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. 2020 Sep;22(3):2329-2341.
doi: 10.3892/mmr.2020.11299. Epub 2020 Jul 6.

Proteomic analysis of differentially expressed proteins in the serum of patients with acute renal allograft rejection using iTRAQ labelling technology

Yue Zhang  1 , Minglin Ou  1 , Hua Lin  1 , Liusheng Lai  1 , Huaizhou Chen  1 , Jiejing Chen  1 , Weiguo Sui  1 , Wen Xue  1 , Ruohan Zhang  1 , Qing Gan  1 , Donge Tang  2 , Xuyong Sun  3 , Jianhui Dong  3 , Qiang Yan  1 , Yong Dai  1
Affiliations

Proteomic analysis of differentially expressed proteins in the serum of patients with acute renal allograft rejection using iTRAQ labelling technology

Yue Zhang et al. Mol Med Rep. 2020 Sep.

Abstract

Transplantation is currently the best treatment for patients with end‑stage renal disease. However, acute rejection (AR) is the major source of failure in renal transplantation. The current best practice for the diagnosis of AR involves renal biopsy, but it is invasive, time‑consuming, costly and inconvenient. Sensitive and less invasive detection of AR episodes in renal transplant patients is essential to preserve allograft function. The present study applied isobaric tags for relative and absolute quantitation (iTRAQ) mass spectrometry to analyze serum protein expression in patients with AR and healthy controls. Overall, 1,399 proteins were identified. Using a cut‑off of Q<0.05 and a fold change of >1.2 for the variation in expression, 109 proteins were identified to be differentially expressed between the AR and control groups, 72 of which were upregulated and 37 were downregulated. Several proteins, including properdin, keratin 1, lipoprotein(a) and vitamin D‑binding protein, may have roles in the pathogenesis of AR. The present study focused on iTRAQ‑based proteomic profiling of serum samples in AR. Insight from the present study may help advance the understanding of the molecular mechanisms of AR and identify potential novel biomarkers of AR for further characterization.

Keywords: acute rejection; renal allograft; isobaric tags with related and absolute quantitation; proteomics; serum.

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Figures

Figure 1.
Figure 1.
Volcano plot of DEPs. This plot depicts the log2 FC (x-axis) vs. -log10 Q value (y-axis, representing the probability that the protein is differentially expressed). Q≤0.05 and FC≥1.2 were set as the significant threshold for differential expression. Dots in red denote significantly upregulated proteins which passed the screening threshold. Dots in green denote significantly downregulated proteins which passed the screening threshold. Gray dots indicate non-significantly DEPs. FC, fold change; up, upregulated; down, downregulated; norm, not differentially expressed; DEP, differentially expressed protein.
Figure 2.
Figure 2.
Bioinformatics analysis of differentially expressed proteins. Gene Ontology terms in the categories (A) Molecular function, (B) Cellular component and (C) Biological process.
Figure 3.
Figure 3.
Statistics of pathway enrichment of DEPs in patients with acute rejection and control subjects. The vertical axis represents the name of the pathway and the horizontal axis represents the corresponding enrichment factors. The enrichment factor is the ratio of the number of DEPs annotated to this pathway term to the total number of proteins annotated to this pathway term. A higher enrichment factor indicates greater intensiveness, a lower P-value means greater intensiveness. The dot size represents the number of DEPs annotated to the pathway. PPAR, peroxisome proliferator-activated receptor; DEP, differentially expressed protein.
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