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Gut and Liver is an international journal of gastroenterology, focusing on the gastrointestinal tract, liver, biliary tree, pancreas, motility, and neurogastroenterology. Gut atnd Liver delivers up-to-date, authoritative papers on both clinical and research-based topics in gastroenterology. The Journal publishes original articles, case reports, brief communications, letters to the editor and invited review articles in the field of gastroenterology. The Journal is operated by internationally renowned editorial boards and designed to provide a global opportunity to promote academic developments in the field of gastroenterology and hepatology. +MORE
Yong Chan Lee |
Professor of Medicine Director, Gastrointestinal Research Laboratory Veterans Affairs Medical Center, Univ. California San Francisco San Francisco, USA |
Jong Pil Im | Seoul National University College of Medicine, Seoul, Korea |
Robert S. Bresalier | University of Texas M. D. Anderson Cancer Center, Houston, USA |
Steven H. Itzkowitz | Mount Sinai Medical Center, NY, USA |
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Na Yuan1,2,3 , Xiaoyan Li2 , Meng Wang4 , Zhilin Zhang3 , Lu Qiao2 , Yamei Gao2 , Xinjian Xu5 , Jie Zhi2 , Yang Li6 , Zhongxin Li7 , Yitao Jia1,2
Correspondence to: Yitao Jia
ORCID https://orcid.org/0000-0003-2610-9330
E-mail jiayitao99@163.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Gut Liver 2022;16(4):575-588. https://doi.org/10.5009/gnl210177
Published online March 23, 2022, Published date July 15, 2022
Copyright © Gut and Liver.
Background/Aims:This study aimed to explore the effect of gut microbiota-regulated Kupffer cells (KCs) on colorectal cancer (CRC) liver metastasis.
Methods: A series of in vivo and in vitro researches were showed to demonstrate the gut microbiota and its possible mechanism in CRC liver metastasis.
Results: Fewer liver metastases were identified in the ampicillin-streptomycin-colistin and colistin groups. Increased proportions of Parabacteroides goldsteinii, Bacteroides vulgatus, Bacteroides thetaiotaomicron, and Bacteroides uniformis were observed in the colistin group. The significant expansion of KCs was identified in the ampicillin-streptomycin-colistin and colistin groups. B. vulgatus levels were positively correlated with KC levels. More liver metastases were observed in the vancomycin group. An increased abundance of Parabacteroides distasonis and Proteus mirabilis and an obvious reduction of KCs were noted in the vancomycin group. P. mirabilis levels were negatively related to KC levels. The number of liver metastatic nodules was increased in the P. mirabilis group and decreased in the B. vulgatus group. The number of KCs decreased in the P. mirabilis group and increased in the B. vulgatus group. In vitro, as P. mirabilis or B. vulgatus doses increased, there was an opposite effect on KC proliferation in dose- and time-dependent manners. P. mirabilis induced CT26 cell migration by controlling KC proliferation, whereas B. vulgatus prevented this migration.
Conclusions: An increased abundance of P. mirabilis and decreased amount of B. vulgatus play key roles in CRC liver metastasis, which might be related to KC reductions in the liver.
Keywords: Colorectal neoplasms, Liver metastasis, Gastrointestinal microbiome, Kupffer cells
Globally, the most prevalent type of malignant tumor is colorectal cancer (CRC),1 and many of the CRC patients (15% to 25%) are diagnosed with metastasis of cancer.2 Despite advancements in the therapeutic strategies of CRC liver metastasis, there is a huge population of patients (>50%) who experience recurrence and metastasis of cancer within 2 years.3 Therefore, exploring the mechanism involved in the CRC liver metastasis is critical in improving the treatment options.
The cancer metastasis is associated with the cancer microenvironment
Although intestinal microbiota conciliate the hepatic natural killer T cell accumulation with both primary hepatic cancer and metastatic hepatic cancers, the possible alterations in the Kupffer cells (KCs), the most copious residential macrophages in the sinusoids of liver, have not been evaluated.9 Among all hepatic non-parenchymal cells in the liver, KCs makeup up 20% of these and have a significant role in tumor phagocytosis.10 KCs regulate the function and activity of the T-cells and natural killer (NK) cells. KCs stimulate the NK cells to produce and secrete the cytokines, for example, granulocyte-macrophage colony-stimulating factor and interferon-γ, that increases the pathogenicity of the KCs.11 It has also been observed that the KCs influence the multiplication of the stimulated CD8+ T-cells during the initial stages of cancer and enhance the apoptosis in the later stages of cancer.12 Several animals studies have proposed that the KCs increase the apoptosis of T-cell via Fas/Fas-L pathway and express the upper levels of programmed death-ligand 1 to obstruct multiplication and functionality of the T-cells by direct contact.13,14 Therefore, the KCs have a complicated role in tumor progression.
KCs are involved in the killing of microbes by phagocytosis that invades from the bloodstream; so killing the
The mice colon tumor cell line colon 26 (CT26; Cell Bank of the Chinese Academy of Sciences, Shanghai, China) and hepatic KC cell line (Guangzhou Jennio Biotech Co., Ltd., Guangzhou, China) were cultured in Roswell Park Memorial Institute 1640 (Gibco, Santa Cruz, CA, USA) complete 10% fetal bovine serum (FBS; Gibco) supplemented medium and having 1% streptomycin along with 1% penicillin (Gibco) at 37°C in a CO2 (5%) incubator.18 Proteus mirabilis (BNCC® 107943) and
In this study, 6 weeks old, 60 male BALB/c specific-pathogen-free mice (Animal Experiment Center of Hebei Medical University, Shijiazhuang, Hebei, China) were used and differentiated into four random groups, each group having 15 mice. Antibiotics were administered in sterile drinking water to each group as per the following tab: administrated in sterile drinking water to control group (without antibiotics); vancomycin (Vanc) group (0.25 mg/mL vancomycin); colistin (Coli) group (2 mg/mL colistin); ampicillin-streptomycin-colistin (ASC) group (1 mg/mL ampicillin, 5 mg/mL streptomycin and 1 mg/mL colistin) (Table 1).19
Table 1 Groups of Mice with Antibiotic Protocols19
Group | Treatment |
---|---|
Control group | No antibiotics |
Vanc group | Vancomycin (0.25 mg/mL) |
Coli group | Colistin (2 mg/mL) |
ASC group | Ampicillin (1 mg/mL) |
Streptomycin (5 mg/mL) | |
Colistin (1 mg/mL) |
Fecal sample collection was done 2 weeks after the antibiotic treatment and later 16S rDNA sequencing was done. The antimicrobial effects of antibiotics, used in this study, were observed and a model of CRC liver metastasis was made via splenic inoculation of CT26 cells (1×105) as mentioned earlier (Fig. 1A).20
Fifty-six specific-pathogen-free male BALB/c mice (aged 6 weeks) were bred in the specific-pathogen-free Laboratory Animal Center of Hebei General Hospital. Before the experiment, antibiotics were administered to mice of all groups through sterile drinking water containing 0.2 g/L ampicillin, neomycin, and metronidazole and 0.1 g/L vancomycin daily for 2 weeks.21 Then, mice were divided into seven groups (n=8) based on intragastric gavage twice weekly with 2×108 colony-forming units (CFU)/0.2 mL
Mice fecal pellets were collected and gene sequencing of 16S rDNA was done before making the CT26 tumor-bearing model. The 16S rDNA was done following the evaluation of antibacterial effect in groups.
The extraction of DNA was done from fecal samples by the cetyltrimethylammonium bromide (Sigma-Aldrich, St. Louis, Mo, USA) method. Agarose gel electrophoresis was utilized to detect the concentration and purity of DNA extracted from the fecal samples of mice. A suitable quantity of DNA was taken in a centrifuge tube and dilution was made up to 1 ng/μL in sterile water, then primer sequence 806R (5’-GGA CTA CNN GGG TAT CTA AT-3’) and 515F (5’-GTG CCA GCM GCC GCG GTA A-3’) were used to amplify the V3-V4 regions by polymerase chain reaction.
The DNA library was developed with the Ion Plus Fragment Library Kit (48 reactions; Thermo Fisher Scientific, Waltham, MA, USA). This developed DNA library was quantified by a Qubit (Thermo Fisher Scientific) as well as sequenced with an Ion S5TMXL (Thermo Fisher Scientific). Then the comparison of the reading sequence was done with the species annotation database Aby (https://github.com/torognes/vsearch/) along with the examination of chimera sequences. Clean reads were left only after the removal of the chimera sequence. Clustering was done with Uparse software (Uparse v7.0.1001, https://drive5.com/uparse/) with 97% similarity, and the operational taxonomic units regarding the species categorization were acquired following chimera filtering the clustered sequence.
The filter value was defined by the linear discriminant analysis effect size software and it was 4. Following the assurance of the operational taxonomic unit annotation data from the SILVA database (SILVA SSU 138.1, https://www.arb-silva.de), the entire information of the functional genomic for the prokaryotes in the Kyoto Encyclopedia of Genes and Genomes database was interpreted through UProC, and then by using the DNA aligner, the proteins were aligned in association with the SILVA database, in this way the functional prediction of Tax4Fun was perceived.
The liver samples from mice were collected, fixed, impregnated, and embedded in paraffin wax, and sections were made (4 µm). A monoclonal rabbit anti-mouse F4/80 primary antibody (1:500; Servicebio, Wuhan, China) was added to the sections following the dewaxing & hydrating the sample, antigen repair, and 15 minutes incubating in H2O2 solution (3%). A 3,3’-diaminobenzidine staining solution IHC kit (Zsbio, Beijing, China) was used for color development. Three high-power microscopic fields were selected randomly and micrographs were taken with the microscope (Nikon, Tokyo, Japan). Image J (Ver-1.8.0, National Institutes of Health, Bethesda, MD, USA) was used to calculate the optical densities of proteins.
