De novo sequencing, assembly and analysis of the genome of the laboratory strain Saccharomyces cerevisiae CEN.PK113-7D, a model for modern industrial biotechnology.

Saccharomyces cerevisiae CEN.PK 113-7D is widely used for metabolic engineering and systems biology research in industry and academia. We sequenced, assembled, annotated and analyzed its genome. Single-nucleotide variations (SNV), insertions/deletions (indels) and differences in genome organization compared to the reference strain S. cerevisiae S288C were analyzed. In addition to a few large deletions and duplications, nearly 3000 indels were identified in the CEN.PK113-7D genome relative to S288C. These differences were overrepresented in genes whose functions are related to transcriptional regulation and chromatin remodelling. Some of these variations were caused by unstable tandem repeats, suggesting an innate evolvability of the corresponding genes. Besides a previously characterized mutation in adenylate cyclase, the CEN.PK113-7D genome sequence revealed a significant enrichment of non-synonymous mutations in genes encoding for components of the cAMP signalling pathway. Some phenotypic characteristics of the CEN.PK113-7D strains were explained by the presence of additional specific metabolic genes relative to S288C. In particular, the presence of the BIO1 and BIO6 genes correlated with a biotin prototrophy of CEN.PK113-7D. Furthermore, the copy number, chromosomal location and sequences of the MAL loci were resolved. The assembled sequence reveals that CEN.PK113-7D has a mosaic genome that combines characteristics of laboratory strains and wild-industrial strains.

Molecular characterization of trimellitic anhydride-induced respiratory allergy in Brown Norway rats.

To contribute to the hazard identification of low molecular weight (LMW) respiratory allergens, respiratory allergy induced by trimellitic anhydride (TMA) was characterized by whole genome analysis of lung tissue and blood proteomics in Brown Norway rats. Dermal sensitization (50% and 25% w/v) with TMA and an inhalation challenge of 15 mg/m(3) TMA-induced apneas, laryngeal inflammation, increased numbers of eosinophils, neutrophils and macrophages in bronchoalveolar lavage (BAL), and increased immunoglobulin E levels in serum and lung tissue. Whole genome analysis of lung, sampled 24 hours after challenge, showed expression changes of not only genes belonging to several Gene Ontology groups with up-regulation of inflammatory-associated genes and those associated with lung remodeling but also genes involved in downsizing these processes. Blood proteomics reflected activation of inflammation-inhibiting pathways. Unsensitized animals challenged with TMA exhibited also an increased number of macrophages in BAL, but gene expression in the above-mentioned gene pathways was unchanged or down-regulated. The authors conclude that parameters for lung remodeling can be a valuable tool in hazard identification of LMW respiratory allergens.

Systems toxicology: applications of toxicogenomics, transcriptomics, proteomics and metabolomics in toxicology.

Toxicogenomics can facilitate the identification and characterization of toxicity, as illustrated in this review. Toxicogenomics, the application of the functional genomics technologies (transcriptomics, proteomics and metabolomics) in toxicology enables the study of adverse effects of xenobiotic substances in relation to structure and activity of the genome. The advantages and limitations of the different technologies are evaluated, and the prospects for integration of the technologies into a systems biology or systems toxicology approach are discussed. Applications of toxicogenomics in various laboratories around the world show that the crucial steps and sequence of events at the molecular level can be studied to provide detailed insights into mechanisms of toxic action. Toxicogenomics allowed for more sensitive and earlier detection of adverse effects in (animal) toxicity studies. Furthermore, the effects of exposure to mixtures could be studied in more detail. This review argues that in the (near) future, human health risk assessment will truly benefit from toxicogenomics (systems toxicology).

Toxicogenomic analysis of gene expression changes in rat liver after a 28-day oral benzene exposure.

Benzene is an industrial chemical, component of automobile exhaust and cigarette smoke. After hepatic bioactivation benzene induces bone marrow, blood and hepatic toxicity. Using a toxicogenomics approach this study analysed the effects of benzene at three dose levels on gene expression in the liver after 28 daily doses. NMR based metabolomics was used to assess benzene exposure by identification of characteristic benzene metabolite profiles in urine. The 28-day oral exposure to 200 and 800 mg/kg/day but not 10 mg/kg/day benzene-induced hematotoxicity in male Fisher rats. Additionally these upper dose levels slightly reduced body weight and increased relative liver weights. Changes in hepatic gene expression were identified with oligonucleotide microarrays at all dose levels including the 10 mg/kg/day dose level where no toxicity was detected by other methods. The benzene-induced gene expression changes were related to pathways of biotransformation, glutathione synthesis, fatty acid and cholesterol metabolism and others. Some of the effects on gene expression observed here have previously been observed after induction of acute hepatic necrosis with bromobenzene and acetaminophen. In conclusion, changes in hepatic gene expression were found after treatment with benzene both at the toxic and non-toxic doses. The results from this study show that toxicogenomics identified hepatic effects of benzene exposure possibly related to toxicity. The findings aid to interpret the relevance of hepatic gene expression changes in response to exposure to xenobiotics. In addition, the results have the potential to inform on the mechanisms of response to benzene exposure.

Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach.

Toxicogenomics is a novel approach integrating the expression analysis of thousands of genes (transcriptomics) or proteins (proteomics) with classical methods in toxicology. Effects at the molecular level are related to pathophysiological changes of the organisms, enabling detailed comparison of mechanisms and early detection and prediction of toxicity. This report addresses the value of the combined use of transcriptomics and proteomics technologies in toxicology. Acute hepatotoxicity was induced in rats by bromobenzene administration resulting in depleted glutathione levels and reduced average body weights, 24hr after dosage. These physiological symptoms coincided with many changes of hepatic mRNA and protein content. Gene induction confirmed involvement of glutathione-S-transferase isozymes and epoxide hydrolase in bromobenzene metabolism and identified many genes possibly relevant in bromobenzene toxicity. Observed glutathione depletion coincided with induction of the key enzyme in glutathione biosynthesis, gamma-glutamylcysteine synthetase. Oxidative stress was apparent from strong upregulation of heme oxygenase, peroxiredoxin 1 and other genes. Bromobenzene-induced protein degradation was suggested from two-dimensional gel electrophoresis, upregulated mRNA levels for proteasome subunits and lysosomal cathepsin L, whereas also genes were upregulated with a role in protein synthesis. Both protein and gene expression profiles from treated rats were clearly distinct from controls as shown by principal component analysis, and several proteins found to significantly change upon bromobenzene treatment were identified by mass spectrometry. A modest overlap in results from proteomics and transcriptomics was found. This work indicates that transcriptomics and proteomics technologies are complementary to each other and provide new possibilities in molecular toxicology.

Bromobenzene-induced hepatotoxicity at the transcriptome level.

Rats were exposed to three levels of bromobenzene, sampled at 6, 24, and 48 h, and liver gene expression profiles were determined to identify dose and time-related changes. Expression of many genes changed transiently, and dependent on the dose. Few changes were identified after 6 h, but many genes were differentially expressed after 24 h, while after 48 h, only the high dose elicited large effects. Differentially expressed genes were involved in drug metabolism (upregulated GSTs, mEH, NQO1, Mrps, downregulated CYPs, sulfotransferases), oxidative stress (induced HO-1, peroxiredoxin, ferritin), GSH depletion (induced GCS-l, GSTA, GSTM) the acute phase response, and in processes like cholesterol, fatty acid and protein metabolism, and intracellular signaling. Trancriptional regulation via the electrophile and sterol response elements seemed to mediate part of the response to bromobenzene. Recovery of the liver was suggested in response to BB by the altered expression of genes involved in protein synthesis and cytoskeleton rearrangement. Furthermore, after 48 h, rats in the mid dose group showed no toxicity, and gene expression patterns resembled the normal situation. For certain genes (e.g., CYP4A, metallothioneins), intraday variation in expression levels was found, regardless of the treatment. Selected cDNA microarray measurements were confirmed using the specific and sensitive branched DNA signal amplification assay.

Toxicology of chemical mixtures: a challenging quest along empirical sciences.

This paper describes the "quest" of our institute trying to assess the toxicology of chemical mixtures. In this overview, we will discuss some critical developments in hazard identification and risk assessment of chemical mixtures during these past 15 years. We will stand still at empirical and mechanistic modeling. "Empirical" means that only information on doses or concentrations and effects is available in addition to an often empirically selected quantitative dose-response relationship. Empirical models have played a dominant role in the last decade to identify health and safety characteristics of chemical mixtures. Many of these models are based on the work of pioneers in mixture toxicology who defined three basic types of action for combinations of chemicals: simple similar action, simple dissimilar action and interaction. Nowadays, empirical models are mainly based on response-surface analysis and make use of advanced statistical designs. However, possible interactions between components in a mixture can also be given in terms of mechanistic models. In terms of "mechanistic" (or biological) understanding, interactions between compounds may occur in the kinetic phase (processes of uptake, distribution, metabolism and excretion) or in the dynamic phase (effects of chemicals on the receptor, cellular target or organ). A biological phenomenon such as competitive agonism as described for mixtures of drugs (biotransformation enzymes) or sensory irritants (nerve receptors) can accurately predict the effect of any of these mixtures. Thus, far mechanistic and empirical analyses of interactions are usually unrelated. It is one of the future challenges for mixtures research to combine information from both approaches. Also, our current biology-based models have their limitations, since they cannot integrate every relevant biological mechanism. In this respect, mechanistic modeling of mixtures may benefit from the developments coming from the arena of molecular biology (toxicogenomics) which offers an in-depth analysis of several involved enzymatic pathways in parallel through the use of a systems biology approach. This was illustrated with mixtures of food additives known to affect the liver. Key to further maturation of mixture toxicology is collaboration of experimental toxicologists, biomathematicians, biologists, pharmacologists, model developers, molecular biologists and bioinformaticians to ensure parallel and coordinated research in this challenging area of toxicology. For this reason, the next sequel will be even more challenging and exciting to that first 15 years of empirical testing.

