Home
JournalsCollections
For Authors For Reviewers For Editorial Board Members
Article Processing Charges Open Access
Ethics Advertising Policy
Editorial Policy Resource Center
Company Information Contact Us
OPEN ACCESS

The Presence of Periodontal Pathogens in Gastric Cancer

  • Marcel A. de Leeuw1,* and
  • Manuel X. Duval2
Exploratory Research and Hypothesis in Medicine   2020;5(3):87-96

doi: 10.14218/ERHM.2020.00024

Received:

Revised:

Accepted:

Published online:

 Author information

Citation: de Leeuw MA, Duval MX. The Presence of Periodontal Pathogens in Gastric Cancer. Explor Res Hypothesis Med. 2020;5(3):87-96. doi: 10.14218/ERHM.2020.00024.

Abstract

Background and objective

Microbiota are thought to play a role in the development of gastric cancer (GC). Several studies have put forward putatively carcinogenic species in addition to Helicobacter pylori but are not in perfect alignment, possibly due to variable parameters in the experiments. Meta-analyses on this subject have not been published so far. Therefore, there is a lack of clinical guidance beyond H. pylori eradication therapy.

Methods

Here, we analyzed gastric mucosa samples from nine public datasets, including GC samples. We defined fine grain bacterial networks of gastric mucosa and identified the species associated with the tumor status of samples.

Results

Despite study-specific variability, the periodontal species Fusobacterium nucleatum, Parvimonas micra and Peptostreptococcus stomatis were found in association with tumor status in several datasets. The three species were predicted to be in interaction by ecological network analysis and also formed the intersection of tumor-associated species between four GC datasets and five colorectal cancer datasets we reanalyzed. We formulated a probiotic composition putatively competing with the GC pathogen spectrum, from correlation analysis in a large superset of gut samples (n = 17,800) from clinical- and crowd-sourced studies.

Conclusions

The overlapping pathogen spectrum between two gastrointestinal tumor types, GC and colorectal cancer, has implications for etiology, treatment and prevention. In vitro testing results reported in the literature suggest H. pylori eradication treatment should be efficient against the GC pathogen spectrum, yet the existence of an upstream periodontal reservoir is of concern. To address this, we propose use of the formulated probiotics composition.

Keywords

Gastric cancer, Biopsy, Periodontal pathogens, Biofilm, Fusobacterium nucleatum

Introduction

Gastric cancer (GC) is the sixth most common cancer in the world, with more than 70% of cases occurring in the developing world. GC is the third leading cause of cancer deaths worldwide (source: WHO, 2018). More than 50% of cases occur in Eastern Asia. In Asia, GC is the third most common cancer after breast and lung and is the second most common cause of cancer death after lung cancer.1

The seroprevalence of Helicobacter pylori is closely related to the incidence of GC.2–4 In recent years, other bacteria have been proposed as risk factors for GC, including Propionibacterium acnes and Prevotella copri,5Fusobacterium nucleatum6,7 and Leptotrichia wadei.8Prevotella melaninogenica, Streptococcus anginosus and P. acnes have been reported as increased in the tumoral microhabitat.9 The centrality of Peptostreptococcus stomatis, S. anginosus, Parvimonas micra, Slackia exigua and Dialister pneumosintes in GC tissue has also been reported.10 Furthermore, P. acnes has also been associated with lymphocytic gastritis.11 The association between periodontal pathogens and GC has been questioned, and answered so far negatively regarding the gastric microbiome12,13 but positively regarding the oral microbiome.14

The availability of a number of these studies in the form of raw microbiome sequence reads offers the possibility to revisit the GC microbiome using a uniform bioinformatics approach, to obtain a consensus of additional species possibly involved in GC and address therapeutic options beyond H. pylori eradication therapy.

Materials and methods

We identified a total of 12 eligible datasets from the literature and the NCBI BioProject repository. Dataset SRP080738 was excluded due to mismatch of paired-end sequences as submitted. Dataset SRP224905 was excluded because the variable regions sequenced were not documented. Dataset SRP109017 was excluded because of the extreme amount of non-specific human DNA amplification. Most eligible datasets are from China (Table 1). Scientific publication has been issued for the following projects: PRJEB21497,15 PRJEB21104,16 PRJEB22107,17 PRJNA428883,9 and PRJNA495436.18 For the purpose of comparison, we also included all five colorectal cancer (CRC) mucosa biopsy datasets we had previously analyzed (Supplementary Material, Table S1).

Table 1

Gastric mucosa samples used in this study.

BioProjectSRAn16SStudy metadataRegion
PRJEB21104ERP02333493V1-V2disease progressUK
PRJEB21497ERP02375334V4disease progressMalaysia
PRJEB22107ERP02444030V1-V2Hp+/−, CagA+/−Austria
PRJNA313391SRP070925119V3-V4disease progressChina, Qingdao
PRJNA428883SRP128749669V3-V4disease locationChina, Zhejiang
PRJNA481413SRP154244301V4anatomic locationChina, Nanchang
PRJNA495436SRP16521332V3-V4pre/post-Hp eradicationChina, Nanchang
PRJNA508819SRP172818173V3-V4disease locationChina, Zhejiang
PRJNA545207SRP20016963V3-V4healthy onlyChina, Nanchang
Total1,514

Data analysis

Amplicon sequence variants (ASVs) were generated with the R Bioconductor package dada2,19 version 1.12.1, with recommended parameters, involving quality trimming, discarding of sequences with N’s, assembly of forward and reverse sequences and chimera removal, as described previously.20 ASVs per dataset were subject to further analysis, involving multiple alignment with mafft, version 6.603b21 and approximately-maximum-likelihood phylogenetic tree generation with FastTreeMP, version 2.1.11,22 both used with default settings.

Taxonomic classification of ASVs were performed by an in-house Python and R program using random forest-based supervised learning on RDP release 11.5. The classifier assigns a species or higher level taxonomic identity to each ASV. Resulting classifications are available from the github repository https://github.com/GeneCreek/GC-manuscript in the form of R data objects.

