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A new method for classifying different phenotypes of kidney transplantation

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Abstract

For end-stage renal diseases, kidney transplantation is the most efficient treatment. However, the unexpected rejection caused by inflammation usually leads to allograft failure. Thus, a systems-level characterization of inflammation factors can provide potentially diagnostic biomarkers for predicting renal allograft rejection. Serum of kidney transplant patients with different immune status were collected and classified as transplant patients with stable renal function (ST), impaired renal function with negative biopsy pathology (UNST), acute rejection (AR), and chronic rejection (CR). The expression profiles of 40 inflammatory proteins were measured by quantitative protein microarrays and reduced to a lower dimensional space by the partial least squares (PLS) model. The determined principal components (PCs) were then trained by the support vector machines (SVMs) algorithm for classifying different phenotypes of kidney transplantation. There were 30, 16, and 13 inflammation proteins that showed statistically significant differences between CR and ST, CR and AR, and CR and UNST patients. Further analysis revealed a protein-protein interaction (PPI) network among 33 inflammatory proteins and proposed a potential role of intracellular adhesion molecule-1 (ICAM-1) in CR. Based on the network analysis and protein expression information, two PCs were determined as the major contributors and trained by the PLS-SVMs method, with a promising accuracy of 77.5 % for classification of chronic rejection after kidney transplantation. For convenience, we also developed software packages of GPS-CKT (Classification phenotype of Kidney Transplantation Predictor) for classifying phenotypes. By confirming a strong correlation between inflammation and kidney transplantation, our results suggested that the network biomarker but not single factors can potentially classify different phenotypes in kidney transplantation.

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Abbreviations

AR:

Acute rejection

BLC:

B lymphocyte chemoattractant

CR:

Chronic rejection

G-CSF:

Granulocyte colony-stimulating factor

GM-CSF:

Granulocyte/macrophage colony-stimulating factor

UNST:

Impaired renal function with negative biopsy pathology

ICAM-1:

Intracellular adhesion molecule-1

LOO:

Leave-one-out

PLS:

Partial least squares

PCs:

Principal components

PPI:

Protein-protein interaction

M-CSF:

Macrophage colony-stimulating factor-1

TIMP-2:

Metalloproteinase inhibitor 2

MCP-1:

Monocyte chemoattractant protein-1

ROC:

Receiver operation characteristic

ST:

Stable renal function

SVMs:

Support vector machines

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Acknowledgments

This work was sponsored by the National Natural Science Foundation of China (81500569) to D.Z.; National Natural Science Foundation of China (81100534) and Shanghai Rising-Star Program (13QA1400800) to D.W.; grants from the National Basic Research Program (973 project) (2012CB910101 and 2013CB933903), and Natural Science Foundation of China (31171263, 81272578) to Y.X. The authors have no commercial or other associations that might pose a conflict of interest in connection with the submitted material.

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    Authors and Affiliations

    1. Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China

      Dong Zhu, Tongyu Zhu & Duojiao Wu

    2. Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China

      Zexian Liu, Zhicheng Pan, Yu Xue & Duojiao Wu

    3. Biomedical Research Center, Zhongshan Hospital, Fudan University, Shanghai, China

      Mengjia Qian & Linyan Wang

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    1. Dong Zhu
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    2. Zexian Liu
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    3. Zhicheng Pan
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    4. Mengjia Qian
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    5. Linyan Wang
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    6. Tongyu Zhu
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    7. Yu Xue
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    8. Duojiao Wu
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    Correspondence to Yu Xue or Duojiao Wu.

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    All samples were obtained with informed consent and ethics approval by the ethics board of Zhongshan Hospital, Fudan University. The study has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans.

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    Dong Zhu and Zexian Liu contributed equally to this work.

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    Cite this article

    Zhu, D., Liu, Z., Pan, Z. et al. A new method for classifying different phenotypes of kidney transplantation. Cell Biol Toxicol 32, 323–332 (2016). https://doi.org/10.1007/s10565-016-9337-x

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