Sample preparation and biostatistics for integrated genomics approaches.

Genomics is based on the ability to determine the transcriptome, proteome, and metabolome of a cell. These technologies only have added value when they are integrated and based on robust and reproducible workflows. This chapter describes the experimental design, sampling, sample pretreatment, data evaluation, integration, and interpretation. The actual generation of the data is not covered in this chapter since it is highly depended on available equipment and infrastructure. The enormous amount of data generated by these technologies are integrated and interpreted inorder to generate leads for strain and process improvement. Biostatistics are becoming very important for the whole work flow therefore, some general recommendations how to set up experimental design and how to use biostatistics in enhancing the quality of the data and the selection of biological relevant leads for strain engineering and target identification are described.

Genome-wide gene expression changes in an industrial clavulanic acid overproduction strain of Streptomyces clavuligerus.

To increase production of the important pharmaceutical compound clavulanic acid, a ß-lactamase inhibitor, both random mutagenesis approaches and rational engineering of Streptomyces clavuligerus strains have been extensively applied. Here, for the first time, we compared genome-wide gene expression of an industrial S. clavuligerus strain, obtained through iterative mutagenesis, with that of the wild-type strain. Intriguingly, we found that the majority of the changes contributed not to a complex rewiring of primary metabolism but consisted of a simple upregulation of various antibiotic biosynthesis gene clusters. A few additional transcriptional changes in primary metabolism at key points seem to divert metabolic fluxes to the biosynthetic precursors for clavulanic acid. In general, the observed changes largely coincide with genes that have been targeted by rational engineering in recent years, yet the presence of a number of previously unexplored genes clearly demonstrates that functional genomic analysis can provide new leads for strain improvement in biotechnology.

Genome sequencing and analysis of the filamentous fungus Penicillium chrysogenum.

Industrial penicillin production with the filamentous fungus Penicillium chrysogenum is based on an unprecedented effort in microbial strain improvement. To gain more insight into penicillin synthesis, we sequenced the 32.19 Mb genome of P. chrysogenum Wisconsin54-1255 and identified numerous genes responsible for key steps in penicillin production. DNA microarrays were used to compare the transcriptomes of the sequenced strain and a penicillinG high-producing strain, grown in the presence and absence of the side-chain precursor phenylacetic acid. Transcription of genes involved in biosynthesis of valine, cysteine and alpha-aminoadipic acid-precursors for penicillin biosynthesis-as well as of genes encoding microbody proteins, was increased in the high-producing strain. Some gene products were shown to be directly controlling beta-lactam output. Many key cellular transport processes involving penicillins and intermediates remain to be characterized at the molecular level. Genes predicted to encode transporters were strongly overrepresented among the genes transcriptionally upregulated under conditions that stimulate penicillinG production, illustrating potential for future genomics-driven metabolic engineering.

Profiles of metabolites and gene expression in rats with chemically induced hepatic necrosis.

This study investigated whether integrated analysis of transcriptomics and metabolomics data increased the sensitivity of detection and provided new insight in the mechanisms of hepatotoxicity. Metabolite levels in plasma or urine were analyzed in relation to changes in hepatic gene expression in rats that received bromobenzene to induce acute hepatic centrilobular necrosis. Bromobenzene-induced lesions were only observed after treatment with the highest of 3 dose levels. Multivariate statistical analysis showed that metabolite profiles of blood plasma were largely different from controls when the rats were treated with bromobenzene, also at doses that did not elicit histopathological changes. Changes in levels of genes and metabolites were related to the degree of necrosis, providing putative novel markers of hepatotoxicity. Levels of endogenous metabolites like alanine, lactate, tyrosine and dimethylglycine differed in plasma from treated and control rats. The metabolite profiles of urine were found to be reflective of the exposure levels. This integrated analysis of hepatic transcriptomics and plasma metabolomics was able to more sensitively detect changes related to hepatotoxicity and discover novel markers. The relation between gene expression and metabolite levels was explored and additional insight in the role of various biological pathways in bromobenzene-induced hepatic necrosis was obtained, including the involvement of apoptosis and changes in glycolysis and amino acid metabolism. The complete Table 2 is available as a supplemental file online at http://taylorandfrancis.metapress.com/openurlasp?genre=journal&issn=0192-6233. To access the file, click on the issue link for 33(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.