Comparative analysis of mucosa-associated and luminal gut microbiota in pediatric ulcerative colitis

doi:10.21203/rs.3.rs-5301915/v1

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Research Article

Comparative analysis of mucosa-associated and luminal gut microbiota in pediatric ulcerative colitis

https://doi.org/10.21203/rs.3.rs-5301915/v1

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Background

Inflammatory bowel diseases (IBD), including ulcerative colitis (UC) and Crohn’s disease, are chronic disorders relating to gut microbiota dysbiosis. Despite severe pancolitis being more prevalent in pediatric UC than in adult cases, alterations in the colon mucosa-associated microbiota (MAM) and their association with disease severity remain to be elucidated. The present study aimed to compare the gut microbiota in colon lavage fluids (CLFs) and fecal samples from pediatric UC patients.

Results

A total of 140 CLFs and 23 fecal samples from 19 each of pediatric UC and non-IBD patients were analyzed. CLFs were collected by aspirating intestinal fluid after washing the colonic mucosa using an endoscope with a waterjet function. Microbiota profiles of each sample were analyzed by 16S rRNA gene amplicon sequencing. The community structure of MAM was similar throughout the colon in both pediatric UC and non-IBD. Bacterial compositions between MAM and feces were significantly different in non-IBD while no difference was observed in pediatric UC, indicating a compromised mucous layer that could not sufficiently separate the MAM and luminal microbiota in UC. In pediatric UC, homogenous distribution of MAM was gradually disordered with increases in disease activity or mucosal inflammation, and the bacterial groups that usually colonize the upper digestive tract or have environmental origin were more abundant in MAM. To potentially distinguish pediatric UC from non-IBD, we identified the key bacterial genera in MAM; they included Lactobacillus, Enterococcus, Blautia, Parabacteroides, Faecalibacterium and Fusobacterium.

Conclusion

Compared with feces, MAM is more enriched in specific bacterial groups in non-IBD pediatric patients, whereas the feces and MAM microbiota are similar in pediatric UC. Our results indicate that the fecal microbiota reflect the status of MAM in pediatric UC. Monitoring the key fecal bacteria that are specifically increased in MAM depending on disease activity might be useful for evaluation of patient prognosis in pediatric UC. Further studies on MAM are needed to elucidate the contribution of its community structure to the pathophysiology of pediatric UC.

Pediatric ulcerative colitis

mucosa-associated microbes

16S rRNA gene analysis

colonoscopy

intestinal lavage fluids

Inflammatory bowel diseases (IBD), which include ulcerative colitis (UC) and Crohn’s disease (CD), are chronic inflammatory conditions of the gastrointestinal tract with unknown etiology. Complex factors such as disruption of gut mucosal barrier function, dysbiosis of gut microbiota, excessive gut immune response to resident microbes and Westernized lifestyle are considered to predispose patients to IBD [1]. Genetic background also seems to be involved in IBD pathogenesis. Genome-wide association studies have revealed 163 distinct genetic loci associated with IBD; some of these loci are related to mucosal barrier function and host innate immunity to gut microbiota [2].

The prevalence of childhood-onset IBD has increased rapidly over the last few decades [3, 4, 5]. Pediatric IBD patients show several clinical features that differ from those in adults. Among pediatric UC patients, pancolitis is more common and disease severity is higher than in adults [6]. In addition, the control of disease activity is often more difficult due to the longer duration of gut inflammation [7].

In recent years, metagenomics and metabolomics have brought novel insights regarding the relationships of the human microbiomes with various diseases. There is growing evidence showing that the reduction of biodiversity in gut microbiota is prolonged in IBD [8]. This reduction leads to gut mucosal inflammation due to insufficient supply of health-promoting metabolites from the microbiota, colonization of potentially pathogenic microorganisms, and thinning of the intestinal mucus layer allowing the gut microbes to directly adhere to epithelial cells [9]. This condition is called “dysbiosis,” which is considered to play an important role in IBD pathophysiology. Dysbiosis induces alteration of microbiota composition. Previous studies have also shown that the gut microbial communities of patients with IBD are characterized by reduced diversity as well as concomitant decrease and increase in the phyla Firmicutes and Proteobacteria, respectively [10, 11, 12].

The gut microbiota consist of luminal and mucosa-associated microbiota (MAM). Compared with luminal microbiota, MAM have been reported to play a more important role in the maintenance of intestinal homeostasis and host immunity in patients with IBD [13]. Some authors have reported that compared with healthy controls, patients with IBD have higher amounts of bacteria attaching to their gut epithelial surfaces, even in the non-inflamed mucosa [14.15].

