As the majority of metabolic enzymes remains active in cultured h

As the majority of metabolic enzymes remains active in cultured those hepatocytes (although at a reduced level), the most widely-used on/off model to relate transcripts

to metabolic networks [13,14] is not suitable here. Therefore, gene changes are analyzed without an a priori threshold (amplitude or significance level). Gene changes with a lesser amplitude may be ignored (if the metabolic function scores low in the rankings), but may also become integrated if complementary genes with respect Inhibitors,research,lifescience,medical to a particular function occur with a higher amplitude. In this study, ModeScore [15] was applied for a plethora of metabolic functions—a novel approach, which relates RNA differences to functional flux distributions [16] computed Inhibitors,research,lifescience,medical in the stoichiometric network of hepatocyte metabolism [17]. 2. Results 2.1. General Observations The average expression of genes associated to metabolic functions shows a 4-fold (2 on log2 scale) higher expression as compared with the rest of the genes (see Figure 1A). The difference between the average expression of metabolic versus non-metabolic genes is smaller for later time points and for TGFβ treated examples. Figure 1 A Average expression values of metabolic genes (upon mapping

to HepatoNet1) vs. all other genes, by expression profile (C = control, T = TGFβ treated). In B, the set of metabolic genes is split into genes encoding enzymes, transporters, and selected … When comparing transcript profiles, two types Inhibitors,research,lifescience,medical are evaluated—changes in the time course and changes induced by TGFβ treatment. For changes with time,

differences between transcript abundances Inhibitors,research,lifescience,medical from 1 h to 24 h, 1 h to 6 h, and 6 h to 24 h in the control experiment (C1 h/24 h, C1h/6 h, and C6h/24 h) and the TGFβ treated hepatocytes (T1h/24 h, T1h/6 h, and T6 h/24 h) are considered. For treatment-induced changes, the differences between abundances of the control and TGFβ treated hepatocytes at 6 h and 24 h (C/T 6 h and C/T 24 h) are evaluated. Inhibitors,research,lifescience,medical The difference at 1 h is negligible and not considered. In Figure 1C, a difference analysis of the average expression is presented. A Welch’s t-test [18] was performed to assert whether the averages differ significantly. For the non-metabolic genes, there is no significant difference. For the metabolic genes, the averages of T6 h/24 h and C/T 24 h comparisons are considerably different with high significance, Brefeldin_A whereas the averages of the C6 h/24 h comparison are of low significant differences. Figure 1B presents a finer distinction in classes of genes associated to HepatoNet1. Excretion proteins such as albumin, haptoglobin, and collagens display the highest expression (14-fold higher than the non-metabolic genes—4.3 in log2 scale), which decreases with time but is not affected by TGFβ treatment. Transporters show the 2nd highest expression also decreasing with time and further decreasing upon TGFβ treatment. Expression of enzymes is at a lower level, but still more than threefold (1.

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