Patent Application
20090202995
The contents of the submission on compact discs submitted herewith are incorporated herein by reference in their entirety: A compact disc copy of the Sequence Listing (COPY 1) (filename: GENE 127 01WO SeqList.txt, date recorded: Aug. 24, 2006, file size 2,853 kilobytes); a duplicate compact disc copy of the Sequence Listing (COPY 2) (filename: GENE 127 01WO SeqList.txt, date recorded: Aug. 24, 2006, file size 2,853 kilobytes); a duplicate compact disc copy of the Sequence Listing (COPY 3) (filename: GENE 127 01WO SeqList.txt, date recorded: Aug. 24, 2006, file size 2,853 kilobytes); a computer readable format copy of the Sequence Listing (CRF COPY) (filename: GENE 127 01WO SeqList.txt, date recorded: Aug. 24, 2006, file size 2,853 kilobytes).
The need for methods of assessing the toxic impact of a compound, pharmaceutical agent or environmental pollutant on a cell or living organism has led to the development of procedures which utilize living organisms as biological monitors. The simplest and most convenient of these systems utilize unicellular microorganisms such as yeast and bacteria, since they are the most easily maintained and manipulated. In addition, unicellular screening systems often use easily detectable changes in phenotype to monitor the effect of test compounds on the cell. Unicellular organisms, however, are inadequate models for estimating the potential effects of many compounds on complex multicellular animals, as they do not have the ability to carry out biotransformations.
The biotransformation of chemical compounds by multicellular organisms is a significant factor in determining the overall toxicity of agents to which they are exposed. Accordingly, 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 pharmaceutical industry spends significant resources to ensure that therapeutic compounds of interest are not toxic to human beings. This process is lengthy as well as expensive and involves testing in a series of organisms starting with rodents and progressing to dogs or non-human primates. Moreover, modeling methods for designing candidate pharmaceuticals and their synthesis in nucleic acid, peptide or organic compound libraries has increased the need for inexpensive, fast and accurate methods to predict toxic responses. Toxicity modeling methods based on nucleic acid hybridization platforms would allow the use of biological samples from compound-exposed animal tissue or cell samples, such as rat tissues or cells, to detect human organ toxicity much earlier than has been possible to date.
The present invention is based, in part, on the elucidation of the global changes in gene expression in tissues or cells exposed to known toxins, in particular cardiotoxins, as compared to unexposed tissues or cells as well as the identification of individual genes that are differentially expressed upon toxin exposure.
The invention includes methods of predicting at least one toxic effect of a compound, comprising: detecting the level of expression in cardiac tissues or cells exposed to the compound of two or more genes from Table 1, 2 or 4 and presenting information related to the detection; wherein differential expression of the genes in Table 1, 2 or 4 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 profile from tissues or cells exposed to the test compound; and comparing the gene expression profile to a database comprising quantitative gene expression information for at least one gene or gene fragment of Table 1, 2 or 4 from cardiac tissues or cells that have been exposed to at least one toxin and quantitative gene expression information for at least one gene or gene fragment of Table 1, 2 or 4 from control tissues or cells exposed to the excipients in the toxin formulation, thereby predicting at least one toxic effect of the test compound.
In various aspects, the invention also includes methods of predicting at least one toxic effect of a test agent by comparing gene expression information from agent-exposed cardiac samples to a database of gene expression information from toxin-exposed and control cardiac samples (vehicle-exposed samples or samples exposed to a non-toxic compound or experimental condition 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. This sample prediction score is then compared to a reference prediction score for one or more toxicity models. The sample prediction score can be generated 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.
In various aspects, 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 cardiac tissues or cells exposed to a toxin and tissues or cells 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.
The invention further provides a set of genes or gene fragments, listed in Tables 1, 2 and 4, from which probes can be made and attached to solid supports. These genes serve as a preferred set of markers of cardiotoxicity 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.
In other aspects, 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.
In further aspects of the invention, the gene expression information from test agent-exposed tissues or cells may be prepared and 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: 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 and Unigene cluster ID.
Table 2: Table 2 presents the PLS weight scores (index scores) for each gene from a series of cardiotoxicity models.
Table 3: Table 3 lists the toxins and negative control compounds used to build and train each cardiotoxicity model. 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 “Not Used” indicates that that compound's data (at the dose indicated) was not used in building the particular model.
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.
For the pathology or other compound-grouped models, “1” indicates compounds that cause that pathology or are a part of the compound group being assayed for in humans or, if the pathology or other factor is known to be a rat-specific event, compounds that cause that pathology in rats; “−1” indicates compounds that do not cause that pathology in humans or, if the pathology is known to be a rat-specific event, compounds that do not cause that pathology in rats.
Table 4: Table 4 supplies information concerning the metabolic pathways in which the genes and gene fragments of Tables 1 and 2 function.
Many biological functions are accomplished by altering the expression of various genes through transcriptional (e.g. through control of initiation, provision of RNA precursors, RNA processing, etc.) and/or translational control. For example, fundamental biological processes such as cell cycle, cell differentiation and cell death are often characterized by the variations in the expression levels of groups of genes.
Changes in gene expression are also associated with the effects of various chemicals, drugs, toxins, pharmaceutical agents and pollutants on an organism or cells. For example, the lack of sufficient expression of functional tumor suppressor genes and/or the over expression of oncogene/protooncogenes after exposure to an agent could lead to tumorgenesis or hyperplastic growth of cells (Marshall (1991) Cell 64: 313-326; Weinberg (1991) Science 254:1138-1146). Thus, changes in the expression levels of particular genes (e.g. oncogenes or tumor suppressors) may serve as indicators of the presence and/or progression of toxicity or other cellular responses to exposure to a particular compound.
Monitoring changes in gene expression may also provide certain advantages during drug screening and development. Often drugs are screened for the ability to interact with an intended target with little or no regard to other effects the drugs may have on cells. These cellular effects may cause toxicity in the whole animal, which prevents the development and clinical use of the potential drug.
The present inventors have examined cardiac tissue from animals exposed to known cardiotoxins which induce detrimental heart effects in humans and/or nonclinical species, to identify global changes in gene expression and individual changes in gene expression induced by these compounds. These changes in gene expression, which can be detected by producing or obtaining gene expression profiles (an expression level of one or more genes), 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.
As used herein, “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.
As used herein, “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. 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 heart 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. Preferred cells or tissues are cardiac cells or tissues, such as rat cardiac cells or tissues.
As used herein, “test agent” refers to an agent, compound, biologic such as an antibody, or composition that is being tested or analyzed in a method of the invention. For instance, a test agent may be a pharmaceutical candidate for which toxicology data is desired.
As used herein, “pathology” refers to an observable endpoint indicative of toxicity as classified by a pathologist or other practitioner with experience in the field. Most models built from expression data are based on compounds that cause common pathology endpoints. However, some models may be based on other factors for which compound commonality can be derived, including structural or mechanistic factors. The term “pathology” is used as the most common embodiment, but generally includes the other factors of compound commonality.
As used herein, “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.
As used herein, “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.
As used herein, 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.
As used herein, “at least one gene” refers to a nucleic acid molecule detected by the methods of the invention in a sample. The term “gene” as used herein, includes fully characterized open reading frames and the encoded mRNA as well as fragments of expressed RNA that are detectable by any hybridization method in the cell or tissue samples assayed as described herein. For instance, 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 and 4. As used herein, at least one gene includes a “plurality of genes.”
As used herein, “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. For instance, 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.
As used herein, “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.
As used herein, “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.
As used herein, “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.
As used herein, 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., Nucleic Acids Research 31(4) e15 (2003).
As used herein, “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.
As used herein, a “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 Affymetrix GeneChip® Expression Analysis Technical Manual, which is herein incorporated by reference.
As used herein, 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.
As used herein, 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.
As used herein, an “arrhythmia model” refers to a model wherein the condition of the heart is characterized by a disturbance in the electrical activity that manifests as an abnormality in heart rate or heart rhythm. Patients with a cardiac arrhythmia may experience a wide variety of symptoms ranging from palpitations to fainting.
As used herein, a “myocardial necrosis model” refers to a model wherein an area of necrosis of the heart results from an insufficiency of coronary blood supply.
As used herein, a “heart failure model” refers to a model of an abnormality of cardiac function where the heart does not pump blood at the rate needed for the requirements of metabolizing tissues. The heart failure can be caused by any number of factors, including ischemic, congenital, rheumatic, or idiopathic forms.
As used herein, an “adrenergic agonist model” refers a condition where there is ineffective pumping of the heart leading to an accumulation of fluid in the lungs. Typical symptoms include shortness of breath with exertion, difficulty breathing when lying flat and leg or ankle swelling. Causes include chronic hypertension, cardiomyopathy, and myocardial infarction.
As used herein, “vasculature agents” refers to agents that cause physiological change of the vasculature.
To evaluate and identify gene expression changes that are predictive of toxicity, studies using selected compounds with well characterized toxicity may be used to build a model or database of the present invention. In the present studies, the following cardiotoxins and non-cardiotoxins were used to build one or more of the models of the invention: acyclovir, adriamycin, amphotericin B, BI compound, carboplatin, CCl4, cisplatin, clenbuterol, cyclophosphamide, dantrolene, dopamine, epinephrine, epirubicin, famotidine, hydralazine, ifosfamide, imatinib, isoproterenol, minoxidil, monocrotaline, norephinephrine, paroxetine, pentamidine, Pfizer compound, phenylpropanolamine, rosiglitazone, and temozolomide. Methods used to prepare the models of the present invention include an RMA/PLS method (analysis of raw gene expression data by the robust multi-array average algorithm, with evaluation of predictive ability by the partial least squares algorithm).
In general, the models of the invention are built using cardiac tissue and cell samples that are analyzed after exposure to compounds known to exhibit at least one toxic effect. 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 heart pathology. These marker genes may also be used to prepare reference gene expression profiles that characterize a specific cardiotoxic response. To train a toxicity model that is initially built from a database of gene expression information classified as showing a toxic response or not showing a toxic response, information from samples treated with some compounds is removed from the model, while information from samples treated with other compounds is retained. If the model with the retained information also retains the ability of the original model to distinguish between a toxic response and the lack of a toxic response in test samples compared to the model, the genes in the training model whose expression is up- or down-regulated are used to build a specific toxicity model. These genes are used on the tox side of the training model.
The toxins and negative control compounds used to build and train each toxicity model are shown in Table 3. 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. Where 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. Where a particular compound in a particular model has the designation “Not Used,” the compound was not used to train that model.
In the present invention, a toxicity study or “tox study” comprises a set of cardiac tissues or cells 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. As described below, the cell or tissue samples may be exposed to the toxin and control treatments in vivo or in vitro. In some studies, 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. In some studies, 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. These 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 cell or tissue samples), and dose (amount of test compound administered). All cohorts in a tox study typically share the same vehicle control. For example, a cohort may be a set of samples of tissues or cells from laboratory rats that were treated with isoproterenol for 6 hours at a dosage of 0.5 mg/kg. A time-matched vehicle cohort is a set of samples that serve as controls for treated tissues or cells within a tox study, e.g., for 6-hour isoproterenol-treated 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. For instance, a reference database may include data from rat cardiac 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 cardiotoxicity database is a set of cardiotoxicity studies that alone or in combination comprises 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):e15, 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 log2 scale, typically between 4 and 12, for genes that are expressed significantly above or below control levels. These 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 heart or kidney toxicity. In a preferred embodiment, the model is validated by techniques known to those skilled in the art. Preferably, 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, or Partial Least Squares, is 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 al. (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.
From the nucleic acid hybridization data, a gene expression measure is calculated for one or more genes whose level of expression is detected in the nucleic acid hybridization value. As described above, the gene expression measure may comprise an RMA fold change value. The toxicity reference score=ΣwiRFCi. “i” is the index number for each gene in a gene expression profile to be evaluated. “wi” is the PLS weight (or PLS score, see Table 2) for each gene. “RFCi” is the RMA fold-change value for the ith 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, including gene regulation scores calculated for the genes of the attached Tables, in particular Tables 1 and 2 as herein described.
In one embodiment of the invention, 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 tissues or cells exposed to a toxin and tissues or cells 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.
In another preferred embodiment, the model is validated by techniques known to those skilled in the art. Preferably, 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.
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 cardiotoxicity or other tissue toxicity of a test or unknown agent or compound. The gene regulation scores and toxicity prediction scores from heart cell or tissue samples exposed to toxins may also be used to predict the ability of a test agent or compound to induce tissue pathology, such as arrhythmia, 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.
As used, herein, 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 tissue necrosis. Accordingly, the toxic effect includes effects at the molecular and cellular level. Cardiotoxicity, for instance, is an effect as used herein and includes but is not limited to the pathologies of: myocarditis, arrhythmias, tachycardia, myocardial ischemia, myocardial necrosis, heart failure, angina, hypertension, hypotension, dyspnea, and cardiogenic shock.
In general, assays to predict the toxicity of a test agent (or compound or multi-component composition) comprise the steps of exposing a living animal, such as a laboratory rat, to the test agent or compound, isolating the tissues and cells from the animal, 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 4, calculating a sample prediction score and comparing the sample prediction score to one or more toxicology reference scores (see Example 1).
