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10.1080/0886022X.2024.2399749
2399749
Version of Record
Research Article
Artificial Intelligence and Machine Learning
Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study
X. Yuan et al.
Yuan Xiaoli ab
Qing Jianbo c
Zhi Wenqiang a
Wu Feng a
Yan Yan a
https://orcid.org/0000-0002-7500-0959
Li Yafeng bdef
a The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China
b Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan, China
c Department of Nephrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
d Core Laboratory, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan, China
e Medicinal Basic Research Innovation Center of Chronic Kidney Disease, Ministry of Education, Shanxi Medical University, Taiyuan, China
f Academy of Microbial Ecology, Shanxi Medical University, Taiyuan, China
Supplemental data for this article can be accessed online at https://doi.org/10.1080/0886022X.2024.2399749.

CONTACT Yafeng Li dr.yafengli@gmail.com Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan 030000, China
9 9 2024
2024
9 9 2024
46 2 239974919 1 2024
27 8 2024
29 8 2024
KnowledgeWorks Global Ltd.7 9 2024
published online in a building issue7 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Background

IgA nephropathy (IgAN) is intimately linked to mucosal immune responses, with nasopharyngeal and intestinal lymphoid tissues being crucial for its abnormal mucosal immunity. The specific pathogenic bacteria in these sites associated with IgAN, however, remain elusive. Our study employs 16S rRNA sequencing and machine learning (ML) approaches to identify specific pathogenic bacteria in these locations and to investigate common pathogens that may exacerbate IgAN.

Methods

In this cross-sectional analysis, we collected pharyngeal swabs and stool specimens from IgAN patients and healthy controls. We applied 16SrRNA sequencing to identify differential microbial populations. ML algorithms were then used to classify IgAN based on these microbial differences. Spearman correlation analysis was employed to link key bacteria with clinical parameters.

Results

We observed a reduced microbial diversity in IgAN patients compared to healthy controls. In the gut microbiota of IgAN patients, increases in Bacteroides, Escherichia-Shigella, and Parabacteroides, and decreases in Parasutterella, Dialister, Faecalibacterium, and Subdoligranulum were notable. In the respiratory microbiota, increases in Neisseria, Streptococcus, Fusobacterium, Porphyromonas, and Ralstonia, and decreases in Prevotella, Leptotrichia, and Veillonella were observed. Post-immunosuppressive therapy, Oxalobacter and Butyricoccus levels were significantly reduced in the gut, while Neisseria and Actinobacillus levels decreased in the respiratory tract. Veillonella and Fusobacterium appeared to influence IgAN through dual immune loci, with Fusobacterium abundance correlating with IgAN severity.

Conclusions

This study revealing that changes in flora structure could provide important pathological insights for identifying therapeutic targets, and ML could facilitate noninvasive diagnostic methods for IgAN.

Keywords

Mucosal immune response
IgA nephropathy
16SrRNA
Fusobacterium
machine learning
the National Science Foundation of China 82170716 81870333 the Key Laboratory Construction Plan Project of Shanxi Provincial Health Commission the Key Project of Shanxi Provincial Health Commission 2020XM21 This research was funded by the National Science Foundation of China (82170716 and 81870333), the Key Laboratory Construction Plan Project of Shanxi Provincial Health Commission (2020SYS01), the Key Project of Shanxi Provincial Health Commission(2020XM21), the College Science and Technology Innovation Project of Shanxi Education Department and the Special Fund from Medicinal Basic Research Innovation Center of Chronic Kidney Disease, Ministry of Education, Shanxi Medical University (No. CKD/SXMU-2024-06)
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pmc1. Introduction

IgA nephropathy (IgAN) is the most prevalent type of primary glomerulonephritis, characterized by the deposition of multimeric IgA1 in the glomerular mesangial zone. An aberrant mucosal immune response plays a pivotal role in the pathogenesis of IgAN [1]. Despite being generally benign, 20-40% of IgAN patients progress to end-stage renal disease (ESRD), underscoring the urgent need for comprehending its underlying mechanisms and identifying targeted therapeutic strategies. A crucial question in IgAN research is the origin of Gd-IgA1 within the affected tethered zone. Hypothesized to originate from mucosal plasma cells, key induction sites for mucosal immunity include the nasopharynx-associated lymphoid tissue (NALT) and gut-associated lymphoid tissue (GALT). Antigens acquired by the mucosa and B cells through T-cell dependent or independent pathways lead to the conversion of IgA+ B cells to IgA+ plasma cells. These cells produce glycosylation-deficient IgA antibodies in response to BAFF and APRIL, eventually depositing in the glomerular mesangial area and contributing to the development of IgAN [2].

The ‘four-hit hypothesis’ for the pathogenesis of IgAN, although not fully elucidated, is widely acknowledged and substantiated by numerous clinical and basic experiments [3]. Pathogens are known to affect the initial and subsequent immune responses, with previous studies identifying specific pathogens like Porphyromonas gingivalis, Treponema denticola [4], Capnocytophaga, and SR1_genera_incertae_sedis [5] more abundantly in the tonsils of IgAN patients compared to healthy controls (HCs). Changes in gut microorganisms during IgAN progression have also been documented [6,7]. Therefore, both pharyngeal and intestinal microorganisms play a pivotal role in the progression of IgAN.

This study aims to elucidate the role of specific bacteria from the pharynx and gut in initiation and progressing IgAN. Additionally, we explore whether oral flora can exacerbate IgAN by translocating to the gut through functions such as swallowing, and the effects of immunosuppressive therapy on flora structure. Utilizing 16SrRNA sequencing and advanced statistical methods, our study establishes a foundation for unraveling the pathogenesis of IgAN and identifying novel diagnostic and therapeutic approaches.

