Kidney360 Publish Ahead of Print, published on June 9, 2021 as doi:10.34067/KID.0000132021

The Gut and Blood Microbiome in IgA

Nephropathy and Healthy Controls

Neal B. Shaha; Sagar U. Nigwekarb; Sahir Kalimb; Benjamin Lelouvierc; Florence Servantc; Monika

Dalala; Scott Krinskyb; Alessio Fasanod; Nina Tolkoff-Rubinb; Andrew S. Allegrettib

aDepartment of Medicine, Division of Hospital Medicine, Johns Hopkins Bayview Medical Center,

Baltimore, Maryland, USA

bDivision of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston,

Massachusetts, USA

cVaiomer SAS, Labège, France

dDivision of Pediatric Gastroenterology and Nutrition, Center for Celiac Research, MassGeneral Hospital

for Children, Boston, MA, USA

Corresponding author:

Neal B. Shah

Department of Medicine, Division of Hospital Medicine,

Johns Hopkins Bayview Medical Center

5200 Eastern Avenue, MFL East Tower, room 260, Baltimore, MD 21224, USA.

Tel: (410) 550-5018. Fax: (410) 550-2972.

E-mail: [email protected].

1

Copyright 2021 by American Society of Nephrology. KEY POINTS

 A higher microbiome load possibly originating from different body sites may be playing a

pathogenic role in IgA Nephropathy.

 Several microbiome taxonomic differences between IgA Nephropathy and healthy controls are

observed in blood and stool.

 Striking differences between the blood and gut microbiome confirm that the blood microbiome

does not directly reflect the gut microbiome.

ABSTRACT

Background:

IgA nephropathy (IgAN) has been associated with gut dysbiosis, intestinal membrane disruption and translocation of into blood. Our study aimed to understand the association of gut and blood microbiomes in IgAN patients in relation to healthy controls.

Methods:

We conducted a case control study with 20 progressive IgAN patients matched with 20 healthy controls, analyzing bacterial DNA quantitatively in blood by 16S PCR and qualitatively in blood and stool by 16S metagenomic sequencing. Between group comparisons as well as comparisons between the blood and gut microbiomes were conducted.

Results:

Higher median 16S bacterial DNA in blood was found in the IgAN group compared to the healthy controls group (7410 vs 6030 16SrDNA copies/uL blood, p = 0.04). Alpha and beta diversity in both blood and stool was largely similar between the IgAN and healthy groups.. Higher proportions of class

Coriobacteriia, and of genera legionella, Enhydrobacter and parabacteroides in blood; and species

2 of genera Bacteroides, Escherichia-Shigella and some Ruminococcus in stool were observed in IgAN patients in comparison with healthy controls. Taxa distribution were markedly different between the blood and stool samples of each subject in both IgAN and healthy groups without any significant correlation between corresponding gut and blood phyla.

Conclusions:

Important bacterial taxonomic differences quantitatively in blood and qualitatively in both blood and stool samples detected between IgAN and healthy groups warrant further investigation into their roles in the pathogenesis of IgAN. While gut bacterial translocation into blood may be one of the potential sources of the blood microbiome, marked taxonomic differences between gut and blood samples in each subject in both groups confirms that the blood microbiome does not directly reflect the gut microbiome. Further research is needed into other possible sites of origin and internal regulation of the blood microbiome.

3

INTRODUCTION:

IgA Nephropathy (IgAN) is the most common primary glomerulopathy and is characterized by deposition of IgA antibodies usually in the kidney mesangium1. While the exact pathogenesis remains unclear, antigens are believed to stimulate production of poorly galactosylated IgA1 in susceptible hosts resulting in glomerular mesangium immune complex deposition, thus eliciting inflammation and tissue damage2. A genome-wide association study (GWAS) showed that genes involved in IgAN were associated with the ability of the gut-associated lymphoid tissue (GALT) to regulate intestinal pathogens and maintain integrity of the intestinal barrier3. These results have generated interest in the association and role of gut microbes in IgAN.

Previous gut microbiome studies have shown that the gut microbiome plays a vital role in host nutrition and development of the immune system4,5. This gut microbiome tends to become imbalanced (dysbiotic) in various disease states including chronic kidney disease (CKD)6,7. Gut dysbiosis associated with disruption of intestinal membrane barrier resulting in translocation of gut bacteria and toxins into blood has been observed in CKD8,9. Strong evidence of gut renal axis has been recently reported to be associated with the pathogenesis of IgAN10. As a major immunoglobulin of the gut mucosal immune system, IgA in secretory form plays a crucial role in controlling mucosal inflammation by linking to specific gut microbiota11. A recent study by De Angelis et al has shown significant differences in gut microbiota between IgAN and healthy subjects with a higher proportion of species and genera of families

Ruminococcaceae, Lachnospiraceae, Streptococcaceae and others identified in patients with IgAN12.

Subsequent gut microbiome studies in Chinese population with IgAN have additionally noted higher prevalence of genera Escherichia-Shigella and Bacteroides in stool when compared with healthy controls13,14. We were interested in understanding if such microbiota may be mediating their pathogenic effects by translocating into blood via a disrupted intestinal barrier. A study analyzing simultaneously both blood and gut microbiome in IgAN has not been conducted previously. We hypothesized that the blood microbiome in IgAN will reflect dysbiosis analogous to gut and differ from healthy controls. Our

4 study aimed at comparing the blood bacterial quantity of 16S ribosomal DNA (16S rDNA) and blood and stool metagenomic qualitative profiles between IgAN patients and healthy controls. Analyzing human stool and blood microbiomes simultaneously for the first time in IgAN, we also compared concurrent stool and blood microbiome samples to better understand the relationship of gut microbiota translocating into blood.

METHODS:

Study design:

A case control study was conducted involving blood and stool microbiome testing of 20 IgAN cases and

20 healthy control subjects. The study was approved by our Partners Institutional Review Board (IRB) and adhered to the Declaration of Helsinki.

Enrollment of study participants:

Twenty adult patients in each group aged 18-65 years and enrolled in our hospital electronic medical record (EMR) system were recruited (figure 1). IgAN cases were identified by reviewing kidney biopsy reports and patient charts of patients followed at Massachusetts General Hospital. Cases were biopsy proven IgAN with progressive disease at various stages and estimated Glomerular Filtration Rate (eGFR)

≥15ml/min by CKD-EPI formula15 who were not on any oral or systemic immunosuppressants and never had dialysis or transplant. Healthy controls were frequency matched by age and sex. They were recruited primarily via advertisement of study using an online platform named ‘Rally’ which is approved by IRB to foster collaboration between public and the research community. We excluded subjects with diagnosed diabetes, any malignancy, inflammatory bowel disease, history of colon surgery or intake of antibiotics or probiotics within 30 days of the study visit. Dietary assessment was not performed due to unclear effects of different foods on the microbiome. Notably, previous studies have demonstrated that the overall composition of the gut microbiome at phylum level remains relatively stable despite some diurnal variations16.

