Abstract:
The present invention includes methods of identifying genes whose expression level is invariant among cell or tissue types. The methods of the invention can be used in the diagnosis of disease, in quality control in evaluating external data or databases, and in normalization of external data for comparative purposes. The genes of the invention can be used to produce microarrays that generate data with improved reliability.

Description:
RELATED APPLICATIONS  
       [0001]    This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application No. 60/396,145, filed Jul. 17, 2002, which is herein incorporated by reference in its entirety. 
     
    
     
       FIELD OF THE INVENTION  
         [0002]    The invention relates generally to control genes that may be utilized for normalizing hybridization and/or amplification reactions, as well as methods of identifying these genes that may be used in toxicology studies and in analyzing gene expression data sets for quality and compatibility with other data sets.  
         BACKGROUND OF THE INVENTION  
         [0003]    Nucleic acid hybridization and other quantitative nucleic acid detection assays are routinely used in medical and biotechnological research and development, diagnostic testing, drug development and forensics. Such technologies have been used to identify genes which are up- or down-regulated in various disease or physiological states, to analyze the roles of the members of cellular signaling cascades and to identify drugable targets for various disease and pathology states.  
           [0004]    Examples of technologies commonly used for the detection and/or quantification of nucleic acids include Northern blotting (Krumlauf (1994),  Mol Biotechnol  2:227-242), in situ hybridization (Parker &amp; Barnes (1999),  Methods Mol Biol  106:247-283), RNAse protection assays (Hod (1992),  Biotechniques  13:852-854; Saccomanno et al. (1992),  Biotechniques  13:846-850), microarrays, and reverse transcription polymerase chain reaction (RT-PCR) (see Bustin (2000),  J Mol Endocrin  25:169-193).  
           [0005]    The reliability of these nucleic acid detection methods depend on the availability of accurate means for accounting for variations between analyses. For example, variations in hybridization conditions, label intensity, reading and detector efficiency, sample concentration and quality, background effects, and image processing effects each contribute to signal heterogeneity (Hegde et al. (2000),  Biotechniques  29:548-562; Berger et al. (2000), WO 00/04188). Normalization procedures used to overcome these variations often rely on control hybridizations to housekeeping genes such as β-actin, glyceraldehyde-3-phosphate dehydrogenase (GADPH), and the transferrin receptor gene (Eickhoff et al. (1999),  Nuc Acids Res  27:e33; Spiess et al. (1999),  Biotechniques  26: 46-50). These methods, however, generally do not provide the signal linearity sufficient to detect small but significant changes in transcription or gene expression (Spiess et al. (1999),  Biotechniques  26: 46-50). In addition, the steady state levels of many housekeeping genes are susceptible to alterations in expression levels that are dependent on cell differentiation, nutritional state, specific experimental and stimulation protocols (Eickhoff et al. (1999),  Nuc Acids Res  27:e33; Spiess et al. (1999),  Biotechniques  26:46-50; Hegde et al. (2000),  Biotechniques  29:548-562; and Berger et al. (2000), WO 00/04188). Consequently, there exists a need for the identification and use of additional genes that may serve as effective controls in nucleic acid detection assays.  
         SUMMARY OF THE INVENTION  
         [0006]    The present invention includes methods of identifying at least one gene that is consistently or invariantly expressed across different cell or tissue types in an organism, comprising: preparing gene expression profiles for different cell or tissue types from the organism; calculating a percent variability of expression for at least one gene in each of the profiles across the different cell or tissue types; and selecting any gene whose percent variability of expression indicates that the gene is consistently or invariantly expressed across the different cell or tissue types. The percent variability of expression may be determined by a one-factor or two-factor analysis of variance (ANOVA) wherein the R 2  value is a measure of percent variability of expression.  
           [0007]    The invention, in another embodiment, includes methods of normalizing the data from a nucleic acid detection assay comprising: detecting the expression level for at least one gene in a nucleic acid sample; and normalizing the expression of said at least one gene with the detected expression of at least one control gene of Table 1. The number of control genes used to normalize gene expression data may comprise about 10, 25, 50, 100, 500 or more of the control genes herein identified.  
           [0008]    In another embodiment, the invention includes a set of probes comprising at least two probes that specifically hybridize to a gene of Table 1. The set may comprise at least about 10, 25, 50, 100, 500 or more of the control genes of Table 1. The sets of probes may or may not be attached to a solid substrate such as a chip.  
         DETAILED DESCRIPTION  
         [0009]    The present Inventors have identified rat control genes that may be monitored in nucleic acid detection assays and whose expression levels may be used to normalize gene expression data or evaluate the suitability of test data to compare to or to include in a database of like data. Normalization of gene expression data from a cell or tissue sample with the expression level(s) of the identified control genes allows the accurate assessment of the expression level(s) for genes that are differentially regulated between samples, tissues, treatment conditions, etc. These control genes may be used across a broad spectrum of assay formats, but are particularly useful in microarray or hybridization based assay formats.  
           [0010]    A. Nucleic Acid Detection Assay Controls  
           [0011]    1. Selection of Control Genes  
           [0012]    As used herein, the genes selected by the disclosed methods as well as the rat genes and nucleic acids of Table 1 (identified by ANOVA methods, discussed below) are referred to as “invariant” or “control genes.” Control genes of the invention may be produced by a method comprising preparing gene expression profiles (a representation of the expression level for at least one gene, preferably 10, 25, 50, 100, 500 or more, or, most preferably, nearly all or all expressed genes in a sample) from at least two (or a variety) of cell or tissue types, or from a set of samples of at least one cell or tissue type in which the set contains normal samples (from healthy animals), disease state samples, toxin-exposed samples, etc., measuring the level of expression for at least one gene in each of the gene expression profiles to produce gene expression data, calculating the variation in expression level (R 2 ) from the gene expression data for each gene and selecting genes whose variation in expression level indicates that the gene is consistently expressed at about the same level in the different cell or tissue types. In one embodiment, such genes that are expressed at about the same level, or are invariantly expressed, are those genes that have a percent variability in expression level (R 2 ) less than or equal to about 12.  
