A Comparative Study of the Gut Microbiota Associated with Immunoglobulin A Nephropathy and Membranous Nephropathy

Background

The gut microbiome, residing at intestinal epithelial barriers, is recognized as an important element that contributes to health and disease¹. Dysregulation in the interactions between the gut microbial ecosystem and the adjacent mucosal immune system have been identified in Crohn’s disease (CD)² and ulcerative colitis (UC)³. Increasing evidence indicates that gut dysbiosis is associated with other immune-mediated diseases, including systemic lupus erythematous (SLE)⁴, ankylosing spondylitis⁵ and rheumatoid arthritis(RA)⁶, where abnormal immune response affect sites distant from the gut. However, a mechanistic connection between the gut microbiota and extra-intestinal immune-mediated diseases remains unclear.

IgA nephropathy (IgAN), the most prevalent primary glomerulonephritis⁷, is characterized by the deposition of IgA1(particularly, galactose-deficient IgA1) in the glomerular mesangium⁸. Galactose-deficient IgA1, supposed to be produced by Peyer patches in the mucosa-associated lymphoid tissue(MALT)⁸, is triggered by exposure to commensal or pathogenic bacteria⁹ and is involved in the initial step in the pathogenesis of IgAN. A previous study showed that certain new risk loci for IgA nephropathy are associated with the maintenance of the intestinal epithelial barrier and response to mucosal pathogens¹⁰. The complex crosstalk between the gut and the kidney in IgAN has been elucidated and the gut-kidney axis has been supposed¹¹. Studies have suggested that gut dysbiosis may contribute to the onset and progression of IgAN; however, no clear causal associations have been demonstrated so far.

Membranous nephropathy (MN), the most common cause of adult-onset nephrotic syndrome worldwide, is an immune-mediated glomerular disease¹². Muscle-type phospholipase A2 receptor (PLA2R)¹³ and thrombospondintype-1 domain-containing 7A (THSD7A)¹⁴ were identified as the target antigens. Autoantibodies to both target antigens are predominantly of the IgG4 subclass¹⁵. Cationic bovine serum albumin, identified as the main antigen in early-childhood membranous nephropathy, may derive from microbiota, intestinal cells and milk formula¹⁶. Clonal expansion of B cells, promoted by MALT ¹⁹, may induce autoantibody production by interacting with genetic polymorphisms²⁰. With this background, therefore, we hypothesized that gut dysbiosis is possibly involved in the etiopathogenesis of MN.In this study, we aimed to investigate the association between the gut microbiota and the pathogenesis of IgAN and MN; toward this, we analyzed the bacterial community composition and diversity in patients and healthy controls using 16S ribosomal DNA Miseq sequencing. We report herein substantial differences in the composition of the gut microbiota between patients and healthy controls. Furthermore, we identify taxonomic biomarkers associated with clinical parameters. This study presents a picture of the microbiota in patients with IgAN and MN and offers specific biomarkers that may potentially contribute to the pathogenesis of IgAN and MN.

Methods

Study Population

We enrolled 44 patients with kidney biopsy-proven IgAN and 40 patients with kidney biopsy-proven MN who did not receive corticosteroids and/or immunosuppressive therapies prior to sampling, along with 30 matched healthy controls. All patients with IgAN and MN were recruited between September 2017 and May 2019 from the inpatient department of the Xijing Hospital. Patients with secondary glomerulonephritis, renovascular disease, gastro-intestinal diseases, pregnancy, and other autoimmune disorders were excluded. Healthy controls were enrolled from physical examination center volunteers. None of the individuals had taken antibiotics or probiotics/prebiotics for at least 3 months prior to sample collection. All individuals confirmed that there were no significant differences in meals and other lifestyle-related factors for at least 2 weeks. The demographic data, including gender, age, and body mass index (BMI), clinical features, results of biochemical examination, and biopsy-based pathological manifestations were recorded (Supplemental Table 1).

