WO2007022419A2 - Modeles de toxicite moleculaire developpes a partir d'hepatocytes isoles - Google Patents

Modeles de toxicite moleculaire developpes a partir d'hepatocytes isoles Download PDF

Info

Publication number
WO2007022419A2
WO2007022419A2 PCT/US2006/032336 US2006032336W WO2007022419A2 WO 2007022419 A2 WO2007022419 A2 WO 2007022419A2 US 2006032336 W US2006032336 W US 2006032336W WO 2007022419 A2 WO2007022419 A2 WO 2007022419A2
Authority
WO
WIPO (PCT)
Prior art keywords
gene
model
toxicity
genes
score
Prior art date
Application number
PCT/US2006/032336
Other languages
English (en)
Other versions
WO2007022419A3 (fr
Inventor
Donna L. Mendrick
Michael Elashoff
Michael S. Orr
Mark W. Porter
Original Assignee
Gene Logic, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Gene Logic, Inc. filed Critical Gene Logic, Inc.
Publication of WO2007022419A2 publication Critical patent/WO2007022419A2/fr
Publication of WO2007022419A3 publication Critical patent/WO2007022419A3/fr

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5014—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing toxicity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5044—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics involving specific cell types
    • G01N33/5067—Liver cells
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/136—Screening for pharmacological compounds
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/142—Toxicological screening, e.g. expression profiles which identify toxicity
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/166—Oligonucleotides used as internal standards, controls or normalisation probes

