title: Network analysis reveals roles of inflammatory factors in different phenotypes of kidney transplant patients.
abstract: BACKGROUND
  Systems-level characterization of inflammation in kidney transplantation remains incomplete. By stratifying kidney transplant patients based on phenotypes, the present study sought to identify the role of inflammatory proteins in disease progress and assess potential biomarkers for allograft monitoring.
  
  
  METHODS
  Kidney transplant patients with different allograft status were enrolled in the study: stable renal function (ST), impaired renal function (UNST), acute rejection (AR), and chronic rejection (CR). We stratified the patients into 3 phenotype levels according to their symptoms and pathogenesis. Serum protein concentrations were measured by a quantitative protein array. All differentially expressed proteins were analyzed by protein-protein interaction networks (PPINs) to highlight protein interactions in patients with the above dysfunction levels. We identified level-related proteins and evaluated the classification efficiency of these biomarkers based on leave-one-out validation. The candidate proteins related to phenotype transforming were annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.
  
  
  RESULTS
  Based on the hypothesis that common proteins and their up- or down-regulation promote disease progress, we obtained 12 common proteins and 11 level-specific proteins from the phenotype-related PPINs. The common proteins were annotated for KEGG enrichment: (1) cytokine-cytokine receptor interaction; (2) hematopoietic cell lineage; (3) Jak-STAT signaling pathway; (4) allograft rejection; and (5) T cell receptor signaling pathway. The level-specific proteins could be potential biomarkers with diagnostic value. The classification potency of the 11 level-specific proteins (IL-1R-1, IL-16, TIMP-1, G-CSF, MIG, IL-11, BLC, TNF-β, Eotaxin-2, I-309 and IL-6 sR) was better than the performance using all 40 proteins.
  
  
  CONCLUSIONS
  The study demonstrated the potential value of PPINs-based approach to understanding inflammation-derived mechanisms and developing diagnostic biomarkers. Independent evaluations are required to further estimate the clinical relevance of the new diagnostic biomarkers.
authors.affiliation: Qingpu Branch, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China.
authors.forename: Duojiao
authors.initials: D
authors.lastname: Wu
authors.affiliation: Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Liu
authors.affiliation: Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Liu
authors.affiliation: Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Qingpu Branch, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Xu
authors.affiliation: Qingpu Branch, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China.
authors.forename: Ruiming
authors.initials: R
authors.lastname: Rong
authors.affiliation: Qingpu Branch, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China.
authors.forename: Mengjia
authors.initials: M
authors.lastname: Qian
authors.affiliation: Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. Electronic address: lnchen@sibs.ac.cn.
authors.forename: Luonan
authors.initials: L
authors.lastname: Chen
authors.affiliation: Qingpu Branch, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China. Electronic address: tyzhu@fudan.edu.cn.
authors.forename: Tongyu
authors.initials: T
authors.lastname: Zhu
mesh_terms: D000328:Adult; D000906:Antibodies / Q000737:chemistry; D015415:Biomarkers / Q000378:metabolism; D019070:Cell Lineage; D049109:Cell Proliferation; D016207:Cytokines / Q000378:metabolism; D005189:False Positive Reactions; D005260:Female; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006084:Graft Rejection; D006680:HLA Antigens / Q000737:chemistry; D006801:Humans; D007249:Inflammation; D007676:Kidney Failure, Chronic / Q000378:metabolism* / Q000473:pathology; D016030:Kidney Transplantation; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D025941:Protein Interaction Mapping / Q000379:methods; D049490:Systems Biology
chemical_list: D000906:Antibodies; D015415:Biomarkers; D016207:Cytokines; D006680:HLA Antigens
keywords: Biomarker; Immune rejection; Inflammation; Kidney transplantation; Protein–protein interaction network