5

Study visit and sample collection:

The study visit involved obtaining written informed consent per ICMJE recommendations and obtaining blood and urine samples. Blood was tested for routine chemistries and microbiome. A pre-prepared stool kit was given to subjects and all samples were either dropped off personally or mailed to us via overnight shipping within 1-2 days of sample collection. Samples were collected within two weeks of signing informed consent. Nine subjects provided stool specimen on the same day as the study visit out of which five were provided after using the laboratory restroom within minutes of blood collection. Blood and stool microbiome samples were stored in a -80C freezer until study completion and then shipped for batch testing.

Microbiome testing:

DNA extraction and 16S quantification:

After sterilizing skin prior to venipuncture, three ml of whole blood was drawn for microbiome testing in an EDTA tube midway amongst other blood draws to eliminate chances of skin contamination. Total

DNA was extracted from 100 µl of whole blood using a specific Vaiomer protocol carefully designed to minimize any risk of contamination as described previously17–19. To ensure a low background signal from bacterial contamination of reagents and consumables, negative controls consisting of molecular grade water were added in an empty tube separately at the DNA extraction step (extraction negative control) and PCR step (PCR negative control) and amplified and sequenced at the same time as the extracted DNA of the blood samples. Beta diversity analysis show a clear separation between negative controls and both blood samples (Supplementary Figure 1). These controls confirm that bacterial contamination was well contained in our pipeline and had a negligible impact on the taxonomic profiles of the samples of this study. DNA was extracted from stool samples as described previously20.

16SrDNA amplification and measurement:

6

Total 16S rDNA quantity in DNA extracted from blood samples was measured by qPCR in triplicates using 16S universal primers targeting the V3-V4 region of the bacterial 16S ribosomal gene and normalized using a plasmid-based standard scale18. The efficiency calculated from the standard curve was 91.75% (Normal 80-120%), and the R2 of the standard curve was 0.99

(Normal > 0.98). After successful extraction and amplification, 16S rDNA was measured as number of 16S copies per microliter of blood in triplicates and fell within the standard curve range.

16S Metagenomic sequencing:

The sequencing was performed using the Illumina® MiSeq technology after a two-step PCR library preparation as described previously20,21. The V3-V4 16S region from both blood and stool microbiota were analyzed using the bioinformatics pipeline established by Vaiomer (Toulouse, France) from the

FROGS guidelines22. The taxonomic assignment was performed against the Silva v132 database to determine community profiles. 4,651,231 raw read pairs were generated; 3,304,534 were kept after quality filters; and 2,447,442 were clustered in Operational taxonomic units (OTU). The following specific filters were applied for this analysis to obtain the best results: a) The last 10 bases of reads R1 were removed; b) The last 40 bases of reads R2 were removed; c) Amplicons with a length of <350 or

>500 nucleotides were removed; d) OTUs with abundance lower than 0.005% of the whole dataset abundance were removed. To increase the specificity of bacterial taxa truly different between the IgAN and healthy groups, we lowered the sensitivity by eliminating taxa having proportions <0.005% in more than half subjects in both groups and restricting statistical analysis to the genus level since approximately

>70% of taxa at species level were either unknown or had multiple affiliations.

Alpha and beta diversities were compared between the two groups23. Alpha diversity using Shannon index

(measuring the richness and evenness of distribution of taxa) within each sample was used24. Beta diversity (comparing differences in the microbial community between groups) was measured using the

7 weighted UniFrac technique, which calculates the distance between pairs of samples based on the abundance and phylogenetic relatedness of observed taxa25. Individual bacterial taxonomic differences between groups were compared by comparing OTUs generated using the Linear discriminant analysis effect size (LEfSe) algorithm26.

Statistical analysis:

Demographic characteristics between IgAN and healthy groups were compared by using t-test, Mann-

Whitney U test and Chi-squared test as appropriate. Differences in blood total 16S rDNA levels between the two groups was compared using Mann-Whitney U test. Adjusted analysis was done using multivariable regression modeling, adjusting for age, albumin, BMI, and WBC count. Differences in alpha diversity as well as individual taxonomic differences between groups were compared between groups using Mann-Whitney U test. Spearman test was used for correlation analysis. IgAN patients were also stratified by eGFR levels ≤60ml/min (n=11) and eGFR levels>60ml/min (n=9). Bacterial 16S rDNA quantity as well as individual taxa in both eGFR groups were compared with the healthy control groups separately to limit confounding by eGFR. Separate analysis was done for blood and stool samples when comparing the IgAN and healthy groups. For all analysis, SAS v 9.4 was used and two tailed p-values of

<0.05 were deemed statistically significant. For microbiome differences conducted using LEfSe, significance was also determined based on effect size of microbiota.

RESULTS:

Baseline characteristics:

Baseline characteristics of the 40 subjects are shown in table 1. Demographic characteristics were similar between groups. IgAN group had statistically significant higher white blood cell (WBC) count (p=0.04) and lower serum albumin levels (p=0.03) compared to healthy controls. As expected, proteinuria and

8 eGFR was significantly worse in the IgAN group. Median eGFR was similar between the healthy and

IgAN group with eGFR>60ml/min (100 vs 91 ml/min).

Blood 16S rDNA quantitative testing:

Median blood bacterial 16S rDNA concentration was significantly higher in the IgAN group compared to the healthy group (7410 vs 6030 16S rDNA copies/uL blood, p = 0.04; see supplementary figure 2). After stratifying by eGFR, median blood 16S rDNA concentration remained significantly higher in IgAN patients with eGFR >60ml/min when compared to healthy controls (7730 vs 6030 16S rDNA copies/uL blood, p = 0.04). This significance was lost when comparing IgAN patients with eGFR≤60ml/min to healthy controls (7343 vs 6030 16S rDNA copies/uL blood, p = 0.22).

After multivariable adjustment, especially for WBC count, the differences in 16S rDNA between IgAN and healthy groups were no longer significant (p=0.24). There was a strong positive correlation between

16S rDNA copies and WBC count (r=0.7, p<0.001).

16S metagenomic sequencing:

1) Alpha and beta diversity:

Alpha diversity (figure 2A) by Shannon index between the IgAN and healthy groups was not significantly different in blood (2.65 vs 2.68, p = 0.79) or stool (3.25 vs 3.31, p = 0.82). No significant differences were observed even after stratification of IgAN patients by eGFR. The overall beta diversity measured by weighted UniFrac technique was largely similar between IgAN and healthy groups in both blood and stool (indicated by the high degree of overlap in figure 2B).

2) Taxonomic signature analysis:

Analyses of differences in individual taxa proportions between biological groups were performed using the LEfSe algorithm which combines statistical significance with biological effect size. There were 10

OTU differences in blood and 41 OTU differences in the gut between the IgAN and healthy groups (table

9

2). After eliminating OTUs with proportions <0.005% and restricting analysis to genus level to increase specificity, significantly higher prevalence of class (p=0.01), as well as Legionella

(p=0.05) and Enhydrobacter (p=0.04) genera were present in blood of those in the IgAN group (table 3).

