholic liver disease [50]. Additionally, alterations in GGT, BUN, ALP, lipase, and amylase levels reflect stress on hepatic and renal systems, as well as the pancreas [51, 52]. The reduction in CREA levels, although seemingly paradoxical, may be linked to muscle atrophy or malnutrition, conditions frequently associated with chronic alcoholism [53]. Moreover, imbalances in amino acid aggravate the pathological process of pancreatitis [45]. Key amino acids involved in transamination, such as glutamate, aspartate, and alanine, are catalyzed by ALT and AST [45]. AP rats exhibited enhanced levels of Glutamate and aspartate [54], in line with our findings of increased levels of ALT, AST, N-Acetyl-L-glutamate, N-carbamylglutamate, N-acetyl-aspartic acid, L-asparagine, Aspartic acid, and L-Aspartic acid. Notably, Homocysteine is a key metabolite that bridges the methylation, remethylation and transsulfuration pathways [55], with increased levels observed in AP [56]. Guo et al. [57] identified 19 DEMs of AP by UPLC-MS metabolomics, including increased L-Phenylalanine, L-Tyrosine, Dihydroxyacetone, L-Valine, 3-Hydroxybutyric acid, Uric acid, and (R)−3-Hydroxy-hexadecanoic acid, as well as reduced d-Glutamine. Our results further validated the above findings. Agmatine, a metabolite derived from L-arginine and an endogenous polyamine, is primarily produced by the metabolism of intestinal flora such as Escherichia, Bacteroides, and Enterobacteriaceae [58]. Meanwhile, as a nitric oxide synthase inhibitor, Agmatine exerts anti-inflammatory and antioxidant properties [59]. The progression of experimental AP was attenuated with Agmatine treatment by inhibiting inflammatory responses and oxidative stress, as evidenced by decreased levels of amylase, glutathione peroxidase, TNF-α, and transforming growth factor-β [59]. In our comparative analysis of the Alcohol_CI3 and Control_8w groups, the Alcohol_8w and Control_8w groups, as well as the Alcohol_CI3 and Alcohol_8w groups, Agmatine was reduced in our study.

The present investigation offers a more detailed mechanistic insight into alcoholic AP, particularly with respect to the associated molecular and epigenetic modifications. However, this study has certain limitations, mainly including the lack of validation in female rat models and human subjects. Furthermore, the present study dose not incorporate the interventional studies, which are essential for thoroughly evaluating the clinical applicability of the identified mechanism and for validating the practical effect of the potential targets. This omission contributes to an increased level of uncertainty and risk regarding clinical implications. Therefore, it is crucial that future research encompasses diverse models and clinical trials to corroborate the findings of this study.

Conclusions

In conclusion, our study confirmed the detrimental effects of alcohol combined with CI on liver, kidney, and pancreatic function, and highlighted the reduced HIF-1α DNA methylation and the potential biomarkers in AP. HIF-1α−25_CpG_35 methylation, intestinal flora, and metabolites involved in regulating AP pathogenesis. Methanobrevibacter_A, Ruminococcus_C_59129, Lactobacillus, Eubacterium_Q, Phascolarctobacterium_A, Homocysteine, Aspartic acid, Agmatine, 6-Ketoprostaglandin e1, Stearic acid, and Palmitic acid were the potential biomarkers, offering new insights into the pathogenesis of AP and the development of effective therapeutic strategies in AP.

Abbreviations

AP

Acute Pancreatitis

CP

Chronic pancreatitis

SAP

Severe acute pancreatitis

GC-MS

Gas chromatography-mass spectrometry

AST

Aspartate Aminotransferase

ALT

Alanine Aminotransferase

GGT

Gamma-Glutamyl Transferase

BUN

Blood Urea Nitrogen

ALP

Alkaline Phosphatase

CREA

Creatinine

HE

Hematoxylin and Eosin

ELISA

Enzyme-Linked Immunosorbent Assay

HIF-1α

Hypoxia-inducible factor 1 alpha

VEGF

Vascular Endothelial Growth Factor

PCoA

Principal Coordinates Analysis

OPLS-DA

Orthogonal partial least squares discriminant analysis

DEMs

Differential metabolites

CI

Cerulein

TNF-α

Tumor Necrosis Factor-alpha

IL-6

Interleukin 6

IL-1β

Interleukin 1 beta

MSP

Methylation-Specific PCR (Polymerase Chain Reaction)

mTOR

Mammalian Target of Rapamycin

p-mTOR

Phosphorylated Mammalian Target of Rapamycin

NMDS

Non-metric Multidimensional Scaling

Authors' contributions

Haicheng Dong: Conceptualization, Data curation, Formal analysis, Investigation, Project administration, Writing-original draft. Weixing Ying: Formal analysis, Investigation, Methodology, Validation, Visualization, Writing-review & editing. Shifeng Zhu: Conceptualization, Formal analysis, Funding acquisition, Project administration, Supervision, Writing-review & editing.

Funding

This work was supported by National Natural Science Foundation of China (NSFC) (grant number 82104711).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the principles of ethical research practice: Animal Experimentation Ethics Committee of Zhejiang Eyong Pharmaceutical Research and Development Center, SYXK (Zhe) 2021-0033. (Date: 2022-Oct-24/No. ZJEY-20221024-06)

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.