Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function.PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis.We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics. Caenorhabditis elegans has established that coordinate transcriptional regulation of functionally related genes occurs on a broader scale than was previously recognized, encompassing at least half of all cellular processes in yeast Click here for additional data file175 lists of genes that are expressed in individual tissues, highest in individual tissues, or specific to individual tissuesClick here for additional data file


Fundulus heteroclitus was examined. Only a small subset (31%) of tissue-specific differences was consistent in all three populations, indicating that many tissue-specific differences in gene expression are unique to one population and thus are unlikely to contribute to fundamental differences between tissue types.The expression of a selected suite of 192 metabolic genes in brain, heart and liver in three populations of the teleost fish  Fundulus heteroclitus using a highly replicated experimental design.Variation in gene expression is extensive among tissues, individuals, strains, populations and species. The interactions among these sources of variation are relevant for physiological studies such as disease or toxic stress; for example, it is common for pathologies such as cancer, heart failure and metabolic disease to be associated with changes in tissue-specific gene expression or changes in metabolic gene expression. But how conserved these differences are among outbred individuals and among populations has not been well documented. To address this we examined the expression of a selected suite of 192 metabolic genes in brain, heart and liver in three populations of the teleost fish Half of the genes (48%) were differentially expressed among individuals within a population-tissue group and 76% were differentially expressed among tissues. Differences among tissues reflected well established tissue-specific metabolic requirements, suggesting that these measures of gene expression accurately reflect changes in proteins and their phenotypic effects. Remarkably, only a small subset (31%) of tissue-specific differences was consistent in all three populations.These data indicate that many tissue-specific differences in gene expression are unique to one population and thus are unlikely to contribute to fundamental differences between tissue types. We suggest that those subsets of treatment-specific gene expression patterns that are conserved between taxa are most likely to be functionally related to the physiological state in question. The regulation of gene expression varies extensively among tissues, individuals, strains, populations and species -6 and vaAlthough tissue-specific gene expression patterns are often used as a method to identify functionally relevant genes, how conserved these differences are among outbred individuals and among populations has not been well documented. It is possible that many of these changes represent polymorphism among individuals or populations and are not specifically associated with disease. To address this we used a well established system (tissue-specific gene expression) and genes with well defined function and tissue-specific distributions (metabolic genes).Fundulus heteroclitus. A cDNA microarray was used to measure levels of expression in normal healthy male fish for 192 genes involved in central metabolic pathways. We used this compact array in order to impose a high degree of technical and biological replication . Also, this array was used because metabolic genes are essential, are known to have tissue-specific expression, especially in fish, and are often misused as controls with little characterization of variation in expression among individuals or tissues. Analysis of variance (ANOVA) was used as a statistical test to determine which genes were differentially expressed among tissues and populations. Tissue-specific patterns of gene expression were compared among populations. As expected, we detected extensive variation in gene expression among tissues. Unexpectedly, only a fraction (31%) of tissue-specific differences was conserved between all populations.Given the high variance in gene expression among individuals and populations, our goal was to examine the conservation of tissue-specific gene expression among populations of the same species. Specifically, we assessed the among-population variance of tissue-specific patterns of gene expression  in the teleost fish p < 0.05) among individuals within populations and tissues  differentially expressed among brains, hearts and livers . Selecting the α level at which differences between treatments are considered significant is problematic because of the large number of comparisons performed. As such, we present a volcano plot to illustrate the range of expression differences between tissues and associated p-values , the proportion of significant genes is 67% (129 genes), 50% (96) and 39% (75), respectively. Significant differences in expression ranged from less than 1.2-fold to nearly 16-fold , hepatocyte nuclear factor 4-alpha (a transcription factor) was more highly expressed in liver than in other tissues (p < 0.001), and two genes involved in glycerolipid metabolism -lipoprotein lipase and phopholipase XIII A2 - were more highly expressed in liver than other tissues (p < 0.001 for both genes).Many expected tissue-specific patterns emerged. For example, the brain-specific fatty-acid-binding protein was typically more highly expressed in the brain than in other tissues . However, it should be noted that although the split-plot design is powerful for detecting differences between split-plot factors (tissues), it is considered to have low power for detecting differences between blocks (populations)  α[× SEMSR = Qkv] α[is the studentized range [k = number of groups in the comparison , v = degrees of freedom of MStissue-by-individual within population, and SE is the standard error among tissue-by-individual samples within populations. The T-method following ANOVA was used to identify genes differentially expressed among tissues in each population. These data were then used to contrast tissue-specific and population-specific expression patterns. Robustness of ANOVA data was tested using a permutation test; means for the 27 biological samples were randomly permuted 1,000 times between population and tissue and test statistics were recalculated for differences among populations, tissues and tissue-by-population interaction. Agreement between ANOVA and permutation test results would indicate the robustness of the ANOVA model. Finally, in order to graphically illustrate expression similarity among tissues, expression distance between samples was calculated as the sum of differences of log2 expression values over all genes, and neighbor-joining trees of global similarity of expression patterns among tissues  were constructed [where the critical value Qed range , k = numstructed  for eachp-values associated with statistical tests for differences in expression between populations, tissues, tissue-by-population interaction, and among individuals within populations. Also listed are mean expression for each sample, and columns comparing differences in expression between tissues within each population. Final columns tabulate whether a tissue difference was detected for each comparison, whether this difference was consistent between populations, and whether significant interaction was detected for that gene.The following additional data are available with the online version of this paper. Additional data file The results from statistical analyses for all genesClick here for additional data file


This indicates that sustained post hoc analyses in regards to efficacy of disruption for every single study group member may be required.The Cre/loxP-system has become the system of choice for the generation of conditional so-called knockout mouse strains, i.e. the tissue-specific disruption of expression of a certain target gene. We here report the loss of expression of Cre recombinase in a transgenic mouse strain with increasing number of generations. This eventually led to the complete abrogation of gene expression of the inserted Cre cDNA while still being detectable at the genomic level. Conversely, loss of Cre expression caused an incomplete or even complete lack of disruption for the protein under investigation. As Cre expression in the tissue of interest in most cases cannot be addressed  In recent years, the Cre/loxP-system has become the system of choice for the generation of conditional so-called knockout mouse strains, i.e. the tissue-specific disruption of expression of a certain target gene Any deliberately chosen DNA sequence can be flanked with loxP-elements. LoxP-sites are inserted in the intronic spacers of exons which encode vital structures of the protein of interest. Cre mediated recombination subsequently leads to the deletion of the sequence between two loxP-elements and a truncated gene product if any at all.Cre recombinase can be expressed in a given tissue or cell type under the control of a defined promoter fragment. Theoretically, expression occurs exclusively in the cell type, where the promoter usually is active The mechanisms causing these undesired effects are widely unknown. In the first place, the activity of many promoters in most cases is not fully understood. Hence, there might be developmental stages or other environmental factors affecting the activity of a promoter that have not yet been characterized. When Cre expression occurs within an early embryonic stage, all cells derived from this lineage will carry the deleted gene.Secondly, Cre transgenic mouse strains are mostly generated by random integration of a DNA construct comprising promoter and Cre cDNA into the host DNA. This may lead to unspecified genetic interactions, i.e. transactivations, at the site of integration which might be concurrently causative for unspecific Cre expression in many cell types.position-effect variegation (PEV), the phenomenon of mosaic expression has originally been described as the genetic cause of heterogeneously coloured eye-discs in mutants of Drosophila melanogasterThirdly, a number of articles on the effects of integration site on the expression pattern of a given transgene have been published. Termed Silencing is assumed to be due to site specific effects such as condensation of chromatin These previous publications altogether suggest that various processes may cause impaired expression of transgenes. We here demonstrated that this might, at least in some cases, precludes reliable prediction of tissue-specific gene disruption by tail-biopsy genotyping.Alb) have been generated Alb promoter driven expression of Cre recombinase, it has been reported, that the maximum level of recombination in hepatocytes occurs at two weeks of age bona fide knock-outs. Subsequently and to generate animals for several follow-up studies we observed inconsistencies between the phenotype initially observed Alb Cre construct.By means of site directed recombination, mouse models with a liver specific inactivation of various proteins employing Cre expression under the control of the Albumin promoter  responsible for these effects. These findings highlight the importance of sustained and comprehensive monitoring of the presence of disruption on a transcriptional or translational level for tissue-specific knock-out models.Animals were generated Isolation of genomic DNA and RNA was performed as previously described Protein samples were prepared as described


A genome-wide analysis of promoters was carried out in the context of gene expression patterns in tissue surveys using human microarray and EST-based expression data. The study revealed that most genes show statistically significant tissue-dependent variations of expression level and identified components of promoters that distinguish tissue-specific from ubiquitous genes. The regulatory mechanisms underlying tissue specificity are a crucial part of the development and maintenance of multicellular organisms. A genome-wide analysis of promoters in the context of gene-expression patterns in tissue surveys provides a means of identifying the general principles for these mechanisms.We introduce a definition of tissue specificity based on Shannon entropy to rank human genes according to their overall tissue specificity and by their specificity to particular tissues. We apply our definition to microarray-based and expressed sequence tag (EST)-based expression data for human genes and use similar data for mouse genes to validate our results. We show that most genes show statistically significant tissue-dependent variations in expression level. We find that the most tissue-specific genes typically have a TATA box, no CpG island, and often code for extracellular proteins. As expected, CpG islands are found in most of the least tissue-specific genes, which often code for proteins located in the nucleus or mitochondrion. The class of genes with no CpG island or TATA box are the most common mid-specificity genes and commonly code for proteins located in a membrane. Sp1 was found to be a weak indicator of less-specific expression. YY1 binding sites, either as initiators or as downstream sites, were strongly associated with the least-specific genes.We have begun to understand the components of promoters that distinguish tissue-specific from ubiquitous genes, to identify associations that can predict the broad class of gene expression from sequence data alone. The development of an adult from the single cell of a fertilized egg requires a complex orchestration of genes to be expressed at the right time, place, and level. Basic cellular functions require the expression of certain genes in all cells and tissues  while specialized functions require restricted expression of other genes in a single or small number of cells and tissues . Both types of genes may be needed for embryonic development as well as for the function of adult cells and tissues. While the details of regulatory mechanisms will vary for individual genes, general features of promoters (and here we will restrict our focus to RNA polymerase II (Pol II) promoters) are likely to facilitate whether a gene will be expressed widely or in a restricted manner. For example, based on the limited number of genes available at the time of the analysis, promoters with CpG islands have been associated with housekeeping genes ,2. It isFurthermore, it would also be informative to examine the relationship of CpG islands to the base composition of promoters, and the distribution of motifs thought to be bound by factors closely involved with (or part of) the basal transcription complex. The distribution of major components of the core promoter, the TATA box (TBP/TFIID binding site) and initiator element  , and proInvestigators have searched for combinations of transcription-factor-binding sites that confer tissue-specific expression on particular cell types such as muscle  or liverMeasures have been developed for overall tissue specificity ,27,28 thH) measures the degree of overall tissue specificity of a gene, but does not indicate whether it is specific to a particular tissue. To quantify categorical tissue specificity, we introduce a new statistic (Q) that incorporates overall tissue specificity and relative expression level. We demonstrate that H and Q are effective metrics for ranking and selecting genes according to tissue specificity and then proceed to use them to investigate promoter features  that may be used distinguish tissue-specific genes from nonspecific genes. The association of promoter features with a quantitative assessment of tissue specificity using H and Q is an important step towards developing models for promoter function.A metric for characterizing the breadth and uniformity of the expression pattern of a gene that meets our criteria is the Shannon information theoretic measure entropy. Although entropy has been used previously to identify potential drug targets ,31 by coWe begin by defining the measurement of two kinds of tissue specificity, 'overall' tissue specificity and 'categorical' tissue specificity.  Overall tissue specificity ranks a gene according to the degree to which its expression pattern differs from ubiquitous uniform expression. We use the term 'ubiquitous' expression to mean expression at any level above background in all tissues. Categorical tissue specificity places special emphasis on a particular tissue of interest and ranks a gene according to the degree to which its expression pattern is skewed toward expression in only that particular tissue. In both cases, a gene's specificity to a tissue, cell type or other condition is decreased as the gene is more uniformly expressed in a wider variety of conditions. In addition, the categorical tissue specificity should decrease as the tissue of interest becomes a smaller component of the overall expression pattern of the gene.Given a static multi-tissue expression profile for a gene, there are at least two dimensions along which we can assess the profile to measure tissue specificity. The first dimension is the number of tissues that express the gene above some background level. It can be argued that this dimension measures tissue restriction, that is, a gene shows restricted expression if it is expressed in only a subset of tissues. The second dimension is the uniformity of expression over all tissues that express the gene. A gene that shows significant non-uniform expression is exhibiting tissue-dependent regulation, in addition to any tissue restriction that may be occurring. We assume that a gene that exhibits no tissue-specific regulation will be expressed at the same level in every tissue. We do not assert that such genes are not regulated, only that they are regulated in a way that is not sensitive to tissue.The term 'most tissue-specific' will refer to the range of genes that are closer to the extreme of expression in a single tissue than to the extreme of ubiquitous uniform expression. We will refer to genes close to the uniform and ubiquitous end as either 'least tissue-specific' or 'nonspecific' though the latter term may not be strictly true. The range in the middle will be termed 'semi-tissue specific'. The term 'housekeeping' has been applied to genes that are widely expressed and may show little tissue-specific changes in expression level. We can use such genes as an example of genes that will tend to be ubiquitously and uniformly expressed and thus ought to be nonspecific on average. We will use the phrase 'gene sharing' to refer to the situation that occurs when a gene is tissue-specific, and is expressed in a small number of tissues that can be said to share the gene.N tissues, we defined the relative expression of a gene g in a tissue t as pt|g = wg,t/∑t ≤ N1 ≤ wg,t where wg,t is the expression level of the gene in the tissue. The entropy [Hg = ∑t ≤ N 1 ≤ - pt|g log2(pt|g). Hg has units of bits and ranges from zero for genes expressed in a single tissue to log2(N) for genes expressed uniformly in all tissues considered. The maximum value of Hg depends on the number of tissues considered so we will report this number when appropriate. Because we use relative expression the entropy of a gene is not sensitive to the absolute expression levels. To measure categorical tissue specificity we define Qg|t = Hg - log2(pt|g). The quantity -log2(pt|g) also has units of bits and has a minimum of zero that occurs when a gene is expressed in a single tissue and grows unboundedly as the relative expression level drops to zero. Thus Qg|t is near its minimum of zero bits when a gene is relatively highly expressed in a small number of tissues including the tissue of interest, and becomes higher as either the number of tissues expressing the gene becomes higher, or as the relative contribution of the tissue to the gene's overall pattern becomes smaller. By itself, the term -log2(pt|g) is equivalent to pt|g. Adding the entropy term serves to favor genes that are not expressed highly in the tissue of interest, but are expressed only in a small number of other tissues. As described earlier, we want to consider such genes as categorically tissue-specific since their expression pattern is very restricted. Figure Hg and Qg|t. The top five genes specific to mouse amygdala, lymph node, and liver as assessed by this data are listed in Table Hg and Qg|t values for all genes in all tissues in the GNF-GEA datasets are available in Additional data files 1 and 2.We used two gene-expression datasets to evaluate our methods; Affymetrix-based data from the GNF Gene Expression Atlas (GNF-GEA)  and the  entropy  of a genHg as shown in Figure Hg ≥ 4 bits, which implies expression in at least 16 tissues and typically corresponds to wider, but uneven, expression. Only 87 (2%) of genes had Hg ≤ 1.5 bits, which corresponds to expression in as few as three tissues. Both microarray- and EST-based data yielded similar overall curves. The EST curve peaked at a lower Hg than the microarray curve. This was due to the small numbers of EST sequences in some of the tissues we considered; EST counts for tissues ranged from 1,933 in the adrenal gland to 331,582 in the central nervous system (CNS). Genes that are ubiquitously expressed may not have ESTs from several of the lightly sequenced tissues, making them appear to have more restricted expression, and hence a lower entropy, than they really do. Figure Hg derived from microarray and EST data. Visual inspection of the plot reveals that while there are no strong contradictions between the two methods, quantitative agreement is limited. Detailed analysis shows that the standard deviation of the difference of paired Hg values is 0.61 bits. Under the null hypothesis that the estimates from the two data sources are totally uncorrelated the average standard deviation was found to be 0.91 bits. We can reject the null hypothesis (P < 10-5 as estimated by Monte Carlo methods). The distribution of Qg|t for selected tissues is shown in Figure To compare results from microarray and EST-based expression data we mapped the tissues from the GNF-GEA study to the hierarchical controlled vocabulary of anatomical terms used by DoTS and chose a set of 45 tissue terms grouped into 32 groups shown in Table Hg and Qg|t statistics can be estimated from a dataset to determine the smallest meaningful difference in scores and to guide interpretation of gene rankings. To assess the standard deviations of and Hg and Qg|t, we sampled from the replicates in the GNF-GEA microarray data to compute a large number of Hg values for each probe set. We found that the standard deviation for Hg was less than 0.2 bits for 97% of genes. Qg|t was not estimated as well; the standard deviation was 1 bit or less for 95% of gene and tissue pairs. This was probably due to the high standard deviation of the -log2(pt|g) term for low expressing gene-tissue pairs. We found much more variation when we measure reproducibility by considering genes that have two or more probe sets (and therefore two or more different transcripts) in the microarray data. In this case, the standard deviation of Hg estimates was as high as 1 bit for 97% of the genes but less than 0.3 bits for about 70-80% of the genes. We chose a minimum of 1 bit for Hg bins and 2 bits for Q bins in the rest of the analyses that require binning. This bin size ensured that most of the genes are in the proper bin and thus the bin could be reliably used to determine associations with the tissue specificity of a class of genes.It is important to determine how well the Hg and Qg|t statistics is to determine values for a set of nonspecific genes such as housekeeping genes. A list of 797 human housekeeping genes [Hg = 4.6 ± 0.27 bits in a set of 27 tissues with a maximum H = lg(27) = 4.75 bits; thus they are nonspecific as expected. Interestingly, a small number of these genes did show some degree of tissue specificity yet were ubiquitously expressed. For example, the median expression of NM_021983 the major histocompatibility complex, class II DR beta 4 gene (32035_at) is approximately 200 AU, but it shows much higher expression in a small set of tissues , which lowered its entropy. A more extreme case is NM_001502 glycoprotein 2 (zymogen granule membrane protein 2), which is expressed between 250 and 1,000 AU in all tissues except pancreas, where it is expressed at 34,183 AU. This is a ubiquitously expressed gene that entropy categorizes as specific since it showed such extreme tissue-specific induction. The housekeeping genes had a mean Qg|t = 9.5 ± 0.14 bits in the same set of tissues. The expected Q value for a uniformly and ubiquitously expressed gene is 2 lg(27) = 9.5 bits. Thus, the Hg and Qg|t statistics successfully captured the expected expression properties of housekeeping genes.A test of the ng genes  was evals = 0.5, which allows for a relatively large amount of variation; up to 1.4-fold tissue-to-tissue variation around the mean expression level in about 63% of tissues and larger changes in the remaining tissues. As a threshold for selecting genes with tissue-dependent expression, we choose Hg = 4.52 bits which has a p-value of 0.005 under the null hypothesis that all genes are uniform. We then find that 5,837/8,703 (67%) of human genes have entropies less than this and so are probably regulated in a tissue-dependent manner. If we use a more stringent definition of uniform expression that allows half as much variation in tissue-to-tissue expression levels (s = 0.25), then the threshold is Hg = 4.62 bits and we find that 7,584/8,703 (87%) of human genes show evidence of tissue-dependent regulation. Similar results are found in mouse using all 42 distinct tissues, where the corresponding thresholds are Hg = 5.24 bits (s = 0.5) and Hg = 5.35 bits (s = 0.25) and the fractions of genes showing tissue-dependent expression are 5,467/7,913 (69%) and 7,482/7,913 (94%) respectively. Thus we conclude that most genes show evidence of tissue-dependent expression levels.Although the housekeeping genes assessed above have relatively high entropies, they do show some small degree of overall tissue specificity. We therefore sought to determine how many genes show evidence of tissue-dependent regulation. Since random biological and experimental variation introduce fluctuations in the expression levels of genes, we made a probability model of the effect of these fluctuations on the observed entropy. The experimental variability was estimated from the GNF-GEA data using all normal tissues. The random tissue-to-tissue biological variability was modeled by assuming that each gene has an average expression level across all tissues and that the log base 2 of the tissue-dependent fold changes from the average level follow a normal distribution with mean equal to zero and some unknown, but 'small', standard deviation(s). We obtain a conservative estimate of the number of genes showing evidence of tissue-dependent regulation by using Qg|t with respect to specific genes is to evaluate the tissues in which they rank highly  for consistency. This was accomplished by clustering tissues with similar tissue-specific genes and inspecting the clusters formed. We used 27 normal human tissues and, separately, 39 tissues