After stratifying IgAN patients by eGFR, such differences were no longer significant despite having higher prevalence in both eGFR stratified IgAN groups when compared to healthy controls. Proportion of genus legionella was higher in IgAN patients with eGFR≤60ml/min compared to eGFR>60ml/min.

Additionally, proportions of both Staphylococcus and Streptococcus genera from class Bacilli of phylum

Firmicutes were higher in IgAN patients with eGFR ≤60ml/min in comparison to both eGFR>60ml/min group as well as the healthy group.

Significantly higher levels of genus Bacteroides (p=0.01) of family Bacteroidaceae and genus

Escherichia-Shigella (p=0.01) of family Enterobacteriaceae were observed in the stool of the IgAN group

(table 4). Although differences were no longer significant after stratifying IgAN patients by eGFR, they continued to remain at higher proportions in both eGFR IgAN groups when compared to healthy controls group despite being more prominent in the low eGFR IgAN group. The proportion of genus Bacteroides was much higher in patients with eGFR<60ml/min with a significantly higher ratio of phylum

Bacteroidetes to when compared to healthy controls. Healthy group had significantly higher abundance of Genera Prevotella9 (p=0.02), and Ruminococcaceae groups NK4A214 (p=0.001) and

UCG002 (p=0.04). Major taxonomic comparisons between IgAN and healthy groups up to genus level in blood and stool are outlined in tables 3 and 4, respectively.

When comparing taxonomic differences between blood and stool samples, both IgAN and healthy groups exhibited large bacterial microbiome differences at all taxonomic levels with several blood microbiome taxa that were absent in stool (Figure 3).

Correlation analyses:

a) Correlation of microbiome with clinical parameters of IgA Nephropathy

10

Blood and stool microbiota correlations were performed with clinical characteristics of IgAN such as urine albumin to creatinine ratio, blood levels of eGFR, C-reactive protein, IgA level, albumin, WBC count, 16SrDNA and histologic MEST-C score27. These correlations are detailed in table 5. Due to wide inter-individual variations in eGFRs>60ml/min in the IgAN group, we restricted our analysis to CKD patients with GFR ≤60ml/min, which included 11 IgAN patients. eGFR levels did not correlate with 16S rDNA levels (r -0.14, p 0.7). Notably, blood IgA levels were available only for 12 IgAN patients while

MEST-C score report was available only for 16 IgAN patients as 4 patients were biopsied at other places or countries in the past from where record could not be obtained. Given the variable timeline of biopsies in relation to study enrollment, interpretations are limited.

b) Correlation between blood and stool microbiota

Proportions of major taxa were significantly different between blood and stool samples in both IgAN and healthy groups (Figure 4A). There was no correlation found between blood and stool samples in major phyla (r = 0.21, p = 0.19), (r = -0.24, p = 0.13), (r = -0.25, p =

0.11) and Firmicutes (r = -0.07, p = 0.65). Even at genus level, top 15 taxa observed in blood were different from those observed in stool in both IgAN and healthy groups (Figure 4B).

Outlying Patients:

Three subjects (1 healthy and 2 IgAN) did not pass the technical quality control of sequencing and had abnormal taxonomic profiles with high proportion of Lachnospiraceae and Ruminococcaceae families from Clostridia class and Firmicutes phylum when compared to remaining 37 subjects (figure 5). We conducted our statistical analysis both including and excluding these three outliers in blood samples.

Alpha and beta diversities as well as correlation analyses remained largely unchanged. Only 5 out of 10

OTUs in blood remained significantly different between the two groups (table 2). Significance was lost after stratification by eGFR.

Outlying proportions of blood microbiota:

11

Several IgAN patients had certain blood microbiota in disproportionately high abundance that were not statistically significant between groups due to being absent in most subjects. We excluded the 3 outliers for this observation. Most subjects had streptococcus and staphylococcus genera proportion undetectable to <1%, but 4 IgAN patients had streptococcus proportions of 2-6% and 1 IgAN patient had staphylococcus proportion of 9.5%. Several other minor outlying genera in different IgAN patients included Parabacteroides, Phyllobacterium, Parasutterella, Ruminococcus UCG005 and UCG013,

Romboutsia and Sphingomonas.

DISCUSSION:

This study simultaneously measured the blood and gut microbiome in IgAN patients and compared them to healthy controls in order to identify potential bacterial microbiota that may be implicated in the pathogenesis of IgAN. We found a higher quantity of blood 16SrDNA in the IgAN group. No significant difference in the alpha diversity was observed between the IgAN and healthy groups in either blood or stool. Beta diversity was largely similar between the IgAN and healthy groups except for 10 OTU differences in blood and 42 OTU differences in the gut. Interestingly, we detected a striking difference between the blood and gut microbiota across all subjects without any direct correlation in corresponding phyla between the two samples, confirming that the blood microbiome does not directly reflect the gut microbiome, as also observed previously28,29.

A higher quantity of 16S rDNA was detected in blood of the IgAN group when compared to healthy controls group even after adjustment for eGFR. Since this blood microbiome does not seem to reflect the gut microbiome; other body sites including oropharynx and respiratory tract may also be possible sources suggested by observation of certain taxa in blood that were not observed in stool. A strong correlation between 16S rDNA quantity and WBC count could suggest a leucocytic response to the invading microbiome. Alternatively, higher 16SrDNA quantity could be an outcome of higher mean WBC count in the IgAN group, since majority of blood microbiome has been observed in buffy coat18. Given the cross- sectional measurement nature of this study, it is difficult to establish a temporal cause effect relationship.

12

Alpha diversity was similar between the IgAN and healthy groups in both blood and stool (figure 2a).

Previous studies have shown direct correlation of alpha diversity with stronger immunity while lower diversity has been associated with diseases including advanced CKD21,30. Alpha diversity studies in early

CKD stages are limited and possibly may be minimally affected, similar to our study findings. In both

IgAN and healthy groups, a significantly higher alpha diversity is observed in stool compared to blood suggesting larger variety or complexity of bacteria in gut compared to blood. Overall, alpha diversity does not seem to suggest a pathogenic role in IgAN.

Beta diversity measures the overall microbial community within a sample. Large overlap between the

IgAN and healthy groups in both blood and stool suggests that majority of bacterial taxa (except a few differences) were similar between the two groups (figure 2b). Lack of an exact overlap indicates that there were subtle bacterial differences between the two groups that may correlate with a diseased state. Again, striking differences in beta diversity between blood and stool in both groups indicate that the bacterial communities residing in the gut and blood are remarkably different as also observed previously28,29.

Consistent with the findings of the Human Microbiome Project, beta diversity (bacterial community) tends to be more similar between individuals within the same body site than between different body sites within an individual suggesting that the microbial communities tend to adapt to specific body sites31.

Among several other taxonomic differences, a higher prevalence of class Coriobacteriia and Bacilli in blood, genera Legionella, Enhydrobacter, Staphylococcus and Streptococcus in blood; and genera

Bacteroides and Escherichia-Shigella in stool were observed in the IgAN group compared to healthy controls. These genera have been implicated previously as bacterial antigens in IgAN32–34. Genera

Prevotella, Ruminicoccus NK4214 group, Barnesiella, Bifidobacterium and Coprococcus in stool have been observed to have higher prevalence among healthy individuals when compared to IgAN in Chinese population13,14. In accordance with these studies, we observe a similar trend despite not all of them achieving statistical significance.

13

Other minor genera with high proportions in blood of certain IgAN patients but not in healthy subjects included Parabacteroides, Parasutterella, Phyllobacterium, Romboutsia, Sphingomonas and few genera from Ruminococcaceae family. However, they were neither statistically significant nor uniform across all

IgAN patients. Inter-individual variations were observed among IgAN patients. Each patient had 2-5 distinct genera with collective proportions as high as 5-15% which differed from other subjects

(represented as ‘others’ in Fig 4B). Given the heterogeneity of IgAN with multi-hit pathogenesis and different microbes implicated previously, these microbiota may not be statistically significant across all

IgAN patients but may still hold clinical significance within an individual. He et al observed that certain human genetic variants may be associated with certain microbiota35. This can explain some of the observed heterogeneity. Combining above genera in blood and stool that have consistently shown differences between IgAN and healthy subjects can be used as biomarkers in a predictive model for diagnosis and prognosis of IgAN. Correlation of microbiota with clinical parameters of IgAN can further strengthen this model and serve as potential personalized intervention targets.

Striking differences between stool and blood microbiota with lack of correlation between corresponding taxa could suggest other potential sources of origin of certain blood microbiota. Piccolo et al compared the salivary microbiota between IgA nephropathy patients and healthy controls and observed Firmicutes as the dominant phyla with proportions 30-40% in both groups and a lower Firmicutes to Proteobacteria ratio in IgA nephropathy patients36. Microbiome measurement from tonsillar crypts of IgAN patients who underwent tonsillectomy showed no significant difference than those without IgAN37. Further, the 7 predominant genera observed were markedly different from blood microbiome genera observed in our study (figure 4b). Subsequently, Park et al conducted another study comparing microbiome from tonsillar swabs of IgAN patients with healthy controls as well as diabetic nephropathy and membranous nephropathy and observed several microbiome differences between each of the groups, especially higher abundances of Rahnella, Ruminococcus_g2 and Clostridium_g21 genera when compared to healthy controls38. Overall, microbiota composition and differences observed in saliva and tonsils of IgAN

14 patients are different than what we observe in blood, suggesting that the blood microbiota may not be reflecting tonsillar microbiota.

While this study was not designed to examine mechanisms accounting for the differences between blood and stool microbiome, learning why such striking differences exist can improve our understanding of the physiologic mechanisms involved in the regulation of the human blood microbiome as well as explain differences between blood and stool microbiome observed in this study. Such differences were observed across all subjects and not specific to IgAN. Whittle et al compared their blood microbiome data with stool, oral cavity and skin microbiome data of the Human Microbiome Project and showed that blood microbiome resembled more closely to skin and oral cavity compared to gut39. Despite this similarity in microbiota, the proportions of phyla Firmicutes (30-40% in skin and oropharynx compared to10% in blood) and Proteobacteria (30% in skin and oropharynx compared to 70-80% in blood) were remarkably different40,41. These studies suggest that skin and oral microbiota entering into blood may be undergoing further regulation to maintain a characteristic composition of the blood microbiome dominated by phylum

Proteobacteria followed by phyla Actinobacteria and Firmicutes, as also observed in other blood microbiome studies18,39,42,43. While several prior studies have observed gut translocation of bacteria into blood during a dysbiotic state, the proportions of gut and blood microbiota have not been compared8,44,45.

Studies comparing blood and stool microbiome have observed striking differences in proportions of major phyla similar to our study findings28,29. These studies suggest that microbiota from different body sites likely undergo further regulation after entering blood and can explain the differences observed between the blood and stool microbiome without eliminating the possibility of gut bacterial translocation into blood.

The blood microbiome could also be representing previously reported dormant and cell wall deficient nonculturable L-form bacteria that have been reported to possess the ability of undergoing pleomorphic adaptation to its milieu46–48. A characteristic composition of the blood microbiome phyla differing from those reported at other body sites suggests that microbiota in blood may be undergoing pleomorphic

15 adaptation similar to L-form bacteria. The intestinal wall, immune system and liver have been hypothesized to play key roles in the filtering of microbes and regulating the composition of the blood microbiome49. Further studies are needed to explore these regulating mechanisms.

Our study has several limitations. Being a cross sectional study with small sample size, causality and generalizability of microbiome differences is limited. Our IgAN subjects are heterogenous with varying renal function and varying biopsy findings and time points. Three outlying blood microbiome samples were excluded as discussed earlier. We did not measure galactosylated IgA levels. Factors influencing the microbiome (including diet) have not been measured. Finally, our results are based on 16S metagenomics sequencing of the bacterial DNA and limited on the interpretation of viability, and potential functional or causal roles of these bacteria in IgA nephropathy. Nevertheless, we observe important microbiome differences between groups as well as between gut and blood samples and confirm that the blood microbiome does not directly reflect the gut.

In conclusion, our study demonstrates higher quantities of bacterial DNA in the blood of IgAN patients with several blood and gut microbiota differences between IgAN and healthy groups as well as important correlations with clinical parameters of IgAN that could have potential diagnostic, prognostic and therapeutic implications in future. Striking differences between gut and blood microbiota suggest that gut microbiota relevant in IgAN may not be mediating its effects via translocation into blood. Characteristic phylum composition of the blood microbiome compared to previously reported microbiome composition at other body sites suggests internal regulation of the invading microbiome into blood that could explain differences observed between the blood and stool microbiome without eliminating the possibility of gut bacterial translocation. Further large-scale longitudinal research studies are needed to understand factors influencing these microbiome changes and determine their functional or causal roles in IgA nephropathy.

16

Disclosures:

A. Fasano reports personal fees from AbbVie, other funding from Alba Therapeutics, personal fees from

Innovate Biopharmaceuticals, personal fees from Mead Johnson Nutrition, personal fees from Takeda, and personal fees from uBiome, outside the submitted work. A. Allegretti reports the following:

Consultancy Agreements: Mallinckrodt Pharmaceuticals, Cymabay Therapeutics; Research Funding:

Mallinckrodt Pharmaceuticals. S. Nigwekar reports the following: Consultancy Agreements: Becker

Professional Education, Allena Pharma, Epizon Pharma, Laboratoris Sanifit; Research Funding: Hope

Pharma, Allena Pharma; Honoraria: Sanofi-Aventis, Guidpoint; Scientific Advisor or Membership: Vifor pharma. N. Tolkoff-Rubin reports the following: Consultancy Agreements: Best Doctors; Honoraria: Best

Doctors. All remaining authors have nothing to disclose.

