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Esophageal microbial dysbiosis impairs mucosal barrier integrity via toll-like receptor 2 pathway in patients with gastroesophageal reflux symptoms

Journal of Translational Medicine volume 22, Article number: 1145 (2024) Cite this article

Abstract

Background

Previous research on the lower gastrointestinal tract has proved that microbial dysbiosis can lead to intestinal barrier dysfunction and enhanced visceral sensitivity, thus triggering bowel symptoms. Whether esophageal microbial dysbiosis also contributes to the development of gastroesophageal reflux (GER) symptoms, which are known to be associated with impaired esophageal barrier integrity, remains to be explored.

Methods

Patients with GER symptoms (gastroesophageal reflux disease [GERD] and functional esophageal disorders [FED]), duodenal ulcer patients and healthy controls were prospectively included for esophageal microbial analysis. The expression of toll-like receptors (TLRs) and tight junction proteins and intercellular spaces were assessed through transcriptome analysis and immunohistochemistry. The human esophageal epithelial cell (HEEC) line was used to explore how esophageal microbial dysbiosis induced GER symptoms.

Results

Patients with GER symptoms, whether GERD or FED, had a very similar pattern of microbial composition, which showed a significantly increased proportion of Gram-negative bacteria than controls. Patients with GER symptoms (GERD and FED) also exhibited significantly higher TLR2 expression, reduced claudin-1 expression and dilated intercellular spaces (DIS). In vitro, exposure of HEECs to lipopolysaccharide resulted in marked up-regulation of TLR2 and interleukin (IL)-6, down-regulation of claudin-1 and DIS. These effects were mitigated by blocking TLR2 or IL-6.

Conclusion

This study demonstrated that regardless of objective evidence of reflux, patients with GER symptoms presented esophageal microbial dysbiosis characterized by an elevated proportion of Gram-negative bacteria. Enriched Gram-negative bacteria could induce esophageal barrier dysfunction via LPS-TLR2-IL-6-claudin-1-DIS pathway.

Introduction

Gastroesophageal reflux (GER) symptoms (heartburn and/or reflux) are one of the most prevalent complaints encountered in gastrointestinal clinics. Epidemiological studies have reported that approximately 13.3% of the global population had GER symptoms at least weekly, and the incidence are still rising rapidly [1, 2]. These symptoms significantly impair patients’ quality of life and impose a substantial socioeconomic burden, with annual direct pharmacotherapy costs ranging from $26,000 to $41,000 per person [3, 4].

Previous studies have shown that GER symptoms are caused by the retrograde flow of gastric acid into the esophagus [5]. Thus, acid suppressive therapy has long been the first-line treatment for controlling GER symptoms. However, up to 50% of patients reported incomplete symptom relief despite daily use of acid suppressants [6]. Furthermore, GER symptoms can be presented either in gastroesophageal reflux disease (GERD) or functional esophageal disorders (FED) (reflux hypersensitive and functional heartburn) [7]. While GERD is characterized by pathological reflux burden, FED is defined as the presence of GER symptoms without abnormal esophageal acid exposure and mechanical abnormalities. Therefore, apart from acid reflux, there are still other mechanisms involved in the symptom onset.

In order to find the direct causes of GER symptoms, some studies have tried to find the common pathophysiological manifestations of GERD and FED. The results demonstrated that both GERD and FED had impairment of esophageal mucosal barrier function, manifested as dilated intercellular spaces (DIS) [8,9,10]. Normally, the integrity of esophageal mucosa is sustained by tightly-connected squamous epitheliums, which prevents the invasion of harmful stimulants. DIS could cause irritating substances to enter the submucosa and stimulate esophageal sensory neurons more easily, indicating that physiological reflux burden alone is sufficient to trigger GER symptoms [11, 12]. Taken together, DIS might be one of the direct factors resulting in GER symptoms. However, the causes of DIS in patients with GER symptoms remain unclear.

Research on the lower gastrointestinal tract showed that intestinal microbial dysbiosis played an important role in the generation of bowel symptoms. Patients with irritable bowel syndrome (IBS) or functional constipation both had a significantly higher proportion of Gram-negative (G-) bacteria [13, 14]. Mechanism studies further proved that, lipopolysaccharide (LPS), the major component of the outer membrane of G- bacteria, could induce bowel symptoms by binding to toll-like receptors (TLRs), which promoted the production of cytokines and the impairment of intestinal mucosal barrier function [15,16,17,18]. Drawing on the findings in the lower digestive tract, we speculated that the dysbiosis of esophageal microbiota might be the upstream factor of esophageal DIS. Currently, research on esophageal microbiota still mainly focuses on organic diseases [19,20,21,22]. Although some studies have demonstrated an increased proportion of G- bacteria in patients with reflux esophagitis [19, 23], considering that these patients all had excessive acid exposure in the esophageal lumen, the observed microbial dysbiosis could either be a cause or a consequence of the disease. Therefore, it still remains challenging to establish a causal relationship between esophageal microbial dysbiosis and GER symptoms.

We hypothesized that esophageal microbial dysbiosis could induce cell barrier dysfunction via the TLR-inflammation pathway (as observed in bowel symptom patients), which led to the exposure of submucosal sensory neurons to irritants, thereby triggering GER symptoms (Fig. 1). The aim of this study was to elucidate the relationship between esophageal microbial dysbiosis and GER symptoms by comparing the differences in esophageal microbiota among GERD, FED and controls. We also aimed to explore the potential mechanisms of esophageal microbial dysbiosis causing GER symptoms through cell model experiments. This study might provide new therapeutic targets for refractory GER symptoms.