KCs were cultured in tissue plates with 96 wells and these were used as at 5×103 cells in each well, then allowed to settle and adhere. Grown to late-log phase in 1640 complete medium supplemented with 10% FBS,
KCs (1×105) were inoculated on 24-well plates (lower chamber) with 500 µL 1640 medium and cultured for 24 hours; 50 µL of phosphate-buffered saline, 1×103, 1×104, 1×105, 1×106, or 1×107 CFU/mL
The data, from this study, were analyzed with SPSS software SPSS version 19.0 (IBM Corp., Armonk, NY, USA) and the data are expressed as the mean±standard deviation. Limited slip differential and one-way analysis of variance were utilized for multiple comparisons after testing the variance homogeneity and normal distribution of data. The correlation between differential bacteria and KCs were analyzed by using the Pearson correlation analysis between the Vanc group, Coli group, and control group. The differences of intestinal microbiome between different groups were statistically investigated by R software (R Foundation for Statistical Computing, Vienna, Austria). The comparisons of more than two groups were done with the Wilcoxon tests and the Tukey
The splenic tumor injection resulted in sudden CRC liver metastasis in BALB/c mice as mentioned earlier (Fig. 1A).9 In the ASC group, antibiotics mixed in drinking water affected the intestinal commensal population negatively (Fig. 1B).23 Fewer liver metastases were observed in the ASC group (p=0.003). Vancomycin promoted the CRC liver metastases (p=0.028) while colistin inhibited the CRC liver metastases (p=0.041). Vancomycin-treated mice group targeting the Gram-positive bacteria showed greater (p<0.001) liver metastases than the colistin-treated group of mice targeting the Gram-negative bacteria; comparing with this, a vigorous decrease (p<0.001) was identified in the liver metastasis in ASC group as compared to Vanc group. In this experiment, the liver volume showed identical results; liver volume was larger (p=0.028) in the Vanc group but it was in the ASC group, the liver volume was smaller (p=0.009). Furthermore, liver volume was greater (p=0.037) in the Vanc group as compared to the ASC group and Coli group (Fig. 1B and C).
To better understand the part of intestinal microbiome in response to the CRC liver metastasis, six fecal samples were collected from each group including the Coli, Vanc, ASC, and control groups, and 16S rDNA sequencing was done to assess the microbiota landscape in the fecal samples. Among all treatment groups, ASC treatment showed significant effects on intestinal commensal bacteria by eliminating them from the intestines. A Venn diagram graph showed that 220 species of bacteria were common among all species identified from the control group (n=395), Vanc group (n=258), and Coli group (n=373) in this study (Fig. 1D). According to the principal coordinate analysis, the microbial population structure was visibly different between the Coli group and Vanc groups, while there was no clear separation between control group and Coli group (Fig. 1E). Additionally, the alpha diversity of the intestinal microbiome was significantly different between Vanc group and Coli group which was observed by certain methods including Shannon, Chao1, and Simpson; moreover, a remarkable elevation in the microbial diversity of the Coli group was observed by Simpson and Shannon diversity (Fig. 1F). According to the beta diversity analysis, the different coefficients of intestinal microbiota diversity in the Vanc group and the Coli group were 0.509 and 0.145, respectively. Moreover, the correlation coefficient of commensal bacteria between the Vanc group and the Coli group was 0.466 (Fig. 1G). These results of this study suggested the fact that modulating the intestinal commensal bacteria influenced the CRC liver metastasis in a way that the increased diversity and community richness might suppress the CRC liver metastasis.
In this study, the disagreement between the intestinal microbiome of all three groups were evaluated and high-dimensional class comparisons regarding the common taxa of intestinal bacteria were determined via linear discriminant analysis effect size bar and cladogram analysis. The Coli group had abundant Bacteroidetes and Firmicutes; while the Vanc group had abundant Proteobacteria, considering the phylum level. Additionally for the species levels, the Coli group was found to be rich with
The functional prediction was done with Tax4Fun to assess the mechanism for the role of commensal bacteria in causing liver metastasis. The differential gene functions in all three groups contained the genes, immune system, cell motility, transport, and various types of metabolism (Fig. 3A). To detect the difference in immune responses, pair-wise comparisons were done, which revealed that clear differences were observed in the immune system between the control group and Vanc group and between control and Coli groups. These differential functions between the three groups consisted of bacterial secretion system, NOD-like receptor signaling, bacterial motility protein, interleukin (IL)-17 signaling, and two-component system (Fig. 3B and C, Supplementary Fig. 1A). This suggested that the alterations in the intestinal microbiota influenced the CRC liver metastasis and it was associated with the IL-17 signaling.