Funding:

The study was internally funded by Massachusetts General Hospital Nephrology Divisional funds.

Acknowledgements:

We acknowledge the Biostatistics, Epidemiology and Data Management (BEAD) core consulting service at Johns Hopkins University for assistance with biostatistical analysis involved with this study.

Author Contributions:

N Shah: Conceptualization; Methodology; Project administration; Writing - original draft; Writing - review and editing

S Nigwekar: Conceptualization; Resources; Writing - review and editing

S Kalim: Conceptualization; Resources; Writing - review and editing

B Lelouvier: Data curation; Methodology; Software; Writing - review and editing

F Servant: Formal analysis; Methodology; Software

17

M Dalal: Formal analysis; Software

S Krinsky: Methodology; Project administration; Resources

A Fasano: Supervision

N Tolkoff-Rubin: Conceptualization; Funding acquisition; Supervision; Writing - review and editing

A Allegretti: Conceptualization; Supervision; Validation; Writing - review and editing

All authors approved the final version of the manuscript. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Supplemental Materials: Methods Supplementary figure 1 Supplementary figure 2

18

References: 1. Wyatt RJ, Julian BA. MEdical progress: IgA nephropathy. N Engl J Med. Published online 2013. doi:10.1056/NEJMra1206793 2. Suzuki H, Kiryluk K, Novak J, et al. The pathophysiology of IgA nephropathy. J Am Soc Nephrol. Published online 2011. doi:10.1681/ASN.2011050464 3. Kiryluk K, Li Y, Scolari F, et al. Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens. Nat Genet. 2014;46(11):1187-1196. doi:10.1038/ng.3118 4. Levy M, Kolodziejczyk AA, Thaiss CA, Elinav E. Dysbiosis and the immune system. Nat Rev Immunol. Published online 2017. doi:10.1038/nri.2017.7 5. Levy M, Thaiss CA, Zeevi D, et al. Microbiota-Modulated Metabolites Shape the Intestinal Microenvironment by Regulating NLRP6 Inflammasome Signaling. Cell. Published online 2015. doi:10.1016/j.cell.2015.10.048 6. Wang B, Yao M, Lv L, Ling Z, Li L. The Human Microbiota in Health and Disease. Engineering. Published online 2017. doi:10.1016/J.ENG.2017.01.008 7. Vaziri ND, Wong J, Pahl M, et al. Chronic kidney disease alters intestinal microbial flora. Kidney Int. 2013;83(2):308-315. doi:10.1038/ki.2012.345 8. Vaziri ND, Zhao YY, Pahl M V. Altered intestinal microbial flora and impaired epithelial barrier structure and function in CKD: The nature, mechanisms, consequences and potential treatment. Nephrol Dial Transplant. 2016;31(5):737-746. doi:10.1093/ndt/gfv095 9. Anders HJ, Andersen K, Stecher B. The intestinal microbiota, a leaky gut, and abnormal immunity in kidney disease. Kidney Int. 2013;83(6):1010-1016. doi:10.1038/ki.2012.440 10. Coppo R. The Gut-Renal Connection in IgA Nephropathy. Semin Nephrol. Published online 2018. doi:10.1016/j.semnephrol.2018.05.020 11. Salerno-Goncalves R, Safavie F, Fasano A, Sztein MB. Free and complexed-secretory immunoglobulin A triggers distinct intestinal epithelial cell responses. Clin Exp Immunol. Published online 2016. doi:10.1111/cei.12801 12. De Angelis M, Montemurno E, Piccolo M, et al. Microbiota and metabolome associated with Immunoglobulin A Nephropathy (IgAN). PLoS One. Published online 2014. doi:10.1371/journal.pone.0099006 13. Zhong ZX, Tan JX, Tan L, et al. Modifications of gut microbiota are associated with the severity of IgA nephropathy in the Chinese population. Int Immunopharmacol. Published online 2020. doi:10.1016/j.intimp.2020.107085 14. Hu X, Du J, Xie Y, et al. Fecal microbiota characteristics of Chinese patients with primary IgA nephropathy: A cross-sectional study. BMC Nephrol. Published online 2020. doi:10.1186/s12882- 020-01741-9 15. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006 16. Liang X, Bushman FD, FitzGerald GA. Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock. Proc Natl Acad Sci U S A. Published online 2015. doi:10.1073/pnas.1501305112

19

17. Shah NB, Allegretti AS, Nigwekar SU, et al. Blood Microbiome Profile in CKD. Clin J Am Soc Nephrol. 2019;14(5):692-701. doi:10.2215/CJN.12161018 18. Païssé S, Valle C, Servant F, et al. Comprehensive description of blood microbiome from healthy donors assessed by 16S targeted metagenomic sequencing. Transfusion. 2016;56(5):1138-1147. doi:10.1111/trf.13477 19. Schierwagen R, Alvarez-Silva C, Servant F, Trebicka J, Lelouvier B, Arumugam M. Trust is good, control is better: Technical considerations in blood microbiome analysis. Gut. Published online 2020. doi:10.1136/gutjnl-2019-319123 20. Lluch J, Servant F, Païssé S, et al. The characterization of novel tissue microbiota using an optimized 16S metagenomic sequencing pipeline. PLoS One. 2015;10(11). doi:10.1371/journal.pone.0142334 21. Shah NB, Allegretti AS, Nigwekar SU, et al. Blood Microbiome Profile in CKD. Clin J Am Soc Nephrol. Published online April 8, 2019:CJN.12161018. doi:10.2215/CJN.12161018 22. Escudié F, Auer L, Bernard M, et al. FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics. Published online 2018. doi:10.1093/bioinformatics/btx791 23. Huttenhower C, Gevers D, Knight R, et al. Structure, function and diversity of the healthy human microbiome. Nature. Published online 2012. doi:10.1038/nature11234 24. Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJ. Counting the uncountable: statistical approaches to estimating microbial diversity. Appl Environ Microbiol. 2001;67(10):4399-4406. doi:10.1128/AEM.67.10.4399-4406.2001 25. Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol. 2007;73(5):1576-1585. doi:10.1128/AEM.01996-06 26. Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biol. Published online 2011. doi:10.1186/gb-2011-12-6-r60 27. Markowitz G. Glomerular disease: Updated Oxford Classification of IgA nephropathy: A new MEST-C score. Nat Rev Nephrol. Published online 2017. doi:10.1038/nrneph.2017.67 28. Lelouvier B, Servant F, Paisse S, et al. Changes in blood microbiota profiles associated with liver fibrosis in obese patients: A pilot analysis. Hepatology. 2016;64(6):2015-2027. doi:10.1002/hep.28829 29. Serena G, Davies C, Cetinbas M, Sadreyev RI, Fasano A. Analysis of blood and fecal microbiome profile in patients with celiac disease. Hum Microbiome J. Published online 2019. doi:10.1016/j.humic.2018.12.001 30. Mosca A, Leclerc M, Hugot JP. Gut microbiota diversity and human diseases: Should we reintroduce key predators in our ecosystem? Front Microbiol. 2016;7(MAR). doi:10.3389/fmicb.2016.00455 31. Consortium THMP, The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207-214. doi:10.1038/nature11234 32. Hirabayashi A, Yorioka N, Oda H, et al. Involvement of bacterial antigens in immunoglobulin A nephropathy. Hiroshima J Med Sci. 1996;45(4):113-117. Accessed March 5, 2020.