           [0013]    In preferred embodiments, the statistical measure referred to herein as the percent variability in expression level (R 2 ) is calculated on a gene by gene basis across a number of samples or across a reference database to find the least variant genes with respect to a number of cell or tissue types or sample treatments. A two-factor ANOVA model is applied to all cell and tissue sample sets where both control and disease, pathology or treatment groups exist. The factors for this model were normal state (control or affected tissue) and tissue type. A one factor ANOVA was also used to examine the effects of tissue kind alone. Genes are ranked according to R-squared values. The R-squared value can be interpreted as the percent variability of expression that can be explained by the underlying factors. Cut-off values are also selected for the alpha error p-values for each factor and the interaction of these two factors. A cut-off value for both one factor and two factor R 2  values of less than or equal to about 14, preferably less than about 12, may be used, and genes with R 2  values less than or equal to 14, preferably less than or equal to 12, may be selected as control genes or considered as genes that are consistently expressed across the different cell or tissue types tested. In addition, any gene with large known regulation events within tissues may be removed and any co-clustered Unigene fragments may be examined for consistency in R 2  values. A probe set is also selected using the following supplemental criteria: (a) Mean Average Differential over all rat samples less than or equal to about 20, (b) Present Frequency over all rat samples less than or equal to about 75% and (c) no probe sets exhibiting saturation.  
             E   ij   =u+T   j +error  Model 1  
           [0014]    (E ij  is the expression value of the i th  gene in the j th  sample)  
           [0015]    (T j  is the tissue type of the j th  sample)  
           [0016]    For each gene, model fitting produces a p-value for the T factor, as well as a sum of squares attributable to this factor. This sum of squares is the model sum of squares. The R 2  value is then the ratio of the model sum of squares to the total sum of squares  
         ∑   j                                 (       E   ij     -       E   _     i       )     2     .                           
  E   ij   =u+T   j   +N   j   +T   j   *N   j +error  Model 2  
           [0017]    (E ij  is the expression value of the i th  gene in the j th  sample)  
           [0018]    (T j  is the tissue type of the j th  sample)  
           [0019]    (N j  is the state of the j th  sample (N j =0 for normal, 1 otherwise))  
           [0020]    The model fitting yields, for each gene, a p-value for the T factor, the N factor, and the T*N factor, as well as a sum of squares attributable to each of these factors. Adding the three sums of squares gives the model sum of squares. The R 2  value is then the ratio of the model sum of squares to the total sum of squares  
         ∑   j                                 (       E   ij     -       E   _     i       )     2     .                           
 
           [0021]    Further, the ANOVA-based methods of the invention are particularly useful for determining the compatibility of a test sample to an entire set of samples, or an existing database derived from those samples. For instance, an R 2  value for genes that have been shown to be the most resistant to variability is calculated for all samples within a test group or test database. These R 2  values are then compared to those from a standard reference database. Accordingly, a closeness distribution of all individual samples in the test database to the reference database as a whole can be generated to evaluate the compatibility of new samples. The genes identified in Table 1 show invariant patterns of expression and can be used to assess compatibility and reliability of gene expression experiments and predictive modeling experiments. These genes show low variability both in control groups from many different experiments and in studies of disruptions of gene expression, such as those occurring in disease states. As a result, these genes can be used as an internal standard for comparing gene expression data. Measurements of expression level of these genes are used to determine the extent of compatibility of data from different sources and the need, or lack thereof, for normalization or further quality control and adjustments. These measurements also provide an internal standard that supplies a reference point for highly disrupted patterns of gene expression. These genes are also of critical importance for determining relative expression if small numbers of markers are used in custom microarrays.  
           [0022]    In some embodiments of the invention, the percent variability of expression may be calculated from data that has been normalized to control for the mechanics of hybridization, such as data normalized or controlled for background noise due to non-specific hybridization. Such data typically include, but are not limited to, fluorescence readings from microarray based hybridizations, densitometry readings produced from assays that rely on radiological labels to detect and quantify gene expression and data produced from quantitative or semi-quantitative amplification assays.  
           [0023]    In the methods of the invention, gene expression profiles may be produced by any means of quantifying gene expression for at least one gene in the tissue or cell sample. In preferred methods, gene expression is quantified by a method selected from the group consisting of a hybridization assay or an amplification assay. Hybridization assays may be any assay format that relies on the hybridization of a probe or primer to a nucleic acid molecule in the sample. Such formats include, but are not limited to, differential display formats and microarray hybridization, including microarrays produced in chip format. Amplification assays include, but are not limited to, quantitative PCR, semiquantitative PCR and assays that rely on amplification of nucleic acids subsequent to the hybridization of the nucleic acid to a probe or primer. Such assays include the amplification of nucleic acid molecules from a sample that are bound to a microarray or chip.  
           [0024]    In other circumstances, gene expression profiles may be produced by querying a gene expression database comprising expression results for genes from various cell or tissue samples. The gene expression results in the database may be produced by any available method, such as differential display methods and microarray-based hybridization methods. The gene expression profile is typically produced by the step of querying the database with the identity of a specific cell or tissue type for the genes that are expressed in the cell or tissue type and/or the genes that are differentially regulated compared to a control cell or tissue sample. Available databases include, but are not limited to, the Gene Logic ToxExpress® database, the Gene Expression Omnibus gene expression and hybridization array repository available through NCBI (www.ncbi.nlm.nih.gov/entrez) and the SAGE™ gene expression database.  
           [0025]    The cell or tissue samples that are used to prepare gene expression profiles may include any cell or tissue sample available. Such samples include, but are not limited to, tissues removed as surgical samples, diseased or normal tissues, in vitro or in vivo grown cells, and cell cultures and cells or tissues from animals exposed to an agent such as a toxin. The number of samples that may be used to calculate absolute R 2  values is variable, but may include about 3, 10, 25, 50, 100, 200, 500 or more cell or tissue samples. The cell or tissue samples may be derived from an animal or plant, preferably a mammal, most preferably a rat. In some instances, the cell or tissue samples may be human, canine (dog), or mouse in origin.  
           [0026]    As used herein, “background” refers to signals associated with non-specific binding (cross-hybridization). In addition to cross-hybridization, background may also be produced by intrinsic fluorescence of the hybridization format components themselves.  
           [0027]    “Bind(s) substantially” refers to complementary hybridization between an oligonucleotide probe and a nucleic acid sample and embraces minor mismatches that can be accommodated by reducing the stringency of the hybridization media to achieve the desired detection of the nucleic acid sample.  
           [0028]    The phrase “hybridizing specifically to” refers to the binding, duplexing or hybridizing of a molecule substantially to or only to a particular nucleotide sequence or sequences under stringent conditions when that sequence is present in a complex mixture (e.g., total cellular) DNA or RNA.  
           [0029]    2. Preparation of Controls Genes, Probes and Primers  
           [0030]    The control genes listed in Table 1 may be obtained from a variety of natural sources such as organisms, organs, tissues and cells. The sequences of known genes are in the public databases. The GenBank Accession Number corresponding to the Normalization Control Genes can be found in Table 1. The sequences of the genes in GenBank (http://www.ncbi.nlm.nih.gov/) are herein incorporated by reference in their entirety as of the priority date of this application.  
