Comparative analysis of the tonsillar microbiota in IgA nephropathy and other glomerular diseases

Immunoglobulin A nephropathy (IgAN) involves repeated events of gross haematuria with concurrent upper airway infections. The mucosal immune system, especially the tonsil, is considered the initial site of inflammation, although the role of the tonsillar microbiota has not been established in IgAN. In this study, we compared the tonsillar microbiota of patients with IgAN (n = 21) and other glomerular diseases (n = 36) as well as, healthy controls (n = 23) from three medical centres in Korea. The microbiota was analysed from tonsil swabs using the Illumina MiSeq system based on 16S rRNA gene. Tonsillar bacterial diversity was higher in IgAN than in other glomerular diseases, although it did not differ from that of healthy controls. Principal coordinates analysis revealed differences between the tonsillar microbiota of IgAN and both healthy and disease controls. The proportions of Rahnella, Ruminococcus_g2, and Clostridium_g21 were significantly higher in patients with IgAN than in healthy controls (corrected p < 0.05). The relative abundances of several taxa were correlated with the estimated glomerular filtration rate, blood urea nitrogen, haemoglobin, and serum albumin levels. Based on our findings, tonsillar microbiota may be associated with clinical features and possible immunologic pathogenesis of IgAN.

the intestinal mucosa of a mouse model 10 . Significant loci for IgAN are associated with the maintenance of the mucosal barrier and response to mucosal pathogens based on a genome-wide association study 11 .
The tonsil is a central site for antigen processing in the mucosal immune system and the first defensive organ against gastrointestinal entry. A common clinical manifestation of IgAN is macroscopic haematuria, which often coincides with tonsillitis 8,12 . Accordingly, the tonsil is considered the initial site of inflammation, and tonsillectomy has well-established beneficial effects in patients with IgAN suffering from recurrent tonsillitis 13,14 , either alone or in combination with steroid therapy [14][15][16][17][18] . Furthermore, several bacterial antigens are known to induce IgAN [19][20][21][22] .
The microbiota signature in the tonsils of patients with IgAN has not been fully elucidated. A recent study of the tonsillar microbiota did not show any difference among IgAN, recurrent tonsillitis, and tonsillar hypertrophy 23 . However, this previous study was based on single-centre data, and urinalysis data were lacking for paediatric patients in the control group, making it impossible to exclude hidden glomerular diseases. In this study, we characterised the tonsillar microbiota in patients with IgAN in comparison with that in healthy controlsconsisting of live kidney donors without evidence of kidney disease-and disease controls with biopsy-proven diabetic nephropathy (DN) and membranous nephropathy (MN) in multiple centres. Our findings extend our understanding of the role of the tonsillar microbiota in the pathogenesis of IgAN.  Table 1. The mean age was lower in the healthy control group (32.8 ± 5.8 years) than in the disease groups (53.2 ± 14.4 years, p < 0.001). Hypertension was more prevalent in all disease groups than in healthy controls (p < 0.001). The levels of plasma haemoglobin, serum albumin, and serum calcium were lower in IgAN group than in healthy controls (p < 0.001). Patients with MN showed features of nephrotic syndrome, including hypercholesterolaemia, hypoalbuminaemia, and large amounts of proteinuria. Kidney function, as evaluated by the estimated glomerular filtration rate (eGFR), was lowest in DN, followed by IgAN. Proteinuria and haematuria Table 1. Baseline characteristics of subjects. SBP systolic blood pressure, DBP diastolic blood pressure, eGFR; estimated glomerular filtration rate, HPF; high-power field. Data are presented as mean ± standard deviation or number (percentage); the p-value for comparison of all four groups.   Supplementary Table S1. Although the mean age differed significantly among groups (Table 1), there were no significant differences in bacterial diversity according to age in each group (p > 0.05; Supplementary Fig. S1). As shown in Fig. 1, we detected more operational taxonomic units (OTUs) in the IgAN and healthy control groups than in the MN and DN groups (p < 0.01). The compositions of the microbiota differed among groups in a Principal coordinates analysis (PcoA) based on Bray-Curtis distances (p < 0.01 by permutation tests; Fig. 1C). The microbiota of MN was more similar to that of DN than to those of the IgAN and healthy control groups. Individual variation was higher in the IgAN group than in the other groups. Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria were the dominant phyla in all groups (Fig. 1D). The relative abundance of Proteobacteria (55.7% of the total microbiota) was higher in the healthy control than in the other groups (33.8% to 40.8%), whereas the proportions of Firmicutes and Bacteroidetes were higher in all disease groups than in healthy control groups. Although the average proportions of each phylum differed among groups, these differences were not statistically significant (corrected p > 0.05).