from the GNF-GEA data for mouse and selected the genes  that express at least 200 AU in at least one tissue and have Qg|t = 7 in at least one tissue. With these genes, we made a consensus hierarchical clustering of the tissues as shown in Figure Qg|t is correctly identifying tissue-specific genes. Interestingly, testis is an outlier in both trees, indicating that the collection of genes expressed in testis are distinct from any other tissue or organ. Furthermore, Hg and Qg|t can also be used in conjunction with a tissue hierarchy to answer more complex questions about the tissue distribution of genes such as 'what genes are specific to the brain but are widely expressed throughout the brain?' In Table A test of Hg. We considered only predicted CpG islands that span the start of transcription . Therefore, a gene expressed in the adult was 2.8 (= 0.39/0.14) times more likely to be expressed in the early embryo if it contained a start CpG island. Furthermore, the most tissue-specific genes expressed in the adult were four times more likely to have been expressed in the early embryo if their promoter contained a start CpG island. These results strongly suggest that CpG islands are promoter features for both embryonic and the least tissue-specific genes.Another group of genes observed to be associated with CpG islands are those expressed in the early embryo  from theHg ≤ 3.5 bits) and low (4.4 ≤ Hg ≤ 4.71 bits) tissue-specificity genes. We considered CGI+ and CGI- genes separately, as it is clear the presence of a CpG island will strongly influence the base composition and that the fraction of start CpG islands varies with entropy. In addition, the presence of a start CpG island may indicate a different regulation mechanism related to either tissue specificity or embryonic expression (or both). The number of promoters from DBTSS in these four classes that were used in the analysis were: 310 CGI- and 129 CGI+ high specificity; 342 CGI- and 1,501 CGI+ low specificity. Genes that have only non-start CpG islands represented a minor component and were not included in this analysis. We used the full set of normal tissues in the first GNF-GEA microarray study for human and mouse. Base composition profiles with 10 base-pair (bp) windows are shown in Figure 10 which is equivalent to 0.001. Promoters of CGI+ genes  region for all classes but was most pronounced in the tissue-specific promoters Figure . These sHg calculated from the GNF-GEA data. Second, we searched for core motif consensus sites in promoters of genes selected using Qg|t calculated from EST data.We next examined the distribution of basic core promoter features: the TATA box, the initiator element, and two binding sites for selected ubiquitous transcription factors, Sp1 and YY1, to see if their presence in the proximal promoter was correlated with the tissue specificity of a gene. Two approaches were taken using different datasets and motif-searching methods that gave similar results, providing independent confirmation of results. First, we searched for core motifs using weight matrix hits in promoters of genes selected using We grouped the human genes that expressed at least 200 AU  in the GNF-GEA data by entropy and start CpG island status. The number of genes in each category is shown in Table P ≈ 0 exact binomial). Similar numbers are found in mouse (52%/11% = 4.7) This trend also holds within CGI- genes and CGI+ genes. The most specific CGI- genes were three times more likely to have a TATA box than the least specific CGI- genes . While less common in CGI+ genes, TATA boxes were still almost four times as likely to be found in the most specific CGI+ genes than the least specific CGI+ genes . Thus TATA boxes are clearly associated with tissue-specific genes and provide a second axis (with CpG islands) for distinguishing between the most and least specific genes.We searched for the TATA box in the -45, -10) region where the average observed/expected ratio for the TATA box was 3.1. As shown in Table , -10 regIn contrast, the frequency of occurrences of the initiator element (Pol II binding site) was roughly constant across all tissue-specificity classes for both CGI+ and CGI- genes. We searched for the initiator element in the  region. It occurred in 762 of 1,118 (68%) of CGI- genes and 1,273 of 2,434 (52%) of CGI+ genes. Similarly, it occurred in 149 of 215 (69%) of the most specific genes and 388 of 607 (64%) of CGI+ genes. The observed frequency of TATA+/Inr+ promoters was not significantly different from the expected rate assuming independence of the two individual features (data not shown).Sp1 ,38 is a P = 0.016). Similar numbers are found in the mouse; 38% of the least specific and 26% of the most specific promoters have Sp1 sites. Thus, although Sp1 shows a preference for the least tissue-specific promoters, it is not a strong predictor of the tissue specificity of a gene.Sp1 sites are associated with CpG islands but are an important component of GGI- promoters as well. Considering just the  region, Sp1 sites occurred in 1,105/2,434 (45%) of human CGI+ gene promoters, and 316/1,118 (28%) of CGI- genes at about 2.5 to 3.0 times the expected frequency in both cases. Frequencies in mouse are 927/2075 (45%) of CGI+ promoters and 464/1652 (28%) CGI- promoters. Sp1 sites were also weakly associated with the least specific genes occurring in 1,105/2,679 (41%) of these genes as compared to 94/271 (32%) in the most tissue-specific genes (r) were preferentially located in the  region but with some elevated levels to +80 bp. Start positions of sites in the forward orientation (YY1f) showed a very sharp preference for -3 bp, which probably represents a YY1-like initiator sequence reviewed elsewhere [f initiator sites are rare; only 55/2,679 (2%) were found above background in human low-specificity genes. The rate in mouse, 22/2,832 (0.8%) of low-specificity promoters, is even lower. The YY1r sites are more common and were found above background in 217 (8%) of the 2,679 least specific genes. YY1r sites were more common in CGI+ genes than in CGI- genes  versus 15/607 (2%) P = 3.7 × 10-9 two-population binomial). The corresponding rates in mouse confirm these observations; 178/2,832 (6%) for all low-specificity genes and 152/1,779 (9%) in CGI+ and 26/1,053 (2%) of CGI- low-specificity promoters. These YY1-like sites therefore constitute a feature strongly associated with the least specific genes and may partially explain the observed G > C ratio in the  region.The transcription factor YY1 -8 is alslsewhere . Both orhe +1, +2 region bQ and is robust to using EST data as well as promoters that did not specifically rely on full-length cDNA clones. The definition of Q implies that genes with a particular Q-value can have a variety of Hg values and thus it may be more difficult to identify features related to tissue specificity. We tabulated all DoTS genes that contained at least two ESTs from an islet-cell library then ranked the genes by Qpancreas computed using EST counts. We used Qpancreas ≤ 7 bits as the criterion for selecting pancreas-specific genes which we grouped into 2-bit Q intervals. For comparison we selected 50 genes with Qpancreas = 8.5 bits, and 50 genes with 10 ≤ Qpancreas ≤ 10.6 bits. Genes with high specificity for the pancreas  preferentially had TATA boxes (8 of 9) with half of these also having an initiation element  across all specificity classes  is also found for the same set of genes in another mammal. We also added bins of genes with higher Q-values that represent more widely expressed genes. For each human gene, the orthologous mouse gene was determined  and analyzed as described above. Overall, 18.8% of the human genes and 22.9% of the mouse genes that were analyzed carry the TATA box motif. Except for the last group (Q >10 bits) the percentage of the genes with TATA box motifs decreases with the increase in the Q-value. This is to be expected since genes with high Q may be specific to other tissues and hence are more likely to have a TATA box. Discrepancies between human and mouse promoters were noted for only about 10% of all human-mouse pairs analyzed and may reflect sequence differences and possible annotation discrepancies for the transcription start site. Nevertheless, there is overall excellent agreement for the presence of TATA motifs in human and mouse genes. Thus, our assessment of preferential presence of transcription regulatory motifs in the human pancreas-expressed genes also applies to their mouse orthologs. We conclude that genes expressed with restricted tissue-distribution may be preferentially regulated via TATA-mediated transcription, and that genes with broader expression profiles are more likely to be regulated by non-TATA mediated mechanisms (such as YY1).The consistency of findings for the TATA box with human islet genes based on Since the presence or absence of a start CpG island and a TATA box appear to be the primary sequence feature that correlate with tissue specificity, we consider them in more detail. We observe that CpG islands and TATA boxes are not mutually exclusive features of promoters and so we consider all possible