Deregulation from CD4+ memory T cells to regulatory cells in patients with chronic renal failure: a pilot study

A Deep Insight Into Regulatory T Cell Metabolism in Renal Disease: Facts and Perspectives

Kidney disease encompasses a complex set of diseases that can aggravate or start systemic pathophysiological processes through their complex metabolic mechanisms and effects on body homoeostasis. The prevalence of kidney disease has increased dramatically over the last two decades. CD4⁺CD25⁺ regulatory T (Treg) cells that express the transcription factor forkhead box protein 3 (Foxp3) are critical for maintaining immune homeostasis and preventing autoimmune disease and tissue damage caused by excessive or unnecessary immune activation, including autoimmune kidney diseases. Recent studies have highlighted the critical role of metabolic reprogramming in controlling the plasticity, stability, and function of Treg cells. They are also likely to play a vital role in limiting kidney transplant rejection and potentially promoting transplant tolerance. Metabolic pathways, such as mitochondrial function, glycolysis, lipid synthesis, glutaminolysis, and mammalian target of rapamycin (mTOR) activation, are involved in the development of renal diseases by modulating the function and proliferation of Treg cells. Targeting metabolic pathways to alter Treg cells can offer a promising method for renal disease therapy. In this review, we provide a new perspective on the role of Treg cell metabolism in renal diseases by presenting the renal microenvironment、relevant metabolites of Treg cell metabolism, and the role of Treg cell metabolism in various kidney diseases.

Identification of key biomarkers and signaling pathways and analysis of their association with immune cells in immunoglobulin A nephropathy

Introduction

Immunoglobulin A nephropathy (IgAN) is the most common glomerular disease worldwide, with a poor prognosis. The aim of our study was to identify key biomarkers and their associations with immune cells to aid in the study of IgAN pathology and immunotherapy.

Material and methods

The data of IgAN were downloaded from a public database. The metaMA package and limma package were used to identify differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs), respectively. Biological functions of the DEmRNAs were analyzed. Machine learning was used to screen the mRNA biomarkers of IgAN. Pearson's correlation coefficient was used to analyze the correlation between mRNA biomarkers, immune cells and signaling pathways. Moreover, we constructed a miRNAs-mRNAs targeted regulatory network. Finally, we performed in vitro validation of the identified miRNAs and mRNAs.

Results

1205 DEmRNAs and 125 DEmiRNAs were identified. In gene set enrichment analysis (GSEA), tumor necrosis factor α (TNF-α) signaling via nuclear factor κB (NF-κB), apoptosis and MTORC-1 signaling were inhibited in IgAN. 8 mRNA biomarkers were screened by machine learning. In addition, the distribution of 8 immune cell types was found to be significantly different between normal controls and IgAN by difference analysis. Pearson correlation coefficient analysis demonstrated that AKAP8L was significantly negatively correlated with CD4⁺ memory T-cells. AKAP8L was also significantly negatively correlated with TNF-α signaling via NF-κB, apoptosis, and MTORC-1 signaling. Subsequently, 5 mRNA biomarkers predicted corresponding negative regulatory miRNAs.

Conclusions

The identification of 8 important biomarkers and their correlation with immune cells and biological signaling pathways provides new ideas for further study of IgAN.