Studies using biopsy samples and brush scrapings collected by endoscopy have been conducted in adults; these studies found that the composition of intestinal MAM differs greatly from those of fecal samples [16]. Various reports have shown differences in the diversity and composition of the microbiota between the feces and biopsy samples from healthy adults [17, 18]. Similar studies have been conducted in IBD. Chen reported differences in microbiota composition in stool and intestinal MAM between healthy subjects and UC patients [19]. Another study compared the microbiome in stool and colonic biopsy samples from healthy adults and IBD patients, reporting that the microbiota in fecal samples from healthy subjects and IBD patients were similar, but the differences in colonic MAM between the groups were evident [20].

Most of the previous studies on intestinal MAM used biopsy samples. However, the procedure is invasive, particularly for children, and biopsy samples reflect the intestinal MAM at limited mucosal regions. Less invasive sampling such as brush scrapings or lavage fluids collection has been employed to examine the differences of intestinal MAM between healthy adults and patients with IBD [21]. At present, only limited information is available for intestinal MAM in children, in which biopsy samples [22, 23, 24] or intestinal lavage fluid [25] were examined in pediatric UC. However, the latter study compared the duodenal MAM between pediatric UC patients and healthy controls. There have been no reports so far on studies that examined the colon lavage fluids in pediatric UC.

In the present study, we performed a 16S rRNA gene-based metagenomic analysis of the MAM using endoscopic lavage fluids collected throughout the colon (ascending colon, transverse colon, sigmoid colon and rectum) in pediatric UC patients. These microbiome data were compared with those of fecal samples. We also aimed to identify the microbes that correlate with the clinical activity of UC or the serological inflammatory markers.

1. Patient selection

This study was a single-center, prospective case series. All patients were enrolled from Kagawa University Hospital inpatient wards and outpatient pediatric clinics within the period 2020–2021. To participate in the study, patients had to have undergone colonoscopy for UC, CD or suspected IBD; be willing to participate; and be able to maintain close follow-up. This study was approved by the Ethics Committee of Kagawa University (Approved no: 2020 − 175). All patients and families gave written informed consent and assent in accordance with the Declaration of Helsinki.

2. Colon lavage collection

Lavage samples were obtained from 4 sites (ascending colon, transverse colon, sigmoid colon and rectum) by colonoscopy at active or remission phases of UC, or at the first colonoscopy in the cases for suspected IBD. Before the examination, patients ingested sodium picosulfate hydrate for colon cleansing the day before colonoscopy, and a bowel-cleansing agent was given orally on the day of the examination. If the patient could not take these agents or the patient’s bowel was not clear or fecal matter remained, an additional enema was performed. These drugs were used according to the age and acceptability of the patient. During colonoscopy, we washed colonic mucosa, and the lavage samples were aspirated via a sterile tube and sampled using the working channel of the colonoscope. Each fluid sample was collected and submitted to the study coordinator at room temperature and processed within 24 h after collection. Before or after colonoscopy, we also collected fecal samples from the same patients when available.

3. DNA preparation

Colon lavage and fecal samples (0.1 g/ml in 1xPBS) were centrifuged at 12,000 g for 5 min to collect the bacterial cells, and the pellets were washed with 1xPBS (pH7.4). The suspensions were centrifuged again and the pellets resuspended in Tris-EDTA buffer (pH8.0). For 16S rRNA gene sequencing, DNA samples were extracted using the NucleoSpin DNA Stool kit (MACHEREY-NAGEL, Germany) according to the manufacturer’s instructions. DNA concentrations were determined using a Qubit dsDNA BR (Broad-Range) Assay kit (Thermo Fisher Scientific K.K., USA).

4. 16S rRNA gene analysis

Sequencing libraries were prepared by amplifying the V3-V4 hypervariable region of the 16S rRNA gene using the primer sets of 341F and 805R with Illumina Miseq adaptor sequence as previously reported [26]. Libraries were sequenced on the Illumina MiSeq platform using 300-bp paired-end V3 chemistry (Illumina Inc., San Diego, USA) with addition of 5% PhiX. Operational taxonomic unit (OTU) clustering of the sequencing reads was performed with Qiime2 with UCLUST algorithm at 97% similarity against the Greengenes reference database (v.13.8).

