Jian Gan powder exerts anti-inflammatory and pro-proliferative effects and restores the dysbiosis of the gut microbiota and metabolism of the immunological liver injury mice

doi:10.21203/rs.3.rs-1575901/v1

Download PDF

Research Article

Jian Gan powder exerts anti-inflammatory and pro-proliferative effects and restores the dysbiosis of the gut microbiota and metabolism of the immunological liver injury mice

https://doi.org/10.21203/rs.3.rs-1575901/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Jian Gan powder (JGP), a traditional Chinese medicine (TCM) formula, has shown a considerable protective and therapeutic effect on hepatitis virus-induced immunological liver injury (ILI) in the clinic. However, the underlying mechanisms of the protective effects of JGP on ILI remain unknown. Evidence suggests that TCM or TCM formula can affect the intestinal microbial community and metabolic function to control liver disease progression.

Results

JGP treatment reduced inflammatory cytokine expression and promoted liver cell proliferation. The fecal 16S rRNA gene sequencing and LC-MS-based metabolomics analysis revealed that JGP could notably shape the gut microbiota structure and modify metabolic profiles. JGP treatment could restore the dysbiosis of Alloprevotella, Burkholderia-Caballeronia-Paraburkholderia, Muribaculum, Streptococcus, and Stenotrophomonas. Moreover, fecal metabolite profiles reflecting microbial activities showed that JGP modulated the disorder of biotin metabolism and steroid hormone biosynthesis in ILI mice. What’s more, the metabolites, Allylestrenol, Eplerenone, PE (P-20:0/0:0), SM d27:1, Soyasapogenol C, Chrysin, and Soyasaponin I were notably changed and remarkably reversed after JGP treatment.

Conclusion

JGP exerts anti-inflammatory and pro-proliferative effects and restores the dysbiosis of the gut microbiota and metabolism of the immunological liver injury mice. These results provided a new perspective that intestinal microbiota and their metabolites might be potential targets for elucidating the mechanism of JGP on the improvement of ILI.

Immunological liver injury (ILI)

Jian Gan powder (JGP)

Gut microbiota

Metabolites

The liver is a vital organ involved in physiologic homeostasis and various metabolic processes^[1]. In recent years, liver injury caused by stress insults, alcohol abuse, drug exposure, viral infection, and other factors has been increased in prevalence. The most common liver injury is immunological liver injury(ILI)which is a crucial process in the pathogenesis of acute liver failure, chronic hepatitis, and liver fibrosis^[2]. Currently, there is a lack of effective drugs for ILI treatment. So, it is crucial to seek a protective and effective therapeutic method to treat ILI.

Gut microbiota may potentially become a novel therapeutic target in ILI. Growing evidence indicates that the alteration of gut flora is strongly related to human liver disease^[3]. Gut microbiota communicates with the liver through the gut-liver axis. The liver dysfunction can remodel intestinal microbial communities, and the feedback route of gut-derived products secretion to the liver changed, thereby aggravating the development of liver disease^[4]. Therefore, the homeostasis of the gut-liver axis is essential for gut and liver function.

More recently, studies have shown that traditional Chinese medicine (TCM) or TCM formula can influence the intestinal microbial community and metabolic function to control liver disease progression^{[5, 6]}. TCM has been widely used and shown effective in the prevention and treatment of the immunological liver injury. Jian Gan powder (JGP) is an empirical formula for treating ILI induced by hepatitis B Virus infection, which consists of 11 kinds of herbs and has been widely used over recent years. However, the underlying mechanism of the preventive effect on ILI of JGP remains unknown. Given emerging reports that link TCM to gut microbiota structure and metabolism concerning liver disease, we hypothesize that the mechanism of JGP on the amelioration of ILI is associated with modifying gut microbiota structure and its metabolites.

In this study, the ILI mouse model was successfully established by Bacillus Calmette-Guérin (BCG) plus Lipopolysaccharide (LPS). 16S rRNA gene sequencing was firstly used to explore the changes of intestinal microflora in ILI mice after JGP treatment. Then, the metabolites from intestinal microflora were analyzed by untargeted metabolomics. To sum up, the results provided a novel insight into the mechanisms of JGP in treating ILI and the scientific basis to support its clinical application.

