Microbial genetic markers for discriminating diabetic nephropathy and membranous nephropathy and use thereof

Abstract

The application belongs to the technical field of kidney disease differential diagnosis, and particularly relates to a microbial gene marker for differentiating diabetic nephropathy and membranous nephropathy and application thereof. The application provides sufficient evidence for the differentiation and treatment of clinical diagnosis of diabetic nephropathy and pathological diagnosis of membranous nephropathy based on four microbial markers of Blautia, Akkermansia, Sphingomonas and Granulicatella.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of differential diagnosis of kidney diseases, and particularly relates to a microbial gene marker for differentiating diabetic kidney disease and membranous nephropathy and application thereof. BACKGROUND

[0002] Diabetic kidney disease (DKD) and membranous nephropathy (MN) are the most common causes of primary or secondary glomerular diseases worldwide, and have similar clinical manifestations characterized by nephrotic syndrome. In the past decade, the incidence of DKD has shown a pandemic increase, mainly due to the rising prevalence of diabetes mellitus (DM) worldwide. Increasing evidence suggests that DKD is the leading cause of chronic kidney disease (CKD) to end-stage renal disease (ESRD) in the world (about 50%), followed by MN, accounting for 30% of the proportion. Although clinical indicators depending on urinary albumin excretion and DM course play a key role in the identification and prognosis prediction of DKD, there is still an urgent need to find the deficiency of early diagnosis and the non-specificity of DKD differential diagnosis. Similarly, the remaining cases of MN (accounting for 20%) cannot be determined due to their negative reaction to phospholipase A2 receptor (PLA2R) and thrombospondin type 1 domain-containing protein 7A (THSD7A) targeted antigens. At present, kidney biopsy is the gold standard for distinguishing the two diseases. However, due to the related contraindications of patients (such as coagulation dysfunction), a large number of patients cannot be subjected to kidney biopsy. In addition, a part of patients are unwilling to accept kidney biopsy. In this case, there is no effective method to provide diagnostic evidence to distinguish DKD patients and MN. Therefore, it is urgent to try to explore a new method of DKD / MN identification with wider coverage.

[0003] The characteristics of intestinal microecology or the discriminant model based on intestinal microorganisms as a distinguishing tool for specific diseases or tumors are increasingly widely reported and recognized. Recent studies have reported the progress of intestinal microbiome in the diagnosis, pathogenesis, and treatment of DM and DKD. For example, a fiber diet targeting the intestinal flora can reduce insulin resistance and improve hyperglycemia in diabetic populations. Li and his colleagues (The potential role of the gut microbiota in modulating renal function in experimental diabetic nephropathy murine models established in same environment. Biochim Biophys Acta Mol Basis Dis, 2020) demonstrated that the genus Blautia had a reverse protective effect on the development of kidney function from microalbuminuria to massive albuminuria in a mouse model with DKD. The inhibitory effect of phenol sulfate synthesis produced by intestinal tyrosine fermentation may reduce the urinary protein level of DKD mice. Notably, Tao's study (Understanding the gut-kidney axis among biopsy-proven diabetic nephropathy, type 2 diabetes mellitus and healthy controls: an analysis of the gut microbiota composition. Acta Diabetol, 2019 May) proposed the idea of using intestinal microbiome as a target for biomarkers for high-accuracy differential diagnosis of DKD and DM. Although the role of intestinal microbiome in DKD mouse models has been studied, more studies in human subjects are needed for validation. However, intestinal microbial models for differentiating diabetic nephropathy and membranous nephropathy have not been reported. SUMMARY

[0004] To this end, the technical problem to be solved by the present application is to provide a microbial genetic marker for differentiating diabetic nephropathy and membranous nephropathy, a method for making differential diagnosis of diabetic nephropathy and membranous nephropathy using intestinal microorganisms for clinical diagnosis, and a target for targeted therapy of membranous nephropathy, which can also be used as a non-invasive tool for differentiating diabetic nephropathy and membranous nephropathy, and provides the possibility of using intestinal microbial markers to differentiate different kidney diseases.

[0005] The present application adopts the following technical solutions:

[0006] The application provides a microbial gene marker for differential diagnosis of diabetic nephropathy and membranous nephropathy, which comprises a mixture of Blautia, Akkermansia, Sphingomonas and Granulicatella.

[0007] Further, the microbial gene marker for differential diagnosis of diabetic nephropathy and membranous nephropathy comprises:

[0008] The Blautia comprises OTU612;

[0009] The Akkermansia comprises OTU668;

[0010] The Sphingomonas comprises OTU900;

[0011] The Granulicatella comprises OTU692;

[0012] The OTU612 comprises a nucleotide sequence structure as shown in SEQ ID No. 1;

[0013] The OTU668 comprises a nucleotide sequence structure as shown in SEQ ID No. 2;

[0014] The OTU900 comprises a nucleotide sequence structure as shown in SEQ ID No. 3;

[0015] The OTU692 comprises a nucleotide sequence structure as shown in SEQ ID No. 4.

[0016] Further, the OTU612 and OTU668 are dominant bacteria genera of diabetic nephropathy, and the OTU900 and OTU692 are dominant bacteria genera of membranous nephropathy.

[0017] The application further discloses use of the microbial gene marker in preparation of a differential diagnosis reagent for diabetic nephropathy and membranous nephropathy, or a microbial targeted treatment drug for membranous nephropathy.

[0018] The application further discloses a kit for differential diagnosis of diabetic nephropathy and membranous nephropathy, comprising primers of the microbial gene marker.

[0019] Further, the primers comprise:

[0020] The upstream primer is 341F 5'-CCTACGGGNGGCWGCAG-3';

[0021] The downstream primer is 805R 5'-GACTACHVGGGTATCTAAG-3';

[0022] Downstream primer: 805R-GACTACHVGGGTATCTAATCC-3'.

[0023] The application also discloses a composition for targeting therapy of membranous nephropathy by microorganisms, namely, the microorganism marker is included, and the inflammation progress of membranous nephropathy is improved by increasing the relative content of the Sphingomonas and Granulicatella.

[0024] The application also discloses a model construction method for identifying diabetic nephropathy and membranous nephropathy, comprising the following steps:

[0025] (1) Collecting the feces of patients clinically diagnosed as diabetic nephropathy and patients pathologically diagnosed as membranous nephropathy respectively, and completing 16S rRNA gene sequencing of the patients;

[0026] (2) According to the similarity of 97±1%, OTU clustering and OTU abundance determination are completed on the obtained gene sequences; further, the RDP classifier Bayesian algorithm is used for taxonomic analysis on the representative sequences of the OTU with the similarity of 97±1%, and the community composition of each sample is counted at each taxonomic level;

[0027] (3) Using statistical analysis, the differential OTU is included in the random forest model, and the differential key OTU combination capable of distinguishing diabetic nephropathy and membranous nephropathy is found out;

[0028] (4) Based on the OTU combination obtained in step (3), the sum of the abundances of the dominant genera of diabetic nephropathy and the dominant genera of membranous nephropathy is calculated respectively, and the individual average abundance index is further calculated by calculating the abundance difference between the two dominant flora; the individual average abundance index of all individuals of the diabetic nephropathy group and the membranous nephropathy group is combined, an ROC curve is constructed, and the distinguishing ability of the OTU combination marker is determined.

[0029] Further, step (3) further comprises the step of cross-validation analysis on the obtained differential key OTU combination, so as to find out the least OTU combination capable of most accurately distinguishing diabetic nephropathy and membranous nephropathy.

[0030] The cross-validation refers to that all data are divided into 5 subsets, each subset is used as a test set, and the rest is used as a training set. Each time, one subset is selected as a test set, the cross-validation is repeated 5 times, and the average cross-validation correct recognition rate of 5 times is taken as a result. All samples are used as a training set and a test set, and each sample is verified once. The key OTU combination screened by the random forest method is traversed, so as to construct a low-error efficient classifier by using the least OTU number combination.

[0031] Further, the method for constructing the model for predicting diabetic nephropathy further comprises the step of performing gene detection on the feces of the patient to be diagnosed according to the microbial genetic markers.

[0032] The method for constructing the model for predicting diabetic nephropathy collects qualified fecal samples for high-throughput sequencing, and based on the high-throughput sequencing data, a microbial differential model of the diabetic nephropathy group and the membranous nephropathy group is established in a cohort, and a probability of disease (POD) index is established.