Sample prediction scores may be calculated as follows: sample prediction score=ΣwiRFCi. “i” is the index number for each gene in a gene expression profile to be evaluated. “wi” is the PLS weight (or PLS score) for each gene derived from a toxicity model. “RFCi” is the RMA fold-change value for the ith 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. A sample 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 (or see the numbers of genes below), including gene regulation scores calculated for the genes of the attached Tables, in particulare Tables 1 and 2 as herein described.
Nucleic acid hybridization data or methods of the invention may include any measurement of the hybridization of sample nucleic acids to probes or gene expression levels 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 4. 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 4. 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. These same numbers of genes may be used a taught herein in any step of the disclosed methods or a genes in a gene expression database as appropriate.
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. For instance, 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.
In another embodiment of the invention, cell or tissue samples are exposed to a test agent or compound by administering the agent to laboratory rats or to cultured heart cells 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 product(s) 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. For instance, 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.
Once data is received, the nucleic acid hybridization data may be screened for database compatibility by any available means. In one embodiment, 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. In addition, various data QC metrics can be applied, including one or more disclosed in PCT/US03/24160, filed Aug. 1, 2003, which claims priority to U.S. provisional application 60/399,727.
As described above, the cell population that is exposed to the test agent, compound or composition may be exposed in vitro or in vivo. For instance, cultured or freshly isolated heart cells, in particular rat heart cells, may be exposed to the agent under standard laboratory and cell culture conditions. In another assay format, in vivo exposure may be accomplished by administration of the agent to a living animal, for instance a laboratory rat.
Procedures for designing and conducting toxicity tests in in vitro and in vivo systems are well known, and are described in many texts on the subject, such as Loomis et al., Loomis's Essentials of Toxicology, 4th Ed., Academic Press, New York, 1996; Echobichon, The Basics of Toxicity Testing, CRC Press, Boca Raton, 1992; Frazier, editor, In Vitro Toxicity Testing, Marcel Dekker, New York, 1992; and the like.
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.
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. When this is not possible because of solubility limitations, vegetable oils such as corn oil or organic solvents such as propylene glycol may be used.
Regardless of the route of administration, 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. When rats or mice are used, the volume administered by the oral route generally should not exceed about 0.005 ml per gram of animal. Even when aqueous or physiological saline solutions are used for parenteral injection the volumes that are tolerated are limited, although such solutions are ordinarily thought of as being innocuous. The intravenous LD50 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. In some instances, 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 humans for therapeutic purposes.
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 μm the particles will not reach the terminal alveolar sacs in the lungs. A variety of apparati and chambers are available to perform studies for detecting effects of irritant or other toxic endpoints when they are administered by inhalation. The preferred method of administering an agent to animals is via the oral route, either by intubation or by incorporating the agent in the feed.
When the agent is exposed to cells in vitro or in cell culture, 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. In some preferred embodiments of the methods of the invention, the cells to be exposed to the agent are derived from heart tissue. For instance, cultured or freshly isolated rat heart cells 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 arrhythmias, myocardial necrosis, heart failure, 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. In addition, 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.
As discussed above, 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. Methods for the configuration and construction of such databases and computer-readable media containing such databases are widely available, for instance, see U.S. Publication No. 2003/0171876 (Ser. No. 10/090,144), filed Mar. 5, 2002, PCT Publication No. WO 02/095659, published Nov. 23, 2002, and U.S. Pat. No. 5,953,727, which are herein incorporated by reference in their entirety. In a preferred embodiment, the database is a ToxExpress® or BioExpress database marketed by Gene Logic Inc., Gaithersburg, Md. A toxicology database of the invention may include gene expression information for about or at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 50, 75, 100, 200, 500, 1000 or more genes from Table 2 (or Table 1), wherein the gene expression information is from cardiac tissues or cells exposed in vivo or in vitro to one or more of the toxins or controls as described herein.
The databases of the invention may be linked to an outside or external database such as GenBank (www.ncbi.nlm.nih.gov/entrez.index.html); KEGG (www.genome.ad.jp/kegg); SPAD (www.grt.kyushu-u.ac.jp/spad/index.html); HUGO (www.gene.ucl.ac.uk/hugo); Swiss-Prot (www.expasy.ch.sprot); Prosite (www.expasy.ch/tools/scnpsitl.html); OMIM (www.ncbi.nlm.nih.gov/omim); and GDB (www.gdb.org). In a preferred embodiment, the external database is GenBank and the associated databases maintained by the National Center for Biotechnology Information (NCBI) (www.ncbi.nlm.nih.gov).
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, eNortherns™ 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.
As described above, the methods, databases and computer systems of the invention can be used to produce, deliver and/or send a toxicity, cardiotoxicity or toxicology report. As consistent with the use of the terms “toxicity” and “toxicology” as used herein, 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. For instance, 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.
As an exemplary, non-limiting example, a toxicity report of the invention may be in a form such as the reports disclosed in PCT/US02/22701, filed Jul. 18, 2002, which is herein incorporated by reference in its entirety. As described elsewhere in this specification, 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. For example, 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. In 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.
To produce nucleic acid hybridization data, 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 4 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; McGall et al. (1996), Proc Nat Acad Sci USA 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 4. For instance, 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 4. As used herein, “substantially all” of the genes in Table 1, 2 or 4 refers to a set of genes or gene fragments containing at least 80% of the genes or gene fragments in Table 1, 2 or 4. In another preferred embodiment, arrays are constructed that contain oligonucleotides to detect all or nearly all of the genes in Table 1, 2 or 4, or a single model of Table 1, 2 or 4, on a single solid support substrate, such as a chip.
The sequences of the genes and gene fragments of Table 1, 2 or 4 are in the public databases. Table 1 provides the SEQ ID NO: and GenBank Accession Number (NCBI RefSeq ID) for each of the sequences (see www.ncbi.nlm.nih.gov/), as well as the title for the cluster of which gene is part. The 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. As described above, in addition to the sequences of the GenBank Accession Numbers disclosed in the Table 1, 2 or 4, sequences such as naturally occurring variant or polymorphic sequences may be used in the methods and compositions of the invention. For instance, expression levels of various allelic or homologous forms of a gene or gene fragment disclosed in Table 1, 2 or 4 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 4, 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.
As used herein, oligonucleotide sequences that are complementary to one or more of the genes or gene fragments described in Table 1, 2 or 4 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).
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 1, 2 or 4 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. In addition, any available software may be used to produce specific probe sequences, including, for instance, software available from Molecular Biology Insights, Olympus Optical Co. and Biosoft International. In a preferred embodiment, the array will also include one or more control probes.
High density array chips of the invention include “test probes.” 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.
In addition to test probes that bind the target nucleic acid(s) of interest, 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. In a preferred embodiment, 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. However, it is recognized that hybridization efficiency varies with base composition and probe length. 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. application Ser. Nos. 10/479,866, 10/483,889, 10/620,765 and 10/629,618. 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. Thus, for example, where a probe is a 20 mer, a corresponding mismatch probe will have the identical sequence except for a single base mismatch (e.g., substituting a G, a C or a T for an A) at any of positions 6 through 14 (the 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 4. The difference in intensity between the perfect match and the mismatch probe provides a good measure of the concentration of the hybridized material.
The terms “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. In a preferred embodiment, 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. Of course, one of skill in the art will appreciate that where the probes to a particular gene hybridize well and thus appear to be specifically binding to a target sequence, they should not be used in a background signal calculation. Alternatively, 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.
The phrase “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.
As used herein 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. As used herein, a probe may include natural (i.e., A, G, U, C, or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in probes may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. Thus, probes may be peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages.
Methods of forming high density arrays of oligonucleotides with a minimal number of synthetic steps are known. The 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. Pat. No. 5,143,854).
In brief, the light-directed combinatorial synthesis of oligonucleotide arrays on a glass surface proceeds using automated phosphoramidite chemistry and chip masking techniques. In one specific implementation, 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. 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). Thus, 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.
In addition to the foregoing, additional methods which can be used to generate an array of oligonucleotides on a single substrate are described in PCT Publication Nos. WO 93/09668 and WO 01/23614. 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. When cultured cells or tissues are used, 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. In a preferred format, primary isolates, cultured cell lines or freshly isolated or frozen animal or human heart cells 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. In some assays, it may be preferable, however, to use polyA+ RNA as a source, as it can be used with less processing steps.
As is apparent to one of ordinary skill in the art, 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. One of skill in the art would appreciate that it is desirable to inhibit or destroy RNase present in homogenates before homogenates are used.
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. Thus, specificity of hybridization is reduced at lower stringency. Conversely, at higher stringency (e.g., higher temperature or lower salt) successful hybridization tolerates fewer mismatches. One of skill in the art will appreciate that hybridization conditions may be selected to provide any degree of stringency.
In a preferred embodiment, hybridization is performed at low stringency, in this case in 6×SSPET at 37° C. (0.005% Triton X-100), to ensure hybridization and then subsequent washes are performed at higher stringency (e.g., 1×SSPET at 37° C.) to eliminate mismatched hybrid duplexes. Successive washes may be performed at increasingly higher stringency (e.g., down to as low as 0.25×SSPET at 37° 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.).
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 signal intensity greater than approximately 10% of the background intensity. Thus, in a preferred embodiment, 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 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 databases that may be packaged with the kits are described above. In particular, the database software and packaged information may contain the databases saved to a computer-readable medium, or transferred to a user's local server. In another format, 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.
Databases and software designed for use with microarrays are discussed in Balaban et al., U.S. Pat. Nos. 6,229,911, a computer-implemented method for managing information collected from small or large numbers of microarrays, and 6,185,561, a computer-based method with data mining capability for collecting gene expression level data, adding additional attributes and reformatting the data to produce answers to various queries. Chee et al., U.S. Pat. No. 5,974,164, disclose a software-based method for identifying mutations in a nucleic acid sequence based on differences in probe fluorescence intensities between wild type and mutant sequences that hybridize to reference sequences.
As described above, 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. For instance, 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 4 may be compared to the expression levels found in tissues or cells exposed to the toxins described herein. These methods may result in the diagnosis of a physiological state in the cell or may be used to identify the potential toxicity of a compound, for instance a new or unknown compound or agent. The comparison of 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 as described below.
As described above, the genes and gene expression information provided in Table 1, 2 or 4 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. For instance, 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 4 may be compared to the expression levels found in tissue or cells exposed to the cardiotoxins 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.
According to the present invention, the genes and gene fragments identified in Table 1, 2 or 4 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. In various formats, 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. According to the present invention, one can also compare the specificity of a drug's effects by looking at the number of markers which the drug induces and comparing them. More specific drugs will have less transcriptional targets. Similar sets of markers identified for two drugs may indicate a similarity of effects.
Assays to monitor the expression of a marker or markers as defined in Table 1, 2 or 4 may utilize any available means of monitoring for changes in the expression level of the nucleic acids of the invention. As used herein, 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.
In one assay format, gene chips containing probes to one, two or more genes or gene fragments from Table 1, 2 or 4 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 4 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 4 are particularly appropriate markers in these assays as they are differentially expressed in cells upon exposure to a known cardiotoxin.
In another format, 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 4 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 4. 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, N.Y., 2001).
Agents that are assayed in the above methods can be randomly selected or rationally selected or designed. As used herein, an agent is said to be randomly selected when the agent is chosen randomly without considering the specific sequences involved in the association of 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.
As used herein, 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 agent's action. Agents can be rationally selected or rationally designed by utilizing the peptide sequences that make up these sites. For example, 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.
Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the compounds of the present invention and practice the claimed methods. The following working examples therefore, specifically point out the preferred embodiments of the present invention, and are not to be construed as limiting in any way the remainder of the disclosure.
The cardiotoxins and control compositions including, but not limited to, acyclovir, adriamycin, amphotericin B, BI compound, carboplatin, CCl4, cisplatin, clenbuterol, cyclophosphamide, dantrolene, dopamine, epinephrine, epirubicin, famotidine, hydralazine, ifosfamide, imatinib, isoproterenol, minoxidil, monocrotaline, norephinephrine, paroxetine, pentamidine, Pfizer compound, phenylpropanolamine, rosiglitazone, and temozolomide were administered to male Sprague-Dawley rats at various time points using administration diluents, protocols and dosing regimes described above as well as previously described in the art and in the related applications discussed above.
After administration, the dosed animals were observed and tissues were collected as described below, although heart tissues were used in the cardiotoxicity models described herein.
1. Clinical Observations—Twice daily: mortality and moribundity check. Cage Side Observations—skin and fur, eyes and mucous membrane, respiratory system, circulatory system, autonomic and central nervous system, somatomotor pattern, and behavior pattern. Potential signs of toxicity, including tremors, convulsions, salivation, diarrhea, lethargy, coma or other atypical behavior or appearance, were recorded as they occurred and included a time of onset, degree, and duration.
2. Physical Examinations—Prior to randomization, prior to initial treatment, and prior to sacrifice.
3. Body Weights—Prior to randomization, prior to initial treatment, and prior to sacrifice.
Terminal Sacrifice
At the sampling times indicated in Table 3 for each cardiotoxin, and as previously described in the related applications mentioned above, rats were weighed, physically examined, sacrificed by decapitation, and exsanguinated. The animals were necropsied within approximately five minutes of sacrifice. Separate sterile, disposable instruments were used for each animal, with the exception of bone cutters, which were used to open the skull cap. The bone cutters were dipped in disinfectant solution between animals.