Fig. 1
figure 1

The potential mechanisms that esophageal microbial dysbiosis induced GER symptoms. Esophageal microbial dysbiosis induced esophageal barrier dysfunction via LPS-TLR2-IL-6 pathway, thus triggering GER symptoms. Abbreviation: G-: Gram-negative; LPS: lipopolysaccharide; TLR: toll-like receptor; IL: interleukin; GER: gastroesophageal reflux

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Materials and methods

Study subjects

Patients with chief complaints of GER symptoms (heartburn and reflux) who received endoscopy, high-resolution manometry (HRM) and 24-hour multichannel pH-impedance monitoring were prospectively enrolled from June 2019 to December 2020. We also included healthy volunteers as negative controls and duodenal ulcer patients (another acid-related disease of which the lesion is in the duodenum rather than the esophagus) as disease controls to demonstrate that the changes of esophageal microbiota are disease-specific. All participants underwent endoscopic examinations. GER symptom patients and healthy volunteers further completed esophageal function tests (HRM and reflux monitoring). This study was approved by the Clinical Research Ethics Committees of The First Affiliated Hospital of Sun Yat-sen University (the approval number 2019[290]). Informed consent was also obtained from all participants.

Inclusion criteria were as follows: (i) Patients with GER symptoms: (1) aged between 18 and 75 years old; (2) having heartburn or reflux symptoms for more than 3 months, occurring more than 2 times per week. (ii) Healthy volunteers: (1) aged between 18 and 75 years old; (2) absence of upper gastrointestinal symptoms in the past 6 months; (3) without endoscopic lesions. (iii) Patients with duodenal ulcer: (1) aged between 18 and 75 years old; (2) no heartburn or reflux symptoms; (3) presence of active duodenal bulb ulcer under endoscopy evaluation. Exclusion criteria included: (1) use of antibiotics, probiotics, prebiotics or synbiotics within the past 2 months; (2) discontinuation of acid suppressants, prokinetic drugs or antacids less than 2 weeks; (3) presence of major motility disorders (achalasia, absent contractility, distal esophageal spasm and hypercontractile esophagus) under HRM evaluation; (4) history of prior foregut surgery or gastrointestinal tumors; (5) existence of organic lesions other than esophagitis (including upper gastrointestinal tumors, ulcers, eosinophilic esophagitis, Barrett’s esophagus, esophageal strictures, etc.); (6) presence of oral, gastrointestinal or systemic infections; (7) pregnant women; (8) contraindications to endoscopy or esophageal function testing.

Patients with GER symptoms were subsequently classified into two groups: (1) GERD, manifested with Los Angeles (LA) B/C/D esophagitis or pathological esophageal reflux burden (acid exposure time > 6%); (2) FED, characterized by absence of esophagitis and normal reflux burden (acid exposure time < 4%) [24].

Human tissue collection

All patients and healthy volunteers underwent endoscopic examinations and then esophageal mucosa biopsies were obtained from 2 to 5 cm above the lower esophageal sphincter. To minimize bacterial contamination, all biopsies were collected using disposable biopsy forceps during the endoscopic insertion procedure (avoiding the use of the endoscopic suction function) [25]. The esophageal mucosal biopsies were then transferred into sterile cryovials using disposable needles and immediately frozen in liquid nitrogen and subsequently stored at -80 °C.

16 S rRNA sequencing

Biopsy DNA was isolated using a DNA extraction kit (QIAGEN, 51304) following the manufacturer’s protocol. After extracting the total DNA, the V3-V4 region of the 16 S rRNA gene was amplified using the primers 338 F and 806R via polymerase chain reaction (PCR). The quality of amplified DNA was checked by agarose gel electrophoresis while its quantity was determined using the QuantiFluor™-ST system (Promega, USA). The PCR products were then subjected to MiSeq sequencing (Illumina, USA) with paired-end reads (2 × 300 bp). The paired-end reads obtained from MiSeq sequencing were first merged based on overlapping regions, with stringent quality control and filtering applied. Post-merging, sequences were demultiplexed by sample and subjected to operational taxonomic units (OTU) clustering and taxonomic classification [26].

Immunohistochemistry (IHC)

IHC was performed as described in previous articles [27]. The diluted primary antibodies (TLR2, TLR4, occludin, E-cadherin, claudin-1 and zonula occludens [ZO-1]) added to the slices were shown in Supplementary Table 1. Three images were randomly collected under 400 times magnification. Staining intensity was scored as follows: 0 (no staining), 1 (weak staining), 2 (moderate staining) and 3 (intense staining). The staining area was scored as 0 (< 5%), 1 (5-25%), 2 (25-50%), 3 (50-75%) and 4 (> 75%). The overall intensity score was calculated by multiplying the intensity score by the area score [28, 29]. The average scores of the three fields were calculated as the IHC score for each patient.

Transcriptome analysis

Sequencing was performed using Illumina HiSeqTM2000 (FOREVERGEN). Quality control of the raw data was conducted using FastQC. After filtering out low-quality reads, the remaining high-quality sequences, or “clean reads,” were obtained for further analysis. Hisat2 was used to efficiently and accurately align the reads to the reference genome. Gene expression levels were then quantified and formed the basis of downstream analysis. Transcripts Per Kilobase Million (TPM) was used to determine normalization and differential expression.

Cell culture and experiment design

The normal human esophageal epithelial cell (HEEC) line (BNCC, BNCC359279) was cultured in complete medium containing 90% DMEM (ThermoFisher, C11995500BT), 10% fetal bovine serum (ThermoFisher, 10099141 C), 100U/ml penicillin G and 0.1 mg/ml streptomycin (ThermoFisher, 15140-122) at 37 °C incubator.

LPS (Sigma Aldrich, L3129-10MG) at different concentrations (0 µg/ml, 50 µg/ml, 75 µg/ml, 100 µg/ml, 200 µg/ml and 250 µg/ml) was used to stimulate HEECs to investigate its direct effects on barrier proteins (Supplementary Fig. 1A). Anti-hTLR2-IgA (Invivogen, maba2-htlr2-2) at a concentration of 5 µg/ml, 50 µg/ml and 100 µg/ml was used to block the binding sites of TLR2 [30]. Various concentrations of human IL-6 protein (MCE, HY-P7044; 0 ng/ml, 10 ng/ml, 50 ng/ml, 100 ng/ml, 200 ng/ml and 400 ng/ml) was used to investigate its direct effects on claudin-1 expression and intercellular spaces (Supplementary Fig. 1B). IL-6 antibody (MCE, HY-P9956) at a concentration of 0.01 ng/ml, 0.1 ng/ml, 1 ng/ml or 10 ng/ml was used to neutralize IL-6 secretion. Claudin-1 siRNA (HANBIO, HH20240401FTM-SI04) was used to knockdown claudin-1 expression. Final concentrations were 100 nM for siRNA and 7.5 µL/mL for RNAiFit. Cells were stimulated for 48 h for RNA isolation and 72 h for protein extraction.