20

http://www.ncbi.nlm.nih.gov/pubmed/9119709 33. Rollino C, Vischini G, Coppo R. IgA nephropathy and infections. J Nephrol. 2016;29(4):463-468. doi:10.1007/s40620-016-0265-x 34. Fergusson RJ, Mine LT. Legionnaires’ disease and IgA nephropathy. Scott Med J. 1986;31(2):114-116. doi:10.1177/003693308603100214 35. He J-W, Zhou X-J, Li Y-F, et al. Associations of Genetic Variants Contributing to Gut Microbiota Composition in Immunoglobin A Nephropathy. mSystems. Published online 2021. doi:10.1128/msystems.00819-20 36. Piccolo M, De Angelis M, Lauriero G, et al. Salivary Microbiota Associated with Immunoglobulin A Nephropathy. Microb Ecol. Published online 2015. doi:10.1007/s00248-015-0592-9 37. Watanabe H, Goto S, Mori H, et al. Comprehensive microbiome analysis of tonsillar crypts in IgA nephropathy. Nephrol Dial Transplant. Published online 2017. doi:10.1093/ndt/gfw343 38. Park JI, Kim TY, Oh B, et al. Comparative analysis of the tonsillar microbiota in IgA nephropathy and other glomerular diseases. Sci Rep. Published online 2020. doi:10.1038/s41598-020-73035-x 39. Whittle E, Leonard MO, Harrison R, Gant TW, Tonge DP. Multi-method characterization of the human circulating microbiome. Front Microbiol. Published online 2019. doi:10.3389/fmicb.2018.03266 40. Grice EA, Segre JA. The skin microbiome. Nat Rev Microbiol. Published online 2011. doi:10.1038/nrmicro2537 41. Dewhirst FE, Chen T, Izard J, et al. The human oral microbiome. J Bacteriol. Published online 2010. doi:10.1128/JB.00542-10 42. Schierwagen R, Alvarez-Silva C, Madsen MSA, et al. Circulating microbiome in blood of different circulatory compartments. Gut. 2018. 43. Olde Loohuis LM, Mangul S, Ori APS, et al. Transcriptome analysis in whole blood reveals increased microbial diversity in schizophrenia. Transl Psychiatry. Published online 2018. doi:10.1038/s41398-018-0107-9 44. Sato J, Kanazawa A, Ikeda F, et al. Gut dysbiosis and detection of “Live gut bacteria” in blood of Japanese patients with type 2 diabetes. Diabetes Care. Published online 2014. doi:10.2337/dc13- 2817 45. Spadoni I, Zagato E, Bertocchi A, et al. A gut-vascular barrier controls the systemic dissemination of bacteria. Science (80- ). Published online 2015. doi:10.1126/science.aad0135 46. Potgieter M, Bester J, Kell DB, Pretorius E. The dormant blood microbiome in chronic, inflammatory diseases. FEMS Microbiol Rev. 2015;39(4):567-591. doi:10.1093/femsre/fuv013 47. Markova ND. L-form bacteria cohabitants in human blood: significance for health and diseases. Discov Med. Published online 2017. 48. Errington J, Mickiewicz K, Kawai Y, Wu LJ. L-form bacteria, chronic diseases and the origins of life. Philos Trans R Soc B Biol Sci. 2016;371(1707). doi:10.1098/rstb.2015.0494 49. Castillo DJ, Rifkin RF, Cowan DA, Potgieter M. The healthy human blood microbiome: Fact or fiction? Front Cell Infect Microbiol. 2019;9(MAY). doi:10.3389/fcimb.2019.00148

21

22

Table 1: Baseline characteristics of the study groups Variable IgAN (n=20) Healthy (n=20) p-value

Age, years 37 (34, 50) 38 (30, 55) 0.98

Males, n (%) 9 (45) 9 (45) 1.0

Caucasian, n (%) 13 (65) 13 (65) 1.0

Body Mass Index, kg/m2 29.3 ± 5.8 26.2 ± 4.4 0.06

White blood cell, per mcL 7.5 ± 2.0 6.3 ± 1.5 0.04

Serum albumin g/dL 4.2 (3.9, 4.4) 4.4 (4.2, 4.7) 0.03

Urine Microalbumin/Creatinine, 545.6 (134.8, 1168.6) 2.3 (0.7, 5.9) <0.01 mg/g

CRP, mg/L 2.6 (1.0, 6.5) 1.6 (0.7, 3.0) 0.13 eGFR, ml/min 55 (27.5, 88) 100 (81, 110) <0.01

Allergy history, n (%)a 10 (50) 6 (30) 0.19

Perceived stress scoreb 15.2 ± 6.2 6.7 ± 3.7 <0.01

16S DNA, copies/uL blood c (6370, 8695) 6030 (4796, 7505) 0.04

Data expressed as median (25th, 75th quartile), mean ± SD or n (%) as appropriate. Significant differences between groups detected in White blood cell count, serum albumin, urine microalbumin/creatinine, eGFR, perceived stress score and 16S DNA copies. eGFR determined using CKD Epidemiology Collaboration Equation. aReported or documented allergy to food product or drugs bTwo IgAN and 1 healthy subject excluded due to language or understanding barrier

23

Table 2: Taxonomic differences between IgAN and healthy groups Sample High in group Phylum Class Order Family Genus Species

