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Temporal Gene Expression Analysis of Monolayer Cultured Rat Hepatocytes Thomas K. Baker, Mark A. Carfagna, Hong Gao, Ernst R. Dow,† Qingqin Li,‡ George H. Searfoss, and Timothy P. Ryan* Department of Investigative Toxicology, Lilly Research Laboratories, A Division of Eli Lilly and Company, Greenfield, Indiana 46140 Received April 25, 2001

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.

Introduction Parenchymal hepatocytes are complex, highly differentiated cells that comprise over 80% of the normal liver mass and 60% of the total liver cell population (1, 2). The parenchymal hepatocyte performs most specialized functions of the liver, including lipid metabolism, detoxification of exogenous xenobiotics, regulation of urea, and production of plasma proteins. The majority of proteins involved in liver homeostasis are produced in parenchymal hepatocytes, and their abundance is primarily regulated at the transcriptional level (3). In the normal liver, most differentiated hepatocytes are quiescent. With the development of suitable enzymatic methods for the isolation of parenchymal hepatocytes (4-6), * To whom correspondence should be addressed. Phone: (317) 4333462. Fax: (317) 277-6770. E-mail: [email protected]. † Information Technology, Biology Research Technologies, Lilly Research Laboratories. ‡ Biology Research Technologies/Proteins, Lilly Research Laboratories.

researchers in a variety of scientific disciplines have demonstrated the utility of in vitro hepatocyte models. These models have been shown to reduce animal utilization, provide a bridge for extrapolation to human effects, allow for quicker experimental analysis, and reduce costs. Isolated hepatocytes have proven useful in the evaluation of drug binding (7), metabolism (8-10), mechanisms of xenobiotic action (11, 12), oxidative stress (13-16), apoptosis (17-19), and neoplasia (20, 21). Although hepatocyte models are useful mechanistic tools, researchers have been challenged with a fundamental question regarding the loss of specialized liver function in cultured hepatocytessa process often referred to as dedifferentiation. For example, much effort has been expended to better understand the affects of cell culture on cytochrome P450 expression and metabolism of xenobiotics. These studies show that, in isolated hepatocytes, there is a progressive loss of P450 mRNA and protein content with time in culture (22, 23). Following isolation, hepatocytes must respond to new environmental stresses through physiological and mor-

10.1021/tx015518a CCC: $20.00 © 2001 American Chemical Society Published on Web 08/30/2001

Gene Expression Analysis of Monolayer Cultured Rat Hepatocytes

phological adaptations, in which an altered steady state is achieved to preserve cellular viability. From a transcriptional standpoint, adaptation includes the induction and repression of genes that are needed for cellular survival in their new environment. Comprehensive evaluation of the underlying processes driving physiological and morphological hepatocyte adaptations have not been conducted. Recent advances in genome sequencing programs coupled with advances in gene expression technology have provided tools to perform comprehensive, quantitative comparisons at the transcriptional level of thousands of genes simultaneously. Currently, gene microarrays can be constructed with cloned cDNA probes (24) or with synthetic oligonucleotide probes (25, 26). By labeling cellular RNA and monitoring hybridization to arrayed probes, this technology provides the opportunity to thoroughly evaluate the processes of hepatocyte dedifferentiation and adaptation. Herein, we demonstrate that valuable insight can be gained regarding cellular dedifferentiation and adaptation to the culturing process via gene expression analysis. Dedifferentiation was confirmed by changes observed with phase I metabolizing enzymes that have been described previously. Genes involved in maintaining cellular processes such as glutathione status, sugar metabolism, and production of extracellular matrix proteins demonstrated changes in expression consistent with adaptation to the culture environment. Evaluation of genes involved in proliferation revealed expression patterns representative of maintenance of hepatocyte cultures in a quiescent, nonproliferative state. These striking findings of hepatocyte adaptation to the culture environment provide a novel, comprehensive, and dynamic picture of monolayer cultured hepatocytes as revealed by a global analysis of gene expression.