[0033] The marker abundance difference at the OTU level is achieved by 16S rRNA gene sequencing. The content determination method of the microbial marker is preferably 16S rRNA gene sequencing, which is mature and advanced. The 16S rRNA gene is a gene encoding the small subunit of the ribosome of prokaryotes. The 16S rDNA is a gene (about 1.5 kb) encoding the subunit, which has 9 variable regions, V1-V9. Due to the limitation of the read length of the sequencing platform, the V3-V4 variable region is generally selected for amplification and sequencing to understand the types and abundance of bacteria. The microbial sequencing in the present application is mainly carried out on the Shanghai Illumina platform using Miseq sequencing. Miseq sequencing obtains double-end sequence data, and has larger sequencing throughput and shorter sequencing time compared with other similar sequencing methods, and is widely used in intestinal flora sequencing. The gene sequence obtained by Miseq sequencing is clustered according to 97% similarity, and the types of OTUs in the individual and the abundance value of each OTU can also be obtained in the clustering process.

[0034] In addition, the representative sequence of each OTU is selected, and compared with the RDP database, the taxonomic annotation of the OTU can be completed, and the community composition of each sample is counted at each taxonomic level: domain, kingdom, phylum, class, order, family, and genus. In comparison, the OTU level is relatively more accurate than the species, and the OTU is preferably used as the microbial marker, and other more macroscopic taxonomy mainly provides taxonomic annotation for the OTU.

[0035] The relative content determination method of the microbial marker combination is preferably Miseq sequencing, the determined OTU types are constant and the abundance in the diseased sample is basically stable, and the output POD value also has higher sensitivity, which can assist in the diagnosis of diabetic nephropathy.

[0036] The present application scheme detects the relative content of fecal microorganisms of 129 patients clinically diagnosed as diabetic nephropathy and 142 patients pathologically diagnosed as membranous nephropathy. The microbial composition in the two groups is obtained by 16S rRNA gene sequencing method. LEfSE is used to analyze the difference of microbial composition at the taxonomic level to find out the community or species that has significant difference on sample division. The LDA effect value of 4 genera is 2.0, which is defined as a marker. Based on the obtained MAI value, the discrimination ability of 129 DKD patients and 142 MN patients is evaluated by ROC curve. The area under the ROC curve is 92.28%, which proves that this microbial combination has good identification ability.

[0037] The present application first determines the intestinal microbial composition of diabetic nephropathy and membranous nephropathy by 16S rRNA gene sequencing, and combines the linear discriminant analysis (LEfSe) of effect value for screening microbial gene markers, and further statistical analysis to determine the different microbial genes. Based on 4 microbial gene markers, the present application can be divided into two categories of microbial markers, namely DN group dominant genus and MN group dominant genus. The DKD group dominant genus includes Blautia and Akkermansia; the MN group dominant genus includes Sphingomonas and Granulicatella. The four intestinal microbial gene markers have good discrimination for the two diseases, and provide sufficient basis for the microbial related differential diagnosis and treatment of diabetic nephropathy and membranous nephropathy. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to make the content of the present application more easily understood, the present application will be further described in detail below according to specific embodiments of the present application and in conjunction with the drawings, wherein,

[0039] Figure 1 Flow chart for determining microbial species and relative content;

[0040] Figure 2 ROC curve example graph, wherein the left side is the ROC curve graph of the training set in the experiment, and the right side is the ROC curve graph of the validation set. DETAILED DESCRIPTION

[0041] In the following examples of the present application, the methods used are conventional methods unless otherwise specified. The materials or reagents required in the following examples are obtained from public commercial channels unless otherwise specified; the experimental process is completed by Shanghai Mubai Biological Technology Co., Ltd. Illumina platform.

[0042] Example 1

[0043] Clinical collection of 129 patients with clinical diagnosis of diabetic nephropathy and 142 patients with pathological diagnosis of membranous nephropathy, and the relative content of microorganisms is detected and determined, and the specific operation process is shown in the accompanying Figure 1 The specific steps include the following steps:

[0044] Sample aliquot freezing: the fresh stool sample is stored in a -80°C refrigerator immediately after collection, and repeated freezing is avoided during transportation; this step is completed on the Shanghai illumina platform.

[0045] The DNA extraction operation includes:

[0046] (1) Chemical lysis method to break cell membrane and nuclear membrane: add 790ul of lysis solution (4mmol / L thiocyanate guanidine, 250ul; 10% N-lauroyl amino acid, 40ul; 5% N-lauroyl amino acid, 0.1mmol / L PBS buffer [pH8.0], 500ul) to each sample, vortex vigorously, and incubate at 70°C for 1h;

[0047] (2) Physical beating method (Beads-Beating) to fully break the cell membrane and nuclear membrane: after incubation, add glass beads (0.1mm, 500-750ul) and mix, beat for 10min (25HZ / S);

[0048] (3) Follow-up extraction according to the instructions of the extraction kit (E.Z.N.A.○RStool DNA Kit);

[0049] (4) Centrifugation (14000g, 3min) to take the supernatant and place it in a 2ml centrifuge tube, add 200ul of SP2 buffer, vortex and ice bath for 5min;

[0050] (5) Centrifugation (14000g, 3min) to take the supernatant and place it in a new 2ml centrifuge tube, add 200ul of cHTR Reagent (shake well before use), mix well and place at room temperature for 2min;

[0051] (6) Centrifugation (14000g, 2min) to take 500ul of supernatant and place it in a new 2ml centrifuge tube, add 20ul of proteinase K (20mg / ml), 500ul of BL buffer, vortex to mix, incubate at 70°C for 10min; after incubation, add 500ul of anhydrous ethanol, vortex and perform column: Insert the DNA mini column into a 2mL collection tube;

[0052] PCR amplification and sequencing:

[0053] The DNA extracted from each sample was used as a template to amplify the V3-V4 region of the 16S rRNA gene, and the sequencing primer sequence 341F_805R included:

[0054] Upstream primer: 341F 5'-CCTACGGGNGGCWGCAG-3';

[0055] Downstream primer: 805R-GACTACHVGGGTATCTAATCC-3';

[0056] The specific reaction conditions and system are shown in Table 1 below:

[0057] Table 1 PCR reaction conditions and system

[0058]

[0059] The PCR products were mixed in the same proportion, and sequencing was completed on the Illumina Miseq platform.

[0060] Example 2 OTU clustering and annotation and ROC analysis

[0061] Through the classification operation, the sequences were classified into many small groups according to their similarity to each other, and a group was an OTU. According to different similarity levels, all sequences were divided into OTUs, and usually OTUs at a similarity level of 97% were subjected to bioinformatics statistical analysis (Usearch software). In order to obtain the species classification information corresponding to each OTU, the RDP classifier Bayesian algorithm was used to perform taxonomic analysis on the representative sequences of the OTUs at a similarity level of 97%, and the community composition of each sample was statistically analyzed at each classification level: domain (domain), kingdom (kingdom), phylum (phylum), class (class), order (order), family (family), genus (genus), and species (species).

[0062] On the basis of embodiment 1, the detected and screened flora is Blautia, Akkermansia, Sphingomonas, Catonella, Blautia includes OTU612, OTU612 includes a nucleotide sequence structure as shown in SEQ ID No. 1; Akkermansia includes OTU668, OTU668 includes a nucleotide sequence structure as shown in SEQ ID No. 2; Sphingomonas includes OTU900, OTU900 includes a nucleotide sequence structure as shown in SEQ ID No. 3; and Catonella includes OTU692, OTU692 includes a nucleotide sequence structure as shown in SEQ ID No. 4. Community composition difference analysis: preliminary Wilcoxon rank sum test to screen for different flora (R language); random forest analysis can find out the key OTUs that can distinguish the difference between the two groups of samples and assign importance values (importance) (Qiime software). Generally, for the key OTUs screened by random forest analysis, 5-fold cross-validation analysis is performed according to different combinations to find the least combination of OTUs that can most accurately distinguish between groups, and further analysis such as ROC analysis is performed.

[0063] The abundance of the four OTUs in the validation queue as described above is incorporated into the machine learning model to obtain the POD value of the individual. The POD values of the training queue are shown in Table 2 below, and the POD values of the validation queue are shown in Table 3 below. According to professional knowledge, the results of the disease group and the reference group can be analyzed to determine the upper and lower limits of the measured values, the group interval, and the cut-off point (threshold, cut-off point). The cumulative frequency distribution table is listed according to the selected group interval, and the sensitivity (Sensetivity), specificity and false positive rate (1-specificity: Specificity) of all cut-off points are calculated. The sensitivity is taken as the vertical coordinate to represent the true positive rate, and (1-specificity) is taken as the horizontal coordinate to represent the false positive rate. Plot the ROC curve. The area value (AUC) under the ROC curve is between 1.0 and 0.5, and the closer to the upper left corner, the higher the accuracy of the diagnosis. In the case of AUC>0.5, the closer the AUC is to 1, the better the diagnostic effect. AUC is 0.5-0.7, which has lower accuracy, AUC is 0.7-0.9, which has certain accuracy, and AUC is above 0.9, which has higher accuracy.

