Temporal Gene Expression Analysis of Monolayer Cultured Rat Hepatocytes

Abstract

The use of cultured primary hepatocytes within toxicology has proven to be a valuable tool for researchers, however, questions remain with regard to functional differences observed in these hepatocytes relative to the intact liver. Cultured hepatocytes have typically been described as dedifferentiated, a classification based upon the investigation of a few key cellular processes or hepatocellular markers. In the present study, parallel expression monitoring of approximately 8700 rat genes was used to characterize mRNA changes over time in hepatocyte cultures using Affymetrix microarrays. We isolated and labeled mRNA from whole rat livers, hepatocyteenriched cell pellets, and primary cultured hepatocytes (4,12,24,48, and 72 h postplating), and hybridized these samples to microarrays. From these data, several pairwise and temporal gene expression comparisons were made. Gene expression changes were confirmed by RT/ PCR and by performing replicate experiments and repeated hybridizations using a rat toxicology sub-array that contained a 900-gene subset of the 8700-gene rat genomic microarray. PCR data qualitatively reproduced the temporal patterns of gene expression observed with microarrays. Cluster analysis of time course data using self-organizing maps (SOM) revealed a classic hepatocyte dedifferentiation response. Functional grouping of genes with similar transcriptional patterns showed time-dependent regulation of phase I and phase II metabolizing enzymes. In general, cytochrome P450 mRNA expression was repressed, but expression of phase II metabolizing enzymes varied by class (upregulation of glucuronidation, downregulation of sulfation). Potential metabolic targets for toxic insult, such as glutathione metabolism, gluconeogenesis, and glycolysis, were also affected at the transcriptional level. Progressive induction of several genes associated with the cellular cytoskeleton and extracellular matrix was observed in accord with physical changes in cell shape and connectivity associated with cellular adhesion. Finally, many transcriptional changes of genes involved in critical checkpoints throughout the hepatocyte cell cycle and differentiation process were observed. In total, these data establish a more comprehensive understanding of hepatocellular dedifferentiation and reveal many novel aspects of physiological and morphological hepatocyte adaptation. An assembly of all transcripts that demonstrated differential expression in this study can be found in the Supporting Information.

Cell culture, oxidative stress, and antioxidants: Avoiding pitfalls

“…[16,17] The cells which survive the culture shock appear to be those that have adapted rapidly, with multiple changes in gene expression, metabolic activity, and the levels of many enzymes. [13,[17][18][19] Some enzyme levels are increased, other swiftly decreased. [19] Thus, only a fraction, and probably an unrepresentative fraction, of the initially plated cells survives.…”

“…[13,[17][18][19] Some enzyme levels are increased, other swiftly decreased. [19] Thus, only a fraction, and probably an unrepresentative fraction, of the initially plated cells survives. As an example, p53 is not expressed in 14-day mouse embryos, but in culture of fibroblasts from them, the cells that survived were those that had begun to express it.…”

A Comparative Study of Genome-Wide Transcriptional Profiles of Primary Hepatocytes in Collagen Sandwich and Monolayer Cultures

“…Another study that monitored temporal gene expression in HMs cultured over a 3-day period revealed the down-regulation of cytochrome-P450 expression. 7 However, neither did this study investigate longer time points nor did it compare monolayers to other, more stable culture conditions. DNA microarray measurements have also been used to study specific pathways through which toxicity was conferred in human hepatoblastoma cells 8 and to understand the effects of nonparenchymal cells in 2D cocultures of hepatocytes with fibroblasts or sinusoidal endothelial cells.…”

Nonlinear classification of hERG channel inhibitory activity by unsupervised classification method

“…In our models, SOM decreases the number of information dimensions and visually classifies the compounds according to their similarities. The SOM approach has been successfully applied to the analysis of microarray and multivariate data (Törönen et al, 1999;Baker et al, 2001;Zollanvari et al, 2009). In the application to microarray, the gene cluster that has a similar appearance profile can be made a cluster, and the classification and the distribution of data can visually be understood.…”