Chemical profiling of JGP water extract

Totals of 29 well-separated chromatographic peaks in JGP water extract could be found in the positive and negative ions mode of BPC (Fig. S1). 35 compounds were tentatively identified by their accurate mass data in comparison with reported references (Table S1-1, S1-2). In the positive ion mode, peaks 3, 4, 5, and 6 were tentatively identified to be alkaloids compounds, 8 was tentatively identified to be monoterpenoid, peak 14 was tentatively identified to be ginsenoside. In the negative ion mode, peak 1 was tentatively identified to be quinones compound, 3, 4, and 5 were tentatively identified to be monoterpenoids, 6 and 8 were tentatively identified to be flavonoid glycosides, 7 and 9 were tentatively identified to be salvianolic acids, 12 and 14 were tentatively identified to be flavonoids, 13 was tentatively identified to be ginsenosides, and 15 was tentatively identified to be glycyrrhizin.

Although some small molecules were identified in JGP water extract, polysaccharides and proteins may also exist because these large molecules could be extracted by water. Then the contents of carbohydrates and protein in JGP water extract were determined. Total polysaccharide content in JGP water extract was determined at 490 nm by phenol-sulfuric method, calculated with linear regression equation (y = 6.8308 x - 0.0053, R2 = 0.9995), whose linear range of glucose concentration was 0.005-0.1 mg/mL. Total polysaccharide content was 12.03 mg/g. The content of protein in JGP water extract was measured at 562 nm by BCA protein assay kits according to the manufacturer’s instructions with detection range 0.025-0.5 mg/mL (y = 1.0112 x + 0.023, R2 = 0.9998). Total protein content was 60.65 mg/g.

JGP has an anti-inflammatory effect and promotes liver cell proliferation

In this study, the ILI mouse model was successfully established to explore whether JGP offered protection against the liver (Fig. 1). Then the IFN-γ, IL-6, IL-10, and IL-22 contents in the serum were measured by ELISA. The levels of IFN-γ, IL-6, IL-10, and IL-22 significantly increased in the induced group compared with the normal group. Compared with the induced group, all doses of JGP treatments could effectively decrease serum levels of IFN-γ and IL-22. In addition, high dose JGP treatment significantly reduced the level of IL-6 compared with that in the induced group (Fig. 2A). Western blot was used to assess the levels of STAT3 and p-STAT3 expression in the liver. STAT3 and p-STAT3 expression significantly increased in the induced group compared with the normal group. Different doses of JGP treatment did not influence STAT3 expression, while JGP-M and JGP-H treatment inhibited p-STAT3 expression compared with the induced group (Fig. 2B). Furthermore, STAT3 staining data were consistent with the findings of WB experiments (Fig. S2). To further explore the effect of JGP on ILI, we also studied its effects on liver cell proliferation. Proliferating cell nuclear antigen (PCNA) is commonly accepted as a cell proliferation marker^{[7, 8]}. PCNA staining revealed that the percentage of PCNA positive cells significantly increased in the control group compared with the normal group. What’s more, medium and high doses of JGP treatment elevated the percentage of PCNA positive cells compared with the induced group (Fig. 2C).

Collectively, these results suggested that JGP had anti-inflammatory properties and could promote the proliferation of liver cells in BCG+LPS-induced mice.

Overall structural modulation of gut microbiota after JGP treatment

Sequencing data of bacterial 16S rRNA in colonic feces of mice show that the total number of generated tags was 2497,891, with an average of 83,263 tags per sample. The rarefaction curves for most samples tended to be saturated, suggesting that the sequencing depth already covered most of the diversity and rare phylotypes. (Fig. S3A). The alpha diversity analysis showed that the microbial community richness and diversity indicated by Chao1 estimators and Shannon index was significantly increased in induced, control, JGP-L, JGP-M groups compared with the normal group. In contrast, it was reversed by the JGP-H treatment relative to the induced group (Fig. 3A-B). Venn diagrams (Fig. S3B) showed that the number of OUTs in the induced and control groups was more than that in the normal group. Simultaneously, the number of OTUs in the JGP-H group was less than that in the induced group, which indicated that JGP treatment could influence the diversity of gut microbiota in ILI mice. Ordination of Bray–Curtis dissimilarity by principal coordinate analysis (PCoA) revealed the separation of the six groups (Fig. 3C). Furthermore, when compared to the induced group, different doses of JGP groups exhibited substantial similarities in bacterial members with the normal group, suggesting that JGP's effects on ILI might be attributable to its modification of gut microbiota structure. In addition, the sample clustering tree showed that a significant difference existed between the six groups, and the level of the JGP treated groups was close to that of the normal group (Fig. 3D).