2. Methods

2.1. Study design and participants

This cross-sectional study was conducted from December 2021 to December 2022 at Shanxi Provincial People’s Hospital. It involved collecting specimens from 87 patients diagnosed with primary IgAN. To establish a robust comparison, we included a healthy control (HC) group, matched in age and gender with the IgAN cohort, and recruited from the hospital’s physical examination center during the same period. Specimen collection encompassed stool samples from 68 HCs and 61 IgAN patients, and throat swab specimens from 94 HCs and 87 IgAN patients. The specimens were not all from the same population because some patients did not cooperate with the retention of stool specimens, which had to be agreed upon by the patient for inclusion. Notably, 60 individuals provided both throat swab and stool samples. Among these, we found two overlapping microbial taxa, Veillonella and Fusobacterium, in the same individuals’ samples from different body sites. To further investigate whether the therapeutic effects of immunosuppressants on IgAN are related to changes in microbiota structure, the IgAN patients were categorized into two groups: those sampled before medication initiation (i.e. from hospital admission until the commencement of steroids and/or immunosuppressants) and those sampled after approximately 2 mouths of using steroids and/or immunosuppressants. There were 66 IgAN patients in the pre-medication group and 21 in the post-medication group. Among the post-medication group, the following treatments were observed: 4 patients received Glucocorticoids alone, 3 were treated with Angiotensin-Converting Enzyme Inhibitors/Angiotensin II Receptor Blockers (ACEI/ARB), 5 received a combination of glucocorticoids and ACEI/ARB, 2 were given glucocorticoids combined with ACEI/ARB and MMycophenolate mofetil (MMF), 3 received glucocorticoids combined with ACEI/ARB and hydroxychloroquine, and 4 patients were treated with glucocorticoids combined with ACEI/ARB and cyclophosphamide.

Eligibility for inclusion required a confirmed diagnosis of IgAN through renal biopsy, conducted by an expert nephrologist. Exclusion criteria encompassed severe hepatic and renal insufficiency, immunodeficiency, comorbid autoimmune diseases, history of other immune diseases, recent treatment with immunosuppressants or antibiotics and probiotics (within the past two months), diabetes, stage 4-5 chronic kidney disease, and special dietary habits, which include eating seafood rich in parasites, such as sashimi and other foods, and living in extremely unsanitary conditions.

Ethical clearance was duly obtained from the Ethics Committee of Shanxi Provincial People’s Hospital (Ethics No.202440), and informed consent was secured in writing from all participants.

2.2. Sample collection

First, we recorded clinical parameters including age, sex, 24-h proteinuria (upro-24), serum albumin (ALB), creatinine (Cr), blood urea nitrogen (BUN), blood urine (bld), and the Oxford classification of IgAN for the study subjects. Besides, a trained physician collected pharyngeal specimens, employing a sterile cotton swab for brushing each side of the pharyngeal crypt 2-3 times to gather pathogenic microorganisms. Afterwards, the collected stool samples were promptly preserved in a protective solution and stored at −80 °C for subsequent sequencing.

2.3. DNA extraction, amplicon generation, PCR products quantification and qualification, library preparation and sequencing

Stool DNA was extracted using the Magnetic Soil and Stool DNA Kit (TianGen, China, Catalog #: DP712). For respiratory secretions, the CTAB extraction method was employed. The amplification region for this project is 16SV34, utilizing primers V3 + V4. The primer sequences are as follows: for the forward primer, 341 F with the sequence CCTAYGGGRBGCASCAG, and for the reverse primer, 806 R with the sequence GGACTACNNGGGTATCTAAT. PCR was conducted with Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 µM of primers, and ∼10 ng template DNA. The thermal cycling protocol included initial denaturation at 98°C for 1 min, 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s, and elongation at 72 °C for 30 s and 72 °C for 5 min.

PCR products underwent electrophoresis in 2% agarose gel, mixed in equidensity ratios, and purified using a Universal DNA Purification Kit. Sequencing libraries, prepared with NEB Next® Ultra™ II FS DNA PCR-free Library Prep Kit (New England Biolabs, USA, Catalog #: E7430L), were quantified using Qubit and real-time PCR, and size distribution was assessed with a bioanalyzer. The prepared libraries were sequenced on Illumina platforms based on library concentration and required data quantity.

2.4. Bioinformatics analysis

QIIME2 software was used for species annotation, and we used DADA2 for noise reduction to obtain the final ASVs. Microbiota composition at genus and species levels was quantified. Initial calculations of alpha diversity at both levels provided insights into microbial community diversity, using indices like Chao1 and Shannon. Chao1 is an index used to estimate the species richness of a community based on the relationship between the number of species occurring in the community and the number of low-frequency species within a single sample. A higher Chao1 index indicates a higher species richness in the community. Shannon index, on the other hand, is used to assess the species diversity of a community, taking into account both species richness and evenness, with a higher Shannon’s index indicating higher diversity, i.e. a more even chance of occurrence of the various species in the community. Beta diversity, on the other hand, explores whether the diversity of organisms between the IgAN and HC groups can be distinguished from each other from a downscaling perspective, and its analyses include principal coordinates analysis (PCoA). The PCoA allows us to reduce the high-dimensional colony data to two or three dimensions, facilitating visual comparisons and analyses of similarities and differences between different samples.