BLOOD IgAN Actinobacteria Actinobacteria Collinsella 1

Actinobacteria Coriobacteriia Eggerthellales Eggerthellaceae Unknown 1

Actinobacteria Actinobacteria 1*

Bacteroidetes Bacteroidia Parabacteroides 1

Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae Unknown 1*

Proteobacteria Gammaproteobacteria Legionellales Legionellaceae Legionella 1

Firmicutes Clostridia Clostridiales Lachnospiraceae Multiaffiliation 1*

Firmicutes Clostridia Clostridiales Lachnospiraceae Coprococcus 1*

Firmicutes Clostridia Clostridiales Ruminococcaceae Ruminococcus-1 1*

Healthy Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Delftia 1

STOOL IgAN Bacteroidetes Bacteroidia Bacteroidales Bacteroidaceae Bacteroides 13

Firmicutes Clostridia Clostridiales Lachnospiraceae Lachnoclostridium 2

Firmicutes Clostridia Clostridiales Lachnospiraceae Hungatella 1

Firmicutes Clostridia Clostridiales Lachnospiraceae Unknown 1

Firmicutes Clostridia Clostridiales Ruminococcaceae Ruminiclostridium 1

Firmicutes Clostridia Clostridiales Ruminococcaceae Unknown 1

Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae Escherichia-Shigella 1

Healthy Bacteroidetes Bacteroidia Bacteroidales Bacteroidaceae Bacteroides 2

Bacteroidetes Bacteroidia Bacteroidales Rikenellaceae Alistipes 2

Bacteroidetes Bacteroidia Bacteroidales Tannerellaceae Parabacteroides 1

Bacteroidetes Bacteroidia Bacteroidales Prevotellaceae Prevotella-9 3

Bacteroidetes Bacteroidia Bacteroidales Prevotellaceae Prevotella-2 1

Bacteroidetes Bacteroidia Bacteroidales Prevotellaceae Paraprevotella 1

Firmicutes Clostridia Clostridiales Lachnospiraceae CAG-56 1

Firmicutes Clostridia Clostridiales Ruminococcaceae NK4A214group 1

Firmicutes Clostridia Clostridiales Lachnospiraceae NK4A136 1

Firmicutes Clostridia Clostridiales Lachnospiraceae Lachnoclostridium 1

Firmicutes Clostridia Clostridiales Ruminococcaceae UCG-002 1

Firmicutes Clostridia Clostridiales Lachnospiraceae Multiaffiliation/unknown 5

Firmicutes Selenomonadales Allisonella 1

24

Taxonomic differences generated using Linear discriminant analysis effect size (LEfSe) algorithm representing statistical and biological differences between groups. Numbers in the species column indicate number of species different between groups. Exact species difficult to identify due to clustering and multiple affiliations of organisms * Differences lost after excluding 3 outliers

25

Table 3: Taxonomic proportions of blood microbiota in IgA Nephropathy and Healthy groups PHYLUM CLASS ORDER FAMILY GENUS Bifidobacteriales Bifidobacterium (H=0.005, I=0.005) (H=0.005, I=0.005) (H=0.005, I=0.003) Corynebacteriaceae Corynebacteriales (H=0.001, I=0.005) (H=0.27, I=0.78) Nocardiaceae Actinobacteria (H=0.001, I=0.005) Actinobacteria (H=18.33, I=15.81) (H=20.15, I=17.17) Micrococcales (H=0.303, I=0.004) (H=7.94, H=8.05) Micrococcaceae (H=4.65, I=6.82) (H=3.73, I=4.82) Propionibacteriales Propionibacteriaceae Cutibacterium (H=2.91, I=0.34) (H=2.70, I=0.188) (H=2.70, I=0.19) Coriobacteriia Coriobacteriales (H=0.000, I=0.004) (H=0.000, I=0.004) Caulobacterales Caulobacteraceae (H=3.34, I=1.25) (H=3.34, I=1.25) Alphaproteobacteria Rhizobiales Rhizobiaceae Phyllobacterium (H=16.88, I=11.83) (H=2.17, I=1.28) (H=0.02, I=0.31) (H=0.005, I=0.172) Sphingomonadales Sphingomonadaceae Sphingomonas (H=2.51, I=2.01) (H=2.51, I=2.06) (H=1.64, I=2.06) Betaproteobacteriales Burkholderiaceae Proteobacteria (H=7.01, I=5.85) (H=5.59, I=5.85) (H=64.17, I=62.74) Enterobacteriales Enterobacteriaceae Escherichia-Shigella (H=2.23, I=1.72) (H=2.23, I=1.72) (H=0.828, I=0.004) Gammaproteobacteria Legionellales Legionellaceae Legionella (H=42.85, I=47.14) (H=0.00, I=0.74) (H=0.00, I=0.74) (H=0.00, I=0.74) Moraxellaceae Enhydrobacter Pseudomonadales (H=0.97, I=1.11) (H=0.000, I=0.003) (H=33.96, I=37.69) Pseudomonadaceae Pseudomonas (H=28.84, I=35.17) (H=28.84, I=35.17) Bacteroidetes Bacteroidia Bacteroidales Bacteroidaceae Bacteroides (H=1.61, I=5.09) (H=1.61, I=5.09) (H=0.02, I=0.98) (H=0.008, I=0.015) (H=0.09, I=0.02) Bacillales Staphylococcaceae Staphylococcus (H=0.01, I=0.06) (H=0.000, I=0.005) (H=0.000, I=0.005) Bacilli Streptococcus (H=1.66, I=2.76) Lactobacillales Lactobacillaceae Firmicutes (H=0.003, I=0.018) (H=0.001, I=0.003) (H=0.000, I=0.001) (H=9.03, I=9.29) Lachnospiraceae Clostridia Clostridiales (H=0.59, I=0.01) (H=4.94, I=7.25) (H=4.94, I=7.25) Ruminococcaceae Faecalibacterium (H=0.01, I=0.78) (H=0.000, I=0.006) Oxyphotobacteria Chloroplast (H=0.00, I=0.03) (H=0.00, I=0.03) (H=0.00, I=0.03) H=Healthy, I=IgA Nephropathy, all numbers represent median taxa proportions expressed in percentages Bolded type were significantly different between the IgAN and healthy groups

