https://orcid.org/0000-0001-9577-8373
Figures
Abstract
Introduction
Inadequate antimicrobial treatment has led to multidrug-resistant (MDR) bacteria, including Helicobacter pylori (H. pylori), which one of the notable pathogens in the stomach. Antibiotic-induced changes in the microbiota can negatively affect the host. This study aimed to determine the influence of H. pylori resistance on the diversity and abundance of the stomach microbiome.
Methods
Bacterial DNA was extracted from biopsy samples of patients presenting dyspepsia symptoms with H. pylori positive from cultures and histology. DNA was amplified from the V3-V4 regions of the 16S rRNA gene. In-vitro E-test was used to detect antibiotic resistance. Microbiome community analysis was conducted through α-diversity, β-diversity, and relative abundance.
Results
Sixty-nine H. pylori positive samples were eligible after quality filtering. Following resistance status to five antibiotics, samples were classified into 24 sensitive, 24 single resistance, 16 double resistance, 5 triple resistance. Samples were mostly resistant to metronidazole (73.33%; 33/45). Comparation of four groups displayed significantly elevated α-diversity parameters under the multidrug resistance condition (all P <0.05). A notable change was observed in triple-resistant compared to sensitive (P <0.05) and double-resistant (P <0.05) groups. Differences in β-diversity by UniFrac and Jaccard were not significant in terms of the resistance (P = 0.113 and P = 0.275, respectively). In the triple-resistant group, the relative abundance of Helicobacter genera was lower, whereas that of Streptococcus increased. Moreover, the linear discriminant analysis effect size (LEfSe) was associated with the presence of Corynebacterium and Saccharimonadales in the single-resistant group and Pseudomonas and Cloacibacterium in the triple-resistant group.
Conclusion
Our results suggest that the resistant samples showed a higher trend of diversity and evenness than the sensitive samples. The abundance of H. pylori in the triple-resistant samples decreased with increasing cohabitation of pathogenic bacteria, which may support antimicrobial resistance. However, antibiotic susceptibility determined by the E-test may not completely represent the resistance status.
Citation: Dewayani A, Afrida Fauzia K, Alfaray RI, Waskito LA, Doohan D, Rejeki PS, et al. (2023) Gastric microbiome changes in relation with Helicobacter pylori resistance. PLoS ONE 18(5): e0284958. https://doi.org/10.1371/journal.pone.0284958
Editor: Farah Al-Marzooq, UAE University: United Arab Emirates University, UNITED ARAB EMIRATES
Received: April 23, 2022; Accepted: April 12, 2023; Published: May 18, 2023
Copyright: © 2023 Dewayani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: On behalf replication this study, the dataset and result of analysis can be access on https://figshare.com/articles/dataset/Gastric_microbiome_changes_in_relation_with_em_Helicobacter_pylori_em_resistance_Analysis/22767077.
Funding: This study was supported in part by International Research Collaboration, Universitas Airlangga, Surabaya, Indonesia (1526/UN3.15/PT/2021) (PSR, MM, MAMA, DD) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: All authors declare there is no conflict of interest.
Introduction
Antibiotic resistance has been observed in healthcare-associated and community-acquired infections and has become a global public health challenge. The World Health Organization has acknowledged studies demonstrating a longer duration of hospitalization and increased 30-day mortality in patients with resistant bacteria [1]. The estimated annual cost of this burden is $55 billion every year in United States, namely, $20 and $45 billion in healthcare and loss of productivity, respectively. The cumulative burden could decrease the annual global gross domestic product (GDP) by approximately 1%, with developing countries experiencing losses of 5–7% by 2050 [2]. Moreover, this burden is doubled in the case of multidrug-resistant (MDR) bacteria. MDR strains are often harder and more expensive to handle [3,4]. MDR infections result in 700.000 deaths per year, that may rise to 10 million by 2050 depending on the evolution of resistance patterns and the development of potent antibiotics. The anticipated associated costs amount to 3.8% of the annual GDP (additional 1.2 trillion USD) [5]. Despite global efforts to decrease the overuse of antibiotics, the use of antibiotic classes based on their spectrum of activity is not solely associated with the spread of resistance [6]. Several studies have demonstrated that antibiotics affect the composition and functionality of human microbiota. This is correlated with dysbiosis and the domination of the bacterial composition by pathogenic bacteria, selection of resistant bacteria, and susceptibility to recurrent infections [7]. The relative abundance of gut colonization has been linked to the development of MDR bacteria [8].
