Transformation of SV40-immortalized human uroepithelial cells by 3-methylcholanthrene increases IFN- and Large T Antigen-induced transcripts.

BACKGROUND: Simian Virus 40 (SV40) immortalization followed by treatment of cells with 3-methylcholanthrene (3-MC) has been used to elicit tumors in athymic mice. 3-MC carcinogenesis has been thoroughly studied, however gene-level interactions between 3-MC and SV40 that could have produced the observed tumors have not been explored. The commercially-available human uroepithelial cell lines were either SV40-immortalized (HUC) or SV40-immortalized and then 3-MC-transformed (HUC-TC). RESULTS: To characterize the SV40 - 3MC interaction, we compared human gene expression in these cell lines using a human cancer array and confirmed selected changes by RT-PCR. Many viral Large T Antigen (Tag) expression-related changes occurred in HUC-TC, and it is concluded that SV40 and 3-MC may act synergistically to transform cells. Changes noted in IFP 9-27, 2'-5' OAS, IF 56, MxA and MxAB were typical of those that occur in response to viral exposure and are part of the innate immune response. Because interferon is crucial to innate immune host defenses and many gene changes were interferon-related, we explored cellular growth responses to exogenous IFN-gamma and found that treatment impeded growth in tumor, but not immortalized HUC on days 4 - 7. Cellular metabolism however, was inhibited in both cell types. We conclude that IFN-gamma metabolic responses were functional in both cell lines, but IFN-gamma anti-proliferative responses functioned only in tumor cells. CONCLUSIONS: Synergism of SV40 with 3-MC or other environmental carcinogens may be of concern as SV40 is now endemic in 2-5.9% of the U.S. population. In addition, SV40-immortalization is a generally-accepted method used in many research materials, but the possibility of off-target effects in studies carried out using these cells has not been considered. We hope that our work will stimulate further study of this important phenomenon.

Murine J774 macrophages recognize LPS/IFN-g, non-CpG DNA or two-CpG DNA-containing sequences as immunologically distinct.

Specific bacterial lipopolysaccharides (LPS), IFN-gamma, and unmethylated cytosine or guanosine-phosphorothioate containing DNAs (CpG) activate host immunity, influencing infectious responses. Macrophages detect, inactivate and destroy infectious particles, and synthetic CpG sequences invoke similar responses of the innate immune system. Previously, murine macrophage J774 cells treated with CpG induced the expression of nitric oxide synthase 2 (NOS2) and cyclo-oxygenase 2 (COX2) mRNA and protein. In this study murine J774 macrophages were exposed to vehicle, interferon gamma+lipopolysaccharide (IFN-g/LPS), non-CpG (SAK1), or two-CpG sequence-containing DNA (SAK2) for 0-18h and gene expression changes measured. A large number of immunostimulatory and inflammatory changes were observed. SAK2 was a stronger activator of TNFalpha- and chemokine expression-related changes than LPS/IFN-g. Up regulation included tumor necrosis factor receptor superfamily genes (TNFRSF's), IL-1 receptor signaling via stress-activated protein kinase (SAPK), NF-kappaB activation, hemopoietic maturation factors and sonic hedgehog/wingless integration site (SHH/Wnt) pathway genes. Genes of the TGF-beta pathway were down regulated. In contrast, LPS/IFN-g-treated cells showed increased levels for TGF-beta signaling genes, which may be linked to the observed up regulation of numerous collagens and down regulation of Wnt pathway genes. SAK1 produced distinct changes from LPS/IFN-g or SAK2. Therefore, J774 macrophages recognize LPS/IFN-g, non-CpG DNA or two-CpG DNA-containing sequences as immunologically distinct.

Frequent sampling reveals dynamic responses by the transcriptome to routine media replacement in HepG2 cells.

Cultured cell lines are employed extensively for biological research. Large-scale differential gene expression (LSDGE) is being used to study mechanisms of toxicity in such cultures. 'Normal' gene expression dynamics could have a major impact on the design and interpretation of these studies. In order to provide understanding of such dynamics, we investigated LSDGE responses to media replacement in human hepatoblastoma cells (HepG2) using 5-minute sampling frequencies for 6 hours post routine media replacement. Each mRNA transcript was found to exhibit a characteristic 'operating range' based on signal intensity. Following media replacement, which replenishes nutrients (eg, glucose and glutamate) and removes excretory products (eg, lactate), a complex set of gene expression changes was observed. Some transcripts appeared to switch on from a quiescent state to a very active one (eg, CYP1A1), others exhibited 'clocklike' oscillations (eg, asparagine synthetase), or a synchronous burst (chirp) of expression up regulation (eg, timeless). Mathematical analysis (Fourier Transform, Singular Value Decomposition, Wavelets, Phase Analysis) of oscillating expression patterns identified cycle lengths ranging from 11.8 to 210 minutes. There were prominent 36.5- and 17.4-minute cycles, for subsets of genes, and transcript-specific differences in phase angle with respect to these cycles. The functional consequences of these novel observations remain to be determined. It is clear that dense time-course studies provide a valuable approach to the investigation of physiological responses to nutrients, toxicants, and other environmental variables. This research also highlights the need for an understanding of biological dynamics when using cell culture systems. An Excel data file representing individual transcripts from the respective Clontech cDNA arrays referred to in this article is available at http://taylorandfrancis.metapress.com/openurl.asp?genre=journal&issn=0192-6233. Rows represent data for individual transcripts and columns represent the time-points from 0 to 360 minutes. To access this file, click on the issue link for 31(4), then select this article. In order to access the full article online, you must either have an individual subscription or a member subscription accessed through www.toxpath.org.