           [0031]    Probes or primers for the nucleic acid detection assays described herein that specifically hybridize to a control gene may be produced by any available means. For instance, probe sequences may be prepared by cleaving DNA molecules produced by standard procedures with commercially available restriction endonucleases or other cleaving agents. Following isolation and purification, these resultant normalization control gene fragments can be used directly, amplified by PCR methods or amplified by replication on or expression from a vector.  
           [0032]    Control genes and control gene probes or primers (i.e., synthetic oligonucleotides and polynucleotides) are most easily synthesized by chemical techniques, for example, the phosphoramidite method of Matteucci, et al. ((1981)  J Am Chem Soc  103:3185-3191) or using automated synthesis methods using the GenBank sequences disclosed in Table 1. Probes for attachment to microarrays or for use as primers in amplification assays may be produced from the sequences of the genes identified herein using any available software, including, for instance, software available from Molecular Biology Insights, Olympus Optical Co. and Premier Biosoft International.  
           [0033]    In addition, larger nucleic acids can readily be prepared by well known methods, such as synthesis of a group of oligonucleotides that define various modular segments of the normalization control genes and normalization control gene segments, followed by ligation of oligonucleotides to build the complete nucleic acid molecule.  
           [0034]    B. Normalization Methods  
           [0035]    Gene expression data produced from the control genes in a given sample or samples may be used to normalize the gene expression data from other genes using any available arithmatic or calculative means. In particular, gene expression data from the control genes in Table 1 are useful to normalize gene expression data for toxicology testing or modeling in an animal model, preferably in a rat. Such methods include, but are not limited, methods of data analysis described by Hegde et al. (2000),  Biotechniques  29:548-562; Winzeller et al. (1999),  Meth Enzymol  306:3-18; Tkatchenko et al. (2000),  Biochimica et Biophysica Acta  1500:17-30; Berger et al. (2000), WO 00/04188; Schuchhardt et al. (2000),  Nuc Acids Res  28:e47; Eickhoff et al. (1999),  Nuc Acids Res  27:e33. Micro-array data analysis and image processing software packages and protocols, including normalization methods, are also available from BioDiscovery (http://www.biodiscovery.com), Silicon Graphics (http://www.sigenetics.com), Spotfire (http://www.spotfire.com), Stanford University (http://rana.Stanford.EDU/software), National Human Genome Research Institute (http://www.nhgri.nih.gov/DIR/LCG/15K/HTML/img_analysis.html), TIGR (http://www.tigr.org/softlab), and Affymetrix (affy and maffy packages), among others.  
           [0036]    C. Assay or Hybridization Formats  
           [0037]    The control genes of the present invention may be used in any nucleic acid detection assay format, including solution-based and solid support-based assay formats. As used herein, “hybridization assay format(s)” refer to the organization of the oligonucleotide probes relative to the nucleic acid sample. The hybridization assay formats that may be used with the control genes and methods of the present invention include assays where the nucleic acid sample is labeled with one or more detectable labels, assays where the probes are labeled with one or more detectable labels, and assays where the sample or the probes are immobilized. Hybridization assay formats include but are not limited to: Northern blots, Southern blots, dot blots, solution-based assays, branched-DNA assays, PCR, RT-PCR, quantitative or semi-quantitative RT-PCR, microarrays and biochips.  
           [0038]    As used herein, “nucleic acid hybridization” simply involves contacting a probe and nucleic acid sample under conditions where the probe and its complementary target can form stable hybrid duplexes through complementary base pairing (see Lockhart et al., (1999) WO 99/32660). The nucleic acids that do not form hybrid duplexes are then washed away leaving the hybridized nucleic acids to be detected, typically through detection of an attached detectable label.  
           [0039]    It is generally recognized that nucleic acids are denatured by increasing the temperature or decreasing the salt concentration of the buffer containing the nucleic acids. Under low stringency conditions (e.g., low temperature and/or high salt) hybrid duplexes (e.g., DNA-DNA, RNA-RNA or RNA-DNA) will form even where the annealed sequences are not perfectly complementary. Thus, specificity of hybridization is reduced at lower stringency. Conversely, at higher stringency (e.g., higher temperature or lower salt) successful hybridization requires fewer mismatches. One of skill in the art will appreciate that hybridization conditions may be selected to provide any degree of stringency. In a preferred embodiment, hybridization is performed at low stringency, in this case in 6×SSPE-T at 37° C. (0.005% Triton x-100) to ensure hybridization, and then subsequent washes are performed at higher stringency (e.g., 1×SSPE-T at 37° C.) to eliminate mismatched hybrid duplexes. Successive washes may be performed at increasingly higher stringency (e.g., down to as low as 0.25×SSPET at 37° C. to 50° C. until a desired level of hybridization specificity is obtained. Stringency can also be increased by addition of agents such as formamide. Hybridization specificity may be evaluated by comparison of hybridization to the test probes with hybridization to the various controls that can be present (e.g., expression level control, normalization control, mismatch controls, etc.).  
           [0040]    As used herein, the term “stringent conditions” refers to conditions under which a probe will hybridize to a complementary control nucleic acid, but with only insubstantial hybridization to other sequences. Stringent conditions are sequence-dependent and will be different under different circumstances. Longer sequences hybridize specifically at higher temperatures. Generally, stringent conditions are selected to be about 5° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH.  
           [0041]    Typically, stringent conditions will be those in which the salt concentration is at least about 0.01 to 1.0 M sodium ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 30° C. for short probes (e.g., 10 to 50 nucleotides). Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide.  
           [0042]    In general, there is a tradeoff between hybridization specificity (stringency) and signal intensity. Thus, in a preferred embodiment, the wash is performed at the highest stringency that produces consistent results and that provides a signal intensity greater than approximately 10% of the background intensity. Thus, in a preferred embodiment, the hybridized array may be washed at successively higher stringency solutions and read between each wash. Analysis of the data sets thus produced will reveal a wash stringency above that the hybridization pattern is not appreciably altered and which provides adequate signal for the particular oligonucleotide probes of interest.  