Real-time quantitative polymerase chain reaction (RT-qPCR)

The procedures of RNA isolation, reverse transcription and RT-qPCR were conducted as previously described [31]. The expression of the following genes was evaluated: TLR2, TLR4, claudin-1, occludin, E-cadherin, ZO-1, tumor necrosis factor [TNF]-α, IL-1β, IL-4, IL-8, IL-6 and IL-10. The sequences of the forward (F) and reverse (R) primers were listed in Supplementary Table 2.

Western blot

This procedure followed the protocol described in earlier studies [27]. The primary antibodies used were shown in Supplementary Table 3. The band pictures were then analyzed using Image J software to obtain protein expression data.

Hematoxylin and eosin (H&E) staining

Cells were seeded at a concentration of 4 × 10^5 per well in culture chambers (Thermofisher, 154453PK). After exposure to certain stimulations for 72 h, the slides were stained as previously described [27].

Pictures were captured from 3 randomly selected fields under 400 times magnification and later magnified to 1000 times using Image J software for precise DIS measurement. Ten non-adjacent cells were randomly selected from each of the three images. Five vertical lines perpendicular to the cell membrane were drawn along each selected cell (Supplementary Fig. 2). The distances between the selected cell and its neighboring cells were measured along these lines. Finally, the total average of the five measurements per cell was calculated as the intercellular distance for each group [8,9,10].

Enzyme-linked immunosorbent assay (ELISA)

Cell culture supernatant was collected and centrifuged at 2–8 ℃ for about 20 min (2000–3000 rpm). According to the instructions of the ELISA kit, the concentration of TNF-α, IL-1β, IL-4, IL-8, IL-6 and IL-10 was calculated. The specific ELISA kits used were shown in Supplementary Table 4.

Statistical analysis

For microbial analysis: (1) species annotation: OTU clustering was conducted with a 97% similarity threshold. The resultant OTUs were then matched against the Silva database to obtain precise species annotations; (2) α diversity analysis: Chao index, ace index, shannon index and simpson index were used to evaluate α diversity; (3) comparison of overall microbial composition among groups: Principal coordinates analysis (PCoA) and analysis of similarities (ANOSIM) was used; (4) microbial typing analysis: Unsupervised clustering analysis was applied; (5) differential microbial analysis: The top 10 microbiota (at different classification level) with relative abundance were selected for difference analysis. Statistical significance was set at P < 0.05.

For transcriptome analysis, differentially expressed genes (DEGs) were identified based on the following criteria: P ≤ 0.05; the expression level in GER symptom patients was either more than twice or less than half of that in healthy volunteers. To account for multiple testing, adjusted P-values were calculated using the Benjamini-Hochberg method to control the false discovery rate (FDR). The analysis was conducted using the limma package (3.54.2) in R 4.2.2, with statistical significance evaluated by Wilcoxon test. The sequencing data were shown in supplementary material.

For clinical and experimental data: The data were analyzed using GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, CA). Variables were compared by Kruskal-Wallis test or one-way ANOVA among groups. Statistically significant difference was set at P < 0.05.

Results

The microbiota composition of patients with GER symptoms

For the microbial analysis, 12 patients with GERD, 16 with FED, 10 with duodenal ulcer and 10 healthy volunteers were included. Clinical characteristics were shown in Supplementary Table 5. Using 16 S rRNA sequencing, we identified a total of 2691 OTUs. Taxonomic classification further divided these OTUs into 780 distinct genera. Microbial diversity analysis (α diversity) found no significant difference in ace, chao, shannon and simpson indices between patients with GER symptoms and those with duodenal ulcer or healthy volunteers (Supplementary Fig. 3). However, microbial composition analysis demonstrated that the overall microbial composition of GERD and FED patients was remarkably similar, both distinctly differing from those of healthy volunteers and duodenal ulcer patients (Fig. 2A).

Fig. 2
figure 2

The microbiota composition of patients with GER symptoms. A. Microbial composition analysis. B. Unsupervised clustering of esophageal microbiota. C. Analysis of relative abundance of microbiota among different groups. Proteobacteria: G vs. F vs. H vs. D = 38.1% vs. 56.1% vs. 11.7% vs. 16.6%, P < 0.001; Pseudomonas: G vs. F vs. H vs. D = 25.5% vs. 41.9% vs. 0.2% vs. 1.2%, P < 0.05. Abbreviation: G: gastroesophageal reflux disease; F: functional esophageal disorders; D: duodenal ulcer; H: healthy volunteers; *: P < 0.05; **: P < 0.01; ***: P < 0.0001

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Microbial cluster typing analysis revealed two distinct clusters of esophageal microbiota (Fig. 2B). At the phylum level, type I was predominantly composed of Firmicutes (Gram-positive [G+]; 46.72%), while type II was dominated by Proteobacteria (G-; 77.85%). At the genus level, type I was characterized by the enrichment of Streptococcus (G+; 26.76%), whereas type II was primarily composed of Pseudomonas (G-; 62.12%). All healthy volunteers and patients with duodenal ulcer were categorized into type I. By contrast, a significantly higher proportion of patients with GERD or FED was classified into type II (GERD vs. FED vs. duodenal ulcer vs. healthy volunteers: 41.7% vs. 68.8% vs. 0% vs. 0%, P < 0.001).