Experimental Procedures Chemicals. Gentamicin, LD-L lactate dehydrogenase reagent, Leibovitz’s L-15, Hank’s balanced salt solution magnesium and calcium free sodium bicarbonate, glucose, N-[2hydroxyethyl]piperazine-N′-[2-ethanesulfonic acid] (HEPES),1 ethylene glycol-bis[β-aminoethyl ether]-N,N,N′,N′-tetraacetic acid (EGTA), and L-glutamine were purchased from Sigma Chemical Co. (St. Louis, MO). Collagenase type D and insulintransferrin-sodium selenite supplement were purchased from Boehringer Mannheim (Indianapolis, IN). William’s media E (WE) and certified fetal bovine serum (FBS) were purchased from Gibco BRL (Gaithersburg, MD). Hepatocyte maintenance media (HMM) was purchased from Clonetics (San Diego, CA). All other chemicals not specifically mentioned were purchased at the highest available quality through Sigma Chemical Co. (St. Louis, MO). 1 Abbreviations: HEPES, N-[2-hydroxyethyl]piperazine-N′-[2-ethanesulfonic acid]; EGTA, ethylene glycol-bis[β-aminoethyl ether]-N,N,N′,N′tetraacetic acid; WE, Williams Media E; HMM, hepatocyte maintenance media; WE complete, WE containing 2% FBS, 5.0 µg/mL insulin, 5.0 µg/mL transferrin, 5.0 ng/mL sodium selinite, 10 ng/mL dexamethasone, 2 mM L-glutamine, and 50 µg/mL gentamicin; LDH, lactate dehydrogenase; RT-PCR, reverse transcription polymerase chain reaction; DMT, data mining tool; SOM, self-organizing maps; CYP, cytochrome P450; GST, glutathione S-transferase; γ-GCS, γ-glutamylcysteine synthetase; EM, extracellular matrix; IL-6, interleukin-6; STAT-3, signal transducer and activator of transcription-3; NF-κB, nuclear factor κB; C/EBP, CCAAT/enhancer binding protein; HNF, hepatocyte nuclear factor; CL-6, clone-6; HRS, hepatic arg-ser protein; Bcl-x, B-cell leukemia/lymphoma x gene; Gos-2, G0/G1 switch gene; GGT, γ-glutamyl transpeptidase; TAT, tyrosine aminotransferase; Cx, connexon; SDH, sorbitol dehydrogenase; UDP-GT, Uridine-5′diphospho-glucuronosyltransferases; ST, sulfotransferases; Rb, retinoblastoma gene; CDK, cyclin-dependent kinase.

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Hepatocyte Isolation and Culture. Hepatocytes were isolated from 8 to 10 week old male F344 rats (Harlan SpragueDawley, Indianapolis, IN) using a modified two-stage portal vein perfusion technique (4-6, 27). Hepatocyte viability was assessed to be greater than 90% using trypan blue dye exclusion. Cells were plated at a density of 2.75 × 106 cells per Falcon Primaria 100 mm culture dish (Lincoln Park, NJ) in WE containing 2% FBS, 5.0 µg/mL insulin, 5.0 µg/mL transferrin, 5.0 ng/mL sodium selenite, 10 ng/mL dexamethasone, 2 mM L-glutamine, and 50 µg/mL gentamicin (WE complete) and incubated in a 37 °C, 100% humidified environment (5% CO2, 95% air). Following an attachment period of 4 h, the culture media was replaced with serum-free HMM containing 0.5 µg/mL insulin, 39 ng/mL dexamethasone, 50 µg/mL gentamicin and 50 µg/mL amphotericin. Cellular Viability Assessment. Fifty µL samples of cell culture media were collected and examined for lactate dehydrogenase (LDH) leakage using a Roche, Cobas Mira clinical chemistry analyzer (Montclair, NJ) with Sigma LD-L lactate dehydrogenase reagent. Sample Collection. Samples were collected for analysis at various time points throughout the isolation process. Whole liver samples were collected prior to cannulation by ligating and removing the caudate lobe. The next samples were collected from the cell suspensions following a 50g centrifugation. Subsequently, samples were collected following a 4 h attachment period in WE complete. The remaining samples were collected at 12, 24, 48, and 72 h post plating. Hepatocyte viability analysis showed that all cultures maintained greater than 95% viability over the 72-h culture duration (data not shown). Sample Analysis. Total RNA was isolated from whole livers by direct homogenization in 1.0 mL RNA STAT-60 (Tel-Test, Friendswood, TX) or from cell pellets or hepatocyte cultures by first removing media, then adding 1.0 mL of RNA STAT-60 to each centrifuge tube or 100 mm culture dish. Cells were collected by scraping, and further steps in the isolation process were performed according to the manufacturer’s recommended protocol. Subsequent purification and size selection of total RNA employed RNeasy columns (Qiagen, Valencia, CA) as suggested by the manufacturer. Reverse transcription polymerase chain reaction (RT-PCR) was performed for transcript confirmation using a hot start technique (Superscript II RT + Platinum Taq polymerase, Gibco, Gaithersburg, MD), with 1 µg of total RNA as starting material, and an annealing temperature of 55 °C for 25-35 cycles, depending on the individual transcript being amplified. Labeling of total RNA for microarray analysis was performed as described below. Double-stranded cDNA was synthesized from 22 µg of total RNA using Superscript II (Gibco, Gaithersburg, MD) and an oligo T-7-(dT)24 primer. cRNA was synthesized using a primer that contained a T-7 RNA polymerase site that was labeled with biotin-11-CTP and biotin-16-UTP using a BioArray T-7 polymerase labeling kit (Enzo, Farmingdale, NY). Hybridizing, washing, antibody amplification, and staining of probe arrays were performed as per the Affymetrix Technical Manual (revision four). Experiments were performed using either rat genomic microarrays (RGU34A) or rat toxicology subarrays (RTU34) and visualized using version 3.3 GeneChip software (Affymetrix, Santa Clara, CA). Chip fluorescence was normalized by scaling total chip fluorescence intensities to a common value of 1500 prior to comparison. Because Affymetrix oligonucleotide microarrays utilize multiple perfect-match and mismatch oligonucleotides to determine expression levels, an algorithm within GeneChip software was used to determine the presence and abundance of each individual transcript. This absolute analysis of each individual microarray was performed to determine expression levels of individual genes. Absolute analyses of individual microarrays were followed by comparison analyses between individual microarrays within the GeneChip software to determine expression differences between samples. Data filtering was performed using Data Mining Tool (DMT) software (Affymetrix, Santa Clara, CA). Once comparisons between individual microarrays were performed, time course data was organized SOM (28, 29), a nonlinear generalization of principal components

