# Patent

## Title
Reagent sets and gene signatures for renal tubule injury

## Abstract

The invention discloses reagent sets and gene signatures for predicting onset of renal tubule injury in a subject. The invention also provides a necessary set of 186 genes useful for generating signatures of varying size and performance capable of predicting onset of renal tubule injury. The invention also provides methods, apparatuses and reagents useful for predicting future renal tubule injury based on expression levels of genes in the signatures. In one particular embodiment the invention provides a method for predict whether a compound will induce renal tubule injury using gene expression data from sub-acute treatments.

## Background
CROSS REFERENCE TO RELATED APPLICATIONS
This application claims priority from U.S. Provisional Application No. 60/589,409, filed Jul. 19, 2004, which is hereby incorporated by reference in its entirety.


FIELD OF THE INVENTION
This invention relates to reagent sets and gene signatures useful for predicting the onset of renal tubule injury (RTI) in a subject. The invention also provides methods, apparatuses and kits useful for predicting occurrence of renal tubule injury based on expression levels of genes in the signatures. In one embodiment the invention provides a method for predicting whether a compound will induce renal tubule injury using gene expression data from sub-acute treatments.
BACKGROUND OF THE INVENTION
Renal tubule injury (also referred to herein as, “tubular nephrosis”) is a common drug-induced toxicity that includes degenerative lesions of the renal tubules, such as acute tubular dilation, vacuolation and necrosis. Necrotic lesions of the tubules can arise as a consequence of septic, toxic or ischemic insult, and is a frequent cause of renal failure among hospitalized patients. Recognition is hampered by the lack of accurate markers and the shortcomings and over-reliance of serum markers of impaired glomerular filtration rate (i.e., serum creatinine and blood urea nitrogen) (see e.g., Schrier et al., “Acute renal failure: definitions, diagnosis, pathogenesis, and therapy,” J Clin Invest, 114(1):5-14 (2004)). Drugs associated with the development of tubular nephrosis include aminoglycoside antibiotics, antifungals, antineoplastics, immunosuppresants and radiocontrast dyes, among others.
Similarly to the human clinical setting, long-term treatment of rats during preclinical drug development with relatively low doses of aminoglycoside antibiotics, heavy metal toxicants or antineoplastic drugs, for example, leads to the development of degenerative lesions of the renal tubules. However, histopathological or clinical indications of kidney injury are not readily apparent in the early course of treatment, thus necessitating expensive and lengthy studies.
The development of methods to predict the future onset of renal tubule injury (RTI) and gain a greater understanding of the underlying mechanism, would facilitate the development more reliable clinical diagnostics and safer therapeutic drugs. In addition, improved preclinical markers for RTI would dramatically reduce the time, cost, and amount of compound required in order to prioritize and select lead candidates for progression through drug development.
SUMMARY OF THE INVENTION
The present invention provides methods, reagent sets, gene sets, and associated apparatuses and kits, that allow one to determine the early onset of renal tubule injury (or nephrotoxicity) by measuring gene expression levels. In one particular embodiment, the invention provides a RTI “necessary set” of 186 genes mined from a chemogenomic dataset. These genes are information-rich with respect to classifying biological samples for onset of RTI, even at sub-acute doses and time points of 5 days or earlier, where clinical and histopathological evidence of RTI are not manifested. Further, the invention discloses that the necessary set for RTI classification has the functional characteristic of reviving the performance of a fully depleted set of genes (for classifying RTI) by supplementation with random selections of as few as 10% of the genes from the set of 186. In addition, the invention discloses that selections from the necessary set made based on percentage impact of the selected genes may be used to generate high-performing linear classifiers for RTI that include as few as 4 genes. In one embodiment, the invention provides several different linear classifiers (or gene signatures) for RTI. For all of the disclosed embodiments based on the necessary set of 186 genes, the invention also provides reagent sets and kits comprising polynucleotides and/or polypeptides that represent a plurality of genes selected from the necessary set.
In one embodiment, the present invention provides a method for testing whether a compound will induce renal tubule injury in a test subject, the method comprising: administering a dose of a compound to at least one test subject; after a selected time period, obtaining a biological sample from the at least one test subject; measuring the expression levels in the biological sample of at least a plurality of genes selected from those listed in Table 4; determining whether the sample is in the positive class for renal tubule injury using a classifier comprising at least the plurality of genes for which the expression levels are measured. In one embodiment, the method is carried out wherein the test subject is a mammal selected from the group consisting of a human, cat, dog, monkey, mouse, pig, rabbit, and rat. In one preferred embodiment the test subject is a rat. In one embodiment, the biological sample comprises kidney tissue. In one embodiment, the method is carried out wherein the test compound is administered to the subject intravenously (IV), orally (PO, per os), or intraperitoneally (IP). In one embodiment, the method is carried out wherein the dose administered does not cause histological or clinical evidence of renal tubule injury at about 5 days, about 7 days, about 14 days, or even about 21 days. In one embodiment, the method is carried out wherein the expression levels are measured as log10 ratios of compound-treated biological sample to a compound-untreated biological sample. In one embodiment, the method of the invention is carried out wherein the classifier is a linear classifier. In alternative embodiments, the classifier may be a non-linear classifier. In one embodiment, the method is carried out wherein the selected period of time is about 5 days or fewer, 7 days or fewer, 14 days or fewer, or even 21 days or fewer. In one embodiment of the method, the selected period of time is at least about 28 days.
In one embodiment, the method is carried out wherein the classifier comprises the genes and weights corresponding to any one of iterations 1 through 5 in Table 4. In one embodiment, the method of the invention is carried out wherein the classifier for renal tubule injury classifies each of the 64 compounds listed in Table 2 according to its label as nephrotoxic and non-nephrotoxic.
In one embodiment, the method is carried out wherein the linear classifier for renal tubule injury is capable of classifying a true label set with a log odds ratio at least 2 standard deviations greater than its performance classifying a random label set. In preferred embodiments of the method, the linear classifier for renal tubule injury is capable of performing with a training log odds ratio of greater than or equal to 4.35. In another embodiment, the plurality of genes includes at least 4 genes selected from those listed in Table 4, the four genes having at least having at least 2, 4, 8, 16, 32, or 64% of the total impact of all of the genes in Table 4.
The present invention also provides a gene sets, and reagent sets based on those gene sets, that are useful for testing whether renal tubule injury will occur in a test subject. In one embodiment, the invention provides a reagent set comprising a plurality of polynucleotides or polypeptides representing a plurality of genes selected from those listed in Table 4. In one embodiment, the reagent set comprises a plurality of genes includes at least 4 genes selected from those listed in Table 4, the 4 genes having at least 2% of the total impact of all of the genes in Table 4. In another embodiment, the reagent set comprises a plurality of genes includes at least 8 genes selected from those listed in Table 4, the 8 genes having at least 4% of the total impact of all of the genes in Table 4. Other embodiments include reagent sets based on subsets of genes randomly selected from Table 4, wherein the subset includes at least 4 genes having at least 1, 2, 4, 8, 16, 32, or 64% of the total impact. In preferred embodiments, the reagent sets of the invention include represent as few genes as possible from Table 4 while maximizing percentage of total impact. In preferred embodiments, the reagent sets of the invention include fewer than 1000, 500, 400, 300, 200, 100, 50, 20, 10, or even 8, polynucleotides or polypeptides representing the plurality of genes from Table 4. In one embodiment, the reagent sets consist essentially of polynucleotides or polypeptides representing the plurality of genes from Table 4. Further, the invention comprises kits comprising the reagent sets as components. In one embodiment, the reagent set is packaged in a single container consisting essentially of polynucleotides or polypeptides representing the plurality of genes from Table 4.
In one embodiment, the reagent sets of the invention comprise polynucleotides or polypeptides representing genes comprising a random selection of at least 10% of the genes from Table 4, wherein the addition of said randomly selected genes to a fully depleted gene set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set to at least about 4.0. In another embodiment, a random selection of at least 20% of the genes from Table 4, wherein the addition of said randomly selected genes to a fully depleted gene set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set to at least about 4.5.
In one embodiment, the invention provides a reagent set for classifying renal tubule injury comprising a set of polynucleotides or polypeptides representing a plurality of genes selected from Table 4, wherein the addition of a random selection of at least 10% of said plurality of genes to the fully depleted set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set by at least 3-fold. In another embodiment, the reagent set includes at least 20% of said plurality of genes to the fully depleted set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set by at least 2-fold.
In another preferred embodiment the plurality of genes are selected from the variables of a linear classifier capable of classifying renal tubule injury with a training log odds ratio of greater than or equal to 4.35. In one preferred embodiment, the plurality of genes is the set of genes in any one of iterations 1 through 5 in Table 4. In another embodiment, the plurality of genes is the set of genes in any one of Tables 7, 8, 10, and 11. In one embodiment the reagents are polynucleotide probes capable of hybridizing to a plurality of genes selected from those listed in Table 4, and in a preferred embodiment, the polynucleotide probes are labeled.
In another embodiment, the reagents are primers for amplification of the plurality of genes. In one embodiment the reagents are polypeptides encoded by a plurality of genes selected from those listed in Table 4. Preferably the reagents are polypeptides that bind to a plurality proteins encoded by a plurality of genes selected from those listed in Table 4. In one preferred embodiment, the reagent set comprises secreted proteins encoded by genes listed in Table 4.
The present invention also provides an apparatus for predicting whether renal tubule injury will occur in a test subject comprising a reagent set as described above. In preferred embodiments, the apparatus comprises a device with reagents for detecting polynucleotides, wherein the reagents comprise or consist essentially of a reagent set for testing whether renal tubule injury will occur in a test subject as described above.
In one embodiment, the apparatus comprises at least a plurality of polynucleotides or polypeptides representing a plurality of genes selected from those listed in Table 4. In one embodiment the apparatus comprises a plurality of genes includes at least 4 genes selected from those listed in Table 4, the four genes having at least 2% of the total impact of the genes in Table 4. In another preferred embodiment the plurality of genes are variables in a linear classifier capable of classifying renal tubule injury with a training log odds ratio of greater than or equal to 4.35. In one embodiment, the apparatus comprises the plurality of genes listed in any one of iterations 1 through 5 in Table 4. In one preferred embodiment, the apparatus comprises polynucleotide probes capable of hybridizing to a plurality of genes selected from those listed in Table 4. In preferred embodiments, the apparatus comprises a plurality of polynucleotide probes bound to one or more solid surfaces. In one embodiment, the plurality of probes are bound to a single solid surface in an array. Alternatively, the plurality of probes are bound to the solid surface on a plurality of beads. In another preferred embodiment, the apparatus comprises polypeptides encoded by a plurality of genes selected from those listed in Table 4. In one preferred embodiment, the polypeptides are secreted proteins encoded by genes listed in Table 4.
The present invention also provides a method for predicting renal tubule injury in an individual comprising: obtaining a biological sample from the individual after short-term treatment with compound; measuring the expression levels in the biological sample of at least a plurality of genes selected from Table 4; and determining whether the sample is in the positive class for renal tubule injury using a linear classifier comprising at least the plurality of genes for which the expression levels are measured; wherein a sample in the positive class indicates that the individual will have renal tubule injury following sub-chronic treatment with compound. In one preferred embodiment, the method for predicting renal tubule injury is carried out wherein the genes encode secreted proteins. In a preferred embodiment, the individual is a mammal, and preferably a rat. In another preferred embodiment, the biological sample is selected from blood, urine, hair or saliva. In another preferred embodiment of the method, the expression log10 ratio is measured using an array of polynucleotides.
In another embodiment, the invention provides a method for monitoring treatment of an individual for renal tubule injury, or with a compound suspected of causing renal tubule injury, said method comprising: obtaining a biological sample from the individual after short-term treatment with compound; measuring the expression levels in the biological sample of at least a plurality of genes selected from Table 4; and determining whether the sample is in the positive class for renal tubule injury using a linear classifier comprising at least the plurality of genes for which the expression levels are measured; wherein a sample in the positive class indicates that the individual will have renal tubule injury. In a preferred embodiment, the individual is a mammal, and preferably a rat. In another preferred embodiment, the biological sample is selected from blood, urine, hair or saliva. In another preferred embodiment of the method, the expression log10 ratio is measured using an array of polynucleotides.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 depicts the 35 genes in the first iteration RTI signature derived according to the method of Example 3, their corresponding weights, and their average expression log10 ratio in the 15 compound training positive class.
FIG. 2 depicts a plots of training and test logodds ratios for prediction of renal tubule injury for 20 subsets of genes randomly selected from the necessary set. A training or test LOR of 4.00 could be achieved by signatures of as few as 4 and 7 genes, respectively.



DETAILED DESCRIPTION OF THE INVENTION
I. Overview
The present invention provides methods for predicting whether compound treatments induce future renal tubular injury following sub-chronic or long-term treatment using expression data from sub-acute or short-term treatments. The invention provides necessary and sufficient sets of genes and specific signatures comprising these genes that allow gene expression data to be used to identify the ability of a compound treatment to induce late onset renal tubule injury before the actual histological or clinical indication of the toxicity. Further, the invention provides reagent sets and diagnostic devices comprising the disclosed gene sets and signatures that may be used to deduce compound toxicity using short term studies, and avoiding lengthy and costly long term studies.
II. Definitions
“Multivariate dataset” as used herein, refers to any dataset comprising a plurality of different variables including but not limited to chemogenomic datasets comprising logratios from differential gene expression experiments, such as those carried out on polynucleotide microarrays, or multiple protein binding affinities measured using a protein chip. Other examples of multivariate data include assemblies of data from a plurality of standard toxicological or pharmacological assays (e.g., blood analytes measured using enzymatic assays, antibody based ELISA or other detection techniques).
“Variable” as used herein, refers to any value that may vary. For example, variables may include relative or absolute amounts of biological molecules, such as mRNA or proteins, or other biological metabolites. Variables may also include dosing amounts of test compounds.
“Classifier” as used herein, refers to a function of a set of variables that is capable of answering a classification question. A “classification question” may be of any type susceptible to yielding a yes or no answer (e.g., “Is the unknown a member of the class or does it belong with everything else outside the class?”). “Linear classifiers” refers to classifiers comprising a first order function of a set of variables, for example, a summation of a weighted set of gene expression logratios. A valid classifier is defined as a classifier capable of achieving a performance for its classification task at or above a selected threshold value. For example, a log odds ratio≧4.00 represents a preferred threshold of the present invention. Higher or lower threshold values may be selected depending of the specific classification task.
“Signature” as used herein, refers to a combination of variables, weighting factors, and other constants that provides a unique value or function capable of answering a classification question. A signature may include as few as one variable. Signatures include but are not limited to linear classifiers comprising sums of the product of gene expression logratios by weighting factors and a bias term.
“Weighting factor” (or “weight”) as used herein, refers to a value used by an algorithm in combination with a variable in order to adjust the contribution of the variable.
“Impact factor” or “Impact” as used herein in the context of classifiers or signatures refers to the product of the weighting factor by the average value of the variable of interest. For example, where gene expression logratios are the variables, the product of the gene's weighting factor and the gene's measured expression log10 ratio yields the gene's impact. The sum of the impacts of all of the variables (e.g., genes) in a set yields the “total impact” for that set.
“Scalar product” (or “Signature score”) as used herein refers to the sum of impacts for all genes in a signature less the bias for that signature. A positive scalar product for a sample indicates that it is positive for (i.e., a member of) the classification that is determined by the classifier or signature.
“Sufficient set” as used herein is a set of variables (e.g., genes, weights, bias factors) whose cross-validated performance for answering a specific classification question is greater than an arbitrary threshold (e.g., a log odds ratio≧4.0).
“Necessary set” as used herein is a set of variables whose removal from the full set of all variables results in a depleted set whose performance for answering a specific classification question does not rise above an arbitrarily defined minimum level (e.g., log odds ratio≧4.00).
“Log odds ratio” or “LOR” is used herein to summarize the performance of classifiers or signatures. LOR is defined generally as the natural log of the ratio of the odds of predicting a subject to be positive when it is positive, versus the odds of predicting a subject to be positive when it is negative. LOR is estimated herein using a set of training or test cross-validation partitions according to the following equation,




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where c (typically c=40 as described herein) equals the number of partitions, and TPi, TNi, FPi, and FNi represent the number of true positive, true negative, false positive, and false negative occurrences in the test cases of the ith partition, respectively.

“Array” as used herein, refers to a set of different biological molecules (e.g., polynucleotides, peptides, carbohydrates, etc.). An array may be immobilized in or on one or more solid substrates (e.g., glass slides, beads, or gels) or may be a collection of different molecules in solution (e.g., a set of PCR primers). An array may include a plurality of biological polymers of a single class (e.g., polynucleotides) or a mixture of different classes of biopolymers (e.g., an array including both proteins and nucleic acids immobilized on a single substrate).
“Array data” as used herein refers to any set of constants and/or variables that may be observed, measured or otherwise derived from an experiment using an array, including but not limited to: fluorescence (or other signaling moiety) intensity ratios, binding affinities, hybridization stringency, temperature, buffer concentrations.
“Proteomic data” as used herein refers to any set of constants and/or variables that may be observed, measured or otherwise derived from an experiment involving a plurality of mRNA translation products (e.g., proteins, peptides, etc) and/or small molecular weight metabolites or exhaled gases associated with these translation products.
III. General Methods of the Invention
The present invention provides a method to derive multiple non-overlapping gene signatures for renal tubule injury. These non-overlapping signatures use different genes and thus each may be used independently in a predictive assay to confirm that an individual will suffer renal tubule injury. Furthermore, this method for identifying non-overlapping gene signatures also provides the list of all genes “necessary” to create a signature that performs above a certain minimal threshold level for a specific predicting renal tubule injury. This necessary set of genes also may be used to derive additional signatures with varying numbers of genes and levels of performance for particular applications (e.g., diagnostic assays and devices).
Classifiers comprising genes as variables and accompanying weighting factors may be used to classify large datasets compiled from DNA microarray experiments. Of particular preference are sparse linear classifiers. Sparse as used here means that the vast majority of the genes measured in the expression experiment have zero weight in the final linear classifier. Sparsity ensures that the sufficient and necessary gene lists produced by the methodology described herein are as short as possible. These short weighted gene lists (i.e., a gene signature) are capable of assigning an unknown compound treatment to one of two classes.
The sparsity and linearity of the classifiers are important features. The linearity of the classifier facilitates the interpretation of the signature—the contribution of each gene to the classifier corresponds to the product of its weight and the value (i.e., log10 ratio) from the micro array experiment. The property of sparsity ensures that the classifier uses only a few genes, which also helps in the interpretation. More importantly, the sparsity of the classifier may be reduced to a practical diagnostic apparatus or device comprising a relatively small set of reagents representing genes.
A. Gene Expression Related Datasets
a. Various Useful Data Types
The present invention may be used with a wide range of gene expression related data types to generate necessary and sufficient sets of genes useful for renal tubule injury signatures. In a preferred embodiment, the present invention utilizes data generated by high-throughput biological assays such as DNA microarray experiments, or proteomic assays. The datasets are not limited to gene expression related data but also may include any sort of molecular characterization information including, e.g., spectroscopic data (e.g., UV-Vis, NMR, IR, mass spectrometry, etc.), structural data (e.g., three-dimensional coordinates) and functional data (e.g., activity assays, binding assays). The gene sets and signatures produced by using the present invention may be applied in a multitude of analytical contexts, including the development and manufacture of detection devices (i.e., diagnostics).
b. Construction of a Gene Expression Dataset
The present invention may be used to identify necessary and sufficient sets of responsive genes within a gene expression dataset that are useful for predicting renal tubule injury. In a preferred embodiment, a chemogenomic dataset is used. For example, the data may correspond to treatments of organisms (e.g., cells, worms, frogs, mice, rats, primates, or humans etc.) with chemical compounds at varying dosages and times followed by gene expression profiling of the organism's transcriptome (e.g., measuring mRNA levels) or proteome (e.g., measuring protein levels). In the case of multicellular organisms (e.g., mammals) the expression profiling may be carried out on various tissues of interest (e.g., liver, kidney, marrow, spleen, heart, brain, intestine). Typically, valid sufficient classifiers or signatures may be generated that answer questions relevant to classifying treatments in a single tissue type. The present specification describes examples of necessary and sufficient gene signatures useful for classifying chemogenomic data in liver tissue. The methods of the present invention may also be used however, to generate signatures in any tissue type. In some embodiments, classifiers or signatures may be useful in more than one tissue type. Indeed, a large chemogenomic dataset, like that exemplified in the present invention may reveal gene signatures in one tissue type (e.g., liver) that also classify pathologies in other tissues (e.g., intestine).
In addition to the expression profile data, the present invention may be useful with chemogenomic datasets including additional data types such as data from classic biochemistry assays carried out on the organisms and/or tissues of interest. Other data included in a large multivariate dataset may include histopathology, pharmacology assays, and structural data for the chemical compounds of interest.
One example of a chemogenomic multivariate dataset particularly useful with the present invention is a dataset based on DNA array expression profiling data as described in U.S. patent publication 2002/0174096 A1, published Nov. 21, 2002 (titled “Interactive Correlation of Compound Information and Genomic Information”), which is hereby incorporated by reference for all purposes. Microarrays are well known in the art and consist of a substrate to which probes that correspond in sequence to genes or gene products (e.g., cDNAs, mRNAs, cRNAs, polypeptides, and fragments thereof), can be specifically hybridized or bound at a known position. The microarray is an array (i.e., a matrix) in which each position represents a discrete binding site for a gene or gene product (e.g., a DNA or protein), and in which binding sites are present for many or all of the genes in an organism's genome.
As disclosed above, a treatment may include but is not limited to the exposure of a biological sample or organism (e.g., a rat) to a drug candidate (or other chemical compound), the introduction of an exogenous gene into a biological sample, the deletion of a gene from the biological sample, or changes in the culture conditions of the biological sample. Responsive to a treatment, a gene corresponding to a microarray site may, to varying degrees, be (a) up-regulated, in which more mRNA corresponding to that gene may be present, (b) down-regulated, in which less mRNA corresponding to that gene may be present, or (c) unchanged. The amount of up-regulation or down-regulation for a particular matrix location is made capable of machine measurement using known methods (e.g., fluorescence intensity measurement). For example, a two-color fluorescence detection scheme is disclosed in U.S. Pat. Nos. 5,474,796 and 5,807,522, both of which are hereby incorporated by reference herein. Single color schemes are also well known in the art, wherein the amount of up- or down-regulation is determined in silico by calculating the ratio of the intensities from the test array divided by those from a control.
After treatment and appropriate processing of the microarray, the photon emissions are scanned into numerical form, and an image of the entire microarray is stored in the form of an image representation such as a color JPEG or TIFF format. The presence and degree of up-regulation or down-regulation of the gene at each microarray site represents, for the perturbation imposed on that site, the relevant output data for that experimental run or scan.
The methods for reducing datasets disclosed herein are broadly applicable to other gene and protein expression data. For example, in addition to microarray data, biological response data including gene expression level data generated from serial analysis of gene expression (SAGE, supra) (Velculescu et al., 1995, Science, 270:484) and related technologies are within the scope of the multivariate data suitable for analysis according to the method of the invention. Other methods of generating biological response signals suitable for the preferred embodiments include, but are not limited to: traditional Northern and Southern blot analysis; antibody studies; chemiluminescence studies based on reporter genes such as luciferase or green fluorescent protein; Lynx; READS (GeneLogic); and methods similar to those disclosed in U.S. Pat. No. 5,569,588 to Ashby et. al., “Methods for drug screening,” the contents of which are hereby incorporated by reference into the present disclosure.
In another preferred embodiment, the large multivariate dataset may include genotyping (e.g., single-nucleotide polymorphism) data. The present invention may be used to generate necessary and sufficient sets of variables capable of classifying genotype information. These signatures would include specific high-impact SNPs that could be used in a genetic diagnostic or pharmacogenomic assay.
The method of generating classifiers from a multivariate dataset according to the present invention may be aided by the use of relational database systems (e.g., in a computing system) for storing and retrieving large amounts of data. The advent of high-speed wide area networks and the internet, together with the client/server based model of relational database management systems, is particularly well-suited for meaningfully analyzing large amounts of multivariate data given the appropriate hardware and software computing tools. Computerized analysis tools are particularly useful in experimental environments involving biological response signals (e.g., absolute or relative gene expression levels). Generally, multivariate data may be obtained and/or gathered using typical biological response signals. Responses to biological or environmental stimuli may be measured and analyzed in a large-scale fashion through computer-based scanning of the machine-readable signals, e.g., photons or electrical signals, into numerical matrices, and through the storage of the numerical data into relational databases. For example a large chemogenomic dataset may be constructed as described in U.S. patent publication 2005/0060102, published Mar. 17, 2005, which is hereby incorporated by reference for all purposes.
B. Generating Valid Gene Signatures from a Chemogenomic Dataset
a. Mining a Large Chemogenomic Dataset
Generally classifiers or signatures are generated (i.e., mined) from a large multivariate dataset by first labeling the full dataset according to known classifications and then applying an algorithm to the full dataset that produces a linear classifier for each particular classification question. Each signature so generated is then cross-validated using a standard split sample procedure.
The initial questions used to classify (i.e., the classification questions) a large multivariate dataset may be of any type susceptible to yielding a yes or no answer. The general form of such questions is: “Is the unknown a member of the class or does it belong with everything else outside the class?” For example, in the area of chemogenomic datasets, classification questions may include “mode-of-action” questions such as “All treatments with drugs belonging to a particular structural class versus the rest of the treatments” or pathology questions such as “All treatments resulting in a measurable pathology versus all other treatments.” In the specific case of chemogenomic datasets based on gene expression, it is preferred that the classification questions are further categorized based on the tissue source of the gene expression data. Similarly, it may be helpful to subdivide other types of large data sets so that specific classification questions are limited to particular subsets of data (e.g., data obtained at a certain time or dose of test compound). Typically, the significance of subdividing data within large datasets become apparent upon initial attempts to classify the complete dataset. A principal component analysis of the complete data set may be used to identify the subdivisions in a large dataset (see e.g., U.S. 2003/0180808 A1, published Sep. 25, 2003, which is hereby incorporated by reference herein.) Methods of using classifiers to identify information rich genes in large chemogenomic datasets is also described in U.S. Ser. No. 11/114,998, filed Apr. 25, 2005, which is hereby incorporated by reference herein for all purposes.
Labels are assigned to each individual (e.g., each compound treatment) in the dataset according to a rigorous rule-based system. The +1 label indicates that a treatment falls in the class of interest, while a −1 label indicates that the variable is outside the class. Thus, with respect to the 64 compound treatments shown in Table 2 (see Example 2 below) used in generating an RTI signature, the “nephrotoxic” treatments were labeled +1, whereas the “non-nephrotoxic” were labeled −1. Information used in assigning labels to the various individuals to classify may include annotations from the literature related to the dataset (e.g., known information regarding the compounds used in the treatment), or experimental measurements on the exact same animals (e.g., results of clinical chemistry or histopathology assays performed on the same animal). A more detailed description of the general method for using classification questions to mine a chemogenomic dataset for signatures is described in U.S. Ser. No. 11/149,612, filed Jun. 10, 2005, and PCT/US2005/020695, filed Jun. 10, 2005, each of which is hereby incorporated in its entirety by reference herein.
b. Algorithms for Generating Valid Gene Signatures
Dataset classification may be carried out manually, that is by evaluating the dataset by eye and classifying the data accordingly. However, because the dataset may involve tens of thousands (or more) individual variables, more typically, querying the full dataset with a classification question is carried out in a computer employing any of the well-known data classification algorithms.
In preferred embodiments, algorithms are used to query the full dataset that generate linear classifiers. In particularly preferred embodiments the algorithm is selected from the group consisting of: SPLP, SPLR and SPMPM. These algorithms are based respectively on Support Vector Machines (SVM), Logistic Regression (LR) and Minimax Probability Machine (MPM). They have been described in detail elsewhere (See e.g., El Ghaoui et al., op. cit; Brown, M. P., W. N. Grundy, D. Lin, N. Cristianini, C. W. Sugnet, T. S. Furey, M. Ares, Jr., and D. Haussler, “Knowledge-based analysis of microarray gene expression data by using support vector machines,” Proc Natl Acad Sci USA 97: 262-267 (2000)).
Generally, the sparse classification methods SPLP, SPLR, SPMPM are linear classification algorithms in that they determine the optimal hyperplane separating a positive and a negative class. This hyperplane, H can be characterized by a vectorial parameter, w (the weight vector) and a scalar parameter, b (the bias): H={x|wTx+b=0}.
For all proposed algorithms, determining the optimal hyperplane reduces to optimizing the error on the provided training data points, computed according to some loss function (e.g., the “Hinge loss,” i.e., the loss function used in 1-norm SVMs; the “LR loss;” or the “MPM loss” augmented with a 1-norm regularization on the signature, w. Regularization helps to provide a sparse, short signature. Moreover, this 1-norm penalty on the signature will be weighted by the average standard error per gene. That is, genes that have been measured with more uncertainty will be less likely to get a high weight in the signature. Consequently, the proposed algorithms lead to sparse signatures, and take into account the average standard error information.
Mathematically, the algorithms can be described by the cost functions (shown below for SPLP, SPLR and SPMPM) that they actually minimize to determine the parameters w and b.