Collection of Faecal Samples and DNA Extraction

Fresh faecal samples were collected from the recruited individuals in the morning. After collection, the samples were immediately frozen and stored at -80℃ prior to analyses. The faecal sample was added to a 2ml screwcap vial containing 1g glass beads (0.1mm BioSpec Products, Inc., USA) and was suspended in 790μl sterile lysis buffer (4M guanidine thiocyanate; 10% N-lauroylsarcosine; 5% N-lauroyl sarcosine-0.1 M phosphate buffer, pH 8.0). The samples were subjected to bead beating for 10 min at maximum speed prior to incubation at70℃ for 1 h. Microbial DNA was extracted using the E.Z.N.A.®Stool DNA kit (Omega Bio-tek, Inc., GA,USA).

Polymerase Chain Reaction (PCR), Miseq Sequencing and Sequence Data Processing
The V3-V4 hypervariable regions of the 16S rDNA were amplified with primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′) using an EasyCycler 96 (PCR) system (Analytik Jena Corp., AG, Germany). PCR was conducted using the following program: 3 min of denaturation at 95℃, 21 cycles of 0.5 min at 94℃ (denaturation), 0.5 min for annealing at 58℃, 0.5 min at 72℃, and 5 min at 72℃ for a final extension. PCR was performed in a 20-μl reaction system containing 4μl 5×Fastpfu buffer, 2 μl 2.5 mM deoxynucleotide triphosphates (dNTPs), 0.8 µl of each primer (5 μM), 0.4 µl TransStart Fastpfu DNA polymerase (TransGen Biotech, Beijing, China), and 10 ng template DNA. The PCR products were detected on a 2% agarose gel, and the band was extracted and purified using the AxyPrepDNA gel (Axygen, CA, USA) and PCR clean-up system. The purified PCR products were mixed. Sequencing was performed on an Illumina MiSeq platform according to the standard protocols of the Shanghai Mobio Biomedical Technology Co. Ltd., China. The raw read data of all the samples have been deposited in the European Bioinformatics Institute European Nucleotide Archive database under the accession number: PRJNA574226.

Bioinformatics and Statistical analysis

The raw data were processed using FLASH with the following criteria: (1) longer than 200bp; (2) less than two mismatches of primers; (3) no ambiguous bases; (4) longer than 10 bp overlap in sequences that were merged according to their sequence.

Operational taxonomic units (OTUs) were clustered with 97% similarity cutoff using UPARSE(version 7.1 http://drive5.com/uparse/) with the following steps: (1) non-repeating sequences were extracted from the optimized sequences; (2) singletons of non-repeating sequences were removed; (3) OTUs were picked at 97% similarity cutoff; (4) OTU composition was created. The Ribosomal Database Project (RDP) Classifier algorithm(http://rdp.cme.msu.edu) was applied to analyze the taxonomy of each 16S rDNA sequence using the SILVA database(http://www.arb-silva.de). After filtering, we generated an average of 49,775 reads for each sample.

Species accumulation curve was plotted to evaluate the sufficiency of sample size and estimate bacterial richness. α-Diversity was determined using OTU analysis and presented using ACE index, chao index, Shannon index, and Simpson index, which were analyzed using the implemented method in the R package ‘vegan’. The details are showed in the Supplemental Table 2. Bacterial taxonomic comparison at the phylum and genus levels was tested between two groups using Wilcoxon rank sum test. β-Diversity was measured using unweighted UniFrac distances. Principal coordinate analysis (PCoA) was performed to display the space between samples. Linear discriminant analysis (LDA) effect size (LEfSe) was used to identify the characteristic microbiota and explain the differences between the patients and healthy controls. Different characterizations were performed with an LDA cut-off of 2.0. For correlation analysis, Spearman’s rank test was performed. The two-tailed t-test was used to evaluate continuous variables and the Chi-square test was used to compare categorical variables between the two groups. Statistical analyses were performed using SPSS V.20.0 for Windows (SPSS, Chicago, Illinois, USA).P values were considered significant at P<0.05.

Results

Clinical Characteristics of all Participants

We recruited 44 patients with IgAN, 40 patients with MN, and 30 healthy controls. All the patients were newly diagnosed with the conditions using kidney biopsy. The clinical characteristics of the participants are shown in Table 1. Age, gender, and BMI were matched between the patients and healthy controls. As expected, serum creatinine level was higher in patients with IgAN than in healthy controls, while the serum levels of total protein and albumin were significantly lower in patients with MN than in healthy controls (Table 1). The median proteinuria of patients with IgAN was 776mg/24h, whereas that of patients with MN was 3,623mg/24h. About 72.5% of patients with MN presented with positivity of serum anti-PLA2R antibodies. According to the Oxford classification of IgAN, there was modest glomerulonephritis with mild tubulointerstitial injury. Approximately 35(87.5%) patients presented with stage Ⅱ MN (Table 2).