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Figure 1. PCR primers were designed from sequences defined by Affymetrix for each transcript and used in RT/PCR confirmation studies. (A) Reactions contained 1.0 µg of total RNA, and were conducted with an annealing temperature of 55 °C for 25 (β-actin), 30 (ST1C1, Annexin II, CYP2C23), or 35 (GST π) cycles. 18s ribosomal RNA was used as a control gene for visual normalization. (B) Comparative analyses of the same five transcript levels as determined by oligonucleotide arrays. All values were expressed as “fold change” relative to cell pellet as described in the Experimental Procedures. analysis (30) and hierarchical clustering developed in house and written in C and Perl. When utilizing SOM as a clustering tool, it was important to prevent extremely large fold change values from skewing the results, so fold changes that were greater than 20 and less than -20 were set to 20 and -20, respectively. Because DMT software calculates both positive and negative fold changes, a fold change of -1 is equal to a fold change of +1, making two probe sets with potentially the same fold change appear very different. To make such probe sets equivalent for both clustering and display purposes, the value 1 was subtracted from each positive fold change and 1 was added to each negative fold change. The data were normalized separately for each experiment with the maximum value set to +1 and the minimum value set to -1. This effectively filtered the data so probe sets that had very little change were grouped together in neighboring clusters. SOM were run for 100 epochs using a time-varying learning rate (28, 31, 32) and a time-varying Gaussian neighborhood function (28, 33, 34). Due to the nature of the SOM

algorithm, the number of clusters for the SOM was specified a priori. For these data, an 8 × 8 matrix producing 64 clusters was specified. This choice was a compromise between too many clusters which would negate the effect of clustering, and too few which would allow too much variance within each cluster. The Gaussian neighborhood function imparted a two-dimensional ordering on clusters, so nearby clusters contained transcripts that were more similar than clusters that are farther apart on the map.

Results Three approaches were used to verify the array data collected in this study. First, RT-PCR of select transcripts with various expression patterns was performed with the same RNA used with rat genomic microarrays. As can be seen in Figure 1, the qualitative expression patterns of five genes chosen for RT-PCR analysis match the



Table 1. Gene Expression Changes at Critical Times in the Culture Processa

total probes detectable transcripts 4 h postplating 12 h postplating 24 h postplating 48 h postplating 72 h postplating

genomic array

toxicology sub-array

agreement between common genes

∼8700 27.7 ( 2.3%

∼900 33.9 ( 5.5%

∼900 in common 28.3 ( 5.7%

D ) 92 I ) 170 D ) 393 I ) 253 D ) 405 I ) 406 D ) 361 I ) 464 D ) 360 I ) 468

D ) 18 I ) 40 D ) 90 I ) 35 D ) 116 I ) 46 D ) 105 I ) 85 D ) 100 I ) 74

D ) 28% I ) 60% D ) 69% I ) 66% D ) 74% I ) 61% D ) 80% I ) 45% D ) 79% I ) 49%

a The absolute number of transcripts determined to be increased (I) and decreased (D) at each time point were determined for both the primary experiment performed on the rat genomic arrays, and the secondary experiments performed on the rat toxicology sub-arrays. Agreement was determined by comparing transcript changes (I or D) from both toxicology and genomic arrays/the number of transcript changes (I or D) from toxicology sub-array using the criteria described in Experimental Procedures (>2×).

expression patterns from the microarray analyses. This includes mRNAs that were temporally upregulated (Annexin, GST π, β-actin), and mRNAs whose expression was temporally downregulated (CYP 2C23, ST1C1). RTPCR of 18s ribosomal RNA served as a control gene whose transcript levels did not vary at any times investigated in this study. A second method used to support data from the primary microarray experiment utilized a separate RNA preparation (different animal/cultures) hybridized to rat toxicology sub-arrays. These sub-arrays (designed by Affymetrix) contained a 900 probe subset of the larger rat genomic arrays and were utilized as a means to confirm expression on a scale that could not be achieved with transcript-by-transcript methods, such as RT-PCR. The overall expression levels (percent of total detectable transcripts) observed in both experiments were in agreement with other experiments analyzing hepatocyte gene expression with Affymetrix arrays in our laboratory. On average, 28% of the transcripts probed were detectable in experiments employing genomic arrays and 34% of the transcripts probed were detectable in experiments employing toxicology sub-arrays (Table 1). The design of this sub-array focused upon genes whose expression would be of interest to toxicologists, resulting in a probe bias for highly regulatable and liver specific genes. When employing these sub-arrays in hepatic studies, our laboratory consistently detects a greater percentage of transcripts than with arrays designed for genomic coverage. As an additional measure of reproducibility, we found that 79% of the transcripts were detectable in both primary and repeat experiments, demonstrating reasonable experimental reproducibility between studies (data not shown). A summary of the number of transcripts whose expression levels changed at critical points in both the primary (Table 1, column 2) and repeat experiments (Table 1, column 3) can be found in Table 1. This table was constructed by first removing transcripts from the data sets whose expression was undetectable in both compared samples, followed by setting a fold change threshold of two as criteria for inclusion. At the earliest time point, the number of transcripts whose levels were elevated exceeded the number that were repressed on both the rat genomic microarray and rat toxicology subarray. At later time points, induced and repressed transcripts were somewhat equalized on the rat genomic microarray. On the rat toxicology sub-array, however,