Results
Significantly different microbes among groups. To detect significantly different genera, frequently detected genera were compared among groups ( Supplementary Fig. S2). Frequently detected genera were defined as genera that comprised > 0.5% (mean value) of the microbiota in more than 50% of the samples in each group. Streptococcus, Pseudomonas, Neisseria, and Haemophilus were the dominant genera (> 5% in each group) in all groups. The relative abundances of Rahnella, Ruminococcus_g2, and Clostridium_g21 were significantly higher in the IgAN group than in the healthy control group (corrected p < 0.05; Fig. 2). Other genera that differed significantly between healthy control and disease control groups or between IgAN and disease control groups are summarised in Supplementary correlation between genera and clinical features. We analysed the correlations between the relative abundances of genera and clinical features using Spearman's rank correlation coefficients with corrected p-values. Four genera having significantly different abundance between the IgAN and healthy control were also correlated with clinical features (corrected p < 0.05). The relative abundances of two genera, Acinetobacter and uncultured Moraxellaceae, were correlated with kidney function represented by eGFR and blood urea nitrogen (BUN) (Fig. 3). In particular, they were positively correlated with eGFR values and negatively correlated with BUN values. In addition, Acinetobacter, uncultured Moraxellaceae, and Enterobacter were positively correlated with plasma haemoglobin values ( Supplementary Fig. S3). Acinetobacter, uncultured Moraxellaceae, and Delftia  Fig. S3). Three genera with significant differences in abundance between the healthy control or IgAN and disease control groups were correlated with eGFR, albumin, BUN, and haemoglobin values ( Supplementary Fig. S4). Tannerella was negatively correlated with eGFR values, and Citrobacter was positively correlated with albumin values. Capnocytophaga was positively correlated with BUN values but negatively correlated with haemoglobin values.

Discussion
We detected genus-level differences in the microbiota of the tonsils of patients with IgAN, patients with other kidney diseases (MN and DN), and healthy controls. Rahnella, Ruminococcus_g2, and Clostridium_g21 were relatively abundant in the tonsils of patients with IgAN. In addition, the tonsillar microbiota was related to  The dominant phyla in tonsil samples were Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes, consistent with previous findings (Fig. 1) 24 . Bacterial richness was higher in the IgAN and healthy control groups than in the MN and DN groups. In previous studies, Haemophilus parainfluenzae and Staphylococcus aureus have been identified as candidates for the induction of IgAN [19][20][21][22] . Treponema and Campylobacter rectus are also associated with the development and progression of IgAN 25 . Prevotella, Porphyromonas, and Treponema are more abundant in patients with IgAN than in patients with tonsillar hyperplasia 23 . However, in our study, Haemophilus was the dominant genus in all groups; Staphylococcus, Treponema, and Campylobacter were detected in all groups ( Supplementary Fig. S2) and differences in relative abundances were not significant. We found that the relative abundances of Rahnella, Ruminococcus_g2, and Clostridium_g21 were significantly higher in IgAN than in healthy controls (corrected p < 0.05; Fig. 2). These inconsistencies with previous studies have several potential explanations, including differences in sampling sites, methods, and the ethnicity of subjects. Differences in the composition of the microbiota between the tonsil surface and tissues have been observed 26 . Furthermore, most previous studies have focused on specific bacteria in the pathogenesis of IgAN. Our high-throughput sequencing approach provided an overview of the comprehensive bacterial microbiota. Differences between our results and those of a previous study using a high-throughput sequencing approach 23 could be explained by differences in sample collection and study populations, which are known to effect the human microbiota 27,28 .