Analysis of the relative abundance of microbiota among different groups demonstrated that the abundance of Proteobacteria (at the phylum level) was significantly higher in patients with GER symptoms (GERD vs. FED vs. duodenal ulcer vs. healthy volunteers: 38.1% vs. 56.1% vs. 16.6% vs. 11.7%, P < 0.001; Fig. 2C). At the genus level, patients with GER symptoms exhibited significantly increased abundance of Pseudomonas (GERD vs. FED vs. duodenal ulcer vs. healthy volunteers: 25.5% vs. 41.9% vs. 1.2% vs. 0.2%, P < 0.001; Fig. 2C). Notably, the abundance of G- bacteria was markedly enriched in GERD (55.7%) and FED (71.8%), as compared with patients with duodenal ulcer (46.7%) and healthy subjects (39.8%) (P < 0.001). Taken together, these results suggested that, regardless of pathological reflux, patients with GER symptoms all presented microbial dysbiosis characterized by the elevated proportion of G- bacteria. The change of esophageal microbiota might be one of the causes of GER symptoms.

Immunohistochemistry and transcriptomic features of patients with GER symptoms

Specimens from 5 GERD patients, 5 FED patients and 5 healthy volunteers were further used to investigate the potential downstream effects of microbial dysbiosis. Clinical characteristics were shown in Supplementary Table 6. Given that previous research has suggested that TLRs and cell barrier function might serve as downstream factors affected by microbial dysbiosis [18], we specifically examined the expression of TLRs (TLR2 and TLR4) and cell barrier proteins (E-cadherin, ZO-1, claudin-1 and occludin). IHC analysis showed that compared to healthy subjects, TLR2 and ZO-1 were significantly up-regulated while claudin-1 was markedly down-regulated in patients with GER symptoms (GERD and FED) (Fig. 3A and Supplementary Fig. 4). No significant difference was observed in other proteins. Intercellular spaces analysis demonstrated the presence of DIS in GER symptom patients (GERD and FED) (Fig. 3B).

Fig. 3
figure 3

Immunohistochemical features of patients with GER symptoms. A. The protein expression of TLR2 and claudin-1. TLR2: GERD vs. FED vs. HV = 7.4 ± 1.9 vs. 4.5 ± 1.6 vs. 1.3 ± 1.0, P < 0.001; claudin-1: GERD vs. FED vs. HV = 0.4 ± 0.3 vs. 0.9 ± 1.4 vs. 4.8 ± 1.5, P < 0.001. B. The intercellular space shown by H&E staining. GERD vs. FED vs. HV = 1.4 ± 0.1 μm vs. 0.8 ± 0.1 μm vs. 0.2 ± 0.1 μm, P < 0.001. Abbreviation: GERD: gastroesophageal reflux disease; FED: functional esophageal disorders; HV: healthy volunteers; TLR: toll-like receptor; DIS: dilated intercellular space; H&E staining: hematoxylin and eosin staining; DEGs: differentially expressed genes; *: P < 0.05; **: P < 0.01; ***: P < 0.001

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Relying exclusively on IHC to identify downstream pathways might result in missing some critical molecules. Thus, specimens from 6 GERD patients, 4 FED patients and 5 healthy volunteers were subjected to transcriptome analysis. Clinical characteristics were shown in Supplementary Table 7. There were 1504 DEGs identified in GERD patients compared with healthy subjects, with 760 up-regulated and 744 down-regulated (Fig. 4A). In the FED group, 278 DEGs were identified compared with healthy controls, of which 126 were up-regulated and 152 were down-regulated (Fig. 4B). Notably, we observed that a total of 64 genes showed concurrent changes in both GERD and FED groups, including TLR2, which aligned with above IHC results (GERD vs. controls: 26.8 ± 3.4 vs. 2.2 ± 0.4, P < 0.001; FED vs. controls: 30.2 ± 3.9 vs. 2.2 ± 0.4, P < 0.001; Fig. 4C,D). After adjusting for multiple testing using the Benjamini-Hochberg method, TLR2 was identified as the only gene that retained statistical significance (Adjusted P < 0.01). These observations highlighted the critical role of TLR2 up-regulation in the generation of GER symptoms.

Fig. 4
figure 4

Transcriptome analysis between GERD/FED and healthy volunteers. A. DEGs in GERD patients compared with HV. B. DEGs in FED patients compared with HV. C. Fold change of TLR2 expression in GERD patients compared to healthy volunteers. GERD vs. HV = 26.8 ± 3.4 vs 2.2 ± 0.4, P < 0.001. D. Fold change of TLR2 expression in FED patients compared to healthy volunteers. FED vs. HV = 30.2 ± 3.9 vs 2.2 ± 0.4, P < 0.001. Abbreviation: GERD: gastroesophageal reflux disease; FED: functional esophageal disorders; HV: healthy volunteers; TLR: toll-like receptor; DEGs: differentially expressed genes; ***: P < 0.001

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Exposure of HEECs to LPS

To explore the mechanisms by which G- bacteria caused esophageal barrier dysfunction, HEECs were exposed to LPS in vitro, simulating the increased proportion of G- bacteria observed in patients with GER symptoms. Following LPS exposure at a concentration of 200 µg/ml, the expression of TLR2 was significantly increased both in the RNA and protein level (Fig. 5A); while no significant change was found in TLR4 protein level (Supplementary Fig. 5A). A possible explanation is that the TLR4 expression in esophagus is relatively low, thus it does not play the dominant role (e.g. in intestinal mucosal barrier dysfunction) in esophageal mucosal barrier dysfunction [30]. Regarding tight junction proteins, LPS resulted in down-regulation of claudin-1, occludin, E-cadherin and ZO-1 in the RNA level. However, only claudin-1 was found to be down-regulated in the protein level (Fig. 5B and Supplementary Fig. 5B). For cytokines, LPS led to significant RNA up-regulation of IL-6, IL-8 and TNF-α, while only IL-6 was found to be elevated in the protein level (Fig. 5D and Supplementary Fig. 5C). Additionally, DIS was observed after LPS treatment (Fig. 5C).