5. Statistical analysis

Statistical analyses were performed using the phyloseq package (v.1.35.0) in R (v.4.2.1). Calculation and difference analyses of alpha-diversity indices were done using the MicrobiotaProcess package. Beta-diversity (Bray-Curtis distances) among samples was calculated with vegan R package (v.2.5.7) based on the relative abundances of observed OTUs. Clustering of the samples was visualized by performing a principal coordinates analysis (PCoA) using the microbial distances obtained among the samples. A permutational multivariate analysis of variance (PERMANOVA) based on Bray-Curtis distances was performed using the adonis function of vegan with 9,999 permutations. The association between clinical metadata and bacterial taxa was analyzed using the MaAsLin2 package. The “coefficient value” represents the strength of the association as calculated by MaAsLin2, and a p-value < 0.05 was considered statistically significant. Receiver operating curve (ROC) analysis was performed with the pROC package. Differential analyses of OTU abundances were performed with the DESeq2 package and linear discriminant analysis effect size (LEfSe) implemented in the MicrobiotaProcess package [27]. Differences between groups were tested using the Wilcoxon rank-sum test (Mann-Whitney U-test). Paired differences were determined using the Wilcoxon signed-rank test with multiple comparisons adjusted for a false discovery rate of 1.0%.

Cohort and sample characteristics

The demographic and clinical characteristics of the patients are summarized in Table 1. A total of 38 children, aged 3–19 years (11.4 ± 3.6), were enrolled, and 140 colonic lavage fluid (CLF) samples were collected from different sites of the large intestine (ascending colon, transverse colon and sigmoid colon and rectum). Twenty-three fecal samples were also collected. UC patients were treated according to the guidelines [28, 29]. Three UC and one non-IBD patients had received antibiotics within 4 weeks before sampling.

Table 1. Characteristics of enrolled pediatric UC and non-IBD patients.

Among the non-IBD group, 6 patients had irritable bowel syndrome, 4 had functional abdominal pain syndrome, 4 had eosinophilic gastroenteritis, 3 had juvenile polyp and 2 had constipation ± hemorrhoid. UC disease activity was scored by the Pediatric Inflammatory Ulcerative Colitis Index (active >10, remission <10). UC disease type was categorized by Paris classification. Endoscopic findings of UC were categorized according to Matts classification (grade 1: normal or inactive, grade 2: mild, grade 3: moderate, and grade 4: severe). Antibiotics had been administered within 4 weeks before the examination.

Abbreviations: CLF, colon lavage fluids. FOB: fecal occult blood. IBD: inflammatory bowel disease. UC: ulcerative colitis

* means ± standard deviations; ** median (inter quantile range)

Association of host factors with gut microbial taxonomic variation in UC and non-IBD

We firstly conducted the association analysis using microbial abundance data at the family level and sample metadata to identify the factors that impact the gut microbiome in pediatric UC (Fig. 1). As seen in “Subject ID”, which specified the patient, inter-individual difference explained > 50% of the variation of the gut microbiome in both UC and non-IBD (Fig. 1A). Inter-individual difference was more evident in non-IBD than UC, which contributed to 83.8% and 56.8% variation of the gut microbial community structure, respectively. The community structure in the MAM was similar throughout the colon in both UC and non-IBD. Of note, microbiota composition in CLF and feces was similar in UC (see CLF vs F in Fig. 1A), and no significant difference was detected in both α-diversity (number of observed OTU, Chao1 and Shannon and Simpson indices) and β-diversity among the sampling sites or sample types (Additional file 2). On the other hand, the community structures in MAM and fecal microbiota were different in non-IBD pediatric patients. Although PERMANOVA of the microbial abundance data showed no statistically significant difference between MAM and fecal microbiota in non-IBD patients (Additional file 2), the fecal microbiota tended to show a different pattern from MAM in PCoA. The α-diversity of MAM was unexpectedly higher than that of fecal microbiota in non-IBD patients. The association analysis showed a significant association of disease severity and antibiotics treatment with the variation in the gut microbiome, which explained more than 10% of the variation of gut microbial taxonomy in UC.

Among the blood and serological examinations, serum levels of acute phase proteins (fibrinogen, C-reactive protein [CRP] and serum amyloid A [SAA]) showed a strong association with microbiome composition in both CLF and feces, with fibrinogen accounting for more than 50% of the variation in microbial taxonomy (Fig. 1B). The levels of immunoglobulins, especially IgM, and autoantibody (pANCA and cANCA) showed significant association with the microbial taxonomic variation in CLF collected from UC.