Definitions

  • multicellular screening systems may be preferred or required to detect the toxic effects of compounds.
  • the use of multicellular organisms as toxicology screening tools has been significantly hampered, however, by the lack of convenient screening mechanisms or endpoints, such as those available in yeast or bacterial systems. Additionally, certain previous attempts to produce toxicology prediction systems have failed to provide the necessary modeling data and statistical information to accurately predict toxic responses ⁇ e.g., WO 00/12760, WO 00/47761, WO 00/63435, WO 01/32928, and WO 01/38579).
  • the invention includes methods of predicting at least one toxic effect of a compound, comprising: detecting the level of expression in isolated hepatocytes exposed to the compound of two or more genes or proteins from Table I 5 2 or 5; wherein differential expression of the genes or proteins in Table 1, 2 or 5 is indicative of at least one toxic effect.
  • the invention also includes methods of predicting at least one toxic effect of a test compound, comprising: preparing a gene or protein expression profile from isolated hepatocytes exposed to the test compound; and comparing the gene or protein expression profile to a database comprising quantitative gene or protein expression information for at least one gene or gene fragment or protein of Table 1, 2 or 5 from isolated hepatocytes that have been exposed to at least one toxin and quantitative gene or protein expression information for at least one gene or gene fragment or protein of Table 1, 2 or 5 from a control isolated hepatocytes exposed to the toxin excipient, thereby predicting at least one toxic effect of the test compound.
  • the invention also includes methods of predicting at least one toxic effect of a test agent by comparing gene or protein expression information from agent- exposed samples to a database of gene or protein expression information from toxin- exposed and control samples (vehicle-exposed samples or samples exposed to a non-toxic compound or low levels of a toxic compound).
  • These methods comprise providing or generating quantitative gene expression information from the samples, converting the gene expression information to matrices of logged fold-change values by a robust multi-array (RMA) algorithm, generating a gene regulation score for each gene that is differentially expressed upon exposure to the test agent by a partial least squares (PLS) algorithm, and calculating a sample prediction score for the test agent.
  • RMA robust multi-array
  • PLS partial least squares
  • the invention includes methods of creating a toxicity model. These methods comprise providing or generating quantitative nucleic acid hybridization data for a plurality of genes from isolated hepatocytes exposed to a toxin and isolated hepatocytes exposed to the toxin vehicle, converting the hybridization data from at least one gene to a gene expression measure, such as logged fold-change value, by a robust multi- array (RMA) algorithm, generating a gene regulation score from gene expression measure for the at least one gene by a partial least squares (PLS) algorithm, and generating a toxicity reference prediction score for the toxin, thereby creating a toxicity model.
  • RMA robust multi- array
  • PLS partial least squares
  • the invention further provides a set of genes or gene fragments, listed in Table 1 , 2 or 5, from which probes can be made and attached to solid supports. These core genes serve as a preferred set of markers of liver toxicity and can be used with the methods of the invention to predict or monitor a toxic effect of a compound or to modulate the onset or progression of a toxic response.
  • the invention includes a computer system comprising a computer readable medium containing a toxicity model for predicting the toxicity of a test agent and software that allows a user to predict at least one toxic effect of a test agent by comparing a sample prediction score for the test agent to a toxicity reference prediction score for the toxicity model.
  • the gene expression information from test agent- exposed tissues or cells may be prepared transmitted via the Internet for analysis and comparisons to the toxicity models stored on a remote, central server. After processing, the user that sent the text files receives a report indicating the toxicity or non-toxicity of the test agent.
  • Table 1 provides the GLGC identifier (fragment names from Table 2) in relation to the SEQ ID NO. and GenBank Accession number for each of the gene or gene fragments listed in Table 2 (all of which are herein incorporated by reference and replicated in the attached sequence listing). Also included in the Table are gene names, Unigene cluster titles, names of proteins encoded by the respective genes, protein ID numbers, and secretory properties of the proteins.
  • Table 2 Table 2 presents the PLS weight scores (index scores) for each gene from a series of hepatocyte toxicity models.
  • Table 3 Table 3 lists the toxins in high dose and negative control compounds used to build and train each toxicity model.
  • Table 4 lists the toxins in low dose and negative control compounds used to build and train each toxicity model. Each compound's high and low doses were used independently by the predictive models.
  • the designation "1" for a particular compound in a particular model indicates that the compound (at the dose indicated) was used to train that model on the "Tox" portion of the model. It means that this compound is known to cause general toxicity and/or the pathology(ies) indicated.
  • the designation "-1" for a particular compound in a particular model indicates that the compound (at the dose indicated) was used to train that model on the "Non-Tox" portion of the model. It means that this compound is known not to cause general toxicity and/or the pathology(ies) indicated.
  • the designation "0" for a particular compound in a particular model indicates that the compound (at the dose indicated) was used to train that model at an intermediate value between the "Tox" and “Non-Tox” portions of the model. This value only applies to the low doses of toxicants, which were used in the general model to train in that intermediate capacity. It applies to no other dose and no other model.
  • the designation "Not Used” indicates that that compound's data (at the dose indicated) was not used in building the particular model. [0017] For the general model, "1” indicates compounds that cause toxicity in humans but may or may not cause toxicity in rats; “-1” indicates compounds that do not cause toxicity in humans, but may or may not cause toxicity in rats
  • the present inventors have examined isolated rat hepatocytes exposed to the known hepatotoxins which induce detrimental liver effects and to non-hepatotoxins which are not known to induce detrimental liver effects, to identify changes in gene or protein expression differentially induced between the two sets of hepatotoxins and non-hepatotoxins. These changes in gene or protein expression, which can be detected by the production of gene or protein expression profiles, provide useful toxicity markers that can be used to monitor toxicity and/or toxicity progression by a test compound. Some of these markers may also be used to monitor or detect various disease or physiological states, disease progression, drug efficacy and drug metabolism.
  • nucleic acid hybridization data refers to any data derived from the hybridization of a sample of nucleic acids to a one or more of a series of reference nucleic acids. Such reference nucleic acids may be in the form of probes on a microarray or may be in the form of primers that are used in polymerization reactions, such as PCR amplification, to detect hybridization of the primers to the sample nucleic acids.
  • Nucleic hybridization data may be in the form of numerical representations of the hybridization and may be derived from quantitative, semi-quantitative or non-quantitative analysis techniques or technology platforms. Nucleic acid hybridization data includes, but is not limited to gene expression data.
  • the data may be in any form, including florescence data or measurements of fluorescence probe intensities from a microarray or other hybridization technology platform.
  • the nucleic acid hybridization data may be raw data or may be normalized to correct for, or take into account, background or raw noise values, including background generated by microarray high/low intensity spots, scratches, high regional or overall background and raw noise generated by scanner electrical noise and sample quality fluctuation.
  • cell or tissue samples refers to one or more samples comprising cell or tissue from an animal or other organism, including laboratory animals such as rats or mice.
  • the cell or tissue sample may comprise a mixed population of cells or tissues or may be substantially a single cell or tissue type, such as liver hepatocytes or tissue.
  • Cell or tissue samples as used herein may also be in vitro grown cells or tissue, such as primary cell cultures, immortalized cell cultures, cultured hepatocytes, cultured liver tissue, etc..
  • Cells or tissue may be derived from any organ, including but not limited to, liver, kidney, cardiac, muscle (skeletal or cardiac) or brain.
  • isolated hepatocytes refers to a single or populations of cells cultured or freshly isolated from liver tissue of an animal or other organism, including laboratory animals such as rats or mice.
  • test agent refers to an agent, compound, biologic such as an antiboy, or composition that is being tested or analyzed in a method of the invention.
  • a test agent may be a pharmaceutical candidate for which toxicology data is desired.
  • test agent vehicle refers to the diluent or carrier in which the test agent is dissolved, suspended in or administered in, to an animal, organism or cells.
  • toxin vehicle refers to the diluent or carrier in which a toxin is dissolved, suspended in or administered in, to an animal, organism or cells.
  • a “gene expression measure” refers to any numerical representation of the expression level of a gene or gene fragment in a cell or tissue sample.
  • a “gene expression measure” includes, but is not limited to, a fold change value.
  • At least one gene refers to a nucleic acid molecule detected by the methods of the invention in a sample.
  • a “gene” includes any species of nucleic acid that is detectable by hybridization to a probe in a microarray, such as the "genes" of Tables 1, 2 or 5.
  • at least one gene includes a "plurality of genes.”
  • fold change value refers to a numerical representation of the expression level of a gene, genes or gene fragments between experimental paradigms, such as a test or treated cell or tissue sample, compared to any standard or control.
  • a fold change value may be presented as microarray-derived florescence or probe intensities for a gene or genes from a test cell or tissue sample compared to a control, such as an unexposed cell or tissue sample or a vehicle-exposed cell or tissue sample.
  • An RMA logged fold change value as described herein is a non-limiting example of a fold change value calculated by methods of the invention.
  • gene regulation score refers to a quantitative measure of gene expression for a gene or gene fragment as derived from a weighted index score or PLS score for each gene and the fold change value from treated vs. control samples.
  • sample prediction score refers to a numerical score produced via methods of the invention as herein described. For instance, a “sample prediction score” may be calculated using the weighted index score or PLS score for at least one gene in a gene expression profile generated from the sample and the RMA fold change value for that same gene. A “sample prediction score” is derived from summing the individual gene regulation scores calculated for a given sample.
  • toxicity reference prediction score refers to a numerical score generated from a toxicity model that can be used as a cut-off score to predict at least one toxic effect of a test agent. For instance, a sample prediction score can be compared to a toxicity reference prediction score to determine if the sample score is above or below the toxicity reference prediction score. Sample prediction scores falling below the value of a toxicity reference prediction score are scored as not exhibiting at least one toxic effect and sample prediction scores above the value if a toxicity reference prediction score are scored as exhibiting at least one toxic effect.
  • a log scale linear additive model includes any log scale linear model such as log scale robust multi-array analysis or RMA (see, for example, Irizarry et al. ,
  • remote connection refers to a connection to a server by a means other than a direct hard-wired connection. This term includes, but is not limited to, connection to a server through a dial-up line, broadband connection, Wi-Fi connection, or through the Internet.
  • CEL file refers to a file that contains the average probe intensities associated with a coordinate position, cell or feature on a microarray. See the
  • a "gene expression profile” comprises any quantitative representation of the expression of at least one mRNA species in a cell sample or population and includes profiles made by various methods such as differential display, PCR, microarray and other hybridization analysis, etc.
  • a "general toxicity model” refers to a model that is not limited to a specific pathology or mechanism. This category classifies compounds by their ability to induce toxicity in one or more species, including humans. Compounds that only cause toxicity in the rat are excluded by this definition.