UniFrac distances were computed using the R Bioconductor package phyloseq, version 1.28.023 on raw ASVs. Further analysis used counts and relative abundances summarized at the species level, using the provided taxonomic classifications.

Dirichlet multinomial mixtures were computed with the R bioconductor package DirichletMultinomial, version 1.26.0,24 using default parameters. The required processing steps are provided on https://github.com/GeneCreek/GC-manuscript/blob/master/scripts/dmm_community_types.Rmd .

Classification prediction was performed using the R caret package, version 6.0.84, provided random forest model. Variable (taxa) importance was estimated using the mean decrease in node impurity. Multiclass area-under-the-curve (AUC)25 was computed by the R package pROC, version 1.15.3.

Ecological networks were computed using inverse covariance with SPIEC-EASI26 as incorporated in the R Bioconductor package SpiecEasi, version 1.0.7, using default parameters.

For the nitrosating status of species, we required that at least one non-redundant genome for the species carries a UniProt annotated nitrate reductase alpha unit gene (narG).27

Prevalence difference analysis across disease progress, disease state and H. pylori eradication state was computed using Pearson’s χ2 testing as implemented by the R stats package provided chisq.test, with Monte Carlo simulation-based computation of p-values.28

Co-exclusion and co-occurrence between species for probiotics composition were computed using χ2 testing on detectable presence of species in samples (n = 17,844) from a set of 30 clinical- and crowd-sourced 16S studies, all performed on the Illumina platform (Table 1 and Supplementary Material, Table S1).

A full-stack analysis script for dataset SRP128749 is provided on https://github.com/GeneCreek/GC-manuscript as a detailed processing example.

Results

Pathogens in gastric mucosa

Among the species with highest prevalence in gastric mucosa of healthy individuals (n = 85), we found a substantial number of opportunistic pathogens, with the majority being known as periodontal pathogens. Figure 1 depicts the distribution of prevalence and relative abundances of the top 20 periodontal and other pathogens. Whereas the position of H. pylori is obviously not a surprise, the 60% prevalence of the skin pathogen P. acnes (recently renamed to Cutibacterium acnes) was unexpected. The position of F. nucleatum, a known CRC-associated pathogen, among the top four pathogens is also remarkable. We found 17 distinct ASVs assigned to P. acnes and 53 distinct ASVs assigned to F. nucleatum in this dataset.

Distribution of prevalence and relative abundance of pathogens in gastric biopsies of healthy individuals.
Fig. 1  Distribution of prevalence and relative abundance of pathogens in gastric biopsies of healthy individuals.

Gastric mucosa community analysis

We applied unsupervised clustering to investigate microbial gastric mucosa community structure, irrespective of sample disease status. In brief, using Dirichlet multinomial mixtures, we obtained an optimal goodness of fit at k = 5 communities according to the Laplace and Akaike information criterion evaluations (Supplementary Material, Fig. S1). Assigning per sample community types accordingly, we then retrieved the top 100 most important species. We assigned species to community types by maximum contribution. Putative interactions between these species were retrieved from the SPIEC-EASI ecological network constructor, which operated independently from the community structure on all 1,544 samples. Figure S2 in the Supplementary Material depicts the correspondence between species community types and the correlation network.

For community types one and two, the dominating species was H. pylori, with levels exceeding 50% (Supplementary Material, Fig. S3). Community type two had the lowest phylogenetic diversity of all community types (Supplementary Material, Fig S4). Community type four received the majority of periodontal pathogens, whereas community types three and four harbored the most abundant nitrosating species (Table 2).

Table 2

Distribution of periodontal and other pathogens and nitrosating bacteria over community types

Community typePeriodontalOtherNitrosating
dmm 132
dmm 21
dmm 339
dmm 42058
dmm 521

Anatomical locations

Dataset SRP154244 presents samples from different anatomical gastric locations in patients with gastritis, intestinal metaplasia, and GC. We investigated if microbial signatures cluster by gastric location using random forest models and ecological networks (Supplementary Material, Table S5 and Fig. S5). Although we observed segregation between interacting antral curvature species on the one hand and corpus/antrum species on the other hand, it does not seem we can explain the distribution of datasets over the community types by difference in anatomical location alone.

Disease progress

Dataset SRP070925 contains gastric mucosa samples (n = 119) from patients with gastritis, intestinal metaplasia, early GC and advanced GC. We combined this dataset with dataset SRP200169, containing gastric mucosa samples (n = 63) from healthy subjects. Both are from Chinese cohorts and have been analyzed using the 16S variable regions V3-V4 combined on the Illumina MiSeq. Performing multi-dimensional scaling on unweighted UniFrac distances, we found the disease stages are well separated (Supplementary Material, Fig. S6).

We performed supervised learning of disease progress status with random forests on two-thirds of the combined dataset, with evaluation on the remaining third. Relative abundances summarized at the species level were used as the analysis substrate. Table S6, Supplementary Material provides the classification results. Metaplasia samples were confounded with gastritis and early cancer, whereas advanced cancer samples were in part classified as early cancer. Healthy, gastritis and early cancer samples were well classified, resulting in an overall multi-class AUC of 0.936.

Sample disease location

Dataset SRP128749 contains gastric mucosa samples (n = 669) from GC patients and comprises triplet tumor, peripherical and normal samples. We added biopsies from healthy subjects to this cohort, again using dataset SRP200169, to challenge the idea that GC normal reflects entirely healthy tissue. Performing multi-dimensional scaling on unweighted UniFrac distances, we found the disease locations show interesting separation (Supplementary Material, Fig. S9). We performed two supervised learning experiments on the combined dataset, one with a two-thirds training, one-third evaluation setup and a second using one additional dataset SRP172818 (n = 173) also containing triplets as the cross-validation set. All three datasets are from Chinese cohorts and have been analyzed using the 16S variable regions V3-V4 combined on the Illumina MiSeq.

