bims-lorfki 2022-03-20 papers

Front Immunol. 2022 ;13 835879

Excessive Activation of Notch Signaling in Macrophages Promote Kidney Inflammation, Fibrosis, and Necroptosis.

Tiankui Ma, Xin Li, Yonghong Zhu, Shufan Yu, Tianyan Liu, Xiaodan Zhang, Dong Chen, Shuyan Du, Tong Chen, Shuo Chen, Yanyan Xu, Qiuling Fan.

Diabetic nephropathy (DN) is one of the main causes of end-stage renal disease (ESRD). Existing treatments cannot control the progression of diabetic nephropathy very well. In diabetic nephropathy, Many monocytes and macrophages infiltrate kidney tissue. However, the role of these cells in the pathogenesis of diabetic nephropathy has not been fully elucidated. In this study, we analyzed patient kidney biopsy specimens, diabetic nephropathy model animals. Meanwhile, we cocultured cells and found that in diabetic nephropathy, damaged intrinsic renal cells (glomerular mesangial cells and renal tubular epithelial cells) recruited monocytes/macrophages to the area of tissue damage to defend against and clear cell damage. This process often involved the activation of different types of macrophages. Interestingly, the infiltrating macrophages were mainly M1 (CD68+iNOS+) macrophages. In diabetic nephropathy, crosstalk between the Notch pathway and NF-κB signaling in macrophages contributed to the polarization of macrophages. Hyperpolarized macrophages secreted large amounts of inflammatory cytokines and exacerbated the inflammatory response, extracellular matrix secretion, fibrosis, and necroptosis of intrinsic kidney cells. Additionally, macrophage depletion therapy with clodronate liposomes and inhibition of the Notch pathway in macrophages alleviated the pathological changes in kidney cells. This study provides new information regarding diabetic nephropathy-related renal inflammation, the causes of macrophage polarization, and therapeutic targets for diabetic nephropathy.

Keywords: NF-κB; Notch; diabetic kidney disease; diabetic nephropathy; kidney inflammation; macrophages; necroptosis; renal fibrosis

DOI: https://doi.org/10.3389/fimmu.2022.835879

BMC Endocr Disord. 2022 Mar 15. 22(1): 67

Circ_0000064 promotes high glucose-induced renal tubular epithelial cells injury to facilitate diabetic nephropathy progression through miR-532-3p/ROCK1 axis.

Huanlan Wang, Shenghua Huang, Taotao Hu, Shizhi Fei, Huanqiao Zhang.

BACKGROUND: Circular RNA (circRNA) has been shown to mediate diabetic nephropathy (DN) development by regulating renal tubular epithelial cells (RTECs) injury. However, the role and mechanism of circ_0000064 in high glucose (HG)-induced RTECs injury have not been fully elucidated.
METHODS: Human RTECs (HK-2) were exposed to HG to induce cell injury. Cell oxidative stress was assessed by detecting the levels of oxidative stress-markers. Moreover, cell proliferation and apoptosis were determined by CCK8 assay, EDU assay and flow cytometry. The protein levels of proliferation markers, apoptosis markers and Rho-associated coiled-coil-containing kinase 1 (ROCK1) were measured using western blot analysis. Furthermore, quantitative real-time PCR was performed to assess the expression of circ_0000064, microRNA (miR)-532-3p and ROCK1. The interaction between miR-532-3p and circ_0000064 or ROCK1 was confirmed by dual-luciferase reporter assay and RNA pull-down assay.
RESULTS: Our results revealed that HG treatment could promote HK-2 cells oxidative stress, apoptosis, fibrosis, and inhibit proliferation. Circ_0000064 expression was increased in the serum of DN patients and HG-induced HK-2 cells, and silenced circ_0000064 could relieve HG-induced HK-2 cells injury. MiR-532-3p could be sponged by circ_0000064, and its overexpression also alleviated HG-induced HK-2 cells injury. Besides, the regulation of circ_0000064 knockdown on HG-induced HK-2 cells injury could be reversed by miR-532-3p inhibitor. Additionally, ROCK1 was a target of miR-532-3p, and its expression was inhibited by circ_0000064 knockdown. The inhibition effect of circ_0000064 knockdown on HG-induced HK-2 cells injury also could be reversed by overexpressing ROCK1.
CONCLUSION: In summary, circ_0000064 knockdown might alleviate HG-induced HK-2 cells injury via regulating the miR-532-3p/ROCK1 axis, which provided a new perspective for DN treatment.

Keywords: Circ_0000064; Diabetic nephropathy; High glucose; ROCK1; miR-532-3p

DOI: https://doi.org/10.1186/s12902-022-00968-x

J Biomed Inform. 2022 Mar 11. pii: S1532-0464(22)00065-X. [Epub ahead of print]128 104049

Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma.

Meng Huang, Xiucai Ye, Akira Imakura, Tetsuya Sakurai.

Renal cell carcinoma (RCC) is one of the deadliest cancers and mainly consists of three subtypes: kidney clear cell carcinoma (KIRC), kidney papillary cell carcinoma (KIRP), and kidney chromophobe (KICH). Gene signature identification plays an important role in the precise classification of RCC subtypes and personalized treatment. However, most of the existing gene selection methods focus on statically selecting the same informative genes for each subtype, and fail to consider the heterogeneity of patients which causes pattern differences in each subtype. In this work, to explore different informative gene subsets for each subtype, we propose a novel gene selection method, named sequential reinforcement active feature learning (SRAFL), which dynamically acquire the different genes in each sample to identify the different gene signatures for each subtype. The proposed SRAFL method combines the cancer subtype classifier with the reinforcement learning (RL) agent, which sequentially select the active genes in each sample from three mixed RCC subtypes in a cost-sensitive manner. Moreover, the module-based gene filtering is run before gene selection to filter the redundant genes. We mainly evaluate the proposed SRAFL method based on mRNA and long non-coding RNA (lncRNA) expression profiles of RCC datasets from The Cancer Genome Atlas (TCGA). The experimental results demonstrate that the proposed method can automatically identify different gene signatures for different subtypes to accurately classify RCC subtypes. More importantly, we here for the first time show the proposed SRAFL method can consider the heterogeneity of samples to select different gene signatures for different RCC subtypes, which shows more potential for the precision-based RCC care in the future.

Keywords: Active feature selection; Precision medicine; Reinforcement learning (RL); Renal cell carcinoma (RCC); Subtypes classification

DOI: https://doi.org/10.1016/j.jbi.2022.104049