combinations of these features.Hg in human  of TATA+ genes express at least 1,000 AU in one or more tissues, whereas only 1,321/3,773 (35%) of TATA- genes express that highly . A second group of CGI-/TATA+ that is common, but with a p-value just over the p-value cutoff are the muscle contraction-related genes, actin, troponin and members of the myosin family. Products of these genes are also required in large amounts to create the contractile apparatus but are only produced in a few cell types. The biological processes that are enriched in the CGI-/TATA+ class differ between human and mouse, but nearly all of them are descendants of the GO term 'response to stimulus' (GO:0050896).Products of genes in the CGI-/TATA+ class were often (70/198) located extracellularly. Examples of such genes are the insulin-like growth factor family, serum albumin and chymotrypsin. Some extracellular CGI-/TATA+ genes, such as luteinizing hormone beta (LHB) and bone morphogenetic protein 10 (Bmp10) in the mouse, have a high Hg ≤ 5.57 bits), we find cellular locations (including the nucleus) and biological processes that match the human results.The CGI+/TATA- promoters produce proteins that are typically located in the cell, especially in the cytoplasm and mitochondrion. These locations are consistent with many housekeeping functions. The human results for biological process suggests a large number of housekeeping processes, but these were not confirmed in the mouse using all CGI+/TATA- genes. When we consider just the least specific CGI+/TATA- mouse genes  and support signaling and response to the environment. Such products, for example, bradykinin receptor B2, prolactin receptor or protocadherin 9, may be expressed in a tissue-specific pattern, but not at the high levels required for secreted proteins. The exact biological process GO terms that are statistically significant vary between mouse and human, but a common core includes defense response (GO:0006952), immune response (GO:0006955) and response to stimulus (GO:0050896). Thus these genes are similar to CGI-/TATA+ genes in that they are involved in response, but are not  required to be expressed at such high levels.Q to assess the categorical specificity of a gene for a particular tissue. We have evaluated the performance of entropy on microarray-and EST-based estimates of tissue-specific expression and found that it correctly identifies both tissue-specific and housekeeping genes. Ranking and binning genes by entropy allowed us to begin to deconstruct core promoters into features directing when and where the gene will be expressed. We verified and extended previous observations [We have applied Shannon entropy as a novel measure of overall tissue specificity of gene expression and have created a new statistic rvations  about thHg and Q have allowed us to discover fundamental properties of mammalian Pol II promoters and should allow serve to aid understanding of expression in particular tissues of interest.The identification of an association between promoter type and cellular location and biological function, while an important step in a fundamental understanding of biology, also has practical significance, as the genes in the CGI-/TATA+ and CGI-/TATA- classes are enriched for tissue-specific extracellular and cell surface proteins. Such genes are likely to be useful drug targets. Thus entropy Qg|t is properly identifying genes that are specific to a tissue. The GNF-GEA expression data we analyzed was processed with the MAS4 [H, in the RMA-processed data compared to the reported values. Although this affects some of the precise values of numbers we have reported it does not alter any of the fundamental trends or results. We include tissue specificities based on both analyses in Additional data files 1 and 2.The validity of our approach is supported by findings in other work and by the fact that they are robust with respect to the algorithm used to process the expression data. Our finding that most genes are regulated in a tissue-dependent manner is consistent with another analysis of gene expression , which fthe MAS4  algoriththe MAS4 . This alOur analysis focused on only a few sequence features and although we found good correlations, two aspects of our results indicate that there are other regulatory mechanisms not yet identified. First, there is a gradual transition in the frequency of the TATA box and CpG islands between the most and least tissue-specific genes. Second, while these features are strong indicators of high and low specificity, they are far from perfect predictors. Indeed, the middle range of entropies contains a mix of all promoter classes in large numbers, indicating that it is possible to achieve tissue-specific expression with any promoter class. YY1 may be an example of such a supplementary mechanism. While occurring in only 16% of genes, it is very strictly confined to low-specificity genes and is a better indicator of low specificity than CpG islands. We expect that other such signals will be found.Anatomical resolution is an issue with the datasets used in this study. For example, the pancreas consists of exocrine cells, ductal cells and islet cells of several types. The bulk pancreas was used to generate the GNF-GEA data, so the reported expression level is the average mRNA concentrations weighted by the cell-type count. This approximation reduces the maximum possible entropy and, more significantly, can make the apparent entropy different from the true entropy. Genes highly and specifically expressed in a cell type with a small population may currently appear to be ubiquitous with very low overall expression. Genes expressed in a few tissues may be revealed to be less tissue specific as more cell types are measured in detail. Genes that appear to be ubiquitously expressed may turn out to not to be expressed in a few cell types. It will be interesting to see whether data with higher anatomical resolution will help to increase the accuracy of the rules we have identified here for identifying tissue-specific and nonspecific promoters.in situ hybridization data. SAGE has the advantage of sensitivity, as these studies generally sequence to much greater depths than EST libraries [In situ hybridization data may increase the anatomical resolution of the data. Qualitative intensities, for example, '0', '+', or '+++', can be converted to representative numeric values as appropriate. Our method can also be applied to other collections of conditions beside normal tissues, for example, different types of cancers or samples of the same cancer from multiple patients. Modification of our method to account for temporal changes in tissue specificity represents another direction for future work.Our method can be also applied to other sources of expression data including SAGE, reverse transcription PCR (RT-PCR) and ibraries . In situThe analysis presented here focuses on genes rather than on transcripts generated from different promoters from the same gene. The rate of the occurrence of alternative transcription start sites is at least 9%  and may Our results for CpG island frequency in very tissue-specific genes are lower than recent reports  that werThese results present an initial look at the correlation between tissue specificity, CpG islands and binding sites for selected transcription factors that interact with the basal transcription apparatus. Using a novel approach with entropy-based metrics, we have begun to lay out the framework for promoter function by identifying strong correlations between tissue-specific or ubiquitous expression and a number of these sequence features. We plan to extend this work in several ways. First, we plan to identify correlations with other known transcription-factor-binding sites and novel motifs identified as over-represented in promoter regions . Second,We have used Shannon entropy to quantify and rank the tissue specificity of genes using tissue-survey data. First, this has allowed us to assess the prevalence of tissue-specific regulation; we find that most genes show evidence of some degree of tissue-dependent variation in expression levels. It has also allowed us to find and evaluate associations between promoter features and tissue specificity. We have verified and extended understanding of known associations between, on the one hand, CpG islands and the least tissue-specific genes and, on the other hand, the TATA box and the most tissue-specific genes. However, they are not the sole determinants of tissue-specific expression, as indicated by mid-specificity genes that exhibit a mix of all promoter classes. The class of CGI-/TATA- promoters has emerged as the second most common class of promoter overall and the most common promoter class in mid-specificity genes. Therefore, additional determinants of tissue specificity remain to be found. We have identified one potential determinant, a downstream YY1 site, which is very strongly associated with the least tissue-specific genes but is a relatively rare feature of these promoters. Finally, we have also been able to associate trends in the localization and function of protein products