  
    
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The first term minimizes the training set error, while the second term is the 1-norm penalty on the signature w, weighted by the average standard error information per gene given by sigma. The training set error is computed according to the so-called Hinge loss, as defined in the constraints. This loss function penalizes every data point that is closer than “1” to the separating hyperplane H, or is on the wrong side of H. Notice how the hyperparameter rho allows trade-off between training set error and sparsity of the signature w.



  
    
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Here, the first two terms, together with the constraint are related to the misclassification error, while the third term will induce sparsity, as before. The symbols with a hat are empirical estimates of the covariances and means of the positive and the negative class. Given those estimates, the misclassification error is controlled by determining w and b such that even for the worst-case distributions for the positive and negative class (which we do not exactly know here) with those means and covariances, the classifier will still perform well. More details on how this exactly relates to the previous cost function can be found in e.g., El Ghaoui, L., G. R. G. Lanckriet, and G. Natsoulis, 2003, “Robust classifiers with interval data” Report # UCB/CSD-03-1279. Computer Science Division (EECS), University of California, Berkeley, Calif.
As mentioned above, classification algorithms capable of producing linear classifiers are preferred for use with the present invention. In the context of chemogenomic datasets, linear classifiers may be used to generate one or more valid signatures capable of answering a classification question comprising a series of genes and associated weighting factors. Linear classification algorithms are particularly useful with DNA array or proteomic datasets because they provide simplified signatures useful for answering a wide variety of questions related to biological function and pharmacological/toxicological effects associated with genes or proteins. These signatures are particularly useful because they are easily incorporated into wide variety of DNA- or protein-based diagnostic assays (e.g., DNA microarrays).
However, some classes of non-linear classifiers, so called kernel methods, may also be used to develop short gene lists, weights and algorithms that may be used in diagnostic device development; while the preferred embodiment described here uses linear classification methods, it specifically contemplates that non-linear methods may also be suitable.
Classifications may also be carried using principle component analysis and/or discrimination metric algorithms well-known in the art (see e.g., U.S. 2003/0180808 A1, published Sep. 25, 2003, which is hereby incorporated by reference herein).
Additional statistical techniques, or algorithms, are known in the art for generating classifiers. Some algorithms produce linear classifiers, which are convenient in many diagnostic applications because they may be represented as a weighted list of variables. In other cases non-linear classifier functions of the initial variables may be used. Other types of classifiers include decision trees and neural networks. Neural networks are universal approximators (Hornik, K., M. Stinchcombe, and H. White. 1989. “Multilayer feedforward networks are universal approximators,” Neural Networks 2: 359-366); they can approximate any measurable function arbitrarily well, and they can readily be used to model classification functions as well. They perform well on several biological problems, e.g., protein structure prediction, protein classification, and cancer classification using gene expression data (see, e.g., Bishop, C. M. 1996. Neural Networks for Pattern Recognition. Oxford University Press; Khan, J., J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, and P. S. Meltzer. 2001. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7: 673-679; Wu, C. H., M. Berry, S. Shivakumar, and J. McLarty. 1995. Neural networks for full-scale protein sequence classification: sequence encoding with singular value decomposition. Machine Learning 21: 177-193).
c. Cross-Validation of Gene Signatures
Cross-validation of a gene signature's performance is an important step for determining whether the signature is sufficient. Cross-validation may be carried out by first randomly splitting the full dataset (e.g., a 60/40 split). A training signature is derived from the training set composed of 60% of the samples and used to classify both the training set and the remaining 40% of the data, referred to herein as the test set. In addition, a complete signature is derived using all the data. The performance of these signatures can be measured in terms of log odds ratio (LOR) or the error rate (ER) defined as:

LOR=ln (((TP+0.5)*(TN+0.5))/((FP+0.5)*(FN+0.5)))

and

ER=(FP+FN)/N; 

where TP, TN, FP, FN, and N are true positives, true negatives, false positives, false negatives, and total number of samples to classify, respectively, summed across all the cross validation trials. The performance measures are used to characterize the complete signature, the average of the training or the average of the test signatures.
The SVM algorithms described above are capable of generating a plurality of gene signatures with varying degrees of performance for the classification task. In order to identify that signatures that are to be considered “valid,” a threshold performance is selected for the particular classification question. In one preferred embodiment, the classifier threshold performance is set as log odds ratio greater than or equal to 4.00 (i.e., LOR≧4.00). However, higher or lower thresholds may be used depending on the particular dataset and the desired properties of the signatures that are obtained. Of course many queries of a chemogenomic dataset with a classification question will not generate a valid gene signature.
Two or more valid gene signatures may be generated that are redundant or synonymous for a variety of reasons. Different classification questions (i.e., class definitions) may result in identical classes and therefore identical signatures. For instance, the following two class definitions define the exact same treatments in the database: (1) all treatments with molecules structurally related to statins; and (2) all treatments with molecules having an IC50<1 μM for inhibition of the enzyme HMG CoA reductase.
In addition, when a large dataset is queried with the same classification question using different algorithms (or even the same algorithm under slightly different conditions) different, valid signatures may be obtained. These different signatures may or may not comprise overlapping sets of variables; however, they each can accurately identify members of the class of interest.
For example, as illustrated in Table 1, two equally performing gene signatures (LOR= ̃7.0) for the fibrate class of compounds may be generated by querying a chemogenomic dataset with two different algorithms: SPLP and SPLR. Genes are designated by their accession number and a brief description. The weights associated with each gene are also indicated. Each signature was trained on the exact same 60% of the multivariate dataset and then cross validated on the exact same remaining 40% of the dataset. Both signatures were shown to exhibit the exact same level of performance as classifiers: two errors on the cross validation data set. The SPLP derived signature consists of 20 genes. The SPLR derived signature consists of eight genes. Only three of the genes from the SPLP signature are present in the eight gene SPLR signature.







TABLE 1







Two Gene Signatures for the Fibrate Class of Drugs











Accession
Weight
Unigene name













RLPC
K03249
1.1572
enoyl-Co A, hydratase/3-hydroxyacyl Co A dehydrogenase



AW916833
1.0876
hypothetical protein RMT-7



BF387347
0.4769
ESTs



BF282712
0.4634
ESTs



AF034577
0.3684
pyruvate dehydrogenate kinase 4



NM_019292
0.3107
carbonic anhydrase 3



AI179988
0.2735
ectodermal-neural cortex (with BTB-like domain)



AI715955
0.211
Stac protein (SRC homology 3 and cysteine-rich domain protein)



BE110695
0.2026
activating transcription factor 1



J03752
0.0953
microsomal glutathione S-transferase 1



D86580
0.0731
nuclear receptor subfamily 0, group B, member 2



BF550426
0.0391
KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein retention receptor 2



AA818999
0.0296
muscleblind-like 2



NM_019125
0.0167
probasin



AF150082
−0.0141
translocase of inner mitochondrial membrane 8 (yeast) homolog A



BE118425
−0.0781
Arsenical pump-driving ATPase



NM−017136
−0.126
squalene epoxidase



AI171367
−0.3222
HSPC154 protein



NM−019369
−0.637
inter alpha-trypsin inhibitor, heavy chain 4



AI137259
−0.7962
ESTs


SPLR
NM_017340
5.3688
acyl-coA oxidase



BF282712
4.1052
ESTs



NM_012489
3.8462
acetyl-Co A acyltransferase 1 (peroxisomal 3-oxoacyl-Co A thiolase)