Fig. 5
figure 5

Effects of LPS stimulation on HEECs. A. The mRNA and protein expression of TLR2 following LPS exposure (200 µg/ml). TLR2 mRNA expression: Ctrl vs. LPS = 1.0 ± 0.1 vs. 1.9 ± 0.1, P < 0.05; TLR2 protein expression: Ctrl vs. LPS = 0.7 ± 0.4 vs. 1.5 ± 0.1, P < 0.01. B. The mRNA and protein expression of claudin-1 following LPS exposure (200 µg/ml). Claudin-1 mRNA expression: Ctrl vs. LPS = 1.0 ± 0.0 vs. 0.4 ± 0.0, P < 0.001; claudin-1 protein expression: Ctrl vs. LPS = 1.5 ± 0.1 vs. 1.0 ± 0.1, P < 0.05. C. The intercellular space shown by H&E staining following LPS exposure (200 µg/ml): Ctrl vs. LPS = 0.4 ± 0.1 μm vs. 1.5 ± 0.2 μm, P < 0.001. D. The mRNA and protein expression of IL-6 following LPS exposure (200 µg/ml). IL-6 mRNA expression: Ctrl vs. LPS = 1.0 ± 0.2 vs. 3.9 ± 0.0, P < 0.001; IL-6 protein expression: Ctrl vs. LPS = 189.3 ± 5.4 pg/ml vs. 473.5 ± 7.9 pg/ml, P < 0.001. Abbreviation: Ctrl: control; LPS: lipopolysaccharide; IL: interleukin; DIS: dilated intercellular space; HEEC: human esophageal epithelial cell line; *: P < 0.05; **: P < 0.01; ***: P < 0.001

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TLR2 blockade attenuated the effects of LPS

To confirm that TLR2 is the intermediate mediator between LPS and barrier integrity impairment, TLR2 neutralizing antibody was added to complete medium before LPS treatment. The results showed that at the concentration of 5 µg/ml, TLR2 neutralizing antibody could significantly inhibit IL-6 secretion. Furthermore, claudin-1 down-regulation and DIS were markedly restored (Fig. 6A, B and C).

Fig. 6
figure 6

TLR2 blockade attenuated the effects of LPS. A. The mRNA and protein expression of IL-6 after blocking TLR2 (5 µg/ml). IL-6 mRNA expression: Ctrl vs. LPS vs. LPS + IgA vs. IgA = 1.0 ± 0.3 vs. 9.0 ± 1.3 vs. 4.8 ± 0.4 vs. 1.0 ± 0.1, P < 0.001; IL-6 protein expression: Ctrl vs. LPS vs. LPS + IgA vs. IgA = 62.4 ± 11.8 pg/ml vs. 210.6 ± 14.7 pg/ml vs. 61.1 ± 30.5 pg/ml vs. 49.9 ± 12.4 pg/ml, P < 0.001. B. The mRNA and protein expression of claudin-1 after blocking TLR2 (5 µg/ml). Claudin-1 mRNA expression: Ctrl vs. LPS vs. LPS + IgA vs. IgA = 1.0 ± 0.0 vs. 0.8 ± 0.0 vs. 0.9 ± 0.0 vs. 0.9 ± 0.0, P < 0.001; claudin-1 protein expression: Ctrl vs. LPS vs. LPS + IgA vs. IgA = 1.0 ± 0.1 vs. 0.6 ± 0.1 vs. 1.0 ± 0.1 vs. 0.9 ± 0.1, P < 0.001. C. The intercellular space shown by H&E staining after blocking TLR2 (5 µg/ml). Ctrl vs. LPS vs. LPS + IgA vs. IgA = 0.4 ± 0.2 μm vs. 1.7 ± 0.5 μm vs. 0.5 ± 0.2 μm vs. 0.6 ± 0.2 μm, P < 0.001. Abbreviation: Ctrl: control; LPS: lipopolysaccharide; TLR: toll-like receptor; ***: P < 0.001

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IL-6 production is crucial for LPS-induced effects

To further clarify the impacts of IL-6 on LPS-induced barrier dysfunction, we exposed cells to IL-6. The results showed that at the concentration of 400 ng/ml, IL-6 significantly reduced claudin-1 expression both in the RNA and protein levels (Fig. 7A). H&E staining also showed DIS following IL-6 stimulation (Fig. 7B). Subsequently, we treated HEECs with IL-6 neutralizing antibody prior to LPS exposure. IL-6 antibody at the concentration of 0.1 ng/ml significantly attenuated the effects of LPS on claudin-1 expression and intercellular spaces (Fig. 7C, D). These results highlighted the pivotal role of IL-6 as a mediator of LPS-induced esophageal barrier dysfunction.

Fig. 7
figure 7

IL-6 production is crucial for LPS-induced effects. A. The mRNA and protein expression of claudin-1 following IL-6 stimulation (400 ng/ml). Claudin-1 mRNA expression: Ctrl vs. IL-6: 1.0 ± 0.0 vs. 0.8 ± 0.0, P < 0.001; claudin-1 protein expression: Ctrl vs. IL-6: 1.2 ± 0.3 vs. 0.8 ± 0.1, P < 0.01. B. The intercellular space shown by H&E staining following IL-6 stimulation (400 ng/ml). Ctrl vs. IL-6: 0.4 ± 0.3 μm vs. 1.3 ± 0.3 μm, P < 0.01. C. The mRNA and protein expression of claudin-1 after treated with IL-6 antibody (0.1 ng/ml). Claudin-1 mRNA expression: Ctrl vs. LPS vs. LPS ± IL-6 IgG vs. IgG = 1.0 ± 0.0 vs. 0.8 ± 0.0 vs. 1.1 ± 0.0 vs. 1.0 ± 0.0, P < 0.001; claudin-1 protein expression: Ctrl vs. LPS vs. LPS ± IL-6 IgG vs. IgG = 1.2 ± 0.1 vs. 0.8 ± 0.1 vs. 1.2 ± 0.3 vs. 1.1 ± 0.1, P < 0.05. D The intercellular space shown by H&E staining after treated with IL-6 antibody (0.1 ng/ml). Ctrl vs. LPS vs. LPS ± IL-6 IgG vs. IgG = 0.2 ± 0.1 μm vs. 1.4 ± 0.0 μm vs. 0.4 ± 0.1 μm vs. 0.2 ± 0.1 μm, P < 0.001. Abbreviation: Ctrl: control; IL: interleukin; LPS: lipopolysaccharide; DIS: dilated intercellular space; *: P < 0.05; **: P < 0.01; ***: P < 0.001

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Clauidin-1 knockdown induced DIS

Among all tight junction proteins in patients with GER symptoms, only claudin-1 was observed to be down-regulated. Thus, we wanted to figure out whether the reduction merely in claudin-1 is sufficient to cause DIS. This study established a stable claudin-1 knockdown model using claudin-1 siRNA (100 nM) (Fig. 8A). H&E staining revealed the presence of DIS following claudin-1 knockdown compared with that in blank control (Fig. 8B).

