Characterization and diagnostic value of the gut microbial composition in patients with minimal change disease

Abstract

Background: Minimal change disease (MCD) is one of the most common causes of primary nephrotic syndrome with high morbidity. This study aimed to explore the typical alterations of gut microbiota in MCD and establish a non-invasive classifier using key gut microbiome. We also aimed to evaluate the therapeutic efficiency of gut microbiota intervention in MCD through animal experiments. Methods: A total of 222 stool samples were collected from MCD patients and healthy controls at the First Affiliated Hospital of Zhengzhou University and Shandong Provincial Hospital for 16S rRNA sequencing. Optimum operational taxonomic units (OTUs) were obtained for constructing a diagnostic model. MCD rat models were established using doxorubicin hydrochloride for exploring the therapeutic efficiency of gut microbial intervention through fecal microbiota transplantation (FMT). Results: The α-diversity of gut microbiota decreased in MCD patients when compared with healthy controls. The relative abundance of bacterial species also changed significantly. We constructed a diagnostic model based on eight optimal OTUs and it achieved efficiency of 97.81% in discovery cohort. The high efficiency of diagnostic model was also validated in the patients with different disease states and cross-regional cohorts. The treatment partially recovered the gut microbial dysbiosis in patients with MCD. In animal experiments, likewise, the gut microbiota changed sharply in MCD rats. However, gut microbial interventions did not reduce urinary protein or pathological kidney damage. Conclusion: Gut Microbiota shifts sharply in both patients and rats with MCD. Typical microbial changes can be used as biomarkers for MCD diagnosis. The gut microbiota compositions in patients with MCD tended to normalize after treatment. However, the intervention of gut microbiota seems to have no therapeutic effect on MCD.

Keywords: fecal microbiota transplantation; gut microbiota; gut-kidney axis; minimal change disease; non-invasive diagnosis.

FIGURE 1

Study design and flow diagram. The fecal samples including MCD patients and healthy controls from two medicine centers of China were collected. After exclusion, a total of 222 fecal samples were enrolled in the study. In 111 fecal samples from patients with MCD, 101 were from the First Affiliated hospital of Zhengzhou University in Henan province and 10 were from Shandong Provincial Hospital. In 101 HN_MCD patients, 46 of them were first onset MCD without treatment and were defined as UMCD, while the rest was composed of 45 treated MCD, eight relapse MCD and two other types of MCD. Ten MCD patients from Jinan were also without a history of application of drugs. Except one sample with too little stool, 45 UMCDs and matched HCs were randomly divided into discovery cohort and validation cohort. We compared the gut microbial alteration in discovery cohort and selected key OTUs as biomarker to construct diagnostic model. Then we tested the efficiency in validation cohort. We also validated the diagnostic efficiency in SD_MCD, Re_MCD and TMCD cohorts to explore the applicable population and scope. MCD, minimal change disease; HN_MCD, MCD patients from Henan province; SD_MCD, MCD patients from Shandong province; HC, healthy controls; UMCD, untreated MCD; TMCD, treated MCD; Re_MCD, relapse MCD.

FIGURE 2

Comparison of gut microbiota composition in discovery cohort (UMCD = 30, HC = 31). (A) The ace index (p < 0.001) and observed OTUs (p < 0.001) showed α-diversity decreased in UMCD. (B) PCoA diagram showed the weighted UniFrac distance had obviously difference between UMCD and HCs. (C) NMDS showed different distances in composition of gut microbiota. (D) ANOSIM diagram showed significant differences between HCs and UMCDs. (E) Average relative abundance at genus level in HCs and UMCDs were compared. PCoA, principal coordinate analysis; NMDs, Non-metric multidimensional scaling; ANOSIM, analysis of similarity.