  • a "peroxisome proliferation model” refers to a pathology or mechanism evidenced by an increase in the number of peroxisomes within cells. Humans are refractory to the development of this phenotype.
  • an "inducer/liver enlargement model” refers to a pathology evidenced by an increase in liver size in general.
  • the liver can increase in size due to either hypertrophy, an increase in size of the cells, or hyperplasia, an increase in cell number as observed in multiple species.
  • a "cholestasis model” refers to a pathology that results from an impairment in bile flow as observed in multiple species. See Cotran, R. S., V. Kumar, and
  • the models of the invention are built using isolated rat hepatocytes that are analyzed after exposure to compounds known to exhibit at least one toxic effect. Low doses of these compounds, or the vehicles in which they were prepared, are used as negative controls. Compounds that are known not to exhibit at least one toxic effect may also be used as negative controls.
  • the changes in gene expression levels in samples treated with the compound were considered to represent a specific toxic response, and the genes whose expression was up- or down-regulated upon treatment with the compound were classified as marker genes that may be used as indicators of a specific type of toxic response, i.e., a specific type of liver pathology. These marker genes may also be used to prepare reference gene expression profiles that characterize a specific hepatotoxic response.
  • the designation "1" for a particular compound in a particular model indicates that the compound was used on the toxicity/pathology (tox) side for training the model.
  • a particular compound in a particular model has the designation of "-1”
  • the gene expression information from samples treated with that compound is considered to represent the absence of a toxic response or pathology. This information was used on the non-tox side, or negative control side, for training a model to produce a specific toxicity model.
  • the genes analyzed in these samples are considered not to be markers of toxicity.
  • a particular compound in a particular model has the designation "0,” the compound was not used to train that model.
  • a toxicity study or "tox study” comprises a set of isolated hepatocytes that have been exposed to one or more toxins and may include matched samples exposed to the toxin vehicle or a low, non-toxic, dose of the toxin.
  • the cell or tissue samples may be exposed to the toxin and control treatments in vivo or in vitro.
  • toxin and control exposure to the cell or tissue samples may take place by administering an appropriate dose to an animal model, such as a laboratory rat.
  • toxin and control exposure to the cell or tissue samples may take place by administering an appropriate dose to a sample of in vitro grown cells or tissue, such as primary rat or human hepatocytes.
  • RNA samples are typically organized into cohorts by test compound, time (for instance, time from initial test compound dosage to time at which rats are sacrificed or the time at which RNA is harvested from hepatocyte cultures), and dose (amount of test compound administered). All cohorts in a tox study typically share the same vehicle control.
  • a cohort may be a set of samples of isolated hepatocytes that were treated with amiodarone for 6 hours at a high dosage (60 ⁇ M).
  • a time-matched vehicle cohort is a set of samples that serve as controls for treated isolated hepatocytes within a tox study, e.g., for 6-hour amiodarone -treated high dose samples the time-matched vehicle cohort would be the 6-hour vehicle-treated samples with that study.
  • a toxicity database or "tox database” is a set of tox studies that alone or in combination comprise a reference database.
  • a reference database may include data from rat tissue and cell samples from rats that were treated with different test compounds at different dosages and exposed to the test compounds for varying lengths of time.
  • a hepatotoxicity database is a set of hepatotoxicity studies that alone or in combination comprise a reference database.
  • RMA or robust multi-array average, is an algorithm that converts raw fluorescence intensities, such as those derived from hybridization of sample nucleic acids to an Affymetrix GeneChip ® microarray, into expression values, one value for each gene fragment on a chip (see, for example, Irizarry et al. (2003), Nucleic Acids Res. 31(4):el5, 8 pp.; and Irizarry et al. (2003) "Exploration, normalization, and summaries of high density oligonucleotide array probe level data," Biostatistics 4(2): 249-264). RMA produces values on a Iog2 scale, typically between 4 and 12, for genes that are expressed significantly above or below control levels.
  • RMA values can be positive or negative and are centered around zero for a fold-change of about 1.
  • a matrix of gene expression values generated by RMA can be subjected to PLS to produce a model for prediction of toxic responses, e.g., a model for predicting liver or kidney toxicity.
  • the model is validated by techniques known to those skilled in the art.
  • a cross-validation technique is used. In such a technique, the data is broken into training and test sets several times until an acceptable model success rate is determined. Most preferably, such technique uses a "compound drop" cross-validation, where each compound's set of data is dropped and the data from the remaining compounds are used to rebuild the model.
  • PLS Partial Least Squares
  • a modeling algorithm that takes as inputs a matrix of predictors and a vector of supervised scores to generate a set of prediction weights for each of the input predictors (see, for example, Nguyen et ⁇ l. (2002), Bioinformatics 18:39- 50). These prediction weights are then used to calculate a gene regulation score to indicate the ability of each analyzed gene to predict a toxic response. As described in the examples, the gene regulation scores may then be used to calculate a toxicity reference prediction score.
  • a gene expression measure is calculated for one or more genes whose level of expression is detected in the nucleic acid hybridization value.
  • the gene expression measure may comprise an RMA fold change value.
  • the toxicity reference score ⁇ w i R FCi .
  • "i" is the index number for each gene in a gene expression profile to be evaluated, "w i is the PLS weight (or PLS score, see Table 2) for each gene.
  • R FCi is the RMA fold-change value for the i th gene, as determined from a normalized RMA matrix of gene expression data from the sample (described above).
  • the PLS weight multiplied by the RMA fold-change value gives a gene regulation score for each gene, and the regulation scores for all the individual genes are added to give a toxicity reference prediction score for a sample or cohort of sample.
  • a toxicity reference prediction score can be calculated from at least one gene regulation score, or at least about 5, 10, 25, 50, 100, 500 or about 1,000 or more gene regulation scores.
  • a toxicology or toxicity model of the invention is prepared or created by the steps of (a) providing nucleic acid hybridization data for a plurality of genes from isolated hepatocytes exposed to a toxin and isolated hepatocytes exposed to the toxin vehicle; (b) converting the hybridization data from at least one gene to a gene expression measure; (c) generating a gene regulation score from gene expression measure for said at least one gene; and (d) generating a toxicity reference prediction score for the toxin, thereby creating a toxicity model.
  • the gene expression measure may be a gene fold change value calculated by a log scale linear additive model such as RMA and the toxicity reference prediction score may be generated with PLS.
  • the toxicity reference prediction score may then be added to a toxicity model or database and be used to predict at least one toxic effect of an unknown test agent or compound.
  • the model is validated by techniques known to those skilled in the art.
  • a cross-validation technique is used.
  • the data is broken into training and test sets several times until an acceptable model success rate is determined.
  • such technique uses a "compound drop" cross-validation, where each compound's set of data is dropped and the data from the remaining compounds are used to rebuild the model.
  • the gene regulation scores and toxicity prediction scores derived from cell or tissue samples exposed to toxins may be used to predict at least one toxic effect, including the hepatotoxicity, renal toxicity or other tissue toxicity of a test or unknown agent or compound.
  • the gene regulation scores and toxicity prediction scores from liver cell or tissue samples exposed to toxins may also be used to predict the ability of a test agent or compound to induce a tissue pathology, such as liver cholestasis, in a sample.
  • the toxicology prediction methods of the invention are limited only by the availability of the appropriate toxicity model and toxicology prediction scores. For instance, the prediction methods of a given system, such as a computer system or database of the invention, can be expanded simply by running new toxicology studies and models of the invention using additional toxins or specific tissue pathology inducing agents and the appropriate cell or tissue samples.
  • At least one toxic effect includes, but is not limited to, a detrimental change in the physiological status of a cell or organism.
  • the response may be, but is not required to be, associated with a particular pathology, such as liver cholestasis.
  • the toxic effect includes effects at the molecular and cellular level.
  • Hepatotoxicity for instance, is an effect as used herein and includes but is not limited to the pathologies of: cholestasis, induction or liver enlargement, peroxisome proliferation, and general hepatotoxicity.
  • assays to predict the toxicity of a test agent comprise the steps of exposing a liver cell or tissue sample, such as a sample of isolated or cultured hepatocytes, or population of cell or tissue samples to the test agent or compound, providing nucleic acid hybridization data for at least one gene from the test agent exposed cell or tissue sample(s), by, for instance, assaying or measuring the level of relative or absolute gene expression of one or more of the genes, such as one or more of the genes in Table 1, 2 or 5, calculating a sample prediction score and comparing the sample prediction score to one or more toxicology reference scores (see Example 1).
  • i is the index number for each gene in a gene expression profile to be evaluated.
  • Wj is the PLS weight (or PLS score) for each gene derived from a toxicity model.
  • R FCi is the RMA fold-change value for the i th gene, as determined from a normalized RMA matrix of gene expression data from the sample (described above). The PLS weight from a given model multiplied by the RMA fold-change value gives a gene regulation score for each gene, and the regulation scores for all the individual genes are added to give a prediction score for the sample.
  • Nucleic acid hybridization data or methods of the invention may include any measurement of the hybridization or gene expression levels, of sample nucleic acids to probes corresponding to about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 50, 75, 100, 200, 500, 1000 or more genes, or ranges of these numbers, such as about 2-10, about 10-20, about 20- 50, about 50-100, about 100-200, about 200-500 or about 500-1000 genes of Table 1, 2 or 5.
  • PCR technology may be used to measure gene expression levels for these same numbers of genes from Table 1, 2 or 5.
  • Nucleic acid hybridization data for toxicity prediction may also include the measurement of nearly all the genes in a toxicity model. "Nearly all" the genes may be considered to mean at least about 80% of the genes in any one toxicity model.
  • the methods of the invention to predict at least one toxic effect of a test agent or compound may be practiced by one individual or at one location, or may be practiced by more than one individual or at more than one location.
  • methods of the invention include steps wherein the exposure of a test agent or compound to a cell or tissue sample(s) is accomplished in one location, nucleic acid processing and the generation of nucleic acid hybridization data takes place at another location and gene regulation and sample prediction scores calculated or generated at another location.
  • cell or tissue samples are exposed to a test agent or compound by administering the agent to laboratory rats or to cultured hepatocytes and nucleic acids are processed from selected tissues and hybridized to a microarray to produce nucleic acid hybridization data.
  • the nucleic acid hybridization data is then sent to a remote server comprising a toxicology reference database and software that enables generation of individual gene regulation scores and one or more sample prediction scores from the nucleic acid hybridization data.
  • the software may also enable to user to pre-select specific toxicity models and to compare the generated sample prediction scores to one or more toxicology reference scores contained within a database of such scores. The user may then generate or order an appropriate output produces) that presents or represents the results of the data analysis, generation of gene regulation scores, sample prediction scores and/or comparisons to one or more toxicology reference scores.
  • Data including nucleic acid hybridization data, may be transmitted to a server via any means available, including a secure direct dial-up or a secure or unsecured internet connection. Toxicology prediction reports or any result of the methods herein may also be transmitted via these same mechanisms. For instance, a first user may transmit nucleic acid hybridization data to a remote server via a secure password protected internet link and then request transmission of a toxicology report from the server via that same internet link.