2.5. Statistical analysis

Statistical analyses were performed using R software (version 4.2.0). Quantitative variables were expressed as means or medians (lower quartile, upper quartile) as appropriate. Group comparisons utilized the two-sample t-test or Wilcoxon rank sum test for continuous variables and the chi-square test or Fisher exact test for categorical variables. False Discovery Rate (FDR) was used to reduce the risk of false positives. A p-value < 0.05 was considered statistically significant. Intestinal and respiratory flora differences between groups were incorporated into ML models. Data were split into a training set (70%) and a test set (30%), with the training set employed for constructing ML models. Packages in R software such as ‘vegan’, ‘reshape2’, ‘rstatix’, ‘autoReg’, ‘magrittr’, ‘ape’, ‘ggplot2’, ‘tidyverse’, ‘xgboost’, ‘caret’, ‘rpart’, ‘randomForest’, ‘e1071’, ‘pROC’ were used for analysis in this study.

2.6. Four classifiers for IgA nephropathy

To investigate whether the intestinal and respiratory flora associated with IgAN can effectively identify the condition, this study utilized four ML algorithms. These included the Classification and Regression Tree (CART) [8], Support Vector Machine (SVM) [9], Extreme Gradient Boosting (XGBoost) [10], and the Random Forest (RF) algorithm [11]. Model evaluation was conducted using metrics such as Accuracy, Specificity, Sensitivity, and Area Under the Receiver Operating Curve (AUC).

3. Results

3.1. Baseline characteristics

Table 1 presented a comparative analysis of demographic and clinical characteristics between healthy controls (HCs) and patients with IgAN in both the respiratory microbiota and gut microbiota cohorts. The results showed no significant differences in age (p = 0.065 for respiratory, p = 0.121 for gut), sex (p = 0.729 for respiratory, p = 1.000 for gut), smoking status (p = 0.249 for respiratory, p = 0.119 for gut), and drinking status (p = 1.000 for respiratory, p = 0.357 for gut) between the two groups. However, IgAN patients exhibited significantly elevated levels of blood urea nitrogen (BUN), (p < 0.001 for both microbiota groups) and creatinine (Cr) (p < 0.001 for both microbiota groups) compared to the HC group, while the glomerular filtration rate (GFR) was significantly reduced in the IgAN group (p < 0.001 for both microbiota groups). Additionally, the prevalence of hypertension was significantly more prevalent among IgAN patients than in the HC group (p < 0.001 for both microbiota groups). Although there were no significant differences in blood parameters such as white blood cell (WBC) count, red blood cell (RBC) count, and platelet (PLT) count between the two groups, IgAN patients within the respiratory microbiota cohort exhibited slightly elevated hemoglobin (HGB) levels (p = 0.027), while no significant difference was observed in the gut microbiota cohort (p = 0.244) (Table 1).

Table 1. Baseline characteristics among groups in respiratory microbiota and gut microbiota.

 	Respiratory microbiota	Gut microbiota	
Variables	IgAN (N = 87)	HC (N = 94)	p	IgAN (N = 61)	HC (N = 68)	P	
Age (year)
Median	39.00 (29.00, 53.50)	46.00 (35.00, 53.00)	0.065	39.00 (29.00, 54.00)	45.50 (35.50, 52.00)	0.121	
Sex	 	 	0.729	 	 	1.000	
Male
n (%)	43 (49.4%)	43 (45.7%)	 	32 (52.5%)	35
(51.5%)	 	
Female
n (%)	44 (50.6%)	51 (54.3%)	 	29 (47.5%)	33
(48.5%)	 	
Smoking
n (%)	 	 	0.249	 	 	0.119	
 No	59 (67.8%)	72 (76.6%)	 	38 (62.3%)	52
(76.5%)	 	
 Yes	28 (32.2%)	22 (23.4%)	 	23 (37.7%)	16
(23.5%)	 	
Drinking
n (%)	 	 	1.000	 	 	0.357	
 No	50 (57.5%)	53 (56.4%)	 	30 (49.2%)	40
(58.8%)	 	
 Yes	37 (42.5%)	41 (43.6%)	 	31 (50.8%)	28
(41.2%)	 	
BUN
(mmol/L)
 Median	9.25 (7.33, 11.81)	5.15 (3.77, 6.61)	<0.001	8.54 (6.38, 11.82)	5.15
(3.60,
7.27)	<0.001	
Cr
(umol/L)
 mean ± SD	90.93 ± 11.42	70.22 ± 8.55	<0.001	87.24 ± 13.42	73.40 ± 11.04	<0.001	
GFR
(ml/min/1.73m2)
 mean ± SD	66.85 ± 11.80	94.46 ± 12.37	<0.001	75.07 ± 15.31	90.72 ± 15.57	<0.001	
Hypertensionn (%)	 	 	<0.001	 	 	<0.001	
 No	31 (35.6%)	76 (80.9%)	 	28 (45.9%)	52
(76.5%)	 	
 Yes	56 (64.4%)	18 (19.1%)	 	33 (54.1%)	16
(23.5%)	 	
AST (IU/L)
 Median	17.83 (11.44, 26.38)	19.94 (14.72, 26.23)	0.130	20.97 ± 10.21	19.91 ± 9.39	0.540	
ALT (IU/L)
 Mean ± SD	19.94 ± 8.78	21.64 ± 9.69	0.217	18.78 ± 9.06	20.74 ± 9.66	0.240	
WBC
(×109/L)
 mean ± SD	6.00 ± 1.64	5.83 ± 1.63	0.489	5.81 ± 1.53	5.75 ±
1.56	0.833	
RBC
(×1012/L)
 mean ± SD	4.51 ± 0.31	4.49 ± 0.29	0.514	4.55 ± 0.32	4.50 ±
0.24	0.248	
HGB
(g/L)
 mean ± SD	128.87 ± 10.11	125.55 ± 9.88	0.027	127.44 ± 11.15	125.32 ± 9.47	0.244	
PLT
(×109/L)
 mean ± SD	247.83 ± 41.60	246.41 ± 48.21	0.834	252.22 ± 37.41	242.83 ± 49.76	0.225	
upro-24
(g/24h)
 mean ± SD	1.83 ± 1.49	 	 	2.40 ± 1.81	 	 	
bld
(count/HP)
 mean ± SD	30.23 ± 4.49	 	 	30.56 ± 5.48	 	 	
M lesion score
n (%)	 	 	 	 	 	 	
 M0	48
(55.17%)	 	 	32 (52.46%)	 	 	
 M1	39 (44.82%)	 	 	29
(47.54%)	 	 	
E lesion score
n (%)	 	 	 	 	 	 	
 E0	62 (71.26%)	 	 	47 (77.05%)	 	 	
 E1	25 (28.74%)	 	 	14 (22.95%)	 	 	
S lesion score
n (%)	 	 	 	 	 	 	
 S0	49
(56.32%)	 	 	37 (60.66%)	 	 	
 S1	38
(43.68%)	 	 	24
(39.34%)	 	 	
T lesion score
n (%)	 	 	 	 	 	 	
 T0	35 (40.23%)	 	 	24 (39.34%)	 	 	
 T1	44 (50.57%)	 	 	26 (42.62%)	 	 	
 T2	8 (9.20%)	 	 	11 (18.03%)	 	 	
C lesion score
n (%)	 	 	 	 	 	 	
 C0	47 (54.02%)	 	 	33 (54.10%)	 	 	
 C1	40 (45.98%)	 	 	26 (42.62%)	 	 	
 C2	–	 	 	2 (3.28%)	 	 	
BUN: blood urea nitrogen; Cr: creatinine; GFR: Glomerular filtration rate; ALT: Alanine aminotransferase; AST: Aspartic transaminase; WBC: White blood cell; RBC: Red blood cell; PLT: Platelet; HGB: Hemoglobin; upro-24: 24-h proteinuria; bld: blood urine; M: mesangial hypercellularity; M0: <50% of glomeruli; M1: >50% of glomeruli; E: endocapillary hypercellularity; E0: absent; E1: any glomeruli showing endocapillary hypercellularity; S: segmental glomerulosclerosis; S0: absent; S1: present in any glomeruli; 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; HP: high power field.