FIGURE 3

Construction and validation of discriminative model. (A) Random forest model showed when containing eight OTUs, the model had lower CV error with fewer variables. (B) Mean decrease accuracy and mean decrease gini showed the contribution values of eight selected OTUs. (C) Probability of disease was obvious higher in UMCDs (n = 30) than that in HCs (n = 31) in discovery cohort. (D) Discriminative model based on obtained microbial biomarkers showed good diagnostic efficiency with an AUC of 0.9781 in discovery cohort. (E) Probability of disease was obvious higher in UMCDs (n = 15) than that in HCs (n = 15) in validation cohort. (F) Discriminative model showed good diagnostic efficiency with an AUC of 0.8143 in validation cohort. (G) Probability of disease was obvious higher in JN_MCDs (n = 10) than that in HCs (n = 10). (H) Discriminative model showed good diagnostic efficiency with an AUC of 0.95 in JN_MCDs and HCs. (I) Probability of disease was obvious higher in Re_MCDs (n = 8) than that in HCs (n = 8). (J) Discriminative model showed good diagnostic efficiency with an AUC of 0.8125 in Re_MCDs and HCs. (K) Probability of disease was obvious higher in TMCDs (n = 45) than that in HCs (n = 45). (L) Discriminative model showed good diagnostic efficiency with an AUC of 0.916 in TMCDs and HCs. CV, coefficient of variation; OTU, operational taxonomic unit POD, possibility of disease; ROC, receiving operational curve; AUC, area under curve. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

FIGURE 4

Comparison of gut microbiota compositions among UMCD (n = 45), TMCD (n = 45) and HC groups (n = 91). (A) The ace indexes and observed OTUs had no difference in UMCDs and TMCDs, and both of them were lower than those in HCs (Ace index: UMCD vs. HC, p < 0.001, TMCD vs. HC, p < 0.01; observed OTUs, UMCD vs. HC, p < 0.001, TMCD vs. HC, p < 0.001). (B) PCoA diagram showed that the weighted UniFrac distance got similar to HCs after treatment in patients with MCD. (C) NMDs diagram showed the compositions of gut microbiota were similar to HCs after treatment. (D) ANOSIM diagram showed significant difference among three groups. Average relative abundance at phylum (E) and genus (F) levels among three groups were compared.

FIGURE 5

The Strong, Prosperous, and Resilient Communities Challenge (SPARCC) analysis was used to analyze the degree of correlation between key OTUs used for model construction and clinical indicators. ALB, albumin; 24 h_Pro, 24 h urine protein; SCr, serum creatinine. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

FIGURE 6

Modeling process and changes of urinary protein in Con group (n = 7) and MCD group (n = 7). (A) DOX injected via tail-vein at a dose of 4 mg/kg body weight in week 0 and 3.5 mg/kg body weight in week one in MCD group. The Con group was given the same dose NS. (B) Line chart showed the alteration of urinary protein after modeling. (C) HE, PAS and Masson staining of glomerulus in different groups. (D) Electron microscopy showed detailed pathological injury in each group. DOX, doxorubicin hydrochloride; NS, normal saline. HE, hematoxylin-eosin staining; PAS, periodic acid-schiff staining.

FIGURE 7

Comparison of gut microbiota composition of rats in Con group (n = 7) and MCD group (n = 7). (A) PCoA diagram showed the weighted UniFrac distance had obviously difference between Con group and MCD group. (B) NMDS diagram showed the difference in composition of gut microbiota. (C) There genera of bacteria changed markedly in MCD group. (D) Heatmap showed difference in relative abundance of key OTUs.

FIGURE 8

Modeling process and Pathological injury in MCD + NS group (n = 7), MCD + FC group (n = 7) and MCD + FM group (n = 7). (A) After modeling, rats in three groups were given a two-week gut microbiota cleaning. Then FMT continued from week three to week eight in three groups. (B) Line chart showed the alteration of urinary protein after modeling. (C) HE, PAS and Masson staining of glomerulus in different groups. (D) Electron microscopy showed detailed pathological injury in each group. FMT, fecal microbiota transplantation; FC, fecal microbiota transplantation with feces from Con group; FM, fecal microbiota transplantation with feces from MCD group.