           [0043]    The “percentage of sequence identity” or “sequence identity” is determined by comparing two optimally aligned sequences or subsequences over a comparison window or span, wherein the portion of the polynucleotide sequence in the comparison window may optionally comprise additions or deletions (i.e., gaps) as compared to the reference sequence (which does not comprise additions or deletions) for optimal alignment of the two sequences. The percentage is calculated by determining the number of positions at which the identical residue (e.g., nucleic acid base or amino acid residue) occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the window of comparison and multiplying the result by 100 to yield the percentage of sequence identity. Percentage sequence identity when calculated using the programs GAP or BESTFIT (see below) is calculated using default gap weights. Sequences corresponding to the control genes of Table 1 may comprise at least about 70% sequence identity to the GenBank IDs of the genes in the Tables, preferably about 75%, 80% or 85% or more preferably, about 90% or 95% or more identity.  
           [0044]    Homology or identity is determined by BLAST (Basic Local Alignment Search Tool) analysis using the algorithm employed by the programs blastp, blastn, blastx, tblastn and tblastx (Karlin et al. (1990),  Proc Natl Acad Sci USA  87:2264-2268 and Altschul (1993),  J Mol Evol  36:290-300, fully incorporated by reference) which are tailored for sequence similarity searching. The approach used by the BLAST program is first to consider similar segments between a query sequence and a database sequence, then to evaluate the statistical significance of all matches that are identified and finally to summarize only those matches which satisfy a preselected threshold of significance. For a discussion of basic issues in similarity searching of sequence databases, see Altschul et al. (1994),  Nat Genet  6:119-129) which is fully incorporated by reference. The search parameters for histogram, descriptions, alignments, expect (i.e., the statistical significance threshold for reporting matches against database sequences), cutoff, matrix and filter are at the default settings. The default scoring matrix used by blastp, blastx, tblastn, and tblastx is the BLOSUM62 matrix (Henikoff et al. (1992),  Proc Natl Acad Sci USA  89:10915-10919, fully incorporated by reference). Four blastn parameters were adjusted as follows: Q=10 (gap creation penalty); R=10 (gap extension penalty); wink=1 (generates word hits at every wink th  position along the query); and gapw=16 (sets the window width within which gapped alignments are generated). The equivalent Blastp parameter settings were Q=9; R=2; wink=1; and gapw=32. A Bestfit comparison between sequences, available in the GCG package version 10.0, uses DNA parameters GAP=50 (gap creation penalty) and LEN=3 (gap extension penalty) and the equivalent settings in protein comparisons are GAP=8 and LEN=2.  
           [0045]    As used herein a “probe” or “oligonucleotide probe” is defined as a nucleic acid, capable of binding to a nucleic acid sample or complementary control gene nucleic acid through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation. As used herein, a probe may include natural (i.e., A, G, U, C or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in probes may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. Thus, probes may be peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages.  
           [0046]    Probe arrays may contain at least two or more oligonucleotides that are complementary to or hybridize to one or more of the control genes described herein. Such arrays may also contain oligonucleotides that are complementary or hybridize to at least about 2, 3, 5, 7, 10, 50, 100 or more the genes described herein. Any solid surface to which oligonucleotides or nucleic acid sample can be bound, either directly or indirectly, either covalently or non-covalently, can be used. For example, solid supports for various hybridization assay formats can be filters, polyvinyl chloride dishes, silicon or glass based chips, etc. Glass-based solid supports, for example, are widely available, as well as associated hybridization protocols. (see, e.g., Beattie, WO 95/11755).  
           [0047]    A preferred solid support is a high density array or DNA chip. This contains an oligonucleotide probe of a particular nucleotide sequence at a particular location on the array. Each particular location may contain more than one molecule of the probe, but each molecule within the particular location has an identical sequence. Such particular locations are termed features. There may be, for example, 2, 10, 100, 1000, 10,000, 100,000, 400,000, 1,000,000 or more such features on a single solid support. The solid support, or more specifically, the area wherein the probes are attached, may be on the order of a square centimeter.  
           [0048]    1. Dot Blots  
           [0049]    The control genes listed in Table 1 and methods of the present invention may be utilized in numerous hybridization formats such as dot blots, dipstick, branched DNA sandwich and ELISA assays. Dot blot hybridization assays provide a convenient and efficient method of rapidly analyzing nucleic acid samples in a sensitive manner. Dot blots are generally as sensitive as enzyme-linked immunoassays. Dot blot hybridization analyses are well known in the art and detailed methods of conducting and optimizing these assays are detailed in U.S. Pat. Nos. 6,130,042 and 6,129,828, and Tkatchenko et al. (2000),  Biochimica et Biophysica Acta  1500:17-30. Specifically, a labeled or unlabeled nucleic acid sample is denatured, bound to a membrane (i.e., nitrocellulose) and then contacted with unlabeled or labeled oligonucleotide probes. Buffer and temperature conditions can be adjusted to vary the degree of identity between the oligonucleotide probes and nucleic acid sample necessary for hybridization.  
           [0050]    Several modifications of the basic Dot blot hybridization format have been devised. For example, Reverse Dot blot analyses employ the same strategy as the Dot blot method, except that the oligonucleotide probes are bound to the membrane and the nucleic acid sample is applied and hybridized to the bound probes. Similarly, the Dot blot hybridization format can be modified to include formats where either the nucleic acid sample or the oligonucleotide probe is applied to microtiter plates, microbeads or other solid substrates.  
           [0051]    2. Membrane-Based Formats  
           [0052]    Although each membrane-based format is essentially a variation of the Dot blot hybridization format, several types of these formats are preferred. Specifically, the methods of the present invention may be used in Northern and Southern blot hybridization assays. Although the methods of the present invention are generally used in quantitative nucleic acid hybridization assays, these methods may be used in qualitative or semiquantitative assays such as Southern blots, in order to facilitate comparison of blots. Southern blot hybridization, for example, involves cleavage of either genomic or cDNA with restriction endonucleases followed by separation of the resultant fragments on a polyacrylamide or agarose gel and transfer of the nucleic acid fragments to a membrane filter. Labeled oligonucleotide probes are then hybridized to the membrane-bound nucleic acid fragments. In addition, intact cDNA molecules may also be used, separated by electrophoresis, transferred to a membrane and analyzed by hybridization to labeled probes. Northern analyses, similarly, are conducted on nucleic acids, either intact or fragmented, that are bound to a membrane. The nucleic acids in Northern analyses, however, are generally RNA.  
