Gastrointestinal tract bacteriomes in patients with gastroesophageal reflux disease: a multicenter study

Gastrointestinal tract bacteriomes in patients with gastroesophageal reflux disease: a multicenter study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gastrointestinal tract bacteriomes in patients with gastroesophageal reflux disease: a multicenter study Natalie Mlcuchova, Jan Bohm, Lumir Kunovsky, Tomas Harustiak, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8385155/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims: Gastroesophageal reflux disease (GERD) and its complications–reflux esophagitis (RE), Barrett’s esophagus (BE), and esophageal adenocarcinoma (EAC)–have been associated with bacterial dysbiosis. However, comprehensive characterization of the gastrointestinal tract (GIT) bacteriome across these GERD complications is lacking. This multicenter observational study aimed to fill this gap and evaluate the possibility of identifying non-invasive oral or rectal biomarkers for advanced GERD complications. Methods: Biopsies from patient-matched pathological esophageal tissues, gastric body and antrum, duodenum, and oral and rectal swabs were collected from 158 patients with histologically confirmed RE, BE, or EAC. DNA isolates were analyzed by qPCR and 16S rRNA amplicon sequencing. Anaerobes/aerobes and gramnegative/grampositive ratios, as well as relative abundances of multiple genera, were compared among GIT sites and study groups. Results: Bacterial load, richness, and evenness in esophageal biopsies increased with the severity of GERD complications. The relative abundance of the commensal genus Streptococcus was significantly higher in esophageal samples of RE than in BE and EAC ( P = .037 and P = .017, respectively). Interestingly, BE samples showed distinct bacteriome features deviating from the RE–EAC progression trend. Strong inter-site correlations were identified throughout the GIT bacteriomes. Relative abundances of the genus Porphyromonas ( P = .034) and other gramnegative periodontal pathogens in oral swabs and a probiotic genus Faecalibacterium in rectal swabs were associated with EAC ( P = .019). Conclusions: Comprehensive GIT profiling of 923 samples revealed significant bacteriome alterations associated with the GERD complications severity. Some bacterial genera showed promise for potential use as non-invasive biomarkers for predicting the risk of severe GERD complications. microbiota adenocarcinoma esophagus metagenomics biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 What You Need to Know BACKGROUND AND CONTEXT The bacterial shift may play a role in the pathogenesis and prognosis of patients with gastroesophageal reflux disease and its complications. NEW FINDINGS The relative abundance of the genus Porphyromonas and other gramnegative periodontal pathogens in oral swabs and the probiotic genus Faecalibacterium in rectal swabs were associated with esophageal adenocarcinoma. LIMITATIONS For ethical reasons, healthy controls could not be included in the study. Patient groups were not evenly distributed in the proton pump inhibitors usage, which was, however, addressed in statistical analysis. CLINICAL RESEARCH RELEVANCE We found new potential microbial non-invasive biomarkers that could be potentially used for screening of esophageal adenocarcinoma and evaluation of prognosis in patients with gastroesophageal reflux disease. BASIC RESEARCH RELEVANCE The gastrointestinal bacteriomes in the oral cavity, pathological esophagus, gastric body, gastric antrum, duodenum, and rectum were described and compared in patients with reflux esophagitis, Barrett’s esophagus, and esophageal adenocarcinoma. Lay Summary: In patients with esophageal disorders, we described bacteriomes from multiple sites of the gastrointestinal tract and found potential non-invasive bacterial biomarkers that might be, providing confirmation, used in clinical practice in the future. Design and Analysis: In this multicentre observational study, bacteriome compositions were analyzed using qPCR and 16S rRNA amplicon sequencing in samples from patient-matched pathological and physiological matrices collected across the gastrointestinal tract of patients with reflux esophagitis, Barrett’s esophagus, and esophageal adenocarcinoma. Subsequently, patient-matched analysis was performed, and differences among groups with different pathologies were compared. The reporting pattern is consistent with the STROBE guidelines. Introduction Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal tract (GIT) disorders, with prevalence reaching up to 20% in Western countries. 1 , 2 GERD complications include an inflammation of the esophagus, known as reflux esophagitis (RE), Barrett´s esophagus (BE), and esophageal adenocarcinoma (EAC); 3 however, only a minority of patients with GERD develop BE or EAC. The long-term exposure of the normal squamous epithelium to gastroduodenal fluid results in an inflammation of the esophagus and, subsequently, in replacement of squamous epithelium with columnar epithelium (intestinal metaplasia) in the lower esophagus. 4 Intestinal metaplasia can then lead to the development of BE, which is acknowledged as a pre-cancerous lesion for EAC development; the exact mechanism and conditions of the conversion in this inflammation-related carcinogenesis have, however, not been elucidated yet. 5 The pathogenesis of GERD includes multiple external and internal exposure factors, including obesity, lack of exercise, unhealthy lifestyle (alcohol abuse, smoking, etc.), 6 and genetic predispositions. 1 , 7 Most patients with GERD, especially those with EAC, are older men. 8 Besides these factors, esophageal microbiota has also been associated with GERD pathogenesis. 9 , 10 While the quantity of microbiota in the healthy esophagus is lower than in healthy oral mucosa, the composition of these sites is very similar, 11 with the predominance of grampositive aerobic bacterial genera, especially by the genus Streptococcus . Specimens from the esophagus with GERD-induced pathological changes have, however, been associated with higher relative abundances of gramnegative anaerobes or microaerophiles, such as Veillonella , Prevotella , Haemophilus , Neisseria , Rothia , Granulicatella , Campylobacter , Porphyromonas , Fusobacterium , and Actinomyces . 12 – 14 In this context, the role of pharmaceuticals, such as proton pump inhibitors (PPIs) that are commonly used by these patients for reducing stomach acid and/or Helicobacter pylori eradication (in combination with antibiotics), must also be considered, as they can influence the GIT microbiome diversity and composition. 15 , 16 Although several bacteria were associated with GERD and its complications, scientific literature still lacks a comprehensive description of the GIT bacteriome (i.e., complete genetic information of bacteria in the entire GIT), which could shed light on the interconnectedness of different parts of the GIT in these patients. Identification of bacteriome features specific to these patient groups could help us to better understand the pathogenesis of the disease and the relationships among GERD complications. Here, we aimed to describe, compare, and correlate bacteriomes in biopsies from macroscopically pathological esophageal tissues and those from other parts of the GIT (oral and rectal swabs, gastric body and gastric antrum biopsies, and duodenal biopsies) in patients with RE, BE, and EAC. In addition, as several studies have suggested the possible use of some oral bacteriome characteristics as non-invasive predictive biomarkers for BE/EAC, 17–19 we also aimed to address this hypothesis by comparing relative abundances of bacterial genera in oral and rectal swabs taken from patients with RE/BE/EAC. Methods Study design, clinical examinations, and sample collection Prospective enrollment of patients in our multicenter observational study was conducted from May 2020 to March 2023 at the departments specialized in gastrointestinal disorders of three large university hospitals in the Czech Republic (University Hospital Brno, University Hospital Motol Prague, and University Hospital Olomouc). The study was performed in compliance with the principles of the Helsinki Declaration and approved by the Ethics Committees of all participating institutions. A signed informed consent was obtained from each patient before their inclusion in the study. In this study, we intended to recruit approximately 150 patients with GERD and one of its three major complications (RE, BE, or EAC). Matching for sex, age, and body mass index (BMI) was performed on recruitment. Further inclusion criteria were Czech or Slovak origin (to maintain the genetic homogeneity) and ≥ 30 years of age. Exclusion criteria were hepatic/renal failure, other types of tumors, ongoing pregnancy and lactation, and use of antibiotics and/or probiotics, major change of diet (such as becoming vegan), or a respiratory tract infection within eight weeks before sample collection. A detailed patient history, including pharmacotherapy with PPIs, was obtained. RE and BE were diagnosed endoscopically. RE was evaluated according to the Savary–Miller (SM) 20 and Los Angeles (LA) classifications, 21,22 and BE according to the Prague criteria. 23 – 26 All diagnoses were confirmed histologically. Patients were requested to avoid food and liquid consumption as well as oral hygiene for at least one hour prior to sampling. Oral (O) and rectal (R) swabs were collected from each patient. In addition, biopsies from four different GIT sites were endoscopically obtained: macroscopically pathological esophageal tissue (with RE, BE, or EAC) and biopsies from the gastric body (GB), gastric antrum (GA), and duodenum (D). Histopathological examination and metagenomic analysis One part of the biopsy from each site was processed to formalin-fixed paraffin-embedded (FFPE) blocks and examined by pathologists for histological confirmation of RE, BE, or EAC (patients with negative or inconclusive results were excluded from the study); standard immunohistochemical analysis for H. pylori was performed. The other part of the biopsy from each site, as well as the oral and rectal swabs, were placed in RLT buffer (from the AllPrep DNA/RNA Mini Kit; QIAGEN, Germany) and immediately stored at -80°C until the extraction of nucleic acids. DNA was isolated from all samples (including 46 negative controls, NCs, and internal standard) using the AllPrep DNA/RNA Mini Kit (QIAGEN, Germany). In all matrices, the total content of bacterial DNA was established using qPCR targeting the 16S rRNA region. Subsequently, all samples and NCs were spiked with an internal standard and analyzed by 16S RNA amplicon sequencing on the Illumina platform. Details on laboratory and bioinformatic metagenomic analyses were described in our previous work. 27 Statistical analyses Statistical analyses were carried out in R (v4.1.2). A multi-step filtering of samples was performed prior to the analysis. A threshold of 5,000 sequencing reads was established to prevent analysis of samples with an insufficient sequencing depth. Next, all reads originating from the internal standard were removed from the dataset, and every sample was tested for dissimilarity in bacteriome composition against NCs. Additional exclusion criteria based on the quality of the obtained data are shown in the study workflow in Fig. 1. The genera present in ≥ 20 samples from any single GIT location (or NCs) were analyzed using the ALDEx2 framework. Clustering analysis of bacterial genera was also performed to identify the similarity between individual GIT sites. Sequencing depth and alpha diversity, including the number of distinct amplicon sequence variants (ASVs) and Shannon diversity index, were then calculated on the filtered samples using the vegan package. For all downstream analyses, counts were collapsed to the genus level; any ASVs that could not be assigned a genus were grouped under the category “other”. An overview table of clinical variables was generated using an on-site tool based on the gt package. The ratios of relative abundances of anaerobic to aerobic bacterial genera (anaerobes/aerobes ratio) and gramnegative to grampositive bacterial genera (G - /G + ratio) were computed based on all determined genera for which both statuses (i.e., aerobicity and Gram staining) were known, using, on average, 82% of the total