Table S7, Supplementary Material provides the classification results on the combined SRP128749 and SRP200169 dataset. The model performs with a multi-class AUC of 0.842. Just one normal sample is confounded with healthy samples. The model performance increased to an AUC of 0.906 when trained on the whole combined dataset and cross-validated on the SRP172818 dataset (Supplementary Material, Table S8). None of the GC normal samples were confounded with samples from healthy donors.

Species relevant in GC

We disposed of four datasets having the metadata required for the association of species with tumor status, whether from a disease progress or disease location standpoint. In brief, we processed datasets individually and retrieved the top 50 differentiating species from the random forest models, trained on the dataset as a whole. We generated ecological networks using these top species, retaining only connected nodes for display.

Figure 2 provides the putative interaction network of the disease location datasets SRP172818 and SRP128749, showing reproducible tumor association of, and possible interaction between, the oral species F. nucleatum, P. micra, P. stomatis and Catonella morbi. Correlation indicates the interaction would be cooperative. Figures S10 and S11, Supplementary Material provide the same analysis for the disease progress datasets SRP070925 and ERP023334, respectively; in the first of which, we found P. melaninogenica associated with advanced cancer status and in the second F. nucleatum with cancer status.

Disease status discriminating species.
Fig. 2  Disease status discriminating species.

Datasets (a) SRP172818 and (b) SRP128749. Only species with interactions are displayed. Location associations are based on maximum mean relative abundance. Co-exclusion is indicated in red.

Prevalence differences

An alternative take on the species differentiating between disease states, using χ2 testing of difference in prevalence, is presented in Tables S9–S13, Supplementary Material. P. acnes was reproducibly found at over 61% in GC tumor samples, whereas P. stomatis was found at over 54%, P. micra over 37% and F. nucleatum over 35% in GC tumor samples. The presence of all four roughly doubled over their baseline prevalence in normal samples (Supplementary Material, Tables S9 and S10).

Comparison with CRC

We tested five previously analyzed CRC datasets for presence and interactions of F. nucleatum, P. micra and P. stomatis. All five datasets SRP117763 (n = 34, tumor-only),29 SRP137015 (n = 211, tumor/peripherical/normal),30,31 SRP076561 (n = 50, tumor/normal),32 ERP005534 (n = 96, tumor/normal)33 and SRP064975 (n = 98, tumor/peripherical/normal)34 have been published. We found F. nucleatum in interaction with P. stomatis in SRP137015 and P. micra in interaction with P. stomatis in datasets SRP117763 and SRP076561 (Supplementary Material, Fig. S12). Prevalence of F. nucleatum was found at 70% or more in tumor samples in SRP117763 (Supplementary Material, Table S14), at 48% in tumor samples in SRP137015 (Supplementary Material, Table S15), and at 73% in tumor samples in SRP076561 (Supplementary Material, Table S16). Listing the most abundant cancer-associated species in GC and CRC, the intersection between the two cancer types was formed by F. nucleatum, P. micra and P. stomatis (Table 3).

Table 3

Comparison of GC- and CRC tumor associated species

SpeciesGCCRC
Bacteroides fragilis2
Bacteroides ovatus3
Brevundimonas vesicularis2
Escherichia coli2
Fusobacterium nucleatum33
Gemella morbillorum3
Parvimonas micra23
Peptostreptococcus stomatis22
Prevotella intermedia2
Propionibacterium acnes2

Eradication therapy

Dataset SRP165213 provides mucosa samples, pre- and post-bismuth quadruple H. pylori eradication therapy. Using χ2 testing of difference in prevalence, we found several bacteria, including the expected H. pylori, exhibited an important drop in prevalence (Table 4). P. stomatis, P. micra and F. nucleatum, on the other hand, showed a moderately significant prevalence increase.

Table 4

Pre- and post-eradication therapy prevalence differences, dataset SRP165213

SpeciesAssociationp valuePrePostCount
Helicobacter pyloripre1.0e-03***17/17 (100.0%)2/15 (13.3%)19
Brevundimonas diminutapre1.0e-03***17/17 (100.0%)3/15 (20.0%)20
Sphingobium yanoikuyaepre1.0e-03**13/17 (76.5%)2/15 (13.3%)15
Sphingomonas yabuuchiaepre2.0e-03**13/17 (76.5%)3/15 (20.0%)16
Sphingobium xenophagumpre3.0e-03**11/17 (64.7%)2/15 (13.3%)13
Propionibacterium acnespre1.0e+0014/17 (82.4%)12/15 (80.0%)26
Bifidobacterium adolescentispost1.0e-03***2/17 (11.8%)13/15 (86.7%)15
Ruminococcus bromiipost1.0e-03***4/17 (23.5%)14/15 (93.3%)18
Dorea longicatenapost1.0e-03***1/17 (5.9%)11/15 (73.3%)12
Leptotrichia wadeipost2.0e-03**0/17 (0.0%)7/15 (46.7%)7
Parvimonas micrapost2.8e-02*0/17 (0.0%)4/15 (26.7%)4
Peptostreptococcus stomatispost3.0e-02*5/17 (29.4%)11/15 (73.3%)16
Fusobacterium nucleatumpost4.6e-015/17 (29.4%)7/15 (46.7%)12

Modulation of the gastric mucosa microbiome

Using prevalence data from 17,844 samples, including the samples used in this study, we probed for qualified presumption of safety (referred to here as QPS) species found in co-exclusion with the species of interest panel identified above (Fig. 3). Bifidobacterium longum appears as the most promising QPS species, followed by Streptococcus salivarius; both of these are being used in probiotic products and are actually detectable in gastric mucosa samples (see Fig. 2b for B. longum). In the healthy dataset SRP200169, we found 27 ASVs for B. longum but none for S. salivarius, indicating that the latter is possibly not commensal in the stomach in healthy individuals.