Table 2. Gene Expression Changes Common in Repeat and Replicate Experimentsa fold change threshold 0-fold 2-fold 5-fold

rehybrization experiment % agreement

repeat experiment % agreement

D ) 82 I ) 34 D ) 75 I ) 43 D ) 98 I ) 95

D ) 74 I ) 27 D ) 63 I ) 58 D ) 85 I ) 89

a Transcripts were determined to be differentially expressed using Affymetrix Gene Chip software as described in the Experimental Procedures and sorted by the fold change score for increased (I) and decreased (D) transcripts. Differentially expressed transcripts were then compared to corresponding changes in the primary experiment employing rat genomic arrays using the same criteria, and expressed as % agreement.

this trend actually reversed at all time points beyond 12 h, reflecting the bias on the sub-array for probes associated with drug metabolism, which were generally repressed in this study. Because every gene on the toxicology sub-array is present on the larger rat genomic microarray, a direct comparison of transcript changes in repeat experiments could be made. As can be seen in Table 1, changes in gene expression between experiments at each time agreed quite well, with nearly 80% of repressed transcripts reproducibly changing using the relatively stringent criteria in these separate studies. A third method of investigating experimental variability was performed by rehybridizing labeled RNA from initial experiments to rat toxicology sub-arrays. As can be seen in Table 2, transcript levels were similar between separate hybridizations of the same RNA sample. Transcripts that were highly up or downregulated (fold change threshold g 5), behaved similarly in rehybridization experiments (98% decrease, 95% increase), and in repeat experiments (85% decrease, 89% increase) (Table 2). Because agreement was higher when the same samples were hybridized to different arrays than when the experiment was repeated in entirety, it can be inferred that some of the variability observed between experiments is biological in nature. This conclusion was supported by other experiments using this technology to monitor cellular responses to a pathogenic microorganism (35). The data obtained from each comparison outlined in Table 1 were grouped using SOM, and individual tran-

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Figure 2. Cluster analysis of transcripts over time using SOM. Two representative clusters showing genes whose transcript levels increase (A) and decrease (B) with time. Filtering and clustering analysis was performed as described in Experimental Procedures. All transcripts represented in each cluster are listed with Genebank accession number. In total, 64 clusters with different transcriptional patterns were assembled, all of which can be viewed (including entire population of transcripts with expression change values at each time point) in the Supporting Information.

scripts were placed into one of 64 clusters based upon their temporal expression pattern as described in Experimental Procedures (the entire group of all genes that changed [64 clusters] is contained in the Supporting Information). Given that thousands of transcripts changed in these studies, only general trends are discussed herein. Figure 2 contains two representative clusters from the 64 cluster diagram and the transcript expression patterns at each time point investigated. These clusters were chosen because they contain many transcripts associated with either the extracellular matrix (Figure 2A) or the cytochrome P450 (CYP) family (Figure 2B)stwo protein groups discussed extensively below. To address the dedifferentiation and adaptation processes, we compared expression profiles between the enriched hepatocyte pellet (baseline sample) and various times in culture (variable sample) to determine fold change values. Comparison of the enriched hepatocyte pellet to whole liver tissue revealed gene expression changes associated with removal of cell types during the isolation process, and these types of changes were considered to be beyond the scope and purpose of this communication (data not shown).

Trends in expression patterns were observed in many functional classes of genes, and those observed with phase I enzymes were exemplified by the CYP superfamily presented in Figure 3. As can be seen in Figure 3, most CYP genes were downregulated over time. CYP1 gene expression (green bars) was substantially downregulated between 24 and 48 h, while CYP2 and CYP4 families (red and yellow bars) were downregulated earlier, with the most dramatic changes being observed, between the 12 and 24 h time points. The expression of CYP3 family members (blue bars) remained constant throughout the 72-h culture period. Transcript changes for phase II biotransformation enzymes were grouped by family and further organized by sub-family where appropriate (Table 3). Unlike the CYPs, which were predominately downregulated over time, phase II enzymes demonstrated variable expression patterns. In general, expression of glucuronyl transferases either increased or were relatively unchanged over the 72-h culture period (Table 3A) while sulfoyltransferases and methyltransferases were predominately downregulated (Table 3, parts B and D, respectively) when viewed as a group. Furthermore, individual genes



Figure 3. Evaluation of cyctochrome P450 gene expression at (A) 12, (B) 24, and (C) 48 h post isolation. Data presented as fold change from enriched hepatocyte pellet. Green bars represent CYP1, red bars represent CYP2, blue bars represent CYP3, and yellow bars represent CYP4 transcripts.

were regulated differently within family classifications, as exemplified by transcripts encoding proteins involved in glutathione conjugation (Table 3C). Here, glutathione S-transferase (GST) R transcripts were downregulated with increasing time in culture while GST µ transcripts remained unchanged. Further evaluation of the data set revealed a 7.0-fold upregulation of the fetal expressed GST π isoenzyme transcript following 72 h in culture. An important advantage of global expression profiling compared to individual gene regulation studies is the ability to monitor changes in entire metabolic pathways simultaneously. Three such pathways of particular interest were identified within these data set. The first pathway of note included genes involved in the production and utilization of glutathione. γ-Glutamylcysteine synthetase (γ-GCS) was upregulated 4-fold at 4 h, and

glutathione synthetase was upregulated 10-fold at 24, 48, and 72 h (Table 4). Genes responsible for utilizing GSH in the cellular redox cycle (GSH peroxidase, GSSG reductase, GST), were variably expressed (Table 4). Figures 4 and 5 depict the 24-h gene expression changes in gluconeogenesis and glycolysis, respectively. In general, the genes in the gluconeogenic pathway were downregulated while the genes involved in glycolysis were upregulated. A distinct observation within these data was the consistent and progressive upregulation of genes encoding proteins associated with the cytoskeleton and extracellular matrix (EM). In all, more than 40 different genes were induced, while few were repressed. Included in the list in Table 5 are mRNA coding for proteins involved in the synthesis and maintenance of connective tissue and

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Table 3. Transcript Profiling of Phase II Metabolizing Enzymes by Familya

a Data presented as fold change (green > 2, red < -2) from hepatocyte pellet as determined using Affymetrix data mining tool. (*) Associated cluster number from Supporting Information.

the basement membrane, proteins involved in actin synthesis and function, and others involved in matrix signaling, maintenance of shape, and cell motility. We found it difficult to draw lines of distinction in genes known to have multiple and sometimes diverse function when constructing this particular table. For example, several genes in Table 5 are involved in cell cycle progression through mitosis. Genes involved in hepatocellular proliferation and differentiation were also noted based on their chronologi-

cal involvement in the cell cycle as depicted in Scheme 1 and Table 6. Hepatocellular genes involved in cellular priming were mostly unchanged at the mRNA level throughout the culture process. Exceptions were found that included two insulin responsive genes, insulin growth factor binding protein I (upregulated 10-fold maximally), insulin-like growth factor I (downregulated 20-fold maximally), and interleukin-6 (IL-6) receptor ligand binding protein (upregulation maximal-early) (Table 6A). Transcription factors involved in cell prolif-



Table 4. Transcript Profiling of Genes Involved in Glutathione Maintenance and Utilizationa


Figure 4. Gluconeogenic pathway depicting important catalytic enzymes responsible for the formation of glucose from pyruvate. Fold change in gene expression level is given for each enzyme at the 24-h time point. Fold change in gene expression levels can be found for remaining time points in the Supporting Information.

eration, such as nuclear factor κB (NF-κB), CCAAT/ enhancer binding protein (C/EBP), and hepatocyte nuclear factor (HNF), were found to be variably modulated at the early time points (Table 6B). Immediate-early genes were primarily unaffected or upregulated throughout the culture process. Exceptions included junB (downregulated 6-fold at 48 and 72 h) and Egr-1(Krox 24) (downregulated 4 to 6-fold at 12, 24, 48, and 72 h postplating) (Table 6C). A few delayed-early genes, such as clone-6 (CL-6), hepatic arg-ser protein (HRS) and B-cell leukemia/ lymphoma x gene (Bcl-x) were variably modulated with early upregulation of CL-6, early downregulation of HRS-

Figure 5. Glycolytic pathway and important catalytic enzymes responsible for the breakdown of glucose. Fold change in gene expression level is given for each enzyme at the 24-h time point. Fold change in gene expression levels can be found for remaining time points in the Supporting Information.

and overall upregulation of Bcl-x (Table 6D). Cell cycle progression requires a balance between positive and negative regulatory pathways. Evaluation of genes involved in cell cycle regulatory pathways showed that during culture hepatocytes altered the expression of genes associated with the negative regulatory pathway of the cell cycle. Examples include G0/G1 switch gene (Gos-2) (downregulated 18-fold at 12 h) and p53/p21 (upregulated throughout the culture process) (Table 6, parts C and E).