Fig. 8
figure 8

Clauidin-1 knockdown induced DIS. A. The mRNA and protein expression of claudin-1 following claudin-1 knockdown (100 nM). Claudin-1 mRNA expression: Ctrl vs. siRNA = 1.0 ± 0.1 vs. 0.2 ± 0.0, P < 0.001; claudin-1 protein expression: Ctrl vs. siRNA = 1.0 ± 0.1 vs. 0.4 ± 0.1, P < 0.01. B. The intercellular space shown by H&E staining following claudin-1 knockdown (100 nM). Ctrl vs. siRNA = 0.5 ± 0.5 μm vs. 1.3 ± 0.2 μm, P < 0.05. Abbreviation: Ctrl: control; DIS: dilated intercellular space; *: P < 0.05; **: P < 0.01; ***: P < 0.001

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Discussion

Current management for GER symptoms remains unsatisfactory [32]. Further elucidation of the mechanisms of GER symptoms is expected to provide a new target for the treatment of refractory GER symptoms. In this study, by including both GERD and FED patients, we demonstrated that esophageal microbial dysbiosis was not caused by abnormal reflux burden but was instead, a contributing factor to the development of GER symptoms. We next performed histological analysis and vitro experiments, and demonstrated that LPS from G- bacteria could induce esophageal cell barrier dysfunction via the LPS-TLR2-IL-6 pathway, thereby triggering GER symptoms.

In the past, it was widely accepted that GER symptoms arose from the excessive reflux of acid gastric contents into the esophagus. However, our previous research including 334 physicians and 1409 patients showed that approximately one-third of GER symptom patients and physicians were dissatisfied with symptom relief despite regular use of anti-acid medications [33]. A nationwide survey in the United States also reported that 54.1% of patients on daily acid suppressants still experienced persistent GER symptoms [6]. To clarify whether GER symptoms are directly correlated with reflux, we previously enrolled 233 heartburn patients referred for upper endoscopy and esophageal function tests and found that 75% of patients with GER symptoms did not exhibit esophagitis, and 74.2% had normal esophageal acid exposure [34]. These results indicated that other factors, besides acid reflux, were involved in the development of GER symptoms. Further investigations into the underlying causes of GER symptoms are still needed.

The relationship between lower digestive tract microbiota and bowel symptoms has long been a hot topic of research. However, most of the research on esophageal microbiota still focus on organic diseases [19,20,21,22]. Yang et al. conducted 16 S rRNA sequencing on esophageal biopsies from 34 patients [19]. Using the unsupervised cluster analysis, esophageal microbiome was divided into type I, dominated by genus Streptococcus (G+) and type II, characterized by a greater proportion of G- anaerobes/microaerophiles. Type I was concentrated in the normal esophagus whereas type II was associated with esophagitis and Barrett’s esophagus. Another study by Liu et al. found that Proteobacteria (43%) was the most prevalent phylum in reflux esophagitis [23]. These studies only included patients with existing esophageal diseases, where the esophageal intraluminal environment has been altered, indicating that the observed microbial dysbiosis could either be a cause or a consequence of the disease. In our study, we not only included GERD but also FED patients. The results showed that GER symptom patients, GERD or FED, all exhibited a higher proportion of G- bacteria. The esophageal reflux burden of FED is comparable to that of normal control, and there is no organic abnormality in FED. Thus, our results demonstrated that above microbial dysbiosis was not a consequence of the altered esophageal microenvironment under the disease background, but rather a potential cause of GER symptoms.

G-bacteria are characterized by an outer membrane composed of lipids, proteins and LPS, which distinguishes them from G + bacteria [35]. Multiple studies have shown that various gastrointestinal diseases, including inflammatory bowel disease, IBS and chronic liver disease, were associated with an increased proportion of G- bacteria in gut microbiota [36]. G- bacteria exert their effects mainly through mechanisms involving flagella, capsules, LPS and various secreted proteins [37]. Among these virulence factors, LPS has long been proven to be a potential cause of gastrointestinal symptoms [15, 38]. A recent study by Stephens M et al. investigated the effects of LPS from different species on intestinal epithelial cells. They found that irrespective of the species origin, LPS could up-regulate TLR and promote cytokine production, thereby impairing cell barrier function and inducing intestinal symptoms [18]. Regarding GER symptom patients, several studies performed transmission electron microscopy or H&E staining and showed that regardless of excessive reflux, these patients displayed esophageal cell barrier dysfunction, manifested as DIS [8, 9, 39]. Therefore, we applied LPS as the external stimulus to explore whether G- bacteria exert their effects through LPS in patients with GER symptoms. Subsequently, we examined the expression of TLRs, inflammatory cytokines and barrier proteins, and discovered that LPS from G- bacteria increased IL-6 secretion by binding to TLR2, which then reduced claudin-1 expression, ultimately resulting in DIS.

