CiteULike: jdiggans's library [95 articles]

Human SULT1A1 Gene: Copy Number Differences and Functional Implications.

Scott J Hebbring — 2007-01-23T22:08:12-00:00

Hum Mol Genet (22 December 2006)

SULT1A1, which catalyzes the sulfate-conjugation of a wide variety of natural and synthetic compounds, is genetically polymorphic. Biochemical and pharmacogenetic studies have demonstrated that individual variation in the level of enzyme activity is inherited. Common single nucleotide polymorphisms (SNPs) located in the open reading frame and in the 5'-flanking region (5'-FR) may account for a portion of this individual variation. In this study, we demonstrate the presence of SULT1A1 gene deletions and duplications, representing an additional source of variability in the metabolic activity of this enzyme. A quantitative multiplex PCR assay was used to measure the extent of copy number differences and the frequency of these events in different populations. An analysis of DNA from 362 Caucasian-American and 99 African-American showed the presence of 1 to approximately 5 copies of SULT1A1 in individual samples: 5% of Caucasian subjects contained a single copy of the gene and 26% had three or more copies, while 63% of African-American subjects had three or more copies. Analysis of the genomic region surrounding the SULT1A1 gene in 3 separate cases with a deletion demonstrated that the entire SULT1A1 gene was affected. Reporter assays, constructed for each of the various 5'-FR SNP haplotypes, suggest that these may also play a role in SULT1A1 activity. However, the variability in the level of enzyme activity among 23 human platelet and 267 human liver samples was best explained by gene copy number differences when all sources of genetic variability were considered (p<0.0001). Overall, these observations have obvious implications for the effectiveness of SULT1A1 as a drug and hormone metabolizing enzyme and its potential role as a risk factor for disease.

Toxicogenomics--a new systems toxicology approach to understanding of gene-environment interactions.

K Olden — 2007-01-02T20:06:27-00:00

Ann N Y Acad Sci, Vol. 1076 (September 2006), pp. 703-706.

Toxicogenomics is a new interdisciplinary area of research being developed to monitor the expression of multiple genes, proteins, and metabolites simultaneously. It combines new technologies in genomics, proteomics, and metabolomics with traditional tools of pathology and toxicology to study biological response to drugs and other environmental xenobiotics. The biological response to environmental exposure is so complex and involves so many interactive factors that the use of a systems biology analytical approach is required. In my opinion, the development of the field of toxicogenomics will provide powerful and relatively inexpensive tools to identify biomarkers and to relate exposure and biological events during disease progression.

Application of visualization tools to the analysis of histopathological data enhances biological insight and interpretation.

EK Lobenhofer — 2007-01-02T20:00:36-00:00

Toxicol Pathol, Vol. 34, No. 7. (2006), pp. 921-928.

Gene expression profiling, metabolomic screens, and other high-dimensional methods have become an integral part of many biological investigations. To facilitate interpretation of these data, it is important to have detailed phenotypic data-including histopathology-to which these data can be associated, or anchored. However, as the amount of phenotypic data increases, associations within and across these data can be difficult to visualize and interpret. We have developed an approach for categorizing and clustering biologically related histopathological diagnoses to facilitate their visualization, thereby increasing the possibility of identifying associations and facilitating the comparison with other data streams. In this study, we utilize histopathological data generated as part of a standardized toxicogenomics compendium study to generate composite histopathological scores and to develop visualizations that facilitate biological insight. The validity of this approach is illustrated by the identification of transcripts that correlate with the pathology diagnoses that comprise the categories of "response to hepatocellular injury" and "repair." This approach is broadly applicable to studies in which histopathology is used to phenotypically anchor other data, and results in visualizations that facilitate biological interpretation and the identification of associations and relationships within the data.

GOLEM: an interactive graph-based gene-ontology navigation and analysis tool

Rachel Sealfon — 2006-10-10T23:31:19-00:00

BMC Bioinformatics, Vol. 7 (10 October 2006), 443.

Clinical proteomics and biomarker discovery.

DJ Johann — 2006-10-16T23:44:17-00:00

Ann N Y Acad Sci, Vol. 1022 (June 2004), pp. 295-305.

Early detection of disease generally provides much-improved outcomes by a definitive medical procedure or through lifestyle modification along with specific medical management strategies. For serum biomarkers, which are central to the diagnosis of many diseases, to become truly useful sentinels of pathogenesis, their sensitivity and specificity in both early detection and recurrence monitoring must be improved. Currently, the detection and monitoring of disease markers is based on solitary proteins, and this approach is not always reliable. New classes of biomarkers derived from mass spectroscopy analysis of the low molecular weight proteome have shown improved abilities in the early detection of disease and hence in patient risk stratification and outcome. The development of a modular platform technology with sufficient flexibility and design abstractions allowing for concurrent experimentation, test, and refinement will help speed the progress of mass spectroscopy-derived proteomic pattern-based diagnostics from the scientific laboratory to the medical clinic. For acceptance by scientists, physicians, and regulatory personnel, new bioinformatic tools are essential system components for data management, analysis, and intuitive display of these new and complex data. Clinically engineered mass spectroscopy systems are essential for the further development and validation of multiplexed biomarkers that have shown tremendous promise for the early detection of disease.

Medical applications of microarray technologies: a regulatory science perspective.

EF Petricoin — 2006-10-16T23:43:19-00:00

Nat Genet, Vol. 32 Suppl (December 2002), pp. 474-479.