3.2. Comparison of clinical remission and non-clinical remission groups in IgAN patients after treatment

The study analyzed 21 patients with IgAN, who were categorized into a non-clinical remission group (n = 8) and a clinical remission group (n = 13). The average age was comparable between the two groups (41.50 ± 13.05 years in the non-clinical remission group and 44.08 ± 9.56 years in the clinical remission group, p = 0.607). The gender distribution did not exhibit significant differences, with males comprising 37.5% of the non-clinical remission group and 53.8% of the clinical remission group (p = 0.781). The prevalence of hypertension was prevalent in both groups, with 75% in the non-clinical remission group and 76.9% in the clinical remission group (p = 1.000). Other clinical variables such as BUN (8.20 ± 4.43 mmol/L vs. 10.96 ± 4.50 mmol/L, p = 0.186), Cr (92.39 ± 14.59 µmol/L vs. 94.24 ± 12.19 µmol/L, p = 0.757), and GFR (64.18 ± 9.98 mL/min/1.73m2 vs. 66.19 ± 10.61 mL/min/1.73m2, p = 0.671) showed no significant differences between the two groups.

Liver function markers, AST (23.17 ± 9.58 U/L vs. 19.84 ± 11.42 U/L, p = 0.499) and ALT (23.32 ± 6.16 U/L vs. 21.44 ± 6.21 U/L, p = 0.506), along with blood parameters like WBC (6.25 ± 2.05 x109/L vs. 6.15 ± 1.52 x109/L, p = 0.903), RBC (median 4.50 [4.00, 5.00] vs. 5.00 [4.00, 5.00], p = 0.643), HGB (125.54 ± 7.96 g/L vs. 128.84 ± 11.24 g/L, p = 0.478), and PLT (268.77 ± 32.58 x109/L vs. 253.05 ± 30.48 x109/L, p = 0.277), also did not differ significantly. In terms of lifestyle factors, smoking was slightly more common in the non-clinical remission group (50% vs. 23.1%, p = 0.427), while drinking was more prevalent among the non-clinical remission group (75% vs. 38.5%, p = 0.239), though these differences were not statistically significant.

However, there were significant differences observed in upro-24 levels, with the non-clinical remission group exhibiting higher median levels (1.58 [0.79, 3.49] g/24h) compared to the clinical remission group (0.27 [0.17, 1.05] g/24h, p = 0.009). Additionally, Bld was significantly elevated in the non-clinical remission group (median 28.50 [25.50, 33.00] vs. 8.00 [5.00, 15.00], p < 0.001), as presented in Table 2.

Table 2. Comparison of clinical remission and non-clinical remission groups in IgAN patients after treatment.