JGP regulates the structural segregation of gut microbiota in mice

The microbial species and their relative abundance at the phylum and genus levels were shown in Fig. 4. At the phylum level, Firmicutes were the most abundant phyla in normal, control, and JGP-M groups. Bacteroidetes were the dominant microbiota in induced and JGP-L groups. Interestingly, in the JGP-H group, the most abundant phyla were Proteobacteria (Fig. 4A). The alterations of the core microbiota at the genus level are displayed in Fig. 4B-C. In the induced group, the relative abundances of Streptococcus, Variovorax, Lactobacillus, Burkholderia-Caballeronia-Paraburkholderia, Stenotrophomonas were substantially decreased while the relative abundances of Muribaculum and Alloprevotella were significantly increased compared with the normal group. Notably, the relative abundances changes of Alloprevotella, Burkholderia-Caballeronia-Paraburkholderia, Muribaculum, Streptococcus, and Stenotrophomonas could be reversed by JGP treatment. Moreover, JGP-M treatment could increase the relative abundance of the Lachnospiraceae_NK4A136 group.

Next, a linear discriminant analysis (LDA) effect size (LEfSe) analysis was carried out to identify the specific bacterium associated with ILI and JGP treatment. With an LDA score >4.0, the discriminative features of the bacterial taxa were identified (Fig. 5A-B). The results show that 2 taxa were found in the normal group. p__Firmicutes and s__Streptococcus_danieliae had a great influence on the dominant community. Dominant communities of 4 taxa and 3 taxa were found in the control and induced groups, respectively. Among them, s_Lachnospiraceae_NK4A136_group_unclassified, g_Lachnospiraceae_NK4A136_group, s__Anaerotignum_sp_, and g__Anaerotignum had an important influence on the control group. f__Prevotellaceae, g__Alloprevotella, and s__Alloprevotella_unclassified were the dominant flora in the induced group. After treatment, the mice of the JGP-L group were enriched with s__Candidatus_Arthromitus_unclassified, f__Clostridiaceae_1, and g__Candidatus_Arthromitus. The mice of the JGP-M group were enriched with g__Prevotellaceae_NK3B31_group and s__Prevotellaceae_NK3B31_group_unclassified. Dominant communities of 14 taxa were found in the JGP-H group. Among them, p__Proteobacteria, g__Bacteroides, f__Bacteroidaceae, and s__Streptococcus_unclassified had an important influence on the control group.

Furthermore, correlation analyses were found based on the bacterial taxa at the genus level. Streptococcus was found to be positively associated with Lactobacillus and Burkholderia-Caballeronia-Paraburkholderia. Muribaculaceae-unclassified showed a positive correlation with Muribaculum and Alloprevotella and a negative correlation with Streptococcus and Burkholderia-Caballeronia-Paraburkholderia (Fig. S4).

Effects of JGP on fecal metabolic profiling of ILI mice

Table 1

The identified and change trend of the potential biomarkers of ILI mice intervened by JGP.

No. VIP	Metabolites	Induced	JGP-L	JGP-M	JGP-H
1 1.99	Allylestrenol	^#	ns	ns	**
2 1.94	Eplerenone	^#	**	*	ns
3 1.37	PE (P-20:0/0:0)	^#	ns	*	ns
4 1.78	SM d27:1	^#	*	ns	ns
5 1.84	Soyasapogenol C	^#	*	ns	ns
6 2.17	Chrysin	^#	ns	*	ns
7 1.66	Soyasaponin I	^##	ns	***	ns

# indicates a significant change between the normal and induced groups (^#P< 0.05, ^##P< 0.01), ∗ indicates significant change of different treatment groups vs Induced group (^*P < 0.05, ^***P < 0.001). “ns” represents not significant.

Alterations in the gut microbiota composition are always accompanied by modifications in the metabolic phenotype of the intestinal flora. Hence, we further investigated the metabolites in the fecal samples between different groups. The OPLS-DA approach was used to assess differences across groups. As shown in Fig. 6A, the induced group was substantially separated from the control group, indicating that the metabolic profiles of the induced and the control groups differed. Notably, the PLS-DA and PCA analysis showed that the control and three dosages of JGP-treated groups were also significantly separated from the normal group (Fig. 6B-C). In comparison three dosages of JGP-treated groups showed partially coincided with the control group, suggesting that JGP treatment could change metabolic profiles, unlike the induced group.

Simultaneously, the differential metabolites were identified by combining the P-value and the fold change. A total of 69 major metabolites were identified to be substantially different between the normal and induced groups (Fig. S5). Moreover, 133 main metabolites altered dramatically between the induced and JGP-L groups. And 249 altered metabolites were found between the induced and JGP-M groups. The number of differential metabolites between the induced and JGP-H groups was 226. Among them, seven fecal metabolites Allylestrenol, Eplerenone, PE (P-20:0/0:0), SM d27:1, Soyasapogenol C, Chrysin and Soyasaponin I altered due to BCG+LPS administration could be reversed by JGP treatment (Table 1), and these metabolites were thought to be the most important metabolites of JGP influencing ILI.

The meaningful metabolic pathways were screened by Metabo Analyst 5.0 (P-value < 0.05, impact value >0.1). As shown in Fig. 7A-D, the most meaningful metabolic pathways between the normal and induced groups were biotin metabolism and steroid hormone biosynthesis, which were severely disrupted in ILI mice and modulated by JGP treatment. Interestingly, the most meaningful metabolic pathways differed for these three doses of JGP compared with the induced group. The most meaningful metabolic pathways between the JGP-L group and the induced group were alpha-Linolenic acid metabolism. And the most meaningful metabolic pathways between the JGP-M group and the induced group were Starch and sucrose metabolism, Porphyrin and chlorophyll metabolism, and Arginine biosynthesis. Moreover, Arginine biosynthesis, Purine metabolism, and Pyrimidine metabolism were the most meaningful metabolic pathways between the JGP-H group and the induced group. These findings suggested that JGP could control the dysregulation of metabolites and their associated metabolic pathways, which facilitated ILI development.

Correlation among gut microbiota and metabolites

To further comprehend the functional relationship between gut microbiota alterations and metabolic changes, Spearman correlations were performed to correlate species at the genus level with specific metabolites, which finally revealed strong correlations. As shown in Fig. 8, Allylestrenol, which was decreased in the induced group was positively correlated with Stenotrophomonas, Burkholderia-Caballeronia-Paraburkholderia, and Sphingopyxis, while it was negatively correlated with Muribaculaceae_unclassified, Lachnospiraceae_NK4A136_group, Muribaculum, and Enterorhabdus. In addition, Chrysin had a great relationship with Candidatus_Saccharimonas.

In this study, we demonstrated that JGP exerted anti-inflammatory effects and restored the dysbiosis of the gut microbiota and metabolism of the liver immunological injury mice. To clarify the impact of JGP on gut microbiota, an ILI mice model induced by BCG plus LPS was established. Next, the effects of JGP on the gut microbiota and the relationship between gut microbiota and metabolism in ILI mice were investigated using a multi-group technique of 16S rRNA gene sequencing coupled with UPLC-MS-based metabolomics. JGP's therapeutic mechanisms in ILI mice were likely connected to correcting the dysbiosis of Alloprevotella, Burkholderia-Caballeronia-Paraburkholderia, Muribaculum, Streptococcus, and Stenotrophomonas, as well as controlling the abnormality of biotin metabolism and steroid hormone production. Increasing the abundance of bacteria that produce Allylestrenol and Chrysin might be a potential therapeutic mechanism for JGP in ILI. Collectively, our findings provide a new perspective that intestinal microbiota and their metabolites might be potential targets for elucidating the mechanism of JGP on the improvement of ILI.

It has been shown that severe hepatitis immunological liver damage caused by injecting a tiny amount of bacterial LPS into BCG-pretreated mice can accurately mirror the clinical state of viral fulminant hepatitis^{[9, 10]}. The important feature is that many inflammatory cells like Kupffer cells (KCs) gather in the liver tissue and then synthesize and release many inflammatory cytokines, excessive inflammatory cells, and factors that mediate acute inflammatory reactions and intense immune responses in the liver. It can cause severe damage to liver tissue and cells^{[11, 12]}. Therefore, this model is considered perfect for the study of pathological mechanisms of human viral hepatitis liver diseases.

KCs are liver resident macrophages that are composed of 90% tissue macrophages. Activated KCs produce inflammatory cytokines, including IL-6, IL-1, and TNF-α. In our study, we found the serum IFN-γ, IL-6, IL-10, and IL-22 expressions were increased significantly in LPS + BCG induced mice. It is worth noting that the increased secretion of IFN-γ, IL-6, and IL-22 was also alleviated after JGP treatments. These together suggest that JGP suppressed the release of the inflammatory factors in the liver.

Previous studies found hepatic STAT3 was activated in liver injury and served an essential role in liver cell apoptosis^{[13, 14]}, which is consistent with our experimental results. In addition, JGP treatment could decrease p-STAT3 expression, without affecting the levels of STAT3. It suggests that JGP might reduce cell apoptosis by inhibiting the STAT3 signaling pathway.