           [0053]    3. Arrays  
           [0054]    Any microarray platform or technology may be used to produce gene expression data that may be normalized with the control genes and methods of the invention. Oligonucleotide probe arrays can be made and used according to any techniques known in the art (see for example, Lockhart et al., (1996),  Nat Biotechnol  14:1675-1680; McGall et al. (1996),  Proc Natl Acad Sci USA  93:13555-13460). Such probe arrays may contain at least one or more oligonucleotides that are complementary to or hybridize to one or more of the nucleic acids of the nucleic acid sample and/or the control genes of Tables 1-3. Such arrays may also contain oligonucleotides that are complementary or hybridize to at least 2, 3, 5, 7, 10, 25, 50, 100, 500 or more of the control genes listed in Tables 1-3.  
           [0055]    Control oligonucleotide probes of the invention are preferably of sufficient length to specifically hybridize only to appropriate, complementary genes or transcripts. Typically the oligonucleotide probes will be at least about 10, 12, 14, 16, 18, 20 or 25 nucleotides in length. In some cases longer probes of at least 30, 40, or 50 nucleotides will be desirable. The oligonucleotide probes of high density array chips include oligonucleotides that range from about 5 to about 45 or 5 to about 500 nucleotides, more preferably from about 10 to about 40 nucleotides and most preferably from about 15 to about 40 nucleotides in length. In other particularly preferred embodiments, the probes are 20 or 25 nucleotides in length. In another preferred embodiment, probes are double- or single-stranded DNA sequences. The oligonucleotide probes are capable of specifically hybridizing to the control gene nucleic acids in a sample.  
           [0056]    One of skill in the art will appreciate that an enormous number of array designs comprising control probes of the invention are suitable for the practice of this invention. The high density array will typically include a number of probes that specifically hybridize to each control gene nucleic acid, e.g. mRNA or cRNA. (See WO 99/32660 for methods of producing probes for a given gene or genes). Assays and methods comprising control probes of the invention may utilize available formats to simultaneously screen at least about 100, preferably about 1000, more preferably about 10,000 and most preferably about 500,000 or 1,000,000 different nucleic acid hybridizations.  
           [0057]    The methods and control genes of this invention may also be used to normalize gene expression data produced using commercially available oligonucleotide arrays that contain or are modified to contain control gene probes or the invention. A preferred oligonucleotide array may be selected from the Affymetrix, Inc. GeneChip® series of arrays which include the Human Genome Focus Array, Human Genome U133 Set, Human Genome U95 Set, HuGeneFL Array, Human Cancer Array, HuSNP Mapping Array, GenFlex Tag Array, p53 Assay Array, CYP450 Assay Array, Rat Genome U34 Set, Rat Neurobiology U34 Array, Rat Toxicology U34 Array, Murine Genome U74v2 Set, Murine 11K Set, Yeast Genome S98 Array,  E. coli  Antisense Genome Array,  E. coli  Genome Array (Sense), Arabidopsis ATH1 Genome Array, Arabidopsis Genome Array, Drosophila Genome Array,  C. elegans  Genome Array,  P. aeruginosa  Genome Array and  B. subtilis  Genome Array. In another embodiment, an oligonucleotide array may be selected from the Motorola Life Sciences and Amersham Pharmaceuticals CodeLink™ Bioarray System microarrays, including the UniSet Human 20K I, Uniset Human I, ADME-Rat, UniSet Rat I and UniSet Mouse I, or from the Motorola Life Sciences eSensor™ series of microarrays.  
           [0058]    4. RT-PCR  
           [0059]    The control genes and methods of the invention may be used in any type of polymerase chain reaction. A preferred PCR format is reverse transciptase polymerase chain reaction (RT-PCR), an in vitro method for enzymatically amplifying defined sequences of RNA (Rappolee et al. (1988),  Science  241:708-712) permitting the analysis of different samples from as little as one cell in the same experiment (See Ambion: RT-PCR: The Basics; M. J. McPherson and S. G. Møller,  PCR  BIOS Scientific Publishers Ltd., Oxford, OX4 1RE, 2000; Dieffenbach et al.,  PCR Primer: A Laboratory Manual,  Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 1995, for review). One of ordinary skill in the art may appreciate the enormous number of variations in RT-PCR platforms that are suitable for the practice of the invention, including complex variations aimed at increasing sensitivity such as semi-nested (Wasserman et al. (1999),  Mol Diag  4:21-28), nested (Israeli et al. (1994),  Cancer Res  54:6303-6310; Soeth et al. (1996),  Int J Cancer  69:278-282), and even three-step nested (Funaki et al. (1997),  Life Sci  60:643-652; Funaki et al. (1998),  Brit J Cancer  77:1327-1332).  
           [0060]    In one embodiment of the invention, separate enzymes are used for reverse transcription and PCR amplification. Two commonly used reverse transcriptases, for example, are avian myeloblastosis virus and Moloney murine leukaemia virus. For amplification, a number of thermostable DNA-dependent DNA polymerases are currently available, although they differ in processivity, fidelity, thermal stability and ability to read modified triphosphates such as deoxyuridine and deoxyinosine in the template strand (Adams et al. (1994),  Bioorg Med Chem  2:659-667; Perler et al. (1996),  Adv Prot Chem  48:377-435). The most commonly used enzyme, Taq DNA polymerase, has a 5′-3′ nuclease activity but lacks a 3′-5′ proofreading exonuclease activity. When fidelity is required, proofreading exonucleases such as Vent and Deep Vent (New England Biolabs) or Pfu (Stratagene) may be used (Cline et al. (1996),  Nuc Acids Res  24:3456-3551). In another embodiment of the invention, a single enzyme approach may be used involving a DNA polymerase with intrinsic reverse transcriptase activity, such as  Thermus thermophilus  (Tth) polymerase (Bustin (2000),  J Mol Endo  25:169-193). A skilled artisan may appreciate the variety of enzymes available for use in the present invention.  
           [0061]    The methodologies and control gene primers of the present invention may be used, for example, in any kinetic RT-PCR methodology, including those that combine fluorescence techniques with instrumentation capable of combining amplification, detection and quantification (Orlando et al. (1998),  Clin Chem Lab Med  36:255-269). The choice of instrumentation is particularly important in multiplex RT-PCR, wherein multiple primer sets are used to amplify multiple specific targets simultaneously. This requires simultaneous detection of multiple fluorescent dyes. Accurate quantitation while maintaining a broad dynamic range of sensitivity across mRNA levels is the focus of upcoming technologies, any of which are applicable for use in the present invention. Preferred instrumentation may be selected from the ABI Prism 7700 (Perkin-Elmer-Applied Biosystems), the Lightcycler (Roche Molecular Biochemicals) and iCycler Thermal Cycler. Featured aspects of these products include high-throughput capacities or unique photodetection devices.  
           [0062]    Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, practice the methods and use the control genes of the present invention. The following examples therefore, specifically point out the preferred embodiments of the present invention, and are not to be construed as limiting in any way the remainder of the disclosure. 
       
    
    
     EXAMPLES  
     Example 1  
     Selection of Control Genes  
       [0063]    The control genes were selected by querying a Gene Logic rat tissue database to create expression profiles from a variety of rat cell and tissue samples.  
         [0064]    This database was produced from data derived from screening various cell or tissue samples using the Affymetrix rat GeneChip® set. The rat cell and tissue samples that were analyzed include those that were not treated at all and can be referred to as “normal,” as they represent the laboratory rat population that has not been manipulated outside of normal daily activity within that setting. In general, tissue and cell samples were processed following the Affymetrix GeneChip® Expression Analysis Manual. Frozen cells were ground to a powder using a Spex Certiprep 6800 Freezer Mill. Total RNA was extracted with Trizol (GibcoBRL) utilizing the manufacturer&#39;s protocol. The total RNA yield for each sample was 200-500 μg per 300 mg cells. mRNA was isolated using the Oligotex mRNA Midi kit (Qiagen) followed by ethanol precipitation. Double stranded cDNA was generated from mRNA using the SuperScript Choice system (GibcoBRL). First strand cDNA synthesis was primed with a T7-(dT24) oligonucleotide. The cDNA was phenol-chloroform extracted and ethanol precipitated to a final concentration of 1 μg/ml. From 2 μg of cDNA, cRNA was synthesized using Ambion&#39;s T7 MegaScript in vitro Transcription Kit.  
         [0065]    To biotin label the cRNA, nucleotides Bio-11-CTP and Bio-16-UTP (Enzo Diagnostics) were added to the reaction. Following a 37° C. incubation for six hours, impurities were removed from the labeled cRNA following the RNeasy Mini kit protocol (Qiagen). cRNA was fragmented (fragmentation buffer consisting of 200 mM Tris-acetate, pH 8.1, 500 mM KOAc, 150 mM MgOAc) for thirty-five minutes at 94° C. Following the Affymetrix protocol, 55 μg of fragmented cRNA was hybridized on the Affymetrix rat array set for twenty-four hours at 60 rpm in a 45° C. hybridization oven. The chips were washed and stained with Streptavidin Phycoerythrin (SAPE) (Molecular Probes) in Affymetrix fluidics stations. To amplify staining, SAPE solution was added twice with an anti-streptavidin biotinylated antibody (Vector Laboratories) staining step in between. Hybridization to the probe arrays was detected by fluorometric scanning (Hewlett Packard Gene Array Scanner). Data was analyzed using Affymetrix GeneChip® version 3.0 and Expression Data Mining Tool (EDMT) software (version 1.0), S-Plus, and the GeneExpress® software system. Microarrays were scanned on a high photomultiplier tube (PMT) settings.  
         [0066]    To prepare tissue samples from animals, e.g. rats, sterile instruments were used to sacrifice the animals, and fresh and sterile disposable instruments were used to collect tissues. Gloves were worn at all times when handling tissues or vials. All tissues were collected and frozen within approximately 5 minutes of the animal&#39;s death. The liver sections and kidneys were frozen within approximately 3-5 minutes of the animal&#39;s death. The time of euthanasia, an interim time point at freezing of liver sections and kidneys, and time at completion of necropsy were recorded. Tissues were stored at approximately −80° C. or preserved in 10% neutral buffered formalin.  
         [0067]    Tissues were collected and processed as follows.  
         [0068]    Liver  
         [0069]    1. Right medial lobe—snap frozen in liquid nitrogen and stored at ˜−80° C.  
         [0070]    2. Left medial lobe—Preserved in 10% neutral-buffered formalin (NBF) and evaluated for gross and microscopic pathology.  
         [0071]    3. Left lateral lobe—snap frozen in liquid nitrogen and stored at ˜−80° C.  
         [0072]    Heart—A sagittal cross-section containing portions of the two atria and of the two ventricles was preserved in 10% NBF. The remaining heart was frozen in liquid nitrogen and stored at ˜−80° C.  
         [0073]    Kidneys (Both)  
         [0074]    1. Left—Hemi-dissected; half was preserved in 10% NBF and the remaining half was frozen in liquid nitrogen and stored at ˜−80° C.  
         [0075]    2. Right—Hemi-dissected; half was preserved in 10% NBF and the remaining half was frozen in liquid nitrogen and stored at ˜−80° C.  
         [0076]    Testes (both)—A sagittal cross-section of each testis was preserved in 10% NBF. The remaining testes were frozen together in liquid nitrogen and stored at ˜−80° C.  
         [0077]    Brain (whole)—A cross-section of the cerebral hemispheres and of the diencephalon was preserved in 10% NBF, and the rest of the brain was frozen in liquid nitrogen and stored at ˜−80° C.  
         [0078]    Gene expression data were then analyzed to identify those genes that were consistently expressed across a set of about 5,000 different tissue samples. Table 1 provides a list of approximately 128 genes whose expression, as determined by ANOVA, is considered not to vary across the normal and treated samples studied. Table 1 also provides a GenBank Accession number (fragment name), present frequency and mean average differential for each of the genes. The GenBank Accession Nos. can be used to locate the publicly available sequences, each of which is herein incorporated by reference as of the priority date of this application (Jul. 17, 2002).  
         [0079]    A two-factor ANOVA model was applied to all cell and tissues samples where both control and disease, pathology or treatment groups existed. The factors for this model were normal state (control or affected tissue) and cell or tissue type. A one factor ANOVA was also used to examine the effects of tissue kind alone. Genes were ranked according to R-squared values. The R-squared value can be interpreted as the percent variability of expression that can be explained by the underlying factors. Cut-off values were also selected for the alpha error p-values for each factor and the interaction of these two factors. A cut-off value for both one factor and two factor R-squared values of less than or equal to 12 was used. In addition, any gene with large known regulation events within tissues was removed and any co-clustered Unigene fragments were examined for consistency in R-Squared values. The probe set was also selected using the following supplemental criteria: (a) Mean Average Differential over all rat samples less than or equal to about 20, (b) Present Frequency over all rat samples less than or equal to about 75% and (c) no probe sets exhibiting saturation.  
           E   ij   =u+T   j +error  Model 1  
         [0080]    (E ij  is the expression value of the i th  gene in the j th  sample)  
         [0081]    (T j  is the tissue type of the j th  sample)  
         [0082]    The model fitting yields, for each gene, a p-value for the T factor, as well as a sum of squares attributable to this factor. This sum of squares is the model sum of squares. The R 2  value is then the ratio of the model sum of squares to the total sum of squares  
         ∑   j                                 (       E   ij     -       E   _     i       )     2     .                           