bacterial DNA present in the sample. Dissimilarity in bacteriome composition was calculated as the sum of differences in relative abundances weighted by the difference in quantiles across all genera. This dissimilarity index was used in non-metric multidimensional scaling (nMDS) and to test differences in composition between non-NC samples and negative controls. Comparisons of continuous variables between study groups, such as the total content of bacterial DNA and alpha diversity metrics, were carried out using two-sample Wilcoxon rank-sum tests, performed separately for each sample matrix. P -values from these tests were adjusted for multiple comparisons within the sample matrix by the Benjamini–Hochberg false discovery rate procedure. Results were visualized as boxplots in R using ggplot2, with styled significance annotations added via ggtext. Adjusted p -values are indicated by asterisks: * for P < .05, ** for P < .01, and *** for P < .001. We used ALDEx2 to assess bacteriome composition differences. Read counts were transformed using the central log-ratio transformation using ALDEx2::aldex.clr with 256 Monte Carlo replicates and the scale parameter gamma = 0.25. To detect genera present in the analyzed matrices, a general linear model was built with NCs serving as a baseline. Only genera with significantly higher CLR-transformed relative abundances in the given matrix compared to NCs were considered as present and used in further analysis. Next, a series of Wilcoxon rank-sum tests was performed for every sample matrix, comparing study groups using the ALDEx2::ttest. The beta diversity was visualized using t-distributed stochastic neighbor embedding (t-SNE) with the Rtsne library. Pairwise Pearson correlations were calculated on CLR-transformed abundance data, and p ‐values were adjusted for multiple testing using the Benjamini–Hochberg procedure. Correlations with an absolute coefficient greater than 0.4 and an adjusted P ‐value below .05 were retained and visualized as a network in R using the igraph package. Results Clinical variables The final dataset included 52 RE, 55 BE, and 51 EAC patients, whose descriptive characteristics are shown in Table 1. Men were overrepresented in all three study groups (124 vs. 34), especially in EAC patients. The mean age for individual groups ranged from 59 (RE) to 64 (EAC) years. Patients’ BMIs indicated a prevalence of overweight people in all study groups. Distributions of alcohol consumption, smoking habits, and H. pylori positivity in GIT biopsies were similar among groups ( P > .05, Fisher's exact tests). The use of PPIs significantly differed among groups; while 94% of patients with RE and 100% of those with BE were on PPIs, only 49% of patients with EAC used PPIs ( P < .001, Fisher's exact test). Pre-analysis of bacteriome data All 923 samples reached at least 5,000 reads and significantly differed from the NCs in the dissimilarity test of bacterial composition. The relative abundances of bacterial genera, as well as sequencing depth and total content of bacterial DNA in NCs, are presented as a heatmap in Supplementary Figure S1. NCs were significantly different ( P < .05) from other matrices in all studied bacteriome properties (alpha diversity, G - /G + ratio, and anaerobes/aerobes ratio; data not shown). Results from the ALDEx2 framework are shown in Supplementary Figure S2. Rectal swabs contained the highest number of bacterial genera (N = 41), of which 34 were unique to this location. Upper GIT samples shared 21 bacterial genera. Clustering analysis identified two distinct upper GIT bacteriome clusters: (I) O, D, and E; and (II) GB and GA. No significant differences in alpha diversity or the anaerobes/aerobes ratio were observed between samples from patients with EAC who used PPIs and those who did not. The G - /G + ratio was the only parameter that showed any statistical difference, and even that was observed only at a single site (the gastric body; P = .011 before adjustment), which allowed us to treat all EAC patients as one group in further analyses. Bacteriome profiles in GIT samples The total content of bacterial DNA in oral samples of the BE group was significantly higher than in the RE ( P ADJ < .05) and EAC ( P ADJ < .001, Figure 2A) groups. In esophageal samples, the total content of bacterial DNA increased with the severity of GERD complications ( P ADJ < .001 for both RE-EAC and BE-EAC contrasts). It was also higher in duodenal samples from patients with EAC than with BE. The analysis of sequencing depth (Figure 2B) revealed a similar pattern in esophageal samples – the sequencing depth was increased in EAC samples compared to groups with other GERD complications ( P ADJ < .01 for RE-EAC and < .05 for BE-EAC). The same result was observed in the gastric antrum biopsy ( P ADJ < .05 for both RE-EAC and BE-EAC). The esophagus was the only matrix in which a significant difference in bacteriome richness (measured as the number of distinct ASVs) was detected between GERD complications, with EAC samples containing more distinct ASVs than RE samples ( P ADJ < .01, Figure 2C). An even stronger result was obtained in Shannon diversity (bacteriome evenness), where esophageal samples from patients with EAC showed higher diversity than both RE ( P ADJ < .001) and BE ( P ADJ < 0.05) samples, see Figure 2D. The anaerobes/aerobes ratio (Figure 2E) was significantly higher in oral swabs from patients with BE compared to both RE ( P ADJ < .05) and EAC ( P ADJ < 0.01) patient groups. Finally, the G - /G + ratio was significantly higher in esophageal samples from patients with BE compared to those with RE ( P ADJ < 0.05). In gastric body biopsies, this ratio was significantly higher in samples from patients with BE than with EAC ( p ADJ < 0.05), see Figure 2F. The overall bacterial composition is visualized using t-SNE in Figure 3, showing a distinct cluster of rectal swabs and separation of oral samples from the rest of the GIT. The esophageal, gastric, and duodenal biopsy samples were overlapping and, in two dimensions, unseparable. Supplementary Table S1 shows the ten bacterial genera (based on their median relative abundance) that were most abundant in each of the six GIT sites. Genera Streptococcus, Veillonella, Haemophilus, and Prevotella are among the five most abundant genera in all GIT samples except rectal swabs (although Prevotella is among the top five genera even in the latter matrix). Figure 4 shows an MDS plot of mean dissimilarity in bacteriome composition between individual GIT sites based on pairwise testing of esophageal samples with different severities of GERD complications and individual-matched samples from other locations. Note that in this plot, the mutual distances of esophageal samples for individual diagnoses do not indicate their true (dis)similarity as their mutual pairwise testing was, obviously, impossible. In esophageal tissues, bacteriome compositions of the EAC and RE were relatively similar to oral samples, while high dissimilarity was observed between the BE and oral samples. Bacteriome composition of duodenal samples from all three study groups was relatively similar to that of esophageal samples. Rectal swabs were omitted from this analysis due to the very low intersection of bacterial genera with other GIT sites (leading to a high dissimilarity to all other tissues, preventing a meaningful depiction). Identification of bacterial genera associated with GERD complications The ALDEx2 framework was applied independently to each sample matrix. Across all matrices, a total of 27 bacterial genera/sample matrix combinations yielded at least one statistically significant contrast. All selected genera and their CLR-transformed abundance profiles across matrices and study groups are presented in Supplementary Figure 2. The highest number of significant contrasts between study groups for individual genera (12 genera, 18 contrasts in total) was found in oral samples. Porphyromonas was the only genus showing significant contrasts for both RE-EAC and BE-EAC in oral swabs; the same can be said of Veillonella in gastric body as well as gastric antrum samples. Significant contrasts were found also for both RE-EAC and RE-BE comparisons for Streptococcus in esophageal samples. In rectal swabs, differences between patients with EAC and RE were found only for Faecalibacterium . For full results including possible species classification, see Table 2 and Supplementary Figure S3, depicting distributions and median values of CLR-transformed relative abundances of genera with at least one significant contrast ( p 0.6) are depicted. All the strong correlations between (CLR-transformed) relative abundances were positive. Most matrices (with the exception of the gastric body and gastric antrum) formed distinct clusters. Additionally, the relative abundances of Neisseria in all non-rectal matrices mutually correlated. Prevotella showed the highest number of significant correlations in esophageal, gastric, and duodenal samples, followed by Veillonella. While correlation networks of esophageal, gastric, and duodenal samples are highly connected, the correlation pattern of rectal samples is scattered into multiple subgraphs. The same can also be said of the genera in oral samples, which generally have the weakest correlation structure, with only 4 significant inter-matrix correlations (Figure 5). Discussion The association of several bacteriome characteristics in multiple parts of the GIT with different severity of GERD complications may be caused by an interplay of the pathological mucosal changes and the bidirectional distribution of bacteria through the entire GIT (both due to swallowing and gastroesophageal reflux). 14 In all, 923 samples collected from the entire GIT of 158 patients were analyzed in this study, providing a comprehensive picture of the bacteriomes in each patient, while also enabling us to compare findings from each location. We found that some bacteriome characteristics in biopsies from macroscopically pathological esophageal tissues differed among groups of patients with RE, BE, and EAC. Moreover, strong correlations were detected for some bacterial genera, such as Prevotella , throughout the entire GIT. More interestingly, high or low relative abundances of some bacterial genera, such as Porphyromonas on the oral mucosa and Faecalibacterium in fecal samples, were associated with EAC. GIT bacteriome characteristics and GERD complications Bacteriomes of rectal swabs greatly differed from those found in the other parts of the GIT. The high bacteriome load and diversity found in rectal and oral swabs are consistent with the literature, 28 as are the five most abundant genera present in individual GIT sites. 29 Importantly, the similarity of the oral environment and of the pathological esophageal tissue in both these parameters was high in patients with EAC but not in patients with RE or BE. The total content of bacterial DNA, as well as bacteriome richness and evenness in esophageal biopsies, significantly increased with the severity of the GERD complication, which indicates that the increasing damage to the esophageal mucosa is associated with a significantly greater enrichment of the tissue with bacterial DNA. Although higher richness and evenness in oral and fecal samples are generally associated with health, 30–32 the opposite finding in our study may be caused by the fact that the esophageal tissue is physiologically poor in microbiota and the mucosal damage supports bacterial colonization. 33 If dysbiosis is present in the oral environment, the aforementioned changes in the esophageal mucosa may facilitate its colonization (or even invasion) by potentially pathogenic bacteria from the oral cavity as they pass through the digestive tract. 34 This can lead to esophageal dysbiosis, manifesting as a reduction of the relative abundance of G + commensals (such as the genus Streptococcus ). 30 , 35 In our study, the G − /G + ratio was higher in the esophageal biopsies from patients with BE than in those from RE. This was further corroborated by the fact that the relative abundance of grampositive commensal bacteria of the genus Streptococcus was lower in the esophageal biopsies from patients with BE and EAC compared to those with RE, as observed previously by Lopetuso et al. 36 Besides, Gall et al. reported that the Streptococcus : Prevotella ratio in esophageal samples may constitute a risk factor for BE development. 