Based on the Tax4Fun prediction data, the immune signals particularly the IL-17, were clearly different between all the three groups. IHC was used to detect the count of KCs count in CT26 cancer-bearing mice, to evaluate the mechanism involved in the tumor suppression. The KCs were significantly high in the Coli and ASC groups (p=0.038, p=0.001, respectively), whereas the KCs were significantly less (p=0.027) in the Vanc group. There was a similarity in the KCs number with liver volume and liver metastasis results that the KCs were more in the mice of Coli group and ASC group as compared to that in the Vanc group (p<0.001, p<0.001, respectively) (Fig. 3D).
The results here highlighted the KC landscape of CRC liver metastasis and the huge remodeling after gut microbiota alterations.
The relation of KCs and differential count of bacteria were evaluated to detect the definitive role of the intestinal microbiome in regulating hepatic KC accumulation. A positive was found between KC contents and
To validate the effect of
Given the changes in hepatic KCs in CRC liver metastasis after antibiotic administration, we utilized IHC to estimate the KC content in the liver tissues of tumor-bearing mice pre-transplanted with
To pinpoint the relationship between
Considering that
To summarize,
The mechanisms involved in CRC liver metastasis are unexplained yet. A recent theory of the gut-liver axis put forward the foundation for exploring the correlation between intestinal diseases and the liver. The hepatic portal venous system carries the microbiota into the liver that can bring changes in the liver microenvironment and may influence the CRC liver metastasis.17 In the present study, different models of CRC liver metastasis were created by using different mouse-administered antibiotics. Comparatively, more liver metastasis was detected in the Vanc group comparing with those in the Coli group and mixed treatment group. It was also observed that the population of
The CRC hepatic metastasis modules were created in mice by various intestinal microflora. In the current study, the liver metastases were higher in the Vanc group as compared to the control group. Another study stated a remarkable decrease in the liver metastases in the Vanc group of mice.9 This variation in the results can be due to different doses of antibiotic, treatment duration of antibiotic, feeding conditions of animals, selected strains of animals, or the number of cancerous cells inoculated through the spleen. Moreover, ASC group showed lesser liver metastases as compared to control group, which can be correlated with the depleted symbiotic bacteria in the intestine due to antibiotics. These results are in coordination with an earlier study by Sethi
A definite separation in bacterial populations was noticed in three groups of mice as per the principal coordinate analysis, alpha diversity analysis, and beta diversity analysis of intestinal microflora. Many differences between the control group and the Vanc group and between the control group and the Coli group were analyzed for better identification of commensal bacterial populations responsible for CRC liver metastasis. Raised count of
The functional difference between the three mice groups was found to be correlated with immune signaling based on the prediction. Specifically, IL-17 promotes tumor progression that modulates the inflammatory responses in KCs according to an alcohol-induced hepatocellular carcinoma model.31 The premetastatic niche development in the CRC liver metastasis comprises various cells, for example, cells of bone marrow origin and other resident cells like KCs, liver sinusoidal cells, and hepatic stellate cells. Among all these types of cells, KCs have an important role in CRC liver metastasis.32 In the present study, having a similarity with the liver metastasis results, there was an increased population of KCs in the ASC group comparing with that in the control group which indicates that KCs aggregation was improved due to decreased commensal bacteria in the intestine. Significantly, it was observed that the KC population was reduced in the mouse of the Vanc group and increased in the mice of the Coli group.
Previous studies reported that vancomycin causes increase in IL-25 levels,
In the present study, in the Coli group KCs were abundant and these were significantly low in the Vanc group than in the control group indicating that CRC liver metastasis could be inhibited by the KC accumulation. Moreover, decreased
Changes in the liver microenvironment induced by commensal microbes could be beneficial for patients suffering from CRC liver metastasis. Here, more liver metastatic nodules were identified in the
Evidence has shown that several members of the intestinal microbiota, especially
Moreover, we found that
However, the current study had certain limitations. In this study, one cell line (CT26) was utilized, the KC subtype was not recognized, and certain other cells of hepatic immune response were also not exposed. Moreover, the precise mechanism involved in the observed effects needs additional studies.
In conclusion, alterations to the gut microflora diversity influence CRC liver metastasis. An increased abundance of
Supplementary materials can be accessed at https://doi.org/10.5009/gnl210177.
gnl-16-4-575-supple.pdfThis work was supported by The Specialist Capacity Building and Leader Development Program Funded from the 2018 Hebei Government (grant number: 361003).
The authors of this paper wish to pay regards to all who have contributed in writing this paper. We thank Dr. Li, the Department of General Surgery in the First Hospital of Hebei Medical University, for designing the study.
No potential conflict of interest relevant to this article was reported.