Variables	Non-Clinical remission
(N = 8)	Clinical remission
(N = 13)	P	
Age (year)
 mean ± SD	41.50 ± 13.05	44.08 ± 9.56	0.607	
Sex	 	 	0.781	
Male
 n (%)	3 (37.5%)	7 (53.8%)	 	
Female
 n (%)	5 (62.5%)	6 (46.2%)	 	
Hypertension
 n (%)	 	 	1.000	
No	2 (25%)	3 (23.1%)	 	
Yes	6 (75%)	10 (76.9%)	 	
BUN
 (mmol/L)
 mean ± SD	8.20 ± 4.43	10.96 ± 4.50	0.186	
Cr
 (umol/L)
 mean ± SD	92.39 ± 14.59	94.24 ± 12.19	0.757	
GFR
 (ml/min/1.73m2)
 mean ± SD	64.18 ± 9.98	66.19 ± 10.61	0.671	
AST (IU/L)
 mean ± SD	23.17 ± 9.58	19.84 ± 11.42	0.499	
ALT (IU/L)
 mean ± SD	23.32 ± 6.16	21.44 ± 6.21	0.506	
WBC (×109/L)
 mean ± SD	6.25 ± 2.05	6.15 ± 1.52	0.903	
RBC (×1012/L)
 Median	4.50 (4.00, 5.00)	5.00 (4.00, 5.00)	0.643	
HGB (g/L)
 mean ± SD	125.54 ± 7.96	128.84 ± 11.24	0.478	
PLT (×109/L)
 mean ± SD	268.77 ± 32.58	253.05 ± 30.48	0.277	
 Smoking
n (%)	 	 	 	
No	4 (50%)	10 (76.9%)	0.427	
Yes	4 (50%)	3 (23.1%)	 	
Drinking
 n (%)	 	 	 	
No	2 (25%)	8 (61.5%)	0.239	
Yes	6 (75%)	5 (38.5%)	 	
Upro-24
 (g/24h)
Median	1.58 (0.79, 3.49)	0.27 (0.17, 1.05)	0.009	
Bld
 (count/HP)
 Median	28.50 (25.50, 33.00)	8.00 (5.00, 15.00)	<0.001	

3.3. Taxonomic results and diversity comparison

The QIIME2 software was employed for species annotation classification in this study. This project uses DADA2 noise reduction to obtain the final ASVs, resulting in the identification of 348 taxonomic units at the genus level in the enteric flora and 513 in the respiratory flora. A comprehensive analysis of the microbial communities in both respiratory and intestinal flora was conducted. In the intestinal flora, alpha diversity indices, Chao1 and Shannon, were significantly reduced in the IgAN group (p < 0.05), as depicted in Figure 1A,B. This trend was mirrored in the respiratory microbiota, where Chao1 and Shannon indices exhibited significant reductions in the IgAN group (p < 0.05), indicating a reduced diversity in the respiratory microbiota of IgAN patients, as illustrated in Figure 1C,D. Further exploration through beta-diversity analysis using PCoA underscored significant disparities in the composition of gut and respiratory microbiota composition between IgAN patients and HCs. In the intestinal flora, PCoA revealed substantial sample clustering, with a notable statistical difference (p = 0.021) as determined by Permutational Multivariate Analysis of Variance (PERMANOVA), as evident in Figure 1E. Similarly, the respiratory microbiota demonstrated a significant divergence (p = 0.001) between the IgAN and HC groups, as shown in Figure 1F. These observations underscore the distinct microbial community structures in both the gut and respiratory microbiota of IgAN patients, bolstering the premise for further investigation into their microbiological profiles.

Figure 1. Comparison of alpha and beta diversity between IgAN and HC groups. The points in the figure represent samples; *: p < 0.05, ****: p < 0.0001; A-B. Intestinal microbiota diversity index comparison; C-D. respiratory microbiota diversity index comparison. E-F. Principal Co-ordinates Analysis for intestinal microbiota and respiratory microbiota, respectively; the closer the distance between samples, the more similar the species. The diversity differences were compared using wilcoxon rank sum test and beta differences were compared with Permutational Multivariate Analysis of Variance.

3.4. Comparative analysis of top 15 microbial abundances in gut and respiratory microbiota

Our investigation focused on characterizing the ten most highly abundant microbial taxa in both the gut and respiratory microbiota ecosystems. In the gut microbiota, the most prevalent taxa were Bacteroides, Prevotella_9, Faecalibacterium, Lachnospira, Roseburia, Subdoligranulum, Parasutterella, Agathobacter, Parabacteroides, and Lachnoclostridium (Figure 2A). This distinct profile highlights the specific microbial environment within the gastrointestinal tract. Conversely, In the respiratory microbiota, the predominant taxa comprised Prevotella, Neisseria, Streptococcus, Fusobacterium, Leptotrichia, Veillonella, Alloprevotella, Haemophilus, Porphyromonas, and Ralstonia (Figure 2B). This composition underlines a unique microbial signature characteristic of the respiratory system.

Figure 2. Altered microbial composition in IgAN. Stacked bar plots of top 15 abundant genera profiling in intestinal microbiota (A) and respiratory microbiota (B).

3.5. Differential abundances of respiratory and gut microbiota in IgA nephropathy

In our study, we utilized the Wilcoxon rank-sum test to identify variations in microbial taxa within the respiratory and gut microbiota related to IgAN. Our analysis unveiled significant alterations in 98 taxa within the respiratory microbiota (p.adj < 0.01). The most obviously significant 15 ones represented Eubacterium_nodatum_group, Megasphaera, Family_XIII_UCG-001, Mogibacterium, Atopobium, Oribacterium, Butyrivibrio, Akkermansia, Solobacterium, Selenomonas, Acinetobacter, Abiotrophia, Clostridium_sensu_stricto_1, Prevotella, Bryobacter, as illustrated in Figure 3A. Regarding the gut microbiota, our findings showed notable differences in 18 taxa. This group included Allisonella, Anaerotruncus, lastomonas, Candidatus_Saccharimonas, Caulobacter, Gardnerella, Gemella, Hafnia-Obesumbacterium, Lactobacillus, Ligilactobacillus, Mycoplasma, Neisseria, Parasutterella, Pygmaiobacter, Ralstonia, Romboutsia, Sphingobium, Sphingomonas (p.adj < 0.01), detailed in Figure 3B.

Figure 3. Differential microbiota for IgAN. (A) Respiratory microbiota. (B) Intestinal microbiota.