37 In our study, we have not observed significant differences in the relative abundances of the genus Prevotella among study groups in esophageal samples, but found such differences in the oral cavity. Interestingly, bacteriomes of esophageal biopsies from RE and EAC patients were similar to their oral bacteriomes (although with major differences in several genera, see Fig. 4), which cannot be said of esophageal biopsies from patients with BE. The same trends can be also observed in the total content of bacterial DNA and the anaerobes/aerobes ratio (Fig. 2) as well as in relative abundances of individual bacterial genera (Figure S3), where numerous bacteriome characteristics of samples from patients with BE do not lie between RE and EAC groups as expected, i.e, they deviate from the trend of the increasing severity of pathology. Association of individual bacterial genera with pathology Previously, oral dysbiosis in subgingival and buccal mucosa sites and specific periodontal pathogens (gramnegative anaerobes such as Porphyromonas ) in oral samples were associated with BE and esophageal carcinoma. 17 , 18 , 38 , 39 The genus Porphyromonas has also been associated with oral squamous cell carcinoma, 40,41 esophageal squamous cell carcinoma, 17 and pancreatic cancer. 42 Our results also showed that the relative abundance of the genus Porphyromonas was significantly higher in the oral swabs from patients with EAC than in those from patients with BE or RE; however, the differences are not of a magnitude that could suggest its successful diagnostic use. On the other hand, certain differences were also observed for other periodontal pathogens, such as the genera Prevotella , Treponema , or Fretibacterium , suggesting that the diagnostic use of some indices based on their multiplex detection and quantification might be a promising direction for future research. In our study, the relative abundance of the genus Faecalibacterium in rectal swabs from patients with RE was significantly higher than in those with EAC. Considering that Faecalibacterium is a probiotic genus, 43 its lack is likely to be rather a general indicator of GIT dysbiosis in patients with GERD. Notably, this difference was rather caused by the higher percentage of individuals with a very low representation of this genus among patients with EAC than a systematically lower relative abundance. Changes in the abundance of Faecalibacterium prausnitzii have been previously studied in association with inflammation, 44 diabetes mellitus, 45 and cancer, 46,47 and this bacterium is considered for its therapeutic potential in patients with malignancies. 48 However, the association between the reduction of Faecalibacterium in fecal samples and diseases must be interpreted with care, as this bacterium was previously shown to be negatively affected by many other factors, such as the use of PPIs 49 and chronic alcohol consumption. 50 Knowing that the EAC group was the only one with a major representation of patients not taking PPIs and that an additional comparison of EAC patients with and without PPI revealed no significant difference between these subgroups from the perspective of the relative abundance of the genus Faecalibacterium (data not shown), it appears that the diagnosis plays a greater role than PPI usage. Limitations and strengths The studied patient groups were not evenly distributed from the perspective of the PPIs usage – the proportion of patients with EAC not using PPIs was higher than in the remaining groups. We have, however, addressed that issue by testing differences in basic bacteriome characteristics between EAC PPI users and non-users. Besides, other external and internal factors (e.g., other medications or diagnoses) might have also influenced the GIT microbiomes; we have, however, not analyzed this due to the high granularity of the individual study groups. The lack of a healthy control group might also be perceived as a limitation; however, taking biopsies from the entire GIT of healthy controls would be unethical, and for this reason, we had to consider the least severe complication of GERD (RE) as a reference. Lastly, untargeted metagenomic sequencing could not be performed due to the use of human biopsies with low microbial load and high human DNA content, which limited the analysis only to targeted metagenomic approaches. On the plus side, the use of negative controls as well as the internal standard throughout the entire analysis is a strength of this study, preventing misinterpretation of the results due to any bias caused by contamination from reagents or other causes during sample processing. The library preparations were done in the same laboratory and processed by the same researcher to minimize any interindividual or interlaboratory variability. The relatively large numbers of patients in study groups constitute another strength of this study, especially considering the complexity of sampling and the unique fact that patient-matched samples from six GIT sites were collected and compared. Conclusion Our study identified similarities and differences among multiple GIT sites and groups of patients with RE, BE, and EAC. A gradual trend in several parameters (increase in the total content of bacterial DNA and alpha diversity, and decrease in the relative abundance of the commensal Streptococcus ) was observed in esophageal tissues. It should be, however, noted that several bacteriome parameters in patients with BE deviated from this trend. We have also identified some bacterial genera that might (pending further studies) be potentially useful as non-invasive biomarkers or predictors of progression of GERD complications. In particular, periodontal pathogens (such as Porphyromonas ) in the oral swabs show promise in this respect, warranting the need for further research in this direction. Very low relative abundance of the genus Faecalibacterium in rectal swabs of patients with EAC may, pending further studies, also potentially serve as an additional indicator of increased risk for EAC development. Abbreviations ASVs amplicon sequence variants BE Barrett’s esophagus D duodenum EAC esophageal adenocarcinoma FFPE formalin-fixed paraffin-embedded GA gastric antrum GB gastric body GERD gastroesophageal reflux disease GIT gastrointestinal tract LA Los Angeles classification NCs negative controls nMDS non-metric multidimensional scaling O oral swab PPIs proton pump inhibitors R rectal swab RE reflux esophagitis SM Savary-Miller classification Declarations Ethics declarations The study was approved by the Ethics Committees of the Faculty of Medicine, Masaryk University (No. 09/2020, March 11 th , 2020), University Hospital Brno (No. 05-101019/EK, May 15 th , 2019), University Hospital Motol, Prague (without number, June 19 th , 2019), and University Hospital Olomouc (No. 104/19, June 25 th , 2019). Written informed consent was obtained from all participants before inclusion in the study, and the study is in line with the Helsinki declaration. Grant support: This study was supported by the Ministry of Health of the Czech Republic, grant nr. NU20-03-00126 and by Ministry of Health of the Czech Republic – conceptual development of research organization (FNBr, 65269705). This work has been funded by grant SALVAGE (CZ.02.01.01/00/22_008/0004644 from the Programme JAC under the MEYS CZ – Co-funded by the European Union. Authors thank the RECETOX Research Infrastructure (No LM2023069) financed by MEYS for supportive background. This work was supported from the European Union’s Horizon 2020 research and innovation program under grant agreement No 857560 (CETOCOEN Excellence). This publication reflects only the author's view, and the European Commission is not responsible for any use that may be made of the information it contains. Sequencing was carried out in the laboratories of the Institute of Applied Biotechnologies a.s. Computational resources were provided by the e-INFRA CZ project (ID:90254), supported by the Ministry of Education, Youth and Sports of the Czech Republic. This work was carried out with the support of RECETOX Research Infrastructure (ID LM2023069, Ministry of Education, Youth and Sports of the Czech Republic, 2023-2026). We would like to thank Dr. Jaroslav Janosek for his valuable comments and our colleagues from RECETOX (Dr. Eva Budinska and Vojtech Barton) for data processing and consultations. Conflict of interest: All authors declare no conflict of interest. Author contributions to the manuscript: Natalie Mlcuchova – Data curation, Investigation, Methodology, Visualization, Writing – original draft Jan Bohm – Data curation, Formal analysis, Methodology, Visualization, Writing – original draft Lumir Kunovsky – Data curation, Investigation, Writing – review & editing Tomas Harustiak – Data curation, Investigation, Writing – review & editing Annemarie Boleij – Writing – review & editing Petra Borilova Linhartova – Conceptualization, Funding acquisition, Resources, Methodology, Project administration, Supervision, Writing – original draft The Czech GERD Collaborative Group: Zdenek Kala – Funding acquisition, Supervision, Writing – review & editing Tomas Grolich – Investigation, Project administration, Writing – review & editing Vladimir Prochazka – Investigation, Writing – review & editing Ondrej Urban – Funding acquisition, Supervision, Writing – review & editing Vit Navratil – Investigation, Writing – review & editing Robert Lischke – Funding acquisition, Supervision, Writing – review & editing Jiri Dolina – Investigation, Writing – review & editing Radek Kroupa – Investigation, Writing – review & editing Vaculova Jitka – Investigation, Writing – review & editing Zdenek Pavlovsky – Investigation, Writing – review & editing Jakub Kovarik – Investigation, Writing – review & editing Tereza Deissova – Investigation (pilot analysis), Writing – review & editing Jaroslav Janosek – Writing – review & editing Eva Budinska – Writing – review & editing Jan Lochman – Investigation (pilot analysis), Writing – review & editing Lydie Izakovicova Holla – Writing – review & editing Ondrej Slaby – Writing – review & editing Data Transparency Statement: The data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB104439 (https://www.ebi.ac.uk/ena/browser/view/PRJEB104439). 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Gut 65:740–748 Choi HS, Kim MS, Yu MH et al (2025) Long-term Effects of Potassium-competitive Acid Blockers and Proton Pump Inhibitors on Gastrin, Gastric Emptying Rate, and Small Intestinal Microbiota in Rats. J Neurogastroenterol Motil 31:384–395 Peters BA, Wu J, Pei Z et al (2017) Oral Microbiome Composition Reflects Prospective Risk for Esophageal Cancers. Cancer Res 77:6777–6787 Li H, Luo Z, Zhang H et al (2021) Characteristics of Oral Microbiota in Patients with Esophageal Cancer in China Aziz AUR. ed BioMed Res Int 2021:1–9 Snider EJ, Freedberg DE, Abrams JA (2016) Potential Role of the Microbiome in Barrett’s Esophagus and Esophageal Adenocarcinoma. Dig Dis Sci 61:2217–2225 Savary M, Miller G (1978) The esophagus: Handbook and Atlas of Endoscopy. Gassmann, Solothurn, Switzerland Lundell LR, Dent J, Bennett JR et al (1999) Endoscopic assessment of oesophagitis: clinical and functional correlates and further validation of the Los Angeles classification. Gut 45:172–180 Armstrong D, Bennett J, Blum A et al (1996) The endoscopic assessment of esophagitis: A progress report on observer agreement. Gastroenterology 111:85–92 Sharma P, Dent J, Armstrong D et al (2006) The Development and Validation of an Endoscopic Grading System for Barrett’s Esophagus: The Prague C & M Criteria. Gastroenterology 131:1392–1399 Lee Y, Cook M, Bhatia S et al (2010) Interobserver reliability in the endoscopic diagnosis and grading of Barrett’s esophagus: an Asian multinational study. Endoscopy 42:699–704 Alvarez Herrero L, Curvers W, Van Vilsteren F et al (2013) Validation of the Prague C&M classification of Barrett’s esophagus in clinical practice. Endoscopy 45:876–882 Anand O, Wani S, Sharma P (2008) When and how to grade Barrett’s columnar metaplasia: The Prague system. Best Pract Res Clin Gastroenterol 22:661–669 Szaraz D, Bohm J, Cerulova S et al (2025) Bacterial genera in the fluids from apical periodontitis-related radicular cysts: An observational study. Int Endodontic J 58:902–915 Manson JM, Rauch M, Gilmore MS (2008) The Commensal Microbiology of the Gastrointestinal Tract. In: Huffnagle GB, Noverr MC, eds. GI Microbiota and Regulation of the Immune System .Vol 635. Advances in Experimental Medicine and Biology. New York, NY: Springer New York; :15–28. Available at: http://link.springer.com/ 10.1007/978-0-387-09550-9_2 [Accessed January 11, 2024] Ruan W, Engevik MA, Spinler JK et al (2020) Healthy Human Gastrointestinal Microbiome: Composition and Function After a Decade of Exploration. Dig Dis Sci 65:695–705 Li Y, Wei B, Xue X et al Microbiome changes in esophageal cancer: implications for pathogenesis and prognosis. 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Int J Oral Sci 3:209–215 Vyhnalova T, Danek Z, Gachova D et al (2021) The Role of the Oral Microbiota in the Etiopathogenesis of Oral Squamous Cell Carcinoma. Microorganisms 9:1549 Torres PJ, Fletcher EM, Gibbons SM et al (2015) Characterization of the salivary microbiome in patients with pancreatic cancer. PeerJ 3:e1373 Martín R, Rios-Covian D, Huillet E et al (2023) Faecalibacterium : a bacterial genus with promising human health applications. FEMS Microbiol Rev 47:fuad039 Miquel S, Leclerc M, Martin R et al (ed. mBio 2015) Identification of Metabolic Signatures Linked to Anti-Inflammatory Effects of Faecalibacterium prausnitzii Blaser MJ. 6:e00300–e00315 Xuan W, Ou Y, Chen W et al (2023) Faecalibacterium prausnitzii Improves Lipid Metabolism Disorder and Insulin Resistance in Type 2 Diabetic Mice. Br J Biomed Sci 80:10794 Balamurugan R, Rajendiran E, George S et al (2008) Real-time polymerase chain reaction quantification of specific butyrate‐producing bacteria, Desulfovibrio and Enterococcus faecalis in the feces of patients with colorectal cancer. J Gastro Hepatol 23:1298–1303 De Muinck EJ, Trosvik P, Nguyen N et al (2023) Reduced abundance of Faecalibacterium prausnitzii in the gut microbiota of children diagnosed with cancer, a pilot study. Front Microbiomes 2:1151889 Shi Z, Li M, Zhang C et al (2025) Butyrate-producing Faecalibacterium prausnitzii suppresses natural killer/T-cell lymphoma by dampening the JAK-STAT pathway. Gut 74:557–570 Takagi T, Naito Y, Inoue R et al (2018) The influence of long-term use of proton pump inhibitors on the gut microbiota: an age-sex-matched case-control study. J Clin Biochem Nutr 62:100–105 Leclercq S, Matamoros S, Cani PD et al (2014) Intestinal permeability, gut-bacterial dysbiosis, and behavioral markers of alcohol-dependence severity. Proc Natl Acad Sci USA. ;111. Available at: https://pnas.org/doi/full/10.1073/pnas.1415174111 [Accessed November 6, 2025] Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTableS1.xlsx Supplementary material Supplementary Table S1. Median relative abundancies of the ten most abundant genera observed in gastrointestinal tract sites SupplementaryFigureS1.pdf Supplementary Figure S1. Heatmap showing relative abundances of 20 bacterial genera in negative controls (N = 46). Clustering of samples, alpha diversity, and the number of MOCK (internal standard) and non-MOCK (possible contaminants) reads is shown. N, number. SupplementaryFigureS2.pdf Supplementary Figure S2. Heatmap indicating the presence/absence of individual bacterial genera in individual GIT sites of patients with GERD and in negative controls. Clustering analysis indicates the similarity of individual matrices. GERD, gastroesophageal reflux disease; GIT, gastrointestinal tract; N, number. SupplementaryFigureS3.pdf Supplementary Figure S3.CLR-transformed relative abundances of bacterial genera with at least one significant contrast between different study groups of patients with GERD (based on ALDEx2 framework). Color-coded according to the respective matrices in which the significant difference was observed. Vertical lines link the study groups with significant contrasts; the significance is indicated by the number of asterisks (* P < .05; ** P < .01; *** P < .001). GERD, gastroesophageal reflux disease; RE, reflux esophagitis; BE, Barrett’s esophagus; EAC, esophageal adenocarcinoma. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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12:30:36","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173667,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/ba94674beb1e5f33a19570c4.html"},{"id":98765652,"identity":"2ebd8a63-369d-4ee4-b0da-24039de83d82","added_by":"auto","created_at":"2025-12-22 10:12:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":574702,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy workflow and selection of the final dataset of patients with GERD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eNot all the required samples were taken during the clinical examination.\u003c/p\u003e\n\u003cp\u003eGERD, gastroesophageal reflux disease; RE, reflux esophagitis; BE, Barrett’s esophagus; EAC, esophageal adenocarcinoma; N, number.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/f80262247112f826bae7a99b.png"},{"id":98765658,"identity":"653899c1-8340-4b15-94f1-079648559054","added_by":"auto","created_at":"2025-12-22 10:12:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":440190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplots of bacteriome characteristics in GIT sites compared between study groups with GERD.\u003c/strong\u003eIn each boxplot, the horizontal line shows the median, the box spans the interquartile range (IQR), and the whiskers extend to 1.5 × IQR (or to the minimum/maximum data point). Outliers are shown as individual points. For each matrix, three pairwise Wilcoxon rank-sum tests were performed, and significant differences after Benjamini–Hochberg adjustment are indicated by lines connecting statistically different groups and asterisks (* \u003cem\u003eP\u003c/em\u003e \u0026lt; .05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; .01; *** \u003cem\u003eP\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eGERD, gastroesophageal reflux disease; GIT, gastrointestinal tract; RE, reflux esophagitis; BE, Barrett’s esophagus; EAC, esophageal adenocarcinoma; N, number.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/acc6123236aca933e7e9826c.png"},{"id":98778348,"identity":"d56d7478-a0ba-40f3-96d5-5ca978c8405c","added_by":"auto","created_at":"2025-12-22 12:29:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":527869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDimension reduction plot of bacteriome compositions of samples from different GIT sites of patients with GERD. \u003c/strong\u003eThe plot was created\u003cstrong\u003e \u003c/strong\u003eusing t-distributed stochastic neighbor embedding into two dimensions, indicating the similarity of bacteriome compositions of samples from individual gastrointestinal tract sites (color-coded).\u003c/p\u003e\n\u003cp\u003eGERD, gastroesophageal reflux disease; GIT, gastrointestinal tract; RE, reflux esophagitis; BE, Barrett’s esophagus; EAC, esophageal adenocarcinoma.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/e6d7b67f3bc5b4de75961335.png"},{"id":98765659,"identity":"55edd6c5-35b0-4be1-85d4-758b08693bab","added_by":"auto","created_at":"2025-12-22 10:12:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":259356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean dissimilarity of GIT sites in patients with GERD based on non-metric multidimensional scaling. \u003c/strong\u003eThe distances between sites reflect the mean paired dissimilarities between patient-matched samples from individual GIT sites. The bacterial genera with high contribution to the dissimilarity between individual sites are listed next to the respective connecting lines. Note that esophageal tissues with different levels of pathology (RE, BE, and EAC) could not be tested pairwise, and their mutual distances, therefore, do not indicate their true (dis)similarity.\u003c/p\u003e\n\u003cp\u003eGERD, gastroesophageal reflux disease; GIT, gastrointestinal tract; RE, reflux esophagitis biopsy; BE, Barrett’s esophagus biopsy; EAC, esophageal adenocarcinoma biopsy; O, oral swab; GB, gastric body biopsy; GA, gastric antrum biopsy; D, duodenal biopsy; R, rectal swab.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/e7f5c12a2efd1301f611e7c5.png"},{"id":98765666,"identity":"eb9bffe5-5662-4409-8edf-ec6016285a55","added_by":"auto","created_at":"2025-12-22 10:12:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":897018,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation network of bacterial genera in oral swabs and esophageal biopsies from patients with GERD. Only correlations with Benjamini–Hochberg–adjusted \u003cem\u003eP\u003c/em\u003e\u0026lt; .05 and |r| \u0026gt; .6 are shown (Pearson correlation coefficient on CLR-transformed data). The color indicates the matrix, all presented correlations are positive.\u003c/p\u003e\n\u003cp\u003eGERD, gastroesophageal reflux disease; RE, reflux esophagitis; BE, Barrett’s esophagus; EAC, esophageal adenocarcinoma.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/ac82cc9544bc975ad2c6b771.png"},{"id":98783552,"identity":"c7db701f-ff81-499b-8346-30fa39ab188c","added_by":"auto","created_at":"2025-12-22 12:42:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3162118,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/0aab4a45-2da2-47d6-907d-85bd97a857bc.pdf"},{"id":98778309,"identity":"11b721f9-d29a-4b27-9fa9-d2f9f67be26d","added_by":"auto","created_at":"2025-12-22 12:29:09","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S1. Median relative abundancies of the ten most abundant genera observed in gastrointestinal tract sites\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/e7a46fd197d8455428a5138a.xlsx"},{"id":98779994,"identity":"119625f9-435b-4b59-8a15-ab66f229a892","added_by":"auto","created_at":"2025-12-22 12:30:58","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":59052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. Heatmap showing relative abundances of 20 bacterial genera in negative controls (N = 46). \u003c/strong\u003eClustering of samples, alpha diversity, and the number of MOCK (internal standard) and non-MOCK (possible contaminants) reads is shown.\u003c/p\u003e\n\u003cp\u003eN, number.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/1e586d4aa272de9442b1e170.pdf"},{"id":98780436,"identity":"e584b36f-e36e-44e0-8855-977f151a6b71","added_by":"auto","created_at":"2025-12-22 12:31:20","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":51630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2.\u003c/strong\u003e Heatmap indicating the presence/absence of individual bacterial genera in individual GIT sites of patients with GERD and in negative controls. Clustering analysis indicates the similarity of individual matrices.\u003c/p\u003e\n\u003cp\u003eGERD, gastroesophageal reflux disease; GIT, gastrointestinal tract; N, number.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/2269eafb1c472f68211846c0.pdf"},{"id":98779487,"identity":"b1c7f7c6-30ec-4e97-a585-02bcb72fb785","added_by":"auto","created_at":"2025-12-22 12:30:24","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":769876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S3.CLR-transformed relative abundances of bacterial genera with at least one significant contrast between different study groups of patients with GERD\u003c/strong\u003e (based on ALDEx2 framework). Color-coded according to the respective matrices in which the significant difference was observed. Vertical lines link the study groups with significant contrasts; the significance is indicated by the number of asterisks (* \u003cem\u003eP\u003c/em\u003e \u0026lt; .05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; .01; *** \u003cem\u003eP\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eGERD, gastroesophageal reflux disease; RE, reflux esophagitis; BE, Barrett’s esophagus; EAC, esophageal adenocarcinoma.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8385155/v1/b34181c1fc388cc3508e4208.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGastrointestinal tract bacteriomes in patients with gastroesophageal reflux disease: a multicenter study\u003c/p\u003e","fulltext":[{"header":"What You Need to Know","content":"\u003cp\u003eBACKGROUND AND CONTEXT\u003c/p\u003e\n\u003cp\u003eThe bacterial shift may play a role in the pathogenesis and prognosis of patients with gastroesophageal reflux disease and its complications.