Co-exclusion by and co-occurrence with QPS species of gastric cancer-associated species.
Fig. 3  Co-exclusion by and co-occurrence with QPS species of gastric cancer-associated species.

Putative inhibition is shown in shades of red, potential synergy in shades of green. White reflects neutrality or too little combined prevalence to make a call. Genera are abbreviated as follows: Bcl., Bacillus; Bf., Bifidobacterium; Gb., Geobacillus; Lcn., Leuconostic; Lctb., Lactobacillus; Lctc., Lactococcus; Pd., Pediococcus; S., Streptococcus. QPS, qualified presumption of safety.

Discussion

In this study, we revisited public gastric mucosa and CRC datasets, taking into account recent advances in processing of amplicon metagenomic sequences,35 establishing species level taxonomic classification.

Limitations

Use of a healthy cohort analyzed as a separate batch and from a different regional cohort does not allow for control of batch or regional effects in supervised learning. Regional clustering of GC microbiota has been reported previously.36 So, our hypothesis that samples from healthy donors are distinct from GC normal samples in GC patients is delicate. For confirmation of this hypothesis, healthy donors need to be recruited from the same population as the GC patients.

Four subspecies are known for F. nucleatum. Our taxonomic classifier does not resolve down to the level of subspecies, so all counts and relative abundances for F. nucleatum may conceal different subspecies, moreover so since in CRC, multiple subspecies have been isolated from biopsies37 and since we detected several tens of distinct ASVs associated with F. nucleatum.

Low biomass and contamination

P. acnes has been proposed as a possible contaminant of many experiments.38 This is particularly relevant for gastric samples which are of low biomass as compared to biopsies from the lower gastrointestinal tract. That does not mean we need to discard the bacterium altogether, notably not if it shows significant increase in tumor sample locations as in datasets SRP172818 and SRP128749, but it could mean its baseline presence is overestimated and hence its status as a gastric mucosa commensal.39 Its position as a prevalent but low abundant species in healthy subjects gives credit to the contamination thesis. However, the number of ASVs associated with P. acnes suggests that if there is contamination, it originates from multiple individuals. The fact that the bacterium never reached high abundance in the experiments means that it did not contaminate low biomass samples in particular.

H. pylori

In all datasets, we found gastric mucosa samples completely exempt of H. pylori, including in normal and peripherical samples, which opens the possibility that other pathogens play a role in GC. We did not find H. pylori in significant interaction, which is unexpected and discrepant to findings reported from the same dataset SRP128749.9 We attribute this discrepancy to the use of a more stringent ecological network inference.26 On the other hand, report has been made that H. pylori presence did not affect microbial community composition.40 So, it seems that although H. pylori may create oncogenic conditions through host interaction, there does not seem to be a direct benefit or detriment of such conditions for other bacteria.

Cohort-specific species

Our results show species found in gastric mucosa have a strong cohort-specific distribution of species. Within cohort prediction of sample disease status or location status based on the microbiome composition is performing well (with AUCs > 0.8); so, despite its diversity, there is a clear sample status signature in the microbiome composition.

Nitrosating species

Nitrosating bacteria convert nitrogen compounds in gastric fluid to potentially carcinogenic N-nitroso compounds, which are believed to contribute to GC.41–45 We found nitrosating bacteria were not uniformly distributed over gastric mucosa community types. Community type four combines nitrosating species with periodontal pathogens and can be considered as the highest GC risk community type.

Periodontal and CRC pathogens

It has been reported that among patients with periodontal disease, high levels of colonization of periodontal pathogens are associated with an increased risk of gastric precancerous lesions.13 We found the periodontal pathogens F. nucleatum, P. micra and P. stomatis to be commensal but also associated with tumor status and in direct interaction in several datasets. These three species were also found in association with tumor status in CRC datasets revisited and correspond with a CRC subtype with strong immune signature.29 Revisiting the CRC datasets, we found in part the same interactions as in GC. Two recent meta-analysis of CRC case-control studies placed F. nucleatum, P. micra and P. stomatis among the top five carcinoma-enriched species.32,46F. nucleatum and P. stomatis have also been proposed among a panel of species for early detection of CRC.33

Virulence

The Gram-negative bacterium F. nucleatum promotes tumor development by inducing inflammation and host immune response in the CRC microenvironment. Its adhesion to the intestinal epithelium can cause the host to produce inflammatory factors and recruit inflammatory cells, creating an environment which favors tumor growth. Treatment of mice bearing a colon cancer xenograft with the antibiotic metronidazole reduced Fusobacterium load, cancer cell proliferation, and overall tumor growth.47F. nucleatum can induce immune suppression in gut mucosa, contributing to the progression of CRC.48 In CRC, F. nucleatum is predicted to produce hydrogen sulfide,30 which is a metabolite with a dual role, both carcinogenic and anti-inflammatory. Epithelial cells react to F. nucleatum by activation of multiple cell signaling pathways that lead to production of collagenase-3, increased cell migration, formation of lysosome-related structures, and cell survival.49

Furthermore, it is predicted that F. nucleatum infection regulates multiple signaling cascades, which could lead to up-regulation of proinflammatory responses, oncogenes, modulation of host immune defense mechanism, and suppression of the DNA repair system.50 There does not seem to be a reason why F. nucleatum would not be pathogenic in gastric tissue whereas it is in periodontal, respiratory tract, tonsils, appendix, colonic and other tissues.51

The Gram-positive anaerobe P. stomatis has been isolated from a variety of periodontal and endodontic infections, as well as infections in other bodyparts.52 The species has been found associated with oral squamous cell carcinoma.53 At present, little is known about the specifics of its pathogenicity. The type strain (DSM 17678) genome harbors a gene (mprF, phosphatidylglycerol lysyltransferase) producing lysylphosphatidylglycerol (termed LPG), a major component of the bacterial membrane with a positive net charge. LPG synthesis contributes to bacterial virulence, as it is involved in the resistance mechanism against cationic antimicrobial peptides produced by the host’s immune system and by competing microorganisms. Contrary to other Peptostreptococci, P. stomatis does not produce intestinal barrier enforcing indole-3-propionic acid or indoleacrylic acid.54