Concept and design: Z.L., Y.J. Data acquisition: N.Y., X.L., M.W., L.Q., Y.G. Data analysis and interpretation: N.Y., X.X., J.Z., Y.L. Drafting of the manuscript; critical revision of the manuscript for important intellectual content: N.Y., Z.L., Y.J. Statistical analysis: N.Y. Obtained funding: Y.J. Administrative, technical, or material support; study supervision: Z.L., Z.Z., Y.J. Approval of final manuscript: all authors.
Gut and Liver 2022; 16(4): 575-588
Published online July 15, 2022 https://doi.org/10.5009/gnl210177
Copyright © Gut and Liver.
Na Yuan1,2,3 , Xiaoyan Li2 , Meng Wang4 , Zhilin Zhang3 , Lu Qiao2 , Yamei Gao2 , Xinjian Xu5 , Jie Zhi2 , Yang Li6 , Zhongxin Li7 , Yitao Jia1,2
1Department of Oncology, Hebei Medical University, 2The Third Department of Oncology, Hebei General Hospital, Shijiazhuang, 3Department of Radiotherapy, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 4Department of Clinical Psychology, Baoding No.1 Central Hospital, Baoding, 5Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 6Department of Oncology, Affiliated Hospital of Hebei University, Baoding, and 7Department of General Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, China
Correspondence to:Yitao Jia
ORCID https://orcid.org/0000-0003-2610-9330
E-mail jiayitao99@163.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background/Aims:This study aimed to explore the effect of gut microbiota-regulated Kupffer cells (KCs) on colorectal cancer (CRC) liver metastasis.
Methods: A series of in vivo and in vitro researches were showed to demonstrate the gut microbiota and its possible mechanism in CRC liver metastasis.
Results: Fewer liver metastases were identified in the ampicillin-streptomycin-colistin and colistin groups. Increased proportions of Parabacteroides goldsteinii, Bacteroides vulgatus, Bacteroides thetaiotaomicron, and Bacteroides uniformis were observed in the colistin group. The significant expansion of KCs was identified in the ampicillin-streptomycin-colistin and colistin groups. B. vulgatus levels were positively correlated with KC levels. More liver metastases were observed in the vancomycin group. An increased abundance of Parabacteroides distasonis and Proteus mirabilis and an obvious reduction of KCs were noted in the vancomycin group. P. mirabilis levels were negatively related to KC levels. The number of liver metastatic nodules was increased in the P. mirabilis group and decreased in the B. vulgatus group. The number of KCs decreased in the P. mirabilis group and increased in the B. vulgatus group. In vitro, as P. mirabilis or B. vulgatus doses increased, there was an opposite effect on KC proliferation in dose- and time-dependent manners. P. mirabilis induced CT26 cell migration by controlling KC proliferation, whereas B. vulgatus prevented this migration.
Conclusions: An increased abundance of P. mirabilis and decreased amount of B. vulgatus play key roles in CRC liver metastasis, which might be related to KC reductions in the liver.
Keywords: Colorectal neoplasms, Liver metastasis, Gastrointestinal microbiome, Kupffer cells
Globally, the most prevalent type of malignant tumor is colorectal cancer (CRC),1 and many of the CRC patients (15% to 25%) are diagnosed with metastasis of cancer.2 Despite advancements in the therapeutic strategies of CRC liver metastasis, there is a huge population of patients (>50%) who experience recurrence and metastasis of cancer within 2 years.3 Therefore, exploring the mechanism involved in the CRC liver metastasis is critical in improving the treatment options.
The cancer metastasis is associated with the cancer microenvironment
Although intestinal microbiota conciliate the hepatic natural killer T cell accumulation with both primary hepatic cancer and metastatic hepatic cancers, the possible alterations in the Kupffer cells (KCs), the most copious residential macrophages in the sinusoids of liver, have not been evaluated.9 Among all hepatic non-parenchymal cells in the liver, KCs makeup up 20% of these and have a significant role in tumor phagocytosis.10 KCs regulate the function and activity of the T-cells and natural killer (NK) cells. KCs stimulate the NK cells to produce and secrete the cytokines, for example, granulocyte-macrophage colony-stimulating factor and interferon-γ, that increases the pathogenicity of the KCs.11 It has also been observed that the KCs influence the multiplication of the stimulated CD8+ T-cells during the initial stages of cancer and enhance the apoptosis in the later stages of cancer.12 Several animals studies have proposed that the KCs increase the apoptosis of T-cell via Fas/Fas-L pathway and express the upper levels of programmed death-ligand 1 to obstruct multiplication and functionality of the T-cells by direct contact.13,14 Therefore, the KCs have a complicated role in tumor progression.
