Two commonly used culture systems in hepatic tissue engineering are the collagen sandwich (CS) and monolayers of cells. In this study, genome-wide gene expression profiles of primary hepatocytes were measured over an 8-day period for each cell culture system using Affymetrix GeneChips and compared via gene set enrichment analysis to elicit biologically meaningful information at the level of gene sets. Our results demonstrate that gene expression in hepatocytes in CS cultures steadily and comprehensively diverges from that in monolayer cultures. Gene sets up-regulated in CS cultures include several associated with liver metabolic and synthesis functions, such as metabolism of lipids, amino acids, carbohydrates, and alcohol, and synthesis of bile acids. Monooxygenases such as Cytochrome-P450 enzymes do not show any change between the culture systems after 1 day, but exhibit significant up-regulation in CS cultures after 3 days in comparison to hepatocyte monolayers. These data provide insights into the up-and down-regulation of several liver-critical gene sets and their subsequent effects on liver-specific functions. These results provide a baseline for further explorations into the systems biology of engineered liver mimics.

The liver plays a vital role in glucose homeostasis, the synthesis of bile acids and the detoxification of foreign substances. Liver culture systems are widely used to test adverse effects of drugs and environmental toxicants. The two most prevalent liver culture systems are hepatocyte monolayers (HMs) and collagen sandwiches (CS). Despite their wide use, comprehensive transcriptional programs and interaction networks in these culture systems have not been systematically investigated. We integrated an existing temporal transcriptional dataset for HM and CS cultures of rat hepatocytes with a functional interaction network of rat genes. We aimed to exploit the functional interactions to identify statistically significant linkages between perturbed biological processes. To this end, we developed a novel approach to compute Contextual Biological Process Linkage Networks (CBPLNs). CBPLNs revealed numerous meaningful connections between different biological processes and gene sets, which we were successful in interpreting within the context of liver metabolism. Multiple phenomena captured by CBPLNs at the process level such as regulation, downstream effects, and feedback loops have well described counterparts at the gene and protein level. CBPLNs reveal high-level linkages between pathways and processes, making the identification of important biological trends more tractable than through interactions between individual genes and molecules alone. Our approach may provide a new route to explore, analyze, and understand cellular responses to internal and external cues within the context of the intricate networks of molecular interactions that control cellular behavior.

BackgroundMicroarray experiments can simultaneously identify thousands of genes that show significant perturbation in expression between two experimental conditions. Response networks, computed through the integration of gene interaction networks with expression perturbation data, may themselves contain tens of thousands of interactions. Gene set enrichment has become standard for summarizing the results of these analyses in terms functionally coherent collections of genes such as biological processes. However, even these methods can yield hundreds of enriched functions that may overlap considerably.ResultsWe describe a new technique called Markov chain Monte Carlo Biological Process Networks (MCMC-BPN) capable of reporting a highly non-redundant set of links between processes that describe the molecular interactions that are perturbed under a specific biological context. Each link in the BPN represents the perturbed interactions that serve as the interfaces between the two processes connected by the link.We apply MCMC-BPN to publicly available liver-related datasets to demonstrate that the networks formed by the most probable inter-process links reported by MCMC-BPN show high relevance to each biological condition. We show that MCMC-BPN’s ability to discern the few key links from in a very large solution space by comparing results from two other methods for detecting inter-process links.ConclusionsMCMC-BPN is successful in using few inter-process links to explain as many of the perturbed gene-gene interactions as possible. Thereby, BPNs summarize the important biological trends within a response network by reporting a digestible number of inter-process links that can be explored in greater detail.

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