26

Table 4: Taxonomic proportions of stool microbiota in IgA Nephropathy and Healthy groups PHYLUM CLASS ORDER FAMILY GENUS Actinobacteria Bifidobacteriales Bifidobacteriaceae Bifidobacterium (H=0.17, I=0.15) (H=0.16, I=0.12) (H=0.16, I=0.12) (H=0.16, I=0.12) Actinobacteria Coriobacteriaceae Collinsella (H=0.41, I=0.23) Coriobacteriia Coriobacteriales (H=0.04, I=0.01) (H=0.04, I=0.01) (H=0.10, I=0.06) (H=0.10, I=0.06) Eggerthellaceae Adlercreutzia (H=0.02, I=0.03) (H=0.000, I=0.007) Alphaproteobacteria Caulobacterales Caulobacteraceae (H=0.02, I=0.03) (H=0.008, I=0.009) (H=0.008, I=0.009) Betaproteobacteriales Burkholderiaceae Sutterella and Parasutterella (H=1.09, I=1.75) (H=1.09, I=1.75) Enterobacteriales Enterobacteriaceae Escherichia-Shigella Proteobacteria Gammaproteobacteria (H=0.02, I=0.07) (H=2.23, I=1.72) (H=0.006, I=0.069) (H=3.35, I=3.34) (H=2.78, I=2.33) Pasteurellales Pasteurellaceae Haemophilus (H=0.008, I=0.004) (H=0.008, I=0.004) (H=0.008, I=0.004) Pseudomonadales Pseudomonadaceae Pseudomonas (H=0.01, I=0.01) (H=0.008, I=0.006) (H=0.008, I=0.006) Deltaproteobacteria Desulfovibrionales Desulfovibrionaceae Bilophila (H=0.10, I=0.21) (H=0.10, I=0.21) (H=0.10, I=0.21) (H=0.02, I=0.12) Bacteroidaceae Bacteroides (H=33.36, I=59.32) (H=33.36, I=59.32) Barnesiellaceae Barnesiella (H=0.62, I=0.01) (H=0.62, I=0.005) Marinifilaceae Bacteroidetes Butyricimonas and Odoribacter Bacteroidia Bacteroidales (H=0.42, I=0.48) (H=69.37, (H=69.37, I=73.95) (H=69.37, I=73.95) Prevotellaceae Prevotella9 I=73.95) (H=1.34, I=0.006) (H=0.173, I=0.003) Rikenellaceae Alistipes (H=4.58, I=2.94) (H=4.00, I=2.94) Tannerellaceae Parabacteroides (H=2.17, I=3.10) (H=2.17, I=3.10) Negativicutes Selemonadales Veillonellaceae Dialister (H=0.04, I=0.06) (H=0.04, I=0.06) (H=0.009, I=0.005) (H=0.000, I=0.005) Staphylococcaceae Staphylococcus Bacilli Lactobacillales (H=0.000, I=0.005) (H=0.000, I=0.005) (H=0.04, I=0.02) (H=0.04, I=0.02) Streptococcaceae Streptococcus (H=0.02, I=0.01) (H=0.02, I=0.01) Eubacterium eligens group (H=0.24, I=0.06) Lachnospiraceae OTHERS: Agathobacter, Anaerostipes, (H=11.43, I=9.74) Blautia, CAG-56, Coprococcus, Dorea, Fusicatenibacter, Lachnoclostridium, Lachnospira, Firmicutes Roseburia, other Eubacterium groups (H=26.18, Christensenellaceae ChristensenellaceaeR7group I=21.05) (H=0.16, I=0.06) (H=0.16, I=0.06) Peptostreptococcaceae Romboutsia Clostridia Clostridiales (H=0.03, I=0.03) (H=0.005, I=0.002) (H=25.98, I=20.68) (H=25.98, I=20.68) NK4A214group (H=0.05, I=0.00) UCG002 (H=1.74, I=0.58) Ruminococcaceae (H=12.99, I=10.84) OTHERS: Butyricicoccus, DTU089, Faecalibacterium, Flavonifractor, Oscillibacter, Oscillospira, Ruminiclostridium, other UCG groups, Ruminococcus, Subdoligranulum, UBA1819 H=Healthy, I=IgA Nephropathy, all numbers represent median taxa proportions expressed in percentages Bolded type were significantly different between the IgAN and healthy groups

27

Table 5: Blood and stool microbiota correlations with clinical parameters of IgA Nephropathy Clinical Taxonomic BLOOD STOOL variable level Positive correlation Negative correlation Positive correlation Negative correlation Urine albumin to Phylum Bacteroidetes Bacteroidetes creatinine ratio Class Clostridia Order Legionellales Bacteroidales, Clostridiales Enterobacteriales Family Legionellaceae, Bacteroidaceae, Christensenellaceae, Staphylococcaceae Enterobacteriaceae Lachnospiraceae Genus Legionella Bacteroides CAG-56 (Lachnospiraceae) Staphylococcus Escherichia-Shigella ChristensenellaceaeR7 group, Cutibacterium, Prevotella9, Ruminococcaceae NK4A214 group, Ruminococcaceae UCG005, Eubacterium eligens group Glomerular Phylum Cyanobacteria* Proteobacteria* Filtration rate Class Coriobacteriia*, Bacilli* (GFR) Oxyphotobacteria* ≤60ml/min Order Bifidobacteriales*, Negativicutes*, (n=11) Chloroplast*, Selenomonadales* Coriobacteriales* Family Bifidobacteriaceae* Veillonellaceae* Genus Arhtrobacter* DTU089* (family Ruminococcaceae), Dialister* C reactive Phylum Actinobacteria protein (mg/L) Class Coriobacteriia Actinobacteria Order Coriobacteriales Caulobacterales Bifidobacteriales Family Caulobacteriaceae Bacteroidaceae, Marinifilaceae Bifidobacteriaceae Genus Bacteroides, CAG-56 (Lachnospiraceae) Bifidobacterium MEST-C score+ Class Gammaproteobacteria (n=16) Family Rikenellaceae* Genus Alistepes*, Anaerostipes* Serum Order Bacteroidales* Immunoglobulin Family Bacteroidaceae* Bacteroidaceae* A (IgA) level Genus Bacteroides*, (mg/dl)+ (n=12) Butyricimonas* Serum albumin Genus Ruminiclostridium6, (g/dl) Ruminococcus2 White blood cell Family Rhizobiaceae (WBC) count in Genus CAG-56 (Lachnospiraceae), blood Dorea, Ruminococcaceae NK4A214 group, Eubacterium eligens group 16SrDNA Order Micrococcales Bifidobacteriales (copies/ul blood) Family Bifidobacteriacaeae *Strong correlation with correlation coefficient ≥0.6, others without (*) have correlation coefficient <0.6 +Missing values and historical measures that may have changed with time (interpret with caution)

28

Figure legends:

Figure 1: Enrollment of study subjects

Figure 2: Similar alpha (Shannon index) and beta (weighted unifrac index) diversities between IgA nephropathy and healthy subjects both in blood and stool samples. Different alpha and beta diversities between blood and stool samples in all subjects

Figure 3: Linear discriminant analysis Effect Size (LEfSe) cladogram showing large microbiome differences at various taxonomic levels between Stool (S) and Whole blood (WB) samples in Healthy (A) and IgA Nephropathy (B) groups.

Figure 4: A) Major phyla distribution in blood and stool samples of IgA nephropathy and healthy groups showing similar distribution within the same body site but different between blood and stool body sites. B) Bacterial composition at genus level showing top 15 genera markedly different between blood and stool of Healthy (Group A) and IgA nephropathy (group B) groups. Three outliers highlighted by arrows.

Figure 5: Phylum distribution in blood and stool of IgA nephropathy and Healthy groups with 3 outliers highlighted by arrows

29

Figures

136 total subjects screened

62 IgA nephropathy patients 74 Healthy subjects

42 excluded -Refused participation (n=16) -ESRD or transplant (n=5) 54 excluded -Age<18 or >65yrs (n=5) -Refused participation (n=31) -On immunosuppression (n=5) -Did not meet inclusion criteria -Diabetes (n=3) (antibiotic use, abnormal -Proteinuria <0.3g (n=3) urinalysis, pregnant) or not -Others (n=5) matching by age (n=23)

20 IgA nephropathy included 20 healthy included Figures

A ALPHA B DIVERSITY BETA DIVERSITY

Healthy blood Healthy stool IgAN blood IgAN stool Figures

A) Healthy B) IgA Nephropathy Figures Bacterial composition in stool (Top 15 genus) A) B)

100.0

90.0

80.0

70.0

60.0

50.0

40.0 Bacterial composition in blood (Top 15 genus) 30.0 Phylum proportion (%) Phylum proportion 20.0

10.0

0.0 Healthy IgAN blood Healthy IgAN stool blood stool Groups Actinobacteria Bacteroidetes

Firmicutes Proteobacteria Figures

Healthy-Whole blood Healthy-Stool

IgAN-Whole blood IgAN-Stool