Helicobacter pylori (H. pylori) is one of the most common human pathogens, and 50% of the global population is estimated to be infected by it. H. pylori triggers numerous pathological alterations in the stomach, including peptic ulcer disease, primary gastritis, and gastric cancer [9]. Thus, the eradication of infection can reduce the risk of gastric cancer. The first-line regimen was a triple combination of proton pump inhibitor (PPI) and two antibiotic options: amoxicillin, clarithromycin, metronidazole, and clarithromycin [10]. However, since the late 2000s, H. pylori eradication rates have decreased because of its increasing resistance to one or more antibiotics [11,12]. A systematic review of Asia-Pacific countries showed that the mean primary H. pylori resistance rates were 17, 44, 18, 3, and 4% for clarithromycin, metronidazole, levofloxacin, amoxicillin, and tetracycline, respectively [13]. Moreover, triple- and quintuple-MDR H. pylori strains have been reported in China [14]. These data highlight the worldwide emergence of H. pylori antibiotic resistance.
The advancement of 16S rRNA sequencing over the years has helped reveal the composition of the gastric microbiota, which helps understand the complex interactions between H. pylori and other gastric microbiota communities in the gastric microenvironment. Previous studies have linked changes in the gastric microbiome composition between H. pylori-negative and H. pylori-positive individuals [15,16]. The existence of the Helicobacter genus is associated with an increase in the abundance of other pathogenic populations. Additionally, the gastric microbiota composition is altered during the progression of gastritis to gastric cancer [17,18]. Although antibiotic resistance status is known to affect the gut microbiota [19,20], data related to the gastric environment are still lacking. Therefore, the cohabitation and interplay of the gastric microbiota regarding the antibiotic resistance requires further examination.
Here, we conduct the first microbiome community study on the resistance of H. pylori-positive to five antibiotics in gastric mucosa samples. H. pylori-positive biopsy samples with a resistant status were analyzed for diversity and richness. Subsequently, linear discriminant analysis effect size (LEfSe) analysis was conducted to determine the abundance of microbiota.
Material and methods
Patients and samples
The H. pylori infection study was conducted from 2014 to 2016 in Indonesia; a total of 1074 samples were obtained through endoscopy and biopsy sampling combined with our previous results [21]. All subjects were > 18 years old and had dyspeptic symptoms (postprandial fullness, early satiety, epigastric pain, and heartburn) with or without H. pylori treatment prior to examination. We collected demographic data (age and sex) and a simple diagnosis of dyspepsia using a questionnaire, as described in our previous study [22]. Patients with incomplete specimens were excluded from our study.
We investigated the microbiome on gastric mucosal specimens obtained from the lesser curvature of the antrum, approximately 3 cm from the pyloric ring. The biopsy samples were stored in a transport medium containing 10% glycerol. For the H. pylori culture, the specimens were homogenized in 500 μL of phosphate-buffered saline, and the remaining homogenized specimens then stored at a temperature of −80°C for DNA extraction later. Histological examination was conducted using biopsy specimens from the corpus and antrum. All the specimens were obtained using Radial Jaw 4 forceps (Boston Scientific).
Owing to the low prevalence of H. pylori infection, we included 80 specimens of H. pylori-positive from the culture, and the histological analysis involved DNA extraction and sequencing. The final analysis included 69 specimens after quality filtering (Fig 1).
Of the 1074 samples, 80 samples showed adequate quality and quantity for H. pylori DNA extraction. Final analysis after determining the sequence quality included 69 samples.
All the participants provided written and verbal consent after signing an informed consent form. The study concept and protocol were approved by the Ethics Committee of the Dr. Soetomo Teaching Hospital (221/Panke. KKE/IX/2012; Surabaya, Indonesia) and the Oita University Faculty of Medicine (Yufu, Japan). All protocols followed the principles of the Declaration of Helsinki involving human subjects.
Determination of H. pylori infection
Using cultures of antral biopsy specimens, we determined the infection status based on the H. pylori culture and histological examination. H. pylori was isolated from the antral tissues, cultured, and incubated at 37°C for 10 days on a selective agar plate for H. pylori containing equine serum, soybean peptide, sodium chloride, and selective antibiotics (Nissui Pharmaceutical Co., Japan) in a microaerophilic environment. Colonies were then sub-cultured in antibiotic-free agar medium (Mueller-Hinton II) supplemented with 10% horse blood (Becton Dickinson) in microaerophilic conditions; subsequently, the H. pylori stock was stored at −80°C in Brucella broth (Difco). All the biopsy specimens were preserved in 10% buffered formalin and embedded in paraffin for histological evaluation. Serial sections were stained with hematoxylin-eosin and May-Giemsa stains, and H. pylori was detected in samples with bacterial burdens of grade 1 or above.