P. micra, previously known as (Pepto)streptococcus micros, is a Gram-positive anaerobe known to be involved in periodontal infections. It has also been isolated from oral squamous cell carcinoma.55 It is a producer of collagenase and exhibits limited elastolytic and hemolytic activity.56 In a mouse CRC model, P. micra elicited increased Th2 and Th17 cells, decreased Th1 cells and increased inflammation.57

The oral cavity as a reservoir

It has been shown that in a number of cases (6/14, 43%) identical F. nucleatum strains could be recovered from CRC and saliva of the same patients.58 Furthermore, the oral microbiome composition is to a certain extent predictive for CRC disease progress status.59 It is tempting to speculate that a similar relationship could be explored for disease progress in GC.

Biofilm formation

F. nucleatum is regarded as a central organism for dental biofilm maturation, due to its wide ability to aggregate with other microorganisms, such as Porphyromonas gingivalis.60 It is considered as a bridge bacterium between early and late colonizers in dental plaque.61 The eventuality of H. pylori- and non-H. pylori biofilm formation in the gastric environment has been raised.62 Our ecological interaction networks suggests F. nucleatum and other bacteria but not H. pylori could indeed engage in gastric mucosa biofilms and more particularly in GC biofilms.

Antibiotherapy

H. pylori eradication therapy has been shown to have a prophylactic effect against GC.63 The first-line therapy consists of a proton pump inhibitor or ranitidine bismuth citrate, with any two antibiotics among amoxicillin, clarithromycin and metronidazole. In vitro testing has shown P. stomatis is sensitive to amoxicillin and metronidazole.64F. nucleatum is sensitive to amoxicillin or amoxicillin/clavulanate combination therapy65 and to metronidazole.47,66P. micra is sensitive to amoxicillin/clavulanate and metronidazole.67In vivo sensitivity of the species may differ and in addition, with the oral cavity as a reservoir, periodontal pathogens could recolonize the gastric environment and take advantage of the space cleared by H. pylori, which is what our data suggests.

Probiotics use

We predicted in silico that several QPS species could be effective against the spectrum of H. pylori and the periodontal pathogens discussed above. Our findings are coherent with the report that probiotics including B. longum, Lactobacillus acidophilus, and Enterococcus faecalis significantly reduced the abundance of F. nucleatum in CRC surgery patients by nearly 5-fold, whilst normalizing dysbiosis.68In vitro adhesion inhibition of Gram-negative species by B. longum has been reported.69 Other than adhesion inhibitors, Bifidobacteria produce acetate and lactate as well as vitamins, antioxidants, polyphenols, and conjugated linoleic acids which have been proposed to act as chemical barrier against pathogen proliferation.70S. salivarius not only inhibits adhesion of pathogens to epithelial cells but also produces bacteriocins.71

Future directions

In future GC microbiome studies, it appears imperative to include normal controls from healthy subjects so that normal samples from GC patients can be properly compared against samples from healthy subjects. Fluorescent in situ hybridization could be used in case of gastrectomy to confirm biofilm status of the aforementioned pathogen spectrum. A long-term maintenance formula using probiotics after an antibiotics eradication course can be of interest as a treatment option. A variety of B. longum strains are used in several probiotic preparations commercially available, whereas S. salivarius strain K1272 is also commercially available.

Conclusions

In conclusion, we found disease progress and sample disease status is not reflected in the overall bacterial community type of mucosa. Rather, community types are populated by potentially regionally distinct species. Despite this diversity, we found periodontal pathogens as a common denominator. These pathogens were also identified in CRC, establishing possible microbial similarities between subtypes of GC and CRC, with implications for etiology, treatment and prevention. Correlation networks suggest these species, as in dental plaque and in CRC, engage in biofilm formation in gastric mucosa. Probiotics should be considered as a treatment option, after H. pylori eradication therapy, to avoid recolonization by periodontal pathogens.

Supporting information

Supplementary material for this article is available at https://doi.org/10.14218/ERHM.2020.00024 .

Supplementary Material

The Presence of Periodontal Pathogens in Gastric Cancer.

(PDF)

Abbreviations

AIC: 

Akaike information criterion

ASV: 

amplicon sequence variant

AUC: 

area under the curve

CRC: 

colorectal cancer

GC: 

gastric cancer

LPG: 

lysylphosphatidylglycerol

QPS: 

qualified presumption of safety

Declarations

Acknowledgement

The authors acknowledge the contributions to the Short Read Archive made by the respective institutions and acknowledge scientific journals for enforcing this practice.

Data availability

The input data files used for secondary analysis as well as R analysis scripts are available from https://github.com/GeneCreek/GC-manuscript.

Funding

The authors received no financial support for this study.

Conflict of interest

ML and MD are co-founders of GeneCreek, Inc. and own shares.

Authors’ contributions

Study design, data collection, data analysis and writing of the manuscript (ML); data analysis and writing of the manuscript (MD).