3.6. ML Performance using differential abundances of respiratory and gut microbiota in identifying IgAN

In the context of identifying IgAN through gut microbiota analysis, the RF algorithm showcased impressive effectiveness, achieving a notable accuracy rate of 83.33%. This underscores its exceptional capability in case classification. With a sensitivity of 66.67%, RF effectively pinpointed true positive IgAN cases, and a specificity of 95.24% ensured accurate identification of healthy individuals. The positive predictive value (PPV) was determined to be 90.91%, demonstrating the model’s exceptional precision in diagnosing IgAN. In contrast, the negative predictive value (NPV) at 80.00% reliably excluded non-IgAN subjects (Table 2). The collective metrics highlight the proficiency of RF in accurately utilizing gut microbiota data for IgAN classification. Additionally, the AUC values for RF, SVM, CART, and XGBoost were 0.855, 0.776, 0.662 and 0.8 respectively, depicted in Figure 4A.

Figure 4. AUCs of the algorithms for intestinal and respiratory microbiota in IgAN. Identification performance of differential microbiota in IgAN patients. AUCs of four algorithms. (A) Differential intestinal microbiota and (B) differential respiratory microbiota.

In the assessment of respiratory microbiota for IgAN detection, our study revealed the substantia efficiency of ML algorithms, with XGBoost demonstrating remarkable prominence. The model achieved an impressive accuracy of 95.56%, showcasing its superior classification abilities for IgAN cases. XGBoost exhibited the highest sensitivity at 90.91%, accurately identifying true-positive cases, while maintaining a specificity of 100% to ensure reliable detection of healthy subjects. This thorough evaluation, in conjunction with a PPV of 100% and an NPV of 92.00%, further underscores the effectiveness of XGBoost in discerning IgAN using respiratory microbiota data, as presented in Table 3. Moreover, the AUCs for RF, SVM, CART, and XGBoost were 0.89, 0.90, 0.87 and 0.99, respectively, as shown in Figure 4B.

Table 3. Parameter comparison of four algorithms in gut and respiratory microbiota.

Microbiota	Algorithms	Accuracy	Sensitivity	Specificity	PPV	NPV	
Gut	RF	0.8333	0.6667	0.9524	0.9091	0.8000	
SVM	0.7778	0.6667	0.8571	0.7692	0.7826	
CART	0.6667	0.6667	0.6667	0.5882	0.7368	
XGBoost	0.6944	0.6429	0.7273	0.6000	0.7619	
Respiratory	RF	0.8889	0.9000	0.8800	0.8571	0.9166	
SVM	0.8889	0.8000	0.9600	0.9412	0.8571	
CART	0.6111	0.4667	0.7143	0.5385	0.6522	
XGBoost	0.9556	0.9091	1.0000	1.0000	0.9200	

3.7. Comparative analysis of top 15 microbial abundances in respiratory and gut microbiota of IgAN patients before and after treatment

This investigation conducted an extensive comparative analysis of the top 15 differentially abundant microbial taxa in both the respiratory and intestinal microbiota of IgAN patients, with particular focus on discerning disparities between individuals undergoing immunosuppressive therapy and those not receiving such treatment.

A comprehensive examination of the gut microbiota underscored significant shifts in the top 15 microbial populations in IgAN patients before and after treatment. Noteworthy among these were Parasutterella, Dialister, Subdoligranulum, Romboutsia, and Butyricicoccus, all of which demonstrated marked changes associated with medication. Fascinatingly, the post-treatment group exhibited a reduced presence of potentially deleterious taxa such as Oxalobacter and Butyricicoccus, suggesting a beneficial alteration in the gut microbiota composition as a result of the therapy, as depicted in Figure 5A. Similarly, the respiratory microbiota in IgAN patients revealed significant variations pre- and post-treatment. The 15 most differentially abundant taxa included Prevotella, Leptotrichia, Rothia, Porphyromonas, Veillonella, and Actinobacillus, all undergoing notable changes in microbial makeup in response to the drug therapy, as shown in Figure 5B. Particularly, a decline in the abundance of Actinobacillus and Neisseria was observed in the post-treatment group, indicating an adjustment in the respiratory flora due to medical intervention.

Figure 5. Stacked bar plots of the top 15 abundant genera profiling in intestinal microbiota. A. for respiratory microbiota B. for IgAN patients before and after drug administration.

3.8. Comparative analysis of microbial compositions in fecal and respiratory samples

In our study, we meticulously examined the microbial profiles in fecal and pharyngeal swab samples, uncovering fascinating instances of both crossover and non-crossover microbiomes. A total of 258 shared microbiomes have been identified, highlighting the presence of core microbiomes in both anatomical sites, thereby indicating their potential conserved role in maintaining overall health. Conversely, the presence of site-specific microbial communities is underscored by 90 unique fecal microbiota and 255 unique respiratory microbiota. Our analysis of the 258 common microbiota showcased the top 15 constituents of both respiratory and gut microbiota. The leading 15 gut microbiota comprised well-recognized species like Bacteroidetes, Faecalibacterium, and Lachnospira, alongside distinctive taxa such as Subdoligranulum and Dialister, which contribute to the diversity of the gut microbiota landscape, as illustrated in Figure 6A. In the respiratory microbiota, key players include Prevotella, Neisseria, and Streptococcus, in addition to a variety of other organisms like Veillonella and Fusobacterium, forming a complex mosaic that impacts respiratory health, shown in Figure 6B. Importantly, the intersection of these microbial communities highlighted Veillonella and Fusobacterium as common bacteria among the top 15 taxa of both respiratory and gut microbiota, as depicted in Figure 6C.

Figure 6. Comparative analysis of microbial compositions in fecal and respiratory samples. (A) Intestinal microbiota; (B) Respiratory microbiota; and (C) Intersected microbiota between microbial compositions in intestinal and respiratory microbiota.