[0064] Table 2 POD values of the training queue

[0065]

[0066]

[0067]

[0068] Table 3 POD values of the validation queue

[0069] Sample POD Sample POD Sample POD DKD102 0.066 MP2_116 0.998 DKD108 0.796 MP2_2 0.965 DKD110 0.015 MP2_21 0.172 DKD112 0 MP2_23 0.967 DKD128 0.401 MP2_26 0.989 DKD131 0.015 MP2_39 0.967 DKD134 0.467 MP2_40 0.824 DKD16 0.626 MP2_43 0.628 DKD18 0.008 MP2_46 0.904 DKD180 0.015 MP2_48 0.19 DKD24 0.015 MP2_49 0.621 DKD27 0.101 MP2_5 0.971 DKD3 0.015 MP2_52 0.991 DKD34 0.017 MP2_53 1 DKD36 0.028 MP2_54 0.962 DKD37 0.001 MP2_59 0.985 DKD41 0.006 MP2_60 0.999 DKD47 0 MP2_64 0.992 DKD49 0.015 MP2_68 0.792 DKD55 0.015 MP2_7 0.718 DKD6 0.17 MP2_71 0.939 DKD63 0.325 MP2_73 0.004 DKD65 0.412 MP2_84 0.903 DKD72 0.002 MP2_9 0.994 DKD82 0 MP2_92 0.631 DKD84 0.156 MP2_95 0.971 DKD87 0.005 MP2_98 0.862 DKD88 0 MP3_11 0.984 DKD90 0 MP3_12 0.445 DKD91 0 MP3_15 0.897 DKD92 0 MP3_17 0.929 DKD93 0.726 MP3_18 0.816 DKD94 0.585 MP3_29 0.506 MP1_10 0.968 MP3_5 1 MP1_6 0.98 MP3_8 0.751 MP1_8 0.987 MP2_10 0.015 MP2_101 0.901 MP2_106 0.999 MP2_108 0.79 MP2_112 0.802 MP2_114 0.917

[0070] To evaluate the diagnostic potential of the above-mentioned microbial genetic markers, ROC curves were constructed to distinguish DKD and MN, as shown in Figure 2 The random forest model determined the relative importance of the four microbial group-targeted markers. We found that OTU900 could maximize the prediction performance (stability index: >40.0 average decrease in Gini coefficient). Using 4 OTUs as identifying biomarkers, 96 DKD were separated from 98 MN, and the area under the ROC curve in the training set was 92.28% (95% confidence interval: 88.15%-96.42%). Consistent with these results, 33 DKD and 44 MN were randomly divided into a validation set to verify the diagnostic potential of DKD and MN, and the average POD value of MN patients was significantly higher than that of DKD patients, with an AUC of 94.59% in the validation set, and a 95% CI of 89.82%-99.36%. All results showed that the microbial group-related markers could be used as an alternative tool for high-precision differentiation of DKD and MN.

[0071] In summary, the combination of the four OTUs of the present application can be used as an auxiliary diagnostic method for identifying clinically diagnosed diabetic nephropathy and pathologically diagnosed membranous nephropathy. This model has good specificity and high sensitivity, and can be used as a non-invasive alternative tool for distinguishing DKD and MN. The present application is a new attempt to identify different diseases through the intestinal microbiome. This also provides the possibility of using intestinal microbial biological agents to distinguish different kidney diseases.