BF387347
1.767
ESTs



K03249
1.7524
enoyl-Co A, hydratase/3-hydroxyacyl Co A dehydrogenase



NM_016986
0.0622
acetyl-co A dehydrogenase, medium chain



AB026291
−0.7456
acetoacetyl-CoA synthetase



AI454943
−1.6738
likely ortholog of mouse porcupine homolog









It is interesting to note that only three genes are common between these two signatures, (K03249, BF282712, and BF387347) and even those are associated with different weights. While many of the genes may be different, some commonalities may nevertheless be discerned. For example, one of the negatively weighted genes in the SPLP derived signature is NM—017136 encoding squalene epoxidase, a well-known cholesterol biosynthesis gene. Squalene epoxidase is not present in the SPLR derived signature but aceto-acteylCoA synthetase, another cholesterol biosynthesis gene is present and is also negatively weighted.
Additional variant signatures may be produced for the same classification task. For example, the average signature length (number of genes) produced by SPLP and SPLR, as well as the other algorithms, may be varied by use of the parameter p (see e.g., El Ghaoui, L., G. R. G. Lanckriet, and G. Natsoulis, 2003, “Robust classifiers with interval data” Report # UCB/CSD-03-1279. Computer Science Division (EECS), University of California, Berkeley, Calif.; and PCT publication WO 2005/017807 A2, published Feb. 24, 2005, each of which is hereby incorporated by reference herein). Varying ρ can produce signatures of different length with comparable test performance (Natsoulis et al., “Classification of a large microarray data set: Algorithm comparison and analysis of drug signatures,” Gen. Res. 15:724-736 (2005)). Those signatures are obviously different and often have no common genes between them (i.e., they do not overlap in terms of genes used).
C. “Stripping” Signatures from a Dataset to Generate the “Necessary” Set
Each individual classifier or signature is capable of classifying a dataset into one of two categories or classes defined by the classification question. Typically, an individual signature with the highest test log odds ratio will be considered as the best classifier for a given task. However, often the second, third (or lower) ranking signatures, in terms of performance, may be useful for confirming the classification of compound treatment, especially where the unknown compound yields a borderline answer based on the best classifier. Furthermore, the additional signatures may identify alternative sources of informational rich data associated with the specific classification question. For example, a slightly lower ranking gene signature from a chemogenomic dataset may include those genes associated with a secondary metabolic pathway affected by the compound treatment. Consequently, for purposes of fully characterizing a class and answering difficult classification questions, it is useful to define the entire set of variables that may be used to produce the plurality of different classifiers capable of answering a given classification question. This set of variables is referred to herein as a “necessary set.” Conversely, the remaining variables from the full dataset are those that collectively cannot be used to produce a valid classifier, and therefore are referred to herein as the “depleted set.”
The general method for identifying a necessary set of variables useful for a classification question involved what is referred to herein as a classifier “stripping” algorithm. The stripping algorithm comprises the following steps: (1) querying the full dataset with a classification question so as to generate a first linear classifier capable of performing with a log odds ratio greater than or equal to 4.0 comprising a first set of variables; (2) removing the variables of the first linear classifier from the full dataset thereby generating a partially depleted dataset; (3) re-querying the partially depleted dataset with the same classification question so as to generate a second linear classifier and cross-validating this second classifier to determine whether it performs with a log odds ratio greater than or equal to 4. If it does not, the process stops and the dataset is fully depleted for variables capable of generating a classifier with an average log odds ratio greater than or equal to 4.0. If the second classifier is validated as performing with a log odds ratio greater than or equal to 4.0, then its variables are stripped from the full dataset and the partially depleted set if re-queried with the classification question. These cycles of stripping and re-querying are repeated until the performance of any remaining set of variables drops below an arbitrarily set LOR. The threshold at which the iterative process is stopped may be arbitrarily adjusted by the user depending on the desired outcome. For example, a user may choose a threshold of LOR=0. This is the value expected by chance alone. Consequently, after repeated stripping until LOR=0 there is no classification information remaining in the depleted set. Of course, selecting a lower value for the threshold will result in a larger necessary set.
Although a preferred cut-off for stripping classifiers is LOR=4.0, this threshold is arbitrary. Other embodiments within the scope of the invention may utilize higher or lower stripping cutoffs e.g., depending on the size or type of dataset, or the classification question being asked. In addition other metrics could be used to assess the performance (e.g., specificity, sensitivity, and others). Also the stripping algorithm removes all variables from a signature if it meets the cutoff. Other procedures may be used within the scope of the invention wherein only the highest weighted or ranking variables are stripped. Such an approach based on variable impact would likely result in a classifier “surviving” more cycles and defining a smaller necessary set.
Other procedures may be used within the scope of the invention wherein only the highest weighted or ranking variables are stripped. Such an approach based on variable impact would likely result in a classifier “surviving” more cycles and defining a smaller necessary set.
In another alternative approach, the genes from signatures may be stripped from the dataset until it is unable to generate a signature capable of classifying the “true label set” with an LOR that is statistically different from its classification of the “random label set.” The “true label set” refers to a training set of compound treatment data that is correctly labeled (e.g., +1 class, −1 class) for the particular classification question. The “random label set” refers to the same set of compound treatment data where the class labels have been randomly assigned. Attempts to use a signature to classify a random label set will result in an average LOR of approximately zero and some standard deviation (SD). These values may be compared to the average LOR and SD for the classifying the true label set, where the SD is calculated based on LOR results across the 20 or 40 splits. The difference in classifying true and random label sets with valid signatures should be significantly greater than random. In such an alternative approach, the selected performance threshold for a signature is a p-value rather than a LOR cutoff.
The resulting fully-depleted set of variables that remains after a classifier is fully stripped from the full dataset cannot generate a classifier for the specific classification question (with the desired level of performance). Consequently, the set of all of the variables in the classifiers that were stripped from the full set are defined as “necessary” for generating a valid classifier.
The stripping method utilizes a classification algorithm at its core. The examples presented here use SPLP for this task. Other algorithms, provided that they are sparse with respect to genes could be employed. SPLR and SPMPM are two alternatives for this functionality (see e.g., El Ghaoui, L., G. R. G. Lanckriet, and G. Natsoulis, 2003, “Robust classifiers with interval data” Report # UCB/CSD-03-1279. Computer Science Division (EECS), University of California, Berkeley, Calif., and PCT publication WO 2005/017807 A2, published Feb. 24, 2005, which is hereby incorporated by reference herein).
In one embodiment, the stripping algorithm may be used on a chemogenomics dataset comprising DNA microarray data. The resulting necessary set of genes comprises a subset of highly informative genes for a particular classification question. Consequently, these genes may be incorporated in diagnostic devices (e.g., polynucleotide arrays) where that particular classification (e.g., renal tubule injury) is of interest. In other exemplary embodiments, the stripping method may be used with datasets from proteomic experiments.
D. Mining the Renal Tubule Injury Necessary Set for Signatures
Besides identifying the “necessary” set of genes for a particular signature (i.e., classifier), another important use of the stripping algorithm is the identification of multiple, non-overlapping sufficient sets of genes useful for answering a particular classification question. These non-overlapping sufficient sets are a direct product of the above-described general method of stripping valid classifiers. Where the application of the method results in a second validated classifier with the desired level of performance, that second classifier by definition does not include any genes in common with the first classifier. Typically, the earlier stripped non-overlapping gene signature yields higher performance with fewer genes. In other words, the earliest identified sufficient set usually comprises the highest impact, most information-rich genes with respect to the particular classification question. The valid classifiers that appear during later iterations of the stripping algorithm typically contain a larger number of genes. However, these later appearing classifiers may provide valuable information regarding normally unrecognized relationships between genes in the dataset. For example, in the case of non-overlapping gene signatures identified by stripping in a chemogenomics dataset, the later appearing signatures may include families of genes not previously recognized as involved in the particular metabolic pathway that is being affected by a particular compound treatment. Thus, functional analysis of a gene signature stripping procedure may identify new metabolic targets associated with a compound treatment.
The necessary set high impact genes generated by the stripping method itself represents a subset of genes that may be mined for further signatures. Hence, the complete set of genes in a necessary set for predicting renal tubule injury may used to randomly generate random subsets of genes of varying size that are capable of generating additional predictive signatures. One preferred method of selecting such subsets is based on percentage of total impact. Thus, subsets of genes are selected whose summed impact factors are a selected percentage of the total impact (i.e., the sum of the impacts of all genes in the necessary set). These percentage impact subsets may be used to generate new signatures for predicting renal tubule injury. For example, a random subset from the necessary set of 9 genes with 4% of the total impact may be used with one of the SVM algorithms to generate a new linear classifier of 8 genes, weighting factors and a bias term that may be used as a signature for renal tubule injury. Thus, the necessary set for a particular classification represents a greatly reduced dataset that can generate new signatures with varying properties such as shorter (or longer) gene lengths and higher (or lower) LOR performance values.
E. Functional Characterization of the Renal Tubule Injury Necessary Set
The stripping method described herein produces a necessary set of genes representing for answering the RTI classification question. The RTI necessary set of genes also may be characterized in functional terms based on the ability of the information rich genes in the set to supplement (i.e., “revive”) the ability of a fully “depleted” set of genes to generate valid RTI signatures. Thus, the necessary set for the RTI classification question corresponds to that set of genes from which any random selection when added to a depleted set (i.e., depleted for RTI classification question) restores the ability of that set to produce RTI signatures with an average LOR (avg. LOR) above a threshold level. The general method for functionally characterizing a necessary set in terms of its ability to revive its depleted set is described in U.S. Ser. No. 11/149,612, filed Jun. 10, 2005, and PCT/US2005/020695, filed Jun. 10, 2005, each of which is hereby incorporated in its entirety by reference herein.
Preferably, the threshold performance used is an avg. LOR greater than or equal to 4.00. Other values for performance, however, may be set. For example, avg. LOR may vary from about 1.0 to as high as 8.0. In preferred embodiments, the avg. LOR threshold may be 3.0 to as high as 7.0 including all integer and half-integer values in that range. The necessary set may then be defined in terms of percentage of randomly selected genes from the necessary set that restore the performance of a depleted set above a certain threshold. Typically, the avg. LOR of the depleted set is  ̃1.20, although as mentioned above, datasets may be depleted more or less depending on the threshold set, and depleted sets with avg. LOR as low as 0.0 may be used. Generally, the depleted set will exhibit an avg. LOR between about 0.5 and 1.5.
The third parameter establishing the functional characteristics of the RTI necessary set of genes for answering the RTI classification question is the percentage of randomly selected genes from that set that result in reviving the threshold performance of the depleted set. Typically, where the threshold avg. LOR is at least 4.00 and the depleted set performs with an avg. LOR of  ̃1.20, typically 16-36% of randomly selected genes from the necessary set are required to restore the average performance of the depleted set to the threshold value. In preferred embodiments, the random supplementation may be achieved using 16, 18, 20, 22, 24, 26, 28, 30, 32, 34 or 36% of the necessary set.
Alternatively, as described above, the necessary set may be characterized based on its ability to randomly generate signatures capable of classifying a true label set with an average performance above those signatures ability to classify a random label set. In preferred embodiments, signatures generated from a random selection of at least 10% of the genes in the necessary set may perform at least 1 standard deviation, and preferably at least 2 standard deviations, better for classifying the true versus the random label set. In other embodiments, the random selection may be of at least 15%, 20%, 25%, 30%, 40%, 50%, and even higher percentages of genes from the set.
F. Using Signatures and the Necessary Set to Generate Diagnostic Assays and Devices for Predicting Renal Tubule Injury
A diagnostic usually consists in performing one or more assays and in assigning a sample to one or more categories based on the results of the assay(s). Desirable attributes of a diagnostic assays include high sensitivity and specificity measured in terms of low false negative and false positive rates and overall accuracy. Because diagnostic assays are often used to assign large number of samples to given categories, the issues of cost per assay and throughput (number of assays per unit time or per worker hour) are of paramount importance.
Typically the development of a diagnostic assay involves the following steps: (1) define the end point to diagnose, e.g., cholestasis, a pathology of the liver (2) identify one or more markers whose alteration correlates with the end point, e.g., elevation of bilirubin in the bloodstream as an indication of cholestasis; and (3) develop a specific, accurate, high-throughput and cost-effective assay for that marker. In order to increase throughput and decrease costs several diagnostics are often combined in a panel of assays, especially when the detection methodologies are compatible. For example several ELISA-based assays, each using different antibodies to ascertain different end points may be combined in a single panel and commercialized as a single kit. Even in this case, however, each of the ELISA-based assays had to be developed individually often requiring the generation of specific reagents.
The present invention provides signatures and methods for identifying additional signatures comprising as few as 4 genes that are useful for determining a therapeutic or toxicological end-point for renal tubule injury. These signatures (and the genes from which they are composed) may also be used in the design of improved diagnostic devices that answer the same questions as a large microarray but using a much smaller fraction of data. Generally, the reduction of information in a large chemogenomic dataset to a simple signature enables much simpler devices compatible with low cost high throughput multi-analyte measurement.
As described herein, a large chemogenomic dataset may be mined for a plurality of informative genes useful for answering classification questions. The size of the classifiers or signatures so generated may be varied according to experimental needs. In addition, multiple non-overlapping classifiers may be generated where independent experimental measures are required to confirm a classification. Generally, the sufficient classifiers result in a substantial reduction of data that needs to be measured to classify a sample. Consequently, the signatures and methods of the present invention provide the ability to produce cheaper, higher throughput, diagnostic measurement methods or strategies. In particular, the invention provides diagnostic reagent sets useful in diagnostic assays and the associated diagnostic devices and kits. As used herein, diagnostic assays includes assays that may be used for patient prognosis and therapeutic monitoring.
Diagnostic reagent sets may include reagents representing the subset of genes found in the necessary set of 186 consisting of less than 50%, 40%, 30%, 20%, 10%, or even less than 5% of the total genes. In one preferred embodiment, the diagnostic reagent set is a plurality of polynucleotides or polypeptides representing specific genes in a sufficient or necessary set of the invention. Such biopolymer reagent sets are immediately applicable in any of the diagnostic assay methods (and the associate kits) well known for polynucleotides and polypeptides (e.g., DNA arrays, RT-PCR, immunoassays or other receptor based assays for polypeptides or proteins). For example, by selecting only those genes found in a smaller yet “sufficient” gene signature, a faster, simpler and cheaper DNA array may be fabricated for that signature's specific classification task. Thus, a very simple diagnostic array may be designed that answers 3 or 4 specific classification questions and includes only 60-80 polynucleotides representing the approximately 20 genes in each of the signatures. Of course, depending on the level of accuracy required the LOR threshold for selecting a sufficient gene signature may be varied. A DNA array may be designed with many more genes per signature if the LOR threshold is set at e.g., 7.00 for a given classification question. The present invention includes diagnostic devices based on gene signatures exhibiting levels of performance varying from less than LOR=3.00 up to LOR=10.00 and greater.
The diagnostic reagent sets of the invention may be provided in kits, wherein the kits may or may not comprise additional reagents or components necessary for the particular diagnostic application in which the reagent set is to be employed. Thus, for a polynucleotide array applications, the diagnostic reagent sets may be provided in a kit which further comprises one or more of the additional requisite reagents for amplifying and/or labeling a microarray probe or target (e.g., polymerases, labeled nucleotides, and the like).
A variety of array formats (for either polynucleotides and/or polypeptides) are well-known in the art and may be used with the methods and subsets produced by the present invention. In one preferred embodiment, photolithographic or micromirror methods may be used to spatially direct light-induced chemical modifications of spacer units or functional groups resulting in attachment at specific localized regions on the surface of the substrate. Light-directed methods of controlling reactivity and immobilizing chemical compounds on solid substrates are well-known in the art and described in U.S. Pat. Nos. 4,562,157, 5,143,854, 5,556,961, 5,968,740, and 6,153,744, and PCT publication WO 99/42813, each of which is hereby incorporated by reference herein.
Alternatively, a plurality of molecules may be attached to a single substrate by precise deposition of chemical reagents. For example, methods for achieving high spatial resolution in depositing small volumes of a liquid reagent on a solid substrate are disclosed in U.S. Pat. Nos. 5,474,796 and 5,807,522, both of which are hereby incorporated by reference herein.
It should also be noted that in many cases a single diagnostic device may not satisfy all needs. However, even for an initial exploratory investigation (e.g., classifying drug-treated rats) DNA arrays with sufficient gene sets of varying size (number of genes), each adapted to a specific follow-up technology, can be created. In addition, in the case of drug-treated rats, different arrays may be defined for each tissue.
Alternatively, a single substrate may be produced with several different small arrays of genes in different areas on the surface of the substrate. Each of these different arrays may represent a sufficient set of genes for the same classification question but with a different optimal gene signature for each different tissue. Thus, a single array could be used for particular diagnostic question regardless of the tissue source of the sample (or even if the sample was from a mixture of tissue sources, e.g., in a forensic sample).
In addition, it may be desirable to investigate classification questions of a different nature in the same tissue using several arrays featuring different non-overlapping gene signatures for a particular classification question.
As described above, the methodology described here is not limited to chemogenomic datasets and DNA microarray data. The invention may be applied to other types of datasets to produce necessary and sufficient sets of variables useful for classifiers. For example, proteomics assay techniques, where protein levels are measured or protein interaction techniques such as yeast 2-hybrid or mass spectrometry also result in large, highly multivariate dataset, which could be classified in the same way described here. The result of all the classification tasks could be submitted to the same methods of signature generation and/or classifier stripping in order to define specific sets of proteins useful as signatures for specific classification questions.
In addition, the invention is useful for many traditional lower throughput diagnostic applications. Indeed the invention teaches methods for generating valid, high-performance classifiers consisting of 5% or less of the total variables in a dataset. This data reduction is critical to providing a useful analytical device. For example, a large chemogenomic dataset may be reduced to a signature comprising less than 5% of the genes in the full dataset. Further reductions of these genes may be made by identifying only those genes whose product is a secreted protein. These secreted proteins may be identified based on known annotation information regarding the genes in the subset. Because the secreted proteins are identified in the sufficient set useful as a signature for a particular classification question, they are most useful in protein based diagnostic assays related to that classification. For example, an antibody-based blood serum assay may be produced using the subset of the secreted proteins found in the sufficient signature set. Hence, the present invention may be used to generate improved protein-based diagnostic assays from DNA array information.
The general method of the invention as described above is exemplified below. The following examples are offered as illustrations of specific embodiments and are not intended to limit the inventions disclosed throughout the whole of the specification.
EXAMPLES
Example 1
Construction of Chemogenomic Reference Database (DrugMatrixTM)
This example illustrates the construction of a large multivariate chemogenomic dataset based on DNA microarray analysis of rat tissues from over 580 different in vivo compound treatments. This dataset was used to generate RTI signatures comprising genes and weights which subsequently were used to generate a necessary set of highly responsive genes that may be incorporated into high throughput diagnostic devices as described in Examples 2-7.
The detailed description of the construction of this chemogenomic dataset is described in Examples 1 and 2 of Published U.S. Pat. Appl. No. 2005/0060102 A1, published Mar. 17, 2005, which is hereby incorporated by reference for all purposes. Briefly, in vivo short-term repeat dose rat studies were conducted on over 580 test compounds, including marketed and withdrawn drugs, environmental and industrial toxicants, and standard biochemical reagents. Rats (three per group) were dosed daily at either a low or high dose. The low dose was an efficacious dose estimated from the literature and the high dose was an empirically-determined maximum tolerated dose, defined as the dose that causes a 50% decrease in body weight gain relative to controls during the course of the 5 day range finding study. Animals were necropsied on days 0.25, 1, 3, and 5 or 7. Up to 13 tissues (e.g., liver, kidney, heart, bone marrow, blood, spleen, brain, intestine, glandular and nonglandular stomach, lung, muscle, and gonads) were collected for histopathological evaluation and microarray expression profiling on the Amersham CodeLinkTM RU1 platform. In addition, a clinical pathology panel consisting of 37 clinical chemistry and hematology parameters was generated from blood samples collected on days 3 and 5.
In order to assure that all of the dataset is of high quality a number of quality metrics and tests are employed. Failure on any test results in rejection of the array and exclusion from the data set. The first tests measure global array parameters: (1) average normalized signal to background, (2) median signal to threshold, (3) fraction of elements with below background signals, and (4) number of empty spots. The second battery of tests examines the array visually for unevenness and agreement of the signals to a tissue specific reference standard formed from a number of historical untreated animal control arrays (correlation coefficient>0.8). Arrays that pass all of these checks are further assessed using principle component analysis versus a dataset containing seven different tissue types; arrays not closely clustering with their appropriate tissue cloud are discarded.
Data collected from the scanner is processed by the Dewarping/DetrendingTM normalization technique, which uses a non-linear centralization normalization procedure (see, Zien, A., T. Aigner, R. Zimmer, and T. Lengauer. 2001. Centralization: A new method for the normalization of gene expression data. Bioinformatics) adapted specifically for the CodeLink microarray platform. The procedure utilizes detrending and dewarping algorithms to adjust for non-biological trends and non-linear patterns in signal response, leading to significant improvements in array data quality.
Log10-ratios are computed for each gene as the difference of the averaged logs of the experimental signals from (usually) three drug-treated animals and the averaged logs of the control signals from (usually) 20 mock vehicle-treated animals. To assign a significance level to each gene expression change, the standard error for the measured change between the experiments and controls is computed. An empirical Bayesian estimate of standard deviation for each measurement is used in calculating the standard error, which is a weighted average of the measurement standard deviation for each experimental condition and a global estimate of measurement standard deviation for each gene determined over thousands of arrays (Carlin, B. P. and T. A. Louis. 2000. “Bayes and empirical Bayes methods for data analysis,” Chapman & Hall/CRC, Boca Raton; Gelman, A. 1995. “Bayesian data analysis,” Chapman & Hall/CRC, Boca Raton). The standard error is used in a t-test to compute a p-value for the significance of each gene expression change. The coefficient of variation (CV) is defined as the ratio of the standard error to the average Log10-ratio, as defined above.
Example 2
Preparation of a Chemogenomic Dataset for Late-Onset Renal Tubule Injury
This example describes methods used to prepare a chemogenomic dataset (i.e., a positive training set) for use deriving a signature for renal tubule injury (i.e., late-onset nephrotoxicity).
Overview
28-day repeat dose studies were conducted on known nephrotoxicants. Doses were chosen that would not cause histological or clinical evidence of renal tubular injury after 5 days of dosing, but would cause histological evidence of tubular injury after 28 days of dosing. Animals were assigned to groups such that mean body weights were within 10% of the mean vehicle control group. Test compounds were administered either orally (10 ml of corn oil/kg body weight) or by intra-peritoneal injection (5 ml of saline/kg body weight). Animals were dosed once daily starting on day 0, and necropsied 24 hrs after the last dose following an overnight fast on day 5 (n=5) and day 28 (n=10). An equivalent number of time- and vehicle-matched control rats were treated concurrently. Likewise, a large set of short-term (day 5/7) treatments that would not cause renal tubular injury (i.e., negative control data) after sub-chronic dosing conditions were selected from the chemogenomic reference database in-vivo studies described in Example 1 (above), to complete the training set. This assertion of the absence of nephrotoxicity for these compounds was based on thorough evaluation of human clinical studies curated in Physicians Desk Reference (PDR) as well as peer-reviewed published literature. Lastly, these treatments did not cause histological evidence of renal tubular injury on day 5/7. Appropriate time and vehicle-matched controls for these negative treatments were also derived from the reference database in vivo studies described in Example 1.
Compound Selection and Dosing
To derive a signature predictive of renal tubular injury, it is necessary to first define both nephrotoxic and non-nephrotoxic treatments from short-term studies devoid of tissue injury that can be used to model the early transcriptional effects that will be predictive of late-onset toxicity. To empirically confirm the late-onset nephrotoxicity of the positive treatments prior to inclusion in the training set, 28-day repeat dose studies were conducted on 15 known nephrotoxicants in adult male Sprague-Dawley rats according to the in vivo methods described in Example 1.
In addition, 49 short-term (day 5/7) compound treatments that would not cause renal tubular injury after sub-chronic dosing conditions were selected from chemogenomic reference database (DrugMatrixTM) to complete the training set. This assertion of the absence of nephrotoxicity for these compounds was based on thorough evaluation of human clinical studies curated in Physicians Desk Reference (PDR) as well as peer-reviewed published literature. These treatments were experimentally confirmed not to cause histological evidence of renal tubular injury at the time of expression analysis.
Doses were chosen that would not cause histological or clinical evidence of renal tubular injury after 5 days of dosing, but would cause histological evidence of tubular injury after 28 days of dosing. This time course of injury was significant to deriving a predictive signature since the presence of injury on day 5 would bias the signature towards a gene expression pattern that are indicative of the presence of a lesion, rather than identifying gene expression events that will predict the future occurrence of the lesion.
The compounds and their doses are listed in Table 2.







TABLE 2







64 in vivo compound treatments used in the training set.













Dose
Time





Compound
(mg/kg/d)
(d)
Vehicle
Route
Class















4-NONYLPHENOL
200
5
Corn oil
PO
Nephrotoxic


AMIKACIN
160
5
Saline
IP
Nephrotoxic


CADMIUM CHLORIDE
2
5
Saline
IP
Nephrotoxic


CARBOPLATIN
5
5
Saline
IP
Nephrotoxic


CISPLATIN
0.5
5
Saline
IP
Nephrotoxic


COBALT (II) CHLORIDE
10
5
Saline
IP
Nephrotoxic


CYCLOSPORIN A
70
5
Corn oil
PO
Nephrotoxic


DAUNORUBICIN
4
5
Saline
IV
Nephrotoxic


DOXORUBICIN
4
5
Saline
IV
Nephrotoxic


GENTAMICIN
40
5
Saline
IP
Nephrotoxic


IDARUBICIN
4
5
Saline
IV
Nephrotoxic


LEAD (II) ACETATE
2
5
Saline
IP
Nephrotoxic


NETILMICIN
40
5
Saline
IP
Nephrotoxic


ROXARSONE
11
5
Corn oil
PO
Nephrotoxic


TOBRAMYCIN
40
5
Saline
IP
Nephrotoxic


6-METHOXY-2-NAPHTHYLACETIC ACID
360
5
Saline
PO
Non-nephrotoxic


ACARBOSE
2000
5
Water
PO
Non-nephrotoxic


AMPRENAVIR
600
5
CMC
PO
Non-nephrotoxic


ANTIPYRINE
1500
5
CMC
PO
Non-nephrotoxic


ASPIRIN
375
5
Corn oil
PO
Non-nephrotoxic


ATORVASTATIN
300
5
Corn oil
PO
Non-nephrotoxic


AZATHIOPRINE
54
5
Water
PO
Non-nephrotoxic


BENAZEPRIL
1750
5
CMC
PO
Non-nephrotoxic


BETAHISTINE
1500
5
Water
PO
Non-nephrotoxic


BISPHENOL A
610
5
Corn oil
PO
Non-nephrotoxic


BITHIONOL
333
5
Corn oil
PO
Non-nephrotoxic


CANDESARTAN
1300
5
CMC
PO
Non-nephrotoxic


CAPTOPRIL
1750
5
Water
PO
Non-nephrotoxic


CELECOXIB
263
5
Corn oil
PO
Non-nephrotoxic


CLINDAMYCIN
161
5
Saline
IV
Non-nephrotoxic


CLOFIBRATE
500
7
Corn oil
PO
Non-nephrotoxic


CROMOLYN
1500
5
Water
PO
Non-nephrotoxic


DEXIBUPROFEN
239
5
CMC
PO
Non-nephrotoxic


ENROFLOXACIN
2000
5
CMC
PO
Non-nephrotoxic


ETHANOL
6000
7
Saline
PO
Non-nephrotoxic


EUCALYPTOL
930
5
Corn oil
PO
Non-nephrotoxic


FENOFIBRATE
215
5
Corn oil
PO
Non-nephrotoxic


FLUVASTATIN
94
5
Corn oil
PO
Non-nephrotoxic


GADOPENTETATE DIMEGLUMINE
125
5
Saline
IV
Non-nephrotoxic


GEMFIBROZIL
700
7
Corn oil
PO
Non-nephrotoxic


GLICLAZIDE
1500
5
CMC
PO
Non-nephrotoxic


GLYCINE
2000
5
CMC
PO
Non-nephrotoxic


INDINAVIR
1000
5
CMC
PO
Non-nephrotoxic


KETOPROFEN
20.4
5
Corn oil
PO
Non-nephrotoxic


LEFLUNOMIDE
60
5
Corn oil
PO
Non-nephrotoxic


LINCOMYCIN
1200
5
CMC
PO
Non-nephrotoxic


LISINOPRIL
2000
5
CMC
PO
Non-nephrotoxic


LOVASTATIN
1500
5
Corn oil
PO
Non-nephrotoxic


N,N-DIMETHYLFORMAMIDE
1400
5
Saline
PO
Non-nephrotoxic


N-NITROSODIETHYLAMINE
34
5
Saline
PO
Non-nephrotoxic


RAMIPRIL
1500
5
CMC
PO
Non-nephrotoxic


RAPAMYCIN
60
5
CMC
PO
Non-nephrotoxic


RIFABUTIN
1500
5
CMC
PO
Non-nephrotoxic


RIFAPENTINE
75
5
Corn oil
PO
Non-nephrotoxic


SULFADIMETHOXINE
1100
5
CMC
PO
Non-nephrotoxic


SULFAMETHOXAZOLE
1000
5
Water
PO
Non-nephrotoxic


SULFINPYRAZONE
269
5
CMC
PO
Non-nephrotoxic


TENIDAP
75
5
Corn oil
PO
Non-nephrotoxic


THIAMPHENICOL
1500
5
Water
PO
Non-nephrotoxic


TRANSPLATIN
0.5
5
Saline
IP
Non-nephrotoxic


VALACYCLOVIR
88
5
CMC
PO
Non-nephrotoxic


VALPROIC ACID
850
5
Water
PO
Non-nephrotoxic


ZILEUTON
450
5
Corn oil
PO
Non-nephrotoxic


ZOMEPIRAC
11
5
Saline
PO
Non-nephrotoxic









In Vivo Studies
Male Sprague-Dawley (Crl:CD®(SD)(IGS)BR) rats (Charles River Laboratories, Portage, Mich.), weight matched, 7 to 8 weeks of age, were housed individually in hanging, stainless steel, wire-bottom cages in a temperature (66-77° F.), light (12-hour dark/light cycle) and humidity (30-70%) controlled room. Water and Certified Rodent Diet #5002 (PMI Feeds, Inc, City, ST) were available ad libitum throughout the 5 day acclimatization period and during the 28 day treatment period. Housing and treatment of the animals were in accordance with regulations outlined in the USDA Animal Welfare Act (9 CFR Parts 1, 2 and 3).
Clinical and Post-mortem Evaluation
All animals were monitored daily for clinical observations approximately 1 hr after dosing. For both the reference database studies (described in Example 1) and the sub-chronic study presented herein, gross necropsy observations and organ weights (liver, kidneys, heart, testes) were recorded for all animals following termination. Paired organs were weighed together. Body weights were recorded pre-test and daily thereafter for reference database (i.e., DrugMatrixTM) studies, and on days 0, 3, 5, 7, 14 and 28 for the sub-chronic studies. Terminal body weights were measured at necropsy and used to calculate relative organ weights and percent body weight gain relative to day 0.
Clinical Pathology
Blood samples were collected at necropsy from the orbital sinus or abdominal aorta under CO2/O2 anesthesia prior to terminal necropsy by exsanguinations and pneumothorax. A panel of clinical chemistry and hematology parameters were analyzed on a Hitachi-911 and a Baker 9000 instrument, respectively.
Histopathology
The right kidney was preserved in 10% buffered formalin for tissue fixation and subsequently embedded in paraffin, sectioned and stained with hematoxylin and eosin. Sections (5 μm thick) were examined under light microscope by Board Certified Pathologists for histopathological lesions. The left kidney was snap frozen in liquid nitrogen for subsequent RNA extraction.
Statistical Analysis of Animal Data
Treatment group means for body and organ weights, and clinical chemistry and hematology measurements were compared to the time-matched vehicle control group by Student's T-test. Significance was declared at p<0.05.
Microarray Expression Profiling
Gene expression profiling, data processing and quality control were performed as previously described in Example 1. Briefly, kidney samples from 3 rats were chosen at random from each treatment and control group on day 5 for expression profile analysis on the Amersham CodeLinkTM RU1 Bioarray (Amersham Biosciences, Piscataway, N.J.). Log transformed signal data for all probes were array-wise normalized used Array Qualifier (Novation Biosciences, Palo Alto, Calif.), a proprietary non-linear centralization normalization procedure adapted for the CodeLink RU1 microarray platform. Expression logratios of base 10 are computed as the difference between the logs of the averaged normalized experimental signals and the averaged normalized time-matched vehicle control signals for each gene.
Results
A few treated animals showed histopathological evidence of early chronic renal nephropathy on day 5, including minimal to mild regeneration of tubular epithelium, interstitial inflammation, pelvic dilation, focal thickening of basement membrane and focal infarcts. Cisplatin induced a high incidence of mild tubular basophilia (4 of 5 rats), while both cisplatin and carboplatin induced a high incidence of karyomegaly (3 and 5 rats, respectively). Mild tubular dilation and proteinaceous casts were also observed in one lead acetate-treated rat. Although considered early signs of tubular injury, these mild and infrequent observations are unlikely to bias the signature since the large majority of the animals treated with the 15 nephrotoxicants were unaffected on day 5. Furthermore, the incidence and severity of findings indicative of tubular injury were markedly increased after 4 weeks of treatment relative to the day 5 time point.
After 4 weeks of dosing, all 15 nephrotoxicants showed evidence of degenerative changes of the renal tubules or early signs of tubular toxicity. Histological findings included tubular necrosis, dilation, vacuolation, basophilia, mineralization and cysts. These lesions were also accompanied by a higher incidence and increased severity of epithelial regeneration and interstitial inflammation, as well as granular and proteinaceous casts. A high incidence of karyomegaly was also noted for cisplatin, carboplatin, lead and cobalt. Consist with the tubular injury was the concurrent observation of hypercholesterolemia and hypoalbuminemia for a number of the nephrotoxic treatments. Although weaker than most other nephrotoxicants, 4-nonylphenol and roxarsone induced clear evidence of tubular injury on day 28. For example, proteinaceous casts, tubular cysts and mineralization were only observed in one roxarsone or 4-nonylphenol treated rat on day 28, yet these treatments did induce a much higher incidence and severity of tubular regeneration (4-6 rats) and interstitial inflammation (6 rats) suggestive of future tubular injury. Since the nephrotoxicity of 4-nonylphenol and roxarsone have previously been described (see, Chapin et al., “The effects of 4-nonylphenol in rats: a multigeneration reproduction study,” Toxicological Science 52(1): 80-91 (1999); Latendresse et al., “Polycystic kidney disease induced in F(1) Sprague-Dawley rats fed para-nonylphenol in a soy-free, casein-containing diet,” Toxicological Science 62(1): 140-7 (2001); Abdo et al., “Toxic responses in F344 rats and B6C3F1 mice given roxarsone in their diets for up to 13 weeks.” Toxicology Letters 45(1): 55-66), and early signs of injury are apparent in the current study, these treatments were included in the positive class.
Example 3
Derivation of a Predictive Renal Tubule Injury Signature
Overview
The support vector machine algorithm was trained to classify experimentally confirmed nephrotoxicants from non-nephrotoxicants using the data acquired in Examples 1 and 2 above. A linear classifier (i.e., gene signature) was derived using kidney expression profiles from rats treated with 15 nephrotoxicants that induce renal tubular injury after 4 weeks of daily dosing, and 49 non-nephrotoxicants known not to induce renal tubular injury under subchronic dosing conditions.
Gene Signature Derivation
To derive the gene signature, a three-step process of data reduction, signature generation and cross-validation of the predictive signature was used. A total of 7478 gene probes from the total of 10,000 on the CodeLinkTM RU1 microarray were pre-selected based on having less than 5% missing values (e.g., invalid measurement or below signal threshold) in either the positive or negative class of the training set. Pre-selection of these genes increases the quality of the starting dataset but is not necessary in order to generate valid signatures according to the methods disclosed herein. These pre-selected genes are listed in Table 3.