Richness and Diversity of the Gut Microbiota

In total, 6,337,125 usable raw reads were obtained from 114 stool samples. After quality filtering and assembly of overlapping paired-end reads, 5,674,350 high-quality reads were generated and 698 operational taxonomic units (OTUs) were obtained based on a 97% homology cutoff. The average number of sequences per sample was 49,775±16,071(range 25,248-142,345). The values of Good’s coverage of all libraries were above 99%. The species accumulation curve showed that the estimated OTU richness already approached saturation at this sequencing depth, suggesting that a vast majority of diversity had been detected (Figure 1A). A Venn diagram showed that 541 of the total 698 OTUs were shared among the three groups, while 31 were unique for IgAN, and 16 were specific for MN (Figure 1B). No significant differences in community richness (estimated by chao and ACE indices) and diversity(measured by Shannon and Simpson indices) were observed between IgAN and the healthy control(Supplemental Table 3, Supplemental Fig. 1A, B, C, D).Similar trends for community richness and diversity were observed between IgAN and MN, consistent with the trends observed between MN and healthy control(Supplemental Table 3; Supplemental Fig. 1A, B, C, D).

Taxonomy-based comparisons of gut microbiota at the phylum and genus levels

At the phylum level, the gut microbiota of the three groups was dominated by Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria, which on average accounted for up to 98% of the relative abundance (Figure 2A). Bacterial genera Bacteroides, Faecalibacterium, Prevotella, and Lachnospiraceae_incertae_sedis, each accounting for up to 5% of the sequences on average, were the dominant populations (Figure 2B). The faecal microbial composition of all samples at the phylum and genus levels is shown in the Supplemental Figure 2A, B.

IgA Nephropathy versus Healthy Controls

Firmicutes was the most predominant phylum, contributing 47.5% and 50.7% of the gut microbiota in patients with IgAN and healthy controls, respectively. Compared to that in the healthy controls, Proteobacteria and Candidate_division_TM7were overrepresented (8.37% vs. 3.41% and 0.026% vs. 0.013%, respectively), while Fusobacteria and Synergistetes were significantly underrepresented in patients with IgAN (0.25% vs. 0.29% and 0.013% vs. 0.018%, respectively, all P<0.05, Figure 2C, Supplemental Table 4). At the genus level, we observed that Escherichia-Shigella and Defluviitaleaceae_incertae_sedis were enriched in IgAN(5.67% vs. 1.04% and 0.17% vs. 0.12%, respectively), while five genera, namely, Roseburia, Lachnospiraceae_unclassified, Clostridium_sensu_stricto_1, Haemophilus, and Fusobacterium were enriched in the healthy control(3.52% vs. 4.28%, 0.39% vs. 0.72%, 0.12% vs. 1.02%, 0.03% vs. 0.22%, and 0.25% vs. 0.29%, respectively, all P<0.05, Figure 2D, Supplemental Table 5).

Membranous Nephropathy versus Healthy Controls

Firmicutes was the dominant phylum in both MN and HC, contributing 47.2% and 50.7% of the gut microbiota, respectively. At the phylum level, the abundance of Proteobacteria increased (9.86% vs. 3.41%), whereas that of Synergistetes decreased in patients with MN compared to that in the healthy controls (0.008% vs. 0.018%, all P<0.05, Figure 2E, Supplemental Table 4). At the genus level, the abundance of five genera, namely, Escherichia-Shigella, Streptococcus, Enterobacteriaceae_unclassified, Peptostreptococcaceae_incertae_sedis, and Enterococcus increased(6.24% vs. 1.04%, 0.61% vs. 0.29%, 1.01% vs. 0.11%, 0.18% vs. 0.10%, and 0.045% vs. 0.007%), whereas the abundance of four genera, namely, Lachnospira, Lachnospiraceae_unclassified, Clostridium_sensu_stricto_1, and Veillonella decreased in patients with MN compared to that in the healthy controls (0.63% vs. 1.83%, 0.43% vs. 0.72%, 0.28% vs. 1.02%, and 0.28% vs. 0.42%, all P<0.05, Figure 2F, Supplemental Table 5).