KCs are involved in the killing of microbes by phagocytosis that invades from the bloodstream; so killing the
The mice colon tumor cell line colon 26 (CT26; Cell Bank of the Chinese Academy of Sciences, Shanghai, China) and hepatic KC cell line (Guangzhou Jennio Biotech Co., Ltd., Guangzhou, China) were cultured in Roswell Park Memorial Institute 1640 (Gibco, Santa Cruz, CA, USA) complete 10% fetal bovine serum (FBS; Gibco) supplemented medium and having 1% streptomycin along with 1% penicillin (Gibco) at 37°C in a CO2 (5%) incubator.18 Proteus mirabilis (BNCC® 107943) and
In this study, 6 weeks old, 60 male BALB/c specific-pathogen-free mice (Animal Experiment Center of Hebei Medical University, Shijiazhuang, Hebei, China) were used and differentiated into four random groups, each group having 15 mice. Antibiotics were administered in sterile drinking water to each group as per the following tab: administrated in sterile drinking water to control group (without antibiotics); vancomycin (Vanc) group (0.25 mg/mL vancomycin); colistin (Coli) group (2 mg/mL colistin); ampicillin-streptomycin-colistin (ASC) group (1 mg/mL ampicillin, 5 mg/mL streptomycin and 1 mg/mL colistin) (Table 1).19
Table 1 . Groups of Mice with Antibiotic Protocols19.
Group | Treatment |
---|---|
Control group | No antibiotics |
Vanc group | Vancomycin (0.25 mg/mL) |
Coli group | Colistin (2 mg/mL) |
ASC group | Ampicillin (1 mg/mL) |
Streptomycin (5 mg/mL) | |
Colistin (1 mg/mL) |
Fecal sample collection was done 2 weeks after the antibiotic treatment and later 16S rDNA sequencing was done. The antimicrobial effects of antibiotics, used in this study, were observed and a model of CRC liver metastasis was made via splenic inoculation of CT26 cells (1×105) as mentioned earlier (Fig. 1A).20
Fifty-six specific-pathogen-free male BALB/c mice (aged 6 weeks) were bred in the specific-pathogen-free Laboratory Animal Center of Hebei General Hospital. Before the experiment, antibiotics were administered to mice of all groups through sterile drinking water containing 0.2 g/L ampicillin, neomycin, and metronidazole and 0.1 g/L vancomycin daily for 2 weeks.21 Then, mice were divided into seven groups (n=8) based on intragastric gavage twice weekly with 2×108 colony-forming units (CFU)/0.2 mL
Mice fecal pellets were collected and gene sequencing of 16S rDNA was done before making the CT26 tumor-bearing model. The 16S rDNA was done following the evaluation of antibacterial effect in groups.
The extraction of DNA was done from fecal samples by the cetyltrimethylammonium bromide (Sigma-Aldrich, St. Louis, Mo, USA) method. Agarose gel electrophoresis was utilized to detect the concentration and purity of DNA extracted from the fecal samples of mice. A suitable quantity of DNA was taken in a centrifuge tube and dilution was made up to 1 ng/μL in sterile water, then primer sequence 806R (5’-GGA CTA CNN GGG TAT CTA AT-3’) and 515F (5’-GTG CCA GCM GCC GCG GTA A-3’) were used to amplify the V3-V4 regions by polymerase chain reaction.
The DNA library was developed with the Ion Plus Fragment Library Kit (48 reactions; Thermo Fisher Scientific, Waltham, MA, USA). This developed DNA library was quantified by a Qubit (Thermo Fisher Scientific) as well as sequenced with an Ion S5TMXL (Thermo Fisher Scientific). Then the comparison of the reading sequence was done with the species annotation database Aby (https://github.com/torognes/vsearch/) along with the examination of chimera sequences. Clean reads were left only after the removal of the chimera sequence. Clustering was done with Uparse software (Uparse v7.0.1001, https://drive5.com/uparse/) with 97% similarity, and the operational taxonomic units regarding the species categorization were acquired following chimera filtering the clustered sequence.
The filter value was defined by the linear discriminant analysis effect size software and it was 4. Following the assurance of the operational taxonomic unit annotation data from the SILVA database (SILVA SSU 138.1, https://www.arb-silva.de), the entire information of the functional genomic for the prokaryotes in the Kyoto Encyclopedia of Genes and Genomes database was interpreted through UProC, and then by using the DNA aligner, the proteins were aligned in association with the SILVA database, in this way the functional prediction of Tax4Fun was perceived.
The liver samples from mice were collected, fixed, impregnated, and embedded in paraffin wax, and sections were made (4 µm). A monoclonal rabbit anti-mouse F4/80 primary antibody (1:500; Servicebio, Wuhan, China) was added to the sections following the dewaxing & hydrating the sample, antigen repair, and 15 minutes incubating in H2O2 solution (3%). A 3,3’-diaminobenzidine staining solution IHC kit (Zsbio, Beijing, China) was used for color development. Three high-power microscopic fields were selected randomly and micrographs were taken with the microscope (Nikon, Tokyo, Japan). Image J (Ver-1.8.0, National Institutes of Health, Bethesda, MD, USA) was used to calculate the optical densities of proteins.