TABLE 3







7478 genes used to derive RTI signatures











Accession #
Accession #
Accession #
Accession #
Accession #





NM_012939
NM_012657
NM_12848
U67914
AW915240


BF415939
J02635
U66707
NM_017354
BF283413


L18948
AA997397
AI236696
D87351
NM_019310


NM_017250
NM_012551
BE109861
AF285078
AI233888


AF150082
M22899
X05884
BF405086
NM_012879


AI511090
AF139809
U94708
U61729
AI105410


AA859352
AI717121
AF014503
BE105137
AA850034


NM_017270
D17310
J02643
NM_017259
AA891826


M63282
NM_019308
AF058786
BE113157
AI176677


M35992
X78997
BE109018
AI574903
NM_012963


AB009636
AF055477
NM_012803
L17127
BF420018


X59132
NM_013052
AW916301
AW914342
BF283381


NM_012824
NM_019242
BE113155
AB012721
U57097


NM_012777
U75924
AF160798
BF403552
BF416240


U24174
M96674
U27518
U80076
NM_012565


NM_013105
BE105381
AF159103
U59245
AB005900


AF057564
NM_019322
D00753
AI598399
AF111268


BE109667
AF034577
AF290213
M94454
BE113285


AF208288
Z17239
AI010583
NM_021693
BE113397


NM_013068
AI029460
AJ237852
AI176739
BF388223


NM_012682
M11814
AI410548
U48596
BE098827


NM_019233
NM_013175
NM_013062
AI412099
M58587


NM_013197
NM_019150
U56863
U46118
U10188


AF151367
AW913878
BF282409
AF027331
AI144646


BF555121
AI171219
U25137
NM_012829
M15327


AI169311
BF405468
D38101
X15741
NM_017117


NM_012738
NM_019348
AI407163
U44091
X94186


NM_012786
AW920818
AW916143
AB017820
AF009329


BF522317
BF399598
NM_012698
AF121670
BF284899


AI180253
NM_019128
AI575641
NM_013060
BF285687


J02657
AI412261
BF400833
NM_013005
AF214647


NM_012764
X06827
J03863
NM_012606
AI172259


AB040031
AF199333
Y13400
NM_013094
NM_020538


AA818643
M74716
NM_012639
AI233903
AA892299


D38381
NM_017014
AI236611
BE115621
AW921456


X83231
K03501
AF120275
L27843
AW917933


AB043981
AA818120
NM_019286
L29259
BF281701


NM_017288
NM_019332
AI009597
Y18567
U75402


U22520
X63369
AW915049
BF287903
AW915454


BE113181
AI412259
NM_012567
NM_021836
BF567847


AB013732
AI011505
AB000215
AI111796
BF395192


D50671
NM_012878
AF254802
AW917212
AF105368


AF202887
NM_019298
AW141051
AI010950
BF283340


BE114586
AB025431
BF403190
NM_012771
AF247450


AJ011607
M62832
NM_017123
NM_017011
NM_013008


NM_019126
AA849028
AF227439
X81395
U39943


D38494
AA858817
BE107840
NM_012794
AW528830


M18847
AI175530
M26199
NM_017289
X56846


U04317
U16253
AB036792
AF144756
BF551250


AJ276893
AW917537
AW143005
M34052
NM_021680


AI233740
AB042598
NM_012498
AF086607
X06889


BE100918
M81681
BF283270
AF112256
L19031


AF053312
AI172112
BF387347
BE112719
NM_013086


AF044264
AF306458
AA891470
NM_012735
U08290


NM_012633
U24441
NM_012881
AI227829
AJ242926


AB032419
U09838
AA925167
AA901342
AI412418


NM_012810
AF060173
NM_019295
X76723
AJ011035


J03734
NM_012603
AI234119
AF093567
M33936


X01976
AW143537
BE109691
AI237640
BE109016


BF289266
AI007992
J02752
L06821
NM_020084


D89731
AI008376
NM_012806
X14788
AI408348


M91563
AI012611
BF405917
AW918179
M37828


NM_012654
NM_013217
AI228222
AI716265
U15098


NM_012870
U49066
AI010917
BF551328
AI144797


AA819103
AF015304
NM_012533
BF554744
AI176553


NM_012757
AI101595
BF401614
D49977
U65656


AF063103
AI137819
D90109
NM_019329
NM_019339


AF312687
AW252871
BF542912
U31866
BE108896


BE111688
NM_012580
U45965
AI412108
AF249673


NM_012720
AI176730
AI172281
BF285185
AI171162


AI103158
AI603128
AW917780
BF556736
AW523849


X68640
U15425
AW917985
NM_012627
BF400832


AA998157
Z17223
M15882
AF295535
AA849743


AW251703
AA946230
BF284124
NM_012825
AW143179


NM_012584
BF286009
AW915415
AI169596
NM_012842


BE099881
U55995
AW523614
AJ131563
U07971


AA848355
X87107
AI407487
M16235
AW251791


AF158186
AF068268
M84416
NM_017237
NM_019204


Y00065
U20796
AI180421
AW915996
U12309


AF133037
U41663
AW142880
BF283556
BE095878


AW920606
AW434178
BE113060
BF413176
BF282961


NM_017195
AB022883
AI101117
U41453
NM_021691


AI171656
NM_017019
BF282796
BF402407
NM_012708


AI598316
NM_017208
BF413152
AF086630
BF405035


AF109643
BF393825
X89603
AI407719
AA955213


AI411981
AA800341
X68878
NM_012938
M10161


NM_019230
AA946485
AI412460
BF398155
NM_017275


NM_017331
AI144771
NM_012833
AA817877
U07560


AI071251
AI555029
AA945100
AI172302
BE109271


AW143506
AI407201
BF281697
BF562755
NM_021746


AI408713
AI411941
AA850910
AF029107
M19651


U26686
AF154114
U21871
AW862653
AF007212


AW915739
NM_021869
NM_012564
NM_012779
AF015953


NM_017097
AA892549
AF089825
J02627
AW915613


AW144649
NM_012618
AI171800
X97477
BE119628


J03886
AW917460
BF396132
AF038591
BF285565


AF184983
U67082
AI176814
M94064
NM_012908


BF414043
X84039
NM_013064
AI535126
AI170799


D83231
NM_012597
AW527509
AI059223
AW144399


AI227912
AA819832
AW914004
AI234024
U12623


AI408286
AI111954
NM_017115
AI599016
AF009133


AA964744
AI716469
AW523875
NM_017113
AI103572


BF288765
BE105618
AW919125
AW917133
Z78279


AA817759
L19656
M35297
AA851926
BF391604


BF557871
U97146
U44845
AW919210
D16237


AW920017
AA893596
X83399
AI556458
AA892049


NM_013029
AJ001713
NM_021763
AA925375
BE109730


AF107723
AI180010
AI008409
BE106971
BE117330


AW142962
NM_017215
D50664
AF179679
NM_012621


BF525022
AI178784
NM_017122
BE109520
AW520812


AI409934
BE112216
AI172222
BF396293
AA800587


NM_019344
D13555
BF389915
Y17606
AF193014


L05435
NM_020087
AI549393
U35371
BF282980


NM_017279
AA800292
Z30584
U32679
AA858900


NM_012614
BF399627
AF102854
AW526005
AW915775


AF277452
AW142290
AI177015
D88586
AI104278


AI102884
BF283610
AW862656
NM_012964
BE111666


AW919995
X91234
BF523561
BF407456
X03369


L46791
AI137339
L15453
NM_017027
AA946394


AF104362
BE108873
AA850740
AA850541
L19341


BF415024
BE113252
AI179990
BF564217
NM_021747


J03583
AI716560
AW918006
AA858862
AW917544


M26744
NM_013139
AF172446
AW142828
U05675


NM_013126
AW915606
AI102047
NM_017335
AI102771


J03093
AW918169
AW918050
NM_013106
BF409724


NM_012588
AJ302650
L14323
U30290
X13016


Y00090
U66470
NM_017180
AI010251
BF403184


AI228970
J03026
AW918529
AI012235
AB000489


NM_019326
AI136740
AW921215
AW143771
AF136584


AI454612
NM_017167
BF285985
BF549490
AI407141


BE107069
AI716512
AF148324
AF009330
AW434228


AF157016
NM_013413
BF282282
AW525762
BE106791


AI411412
BE107234
AI576621
AW919683
BF396602


AI556066
BF550033
X53427
BE117335
BF524971


AW916833
BF563113
AW144705
U22893
AI170400


X14159
NM_012851
AJ132008
BE120016
AI411304


AF198442
AA894092
AJ133104
AA892250
BE103975


AW913932
BF283631
AW143091
AF055286
NM_021595


BE111710
M63122
BF556210
AW529723
BF558694


NM_017074
AA894210
BF562701
AW913917
D87336


U33500
AI411995
U81186
NM_013089
M15797


AI045288
AW913986
AI232183
U66471
AW918776


AI101323
AW919092
BF411166
AA955786
AI409218


AI548591
BF284803
M27223
AI012434
AA891839


AA817798
AA965057
AW917546
AI575699
AF237778


AI230339
AF016297
NM_012835
NM_021587
AI178875


BE108282
BF284475
BF394332
AF118651
AW523888


AA848499
NM_013057
NM_013176
AI385364
U61373


AA892366
AW915287
X77797
AI579216
AB043892


AI406538
BE113142
AA893184
NM_017271
AF045564


U17967
AI168968
AF155196
BE100748
AI011757


AI408557
AA875301
AF171936
BE106832
NM_017090


AI235942
AA964535
NM_012998
U62667
NM_017359


BE109661
AW921399
U40064
AF082535
X85183


NM_012750
X91892
AI764464
BE121120
AF013241


AW251848
AW914178
BF285034
U23407
AI007919


NM_012676
AA850480
NM_012561
AI409065
AI111579


X65747
AI175457
AI012250
M77479
AW531805


AI406941
AI410352
AI408580
X04644
AW921168


AI236771
BE120339
AW144039
BE116867
D85035


AF077354
BF286916
AA800782
AW917160
AW918417


NM_017261
BF566488
AB020520
BF566679
BE109531


AF155910
AF036959
AI102591
AF010293
BF551331


J03627
AF041374
D14015
D12769
NM_017330


L36459
AI137683
NM_012489
M69138
BE095859


AB009686
AI412889
NM_012493
NM_017327
BE113367


BE113132
AJ131848
AW915453
AA818759
M18340


NM_012940
AJ224120
AA944169
AJ222971
M90661


NM_019358
NM_012687
AF013144
AW252812
AB033771


AI008390
U09228
AF169636
BE116233
AF111181


BE120346
AF001417
AI071412
BF406291
AI237075


X12355
AF286595
AI411400
D10699
AI454923


AW919284
AA800719
BF389519
BF284190
AI502229


D85760
AI172189
BF411148
BF409783
AB003587


NM_012668
BF555498
M13646
Z14030
AA800260


AI044740
NM_017089
AB012139
AI237403
AB011528


AW528864
NM_019239
AW251324
NM_019192
AW914789


AA799428
AW524733
AB000776
X04310
J00705


Y18965
AI409871
M80550
AF014827
U44750


M17412
BF288073
NM_012625
AA943824
AI179459


AF248548
NM_017310
BF557923
AF186469
BF412037


AI235546
U39546
AA851370
AW144673
D13963


L25527
U61266
AW916826
BF290076
M60388


NM_019904
AI171646
NM_013226
NM_021868
NM_013134


AI101924
NM_013043
AI176515
U74586
X05883


BF549650
AB023432
BF417292
L35921
AA891690


AB039663
AW524724
D86383
AF133731
AF251305


AI234852
BF412073
AI176773
AA997881
AF163569


AW921038
BF405050
AW914097
AW916609
AI599801


NM_012701
AA859796
AA799676
BF282415
BF413513


Z34264
BE349698
AF087697
D13121
M55636


AW918222
BF284692
BE116886
U44129
NM_012667


AW919395
X99723
AB027143
AF021343
NM_019240


AB027155
AI230728
AI007666
BE097240
AW916836


AI406747
AI236770
AI406697
AI179988
AI103918


AW915643
AI406948
NM_012522
D90404
AW531368


AW917481
BF404362
M83209
NM_017047
BE113242


BE349770
M95058
AI176842
U47280
BF285921


BF550231
AA818471
L04527
AI412625
NM_017340


U63740
AI176476
NM_019168
AI411021
X70871


X55995
AI406342
X53724
BF416285
AI101396


AF327513
AF247451
AW525211
AI172196
AI113104


AI177140
BF553139
AA866351
BE116946
BF281931


AB001075
NM_016998
AF041838
BF398367
U14907


BF556958
AW920082
AF297118
NM_012999
AW253265


U93880
AW535233
AI406968
BE108670
M31363


AI171653
BE099774
BF404452
BF287788
U30789


BF399489
L05175
NM_017185
BF404478
AF038388


AW918022
NM_017029
AF182714
AW141130
AW526289


BF398845
AF214733
BF393972
NM_019306
BE101472


M55601
X89968
BF398716
AI009609
BF285109


NM_012505
AW918674
NM_012530
AW523642
BF400636


NM_013200
NM_013036
U68725
AA901337
BE117156


AA799503
NM_019375
X78606
AI145991
BE120810


AF072935
AW144670
D88666
BE113179
NM_013074


BF398063
BF282712
BE107169
BF398114
AI072384


AI235674
BE107427
AW916745
M38060
D16817


L13600
NM_012942
D13927
U89608
NM_012679


AA892778
NM_019258
NM_017135
AA848820
NM_017193


AW535307
BF283056
AA945724
AF080106
AF199504


NM_019289
NM_017211
AW143008
AW434109
AI102073


AI176591
X70223
BE105967
BE119862
AI105049


M26125
AA850347
BF551345
NM_016987
AW253907


X73371
AI176836
BF554752
NM_017292
BF400042


NM_017222
AW142823
U06755
AW918564
BF542426


NM_021664
NM_012845
U10303
NM_012959
U56936


U26033
Y09945
AI410546
AI179101
AA900654


AB007689
AW918231
L11002
AW920764
AI009727


AF021854
NM_019309
AI169317
U64451
AA900261


AI101181
AB011365
AW915558
AJ293617
X66539


AI102732
AF021348
X03015
L19927
C06844


BF395781
AF192366
AA891535
X99470
AI070270


BF563517
AW527151
AA892500
AA819481
AW520754


AA818353
BE098709
AW524433
AI172146
AW918029


AI176497
BF557244
AW916447
AW915254
NM_019369


AI227885
BF567710
BE107464
AW920478
X95507


AW254369
NM_012501
BE108230
BF550822
AF022085


D25233
U89514
NM_012838
U39571
BF284809


L14617
U91539
NM_019370
U75920
M34643


NM_012532
AA892798
AI169655
AA893172
X76168


NM_019283
AI234149
BE106663
AF314540
AI170766


AB024398
AI171288
BE109059
NM_019243
BF289272


AF151373
AI716500
NM_012715
BF548454
BF389876


AI717736
AW528865
U73525
J05266
BF406752


BE113224
BF288208
AA850319
AA800222
NM_012549


BF420610
NM_012884
AA892852
AB011533
NM_016995


AF230645
AI232272
AI229630
AI103924
U95368


AI178171
AW144339
AI716086
AW529808
X60370


AI716250
AW533508
BE106513
AW916093
X74815


AW434520
AW917427
BF401626
BF282239
AA800184


NM_019186
AY004290
BF404514
BF420074
AF306457


U23377
BE101480
BF415760
L29232
AW144456


AA848451
BE107465
BF420628
NM_017144
BE101108


AF067727
BF549703
BF556874
NM_019214
BF411134


AA998660
AF189019
NM_019143
NM_020096
Z11690


BE113620
BE108837
AW141939
X58465
AF025670


AA818392
AI010272
AW434239
U42627
AW921149


AI071243
L22079
AI030179
AA925353
AA944556


AI177050
AA894335
BE110739
AF059258
AI010281


AW435011
AF036548
NM_012553
NM_021682
AI011455


AW918443
AF157498
NM_012689
AA799526
BF282149


BE106598
AI105080
AA818796
AW251115
BF403319


BF411113
AF320509
AI237118
AW915925
BF416794


AA965219
AI012574
AI639168
AI177397
D10926


AI555002
AW532606
BF282476
BE120725
AI104478


BE098212
AW915048
BF283760
BF564940
AW144344


AF076183
BE108327
BF398053
AB002801
AW534329


BF558592
BF282370
BF410020
AI009657
U59486


U17565
NM_017065
NM_020097
M65148
AA799993


BF415786
NM_017276
AW917098
NM_019216
AI406660


NM_017147
AI169829
NM_021585
AA892770
AI704771


U24175
AW253947
AA957047
AI556534
AW144347


NM_019291
BF409296
AF188608
AW914277
AW913888


AF182949
AI227945
AI169105
BF284137
J05499


AW141292
NM_012678
AW918717
BF563117
AB035201


BF394166
NM_021688
NM_012920
AA848821
AI168941


BF410183
U42975
AF063102
AA943576
AI411510


AB016425
AF011789
AF149118
AI409040
AA899704


BF525016
AI170249
AI011501
AI598976
AA945761


D88250
AW529672
AI235960
U60282
AI406809


U52102
BF283390
AW916210
AA945869
AA874859


AI412423
BF561659
AW917663
AW918611
AI102026


BE095842
AB035306
BE115280
AF190458
AI176993


AW914408
AW921109
BF413244
NM_019153
L78306


BE103689
NM_012880
U15408
AA946474
U10357


AA819268
NM_021846
AA955579
AF007818
AA799664


AW913998
BE104266
NM_021775
AW143818
NM_019333


X69716
BE116564
S57864
U85512
U69279


AF118816
BF562675
AA955206
X07365
X60789


AI234012
NM_013003
AW532179
AW525184
BF284889


BE108973
AI599126
NM_019620
D12516
BF398564


BF550769
U89873
AI172057
AA800029
NM_013012


NM_019262
BE109665
AW252815
AI179538
NM_017034


NM_019282
BF556327
BF285046
AI227894
X82152


U92010
D83036
AA848826
BE116816
AI232098


AF003835
AI178527
AI175508
NM_012739
AF090306


AI111802
AA943995
U58466
X59037
AF097887


AI230699
AI406464
AB036421
AF043642
AI235923


D12678
AI412304
AI104545
AI231761
BF405417


AA848311
AI599520
BF404935
BF559190
D13962


AF286534
BF397840
AB017260
AF259981
L13445


AF011790
AA801208
AB021980
BF396614
NM_019205


AI234142
AA849975
AI012231
D88035
AF046886


AI236084
AI045904
BE120595
NM_012972
AI598942


NM_013004
AI411580
AW533822
AA799476
BE102426


AW917823
M55049
BE106398
AA891746
BE328941


BF406312
X78461
AA817812
AI170114
BF563262


NM_012661
BF403410
BE106693
AI407982
NM_019203


NM_012967
NM_012563
L09656
AW914850
X68199


AA866419
AI413060
BE108277
BF550847
AF021935


BE119802
BF408325
AA944061
X66366
BF403098


AI104484
X06338
AA945771
AI710683
AI011610


AI144644
AA892325
AB024930
AW916287
NM_012841


AI411971
AF207605
AI010965
NM_012558
NM_020072


AW435041
AF263368
AW143130
AI235047
AF095576


BF556879
AW916182
AW523899
BF396316
AW918640


NM_012693
BF419280
BE111805
BF418597
BE119482


AB017696
BF556833
BF282313
AA963234
U96921


BF282034
NM_019293
M17086
AF071003
AA818184


D85100
BE101619
AI010948
AF182717
AI168986


AF100960
AW144663
AW251681
AI574743
AW915209


AJ011811
BF412296
D70816
AI599339
BF286131


BF418869
BF416115
NM_017038
AW914966
NM_019199


NM_017235
X69834
AA944518
AW915217
U16655


AA946430
AI235784
AI411149
AW917653
AW919239


AI104125
AW534151
BF558676
BE113656
BE109746


AI144863
AW919320
Y14933
BF284918
BF558086


AI409756
BE104321
AA850509
NM_012499
L23204


AW140637
AB002111
AI232565
NM_013145
BE098326


AW918085
Y18208
AI407560
BE107259
BF398684


BF283003
BF420720
AI501497
BF283261
AI072493


BF408444
NM_017204
AW251416
BF400209
AF099093


BF418630
AA850896
AW914809
BF404557
AI412054


NM_017337
AI171098
BF287191
M95768
BF282646


NM_021842
AI179021
BF414010
X06564
BF396955


BE110514
AW917461
BF566546
BE097587
BF419234


AF184921
NM_012923
L22191
BG153269
BF556836


AI176970
AI412936
L36884
AA891742
AI045635


BE115041
BE098743
L43592
AF030423
AW144346


AA817769
U94709
NM_017143
AF181259
AW917390


AA955926
AF223677
U06436
AI410906
AW920271


AB012933
AW529756
AA850333
AJ242649
BF281754


AI172465
M35106
AI169058
AW441131
J03190


NM_012666
NM_012869
AI237622
D12498
AB020757


NM_013167
AW143834
NM_017322
J05132
AI009371


AB011529
AW535377
NM_017332
U48249
AI231799


AW531919
BE109179
U69702
AW918418
BF396682


AI228528
M37394
U77697
BF281400
BF417187


AI171994
U13396
AF004218
D16308
M63574


AI231808
U17901
AF058787
AW917550
AA799741


AI412662
AI409316
D31873
BF400606
AA799751


AW524460
AW917557
AF043345
BF547620
AF106860


AW917674
BE113545
U05014
BF563077
BF281149


BF287209
BF405134
AA891447
M61726
AB019791


NM_019257
AA819488
AI412169
NM_017212
AW142328


AB033830
D88450
U55816
AA800476
BF396180


AI409182
AA866477
X55969
AI231210
BF399385


BF289240
AI102735
AA848951
AI235446
D00403


BF396295
AI227769
AI071288
AW531735
NM_017040


BF412673
BE106459
AW530415
BE111795
AA850725


NM_017217
BE113152
AW915057
BE349699
AB041998


NM_021856
BF283382
BE107033
BF284919
AW144170


AF111160
D25224
BF283353
BF396319
AW920501


AI104292
AA850987
BF404908
AF087696
BE108882


AI603127
BF400611
BF407675
AI009647
BF389244


X07320
D28754
L07315
X74832
BF408285


BE113369
M80601
NM_017131
AF002281
NM_019386


BE120386
NM_012524
AI406369
AI013919
Y17048


BF399607
X17037
AF106659
AJ295748
AA944314


BF409977
AW526352
AF277900
AW144385
AI172618


NM_017325
AI044316
AI172003
AW252087
AI180353


U60096
AW915106
AI411352
AW920527
AI235467


AF273025
AW914808
AW921986
BE111888
AI407999


AI411225
BF283797
BE107674
AA875143
AI547463


BE107250
BF403853
BF285066
AF202265
NM_013137


AW915650
BF555793
D49955
AI231444
U37058


BF285467
NM_012816
NM_012497
BF555116
AF157026


BF398403
U60835
AI233257
D87839
AI009074


M29853
AI169629
NM_017075
AW142811
AW526039


NM_012725
AW140983
NM_021740
AW915601
BE109164


X51991
AW916321
BF413765
BF288273
NM_012514


X57970
D50568
BF550866
NM_012721
AI175871


X92069
NM_012903
BF555899
AA892791
AI411930


AA892522
AI228236
M21208
AA899304
AI602125


AF160978
AW143256
AF036537
AI176505
BF522885


AI711516
BE107147
AI600221
AI410099
BF288063


BE119676
AF030091
AW915518
AI709768
BF565795


AA996836
BF287099
AW917510
BE111762
NM_012862


NM_017124
BF550883
BE097210
BF282458
AA800455


AB052170
BF551283
BF388763
BF524872
AF228917


AI598407
D49836
BF550217
NM_013001
AI171736


AJ132846
M18467
M55045
U61157
AW915404


AW141993
NM_013091
NM_012704
AF030253
AW917389


L19118
AA891734
U73458
M83676
BE114160


NM_013148
AI145328
BF557672
AF163477
BF283798


NM_019155
AI171655
X57228
AI175555
BF567631


AI105101
AW917475
BF291167
BF416236
L02530


AI599133
U50185
AI599349
U20195
AF080468


BE107503
NM_017068
AW520770
U31668
AI176944


BF419138
NM_017260
BF555127
AA850037
AI179372


AJ003065
BF290834
AI412627
U67884
AW918208


AW143887
BF412769
AI555466
AW914944
BE109208


AW916474
BF555129
AJ011608
AW916786
BE113233


AW917766
BF557296
NM_017181
NM_012828
BE118580


NM_013071
D13126
AB004329
AA900400
BF396948


NM_017345
M17527
AF205438
AF306394
BF549525


X71068
U17604
AI407017
AW533663
NM_019363


AI411997
AA819306
AW143149
AW915554
AF239674


AA800507
AF005099
AW918238
AW917516
AW915161


AA875261
AW918548
BF386716
BE110577
BE105872


AI231776
X74549
U53855
BF281848
NM_012819


AI599077
AA850490
X57523
D86711
NM_019237


AW526320
AW251878
AF044201
J03819
AI406821


BE116976
AW531902
AW914119
NM_017077
AF084576


BE117878
BF392577
AI102739
AW142947
AI060205


U18771
AI044865
AW920722
AW434045
AI179609


AF065161
AI059079
NM_013070
AI232337
AI408442


AF150091
AI454134
AA800062
AJ277747
AW915550


AI231716
AI454913
AA818952
AW918255
BE113312


BE113635
AW919929
AI012608
AW919873
BF387255


BF546202
D16479
AI137286
BF286478
BF394261


U04738
NM_012545
AI234678
BF388422
AA964824


AI012120
NM_012555
AI406707
NM_012562
AF184883


AI178229
Y00480
AI411501
NM_019211
AI231792


AW253043
AF068202
AW915713
U42209
AW521352


AA818438
AI406502
BE105713
AI412190
AW917064


AI103634
AW253742
BE107247
AI716642
AW918585


AW918605
AW919881
U19967
BF555161
BE101088


BE115947
U62326
U35775
NM_012527
U72353


BE116569
X57764
AF037071
NM_021584
NM_020106


BE118450
BE112952
AI009599
AB021645
X71071


BF389910
NM_013153
AI172198
AI169116
AA955396


BF397834
AA874924
AI227748
AW919190
AI176056


NM_012503
AA943817
AW143395
BE098463
AW916119


NM_012936
AI175978
BE113449
NM_012590
AA998964


NM_019305
AI177748
BF396079
NM_019364
AB012759


X97376
AI178923
M17069
U11038
AI179901


BE106816
NM_012974
U01914
AA850505
AI407827


BE118122
NM_013215
X52498
AA892818
AI716077


BF403852
U09583
AF315378
AI177408
BE098955


D63648
AI233769
BF284065
AI227612
BF388440


NM_021859
AI385171
AI007922
AI412150
AF277903


AA944006
AI409259
AI013500
AW523647
AI058960


AB017702
BE108949
AI170394
AW917504
AI409077


BE100018
BE103916
AI556941
BE095865
AW141921


BF415061
AI227890
AW141985
BE103444
BF285451


NM_021703
AW142560
BF409042
M18028
BF389856


AA944542
BF420172
U06864
NM_012733
BF393807


AF001896
BF557300
U08136
X83579
D16348


AI010954
BF559836
U50707
AA997412
NM_017316


AI012498
BF567496
AF245172
AF151982
U10279


AI176695
D25290
AF272892
AI137817
AF290194


AI180420
M88709
AI103040
AI411605
AI170797


AI406310
U32314
AI227907
AW143854
AI179993


AW531361
X16359
AI412317
AW507078
AI713159


AW916592
AA801139
BF404556
AW525122
AW917818


BE112933
AI407016
NM_013049
AW916054
J00741


AW251204
AW915084
BF288129
AW528898
AF020618


BE095833
AW916127
BF403009
AW915175
AF059311


BE110525
BF404842
BF555971
BF287814
AF090867


BF400666
BF418582
NM_019273
U61261
AI172386


NM_012763
L37380
X02904
AB009463
BF284171


U67080
AA943742
AA800273
AB032164
NM_017220


AF059530
AI168952
AI102064
AF031483
AA925490


AI009650
AI556408
AI171802
D29969
AW142307


AI555351
D50559
AI230430
M34083
AW435429


AI711114
AA945320
AW917132
AA848470
AB012233


AW918076
AF247452
BE108178
AF058791
AI175440


BF284878
AI070113
BE108857
AI176665
AI409380


BF399083
AW915174
BF397872
AI178491
AW520758


BF419406
BE111769
BF543356
AI232898
AW524559


NM_013115
BE116370
BF543478
AI233288
AW534383


NM_021744
BF407344
NM_012655
AW532652
AW915350


AB043870
BF554877
NM_019279
AW915437
BE113217


AI598442
BF556614
U16858
U06099
BE116973


AW253880
U29174
AA851728
U83112
BF282030


AW917588
X85184
AI137188
AA965063
BF284994


BE112998
BF566580
AI177431
AB009999
AI009603


U41853
AI176632
AI555341
AF023657
AI011034


AA817841
AI178935
AI600037
AF135059
AI011713


AA849966
AI406531
AW526346
AW144034
AI171480


AF220760
AW144006
BE108174
AW251238
AI232365


AI102512
NM_017141
BE111925
AW254429
AI408347


AI231088
X04240
BF282876
AW915944
AI716103


AI598315
AB023634
BF557821
BF401313
BE111634


AI713206
AI175008
D31838
J05122
BE112615


AW144044
AI237657
L27081
NM_019135
BF393611


BE117902
AI717113
NM_019167
U12187
BF408081


BF282238
AW252879
AA849738
X03475
NM_012601


BF410755
AW532489
AI235219
AA849719
NM_017342


M22323
AW916092
BE107395
AF268030
NM_021764


M81784
BF564219
BE108776
AI009594
U75689


Z48444
NM_013065
BF282217
AI136848
X60822


AA799832
U49057
BF550270
AW143233
AF063939


AF096835
AA800483
AA859010
AW144502
AI179974


AI008701
AB042407
AI600035
AW251339
BF398016


AI176212
AI170313
AI716255
AW435110
BF525193


AI176625
AW252251
BE102621
AW916721
D10665


AI180275
AW917256
BF284716
BF544951
AB014089


AI412673
AW919062
BF397805
NM_017005
AI175820


BE113146
BF389884
BF400811
U77933
AI237593


BF403136
BF396282
L18889
AA998160
AI409747


NM_021589
NM_012671
NM_013132
AI176825
AW253010


AI059493
AA800010
U68168
AI229849
BF283898


AA942765
AA891944
AF053317
AW918000
BF284303


AI137297
AF000577
AI169878
BE120513
BF555924


AI169001
AF017393
AI178796
BF419158
NM_012795


AW252664
AF065147
BE107485
NM_017269
NM_013135


BF397998
AI100769
AA943552
NM_017365
U40603


AF021923
AI170067
AA943564
NM_019219
U43175


AI230762
AI170405
AF017437
NM_021771
AJ000696


AW141128
AI236618
AI172175
U30381
BF548957


AW534159
AW918017
AW142913
AA892370
BF549697


AW915055
BE096387
AW143093
AB022014
L38615


AF226993
BF413396
AI408960
BF392344
BE126739


BE112913
D00252
AW142588
BF404539
BF288138


D88364
AA799400
BE113966
AF000423
BF396678


NM_013114
AA848776
BE117883
AI010267
BF558506


NM_019255
AI176611
AF075382
AI101199
BE111625


U38253
BE116768
AF087454
AI231787
BF558467


U49055
M77362
AI175048
AI715452
NM_017152


AA998662
AA850728
AI407222
AJ002940
BF419635


AI104846
AI178761
AI599104
AW525049
D21799


BF406522
AW916628
AW141280
AW919666
AI069912


BF413631
BE098366
BF419602
BE107334
AW914758


D38072
BF281544
L02896
NM_012636
AW914939


U75916
BF523059
NM_017262
NM_019284
BF284700


AI227843
D16302
AI406693
AA997435
BF404603


AI411425
NM_021660
NM_021762
AI236090
BF555890


AW531412
AI230918
AA955175
AI575940
M64301


BE115635
BE110626
AW915491
BE349755
Y00102


AI232321
AF169409
K03250
BF281802
AA858509


AI236624
AW535358
AA891859
U56732
AI105215


NM_012984
BE100202
AF106657
AF292116
AI237580


AI009623
BF282629
AI105272
AW918457
AJ293697


AI010235
BF556943
AI170757
BF408873
BF565344


AI179979
BF563933
AI233199
BF409812
NM_012581


AI599143
NM_019362
AI409501
AA800241
AI170786


AJ245707
U55765
AI410700
AF050159
AI231438


AJ306292
AI103327
AI598467
AF313411
BF408022


AW917197
AW144790
NM_017199
AI317840
AI598462


BE103937
BE108865
U09793
AI412209
BF281386


BF545951
BF395678
AA851327
AW919129
AI112622


BF556845
M11563
AA945202
M20406
AI172033


AA944161
NM_017194
AA945634
NM_021857
AI175383


AF021936
NM_017267
AB000491
AF131294
AI409049


AF087674
NM_017344
AI102065
AI234849
AW533060


AI232370
U71293
AI232205
BF415080
AW919094


AI579023
AI716115
AW916594
X58375
AW920600


AW141186
AW141000
AW921320
AI102037
BE101138


AW916805
BE109756
BE114154
AW251849
BE107520


BF291161
BF284075
AI411071
AW527217
NM_012674


D14908
AI716289
AW142171
NM_017066
AA801218


AI176016
AA956784
AW434007
AF007549
AF037199


BE109747
AF058714
AW915587
AI008386
AI145784


BF282271
AF090113
BE109596
AI104546
AI177867


D78482
AF127390
NM_017189
AI176039
BF283802


AA893708
BE113175
AA899898
AI235512
BF396424


AB001982
BE113372
AI410203
AI407464
BF405032


AI716516
BF288088
AI705687
AI549323
BF416249


BF401593
NM_013223
AW535229
AW143285
AI172159


BF413556
U40819
AW915543
AW916783
AI409738


NM_012916
AA800025
BF284679
AW917522
AW915402


AA946011
AA850358
BF285207
NM_012685
L07073


AI170668
AI011497
BF392605
NM_012728
U34985


AI230723
AI070137
BF398046
X53003
AI556488


AI598405
AW532074
AI008125
AF098301
BE115058


AW143111
NM_017186
AI172184
AF199411
BF283742


AW434242
NM_021759
BF550875
AI231432
AI103682


AW920179
U50194
AF009603
AI236772
AW534533


BF406407
AA866426
AI009591
AI408517
AW535909


BE098266
BF396082
U77038
BE108249
AA894259


D90166
M35052
U95727
BE109637
AF022952


U07201
M84009
AA943100
BE113111
AI408852


AA891834
NM_017178
AF022729
BF398605
AJ005113


AA997458
NM_019379
AI137488
NM_012836
BE109161


AI044229
NM_021576
AI145359
NM_013216
BF549710


AI175551
AF267197
AI172450
BF389352
NM_012839


AW921797
AF276940
AI175031
AI071698
NM_021653


BF412792
AI454081
AI234810
AI175474
NM_021865


D13127
AW141938
AI408705
AW917280
AW536019


D89514
AW918816
BE099401
BF551315
BE099732


U55192
BE103359
BE120608
AB016532
D12770


AI045819
BE118465
BF550426
AI230220
AW143273


AW144075
NM_017159
BF561727
AW915159
AW523874


AA945706
NM_017311
BF567649
BE108853
NM_019180


AA945734
AI103954
NM_013103
AA891830
AA874838


AF106945
AA819871
AI103456
AI411897
AF228049


AF142629
AF083418
BF284887
BE110722
AI412591


AF176784
AW918470
BF409560
BE112999
AW434329


AI102248
BF551138
AI235238
D26179
AW914982


BE095605
AA800701
BE109510
L06238
AW917734


BE121438
AF052042
BF525211
NM_017050
BE111098


BE329061
AI013104
AI172460
U03708
BF386111


BF550271
AI407821
AI233875
AW915834
BF397542


L31840
AI598402
AW916561
BF284693
BF549877


X64411
AI599376
BE108405
AA944036
AI172191


AA998893
BF285247
BF282009
AI102429
AI232217


AI101490
BF285980
BF555349
AI171775
AW528823


AW915318
U68726
BF556162
AI406506
BF285991


AW915609
X78604
BF562149
AW531891
BF565628


BF407740
X90710
NM_017241
BE107157
AI235353


D00680
AI179119
U26397
BF404868
AJ300162


AF010131
AI411742
AA900983
D12771
AW918833


X79860
AW142808
AA965117
AA893610
D90102


AA943981
AA817907
AI171654
AB038387
U87305


AW916468
AI179443
AI177089
AI170859
AA892330


NM_017101
AB019693
AI408686
AI234035
AI407409


AA943600
AI578861
M97754
BE105286
AW144331


AF314960
NM_017213
NM_017006
BE111776
AW915847


AI008988
U78889
BE107747
BF281438
BF557668


AI233241
AA891790
AB006461
BF404419
AA848342


AW143117
AA925922
AI234008
L36388
AA942695


BE101096
BF408391
AA944483
X86789
AA955630


BE108272
BF525153
AF322224
AA849782
AF020045


L34821
AI407903
AI763565
AA874906
AI137298


AI177887
AW914881
AW916701
AI169368
AI179370


AI231206
BF409759
BE103152
BF283736
AI410438


BE108849
AA859585
BE108583
NM_017021
AI230134


BF389882
AF109393
NM_017099
AI138061
AI410822


BF550292
AI009274
AA817863
AI412244
BE099629


AI010241
AI013361
AB030644
AW915966
AA801434


BF558976
AI013475
AB042887
BE105397
AA819679


AW915795
AW525285
AI103943
BF417391
AF084241


AA894080
BE103518
AI170377
U18942
AW915444


BE097615
BE114137
AI179991
U75973
AW918431


BE108899
BF289044
AW435010
X62952
BE098309


BE113057
J05030
AW526079
AA848834
BF407209


BF407452
AF036344
BF419074
AF327562
AI178489


BF550795
AI137471
BF552916
AJ238717
AW434972


BF555867
AI145625
M64711
AW918775
AA848526


D13871
AI172211
AA800210
BE104931
AF063447


NM_019220
AW919132
AA850498
BE119692
AF218575


U72994
BE112948
AA893230
BF283247
AI170251


AI170827
BF283612
AF115282
BF555980
AI235480


BE113005
BF284840
AI169619
BF564461
AW144226


BE117511
BF414261
AW531530
NM_013058
AW251666


BF389478
BF522056
AW919429
U48246
AI013913


BF412016
D13061
BE102505
AI013699
AI137301


U57362
U46034
BF419925
AI409741
AJ001184


AI012356
AA799661
M76591
AW142955
AW917946


AI169243
AA875055
NM_013063
BE096047
BE112415


BF281787
AA943094
AF072124
BE101311
AF245040


BF287768
AF037350
AI177645
BE109604
AI136871


BF396114
AF244349
AW918369
BF289328
AI177706


U68544
AI180400
BE120038
BF393085
AI180454


AA800232
AI603627
BF284819
BF551339
AI231601


AI104857
BE095490
BF406693
L37293
BE108326


AI105461
BE109529
NM_012578
AI010342
BE115880


AI230228
BE113119
NM_017353
AA851945
BF394214


AI412612
NM_020089
AI101475
AA943868
BF399328


AW140530
AW140531
AI176781
AA963282
L10072


BF555370
AI176792
AI411194
AJ293948
NM_012592


AI170769
AI236760
AI705731
AW143480
NM_012793


AI171280
AI598324
AW141990
AW915268
AB042599


AI179677
BE107173
AW253902
BE107438
AF156981


AI410505
L46865
AW524571
BF556273
AI176323


BF403332
NM_012987
BF113371
BF559875
AI317817


AW142852
NM_017175
BF285393
M23984
AI599641


BF286955
AA817895
L26450
NM_012997
AW920443


NM_012515
AA859508
M34384
AA892298
BE097245


AI013928
AI010432
NM_020306
AI029960
BE109513


AI176626
AI169228
U15211
AI409930
AA801206


AI233205
AW534781
AA850551
AI716131
AF231010


AW142713
BF390657
AF051895
AW526697
AI413033


AW142877
NM_017207
AI406290
BE100193
AW143939


AW915294
AF090347
AI412323
BE108131
AW531093


BF392695
AF030377
U65007
BE113228
BF282636


BF397773
AI102519
X66842
BF567904
U48247


M32061
AI177143
AB026288
M81766
AA849715


X62528
AI232354
AI717447
Y08981
AF020046


AA849497
AW522044
AW142440
AW144637
AI412580


AB026291
AW917726
AW527204
AI009167
AI600237


AI317813
BE106275
AW915676
AI408865
AW915560


AI407483
BF282212
BE109266
AI575703
AW918480


AI535483
BF401710
BF390003
AW141463
BE111685


AW433595
J02997
AI103914
AW143992
BF281285


BE108976
AA848338
AI170783
AW918108
BF396317


M59742
AI454466
AI713210
BE105452
BF548520


NM_012613
AI555844
BE098845
U19485
D50580


BE113624
BE098873
BE102816
AA946490
U34841


BF406637
BF395080
BF283510
AB040807
Y17319


U92803
BF414124
BF391673
AF039033
AW918273


AA850785
BF546361
X56541
AF092207
BE121325


AB020759
AW144002
AA800172
AI072958
NM_012980


NM_019187
AA893237
AW917796
AI411999
BF396534


NM_020976
AF277902
BE107459
BE109075
BF404409


X96488
AI145039
BF399791
BF399614
J03753


AF272662
AW143197
L31884
M29295
NM_013006


AW144391
AW918637
AI012263
NM_012665
AA817867


BE099953
BF284879
AI233726
Z83868
AA819812


BF282288
BF565705
AI408104
AI101393
AI169599


BF282645
U11685
AI555237
AI547421
AI227919


BF413969
U13253
BF285079
AW143757
AW919050


AA874952
AB017793
BF417363
AW525128
NM_013076


AW915060
AI230988
M84488
BE108832
U09229


BE104111
AI385140
NM_021997
BF403323
AA945103


BF283001
AI407991
AA858786
BF407165
AB018546


BF284914
AW434026
AA894084
BF555033
AF182946


L32591
BE100014
AW918999
M58716
AI180081


AI010234
BE109057
D90036
NM_017188
AI407985


AI233766
BE119961
NM_021684
AB047002
AI410886


AI716240
BF397933
AA800597
AI232269
AW915104


AW254017
AF034214
AA892281
AW918541
BF407878


AW919336
AF190798
AI169225
BF523077
BF414947


BF415023
AI010660
AI234095
L11319
D10655


J05029
AI170570
AI411077
M23601
M92042


NM_019385
AW526160
AI639139
NM_017305
NM_017231


AA799515
AW531675
AW433942
AI411520
NM_019335


AA925559
BE111118
AA892483
BE108919
NM_020073


AI011736
BE118222
AI104485
BF558507
NM_021847


AI102877
BF555119
AI407945
NM_013090
BE109039


AI176623
NM_017025
AI409108
Z83035
AA850736


BE098468
AA892339
BE095970
AB002466
AI407932


BF550402
AB002406
BE101099
AI230056
AJ005425


NM_021770
AF203906
BE107434
AI410833
AW143263


AA819398
AI010233
D32207
AI555566
AW917908


AA946128
AI175028
NM_013034
AI598648
BE106888


AF151377
AI406667
X89963
AI716218
BE111752


AI177663
AI407482
AI231190
BE101448
BF282437


AI412090
AJ242554
AI412736
BE102671
BF290638


AI412292
AW434419
AW433944
BE118605
AF016049


BE102889
AW521367
AW917545
BF555974
AA892897


BF408844
BF283772
BF283384
AA817722
AB015433


BF564899
BF388772
BF420685
AI233194
AI234830


NM_012634
BF400697
AA944568
AI408375
AW141787


AA924526
BF550302
AI072892
BE109600
AW143141


AA944278
NM_017225
AI105210
BF567692
BF548116


AF094821
AB026057
AI236773
NM_013147
NM_012571


AW144383
AI172214
AI406363
AF085693
AI175536


NM_017251
AI235950
AI408954
AI171807
BF554895


U53475
AI409024
AI412011
AI598414
AA892364


AA850801
AW253750
AW915292
AW915580
AB020022


AF012714
AW535136
AW915499
BF550554
AF051561


AF146738
AW917211
BE113053
D63665
AF177478


BE103926
NM_017356
BF282223
AW254190
AF323615


BE109586
AF061266
AI169291
BF555084
AI071688


BF396467
AI012352
BF399098
NM_021754
AJ006295


NM_019381
AI060043
AA946357
AI009029
AW915256


U72660
AI412018
AI008952
AI227700
AI178647


U83897
AI600031
AI103937
AI409145
AF072509


AA851296
AW433866
AI227742
AW525288
AI172156


AI176848
AF139830
BF547641
NM_013190
AW527880


AI407459
AF205604
U93197
NM_021750
BE113354


AI411005
AW252550
AA924945
AI175586
BF407799


AW142370
AW916799
AF000942
AI411060
U07181


AW252152
BE111887
AW535349
AJ001529
AB020504


AW916013
AI102290
BF558075
BF282544
BF283685


AW916792
AI233162
AI411332
BF408448
BF405110


BF387153
AA799789
BF285720
AA851280
U58858


NM_019341
AI011711
BF557889
AA944380
AI172274


BE111801
AI102236
AA944526
AI176442
AF159626


AA892271
AI411240
AB049189
AI237621
BE115860


AI008961
AA799301
AI101322
AI409180
NM_013028


AW918092
AI236816
AI102495
AI410943
AW140640


BF282185
AI409186
AJ277881
AI411979
BF393884


BF395777
AI012573
BF409313
BE108923
NM_013185


BF398045
AI172116
AA818820
BF386302
NM_017024


BF420629
BF282323
AI102873
D30035
AW916148


BF557739
BF283075
AI179142
NM_012586
BE113380


J03637
M69056
AI230778
X56228
BF285301


Y12009
AI105441
BF285078
AF003944
D90035


AI175375
AI407500
NM_012659
AI013474
AF220455


AI230185
AI170752
U18650
AI101500
AI104326


AW251213
AI172417
AI013775
BF284242
AW140537


M81639
AI412239
AI411964
U58857
M94548


AA945090
D85580
BE109603
AI029291
AA924352


BE111755
J03933
BE114159
AI170751
AW916619


BF419380
L27513
M86870
BE112253
AW917712


AI409032
NM_012911
AB005549
NM_021848
BE108877


AW144517
AF281018
AI231193
AI071187
BF284713


AW525342
AI013788
AI385277
BF405880
AA799636


AW914215
BF399587
AI409841
BF548241
AI407904


BE103434
M81687
AW915241
M93271
AW254590


BF389721
AI413058
BF398378
NM_019222
AW917661


BF397663
AF069525
J05214
U82623
BE104941


BF411381
AI060118
NM_012818
AI410415
BF393950


NM_012846
AI407064
AF01909
AW142953
X87885


NM_017216
BF558513
AI104376
AW434978
AI169383


AW435310
AA875011
AI228233
BE100035
AI412413


AW917572
AA891774
AI639162
BE108780
NM_012528


BE108192
AA892554
AW917587
NM_021760
AI412230


U76997
AI715257
BE100208
AA849752
AW525071


AA892567
BE113288
BE108905
AB003042
AF061947


AA999042
BF551361
NM_019280
AI236861
BF389157


AI232065
AA892346
NM_019622
BE099603
AI008969


AI599031
AI234858
AA944162
BF400873
AW142549


AW915803
AI602172
AI137972
BF551369
BE098806


AW916305
AW915466
AW528847
BF563786
BF396191


BE100201
BF417386
AW920802
J00696
M64300


BE105305
BF551118
L35767
NM_012966
NM_017187


D17447
D14013
AW143336
AI236376
AW915800


L02121
NM_012947
AW144084
AI407946
BF282620


M20133
AA998435
AW252169
AW144223
BF401275


M34253
AF080568
AW528454
BF283130
AW917258


AW919017
AI045590
AW915763
L19658
AI233133


AA875129
AI070591
BF419241
AA801116
AI408930


AA900046
AW915160
BF557396
AI011704
AW918153


AA946441
BF285089
M58340
AW144504
BE109152


BF288288
BE109201
AI170384
AB010467
BF416533


BF562779
BE109644
AI410837
AI102685
NM_017246


U54632
BF281325
AA894189
AI177409
AA946356


AW915140
BF523098
AF119667
AI229166
AB017711


BE109575
D30795
AF228307
AW918105
AI178752


AA899489
L09653
AI234719
BE113010
AI599125


AI111840
NM_017105
AI410917
BF281834
AW144760


AI412967
BE103894
AJ001044
BF386665
BE108884


AI575671
AA799981
BE107298
BF394140
BF284699


BE100155
AA943811
AW916684
X13549
AA956764


NM_013055
AF077195
BF389719
U82626
AI112512


NM_019246
AI236778
BE108876
AA943793
BE107281


AI231333
AW143201
AI411399
AI105167
AI176713


AW523114
AW254246
BE118972
AW144315
AI178763


AW523679
AW916618
H35082
AI236054
AJ299016


BF284300
X04959
L34049
BF389493
BF406240


U06713
AA800199
AW143157
BF400662
AF184920


AI105154
AA819716
AW533321
AI230729
AI072236


BE109143
AA946074
BF412594
BE115551
AW917568


BF567763
BF396729
BF567585
AI012951
BE112921


NM_019208
U39044
AW142367
AW917662
M73714


U04933
AI104251
BE121429
BE113247
AI102744


BE096021
AI231564
BF407916
BE099563
AI232494


BE113323
AI231789
M86235
BF548170
AI233702


BE121314
AW253339
AI009759
AA849756
BF284127


BF407511
AW524478
AI407545
AI229596
BF405996


NM_017079
BE110652
AW918385
AF158379
BF522695


NM_017174
BE117114
BE101157
AI170263
AI412601


AA849031
BF404464
AA799507
AI234844
BF412389


AA859343
BF563403
AA818132
AI639157
BF414338


AA943765
D50564
AI102046
AW915774
AA799576


AI175728
NM_017033
AI171975
AI232784
AF296131


AI228548
AI101900
AI172271
AW916344
AI385216


AI230073
AI413051
AI230110
BF408552
BE110949


AW433847
AW917849
BE102814
AI233916
BF284939


AW915824
BE100016
BE118552
AI409258
BF555949


BE098021
BF404932
BF404472
BE098359
BF564549


U79661
BF416377
NM_013221
BF418913
L20900


AI175762
U23443
NM_021592
J04112
Z16415


AW918595
AI408984
AA944463
AA945604
AI229684


BF281282
AI411771
BF281215
AB017544
AI406527


X78949
AF065387
AA894318
AI170948
AI409951


AA963096
AI176933
AI009656
AW143214
BE098713


AA998971
BE101089
AI010721
BF283454
M31788


U84038
M22631
AI012456
BF523555
U14533


AI071470
NM_012609
AI137208
U70825
AI178912


AI172579
AF192757
AI176483
AW144499
BF555429


AI717053
AI170933
NM_013042
AI010430
AI598321


AW918732
AW529588
AA818571
AI706767
BE111696


BF283743
AW530272
AA943149
AW915737
L20822


AI144583
AW918408
AI169160
AW918850
U08141


BE102535
BF283302
AI411217
BF283053
AA800519


AA849729
L20821
BF282194
AA799614
AF016047


NM_017200
U04319
BF401587
AB032899
AI233267


AW913858
BF410753
NM_021594
AI406853
AW527592


AI012474
AF286006
AA891221
AW527606
AI071703


AI412614
AW252511
BF556691
BE112252
AI145019


AI412626
AA892319
M97380
BE101101
AW915155


AJ130946
AF065438
U40628
AA799666
BE108840


BF558459
AI102139
AW914919
AA944403
H35178


X16481
AI236798
BE107373
AI704799
M11942


AI410901
AW916666
NM_017274
BF398009
M73808


AW915787
BE113034
AB008161
D85435
Y12708


BE108235
BF284695
BF288270
AI233765
AB017638


BE108381
U36786
BF397445
AA800539
AI169242


BF549121
AF036760
BF416387
AA892044
AI233232


BF559056
AI177061
NM_013111
AA942808
AI237681


AF051155
NM_019372
NM_019123
AA946508
AW143114


AI412014
AA892300
AF121893
AW915559
AW913929


BE107155
AF032872
Y15748
BF282349
BE113268


BE109130
AI103962
AA800044
BF398332
NM_017048


Y08172
AI176002
AW144441
AW143568
AA957492


AI598359
AW916151
BF414262
AW916347
AF094609


NM_017151
BE111638
AA848367
BF408957
AI009654


AW528874
AB031014
AI178206
AW915669
AI013906


BF550453
AA892294
AI229655
AA800699
AI171617


AA893193
AI177845
AI406371
AI011749
AW531909


AF181992
AI411497
BE117002
AI104431
BF393126


AW144745
AW253895
BF282296
AI170825
X13058


AW252105
AW915264
AW433959
AI575445
Y17326


AW526756
AW916138
AF029310
AW251630
AA801094


BF399124
AB020879
AI103375
BF287135
AI169140


U87627
AI171276
AI176541
BF420680
AI232722


AI104348
AI712840
AI227815
BF548086
AI236270


AI231785
AJ000347
AI411985
AA942949
AW915791


AI411141
AJ292524
AW142847
BF388434
U41803


BE110537
AW142931
BE113048
AA892829
AA893241


M11185
AW144646
AI179335
AB002151
AI228540


AF029690
BF403923
AA801136
AI170414
AI317827


AI010722
BF420067
AA817945
AI233729
AI575026


AW252820
NM_019275
AA850525
AI236101
BE104107


AW914860
BE107208
AA850909
AI412255
BF282890


BF405883
AI103616
AA891818
BE101485
BF287032


L16532
AW144313
AI104296
BE110671
BF398047


AI012438
AW529753
AI231812
BF283122
BF419646


BE329046
AW915952
AW252855
BF414192
NM_017348


AI136513
AW918376
BE103222
NM_017013
AB024333


AI169330
BF404589
BF288776
AI102745
AI105205


AI171772
BF410846
BF394038
NM_021676
AW918593


AI407001
BF419489
BF397229
AA799550
BE100453


AI548694
BF567996
BF558902
AB008538
BE102815


AW920624
X62322
NM_012804
AF334379
BE103430


BE115875
AI044638
NM_016988
AI235934
BF282594


BF564158
AI598988
AI176331
AI408244
BF397523


AA800290
BE112781
AI013800
AI704755
BF408216


AW434213
BF393577
AI412560
BF282119
BF558463


AI231846
BF414252
AW914984
BF392959
NM_017112


AI408197
BF558120
AW919694
BF409371
AA849991


AW525033
Y17325
BE113234
NM_013166
AA892496


BF284076
AI105265
BE113330
X87106
AA894233


M36074
AI112074
BF398543
AI013041
AI010295


U60063
BE099063
M57299
AI172285
AI011448


AI169278
BE101628
NM_016986
AI411057
AI229529


AA801230
BF549638
NM_017153
AW524453
BE105699


BF413204
AW915928
BF550580
AI172029
BE118683


M94040
U21662
U25808
AI180458
AI598320


NM_012669
AA893505
AA924151
BE102485
BF281741


Y12517
AI058276
AB003400
BF550566
BF285339


AA819729
AI172267
AI227672
BF556846
BF549027


AF054826
AI177016
AI406500
NM_013033
X15958


AI180337
AI233728
AW253963
AF030358
AA818203


AI234533
AI406932
AW914642
AI176121
AW916939


BE105565
AI412180
AW918527
AI598881
BE113338


BF564263
AW143212
BE101505
AW143543
BF408856


NM_012866
BE108162
BF282984
AW915481
BF548630


NM_019152
BE111673
C06665
BF399447
BF557395


AA944438
AI060197
U44979
NM_017201
BF568009


AB011531
AI230388
AA942726
NM_017281
M29472


AF110025
AI408502
AA944828
Z83044
U75928


AI145630
BE108850
AI169053
AI058938
AI009818


AI176996
BE329450
AI171242
AI137569
AI317880


AW141869
BF398626
AW915015
AW527421
AI412086


M54926
AA819234
BE110412
NM_012776
AW917096


AI169607
AI103467
BE113269
AI177863
BF285334


AI169746
AI177412
U78977
AI406964
BF288060


AW915955
AI229902
AA848503
AI411212
BF290997


BF282899
AW915152
AF244895
AI556246
BF407158


BF400575
AW916942
AW435017
U62940
BF420447


U64030
AW917815
BE108968
AA800570
BF556463


AF259504
AW919586
BF405135
AA946434
AA998047


AI171230
BF291214
AA850872
AI407954
AI231781


AI229647
AA943831
AA944332
AI170671
AI236726


AI235502
AF034582
AI600085
AI409070
AJ003004


AW523709
AF077000
AI600108
BE113315
AW531275


BE108860
AI412298
AW433865
AA818128
AW918257


BF419854
L12384
AW913942
AB028626
BE108494


L25331
AI102688
AW916661
AW915815
BE111850


AA818113
AI232248
AW921139
BE101212
BE113375


AF056034
BE103304
BE101171
BF559919
BE120015


AI407095
BE109671
BE106523
AA875045
NM_017264


AW915655
BE112899
BE107223
AI137420
AA926279


BF387477
NM_017361
BF522863
AW251313
AA946382


BF549379
AA849734
BF563261
AW915638
AB008571


BF555532
AA924717
AI406651
AW917594
AI013657


L37085
AI232347
BE096104
BF524281
AI176468


AA817752
AJ004912
BE101124
BF556698
AW520324


AA858600
BE095620
BE109118
AI233262
BF283406


AI169490
BF398121
BF280414
AI233718
BF418890


AI575402
BF417396
BF396629
AI598371
BF420754


AW143173
NM_017015
BF403937
AW141873
BE106191


AW529960
X94351
BF411031
AW919578
BE108396


BE095474
AA893217
NM_013222
BE095971
D21800


BE108346
AA943578
NM_019259
BE109900
AA799499


BE109672
AB028934
AI412015
BF283091
AA892127


BE110542
AI172177
AI169353
NM_019206
AA893171


NM_019299
AW251501
AW252811
AA963094
AF311055


AA893811
AW919497
NM_012619
AI012074
AI169365


AI178257
BE111972
NM_012946
AI236754
AI407130


AI711105
BE118440
AA851239
AW918097
AW527971


AW142280
BF283418
AA899150
AW919037
AW916168


AW915107
BF420144
AI171607
AW919937
NM_021745


BE115558
AI232357
AW915146
BE349725
AI103988


AA964789
AI412958
BF412293
BF282686
BE109599


AI169729
AW251310
AB037424
BF549603
BF523605


AI172272
BF417793
BE110618
BF407149
AI175803


AI179472
BF419240
NM_017326
AA924654
AI556502


BF284775
U19614
AI073176
AW144382
AI59995


BF398680
AW525945
AI411198
AW915749
AW917738


BF410951
AA801212
BF398587
BF281388
BF284345


AI175767
AI639285
AA955157
BF282084
M62388


AI599956
AA800191
AI105145
BF283385
AA924152


BE100802
AA800535
AI231011
BF400719
AI600216


BF407563
AW142925
AI236640
AI177621
AW523737


AA893590
BE108810
AI412002
AI575104
NM_019144


AA944576
BF399618
BE110561
BE112007
Y00350


AI169375
X67654
BE111986
AA848795
AA893208


AW521376
AA893532
U15138
AA894262
AI703715


AW918620
AA944158
BF397956
AI230432
AW916925


AW918940
AI105243
AA799709
AI548620
BE099060


BE110557
AI233763
AI070397
AW917543
J05405


NM_012875
AA851386
AI102943
BE115626
AA799331


AF095741
AA866432
AI231777
AI009222
AA944053


AI231196
AA946017
BF551377
BE108018
AF184893


AJ245646
AI105117
AB018791
AI235192
AI172269


AW525089
AI598410
AI008971
BF283084
BE112892


AW528792
AW141364
BE102266
NM_012595
BF419731


BF410042
AW532663
BF399504
AI178818
D50696


NM_017169
AA800763
AA800001
AW525229
AB032178


AI227832
AA998468
AB010954
C06787
AI012381


AI104378
AW142276
AF179370
D83948
AI180252


AI170657
AW914992
AI171990
Z71925
AI228249


AI230061
BF285344
AW915681
AA945915
AI230278


AW921738
BF561196
AW918311
BF287826
AI408770


BF419366
AB017188
NM_017182
M75153
AI409748


AA892780
AI406280
X93352
M83675
AW433870


AA875425
AW915764
AA924980
AA858879
BF419628


AW917015
AA945568
AF172640
AI231773
M61142


BF398144
AI176477
AI101380
AI232273
NM_021849


BE101784
AI599407
AI179992
BE107540
U66322


AI111559
BE113340
AI717425
BE113490
AI406508


AI169149
BF549893
AW916433
BE120629
AW915566


AI175019
AA892273
BE098799
L11004
BE115600


AI177410
AA899959
BF397603
X74226
BE116507


BE107245
AF285103
AI102027
AA858867
AI171632


BE118650
AI176465
AI104258
AA859922
AI007841


NM_012985
AI411365
AI454943
AF067728
AI599286


BF407964
BE100986
AI059108
AW920774
BE349648


AI103129
BF289928
BE101766
BE099950
U61696


AI234816
BF565365
BF282301
BF407170
AA800277


AI175507
AI111991
BF415017
AF110195
AA819086


BE119615
BF286941
BF420639
AI012785
AI172459


BF408841
AF200359
NM_012789
AI412143
BF397894


AI137756
AI009363
NM_017299
AW253985
AI407555


AW434991
AW915716
AI411436
AW914085
AI556546


NM_019238
BF284754
AJ303456
BE112582
AI577393


AF069306
BF523646
AF044058
BF286237
BF281749


AI599945
AA894030
AI410001
BF399633
AF144701


AI137114
AI713140
AW525660
AA945062
AW141326


BF557792
NM_012960
AI412949
BE112384
AI410481


BF420654
AA891821
AI600036
BF285023
AB041723


AI059234
AW917596
AW253367
AA800665
BF281200


AI232643
AI100850
BE104143
AI178806
AA943011


BE113423
AI102689
AA799783
AI406906
BE096986


AA892993
AI179136
AI716491
AJ225623
AI044721


X13817
AW253642
AW921162
AW918039
BE116383


AW915662
BE118414
AF110026
BF407819
BE111699


AB006450
BF404027
AI013011
AA849757
AI104034


AI233857
BF414266
AI411227
AI170714
AI548730


AW915056
AI412024
AI101580
BE109614
BE113022


AI171211
AW919474
AI598381
BE116918
BE113201


AW140925
AA801308
AW920761
AI011510
NM_013102


AF032120
AA818914
BF558116
BE115034
AI170354


AI169648
AF120111
AI555567
NM_012670
BF411424


AW918604
AI102947
BE099224
X96663
J03624


BF397588
AI409731
BE112202
AI145851
NM_012556


NM_019213
AW254068
BE117946
BF547710
AW143082


AA894297
AW913868
BF282388
AW528625
AF199322


BE104415
BF398537
AA800521
BE349838
Z46957


BF282678
AW526283
AA849788
BF389726
L06040


NM_019334
AI412192
AF281304
BF523622
AF180350


AI169328
AI412537
AI010455
BE111787
AB040802


AI172092
AI716902
AI144663
AI170768
BE109138


AW528057
AW434064
AW915194
BE113043
NM_021695


AF026476
BF396493
BF544320
BF282314
NM_019125


AF136585
AA800576
AA944449
M57547
X92495


AW918068
AI579376
AW142350
AA858518
AW141928


NM_019331
BE113316
AW531382
AI575433
X00469


AA925303
BF406661
AW915412
AA818520
AA799329


AI007987
AI233172
AY017337
AA893517
AA818947


AI229046
AF110732
BE096311
AF165892
AI178768


BF420055
AI102991
BF417071
AI179365
AI010317


AW143287
AA891940
AF315374
AI230346
NM_013165


AI105345
BE100586
NM_017177
BF406604
BF563201


BF413977
AI233751
BF404344
BE113454
D10041


BF398712
AW916097
AI555009
AI171781
AF097723


AI408162
BE109950
AW919046
AI179316
U89744


AW523409
AI172301
BF549833
AI171367
M58364


BF283600
AW520767
AA850288
BE109901
BF398051


U69485
BE109512
AI411153
BE329347
AW434139


BE109521
BF420279
AW916463
AI410096
NM_021656


AA944494
BF393934
BF282695
AI411531
AF205717


BF282132
AA800258
AI410079
BE110545
L33916


BF417400
AI171764
AI411278
BE111677
AW527564


NM_012891
AI706892
M62763
AW141664
L27059


AA946375
BE110530
AI412491
AW143711
NM_013021


AA955172
BF410389
AW915621
NM_019359
AI406655


AF255305
AI412276
BE101165
AI411113
U02096


AI169359
AW433846
AI145899
AW913987
M31176


AI408455
AF002251
AW917752
BE095840
U22830


BF396218
AI104146
BE115557
BF411317
AW143269


BF548597
AI454536
AA819400
BE101129
M55050


BF557304
AJ005424
AB049151
BE100823
AI548036


U93692
AI233276
AI172464
BE101292
M98820


AI232657
AI716471
AW141870
U53512
AI007936


BF414143
AI230758
NM_019252
BE113035
AW141286


BF556841
BE109711
AI007877
L27651
U38938


NM_013092
NM_017170
AF311886
AA875041
X06942


AF176351
AI177747
AA859768
AW919685
BF404901


AA943126
AI176502
AF168795
D14437
D45920


AW143102
AI105086
BF284341
X99338
NM_020074


X78689
NM_012971
BE110633
AF016180
AJ000555


BF550800
AW916756
M14952
AI500969
AA859556


NM_013023
L29419
AI411426
BE105541
U69550


NM_012844
BF410589
BF285557
BE108368
AI071605


NM_013191
X65083
U34843
U49235
BF557670


M22926
BE111296
AF007789
U66292
AF188699


AI408780
M63991
D10693
AI236780
BF284311


NM_012822
NM_017044
D88672
AI599365
AB000216


U53449
U76551
AW918103
NM_012896
D79981


BF285022
D00569
AF003598
AI176810
AW919217


M96548
BF399655
NM_019241
NM_012918
NM_012526


NM_012521
Z50144
NM_012694
BF415072
AW921292


AI715955
AI170387
AI233253
AB020019
NM_013078


BF404304
NM_013154
NM_012702
AI170357
BF555189


U21954
AW915339
NM_012716
AI716535
AI045026


Z96106
AW919159
NM_019223
BF405610
U79031


BF550451
AI172174
Y07704
AI176718
M60753


AF135115
U04998
X06423
L27112
BE113295


X92097
AF069770
Z18877
U08255
AI111803


AW918419
D12978
AI170265
Y11490
AI233752


AW251839
BF288153
U28356
BF408271
L07736


AW915423
AI172352
AA945099
D16829
BF401764


AA893251
NM_012744
X95096
AA946492
AF072835


AI229720
U57063
J04628
AI598346
X63995


D49494
NM_019189
BF405027
U28504
BF407531


U44125
L26009
NM_013100
NM_013196
NM_012699


X67859
BE096501
D63834
AA946350
AA891949


BF394161
AW918276
AW920575
BE108246
NM_019371


NM_021669
AW918684
AF203374
AW141135
BF417565


AI406856
NM_012826
BE110695
U81037
NM_012892


BE107032
BF420059
M31155
AA946467
BE107187


NM_017158
AF324043
BE113362
AI412189
BE108224


NM_017081
NM_017076
NM_017058
AI180349
L01702


BF549748
AF013598
AW920993
X95189
AI177168


NM_017136
AF242391
AI176592
BE117941
M59967


AW143169
AI170665
X54467
AW916860
X98517


AF082533
AB018049
AF150106
BF523660
AJ002556


Z14119
AA801173
AJ002745
NM_017192
NM_013172


BE113272
AA818949
BE104375
U92802
NM_017320


BE121346
AJ132352
D83792
BF285568
AA943114


D14048
AA996961
AF082534
BF281914
BF401491


NM_021264
J02811
AF000973
BF289566
U31203


X52477
NM_013098
BE109616
BF410786
AF024622


NM_013104
BF396151
BF419319
AI105417
BF405059


NM_020088
AI408380
AF136583
V01224
BF563404


AI009128
AF035963
J03621
U07609
BF400779


NM_012629
AI231805
U02315
AI168935
NM_019314


NM_013041
BF282647
AW919325
BF281135
BF419671


J04731
AF054586
BF282951
L12025
NM_019179


NM_013178
AB037937
NM_020080
U10697
BF397726


BE111869
NM_017280
X98746
BF564840
NM_020301


AF012891
AI599294
AF154914
AF163321
D38104


NM_019157
AW918535
BF558524
NM_017278
BF405932


AI409500
NM_012587
AI232085
BF398182
BF399649


AF100421
NM_013069
AW143890
AW915563
NM_012707


NM_019290
AW251335
AI175907
AA851914
BF398696


X68400
AW251633
BE097102
U56859
M88469


AF141386
BF542467
BE111729
AA848534
AI236753


BF403998
BF565649
AI172498
AA944398
M83196


NM_019272
AA892824
AW915002
AF022247
AF067793


U12402
BE120309
AW140991
AW434092
AI137506


AI598429
BF388912
BE107195
BE108809
U48592


BF414004
AW434670
BE117687
BF404853
BE113616


BF549324
BE110658
U41164
AB021971
X07467


Z49762
NM_012900
BF284897
BE113076
BF285915


Z50051
AF062594
AI228240
BF414136
BF563467


U37026
BF420163
BF392884
AI145380
D16465


AI577501
U00964
BF546209
AA943794
NM_019318


AI235610
AI044845
AW918841
AI146056
U90888


NM_013224
BE113165
U25967
AI178808
AA899951


BE108347
BF393078
AI137259
BE109381
BF556350


AI137751
BF558742
AA944308
M55534
U17971


AW917981
Z19087
AI407975
BE329415
NM_019196


BE116152
AF150741
AF168362
AB003478
AA874975


BE120545
U57049
AI406533
AF157511
AF110023


AW919982
AA800382
AW141129
BF408990
AJ225654


BE109277
AW917673
AW915546
BF551318
AW915004


U93851
J04811
BF288240
L05084
BE097085


M87053
AW918559
U22424
NM_021741
BF285071


BF283410
BE099796
AA800389
BF553500
AA801331


M91597
AB046606
AJ223599
BF564759
BE109744


AF030378
BE098930
NM_019147
AI176478
AI713217


AJ305049
AF079864
AI045083
AI454928
BF396314


M22253
BF557269
AJ132230
AI599484
NM_012921


AA799450
AA851305
NM_017020
AW917650
U89280


BE108756
AF201901
BE120578
AB001089
AW917574


AF115435
AI411955
NM_017010
BE098025
BE105864


BE109242
AW143142
BF282689
BE101151
U14914


AW533482
BE109664
D17309
BF282700
AI227916


BE111827
AA900180
L36088
M74067
AW920324


NM_017149
AF230638
NM_012700
AI232138
BF281577


AI598306
BE101140
NM_021593
BE110691
AI600068


BE103482
D37979
U48245
BF282674
BF282471


BE104535
NM_012608
NM_017154
AF015949
BF396350


AA924724
AI711110
U89695
AF054870
M91214


NM_013179
NM_017286
AI233818
AI009608
X82021


X13722
NM_019137
BE113599
AI411793
AF062389


AI178476
AI411375
J03025
NM_012600
BE116153


AA800501
BE118055
U59672
BF287843
U56241


AI169399
AF008554
U90829
BE095997
U67137


BF281357
D50864
AA858794
BE109638
BE108748


AW918387
AF020346
AI317854
L14851
AI172248


BF408867
NM_019145
AJ009698
M83679
AW526136


BF420653
NM_021766
NM_012742
U14647
BE107192


U61184
BE101094
AB019120
BF399595
U17253


AB006614
BE109569
AW918468
BE112983
X95577


AW920729
BE117893
J04147
AI232716
AI600081


U86635
L33413
NM_017129
BE100617
AA819316


AA819339
AA818892
U25055
BE101148
BE113655


AI233213
M30596
BF284768
AA943573
Y15054


AI178556
AW142966
BF406261
AI412866
AA946222


BF393799
AI228955
D30666
BF388797
AI511282


BF408425
AW523755
Y00697
BF403842
AW915567


AW142667
AW918188
AB022714
NM_012624
BE108177


BF404316
BF522212
AF132046
U78090
BE115948


BF555947
AF036335
AI178938
AI176298
AW915148


NM_019165
BE120354
M22923
AW523419
BE107295


U92072
AF083269
NM_012591
AW917114
BF395101


AF156878
AI235493
AF093536
M22756
AI146156


BE114418
AI411056
AI406525
U48702
AI599410


AI179460
L27058
AI408017
NM_012780
AW144095


NM_020081
AW253040
BF284776
AA944552
AW915236


AI102524
AA818020
BE113205
AI408249
NM_012969


NM_017150
AW141878
M14050
U25281
AW253398


AB022209
AW527440
AA892918
NM_012889
BF282987


BF285150
BE113660
NM_013050
AB009372
AF234260


AI411991
BF389120
U42413
J00750
BF416935


NM_012734
AI411270
AB016160
NM_012834
BF550779


AW142654
AW918441
AI410127
AA848305
BF553981


NM_012610
BE102251
NM_019384
AI406532
U30831


AI176548
BE109561
AI102061
NM_017110
AI172415


AA850242
NM_017003
AA946349
NM_021774
M64780


BF396462
AI236928
NM_019256
AI103955
AI407187


NM_012913
AA891213
AF008114
AA818197
BF408452


AI145761
AI407992
AI230591
AW520354
BF413245


AI411297
Y09164
BE101290
Z21513
M88096


NM_017060
BF550737
NM_021661
BE097309
NM_017179


AF281635
X05341
NM_017062
BE118454
AF279918


U42388
AI411422
AI408969
BF290106
AI007974


BF409208
AW919170
AW918198
M31837
AW144302


AW142170
AI763826
AA800744
AA955605
BF402472


AW143820
BF288140
AB028461
AW918716
BF416877


BF282574
X96589
AW143077
BF288254
AI411670


AF178689
BF406991
BE101126
NM_012811
X62660


BE107098
AA817836
BF555858
AB010960
NM_017323


BF407134
X15834
BF556693
AF081582
X73653


L39018
BF555544
AI717140
BF393949
AW251852


BE097840
AB046544
AF240784
AI009820
BF281178


BE107410
L13041
AW916911
AI229209
AJ002942


AI227686
AF009511
BF284509
NM_012907
NM_017214


AW916943
BF567426
BF418775
NM_016994
AF177430


AF227741
NM_017161
BF568015
AW533098
NM_017035


NM_021670
U90312
AA800737
BF404778
AF100172


X52196