3.9. Microbial signatures in IgA nephropathy: algorithmic discrimination using respiratory and gut microbiota

Upon the identification of Veillonella and Fusobacterium are microbial entities shared between the respiratory and intestinal microbiota, we conducted a comprehensive investigation into their potential role as discriminators in IgAN. Utilizing sophisticated algorithms like XGBoost, RF, SVM, and CART, we aimed to assess the diagnostic power of these microbial markers. In the gut microbiota, the potential for discrimination was apparent, though with varying degrees of effectiveness as indicated by the AUC values. Specifically, for discrimination IgAN, the AUC values were as follows: RF AUC 0.65, SVM AUC 0.63, CART AUC 0.66, and XGBoost AUC 0.62. These findings underscore the distinctive impact of gut microbial composition on identifying IgAN, particularly influenced by Veillonella and Fusobacterium species (Figure 7A). Regarding the respiratory microbiota, all four algorithms demonstrated significant discriminatory potential. The AUC values for IgAN differentiation were RF AUC 0.66, SVM AUC 0.66, CART AUC 0.67, and XGBoost AUC 0.68. This suggests that Veillonella and Fusobacterium, two microbial compositions found in the respiratory tract, exhibit modest potential for identifying IgAN, as illustrated in Figure 7B.

Figure 7. AUC of the algorithms for Veillonella and Fusobacterium in IgAN. Veillonella and Fusobacterium diagnosis models for IgAN based on intestinal (A) and respiratory (B) microbiota.

3.10. Correlation analysis

In an effort to delve deeper into the association between two key microbiotas, Veillonella and Fusobacterium, and clinical parameters in IgAN, our study employed Spearman correlation analysis. Within the intestinal microbiota, Fusobacterium exhibited a significant positive correlation with S (p < 0.01) and ALB (p < 0.05). Nevertheless, there was no discernible correlation between Fusobacterium and Cr, BUN, and upro.24, as illustrated in Figure 8A. Conversely, Veillonella did not demonstrate a significant relationship with these clinical parameters (p > 0.05), as evidenced in Figure 8B. In the context of the respiratory microbiota, Veillonella, as depicted in Figure 8C, showed a significant positive correlation with upro.24 and Cr (p < 0.05). On the other hand, Fusobacterium demonstrated a significant association with bld and S (p < 0.05), while no significant correlations were observed with upro.24, Cr, ALB, and BUN as presented in Figure 8D.

Figure 8. Correlations between key microbiotas and important clinical parameters. The size of the circle represents the size of the correlation coefficient. ALB: serum albumin; S: IgAN stages.

Discussion

Microorganisms are recognized as primary pathogenic factors that stimulate aberrant immune responses in human mucosa. Numerous studies, including those referenced [4,6,12,13], have identified dysregulated bacterial flora as a pivotal determinant in augmenting susceptibility and exacerbating IgAN. In this study, we investigated the distribution of oropharyngeal and intestinal flora in a cohort matched for sex and age, comparing these findings with those from HCs. We observed dynamic fluctuations in specific flora before and after treatment, establishing a correlation between key microbiota and clinical indicators. This findings serves as a foundation for elucidating the pathogenesis of IgAN and developing innovative therapeutic strategies. Of particular note, the identification of specific pathogenic bacteria may offer a noninvasive diagnostic approach for IgAN.

By employing 16SrRNA sequencing, we conducted a comparative analysis of the nasopharyngeal and intestinal microbiota variations between patients with IgAN and healthy controls. Our findings align with previous research [5], demonstrating a diminished diversity in the IgAN group, while beta-diversity indicated significant differences in flora distribution. Notably, the disease group exhibited an elevated abundance of Bacteroides, Escherichia-Shigella, and Parabacteroides, while experiencing a reduction in Parasutterella, Dialister, Faecalibacterium, and Subdoligranulum among the top 15 gut microorganisms. A prior study [14] corroborated the elevated levels of Bacteroides and Escherichia-Shigella in IgAN patients. Another study [15] has established a correlation between reduced abundance of Dialister and elevated levels of Gd-IgA1 in the blood. In terms of respiratory flora, we noted an increase in Neisseria, Streptococcus, Fusobacterium, Porphyromonas, and Ralstonella, along with a decrease in Prevotella, Leptotrichia, and Veillonella. These findings are consistent with previous studies [16–18], except for an unexpected rise in Prevotella in IgAN, which may be influenced by dietary habits, lifestyle, comorbidities, and oral medication diversity within our study population. Additionally, we observed a general reduction in the diversity and abundance of microbial communities associated with disease groups, which is consistent with earlier research.

The current clinical diagnosis of IgAN largely relies on invasive renal biopsy, presenting a risk of bleeding. Our study’s findings revealing distinct pharyngeal and intestinal flora in IgAN patients suggest the potential utilization of these unique microbial profiles as a novel, noninvasive diagnostic approach. The integration of artificial intelligence and ML in medical research offers promising avenues for disease diagnosis. Leveraging data-driven models such as XGBoost and RF, our study employed four algorithms (RF, SVM, CART, and XGBoost) to evaluate their efficacy in diagnosing IgAN, while assessing sensitivity, specificity, and error rates. The RF method exhibited superior sensitivity and specificity in diagnosing intestinal flora in IgAN, while the XGBoost method was more effective for respiratory flora diagnosis. XGBoost, an enhancement of the Gradient Boosting Decision Tree and part of the ensemble learning boosting method [19], combines a linear model solver with a tree-learning algorithm. By employing a second-order Taylor expansion of the loss function and introducing a regularization term that accounts for the number of leaf nodes and the L2 norm of leaf weights, XGBoost not only refines accuracy but also facilitates customization of the loss function and effectively prevents overfitting [20,21].

