The gut microbiome in microscopic polyangiitis with kidney involvement: common and unique alterations, clinical association and values for disease diagnosis and outcome prediction

Abstract

Background: Microscopic polyangiitis (MPA) is an autoimmune disease characterized by frequent kidney involvement. Imbalance of intestinal flora has been found implicated in multiple immune-mediated disorders. However, the profiling and the role of the gut microbiome in MPA remains unclear.Methods: We performed 16S rRNA amplicon sequencing on fecal samples from 71 MPA patients with kidney involvement (35 at incipient active stage, 36 at remissive stage) and 34 healthy controls (HCs).Microbial diversity and abundance were compared among the three cohorts. The correlation between altered microbes and clinical indices were investigated. Two random forest models based on the profiling of the gut microbiome were constructed for the diagnosis of MPA.Results: Two α-diversity indices, including Simpson and Shannon index, were decreased in MPA patients (P<0.001), especially in those with active disease (P=0.001). β-diversity analysis showed biased microbial composition among the three groups. Genus Actinomyces and Streptococcus were more abundant in both MPA cohorts than those in HCs, while genus Subdoligranulum, Eubacterium hallii, Ruminococcaceae UCG013, Eubacterium ventriosum, Dorea and Butyricicoccus were more abundant in HCs than those in both MPA cohorts. All the 6 genera with decreased abundance belong to short-chain fatty acids (SCFA)-producing taxons. Besides, 1 and 2 operational taxonomic units (OTUs) were enriched in patients with active MPA who needed dialysis at sampling and in patients who progressed to end-stage renal disease during follow up, respectively. Furthermore, the model for diagnosis of MPA incorporated 6 OTU markers and achieved an AUC of 93.45% (95% CI, 88.15-98.74%). Similarly, the model for predicting disease activity incorporated 11 OTU markers and achieved an AUC of 90.71% (95% CI, 82.49-98.94%).Conclusions: Alteration of intestinal flora existed in MPA patients with kidney involvement and was characterized by increased abundance of genus Actinomyces and Streptococcus and decreased abundance of 6 SCFA-producing genera. Gut microbial profiling combined with machining-learning methods showed potentials for diagnosing MPA and predicting disease activity.

Shen-Ling-Bai-Zhu-San (SL) and SL Derived-Polysaccharide (PL) Ameliorate the Severity of Diarrhea-Induced by High Lactose via Modification of Colonic Fermentation

“…SL increases luminal Bifidobacteria, Megamonas, Blautia, Romboutsia, Akkermansia and mucosal Pediococcus, Rhodococcus, and Coriobacteriaceae UCG-002, which are involved in carbohydrate metabolism and SCFA production. In agreement with the results from the literature ( Guo et al, 2021 ; Huang et al, 2021 ; Yu et al, 2021 ) and our 16S rRNA pyrosequencing analysis, Spearman’s correlation assay further proved that the luminal genera Subdoligranulum, Blautia, Ruminococcus_torques_group and Norank_f_Muribaculaceae were positively correlated with cecal luminal SCFA concentrations, including acetate, protratate, butyrate and pH values, while they were negatively correlated with luminal lactate. It is worth mentioning that the genera Bilophila, Desulfovibrio, Alistipes, Akkermansia, and Bacteroides showed a strong negative correlation with luminal lactose, indicating that these genera might play an important role in lactose fermentation.…”

Alterations of the Gut Microbiota in Patients with Diabetic Nephropathy

“…Tao et al have reported that DN and T2DM can be effectively distinguished by Escherichia - Shigella and Prevotella _9, with an AUC equal to 0.86 ( 17 ). Different types of kidney diseases can also be distinguished by the gut microbiota ( 48 , 49 ). In the present study, the AUCs under the ROC curve of Clostridium sp.…”

Findings on the Relationship Between Intestinal Microbiome and Vasculitis

“…Culturing these mice in a germ-free condition or with antibiotic treatment can significantly downregulate the Th17 cells and relieve kidney inflammation, while Citrobacter rodentium infection can expand Th17 cells and aggravate kidney damage in mice (Krebs et al, 2016). Some cross-sectional studies have proved that alterations of gut microbiota (including reduced SCFAproducing taxons) existed in AAV and were also linked to kidney injury (Table 2) by analyzing the fecal samples of patients using 16s rRNA sequencing (Najem et al, 2018;Yu et al, 2021;Yu et al, 2022) (Yu et al, 2021). They also established a random forest model algorithm based on operational taxonomic unit (OUT) markers for the diagnosis and prediction of disease activity in MPA, which showed good predictive power (Yu et al, 2021).…”

“…Some cross-sectional studies have proved that alterations of gut microbiota (including reduced SCFAproducing taxons) existed in AAV and were also linked to kidney injury (Table 2) by analyzing the fecal samples of patients using 16s rRNA sequencing (Najem et al, 2018;Yu et al, 2021;Yu et al, 2022) (Yu et al, 2021). They also established a random forest model algorithm based on operational taxonomic unit (OUT) markers for the diagnosis and prediction of disease activity in MPA, which showed good predictive power (Yu et al, 2021). Another study also reported a gut-kidney axis in AAV, showing that changes in gut microbiota composition are associated with the degree of renal injury in AAV patients; this suggested that targeted regulation of gut dysbiosis may be a new intervention to reduce kidney damage in AAV patients (Yu et al, 2022).…”

“…Yu et al. reported that MPA patients have increased abundance of Actinomyces and Streptococcus and reduced SCFA-producing taxons in the gut microbiome, which may be the basis of the pathogenesis of kidney damage in MPA ( Yu et al., 2021 ). They also established a random forest model algorithm based on operational taxonomic unit (OUT) markers for the diagnosis and prediction of disease activity in MPA, which showed good predictive power ( Yu et al., 2021 ).…”