AF063851
AI146063
AF032925
AW142717


AB025784
BF415013
AI407061
AA800046
AI409727


AI236120
AA946014
BF282483
AA851302
NM_021776


AW915825
BF287827
AA800364
AW914045
U03491


BE113365
BE098800
AA945579
BE113101
X02610


BF393902
AI712686
BF392911
BF407194
AA818377


BF556880
BF404426
NM_019122
D86345
AI599232


AA894099
J02962
AA818602
AI703713
AW915797


BF417476
NM_020471
AI172262
BE109919
BF285528


U53882
X89383
M85299
BE096027
BF566689


AF153012
U25651
NM_012770
AI409037
AI574745


BF288244
AB015308
AA955527
AW143190
BE112950


NM_012773
AW434998
AF222712
BF396115
X65948


AW252115
AW918031
AW251686
AW143156
AI102758


BE097244
BE096652
NM_017300
AW917977
AI231782


NM_021590
BF551160
M64381
X14773
D88190


BF282395
NM_021866
AI104432
AI230732
AI555819


BE116554
AW527690
AW919837
AI412740
D21158


U55836
AI180050
NM_017238
BF406286
M77246


X16262
AW917831
AI012336
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The signature used to predict the presence or absence of future renal tubular injury was derived using a robust linear programming support vector machine (SVM) algorithm as previously described (see e.g., El Ghaoui, L., G. R. G. Lanckriet, and G. Natsoulis, 2003, “Robust classifiers with interval data” Report # UCB/CSD-03-1279. Computer Science Division (EECS), University of California, Berkeley, Calif.; and U.S. provisional applications U.S. Ser. No. 60/495,975, filed Aug. 13, 2003 and U.S. Ser. No. 60/495,081, filed Aug. 13, 2003, each of which is hereby incorporated by reference herein). Briefly, the SVM algorithm finds an optimal linear combination of variables (i.e., gene expression measurements) that best separate the two classes of experiments in m dimensional space, where m is equal to 7479. The general form of this linear-discriminant based classifier is defined by n variables: x1, x2, . . . xn and n associated constants (i.e., weights): a1, a2, . . . an, such that:




  S
  =
  
    
      
        
          ∑
          i
        
        n
      
      ⁢
      
        
          a
          i
        
        ⁢
        
          x
          i
        
      
    
    -
    b
  




where S is the scalar product and b is the bias term. Evaluation of S for a test experiment across the n genes in the signature determines what side of the hyperplane in m dimensional space the test experiment lies, and thus the result of the classification. Experiments with scalar products greater than 0 are considered positive for sub-chronic nephrotoxicity.

Signature Validation
Cross-validation provides a reasonable approximation of the estimated performance on independent test samples. The signature was trained and validated using a split sample cross validation procedure. Within each partition of the data set, 80% of the positives and 20% of the negatives were randomly selected and used as a training set to derive a unique signature, which was subsequently used to classify the remaining test cases of known label. This process was repeated 40 times, and the overall performance of the signature was measured as the percent true positive and true negative rate averaged over the 40 partitions of the data set, which is equivalent to testing 392 samples. Splitting the dataset by other fractions or by leave-one-out cross validation gave similar performance estimates.
Cross validation using 40 random iterative splits (80:20 training:test) resulted in an estimated sensitivity, or true positive rate, of 83.3%, and a specificity, or true negative rate, of 94.0%. Leave-one-out cross-validation produced similar results.
To test whether the algorithm is identifying a true pattern in the training set, but not a random data set, the labels for the 64 experiments were randomly assigned and a signature was derived and subject to cross-validation as above. This process was repeated 99 times. As expected, the average test log odds closely centered about zero (−0.004±0.86), with a range of −2.3 to 2.9. By comparison, the true label set had a log odds ratio of 4.4, which was significantly greater than expected by chance (p<0.0001).
Results
Using 7478 pre-selected genes whose accession numbers are listed in Table 3, the SVM algorithm was trained to produce a gene signature for renal tubule injury comprising 35 genes, their associated weights and a bias term that perfectly classified the training set. The 35 genes and the parameters of the signature are depicted in FIG. 1. Average impact represents the contribution of each gene towards the scalar product, and is calculated as the product of the average log10 ratio and the weight calculated across the 15 nephrotoxicants in the positive class listed in Table 2.
As shown in FIG. 1, the genes are ranked in descending order of percent contribution, which is calculated as the fraction of the average positive impact each gene in the positive training class has relative to the sum of all positive impacts. Genes with a negative average impact are considered penalty genes. The expression log10 ratio of each gene was plotted in the depicted “heat map” across all 15 treatments in the training set. The sum of the impact across all 35 genes for each treatment, and the resulting scalar product are presented along the two rows below the plot. The bias term for the 35 gene signature was 0.58.
The 35 genes identified represent 35 unique Unigene clusters. This 35 gene signature identifies compound treatments that are predicted to cause future renal tubular injury in the rat based on kidney expression data from short term (<=5 days) in vivo studies.
The product of the weight and the average log10 ratio across the 15 positive experiments in the training set indicated that 31 of the 35 genes are considered “reward” genes, as they represent expression changes that positively contribute to the signature score (i.e., the scalar product). The reward genes assure sensitivity of the signature by rewarding expression changes consistent with nephrotoxicity. A positive scalar product indicates the experiment is predicted to be positive for future renal tubular injury, while a negative scalar product indicates the experiment is negative for future renal tubular injury. The remaining 4 genes in the signature are considered “penalty” genes as they represent expression changes that negatively contribute to a scalar product. Penalty genes assure specificity of the signature by penalizing expression changes not consistent with nephrotoxicity.
The genes and bias term in the signature are weighted such that the classification threshold (i.e., zero) is equidistant, by one unit, between the positive class and negative class experiments in the training set.
Of the 31 reward genes, 15 have an average expression log10 ratio greater than zero and are therefore induced on average by the nephrotoxicants, while the remaining 16 are on averaged repressed by the nephrotoxicants. Examination of the expression changes across the 15 nephrotoxicants in the training set reveals that most genes are not consistently altered in the same direction by all treatments (FIG. 1). Instead, it is the sum of the product of the weight and log10 ratio (i.e., impact) across all 35 signature genes, less the bias, that results in an accurate classification. For example, Cyclin-dependent kinase inhibitor 1A (U24174) or the EST AW143082 are induced and repressed to varying degrees by compounds in the positive class, thus indicating that individual genes would be poor classifiers when used individually. This highlights the limitations of using single genes for classification and also illustrates the basis for signature robustness since classification decisions are not dependent on any one gene that may be subject to experimental error.
Example 4
Stripping of Renal Tubule Injury Signatures to Produce a Necessary Set of Genes
In order to understand the biological basis of classification and provide a subset of genes useful in alternative signatures for renal tubule injury, an iterative approach was taken in order to identify all the genes that are necessary and sufficient to classify the training set.
Starting with the 7478 pre-selected genes on the Codelink RU1 microarray, a signature was generated with the SVM algorithm and cross-validated using multiple random partitions (80% training: 20% test) of the data set. The 35 genes identified previously in the first signature (i.e., “iteration 1” in Table 4) as being sufficient to classify the training set were removed and the algorithm repeated to identify additional genes. This identified an additional 37 genes (i.e., the genes in “iteration 2” in Table 4) that were able to classify the training set with a log odds of 3.80. This approach was repeated until the test LOR of the model reached zero, which occurred after 14 iterations and which consumed 622 genes. Based on the first 5 iterations, 186 genes were identified to be necessary to classify the training set with a test LOR of 1.64 (Table 4), which is approximately 2 standard deviations greater than the average LOR achieved with random label sets. Importantly though, it identifies a reasonable number of genes with a demonstrated ability to uniquely discriminate nephrotoxicants with an approximate accuracy of 76%. These genes are listed in Table 4.







TABLE 4







186 genes identified to be necessary and sufficient to classify the training set.


















Mean
Mean








Logratio
Logratio






Positive
Negative
Unigene


Probe
Iteration
Weight
Impact
Class
Class
ID
UniGene Description

















AI105417
1
−0.89
0.261
−0.294
−0.172
Rn.8180
neuronal regeneration









related protein


BF404557
1
−1.36
0.213
−0.156
0.077
Rn.50972
ESTs


U08257
1
0.88
0.149
0.170
0.029
Rn.10049
Glutamate receptor,









ionotropic, kainate 4


BF285022
1
1.46
0.143
0.097
−0.013
Rn.24387
ESTs


AF155910
1
0.55
0.125
0.226
0.002
Rn.92316
heat shock 27 kD protein









family, member 7









(cardiovascular)


AI144646
1
0.63
0.108
0.171
−0.075
Rn.36522
gap junction protein, alpha









12, 47 kDa (Hs.)









(DBSS_strong)


AI105049
1
0.82
0.104
0.126
−0.018
Rn.23565
ESTs


AI227912
1
0.46
0.074
0.160
−0.026
Rn.873
Sorting nexin 3 (SDP3









protein) (Hs.)









(DBSS_strong)


AW916023
1
−0.64
0.074
−0.116
−0.011
Rn.6788
Kelch-like ECH-associated









protein 1 (Cytosolic









inhibitor of Nrf2) (INrf2)









(Rn.) (DBSS_weak)


BF403410
1
0.42
0.068
0.163
0.020
Rn.23087
Homo sapiens clone 25048









mRNA sequence (Hs.)









(DBSS)


Y00697
1
0.63
0.067
0.106
0.048
Rn.1294
Cathepsin L


AW143082
1
−0.30
0.056
−0.186
0.361
Rn.22057
ESTs


AI599126
1
0.36
0.044
0.122
−0.061
Rn.8452
inner centromere protein









(Mm.) (DBSS_strong)


AI102732
1
−0.31
0.035
−0.113
0.064
Rn.7539
ESTs


AI176933
1
0.46
0.035
0.076
−0.048
Rn.23658
ajuba (Mm.) (DBSS)


AF208288
1
−0.27
0.034
−0.127
0.043
Rn.48779
G protein-coupled receptor









26


AF281635
1
0.43
0.021
0.049
0.002
Rn.9264
zinc finger protein 22









(KOX 15)


U24174
1
0.09
0.021
0.219
0.133
Rn.10089
cyclin-dependent kinase









inhibitor 1A


AW142947
1
−0.22
0.019
−0.085
−0.030
Rn.61563
ESTs


BF396132
1
−0.26
0.014
−0.055
0.004
Rn.76362
echinoderm microtubule









associated protein like 2


NM_012610
1
−0.08
0.014
−0.164
0.054
Rn.10980
nerve growth factor









receptor


U57049
1
−0.17
0.013
−0.080
0.000
Rn.10494
methylenetetrahydrofolate









reductase


AW520754
1
−0.08
0.010
−0.124
0.021
Rn.15536
potassium channel,









subfamily K, member 3









(Hs.) (DBSS)


AI231846
1
−0.13
0.008
−0.059
0.032
Rn.27
ESTs


BE116947
1
0.05
0.006
0.126
−0.078
Rn.8045
ESTs


AW917933
1
−0.04
0.005
−0.124
0.039
Rn.28424
ESTs


AW144517
1
−0.05
0.005
−0.097
−0.004
Rn.13780
ESTs


AW920818
1
0.03
0.005
0.177
−0.078
Rn.11702
macrophage activation 2









(Mm.) (DBSS)


AB021980
1
−0.05
0.003
−0.057
0.054
Rn.32872
delta-6 fatty acid









desaturase


AF087454
1
−0.29
0.001
−0.004
0.033
Rn.30019
potassium voltage-gated









channel, subfamily Q,









member 3


BE097309
1
0.41
0.000
0.001
0.004
Rn.46694
Peregrin (Bromodomain









and PHD finger-containing









protein 1) (Hs.)









(DBSS_strong)


AW919837
1
−0.05
0.000
0.010
0.042
Rn.23432
adrenergic, alpha-2A-,









receptor (Hs.) (DBSS)


NM_013197
1
0.03
−0.007
−0.259
−0.286
Rn.32517
aminolevulinic acid









synthase 2


BF396955
1
0.77
−0.050
−0.065
−0.228
Rn.41236
PC4035 cell-cycle-









dependent 350 K nuclear









protein (Hs.) (DBSS_weak)


BF281149
1
1.34
−0.057
−0.042
−0.226
Rn.3137
Hypothetical protein









KIAA0008 (Hs.)









(DBSS_weak)


AI412011
2
3.38
0.279
0.082
0.005
Rn.3738
RIKEN cDNA









0610012G03; expressed









sequence AI839730 (Mm.)









(DBSS_weak)


BF419406
2
−0.94
0.159
−0.168
−0.026
Rn.26560
ESTs


NM_021682
2
−0.53
0.125
−0.234
−0.032
Rn.42884
kilon


AF136583
2
0.66
0.115
0.174
−0.024
Rn.12100
serum-inducible kinase


NM_020308
2
0.94
0.111
0.118
−0.025
Rn.28393
a disintegrin and









metalloproteinase domain









(ADAM) 15 (metargidin)


BE109152
2
1.60
0.103
0.064
0.011
Rn.19642
Red protein (RER protein)









(Mm.) (DBSS_strong)


AI176739
2
0.41
0.083
0.205
0.005
Rn.22359
KIAA1002 protein (Hs.)









(DBSS_moderate)


AI228233
2
0.67
0.076
0.113
−0.017
Rn.25139
epsin 2 (Hs.) (DBSS)


AF007549
2
0.55
0.075
0.136
0.026
Rn.10734
golgi SNAP receptor









complex member 2


AI232347
2
−2.15
0.070
−0.032
0.012
Rn.102
chromosome 14 open









reading frame 114 (Hs.)









(DBSS_moderate)


AW915996
2
−0.48
0.054
−0.114
0.094
Rn.19250
T00260 hypothetical









protein KIAA0605 (Hs.)









(DBSS_strong)


AA819832
2
−0.40
0.054
−0.136
0.141
Rn.34433
period homolog 1









(Drosophila) (Hs.) (DBSS)


AW524724
2
−0.34
0.052
−0.156
−0.002
Rn.95059
ryanodine receptor type 1









(Mm.) (DBSS_strong)


BE103916
2
−0.72
0.046
−0.064
0.020
Rn.26832
ESTs


BF283302
2
0.56
0.046
0.081
−0.008
Rn.226
ESTs


X68878
2
−0.17
0.040
−0.244
−0.050
Rn.11022
synaptosomal-associated









protein, 91 kDa


D00403
2
−0.44
0.039
−0.088
0.031
Rn.12300
Interleukin 1 alpha


AI145385
2
−0.79
0.035
−0.044
−0.025
Rn.3580
ESTs


AI317854
2
−0.22
0.032
−0.143
0.012
Rn.20362
ESTs


AI231432
2
0.58
0.030
0.051
−0.025
Rn.6983
hypermethylated in cancer









1 (Mm.) (DBSS_moderate)


AA996961
2
−0.34
0.029
−0.088
0.071
Rn.12469
DNA-repair protein









complementing XP-A cells









(Hs.) (DBSS_moderate)


NM_012971
2
−0.26
0.025
−0.098
0.058
Rn.9884
potassium voltage gated









channel, shaker related









subfamily, member 4


BF397726
2
0.43
0.020
0.047
−0.076
Rn.18639
NF-E2-related factor 2









(Rn.) (DBSS_weak)


AW527217
2
−0.20
0.017
−0.088
−0.027
Rn.23378
ESTs


AA799789
2
0.25
0.016
0.065
−0.026
Rn.30163
ESTs


NM_013190
2
−0.59
0.015
−0.026
0.001
Rn.4212
Phosphofructokinase, liver,









B-type


AI576621
2
0.16
0.013
0.082
0.027
Rn.24920
ESTs


AA943149
2
0.81
0.010
0.012
−0.002
Rn.7346
ALEX3 protein (Hs.)









(DBSS_strong)


AW253895
2
−0.12
0.006
−0.055
0.011
Rn.3382
BRCA1 associated protein-









1 (ubiquitin carboxy-









terminal hydrolase) (Hs.)









(DBSS_strong)


BF283340
2
−0.09
0.005
−0.057
0.028
Rn.20857
ESTs


AF073379
2
−0.11
0.005
−0.046
0.015
Rn.10169
glutamate receptor,









ionotropic, N-methyl-D-









aspartate 3A


AA799981
2
−0.14
0.005
−0.034
0.032
Rn.6263
ESTs


AF237778
2
−0.18
0.003
−0.017
0.086
Rn.88349
calcium/calmodulin-









dependent protein kinase II









alpha subunit


AI175375
2
−0.14
0.003
−0.019
−0.025
Rn.24087
ESTs


AJ130946
2
0.13
0.002
0.014
−0.096
Rn.2949
karyopherin (importin)









alpha 2


AI012120
2
0.25
−0.004
−0.016
−0.149
Rn.17809
ESTs


AW252871
2
0.54
−0.078
−0.145
−0.370
Rn.12774
cell proliferation antigen









Ki-67 (Mm.)









(DBSS_moderate)


J03863
3
0.70
0.163
0.233
0.208
Rn.9918
serine dehydratase


U19614
3
2.55
0.161
0.063
−0.005
Rn.11373
lamina-associated









polypeptide 1C


M19651
3
0.78
0.131
0.168
0.052
Rn.11306
Fos-like antigen 1


AI407719
3
−1.78
0.111
−0.063
0.161
Rn.20359
ubiquitin specific protease









2 (Hs.) (DBSS)


BF396629
3
2.54
0.111
0.044
−0.051
Rn.16544
patched homolog









(Drosophila) (Hs.) (DBSS)


BF290678
3
2.25
0.109
0.049
−0.015
Rn.40449
heterogeneous nuclear









ribonucleoprotein G (Mm.)









(DBSS)


BE101099
3
−1.84
0.109
−0.059
−0.008
Rn.35019
parathyroid hormone









regulated sequence (215 bp)


AI070303
3
−1.13
0.098
−0.086
0.019
Rn.21284
pancreasin (Hs.)









(DBSS_moderate)


AA925559
3
−1.06
0.078
−0.074
0.031
Rn.25196
RIKEN cDNA









2610027L16 [(Mm.)









(DBSS_strong)


AB005549
3
0.58
0.056
0.097
−0.026
Rn.31803
three-PDZ containing









protein similar to C. elegans









PAR3 (partitioning









defect)


AI717140
3
−0.59
0.043
−0.072
−0.001
Rn.22400
ESTs


AA858817
3
−0.23
0.040
−0.171
0.079
Rn.22047
T46271 hypothetical









protein DKFZp564P1263.1









(Hs.) (DBSS_moderate)


BF284897
3
0.54
0.035
0.064
0.027
Rn.18772
hypothetical protein









FLJ10579 (Hs.)









(DBSS_moderate)


AW914881
3
0.27
0.034
0.123
0.036
Rn.22383
ESTs


BE106459
3
−0.21
0.033
−0.157
−0.037
Rn.20259
ESTs


BF283556
3
−0.14
0.027
−0.188
0.019
Rn.7829
Homo sapiens clone 23785









mRNA sequence (Hs.)









(DBSS)


M63282
3
0.31
0.016
0.050
0.084
Rn.9664
Activating transcription









factor 3


AW533663
3
0.08
0.014
0.174
0.124
Rn.41672
Proline oxidase,









mitochondrial precursor









(Mm.) (DBSS_strong)


L19656
3
−0.92
0.013
−0.014
0.048
Rn.10552
5-hydroxytryptamine









(serotonin) receptor 6


NM_012852
3
0.11
0.009
0.083
−0.008
Rn.34834
5-Hydroxytryptamine









(serotonin) receptor 1D


AA946230
3
−0.22
0.008
−0.039
−0.023
Rn.47222
ESTs


BF405135
3
−0.36
0.008
−0.022
0.018
Rn.51262
ESTs


AA818949
3
−0.14
0.007
−0.052
0.002
Rn.20419
DnaJ homolog subfamily B









member 12 (Hs.)









(DBSS_moderate)


X79860
3
−0.36
0.006
−0.017
0.066
Rn.65877
H1SHR mRNA


AW253907
3
−0.08
0.005
−0.064
0.066
Rn.98601
ESTs


X89603
3
0.05
0.004
0.091
−0.049
Rn.11325
metallothionein 3


AA858649
3
−0.50
−0.002
0.004
0.004
Rn.16864
chromosome 13 open









reading frame 9 (Hs.)









(DBSS_strong)


AW529588
3
0.61
−0.003
−0.005
−0.040
Rn.28180
ESTs


BF550800
3
0.16
−0.004
−0.023
−0.307
Rn.36317
ESTs


BE111296
3
0.18
−0.014
−0.079
−0.174
Rn.19339
ESTs


AI113104
3
1.77
−0.086
−0.048
−0.262
Rn.12343
protein regulator of









cytokinesis 1 (Hs.)









(DBSS_moderate)


U53706
4
−1.14
0.159
−0.139
−0.021
Rn.10288
mevalonate pyrophosphate









decarboxylase


L36459
4
0.89
0.152
0.171
−0.036
Rn.10045
Interleukin 9 receptor


BF410042
4
4.02
0.151
0.038
−0.030
Rn.31227
cardiac lineage protein 1









(Mm.) (DBSS)


AW915655
4
−2.26
0.129
−0.057
0.000
Rn.14962
ESTs


AA944518
4
−1.07
0.102
−0.096
0.019
Rn.34351
ESTs


NM_012939
4
−0.19
0.079
−0.408
−0.002
Rn.1997
Cathepsin H


BF408867
4
−0.37
0.059
−0.157
0.013
Rn.35618
mitochondrial translational









release factor 1-like (Hs.)