Additionally, we explored whether non-flora-targeted treatments (e.g. immunosuppressive therapy) induce alterations in the composition of flora among

IgAN patients, representing a novel area of research. Subsequent analyses following treatment revealed significant reductions in Oxalobacter and Butyricicoccus within the intestinal microbiota, as well as Neisseria and Actinobacteria within the respiratory microbiota. These bacteria may serve as biomarkers for monitoring IgAN progression and treatment efficacy. Our findings align with recent studies identifying Butyricicoccus as a risk factor in IgAN [22], and we observed its reduction post-treatment. Similarly, the decrease in respiratory Neisseria subsequent to immunosuppressive therapy corroborates its substantial presence in IgAN [16], suggesting that the therapeutic effect of immunosuppression might also involve altering microbial composition.

Considering the association between periodontitis and exacerbation of colitis through oral-to-intestinal transmission of Klebsiella and Enterobacter [23], we investigated whether oropharyngeal bacteria could similarly exacerbate IgAN through swallowing. We identified Veillonella and Fusobacterium as common bacteria in both the oropharynx and intestines, with Fusobacterium showing a positive correlation with IgAN stage and serum albumin. Veillonella, in the respiratory mucosa, correlated positively with 24-h proteinuria and creatinine, while Fusobacterium correlated with blood urine and IgAN stage. These two types of bacteria show statistically significant correlations with the staging of IgAN, serum albumin levels, 24-h proteinuria, and creatinine levels. This correlation helps to further elucidate the potential pathogenic roles of these bacteria in the onset and progression of IgAN. The observed variations in correlation may be attributed to insufficient sample size, non-uniformity in sample sources, etc. Our ongoing research plans to expand the sample size in the future, enabling a more comprehensive investigation into the correlation between these two bacteria and clinical indicators, thus further validating their significant role in IgAN. These novel findings suggest that Fusobacterium abundance at these specific sites is significantly associated with the severity of IgAN and may serve as an important indicator.

Veillonella, a normal flora distributed in the oral cavity, pharynx, and digestive tract, exhibits a wide range of antimicrobial activities through the production of various compounds such as bacteriocins and hydrogen peroxide, effectively inhibiting the growth of pathogenic microorganisms. [24]. Fusobacterium (genus Clostridium) is mainly distributed in the mucous membranes of both humans and animals, with two of the most prominent aggregation. Moreover, it has the ability to produce lipopolysaccharide (LPS), which plays a role in promoting diabetic kidney disease (DKD) [25] and IgAN [18]. This is because LPS may play a role in inducing inflammation by accelerating the activation of macrophages, monocytes and neutrophils [26].

In the present study, we observed a coexistence of the aforementioned two bacteria in both the oropharynx and intestines of the IgAN group, exhibiting significant correlations with crucial clinical indicators including blood urine, creatinine, and 24-h proteinuria, respectively. This indicates that the two bacteria play a pivotal role in the oropharynx and intestinal tract, which are crucial mucosal sites implicated in IgAN, and provides a foundation for additional elucidation of the pathogenesis of IgAN and the development of new therapeutic methods for IgAN. Additionally, the integration of statistical methodologies for assessing the microbial composition in both the pharynx and intestines holds promise as a noninvasive approach to diagnose IgAN, thereby offering significant implications for clinical diagnosis and treatment.

However, this study, has certain limitations. Firstly, considering the significant influence of dietary composition, living environment, smoking, alcohol consumption [27], and oral medication use (e.g. proton pump inhibitors, antibiotics, and nonsteroidal anti-inflammatory drugs) [28–30] on individual flora distribution, it is imperative to conduct larger-scale studies in order to elucidate the relationship between microorganisms and IgAN while mitigating the confounding effects of host variables and medications. Furthermore, our study did not include other renal diseases as controls, which would have been valuable to determine if the observed changes in flora are unique to IgAN. Moreover, our analysis is primarily associative, necessitating further validation of the potential roles of these key bacterias in IgAN pathogenesis. In future research by our group will employ animal models to validate these mechanisms.

In summary, the involvement of pathogenic bacteria in the oral cavity and intestinal tract is implicated in the initiation and progression of IgAN through their influence on aberrant mucosal immune responses. The study also addressed the gut and respiratory microbiota landscapes. Moreover, ML algorithms, especially RF and XGBoost could emerge as valuable tools for noninvasive IgAN diagnosis, complementing traditional methods and potentially offering a novel perspective in comprehending and managing this disease.

Supplementary Material

Figures.zip

code.zip

Acknowledgments

I would like to express my deepest appreciation to my supervisor, Yafeng Li, for his guidance, support, and inspiration throughout this research project. Additionally, we are especially grateful for the members of my research team for their collaboration and contributions.

Authors’ contributions

Xiaoli Yuan: Investigation, Conceptualization, Writing Original draft preparation; Methodology, Data curation, Formal analysis; Jianbo Qing: Conceptualization, Data curation, Investigation, Methodology, Writing editing; Wenqiang Zhi: Data curation, Formal analysis; Feng Wu: Conceptualization, Investigation, Writing -review & editing; Yan Yan: Conceptualization, Formal analysis, Methodology, Writing- review & editing; Yafeng Li: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing-review & editing.

Declaration

Ethical clearance was duly obtained from the Ethics Committee of Shanxi Provincial People’s Hospital (Ethics No. 202440), and informed consent was secured in writing from all participants.

Disclosure statement

The authors declare that they have no competing interests.

Data availability statement

Microbiome sequencing data is accessible through the NCBI database, with the accession number ‘PRJNA1121870’. Additional data or code can be obtained via email from the corresponding author.
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