Literature DB >> 34856312

Lysophosphatidylcholine mediates fast decline in kidney function in diabetic kidney disease.

Kentaro Yoshioka1, Yosuke Hirakawa2, Makoto Kurano3, Yuko Ube4, Yoko Ono4, Kensuke Kojima4, Taiga Iwama5, Kuniyuki Kano5, Sho Hasegawa6, Tsuyoshi Inoue7, Takashi Shimada4, Junken Aoki5, Yutaka Yatomi3, Masaomi Nangaku8, Reiko Inagi9.   

Abstract

Some patients with diabetic kidney disease (DKD) show a fast progression of kidney dysfunction and are known as a "fast decliner" (FD). Therefore, it is critical to understand pathomechanisms specific for fast decline. Here, we performed a comprehensive metabolomic analysis of patients with stage G3 DKD and identified increased urinary lysophosphatidylcholine (LPC) in fast decline. This was confirmed by quantification of urinary LPC using mass spectrometry and identified urinary LPC containing saturated fatty acids palmitic (16:0) and stearic (18:0) acids was increased in FDs. The upsurge in urinary LPC levels was correlated with a decline in estimated glomerular filtration rate after 2.5 years. To clarify a pathogenic role of LPC in FD, we studied an accelerated rat model of DKD and observed an increase in LPC (16:0) and (18:0) levels in the urine and kidney tubulointerstitium as the disease progressed. These findings suggested that local dysregulation of lipid metabolism resulted in excessive accumulation of this LPC species in the kidney. Our in vitro studies also confirmed LPC-mediated lipotoxicity in cultured proximal tubular cells. LPC induced accumulation of lipid droplets via activation of peroxisome proliferator-activated receptor-δ followed by upregulation of the lipid droplet membrane protein perilipin 2 and decreased autophagic flux, thereby inducing organelle stress and subsequent apoptosis. Thus, LPC (16:0) and (18:0) may mediate a fast progression of DKD and may serve as a target for novel therapeutic approaches.
Copyright © 2021 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  diabetic kidney disease; lipotoxicity; lysophosphatidylcholine; metabolomic profiling; peroxisome proliferator-activated receptor-δ

Mesh:

Substances:

Year:  2021        PMID: 34856312     DOI: 10.1016/j.kint.2021.10.039

Source DB:  PubMed          Journal:  Kidney Int        ISSN: 0085-2538            Impact factor:   10.612


  7 in total

1.  Choline chloride attenuates the allergic airway disease by inhibiting the lysophosphatidylcholine induced response in mouse model.

Authors:  Preeti Bansal; Naresh Singh; Jayadev Joshi; Naveen Arora; Shailendera N Gaur
Journal:  Curr Res Pharmacol Drug Discov       Date:  2022-05-11

Review 2.  Organelle Stress and Metabolic Derangement in Kidney Disease.

Authors:  Reiko Inagi
Journal:  Int J Mol Sci       Date:  2022-02-02       Impact factor: 5.923

3.  Inhibition of ChREBP ubiquitination via the ROS/Akt-dependent downregulation of Smurf2 contributes to lysophosphatidic acid-induced fibrosis in renal mesangial cells.

Authors:  Donghee Kim; Ga-Young Nam; Eunhui Seo; Hee-Sook Jun
Journal:  J Biomed Sci       Date:  2022-05-10       Impact factor: 12.771

4.  Using network pharmacology to explore the mechanism of Danggui-Shaoyao-San in the treatment of diabetic kidney disease.

Authors:  Jinfei Yang; Chenrui Li; Yan Liu; Yachun Han; Hao Zhao; Shilu Luo; Chanyue Zhao; Na Jiang; Ming Yang; Lin Sun
Journal:  Front Pharmacol       Date:  2022-08-19       Impact factor: 5.988

Review 5.  Lysophosphatidylcholine: Potential Target for the Treatment of Chronic Pain.

Authors:  Jinxuan Ren; Jiaqi Lin; Lina Yu; Min Yan
Journal:  Int J Mol Sci       Date:  2022-07-27       Impact factor: 6.208

6.  Intercellular mitochondrial transfer as a means of revitalizing injured glomerular endothelial cells.

Authors:  Li-Xia Tang; Bing Wei; Lu-Yao Jiang; You-You Ying; Ke Li; Tian-Xi Chen; Ruo-Fei Huang; Miao-Jun Shi; Hang Xu
Journal:  World J Stem Cells       Date:  2022-09-26       Impact factor: 5.247

7.  Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics.

Authors:  Yosuke Hirakawa; Kentaro Yoshioka; Kensuke Kojima; Yasuho Yamashita; Takuma Shibahara; Takehiko Wada; Masaomi Nangaku; Reiko Inagi
Journal:  Sci Rep       Date:  2022-09-29       Impact factor: 4.996

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.