In vitro experiments in this study only showed that LPS from G- bacteria can cause DIS, but yet to further prove that LPS can cause GER symptoms. However, through previous studies, we can find a strong causal relationship between DIS and GER symptoms. The integrity of esophageal mucosa is sustained by the stratified squamous epithelium, which acts as a barrier against noxious components such as acid or bile acids [40]. The submucosal sensory neurons in the esophagus terminate within a few layers of epithelial cells from the esophageal lumen [11]. Normally, irritants cannot penetrate the intact cell barrier to stimulate these sensory neurons, rendering the esophageal mucosa tolerant to routine stimuli. However, when the intercellular spaces are dilated, submucosal sensory neurons can be directly exposed to noxious components, causing esophageal hypersensitivity and thus triggering GER symptom onset [12]. In our previous study involving patients with GERD or FED treated with esomeprazole 40 mg daily for 2 months, we observed a parallel relationship between GER symptoms and DIS [14]. Only when the DIS was reversed, could a patient’s symptoms be relieved.

There were several limitations in the present study. Firstly, the sample size was relatively small. However, all patients samples (GERD and FED) have completed a PPI/PCAB trail and underwent endoscopic examinations and reflux monitoring, as did all healthy volunteers. This strict screening minimized heterogeneity across the study groups. Future research with larger sample size and multi-center data was needed to validate our findings and enhance the generalizability. Secondly, 16 S rRNA sequencing in this study was only accurate to the genus level rather than species level. Further studies using metagenomic or 5R sequencing are still required. Thirdly, we conducted vitro experiments using only a single cell line, primarily due to limitations in obtaining other cell lines in China. Additionally, current reflux animal models, conducted by esophago-duodenal or gastroesophageal anastomosis or esophageal acid perfusion, fail to replicate the reflux patterns of GERD patients and do not reflect the reflux burden in FED patients whose esophageal acid exposure is similar to healthy individuals. The continuous reflux condition in these models cannot mimic the microbial composition observed in GERD/FED patients either. Furthermore, we have not yet determined the specific bacterial species responsible for esophageal dysbiosis, which prevented us from conducting animal studies. Fourthly, although we have confirmed that esophageal microbial dysbiosis can lead to DIS, we have not directly proven that this microbial shift can cause GER symptoms. However, DIS, the key feature of barrier dysfunction, can expose esophageal sensory neurons to reflux contents, which may in turn trigger GER symptoms. Thus, although we did not establish direct causality between microbial dysbiosis and GER symptoms, we provided evidence for a mechanistic link between dysbiosis-induced barrier dysfunction and symptom development. Further study to explore these mechanisms in vivo was a part of our future research efforts. Last but not least, due to the cross-sectional nature of our study design, it was not feasible to establish a clear temporal sequence between microbial changes and the onset or progression of GER symptoms.

Conclusion

This study demonstrated that regardless of objective evidence of reflux, patients with GER symptoms presents microbial dysbiosis characterized by an increased proportion of G- bacteria. Enriched G- bacteria could induce esophageal cell barrier dysfunction via LPS-TLR2-IL-6-claudin-1-DIS pathway.

Data availability

It can be obtained with the consent of the corresponding author.

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Acknowledgements

As authors of this manuscript, we all approve of the material submitted, certify that it has not been previously published, and verify that it is not under consideration for publication elsewhere.

Funding

The study was supported by the National Natural Science Foundation of China (82170577) and the Postdoctoral Fellowship Program of CPSF (GZB20240889).

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Author notes

  1. Songfeng Chen, Dianxuan Jiang and Qianjun Zhuang contributed equally to this work.

Authors and Affiliations

  1. Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, Guangdong, 510080, China

    Songfeng Chen, Dianxuan Jiang, Qianjun Zhuang, Xingyu Jia, Jing Chen, Huiting Lin, Mengyu Zhang, Niandi Tan & Yinglian Xiao

  2. Gastrointestinal Surgery Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China

    Xun Hou

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Contributions

Songfeng Chen, Dianxuan Jiang, Qianjun Zhuang: acquisition of data, completion of experiments, analysis and interpretation of data, drafting of the manuscript; Yinglian Xiao: study concept and design, analysis of data, finalizing and approving the manuscript; other authors: acquisition of data, analysis and interpretation of data.

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This study was approved by the Ethical Review Board of Sun Yat-sen University (IRB no. 2019[290]). Written informed consent was obtained from each participant. The study was registered with ClinicalTrial.gov, number ChiCTR1900025448.

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12967_2024_5878_MOESM1_ESM.png

Supplementary Figure 1: The mRNA expression of tight junction proteins in HEECs upon stimulation of different concentrations of LPS or IL-6 (Caption: (A) The mRNA expression of occludin, ZO-1, claudin-1, and E-cadherin upon stimulation of different concentrations of LPS. (B) The mRNA expression of claudin-1 upon stimulation of different concentrations of IL-6). Abbreviation: Ctrl: control; LPS: lipopolysaccharide; ZO: zonula occludens; IL: interleukin; *: P < 0.05; **: P < 0.01; ***: P < 0.001

12967_2024_5878_MOESM2_ESM.png

Supplementary Figure 2: The measurement of intercellular spaces (Caption: One cell was randomly selected, as shown in the figure, and 5 measurement lines were drawn perpendicular to the cell membrane. The average length of these 5 lines was recorded as the intercellular space distance for that cell)

12967_2024_5878_MOESM3_ESM.png

Supplementary Figure 3: Microbial α diversity analysis (Caption: Abbreviation: G: gastroesophageal reflux disease; F: functional esophageal disorders; D: duodenal ulcer; H: healthy volunteers)

12967_2024_5878_MOESM4_ESM.png

Supplementary Figure 4: The protein expression of ZO-1 was significantly increased in patients with GER symptoms, compared with healthy volunteers. No difference was observed in other tight junction proteins among groups (Caption: TLR4 protein expression: GERD vs. FED vs. HV = 3.7 ± 0.8 vs. 2.8 ± 1.0 vs. 2.6 ± 1.0, P = 0.16; E-cadherin protein expression: GERD vs. FED vs. HV = 2.7 ± 1.8 vs. 3.0 ± 1.3 vs. 3.3 ± 2.7, P = 0.87; ZO-1 protein expression: GERD vs. FED vs. HV = 7.0 ± 1.2 vs. 6.1 ± 2.6 vs. 2.7 ± 1.6, P < 0.01; occludin protein expression: GERD vs. FED vs. HV = 2.8 ± 3.7 vs. 1.1 ± 1.7 vs. 1.0 ± 1.7, P = 0.47). Abbreviation: GERD: gastroesophageal reflux disease; FED: functional esophageal disorders; HV: healthy volunteers; TLR: toll-like receptor; ZO: zonula occludens; *: P < 0.1; **: P < 0.01; ns: non-significant