Three bacteria showed higher abundances in the IgAN group than in the healthy control group. Rahnella sp. has been isolated from the blood, surgical wounds, urine, sputum, bronchial washings, tonsil, and stool 29 . This bacterium can cause bacteraemia from a kidney focus 30 and infection in immunosuppressed individuals 31 . Ruminococcus_g2 and Clostridium_g21 were evaluated by a hierarchical clustering analysis of reference sequences in the EzTaxon-e database (https ://www.ezbio cloud .net/) based on 16S rRNA genes. Ruminococcus_g2 belongs to a cluster including Ruminococcus bromii and uncultured Ruminococcus. R. bromii has been detected in the microbiota of adenoiditis and tonsillitis 32 and in the intestine of patients with HIV-1 infection 33 . Clostridium_g21 belongs to a cluster including Clostridium scindens and uncultured Clostridium. C. scindens can covert glucocorticoids into androgens 34 and is involved in resistance to C. difficile infection 35 . These three bacteria related to tonsillitis are especially abundant in the tonsils of patients with IgAN. They are therefore novel candidates for the pathogenesis of IgAN or for related differences in the immune system status. www.nature.com/scientificreports/ We did not detect genera with significant differences in abundance between the MN and DN groups. We observed more genera differing between the healthy control and MN (17 genera) or DN groups (10) than between the healthy control and IgAN groups (6). The observed differences in the tonsillar microbiota among kidney diseases could be associated with the pathogenesis or progression of each disease. In particular, we detected greater differences between MN and healthy control or IgAN groups than between DN and the healthy control or IgAN groups. The development of DN involves chronic systemic inflammation, whereas primary MN is an autoimmune disease mainly mediated by antibodies to podocyte antigens, the M-type phospholipase A2 receptor and thrombospondin type 1 domain-containing 7A 36 . The tonsil has immunological functions, and the tonsillar microbiota may be more closely related to pathogenesis of MN than to the pathogenesis of DN. Differentially abundant genera in the tonsillar microbiota of each kidney disease are candidates for further functional studies.
We detected correlations between the relative abundances of genera and clinical features of kidney functions ( Fig. 3 and S3). Anaemia is a well-known clinical feature of acute kidney injury or chronic kidney disease 37,38 . Hypoalbuminaemia is common in advanced chronic kidney disease including including end-stage kidney disease 39 and is associated with high mortality in acute kidney injury and chronic kidney disease 40 . In our study, Acinetobacter and uncultured Moraxellaceae were related to better kidney function and higher levels of plasma haemoglobin and serum albumin in all kidney diseases. The similar patterns for the relationships between these parameters and specific bacteria provide a basis for further clinical studies, particularly given the lack of research on the role of tonsillar microbiota in kidney diseases.
In addition, three genera with significant differences in relative abundance between healthy control or IgAN and disease control groups were correlated with eGFR, serum albumin, BUN, and plasma haemoglobin (Supplementary Table S2 and Supplementary Fig. S4). Tannerella, found in the oral cavity and tonsilloliths, is associated with the production of volatile sulphur compounds and periodontitis 41,42 , which is linked to chronic kidney disease 43 . The correlation between a high abundance of Tannerella and decreased kidney function (as evaluated by eGFR) might be related to periodontitis caused by this pathogen. Capnocytophaga is a core component of the microbiota in the oral cavity and palatine tonsil of HIV-infected individuals 44 . C. ochracea produces an immunosuppressive factor and degrades immunoglobulins 45,46 . These results highlight the potential relationship among the tonsil environment, tonsillar microbiota, and clinical features and suggest that the tonsillar microbiota contributes to kidney diseases, including IgAN.
