» Articles » PMID: 37705917

Autophagy Activation in Peripheral Blood Mononuclear Cells of Peritoneal Dialysis Patients

Overview
Journal Kidney Int Rep
Publisher Elsevier
Specialty Nephrology
Date 2023 Sep 14
PMID 37705917
Authors
Affiliations
Soon will be listed here.
Abstract

Introduction: The complete systemic deregulated biological network in patients on peritoneal dialysis (PD) is still only partially defined. High-throughput/omics techniques may offer the possibility to analyze the main biological fingerprints associated with this clinical condition.

Methods: We applied an innovative bioinformatic analysis of gene expression microarray data (mainly based on support vector machine (SVM) learning) to compare the transcriptomic profile of peripheral blood mononuclear cells (PBMCs) of healthy subjects (HS), chronic kidney disease (CKD) patients, and patients on PD divided into a microarray group (5 HS, 9 CKD, and 10 PD) and a validation group (10 HS, 15 CKD, and 15 PD). Classical well-standardized biomolecular approaches (western blotting and flow cytometry) were used to validate the transcriptomic results.

Results: Bioinformatics revealed a distinctive PBMC transcriptomic profiling for PD versus CKD and HS ( = 419 genes). Transcripts encoding for key elements of the autophagic pathway were significantly upregulated in PD, and the autophagy related 5 (ATG5) reached the top level of discrimination [-Log10 value = 11.3, variable importance in projection (VIP) score = 4.8, SVM rank:1]. Protein levels of ATG5 and microtubule associated protein 1 light chain 3 beta (LC3B), an important constituent of the autophagosome, validated microarray results. In addition, the incubation of PBMCs of HS with serum of patients on PD upregulated both proteins. Autophagy in PBMCs from patients on PD was attenuated by N-acetyl-cysteine or Resatorvid treatment.

Conclusions: Our data demonstrated, for the first time, that the autophagy pathway is activated in immune-cells of patients on PD, and this may represent a novel therapeutic target.

Citing Articles

Autophagy and machine learning: Unanswered questions.

Yang Y, Pan Z, Sun J, Welch J, Klionsky D Biochim Biophys Acta Mol Basis Dis. 2024; 1870(6):167263.

PMID: 38801963 PMC: 11886774. DOI: 10.1016/j.bbadis.2024.167263.

References
1.
Mateijsen M, van der Wal A, Hendriks P, Zweers M, Mulder J, Struijk D . Vascular and interstitial changes in the peritoneum of CAPD patients with peritoneal sclerosis. Perit Dial Int. 2000; 19(6):517-25. View

2.
Lin T, Wu V, Wang C . Autophagy in Chronic Kidney Diseases. Cells. 2019; 8(1). PMC: 6357204. DOI: 10.3390/cells8010061. View

3.
Cox J, Mann M . 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics. 2012; 13 Suppl 16:S12. PMC: 3489530. DOI: 10.1186/1471-2105-13-S16-S12. View

4.
Hill N, Fatoba S, Oke J, Hirst J, OCallaghan C, Lasserson D . Global Prevalence of Chronic Kidney Disease - A Systematic Review and Meta-Analysis. PLoS One. 2016; 11(7):e0158765. PMC: 4934905. DOI: 10.1371/journal.pone.0158765. View

5.
Nam S, Kim W, Kim J, Park S, Kim H, Lee M . Autophagy attenuates tubulointerstital fibrosis through regulating transforming growth factor-β and NLRP3 inflammasome signaling pathway. Cell Death Dis. 2019; 10(2):78. PMC: 6349890. DOI: 10.1038/s41419-019-1356-0. View