IgA Nephropathy versus Membranous Nephropathy

Next, we investigated how microbial populations vary between disease cohorts, although no significant differences were observed at the phylum level between IgAN and MN. Compared to that in MN, the abundance of genera Megasphaera and Bilophila increased (1.23% vs. 0.17% and 0.23% vs. 0.09%), whereas those of Veillonella, Klebsiella, Haemophilus, Enterococcus, and Streptococcus decreased in patients with IgAN(0.25% vs. 0.28%, 0.33% vs. 0.91%, 0.03% vs. 0.43, 0.006% vs. 0.04%,and 0.17% vs. 1.01%, all P<0.05, Figure 2G,Supplemental Table 5).

Identification of Key OTUs

To identify key phylotypes distinguishing different groups, OTUs with a median relative abundance larger than 0.01% were analyzed using a Random Forest model. Compared to that in the healthy control, we identified 15 OTUs specific for IgAN, among which six OTUs assigned to Bifidobacterium, Paraprevotella, Parabacteroides, Roseburia, and Defluviitaleaceae_incertae_sedis were enriched, while nine OTUs assigned to Lachnospiraceae_unclassified, Haemophilus, Clostridium_sensu_stricto_1, Bacteroides, Ruminococcaceae_incertae_sedis, Megamonas, Faecalibacterium, and Roseburia were depleted in IgAN. Furthermore, we identified 15 OTUs specific for MN, among which seven OTUs assigned to Bacteroides, Escherichia-Shigella, Streptococcus, and Lachnospiraceae_incertae_sedis were enriched, while eight OTUs assigned to Bacteroides, Lachnospiraceae_unclassified, Clostridium_sensu_stricto_1, Lachnospira, Lachnospiraceae_incertae_sedis, Ruminococcaceae_incertae_sedis, Subdoligranulum, and Ruminococcus were depleted in MN. Furthermore, we found 12 OTUs as key variables between patients with IgAN and MN, among which three OTUs assigned to Flavonifractor, Veillonella and Ruminococcaceae_incertae_sedis were enriched in IgAN, while nine OTUs assigned to Veillonella, Haemophilus, Gemella, Lactobacillus, Bacteroides, Klebsiella, Actinomyces and Streptococcus were enriched in MN(Figure 3A,B,C).

Differentiation of patients Based on Gut Microbiota Profiles

PCoA based on unweighted UniFrac distances revealed that the microbial composition of IgAN deviated from those of the healthy controls (P=0.007, Figure 4A). The patients with MN and healthy control samples also separated when subjected to PCoA (P=0.018, Figure 4B). Conversely, a symmetrical distribution was observed between IgAN and MN when subjected to PCoA based on unweighted UniFrac distances (P=0.633, Supplemental Figure 3). To identify the specific taxa between groups, we analyzed faecal microbiota using LEfSe. A cladogram presented the gut microbial structures and the major differences in taxa between patients with IgAN and healthy controls (Supplemental Figure 4A, B). We also compared the faecal microbiota to identify the specific taxa between patients with MN and healthy controls (Supplemental Figure 4C, D); results showed gut microbial dysbiosis in patients with IgAN and MN. Further, the cladogram of microbial structure obtained after comparison of the faecal microbiota between disease cohorts showed the maximum differences in taxa (Supplemental Figure 4E, F).

Spearman correlation test in patients with IgAN and MN

Pairwise comparisons of clinical factors were shown, with a color gradient denoting Spearman’s correlation coefficients. The clinical factors were mainly focused on the risk factors for kidney prognosis. In IgAN group, significant positive correlation existed between the genera Klebsiella and Enterobacteriaceae_unclassified(ρ=0.82, Figure 5A). In addition, the genus Prevotella showed positive correlation, while Klebsiella, Citrobacter, and Fusobacterium showed negative correlations with serum albumin (ALB) level. Positive correlations existed between Bilophila and Crescents in the Oxford classification of IgAN (Figure 5A, Supplemental Table 6). Correspondingly, in the MN group, significant positive correlations existed in the genera: Alistipes and Ruminococcaceae_uncultured(ρ=0.82), Anaerotruncus and Christensenellaceae_uncultured(ρ=0.87), Citrobacter and Enterobacteriaceae_unclassified(ρ=0.81, Figure 5B). Negative correlation existed between Escherichia-Shigella and proteinuria, Bacteroides and Klebsiella showed positive correlation with the MN stage, while Akkermansia showed negative correlation with IgG4 deposition in the subepithelia, as observed using immunofluorescence (Figure 5B, Supplemental Table 7).