KCs were cultured in tissue plates with 96 wells and these were used as at 5×103 cells in each well, then allowed to settle and adhere. Grown to late-log phase in 1640 complete medium supplemented with 10% FBS,
KCs (1×105) were inoculated on 24-well plates (lower chamber) with 500 µL 1640 medium and cultured for 24 hours; 50 µL of phosphate-buffered saline, 1×103, 1×104, 1×105, 1×106, or 1×107 CFU/mL
The data, from this study, were analyzed with SPSS software SPSS version 19.0 (IBM Corp., Armonk, NY, USA) and the data are expressed as the mean±standard deviation. Limited slip differential and one-way analysis of variance were utilized for multiple comparisons after testing the variance homogeneity and normal distribution of data. The correlation between differential bacteria and KCs were analyzed by using the Pearson correlation analysis between the Vanc group, Coli group, and control group. The differences of intestinal microbiome between different groups were statistically investigated by R software (R Foundation for Statistical Computing, Vienna, Austria). The comparisons of more than two groups were done with the Wilcoxon tests and the Tukey
The splenic tumor injection resulted in sudden CRC liver metastasis in BALB/c mice as mentioned earlier (Fig. 1A).9 In the ASC group, antibiotics mixed in drinking water affected the intestinal commensal population negatively (Fig. 1B).23 Fewer liver metastases were observed in the ASC group (p=0.003). Vancomycin promoted the CRC liver metastases (p=0.028) while colistin inhibited the CRC liver metastases (p=0.041). Vancomycin-treated mice group targeting the Gram-positive bacteria showed greater (p<0.001) liver metastases than the colistin-treated group of mice targeting the Gram-negative bacteria; comparing with this, a vigorous decrease (p<0.001) was identified in the liver metastasis in ASC group as compared to Vanc group. In this experiment, the liver volume showed identical results; liver volume was larger (p=0.028) in the Vanc group but it was in the ASC group, the liver volume was smaller (p=0.009). Furthermore, liver volume was greater (p=0.037) in the Vanc group as compared to the ASC group and Coli group (Fig. 1B and C).
To better understand the part of intestinal microbiome in response to the CRC liver metastasis, six fecal samples were collected from each group including the Coli, Vanc, ASC, and control groups, and 16S rDNA sequencing was done to assess the microbiota landscape in the fecal samples. Among all treatment groups, ASC treatment showed significant effects on intestinal commensal bacteria by eliminating them from the intestines. A Venn diagram graph showed that 220 species of bacteria were common among all species identified from the control group (n=395), Vanc group (n=258), and Coli group (n=373) in this study (Fig. 1D). According to the principal coordinate analysis, the microbial population structure was visibly different between the Coli group and Vanc groups, while there was no clear separation between control group and Coli group (Fig. 1E). Additionally, the alpha diversity of the intestinal microbiome was significantly different between Vanc group and Coli group which was observed by certain methods including Shannon, Chao1, and Simpson; moreover, a remarkable elevation in the microbial diversity of the Coli group was observed by Simpson and Shannon diversity (Fig. 1F). According to the beta diversity analysis, the different coefficients of intestinal microbiota diversity in the Vanc group and the Coli group were 0.509 and 0.145, respectively. Moreover, the correlation coefficient of commensal bacteria between the Vanc group and the Coli group was 0.466 (Fig. 1G). These results of this study suggested the fact that modulating the intestinal commensal bacteria influenced the CRC liver metastasis in a way that the increased diversity and community richness might suppress the CRC liver metastasis.
In this study, the disagreement between the intestinal microbiome of all three groups were evaluated and high-dimensional class comparisons regarding the common taxa of intestinal bacteria were determined via linear discriminant analysis effect size bar and cladogram analysis. The Coli group had abundant Bacteroidetes and Firmicutes; while the Vanc group had abundant Proteobacteria, considering the phylum level. Additionally for the species levels, the Coli group was found to be rich with
The functional prediction was done with Tax4Fun to assess the mechanism for the role of commensal bacteria in causing liver metastasis. The differential gene functions in all three groups contained the genes, immune system, cell motility, transport, and various types of metabolism (Fig. 3A). To detect the difference in immune responses, pair-wise comparisons were done, which revealed that clear differences were observed in the immune system between the control group and Vanc group and between control and Coli groups. These differential functions between the three groups consisted of bacterial secretion system, NOD-like receptor signaling, bacterial motility protein, interleukin (IL)-17 signaling, and two-component system (Fig. 3B and C, Supplementary Fig. 1A). This suggested that the alterations in the intestinal microbiota influenced the CRC liver metastasis and it was associated with the IL-17 signaling.
