Fig. 6

Global metabolic changes from in silico simulation and transcriptional profiling in polycystic kidney disease. a Analysis of metabolic rearrangement in bioenergetic pathways resulting from in silico simulations. The Differential Abundance (DA) score captures the average, gross changes of all metabolic fluxes in a pathway. A score of −1 indicates that all the simulated metabolic fluxes in the pathway decrease, while a score of 1 indicates that all in silico fluxes increase upon comparing the simulations of increased of glucose uptake with the computations of wild-type conditions. b Representation of the results of metabolic changes in the DFA in silico simulations upon increased glucose uptake. Data show that equilibrium is reached when upregulation of GLY, PPP, glutaminolysis, and FAS is achieved, while a reduction of OXPHOS and FAO. The figure contains modified elements from Servier Medical Art (http://smart.servier.com/). c Identification of genes differentially expressed in Pkd1^V/V kidneys versus controls in GLY, PPP, TCA/OXPHOS, FAS, and FAO. D-val represents the output of SAM algorithm launched with the parameter delta set to 1.0. All genes in the panel were differentially expressed between Pkd1^V/V animal model and wild-type model at P10 with a FDR equal to 0.1. Upregulated genes are shown in red, while downregulated ones are reported in blue. d Identification of genes differentially expressed in GLY, PPP, TCA/OXPHOS, FAS, and FAO in human PKD1 microarrays data sets. D-val represents the output of SAM algorithm launched with the parameter delta set to 2.4. All genes in the panel were differentially expressed between cystic (small, medium, and large cysts) versus control tissues (minimal cystic and normal renal cortical tissues) with a FDR equal to zero. n = 4 for Pkd1^V/V animal model, n = 8 (normal and minimal cyst) and n = 13 for large, medium, small cyst for human PKD1 microarrays