Necropsies were conducted on each animal following procedures approved by board-certified pathologists.
Animals not surviving until terminal sacrifice were discarded without necropsy (following euthanasia by carbon dioxide asphyxiation, if moribund). The approximate time of death for moribund or found dead animals was recorded.
Postmortem Procedures
Fresh and sterile disposable instruments were used to collect tissues. Gloves were worn at all times when handling tissues or vials. All tissues were collected and frozen within approximately 5 minutes of the animal's death. The liver sections, kidneys and hearts were frozen within approximately 3-5 minutes of the animal's death. The time of euthanasia, an interim time point at freezing of liver sections and kidneys, and time at completion of necropsy were recorded. Tissues were stored at approximately −80° C. or preserved in 10% neutral buffered formalin.
Tissue Collection and Processing
Liver—
1. Right medial lobe—snap frozen in liquid nitrogen and stored at −80° C.
2. Left medial lobe—Preserved in 10% neutral-buffered formalin (NBF) and evaluated for gross and microscopic pathology.
3. Left lateral lobe—snap frozen in liquid nitrogen and stored at ˜−80° C.
Heart—
A sagittal cross-section containing portions of the two atria and of the two ventricles was preserved in 10% NBF. The remaining heart was frozen in liquid nitrogen and stored at ˜−80° C.
Kidneys (both)—
1. Left—Hemi-dissected; half was preserved in 10% NBF and the remaining half was frozen in liquid nitrogen and stored at ˜−80° C.
2. Right—Hemi-dissected; half was preserved in 10% NBF and the remaining half was frozen in liquid nitrogen and stored at ˜−80° C.
Testes (both)—
A sagittal cross-section of each testis was preserved in 10% NBF. The remaining testes were frozen together in liquid nitrogen and stored at ˜−80° C.
Brain (whole)—
A cross-section of the cerebral hemispheres and of the diencephalon was preserved in 10% NBF, and the rest of the brain was frozen in liquid nitrogen and stored at ˜−80° C.
RNA Collection from Tissues or Cells and Processing
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, Calif.). Frozen cardiac cells are ground to a powder using a Spex Certiprep 6800 Freezer Mill. Total RNA is extracted with Trizol (Invitrogen, Carlsbad Calif.) utilizing the manufacturer's protocol. The total RNA yield for each sample is typically 200-500 μg 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 Calif.). 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 μg/ml. From 2 μg of cDNA, cRNA is synthesized using Ambion's T7 MegaScript in vitro Transcription Kit.
To biotin label the cRNA, nucleotides Bio-11-CTP and Bio-16-UTP (Enzo Diagnostics) are added to the reaction. Following a 37° C. incubation for six hours, 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. Following the Affymetrix protocol, 55 μg of fragmented cRNA is hybridized on the Affymetrix rat array set for twenty-four hours at 60 rpm in a 45° C. hybridization oven. The chips are washed and stained with Streptavidin Phycoerythrin (SAPE) (Molecular Probes) in Affymetrix fluidics stations. To amplify staining, 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®0 and Expression Data Mining (EDMT) software, the GeneExpress® database, and S-Plus® statistical analysis software (Insightful Corp.).
RMA/PLS models are built as follows. From DNA microarray data from one or more studies, a matrix of RMA fold-change expression values is generated (in this study, nucleic acid hybridization from heart tissue exposed to various cardiotoxins or control compounds in used). These values are generated, for example, according to the method of Irizarry et al. (Nucl Acids Res 31 (4):e 15, 2003, which is herein incorporated by reference in its entirety), which uses the following equation to produce a log scale linear additive model: T(PMij)=ei+aj+εij. T represents the transformation that corrects for background and normalizes and converts the PM (perfect match) intensities to a log scale. ei represents the log2 scale expression values found on arrays i=1−I, aj represents the log scale affinity effects for probes j=1−J, and εij represents error (to correct for the differences in variances when using probes that bind with different intensities).
In 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. Next, 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. Next, the absolute value of the mean of these differences is calculated. These absolute mean difference values serve as the base data on which both fragment selection and PLS modeling is calculated.