References

  1. Rahman R, Asombang AW, Ibdah JA. Characteristics of gastric cancer in Asia. World J Gastroenterol 2014;20(16):4483-4490 View Article PubMed/NCBI
  2. Kato M, Asaka M, Shimizu Y, Nobuta A, Takeda H, Sugiyama T, et al. Relationship between Helicobacter pylori infection and the prevalence, site and histological type of gastric cancer. Aliment Pharmacol Ther ;2004(Suppl 1):85-89 View Article PubMed/NCBI
  3. Ferreccio C, Rollán A, Harris PR, Serrano C, Gederlini A, Margozzini P, et al. Gastric cancer is related to early Helicobacter pylori infection in a high-prevalence country. Cancer Epidemiol Biomarkers Prev 2007;16(4):662-667 View Article PubMed/NCBI
  4. Shiota S, Mahachai V, Vilaichone RK, Ratanachu-Ek T, Tshering L, Uchida T, et al. Seroprevalence of Helicobacter pylori infection and gastric mucosal atrophy in Bhutan, a country with a high prevalence of gastric cancer. J Med Microbiol 2013;62(Pt 10):1571-1578 View Article PubMed/NCBI
  5. Gunathilake MN, Lee J, Choi IJ, Kim YI, Ahn Y, Park C, et al. Association between the relative abundance of gastric microbiota and the risk of gastric cancer: a case-control study. Sci Rep 2019;9(1):13589 View Article PubMed/NCBI
  6. Yamamura K, Baba Y, Miyake K, Nakamura K, Shigaki H, Mima K, et al. Fusobacterium nucleatum in gastroenterological cancer: Evaluation of measurement methods using quantitative polymerase chain reaction and a literature review. Oncol Lett 2017;14(6):6373-6378 View Article PubMed/NCBI
  7. Hsieh YY, Tung SY, Pan HY, Yen CW, Xu HW, Lin YJ, et al. Increased Abundance of Clostridium and Fusobacterium in Gastric Microbiota of Patients with Gastric Cancer in Taiwan. Sci Rep 2018;8(1):158 View Article PubMed/NCBI
  8. Yang I, Woltemate S, Piazuelo MB, Bravo LE, Yepez MC, Romero-Gallo J, et al. Different gastric microbiota compositions in two human populations with high and low gastric cancer risk in Colombia. Sci Rep 2016;6:18594 View Article PubMed/NCBI
  9. Liu X, Shao L, Liu X, Ji F, Mei Y, Cheng Y, et al. Alterations of gastric mucosal microbiota across different stomach microhabitats in a cohort of 276 patients with gastric cancer. EBioMedicine 2019;40:336-348 View Article PubMed/NCBI
  10. Coker OO, Dai Z, Nie Y, Zhao G, Cao L, Nakatsu G, et al. Mucosal microbiome dysbiosis in gastric carcinogenesis. Gut 2018;67(6):1024-1032 View Article PubMed/NCBI
  11. Montalban-Arques A, Wurm P, Trajanoski S, Schauer S, Kienesberger S, Halwachs B, et al. Propionibacterium acnes overabundance and natural killer group 2 member D system activation in corpus-dominant lymphocytic gastritis. J Pathol 2016;240(4):425-436 View Article PubMed/NCBI
  12. Sun J, Li Y, Francois F, Corby P, Dasanayake AP, Chen Y. H. pylori, Periodontal Pathogens, and Risk Factors of Gastric Cancer. 2010 AADR/CADR Annual Meeting (Washington, D.C.). Presentation ID: 1497. Available from: https://iadr.abstractarchives.com/abstract/2010dc-131087/h-pylori-periodontal-pathogens-and-risk-factors-of-gastric-cancer Accessed April 30, 2020 View Article PubMed/NCBI
  13. Salazar CR, Sun J, Li Y, Francois F, Corby P, Perez-Perez G, et al. Association between selected oral pathogens and gastric precancerous lesions. PLoS One 2013;8(1):e51604 View Article PubMed/NCBI
  14. Sun JH, Li XL, Yin J, Li YH, Hou BX, Zhang Z. A screening method for gastric cancer by oral microbiome detection. Onco Rep 2018;39(5):2217-2224 View Article PubMed/NCBI
  15. Yap TW, Gan HM, Lee YP, Leow AH, Azmi AN, Francois F, et al. Helicobacter pylori Eradication Causes Perturbation of the Human Gut Microbiome in Young Adults. PLoS One 2016;11(3):e0151893 View Article PubMed/NCBI
  16. Parsons BN, Ijaz UZ, D’Amore R, Burkitt MD, Eccles R, Lenzi L, et al. Comparison of the human gastric microbiota in hypochlorhydric states arising as a result of Helicobacter pylori-induced atrophic gastritis, autoimmune atrophic gastritis and proton pump inhibitor use. PLoS Pathog 2017;13(11):e1006653 View Article PubMed/NCBI
  17. Klymiuk I, Bilgilier C, Stadlmann A, Thannesberger J, Kastner MT, Högenauer C, et al. The Human Gastric Microbiome Is Predicated upon Infection with Helicobacter pylori. Front Microbiol 2017;8:2508 View Article PubMed/NCBI
  18. He C, Peng C, Wang H, Ouyang Y, Zhu Z, Shu X, et al. The eradication of Helicobacter pylori restores rather than disturbs the gastrointestinal microbiota in asymptomatic young adults. Helicobacter 2019;24(4):e12590 View Article PubMed/NCBI
  19. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 2016;13(7):581-583 View Article PubMed/NCBI
  20. Callahan BJ, Sankaran K, Fukuyama JA, McMurdie PJ, Holmes SP. Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses. F1000Res 2016;5:1492 View Article PubMed/NCBI
  21. Katoh K, Asimenos G, Toh H. Multiple alignment of DNA sequences with MAFFT. Methods Mol Biol 2009;537:39-64 View Article PubMed/NCBI
  22. Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One 2010;5(3):e9490 View Article PubMed/NCBI
  23. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013;8(4):e61217 View Article PubMed/NCBI
  24. Holmes I, Harris K, Quince C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 2012;7(2):e30126 View Article PubMed/NCBI
