| Objective:Existing studies have shown that there is colonizing microbiota in the bladder,and bladder microbiota disorders are related to various urinary system diseases such as kidney stones,kidney transplantation,and chronic kidney disease.IgA nephropathy is the most common primary glomerular disease in chronic kidney disease,and the intestinal and tonsil microbiota have been reported to be associated with the occurrence and development of IgA nephropathy,but the urine microbiome characteristics of IgA nephropathy are unclear,especially whether it is related to the progression of the disease needs to be studied.In this study,it is proposed to use 16 s r RNA gene sequencing to study the urine microbiome characteristics of patients with IgA nephropathy,combined with blood and urine metabolomics and renal tissue proteomics’ "multiomics” analyze the correlation between urinary tract microbiome and disease progression in patients with IgA nephropathy,and screen for possible mechanisms of action.In addition,we intend to use microbiome and metabolomics to screen potential biomarkers associated with disease progression.Methods:1.IgA nephropathy patients who underwent renal biopsy surgery in the department of nephrology of the First People’s Hospital of Yunnan Province,Yan’an Hospital of Kunming and the First Affiliated Hospital of Kunming Medical University from June 2022 to October 2022 were collected as the IgAN group.According to the pathological grade of IgA nephropathy,it was divided into IgAN II,IgAN III,IgAN IV,and IgAN V,and healthy volunteers from the health examination clinic were recruited as the control group.Clean mid-urine samples and serum samples from all study subjects and renal biopsy tissue samples from IgAN patients were collected aliquot and stored at-80°C for use.Collect clinical data and clinical test data of research subjects.2.16 s r RNA gene sequencing was used to amplify and sequence the V3-V4 variable region gene encoding the small subunit of ribosomal in urine bacteria,and the richness and diversity characteristics of urine microbiota were explored by αdiversity and β diversity.The difference between IgA nephropathy and healthy controls was explored through Linear discriminant analysis effect Size(LEf Se)analysis and rank sum test.3.Untargeted metabolomics were used to analyze the blood and urine metabolome characteristics of patients with IgA nephropathy and healthy controls,and principal component analysis(PCA)was used to analyze the metabolite difference,and p value values were statistically tested according to one-way analysis of variance.According to the statistical test P value of one-way ANOVA,Orthogonal projections to latent structures discriminant analysis(OPLS-DA)calculated the Variable Importance for the Projection(VIP),and the fold of difference between groups change,FC)screened differential metabolites,metabolites with VIP>1 and p<0.05 as intergroup differential metabolites,and traceability analysis of differential metabolites was carried out to screen metabolites derived from microorganisms.4.Four blood and urine metabolites with the largest VIP values were selected to draw the receiver operating characteristic curve(ROC),the diagnostic efficacy of metabolites in patients with different grades was analyzed,and then the relative abundance of the screened differential microbiota and differential metabolite combination was constructed according to the random Forest 4.6–14 of the R software.The operational characteristic curve(ROC)of the random forest model is plotted,and the area under the curve(AUC)is calculated using p ROC package to evaluate the performance of the model.5.The protein of kidney biopsy tissue of patients with IgA nephropathy was detected by 4D-Fast DIA protein quantification,and the protein with a change in expression > 1.5 times(FC>1.5 or FC<0.67)and p value<0.05 after T test was selected as the differential protein,and the gene ontology(GO)and kyoto encyclopedia genes and genomes(KEGG)were performed on the differential protein by bioinformatics analysis,to analyze the relevant functions of differential proteins.6.Use multi-omics data to analyze the correlation between urine microorganisms and IgAN progression,use the deep Met Origin Analysis analysis mode of the Met Origin platform to analyze the correlation between microorganisms and microbial-derived metabolites(sample one-to-one correspondence),and draw heat maps and related network diagrams to screen important microorganisms and metabolites,and