iTRAQ-based proteomic analysis of plasma reveals abnormalities in lipid metabolism proteins in chronic kidney disease-related atherosclerosis

Abstract

Patients with chronic kidney disease (CKD) have a considerably higher risk of death due to cardiovascular causes. Using an iTRAQ MS/MS approach, we investigated the alterations in plasma protein accumulation in patients with CKD and classical cardiovascular disease (CVD) without CKD. The proteomic analysis led to the identification of 130 differentially expressed proteins among CVD and CKD patients and healthy volunteers. Bioinformatics analysis revealed that 29 differentially expressed proteins were involved in lipid metabolism and atherosclerosis, 20 of which were apolipoproteins and constituents of high-density lipoprotein (HDL) and low-density lipoprotein (LDL). Although dyslipidemia is common in CKD patients, we found that significant changes in apolipoproteins were not strictly associated with changes in plasma lipid levels. A lack of correlation between apoB and LDL concentration and an inverse relationship of some proteins with the HDL level were revealed. An increased level of apolipoprotein AIV, adiponectin, or apolipoprotein C, despite their anti-atherogenic properties, was not associated with a decrease in cardiovascular event risk in CKD patients. The presence of the distinctive pattern of apolipoproteins demonstrated in this study may suggest that lipid abnormalities in CKD are characterized by more qualitative abnormalities and may be related to HDL function rather than HDL deficiency.

Proteomic Analysis of Human Serum from Patients with Chronic Kidney Disease

“…Our results correlate well with the data obtained by other researchers in that blood APOA4, AAT, VIL1, complement component and cytokine concentrations increased in patients with renal disorders [8,[11][12][13][14]28,[32][33][34]43,[69][70][71]. Moreover, we found elevated serum concentrations of potential oncomarkers (CUL5, antigen KI-67) and other intracellular proteins (NKRF, CAPRI, IGSF22, APPBP2, CCD171 and CCD43) in patients with CKD.…”

“…The disadvantage of this method is its poor effectiveness in diagnosing early CKD. There are a number of studies where potential protein biomarkers of CKD were proposed and evaluated in the serum or plasma of patients [7,8,[11][12][13][14]. Peptide biomarkers can also provide insight into CKD diagnosis and progression.…”

Subpopulations of High-Density Lipoprotein: Friends or Foes in Cardiovascular Disease Risk in Chronic Kidney Disease?

“…Florens et al studied non-diabetic HD patients and found that 10 proteins were significantly upregulated (UDP-glucose: glycoprotein glucosyltransferase 1; β-2-microglobulin; SP-B; AMBP; insulin-like growth factor II; immunoglobulin heavy constant alpha 2; immunoglobulin lambda constant 2; HLA class I histocompatibility antigen; B-58 alpha chain; complement factor D; and inter-alpha-trypsin inhibitor heavy chain H1) and nine downregulated (guanylin; calpain-1 catalytic subunit; keratin, type I cytoskeletal 16; Ras-related protein Rab-6B; ganglioside GM2 activator; prostaglandin-H2 D-isomerase; secretoglobin family 3A member; thioredoxin-dependent peroxide reductase, mitochondrial; and solute carrier family 2, facilitated glucose transporter member 2) in HDL from HD patients compared to controls; these proteins are involved in lipid metabolism, hemostasis, wound healing, oxidative stress, and apoptosis pathways [ 71 ]. ITRAC (Isobaric Tag for relative and absolute quantitation)-based proteomics analysis also showed that the HDL proteome is remodeled in CKD, namely in ESRD patients under dialysis treatment [ 72 ]. Based on all these data, a schematic view of the major changes that occur in HDL composition in CKD, especially in ESRD patients, favoring dysfunctionality of HDL, is presented in Figure 2 .…”

iTRAQ-based quantitative proteomic of tobacco (Nicotiana tabacum L.) identified major host proteins involved in photosystems throughout the aging process

“…By extracting and analyzing these PRM spectrum files, quantitative information of the protein can be obtained. By extracting and analyzing these PRM spectrum files, quantitative information of the protein can be obtained [61,62].…”