  E   ij   =u+T   j   +N   j   +T   j   *N   j +error  Model 2  
         [0083]    (E ij  is the expression value of the i th  gene in the j th  sample)  
         [0084]    (T j  is the tissue type of the j th  sample)  
         [0085]    (N j  is the state of the j th  sample (N j =0 for normal, 1 otherwise))  
         [0086]    The model fitting yields, for each gene, a p-value for the T factor, the N factor, and the T*N factor, as well as a sum of squares attributable to each of these factors. Adding the three sums of squares gives the model sum of squares. The R 2  value is then the ratio of the model sum of squares to the total sum of squares  
         ∑   j                                 (       E   ij     -       E   _     i       )     2     .                           
 
                                             TABLE 1                       GLGC   Fragment   Present   Mean Average       Identifier   Name   Frequency   Differential                                102271   AA012709_at   0.9282   190.551       77300   AF029357cds_at   0.9848   119.409       77332   AF034900mRNA_i_at   0.989   203.019       77517   AF081148_s_at   0.9146   52.382       77576   AF091561_at   0.9609   62.252       77615   AF095927_at   0.9521   40.406       77721   AJ132230_g_at   0.7605   62.179       77738   D01046_at   0.8189   70.892       77745   D10587_at   0.8261   103.633       80151   D87840_at   0.9734   83.52       78209   M13100cds#1_g_at   0.9657   192.653       78211   M13100cds#3_f_at   0.9867   265.171       78212   M13100cds#4_f_at   0.9918   128.404       78213   M13100cds#5_s_at   0.9717   179.794       78214   M13100cds#6_f_at   0.9817   338.825       78215   M13101cds_f_at   0.9256   195.555       81802   M25584_at   0.7688   108.344       76571   M27467_at   0.8166   64.614       76597   M74439mRNA_i_at   0.9709   85.002       76604   M76767_s_at   0.9227   148.154       81918   M83680_at   0.9692   151.235       84412   rc_AA799406_at   0.9722   150.886       84486   rc_AA799551_g_at   0.7849   110.294       84567   rc_AA799745_at   0.8588   123.746       84748   rc_AA800684_at   0.8148   47.537       84809   rc_AA800881_at   0.8955   98.88       84830   rc_AA801017_at   0.8557   56.038       84832   rc_AA801025_g_at   0.9197   88.845       84841   rc_AA801181_at   0.8566   101.242       84851   rc_AA801228_g_at   0.9251   113.4       84854   rc_AA801231_at   0.8871   222.933       99702   rc_AA818590_at   0.7573   32.931       98583   rc_AA819268_at   0.9357   347.913       100600   rc_AA819664_at   0.9852   320.9       84964   rc_AA848965_at   0.8342   64.375       85024   rc_AA849525_i_at   0.8484   45.264       85060   rc_AA849730_at   0.8953   66.225       85158   rc_AA850117_at   0.9611   228.531       85262   rc_AA850595_at   0.9132   86.758       85466   rc_AA851405_at   0.9773   114.684       85474   rc_AA851439_at   0.962   229.271       85553   rc_AA851892_at   0.9836   218.25       102013   rc_AA858480_at   0.8612   110.441       101949   rc_AA859201_at   0.9978   275.683       81000   rc_AA859702_at   0.8713   26.883       83140   rc_AA859750_at   0.7544   51.105       83979   rc_AA892504_at   0.82   109.04       81044   rc_AA892895_r_at   0.9972   499.824       84111   rc_AA892959_at   0.8275   37.656       84145   rc_AA893127_at   0.7778   96.525       84310   rc_AA893980_at   0.8572   69.74       84392   rc_AA894340_at   0.8296   31.49       85633   rc_AA899265_at   0.8552   56.148       85635   rc_AA899278_at   0.8469   56.079       85698   rc_AA899664_at   0.9944   414.896       85712   rc_AA899723_at   0.9147   112.458       85771   rc_AA899991_at   0.8249   124.576       85831   rc_AA900348_s_at   0.9502   212.75       85846   rc_AA900422_at   0.9604   404.271       85949   rc_AA900926_at   0.8398   71.065       86913   rc_AA901272_f_at   0.7765   48.604       87063   rc_AA924396_at   0.9271   83.43       76263   rc_AA924542_s_at   0.9604   62.91       87182   rc_AA924830_at   0.7985   40.337       87211   rc_AA924964_at   0.794   393.025       87348   rc_AA925432_at   0.9735   225.799       87443   rc_AA925854_at   0.8516   92.302       86025   rc_AA942964_at   0.9328   494.302       86074   rc_AA943120_at   0.855   233.325       86169   rc_AA943553_g_at   0.9966   665.561       86209   rc_AA943738_g_at   0.9859   137.092       86243   rc_AA943835_at   0.7664   165.778       86314   rc_AA944239_at   0.949   216.561       86524   rc_AA945099_g_at   0.8554   54.104       86629   rc_AA945805_at   0.8566   68.783       86724   rc_AA946166_at   0.9215   75.825       86727   rc_AA946181_at   0.8695   169.878       86837   rc_AA946499_at   0.8446   63.922       86846   rc_AA946528_at   0.9054   279.156       87736   rc_AA955911_at   0.7623   70.604       87993   rc_AA957063_at   0.9941   391.775       88267   rc_AA963170_at   0.987   118.572       88591   rc_AA964611_at   0.9243   128.413       88723   rc_AA965110_at   0.7869   67.276       88766   rc_AA996405_at   0.8167   72.635       88839   rc_AA996701_f_at   0.7552   43.716       89007   rc_AA997745_at   0.7736   45.566       89217   rc_AA997960_at   0.8546   77.485       89360   rc_AA998471_i_at   0.9129   284.784       89468   rc_AA999041_at   0.9482   133.563       89701   rc_AI008674_at   0.8997   100.377       76186   rc_AI009141_at   0.811   67.18       90399   rc_AI011949_at   0.7884   74.517       90427   rc_AI012073_at   0.7986   34.14       90437   rc_AI012103_at   0.7764   479.806       90744   rc_AI013204_at   0.9984   974.703       90764   rc_AI013310_at   0.7918   76.764       81319   rc_AI014135_g_at   0.8066   111.16       91024   rc_AI029274_at   0.8263   59.624       81335   rc_AI029805_at   0.8404   27.604       91371   rc_AI030564_at   0.7837   286.222       91449   rc_AI030813_at   0.7509   52.319       91867   rc_AI044239_i_at   0.8506   43.725       92024   rc_AI044638_at   0.9104   212.046       92444   rc_AI045686_at   0.7798   72.274       92887   rc_AI059209_at   