Association of the specific microbial taxa with clinical metadata

To identify the specific association of microbial taxa (family level) with clinical data, we conducted an association analysis by linear model coefficients (MaAsLin). Figure 2A shows the specific microbial groups strongly associated with disease type (non-IBD vs UC), fasting and antibiotic treatment. In UC, the relative abundances of Enterococcaceae and Lactobacillaceae were higher while those of Porphyromonadaceae, Alcaligenaceae and Fusobacteriaceae were lower than in non-IBD. Antibiotic treatment correlated with the increase in the relative abundances of Oxalobacteraceae, Phyllobacteriaceae, Sphingomonadaceae, Planococcaceae and Staphylococcaceae. Of these, the first three families were negatively correlated with serum albumin and positively correlated with fecal calprotectin levels (Fig. 2B). No microbial taxa were significantly associated with sample type (CLF vs F), mucosal inflammation (Matts), disease severity (PUCAI) or fecal occult blood level. Analysis at the genus level showed that the abundances of Lactobacillus and Enterococcus were higher while those of Blautia, Faecalibacterium and Parabacteroides were lower in UC when compared with non-IBD gut microbiome (Additional file 3). Antibiotic treatment was associated with increased abundances of environmental bacteria such as Ralstonia and Phyllobacterium as well as Staphylococcus. While environmental bacteria such as Ralstonia, Phyllobacterium and Sphingomonas were negatively correlated with serum albumin level, they showed significant associations with inflammatory markers such as CRP and fecal calprotectin levels. Faecalibacterium and Parabacteroides showed positive correlations with hemoglobin (Hb) and hematocrit (Hct) level. Diverse microbial genera were associated with serum fibrinogen level. Interestingly, Lactobacillus and Enterococcus showed significant associations with all classes of immunoglobulins (IgG, IgM and IgA). The levels of autoantibodies pANCA and cANCA were significantly associated with the relative abundances of Staphylococcus and unassigned genus within Enterobacteriaceae in gut microbiome (Additional file 3).

Homogenous distribution of microbiota in MAM along the colon

We investigated the differences between the microbial community structures of MAM and fecal microbiota. Significant differences were observed in α- and β-diversities between MAM and fecal microbiota in non-IBD pediatric patients (Fig. 3A and B). CLF contained a larger number of OTU (family level) than did feces. The abundances of Lachnospiraceae and Bifidobacteriaceae were significantly higher in feces, whereas those of Enterobacteriaceae and Campylobacteriaceae were significantly higher in CLF (Fig. 3C). Oropharyngeal bacteria such as Pasteurellaceae, Fusobacteriaceae, Gemellaceae, Neisseriaceae and Leptotrichiaceae were also significantly more enriched in CLF than in feces in non-IBD pediatric patients.

On the other hand, no significant difference of microbiota diversity between CLF and feces was observed in pediatric UC patients (Additional file 4). Enterobacteriaceae was the only bacterial group that was significantly different in abundance between MAM and fecal microbiota. Consistent with the non-IBD findings described above, LEfSe algorithm analysis showed that genera within Enterobacteriaceae made up the only bacterial group that was higher in MAM than in fecal microbiota in pediatric UC patients (Fig. 4A). In the case of non-IBD patients, the relative abundance of various bacterial groups was significantly different between MAM and fecal microbiota, where Clostridia, Lachnospiraceae, Bifidobacteriaceae, Blautia and Roseburia were more abundant in feces while Enterobacteriaceae, Campylobacteriaceae and oral/oropharyngeal bacteria such as Neisseriaceae, Pasteurellaceae, Gemellaceae and Fusobacteriaceae were more abundant in MAM (Fig. 4B). These results indicate that compartmentalized distribution of gut microbiota along the mucosa-luminal axis was disrupted in the pediatric UC patients.

Microbial community structure of MAM and feces in pediatric UC and non-IBD patients

The microbial community structure of MAM was homogenous across the tested colon sites in both UC and non-IBD patients (Additional file 2). We next addressed the difference in MAM between the patient groups. As shown in Fig. 5A, all of the diversity indices of MAM were significantly lower in UC than in non-IBD. PCoA showed significantly different distributions between UC and non-IBD, where the community structure of MAM was more variable within the UC samples than within non-IBD (Fig. 5B). LEfSe analysis identified the differentially enriched bacterial groups in the MAM between UC and non-IBD patients (Fig. 5C). The abundance of the bacterial groups within Bacteroidales, Lachnospiraceae, Ruminococcaceae, Parabacteroides, Blautia, Porphyromonadaceae, Fusobacterium and Faecalibacterium were higher in MAM of non-IBD while those within Bacilli, Lactobacillus and Enterococcus were higher in MAM of UC patients. The same analysis was conducted with fecal microbiota (Additional file 5). The results showed that microbial diversity (Shannon and Simpson indices) was significantly lower in UC. In addition, no bacterial group enrichment was detected in UC fecal microbiota while typical fecal microbes in healthy subjects such as Lachnospiraceae, Blautia, Parabacteroides, Porphyromonadaceae, Clostridiales and Roseburia were enriched in non-IBD patients.

