2.1. Cell line and cell culture
Cell line BNL 1ME A.7R.1 (referred as BNL in this study) from BALB/C mouse, sensitive to sorafenib, was obtained from Shanghai Xin Yu Biotechnology(Shanghai, China), and maintained in Dulbecco’s minimal essential medium (DMEM) (Gibco) supplemented with 10% heat-inactivated (56°C, 30 min) fetal bovine serum (Gibco) and 100 U/mL penicillin, 100 µg/mL streptomycin (Gibco), L-glutamin (2 mM) in a humidified atmosphere of 5% CO2 at 37°C.
2.2. JHD preparation
JHD is a combination of six medicinal herbs: Atractylodes macrocephala, Curcuma zedoaria, Radix Sophorae flavescentis, Citrus medica, Hedyotis diffusa, Radix Sophorae flavescentis at a rate of 3:3:3:5:5:5 (w/w/w/w/w/w). The six herbs of JHD were purchased from Shou Yi Zhen Yuan (Xiamen, China) and were identified by two experienced pharmacists. For extraction of JHD, the herbs were first soaked in deionized water at tenfold volume (v/w) 30 min and then extracted by decoction two times, 1h for the first time and 45 min for the second time with sixfold volume of deionized water to herbs (v/w). After filtration, the solution was evaporated under reduced pressure to obtain an extract, and then the extract was desiccated to powder at 60°C and stored at -80°C for use.
2.3. Animals and treatment
Male BALB/c mice (5 weeks) were purchased from the Animal Center of Southern Medical University. They were maintained under specific pathogen-free conditions, at 22–26°C, relative humidity of 55 ± 5%, and a 12 h light-dark cycle. A subcutaneous tumor-bearing model was established by infecting 5 × 106/100 µL of BNL cells in the right flank. When the tumor reached a diameter of 100–200 mm3, mice were divided randomly into four group JHD, sorafenib, JHD + sorafenib and vehicle with five mice in each group. Sorafenib (Nexavar™) was purchased from MedChemExpress (Monmouth Junction, NJ,USA) and dissolved in vehicle (10% dimethyl sulfoxide + 40% polyethylene glycol 300 + 5%Tween-80 + 45%saline). JHD was prepared as “2.2” described. Mice were fed with vehicle(10 mL/kg) and JHD (24.96 g/kg) or sorafenib (30 mg/kg/day) or a combination of JHD and sorafenib, or vehicle (100 µL/10 g) by gavage. The study protocol was approved by the Animal Care and Use Committee of Southern Medical University (Guangzhou, China).
2.4. Gut microbiota analysis
Mice fecal samples were collected(23 days after implantation)and stored at − 80°C immediately. Total genome DNA from samples was extracted using CTAB-SDS method. 16S rRNA genes were amplified used the specific primer with the barcode. PCR products was purified with GeneJET Gel Extraction Kit(Thermo Scientific). Sequencing libraries were established using Illumina TruSeq DNA PCR-Free Library Preparation Kit (Illumina, USA) following manufacturer’s instructions and index codes were added. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina NovaSeq platform and 250 bp paired-end reads were generated.
Paired-end reads from the original DNA fragments were merged by using FLASH[11], and was assigned to each sample according to the unique barcodes. Sequences were analyzed using QIIME[12] software package (Quantitative Insights Into Microbial Ecology), and in-house Perl scripts were used to analyze alpha and beta diversity. First, reads were filtered by QIIME quality filters. Then we used pick_de_novo_otus.py to pick operational taxonomic units (OTUs) by making OTU table. Sequences with ≥ 97% similarity were assigned to the same OTUs. We picked representative sequences for each OTU and used the RDP classifier[13] to annotate taxonomic information for each representative sequence. We rarified the OTU table and calculated three metrics Chao1 and Shannon index. We used weighted unifrac, calculated by QIIME, for principal coordinate analysis (PCoA). Significance test was conducted with some statistical analysis methods, including T-test, similarity percentages breakdown (SIMPER) and linear discriminant analysis effect size(LEfSe).
2.5. Western-Blot analysis and qRT-PCR
Total protein and RNA was extracted using extraction kit following the manufacturer's instructions. Protein was quantified, separated, transferred and blocked as previous description. The membranes were incubated with primary antibodies (β-actin, affinity 1 : 5000; STAT3, CST 1 : 1000; pSTAT3, CST 1 : 2000; iNOS, CST 1 : 2000). Protein bands were quantified using ImageJ software with β-actin as the internal control. The expression of mRNA was measured via qRT-PCR using a SYBR PrimeScript RT-PCR Kit (Takara Bio, Shiga, Japan) in accordance with the manufacturer's instructions. We used β-actin as an internal control. The primers used are listed in Table 1. We calculated relative mRNA levels based on the Ct values and normalized using β-actin expression.
Table 1
Sequence of Primers Used in Real-Time Experiments (related to Experimental Procedures)
Gene | Forward Primer | Reverse Primer |
IL-6 | TAGTCCTTCCTACCCCAATTTCC | TTGGTCCTTAGCCACTCCTTC |
β-actin | GGCTGTATTCCCCTCCATCG | CCAGTTGGTAACAATGCCATGT |
2.6.Hematoxylin-eosin staining(H&E), immunohistochemistry(IHC) and immunofluorescence(IF)
For H༆E, the slices were dipped into hematoxylin reagent (Sangon Biotech) for 10 min, rinsed with distilled water for 10 min, and dehydrated with anhydrous ethanol for 5 min. Finally, the slices were dipped into an eosin dye solution (Sangon Biotech) for 3 min, dehydrated with anhydrous ethanol for 3 min, and sealed with neutral gum. All slices were observed under an inverted microscope and images were collected. For immunohistochemistry, the tumor tissue slices were incubated with PCNA (1:500, abcam ab92552), CD31 (1:2000, abcam ab182981), VEGFA (1:250, abcam ab52917) antibody and with Horseradish peroxidase-conjugated secondary antibody. For immunofluorescence, the tumor tissue slices were incubated with IL-6 primary antibody (1:200, CST 12912), JAK2(1:250, abcam ab108596) and with Alexa Fluor 488/594-conjugated secondary antibody (Abbkine, Wuhan, CA).
2.7. High-performance liquid chromatography coupled with mass spectrometry (HPLC-MS)
JHD water extract was analyzed by high-performance liquid chromatography with hybrid linear ion trap Orbitrap mass spectrometry (HPLC-LTQ/Orbitrap) equipped with an ACQUITY BEH C18 column (100 mm × 2.1 mm i.d., 1.7 µm; Waters, Milford, USA). The mobile phases consisted of 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile: isopropanol (1:1, v/v)(solvent B). The sample injection volume was 2 uL and the flow rate was set to 0.4 mL/min. The mass spectrometric data was collected using a UHPLC-Q Exactive Mass Spectrometer (Thermo, USA) equipped with an electrospray ionization source operating in either positive or negative ion mode. The optimal conditions were set as followed: Aus gas heater temperature, 400℃ ; Sheath gas flow rate 40 psi; Aus gas flow rate 30 psi; ion-spray voltage floating,-2800V in negative mode and 3500V in positive mode, respectively; Normalized collision energy, 20-40-60V rolling for MS/MS. Data acquisition was performed with the Data Dependent Acquisition mode. The detection was carried out over a mass range of 70-1050 m/z.
2.8. Statistical analysis
Tumor weight, body weight, the expression of mRNA and protein, the relative abundance of gut microbiota and the numbers of MDSCs were analyzed using graphpad prsim 5 (GraphPad Software, Inc. USA). Part of the 16S rRNA analysis and spearman correlation was carried out in R software. All data was expressed as mean ± S.E.M. ANOVA and t-test were used when data accorded with normal distribution and homogeneity of variance. Tukey test was used multiple comparisons. A p-value < 0.05 indicated that the difference was statistically significant.