The potential medical applications of microarrays have generated much excitement, and some skepticism, within the biomedical community. Some researchers have suggested that within the decade microarrays will be routinely used in the selection, assessment, and quality control of the best drugs for pharmaceutical development, as well as for disease diagnosis and for monitoring desired and adverse outcomes of therapeutic interventions. Realizing this potential will be a challenge for the whole scientific community, as breakthroughs that show great promise at the bench often fail to meet the requirements of clinicians and regulatory scientists. The development of a cooperative framework among regulators, product sponsors, and technology experts will be essential for realizing the revolutionary promise that microarrays hold for drug development, regulatory science, medical practice and public health.

Toxicogenomics: a new revolution in drug safety.

AL Castle — 2006-10-16T23:41:12-00:00

Drug Discov Today, Vol. 7, No. 13. (1 July 2002), pp. 728-736.

New drugs are screened for adverse reactions using a laborious, costly process and still some promising therapeutics are withdrawn from the marketplace because of unforeseen human toxicity. Novel higher throughput methods in toxicology need to be developed. These new approaches should provide more insight into potential human toxicity than current methods. Toxicogenomics, the examination of changes in gene expression following exposure to a toxicant, offers the potential to identify a human toxicant earlier in drug development and to detect human-specific toxicants that cause no adverse reaction in rats.

Pharmaceuticals: A new grammar for drug discovery

Mark Fishman — 2005-09-21T17:54:15-00:00

Nature, Vol. 437, No. 7058. (21 September 2005), pp. 491-493.

Network biology: understanding the cell's functional organization.

AL Barabási — 2004-11-22T00:17:30-00:00

Nat Rev Genet, Vol. 5, No. 2. (February 2004), pp. 101-113.

Tumour biology: Policing of oncogene activity by p53

Alejo Efeyan — 2006-09-14T07:09:19-00:00

Nature, Vol. 443, No. 7108. (13 September 2006), pp. 159-159.

Design principles of molecular networks revealed by global comparisons and composite motifs

Haiyuan Yu — 2006-07-20T03:30:47-00:00

Genome Biology, Vol. 7 (19 July 2006), R55.

Core transcriptional regulatory circuitry in human hepatocytes.

DT Odom — 2006-07-12T13:58:35-00:00

Mol Syst Biol, Vol. 2 (2006)

We mapped the transcriptional regulatory circuitry for six master regulators in human hepatocytes using chromatin immunoprecipitation and high-resolution promoter microarrays. The results show that these regulators form a highly interconnected core circuitry, and reveal the local regulatory network motifs created by regulator-gene interactions. Autoregulation was a prominent theme among these regulators. We found that hepatocyte master regulators tend to bind promoter regions combinatorially and that the number of transcription factors bound to a promoter corresponds with observed gene expression. Our studies reveal portions of the core circuitry of human hepatocytes.

PAGE: parametric analysis of gene set enrichment.

SY Kim — 2006-07-24T16:20:10-00:00

BMC Bioinformatics, Vol. 6 (2005)

BACKGROUND: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate. RESULTS: We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level. CONCLUSION: PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.

On methods for gene function scoring as a means of facilitating the interpretation of microarray results.

N Raghavan — 2006-07-25T15:21:58-00:00

J Comput Biol, Vol. 13, No. 3. (April 2006), pp. 798-809.

As gene annotation databases continue to evolve and improve, it has become feasible to incorporate the functional and pathway information about genes, available in these databases into the analysis of gene expression data, for a better understanding of the underlying mechanisms. A few methods have been proposed in the literature to formally convert individual gene results into gene function results. In this paper, we will compare the various methods, propose and examine some new ones, and offer a structured approach to incorporating gene function or pathway information into the analysis of expression data. We study the performance of the various methods and also compare them on real data, using a case study from the toxicogenomics area. Our results show that the approaches based on gene function scores yield a different, and functionally more interpretable, array of genes than methods that rely solely on individual gene scores. They also suggest that functional class scoring methods appear to perform better and more consistently than overrepresentation analysis and distributional score methods.

Application of a priori established gene sets to discover biologically important differential expression in microarray data

Andrea Bild — 2006-07-26T15:13:39-00:00

PNAS, Vol. 102, No. 43. (25 October 2005), pp. 15278-15279.

10.1073/pnas.0507477102

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles.

E Edelman — 2005-01-20T00:29:54-00:00

Bioinformatics, Vol. 22, No. 14. (July 2006), pp. e108-e116.

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess CONTACT: sayan@stat.duke.edu.

Significance analysis of functional categories in gene expression studies: a structured permutation approach

William Barry — 2005-04-30T22:54:26-00:00

Bioinformatics, Vol. 21, No. 9. (1 May 2005), pp. 1943-1949.

T-profiler: scoring the activity of predefined groups of genes using gene expression data.

A Boorsma — 2006-08-08T16:53:50-00:00

Nucleic Acids Res, Vol. 33, No. Web Server issue. (1 July 2005)

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. Here we describe T-profiler, a tool that uses the t-test to score changes in the average activity of predefined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif or location on the same chromosome. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can upload their microarray data for analysis on the web at http://www.t-profiler.org.

ErmineJ: tool for functional analysis of gene expression data sets.