  25. Hand DJ, Till RJ. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems. Machine Learning 2001;45:171-186 View Article PubMed/NCBI
  26. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol 2015;11(5):e1004226 View Article PubMed/NCBI
  27. Calmels S, Ohshima H, Bartsch H. Nitrosamine formation by denitrifying and non-denitrifying bacteria: implication of nitrite reductase and nitrate reductase in nitrosation catalysis. J Gen Microbiol 1988;134(1):221-226 View Article PubMed/NCBI
  28. Hope ACA. A simplified Monte Carlo Significance Test Procedure. J R Stat Soc Ser B Methodol 1968;30:582-598 View Article PubMed/NCBI
  29. Purcell RV, Visnovska M, Biggs PJ, Schmeier S, Frizelle FA. Distinct gut microbiome patterns associate with consensus molecular subtypes of colorectal cancer. Sci Rep 2017;7(1):11590 View Article PubMed/NCBI
  30. Hale VL, Jeraldo P, Mundy M, Yao J, Keeney G, Scott N, et al. Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. Methods 2018;149:59-68 View Article PubMed/NCBI
  31. Hale VL, Jeraldo P, Chen J, Mundy M, Yao J, Priya S, et al. Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers. Genome Med 2018;10(1):78 View Article PubMed/NCBI
  32. Drewes JL, White JR, Dejea CM, Fathi P, Iyadorai T, Vadivelu J, et al. High-resolution bacterial 16S rRNA gene profile meta-analysis and biofilm status reveal common colorectal cancer consortia. NPJ Biofilms Microbiomes 2017;3:34 View Article PubMed/NCBI
  33. Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR, Costea PI, et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol Syst Biol 2014;10:766 View Article PubMed/NCBI
  34. Lu Y, Chen J, Zheng J, Hu G, Wang J, Huang C, et al. Mucosal adherent bacterial dysbiosis in patients with colorectal adenomas. Sci Rep 2016;6:26337 View Article PubMed/NCBI
  35. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 2017;11(12):2639-2643 View Article PubMed/NCBI
  36. Yu G, Torres J, Hu N, Medrano-Guzman R, Herrera-Goepfert R, Humphrys MS, et al. Molecular Characterization of the Human Stomach Microbiota in Gastric Cancer Patients. Front Cell Infect Microbiol 2017;7:302 View Article PubMed/NCBI
  37. Brennan CA, Garrett WS. Fusobacterium nucleatum - symbiont, opportunist and oncobacterium. Nat Rev Microbiol 2019;17(3):156-166 View Article PubMed/NCBI
  38. Mollerup S, Friis-Nielsen J, Vinner L, Hansen TA, Richter SR, Fridholm H, et al. Propionibacterium acnes: Disease-Causing Agent or Common Contaminant? Detection in Diverse Patient Samples by Next-Generation Sequencing. J Clin Microbiol 2016;54(4):980-987 View Article PubMed/NCBI
  39. Delgado S, Suárez A, Mayo B. Identification, typing and characterisation of Propionibacterium strains from healthy mucosa of the human stomach. Int J Food Microbiol 2011;149(1):65-72 View Article PubMed/NCBI
  40. Bik EM, Eckburg PB, Gill SR, Nelson KE, Purdom EA, Francois F, et al. Molecular analysis of the bacterial microbiota in the human stomach. PNAS 2006;103(3):732-737 View Article PubMed/NCBI
  41. Sharma BK, Santana IA, Wood EC, Walt RP, Pereira M, Noone P, et al. Intragastric bacterial activity and nitrosation before, during, and after treatment with omeprazole. Br Med J (Clin Res Ed) 1984;289(6447):717-719 View Article PubMed/NCBI
  42. Mowat C, Williams C, Gillen D, Hossack M, Gilmour D, Carswell A, et al. Omeprazole, Helicobacter pylori status, and alterations in the intragastric milieu facilitating bacterial N-nitrosation. Gastroenterology 2000;119(2):339-347 View Article PubMed/NCBI
  43. Jo HJ, Kim J, Kim N, Park JH, Nam RH, Seok YJ, et al. Analysis of Gastric Microbiota by Pyrosequencing: Minor Role of Bacteria Other Than Helicobacter pylori in the Gastric Carcinogenesis. Helicobacter 2016;21(5):364-374 View Article PubMed/NCBI
  44. Ferreira RM, Pereira-Marques J, Pinto-Ribeiro I, Costa JL, Carneiro F, Machado JC, et al. Gastric microbial community profiling reveals a dysbiotic cancer-associated microbiota. Gut 2018;67(2):226-236 View Article PubMed/NCBI
  45. Park CH, Lee JG, Lee AR, Eun CS, Han DS. Network construction of gastric microbiome and organization of microbial modules associated with gastric carcinogenesis. Sci Rep 2019;9(1):12444 View Article PubMed/NCBI
  46. Wirbel J, Pyl PT, Kartal E, Zych K, Kashani A, Milanese A, et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat Med 2019;25(4):679-689 View Article PubMed/NCBI
  47. Bullman S, Pedamallu CS, Sicinska E, Clancy TE, Zhang X, Cai D, et al. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 2017;358(6369):1443-1448 View Article PubMed/NCBI
  48. Wu J, Li Q, Fu X. Fusobacterium nucleatum Contributes to the Carcinogenesis of Colorectal Cancer by Inducing Inflammation and Suppressing Host Immunity. Transl Oncol 2019;12(6):846-851 View Article PubMed/NCBI
  49. Uitto VJ, Baillie D, Wu Q, Gendron R, Grenier D, Putnins EE, et al. Fusobacterium nucleatum increases collagenase 3 production and migration of epithelial cells. Infect Immun 2005;73(2):1171-1179 View Article PubMed/NCBI
  50. Kumar A, Thotakura PL, Tiwary BK, Krishna R. Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions. BMC Microbiol 2016;16:84 View Article PubMed/NCBI
