Single-cell transcriptomics: A new tool for studying diabetic kidney disease

Interferon gamma in the pathogenesis of hypertension - recent insights

PURPOSE OF REVIEW

The mounting body of evidence underscores the pivotal role of interferon gamma (IFNγ) in the pathogenesis of hypertension, prompting exploration of the mechanisms by which this cytokine fosters a pro-inflammatory immune milieu, subsequently exacerbating hypertension. In this review, we delve into recent preclinical and clinical studies from the past two years to elucidate how IFNγ participates in the progression of hypertension.

RECENT FINDINGS

IFNγ promotes renal CD8 + T cell accumulation by upregulating tubular PDL1 and MHC-I, intensifying cell-to-cell interaction. Intriguingly, a nucleotide polymorphism in LNK, predisposing towards hypertension, correlates with augmented T cell IFNγ production. Additionally, anti-IFNγ treatment exhibits protective effects against T cell-mediated inflammation during angiotensin II infusion or transverse aortic constriction. Moreover, knockout of the mineralocorticoid receptor in T cells protects against cardiac dysfunction induced by myocardial infarction, correlating with reduced IFNγ and IL-6, decreased macrophage recruitment, and attenuated fibrosis. Interestingly, increased IFNγ production correlates with elevated blood pressure, impacting individuals with type 2 diabetes, nondiabetics, and obese hypertensive patients.

SUMMARY

These revelations spotlight IFNγ as the critical mediator bridging the initial phase of blood pressure elevation with the sustained and exacerbated pathology. Consequently, blocking IFNγ signaling emerges as a promising therapeutic target to improve the management of this 'silent killer.'

New insights into the role of immunity and inflammation in diabetic kidney disease in the omics era

Diabetic kidney disease (DKD) is becoming the leading cause of chronic kidney disease, especially in the industrialized world. Despite mounting evidence has demonstrated that immunity and inflammation are highly involved in the pathogenesis and progression of DKD, the underlying mechanisms remain incompletely understood. Substantial molecules, signaling pathways, and cell types participate in DKD inflammation, by integrating into a complex regulatory network. Most of the studies have focused on individual components, without presenting their importance in the global or system-based processes, which largely hinders clinical translation. Besides, conventional technologies failed to monitor the different behaviors of resident renal cells and immune cells, making it difficult to understand their contributions to inflammation in DKD. Recently, the advancement of omics technologies including genomics, epigenomics, transcriptomics, proteomics, and metabolomics has revolutionized biomedical research, which allows an unbiased global analysis of changes in DNA, RNA, proteins, and metabolites in disease settings, even at single-cell and spatial resolutions. They help us to identify critical regulators of inflammation processes and provide an overview of cell heterogeneity in DKD. This review aims to summarize the application of multiple omics in the field of DKD and emphasize the latest evidence on the interplay of inflammation and DKD revealed by these technologies, which will provide new insights into the role of inflammation in the pathogenesis of DKD and lead to the development of novel therapeutic approaches and diagnostic biomarkers.

Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Background

Our previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes (T2D) in China. This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction, then construct a predictive model based on serum C-peptide and other clinical parameters.

Methods

The patients with T2D and normal renal function at baseline were recruited in this study. The LASSO algorithm was performed to filter potential predictors from the baseline variables. Logistic regression (LR) was performed to construct the predictive model for new-onset renal dysfunction risk. Power analysis was performed to assess the statistical power of the model.

Results

During a 2-year follow-up period, 21.08% (35/166) of subjects with T2D and normal renal function at baseline progressed to renal dysfunction. Six predictors were determined using LASSO regression, including baseline albumin-to-creatinine ratio, glycated hemoglobin, hypertension, retinol-binding protein-to-creatinine ratio, quartiles of fasting C-peptide, and quartiles of fasting C-peptide to 2h postprandial C-peptide ratio. These 6 predictors were incorporated to develop model for renal dysfunction risk prediction using LR. Finally, the LR model achieved a high efficiency, with an AUC of 0.83 (0.76 - 0.91), an accuracy of 75.80%, a sensitivity of 88.60%, and a specificity of 70.80%. According to the power analysis, the statistical power of the LR model was found to be 0.81, which was at a relatively high level. Finally, a nomogram was developed to make the model more available for individualized prediction in clinical practice.

Conclusion

Our results indicated that the baseline level of serum C-peptide had the potential role in the risk prediction of new-onset renal dysfunction. The LR model demonstrated high efficiency and had the potential to guide individualized risk assessments for renal dysfunction in clinical practice.

Single-cell transcriptomic profiles in the pathophysiology within the microenvironment of early diabetic kidney disease

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease, resulting in a huge socio-economic impact. Kidney is a highly complex organ and the pathogenesis underlying kidney organization involves complex cell-to-cell interaction within the heterogeneous kidney milieu. Advanced single-cell RNA sequencing (scRNA-seq) could reveal the complex architecture and interaction with the microenvironment in early DKD. We used scRNA-seq to investigate early changes in the kidney of db/m mice and db/db mice at the 14th week. Uniform Manifold Approximation and Projection were applied to classify cells into different clusters at a proper resolution. Weighted gene co-expression network analysis was used to identify the key molecules specifically expressed in kidney tubules. Information of cell-cell communication within the kidney was obtained using receptor-ligand pairing resources. In vitro model, human subjects, and co-detection by indexing staining were used to identify the pathophysiologic role of the hub genes in DKD. Among four distinct subsets of the proximal tubule (PT), lower percentages of proliferative PT and PT containing AQP4 expression (PT^AQP4+) in db/db mice induced impaired cell repair activity and dysfunction of renin-angiotensin system modulation in early DKD. We found that ferroptosis was involved in DKD progression, and ceruloplasmin acted as a central regulator of the induction of ferroptosis in PT^AQP4+. In addition, lower percentages of thick ascending limbs and collecting ducts with impaired metabolism function were also critical pathogenic features in the kidney of db/db mice. Secreted phosphoprotein 1 (SPP1) mediated pathogenic cross-talk in the tubular microenvironment, as validated by a correlation between urinary SPP1/Cr level and tubular injury. Finally, mesangial cell-derived semaphorin 3C (SEMA3C) further promoted endothelium-mesenchymal transition in glomerular endothelial cells through NRP1 and NRP2, and urinary SEMA3C/Cr level was positively correlated with glomerular injury. These data identified the hub genes involved in pathophysiologic changes within the microenvironment of early DKD.