  • Data transmitted by a remote user of a toxicity database or model may be raw, un- normalized data or may be normalized from various background parameters before transmission. For instance, data from a microarray may be normalized for various chip and background parameters such as those described above, before transmission.
  • the data may be in any form, as long as the data can be recognized and properly formatted by available software or the software provided as part of a database or computer system.
  • microarray data may be provided and transmitted in a CEL file or any other common data files produced from the analysis of microarray based hybridization on commercially available technology platforms (see, for instance, the Affymetrix GeneChip ® Expression Analysis Technical Manual available at www, affymetrix . com) .
  • Such files may or may not be annotated with various information, for instance, but not limited to, information related to the customer or remote user, cell or tissue sample data or information, hybridization technology or platform on which the data was generated and/or test agent data or information.
  • the nucleic acid hybridization data may be screened for database compatibility by any available means.
  • commonly available data quality control metrics can be applied. For instance, outlier analysis methods or techniques may be utilized to identify samples incompatible with the database, for instance, samples exhibiting erroneous florescence values from control probes which are common between the data and the database or toxicity model.
  • various data QC metrics can be applied, including one or more disclosed in PCT/US03/24160, filed August I 5 2003, which claims priority to U.S. provisional application 60/399,727.
  • the cell population that is exposed to the test agent, compound or composition may be exposed in vitro or in vivo.
  • cultured or freshly isolated liver cells in particular rat hepatocytes, may be exposed to the agent under standard laboratory and cell culture conditions.
  • in vivo exposure may be accomplished by administration of the agent to a living animal, for instance a laboratory rat.
  • test organisms In in vivo toxicity testing, two groups of test organisms are usually employed. One group serves as a control, and the other group receives the test compound in a single dose (for acute toxicity tests) or a regimen of doses (for prolonged or chronic toxicity tests). Because, in some cases, the extraction of tissue as called for in the methods of the invention requires sacrificing the test animal, both the control group and the group receiving compound must be large enough to permit removal of animals for sampling tissues, if it is desired to observe the dynamics of gene expression through the duration of an experiment. [0071] In setting up a toxicity study, extensive guidance is provided in the literature for selecting the appropriate test organism for the compound being tested, route of administration, dose ranges, and the like.
  • Water or physiological saline (0.9% NaCl in water) is the solute of choice for the test compound since these solvents permit administration by a variety of routes.
  • vegetable oils such as corn oil or organic solvents such as propylene glycol may be used.
  • the volume required to administer a given dose is limited by the size of the animal that is used. It is desirable to keep the volume of each dose uniform within and between groups of animals.
  • the volume administered by the oral route generally should not exceed about 0.005 ml per gram of animal.
  • the intravenous LD 5O of distilled water in the mouse is approximately 0.044 ml per gram and that of isotonic saline is 0.068 ml per gram of mouse.
  • the route of administration to the test animal should be the same as, or as similar as possible to, the route of administration of the compound to man for therapeutic purposes.
  • a compound When a compound is to be administered by inhalation, special techniques for generating test atmospheres are necessary. The methods usually involve aerosolization or nebulization of fluids containing the compound. If the agent to be tested is a fluid that has an appreciable vapor pressure, it may be administered by passing air through the solution under controlled temperature conditions. Under these conditions, dose is estimated from the volume of air inhaled per unit time, the temperature of the solution, and the vapor pressure of the agent involved. Gases are metered from reservoirs. When particles of a solution are to be administered, unless the particle size is less than about 2 ⁇ m the particles will not reach the terminal alveolar sacs in the lungs.
  • the cell population to be exposed to the agent may be divided into two or more subpopulations, for instance, by dividing the population into two or more identical aliquots.
  • the cells to be exposed to the agent are derived from liver tissue. For instance, cultured or freshly isolated rat hepatocytes may be used.
  • the methods of the invention may be used generally to predict at least one toxic response, and, as described in the Examples, may be used to predict the likelihood that a compound or test agent will induce various specific pathologies, such as liver cholestasis, induction or liver enlargement, peroxisome proliferation, general hepatotoxicity, or other pathologies associated with at least one known toxin.
  • the methods of the invention may also be used to determine the similarity of a toxic response to one or more individual compounds.
  • the methods of the invention may be used to predict or elucidate the potential cellular pathways influenced, induced or modulated by the compound or test agent.
  • Databases and computer systems of the present invention typically comprise one or more data structures, saved to a computer readable medium, comprising toxicity or toxicology models as described herein, including models comprising individual gene or toxicology marker weighted index scores or PLS scores (See Table 2), gene regulation scores, sample prediction scores and/or toxicity reference prediction scores.
  • Such databases and computer systems may also comprise software that allows a user to manipulate the database content or to calculate or generate scores as described herein, including individual gene regulation scores and sample prediction scores from nucleic acid hybridization data.
  • the software may also allow the user to compare one or more sample prediction scores to one or more toxicity reference paradigm scores in at least one toxicity model.
  • the databases and computer systems of the invention may comprise equipment and software that allow access directly or through a remote link, such as direct dial-up access or access via a password protected Internet link.
  • Any available hardware may be used to create computer systems of the invention. Any appropriate computer platform, user interface, etc. may be used to perform the necessary comparisons between sequence information, gene or toxicology marker information and any other information in the database or information provided as an input. For example, a large number of computer workstations are available from a variety of manufacturers. Client/server environments, database servers and networks are also widely available and appropriate platforms for the databases of the invention.
  • the databases may be designed to include different parts, for instance a sequence database and a toxicology reference database.
  • the database is a ToxExpress ® or BioExpressTM database marketed by Gene Logic Inc., Gaithersburg, MD.
  • a toxicology database of the invention may include gene or protein expression information for about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 50, 75, 100, 200, 500, 1000 or more genes or their encoded products from Table 2 (or Table 1), wherein the gene or protein expression information is from isolated hepatocytes exposed to one or more of the toxins or controls as described herein.
  • GenBank www. ncbi.nlm.nikgov/enti'ez. index. html
  • KEGG www.genome.ad.jp/kegg
  • SPAD www.grt.kyushu-u.ac.jp/spad/index.htmt
  • HUGO www.gene.ucl.ac.uk/hugo
  • Swiss-Prot www.
  • the external database is GenBank and the associated databases maintained by the National Center for Biotechnology Information (NCBI) (www.ncbi.nlm.nih.gov).
  • NCBI National Center for Biotechnology Information
  • Any appropriate computer platform, user interface, etc. may be used to perform the necessary comparisons between sequence information, gene expression information and any other information in the database or information provided as an input. For example, a large number of computer workstations are available from a variety of manufacturers, such has those available from Silicon Graphics. Client/server environments, database servers and networks are also widely available and appropriate platforms for the databases of the invention.
  • the databases of the invention may be used to produce, among other things, eNorthernsTM reports (Gene Logic, Inc) that allow the user to determine the cell type or tissue in which a given gene is expressed and to allow determination of the abundance or expression level of a given gene in a particular tissue or cell.
  • eNorthernsTM reports Gene Logic, Inc
  • the methods, databases and computer systems of the invention can be used to produce, deliver and/or send a toxicity, hepatotoxicity or toxicology report.
  • a toxicity report and a “toxicology report” are interchangeable.
  • the toxicity report of the invention typically comprises information or data related to the results of the practice of a method of the invention.
  • the practice of a method of identifying at least one toxic effect of a test agent or compound as herein described may result in the preparation or production of a report describing the results of the method.
  • the report may comprise information related to the toxic effects predicted by the comparison of at least one sample prediction score to at least one toxicity reference prediction score from the database.
  • the report may also present information concerning the nucleic acid hybridization data, such as the integrity of the data as well as information inputted by the user of the database and methods of the invention, such as information used to annotate the nucleic acid hybridization data.
  • a toxicity report of the invention may be in a form such as the reports disclosed in PCT/US02/22701, filed July 18, 2002, which is herein incorporated by reference in its entirety.
  • the report may be generated by a server or computer system to which is loaded nucleic acid hybridization data by a user.
  • the report related to that nucleic acid data may be generated and delivered to the user via remote means such as a password secured environment available over the internet or via available computer communication means such as email.
  • Any assay format to detect gene expression may be used to produce nucleic acid hybridization data.
  • traditional Northern blotting, dot or slot blot, nuclease protection, primer directed amplification, RT- PCR, semi- or quantitative PCR, branched- chain DNA and differential display methods may be used for detecting gene expression levels or producing nucleic acid hybridization data.
  • Those methods are useful for some embodiments of the invention, hi cases where smaller numbers of genes are detected, amplification based assays may be most efficient. Methods and assays of the invention, however, may be most efficiently designed with high-throughput hybridization-based methods for detecting the expression of a large number of genes.
  • any hybridization assay format may be used, including solution-based and solid support-based assay formats.
  • Solid supports containing oligonucleotide probes for differentially expressed genes of the invention can be filters, polyvinyl chloride dishes, particles, beads, microparticles or silicon or glass based chips, etc. Such chips, wafers and hybridization methods are widely available, for example, those disclosed by Beattie (WO 95/11755).
  • any solid surface to which oligonucleotides can be bound, either directly or indirectly, either covalently or non-covalently, can be used.
  • a preferred solid support is a high density array or DNA chip. These contain a particular oligonucleotide probe in a predetermined location on the array. Each predetermined location may contain more than one molecule of the probe, but each molecule within the predetermined location has an identical sequence. Such predetermined locations are termed features. There may be, for example, from 2, 10, 100, 1000 to 10,000, 100,000 or 400,000 or more of such features on a single solid support.
  • the solid support, or the area within which the probes are attached may be on the order of about a square centimeter.
  • Probes corresponding to the genes or gene fragments of Table 1, 2 or 5 may be attached to single or multiple solid support structures, e.g., the probes may be attached to a single chip or to multiple chips to comprise a chip set.
  • the genes or gene fragments described in the related applications mentioned above may also be attached to these solid supports.
  • Oligonucleotide probe arrays for expression monitoring can be made and used according to any techniques known in the art (see for example, Lockhart et al (1996), Nat Biotechnol 14:1675-1680; McGaIl et al. (1996), Proc NatAcadSci OSA 93: 13555-13460).
  • Such probe arrays may contain at least two or more oligonucleotides that are complementary to or hybridize to two or more of the genes or gene fragments described in Table 1, 2 or 5.
