Salivary microbiota analysis of patients with membranous nephropathy

Mol Med Rep. 2022 May;25(5):190. doi: 10.3892/mmr.2022.12706. Epub 2022 Apr 1.

Abstract

The oral microbiota are closely related to human health. Nonetheless, to the best of our knowledge, their relationship with membranous nephropathy (MN) remains unstudied. The saliva microbiota collected from 22 patients with MN and 15 healthy controls were analyzed by next‑generation sequencing, and bioinformatics analysis of the 16S ribosomal RNA gene was subsequently carried out. The Chao1 and Shannon indices in patients with MN were higher than those in healthy controls. Analysis of similarities revealed that the oral microbiota in the patient group were significantly different from those in the healthy controls. At the genus level, the abundance of Alloprevotella, Granulicatella, Prevotella, Streptococcus and Prevotella_7 was markedly higher in patients with MN than in healthy controls. Six operational taxonomic units (OTUs; OTU5, OTU28, OTU9, OTU15, OTU33 and OTU38) were found to be markedly correlated with the clinical factors creatinine, proteinuria in 24 h, estimated glomerular filtration rate and systolic blood pressure. A total of 28 Kyoto Encyclopedia of Genes and Genomes pathways were obtained from the significant OTUs. The oral microbiota of patients with MN were investigated and it was found that OTU5, OTU28, OTU9, OTU15, OTU33 and OTU38 may be used as biomarkers. The present findings may assist in the diagnosis of patients with MN.

Keywords: 16S ribosomal RNA gene; biomarkers; membranous nephropathy; oral microbiota.

MeSH terms

  • Glomerulonephritis, Membranous* / genetics
  • Humans
  • Microbiota* / genetics
  • Prevotella / genetics
  • RNA, Ribosomal, 16S / genetics
  • Saliva

Substances

  • RNA, Ribosomal, 16S

Grants and funding

The work was supported by grants from the National Natural Science Foundation of China (grant no. 82001336), Guangdong Basic and Applied Basic Research Foundation (grant nos. 2019A1515011009, 2021A1515010683, 2020A1515010225 and 2021A1515010955), Shenzhen Foundation of Science and Technology (grant nos. JCYJ20180306172449376, JCYJ20180306172459580 and JCYJ20180306172502097), Nanjing Municipal Health Science and Technology Development Special Fund Project (grant no. YKK19161) and Shenzhen Longhua District Foundation of Science and Technology (grant no. SZLHQJCYJ202002).