Alteration in community structure of MAM depending on UC disease activity

As described above, the community structure of MAM was homogenous along the colon in both UC and non-IBD, although the microbiome patterns were different between the groups. We examined whether microbial abundance in MAM differs according to mucosal inflammation or disease severity of pediatric UC patients. As shown in Fig. 6, intra-individual Bray-Curtis distance increased with the degree of mucosal inflammation and disease severity. These results indicate that mucosal inflammation and disease severity are associated with the regional heterogeneity of MAM in UC.

Profiling of MAM community structure according to UC disease severity

We analyzed the association of the microbial community structure of MAM with UC disease activity. Samples were classified into four groups (inactive, mild, moderate and severe) depending on PUCAI. PCoA analysis of the microbiome data showed that the community structure of MAM changed with exacerbation of the disease (Fig. 7A). Among the genera of relatively high abundance (> 0.1% in either of the samples), Bifidobacterium, Ruminococcus, Blautia, Epulopiscium and unassigned OTU within Clostridiaceae and Enterococcaceae decreased in the MAM with disease severity (Fig. 7B). However, Bacteroides, Phyllobacterium, Enterococcus, Porphyromonas, Sphingomonas, Ralstonia, Prevotella, Staphylococcus, Megamonas, and unassigned OTU within Enterobacteriaceae, Planococcaceae and Gemellaceae were higher in the MAM of the UC patients with active disease (Fig. 7C). DESeq2 analysis was conducted to detect which OTUs in MAM (including minor populations) were differentially enriched (> 4 fold change) depending on disease severity (Additional file 6). Many bacterial groups had different abundances when MAM was compared between inactive and active UC patients. Abundances of Lachnospira, Epulopiscium, and OTU within Enterococcaceae were lower while those of diverse bacterial groups such as Phascolarctobacterium, Edwardsiella, Pediococcus, Planococcus, Sphingomonas, Ralstonia, Phyllobacterium, and unassigned genera within Barnesiellaceae were higher in MAM from active UC. In comparisons between the patient groups, the number of differentially enriched bacterial groups in MAM decreased as disease activity progressed. Edwardsiella, Staphylococcus, Aggregatibacter, and the bacterial groups in Coriobacteriaceae and Enterobacteriaceae were enriched in MAM of the patients with severe disease activity. Interestingly, the abundance of probiotic Lactobacillus and unassigned OTU within Bifidobacteriaceae was higher in mild and moderate disease activity, respectively, when compared with inactive stage disease.

Profiling of MAM community structure according to mucosal inflammation

Mucosal inflammation of the sampling site was endoscopically evaluated by Matts scoring (Additional file 1). To investigate the impact of mucosal inflammation on the microbial community structure of MAM in pediatric UC, PCoA was conducted using the relative abundances of genus-level OTUs. Microbiome patterns of MAM were significantly altered by mucosal inflammation (Fig. 8A). The OTUs within Clostridiaceae, Bifidobacterium, and Epulopiscium decreased in abundance while Phyllobacterium, Sphingomonas, Megamonas, Ralstonia, and the unassigned OTUs within Planococcaceae and Gemellaceae gradually increased depending on Matts grade (Fig. 8B). DESeq2 analysis detected no OTU enrichment in MAM on normal mucosa (M1 vs M2 or M3) except for Roseburia (Fig. 8C). On the other hand, the community structure of MAM on the inflamed mucosa (M2 vs M3) was similar and enriched in bacterial groups that mainly colonize the oral cavity or upper digestive tract (e.g. Parvimonas and Gemella) and that have environmental origins (e.g. Pseudomonas, Phyllobacterium and Ralstonia) compared with normal mucosa (M1). It was noteworthy that Staphylococcus was the most abundant group in MAM on inflamed mucosa (M2 and M3). Consistent with the results shown in Additional file 6, the unknown genera within Bifidobacteriaceae retained their abundances in MAM on inflamed mucosa.

Potential microbial markers in MAM to discriminate pediatric UC from non-IBD

ROC analysis was conducted with relative abundances of bacterial groups in MAM to evaluate their potential to discriminate between pediatric UC and non-IBD patients. We detected nine genera with area under the curve (AUC) > 0.7 (Fig. 9). Lactobacillus, Enterococcus and other genera within Enterococcaceae were identified as potential markers for pediatric UC, with AUCs of 0.792, 0.777 and 0.703, respectively. Lactobacillus showed 51.4% sensitivity and 95.6% specificity for UC when the cut-off value of its relative abundance was > 0.0175%. Among the six potential markers for non-IBD, Blautia showed the highest AUC (0.849), with 84.7% sensitivity and 86.8% specificity when its relative abundance was > 0.1628%.