  • Such arrays may contain oligonucleotides that are complementary to or hybridize to at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 50, 70, 100, 500 or 1,000 or more of the genes described herein.
  • Preferred arrays contain all, or substantially all, of the genes or gene fragments listed in Table 1, 2 or 5.
  • substantially all of the genes in Table 1, 2 or 5 refers to a set of genes or gene fragments containing at least 80% of the genes or gene fragments in Table 1, 2 or 5.
  • arrays are constructed that contain oligonucleotides to detect all or nearly all of the genes in Table 1, 2 or 5, or a single model of Table 1, 2 or 5, on a single solid support substrate, such as a chip.
  • Table 1 provides the SEQ ID NO: and GenBank Accession Number (NCBI RefSeq ID) for each of the sequences (see www.ncbi.nhn.nih.gov/), as well as the title for the cluster of which gene is part.
  • GenBank GenBank Accession Number
  • sequences of the genes in GenBank are expressly herein incorporated by reference in their entirety as of the filing date of this application, as are related sequences, for instance, sequences from the same gene of different lengths, variant sequences, polymorphic sequences, genomic sequences of the genes and related sequences from different species, including the human counterparts, where appropriate.
  • Table 1 also provides protein sequences that correspond to the gene sequences. These proteins can be secreted or non- secreted proteins. Their annotation is based on the databases the proteins are presented. For example, if a protein fragment is linked to a RefSeq nucleotide record, the corresponding RefSeq protein record is used. If a protein fragment is linked to a Swiss-Prot protein record by Gene Index database, this Swiss-Prot protein record is used. A protein fragment could be linked to a Unigene cluster, which may have a list of annotated protein IDs.
  • sequences such as naturally occurring variant or polymorphic sequences may be used in the methods and compositions of the invention.
  • expression levels of various allelic or homologous forms of a gene or gene fragment disclosed in Table 1, 2 or 5 may be assayed.
  • Any and all nucleotide variations that do not alter the functional activity of a gene or gene fragment listed in Table 1, 2 or 5, including all naturally occurring allelic variants of the genes herein disclosed, may be used in the methods and to make the compositions (e.g., arrays) of the invention.
  • Probes based on the sequences of the genes described above may be prepared by any commonly available method.
  • Oligonucleotide probes for screening or assaying a tissue or cell sample are preferably of sufficient length to specifically hybridize only to appropriate, complementary genes or transcripts. Typically the oligonucleotide probes will be at least about 10, 12, 14, 16, 18, 20 or 25 nucleotides in length. In some cases, longer probes of at least 30, 40, or 50 nucleotides will be desirable.
  • oligonucleotide sequences that are complementary to one or more of the genes or gene fragments described in Table 1, 2 or 5 refer to oligonucleotides that are capable of hybridizing under stringent conditions to at least part of the nucleotide sequences of said genes. Such hybridizable oligonucleotides will typically exhibit at least about 75% sequence identity at the nucleotide level to said genes, preferably about 80% or 85% sequence identity or more preferably about 90% or 95% or more sequence identity to said genes (see GeneChip ® Expression Analysis Manual, Affymetrix, Rev. 3, which is herein incorporated by reference in its entirety).
  • Probe Design One of skill in the art will appreciate that an enormous number of array designs are suitable for the practice of this invention.
  • the high density array will typically include a number of test probes that specifically hybridize to the sequences of interest.
  • Probes may be produced from any region of the genes or gene fragments identified in Table 2 and the attached representative sequence listing. In instances where the gene reference in the Tables is a gene fragment, probes may be designed from that sequence or from other regions of the corresponding full-length transcript that may be available in any of the sequence databases, such as those herein described. See WO 99/32660 for methods of producing probes for a given gene or genes.
  • Test probes may be oligonucleotides that range from about 5 to about 500, or about 7 to about 50 nucleotides, more preferably from about 10 to about 40 nucleotides and most preferably from about 15 to about 35 nucleotides in length. In other particularly preferred embodiments, the probes are about 20 or 25 nucleotides in length. In another preferred embodiment, test probes are double or single strand DNA sequences.
  • DNA sequences are isolated or cloned from natural sources or amplified from natural sources using native nucleic acid as templates. These probes have sequences complementary to particular subsequences of the genes whose expression they are designed to detect. Thus, the test probes are capable of specifically hybridizing to the target nucleic acid they are to detect.
  • the high density array can contain a number of control probes.
  • the control probes may fall into three categories referred to herein as 1) normalization controls; 2) expression level controls; and 3) mismatch controls.
  • Normalization controls are oligonucleotide or other nucleic acid probes that are complementary to labeled reference oligonucleotides or other nucleic acid sequences that are added to the nucleic acid sample to be screened.
  • the signals obtained from the normalization controls after hybridization provide a control for variations in hybridization conditions, label intensity, "reading" efficiency and other factors that may cause the signal of a perfect hybridization to vary between arrays.
  • signals (e.g., fluorescence intensity) read from all other probes in the array are divided by the signal (e.g., fluorescence intensity) from the control probes thereby normalizing the measurements.
  • Virtually any probe may serve as a normalization control.
  • Preferred normalization probes are selected to reflect the average length of the other probes present in the array, however, they can be selected to cover a range of lengths.
  • the normalization control(s) can also be selected to reflect the (average) base composition of the other probes in the array, however in a preferred embodiment, only one or a few probes are used and they are selected such that they hybridize well (i.e., no secondary structure) and do not match any target-specific probes.
  • Expression level controls are probes that hybridize specifically with constitutively expressed genes in the biological sample. Virtually any constitutively expressed gene provides a suitable target for expression level controls. Typically expression level control probes have sequences complementary to subsequences of constitutively expressed "housekeeping genes" including, but not limited to the actin gene, the transferrin receptor gene, the GAPDH gene, and the like. Examples of expression level control probes may be found in U.S. Applications 10/479,866, 10/483,889, 10/620,765 and 10/629,618. [00102] Mismatch controls may also be provided for the probes to the target genes, for expression level controls or for normalization controls.
  • Mismatch controls are oligonucleotide probes or other nucleic acid probes identical to their corresponding test or control probes except for the presence of one or more mismatched bases.
  • a mismatched base is a base selected so that it is not complementary to the corresponding base in the target sequence to which the probe would otherwise specifically hybridize.
  • One or more mismatches are selected such that under appropriate hybridization conditions (e.g., stringent conditions) the test or control probe would be expected to hybridize with its target sequence, but the mismatch probe would not hybridize (or would hybridize to a significantly lesser extent).
  • Preferred mismatch probes contain a central mismatch.
  • mismatch probes thus provide a control for non-specific binding or cross hybridization to a nucleic acid in the sample other than the target to which the probe is directed. For example, if the target is present the perfect match probes should be consistently brighter than the mismatch probes. In addition, if all central mismatches are present, the mismatch probes can be used to detect a mutation, for instance, a mutation of a gene or gene fragment in Table 1, 2 or 5. The difference in intensity between the perfect match and the mismatch probe provides a good measure of the concentration of the hybridized material.
  • background or “background signal intensity” refer to hybridization signals resulting from non-specific binding, or other interactions, between the labeled target nucleic acids and components of the oligonucleotide array (e.g., the oligonucleotide probes, control probes, the array substrate, etc.). Background signals may also be produced by intrinsic fluorescence of the array components themselves. A single background signal can be calculated for the entire array, or a different background signal may be calculated for each target nucleic acid.
  • background is calculated as the average hybridization signal intensity for the lowest 5% to 10% of the probes in the array, or, where a different background signal is calculated for each target gene, for the lowest 5% to 10% of the probes for each gene.
  • background may be calculated as the average hybridization signal intensity produced by hybridization to probes that are not complementary to any sequence found in the sample (e.g. probes directed to nucleic acids of the opposite sense or to genes not found in the sample such as bacterial genes where the sample is mammalian nucleic acids). Background can also be calculated as the average signal intensity produced by regions of the array that lack any probes at all.
  • hybridizing specifically to or “specifically hybridizes” refers to the binding, duplexing, or hybridizing of a molecule substantially to or only to a particular nucleotide sequence or sequences under stringent conditions when that sequence is present in a complex mixture (e.g., total cellular) DNA or RNA.
  • a "probe” is defined as a nucleic acid, capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation.
  • a probe may include natural (i.e., A, G, U, C, or T) or modified bases (7- deazaguanosine, inosine, etc.).
  • the bases in probes may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization.
  • probes may be peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages.
  • oligonucleotide analogue array can be synthesized on a single or on multiple solid substrates by a variety of methods, including, but not limited to, light-directed chemical coupling, and mechanically directed coupling (see Pirrung, U.S. Patent No. 5,143,854).
  • a glass surface is derivatized with a silane reagent containing a functional group, e.g., a hydroxyl or amine group blocked by a photolabile protecting group.
  • a functional group e.g., a hydroxyl or amine group blocked by a photolabile protecting group.
  • Photolysis through a photolithogaphic mask is used selectively to expose functional groups which are then ready to react with incoming 5' photoprotected nucleoside phosphoramidites.
  • the phosphoramidites react only with those sites which are illuminated (and thus exposed by removal of the photolabile blocking group).
  • the phosphoramidites only add to those areas selectively exposed from the preceding step. These steps are repeated until the desired array of sequences have been synthesized on the solid surface. Combinatorial synthesis of different oligonucleotide analogues at different locations on the array is determined by the pattern of illumination during synthesis and the order of addition of coupling reagents.
  • High density nucleic acid arrays can also be fabricated by depositing pre-made or natural nucleic acids in predetermined positions. Synthesized or natural nucleic acids are deposited on specific locations of a substrate by light directed targeting and oligonucleotide directed targeting. Another embodiment uses a dispenser that moves from region to region to deposit nucleic acids in specific spots.
  • Cell or tissue samples may be exposed to the test agent in vitro or in vivo.
  • appropriate mammalian cell extracts such as liver extracts, may also be added with the test agent to evaluate agents that may require biotransformation to exhibit toxicity.
  • primary isolates, cultured cell lines or freshly isolated animal or human hepatocytes may be used.
  • the genes which are assayed according to the present invention are typically in the form of mRNA or reverse transcribed mRNA.
  • the genes may or may not be cloned.
  • the genes may or may not be amplified. The cloning and/or amplification do not appear to bias the representation of genes within a population.
  • nucleic acid samples used in the methods and assays of the invention may be prepared by any available method or process. Methods of isolating total mRNA are well known to those of skill in the art. For example, methods of isolation and purification of nucleic acids are described in detail in Chapter 3 of Laboratory Techniques in Biochemistry and Molecular Biology, Vol. 24, Hybridization With Nucleic Acid Probes: Theory and Nucleic Acid Probes, P. Tijssen, Ed., Elsevier Press, New York, 1993.
  • Such samples include RNA samples, but also include cDNA synthesized from a mRNA sample isolated from a cell or tissue of interest. Such samples also include DNA amplified from the cDNA, and RNA transcribed from the amplified DNA.
  • RNA samples include DNA samples, but also include cDNA synthesized from a mRNA sample isolated from a cell or tissue of interest.
  • samples also include DNA amplified from the cDNA, and RNA transcribed from the amplified DNA.
  • Biological samples may be of any biological tissue or fluid or cells from any organism as well as cells raised in vitro, such as cell lines and tissue culture cells. Frequently the sample will be a tissue or cell sample that has been exposed to a compound, agent, drug, pharmaceutical composition, potential environmental pollutant or other composition. In some formats, the sample will be a "clinical sample" which is a sample derived from a patient. Typical clinical samples include, but are not limited to, sputum, blood, blood-cells (e.g., white cells), tissue or fine needle biopsy samples, urine, peritoneal fluid, and pleural fluid, or cells therefrom. Biological samples may also include sections of tissues, such as frozen sections or formalin fixed sections taken for histological purposes.
  • nucleic acid hybridization simply involves contacting a probe and target nucleic acid under conditions where the probe and its complementary target can form stable hybrid duplexes through complementary base pairing. See WO 99/32660. The nucleic acids that do not form hybrid duplexes are then washed away leaving the hybridized nucleic acids to be detected, typically through detection of an attached detectable label. It is generally recognized that nucleic acids are denatured by increasing the temperature or decreasing the salt concentration of the buffer containing the nucleic acids. Under low stringency conditions (e.g., low temperature and/or high salt) hybrid duplexes (e.g., DNA:DNA, RNA:RNA, or RNA:DNA) will form even where the annealed sequences are not perfectly complementary.
  • low stringency conditions e.g., low temperature and/or high salt
  • hybridization is performed at low stringency, in this case in 6x SSPET at 37 0 C (0.005% Triton X-100), to ensure hybridization and then subsequent washes are performed at higher stringency (e.g., Ix SSPET at 37 0 C) to eliminate mismatched hybrid duplexes.
  • Successive washes may be performed at increasingly higher stringency (e.g., down to as low as 0.25x SSPET at 37 0 C to 50°C) until a desired level of hybridization specificity is obtained. Stringency can also be increased by addition of agents such as formamide. Hybridization specificity may be evaluated by comparison of hybridization to the test probes with hybridization to the various controls that can be present (e.g., expression level control, normalization control, mismatch controls, etc.). [00116] In general, there is a tradeoff between hybridization specificity (stringency) and signal intensity. Thus, in a preferred embodiment, the wash is performed at the highest stringency that produces consistent results and that provides a signal intensity greater than approximately 10% of the background intensity.
  • the hybridized array may be washed at successively higher stringency solutions and read between each wash. Analysis of the data sets thus produced will reveal a wash stringency above which the hybridization pattern is not appreciably altered and which provides adequate signal for the particular oligonucleotide probes of interest.
  • the hybridized nucleic acids are typically detected by detecting one or more labels attached to the sample nucleic acids.
  • the labels may be incorporated by any of a number of means well known to those of skill in the art. See WO 99/32660.
  • the present invention further provides methods as described herein that include steps to detect the expression or activity of a protein encoded by a gene of Table 1 , 2 or 5. These assays or methods may include the measurement of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 50, 75, 100, 200, 500, 1000 or more proteins, or ranges of these numbers, such as about 2-10, about 10-20, about 20-50, about 50-100, about 100-200, about 200-500 or about 500-1000 proteins encoded by the genes of Table 1, 2 or 5. In an alternate format, PCR technology may be used to measure gene expression levels for these same numbers of genes from Table 1, 2 or 5.
  • the relative amounts of a protein encoded by a gene between a cell population that has been exposed to the agent to be tested compared to an un- exposed control cell population and a cell population exposed to a known toxin may be assayed.
  • probes such as specific antibodies are used to monitor the differential expression of the protein in the different cell populations.
  • Cell lines or populations are exposed to the agent to be tested under appropriate conditions and time.
  • Cellular lysates may be prepared from the exposed cell line or population and a control, unexposed cell line or population. The cellular lysates are then analyzed with the probe, such as a specific antibody.
  • cellular changes suitable for a method of the present invention comprise directly measuring changes in the function or quantity of proteins encoded by the genes of encoded by a gene of Table 1, 2 or 5, or by measuring downstream effects of those proteins, such as measuring secondary messenger concentrations, changes in transcription or changes in protein levels of genes that are transcriptionally influenced by the test agent.
  • Preferred measurement means include changes in the quantity of the proteins of Tables 1, 2 or 5, or changes in the function or activity of said proteins, changes in the quantity of mRNA, changes in intracellular protein, changes in cell surface protein, or secreted protein, or changes in Ca 2+ , cAMP or GTP concentration.
  • Immunoaffinity, ligand affinity, or enzymatic measurement may be used to quantify changes in levels of protein in cells.
  • Protein-specific affinity beads or specific antibodies may be used to isolate for example 35 S- methionine labelled or unlabelled protein. Labelled protein may be analyzed by SDS- PAGE. Unlabelled protein may be detected by Western blotting, cell surface detection by fluorescent cell sorting, cell image analysis, ELISA or RIA employing specific antibodies. Where the protein is an enzyme, the induction of protein may be monitored by cleavage of a fluoro-genic or calorimetric substrate. [00121] A preferred detection means for secreted proteins that are enzymes such as alkaline phosphatase or proteases, would be fluorescent or calorimetric enzymatic assays.
  • Fluorescent/luminescent/color substrates for alkaline phosphatase are commercially available and such assays are easily adaptable to high throughput multi-well plate screen format. Fluorescent energy transfer based assays are used for protease assays. Fluoro- phore and quencher molecules are incorporated into the two ends of the peptide substrate of the protease. Upon cleavage of the specific substrate, separation of the fluoro-phore and quencher allows the fluorescence to be detectable. When the secreted protein can be measure by radioactive methods, scintillation proximity technology may be used. The substrate of the protein of interest is immobilized either by coating or incorporation on a solid support that contains a fluorescent material.
  • a radioactive molecule brought in close proximity to the solid phase by enzyme reaction, causes the fluorescent material to become excited and emit visible light. Emission of visible light forms the basis of detection of successful ligand/target interaction, and is measured by an appropriate monitoring device.
  • An example of a scintillation proximity assay is disclosed in U.S. Pat. No. 4,568,649, issued Feb. 4, 1986. Materials for these types of assays are commercially available from Dupont NEN R TM. (Boston, Mass.) under the trade name FlashPlateTM.
  • the invention further includes kits combining, in different combinations, high- density oligonucleotide arrays, reagents for use with the arrays, signal detection and array- processing instruments, toxicology databases and analysis and database management software described above.
  • the kits may be used, for example, to predict or model the toxic response of a test compound.
  • the database software and packaged information may contain the databases saved to a computer-readable medium, or transferred to a user's local server.
  • database and software information may be provided in a remote electronic format, such as a website, the address of which may be packaged in the kit.
  • the genes and gene expression information or portfolios of the genes with their expression information as provided in the accompanying Tables may be used as diagnostic markers for the prediction or identification of the physiological state of tissue or cell sample that has been exposed to a compound or to identify or predict the toxic effects of a compound or agent.
  • a tissue sample such as a sample of peripheral blood cells or some other easily obtainable tissue sample may be assayed by any of the methods described above, and the expression levels from a gene or gene fragment of Table 1, 2 or 5 may be compared to the expression levels found in tissues or cells exposed to the toxins described herein.
  • the levels of a gene(s) of Table 1, 2 or 5, its encoded protein(s), or any metabolite produced by the encoded protein may be monitored or detected in a sample, such as a bodily tissue or fluid sample to identify or diagnose a physiological state of an organism.
  • samples may include any tissue or fluid sample, including urine, blood and easily obtainable cells such as peripheral lymphocytes.
  • the genes and gene expression information provided in Table 1, 2 or 5 may also be used as markers for the monitoring of toxicity progression, such as that found after initial exposure to a drug, drug candidate, toxin, pollutant, etc.
  • a tissue or cell sample may be assayed by any of the methods described above, and the expression levels from a gene or gene fragment of Table 1, 2 or 5 may be compared to the expression levels found in tissue or cells exposed to the hepatotoxins described herein.
  • the comparison of the expression data, as well as available sequence or other information may be done by researcher or diagnostician or may be done with the aid of a computer and databases.
  • the genes and gene fragments identified in Table I 3 2 or 5 may be used as markers or drug targets to evaluate the effects of a candidate drug, chemical compound or other agent on a cell or tissue sample.
  • the genes may also be used as drug targets to screen for agents that modulate their expression and/or activity.
  • a candidate drug or agent can be screened for the ability to stimulate the transcription or expression of a given marker or markers or to down-regulate or counteract the transcription or expression of a marker or markers.
  • Assays to monitor the expression of a marker or markers as defined in Table 1, 2 or 5 may utilize any available means of monitoring for changes in the expression level of the nucleic acids of the invention.
  • an agent is said to modulate the expression of a nucleic acid of the invention if it is capable of up- or down-regulating expression of the nucleic acid in a cell.
  • gene chips containing probes to one, two or more genes or gene fragments from Table 1, 2 or 5 may be used to directly monitor or detect changes in gene expression in the treated or exposed cell.
  • Cell lines, tissues or other samples are first exposed to a test agent and in some instances, a known toxin, and the detected expression levels of one or more, or preferably 2 or more of the genes or gene fragments of Table 1, 2 or 5 are compared to the expression levels of those same genes exposed to a known toxin alone.
  • Compounds that modulate the expression patterns of the known toxin(s) would be expected to modulate potential toxic physiological effects in vivo.
  • the genes and gene fragments in Table 1, 2 or 5 are particularly appropriate markers in these assays as they are differentially expressed in cells upon exposure to a known hepatotoxin.
  • cell lines that contain reporter gene fusions between the open reading frame and/or the transcriptional regulatory regions of a gene or gene fragment in Table 1, 2 or 5 and any assayable fusion partner may be prepared. Numerous assayable fusion partners are known and readily available including the firefly luciferase gene and the gene encoding chloramphenicol acetyltransferase (Alam et al. (1990) Anal Biochem 188:245-254). Cell lines containing the reporter gene fusions are then exposed to the agent to be tested under appropriate conditions and time. Differential expression of the reporter gene between samples exposed to the agent and control samples identifies agents which modulate the expression of the nucleic acid.
  • Additional assay formats may be used to monitor the ability of the agent to modulate the expression of a gene identified in Table 1, 2 or 5. For instance, as described above, mRNA expression may be monitored directly by hybridization of probes to the nucleic acids of the invention. Cell lines are exposed to the agent to be tested under appropriate conditions and time and total RNA or mRNA is isolated by standard procedures such those disclosed in Sambrook et al. (Molecular Cloning: A Laboratory Manual, Third Ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 2001). [00133] Another embodiment of the present invention provides methods for identifying agents that modulate at least one activity of a protein(s) encoded by the genes or gene fragments in Table 1, 2 or 5. Such methods or assays may utilize any means of monitoring or detecting the desired activity.
  • Agents that are assayed in the above methods can be randomly selected or rationally selected or designed.
  • an agent is said to be randomly selected when the agent is chosen randomly without considering the specific sequences involved in the association of the a protein of the invention alone or with its associated substrates, binding partners, etc.
  • An example of randomly selected agents is the use a chemical library or a peptide combinatorial library, or a growth broth of an organism.
  • an agent is said to be rationally selected or designed when the agent is chosen on a nonrandom basis which takes into account the sequence of the target site and/or its conformation in connection with the agents action. Agents can be rationally selected or rationally designed by utilizing the peptide sequences that make up these sites.
  • a rationally selected peptide agent can be a peptide whose amino acid sequence is identical to or a derivative of any functional consensus site.
  • the agents of the present invention can be, as examples, peptides, small molecules, vitamin derivatives, as well as carbohydrates. Dominant negative proteins, DNAs encoding these proteins, antibodies to these proteins, peptide fragments of these proteins or mimics of these proteins may be introduced into cells to affect function. "Mimic” used herein refers to the modification of a region or several regions of a peptide molecule to provide a structure chemically different from the parent peptide but topographically and functionally similar to the parent peptide (see G.A. Grant in: Molecular Biology and Biotechnology. Meyers, ed., pp. 659-664, VCH Publishers, New York, 1995). A skilled artisan can readily recognize that there is no limit as to the structural nature of the agents of the present invention.
  • liver toxins non-toxins are administered to isolated hepatocytes from male Sprague-Dawley rats at various time points using administration diluents, protocols, dosing regimes and sampling time points as previously described in the art, as previously described in the related applications discussed above, and at the time points and doses of Tables 3 and 4.
  • the source of the primary rat hepatocytes is Sprague Dawley Outbred CD® Rats (CRL:CD®[SD] IGS BR, Charles River Laboratories). Hepatocyte cultures are obtained in T-75cm 2 matrigel coated flasks for RNA isolation for microarray analysis (187,000 cells/cm 2 ) . Primary rat hepatocytes are received the day after the cells are removed from the animals. After arrival, the cells, the cells are incubated overnight ( ⁇ 15hrs) before the toxin is added to the cultures. The medium used in the toxicity experiments is HIM culture medium (Hepatocyte Incubation Medium, In Vitro Technologies Cat. No. Z90009) containing 0.2% DMSO (Sigma Cat. No.
  • Toxin or vehicle is administered to hepatocyte cultures as follows. For each treatment, i.e., vehicle alone, vehicle + toxin at low dose, or vehicle + toxin at high dose, cells are harvested at various time points after the incubation with the toxin or non-toxin solution or with the vehicle begins.
  • RNA Collection from Isolated Hepatocytes and Processing More than 10 7 cells are typically prepared for each sample. RNA is collected at various time points following addition of the toxin according to the following procedure. [00142] The medium from the flasks is discarded, and the cells are washed once with 20 ml of warm (37 0 C) RPMI-1640 + 1OmM HEPES medium (Life Technologies, Cat. No. 22400- 089). 12 ml of Trizol (Life Technologies, Cat. No. 15596-018) is placed immediately into each T-75 flask. Each flask contains -10-20 million cells.
  • each flask The contents of each flask are mixed vigorously for one minute with a vortex mixer and then aspirated up and down 5 times with a pipette.
  • the contents of each flask ( ⁇ 12 ml each) is collected into a 50 ml conical polypropylene tissue culture tube (Falcon), snap frozen in liquid nitrogen and stored at ⁇ -86° C.
  • Microarray sample preparation is conducted with minor modifications, following the protocols set forth in the Affymetrix GeneChip ® Expression Technical Analysis Manual (Affymetrix, Inc. Santa Clara, CA). Frozen cells are ground to a powder using a Spex Certiprep 6800 Freezer Mill. Total RNA is extracted with Trizol (Invitrogen, Carlsbad CA) utilizing the manufacturer's protocol. The total RNA yield for each sample is typically 200- 500 ⁇ g per 300 mg cells. mRNA is isolated using the Oligotex mRNA Midi kit (Qiagen) followed by ethanol precipitation. Double stranded cDNA is generated from mRNA using the Superscript Choice system (Invitrogen, Carlsbad CA).
  • First strand cDNA synthesis is primed with a T7-(dT24) oligonucleotide.
  • the cDNA is phenol-chloroform extracted and ethanol precipitated to a final concentration of 1 ⁇ g/ml.
  • cRNA is synthesized using Ambion's T7 MegaScript in vitro Transcription Kit.
  • nucleotides Bio- 11 -CTP and Bio-16-UTP Enzo Diagnostics
  • impurities are removed from the labeled cRNA following the RNeasy Mini kit protocol (Qiagen).
  • cRNA is fragmented (fragmentation buffer consisting of 200 mM Tris-acetate, pH 8.1, 500 mM KOAc, 150 mM MgOAc) for thirty-five minutes at 94°C.
  • fragmentation buffer consisting of 200 mM Tris-acetate, pH 8.1, 500 mM KOAc, 150 mM MgOAc
  • 55 ⁇ g of fragmented cRNA is hybridized on the Affymetrix rat array set for twenty-four hours at 60 rpm in a 45 0 C hybridization oven.
  • the chips are washed and stained with Streptavidin Phycoerythrin (SAPE) (Molecular Probes) in Affymetrix fluidics stations.
  • SAPE Streptavidin Phycoerythrin
  • SAPE solution is added twice with an anti-streptavidin biotinylated antibody (Vector Laboratories) staining step in between.
  • Hybridization to the probe arrays is detected by fluorometric scanning (Hewlett Packard Gene Array Scanner). Data is analyzed using Affymetrix GeneChip ® and Expression Data Mining (EDMT) software, the GeneExpress ® database, and S -Plus ® statistical analysis software (Insightful Corp.).
  • EDMT Expression Data Mining
  • T represents the transformation that corrects for background and normalizes and converts the PM (perfect match) intensities to a log scale
  • ⁇ y represents error (to correct for the differences in variances when using probes that bind with different intensities).
  • RMA fold-change matrices the rows represent individual fragments, and the columns are individual samples.
  • a vehicle cohort median matrix is then calculated, in which the rows represent fragments and the columns represent vehicle cohorts, one cohort for each study/time-point combination.
  • the values in this matrix are the median RMA expression values across the samples within those cohorts.
  • a matrix of normalized RMA expression values is generated, in which the rows represent individual fragments and the columns are individual samples.
  • the normalized RMA values are the RMA values minus the value from the vehicle cohort median matrix corresponding to the time-matched vehicle cohort.
  • PLS works by computing a series of PLS components, where each component is a weighted linear combination of fragment values. In this case, the nonlinear iterative partial least squares method is used to compute the PLS components.
  • a vehicle cohort mean matrix is generated, in which the rows represent fragments and the columns represent vehicle cohorts, one cohort for each study/time-point combination. The values in this matrix are the mean RMA expression values across the samples within those cohorts.
  • a treated cohort mean matrix is then generated, in which the rows represent fragments and the columns represent treated (non- vehicle) cohorts, one cohort for each study/time-point/compound/dose combination.
  • the values in this matrix are the mean RMA expression values across the samples within those cohorts.
  • a treated cohort fold-change matrix is generated, in which the rows represent fragments and the columns represent treated cohorts, one cohort for each study/time- point/compound/dose combination.
  • the values in this matrix are the values in the treated cohort mean matrix minus the values in the vehicle cohort mean matrix corresponding to appropriate time-matched vehicle cohorts.
  • a treated cohort p- value matrix is generated, in which the rows represent fragments and the columns represent treated cohorts, one cohort for each study/time-point/compound/dose combination.
  • the values in this matrix are p-values based on two-sample t-tests comparing the treated cohort mean values to the vehicle cohort mean values corresponding to appropriate time-matched vehicle cohorts.
  • This matrix is converted to a binary coding based on the p-values being less than 0.05 (coded as 1) or greater than 0.05 (coded as 0).
  • a separate matrix is constructed where the columns are the individual models being built and the rows are cohorts.
  • the values in this matrix are the "1", “-1", and "0" target training scores based on the compound's assessment as a toxicant, non-toxicant, or intermediate as shown in Table 3 and 4.
  • the p- value matrix and the target training score matrix are then used to correlate gene fragment changes to the target the commonly used Pearson's correlation statistic.
  • PLS modeling and 2/3 / 1/3 cross-validation are then performed based on taking the top N fragments according to the correlation, varying N and the number of PLS components, and recording the model success rate for each combination. N is chosen to be the point at which the cross-validated error rate is minimized.
  • each of those N fragments receives a PLS weight (PLS score) corresponding to the fragment's utility, or predictive ability, in the model (see Table 2 for lists of PLS weight scores for individual genes and gene fragments in the various hepatotoxicity models).
  • PLS score PLS weight
  • Table 2 presents several hepatotoxicity models and includes the gene or gene fragment name for each marker and the corresponding PLS weight or index score for each gene or gene fragment in each model.
  • the models are as follows: General Toxicity, Cholestasis, Inducer or Liver Enlargement, and Peroxisome Proliferation.
  • the model can be trained by setting a score of -1 for each gene that cannot predict a toxic response and by setting a score of +1 for each gene that can predict a toxic response.
  • Cross-validation of RMA/PLS models may be performed by the compound-drop method and by the 2/3:1/3 method.
  • sample data from animals treated with one particular test compound are removed from a model, and the ability of this model to predict toxicity is compared to that of a model containing a full data set.
  • gene expression information from a random third of the genes in the model is removed, and the ability of this subset model to predict toxicity is compared to that of a model containing a full data set.
  • i is the index number for each gene in a gene expression profile to be evaluated, "w;” is the PLS weight score (or PLS index score, see Table 2 for the lists of PLS scores for each hepatotoxicity model) for each gene.
  • R FCi is the RMA fold-change value for the i th gene, as determined from a normalized RMA matrix of gene expression data from the sample (described above). The PLS weight multiplied by the RMA fold-change value gives a gene regulation score for each gene, and the regulation scores for all the individual genes are added to give a prediction score for the sample.
  • QC quality control
  • a Pearson correlation matrix is calculated of the samples to each other. This matrix is samples by samples. For each sample row of the matrix, the mean of all correlation values in that row of the matrix, excluding the diagonal is calculated (which is always 1). This mean is the average correlation for that sample. If the average correlation is less than a threshold (for instance .90), the sample is flagged as a potential outlier. This process is repeated for each row (sample) in the study. Outliers flagged by the average correlation QC check are dropped out of any downstream normalization, prediction or compound similarity steps in the process.
  • the cut-off prediction scores range from about 0.40 to about 0.90, as indicated above. If a sample score, when compared to a particular liver toxicity model, e.g. the cholestasis pathology model, is about 0.40 or above, it can be predicted that the sample shows a toxic response after exposure to the test compound. If the sample score is below 0.40, it can be predicted that the sample does not show a toxic response.
  • a particular liver toxicity model e.g. the cholestasis pathology model
  • a report may be generated comprising information or data related to the results of the methods of predicting at least one toxic effect.
  • the report may comprise information related to the toxic effects predicted by the comparison of at least one sample prediction score to at least one toxicity reference prediction score from the database.
  • the report may also present information concerning the nucleic acid hybridization data, such as the integrity of the data as well as information inputted by the user of the database and methods of the invention, such as information used to annotate the nucleic acid hybridization data. See PCT US02/22701 for a non-limiting example of a toxicity report that may be generated.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Immunology (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Hematology (AREA)
  • Urology & Nephrology (AREA)
  • Analytical Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Microbiology (AREA)
  • Organic Chemistry (AREA)
  • Pathology (AREA)
  • Biotechnology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Toxicology (AREA)
  • Physics & Mathematics (AREA)
  • Cell Biology (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Medicinal Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Genetics & Genomics (AREA)
  • Food Science & Technology (AREA)
  • Tropical Medicine & Parasitology (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Gastroenterology & Hepatology (AREA)
  • Biophysics (AREA)
  • General Engineering & Computer Science (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