then analyze the correlation between differential metabolites and differential proteins according to Spilman correlation analysis.Screen the most strongly correlated signaling pathways to obtain possible signaling pathways associated with IgAN progression by urethral microorganisms.Result:1.After matching by gender,age and BMI,a total of 100 patients were recruited,including 20 patients with IgAN II-V in each group and 20 in the matched healthy control group.100 blood and urine samples were collected from 100 subjects,and 10 cases of kidney biopsy tissue samples were collected,including 3 cases of grade II and 7 cases of grade III.2.microbiome analysis found that there was no statistical difference between the Shannon,Simpson,and pielou indices,but the ACE index,chao1 index and Observe species index of IgAN-III were higher than those in the control group and IgAN-V.(p< 0.05),while β diversity analysis found that IgAN patients and healthy controls were far away.This showed that the microflora communities were highly different between the samples.Analysis of the relative abundance of urine microbial species found that at the phylum level,the dominant phylum of IgAN patients and healthy control groups were basically the same,mainly including Proteobacteria,Firmicutes,Bacteroides and Actinomycetes.At the genus level,the dominant genera species of IgAN patients and healthy controls were basically the same,and the most abundant genus was Halomonas.There were 34 species of bacteria with differences between the groups were found by rank sum test screening,the IgA nephropathy group has increased to include,g＿Enterococcus 、 f＿Enterobacteriaceae 、 g-＿Finegoldia 、g＿Dinghuibacter、g＿Schleiferia、g＿Anaerococcus、f＿Sphingomonadaceae.g-SD04E11、g＿Peptostreptococcus、g＿Lactococcus、g＿Veillonella、g＿Staphylococcus、g＿Aeromona、g＿Lactobacillus.3.Through metabolome analysis,it was found that the unsaturated fatty acids and free amino acids in different grades of IgAN group increased significantly compared with the healthy control group and were positively correlated with the pathological grade of the disease.Among them,244 differential metabolites were found in urine metabolite analysis,100 differential metabolites were found in blood metabolite analysis,and metabolites were traced and analyzed,and a variety of nephrotoxic toxins were found in metabolites,such as indole-3-acetate,indoleacetic acid;Betainealdehyde,isovaleric acid,riboflavin,etc.4.After ROC curve analysis,it was found that 8 metabolites had certain differential diagnostic value among patients with different grades of IgA,which may be related to the progression of the disease.After the establishment of a random forest model,Chenodeoxycholic acid,Glycocholic acid,9,10-Epoxyoctadecenoic acid,2-Ketobutyric acid,and Prostaglandin E1 showed the best performance,5 metabolites may be potential markers associated with disease progression.5.Through the proteomic analysis of biopsy tissues of IgAN patients,82 differential proteins were finally screened,including 53 upregulated proteins and 29 downregulated.Bioinformatics enrichment analysis found that the most significant signaling pathway for differential protein enrichment is oxidative phosphorylation.6.Through multi-omics association analysis,we screened the signaling pathways that may be related to IgAN progression,among which enterococcus-dimethylglycine-platelet factor 4 was the strongest,in addition,L-methionine had statistically significant differences in the detection of metabolites between blood and urine groups,and was positively correlated with the bacteria enriched in the healthy control group,indicating that the antioxidant effect of L-methionine in IgAN patients was weakened,which may be related to IgAN progression.Conclusion:1.IgAN patients have urine flora disorders,enterococcus,Enterobacteriaceae,Macropeptic streptococcus,g＿Enterococcus、f＿Enterobacteriaceae、g＿Finegoldia、g＿Dinghuibacter、g＿Schleiferia、g＿Anaerococcus、f＿Sphingomonadaceae.g-SD04E11、g＿Peptostreptococcus、g＿Lactococcus、g＿Veillonella、g＿Staphylococcus、g＿Aeromona、g＿Lactobacillus increased.2.Multi-omics association analysis found that enterococcus-dimethylglycine-platelet factor 4 may be the signaling pathway most strongly correlated with IgAN progression.3.Random Forest screening of deoxycholic acid,glycinolic acid,9,10-epoxyoctadecenoic acid,2-acetylbutyric acid,and prostaglandin E1 in urine have certain value in predicting IgAN progression. |
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