Although intestinal MAMs have been observed to be involved in the pathophysiology of UC, many of the previous studies focusing on MAM used mucosal biopsy in adults as well as pediatric patients [30, 31, 32]. In this study, we employed CLFs and paired fecal samples to determine specific alterations in MAM according to mucosal inflammation or disease activity. We collected CLFs using colonoscopy via a sterile tube and carried out high-throughput 16S rRNA gene metagenomic analysis. In order to evaluate the microbiota residing proximal to the mucous layer, we aspirated the CLFs immediately after vigorously washing the mucosal surfaces using the waterjet function of the endoscope. This method has advantages for safe sampling from a wide mucosal area along the colon because biopsy at many sites is invasive, especially for children. In addition, the lavage contains a sufficient number of microbial cells, which minimizes the effect of environmental contamination on PCR-based analysis and allowing more efficient isolation of the microbes of interest. To our knowledge, this is the first report to conduct a comparative analysis of microbiota between CLFs and paired samples of feces from pediatric UC patients.

Consistent with other reports [33, 34, 35], substantial inter-individual differences in gut microbiome was observed among the patients (Fig. 1). However, these differences accounted for less variation in the UC than in the non-IBD microbiomes: 56.8% vs 83.8%, respectively. In addition, compared with UC, the microbiome variations in non-IBD correlated more with sampling sites (A, T, S, R or F) and sample types (CLF or feces). Correspondingly, the α-diversity (number of observed OTU and/or Chao1 index) showed significant differences between the CLFs and feces in non-IBD while no such difference was observed in UC (Fig. 2). The microbiome composition (β-diversity) of MAM in UC was homogenous along the colon and similar to that in feces. However, in the cases of non-IBD, the microbiomes in MAM tended to be different from those in feces (Fig. 3). These results suggest that compartmentalized distribution of microbiome along the mucosa-luminal axis is disrupted in UC due to the compromised mucous layer function even in the remission stage. Of note, compared with mucosal inflammation (Matts score), the disease severity (PUCAI) had a stronger association with alteration in MAM. This might indicate that gut physiological dysfunction (diarrhea, bleeding or fast transit time etc.) has a larger impact on the gut microbiome.

Among the blood analysis data, fibrinogen level was associated with nearly half of the variation in the MAM (Fig. 1). Shen et al. has reported that UC patients showed elevated fibrinogen levels, which they therefore proposed as a diagnostic marker for UC [36]. However, we observed a similar association in non-IBD patients, indicating that the fibrinogen is not a UC-specific marker but reflects the nonspecific inflammatory response. Notably, the serum levels of all classes of immunoglobulins (IgA, IgG and IgM) were significantly associated with the variation in the MAM, and the degree of correlation was more evident in UC than in non-IBD. MaAsLin analysis showed that abundances of Enterococcaceae and Lactobacillaceae were strongly associated with these antibody levels, indicating that UC patients developed humoral immune responses to these bacterial groups (Fig. 2). These findings were consistent with the report by Bourgonje et al., in which Streptococcus, Lactobacillus, Lactococcus, Enterococcus, Veillonella and Enterobacteriaceae were enriched among IgG-coated bacteria in adult UC [37]. In UC, levels of the autoantibodies pANCA and cANCA correlated with the relative abundance of Staphylococcus and Enterobacteriaceae (Fig. 2). This association was probably due to the strong inflammatory response evoked by these bacteria. Luo et al. reported that systemic translocation of Staphylococcus was associated with germinal center B cell activation and production of autoantibodies to nuclear antigen (ANA) and double strand DNA [38]. They also showed a higher plasma LPS level in HIV-positive patients with high ANA than with low ANA levels. It was also reported that E. coli-derived caseinolytic protease B induces autoantibody formation [39]. Overall, the levels of serum inflammatory markers and levels of immunoglobulin were more correlated with the MAM than fecal microbiota (Fig. 1), suggesting that the alteration in MAM reflects the host humoral immune response to gut microbiota.

Several studies have reported that the gut microbial diversity and composition differ between feces and mucosal biopsy in healthy adults [17, 18] as well as in adult UC patients [19]. However, to our knowledge, no reports have been published that compared the microbiota in CLFs and the paired feces from pediatric UC patients. Inconsistent with these reports in adult cases, our results showed that MAMs are homogenously distributed along the colon, and no difference in α- and β-diversities among the sites was observed in the pediatric UC as well as the non-IBD patients. Notably, the microbial composition between CLFs and feces was similar, particularly in the pediatric UC patients. These findings (homogenous distribution of intestinal MAM) might account for the higher prevalence of severe pancolitis in pediatric UC than in adult cases [40, 41]. Age-dependent alteration in MAM along the intestine should be investigated to address the precise role of the MAM on the pathophysiology in UC.

