Lipid homeostasis is crucial for proper cellular and systemic functions. A growing number of studies confirm the importance of lipid homeostasis in diabetic kidney disease (DKD). Lipotoxicity caused by imbalance in renal lipid homeostasis can further exasperate renal injury. Large lipid deposits and lipid droplet accumulation are present in the kidneys of DKD patients. Autophagy plays a critical role in DKD lipid homeostasis and is involved in the regulation of lipid content. Inhibition or reduction of autophagy can lead to lipid accumulation, which in turn further affects autophagy. Lipophagy selectively recognizes and degrades lipids and helps to regulate cellular lipid metabolism and maintain intracellular lipid homeostasis. Therefore, we provide a systematic review of fatty acid, cholesterol, and sphingolipid metabolism, and discuss the responses of different renal intrinsic cells to imbalances in lipid homeostasis. Finally, we discuss the mechanism by which autophagy, especially lipophagy, maintains lipid homeostasis to support the development of new DKD drugs targeting lipid homeostasis.

Background: Acteoside, an active ingredient found in various medicinal herbs, is effective in the treatment of diabetic kidney disease (DKD); however, the intrinsic pharmacological mechanism of action of acteoside in the treatment of DKD remains unclear. This study utilizes a combined approach of network pharmacology and experimental validation to investigate the potential molecular mechanism systematically. Methods: First, acteoside potential targets and DKD-associated targets were aggregated from public databases. Subsequently, utilizing protein-protein interaction (PPI) networks, alongside GO and KEGG pathway enrichment analyses, we established target-pathway networks to identify core potential therapeutic targets and pathways. Further, molecular docking facilitated the confirmation of interactions between acteoside and central targets. Finally, the conjectured molecular mechanisms of acteoside against DKD were verified through experimentation on unilateral nephrectomy combined with streptozotocin (STZ) rat model. The underlying downstream mechanisms were further investigated. Results: Network pharmacology identified 129 potential intersected targets of acteoside for DKD treatment, including targets such as AKT1, TNF, Casp3, MMP9, SRC, IGF1, EGFR, HRAS, CASP8, and MAPK8. Enrichment analyses indicated the PI3K-Akt, MAPK, Metabolic, and Relaxin signaling pathways could be involved in this therapeutic context. Molecular docking revealed high-affinity binding of acteoside to PIK3R1, AKT1, and NF-κB1. In vivo studies validated the therapeutic efficacy of acteoside, demonstrating reduced blood glucose levels, improved serum Scr and BUN levels, decreased 24-hour urinary total protein (P< 0.05), alongside mitigated podocyte injury (P< 0.05) and ameliorated renal pathological lesions. Furthermore, this finding indicates that acteoside inhibits the expression of pyroptosis markers NLRP3, Caspase-1, IL-1β, and IL-18 through the modulation of the PI3K/AKT/NF-κB pathway. Conclusion: Acteoside demonstrates renoprotective effects in DKD by regulating the PI3K/AKT/NF-κB signaling pathway and alleviating pyroptosis. This study explores the pharmacological mechanism underlying acteoside’s efficacy in DKD treatment, providing a foundation for further basic and clinical research.

Background: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN. Methods: The gene chip data were retrieved from the GEO database using the search term ' diabetic nephropathy ‘. The ' limma ' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package ‘WGCNA’ was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the ‘ClusterProfiler’ software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the ‘ConsensusClusterPlus ‘R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients. Results: Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P < 0.05). Conclusions: Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN.