Integrating time-course microarray gene expression profiles with cytotoxicity for identification of biomarkers in primary rat hepatocytes exposed to cadmium.

MOTIVATION: DNA microarrays can provide information about the expression levels of thousands of genes simultaneously at the transcriptomic level, while conventional cell viability and cytotoxicity measurement methods provide information about the biological functions at the cellular level. Integrating these data at different levels provides a promising approach for evaluating or predicting how cells respond to chemical exposure. It is important to investigate the multi-scale biological system in a systematic way to better understand the gene regulation networks and signal transduction pathways involved in the cellular responses to environmental factors. RESULTS: Primary rat hepatocytes were exposed to cadmium acetate at 0, 1.25 and 2 microM. mRNA expression profiles at 0, 3, 6, 12 and 24 h were measured using the Affymetrix RatTox U34 GeneChip arrays. Simultaneously, cytotoxicity was assessed by lactase dehydrogenase leakage assay. Gene expression profiles at different time points were used to evaluate cytotoxicity at subsequent time points using partial least squares, and it was found that gene expression profiles at 0 h had the best prediction accuracy for the cytotoxicity observed at 12 h. Some biomarkers whose expression profiles showed strong relationship with cytotoxicity were identified and the underlying pathways were reconstructed to illustrate how hepatocytes respond to cadmium exposure. Permutation studies were also applied to assess the reliability of the predictive models. AVAILABILITY: Matlab source code is available upon request and DNA microarray data are available at GEO (http://www.ncbi.nlm.nih.gov/geo).

Gene expression profiles differ markedly in mouse strains that are (or are not) susceptible to hyperthyroidism induced using thyrotropin receptor-expressing adenovirus.

BALB/c mice are susceptible and C57BL/6 mice are resistant to Graves' hyperthyroidism induced by immunization with adenovirus encoding the thyrotropin receptor (TSHR) A-subunit. Both strains develop comparable levels of TSHR antibodies, but potent TSH blocking antibody activity in C57BL/6 mice likely blocks development of hyperthyroidism. We used microarrays to compare gene expression in spleens of mice immunized with A-subunit adenovirus (TSHR-Ad) or control adenovirus (Con-Ad). To preclude the effects of variable thyroxine (T(4)) levels, mice were studied when euthyroid as follows: BALB/c mice immunized three times with TSHR-Ad or Con-Ad and C57BL/6 mice immunized three times with TSHR-Ad or Con-Ad. Among the 14,000 expressed probe sets, there were no statistically significant differences in gene expression in BALB/c mice immunized with TSHR-Ad versus Con-Ad. In contrast, expression of 57 transcripts (representing 40 genes) changed in response to TSHR-Ad in C57BL/6 mice. Diverse genes were identified, including proteins involved in immune responses, inflammation, and cell cycling as well as heat-shock proteins and proteases. Down-regulation of chitinase 3- and-4 gene expression likely reflects cytokines produced by T-helper 2 (Th2) type cells. Indeed, the immunoglobulin (IgG) subclass for TSHR antibodies reflects a deviation away from Th2 cytokines and toward Th1 in C57BL/6 mice. In conclusion, TSHR-Ad immunization altered gene expression profiles in C57BL/6, but not in BALB/c, mice. This response primarily involved reduced gene expression. In C57BL/6 mice, decreased expression of genes such as cathelicidin, calgranulins, and lipocalin following TSHR A-subunit adenovirus immunization suggests the importance of innate immunity in this response.

Transcriptomic fingerprinting of bone marrow-derived hepatic beta2m-/Thy-1+ stem cells.

The aim of the present study was to determine if the bone marrow (BM) beta2m-/Thy-1+ stem cells isolated from common bile duct ligated (CBDL) rats possess hepatocyte-like characteristics in their global gene expression profiles. The Affymetrix RG U34A arrays were used to conduct transcriptomic profiling on BM beta2m-/Thy-1+ stem cells isolated from CBDL and control rats as well as primary hepatocytes. Forty-one probe sets were up-regulated more than 2-fold in CBDL-derived beta2m-/Thy-1+ BM stem cells compared to control BM stem cells. Twenty-seven probe sets were present in both CBDL-derived beta2m-/Thy-1+ BM stem cells and control hepatocytes but absent in control beta2m-/Thy-1+ BM stem cells, including Tcf1 and Dbp. Compared to the control beta2m-/Thy-1+ BM stem cells, CBDL-derived beta2m-/Thy-1+ BM stem cells shared more commonly expressed genes with hepatocytes. Overall, CBDL-derived beta2m-/Thy-1+ stem cells displayed a different transcriptomic fingerprint compared with beta2m-/Thy-1+ BM stem cells isolated from control rats; and CBDL-derived beta2m-/Thy-1+ stem cells started to express some hepatocyte-like genes.

Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data.

DNA microarray technology provides a promising approach to the diagnosis and prognosis of tumors on a genome-wide scale by monitoring the expression levels of thousands of genes simultaneously. One problem arising from the use of microarray data is the difficulty to analyze the high-dimensional gene expression data, typically with thousands of variables (genes) and much fewer observations (samples), in which severe collinearity is often observed. This makes it difficult to apply directly the classical statistical methods to investigate microarray data. In this paper, total principal component regression (TPCR) was proposed to classify human tumors by extracting the latent variable structure underlying microarray data from the augmented subspace of both independent variables and dependent variables. One of the salient features of our method is that it takes into account not only the latent variable structure but also the errors in the microarray gene expression profiles (independent variables). The prediction performance of TPCR was evaluated by both leave-one-out and leave-half-out cross-validation using four well-known microarray datasets. The stabilities and reliabilities of the classification models were further assessed by re-randomization and permutation studies. A fast kernel algorithm was applied to decrease the computation time dramatically. (MATLAB source code is available upon request.).

Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models.

High-throughput DNA microarray provides an effective approach to the monitoring of expression levels of thousands of genes in a sample simultaneously. One promising application of this technology is the molecular diagnostics of cancer, e.g. to distinguish normal tissue from tumor or to classify tumors into different types or subtypes. One problem arising from the use of microarray data is how to analyze the high-dimensional gene expression data, typically with thousands of variables (genes) and much fewer observations (samples). There is a need to develop reliable classification methods to make full use of microarray data and to evaluate accurately the predictive ability and reliability of such derived models. In this paper, discriminant partial least squares was used to classify the different types of human tumors using four microarray datasets and showed good prediction performance. Four different cross-validation procedures (leave-one-out versus leave-half-out; incomplete versus full) were used to evaluate the classification model. Our results indicate that discriminant partial least squares using leave-half-out cross-validation provides a more realistic estimate of the predictive ability of a classification model, which may be overestimated by some of the cross-validation procedures, and the information obtained from different cross-validation procedures can be used to evaluate the reliability of the classification model.

Quantitative structure-activity relationship modeling of alcohol, ester, aldehyde, and ketone flavor thresholds in beer from molecular features.

Three quantitative structure-activity relationship approaches-principal components regression, partial least-squares regression, and alternating conditional expectations-were used to investigate relationships between the flavor thresholds of 38 alcohols, 40 esters, 45 aldehydes, and 43 ketones in beer and their structures. Strong nonlinear relationships between the logarithm of the flavor threshold and four or five structure descriptors were found for each class of compounds (R2 = 0.920, 0.937, 0.920, and 0.928 for alcohols, esters, aldehydes, and ketones, respectively). Simple nonlinear relationships between the alcohol, ester, and aldehyde thresholds and the numbers of hydrogen atoms in the molecules were also demonstrated.