[0072] Obviously, the above-mentioned embodiments are only examples for the sake of clarity, and are not a limitation on the embodiments. Based on the above description, other different forms of changes or variations can also be made by those of ordinary skill in the art. Here, it is not necessary and impossible to exhaust all embodiments. The obvious changes or variations derived therefrom are still within the protection scope of the present application. SEQUENCE LISTING <110> The First Affiliated Hospital of Zhengzhou University <120> Microbial genetic markers for identifying diabetic nephropathy and membranous nephropathy and applications thereof <160> 4 <170> SIPOSequenceListing 1.0 <210> 1 <211> 440 <212> DNA <213> OTU612 <400> 1 cctacgggtg gctgcagtgg ggaatattgc acaatggggg aaaccctgat gcagcgacgc 60 cgcgtgagtg aagaagtatt tcggtatgta aagctctatc agcagggaag aaaatgacgg 120 tacctgacta agaagccccg gctaactacg tgccagcagc cgcggtaata cgtagggggc 180 aagcgttatc cggatttact gggtgtaaag ggagcgtaga cggcatgaca agccagatgt 240 gaaaacccag ggctcaaccc tgggactgca tttggaactg ccaggctgga gtgcaggaga 300 ggtaagcgga attcctagtg tagcggtgaa atgcgtagat attaggagga acaccagtgg 360 cgaaggcggc ttactggacg atcactgacg ttgaggctcg aaagcgtggg gagcaaacag 420 gattagatac cccagtagtc 440 <210> 2 <211> 446 <212> DNA <213> OTU668 <400> 2 cctacgggtg gctgcagtcg agaatcattc acaatggggg aaaccctgat ggtgcgacgc 60 cgcgtggggg aatgaaggtc ttcggattgt aaacccctgt catgtgggag caaattaaaa 120 agatagtacc acaagaggaa gagacggcta actctgtgcc agcagccgcg gtaatacaga 180 gaaaacccag ggctcaaccc tgggactgca tttggaactg ccaggctgga gtgcaggaga 300ggtctcaagc gttgttcgga atcactgggc gtaaagcgtg cgtaggctt ttcgtaagtc 240 gtgtgtgaaa ggcgcggggct caacccgcgg acggcacatg attackgcgag actagagtaa 300 tggagggga accggaatttc tcggtgtagc agtgaatgc gtagatatcg agaggaacac 360 tcgtggcgaa ggcggttcc tggacattaa ctgacgctga ggcacgaagg ccagggaggc 420 gaaagggatt agatacccta gtagtc 446 <210> 3 <211> 440 <212> DNA <213> OTU900 <400> 3 cctacgggtg gctgcagtgg ggaatattgg acatggcg caagcctgat ccagcaatgc 60 cgcgtgagtg atgaggccc taggttgta aagctctttt acccgggag atatgactg 120 taccgggaga ataagccccg gctaactccg tgccagcagc cgcggtaata cggagggggc 180 tagcgttgtt cggaattact gggcgtaag cgcacgtagg cggcttgta agtcagaggt 240 gaaagcctgg agctcaacc cagaactgcc tttgagactg catcgcttga atccaggaga 300 ggtcagtgga attccgagtg tagaggtgaa attcgtagat attcggaaga acaccagtgg 360 cgaaggcggc tgactggact ggtattgacg ctgaggtgcg aaagcgtggg gagcaaacag 420 gattagatac cctagtagtc 440 <210> 4 <211> 465 <212> DNA <213> OTU692 <400> 4 cctacgggtg gcagcagtag ggaatcttcc gcaatggacg caagtctgac ggagcaacgc 60 cgcgtgagtg aagaaggttt tcggatcgta aaactctgtt gttagagaag aacaagtgct 120 agagtaactg ttagcgcctt gacggtatct aaccagaaag ccacggctaa ctacgtgcca 180 gcagccgcgg taatacgtag gtggcaagcg ttgtccggat ttattgggcg taaagcgagc 240 gcaggcggtt ccttaagtct gatgtgaaag cccccggctc aaccggggag ggtcattgga 300 aactggggaa cttgagtgca gaagaggaga gtggaattcc atgtgtagcg gtgaaatgcg 360 tagatatatg gaggaacacc agtggcgaag gcgactctct ggtctgtaac tgacgctgag 420 gctcgaaagc gtgggtagca aacaggatta gataccctag tagtc 465

Claims

1. The use of a microbial marker for the preparation of a differential diagnosis reagent for diabetic nephropathy and membranous nephropathy, characterized in that: The microbial markers are a mixture of Blautia, Akkermansia, Sphingomonas, and Granulicatella, Blautia and Akkermansia being the dominant genera in diabetic nephropathy and Sphingomonas and Granulicatella being the dominant genera in membranous nephropathy. The microbial markers are a mixture of Blautia, Akkermansia, Sphingomonas, and Granulicatella, Blautia and Akkermansia being the dominant genera in diabetic nephropathy and Sphingomonas and Granulicatella being the dominant genera in membranous nephropathy. The microbial markers are a mixture of Blautia, Akkermansia, Sphingomonas, and Granulicatella, Blautia and Akkermansia being the dominant genera in diabetic nephropathy and Sphingomonas and Granulicatella being the dominant genera in membranous nephropathy. The microbial markers are a mixture of Blautia, Akkermansia, Sphingomonas, and Granulicat

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