of genes according to their promoter class. Many of the CGI-/TATA+ genes code for highly expressed, very tissue specific, extracellular proteins involved in a cell's response to the environment. CGI-/TATA- genes are also involved in response to the environment, but are found more uniformly across the spectrum of tissue specificity, are not as highly expressed as CGI-/TATA+ genes, and very often code for membrane-bound proteins. CGI+/TATA- genes are more likely to be located in the cytoplasm or nucleus and, as expected, carry out housekeeping functions. All of the results we report are found in both human and mouse and so may reflect general principles of all mammalian species.N tissues we define pt|g = wg,t/∑t ≤ N1 ≤ wg,t where wt is the expression level of the gene g in tissue t. DoTS, available through the AllGenes [g. To accommodate the great disparity in sampling depth across tissues we normalized EST counts by tissue. To avoid artificially low entropies for genes that contain relatively few ESTs we used pseudocounts to smooth the data. The expression level of a gene in a tissue is computed as wg,t = /(Nt + Ng) where ng,t is the number of ESTs from libraries for a tissue included in a gene, Nt is the total number of ESTs from a tissue assembled into genes, and Ng is the number of genes. We used different sets of tissues depending on the task. Hg and Q measures in Figure The GNF-GEA data are processed as described . Given aAllGenes  site, coH and Q, we took advantage of tissue replicates in the GNF-GEA data. Using the mouse dataset, we repeatedly sampled one of the measurements from each pair of replicates and computed H for each gene. We then computed the variance of the distribution of the estimates of H for each gene and show the survivor distribution function in Figure Q was computed in a similar manner.To estimate the variance in Q scores for the set of mouse genes with Qg|t ≤ 7 for at least one tissue and expressing at least 200 AU in at least one tissue in the GNF-GEA data. There were 1,786 Affymetrix probe sets selected. The tree in Figure Clustering was based on the Qg,n for the gene at the node. Using Qg,n we can rank genes by specificity to a cluster of tissues just as we can for an individual tissue.The total entropy of all tissues under a node can be computed at each node in the hierarchy using a generalization of the grouping theorem . If the We predicted CpG islands using the program NEWCGREPORT in the EMBOSS  package N CGI+ genes, ne of which are expressed in the embryo, that is, marked as special. The NA tissue-specific genes in the adult are considered a random sample from the original N and we compute the probability of finding that at least (or at most) nae of these were expressed in the embryo.We computed statistical significance of differences between all embryonic-expressed genes and adult-specific rates using a hypergeometric distribution. We start with a collection of s). The standard deviation can be adjusted to control the amount of biological variation a 'uniformly' expressed gene is allowed to show. For example, setting s = 0.5 means that about 68% of the fold changes between a particular tissue and the nominal level are within 1.4 up or down from the nominal level, that is, a twofold change from the lowest to the highest levels. Larger fold changes are expected to occur in 32% of tissues. This model allows significant variation and so is arguably close to the upper limit of variation allowable for a gene that shows no tissue specificity. We also used s = 0.25 as a more stringent definition of uniform expression. We sampled mean expression levels from the distribution of observed mean expression levels and sampled entropy values from the probability model. An entropy threshold was estimated by sampling approximately 5,000 random expression profiles and determining the value for a p-value of 0.002. This process was repeated 10 times and the corresponding thresholds and fraction of genes were computed. The thresholds spanned a range of less than 0.01 bit. The tissue-dependent gene fractions never varied by more than one percentage point in either direction.To model the effect of experimental variability, we computed the distribution of the difference between expression levels of individual replicates for each gene and tissue and the mean expression level across replicates as a function of the mean expression level. This distribution was well fit by an exponential distribution with a parameter that depends on the mean expression level. Thus, given an 'ideal' expression level, we can estimate what the experimental variability will be. To model a uniformly expressed gene, we assume that a gene has some average expression level across all tissues and then allow the expression levels in individual tissues to follow a narrow distribution of random fold changes from that level. Specifically, we assumed that the log base 2 of the fold changes is distributed according to a normal distribution with mean equal to 0 and a standard deviation  n12 hits in a random selection of n2 promoters from a pool of N promoters where n1 of them are 'special'.We estimated the statistical significance of the co-occurrence of motifs using the hypergeometric distribution. Given two motifs with occurrence counts N1 and N2 and positive observations n1 and n2 in each, we computed the probability that the underlying rates are different using an exact calculation of the binomial distribution to compute the probability of seeing at least (or no more) than ni matches in Ni trials where the rate is assumed to be r = nj/Nj. We estimated r using the larger of the two sets.Given two sets of size z-score.We used the normal approximation to the difference of the proportions normalized by their variance to compute a H-based set of analyses used links from Affymetrix probe sets to RefSeq identifiers to select alignments from the DBTSS promoter sequences covering the  region downloaded from the DBTSS website [Q-based analyses of TATA box and initiator elements used genomic locations of DoTS genes on UCSC Golden Path release mm3 [We obtained promoter sequence in two ways. The  website . The Q-bease mm3 ,61 to idease mm3 . The mouH-based analysis used core promoter element models from EPD [Q-based analyses of core motifs used the TATA box motif (TATAA) and initiator element (YYANWYY). Motif searches were carried out using the tool patternmatch from the biological workbench 3.2 [The from EPD ,63. The ench 3.2 . Only thWe used an AlignACE-derived weight matrix  or 0.05/8972 = 0.000006  using the number of GO terms for the corresponding GO divisions in a Bonferroni correction.We submitted Affymetrix probe set ids of interest to the DAVID website ,46 and cWe obtained CEL files for the GNF-GEA study from and re-quantified them using the gcrma package  in the BTwo additional data files are available with the online version of this article. They contain H and Q values for all normal tissues in the GNF-GEA data set for both human (Additional data file A table showing H and Q values for all normal human tissues in the GNF-GEA dataset. H and Q values for all normal tissues in the GNF-GEA dataset for human using both the original MAS4 quantification and our RMA re-quantification. The RMA data were normalized to yield common medians of 3.75 prior to the H and Q calculation. The data for each tissue are placed in separate worksheets. Each worksheet contains H- and Q-values, the expression value of the gene in the worksheet's tissue, and its maximum expression across all tissues in the file, the gene symbol, RefSeq, SwissProt, and Unigene ID, and a description. The rows in each worksheet are sorted by increasing values of Q using the RMA data. Thus the top of each worksheet displays the genes most specific to that worksheet's tissue.Click here for fileA table showing H and Q values for all normal mouse tissues in the GNF-GEA dataset. H and Q values for all normal tissues in the GNF-GEA dataset for mouse using both the original MAS4 quantification and our RMA re-quantification. The RMA data were normalized to yield common medians of 3.22 prior to the H and Q calculation. The data for each tissue are placed in separate worksheets. Each worksheet contains H- and Q-values, the expression value of the gene in the worksheet's tissue, and its maximum expression across all tissues in the file, the gene symbol, RefSeq, SwissProt, and Unigene ID, and a description. The rows in each worksheet are sorted by increasing values of Q using the RMA data. Thus the top of each worksheet displays the genes most specific to that worksheet's tissue.Click here for file


A microaaray analysis of mouse gene expression combined with the proteins functional and phyletic classification suggests that phyletic age (and not function) is the dominant factor shaping the expression profle of a protein. The combination of complete genome sequence information with expression data enables us to characterize the relationship between a protein's evolutionary origin or functional category and its expression pattern. In this study, mouse proteins were assigned into functional and phyletic groups and the gene expression patterns of the different protein groupings were examined by microarray analysis in various mouse tissues.Our results suggest that the proteins that are universally distributed in all tissues are predominantly enzymes and transporters. In contrast, the tissue-specific set is dominated by regulatory proteins . An increased tendency to tissue-specificity is observed for metazoan-specific proteins. As the composition of the phyletic groups highly correlates with that of the functional groups, the data were tested in order to determine which of the two factors - function or phyletic age - is dominant in shaping the expression profile of a protein. The observed differences in expression patterns of genes between functional groups were found mainly to reflect their different phyletic origin. The connection between tissue specificity and phyletic age cannot be explained by the recent rate of evolution. Finally, although metazoan-specific proteins tend to be tissue-specific compared with phyletically conserved proteins present in all domains of life, many such 'universal' proteins are also tissue-specific.The minimal cellular transcriptome of the metazoan cell differs from that of the ancestral unicellular eukaryote: new functions were added (metazoan-specific proteins), whilst other functions became specialized and no longer took place in all cells (tissue-specific pre-metazoan proteins). Higher animals are characterized by differentiated tissue types, where each tissue has its own unique cellular composition and physiological function. Comparative genomic studies have shown that the evolution of the metazoan lineage involves the expansion of those specific protein families known to participate in cellular communication and transcriptional regulation ,2. HowevRecent studies have related several characteristics of a protein to its expression profile. Subramanian and Kumar  have shoFinally, we wanted to verify that the phyletic age of a protein is indeed a major factor in shaping its expression profile rather than merely a reflection of the level of conservation in a protein - a factor that has already been shown to play a role in determining expression -7. To ruIn order to tackle these questions we have studied expression patterns in 14 mouse tissues. Gene expression patterns  were related to the evolutionary origin of the protein as reflected in the distribution of proteins in different phyla. Firstly, we have assigned mouse proteins to one of four functional categories: two regulatory categories  and two metabolic categories (enzymes and transporters). Next, the proteins were assigned to a phyletic category: mammalian-specific proteins, metazoan-specific proteins, eukaryote-specific proteins and universal proteins - present in prokaryote species. Then we compared the expression pattern of the different categories within various mouse tissues and studied the tendency of proteins within these groups to be tissue-specific or ubiquitous. The assignment process is described in Figure For each tissue we counted the number of expressed probe sets. The fraction of probe sets expressed in each tissue ranges from 0.35 (muscle) to 0.55 (eye). Nearly a constant fraction (~60%) of the probe sets in each tissue is mapped to proteins. Similarly a constant fraction (~45%) of the proteins in each tissue can be assigned a Gene Ontology (GO) annotation Figure . We compAs the tissues seem to have almost identical overall composition of functional categories Figure , tissue We further studied the contribution of different functional and phyletic groups to tissue variation. Are some functional categories more tissue-specific than others? We examined the expression profile of proteins from the four functional categories within 14 different mouse tissues. For each group, we calculated the fraction of its protein members expressed in one tissue, two tissues, and so on Figure . SurprisSignal transduction proteins and transcription factors are known to be the main functional categories that were expanded in the metazoa lineage while enzymes and transporter proteins are usually more highly conserved between the different domains of life ,2. Therep value 0.04), suggesting that most of the relationship observed between function and tissue specificity is accounted for by the age of the gene. The relationship between phyletic age and tissue specificity is not explained by a gene's function , nor is the relationship between phyletic age and function explained by the tissue specificity . Therefore, the results imply that enzymes tend to be ubiquitously expressed mainly due to their phyletic universal origin .In order to identify whether a protein's expression pattern better reflects function or age, the inter-relationship between these three factors was compared statistically. After phyletic age was taken into account, only a weak dependence between function and tissue specificity was detected  is one example of a universal enzyme whose expression is limited to few cell types in mammals. Ldh participates in anaerobic glycolysis - a nearly universal pathway that converts glucose into pyruvate. The sequence of reactions in the pathway is similar in all organisms and in all cell types. In contrast, the fate of pyruvate is variable. In a variety of microorganisms, lactate is normally formed from pyruvate in a reaction catalyzed by Ldh. In higher organisms, most cells do not convert pyruvate to lactate and the reaction is limited to few tissues [Ldhc (testis-specific expression). The expression of Ldhc is an example of a function occurring in the ancestral unicellular cell that becomes tissue-specific in multicellular species.Yet, although pre-metazoan proteins tend to be more widely expressed, less than one-third of the pre-metazoan metabolic proteins are expressed in all tissues.  tissues . In germ tissues , we obseG6pd-2) and phosphoglycerate kinase 2 (Pgk-2) - provides a different example for a specific expression of universal enzymes. G6pd-2 and Pgk-2 are believed to arise from their isoenzymes, G6pd and Pgk-1, respectively, by a gene duplication event. G6pd and Pgk-1 are essential, widely expressed, X chromosome-encoded genes. The absence of those two enzymes during the inactivation of the X chromosome in postmeiotic spermatogenic cells is compensated for by the expression of their autosomal testis-specific isoenzymes G6pd-2 and Pgk-2 [The testis-specific expression of two other universal enzymes in our dataset - glucose-6-phosphate dehydrogenase 2 . The chi-squared test statistic for the independence of age and tissue expression given rate in our data was 226.5, whereas the maximum observed statistic in 10,000 random draws, generated as described in Methods, was 84.3. The connection between phyletic age and tissue specificity that we observed in our data cannot be explained purely in terms of both factors' mutual correlation with the recent rate of evolution.Tissue-specific genes tend to evolve more rapidly than broadly expressed ones -7. ThereIt is important to remember that our analysis is based only on those proteins that are present on the Affymetrix chip and have GO annotation. Our dataset covers approximately one-quarter of mouse proteins. Clearly, a better coverage for the expression and annotation of proteins is desirable and could change the conclusion presented below. In order to decrease the probability that our results are arbitrary, we repeated the experiment with a different set of tissues . The results obtained are compatible with the observations we report here (data not shown).We show here that multicellular specific proteins tend to be more tissue-specific than 'ancient' universal proteins. Most of the 'late' evolutionary proteins are transcription factors and signal transduction proteins, categories that have previously been suggested to play a crucial role in tissue differentiation. However, our analysis suggests that more recent enzymes and transporters also contribute to tissue diversity as many of them are tissue-specific Figure . The selDespite this trend, many metazoan-specific proteins are ubiquitous and many universal proteins are tissue-specific. The minimal cellular transcriptome of the metazoan cell differs from that of the ancestral unicellular eukaryote: new functions were added (metazoan-specific proteins), whilst other functions became specialized and no longer took place in all cells (tissue-specific pre-metazoan proteins). The extent of the cellular specialization can be implied from the observation that only one-third of the proteins are expressed in all tissues examined. In some of these cases, functions occurring in the unicellular cell become tissue-specific in multicellular species. In other cases, universal genes that have been duplicated become specific to a tissue whilst a second copy maintains its original expression pattern. Only about one-third of the pre-metazoan metabolic enzymes are expressed in all tissues. Tissue differentiation is at least in part achieved by tissue specialization of metabolism - either by differentially expressing two-thirds of the ancient metabolic proteins, or