Antibiotic susceptibility test
Antibiotic sensitivity measurements were conducted by measuring the minimum inhibitory concentration (MIC) of five antibiotics (amoxicillin, levofloxacin, metronidazole, clarithromycin, and tetracycline), as described in our previous study [23]. Procedural tests were conducted using an E-test (Biomerieux, France). First, the selected H. pylori cultures were sub-cultured thrice on a Brucella agar plate supplemented with 7% horse blood without antibiotics under microaerophilic conditions overnight. Subsequently, H. pylori colonies were collected in phosphate-buffered saline, customized to a 3.0 McFarland concentration, and embedded on 5% horse blood-supplemented Mueller Hinton agar. E-test strips containing five types of antibiotics were patched in the center. The antibiotic concentrations were as follows: 0.016–256 mg/L for clarithromycin, amoxicillin, tetracycline, and metronidazole, respectively, and 0.002–32 mg/L for levofloxacin. The area of inhibition was measured after 72 h. All sensitivities were determined according to the clinical breakpoint of the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [24].
DNA extraction and PCR amplification of 16S rRNA sequences
DNA extraction and PCR amplification were conducted as previously described [25]. DNA was extracted using the QIAGEN DNeasy Blood & Tissue Kit (QIAGEN) and concentrated using the DNA Clean & Concentrator (QIAGEN) (Zymo Research). The DNA was extracted and maintained at a temperature of 20°C. The 16S rRNA gene library was prepared according to the manufacturer’s instructions (Illumina, San Diego, CA, USA). Universal primer 341F was employed to amplify the V3-V4 regions of the bacterial 16S rRNA gene, and 17 polymerase chain reaction (PCR) amplifications were conducted using KAPA HiFi HotStart Ready Mix (KAPA Biosystem Inc. Eight PCR cycles were conducted using the Nextera XT Index kit (Illumina Inc.), and Agencourt AMPure XP magnetic beads (Beckman Coulter) were used for amplicon purification. The DNA library was validated using the MCE-202 MultiNA system (Shimadzu) and QuantiFluor dsDNA system (Promega Corporation). The pooled 5 pM DNA library was denatured with 0.2 N of NaOH and combined with PhiX Control v3 (Illumina Inc.) to a final concentration of 15%, according to the protocol of Illumina. Paired-end sequencing was conducted using the MiSeq platform (Illumina Inc.) and MiSeq Reagent Kit version 3 (2300 bp paired-end reads; Illumina Inc.).
Sequence data analysis
Reads obtained from the Illumina MiSeq platform were analyzed using the QIIME2 (Version 2021.2) pipeline [26]. The demultiplexed reads were uploaded, trimmed, and filtered using Cutadapt to remove low-quality reads. The Deblur pipeline integrated into QIIME2 was used for denoising, chimera removal, and clustering. The output was classified for taxonomy and allocated to the SILVA 138 reference database with 99% identity [27]. These sequences were aligned using MAFFT, and a phylogenetic tree was constructed using FastTree [28,29]. We imported the operational taxonomic unit (OTU) table, representative sequences, and phylogenetic tree as artifacts into the QIIME2 analysis platform (https://qiime2.org). The α-diversity including the observed richness, Simpson and Shannon diversity indexes, and Pielou’s evenness were analyzed using the alpha significance QIIME diversity. The Wilcoxon signed-rank test was used to compare the diversity indexes. The plot was drawn in the “ggplot2” package of the R environment (ver 4.02).
The OTU tables, metadata, taxonomy, and phylogenetic tree were exported into the phyloseq object using biom-convert and “phyloseq” package of the R environment for beta diversity and abundance analysis. The β-diversity analysis measured the unweighted UniFrac and Bray-Curtis distances. Principal component analysis was conducted using the R software. The Adonis function from the “vegan” package was used to conduct permutational analysis of variance (PERMANOVA) with the Bonferroni correction. Using the “phyloseqCompanion” package in R, we conducted LEfSe to determine OTUs that are likely to explain differences between sensitive, single-, double-, and triple-resistant strains. We also used the Kruskal-Wallis test to determine the presence of any differences in the relative abundances of specific OTUs in the resistant strains.
Results
Subjects and antibiotic resistance status
Among the 69 subjects who were chosen for further analysis, 30 (43.47%) and 39 (56.52%) were male and female, respectively. Most patients were diagnosed with gastritis (n = 58, 84.05%) and had no history of H. pylori eradication (n = 48, 69.56%) (Table 1). The drug profiles were based on the E-test of antibiotic susceptibility to five antibiotics: amoxicillin, levofloxacin, metronidazole, clarithromycin, and tetracycline. We found that strains were most resistant to metronidazole (33/45, 73.33%) and least resistant to amoxicillin and tetracycline (4/45, 8.88% and 2/49, 4.44%, respectively). Based on the resistance to five types of antibiotics, 24, 24, 16, and 5 sample cultures were classified as sensitive, single-resistant, double-resistant, and triple-resistant groups.
