Identification of DEPs in urine from kidney disease patients
In order to the identify DEPs in urine from patients with multiple kidney diseases, the urinary proteomes of patients with AKI, AKI+CKD, diabetic CKD, non-diabetic CKD with IgAN, non-diabetic CKD without IgAN, and healthy volunteers were labelled using iTRAQ reagents (Figure 2 Cluster and volcano plot analyses of urinary DEPs identified in the five renal diseases). Cluster analysis of AKI and controls revealed that APOL1, protein S and rheumatoid factor were increased 3-fold, while histone H4, neuroblast differentiation-associated protein AHNAK and calcitonin were own-regulated by more than 4-fold (Figure 2A and Table S1, first image). Among urinary DEPs in AKI+CKD patients, NADH dehydrogenase (ubiquinone) 1 alpha subcomplex subunit 5 and follistatin-related protein 3 were increased 3-fold, while pancreatic alpha-amylase, polymeric immunoglobulin receptor and Golgi integral membrane protein 4 were diminished by more than 4-fold (Figure S1A and Table S2, second image). Numerous urinary DEPs were also identified in diabetic CKD, non-diabetic CKD with IgAN, and non-diabetic CKD without IgAN patients. For example, urinary proteins alpha-adducin and chromogranin-A were markedly upregulated in diabetic CKD patients, as were ephrin type-A receptor 4 and profilin-2 in Non-diabetic CKD with IgAN patients, and alpha-1 globin and apolipoprotein C-I in non-diabetic CKD without IgAN samples. Meanwhile, tubulin-specific chaperone A and pro-epidermal growth factor were significantly downregulated in diabetic CKD patients, as were secreted and transmembrane protein 1 and tumour-associated calcium signal transducer 2 in non-diabetic CKD with IgAN patients, and histone H2B type 1-K and Nectin-4 in non-diabetic CKD without IgAN samples (Figure S2A-S4A and Table S3-S5, images 3-5). Based on analysis of DEPs using volcano plots, 31, 22, 73, 29 and 45 DEPs were significantly increased by over 20% in the five kidney disease groups, while 125, 134, 213, 158 and 139 DEPs were downregulated by over 20% (Figure 2B, Figure S1B-S4B).
Gene ontology annotation of targeted DEPs
Urinary DEPs identified in different renal diseases were subjected to BLAST2GO annotation. Urinary proteins were classified based on biological process (BP), cellular component (CC) and molecular function (MF) categories according to GO assignment. In the BP category, the majority of DEPs in AKI, AKI+CKD, non-diabetic CKD with IgAN, and non-diabetic CKD without IgAN groups were mainly implicated in cellular process, response to stimulus, metabolic process and multicellular organismal processing. Biological processes were primarily associated with cellular process, single-organismal process, biological regulation and response to stimulus subcategories. At the CC level, the identified DEPs were mainly associated with cell, organelle, membrane and extracellular region subcategories for all five renal diseases. MF analysis indicated that binding, catalytic activity and receptor activity were the primary functions of DEPs in these renal diseases (Figure 3A-E GO annotation of urinary DEPs identified among various renal diseases.). Unlike urinary DEPs in other diseases, electron carrier activity was an exclusive MF category in AKI+CKD patients (Figure 3B). In comparison with the AKI group, DEPs mediating MF did not include chemoattractant activity in AKI+CKD, non-diabetic CKD with IgAN, or non-diabetic CKD without IgAN patients (Figure 3A, B and 3D, E). Additionally, there were unique MF subcategories in diabetic CKD patients, including protein binding and nucleic acid binding transcription factor activities (Figure 3C).
GO term enrichment analysis of DEPs in different renal diseases
GO term enrichment analysis of DEPs revealed that all DEPs were linked to a variety of biological functions. The top 10 most significant biological functions in each group are shown in Figure 4 (GO enrichment analysis of DEPs identified in different kidney diseases). As indicated in Figure 4A, single-organism process, protein binding, multicellular organismal process and developmental process were the main biological functions related to urinary DEPs in AKI patients (Figure 4A). Meanwhile, DEPs in AKI+CKD played an important role in developmental processes, system development and biological adhesion (Figure 4B). Among DEPs mediating biological functions, regulation of response to external stimuli, enzyme inhibitor activity, and endopeptidase regulator/inhibitor activity were the most obvious in diabetic CKD patients compared with the control group (Figure 4C). Multicellular organismal process, developmental process, and tissue development are predominant in non-diabetic CKD with IgAN, and single-multicellular organism process, multicellular organismal development, and metal ion binding are the main biological processes in non-diabetic CKD without IgAN patients (Figure 4D, E). These results suggest that the biological functions regulated by DEPs in these renal diseases are diverse and complex.