Results
3.1. Identification of components of JHD
The components of JHD were identified by HPLC-MS. Ten potential compounds, that is, Citropten 7, Formononetin, 1-Kestose, Biochanin A, Xanthohumol, Cytisine, Ferulic acid, Gallic acid Hesperetin, and Quercetin 3-O-glucoside were identified (Supplement 1A, Table 2), which were lined to what we identified before[7]. The characterizations and sources of these compounds are listed in Table 2.
Table 2
Identification of potential components of JHD.
| Identification | Molecular weight M/Z | Molecular Formula | Retention time |
1 | Citropten | 207.0654332 | C11H10O4 | 5.915317 |
2 | Formononetin | 269.0811284 | C16H12O4 | 4.686867 |
3 | 1-Kestose | 543.132835 | C18H32O16 | 0.616733 |
4 | Biochanin A | 283.0617134 | C16H12O5 | 3.5596 |
5 | Xanthohumol | 355.1545057 | C21H22O5 | 6.086867 |
6 | Cytisine | 191.1180483 | C11H14N2O | 0.67545 |
7 | Ferulic acid | 193.0503398 | C10H10O4 | 4.934583 |
8 | Gallic acid | 169.0137157 | C7H6O5 | 1.447133 |
9 | Hesperetin | 301.0723318 | C16H14O6 | 4.408767 |
10 | Quercetin 3-O-glucoside | 465.1035356 | C21H20O12 | 3.540883 |
3.2. JHD inhibited the growth of tumor and enhanced the therapeutic effect of sorafenib in vivo.
To investigate the combined effect of JHD and sorafenib, we determined the anti-HCC effect of sorafenib at a clinical dose (30 mg/kg) in syngeneic mouse models. Obviously, sorafenib inhibited tumor growth, and the combination treatment of sorafenib and JHD showed more dramatically suppression than JHD alone or sorafenib alone(Fig. 1A, B). We also weighed the tumor tissue excised from tumor-bearing mice, and the weight of tumor from the JHD group was lower than that of the vehicle group (Fig. 1C). To further confirmed the synergistic effect of JHD on sorafenib, we assessed the proliferation and angiogenesis of tumor tissue by staining with proliferating cell nuclear antigen (PCNA), CD31 and vascular endothelial growth (VEGF) stain. JHD had slight effect on the proliferation of tumor cells, whereas the sorafenib combined with JHD strongly suppressed cell proliferation in vivo(Fig. 1D). Sorafenib significantly inhibited the expression of VEGFA(Fig. 1G), which is one of the target of sorafenib[14], and decreased tumor angiogenesis(as indicated by reduced microvessel density in tumors). The combined of JHD and sorafenib further inhibited tumor angiogenesis (Fig. 1F). These results indicated that JHD induced a synergistic antitumor effect when combined with sorafenib for HCC treatment.
3.3. JHD protected occurrences of sorafenib-induced diarrhea and subsequent occurrences of body weight loss.
Although tumor growth was efficiently suppressed, sorafenib led to diarrhea and body weight loss, suggesting that side effects were induced. In clinical, ~ 80% of patients treated with sorafenib suffer AEs, such as diarrhea, body weight loss, hand–foot skin reaction, and hypophosphatemia[3, 9].
The most frequent AEs (any grade) were diarrhea. Body weight loss is common in patients experienced diarrhea. In this study, significant reduction in body weight of mice treated with sorafenib was observed. Treatment of JHD exhibited remarkably improvement on the loss of body weight and diarrhea control induced by sorafenib. Moreover, we observed a decrease of diarrhea accompanied by less body weight loss in the mice treated with the combination of sorafenib and JHD compared with that of treatment of sorafenib alone(Fig. 2A, B).