Discussion Primary isolated hepatocytes have become a standard in vitro model used to advance our understanding of liver

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Table 5. Cellular Cytoskeleton and Extracellular Matrixa


Scheme 1

biology and xenobiotic metabolism. Ideally, in vitro hepatocyte models should provide an environment that allows for the maintenance of differentiated hepatocytes. The loss of specialized cellular functions that results from monolayer culturing hepatocytes is referred to as cellular dedifferentiation. Most evaluations have utilized select markers (R-fetoprotein, γ-glutamyl transpeptidase (GGT), GST-π, tyrosine aminotransferase (TAT), connexin (Cx) 26, Cx 43, sorbitol dehydrogenase (SDH) or protein alterations involved in specific hepatocellular processes as general indicators of hepatocyte dedifferentiation (3638). Through the use of these select markers and focused evaluations of hepatocellular processes, a description of hepatocyte dedifferentiation as we currently view it has evolved. Using microarray technology, our objective was to demonstrate the utility of global gene expression analysis to further elucidate changes in monolayer cultured rat hepatocytes over time and to use this new

information to clarify the general understanding of hepatocyte dedifferentiation. The present study focused upon transcripts that demonstrated changes at multiple time points and on transcripts that were part of functionally related sets of genes showing similar expression patterns. For direct confirmation of these temporal transcript changes, five transcripts were chosen for RT-PCR analysis, and all five reproduced the expression trends observed via microarray analysis. We chose genes with varying expression patterns for confirmation analysis to ensure that differences in both induced and repressed transcripts could be reliably reproduced. This confirmation process included multiple RT-PCR analyses of β-actin, whose subtle, yet reproducible, increase in expression over time in culture precluded our initial attempt to use it as a housekeeping gene for normalization of RT-PCR data.



Table 6. Hepatocellular Proliferation and Differentiationa


By inspecting the number of transcripts up or downregulated at different times throughout this study, we observed patterns in the overall transcriptional behavior of hepatocytes in culture (Table 1). Four hours after the

cells were plated, more transcripts were induced than repressed when compared to the hepatocyte-enriched cell pellet. Upon removal from their in situ environment and placement into culture, hepatocytes demonstrated in-

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creased gene expression of many immediate early genes associated with the stress response including metalothionine, heme oxygenase, superoxide dismutase, and heat shock proteins (Supporting Information). As hepatocytes aged in culture beyond 4 h, the trend reversed, and many downregulated transcripts appeared. Down regulation was more pronounced with genes previously determined to be of toxicological significance, as the proportion of downregulated genes was greater on the toxicology sub-array than that on genomic arrays at all of the later time points (Tables 1 and 2). Additionally, the downregulation process appeared to be more reproducible than the upregulation response, as illustrated by the greater percentage of transcripts in agreement between repeat and replicate experiments in both Tables 1 and 2. Collectively, by considering the excellent agreement between transcripts at individual time points and that our analyses focus upon transcriptional changes over time, the trends in gene expression observed in this study can be deemed reliable and reproducible. One caution when using sub-arrays to study gene expression is evident by viewing the data presented in Table 1. As can be seen in this table, data derived from the toxicology sub-arrays is heavily biased toward genes downregulated over time. This is explained by inspecting the probe content of the rat toxicology sub-array. This array is biased with many probes for transcripts encoding proteins studied by toxicologists, which are preferentially downregulated under in vitro conditions utilized in the present study. Using a microarray containing thousands of genes, such as the rat genomic microarray employed in these studies, increases ones aperture of expression analysis. Examples of gene expression changes that would have been missed if these experiments were conducted using the toxicology sub-array include important expression changes in transcripts associated with the cytoskeleton, extracellular matrix, and cell cycle (Tables 5 and 6). In the present study, transcripts were first grouped based upon their expression patterns (Figure 2), followed by functional classification to illustrate the underlying biological changes associated with monolayer culturing of hepatocytes. Maximally, 10% of the genes probed were affected at any singular time point, leaving 90% of the genes unaffected by the culture process. It is not the intent of this communication to describe all transcriptional changes observed in this series of experiments, and for a comprehensive view of all changes observed in the present study, the reader is referred to the Supporting Information contained on the (http://pubs.acs.org/subscribe/ journals/crtoec/supmat/index.html). This supplement contains all transcripts that changed in at least one time point in a comprehensive cluster diagram. Several biological processes were transcriptionally affected in the present study, and a few of these processes are discussed below. Phase I P450 Metabolism. Previous studies have established that many phase I biotransformation enzymes, particularly members of the CYP family, are downregulated with time in culture (22, 23). An example of the downregulation of CYP genes can be seen in Figure 2B, where approximately 30% of the progressively downregulated members in this cluster are transcripts of the P450 family. Although the present study confirms that most genes associated with phase I P450 metabolism are downregulated upon culturing, to generalize that all

Baker et al.