12967_2024_5878_MOESM5_ESM.png

Supplementary Figure 5: LPS did not lead to significant changes in the protein levels of TLR4 and other tight junction proteins (E-cadherin, ZO-1 and occludin). Moreover, LPS failed to increase the mRNA and protein expression of other cytokines (TNF-α, IL-1β, IL-4, IL-8 and IL-10) (Caption: (A) The mRNA and protein expression of TLR4. TLR4 mRNA expression: Ctrl vs. LPS = 1.0 ± 0.1 vs. 0.7 ± 0.0, P < 0.01; TLR4 protein expression: Ctrl vs. LPS = 1.6 ± 0.4 vs. 1.6 ± 0.3, P = 0.36. (B) The mRNA and protein expression of E-cadherin, ZO-1 and occludin. E-cadherin mRNA expression: Ctrl vs. LPS = 1.0 ± 0.0 vs. 0.8 ± 0.0, P < 0.001; E-cadherin protein expression: Ctrl vs. LPS = 1.4 ± 0.0 vs. 1.6 ± 0.2, P = 0.26; ZO-1 mRNA expression: Ctrl vs. LPS = 2.2 ± 0.1 vs. 0.7 ± 0.0, P < 0.001; ZO-1 protein expression: Ctrl vs. LPS = 0.2 ± 0.1 vs. 0.2 ± 0.1, P = 0.90; occludin mRNA expression: Ctrl vs. LPS = 1.5 ± 0.0 vs. 0.6 ± 0.0, P < 0.001; occludin protein expression: Ctrl vs. LPS = 0.9 ± 0.3 vs. 1.1 ± 0.1, P = 0.26. (C) The mRNA and protein expression of TNF-α, IL-1β, IL-4, IL-8 and IL-10. Certain proteins including TNF-α, IL-4 and IL-10 exhibited expression levels too low to be detected by ELISA. TNF-α mRNA expression: Ctrl vs. LPS = 1.0 ± 0.0 vs. 1.5 ± 0.1, P < 0.001; TNF-α protein expression: Ctrl vs. LPS = 0.7 ± 1.2 pg/ml vs. 0.0 ± 0.0 pg/ml, P = 0.37; IL-1β mRNA expression: Ctrl vs. LPS = 1.0 ± 0.0 vs. 0.3 ± 0.0, P < 0.001; IL-1β protein expression: Ctrl vs. LPS = 30.7 ± 1.8 pg/ml vs. 33.8 ± 1.9 pg/ml, P = 0.06; IL-4 mRNA expression: Ctrl vs. LPS = 1.0 ± 0.0 vs. 0.9 ± 0.0, P = 0.17; IL-4 protein expression: Ctrl vs. LPS = 0.1 ± 0.2 pg/ml vs. 0.0 ± 0.0 pg/ml, P = 0.29; IL-8 mRNA expression: Ctrl vs. LPS = 1.0 ± 0.1 vs. 1.7 ± 0.0, P < 0.01; IL-8 protein expression: Ctrl vs. LPS = 1102 ± 56.0 pg/ml vs. 665.8 ± 87.4 pg/ml, P < 0.01; IL-10 mRNA expression: Ctrl vs. LPS = 1.0 ± 0.1 vs. 0.3 ± 0.0, P < 0.01; IL-10 protein expression: Ctrl vs. LPS = 0.0 ± 0.0 pg/ml vs. 0.0 ± 0.0 pg/ml, P = ns). Abbreviation: LPS: lipopolysaccharide; TLR: toll-like receptor; ZO: zonula occludens; TNF: tumor necrosis factor; IL: interleukin; *: P < 0.05; **: P < 0.01; ***: P < 0.001; ns: non-significant

12967_2024_5878_MOESM6_ESM.docx

Supplementary Tables: Supplementary Table 1: The diluted antibodies for immunohistochemistry (Caption: Abbreviation: TLR: toll-like receptor; ZO: zonula occludens). Supplementary Table 2: The primer sequences utilized in RT-qPCR (Caption: Abbreviation: IL: interleukin; TNF: tumor necrosis factor; ZO: zonula occludens; TLR: toll-like receptor). Supplementary Table 3: The diluted antibodies for Western blot (Caption: Abbreviation: GAPDH: Glyceraldehyde-3-Phosphate Dehydrogenase; TLR: toll-like receptor; ZO: zonula occludens). Supplementary Table 4: ELISA kit used in this study (Caption: Abbreviation: TNF: tumor necrosis factor; IL: interleukin). Supplementary Table 5: Clinical characteristics of patients undergoing esophageal microbiota analysis (Caption: Abbreviation: GERD: gastroesophageal reflux disease; FED: functional esophageal disorder; BMI: body mass index; LA: Los Angeles Classification; IEM: ineffective esophageal motility; AET: acid exposure time). Supplementary Table 6: Clinical characteristics of patients undergoing esophageal immunohistochemistry analysis (Caption: Abbreviation: GERD: gastroesophageal reflux disease; FED: functional esophageal disorder; BMI: body mass index; LA: Los Angeles Classification; IEM: ineffective esophageal motility; AET: acid exposure time). Supplementary Table 7: Clinical characteristics of patients undergoing esophageal transcriptome analysis (Caption: Abbreviation: GERD: gastroesophageal reflux disease; FED: functional esophageal disorder; BMI: body mass index; LA: Los Angeles Classification; IEM: ineffective esophageal motility; AET: acid exposure time)

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Chen, S., Jiang, D., Zhuang, Q. et al. Esophageal microbial dysbiosis impairs mucosal barrier integrity via toll-like receptor 2 pathway in patients with gastroesophageal reflux symptoms. J Transl Med 22, 1145 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-024-05878-1

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