Discussion

To our knowledge, our present study represents the characterization of the gut microbiota in patients with IgAN and MN. In our present study, we demonstrated patterns of gut microbiota dysbiosis in two most prevalent glomerulonephritis and identified taxa specific for patients and healthy controls. The results of this study are twofold. First, using differential abundance analyses, we identified that patients with IgAN and MN were characterized by altered composition of the stool microbiota. Second, we showed the dissimilitude of the microbial community between patients with IgAN and MN.

We observed numerous taxonomic differences between disease cohorts and healthy controls. Compared to that in the control, the ethanol-producing genus Escherichia-Shigella²³ was enriched, consistent with the previous study²¹, whereas the butyrate-producing genera Roseburia and Faecalibacterium²⁴^,²⁵were depleted in patients with IgAN. The opportunistic pathogens Escherichia-Shigella occur predominantly in patients with diabetic nephropathy ²²and Crohn’s disease². The increase in Escherichia-Shigella population may exacerbate gut leakiness by decreasing butyrate biosynthesis and increasing oxidative stress to penetrate the intestinal epithelial barrier²⁶.

Compared to that in the healthy controls, the genus Roseburia has been reported to be depleted in Crohn’s disease²⁷. Interestingly, patients with Crohn’s disease on anti-TNFα antibody therapy showed increase in Roseburia abundance²⁸. Underrepresentation of the genus Faecalibacterium has been implicated in several disorders, including inflammatory bowel disease(IBD)²⁹ and obesity³⁰. In vitro, the culture supernatant of Faecalibacterium has been shown to inhibit nuclear factor-κB(NF-κB) activation, which is involved in the pathogenesis of IgAN¹⁰. As the major source of energy in the intestinal mucosa ³¹, butyrate plays an important role in maintaining gut health by exerting anti-inflammatory effects²⁴ and affects regulatory T (Treg) cells, which participate in the pathogenesis of IgAN³². Hence, Roseburia and Faecalibacterium were reported to be potential markers of gut health ^24,25.The reduction in butyrate-producing genera may promote intestinal mucosal destruction³³, which plays a critical step in the pathogenesis of IgAN¹⁰. Increasing evidence from human studies suggests that Roseburia and Faecalibacterium are potential probiotic candidates for the treatment of chronic gut inflammation³⁴.

Interestingly, Bifidobacterium abundance was higher in patients with IgAN than in healthy controls. As probiotic microorganisms, members of the genus Bifidobacterium are beneficial for health as they affect immune regulation³⁵, inhibit pathogens³⁶, and degrade diet-derived carbohydrates³⁷. Out of more than 50 species assigned to Bifidobacterium, only 10 are found in humans. However, a previous study demonstrated the invasive potential of Bifidobacterium in immune compromised host³⁸. Another study identified higher abundance of Bifidobacterium in patients with ulcerative colitis²⁷ than in healthy controls. Thus, we propose that some species may be disease-specific and further investigation regarding their effect on gut homeostasis is required.

Investigation of the relationships between clinical parameters and microbial taxa in the IgAN group revealed that Prevotella was associated with higher level of ALB, while Klebsiella, Citrobacter, and Fusobacterium were associated with lower level of ALB. Prevotella has previously been reported to be associated with improved glucose metabolism and insulin sensitivity³⁹, while Klebsiella correlated with the invasion of epithelial cells²⁵.