\u003c/p\u003e\n\u003cp\u003eNEW FINDINGS\u003c/p\u003e\n\u003cp\u003eThe relative abundance of the genus \u003cem\u003ePorphyromonas\u003c/em\u003e and other gramnegative periodontal pathogens in oral swabs and the probiotic genus\u0026nbsp;\u003cem\u003eFaecalibacterium\u003c/em\u003e in rectal swabs were associated with esophageal adenocarcinoma.\u003c/p\u003e\n\u003cp\u003eLIMITATIONS\u003c/p\u003e\n\u003cp\u003eFor ethical reasons, healthy controls could not be included in the study. Patient groups were not evenly distributed in the proton pump inhibitors usage, which was, however, addressed in statistical analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCLINICAL RESEARCH RELEVANCE\u003c/p\u003e\n\u003cp\u003eWe found new potential microbial non-invasive biomarkers that could be potentially used for screening of esophageal adenocarcinoma and evaluation of prognosis in patients with gastroesophageal reflux disease.\u003c/p\u003e\n\u003cp\u003eBASIC RESEARCH RELEVANCE\u003c/p\u003e\n\u003cp\u003eThe gastrointestinal bacteriomes in the oral cavity, pathological esophagus, gastric body, gastric antrum, duodenum, and rectum were described and compared in patients with reflux esophagitis, Barrett’s esophagus, and esophageal adenocarcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLay Summary:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn patients with esophageal disorders, we described bacteriomes from multiple sites of the gastrointestinal tract and found potential non-invasive bacterial biomarkers that might be, providing confirmation, used in clinical practice in the future.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign and Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this multicentre observational study, bacteriome compositions were analyzed using qPCR and 16S rRNA amplicon sequencing in samples from patient-matched pathological and physiological matrices collected across the gastrointestinal tract of patients with reflux esophagitis, Barrett’s esophagus, and esophageal adenocarcinoma. Subsequently, patient-matched analysis was performed, and differences among groups with different pathologies were compared. The reporting pattern is consistent with the STROBE guidelines.\u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eGastroesophageal reflux disease (GERD) is one of the most common gastrointestinal tract (GIT) disorders, with prevalence reaching up to 20% in Western countries.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e GERD complications include an inflammation of the esophagus, known as reflux esophagitis (RE), Barrett\u0026acute;s esophagus (BE), and esophageal adenocarcinoma (EAC);\u003csup\u003e3\u003c/sup\u003e however, only a minority of patients with GERD develop BE or EAC. The long-term exposure of the normal squamous epithelium to gastroduodenal fluid results in an inflammation of the esophagus and, subsequently, in replacement of squamous epithelium with columnar epithelium (intestinal metaplasia) in the lower esophagus.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Intestinal metaplasia can then lead to the development of BE, which is acknowledged as a pre-cancerous lesion for EAC development; the exact mechanism and conditions of the conversion in this inflammation-related carcinogenesis have, however, not been elucidated yet.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe pathogenesis of GERD includes multiple external and internal exposure factors, including obesity, lack of exercise, unhealthy lifestyle (alcohol abuse, smoking, etc.),\u003csup\u003e6\u003c/sup\u003e and genetic predispositions.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Most patients with GERD, especially those with EAC, are older men.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Besides these factors, esophageal microbiota has also been associated with GERD pathogenesis.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e While the quantity of microbiota in the healthy esophagus is lower than in healthy oral mucosa, the composition of these sites is very similar,\u003csup\u003e11\u003c/sup\u003e with the predominance of grampositive aerobic bacterial genera, especially by the genus \u003cem\u003eStreptococcus\u003c/em\u003e. Specimens from the esophagus with GERD-induced pathological changes have, however, been associated with higher relative abundances of gramnegative anaerobes or microaerophiles, such as \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, \u003cem\u003eGranulicatella\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, and \u003cem\u003eActinomyces\u003c/em\u003e.\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e In this context, the role of pharmaceuticals, such as proton pump inhibitors (PPIs) that are commonly used by these patients for reducing stomach acid and/or \u003cem\u003eHelicobacter pylori\u003c/em\u003e eradication (in combination with antibiotics), must also be considered, as they can influence the GIT microbiome diversity and composition.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough several bacteria were associated with GERD and its complications, scientific literature still lacks a comprehensive description of the GIT bacteriome (i.e., complete genetic information of bacteria in the entire GIT), which could shed light on the interconnectedness of different parts of the GIT in these patients. Identification of bacteriome features specific to these patient groups could help us to better understand the pathogenesis of the disease and the relationships among GERD complications. Here, we aimed to describe, compare, and correlate bacteriomes in biopsies from macroscopically pathological esophageal tissues and those from other parts of the GIT (oral and rectal swabs, gastric body and gastric antrum biopsies, and duodenal biopsies) in patients with RE, BE, and EAC. In addition, as several studies have suggested the possible use of some oral bacteriome characteristics as non-invasive predictive biomarkers for BE/EAC,\u003csup\u003e17\u0026ndash;19\u003c/sup\u003e we also aimed to address this hypothesis by comparing relative abundances of bacterial genera in oral and rectal swabs taken from patients with RE/BE/EAC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, clinical examinations, and sample collection\u003c/h2\u003e \u003cp\u003eProspective enrollment of patients in our multicenter observational study was conducted from May 2020 to March 2023 at the departments specialized in gastrointestinal disorders of three large university hospitals in the Czech Republic (University Hospital Brno, University Hospital Motol Prague, and University Hospital Olomouc). The study was performed in compliance with the principles of the Helsinki Declaration and approved by the Ethics Committees of all participating institutions. A signed informed consent was obtained from each patient before their inclusion in the study.\u003c/p\u003e \u003cp\u003eIn this study, we intended to recruit approximately 150 patients with GERD and one of its three major complications (RE, BE, or EAC). Matching for sex, age, and body mass index (BMI) was performed on recruitment. Further inclusion criteria were Czech or Slovak origin (to maintain the genetic homogeneity) and \u0026ge;\u0026thinsp;30 years of age. Exclusion criteria were hepatic/renal failure, other types of tumors, ongoing pregnancy and lactation, and use of antibiotics and/or probiotics, major change of diet (such as becoming vegan), or a respiratory tract infection within eight weeks before sample collection.\u003c/p\u003e \u003cp\u003eA detailed patient history, including pharmacotherapy with PPIs, was obtained. RE and BE were diagnosed endoscopically. RE was evaluated according to the Savary\u0026ndash;Miller (SM)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and Los Angeles (LA) classifications,\u003csup\u003e21,22\u003c/sup\u003e and BE according to the Prague criteria.\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e All diagnoses were confirmed histologically.\u003c/p\u003e \u003cp\u003e Patients were requested to avoid food and liquid consumption as well as oral hygiene for at least one hour prior to sampling. Oral (O) and rectal (R) swabs were collected from each patient. In addition, biopsies from four different GIT sites were endoscopically obtained: macroscopically pathological esophageal tissue (with RE, BE, or EAC) and biopsies from the gastric body (GB), gastric antrum (GA), and duodenum (D).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHistopathological examination and metagenomic analysis\u003c/h3\u003e\n\u003cp\u003eOne part of the biopsy from each site was processed to formalin-fixed paraffin-embedded (FFPE) blocks and examined by pathologists for histological confirmation of RE, BE, or EAC (patients with negative or inconclusive results were excluded from the study); standard immunohistochemical analysis for \u003cem\u003eH. pylori\u003c/em\u003e was performed. The other part of the biopsy from each site, as well as the oral and rectal swabs, were placed in RLT buffer (from the AllPrep DNA/RNA Mini Kit; QIAGEN, Germany) and immediately stored at -80\u0026deg;C until the extraction of nucleic acids.\u003c/p\u003e \u003cp\u003eDNA was isolated from all samples (including 46 negative controls, NCs, and internal standard) using the AllPrep DNA/RNA Mini Kit (QIAGEN, Germany). In all matrices, the total content of bacterial DNA was established using qPCR targeting the 16S rRNA region. Subsequently, all samples and NCs were spiked with an internal standard and analyzed by 16S RNA amplicon sequencing on the Illumina platform. Details on laboratory and bioinformatic metagenomic analyses were described in our previous work.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were carried out in R (v4.1.2). A multi-step filtering of samples was performed prior to the analysis. A threshold of 5,000 sequencing reads was established to prevent analysis of samples with an insufficient sequencing depth. Next, all reads originating from the internal standard were removed from the dataset, and every sample was tested for dissimilarity in bacteriome composition against NCs. Additional exclusion criteria based on the quality of the obtained data are shown in the study workflow in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eThe genera present in \u0026ge;\u0026thinsp;20 samples from any single GIT location (or NCs) were analyzed using the ALDEx2 framework. Clustering analysis of bacterial genera was also performed to identify the similarity between individual GIT sites.\u003c/p\u003e \u003cp\u003eSequencing depth and alpha diversity, including the number of distinct amplicon sequence variants (ASVs) and Shannon diversity index, were then calculated on the filtered samples using the vegan package. For all downstream analyses, counts were collapsed to the genus level; any ASVs that could not be assigned a genus were grouped under the category \u0026ldquo;other\u0026rdquo;. An overview table of clinical variables was generated using an on-site tool based on the gt package.\u003c/p\u003e \u003cp\u003eThe ratios of relative abundances of anaerobic to aerobic bacterial genera (anaerobes/aerobes ratio) and gramnegative to grampositive bacterial genera (G\u003csup\u003e-\u003c/sup\u003e/G\u003csup\u003e+\u003c/sup\u003e ratio) were computed based on all determined genera for which both statuses (i.e., aerobicity and Gram staining) were known, using, on average, 82% of the total bacterial DNA present in the sample. Dissimilarity in bacteriome composition was calculated as the sum of differences in relative abundances weighted by the difference in quantiles across all genera. This dissimilarity index was used in non-metric multidimensional scaling (nMDS) and to test differences in composition between non-NC samples and negative controls.