The gut-liver axis is bidirectional crosstalk that integrates metabolites with the gut and liver^[15]. More and more studies have shown that changes in intestinal flora are related to almost all known liver diseases^[4]. In this study, the influence of JGP on gut microbiota in ILI mice, as well as the relationship between gut microbiota and host metabolism, were studied using multi-omics. Bacterial 16S rRNA sequencing results demonstrated that the diversity of gut microbiota was significantly increased in ILI mice. JGP treatment could also considerably improve the diversity and richness of gut microbiota compared with the normal group. However, a high dosage of JGP treatment could reduce the diversity and richness of gut microbiota. PCoA analysis revealed substantial variations in the structure of gut microbiota between the normal and induced groups. However, the JGP-M and JGP-H groups showed a similarity to the normal group, revealing that chemically generated ILI altered the overall structure of the intestinal microbiota and that JGP attenuated it to some extent. The results imply JGP treatment can maintain intestinal immune homeostasis associated with alterations of the intestinal flora. At the level of genus, JGP treatment could dramatically restore the relative abundance of Alloprevotella, Burkholderia-Caballeronia-Paraburkholderia, Muribaculum, Streptococcus, and Stenotrophomonas in ILI mice. As a result, these five genera are thought to be responsible for the treatment of JGP. Notably, the changed intestinal flora metabolism in ILI mice could be regulated by JGP treatment. Through metabolic pathway analysis, we discovered that JGP could greatly correct the disorder of two metabolic pathways, including biotin metabolism and steroid hormone biosynthesis. Interestingly, arginine biosynthesis had the highest impact score in the JGP-M and JGP-H groups compared with the induced group. It has already been shown that liver injury can be alleviated by supplementation of arginine^{[16, 17]}. The level of plasma arginine was decreased after acute liver injury. Therefore, blood arginine has been determined as a unique indicator for liver regeneration after acute liver damage^[18–20]. These findings illustrate that arginine biosynthesis may play a role in JGP's hepatoprotective properties. Our observation showed that the levels of Allylestrenol, Eplerenone, PE (P-20:0/0:0), SM d27:1, Soyasapogenol C, Chrysin, and Soyasaponin I were notably changed. However, these alterations were remarkably reversed after JGP treatment. It is worth noting that increased Allylestrenol was significantly associated with several changed genera. And increased Chrysin was a positive correlation with Candidatus_Saccharimonas. Clinically, Allylestrenol (AT) can effectively treat abortion and intrauterine growth retardation. A previous study found that Allylestrenol combined with ritodrine could significantly reduce inflammatory cytokine expressions, such as IL-6 and IL-10^[21]. The reduced expression of p-STAT3 in the JGP treatment group might also be due to the AT change. Additionally, Chrysin has anti-inflammatory and hepatoprotective effects^{[22, 23]}. Therefore, AT and Chrysin and correlated gut microbiota may be the potential mechanisms underlying the effects of JGP on ILI.

In the present study, we found that JGP had an anti-inflammatory effect and promoted liver cell proliferation. At the same time, JGP could substantially restore the dysbiosis of the gut microbiota and metabolism of the BCG + LPS-induced ILI mice. All in all, this study helps clarify the detailed pharmacological mechanism of JGP in the treatment of ILI.

Animals

Male Balb/c mice (6-8 weeks) were purchased from the Laboratory Animal Management Department of Shanghai Family Planning Research Institute (Shanghai, China) and maintained in an environmentally controlled room (22 ± 1 °C, 45% relative humidity, 12-h light/dark cycle). The experiment began after one week of acclimation.

Preparation of JGP suspension

Panax ginseng C.A.Mey. (Araliaceae), Paeonia lactiflora Pall. (Paeoniaceae), Astragalus mongholicus Bunge (Fabaceae), Salvia miltiorrhiza Bunge (Lamiaceae), Curcuma longa L. (Zingiberaceae), Cyperus rotundus L. (Cyperaceae), Citrus × aurantium L. (Rutaceae), Sophora flavescens Aiton (Fabaceae), Artemisia capillaris Thunb. (Asteraceae), Bupleurum falcatum L. (Apiaceae), Glycyrrhiza glabra L. (Fabaceae) were mixed at a ratio of 2:4:4:2:2:1:1:4:2:1:1. Then the mixture was soaked and boiled repeatedly with distilled water. Next, the mixture was filtered and centrifuged to obtain the supernatant, and then the supernatant was concentrated and sterilized. Finally, the JGP with the concentration of raw herb was 1g/mL was obtained and stored in the refrigerator at -20 ℃. The ratio was set aside according to the concentration requirements.

Induction of ILI and animal grouping

After one week of acclimation, the mice were randomly divided into 6 groups (n = 5): a normal control group (Normal), a model control group (Induced), a JGP control group (Control, 15 g/kg/d), a low-dose JGP group (JGP-L, 7.5 g/kg/d), a medium-dose JGP group (JGP-M, 15 g/kg/d) and a high-dose JGP group (JGP-H, 30 g/kg/d). Then the induced group, the JGP-L group, the JGP-M group, and the JGP-H group mice were injected 2.5 mg/200 μL BCG (catalog 202010, REBIO, China) by tail vein and the normal group and the control group mice were injected with the same volume of normal saline through the tail vein. Meanwhile, the mice in the control group, the JGP-L group, the JGP-M group, and the JGP-H group were treated for 12 days by gastric gavage once daily. Normal and model mice were received the same dose of water. On day12, the induced, JGP-L, JGP-M, and JGP-H mice were injected with 7.5 μg/200 μL LPS (catalog L6529, Sigma, USA) by tail vein, and the normal group and the control group mice were injected with the same volume of normal saline through the tail vein. The experimental schedules are shown in Fig. 1.

Western blotting analysis

Hepatocytes in each experimental group were treated with 1 mL precooled Western and IP cell lysate (catalog P0013, Beyotime) and 1% PMSF (catalog ST505, Beyotime), respectively, and frozen for 30 min. The supernatant was extracted by centrifuging for 10 min at 4 ^◦C at the speed of 12000 rpm/min, and the protein concentration was determined by the BCA method (catalog 23225, Thermo). 3 μL was extracted from the original solution and 27 μL PBS was added.

Take 25 μg of denatured protein for each group and load the protein on 10 % SDS-PAGE (concentrated gel voltage 80 V, separated gel voltage 120 V), constant current (300 mA current, 2 h) to 0.45 μm PVDF membrane, add 5% skim milk powder was shaken for 1 h at room temperature and shaker. After washing the membrane with TBST, incubated with the following antibodies at 4^◦C in a shaker overnight: GAPDH (1: 10000, catalog AP0063, Bioworld), p-STAT3 (1: 1000, catalog 9145T, CST), STAT3 (1: 1000, catalog 9139, CST). After washing the membrane with TBST, goat anti-mouse IgG and goat anti-rabbit IgG secondary antibody (1:3000, catalog A0208, Beyotime) were incubated at room temperature for 1 h, place the image in a fully automated chemiluminescence image analysis system (Tanon Technology Co., Shanghai, China), and analyze using Quantity One 4.6.2 software Strip protein gray, calculate relative protein expression based on GAPDH gray value.

Histological assessment

Liver tissue slices were cut at a thickness of 4 μm, deparaffinized, rehydrated, and incubated in 1x citrate buffer for 5 minutes for high-pressure antigen repair. After then, the portions were allowed to cool naturally. After blocking the liver peroxidase with H₂O₂, the sections were treated with a PCNA antibody overnight at 4°C. The next day, the DAB reagent was applied for hematoxylin counterstaining after incubation with the secondary antibody. The slices were then dehydrated in gradient ethanol and xylene and placed on clear, neutral gum before being examined under an optical microscope at 200×.

Fecal DNA extraction and Illumina Miseq sequencing

Total microbial DNA was extracted from fresh fecal samples by OMEGA Stool DNA Kit. The PCR primer was designed against the conserved region to target the variable region of the 16S /ITS2 rDNA gene. After 35 cycles of PCR, sequencing adapters and barcodes were added for amplification. PCR amplification products were detected by 1.5% agarose gel electrophoresis. The target fragments were recovered using the AxyPrep PCR Cleanup Kit. The PCR product was further purified using the Quant-iT PicoGreen dsDNA Assay Kit. The library was quantified on the Promega QuantiFluor fluorescence quantification system. The pooled library was loaded on the Illumina platform using a paired-end sequencing protocol (2 x 250 bp).

Paired-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads was merged using FLASH (v1.2.8) (for 16S)/PEAR (v0.9.6) (for ITS2). Quality filtering on the raw reads was performed under specific filtering conditions to obtain the high-quality clean tags according to the fqtrim (v0.94). Chimeric sequences were filtered using Vsearch software (v2.3.4). After dereplication using DADA2, we obtained the feature table and feature sequence. Alpha diversity and beta diversity were calculated by QIIME2, which the same number of sequences were extracted randomly by reducing the number of sequences to the minimum of some samples, and the relative abundance (X bacteria count/total count) is used in bacteria taxonomy. Alpha diversity and Beta diversity were analyzed by the QIIME2 process, and pictures were drawn by R (v3.5.2). The sequence alignment of species annotation was performed by Blast, and the alignment database was SILVA and NT-16S.

Metabolic profiling using UPLC-Q-TOF/MS

Fecal samples were thawed on ice, and metabolites were extracted with 50% methanol Buffer. All samples were acquired by the LC‐MS system followed machine orders. A high‐resolution tandem mass spectrometer TripleTOF5600plus (SCIEX, UK) was used to detect metabolites eluted from the column.

The acquired MS data pretreatments including peak picking, peak grouping, retention time correction, second peak grouping, and annotation of isotopes and adducts were performed using XCMS software. LC−MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA, and metaX toolbox implemented with the R software. Each ion was identified by combining retention time (RT) and m/z data. Intensities of each peak were recorded and a three-dimensional matrix containing arbitrarily assigned peak indices (retention time‐m/z pairs), sample names (observations), and ion intensity information (variables) was generated.

The online KEGG, HMDB database was used to annotate the metabolites by matching the exact molecular mass data (m/z) of samples with those from the database. If a mass difference between the observed and the database value was less than 10 ppm, the metabolite would be annotated and the molecular formula of metabolites would further be identified and validated by the isotopic distribution measurements. We also used an in‐house fragment spectrum library of metabolites to validate the metabolite identification.

The intensity of peak data was further preprocessed by metaX. Those features that were detected in less than 50% of QC samples or 80% of biological samples were removed, the remaining peaks with missing values were imputed with the k‐nearest neighbor algorithm to further improve the data quality. PCA was performed for outlier detection and batch effects evaluation using the pre‐processed dataset. Quality control‐based robust LOESS signal correction was fitted to the QC data with respect to the order of injection to minimize signal intensity drift over time. In addition, the relative standard deviations of the metabolic features were calculated across all QC samples, and those > 30% were then removed.

Student t‐tests were conducted to detect differences in metabolite concentrations between the 2 phenotypes. The P-value was adjusted for multiple tests using an FDR (Benjamini–Hochberg). Supervised PLS‐DA was conducted through metaX to discriminate the different variables between groups. The VIP value was calculated. A VIP cut‐off value of 1.0 was used to select important features.

Statistical analysis

The mean ± standard errors of the means were used to express all of the data. A one-way ANOVA test was used to examine the multiple organ index, protein expression, H&E, and fecal metabolites. Welch's t-test and LefSe analysis were performed in 16S rRNA analysis to screen for species with significant abundance differences across experimental groups. A p-value < 0.05 was considered statistically significant. The correlation coefficient between key fecal metabolites and gut microbiota at the genus level was calculated by Spearman’s correlation analysis and the resulting heatmaps were displayed using GraphPad Prism 8.0.2.

Ethics approval and consent to participate

The experimental protocol was established, according to the ethical guide-lines of the Basel Declaration and was approved by the Jiangsu Province Hospital on Integration of Chinese and Western Medicine (ethic’s number: AEWC-20220119-185). All experiments were performed in compliance with the ARRIVE guidelines.

Consent to publish

Not applicable.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by the 2020 Scientific and Technological Planning Guiding project of Nantong city, Jiangsu Province (Study on the mechanism of JGP in treating liver immune-mediated inflammation, MSZ20192).

Acknowledgement

Not applicable.

Authors’ Contributions

KL designed and performed the study, KL, YC, XZ, CM, JZ, ZY, and YJ performed analysis and interpreted the data, KL and JZ drafted the manuscript, FZ revised the manuscript, FZ supervised research and provided funding.