3.4. Sorafenib induced increased proinflammatory microbiota
Emerging evidences suggest that GM plays a vital role in progression of HCC[15] and cancer immunotherapy[16]. Meanwhile, microbiota dysbiosis, as indicated by drastic bacterial population changes at the phylum and genus levels is associated with a higher risk of diarrhea and can be a consequence of diarrhea. Then we try to understand these findings by the changes of GM. Overall, 82,887 useable reads and 1,487 operational taxonomic units (OTUs) were obtained from 20 samples(Supplement 2). The relative abundance of GM was analyzed at the phylum level, Bacteroidota and Firmicutes accounted for ༞90% of the total community of GM (Fig. 3A). We noted that Firmicutes/Bacteroidetes (F/B) ratio, associated with disease or imbalance in metabolism, [17, 18], was increased after treated with sorafenib, whereas had a reduction in the combination of sorafenib and JHD group(Fig. 3B).Then, we sought to determine if differences existed in the alpha diversity and beta diversity. The alpha diversity was significantly lower in the sorafenib group than that in the other three groups based on the Shannon index (Fig. 3C), and the beta analysis showed a opposite result with weighted unifrac(Fig. 3D). Four clusters were separated on the principal coordinates analysis (PCoA) plot, in which GM of sorafenib group were far from those of sorafenib + JHD group and JHD group (Fig. 3E). At genes level, the changes of specific microbiota were observed, and we noticed that proinflammatory GM, such as Helicobacter, saccharimonas, faecalibacterium and enterorhabdus were increased in the mice treated with sorafenib.
3.5. JHD modulated GM composition and decreased proinflammatory microbiota.
Following that, we paid our attention on the difference in GM between the sorafenib group and the sorafenib + JHD group. The contribution to the average dissimilarity was investigated by SIMPER procedure. Helicobacter species (21.88%) and Lactobacillus species (14.13%) contributed most at the genus level (Fig. 4A). Although dramatic increase of Helicobacter was shown in the sorafenib group, significance difference was not observed between these two groups(Fig. 4B). The t-test analyses showed bacteria of the genera Muribaculum, Fusicatenibacter and Dorea were enriched in the sorafenib + JHD group(Fig. 4C). At last, Lefse analyses was used to find biomarker, and Helicobacter species was enriched in the sorafenib group, and decreased in mice treated with the combination of sorafenib and JHD(Fig. 4D). Together, these data clearly indicated that sorafenib treatment induced expanded proinflammation microbiota which was suppressed by JHD.
3.6. JHD decreased the infiltration of inflammatory cells and inhibited the IL-6/STAT3 pathway in tumors following the modulation of GM
Pathological changes in the composition of the GM that lead to intestinal inflammation are a common factor for HCC[19]. The GM can gain access to the liver as a result of a chronic inflammation disease associated to dysfunction of the intestinal barrier. Following the result that proinflammatory microbiota was induced by sorafenib, we examined infiltration of inflammatory cells in the main organs, liver, lung, spleen, and tumor tissue of mice by staining (H&E). The liver displayed the most significant changes, and dense punctate inflammatory cells were seen in the liver of sorafenib group, whereas fewer changes were observed in the sorafenib + JHD group and JHD group (Fig. 5A). The “leaky” intestinal membrane allows for translocation of bacteria-derived LPS (gram-negative bacteria) and lipoteichoic acid (LTA, derived from gram-positive bacteria)[20] which initiates inflammatory signaling pathway and ultimately leads to production of the inflammatory cytokines. IL-6/STAT3 signaling pathways link inflammation to cancer and was vital to the progression of HCC. We measured expression of the key signaling pathway IL-6/janus kinase 2 (JAK2)/STAT3 at mRNA and protein levels. Expression of the cytokine and pathway activator IL-6 was downregulated in the sorafenib + JHD and JHD group. Moreover, expression of the downstream molecules JAK2 and pSTAT3 was suppressed (Fig. 5B-E). Expression of the proinflammatory mediator iNOS was increased in tumor cells in sorafenib group. (Fig. 5F, G)