ArticleSaudi journal of biological sciences2024
Identification of renal protective gut microbiome derived-metabolites in diabetic chronic kidney disease: An integrated approach using network pharmacology and molecular docking.
Article in Saudi journal of biological sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Decoding the Neuroinflammatory Potential of Non-Nutritive Sweeteners Through An Integrative Computational Approach.Neurotoxicity research · 2026Article
- Multi-omics analysis: Gut microbial metabolites in ovarian lesions.The Journal of international medical research · 2026Article
- Therapeutic assessment ofFrontiers in oncology · 2026Article
- Integrative multi-omics analysis prioritizes compartment-specific candidate targets of gut microbiota metabolites in diabetic kidney disease.Frontiers in immunology · 2026Article
- Identification and experimental validation of autophagy-related genes in focal segmental glomerulosclerosis by integrating bioinformatics and machine learning.Scientific reports · 2025Article
- Exploring the role of gut probiotic metabolites in the prevention and treatment of otitis media.Frontiers in cellular and infection microbiology · 2025Article
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Authors and funding
2 authors.
Funding
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Abstract
Metabolites from the gut microbiota define molecules in the gut-kidney cross talks. However, the mechanistic pathway by which the kidneys actively sense gut metabolites and their impact on diabetic chronic kidney disease (DCKD) remains unclear. This study is an attempt to investigate the gut microbiome metabolites, their host targeting genes, and their mechanistic action against DCKD. Gut microbiome, metabolites, and host targets were extracted from the gutMgene database and metabolites from the PubChem database. DCKD targets were identified from DisGeNET, GeneCard, NCBI, and OMIM databases. Computational examination such as protein-protein interaction networks, enrichment pathway, identification of metabolites for potential targets using molecular docking, hubgene-microbes-metabolite-samplesource-substrate (HMMSS) network architecture were executed using Network analyst, ShinyGo, GeneMania, Cytoscape, Autodock tools. There were 574 microbial metabolites, 2861 DCKD targets, and 222 microbes targeting host genes. After screening, we obtained 27 final targets, which are used for computational examination. From enrichment analysis, we found NF-ΚB1, AKT1, EGFR, JUN, and RELA as the main regulators in the DCKD development through mitogen activated protein kinase (MAPK) pathway signalling. The (HMMSS) network analysis found
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