Discovering Networks of Perturbed Biological Processes in Hepatocyte Cultures

Abstract

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.

Mechanistic evaluation of primary human hepatocyte culture using global proteomic analysis reveals a selective dedifferentiation profile

“…Global studies offer an insight into how cells react in a holistic sense to a particular condition, and whilst efforts have been made to map changes in the hepatocyte transcriptome during dedifferentiation (Kim et al 2010; Lasher et al 2011), many changes key to the mature phenotype of the hepatocyte are not properly represented in such analysis. This is exemplified by the recent results, showing that only ~40 % of the variation in cell protein levels can be explained by differences in the corresponding mRNA levels (Schwanhausser et al 2011).…”

Summarizing cellular responses as biological process networks

“…Third, for each contrast, we display an example BPN and provide detail on several interesting links in the reported BPNs. Fourth, we demonstrate that the BPNs produced by MCMC-BPN are more informative while also being less redundant than those computed by two previous methods: CBPLN [15] and biological process linkage networks (BPLN) [13]. Finally, we describe some general observations of the behavior of the MCMC and features which affect the performance of our algorithm.…”

“…Motivated by this methodological gap, in earlier work [15] we extended the method of Dotan-Cohen et al [13] by integrating gene expression data with gene-gene interactions to compute what we termed “Contextual Biological Process Linkage Networks” (CBPLNs). A link in a CBPLN indicates not only that the genes of two processes have a significant number of interactions among them, but that genes at the interface exhibit a large amount of perturbation in expression.…”

A New Framework for Bridging the Gap from Protein-Protein Interactions to Biological Process Interactions

“…Identifying GI-linked processes cal lead to a new direction in generic interaction research. As a next step, the method could incorporate gene expression information, to study processes interactions at a treatment-control basis, as proposed in [3,4].…”

“…In [2], processes are considered as interacting when more proteins annotated by them interact than expected by chance. Other methods also require gene expression information [3] to examine how interactions may change in different experimental conditions. In addition, this method incorporates weights at the protein interactions.…”