Alpha diversity of the gastric microbiome in susceptible and resistant samples
The analyzed data were set at a minimum sampling depth of 500 reads for diversity normalization, and no samples were excluded. To investigate the general condition, we analyzed the gastric microbiota community according to the susceptibility status. Overall, we observed a higher tendency for diversity and evenness, but this was not statistically significant in the resistant group compared with the sensitive group. Fig 2 shows the Shannon diversity index (P = 0.54), Simpson index (P = 0.38), and Pielou’s evenness index (P = 0.3664). Bacterial community richness at the OTU level was also comparable between the groups (P = 0.5119).
The boxes represent the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively). The vertical line inside the box denotes the median.
Gastric microbiota diversity among resistant groups
We further investigated the diversity of drug resistance to the five antibiotics using the E-test. Our analysis indicated significant differences in the alpha diversity of the Shannon (P = 0.04) and Simpson (P = 0.032) indexes, and evenness of Pielou’s index (P = 0.025) among the groups. The pair-wise comparison of sensitive and triple-resistant strains showed an increasing trend of diversity richness for MDR strains, and double- and triple-resistant strains showed significant results in the Shannon index (Fig 3A). A similar tendency was observed for the Simpson index, which was concordant with the Shannon index. The pair-wise comparison of the evenness between sensitive and triple-resistant groups (P <0.01) and the comparison between double- and triple-resistant groups (P <0.01) were also conducted. The α-diversities showed that triple-MDR strains had the highest diversity and evenness trends. In contrast, double-resistant group displayed a lower trend in the diversity and evenness compared with the sensitive and other resistant groups. Generally, the observed OTUs were comparable between groups (Fig 3A).
(A). Boxplot displaying the alpha diversity measured by the Shannon index, Simpson index, and Pielou’s index, and observed OTUs. It showed significant differences in the diversity measured by the Shannon and Simpson indexes and the community evenness measured by Pielou’s index. (B) Beta diversity measured by UniFrac and Bray-Curtis distances indicated that the gastric community cannot be separated into particular clusters.
We evaluated differences in the β-diversity analysis distance plot. Both UniFrac and Bray-Curtis distances revealed that the clusters appeared separate but were not significantly correlated with drug resistance (P = 0.113 and P = 0.274, respectively), as indicated by PERMANOVA (Fig 3B).
Relative abundances of gastric microbiota are affected in the presence of resistant H. pylori
As all samples were H. pylori-positive, the comparison of relative abundance indicated that the most abundant phylum in all groups was Campylobacterota. The major human stomach microbiota communities, Actinobacteria, Bacteroidetes, and Firmicutes [30] were observed across the groups. However, an increase in the proportion of Proteobacteria was observed in the triple-resistant group (Fig 4A).
(A) Taxa comparison in terms of the phylum and family. A lower proportion of Campylobacteria was observed with an increase in Proteobacteria from single resistance to triple resistance. (B) Analysis of LDA by LEFse at the genus level found possible biomarkers in resistance strains. Enrichment of Pseudomonas and Cloacibacterium was observed in triple resistance and that of Corynebacterium was observed in single resistance. (C & D) Boxplots illustrating the abundance of two major genera across resistance groups. Depleted abundance of Helicobacter was observed in the triple resistance group.
Linear discriminant analysis (LDA) at the genus level was conducted for taxa with an abundance of at least 2% of the population. Several taxa characterizing each disease group were observed with an LDA score cutoff of 5.0 (Fig 4B). In addition to Helicobacter, single resistance was enriched in Corynebacterium and Saccharimonadales, whereas triple resistance was highly enriched in Pseudomonas and Cloacibacterium. Additionally, at the genus level, the triple-resistant group had a significantly depleted relative abundance of Helicobacter compared with the other groups and higher Streptococcus compared with the sensitive group (Fig 4C and 4D).
Discussion
The stomach was thought to be sterilized by commensal bacteria because of the low pH of gastric acid. The discovery of H. pylori broke the paradigm and subsequently changed perspectives regarding the microbiota community in the gastric niche [31]. H. pylori is a class I carcinogen associated with gastric cancer. This infection alters the physiological conditions of the gastric environment to serve other bacterial habitats [9]. As the contribution of microbiota to human diseases is increasing, it is important to analyze distinct differences in the gastric microbiome diversity, especially in terms of the H. pylori resistance status.