  51. Han YW. Fusobacterium nucleatum: a commensal-turned pathogen. Curr Opin Microbiol 2015;23:141-147 View Article PubMed/NCBI
  52. Downes J, Wade WG. Peptostreptococcus stomatis sp. nov., isolated from the human oral cavity. Int J Syst Evol Microbiol 2006;56(Pt 4):751-754 View Article PubMed/NCBI
  53. Pushalkar S, Ji X, Li Y, Estilo C, Yegnanarayana R, Singh B, et al. Comparison of oral microbiota in tumor and non-tumor tissues of patients with oral squamous cell carcinoma. BMC Microbiol 2012;12:144 View Article PubMed/NCBI
  54. Wlodarska M, Luo C, Kolde R, d’Hennezel E, Annand JW, Heim CE, et al. Indoleacrylic Acid Produced by Commensal Peptostreptococcus Species Suppresses Inflammation. Cell Host Microbe 2017;22(1):25-37.e6 View Article PubMed/NCBI
  55. Hooper SJ, Crean SJ, Fardy MJ, Lewis MAO, Spratt DA, Wade WG, et al. A molecular analysis of the bacteria present within oral squamous cell carcinoma. J Med Microbiol 2007;56(Pt ;12):1651-1659 View Article PubMed/NCBI
  56. Ota-Tsuzuki C, Alves Mayer MP. Collagenase production and hemolytic activity related to 16S rRNA variability among Parvimonas micra oral isolates. Anaerobe 2010;16(1):38-42 View Article PubMed/NCBI
  57. Yu J, Zhao L, Zhao R, Long X, Coker OO, Sung JJY. The role of parvimonas micra in intestinal tumorigenesis in germ-free and conventional apcmin/+ mice. J Clin Oncol 2019;37(4_suppl):531-531 View Article PubMed/NCBI
  58. Komiya Y, Shimomura Y, Higurashi T, Sugi Y, Arimoto J, Umezawa S, et al. Patients with colorectal cancer have identical strains of Fusobacterium nucleatum in their colorectal cancer and oral cavity. Gut 2019;68(7):1335-1337 View Article PubMed/NCBI
  59. Flemer B, Warren RD, Barrett MP, Cisek K, Das A, Jeffery IB, et al. The oral microbiota in colorectal cancer is distinctive and predictive. Gut 2018;67(8):1454-1463 View Article PubMed/NCBI
  60. Tavares LJ, Klein MI, Panariello BHD, Dorigatti de Avila E, Pavarina AC. An in vitro model of Fusobacterium nucleatum and Porphyromonas gingivalis in single- and dual-species biofilms. J Periodontal Implant Sci 2018;48(1):12-21 View Article PubMed/NCBI
  61. He Z, Huang Z, Zhou W, Tang Z, Ma R, Liang J. Anti-biofilm Activities from Resveratrol against Fusobacterium nucleatum. Front Microbiol 2016;7:1065 View Article PubMed/NCBI
  62. Rizzato C, Torres J, Kasamatsu E, Camorlinga-Ponce M, Bravo MM, Canzian F, et al. Potential Role of Biofilm Formation in the Development of Digestive Tract Cancer With Special Reference to Helicobacter pylori Infection. Front Microbiol 2019;10:846 View Article PubMed/NCBI
  63. Kwok A, Lam T, Katelaris P, Leong RW. Helicobacter pylori eradication therapy: indications, efficacy and safety. Expert Opin Drug Saf 2008;7(3):271-281 View Article PubMed/NCBI
  64. Könönen E, Bryk A, Niemi P, Kanervo-Nordström A. Antimicrobial susceptibilities of Peptostreptococcus anaerobius and the newly described Peptostreptococcus stomatis isolated from various human sources. Antimicrob Agents Chemother 2007;51(6):2205-2207 View Article PubMed/NCBI
  65. Jacinto RC, Montagner F, Signoretti FG, Almeida GC, Gomes BP. Frequency, microbial interactions, and antimicrobial susceptibility of Fusobacterium nucleatum and Fusobacterium necrophorum isolated from primary endodontic infections. J Endod 2008;34(12):1451-1456 View Article PubMed/NCBI
  66. Shilnikova II, Dmitrieva NV. Evaluation of antibiotic susceptibility of Bacteroides, Prevotella and Fusobacterium species isolated from patients of the N. N. Blokhin Cancer Research Center, Moscow, Russia. Anaerobe 2015;31:15-18 View Article PubMed/NCBI
  67. Veloo AC, Welling GW, Degener JE. Antimicrobial susceptibility of clinically relevant Gram-positive anaerobic cocci collected over a three-year period in the Netherlands. Antimicrob Agents Chemother 2011;55(3):1199-1203 View Article PubMed/NCBI
  68. Gao Z, Guo B, Gao R, Zhu Q, Wu W, Qin H. Probiotics modify human intestinal mucosa-associated microbiota in patients with colorectal cancer. Mol Med Rep 2015;12(4):6119-6127 View Article PubMed/NCBI
  69. Inturri R, Stivala A, Furneri PM, Blandino G. Growth and adhesion to HT-29 cells inhibition of Gram-negatives by Bifidobacterium longum BB536 e Lactobacillus rhamnosus HN001 alone and in combination. Eur Rev Med Pharmacol Sci 2016;20(23):4943-4949 View Article PubMed/NCBI
  70. Inturri R, Trovato L, Volti GL, Oliveri S, Blandino G. In vitro inhibitory activity of Bifidobacterium longum BB536 and Lactobacillus rhamnosus HN001 alone or in combination against bacterial and Candida reference strains and clinical isolates. Heliyon 2019;5(11):e02891 View Article PubMed/NCBI
  71. Manning J, Dunne EM, Wescombe PA, Hale JD, Mulholland EK, Tagg JR, et al. Investigation of Streptococcus salivarius-mediated inhibition of pneumococcal adherence to pharyngeal epithelial cells. BMC Microbiol 2016;16(1):225 View Article PubMed/NCBI
  72. Burton JP, Wescombe PA, Moore CJ, Chilcott CN, Tagg JR. Safety assessment of the oral cavity probiotic Streptococcus salivarius K12. Appl Environ Microbiol 2006;72(4):3050-3053 View Article PubMed/NCBI