Compared to that in the healthy controls, the abundance of Escherichia-Shigella, Bacteroides, Actinomyces, and Streptococcus increased, while those of Lachnospira and Roseburia decreased in patients with MN. Escherichia-Shigella and Bacteroides have been reported to produce lipopolysaccharide (LPS)⁴⁰, which initiates various pathophysiological cascades⁴¹. High levels of LPS activate the NF-κB pathway and lead to the production of pro-inflammatory cytokines(TNF-α, IL-6, and IL-1)⁴¹. In agreement with this observation, higher abundance of Escherichia-Shigella and Bacteroides increases the circulating levels of pro-inflammatory cytokines such as TNF-α and IL-6, and genetic polymorphisms in these cytokines are associated with the onset/occurrence of MN^42,43. Actinomyces has been identified to be abundant in patients with rheumatoid arthritis⁴⁴ and ulcerative colitis⁴⁵. Members of the genus Streptococcus has recently been associated with numerous immune-mediated inflammatory diseases ^28,46. Furthermore, the presence of Streptococcus may be a predictive marker for the future recurrence of Crohn’s disease². Butyrate-producing Lachnospira and Roseburia are potential markers of health. Akkermansia displayed negative correlations with IgG4 deposition in the subepithelia. Akkermansia muciniphila, which produces the protective mucous lining of the intestine⁴⁷, is associated with health and the absence of autoimmune diseases⁴⁸.

Genetic susceptibilities and immune dysregulation are believed to be involved in the pathogenesis of IgAN and MN. Numerous environmental factors are known to affect the gut microbial community. Thus, it is not surprising that the abundance of certain taxa differs in the gut microbiota of patients and healthy controls. It is noteworthy that compared to that in healthy controls, taxa such as Ruminococcaceae_incertae_sedis were more abundant in IgAN and less abundant in MN. Ruminococcaceae was enriched in patients with hepatic encephalopathy⁴⁹, although evidence linking Ruminococcaceae_incertae_sedis to the disease is lacking. Therefore, it has been hypothesized that the varying abundance of certain taxa may influence autoimmune response and distant organs. Further studies are required to confirm the possible roles these microorganisms in the etiology of glomerulopathy. We identified certain genera (and OTUs) to be uniquely represented in patients compared to in healthy controls. Furthermore, we also identified certain taxa that were significantly different between patients with IgAN and MN. Considering the different pathological patterns of glomerulopathy, it is not surprising that we detected these differences.

Compared to that in the healthy controls, we observed alterations in the gut microbial community in the treatment-naïve IgAN and MN patients, which indicated that certain bacterial candidates may be implicated in disease pathogenesis and can be used as specific biomarkers in the patient cohort.

Despite the promising results, our study has certain limitations. First, we tested a relatively small sample size at the moderate pathological stage; a larger cohort that includes patients at different pathological stages will be necessary to comprehensively study the role of the gut microbiota and further validate these findings. Similarly, sufficient sample size will be required for comparisons across different disease states (active and inactive) and treatments. Data regarding the role of an active immune response and immunomodulating therapies on the gut microbiota are also limited. Second, although age, gender, and BMI were matched in our study, certain confounding effects such as dietary factors, should also be considered. However, the participants in our study were from northwest of China, and the lifestyle factors were similar. Third, we analyzed faecal microbiota, which cannot fully reflect the profiles of mucosal microbiota.

The results of our study demonstrated that alteration of gut microbiota is associated with developments of IgAN and MN, evidenced by the changes in various taxonomic levels. The specific microbes may be potential diagnostic biomarkers and therapeutic targets for IgAN and MN. Potential therapeutic strategies for IgAN and MN that target the gut microbiota by faecal microbiota transplantation are already being investigated.

Declarations

Ethics approval and consent to participate

The protocol of the study was approved by the Ethics Committee of Xijing Hospital of The Fourth Military Medical University. All the subjects provided their written consent for participation in this study. The procedures were followed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Availabilty of data and material

The raw read data of all the samples have been deposited in the European Bioinformatics Institute European Nucleotide Archive database under the accession number: PRJNA574226.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was sponsored by a grant from the discipline boosting program of the Xijing Hospital of the Fourth Military Medical University(XJZT18Z15). Key research and development plan of Shaanxi province, China (No. 2017ZDXM-SF-045).

Author contribution

SS and MB designed and supervised the project. RD and JZ collected samples. DW performed pathological diagnosis. RD contributed to data collection. RD and JZ performed bioinformatics and statistical analysis, and interpreted data. RD drafted the manuscript. SS and MB revised the manuscript for important content. Each author contributed important content, accepts personal accountability for the author’s own contributions, and agrees to ensure that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.