La présente invention concerne des procédés pour pronostiquer l'hépatotoxicité d'agents de test et des procédés pour générer des modèles de pronostic d'hépatotoxicité au moyen d'algorithmes destinés à l'analyse des informations quantitatives d'expression génique. L'invention comprend aussi des micro-réseaux, des systèmes informatiques comprenant des modèles de pronostic de toxicité et des procédés pour utiliser des systèmes informatiques par des utilisateurs distants afin de déterminer la toxicité d'agents de test.
PCT/US2006/032336 2005-08-17 2006-08-17 Modeles de toxicite moleculaire developpes a partir d'hepatocytes isoles WO2007022419A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US70875405P 2005-08-17 2005-08-17
US60/708,754 2005-08-17

Publications (2)

Publication Number Publication Date
WO2007022419A2 true WO2007022419A2 (fr) 2007-02-22
WO2007022419A3 WO2007022419A3 (fr) 2009-04-16

Family

ID=37758436

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2006/032336 WO2007022419A2 (fr) 2005-08-17 2006-08-17 Modeles de toxicite moleculaire developpes a partir d'hepatocytes isoles

Country Status (1)

Country Link
WO (1) WO2007022419A2 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008106430A2 (fr) * 2007-02-26 2008-09-04 The Regents Of The University Of California Expression hétérologue de protéines et de chaperons moléculaires de choc thermique extrémophile dans des microorganismes pour augmenter la tolérance aux composés toxiques
WO2011133284A1 (fr) 2010-04-22 2011-10-27 Oregon Health & Science University Cochons déficients en fumarylacétoacétate hydrolase (fah) et leurs utilisations
WO2013032918A1 (fr) 2011-08-26 2013-03-07 Yecuris Corporation Rats carencés en fumarylacétoacétate hydrolase (fah) et immunodéficients et leurs utilisations
CN103150482A (zh) * 2013-03-28 2013-06-12 湖南中烟工业有限责任公司 一种基于pls确定烘丝工艺参数影响因子的方法
EP3211098A1 (fr) * 2008-11-10 2017-08-30 Battelle Memorial Institute Procédés, compositions et dispositifs utilisant un microarn pour déterminer des conditions physiologiques

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
LIGUORI M.J. ET AL.: 'Microarray Analysis in human hepatocytes suggests a mechanism for hepatotoxicity induced by travafloxacin.' HEPATOLOGY. vol. 41, no. 1, January 2005, pages 177 - 186 *
PIETSCH ET AL.: 'Lovastatin Reduces Expression of the Combined Adhesion and Scavenger Receptor CD36 in Human Monocytic Cells.' BIOCHEMICAL PHARMACOLOGY. vol. 52, 1996, pages 433 - 439 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008106430A2 (fr) * 2007-02-26 2008-09-04 The Regents Of The University Of California Expression hétérologue de protéines et de chaperons moléculaires de choc thermique extrémophile dans des microorganismes pour augmenter la tolérance aux composés toxiques
WO2008106430A3 (fr) * 2007-02-26 2009-01-15 Univ California Expression hétérologue de protéines et de chaperons moléculaires de choc thermique extrémophile dans des microorganismes pour augmenter la tolérance aux composés toxiques
US8685729B2 (en) 2007-02-26 2014-04-01 The Regents Of The University Of California Heterologous expression of extremophile heat shock proteins and chaperones in microorganisms to increase tolerance to toxic compounds
EP3211098A1 (fr) * 2008-11-10 2017-08-30 Battelle Memorial Institute Procédés, compositions et dispositifs utilisant un microarn pour déterminer des conditions physiologiques
WO2011133284A1 (fr) 2010-04-22 2011-10-27 Oregon Health & Science University Cochons déficients en fumarylacétoacétate hydrolase (fah) et leurs utilisations
WO2013032918A1 (fr) 2011-08-26 2013-03-07 Yecuris Corporation Rats carencés en fumarylacétoacétate hydrolase (fah) et immunodéficients et leurs utilisations
EP3578042A1 (fr) 2011-08-26 2019-12-11 Yecuris Corporation Rats carencés en fumarylacétoacétate hydrolase (fah) et immunodéficients et leurs utilisations
CN103150482A (zh) * 2013-03-28 2013-06-12 湖南中烟工业有限责任公司 一种基于pls确定烘丝工艺参数影响因子的方法

Also Published As

Publication number Publication date
WO2007022419A3 (fr) 2009-04-16

Similar Documents

Publication Publication Date Title
Barnes et al. Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms
US7711492B2 (en) Methods for diagnosing lymphoma types
Natsoulis et al. The liver pharmacological and xenobiotic gene response repertoire
WO2006033701A2 (fr) Ensembles de reactifs et signatures genetiques pour une blessure du tubule renal
US20070015148A1 (en) Gene expression profiles in breast tissue
EP2080140A2 (fr) Diagnostic de melanome metastatique et surveillance d'indicateurs d'immunosuppression par analyse de microreseaux de leucocytes sanguins
US20090220970A1 (en) Molecular toxicology modeling
US20070015147A1 (en) Primary rat hepatocyte toxicity modeling
WO2007084187A2 (fr) Modélisation de cardiotoxicologie moléculaire
US20110071767A1 (en) Hepatotoxicity Molecular Models
WO2007022419A2 (fr) Modeles de toxicite moleculaire developpes a partir d'hepatocytes isoles
EP1476747A4 (fr) Modelisation d'hepatotoxicologie moleculaire
EP1583819A2 (fr) Modelage cardiotoxicologique moleculaire
EP1578393A4 (fr) Modelisation de la toxicite de cellules hepatiques primaires de rat
US20090088345A1 (en) Necessary and sufficient reagent sets for chemogenomic analysis
US20060240418A1 (en) Canine gene microarrays
EP1412537A2 (fr) Modelisation toxicologique moleculaire de la cardiotoxine
WO2005052181A2 (fr) Procedes de modelisation de toxicologie moleculaire
US20070055448A1 (en) Primary rat hepatocyte toxicity modeling
WO2006037025A2 (fr) Modeles de toxicite moleculaire obtenus a partir d'hepatocytes isoles
Hoffmann Gene expression patterns in human and mouse B cell development
US20100021885A1 (en) Reagent sets and gene signatures for non-genotoxic hepatocarcinogenicity
Shaughnessy Jr Primer on Medical Genomics Part IX: Scientific and Clinical Applications of DNA Microarrays—Multiple Myeloma as a Disease Model
US20070054269A1 (en) Molecular cardiotoxicology modeling
US20080281526A1 (en) Methods For Molecular Toxicology Modeling

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application
NENP Non-entry into the national phase in:

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 06801849

Country of ref document: EP

Kind code of ref document: A2