(DBSS_moderate)


AW915454
4
−0.26
0.052
−0.204
−0.028
Rn.14822
ESTs


BE113132
4
−0.37
0.042
−0.112
0.124
Rn.22381
guanine nucleotide









exchange factor for Rap1;









M-Ras-regulated GEF









(Hs.) (DBSS)


AW143273
4
0.72
0.040
0.056
−0.020
Rn.11888
Rec8p, a meiotic









recombination and sister









chromatid cohesion









phosphoprotein of the









rad21p family (Hs.)









(DBSS)


AW915107
4
0.70
0.039
0.055
−0.023
Rn.19003
ESTs


BE110577
4
0.96
0.038
0.040
−0.008
Rn.14584
ESTs


AW141985
4
0.39
0.034
0.088
−0.008
Rn.13195
ATP-binding cassette, sub-









family C (CFTR/MRP),









member 4


AW140530
4
−0.35
0.029
−0.083
0.005
Rn.7679
tumor susceptibility protein









101 (tsg101) gene (Mm.)









(DBSS)


BF420720
4
−0.31
0.026
−0.083
0.030
Rn.23998
ESTs


AW144399
4
−0.78
0.025
−0.032
0.068
Rn.15255
hypothetical protein









FLJ10652 (Hs.)









(DBSS_moderate)


AI411605
4
−0.30
0.024
−0.079
−0.095
Rn.20056
ESTs


NM_019123
4
0.38
0.021
0.055
−0.025
Rn.88072
sialyltransferase 7c


AW920802
4
0.50
0.019
0.037
−0.021
Rn.36609
ribosomal protein L5 (Hs.)









(DBSS)


AI228598
4
−0.70
0.018
−0.026
0.036
Rn.11771
ESTs


AI175454
4
0.18
0.013
0.072
−0.002
Rn.17244
procollagen-proline, 2-









oxoglutarate 4-dioxygenase









(proline 4-hydroxylase),









alpha polypeptide II (Hs.)









(DBSS_strong)


AI009623
4
−0.08
0.011
−0.135
−0.073
Rn.13924
ESTs


AI235282
4
−0.20
0.011
−0.053
0.004
Rn.22436
Low-density lipoprotein









receptor-related protein 1









precursor (Hs.)









(DBSS_strong)


NM_012564
4
−0.06
0.009
−0.159
−0.100
Rn.1437
Group-specific component









(vitamin D-binding









protein)


BE095865
4
−0.35
0.009
−0.025
0.104
Rn.21852
calcium channel, voltage-









dependent, alpha 1I subunit









(Hs.) (DBSS)


AF291437
4
−0.40
0.009
−0.022
−0.058
Rn.39124
leucine rich repeat protein









3, neuronal


AF176351
4
−0.26
0.009
−0.032
0.017
Rn.54003
nuclear receptor









coactivator 6


AB027155
4
0.15
0.008
0.057
0.027
Rn.44869
phosphodiesterase 10A


BE116569
4
0.34
0.008
0.024
−0.009
Rn.15835
zinc-finger protein









AY163807 (Hs.)









(DBSS_strong)


AA894210
4
0.05
0.004
0.091
0.082
Rn.85480
ESTs


AJ237852
4
−0.04
0.003
−0.058
0.065
Rn.30023
sodium channel, voltage-









gated, type11, alpha









polypeptide


AJ305049
4
−1.09
0.002
−0.002
0.075
Rn.64632
interleukin 10 receptor,









alpha


NM_017186
4
−0.03
0.002
−0.070
−0.015
Rn.30042
glial cells missing









(Drosophila) homolog a


AA800004
4
0.04
0.001
0.024
−0.063
Rn.6269
Septin 4 (Peanut-like









protein 2) (Brain protein









H5) (Hs.) (DBSS_strong)


NM_012614
4
0.05
0.001
0.012
0.040
Rn.9714
Neuropeptide Y


BF285985
4
−0.06
−0.001
0.016
0.074
Rn.42366
protein tyrosine









phosphatase, receptor type,









f polypeptide (PTPRF),









interacting protein (liprin),









alpha 4


AI412889
4
−0.08
−0.001
0.012
0.105
Rn.23659
monocyte to macrophage









differentiation-associated 2









(Mm.) (DBSS)


AJ002556
4
−0.54
−0.003
0.006
0.050
Rn.37490
microtubule-associated









protein 6


AI179459
4
0.12
−0.011
−0.094
−0.152
Rn.31366
Kell blood group (Mm.)









(DBSS_moderate)


AI603128
4
0.15
−0.019
−0.127
−0.330
Rn.13094
Cyclin A2 (Cyclin A)









(Mm.) (DBSS_strong)


BE111688
4
1.72
−0.082
−0.048
−0.343
Rn.23351
cyclin B2 (Hs.)









(DBSS_strong)


NM_012892
5
−0.70
0.128
−0.184
−0.127
Rn.37523
amiloride-sensitive cation









channel 1


BE098463
5
2.30
0.101
0.044
−0.100
Rn.18203
ESTs


C06844
5
−0.94
0.095
−0.101
0.075
Rn.7159
S49158 complement









protein C1q beta chain









precursor (Rn.)









(DBSS_weak)


AI170114
5
−0.42
0.078
−0.183
−0.112
Rn.91697
ESTs


AI105265
5
−1.53
0.073
−0.048
0.009
Rn.5911
hypothetical protein









FLJ10315 (Hs.)









(DBSS_strong)


BF394214
5
−0.79
0.071
−0.090
−0.014
Rn.58227
ESTs


AA946356
5
−1.08
0.063
−0.058
−0.017
Rn.1435
CGG triplet repeat binding









protein 1 (Hs.) (DBSS)


AW919159
5
1.09
0.056
0.051
−0.022
Rn.41574
A38135 ADP-









ribosylarginine hydrolase









(Rn.) (DBSS_weak)


AI230884
5
1.61
0.053
0.033
−0.034
Rn.9797
Fibroblast growth factor









receptor 1


BF406522
5
0.92
0.052
0.056
−0.019
Rn.3537
cerebellar degeneration-









related protein 2, 62 kDa









(Hs.) (DBSS)


NM_012848
5
0.14
0.048
0.350
0.110
Rn.54447
ferritin, heavy polypeptide 1


AW914090
5
−1.61
0.046
−0.029
0.002
Rn.973
60S acidic ribosomal









protein P1 (Rn.)









(DBSS_strong)


AW142828
5
−0.65
0.044
−0.068
−0.034
Rn.23877
ESTs


AI705731
5
−0.95
0.040
−0.042
0.058
Rn.24919
transcription factor









MTSG1


NM_019126
5
−0.33
0.037
−0.112
0.140
Rn.25723
Carcinoembryonic antigen









gene family (CGM3)


U73503
5
0.64
0.037
0.057
−0.014
Rn.10961
calcium/calmodulin-









dependent protein kinase









(CaM kinase) II gamma


AF017437
5
0.55
0.036
0.066
−0.010
Rn.7409
integrin-associated protein


NM_021869
5
−0.42
0.035
−0.083
0.057
Rn.1993
syntaxin 7


AI144644
5
−0.34
0.030
−0.087
0.024
Rn.12319
ESTs


AA818377
5
0.79
0.029
0.037
−0.033
Rn.34063
hypothetical protein









FLJ22419 (Hs.)









(DBSS_weak)


AI171994
5
0.13
0.027
0.198
0.008
Rn.22380
ESTs


AA925167
5
−0.12
0.022
−0.180
0.106
Rn.8672
ESTs


BF398051
5
−0.38
0.020
−0.053
0.080
Rn.97322
ESTs


AW144075
5
0.48
0.019
0.040
−0.024
Rn.19790
ESTs


U26686
5
−0.09
0.015
−0.158
−0.045
Rn.10400
nitric oxide synthase 2


BF404426
5
−0.07
0.009
−0.128
−0.032
Rn.63325
ESTs


U31866
5
0.24
0.007
0.029
−0.037
Rn.32307
Nclone10 mRNA


AW917475
5
−0.07
0.006
−0.087
0.055
Rn.16643
high-affinity









immunoglobulin gamma Fc









receptor I


AI408517
5
0.44
0.006
0.013
0.021
Rn.2773
protein phosphatase 1,









regulatory (inhibitor) 5









subunit 14B


AF207605
5
−0.34
0.005
−0.015
0.000
Rn.42674
tubulin tyrosine ligase


AI178922
5
−0.41
0.005
−0.012
−0.023
Rn.18670
leucine zipper and









CTNNBIP1 domain









containing (Hs.)









(DBSS_moderate)


BF398403
5
0.41
0.005
0.011
−0.037
Rn.20421
mannosyl-oligosaccharide









1,3-1,6-alpha-mannosidase









(EC 3.2.1.114) (Mm.)









(DBSS_moderate)


M22923
5
0.05
0.004
0.091
−0.019
Rn.10922
membrane-spanning 4-









domains, subfamily A,









member 2


BE107747
5
−0.05
0.004
−0.077
0.041
Rn.29176
ESTs


BF281697
5
0.57
0.004
0.007
−0.024
Rn.7770
potassium voltage-gated









channel, Isk-related family,









member 1-like (Hs.)









(DBSS)


AB006461
5
0.03
0.002
0.059
−0.009
Rn.5653
neurochondrin


AF100960
5
0.03
0.001
0.051
−0.038
Rn.8633
FAT tumor suppressor









(Drosophila) homolog


U79031
5
−0.07
0.000
0.006
0.048
Rn.44299
adrenergic receptor, alpha









2a


NM_017353
5
−0.21
−0.004
0.019
0.045
Rn.32261
tumor-associated protein 1


AI231716
5
1.81
−0.007
−0.004
−0.138
Rn.24598
ESTs


NM_012964
5
0.67
−0.024
−0.036
−0.298
Rn.92304
Hyaluronan mediated









motility receptor









(RHAMM)


L06040
5
0.19
−0.035
−0.183
−0.306
Rn.11318
arachidonate 12-









lipoxygenase









The 186 genes of the necessary set listed in Table 4 correspond to 164 reward genes, of which 72 are induced on average across the nephrotoxicants. Additional genes not necessary for classification, but nonetheless differentially regulated by the nephrotoxicants relative to the negative class, were also considered.
Example 5
Using a Necessary Set to Generate New Signatures for Renal Tubule Injury
As shown above in Examples 1-3, a predictive signature for renal tubule injury comprising 35 genes may be derived using gene expression data from a microarray in the context of a chemogenomic database. Using the signature stripping method described above, four additional high performing predictive signatures for renal tubule injury may also be derived wherein each of the signatures is non-overlapping, i.e., comprises genes not used in any of the other signatures. Together, the union of the genes in these five signatures comprises a set of 186 genes that is necessary for deriving a predictive signature for renal tubule injury capable of classifying the training set above a selected threshold level of LOR=1.64.
This example demonstrates that additional signatures for renal tubule injury may be generated based on the necessary set of 186 genes. In addition, it is shown that at least four genes must be selected from the necessary set in order to generate a signature for renal tubule injury capable of performing above a selected threshold LOR of 4.00.
As listed in Table 4, for each gene from the necessary set of 186, an impact factor was calculated, corresponding to the product of the gene's weight and the gene's expression mean logratio in the positive class (i.e., nephrotoxicants). Subsets of genes were chosen randomly from the necessary set of 186 so that the sum of the impacts of all genes in the subset accounted for 1, 2, 4, 8, 16, 32, or 64% of the total impact. Total impact was defined as the sum of the individual impacts of all 186 genes in the necessary set. This random subset selection procedure was repeated 20 times resulting in 140 gene subsets (i.e., 7 impact thresholds times 20 random choices).
Table 5 shows the average number of genes for each of these seven impact thresholds. This number increases regularly reaching an average of 116 genes for those subsets that account for 64% of the total impact. Each of these random subsets was used as input to compute a renal tubule injury signature using the SPLP algorithm as described in Example 3 above. A training LOR and a 10-fold cross-validated test LOR were calculated for each signature. Table 5 lists average LOR values for the signatures generated in each of the seven percent of total impact thresholds. Based on the results tabulated in Table 5 it may be concluded that signatures for renal tubule injury capable of performing with an average training LOR of 4.30 may be generated starting with random subsets having an average of 4.4 genes that together have only 2% of the total impact of the necessary set. Similarly signatures capable of performing with an average test LOR of 4.41 may be derived from random subsets of the necessary set having an average of 9.15 genes with only 4% of the total impact. Significantly, the average training LOR never drops below 4.00 when a random set of genes having at least 4% impact are selected. As shown in Table 5, comparably higher performing signatures are derived from the necessary set when the random subsets have a percent impact of 8% or higher.







TABLE 5







RTI signatures generated based on randomly selecting necessary set genes


with minimal percentage impact













Signature
LOR




# input genes
Length
(training)
LOR (test)

















percent impact*
avg
min
max
avg
min
max
avg
stdev
avg
stdev




















1
2.85
1
5
2.8
1
5
3.42
1.61
3.01
1.34


2
4.4
1
9
4.3
1
8
4.30
1.61
3.20
1.00


4
9.15
3
17
8.05
3
13
6.82
2.34
4.41
2.43


8
17.3
8
27
12.8
8
18
8.54
0.61
5.91
1.99


16
33.4
22
42
19.2
14
25
8.68
0.00
7.85
2.01


32
61.6
49
76
26.5
22
30
8.68
0.00
7.35
2.03


64
116
100
134
30.7
28
36
8.68
0.00
7.07
1.50





*average of 20 lists chosen from the necessary set






Table 6 shows the parameters for 20 signatures generated from random subsets of genes with 2% of the total impact of the 186 gene necessary set. Tables 7 (subset 8) and 8 (subset 14) illustrate two specific 5 gene signatures (including values for gene weights and bias) for predicting renal tubule injury onset that perform with a training LOR of 4.00 and 7.3, respectively.







TABLE 6







RTI signatures generated based on random selections of necessary set


genes with 2% impact












# Input
Signature
Training
Test


Subset #
Genes
Length
LOR
LOR














14
5
5
7.3
5.0


9
7
7
6.8
3.4


15
5
5
6.2
4.1


7
6
6
6.0
3.2


18
5
5
5.8
3.7


3
4
4
5.5
4.0


10
9
8
5.0
2.8


2
4
3
4.7
1.7


13
3
3
4.5
3.2


19
6
6
4.4
2.6


8
5
5
4.0
2.8


11
5
5
3.8
4.5


4
4
4
3.8
4.0


12
4
4
3.8
5.1


20
4
4
3.2
2.7


5
3
3
2.8
2.6


1
4
4
2.6
2.4


17
3
3
2.2
2.4


6
1
1
2.1
1.6


16
1
1
1.7
2.3


















TABLE 7







Subset 8



















BF283302
15.5



AW920818
5.88



AW141985
5.48



BF403410
4.28



AA858649
−2.3



Bias
1.13



















TABLE 8







Subset 14



















AI176933
43.1



U08257
33.7



BE116947
18.4



AI408517
12.7



AA819832
−2.9



Bias
8.49










Similarly Table 9 shows the parameters for 20 signatures generated from random subsets of genes with 4% of the total impact of the 186 gene necessary set. Tables 10 (subset 18) and 11 (subset 5) illustrate specific 9 and 13 gene signatures for predicting renal tubule injury onset that perform with a test LOR of 4.1 and 10.2, respectively.











TABLE 9






# Input
Signature
Training
Test


Subset #
Genes
Length
LOR
LOR



















5
13
13
8.7
10.2


2
14
11
8.7
8.9


7
11
10
8.7
8.9


9
17
11
8.7
6.2


20
11
9
8.7
5.3


10
14
12
8.7
4.7


11
13
12
8.7
4.6


14
7
6
8.7
4.5


12
9
8
8.7
4.3


18
9
9
8.7
4.1


15
11
9
8.7
3.8


3
6
6
6.2
3.3


19
7
6
6.2
3.2


13
6
6
4.7
3.1


8
11
9
6.8
2.7


4
5
5
4.3
2.7


17
5
5
3.7
2.1


1
7
7
3.7
2.1


6
4
4
3.4
2.0


16
3
3
1.9
1.5


















TABLE 10







Subset 18



















AW143273
55.95



AI599126
29.8



AI705731
19.05



BF406522
16.71



AB027155
−4.12



AW253895
−13.53



AA819832
−14.81



X68878
−17.57



AW140530
−19.85



Bias
8.96



















TABLE 11







Subset 5



















AW144075
4.82



AI113104
4.58



AI171994
4.25



AW920818
3.39



BF281697
3.11



AI012120
1.76



BE110577
1.08



NM 012964
0.87



AI227912
0.74



AW144399
−0.2



AI232347
−2.9



AA944518
−6.4



AW914090
−6.6



Bias
0.68










The results tabulated in Table 5 may also be illustrated graphically. As shown in FIG. 2, which plots training LOR and test LOR versus signature length, a signature performing with an average training LOR of 4.00 may be achieved by randomly selecting on average 4 genes from the necessary set. Similarly, an average test LOR of 4.00 may be achieved by randomly selecting on average 7 genes from the necessary set.
Example 6
Functional Characterization of the Necessary Set of Genes for Renal Tubule Injury by Random Supplementation of a Fully Depleted Set
This example illustrates how the set of 186 genes necessary for classifying renal tubule injury may be functionally characterized by randomly supplementing and thereby restoring the ability of a depleted gene set to generate RTI signatures capable of performing on average above a threshold LOR. In addition to demonstrating the power of the 186 information rich genes in the RTI necessary set, this example illustrates a system for describing any necessary set of genes in terms of its performance parameters.
As described in Example 4, a necessary set of 186 genes (see Table 4) for the RTI classification question was generated via the stripping method. In the process, a corresponding fully depleted set of 7292 genes (i.e., the full dataset of 7478 genes minus 186 genes) was also generated. The fully depleted set of 7292 genes was not able to generate an RTI signature capable of performing with a LOR greater than or equal to 1.28 (based on cross-validation using 40 random 80:20 training:test splits).
A further 186 genes were randomly removed from the fully depleted set. Then a randomly selected set including 10, 20, 40 or 80% of the genes from either: (a) the necessary set; or (b) the set of 186 randomly removed from the fully depleted set; is added back to the depleted set minus 186. The resulting “supplemented” depleted set was then used to generate an RTI signature, and the performance of this signature is cross-validated using 3 random 60:40 training:test splits. This process was repeated 20 times for each of the different percentage supplementations of genes from the necessary set and the random 186 genes removed from the original depleted set. Twenty cross-validated RTI signatures were obtained for each of the various percentage supplementations of the depleted set. Average LOR values were calculated based on the 20 signatures generated for each percentage supplementation.
Results
As shown in Table 12, supplementing the fully depleted set (minus random 186) with as few as 10% of the randomly chosen genes from the necessary set results in significantly improved performance for classifying RTI. The random 10% of genes selected from the depleted 186 yielded signatures performing with an avg. LOR=1.4. In contrast, supplementing the depleted set (minus random 186) with 10% from the necessary set yields RTI signatures performing with an avg. LOR=4.5 (based on 3-fold cross-validation using random 60:40 splits).







TABLE 12







Supplementation with random genes from necessary or depleted sets










Necessary Set
Depleted Set


%
Avg. LOR
Avg. LOR





10
4.51
1.43


20
4.93
2.32


40
4.73
2.63


80
4.10
3.28









Although increasing the percentage of random “depleted” set genes used to supplement resulted in an increase in average performance, even at 80%, the average LOR remained below 4.00, while supplementation with the random 80% “necessary” set genes yielded an average LOR above 4.00.
These results demonstrate how supplementation with a percentage of randomly selected genes from the RTI necessary set of 186 “revives” the performance of a fully depleted set for generating classifiers. Thus, the RTI necessary set of genes may be functionally characterized as the set of genes for which a randomly selected 10% will supplement a set of genes fully depleted for RTI classification (i.e., not capable of producing RTI signatures with avg. LOR> ̃1.4), such that the resulting “revived” gene set generates RTI signatures with an average LOR greater than or equal to 4.00.
Example 7
Construction and Use of a DNA Array for Predicting Renal Tubule Injury
The necessary subset of 186 genes identified to be necessary and sufficient to classify the renal tubule injury training set listed in Table 4 may be used as the basis for a DNA array diagnostic device for predicting renal tubule injury. The device may be used in a therapeutic monitoring context, such as for monitoring the response of an individual to a compound that is suspected of possibly causing renal tubule injury (or related nephrotoxic side effects). Alternatively, smaller sufficient subsets of genes the necessary set, which may be selected according to the methods of Examples 4 and 5 described above, may be used as the basis for a DNA array.
The probe sequences used to represent the 186 (or fewer) genes on the array may be the same ones used on the Amersham CodeLinkTM RU1 platform DNA array used to derive the renal tubule injury signature as described in Examples 1-3. The 186 probes are pre-synthesized in a standard oligonucleotide synthesizer and purified according to standard techniques. The pre-synthesized probes are then deposited onto treated glass slides according to standard methods for array spotting. For example, large numbers of slides, each containing the set of 186 probes, are prepared simultaneously using a robotic pen spotting device as described in U.S. Pat. No. 5,807,522. Alternatively, the 186 probes may be synthesized in situ one or more glass slides from nucleoside precursors according to standard methods well known in the art such as ink-jet deposition or photoactivated synthesis.
The DNA probe arrays made according to this method are then each hybridized with a fluorescently labeled nucleic acid sample. The nucleic acid may be derived from mRNA obtained from a biological fluid (e.g., blood) or a tissue sample from a compound treated individual. Any of the well-known methods for preparing labeled samples for DNA probe array hybridization may be used. The fluorescence intensity data from hybridization of the sample to the DNA array of 186 (or fewer) genes of the necessary set is used to calculate expression log ratios for each of the genes. Depending on the specific gene signature selected for use in predicting renal tubule injury (e.g., the genes in iteration 1 of Table 4), the scalar product for that signature is calculated (i.e., sum of the products of expression log10 ratio and weight for each gene less the bias). If the scalar product is greater than zero then the sample is classified as positive (i.e., onset of renal tubule injury is predicted).
All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference.
Although the foregoing invention has been described in some detail by way of illustration and example for clarity and understanding, it will be readily apparent to one of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit and scope of the appended claims.

## Claims

1. A method for testing whether a compound will induce renal tubule injury in a test subject, the method comprising:
a) administering a dose of compound to at least one test subject, wherein the test subject is a mouse or rat;
b) after a selected time period, obtaining a biological sample from the at least one test subject;
c) measuring the expression levels in the biological sample of at least a plurality of sequences selected from Table 4, wherein the plurality of sequences comprises AI105417, BF404557, U08257, BF285022, and AF155910 and has at least 2% of the total impact of all of the sequences in Table 4; and
d) determining whether the sample is in the positive class for renal tubule injury using a classifier comprising at least the plurality of sequences for which the expression levels are measured.

2. The method of claim 1, wherein the test compound is administered by route of IV, PO, or IP.

3. The method of claim 1, wherein the dose administered does not cause histological or clinical evidence of renal tubule injury at about 5 days.

4. The method of claim 1, wherein the biological sample comprises kidney tissue.

5. The method of claim 1, wherein the selected period of time is about 5 days or fewer.

6. The method of claim 1, wherein said selected period of time is at least 28 days.

7. The method of claim 1, wherein the expression levels are measured as log10 ratios of a compound-treated biological sample to a compound-untreated biological sample.

8. The method of claim 1, wherein the classifier is a non-linear classifier.

9. The method of claim 1, wherein the classifier is a linear classifier.

10. The method of claim 9, wherein the linear classifier comprises the sequences and weights corresponding to any one of iterations 1 through 5 in Table 4.

11. The method of claim 10, wherein the linear classifier for renal tubule injury classifies the nephrotoxic versus non-nephrotoxic compounds listed in Table 2 with a training log odds ratio of greater than or equal to 4.35.

12. The method of claim 1, wherein the plurality of sequences from Table 4 includes at least 8 sequences selected having at least 4% of the total impact of all of the sequences in Table 4.