Acknowledgments

We thank Dr Hongyan Ren(Shanghai Mobiomed Technology Co., Ltd., China) for Miseq sequencing. We also thank the clinical doctors from the Xijing Hospital, the Fourth Military Medical University. We also thank the volunteers who enrolled for the study.

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Tables

Table 1 Baseline characteristics of study individuals.

Clinical indexes	IgAN(n=44)	MN(n=40)	HC (n=30)	P
Age, y	34.89±10.74	43.13±13.81	38.60±12.80	0.012
Male sex	20(45.5%)	26(65.0%)	14(46.7%)	0.150
BMI, kg/m²	22.95±3.17	23.76±3.15	22.78±2.32	0.310
Hypertension	11(25.0%)	12(30.0%)	-	0.005
Systolic BP, mmHg	126.05±18.31	126.98±18.78	112.33±9.98	0.001
Diastolic BP, mmHg	78.86±15.37	76.38±12.42	71.77±8.21	0.067
WBC, 10⁹/L	6.71±1.71	6.55±1.37	5.59±1.22	0.004
Hb, g/L	136.75±22.77	143.25±20.73	146.53±16.38	0.113
PLT, 10⁹/L	249.02±96.07	250.0±64.60	212.30±57.49	0.079
TP, g/L	69.40±6.68^a	51.45±8.24^b	74.45±3.88	<0.001
ALB, g/L	42.10±5.32^c	26.90±6.77^d	48.21±3.35	<0.001
S-Cre, umol/L	102.04±32.33^e	79.73±20.66	83.85±16.99	<0.001
eGFR, mL/min/1.73m²	73.30±23.94	98.15±23.24	85.04±18.24	<0.001
Hematuria	44(100%)	31(77.5%)	-	-
Nephrotic syndrome	2(4.5%)	33(82.5%)	-	-
Proteinuria, mg/24h	776[384-1372]	3623[2000-6493]	-	-
Anti-PLA2R antibody positivity	0(0%)	29(72.5%)	-	-

Note: Continuous data presented as mean±standard deviation or median [interquartile range]; categorical variables were presented as number (%). One-way ANOVA was used to evaluate continuous variables and Chi-square test was used to compare categorical variables among the three groups. Two-tailed t test was used to evaluate continuous variables and Chi-square test was used to compare categorical variables between the two groups.

Abbreviations: BMI, body mass index; BP, blood pressure; WBC, white blood cell count; Hb, hemoglobin; Plt, platelet count; TP, total protein; ALB, albumin; S-Cre, serum creatinine; eGFR, glomerular filtration rate; PLA2R, phospholipase A2 receptor.

^a,c,e P values are from t test between IgAN group and HC group, ^b ^,dP values are from t test between MN group and HC group. They are statistical different.

Table 2 Pathological features of the patients with IgAN and MN.

Pathological features	IgAN(N=44)	Pathological features	MN(N=40)
Oxford classification		Glomerular lesion of MN
Mesangial hypercellularity (M0/M1)	22/22(50%/50%)	Stage Ⅰ,n(%)	2(5%)
Segmental glomerulosclerosis (S0/S1)	10/34(22.7%/77.3%)	Stage Ⅱ,n(%)	35(87.5%)
Endocapillary hypercellularity (E0/E1)	41/3(93.2%/6.8%)	Stage Ⅲ,n(%)	3(7.5%)
Tubular atrophy/interstitial fibrosis (T0/T1/T2)	34/8/2(77.3%/18.2%/4.5%)	Stage Ⅳ,n(%)	0(0%)
Crescents(C0/C1/C2)	37/7/0(84.1%/15.9%/0%)

Note: Categorical variables were presented as number (%).

Abbreviations: M, mesangial hypercellularity (M0, <50% of glomeruli show mesangial hypercellularity; M1, >50% of glomeruli show mesangial hypercellularity); S, segmental glomerulosclerosis (S0, absent; S1, present in any glomeruli); E, endocapillary hypercellularity (E0, no endocapillary hypercellularity; E1, any glomeruli show endocapillary hypercellularity); T, tubular atrophy/interstitial fibrosis (T0, 0%-25% of cortical area; T1, 26%-50% of cortical area; T2, >50% of cortical area); C, crescents (C0: absent; C1, 0%-25% of glomeruli; C2, ≥25% of glomeruli).