Biomedical subjects

Laura Suter

Publications and source records attributed to Laura Suter.

11 recordsLinked to original sources

PubMed2006

Biomarker discovery by imaging mass spectrometry: transthyretin is a biomarker for gentamicin-induced nephrotoxicity in rat.

Adverse drug effects are often associated with pathological changes in tissue. An accurate depiction of the undesired affected area, possibly supported by mechanistic data, is important to classify the effects with regard to relevance for human patients. MALDI imaging MS represents a new analytical tool to directly provide the spatial distribution and the relative abundance of proteins in tissue. Here we evaluate this technique to investigate potential toxicity biomarkers in kidneys of rats that were administered gentamicin, a well known nephrotoxicant. Differential analysis of the mass spectrum profiles revealed a spectral feature at 12,959 Da that strongly correlates with histopathology alterations of the kidney. We unambiguously identified this spectral feature as transthyretin (Ser(28)-Gln(146)) using an innovative combination of tissue microextraction and fractionation by reverse-phase liquid chromatography followed by a top-down tandem mass spectrometric approach. Our findings clearly demonstrate the emerging role of imaging MS in the discovery of toxicity biomarkers and in obtaining mechanistic insights concerning toxicity mechanisms.

Amino Acid Sequence↗

PubMed2006

Gene expression analysis of the hepatotoxicant methapyrilene in primary rat hepatocytes: an interlaboratory study.

Genomics technologies are used in several disciplines, including toxicology. However, these technologies are relatively new, and their applications require further investigations. When investigators apply these technologies to in vitro experiments, two major issues need to be clarified: a) can in vitro toxicity studies, in combination with genomics analyses, be used to predict the toxicity of a compound; and b) are the generated toxicogenomics data reproducible between laboratories? These questions were addressed by an interlaboratory study with laboratories of four pharmaceutical companies. We evaluated gene expression patterns from cultured rat primary hepatocytes after a 24-hr incubation with methapyrilene (MP). Extensive data analysis showed that comparison of genomics data from different sources is complex because both experimental and statistical variability are important confounding factors. However, appropriate statistical tools allowed us to use gene expression profiles to distinguish high-dose-treated cells from vehicle-treated cells. Moreover, we correctly identified MP in an independently generated in vitro database, underlining that in vitro toxicogenomics could be a predictive tool for toxicity. From a mechanistic point of view, despite the observed site-to-site variability, there was good concordance regarding the affected biologic processes. Several subsets of regulated genes were obtained by analyzing the data sets with one method or using different statistical analysis methods. The identified genes are involved in cellular processes that are associated to the exposure of primary hepatocytes to MP. Whether they are specific for MP and are cause or consequence of the toxicity requires further investigations.

Animals↗

PubMed2005

Assessment of hepatotoxic liabilities by transcript profiling.

Male Wistar rats were treated with various model compounds or the appropriate vehicle controls in order to create a reference database for toxicogenomics assessment of novel compounds. Hepatotoxic compounds in the database were either known hepatotoxicants or showed hepatotoxicity during preclinical testing. Histopathology and clinical chemistry data were used to anchor the transcript profiles to an established endpoint (steatosis, cholestasis, direct acting, peroxisomal proliferation or nontoxic/control). These reference data were analyzed using a supervised learning method (support vector machines, SVM) to generate classification rules. This predictive model was subsequently used to assess compounds with regard to a potential hepatotoxic liability. A steatotic and a non-hepatotoxic 5HT(6) receptor antagonist compound from the same series were successfully discriminated by this toxicogenomics model. Additionally, an example is shown where a hepatotoxic liability was correctly recognized in the absence of pathological findings. In vitro experiments and a dog study confirmed the correctness of the toxicogenomics alert. Another interesting observation was that transcript profiles indicate toxicologically relevant changes at an earlier timepoint than routinely used methods. Together, these results support the useful application of toxicogenomics in raising alerts for adverse effects and generating mechanistic hypotheses that can be followed up by confirmatory experiments.

Animals↗

PubMed2005

Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit?

Almost 10 years ago, microarray technology was established as a new powerful tool for large-scale analysis of gene expression. Soon thereafter the new technology was discovered by toxicologists for the purpose of deciphering the molecular events underlying toxicity, and the term "Toxicogenomics" appeared in scientific literature. Ever since, the toxicology community was fascinated by the multiplicity of sophisticated possibilities toxicogenomics seems to offer: genome-wide analysis of toxicant-induced expression profiles may provide a means for prediction of toxicity prior to classical toxicological endpoints such as histopathology or clinical chemistry. Some researchers even speculated of the classical methods being superfluous before long. It was assumed that by using toxicogenomics it would be possible to classify compounds early in drug development and consequently save animals, time, and money in pre-clinical toxicity studies. Moreover, it seemed within reach to unravel the molecular mechanisms underlying toxicity. The feasibility of bridging data derived from in vitro and in vivo systems, identifying new biomarkers, and comparing toxicological responses "across-species" was also excessively praised. After several years of intensive application of microarray technology in the field of toxicology, not only by the pharmaceutical industry, it is now time to survey its achievements and to question how many of these wishes and promises have really come true.

Animals↗

PubMed2004

Toxicogenomics in predictive toxicology in drug development.

The goal of toxicology is the assessment of possible risk to man. An emerging technology with the potential to have a major impact on risk assessment is toxicogenomics. In this review, we provide an overview of the many possibilities for toxicogenomics including technology platforms, data interpretation, and regulatory perspective and we give examples of toxicogenomics investigations. Toxicogenomics is a powerful tool for compound classification, for mechanistic studies, and for the detection of toxicity markers. Thus, toxicogenomics helps in the extrapolation of findings across species and increases predictability. Biomarkers are valuable in the evaluation of compounds at earlier development phases, improving clinical candidate selection. Caution regarding the interpretation of the results is still necessary. Nevertheless, toxicogenomics will accelerate preclinical safety assessments and improve the prediction of toxic liabilities, as well as of potential risk accumulation for drug-drug or drug-disease interactions.

Biomarkers↗

PubMed2004

Interlaboratory evaluation of rat hepatic gene expression changes induced by methapyrilene.

Several studies using microarrays have shown that changes in gene expression provide information about the mechanism of toxicity induced by xenobiotic agents. Nevertheless, the issue of whether gene expression profiles are reproducible across different laboratories remains to be determined. To address this question, several members of the Hepatotoxicity Working Group of the International Life Sciences Institute Health and Environmental Sciences Institute evaluated the liver gene expression profiles of rats treated with methapyrilene (MP). Animals were treated at one facility, and RNA was distributed to five different sites for gene expression analysis. A preliminary evaluation of the number of modulated genes uncovered striking differences between the five different sites. However, additional data analysis demonstrated that these differences had an effect on the absolute gene expression results but not on the outcome of the study. For all users, unsupervised algorithms showed that gene expression allows the distinction of the high dose of MP from controls and low dose. In addition, the use of a supervised analysis method (support vector machines) made it possible to correctly classify samples. In conclusion, the results show that, despite some variability, robust gene expression changes were consistent between sites. In addition, key expression changes related to the mechanism of MP-induced hepatotoxicity were identified. These results provide critical information regarding the consistency of microarray results across different laboratories and shed light on the strengths and limitations of expression profiling in drug safety analysis.

Animals↗

PubMed2004

Discriminating different classes of toxicants by transcript profiling.

Male rats were treated with various model compounds or the appropriate vehicle controls. Most substances were either well-known hepatotoxicants or showed hepatotoxicity during preclinical testing. The aim of the present study was to determine if biological samples from rats treated with various compounds can be classified based on gene expression profiles. In addition to gene expression analysis using microarrays, a complete serum chemistry profile and liver and kidney histopathology were performed. We analyzed hepatic gene expression profiles using a supervised learning method (support vector machines; SVMs) to generate classification rules and combined this with recursive feature elimination to improve classification performance and to identify a compact subset of probe sets with potential use as biomarkers. Two different SVM algorithms were tested, and the models obtained were validated with a compound-based external cross-validation approach. Our predictive models were able to discriminate between hepatotoxic and nonhepatotoxic compounds. Furthermore, they predicted the correct class of hepatotoxicant in most cases. We provide an example showing that a predictive model built on transcript profiles from one rat strain can successfully classify profiles from another rat strain. In addition, we demonstrate that the predictive models identify nonresponders and are able to discriminate between gene changes related to pharmacology and toxicity. This work confirms the hypothesis that compound classification based on gene expression data is feasible.

Algorithms↗

PubMed2003

Gene expression in two hepatic cell lines, cultured primary hepatocytes, and liver slices compared to the in vivo liver gene expression in rats: possible implications for toxicogenomics use of in vitro systems.

Microarray technology allows the simultaneous analysis of mRNA expression levels of thousands of genes. In the field of toxicogenomics, this technology could help to identify potentially unsafe compounds based on the changes in mRNA expression patterns they induce. Rodent in vivo and in vitro systems are currently the experimental models of choice for predictive toxicology, especially in early phases of development. This study characterizes several hepatic in vitro systems based on mRNA expression profiles, comparing them to gene expression in liver tissue. The in vitro systems investigated comprise two rat liver cell lines (BRL3A and NRL clone 9), primary hepatocytes in conventional monolayer or in sandwich culture, and liver slices. The results demonstrate that liver slices exhibit the strongest similarity to liver tissue regarding mRNA expression, whereas the two cell lines are quite different from the whole liver. We were able to identify genes with strong changes in expression levels in all or at least one of the in vitro systems relative to whole liver. In particular, for some cytochrome P450s the differences observed on the mRNA expression level were paralleled by protein expression and enzymatic activity. In addition, the effect of time in culture was assessed. We were able to show a profound effect of the duration of culture. Expression patterns change most rapidly soon after cell isolation and culture initiation and stabilize with time in culture. The findings are discussed with respect to the usefulness of the various hepatic in vitro systems for microarray-based toxicological testing of compounds.

Animals↗

PubMed2002

Proteomic analysis of the rat liver.

Rat is a useful, widely used animal model for biological and toxicity studies. We analyzed total and cytosolic rat liver proteins by applying proteomics technologies. The proteins were separated by two-dimensional electrophoresis employing broad and narrow range immobilized pH gradient strips, followed by MALDI-MS analysis of the tryptic digests. Two hundred and seventy-three different gene products were identified, of which approximately 60% were enzymes with a broad spectrum of catalytic activities. Most of the identified proteins were detected in other rat protein samples as well, which were analyzed in our laboratory. Fifteen gene products were detected for the first time. These were represented by one spot each, whereas most of the frequently detected proteins were represented by multiple spots. In average, approximately five to 10 spots corresponded to one gene product. The database includes a large number of proteins known to be involved in toxicology-relevant pathways and may be useful in toxicity prediction studies.

Animals↗

PubMed2002

Modulation of gene and protein expression by carbon tetrachloride in the rat liver.

The gene and protein expression changes after exposure to a toxic compound might help elucidate its mechanism of action. In this paper we investigated the effect of carbon tetrachloride (CCl(4)) on the gene and protein expression in rat livers. Adult Wistar rats were administered CCl(4) and livers were harvested 6 or 24 h thereafter. The analysis of mitochondrial proteins on 2D gels showed the upregulation of two proteins involved in stress (catalase and uricase). Among the downregulated proteins, enzymes related to the metabolism of lipids and aminoacids were affected. Additionally, alpha-2-macroglobulin and senescence marker protein, two proteins whose decrease in expression has been connected to hepatocyte damage, were decreased. Several of the upregulated genes are involved in stress response, DNA and protein damage, and repair. Genes coding for several enzymes involved in different metabolic pathways, including some P450, were downregulated in the treated animals. In conclusion, a single dose of CCl(4) caused gene and protein expression changes that can be related to its mechanism of toxicity. Results from both technologies support previous publications and provide possible new toxicity markers. However, the correlation between gene and protein expression at a given time point is less apparent, partly as a result of different regulatory mechanisms between gene and protein expression.

Animals↗

PubMed2002

The rat liver mitochondrial proteins.

Subcellular fractionation increases the probability of detection of low-abundance proteins. We prepared a fraction highly enriched in mitochondrial proteins from rat liver. The proteins were analyzed by two-dimensional (2-D) electrophoresis using broad-and narrow-range immobilized pH gradient strips, and identified by matrix assisted laser desorption/ionization-mass spectrometry (MALDI-MS). 192 different gene products were detected, of which approximately 70% were enzymes with a broad spectrum of catalytic activities. Most of the identified proteins were detected in other rat protein samples as well, which were analyzed in our laboratory. Eight gene products were detected for the first time. These were represented by one spot each, whereas most of the frequently detected proteins were represented by multiple spots. In average, approximately 10-15 spots corresponded to one gene product.

Animals↗