\u003c/p\u003e \u003cp\u003eComparisons of continuous variables between study groups, such as the total content of bacterial DNA and alpha diversity metrics, were carried out using two-sample Wilcoxon rank-sum tests, performed separately for each sample matrix. \u003cem\u003eP\u003c/em\u003e-values from these tests were adjusted for multiple comparisons within the sample matrix by the Benjamini\u0026ndash;Hochberg false discovery rate procedure. Results were visualized as boxplots in R using ggplot2, with styled significance annotations added via ggtext. Adjusted \u003cem\u003ep\u003c/em\u003e-values are indicated by asterisks: * for \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** for \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, and *** for \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e \u003cp\u003eWe used ALDEx2 to assess bacteriome composition differences. Read counts were transformed using the central log-ratio transformation using ALDEx2::aldex.clr with 256 Monte Carlo replicates and the scale parameter gamma\u0026thinsp;=\u0026thinsp;0.25. To detect genera present in the analyzed matrices, a general linear model was built with NCs serving as a baseline. Only genera with significantly higher CLR-transformed relative abundances in the given matrix compared to NCs were considered as present and used in further analysis. Next, a series of Wilcoxon rank-sum tests was performed for every sample matrix, comparing study groups using the ALDEx2::ttest. The beta diversity was visualized using t-distributed stochastic neighbor embedding (t-SNE) with the Rtsne library.\u003c/p\u003e \u003cp\u003ePairwise Pearson correlations were calculated on CLR-transformed abundance data, and \u003cem\u003ep\u003c/em\u003e‐values were adjusted for multiple testing using the Benjamini\u0026ndash;Hochberg procedure. Correlations with an absolute coefficient greater than 0.4 and an adjusted \u003cem\u003eP\u003c/em\u003e‐value below .05 were retained and visualized as a network in R using the igraph package.\u003c/p\u003e"},{"header":"Results","content":"\u003ch4\u003eClinical variables\u003c/h4\u003e\n\u003cp\u003eThe final dataset included 52 RE, 55 BE, and 51 EAC patients, whose descriptive characteristics are shown in Table 1. Men were overrepresented in all three study groups (124 vs. 34), especially in EAC patients. The mean age for individual groups ranged from 59 (RE) to 64 (EAC) years. Patients’ BMIs indicated a prevalence of overweight people in all study groups. Distributions of alcohol consumption, smoking habits, and \u003cem\u003eH. pylori\u003c/em\u003e positivity in GIT biopsies were similar among groups (\u003cem\u003eP\u003c/em\u003e \u0026gt; .05, Fisher's exact tests). The use of PPIs significantly differed among groups; while 94% of patients with RE and 100% of those with BE were on PPIs, only 49% of patients with EAC used PPIs (\u003cem\u003eP\u003c/em\u003e \u0026lt; .001, Fisher's exact test).\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003ePre-analysis of bacteriome data\u003c/h4\u003e\n\u003cp\u003eAll 923 samples reached at least 5,000 reads and significantly differed from the NCs in the dissimilarity test of bacterial composition. The relative abundances of bacterial genera, as well as sequencing depth and total content of bacterial DNA in NCs, are presented as a heatmap in Supplementary Figure S1. NCs were significantly different (\u003cem\u003eP\u003c/em\u003e \u0026lt; .05) from other matrices in all studied bacteriome properties (alpha diversity, G\u003csup\u003e-\u003c/sup\u003e/G\u003csup\u003e+\u003c/sup\u003e ratio, and anaerobes/aerobes ratio; data not shown).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults from the ALDEx2 framework are shown in Supplementary Figure S2. Rectal swabs contained the highest number of bacterial genera (N = 41), of which 34 were unique to this location. Upper GIT samples shared 21 bacterial genera. Clustering analysis identified two distinct upper GIT bacteriome clusters: (I) O, D, and E; and (II) GB and GA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo significant differences in alpha diversity or the anaerobes/aerobes ratio were observed between samples from patients with EAC who used PPIs and those who did not. The G\u003csup\u003e-\u003c/sup\u003e/G\u003csup\u003e+\u003c/sup\u003e ratio was the only parameter that showed any statistical difference, and even that was observed only at a single site (the gastric body; \u003cem\u003eP\u003c/em\u003e = .011 before adjustment), which allowed us to treat all EAC patients as one group in further analyses.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eBacteriome profiles in GIT samples\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eThe total content of bacterial DNA in oral samples of the BE group was significantly higher than in the RE (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u003c/sub\u003e \u0026lt; .05) and EAC (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u003c/sub\u003e \u0026lt; .001, Figure 2A) groups. In esophageal samples, the total content of bacterial DNA increased with the severity of GERD complications (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; .001 for both RE-EAC and BE-EAC contrasts). It was also higher in duodenal samples from patients with EAC than with BE. The analysis of sequencing depth (Figure 2B) revealed a similar pattern in esophageal samples – the sequencing depth was increased in EAC samples compared to groups with other GERD complications (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; .01 for RE-EAC and \u0026lt; .05 for BE-EAC). The same result was observed in the gastric antrum biopsy (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; .05 for both RE-EAC and BE-EAC). The esophagus was the only matrix in which a significant difference in bacteriome richness (measured as the number of distinct ASVs) was detected between GERD complications, with EAC samples containing more distinct ASVs than RE samples (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u003c/sub\u003e \u0026lt; .01, Figure 2C). An even stronger result was obtained in Shannon diversity (bacteriome evenness), where esophageal samples from patients with EAC showed higher diversity than both RE (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; .001) and BE (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; 0.05) samples, see Figure 2D. The anaerobes/aerobes ratio (Figure 2E) was significantly higher in oral swabs from patients with BE compared to both RE (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; .05) and EAC (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; 0.01) patient groups. Finally, the G\u003csup\u003e-\u003c/sup\u003e/G\u003csup\u003e+\u003c/sup\u003e ratio was significantly higher in esophageal samples from patients with BE compared to those with RE (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; 0.05). In gastric body biopsies, this ratio was significantly higher in samples from patients with BE than with EAC (\u003cem\u003ep\u003c/em\u003e\u003csub\u003eADJ\u0026nbsp;\u003c/sub\u003e\u0026lt; 0.05), see Figure 2F.\u003c/p\u003e\n\u003cp\u003eThe overall bacterial composition is visualized using t-SNE in Figure 3, showing a distinct cluster of rectal swabs and separation of oral samples from the rest of the GIT. The esophageal, gastric, and duodenal biopsy samples were overlapping and, in two dimensions, unseparable. Supplementary Table S1 shows the ten bacterial genera (based on their median relative abundance) that were most abundant in each of the six GIT sites. Genera \u003cem\u003eStreptococcus, Veillonella, Haemophilus,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Prevotella\u003c/em\u003e are among the five most abundant genera in all GIT samples except rectal swabs (although \u003cem\u003ePrevotella\u003c/em\u003e is among the top five genera even in the latter matrix).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 4 shows an MDS plot of mean dissimilarity in bacteriome composition between individual GIT sites based on pairwise testing of esophageal samples with different severities of GERD complications and individual-matched samples from other locations. Note that in this plot, the mutual distances of esophageal samples for individual diagnoses do not indicate their true (dis)similarity as their mutual pairwise testing was, obviously, impossible. In esophageal tissues, bacteriome compositions of the EAC and RE were relatively similar to oral samples, while high dissimilarity was observed between the BE and oral samples. Bacteriome composition of duodenal samples from all three study groups was relatively similar to that of esophageal samples. Rectal swabs were omitted from this analysis due to the very low intersection of bacterial genera with other GIT sites (leading to a high dissimilarity to all other tissues, preventing a meaningful depiction).\u003c/p\u003e\n\u003ch4\u003eIdentification of bacterial genera associated with GERD complications\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eThe ALDEx2 framework was applied independently to each sample matrix. Across all matrices, a total of 27 bacterial genera/sample matrix combinations yielded at least one statistically significant contrast. All selected genera and their CLR-transformed abundance profiles across matrices and study groups are presented in Supplementary Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe highest number of significant contrasts between study groups for individual genera (12 genera, 18 contrasts in total) was found in oral samples. \u003cem\u003ePorphyromonas\u003c/em\u003e was the only genus showing significant contrasts for both RE-EAC and BE-EAC in oral swabs; the same can be said of \u003cem\u003eVeillonella\u003c/em\u003e in gastric body as well as gastric antrum samples. Significant contrasts were found also for both RE-EAC and RE-BE comparisons for \u003cem\u003eStreptococcus\u003c/em\u003e in esophageal samples. In rectal swabs, differences between patients with EAC and RE were found only for \u003cem\u003eFaecalibacterium\u003c/em\u003e. For full results including possible species classification, see Table 2 and Supplementary Figure S3, depicting distributions and median values of CLR-transformed relative abundances of genera with at least one significant contrast (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05).\u003c/p\u003e\n\u003ch4\u003eCorrelation analysis\u003c/h4\u003e\n\u003cp\u003eFigure 5 shows the result of the correlation analysis; only genus-matrix combinations that showed at least one strong correlation (|r|\u0026gt;0.6) are depicted. All the strong correlations between (CLR-transformed) relative abundances were positive. Most matrices (with the exception of the gastric body and gastric antrum) formed distinct clusters. Additionally, the relative abundances of \u003cem\u003eNeisseria\u003c/em\u003e in all non-rectal matrices mutually correlated. \u003cem\u003ePrevotella\u003c/em\u003e showed the highest number of significant correlations in esophageal, gastric, and duodenal samples, followed by \u003cem\u003eVeillonella.\u0026nbsp;\u003c/em\u003eWhile correlation networks of esophageal, gastric, and duodenal samples are highly connected, the correlation pattern of rectal samples is scattered into multiple subgraphs. The same can also be said of the genera in oral samples, which generally have the weakest correlation structure, with only 4 significant inter-matrix correlations (Figure 5).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe association of several bacteriome characteristics in multiple parts of the GIT with different severity of GERD complications may be caused by an interplay of the pathological mucosal changes and the bidirectional distribution of bacteria through the entire GIT (both due to swallowing and gastroesophageal reflux).\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e In all, 923 samples collected from the entire GIT of 158 patients were analyzed in this study, providing a comprehensive picture of the bacteriomes in each patient, while also enabling us to compare findings from each location. We found that some bacteriome characteristics in biopsies from macroscopically pathological esophageal tissues differed among groups of patients with RE, BE, and EAC. Moreover, strong correlations were detected for some bacterial genera, such as \u003cem\u003ePrevotella\u003c/em\u003e, throughout the entire GIT. More interestingly, high or low relative abundances of some bacterial genera, such as \u003cem\u003ePorphyromonas\u003c/em\u003e on the oral mucosa and \u003cem\u003eFaecalibacterium\u003c/em\u003e in fecal samples, were associated with EAC.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGIT bacteriome characteristics and GERD complications\u003c/h2\u003e \u003cp\u003eBacteriomes of rectal swabs greatly differed from those found in the other parts of the GIT. The high bacteriome load and diversity found in rectal and oral swabs are consistent with the literature,\u003csup\u003e28\u003c/sup\u003e as are the five most abundant genera present in individual GIT sites.