Fragment selection is achieved through several successive steps. Step 1, a “Control Cohort” matrix is created using the absolute mean difference values, where the rows represent fragments and the columns represent vehicle and/or non-cardiotoxin absolute mean difference values for each cohort. Step 2, a “Toxin Cohort” matrix is created using the absolute mean difference values, where the rows represent fragments and the columns represent cardiotoxin absolute mean difference values for each cohort. Step 3, remove fragments from the “Control Cohort” matrix that are uniquely regulated for any single cohort within that matrix. This is done by removing those fragments where the highest absolute mean difference value is 1.25 times greater than the next highest absolute mean difference value. This step is done to reduce the incidence of false-positives due to aberrant unique regulation in the “Control” class. These same fragments are also removed from the “Toxin Cohort” matrix. Step 4, the “Toxin Cohort” matrix is converted to a binary coding based on whether the cardiotoxin absolute mean difference value is 1.25 times greater than or equal to the maximum observed absolute mean difference value in the “Control Cohort” matrix. For each fragment and cohort that meets this criteria, a value of “1” is assigned; otherwise, a value of “0” is assigned. This binary coding is done for each cell of the “Toxin Cohort” matrix. Step 5, a new matrix, the “Toxin Compound” matrix, is created by taking the maximum binary assigned code over each cardiotoxin's cohorts. Therefore, each compound is represented for each fragment with a “1” where any of its treatment cohorts contains a “1” in the “Toxin Cohort” binary matrix, or with a “0” where all of its treatment cohorts contain a “0.” Step 6, each row of the “Toxin Compound” matrix is summed, yielding the number of cardiotoxins that a fragment is regulated by relative to vehicles and non-cardiotoxicants.
PLS modeling is then applied to the absolute mean difference values (a subset by taking certain fragments as described below), using a −1=non-tox, +1=tox supervised score vector as the dependant variable and the rows of normalized RMA matrix as the independent variables. 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.
PLS modeling and compound drop cross-validation are then performed based on taking the top N fragments according to the frequency of regulation observed in the “Toxin Compound” matrix, 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. In the PLS model, 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 cardiotoxicity models). Table 2 presents several cardiotoxicity 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, adrenergic agonist, arrhythmia, heart failure, myocardial necrosis, and vasculature agent.
To establish a toxicity prediction score cut-off value for a toxicity model, the true-positive and false positive rates for each possible score cut-off value are computed, using the scores from all tox and non-tox samples in the training set. This generates an ROC curve, which is used to set the cut-off score at the point on the ROC curve corresponding to −5% false positive rate.
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. In the compound-drop 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. In the 2/3:1/3 method, 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.
To determine whether or not a cardiac cell or tissue sample such as tissues or cells treated with a test agent or compound exhibits at least one toxic effect or response, RNA is prepared from tissues or cells exposed to the agent and hybridized to a DNA microarray, as described in Example 1 above. From the nucleic acid hybridization data, a prediction score is calculated for that sample and compared to a reference score from a toxicity reference database according to the following equation. The sample prediction score=ΣwiRFCi. “i” is the index number for each gene in a gene expression profile to be evaluated. “wi” is the PLS weight score (or PLS index score, see Table 2 for the lists of PLS scores for each cardiotoxicity model) for each gene. “RFCi” is the RMA fold-change value for the ith 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.
As a quality control (QC) check, for each incoming study, an average correlation assessment may be performed. After the RMA matrix is generated (genes by samples), 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 0.90), 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.
In the cardiotoxicity models of Table 2, the cut-off prediction scores range from about 0.80 to about 1.41, as indicated above. If a sample score, when compared to a particular cardiotoxicity model, e.g. the arrhythmia pathology model, is about 1.25 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 1.25, it can be predicted that the sample does not show a toxic response.
Compound similarity is assessed in the following way. In the same manner as described above, a cohort fold change vector for each study/time-point/compound/dose combination is calculated. This vector is reduced to only the fragments used in the PLS predictive models. We then calculate Pearson correlations of that cohort fold change vector to each subsetted cohort vector in our reference database. Finally, Pearson correlations are calculated ranked from highest to lowest and the results are stored in the toxicity model and reported.
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.
Although the present invention has been described in detail with reference to examples above, it is understood that various modifications can be made without departing from the spirit of the invention. Accordingly, the invention is limited only by the following claims. All cited patents, patent applications and publications referred to in this application are herein incorporated by reference in their entirety.
This application is entitled to priority pursuant to 35 U.S.C. §119(e) to U.S. provisional patent application No. 60/711,444, which was filed on Aug. 26, 2005, which is incorporated herein in its entirety. This application is related to, but does not claim priority to PCT/U505/011532, which is herein incorporated by reference in its entirety.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US2006/033712 | 8/28/2006 | WO | 00 | 9/30/2008 |
| Number | Date | Country | |
|---|---|---|---|
| 60711444 | Aug 2005 | US |