P450s are rapidly downregulated is misleading. Our studies reveal that P450 expression levels are heterogeneous in both timing and magnitude over the 72-h culture period (Figure 3 and Supporting Information). For example, expression of the CYP1 family (green bars) is downregulated between 24 and 48 h while the CYP2 and CYP4 families (red and yellow bars) are downregulated between the 12- and 24-h time points. The maintenance of CYP3 (blue bars) expression may be due to inclusion of dexamethasone in the culture media, as dexamethasone is a CYP3 substrate. This interpretation is supported by the dramatic induction of the steroid metabolizing cytochrome P450, CYP17R hydroxylase and other transcripts induced in this study (Supporting Information, ref 23). It should be pointed out that some P450s remain inducible under monolayer culture conditions, despite the baseline gene expression decrease. We, along with other investigators, have previously demonstrated this with members of the CYP4A subfamily following treatment with peroxisome proliferators (data not shown, ref 39). This demonstrates that cultured hepatocytes do not lose their ability to express CYP4A genes, rather, these genes are selectively repressed under these in vitro conditions. In the current studies, many probes for liverspecific markers, in addition to the P450s discussed above, were present on the microarrays. We evaluated several of these genes to determine if cultured hepatocytes downregulate the majority of liver-specific genes, or if only a few of the better described families, such as the CYPs, were affected. Based upon the genes that we evaluated (i.e., R fetoprotein, vitamin D binding protein, R albumin), not all were differentially expressed, making a general classification of liver-specific markers difficult with the current gene expression data set. Phase II Metabolism. Transcriptional changes representing elements of both adaptation and dedifferentiation, as defined earlier, were observed upon monitoring the expression of genes associated with phase II biotransformation. Uridine-5′diphospho-glucuronosyltransferases (UDP-GTs) represent a family of enzymes responsible for the conjugation of many endogenous and exogenous xenobiotics. As can be seen in Table 3, several UDP-GT genes are upregulated during culture. Dexamethasone has previously been shown to induce UDP-GT activity and may be responsible for the changes in UDP-GT mRNA observed here (40). Sulfotransferases (ST) catalyze the conjugation of hydroxyl groups through transfer of inorganic sulfates. ST gene expression was dramatically downregulated following 12 h in culture. Previous work in monolayer cultured rat hepatocytes studying sulfation in the presence of acetaminophen revealed that sulfyltransferases remain active in culture given appropriate sulfate, hormone, and substrate availability (41). The downregulation of ST gene expression in the present study may be related to the absence of these factors, and illustrates the importance of understanding how varying culture environments effects gene expression. GSTs catalyze the initial step of GSH conjugation, where GSH itself (discussed below) serves as a nucleophile for metabolic conversion of xenobiotics. Previous studies using primary hepatocyte cultures have shown that activity levels of GST R and GST µ fall, while GST π increases with time (42). GST R is downregulated and GST π is upregulated in the present study, supporting these observations. In contrast to GST R and GST π, GST µ mRNA levels were unchanged in the current study.


This may represent a situation where mRNA expression levels are not indicative of protein content and, therefore, enzyme activity. The upregulation of GST π is a classic marker of hepatocellular dedifferentiation. This gene is not normally expressed in adult liver, and its induction indicates a recapitulation of the fetal state (38). Methylation is a phase II conjugation reaction catalyzed by methyltransferases, and in the current study methyltransferase gene expression was primarily repressed. In total, expression of phase II metabolism gene expression is illustrative of both dedifferentiation and adaptation to the culturing process employed in these studies. GSH Production and Utilization. Hepatocellular GSH participates in critical cell survival defense mechanisms by detoxifying electrophiles and scavenging free radicals. GSH synthesis requires the catalytic activity of γ-GCS and GSH synthetase and is feedback regulated by competitive inhibition of γ-GCS by GSH. An early increase in γ-GCS mRNA occurred most likely due to lack of available cysteine (43). Cysteine is not stable in culture media used in these studies, so in vitro hepatocytes must generate cysteine intracellularly either through methionine or cystine conversion to cysteine. This lack of cysteine would explain our data showing an early induction γ-GCS followed by a return to baseline (Table 4). Glutathione synthetase, which is normally in excess, was further elevated at the mRNA level in these studies. Recent studies have demonstrated that overexpression of GSH synthetase does not lead to elevated GSH levels and is not damaging to the cell, while an under expression of GSH synthetase will lead to γ-GCS accumulation and metabolic acidosis (44). The antioxidant function of GSH is critical for cell survival due to oxidative stresses associated with aerobic metabolism. In the present study, GSH peroxidase mRNA was decreased along with other antioxidant enzymes such as catalase and Cu, Znsuperoxide dismutase, while GSSG reductase mRNA increased (Table 4). The reduction of these genes in culture may render hepatocytes more susceptible to oxidative damage relative to the intact liver. Glucose Metabolism. Lactic acid, the end product of anaerobic glucose breakdown by glycolysis in the muscle, is converted to pyruvate, and then pyruvate is converted back to glucose in the liver through gluconeogenesis (Figure 4). Expression of genes encoding the enzymes of gluconeogenesis was dramatically downregulated in this study. This change is most likely an adaptive response to the culture system and can be attributed to the high levels of glucose in the culture media (2000 mg/L) and the lack of lactic acid production. Glycolysis converts glucose into pyruvate yielding ATP (Figure 5). In contrast to the enzymes of the gluconeogenic pathway, cultured hepatocytes had elevated expression levels of key glycolytic regulatory enzymes. These data are consistent with a shift from gluconeogenesis to glycolysis and provide evidence of the concomitant regulation of gluconeogenesis and glycolysis as an adaptive response to the culture environment. Cytoskeleton and Extracellular Matrix. Upon culturing, hepatocytes change shape from their in situ cuboidal differentiated form to a more flattened dedifferentiated structure that adheres to the bottom of the plastic culture plate (45). This physical change in cellular shape upon adherence must be accompanied by architectural changes in structural components of the hepatocyte and be controlled by signal transducing systems