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Importantly, the similarity of the oral environment and of the pathological esophageal tissue in both these parameters was high in patients with EAC but not in patients with RE or BE. The total content of bacterial DNA, as well as bacteriome richness and evenness in esophageal biopsies, significantly increased with the severity of the GERD complication, which indicates that the increasing damage to the esophageal mucosa is associated with a significantly greater enrichment of the tissue with bacterial DNA. Although higher richness and evenness in oral and fecal samples are generally associated with health,\u003csup\u003e30\u0026ndash;32\u003c/sup\u003e the opposite finding in our study may be caused by the fact that the esophageal tissue is physiologically poor in microbiota and the mucosal damage supports bacterial colonization.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIf dysbiosis is present in the oral environment, the aforementioned changes in the esophageal mucosa may facilitate its colonization (or even invasion) by potentially pathogenic bacteria from the oral cavity as they pass through the digestive tract.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e This can lead to esophageal dysbiosis, manifesting as a reduction of the relative abundance of G\u003csup\u003e+\u003c/sup\u003e commensals (such as the genus \u003cem\u003eStreptococcus\u003c/em\u003e).\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e In our study, the G\u003csup\u003e\u0026minus;\u003c/sup\u003e/G\u003csup\u003e+\u003c/sup\u003e ratio was higher in the esophageal biopsies from patients with BE than in those from RE. This was further corroborated by the fact that the relative abundance of grampositive commensal bacteria of the genus \u003cem\u003eStreptococcus\u003c/em\u003e was lower in the esophageal biopsies from patients with BE and EAC compared to those with RE, as observed previously by Lopetuso et al.\u003csup\u003e36\u003c/sup\u003e Besides, Gall et al. reported that the \u003cem\u003eStreptococcus\u003c/em\u003e:\u003cem\u003ePrevotella\u003c/em\u003e ratio in esophageal samples may constitute a risk factor for BE development.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e In our study, we have not observed significant differences in the relative abundances of the genus \u003cem\u003ePrevotella\u003c/em\u003e among study groups in esophageal samples, but found such differences in the oral cavity.\u003c/p\u003e \u003cp\u003eInterestingly, bacteriomes of esophageal biopsies from RE and EAC patients were similar to their oral bacteriomes (although with major differences in several genera, see Fig.\u0026nbsp;4), which cannot be said of esophageal biopsies from patients with BE. The same trends can be also observed in the total content of bacterial DNA and the anaerobes/aerobes ratio (Fig.\u0026nbsp;2) as well as in relative abundances of individual bacterial genera (Figure S3), where numerous bacteriome characteristics of samples from patients with BE do not lie between RE and EAC groups as expected, i.e, they deviate from the trend of the increasing severity of pathology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of individual bacterial genera with pathology\u003c/h2\u003e \u003cp\u003ePreviously, oral dysbiosis in subgingival and buccal mucosa sites and specific periodontal pathogens (gramnegative anaerobes such as \u003cem\u003ePorphyromonas\u003c/em\u003e) in oral samples were associated with BE and esophageal carcinoma.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e The genus \u003cem\u003ePorphyromonas\u003c/em\u003e has also been associated with oral squamous cell carcinoma,\u003csup\u003e40,41\u003c/sup\u003e esophageal squamous cell carcinoma,\u003csup\u003e17\u003c/sup\u003e and pancreatic cancer.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Our results also showed that the relative abundance of the genus \u003cem\u003ePorphyromonas\u003c/em\u003e was significantly higher in the oral swabs from patients with EAC than in those from patients with BE or RE; however, the differences are not of a magnitude that could suggest its successful diagnostic use. On the other hand, certain differences were also observed for other periodontal pathogens, such as the genera \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eTreponema\u003c/em\u003e, or \u003cem\u003eFretibacterium\u003c/em\u003e, suggesting that the diagnostic use of some indices based on their multiplex detection and quantification might be a promising direction for future research.\u003c/p\u003e \u003cp\u003eIn our study, the relative abundance of the genus \u003cem\u003eFaecalibacterium\u003c/em\u003e in rectal swabs from patients with RE was significantly higher than in those with EAC. Considering that \u003cem\u003eFaecalibacterium\u003c/em\u003e is a probiotic genus,\u003csup\u003e43\u003c/sup\u003e its lack is likely to be rather a general indicator of GIT dysbiosis in patients with GERD. Notably, this difference was rather caused by the higher percentage of individuals with a very low representation of this genus among patients with EAC than a systematically lower relative abundance. Changes in the abundance of \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e have been previously studied in association with inflammation,\u003csup\u003e44\u003c/sup\u003e diabetes mellitus,\u003csup\u003e45\u003c/sup\u003e and cancer,\u003csup\u003e46,47\u003c/sup\u003e and this bacterium is considered for its therapeutic potential in patients with malignancies.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e However, the association between the reduction of \u003cem\u003eFaecalibacterium\u003c/em\u003e in fecal samples and diseases must be interpreted with care, as this bacterium was previously shown to be negatively affected by many other factors, such as the use of PPIs\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and chronic alcohol consumption.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e Knowing that the EAC group was the only one with a major representation of patients not taking PPIs and that an additional comparison of EAC patients with and without PPI revealed no significant difference between these subgroups from the perspective of the relative abundance of the genus \u003cem\u003eFaecalibacterium\u003c/em\u003e (data not shown), it appears that the diagnosis plays a greater role than PPI usage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and strengths\u003c/h2\u003e \u003cp\u003eThe studied patient groups were not evenly distributed from the perspective of the PPIs usage \u0026ndash; the proportion of patients with EAC not using PPIs was higher than in the remaining groups. We have, however, addressed that issue by testing differences in basic bacteriome characteristics between EAC PPI users and non-users. Besides, other external and internal factors (e.g., other medications or diagnoses) might have also influenced the GIT microbiomes; we have, however, not analyzed this due to the high granularity of the individual study groups. The lack of a healthy control group might also be perceived as a limitation; however, taking biopsies from the entire GIT of healthy controls would be unethical, and for this reason, we had to consider the least severe complication of GERD (RE) as a reference. Lastly, untargeted metagenomic sequencing could not be performed due to the use of human biopsies with low microbial load and high human DNA content, which limited the analysis only to targeted metagenomic approaches.\u003c/p\u003e \u003cp\u003eOn the plus side, the use of negative controls as well as the internal standard throughout the entire analysis is a strength of this study, preventing misinterpretation of the results due to any bias caused by contamination from reagents or other causes during sample processing. The library preparations were done in the same laboratory and processed by the same researcher to minimize any interindividual or interlaboratory variability. The relatively large numbers of patients in study groups constitute another strength of this study, especially considering the complexity of sampling and the unique fact that patient-matched samples from six GIT sites were collected and compared.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study identified similarities and differences among multiple GIT sites and groups of patients with RE, BE, and EAC. A gradual trend in several parameters (increase in the total content of bacterial DNA and alpha diversity, and decrease in the relative abundance of the commensal \u003cem\u003eStreptococcus\u003c/em\u003e) was observed in esophageal tissues. It should be, however, noted that several bacteriome parameters in patients with BE deviated from this trend. We have also identified some bacterial genera that might (pending further studies) be potentially useful as non-invasive biomarkers or predictors of progression of GERD complications. In particular, periodontal pathogens (such as \u003cem\u003ePorphyromonas\u003c/em\u003e) in the oral swabs show promise in this respect, warranting the need for further research in this direction. Very low relative abundance of the genus \u003cem\u003eFaecalibacterium\u003c/em\u003e in rectal swabs of patients with EAC may, pending further studies, also potentially serve as an additional indicator of increased risk for EAC development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASVs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;amplicon sequence variants\u003c/p\u003e\n\u003cp\u003eBE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Barrett\u0026rsquo;s esophagus\u003c/p\u003e\n\u003cp\u003eD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;duodenum\u003c/p\u003e\n\u003cp\u003eEAC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;esophageal adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFFPE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;formalin-fixed paraffin-embedded\u003c/p\u003e\n\u003cp\u003eGA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;gastric antrum\u003c/p\u003e\n\u003cp\u003eGB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;gastric body\u003c/p\u003e\n\u003cp\u003eGERD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;gastroesophageal reflux disease\u003c/p\u003e\n\u003cp\u003eGIT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;gastrointestinal tract\u003c/p\u003e\n\u003cp\u003eLA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Los Angeles classification\u003c/p\u003e\n\u003cp\u003eNCs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;negative controls\u003c/p\u003e\n\u003cp\u003enMDS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;non-metric multidimensional scaling\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;oral swab\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPIs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;proton pump inhibitors\u003c/p\u003e\n\u003cp\u003eR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;rectal swab\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRE reflux esophagitis\u003c/p\u003e\n\u003cp\u003eSM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Savary-Miller classification\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committees of the Faculty of Medicine, Masaryk University (No. 09/2020, March 11\u003csup\u003eth\u003c/sup\u003e, 2020), University Hospital Brno (No. 05-101019/EK, May 15\u003csup\u003eth\u003c/sup\u003e, 2019), University Hospital Motol, Prague (without number, June 19\u003csup\u003eth\u003c/sup\u003e, 2019), and University Hospital Olomouc (No. 104/19, June 25\u003csup\u003eth\u003c/sup\u003e, 2019). Written informed consent was obtained from all participants before inclusion in the study, and the study is in line with the Helsinki declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrant support:\u003c/strong\u003e This study was supported by the Ministry of Health of the Czech Republic, grant nr. NU20-03-00126 and by Ministry of Health of the Czech Republic \u0026ndash; conceptual development of research organization (FNBr, 65269705). This work has been funded by grant SALVAGE (CZ.02.01.01/00/22_008/0004644 from the Programme JAC under the MEYS CZ \u0026ndash; Co-funded by the European Union. Authors thank the RECETOX Research Infrastructure (No LM2023069) financed by MEYS for supportive background. This work was supported from the European Union\u0026rsquo;s Horizon 2020 research and innovation program under grant agreement No 857560 (CETOCOEN Excellence). This publication reflects only the author\u0026apos;s view, and the European Commission is not responsible for any use that may be made of the information it contains.