Screen The Intestinal Flora Related To Hyperuricemia Based On Clinical Cases

Objective Hyperuricemia?HUA?is a metabolic disorder in which blood uric acid levels rise as a result of purine metabolic disorders,excessive production of uric acid?UA?,or reduced excretion.HUA is the result of a combination of genetics,gender,age,lifestyle and environment.HUA has become the second largest metabolic disease after diabetes.At present,the research objects of hyperuricemia and gut microbiota are mostly experimental animals.This study was based on clinical cases,and the abundance of gut microbiota of volunteers was detected by 16S rDNA V3+V4 region second-generation sequencing technology and biological information analysis technology,so as to explore the changes of gut microbiota related to hyperuricemia.Methods Collected the Fecal samples from hyperuricemia and non-hyperuricemia volunteers.In this study,the collected samples were paired and grouped by 1:1 using the propensity score matching method to reduce the interference of confounding factors.The V3-V4 variable region of 16S rDNA was amplified by PCR,and Illumina HiSeq 2500 was used for high-throughput sequencing.DADA2 was used to generate feature table by denoising the original data;The R edgeR and bnlearn packages were used to screen the differential core microbiota and draw the bayesian network.The enrichment analysis of the gut microbiota were carried out at all levels of the phylum,class,order,family and genus.The classification tree was drawn,too.Using the PICRUSt platform,metagenomic function prediction was carried out on the core microbiota,and the difference analysis was carried out on the function prediction results by STAMP software.Results Stool samples of 133 volunteers were collected in this study,including 40 samples of hyperuricemia,93 samples in non-hyperuricemia group.Propensity score matching method matched patients with hyperuricemia and non-hyperuricemia volunteers according to the proportion of 1:1 matching and selected the paired samples of the same gender samples for the final match result.Totally,24 paired samples were matched successfully.The level of UA which was the exposure factors was statistically significant,other factors of the volunteers basic characteristics and clinical indicators were no statistically significant difference between the two groups.DADA2 denoised the original data to generate feature tables,which include a total of 8276 ASV?Amplicon Sequence Variant?.15 phylum,such as Actinobacteria,Firmicutes,Bacteroidetes and 247 genera including Bifidobacterium,Bacteroides Faecalibacterium were identified by species annotation.Difference analysis:28 ASV were screened between the HUA group and the non-HUA group.11 ASV are up-regulated in the HUA group and 17 ASV are up-regulated in the non-HUA group.According to the species annotation information,the different gut microbiota were Bacteroides,Coprococcus₂,Lachnospiraceae_ND3007_group,Lachnoclostridium,Butyricicoccus,Ruminiclostridium₉ and Ruminococcaceae_ucg-005 at the genus level,.mainly belonging to Bacteroidaceae,Lachnospiraceae and Ruminococcaceae.Bayesian network:The inference results of the bayesian network show that the core gut microbiota directly related to the HUA are Bacteroide,Butyricicoccus,Lachnospiraceae?genus:unclassified?,Coprococcus₂,Ruminococcaceae_ucg-005 and Ruminiclostridium₉.Enrichment analysis:Compared with the background microbiota,Bacteroidetes,Bacteroidia,Bacteroidales,Bacteroidaceae,Bacteroides,Coprococcus₂ and Ruminiclostridium₉ all showed enrichment.In the level of classes,orders and families?Bacteroidetes,Bacteroidia,Bacteroidales,Bacteroidaceae?of the genus Bacteroides were all enriched.Functional prediction:37 KEGG pathways were identified by PICRUSt at the KO2 level,11 KEGG pathways be screened out which the difference was statistically significant between HUA group and non-HUA group by differential analysis.Excretory System pathway in the HUA group is higher than the non-HUA group,Immune System Diseases,Biosynthesis of Other Secondary Metabolites,Carbohydrate Metabolism and other KEGG pathways were higher in the non-HUA group.214 KEGG pathways were identified at the K03 level,and a total of 67 different KEGG pathways were screened.16 KEGG pathways in the HUA group were significantly higher than the non-HUA group,including Atrazine metabolism,Amino acid metabolism,Arachidonic acid metabolism,etc.There were 51 KEGG pathways in the non-HUA group higher than HUA,including Primary immunodeficiency,Primary bile acid biosynthesis and Secondary bile acid biosynthesis.ConclusionThrough the study of gut microbiota between hyperuricemia and non-hyperuricemia people,the results showed that there were differences in gut microbiota between hyperuricemia patients and non-hyperuricemia people.Six differential core microbiota closely related to hyperuricemia were selected based on differential analysis and bayesian network inference,including Bacteroides?ASV₂60?,Butyricicoccus?ASV₆00?,and Lachnospiraceae(genus:Unclassified?ASV₅56?,Coprococcus₂?ASV₄85?,Ruminococcaceae_ucg-005?ASV₆34?,Ruminiclostridium₉?ASV₄33?;Bacteroides,Coprococcus₂ and Ruminiclostridium₉ were selected as core microbiota from different gut microbiota from the perspective of species distribution.In this study,three potential biomarkers were identified,among which Bacteroides and Coprococcus₂ was significantly reduced in patients with hyperuricemia,and Ruminiclostridium₉ was significantly increased in patients with hyperuricemia.The pathogenesis is significantly may related to function prediction KEGG pathways,such as Immune System Diseases and Excretory System.In conclusion,there are significant differences in the composition of gut microbiota between the patients with hyperuricemia and the non-hyperuricemia population.Bacteroides,Coprococcus₂ and Ruminiclostridium₉ are the biomarkers of hyperuricemia.The biomarkers and the metabolic functions prediction can provide reference for the subsequent studies on hyperuricemia based on gut microbiota.