in a coordinated manner. Furthermore, the culturing of cells requires severing and reestablishing cellular contacts as cells are disassociated from the liver then plated. Many of these contacts are mediated through proteins that comprise the extracellular matrix. Cells often undergo apoptosis when deprived of an extracellular matrix (46), therefore, for cells to survive the process of culturing, a response to the mechanical disruption of their environment is needed. The extracellular matrix is a dynamic protein and polysaccharide network that provides cells with position and environmental information, while serving as a tissue specific structure that controls cell function. Similarly, the actin cytoskeleton is a structure that underlies the cell membrane and contributes directly to the maintenance of cell shape (46). Because of the intricate interplay of this system, Table 5 was assembled with more than 40 transcripts encoding proteins associated with the cytoskeleton and extracellular matrix. As a general trend in these studies, these transcripts were upregulated dramatically, reflecting the need for cells to reestablish their connectivity and adapt to their new environment. It is interesting to note that some genes are transcriptionally upregulated very early, and these appear to be involved with early process of hepatocellular reorganization. Genes induced at later time points appear to be involved with the later phases of cellular reorganization and connection. For example, the protein b61 is an immediate-early gene that binds to epha tyrosine kinases and has been postulated to be involved in local depolymerization of the actin cytoskeleton (47). This protein and ryudocan, a proteoglycan involved in assembly of focal adhesions, are induced maximally at the earliest time point investigated, presumably so that cells can reassemble their cytoskeletal machinery and make way for integrin signaling and rebuilding of the actin cytoskeleton. Subsequently, synthesis of structural proteins, such as the intermediate filament proteins cytokeratin and vimentin are heavily upregulated at and beyond 12 h of culturing. Finally, gene products that associate with the actin cytoskeleton, such as p41-arc and R-Crystallin, as well as genes involved with cross-linking the newly synthesized actin cytoskeleton with phospholipids are induced. Molecules involved in cellular adhesion and signaling through newly synthesized or rearranged focal adhesions are also upregulated at the latest time points investigated, and include transcripts for cadherins, CARI, and the signal transducing and actin associated protein, Rho A. Collectively, these changes in gene expression clearly demonstrate a reorganization of the extracellular matrix and cellular cytoskeleton that is necessary for cells to adapt and survive in their new culture environment. In addition, it is firmly established that the extracellular matrix profoundly influences the major programs of growth and differentiation. Cell Cycle and Differentiation. Parenchymal hepatocytes in adult liver are quiescent (held in the G0 phase of the cell cycle) and replicate infrequently. In response to insult, as indicated by a change in liver mass/body mass ratios, parenchymal hepatocytes retain the capacity to proliferate. A change in this ratio can occur through chemical, infectious, ischemic, or physical injury, and results in hyperplastic hepatic regeneration. The advancement of hepatocytes through the cell cycle is transcriptionally coordinated, and transcripts responsible for this regulation can be broken into categories of cell priming (hormones, cytokines), transcription (STAT-3,

1230


C/EBP), immediate-early responses (c-fos, c-myc), delayedearly responses (CL-6, HRS) and cell cycle alterations (cyclins, retinoblastoma gene (Rb)) (Scheme 1). Our evaluation of changes in these transcripts revealed an overall pattern indicative of maintaining hepatocytes in a nonproliferative state. These changes include the early downregulation of the immediate-early gene GOS2, followed by the upregulation of the cell cycle inhibitory genes, p53 and p21. Data collected from cultured human blood mononuclear cells suggests that GOS2 is required for the transition of cells into the G1 phase of the cell cycle (48). Evaluations of liver have revealed that the cyclin-dependent kinase (CDK) inhibitor p21 can block the hepatocyte cell cycle in G1 and possibly G2 (49). Expression analysis of p21 in cultured cells and mice has demonstrated a correlation between elevated p21 expression and withdrawal of cells from their proliferative cycle during cellular differentiation (50). This correlative data puts forth the possibility that p21 could play a role in regulating cellular differentiation through negative regulation of cell cycle genes. Therefore, an upregulation in the expression level of p21 in this study may indicate that the hepatocytes are attempting to conserve a differentiated, G0 cell cycle state. The data within this manuscript provide a comprehensive, temporal gene expression analysis of a traditional monolayer cultured hepatocyte system. From these studies, it is clear that transcriptional fingerprints at select points in time can be used to adequately describe biological responses associated with the transition of hepatocytes from the intact liver to an artificial culture environment. This study confirmed some classical and previously reported changes in gene expression of cultured hepatocytes, which can appropriately be described as dedifferentiation. At the same time, we were able to show other, adaptive responses of biological processes that are involved in cell morphology, signaling, and metabolism. Collectively, these observations begin to define the cultured rat hepatocyte, and will allow more informed interpretation of data derived from this model system. Furthermore, these data provide a comprehensive understanding of the hepatocellular dedifferentiated state, and reveal many aspects of physiological and morphological adaptation that have not been previously reported. Supporting Information Available: A very detailed presentation of microarray data underlying this paper. This material is available free of charge via the Internet at http:// pubs.acs.org/subscribe/journals/crtoec/supmat/index.html.

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