Based on the Tax4Fun prediction data, the immune signals particularly the IL-17, were clearly different between all the three groups. IHC was used to detect the count of KCs count in CT26 cancer-bearing mice, to evaluate the mechanism involved in the tumor suppression. The KCs were significantly high in the Coli and ASC groups (p=0.038, p=0.001, respectively), whereas the KCs were significantly less (p=0.027) in the Vanc group. There was a similarity in the KCs number with liver volume and liver metastasis results that the KCs were more in the mice of Coli group and ASC group as compared to that in the Vanc group (p<0.001, p<0.001, respectively) (Fig. 3D).
The results here highlighted the KC landscape of CRC liver metastasis and the huge remodeling after gut microbiota alterations.
The relation of KCs and differential count of bacteria were evaluated to detect the definitive role of the intestinal microbiome in regulating hepatic KC accumulation. A positive was found between KC contents and
To validate the effect of
Given the changes in hepatic KCs in CRC liver metastasis after antibiotic administration, we utilized IHC to estimate the KC content in the liver tissues of tumor-bearing mice pre-transplanted with
To pinpoint the relationship between
Considering that
To summarize,
The mechanisms involved in CRC liver metastasis are unexplained yet. A recent theory of the gut-liver axis put forward the foundation for exploring the correlation between intestinal diseases and the liver. The hepatic portal venous system carries the microbiota into the liver that can bring changes in the liver microenvironment and may influence the CRC liver metastasis.17 In the present study, different models of CRC liver metastasis were created by using different mouse-administered antibiotics. Comparatively, more liver metastasis was detected in the Vanc group comparing with those in the Coli group and mixed treatment group. It was also observed that the population of
The CRC hepatic metastasis modules were created in mice by various intestinal microflora. In the current study, the liver metastases were higher in the Vanc group as compared to the control group. Another study stated a remarkable decrease in the liver metastases in the Vanc group of mice.9 This variation in the results can be due to different doses of antibiotic, treatment duration of antibiotic, feeding conditions of animals, selected strains of animals, or the number of cancerous cells inoculated through the spleen. Moreover, ASC group showed lesser liver metastases as compared to control group, which can be correlated with the depleted symbiotic bacteria in the intestine due to antibiotics. These results are in coordination with an earlier study by Sethi
A definite separation in bacterial populations was noticed in three groups of mice as per the principal coordinate analysis, alpha diversity analysis, and beta diversity analysis of intestinal microflora. Many differences between the control group and the Vanc group and between the control group and the Coli group were analyzed for better identification of commensal bacterial populations responsible for CRC liver metastasis. Raised count of
The functional difference between the three mice groups was found to be correlated with immune signaling based on the prediction. Specifically, IL-17 promotes tumor progression that modulates the inflammatory responses in KCs according to an alcohol-induced hepatocellular carcinoma model.31 The premetastatic niche development in the CRC liver metastasis comprises various cells, for example, cells of bone marrow origin and other resident cells like KCs, liver sinusoidal cells, and hepatic stellate cells. Among all these types of cells, KCs have an important role in CRC liver metastasis.32 In the present study, having a similarity with the liver metastasis results, there was an increased population of KCs in the ASC group comparing with that in the control group which indicates that KCs aggregation was improved due to decreased commensal bacteria in the intestine. Significantly, it was observed that the KC population was reduced in the mouse of the Vanc group and increased in the mice of the Coli group.
Previous studies reported that vancomycin causes increase in IL-25 levels,
In the present study, in the Coli group KCs were abundant and these were significantly low in the Vanc group than in the control group indicating that CRC liver metastasis could be inhibited by the KC accumulation. Moreover, decreased
Changes in the liver microenvironment induced by commensal microbes could be beneficial for patients suffering from CRC liver metastasis. Here, more liver metastatic nodules were identified in the
Evidence has shown that several members of the intestinal microbiota, especially
Moreover, we found that
However, the current study had certain limitations. In this study, one cell line (CT26) was utilized, the KC subtype was not recognized, and certain other cells of hepatic immune response were also not exposed. Moreover, the precise mechanism involved in the observed effects needs additional studies.
In conclusion, alterations to the gut microflora diversity influence CRC liver metastasis. An increased abundance of
Supplementary materials can be accessed at https://doi.org/10.5009/gnl210177.
gnl-16-4-575-supple.pdfThis work was supported by The Specialist Capacity Building and Leader Development Program Funded from the 2018 Hebei Government (grant number: 361003).
The authors of this paper wish to pay regards to all who have contributed in writing this paper. We thank Dr. Li, the Department of General Surgery in the First Hospital of Hebei Medical University, for designing the study.
No potential conflict of interest relevant to this article was reported.
Concept and design: Z.L., Y.J. Data acquisition: N.Y., X.L., M.W., L.Q., Y.G. Data analysis and interpretation: N.Y., X.X., J.Z., Y.L. Drafting of the manuscript; critical revision of the manuscript for important intellectual content: N.Y., Z.L., Y.J. Statistical analysis: N.Y. Obtained funding: Y.J. Administrative, technical, or material support; study supervision: Z.L., Z.Z., Y.J. Approval of final manuscript: all authors.