Table 1.
Summary of the characteristics of the nine expression datasets.
Fig 1.
Glomerular Cytoskeleton Network.
The nodes represent proteins (labeled with the gene name), and the edges represent the interactions of the corresponding proteins. The importance of each node can be differentiated by its size (degree) and color (betweenness centrality). The larger the size of a node, the more interactions it exhibits; in addition the closer its color is to red, the greater is its betweenness centrality.
Table 2.
Summary of the characteristics of GCNet in the five glomerular diseases.
Table 3.
Consistently regulated genes in GCNet.
Fig 2.
Predicted Disease-related Glomerular Cytoskeleton Network.
The 3 up-regulated genes detected in all 5 diseases are colored red, and the 18 down-regulated genes are colored green. The large-sized nodes represent Glomerular Cytoskeleton Network genes, and the small-sized nodes represent those that are not in the Glomerular Cytoskeleton Network gene list but that link the key genes together.
Fig 3.
Validation of potential candidates based on Human Protein Atlas (HPA) staining images.
High-throughput immunohistochemical (IHC) stainings images from HPA (http://www.proteinatlas.org) were used to validate the protein expression of potential candidates, among which allograft inflammatory factor 1 (AIF1), myosin regulatory light chain 9 (MYL9) and protein kinase cAMP-dependent regulatory type II beta (PRKAR2B) were found to be only expressed in the glomeruli but not in the tubules.
Fig 4.
Protein protein interactions between the potential candidates and known glomerular disease genes.
Protein–protein interaction network for 21 candidate proteins (labeled by gene name). Three up-regulated genes in all 5 diseases are colored red, and 18 down-regulated genes are colored green. Proteins are represented by red (up-regulated) and green (down-regulated) nodes, and the known glomerular disease genes selected from the OMIM database are presented in a larger size.
Fig 5.
Predicted diseases associated with the potential candidates.
A total of 6 diseases (square) related to 3 genes (circle) are shown. Diseases linked together share at least one common disease gene; genes are connected by the shortest path in Fig 3 (AHUS1, HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 1, mutation of CFH; DDS, DENYS-DRASH SYNDROME, mutation of WT1; FSGS1, FOCAL SEGMENTAL GLOMERULOSCLEROSIS 1, mutation of ACTN4; NPHP1, NEPHRONOPHTHISIS 1, mutation of NPHP1; NS1, NEPHROTIC SYNDROME, TYPE 1, mutation of NPHS1; NS2, NEPHROTIC SYNDROME, TYPE 2, mutation of NPHS2).
Fig 6.
Candidate mRNA levels in glomeruli isolated from a PAN induced rat nephropathy model.
Except for TMSB15A, the mRNA levels of the other 20 candidates were examined by real-time quantitative PCR. The mRNA levels of 13 candidates were down-regulated, those of 4 candidates were up-regulated, and those of 3 candidates were not statistical significance in the PAN group compared with the control group. (*p, 0.05 vs. control; **p, 0.01 vs. control; ns: no statistical significance; n = 5. PAN: puromycin aminonucleoside).