KEGG pathway analysis of identified DEPs in different kidney diseases
Further analysis of biological pathways using the KEGG database revealed 6, 13, 12, 10 and 14 signalling pathways that were enriched among the five kidney diseases (Figure 5 Multiple signalling pathways involved in different kidney diseases). KEGG pathways in the AKI group included complement and coagulation cascades, microRNAs in cancer, transcriptional misregulation in cancer, cysteine and methionine metabolism, starch and sucrose metabolism, and apoptosis-multiple species (Figure 5A). As shown in Figure 5B, PI3K-Akt signalling, ECM-receptor interaction, and proteoglycans in cancer were the primary signalling networks mediated by DEPs in AKI+CKD patients, implying that urinary DEPs in AKI and AKI+CKD participated in different pathways, although starch and sucrose metabolism were common to both groups (Figure 5B vs. 5A). In the diabetic CKD group, complement and coagulation cascades, PI3K-Akt signalling, lysosome, and proteoglycans in cancer were the main DEPs-mediated networks, indicating similarities in signal transduction and cross-talk among diabetic CKD, AKI and AKI+CKD patients (Figure 5C). Meanwhile, tuberculosis, proteoglycans in cancer, Rap1 signalling, and starch and sucrose metabolism pathways were significantly enriched in the non-diabetic CKD with IgAN group, while tuberculosis, alcoholism, Staphylococcus aureus infection, and transcriptional misregulation in cancer were observed in non-diabetic CKD without IgAN patients (Figure 5D, E). Thus, bioinformatics analysis of KEGG pathways revealed commonalities as well as process-specific differences among these renal diseases. Importantly, these results imply that some unique signalling pathway could be targeted for the development of therapies for different renal diseases.
Biomarker discovery and verification of unique DEPs in different renal diseases
To identify specific protein markers in urine, we performed comparative analysis of all DEPs identified from disease patients to detect specific biomarkers in each of the five diseases (Figure 6 Identification and validation of DEPs specific to different renal diseases). Combined with the results of Venn diagrams, we identified 34 and 35 DEPs that are specifically expressed in AKI and AKI+CKD patients. Upon comparison of AKI and AKI+CKD groups, 17 specific DEPs were identified in diabetic CKD and non-diabetic CKD groups, including non-diabetic CKD with or without IgAN patients. In the CKD group, 91 unique and abnormally expressed proteins were identified, while 14 specific protein markers were identified in non-diabetic CKD patients. By comparison, we identified 38 and 47 unique urinary protein biomarkers in non-diabetic CKD with IgAN and non-diabetic CKD without IgAN groups, respectively (Figure 6A and Table 1). Further biological function analysis indicated that 22 specific DEPs in AKI were closely related to proliferation, apoptosis, survival, inflammation, repair and migration. For example, SAA1 is associated with proliferation, inflammation and migration, while SHBG is involved in progression of proliferation, apoptosis and survival (Table 2). Using this method, 12 unique DEPs including IGHG1 and PAPPA2 were identified in the AKI+CKD group (Table 3). In contrast to AKI and AKI+CKD groups, 12 urinary DEPs from CKD patients participate in the biological processes mentioned above (Table 4). CKD includes diabetic CKD and non-diabetic CKD patients, and 44 and 6 specific urinary protein markers were identified in these group, respectively (Table 5, 6). Among non-diabetic CKD patients, we identified 17 specific DEPs in patients with non-diabetic CKD with IgAN, and 27 in those with non-diabetic CKD without IgAN (Table 7, 8).
Eventually, we chose five urinary protein markers (Serum amyloid A1, SAA1; Complement component 5, C5; Hepatocyte growth factor activator, HGFAC, Since apolipoprotein C-I, APOC1; and Regenerating islet-derived protein 3 alpha, Reg3A) for validation to support our present work. In ELISA assays, an increase in SAA1 was only observed in urine from AKI patients, suggesting that SAA1 could be used as an indicator of AKI disease (Figure 6B). C5 upregulation was observed in all five kidney diseases, implying it is a universal protein marker for all five diseases (Figure 6C). In samples from patients with non-diabetic CKD without IgAN, HGFAC levels were significantly enhanced compared with other groups, which indicates that HGFAC is a potential biomarker for early diagnosis of non-diabetic CKD without IgAN (Figure 6D). Based on the results shown in Figure 6E, we speculated that detection of APOC1, which was upregulated in all renal diseases except diabetic CKD, could be used to distinguish diabetic CKD from other kidney diseases (Figure 6E). Enhancement in Reg3A abundance was observed in both AKI and AKI+CKD groups, which suggests that Reg3A differs significantly between AKI and CKD patients (Figure 6F). Importantly, these results were consistent with our MS data.