Robinson MW, Harmon C, O'Farrelly C: Liver immunology and its role in inflammation and homeostasis. Cell Mol Immunol 2016, 13(3):267–276.
Woolbright BL, Jaeschke H: Role of the inflammasome in acetaminophen-induced liver injury and acute liver failure. J Hepatol 2017, 66(4):836–848.
Albillos A, de Gottardi A, Rescigno M: The gut-liver axis in liver disease: Pathophysiological basis for therapy. J Hepatol 2020, 72(3):558–577.
Tripathi A, Debelius J, Brenner DA, Karin M, Loomba R, Schnabl B, Knight R: The gut-liver axis and the intersection with the microbiome. Nat Rev Gastroenterol Hepatol 2018, 15(7):397–411.
Han R, Qiu H, Zhong J, Zheng N, Li B, Hong Y, Ma J, Wu G, Chen L, Sheng L et al: Si Miao Formula attenuates non-alcoholic fatty liver disease by modulating hepatic lipid metabolism and gut microbiota. Phytomedicine 2021, 85:153544.
Zhang Y, Zhao M, Jiang X, Qiao Q, Liu T, Zhao C, Wang M: Comprehensive Analysis of Fecal Microbiome and Metabolomics in Hepatic Fibrosis Rats Reveal Hepatoprotective Effects of Yinchen Wuling Powder From the Host-Microbial Metabolic Axis. Front Pharmacol 2021, 12:713197.
Wang SC: PCNA: a silent housekeeper or a potential therapeutic target? Trends Pharmacol Sci 2014, 35(4):178–186.
Naryzhny SN: Proliferating cell nuclear antigen: a proteomics view. Cell Mol Life Sci 2008, 65(23):3789–3808.
Wang. H, Wei. W, Shen. Y-X: Protective effect of melatonin against liver injury in mice induced by Bacillus Calmette-Guerin plus lipopolysaccharide. World J Gastroenterol 2004, 10(18):2690–2696.
Xin X, Yang W, Yasen M, Zhao H, Aisa H: The mechanism of hepatoprotective effect of sesquiterpene rich fraction from Cichorum glandulosum Boiss. et Huet on immune reaction-induced liver injury in mice. J Ethnopharmacol 2014, 155(2):1068–1075.
Yao. H-W, Li. J, Chen. J-Q: FR167653 attenuates murine immunological liver injury. World J Gastroenterol 2004, 10(15):2267–2271.
Wang GS, Liu GT: Influences of Kupffer cell stimulation and suppression on immunological liver injury in mice. Zhongguo Yao Li Xue Bao 1997, 18(2):173–176.
Shen K, Zheng SS, Park O, Wang H, Sun Z, Gao B: Activation of innate immunity (NK/IFN-gamma) in rat allogeneic liver transplantation: contribution to liver injury and suppression of hepatocyte proliferation. Am J Physiol Gastrointest Liver Physiol 2008, 294(4):G1070-1077.
Hao X, Gao M, He L, Ye X, Yang J, Zhang F, Liu R, Wei H: Deficiency of O-linked-glycosylation regulates activation of T cells and aggravates Concanavalin A-induced liver injury. Toxicology 2020, 433–434:152411.
El-Mowafy M, Elgaml A, El-Mesery M, Sultan S, Ahmed TAE, Gomaa AI, Aly M, Mottawea W: Changes of Gut-Microbiota-Liver Axis in Hepatitis C Virus Infection. Biology (Basel) 2021, 10(1).
Adawi D, Kasravi FB, Molin G, Jeppsson B: Oral arginine supplementation in acute liver injury. Nutrition 1996, 12(7–8):529–533.
Angele MK, Fitzal F, Smail N, Knöferl MW, Schwacha MG, Ayala A, Wang P, Chaudry IH: L-arginine attenuates trauma-hemorrhage-induced liver injury. Crit Care Med 2000, 28(9):3242–3248.
Miura A, Hosono T, Seki T: Macrophage potentiates the recovery of liver zonation and metabolic function after acute liver injury. Sci Rep 2021, 11(1):9730.
Saitoh W, Yamauchi S, Watanabe K, Takasaki W, Mori K: Metabolomic analysis of arginine metabolism in acute hepatic injury in rats. J Toxicol Sci 2014, 39(1):41–50.
Pan M, Choudry HA, Epler MJ, Meng Q, Karinch A, Lin C, Souba W: Arginine transport in catabolic disease states. J Nutr 2004, 134(10 Suppl):2826S-2829S; discussion 2853S.
Li Q, Li C, Jin H: Efficacy of allylestrenol combined with ritodrine on threatened premature labor and its influence on inflammatory factors in peripheral blood. Exp Ther Med 2020, 19(2):907–912.
Naz S, Imran M, Rauf A, Orhan IE, Shariati MA, Iahtisham Ul H, IqraYasmin, Shahbaz M, Qaisrani TB, Shah ZA et al: Chrysin: Pharmacological and therapeutic properties. Life Sci 2019, 235:116797.
Mani R, Natesan V: Chrysin: Sources, beneficial pharmacological activities, and molecular mechanism of action. Phytochemistry 2018, 145:187–196.

No competing interests reported.

SupplementaryMaterial.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Jian Gan powder exerts anti-inflammatory and pro-proliferative effects and restores the dysbiosis of the gut microbiota and metabolism of the immunological liver injury mice

Status:

Version 1

Abstract

Background

Results

Conclusion

Figures

Introduction

Results

Discussion

Conclusion

Methods

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1