To the best of our knowledge, microbiome analysis of MDR strains remains limited, and this exploration is the first of its kind for gastric. This study highlights a few findings related to the resistance status. Overall, the resistant strains showed a higher trend in diversity and community evenness compared to the sensitive group. Subsequently, based on the resistance type, there was an increase in the α-diversity index among the MDR. The diversity and richness significantly increased in triple-resistant strains compared with sensitive and single-resistant strains (Fig 3). Notably, our results showed that double-resistant strains had the lowest diversity index. Further research is required to determine the effects of resistance on microbial communities. However, the cluster distance in the β-diversity index was not correlated with the resistance status. Our results indicate that dominant bacteria could suppress the commensal microbiota and increase the number of pathogenic bacteria.
The virulence factors CagA and VacA can alter the gastric microbiota and immune phenotypes previously attributed to H. pylori infection in the stomach [32]. Moreover, previous studies have demonstrated that resistance-carrying microbiomes (resistomes) had higher diversity and relative abundance trends. The group found that resistome-carrying healthy outpatient children had more Enterobacteriaceae spp. with extended-spectrum beta-lactamase (ESBL) gene potential [19].
In the recent study, patients were mostly diagnosed with gastritis with no prior eradication history. We could not specify deeper type of any previous dyspeptic therapy nor antibiotic treatment of patients. Antibiotic activity plays a role in the microbiota composition. Strains showed major antibiotic resistant to metronidazole, followed by clarithromycin and levofloxacin in the E-test. Clinically, metronidazole has been extensively used to target anaerobic bacteria, such as Bacteroidetes spp. [33], whereas clarithromycin targets anaerobic and extensive gram-positive bacteria [34]. Investigating H. pylori resistance to treatment in Indonesia revealed a high distribution of metronidazole and levofloxacin resistance. The 23S rRNA A2143G mutation for metronidazole is associated with clarithromycin resistance [35]. This may also combine with PPIs influence towards lower gastric acidity and development of dysbiosis [36]. The consequences and mechanism of any previous therapy of a patient before biopsy were unclear, but those could affect the bacterial culture and susceptibility, resulting in the deviation of the double-resistant out of pattern.
The abundance of phyla in all groups showed that the core microbiome phyla found in healthy individuals (Firmicutes and Bacteroidetes) were suppressed by Campylobacterota dominance, as shown in Fig 4. Most studies agree that H. pylori infection occupies most of the gastric niche [15,37]. Notably, triple resistance was associated with an increased proportion of Proteobacteria, including many common gram-negative pathogens. LDA indicated that Pseudomonas was the most differentially abundant genus in triple-resistant group. A higher abundance of Streptococcus was also observed. There is a possibility of subjects that possessed MDR H. pylori could also be infected with other MDR bacteria and their augmented abundance repressed the growth of H. pylori, as shown in our results. A previous study reported that the abundance of gram-negative bacteria, such as Escherichia coli, Enterobacter cloacae, and Salmonella enterica increased in patients with MDR bacterial infections [20]. The dominance of Streptococcus and various Proteobacteria is a marker of persistent dysbiosis, which subsequently increases bacteremia [38,39]. Those gram-negative bacteria abundance may be related to the transfer of antibiotic resistance [40], wherein gene variants may be present in H. pylori [41]. This is supported by the ability of H. pylori to form biofilms, which are known to reduce susceptibility compared with the planktonic phase [42]. Moreover, a possible association may exist between biofilm formation and interspecies gene transfer [43].
This study had several limitations. First, the findings presented here were analyzed using 16S rRNA sequencing; thus, we could not determine the presence of drug resistance in other organisms. However, this can serve as a preliminary view of the gastric microbial community in patients with MDR strains. Further investigation using the shotgun metagenome is necessary to confirm the presence of antimicrobial resistance genes possessed by all bacteria present in the environment and help better understand the reciprocal effect. Second, our previous prospective population-based study demonstrated the low prevalence of H. pylori in Indonesia [22,44]. Moreover, this was a cross-sectional study where we found low triple-resistant samples (n = 5), and most resistances were toward metronidazole; hence, pre- and post-antibiotic use cohorts are required to provide a more objective perspective.
In conclusion, recent data indicate that dysbiosis due to multidrug use and cohabitation compositions may be correlated. The resistance status of H. pylori was correlated with the enriched diversity of the gastric microbiome composition, where the abundance of non-H. pylori pathogens increased, especially in triple-resistant strains. Given this possibility, clinicians should be mindful of antibiotic combination treatments because drug resistance in the gut environment could affect the gastrointestinal niche and probably induce drug resistance in other bacteria.
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