Unexpectedly, CLFs in non-IBD pediatric patients showed higher microbial diversity compared with feces, and the bacterial families usually residing in oropharynx or upper intestines such as Enterobacteriaceae, Pasteurellaceae, Fusobacteriaceae and Neisseriaceae were significantly more abundant in the MAM. On the other hand, Bifidobacteriaceae and Lachnospiraceae, representative luminal members of gut microbiota, were more abundant in feces (Figs. 3 and 4). Microbiota of oral origin are known to increase in the intestines of IBD patients [42]. Schirmer et al. reported that Veillonella dispar, Veillonella parvula, Aggregatibacter segnis, Haemophilus parainfluenzae, Campylobacter sp., Lachnospiraceae, Megasphaera sp. increased in the intestine of treatment-naïve pediatric UC patients with the disease course [43]. Somineni et al. reported that an increase in oral bacteria before treatment was associated with aggravation of UC [44]. Moreover, Atarashi et al. reported that oral microbiota were significantly more abundant in the fecal samples from UC patients compared with healthy controls. In addition, they showed that Klebsiella sp. isolated from the salivary microbiota are strong inducers of Th1 cells when they colonize in the gut [45].

Originally, normal intestinal microbiota resist colonization of foreign microorganisms, which suppresses the colonization and proliferation of pathogenic bacteria. It has been reported that the intestinal tracts of IBD patients are more likely to be colonized by oral microbiota, resulting from attenuated colonization resistance [46, 47]. In addition, an increase in oral bacteria migrating to the intestinal tract has been observed in those patients. In IBD patients, genetic factors, treatments and intestinal inflammation may increase the abundance of swallowed oral bacteria that reach the lower intestine [48, 49, 50]. However, our data showed that the relative abundances of these bacteria in CLF from non-IBD were higher than those from feces (Fig. 5), indicating that they normally reside proximity to the mucous layer due to their adhesive properties and/or relative tolerance to reactive oxygen species. On the other hand, in MAM from the pediatric UC patients, we detected only enrichment in Enterobacteriaceae. These results indicate that the compromised mucous layer in UC had lost the ability to separate MAM and luminal microbiota, and also that Enterobacteriaceae is more capable of surviving in inflamed mucosa.

In agreement with other reports [51], mucosal inflammation or disease activity correlated with the alteration in MAM (Fig. 6). As Matts score (mucosal inflammation) or PUCAI (disease activity) increased, the Bray-Curtis dissimilarity in MAM among the sites (A, T, S and R) became larger, suggesting that the UC disease severity promotes the intra-individual regional variations in MAM by selective pressures (e.g. oxidative damage or clearance by immune system). We detected the differentially enriched bacterial genera at each disease condition determined by Matts score or PUCAI (Figs. 7 and 8). In terms of disease activity, the relative abundances of representative luminal members such as Bacteroides, Ruminococcus, Bifidobacterium and Blautia decreased with disease severity, while those of skin and environmental bacteria (Phyllobacterium, Sphingomonas, Ralstonia, Planococcaceae and Staphylococcus) and oral and upper intestinal tract origin (Gemellaceae_UKG, Porphyromonas and Prevotella) increased. A similar trend was observed regarding the Matts score. Among the genera belonging to Enterococcaceae (Enterococcus and unassigned genus), some increased and others decreased. This difference is probably due to variations in proinflammatory potential among Enterococcus species, as described previously [52]. A larger number of differentially enriched genera (> 4-fold change) was detected in the analysis based on disease activity compared with Matts score (Additional file 6 and Fig. 8), again suggesting that gut physiological dysfunction, compared with local mucosal inflammation, has a greater impact on MAM in pediatric UC.

Phascolarctobacterium was identified as the genus that was most differentially increased (moderate vs inactive) or second most differentially increased (mild vs inactive and severe vs inactive) in MAM compared with inactive disease (Additional file 6). This bacterial group is known to consume succinate [53, 54, 55], which might explain the increase in the succinate concentration at the mucous layer with mucosal inflammation. Succinate has been reported to act as a danger signal that induces macrophages to produce IL-1β [56]. Among the gut microbes, Bacteroides, Parabacteroides and Prevotella produce succinate as an end product of sugar metabolism. The alteration of succinate metabolism in MAM with disease severity might be involved in mucosal inflammation in pediatric UC.