0.775   148.062       92926   rc_AI059305_at   0.9861   219.211       93077   rc_AI059664_at   0.9072   154.307       93103   rc_AI059728_f_at   0.8303   281.846       93147   rc_AI059883_at   0.8219   61.436       93198   rc_AI060012_at   0.7549   128.285       93390   rc_AI069980_at   0.7936   325.454       93698   rc_AI070712_at   0.9272   121.653       93822   rc_AI071114_at   0.9722   94.206       93870   rc_AI071210_at   0.8462   85.695       93887   rc_AI071243_at   0.9775   164.564       93927   rc_AI071332_at   0.8399   160.424       93955   rc_AI071418_at   0.7542   35.773       94022   rc_AI071563_at   0.7516   42.418       94095   rc_AI071696_f_at   0.8824   255.85       94127   rc_AI071763_at   0.7685   27.537       94183   rc_AJ071902_at   0.8004   29.416       93354   rc_AI071920_at   0.8101   41.866       94624   rc_AI073001_at   0.7888   46.337       94667   rc_AI073105_at   0.8006   41.572       94674   rc_AI073118_at   0.9816   132.82       94690   rc_AI073191_at   0.9111   51.687       96075   rc_AI101659_at   0.9988   627.052       96344   rc_AI102991_at   0.998   389.649       96381   rc_AI103202_at   0.8064   149.589       96436   rc_AI103415_at   0.8165   44.836       94805   rc_AI111950_at   0.941   117.798       81430   rc_AI112391_s_at   0.9029   56.828       95309   rc_AI144587_at   0.8708   39.214       95480   rc_AI145609_at   0.9806   84.399       81469   rc_AI146195_at   0.8938   51.357       95868   rc_AI169293_at   0.9127   64.184       96814   rc_AI169595_at   0.9206   124.878       96999   rc_AI170628_at   0.8098   39.401       97024   rc_AI170715_at   0.7835   50.309       97099   rc_AI170992_at   0.8404   82.011       97125   rc_AI171172_i_at   0.9942   137.021       97394   rc_AI172069_at   0.9579   55.272       97458   rc_AI172218_at   0.9678   136.643       97601   rc_AI172576_at   0.8256   38.281       97690   rc_AI175266_at   0.9973   335.31       97837   rc_AI175830_at   0.7816   27.925       97962   rc_AI176309_at   0.9542   86.007       98068   rc_AI176625_at   0.8551   152.373       98219   rc_AI177089_at   0.7707   28.18       98232   rc_AI177117_at   0.7661   54.616       98277   rc_AI177251_at   0.8129   49.094       98367   rc_AI177595_at   0.8043   52.792       98370   rc_AI177603_at   0.798   37.734       98563   rc_AI178446_at   0.8241   98.564       98796   rc_AI179239_at   0.992   158.966       98850   rc_AI179411_at   0.9052   78.786       99019   rc_AI180081_at   0.9738   389.838       99327   rc_AI228249_at   0.9917   429.5       99339   rc_AI228279_at   0.8721   81.722       99439   rc_AI228722_at   0.8644   49.792       99810   rc_AI230308_at   0.9803   180.54       99878   rc_AI230562_at   0.9277   84.362       81702   rc_AI230572_at   0.8913   58.278       100117   rc_AI231330_at   0.751   40.863       100183   rc_AI231565_at   0.9039   104.091       100394   rc_AI232347_at   0.8852   120.621       100501   rc_AI232722_at   0.8026   180.831       100698   rc_AI233529_f_at   0.8144   72.074       100818   rc_AI233965_at   0.9171   60.938       100819   rc_AI233966_at   0.8467   142.163       101057   rc_AI235032_at   0.9552   125.501       101104   rc_AI235232_at   0.8299   102.496       101115   rc_AI235272_at   0.7574   35.891       101135   rc_AI235315_at   0.7708   60.792       101275   rc_AI235821_f_at   0.7721   181.906       101388   rc_AI236169_at   0.9237   82.826       101477   rc_AI236475_at   0.8718   156.175       101721   rc_AI237366_at   0.9603   63.197       80595   rc_AI639114_at   0.8775   21.093       80849   rc_AI639391_at   0.7655   61.047       80925   rc_AI639465_f_at   0.9602   142.244       83528   rc_H31217_at   0.7871   28.269       83544   rc_H31535_at   0.8248   95.236       78445   S50461_s_at   0.7606   35.999       78545   S70803_at   0.884   93.026       78574   S74572_g_at   0.791   32.907       78678   S90449_at   0.8728   27.837       82688   U37138_at   0.8904   47.73       82488   U49099_at   0.9579   89.613       76764   U61184_at   0.8679   32.322       78926   U87971_g_at   0.8219   29.276       78969   X05472cds#1_s_at   0.923   129.01       78971   X05472cds#3_f_at   0.8638   129.503       79009   X13527cds_s_at   0.7644   118.765       79081   X53581cds#3_f_at   0.908   166.237       79840   X53944_at   0.9981   196.006       79230   X89697cds_at   0.806   34.392                  
 
       Example 2  
     Quantitative PCR Analysis of Expression Levels Using the Control Genes  
       [0087]    The expression levels of one or more genes listed in Table 1 may be used to normalize gene expression data produced using quantitative PCR analysis. For example, the sequences may be used as Taqman probes, along with the forward and reverse primers for a gene in Table 1. Real time PCR detection may be accomplished by the use of the ABI PRISM 7700 Sequence Detection System. The 7700 measures the fluorescence intensity of the sample each cycle and is able to detect the presence of specific amplicons within the PCR reaction. The TaqMan® assay provided by Perkin Elmer may be used to assay quantities of RNA. The primers may be designed from each of the genes identified in Table 1 using Primer Express, a program developed by PE to efficiently find primers and probes for specific sequences. These primers may be used in conjunction with SYBR green (Molecular Probes), a nonspecific double-stranded DNA dye, to measure the expression level mRNA corresponding to the expression levels of each gene. This gene expression data may then be used to normalize gene expression data of other test genes.  
         [0088]    Although the present invention has been described in detail with reference to examples above, it is understood that various modifications can be made without departing from the spirit of the invention. Accordingly, the invention is limited only by the following claims. All cited patents and publications referred to in this application are herein incorporated by reference in their entirety.