Salivary microbiome in kidney diseases: A narrative review

Ramanathan, Kumaresan; Padmanabhan, Giri; Gulilat, Henok; Malik, Tabarak

doi:10.1002/cbf.3864

Cited by 3 publications

(1 citation statement)

References 89 publications

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“…Nevertheless, it remains unclear how the salivary microbiota of DGF patients varies, how it differs from that of IGF patients, and whether the salivary microbiota data of DGF patients possess clinical significance in prediction. While no specific research has yet directly explored the potential connection between the microbiota in the digestive tract and DGF, several studies have identified changes in salivary microbiota accompanying various kidney diseases (Ramanathan et al 2023 ). Furthermore, salivary microbiota has been implicated in the occurrence and progression of various systemic diseases, such as diabetes, cancer (Belstrøm 2020 ), hypertension (Chen et al 2023 ), Alzheimer’s disease (Lu et al 2022 ), and serves as a non-invasive biomarker in their diagnosis (Zhang et al 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of delayed graft function by early salivary microbiota following kidney transplantation

Xiang,

Peng,

Liu

et al. 2024

Appl Microbiol Biotechnol

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Delayed graft function (DGF) is a frequently observed complication following kidney transplantation (KT). Our prior research revealed dynamic shifts in salivary microbiota post-KT with immediate graft function (IGF), yet its behavior during DGF remains unexplored. Five recipients with DGF and 35 recipients with IGF were enrolled. Saliva samples were collected during the perioperative period, and 16S rRNA gene sequencing was performed. The salivary microbiota of IGFs changed significantly and gradually stabilized with the recovery of renal function. The salivary microbiota composition of DGFs was significantly different from that of IGFs, although the trend of variation appeared to be similar to that of IGFs. Salivary microbiota that differed significantly between patients with DGF and IGF at 1 day after transplantation were able to accurately distinguish the two groups in the randomForest algorithm (accuracy = 0.8333, sensitivity = 0.7778, specificity = 1, and area under curve = 0.85), with Selenomonas playing an important role. Bacteroidales (Spearman’s r = − 0.4872 and p = 0.0293) and Veillonella (Spearmen’s r = − 0.5474 and p = 0.0125) were significantly associated with the serum creatinine in DGF patients. Moreover, the significant differences in overall salivary microbiota structure between DGF and IGF patients disappeared upon long-term follow-up. This is the first study to investigate the dynamic changes in salivary microbiota in DGFs. Our findings suggested that salivary microbiota was able to predict DGF in the early stages after kidney transplantation, which might help the perioperative clinical management and early-stage intervention of kidney transplant recipients. Key points • Salivary microbiota on the first day after KT could predict DGF. • Alterations in salivary taxa after KT are related to recovery of renal function.

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Section: Introductionmentioning

confidence: 99%

Prediction of delayed graft function by early salivary microbiota following kidney transplantation

Xiang,

Peng,

Liu

et al. 2024

Appl Microbiol Biotechnol

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Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren’s syndrome based on comprehensive bioinformatics and immunohistochemical analyses

He,

Wei,

Zhang

et al. 2024

Clin Exp Med

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IgA nephropathy (IgAN) and Sjogren's syndrome (SS) are two autoimmune diseases with undetermined etiology and related to abnormal activation of lymphocytes. This study aims to explore the crucial genes, pathways and immune cells between IgAN and SS. Gene expression profiles of IgAN and SS were obtained from the Gene Expression Omnibus and Nephroseq data. Differentially expressed gene (DEG) and weighted gene co-expression network analyses (WGCNA) were done to identify common genes. Enrichment analysis and protein–protein interaction network were used to explore potential molecular pathways and crosstalk genes between IgAN and SS. The results were further verified by external validation and immunohistochemistry (IHC) analysis. Additionally, immune cell analysis and transcription factor prediction were also conducted. The DEG analysis revealed 28 commonly up-regulated genes, while WGCNA identified 98 interactively positive-correlated module genes between IgAN and SS. The enrichment analysis suggested that these genes were mainly involved in the biological processes of response to virus and antigen processing and presentation. The external validation and IHC analysis identified 5 hub genes (PSMB8, PSMB9, IFI44, ISG15, and CD53). In the immune cell analysis, the effector memory CD8 T and T follicular helper cells were significantly activated, and the corresponding proportions showed positively correlations with the expressions of the 5 hub genes in the two autoimmune diseases. Together, our data identified the crosstalk genes, molecular pathways, and immune cells underlying the IgAN and SS, which provides valuable insights into the intricate mechanisms of these diseases and offers potential intervention targets.

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Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren’s syndrome based on comprehensive bioinformatics and immunohistochemical analyses

He,

Wei,

Zhang

et al. 2024

Preprint

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Background IgA nephropathy (IgAN) and Sjogren's syndrome (SS) are two autoimmune diseases with undetermined etiology and related to abnormal activation of lymphocytes. This study aims to explore the crucial genes, pathways and immune cells between IgAN and SS in the context of predictive, preventive, and personalized medicine (PPPM). Methods Gene expression profiles of IgAN and SS were obtained from the Gene Expression Omnibus and Nephroseq data. Differentially expressed gene (DEG) and weighted gene co-expression network analyses (WGCNA) were done to identify common genes. Enrichment analysis and protein-protein interaction network were used to explore potential molecular pathways and crosstalk genes between IgAN and SS. The results were further verified by external validation and immunohistochemistry (IHC) analysis. Additionally, immune cell analysis and transcription factor prediction were also conducted. Results The DEG analysis revealed 28 commonly up-regulated genes, while WGCNA identified 98 interactively positive-correlated module genes between IgAN and SS. The enrichment analysis suggested that these genes were mainly involved in the biological processes of response to virus and antigen processing and presentation. The external validation and IHC analysis identified 5 hub genes (PSMB8, PSMB9, IFI44, ISG15, and CD53). In the immune cell analysis, the effector memory CD8 T and T follicular helper cells were significantly activated, and the corresponding proportions showed positively correlations with the expressions of the 5 hub genes in the two autoimmune diseases. Conclusion Together, our data identified the crosstalk genes, molecular pathways, and immune cells underlying the IgAN and SS, which provides valuable insights into the intricate mechanisms of these diseases and offers potential intervention targets from the perspective of PPPM.

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Salivary microbiome in kidney diseases: A narrative review

Cited by 3 publications

References 89 publications

Prediction of delayed graft function by early salivary microbiota following kidney transplantation

Prediction of delayed graft function by early salivary microbiota following kidney transplantation

Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren’s syndrome based on comprehensive bioinformatics and immunohistochemical analyses

Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren’s syndrome based on comprehensive bioinformatics and immunohistochemical analyses

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