Finally, we identified potential microbial markers in MAM to discriminate UC from non-IBD pediatric patients (Fig. 9). Lactobacillus and Enterococcus were identified as potential markers for UC; and others, including typical luminal bacteria such as butyrate-producing bacteria (Faecalibacterium and Blautia), were identified as potential markers for non-IBD pediatric patients. Fusobacterium of oral origin has been associated with periodontitis [57–60] and colon adenocarcinoma [61–68]. However, our results might indicate that the resident gut Fusobacterium plays an important role to keep homeostasis on host-microbe interaction.

There are some limitations in this study. First, the small cohort size in this study was insufficient to reach statistical power in some analyses. Second, therapeutics may have affected the MAM, but therapeutic information for individual patients was not included in the analysis. Third, non-IBD controls in this study had abdominal symptoms, and healthy children were not enrolled as a control. Fourth, we could not do quantitative analyses (microbial cell density or short chain fatty acid level) due to the difficulty in adjusting the sampling volume. Finally, we did not perform the longitudinal evaluations in all the patients. Since UC is a condition with repeats in relapse and remission, we are now evaluating the longitudinal progress of MAM in individual patients.

We herein analyzed a MAM in pediatric UC patients using CLFs. To the best of our knowledge, this is the first report to investigate the association of MAM and clinical data in pediatric UC patients. Our findings obtained in this study are as follows:

The compositions of MAM are similar along the colon to fecal microbiota in UC, likely due to the compromised mucous layer not separating MAM and luminal microbiota in UC.

MAM contained a more diverse range of OTUs compared with feces in non-IBD while only Enterobacteriaceae was observed in pediatric UC.

Aggravation in UC disease severity or mucosal inflammation promotes inter-regional difference among the colonic MAMs, leading to the enrichment in microbes that usually reside in the oral cavity, oropharynx, upper intestine or that have environmental origins.

Specific microbes in MAMs may be associated with inflammatory markers and humoral immune response including autoantibodies.

We identified nine potential microbial markers to discriminate pediatric UC from non-IBD patients.

We believe that these results provide novel insights into the association of mucosal bacterial communities with pathophysiology of pediatric UC, in which severe pancolitis is prevalent. Analysis of the intestinal lavage samples from pediatric UC patients enables us to evaluate the microbiota residing proximal to the gut mucosa. In addition, the method is non-invasive, inexpensive and suitable for longitudinal study. We are now planning such a longitudinal study with patients with pediatric UC to validate the microbial markers identified in this study for predicting prognosis.

Ethics approval and consent to participate:

This study was conducted according to the protocol approved by Ethics Committees in Kagawa University (Approved no:2020-175). We obtained the written informed consent from the guardians for all of the participants.

Consent for publication:

We obtained consent for publication from the guardians for all of the participants.

Funding:

This study was supported by Japan Society of the Promotion of Science (Grant Number 24K14726 to Kondo T and 23K28020 to Kuwahara T).

Availability of data and materials:

The16S rRNA gene sequencing read data have been deposited into DDBJ (Accession number: DRR609976-DRR610138).

Competing interests:

The authors declare that they have no competing of interests

Author’s contributions:

Kondo T, SK, Kusaka T and Kuwahara T conceptualized and designed this study. Kondo T and SK evaluated the disease activity and endoscopic findings. Kondo T, SK collected the samples. HNI, AT, NT and ME extracted the DNA, analyzed metagenomic data and conducted statistical analyses. Kondo T, SK, Kusaka T, HNI and Kuwahara T interpreted results and wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements:

We are thankful to Dr. Takanori Matsui and Dr. Hideki Kobara (Endoscopy Center of Kagawa University Hospital) for their assistance to collect CLFs from the participants. We also appreciate Mrs. Mina Aiba and Mrs. Kyoko Horiuchi for their assistance to sample management.

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Comparative analysis of mucosa-associated and luminal gut microbiota in pediatric ulcerative colitis

Status:

Version 1

Abstract

Background

Results

Conclusion

Figures

Background

Patients And Methods

1. Patient selection

2. Colon lavage collection

3. DNA preparation

4. 16S rRNA gene analysis

5. Statistical analysis

Results

Cohort and sample characteristics

Association of host factors with gut microbial taxonomic variation in UC and non-IBD

Association of the specific microbial taxa with clinical metadata

Homogenous distribution of microbiota in MAM along the colon

Microbial community structure of MAM and feces in pediatric UC and non-IBD patients

Alteration in community structure of MAM depending on UC disease activity

Profiling of MAM community structure according to UC disease severity

Profiling of MAM community structure according to mucosal inflammation

Potential microbial markers in MAM to discriminate pediatric UC from non-IBD

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1