\u003cem\u003e \u003c/em\u003eSequencing was carried out in the laboratories of the Institute of Applied Biotechnologies a.s. Computational resources were provided by the e-INFRA CZ project (ID:90254), supported by the Ministry of Education, Youth and Sports of the Czech Republic. This work was carried out with the support of RECETOX Research Infrastructure (ID LM2023069, Ministry of Education, Youth and Sports of the Czech Republic, 2023-2026). We would like to thank Dr. Jaroslav Janosek for his valuable comments and our colleagues from RECETOX (Dr. Eva Budinska and Vojtech Barton) for data processing and consultations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions to the manuscript:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNatalie Mlcuchova \u0026ndash; Data curation, Investigation, Methodology, Visualization, Writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eJan Bohm \u0026ndash; Data curation, Formal analysis, Methodology, Visualization, Writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eLumir Kunovsky \u0026ndash; Data curation, Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eTomas Harustiak \u0026ndash; Data curation, Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eAnnemarie Boleij \u0026ndash; Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003ePetra Borilova Linhartova \u0026ndash; Conceptualization, Funding acquisition, Resources, Methodology, Project administration, Supervision, Writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Czech GERD Collaborative Group:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZdenek Kala \u0026ndash; Funding acquisition, Supervision, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eTomas Grolich \u0026ndash; Investigation, Project administration, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eVladimir Prochazka \u0026ndash; Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eOndrej Urban \u0026ndash; Funding acquisition, Supervision, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eVit Navratil \u0026ndash; Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eRobert Lischke \u0026ndash; Funding acquisition, Supervision, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eJiri Dolina \u0026ndash; Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eRadek Kroupa \u0026ndash; Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eVaculova Jitka \u0026ndash; Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eZdenek Pavlovsky \u0026ndash; Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eJakub Kovarik \u0026ndash; Investigation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eTereza Deissova \u0026ndash; Investigation (pilot analysis), Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eJaroslav Janosek \u0026ndash; Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eEva Budinska \u0026ndash; Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eJan Lochman \u0026ndash; Investigation (pilot analysis), Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eLydie Izakovicova Holla \u0026ndash; Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eOndrej Slaby \u0026ndash; Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Transparency Statement: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB104439 (https://www.ebi.ac.uk/ena/browser/view/PRJEB104439).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no disclosures to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntunes C, Aleem A, Curtis SA Gastroesophageal Reflux Disease. In: Vol 2023. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK441938/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK441938/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaqran TM, Ismaeel MM, Alnuaman AA et al Epidemiology, Causes, and Management of Gastro-esophageal Reflux Disease: A Systematic Review. Cureus 2023. 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Best Pract Res Clin Gastroenterol 22:661\u0026ndash;669\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzaraz D, Bohm J, Cerulova S et al (2025) Bacterial genera in the fluids from apical periodontitis-related radicular cysts: An observational study. Int Endodontic J 58:902\u0026ndash;915\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManson JM, Rauch M, Gilmore MS (2008) The Commensal Microbiology of the Gastrointestinal Tract. In: Huffnagle GB, Noverr MC, eds. \u003cem\u003eGI Microbiota and Regulation of the Immune System\u003c/em\u003e.Vol 635. Advances in Experimental Medicine and Biology. New York, NY: Springer New York; :15\u0026ndash;28. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://link.springer.com/\u003c/span\u003e\u003cspan address=\"http://link.springer.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-0-387-09550-9_2\u003c/span\u003e\u003cspan address=\"10.1007/978-0-387-09550-9_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Accessed January 11, 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan W, Engevik MA, Spinler JK et al (2020) Healthy Human Gastrointestinal Microbiome: Composition and Function After a Decade of Exploration. Dig Dis Sci 65:695\u0026ndash;705\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Wei B, Xue X et al Microbiome changes in esophageal cancer: implications for pathogenesis and prognosis. Cancer Biol Med 2023:1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaker JL, Mark Welch JL, Kauffman KM et al (2024) The oral microbiome: diversity, biogeography and human health. Nat Rev Microbiol 22:89\u0026ndash;104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta VK, Kim M, Bakshi U et al (2020) A predictive index for health status using species-level gut microbiome profiling. Nat Commun 11:4635\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEslick GD (2010) Infectious Causes of Esophageal Cancer. Infect Dis Clin N Am 24:845\u0026ndash;852\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnnavajhala MK, May M, Compres G et al (2020) Relationship of the Esophageal Microbiome and Tissue Gene Expression and Links to the Oral Microbiome: A Randomized Clinical Trial. 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Gastroenterology 137:588\u0026ndash;597\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopetuso LR, Severgnini M, Pecere S et al (2020) Esophageal microbiome signature in patients with Barrett\u0026rsquo;s esophagus and esophageal adenocarcinoma Clemons N, edPLoS ONE. ;15:e0231789\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGall A, Fero J, McCoy C et al (2015) Bacterial Composition of the Human Upper Gastrointestinal Tract Microbiome Is Dynamic and Associated with Genomic Instability in a Barrett\u0026rsquo;s Esophagus Cohort Mukaisho K, edPLoS ONE. ;10:e0129055\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadeghi F, Sohrabi A, Zagai U et al (2025) Oral Microbiome Dysbiosis Is Associated With Precancerous Lesions and Disorders of Upper Gastrointestinal Tract: A Population-Based Study. 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Br J Biomed Sci 80:10794\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalamurugan R, Rajendiran E, George S et al (2008) Real-time polymerase chain reaction quantification of specific butyrate‐producing bacteria, \u003cem\u003eDesulfovibrio\u003c/em\u003e and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e in the feces of patients with colorectal cancer. J Gastro Hepatol 23:1298\u0026ndash;1303\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Muinck EJ, Trosvik P, Nguyen N et al (2023) Reduced abundance of Faecalibacterium prausnitzii in the gut microbiota of children diagnosed with cancer, a pilot study. Front Microbiomes 2:1151889\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Z, Li M, Zhang C et al (2025) Butyrate-producing \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e suppresses natural killer/T-cell lymphoma by dampening the JAK-STAT pathway. Gut 74:557\u0026ndash;570\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakagi T, Naito Y, Inoue R et al (2018) The influence of long-term use of proton pump inhibitors on the gut microbiota: an age-sex-matched case-control study. J Clin Biochem Nutr 62:100\u0026ndash;105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeclercq S, Matamoros S, Cani PD et al (2014) Intestinal permeability, gut-bacterial dysbiosis, and behavioral markers of alcohol-dependence severity. Proc Natl Acad Sci USA. ;111. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pnas.org/doi/full/10.1073/pnas.1415174111\u003c/span\u003e\u003cspan address=\"https://pnas.org/doi/full/10.1073/pnas.1415174111\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Accessed November 6, 2025]\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Masaryk University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"microbiota, adenocarcinoma, esophagus, metagenomics, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-8385155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8385155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims:\u003c/h2\u003e \u003cp\u003eGastroesophageal reflux disease (GERD) and its complications\u0026ndash;reflux esophagitis (RE), Barrett\u0026rsquo;s esophagus (BE), and esophageal adenocarcinoma (EAC)\u0026ndash;have been associated with bacterial dysbiosis. However, comprehensive characterization of the gastrointestinal tract (GIT) bacteriome across these GERD complications is lacking. This multicenter observational study aimed to fill this gap and evaluate the possibility of identifying non-invasive oral or rectal biomarkers for advanced GERD complications.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eBiopsies from patient-matched pathological esophageal tissues, gastric body and antrum, duodenum, and oral and rectal swabs were collected from 158 patients with histologically confirmed RE, BE, or EAC. DNA isolates were analyzed by qPCR and 16S rRNA amplicon sequencing. Anaerobes/aerobes and gramnegative/grampositive ratios, as well as relative abundances of multiple genera, were compared among GIT sites and study groups.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eBacterial load, richness, and evenness in esophageal biopsies increased with the severity of GERD complications. The relative abundance of the commensal genus \u003cem\u003eStreptococcus\u003c/em\u003e was significantly higher in esophageal samples of RE than in BE and EAC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.037 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.017, respectively). Interestingly, BE samples showed distinct bacteriome features deviating from the RE\u0026ndash;EAC progression trend. Strong inter-site correlations were identified throughout the GIT bacteriomes. Relative abundances of the genus \u003cem\u003ePorphyromonas\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.034) and other gramnegative periodontal pathogens in oral swabs and a probiotic genus \u003cem\u003eFaecalibacterium\u003c/em\u003e in rectal swabs were associated with EAC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.019).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eComprehensive GIT profiling of 923 samples revealed significant bacteriome alterations associated with the GERD complications severity. Some bacterial genera showed promise for potential use as non-invasive biomarkers for predicting the risk of severe GERD complications.\u003c/p\u003e","manuscriptTitle":"Gastrointestinal tract bacteriomes in patients with gastroesophageal reflux disease: a multicenter study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:12:08","doi":"10.21203/rs.3.rs-8385155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7ee466b-8559-43b9-bbc4-aa2c30aaa972","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T10:12:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:12:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8385155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8385155","identity":"rs-8385155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}