HK Lee — 2006-08-09T14:53:58-00:00

BMC Bioinformatics, Vol. 6 (2005)

BACKGROUND: It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories. The most common method for such analysis uses the hypergeometric distribution (or a related technique) to look for "over-representation" of groups among genes selected as being differentially expressed or otherwise of interest based on a gene-by-gene analysis. However, this method suffers from some limitations, and biologist-friendly tools that implement alternatives have not been reported. RESULTS: We introduce ErmineJ, a multiplatform user-friendly stand-alone software tool for the analysis of functionally-relevant sets of genes in the context of microarray gene expression data. ErmineJ implements multiple algorithms for gene set analysis, including over-representation and resampling-based methods that focus on gene scores or correlation of gene expression profiles. In addition to a graphical user interface, ErmineJ has a command line interface and an application programming interface that can be used to automate analyses. The graphical user interface includes tools for creating and modifying gene sets, visualizing the Gene Ontology as a table or tree, and visualizing gene expression data. ErmineJ comes with a complete user manual, and is open-source software licensed under the Gnu Public License. CONCLUSION: The availability of multiple analysis algorithms, together with a rich feature set and simple graphical interface, should make ErmineJ a useful addition to the biologist's informatics toolbox. ErmineJ is available from http://microarray.cu.genome.org.

stam--a Bioconductor compliant R package for structured analysis of microarray data.

C Lottaz — 2006-08-21T15:54:32-00:00

BMC Bioinformatics, Vol. 6 (2005)

BACKGROUND: Genome wide microarray studies have the potential to unveil novel disease entities. Clinically homogeneous groups of patients can have diverse gene expression profiles. The definition of novel subclasses based on gene expression is a difficult problem not addressed systematically by currently available software tools. RESULTS: We present a computational tool for semi-supervised molecular disease entity detection. It automatically discovers molecular heterogeneities in phenotypically defined disease entities and suggests alternative molecular sub-entities of clinical phenotypes. This is done using both gene expression data and functional gene annotations. We provide stam, a Bioconductor compliant software package for the statistical programming environment R. We demonstrate that our tool detects gene expression patterns, which are characteristic for only a subset of patients from an established disease entity. We call such expression patterns molecular symptoms. Furthermore, stam finds novel sub-group stratifications of patients according to the absence or presence of molecular symptoms. CONCLUSION: Our software is easy to install and can be applied to a wide range of datasets. It provides the potential to reveal so far indistinguishable patient sub-groups of clinical relevance.

Group testing for pathway analysis improves comparability of different microarray data sets.

Theodora Manoli — 2006-08-22T14:19:27-00:00

Bioinformatics (7 August 2006)

MOTIVATION: The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However, comparisons made on the level of gene lists obtained by different statistical methods or from different data sets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes, for example metabolic or signalling pathways. This can be achieved by group testing procedures, e.g. over-representation analysis (ORA), bluefunctional class scoring (FCS), or global tests. RESULTS: Three public prostate cancer data sets obtained with the same microarray platform (HGU95A/HGU95av2) were analyzed. Each data set was subjected to normalization by either variance stabilizing normalization (vsn) or mixed model normalization (MMN). Then, statistical analysis of microarrays (SAM) was applied to the vsn-normalized data and mixed model analsis (MMA) to the data normalized by MMN. For multiple testing adjustment the false discovery rate (FDR) was calculated and the threshold was set to 0.05. Gene lists from the same method applied to different data sets showed overlaps between 42% and 52%, while lists from different methods applied to the same data set had between 63% and 85% of genes in common. A number of six gene lists obtained by the two statistical methods applied to the three data sets was then subjected to group testing by blueFisher's exact test. Group testing by GSEA and global test was applied to the three data sets, as well. Fisher's exact test followed by global test showed more consistent results with respect to the concordance between analyses on gene lists obtained by different methods and different data sets than the GSEA. However, all group testing methods identified pathways that had already been described to be involved in the pathogenesis of prostate cancer. Moreover, pathways recurrently identified in these analyses are more likely to be reliable than those from a single analysis on a single data set. Supplementary Info: Supplementary Figure 1 and Supplementary Tables 1-4 are available from the Journal's website.

To permute or not to permute

Yifan Huang — 2006-09-07T11:10:22-00:00

Bioinformatics, Vol. 22, No. 18. (15 September 2006), pp. 2244-2248.

Permutation test is a popular technique for testing a hypothesis of no effect, when the distribution of the test statistic is unknown. To test the equality of two means, a permutation test might use a test statistic which is the difference of the two sample means in the univariate case. In the multivariate case, it might use a test statistic which is the maximum of the univariate test statistics. A permutation test then estimates the null distribution of the test statistic by permuting the observations between the two samples. We will show that, for such tests, if the two distributions are not identical (as for example when they have unequal variances, correlations or skewness), then a permutation test for equality of means based on difference of sample means can have an inflated Type I error rate even when the means are equal. Our results illustrate permutation testing should be confined to testing for non-identical distributions. Contact: calian@raunvis.hi.is 10.1093/bioinformatics/btl383

ADGO: analysis of differentially expressed gene sets using composite GO annotation

Dougu Nam — 2006-09-07T11:14:47-00:00

Bioinformatics, Vol. 22, No. 18. (15 September 2006), pp. 2249-2253.

Motivation: Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. Results: Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many filtered' composite terms the number of which reached [~]34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. Availability: We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. Contact: chu@kribb.re.kr Supplementary information: http://array.kobic.re.kr/ADGO 10.1093/bioinformatics/btl378

OrderedList--a bioconductor package for detecting similarity in ordered gene lists

Claudio Lottaz — 2006-09-07T11:21:38-00:00

Bioinformatics, Vol. 22, No. 18. (15 September 2006), pp. 2315-2316.

Summary: OrderedList is a Bioconductor compliant package for meta-analysis based on ordered gene lists like those resulting from differential gene expression analysis. Our package quantifies the similarity between gene lists. The significance of the similarity score is estimated from random scores computed on perturbed data. OrderedList illustrates list similarity in intuitive plots and determines the score-driving genes for further analysis. Availability: http://www.bioconductor.org Contact: claudio.lottaz@molgen.mpg.de Supplementary information: Please visit our webpage on http://compdiag.molgen.mpg.de/software 10.1093/bioinformatics/btl385

A rapid method for microarray cross platform comparisons using gene expression signatures.

Chris Cheadle — 2006-09-22T13:41:24-00:00

Mol Cell Probes (10 August 2006)

Microarray technology has become highly valuable for identifying complex changes in global gene expression patterns. The inevitable use of a variety of different platforms has compounded the difficulty of effectively comparing data between projects, laboratories, and public access databases. The need for consistent, believable results across platforms is fundamental and methods for comparing results across platforms should be as straightforward as possible. We present the results of a study comparing three major, commercially available, microarray platforms (Affymetrix, Agilent, and Illumina). Concordance estimates between platforms was based on mapping of probes to Human Gene Organization (HUGO) gene names. Appropriate data normalization procedures were applied to each dataset followed by the generation of lists of regulated genes using a common significance threshold for all three platforms. As expected, concordance measured by directly comparing genelists was relatively low (an average 22.8% for all platforms across all possible comparisons). However, when statistical tests (gene set enrichment analysis-GSEA, parametric analysis of gene enrichment-PAGE) which align genelists with continuous measures of differential gene expression were applied to the cross platform datasets using significant genelists to poll entire datasets, the relatedness of the results from all three platforms was specific, obvious, and profound.

Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data

Alex Lewin — 2006-10-04T23:09:54-00:00

BMC Bioinformatics, Vol. 7 (03 October 2006), 426.

DrugBank: a comprehensive resource for in silico drug discovery and exploration.

DS Wishart — 2006-01-09T08:58:58-00:00

Nucleic Acids Res, Vol. 34, No. Database issue. (1 January 2006)

DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains >4100 drug entries including >800 FDA approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, >14 000 protein or drug target sequences are linked to these drug entries. Each DrugCard entry contains >80 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. Many data fields are hyperlinked to other databases (KEGG, PubChem, ChEBI, PDB, Swiss-Prot and GenBank) and a variety of structure viewing applets. The database is fully searchable supporting extensive text, sequence, chemical structure and relational query searches. Potential applications of DrugBank include in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. DrugBank is available at http://redpoll.pharmacy.ualberta.ca/drugbank/.

Exploring the diversity of complex metabolic networks.

Vassily Hatzimanikatis — 2005-01-10T04:01:09-00:00

Bioinformatics (4 January 2005)

MOTIVATION: Metabolism, the network of chemical reactions that make life possible, is one of the most complex processes in nature. We describe here the development of a computational approach for the identification of every possible biochemical reaction from a given set of enzyme reaction rules that allows the de novo synthesis of metabolic pathways composed of these reactions, and the evaluation of these novel pathways with respect to their thermodynamic properties. RESULTS: We applied this framework to the analysis of the aromatic amino acid pathways and we discovered almost 75,000 novel biochemical routes from chorismate to phenylalanine, more than 350,000 from chorismate to tyrosine, and only 13 from chorismate to tryptophan. Thermodynamic analysis of these pathways suggests that the native pathways are thermodynamically more favorable than the alternative possible pathways. The pathways generated involve compounds that exist in biological databases, as well as compounds that exist in chemical databases and novel compounds, suggesting novel biochemical routes for these compounds and the existence of biochemical compounds that remain to be discovered or synthesized through enzyme and pathway engineering. AVAILABILITY: Framework will be available via web interface at http://systemsbiology.northwestern.edu/BNICE (site under construction).

Information-based clustering.

Noam Slonim — 2005-12-20T23:19:18-00:00

Proc Natl Acad Sci U S A (13 December 2005)

In an age of increasingly large data sets, investigators in many different disciplines have turned to clustering as a tool for data analysis and exploration. Existing clustering methods, however, typically depend on several nontrivial assumptions about the structure of data. Here, we reformulate the clustering problem from an information theoretic perspective that avoids many of these assumptions. In particular, our formulation obviates the need for defining a cluster "prototype," does not require an a priori similarity metric, is invariant to changes in the representation of the data, and naturally captures nonlinear relations. We apply this approach to different domains and find that it consistently produces clusters that are more coherent than those extracted by existing algorithms. Finally, our approach provides a way of clustering based on collective notions of similarity rather than the traditional pairwise measures.

Drug discovery: a historical perspective.

J Drews — 2006-02-14T16:06:58-00:00

Science, Vol. 287, No. 5460. (17 March 2000), pp. 1960-1964.

Driven by chemistry but increasingly guided by pharmacology and the clinical sciences, drug research has contributed more to the progress of medicine during the past century than any other scientific factor. The advent of molecular biology and, in particular, of genomic sciences is having a deep impact on drug discovery. Recombinant proteins and monoclonal antibodies have greatly enriched our therapeutic armamentarium. Genome sciences, combined with bioinformatic tools, allow us to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future medicines, thereby increasing the number of treatment options. The dramatic increase in the complexity of drug research is enforcing changes in the institutional basis of this interdisciplinary endeavor. The biotech industry is establishing itself as the discovery arm of the pharmaceutical industry. In bridging the gap between academia and large pharmaceutical companies, the biotech firms have been effective instruments of technology transfer.

Database resources of the National Center for Biotechnology Information.

DL Wheeler — 2006-01-24T22:16:12-00:00

Nucleic Acids Res, Vol. 34, No. Database issue. (1 January 2006)

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups, Retroviral Genotyping Tools, HIV-1, Human Protein Interaction Database, SAGEmap, Gene Expression Omnibus, Entrez Probe, GENSAT, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov.

Literature mining for the biologist: from information retrieval to biological discovery

Lars Jensen — 2006-01-18T16:36:02-00:00

Nature Reviews Genetics, Vol. 7, No. 2. (2006), pp. 119-129.

The effect of oxygen on biochemical networks and the evolution of complex life.

J Raymond — 2006-03-25T16:00:29-00:00

Science, Vol. 311, No. 5768. (24 March 2006), pp. 1764-1767.

The evolution of oxygenic photosynthesis and ensuing oxygenation of Earth's atmosphere represent a major transition in the history of life. Although many organisms retreated to anoxic environments, others evolved to use oxygen as a high-potential redox couple while concomitantly mitigating its toxicity. To understand the changes in biochemistry and enzymology that accompanied adaptation to O2, we integrated network analysis with information on enzyme evolution to infer how oxygen availability changed the architecture of metabolic networks. Our analysis revealed the existence of four discrete groups of networks of increasing complexity, with transitions between groups being contingent on the presence of key metabolites, including molecular oxygen, which was required for transition into the largest networks.

Interactome: gateway into systems biology.

ME Cusick — 2006-01-21T21:47:31-00:00

Hum Mol Genet, Vol. 14 Spec No. 2 (15 October 2005)

Protein-protein interactions are fundamental to all biological processes, and a comprehensive determination of all protein-protein interactions that can take place in an organism provides a framework for understanding biology as an integrated system. The availability of genome-scale sets of cloned open reading frames has facilitated systematic efforts at creating proteome-scale data sets of protein-protein interactions, which are represented as complex networks or 'interactome' maps. Protein-protein interaction mapping projects that follow stringent criteria, coupled with experimental validation in orthogonal systems, provide high-confidence data sets immanently useful for interrogating developmental and disease mechanisms at a system level as well as elucidating individual protein function and interactome network topology. Although far from complete, currently available maps provide insight into how biochemical properties of proteins and protein complexes are integrated into biological systems. Such maps are also a useful resource to predict the function(s) of thousands of genes.

Systematic Association of Genes to Phenotypes by Genome and Literature Mining.

Jan O Korbel — 2005-04-19T11:02:11-00:00

PLoS Biol, Vol. 3, No. 5. (5 April 2005)

One of the major challenges of functional genomics is to unravel the connection between genotype and phenotype. So far no global analysis has attempted to explore those connections in the light of the large phenotypic variability seen in nature. Here, we use an unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis. We first mine the MEDLINE literature database for terms that reflect phenotypic similarities of species. Subsequently we predict the likely genomic determinants: genes specifically present in the respective genomes. In a global analysis involving 92 prokaryotic genomes we retrieve 323 clusters containing a total of 2,700 significant gene-phenotype associations. Some clusters contain mostly known relationships, such as genes involved in motility or plant degradation, often with additional hypothetical proteins associated with those phenotypes. Other clusters comprise unexpected associations; for example, a group of terms related to food and spoilage is linked to genes predicted to be involved in bacterial food poisoning. Among the clusters, we observe an enrichment of pathogenicity-related associations, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases.

Leveraging enzyme structure-function relationships for functional inference and experimental design: the structure-function linkage database.

SC Pegg — 2006-05-06T04:56:25-00:00

Biochemistry, Vol. 45, No. 8. (28 February 2006), pp. 2545-2555.

The study of mechanistically diverse enzyme superfamilies-collections of enzymes that perform different overall reactions but share both a common fold and a distinct mechanistic step performed by key conserved residues-helps elucidate the structure-function relationships of enzymes. We have developed a resource, the structure-function linkage database (SFLD), to analyze these structure-function relationships. Unique to the SFLD is its hierarchical classification scheme based on linking the specific partial reactions (or other chemical capabilities) that are conserved at the superfamily, subgroup, and family levels with the conserved structural elements that mediate them. We present the results of analyses using the SFLD in correcting misannotations, guiding protein engineering experiments, and elucidating the function of recently solved enzyme structures from the structural genomics initiative. The SFLD is freely accessible at http://sfld.rbvi.ucsf.edu.

Observing local and global properties of metabolic pathways: "load points" and "choke points" in the metabolic networks.

Syed Asad Rahman — 2006-05-19T12:44:37-00:00

Bioinformatics (8 May 2006)

MOTIVATION: The local and global aspects of metabolic network analyses allow us to identify enzymes or reactions that are crucial for the survival of the organism(s), therefore directing us towards the discovery of potential drug targets. RESULTS: We demonstrate a new method ("load points") to rank the enzymes/metabolites in the metabolic network and propose a model to determine and rank the biochemical lethality in metabolic networks (enzymes/metabolites) through "choke points". Based on an extended form of the graph theory model of metabolic networks, metabolite structural information was used to calculate the k-shortest paths between metabolites (the presence of more than one competing path between substrate and product). On the basis of these paths and connectivity information, load points were calculated and used to empirically rank the importance of metabolites/enzymes in the metabolic network. The load point analysis emphasizes the role that the biochemical structure of a metabolite, rather than its connectivity (hubs), plays in the conversion pathway. In order to identify potential drug targets (based on the biochemical lethality of metabolic networks), the concept of "choke points" and "load points" was used to find enzymes (edges) which uniquely consume or produce a particular metabolite (nodes). A non-pathogenic bacterial strain Bacillus subtilis 168 (lactic acid producing bacteria) and a related pathogenic bacterial strain Bacillus anthracis Sterne (avirulent but toxigenic strain, producing the toxin Anthrax) were selected as model organisms. The choke point strategy was implemented on the pathogen bacterial network of Bacillus anthracis Sterne. Potential drug targets are proposed based on the analysis of the top 10 choke points in the bacterial network. A comparative study between the reported top 10 bacterial choke points and the human metabolic network was performed. Further biological inferences were made on results obtained by performing a homology search against the human genome. AVAILABILITY: The load and choke point modules are introduced in the Pathway Hunter Tool (PHT), the basic version of which is available on http://www.pht.uni-koeln.de.

Systems modeling: a pathway to drug discovery.

P Rajasethupathy — 2005-12-20T14:50:11-00:00

Curr Opin Chem Biol, Vol. 9, No. 4. (August 2005), pp. 400-406.

Systems modeling is emerging as a valuable tool in therapeutics. This is seen by the increasing use of clinically relevant computational models and a rise in systems biology companies working with the pharmaceutical industry. Systems models have helped understand the effects of pharmacological intervention at receptor, intracellular and intercellular communication stages of cell signaling. For instance, angiogenesis models at the ligand-receptor interaction level have suggested explanations for the failure of therapies for cardiovascular disease. Intracellular models of myeloma signaling have been used to explore alternative drug targets and treatment schedules. Finally, modeling has suggested novel approaches to treating disorders of intercellular communication, such as diabetes. Systems modeling can thus fill an important niche in therapeutics by making drug discovery a faster and more systematic process.

Gene expression analysis reveals chemical-specific profiles.

HK Hamadeh — 2006-10-16T03:28:35-00:00

Toxicol Sci, Vol. 67, No. 2. (June 2002), pp. 219-231.

The application of gene expression profiling technology to examine multiple genes and signaling pathways simultaneously promises a significant advance in understanding toxic mechanisms to ultimately aid in protection of public health. Public and private efforts in the new field of toxicogenomics are focused on populating databases with gene expression profiles of compounds where toxicological and pathological endpoints are well characterized. The validity and utility of a toxicogenomics is dependent on whether gene expression profiles that correspond to different chemicals can be distinguished. The principal hypothesis underlying a toxicogenomic or pharmacogenomic strategy is that chemical-specific patterns of altered gene expression will be revealed using high-density microarray analysis of tissues from exposed organisms. Analyses of these patterns should allow classification of toxicants and provide important mechanistic insights. This report provides a verification of this hypothesis. Patterns of gene expression corresponding to liver tissue derived from chemically exposed rats revealed similarity in gene expression profiles between animals treated with different agents from a common class of compounds, peroxisome proliferators [clofibrate (ethyl-p-chlorophenoxyisobutyrate), Wyeth 14,643 ([4-chloro-6(2,3-xylidino)-2-pyrimidinylthio]acetic acid), and gemfibrozil (5-2[2,5-dimethylphenoxy]2-2-dimethylpentanoic acid)], but a very distinct gene expression profile was produced using a compound from another class, enzyme inducers (phenobarbital).

The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.

J Lamb — 2006-10-02T08:35:35-00:00

Science, Vol. 313, No. 5795. (29 September 2006), pp. 1929-1935.

To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.

Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

G Natsoulis — 2005-06-14T14:37:59-00:00

Genome Res, Vol. 15, No. 5. (May 2005), pp. 724-736.

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as "rewards" for the class-of-interest) while others have a negative contribution (act as "penalties") to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class.

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

B Ganter — 2006-10-16T03:22:02-00:00

J Biotechnol, Vol. 119, No. 3. (29 September 2005), pp. 219-244.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.

The Comparative Toxicogenomics Database: A Cross-Species Resource for Building Chemical-Gene Interaction Networks

Mattingly — 2006-07-12T15:10:46-00:00

Toxicological Sciences, Vol. 92, No. 2. (August 2006), pp. 587-595.

Database development in toxicogenomics: issues and efforts.

WB Mattes — 2006-10-16T03:14:00-00:00

Environ Health Perspect, Vol. 112, No. 4. (March 2004), pp. 495-505.

The marriage of toxicology and genomics has created not only opportunities but also novel informatics challenges. As with the larger field of gene expression analysis, toxicogenomics faces the problems of probe annotation and data comparison across different array platforms. Toxicogenomics studies are generally built on standard toxicology studies generating biological end point data, and as such, one goal of toxicogenomics is to detect relationships between changes in gene expression and in those biological parameters. These challenges are best addressed through data collection into a well-designed toxicogenomics database. A successful publicly accessible toxicogenomics database will serve as a repository for data sharing and as a resource for analysis, data mining, and discussion. It will offer a vehicle for harmonizing nomenclature and analytical approaches and serve as a reference for regulatory organizations to evaluate toxicogenomics data submitted as part of registrations. Such a database would capture the experimental context of in vivo studies with great fidelity such that the dynamics of the dose response could be probed statistically with confidence. This review presents the collaborative efforts between the European Molecular Biology Laboratory-European Bioinformatics Institute ArrayExpress, the International Life Sciences Institute Health and Environmental Science Institute, and the National Institute of Environmental Health Sciences National Center for Toxigenomics Chemical Effects in Biological Systems knowledge base. The goal of this collaboration is to establish public infrastructure on an international scale and examine other developments aimed at establishing toxicogenomics databases. In this review we discuss several issues common to such databases: the requirement for identifying minimal descriptors to represent the experiment, the demand for standardizing data storage and exchange formats, the challenge of creating standardized nomenclature and ontologies to describe biological data, the technical problems involved in data upload, the necessity of defining parameters that assess and record data quality, and the development of standardized analytical approaches.

Clofibrate-induced gene expression changes in rat liver: a cross-laboratory analysis using membrane cDNA arrays.

VA Baker — 2006-10-16T02:46:53-00:00

Environ Health Perspect, Vol. 112, No. 4. (March 2004), pp. 428-438.

Microarrays have the potential to significantly impact our ability to identify toxic hazards by the identification of mechanistically relevant markers of toxicity. To be useful for risk assessment, however, microarray data must be challenged to determine reliability and interlaboratory reproducibility. As part of a series of studies conducted by the International Life Sciences Institute Health and Environmental Science Institute Technical Committee on the Application of Genomics to Mechanism-Based Risk Assessment, the biological response in rats to the hepatotoxin clofibrate was investigated. Animals were treated with high (250 mg/kg/day) or low (25 mg/kg/day) doses for 1, 3, or 7 days in two laboratories. Clinical chemistry parameters were measured, livers removed for histopathological assessment, and gene expression analysis was conducted using cDNA arrays. Expression changes in genes involved in fatty acid metabolism (e.g., acyl-CoA oxidase), cell proliferation (e.g., topoisomerase II-Alpha), and fatty acid oxidation (e.g., cytochrome P450 4A1), consistent with the mechanism of clofibrate hepatotoxicity, were detected. Observed differences in gene expression levels correlated with the level of biological response induced in the two in vivo studies. Generally, there was a high level of concordance between the gene expression profiles generated from pooled and individual RNA samples. Quantitative real-time polymerase chain reaction was used to confirm modulations for a number of peroxisome proliferator marker genes. Though the results indicate some variability in the quantitative nature of the microarray data, this appears due largely to differences in experimental and data analysis procedures used within each laboratory. In summary, this study demonstrates the potential for gene expression profiling to identify toxic hazards by the identification of mechanistically relevant markers of toxicity.

Overview of an interlaboratory collaboration on evaluating the effects of model hepatotoxicants on hepatic gene expression.

RG Ulrich — 2006-10-16T02:45:59-00:00

Environ Health Perspect, Vol. 112, No. 4. (March 2004), pp. 423-427.

DNA microarrays and related tools offer promise for identification of pathways involved in toxic responses to xenobiotics. To be useful for risk assessment, experimental data must be challenged for reliability and interlaboratory reproducibility. Toward this goal, the Hepatotoxicity Working Group of the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Technical Committee on Application of Genomics to Mechanism-Based Risk Assessment evaluated and compared biological and gene expression responses in rats exposed to two model hepatotoxins--clofibrate and methapyrilene. This collaborative effort provided an unprecedented opportunity for the working group to evaluate and compare multiple biological, genomic, and toxicological parameters across different laboratories and microarray platforms. Many of the results from this collaboration are presented in accompanying articles in this mini-monograph, whereas others have been published previously. (Italic)In vivo(/Italic) studies for both compounds were conducted in two laboratories using a standard experimental protocol, and RNA samples were distributed to 16 laboratories for analysis on six microarray platforms. Histopathology, clinical chemistry, and organ weight changes were consistent with reported effects. Gene expression results demonstrated reasonable agreement between laboratories and across platforms. Discrepancies in expression profiles of some individual genes were largely due to platform differences and approaches to data analysis rather than to biological or interlaboratory variability. Despite these discrepancies there was overall agreement in the biological pathways affected by these compounds, demonstrating that transcriptional profiling is reproducible between laboratories and can reliably identify affected pathways necessary to provide mechanistic insight. This effort represents an important first step toward the use of transcriptional profiling in risk assessment.

Comparison of oxidative stress and changes of xenobiotic metabolizing enzymes induced by phthalates in rats.

KW Seo — 2006-10-16T02:45:30-00:00

Food Chem Toxicol, Vol. 42, No. 1. (January 2004), pp. 107-114.

Phthalates are widely used as a plasticizer and cause a peroxisome proliferation. Peroxisome proliferators (PPs), such as di-2-ethylhexyl phthalate (DEHP) and clofibrate (CF) are known to have a hepatocarcinogenic potential in rodents. It has been proposed that these PPs may cause hepatocellular cancer by an oxidative damage-mediated mechanism(s). The primary purpose of this study is to find whether there is a difference between the oxidative damage by hepatocarcinogenic PPs (DEHP and CF) and the oxidative damage by weak PPs [di-n-butyl phthalate (DBP) and n-butylbenzyl phthalate (BBP)]. The second purpose is to investigate if phthalates can affect the phase I/phase II enzymes, and if the effect of PPs on metabolizing enzymes correlates with peroxisome proliferation or not. After rats were treated with PPs (DEHP, DBP and BBP; 50, 200, 1000 mg/kg, CF; 100 mg/kg, p.o., for 14 days), the activities of metabolizing enzymes and peroxisomal enzymes were investigated, and the oxidative damage was measured using 8-hydroxydeoxyguanosine (8-OHdG) in the DNA and malonedialdehyde (MDA) in the livers. These four PPs significantly increased the relative liver weights, palmitoyl-CoA oxidation and activity of carnitine acetyltransferase. DEHP was found to be the most potent PP among three phthalates. A dramatic and dose-dependent increase in hepatic MDA levels was observed in CF (100 mg/kg), DEHP (>or=50 mg/kg), DBP and BBP (>or=200 mg/kg) groups. However, the 8-OHdG in hepatic DNA was increased only in DEHP (1000 mg/kg) and CF groups. Activities of cytochrome p4501A1, 1A2, 3A4, UDP-glucuronosyl transferase and glutathione S-transferase were decreased overall by PPs, but there is no correlation between the inhibitory effect on metabolizing enzymes and the peroxisome proliferation. These results indicate that 8-OHdG positively correlates with carcinogenic potential of PPs, but other factors as well as peroxisomal H(2)O(2) could be involved in the generation of 8-OHdG and the carcinogenesis of PPs. The present findings also demonstrate that the effect of PPs on xenobiotic metabolizing enzymes may be independent of the peroxisome proliferation and the oxidative stress. Thus it is possible that the PPs affect the hepatic toxification/detoxification capacity even in humans.

Mechanism of clofibrate hepatotoxicity: mitochondrial damage and oxidative stress in hepatocytes.

B Qu — 2006-10-16T02:45:09-00:00

Free Radic Biol Med, Vol. 31, No. 5. (1 September 2001), pp. 659-669.

Peroxisome proliferators have been found to induce hepatocarcinogenesis in rodents, and may cause mitochondrial damage. Consistent with this, clofibrate increased hepatic mitochondrial oxidative DNA and protein damage in mice. The present investigation aimed to study the mechanism by which this might occur by examining the effect of clofibrate on freshly isolated mouse liver mitochondria and a cultured hepatocyte cell line, AML-12. Mitochondrial membrane potential (Delta Psi(m)) was determined by using the fluorescent dye 5,5',6,6'-tetrachloro-1,1', 3,3'-tetraethyl-benzimidazolylcarbocyanine iodide (JC-1) and tetramethylrhodamine methyl ester (TMRM). Application of clofibrate at concentrations greater than 0.3 mM rapidly collapsed the Delta Psi(m) both in liver cells and in isolated mitochondria. The loss of Delta Psi(m) occurred prior to cell death and appeared to involve the mitochondrial permeability transition (MPT), as revealed by calcein fluorescence studies and the protective effect of cyclosporin A (CsA) on the decrease in Delta Psi(m). Levels of reactive oxygen species (ROS) were measured with the fluorescent probes 5-(and-6)-carboxy-2',7'-dichlorofluorescein diacetate (DCFDA) and dihydrorhodamine 123 (DHR123). Treatment of the hepatocytes with clofibrate caused a significant increase in intracellular and mitochondrial ROS. Antioxidants such as vitamin C, deferoxamine, and catalase were able to protect the cells against the clofibrate-induced loss of viability, as was CsA, but to a lesser extent. These results suggest that one action of clofibrate might be to impair mitochondrial function, so stimulating formation of ROS, which eventually contribute to cell death.

PPAR-alpha: a key to the mechanism of hepatoprotection by clofibrate.

HM Mehendale — 2006-10-16T02:44:51-00:00

Toxicol Sci, Vol. 57, No. 2. (October 2000), pp. 187-190.

The article highlighted in this issue is "Peroxisome Proliferator-Activated Receptor Alpha-Null Mice Lack Resistance to Acetaminophen Hepatotoxicity Following Clofibrate Exposure" by Chuan Chen, Gayle E. Hennig, Herbert E. Whiteley, J Christopher Corton, and José E. Manautou (pp. 338-344).

Clofibrate-induced in vitro hepatoprotection against acetaminophen is not due to altered glutathione homeostasis.

FA Nicholls-Grzemski — 2006-10-16T02:44:18-00:00

Toxicol Sci, Vol. 56, No. 1. (July 2000), pp. 220-228.

Prior induction of peroxisome proliferation protects mice against the in vivo hepatotoxicity of acetaminophen and various other bioactivation-dependent toxicants. The mechanisms underlying such chemoresistance are poorly understood, although they have been suggested to involve alterations in glutathione homeostasis. To clarify the role of glutathione in this phenomenon, we isolated hepatocytes from mice in which hepatic peroxisome proliferation had been induced with clofibrate. The cells were incubated with a range of acetaminophen concentrations and the extent of cell killing after up to 8 h was assessed by measuring lactate dehydrogenase leakage from the cells. Hepatocytes from clofibrate-pretreated mice were much less susceptible to acetaminophen than cells from vehicle-treated controls. However, the extent of glutathione depletion during exposure to acetaminophen was similar in both cell types, as were rates of excretion of the product of glutathione-mediated detoxication of acetaminophen's quinoneimine metabolite, 3-glutathionyl-acetaminophen. The glutathione-replenishing ability of clofibrate-pretreated cells after a brief exposure to diethyl maleate also resembled that of control cells. More importantly, prior depletion of glutathione by diethyl maleate did not abolish the resistance of clofibrate-pretreated cells to acetaminophen. Taken together, these findings indicate that although glutathione-dependent pathways may contribute to hepatoprotection during peroxisome proliferation, the resistance phenomenon is not due exclusively to this mechanism.