Application of cDNA microarray technology to in vitro toxicology and the selection of genes for a real-time RT-PCR-based screen for oxidative stress in Hep-G2 cells.

Large-scale analysis of gene expression using cDNA microarrays promises the rapid detection of the mode of toxicity for drugs and other chemicals. cDNA microarrays were used to examine chemically induced alterations of gene expression in HepG2 cells exposed to a diverse group of toxicants at an equitoxic exposure concentration. The treatments were ouabain (43 microM), lauryl sulfate (260 microM), dimethylsulfoxide (1.28 M), cycloheximide (62.5 microM), tolbutamide (12.8 mM), sodium fluoride (3 mM), diethyl maleate (1.25 mM), buthionine sulfoximine (30 mM), potassium bromate (2.5 mM), sodium selenite (30 microM), alloxan (130 mM), adriamycin (40 microM), hydrogen peroxide (4 mM), and heat stress (45 degrees C x 30 minutes). Patterns of gene expression were correlated with morphologic and biochemical indicators of toxicity. Gene expression responses were characteristically different for each treatment. Patterns of expression were consistent with cell cycle arrest, DNA damage, diminished protein synthesis, and oxidative stress. Based upon these results, we concluded that gene expression changes provide a useful indicator of oxidative stress, as assessed by the GSH:GSSG ratio. Under the conditions of this cell culture test system, oxidative stress upregulated 5 genes, HMOX1, p21(waf1/cip1), GCLM, GR, TXNR1 while downregulating CYP1A1 and TOPO2A. Primers and probes for these genes were incorporated into the design of a 7-gene plate for RT-PCR. The plate design permitted statistical analysis and allowed clear discrimination between chemicals inducing oxidative vs nonoxidative stress. A simple oxidative stress score (0-1), based on the responses by the 7 genes (including p-value) on the RT-PCR plate, was correlated with the GSH:GSSG ratio using linear regression and ranking (Pearson product) procedures. These analyses yielded correlation coefficients of 0.74 and 0.87, respectively, for the treatments tested (when 1 outlier was excluded), indicating a good correlation between the biochemical and transcriptional measures of oxidative stress. We conclude that it is essential to measure the mechanism of interest directly in the test system being used when assessing gene expression as a tool for toxicology. Tables 1-15, referenced in this paper, are not printed in this issue of Toxicologic Pathology. They are available as downloadable text files at http://taylorandfrancis.metapress.com/openurl.asp?genre=journal&issn=0192-6233. To access them, click on the issue link for 30(4), then select this article. A download option appears at the bottom of this abstract. In order to access the full article online, you must either have an individual subscription or a member subscription accessed through www.toxpath.org.

Toxicogenomics, drug discovery, and the pathologist.

The field of toxicogenomics, which currently focuses on the application of large-scale differential gene expression (DGE) data to toxicology, is starting to influence drug discovery and development in the pharmaceutical industry. Toxicological pathologists, who play key roles in the development of therapeutic agents, have much to contribute to DGE studies, especially in the experimental design and interpretation phases. The intelligent application of DGE to drug discovery can reveal the potential for both desired (therapeutic) and undesired (toxic) responses. The pathologist's understanding of anatomic, physiologic, biochemical, immune, and other underlying factors that drive mechanisms of tissue responses to noxious agents turns a bewildering array of gene expression data into focused research programs. The latter process is critical for the successful application of DGE to toxicology. Pattern recognition is a useful first step, but mechanistically based DGE interpretation is where the long-term future of these new technologies lies. Pathologists trained to carry out such interpretations will become important members of the research teams needed to successfully apply these technologies to drug discovery and safety assessment. As a pathologist using DGE, you will need to learn to read DGE data in the same way you learned to read glass slides, patiently and with a desire to learn and, later, to teach. In return, you will gain a greater depth of understanding of cell and tissue function, both in health and disease.