Our study had several limitations. First, the number of subjects was relatively small. Considering the high variability in microbiota composition, caution is needed when applying these results to the general population. Second, there was an age difference between the healthy control group and other groups. Though we showed that there was no obvious difference in bacterial diversity according to age, age is still a powerful parameter and may affect the composition of the tonsillar microbiota. Age-and sex-matched subjects should be compared in a future study. Third, we obtained samples by tonsillar swabs, which may only reflect superficial bacteria. There was also a lack of negative sequencing controls to check potential contamination of sequencing reagents, however we evaluated potential contamination at every experimental step using negative controls (distilled water) and empty swabs. Despite these limitations, our results provide key insights into the tonsillar microbiota in IgAN, including correlations between taxon abundances and clinical features.
In conclusion, the microbiota in the tonsils of patients with IgAN differed from those of patients with other kidney diseases and healthy controls. The high relative abundances of Rahnella, Ruminococcus_g2, and Clostrid-ium_g21 in patients with IgAN could be related to the immune status and pathogenesis of the disease. Further studies with larger sample sizes and systemic analyses of the gut microbiota and immunological features are necessary to understand the role of the microbiota in IgAN development.

Methods
Study subjects and sample collection. Patients who were admitted to undergo a kidney biopsy at three medical centres (Seoul National University Hospital, Seoul National University Boramae Medical Center, and Kangwon National University Hospital) in South Korea were enrolled. Patients were assigned to IgAN, DN, and MN groups based on the pathological evaluation. The DN and MN groups were used as disease control groups. Participants were enrolled at the time of kidney biopsy, before treatment with steroids or other immunosuppressants. Subjects who visited the Seoul National Boramae Medical Center for a regular health check-up and had normal kidney function and no underlying disease served as healthy controls. All subjects were over 18 years-old, and subjects who had undergone tonsillectomy were excluded. The demographic and clinical data for subjects were collected from hospital electronic medical records. The eGFR was calculated using the Chronic Kidney Disease-Epidemiology Collaboration equation 47 . A histological evaluation was performed according to the Oxford classification 48 . Tonsil swab samples were collected by rubbing each palatine tonsil twice using a cotton swab (Easy Swab; Synergy Innovation, Seongnam, South Korea). To check a potential contamination in cotton swab, empty swabs were also analysed along with tonsil swab samples. The samples were immediately stored at 4 °C, delivered to the laboratory within 24 h, and then stored at − 80 °C until DNA extraction. DNA extraction and MiSeq sequencing. Metagenomic DNA was extracted from 80 swab specimens using a FastDNA SPIN Extraction Kit (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer's instructions. For MiSeq sequencing, samples were prepared as described previously 49,50 . Briefly, the V4-5 variable region of the 16S rRNA gene was amplified using extracted DNA, and amplification was performed accord- Sequence data analysis. Sequence reads were analysed using CLC genomics workbench v.11.0.1 with the Microbial Genomic Module (Qiagen, Aarhus, Denmark) as described previously 49,51 . Sequence reads were merged, and reads with short lengths (merged reads of < 200 bp) or low-quality scores (Q < 25) and primer sequences were removed from the merged sequences using the USEARCH pipeline v.10.0.240 (https ://www. drive 5.com/usear ch). Chimeric sequences were removed using the UPARSE tool. Resultant sequences were clustered into OTUs based on97% identity. Taxonomic positions of representative sequences in each OTU cluster were assigned using the EzTaxon-e database 52 . To compare diversity indices among samples, read numbers were normalised by random subsampling and indices were calculated using MOTHUR 53 . PCoA plots were generated to compare the microbiota among samples using Calypso 54 .

Statistical analyses.
Clinical characteristics of subjects were compared by analysis of variance (ANOVA) and chi-square tests. P-values < 0.05 were considered statistically significant. Permutation tests were used to calculate statistical significance in the PCoA. Differences in microbial taxa between samples were evaluated by the Mann-Whitney U test and Kruskal-Wallis test implemented in R. The correlations between clinical features and relative abundances of specific microbes were analysed using Spearman's rank correlation tests in SPSS (version 22). Corrections for multiple testing were performed using the Benjamini-Hochberg method for false discovery rate adjustment. Results with corrected p-values of < 0.05 were considered statistically significant.