by encoding new metabolic proteins. Presumably, the additional transcription-related proteins provide the necessary control. We aim to further characterize the expression patterns of processes that exist in the ancestral metazoa and those that are specific to metazoa. In particular, we are interested in studying the contribution of function differentiation versus gene duplication to tissue diversity in multicellular species.Tissues were dissected and snap frozen from 8-12-week old C57/BL6 male mice with the exception of the ovaries, which were taken from females. Tissue was pooled from between four to six animals, the RNA extracted and 10 mg of total RNA was labeled and then hybridized to the Affymetrix U74AV2 GeneChip using standard protocols ; complete experimental details for each of these stages are given at . ExpressP value < 0.075 for absent/present flags labeling has no effect on the analysis. Only absent/present calls were used to define tissue-specificity and expression levels have not been a factor in this analysis.A subset of 14 samples representing distinct non-redundant organs was chosen out of the complete dataset. The tissues are listed in Figure Out of 12,487 probe sets, 8,218 were mapped into EnsEmbl mouse transcripts using Ensmart ). MappinA total of 4,918 proteins were assigned with a GO annotation . For simWe used four categories to describe the evolutionary origin of mouse proteins: universal proteins, that is, ubiquitous in the three domains of life , eukaryote-specific proteins, metazoan-specific proteins and mammalian-specific proteins. The 6,242 mouse proteins were classified into the phyletic categories according to the results of a BLAST  databaseThe observations reported here are maintained using different cut-offs within the range of 1e-10 < e-score < 1e-1. The observations are also maintained when a universal protein is defined as a protein with a hit in at least a single prokaryote species or when it defined as a protein with a hit in at least ten prokaryote species . Additional tests were performed in order to assure that the phyletic distribution truly describes a complete sequence distribution rather than domain distribution. The classification of a protein to a phyletic group was done when additional filters were added. This was in order to discard those cases where a match between query and hit is based only on recognition of a conserved domain rather than a complete sequence (e-score <1e-3). Firstly, pfam domain composition: all query-hit pairs that do not share an identical pfam domain composition were discarded from our dataset. Secondly, full coverage: all query-hit pairs where the alignment does not cover the full length (80%) of both proteins were discarded from our dataset.When repeating the analysis with the filtered data, the results confirm that the trends reported here are maintained (not shown). Similar results were also obtained when using the homologous clusters database STRING  for a pha (the number of non-synonymous substitutions per non-synonymous site) to Ks (the number of synonymous substitutions per synonymous site) was calculated using the codeml program from the PAML 3.13d package [Ka/Ks ratio could not be reliably estimated. These sequences were discarded from the analysis. In total, the Ka/Ks ratio was calculated for 4,056 mouse proteins from the 5,501 proteins classified to a phyletic category and expressed in at least a single tissue  and then shuffling the phyletic ages within each group, sample sets of data can be created which have no connection between phyletic age and tissue expression other than through their common connection to recent rate of evolution. The connection between rate and tissue specificity in each of these sample sets is identical to the observed data and, because all genes in each bin have a similar rate of evolution, the connection between age and rate is similar to the observed data. By generating random samples in the manner as described above, the expected contingency table of age/expression dependence and the null distribution of the chi-squared test statistic can be estimated. Using the estimated expected table, the chi-squared statistic for the observed data can be calculated. The significance of the observation was assessed by comparing the observed test statistic with those from 10,000 sets of data, of equal size to the observed data, randomly generated according to the expected contingency table (and so satisfying the null hypothesis).By grouping the genes into equal bins of similar recent rates of evolution (The relationship between tissue specificity and phyletic age and function was investigated using a contingency table test under the null hypothesis that function and specificity are independent of given age. Conceptually, the genes are divided up according to age and a separate contingency table for specificity and function is formed for each group. The chi-squared test statistic  for indeThe following additional data are available with the online version of this paper. Additional data file Functional assignments of the proteins used in the analysis. Functional assignments of the proteins used in the analysisClick here for filePhyletic assignments of the proteins used in the analysis. Phyletic assignments of the proteins used in the analysisClick here for fileThe sequences of the proteins used in the analysis. The sequences of the proteins used in the analysisClick here for file


UDP-glucuronosyltransferases 1A isoforms belong to a superfamily of microsomal enzymes responsible for glucuronidation of numerous endogenous and exogenous compounds. The nine functional UGT1A isoforms are encoded by a single UGT1A gene locus with multiple first exons. The expression of the UGT1A transcripts was measured by quantitative RT-PCR in 23 normal human tissues. The tissue-specific expression patterns were observed in 13 tissues. To understand the regulation mechanism that is responsible for the tissue-specific expression patterns, we scanned the DNA sequence alignments of the putative promoter regions, exon 1 sequences and intron 1 sequences for those expression-pattern-linked nucleotides. Using one of the expression-pattern-linked nucleotides for livers as an example, we showed that a database comprised of these expression-pattern-linked nucleotides could be used to generate focused hypotheses on the problem of tissue-specific expression, which is critical for tissue-specific pharmacodynamics of anticancer drugs. Human UDP-glucuronosyltransferase (UGT) 1A is a subfamily of UGT enzymes that glucuronidate xeno-/endobiotics and many other substrates such as steroids and bilirubin to make the metabolic products more easily excreted from the body via the urinary and biliary tracts in silico approach to search for the expression-pattern-linked nucleotides. Due to the unique structure of UGT1A locus, specific nucleotides in promoter regions, exon 1 sequences, intron 1 sequences may (jointly) contribute to the observed tissue-specific expression patterns. Some possible mechanisms include promoter efficiency through transcription factor binding and alternative or false splicing. Specifically, we analyzed the putative promoter regions, exon 1 sequences and intron 1 sequences for nucleotides associated with tissue-specific expression, focusing on the eleven tissues that had more than two expressed UGT1A isoforms. The resulting database or pool of the expression-pattern-linked nucleotides then could be used to generate focused hypotheses on the regulation of the tissue-specific expression of UGT1A isoforms.Reports regarding UGT1A mRNA expression profiles indicate that each tissue contains a selective complement of UGT1A gene products We measured the expression levels of the nine functional UGT1A isoforms in 23 normal tissues. UGT1A genes were not expressed in 10 tissues , while the tissue-specific expression patterns were observed in other 13 tissues, among which placenta and lung had only one expressed isoform (UGT1A6) . Some exUGT1A isoforms showed specific expression pattern in different tissues. For example, in livers, only 6 UGT1A isoforms  had expression detected at different levels. Our major aim was to explore an approach to study the mechanism responsible for the observed tissue-specific expression patterns. Due to the unique structure of the UGT1A cluster, we hypothesized that the nucleotides that were linked to the expression patterns might (jointly) contribute to the regulation of expression in different tissues. A database or pool of expression-pattern-linked nucleotides was constructed by scanning the putative promoters, intron 1 sequences as well as exon 1 sequences of the nine functional UGT1A isoforms. The CLUSTAL Wth bp in the multiple alignment of 3447 bp of the putative promoters for the six UGT1A isoforms with hepatic expression and either a gap or a G for the three UGT1A isoforms not expressed in liver . The Match program identified a TFBS for AP-1 (activator protein 1) for three of the six hepatically-expressed UGT1A isoforms . A testable hypothesis then could be that the binding site for AP-1 close to the TSS may be contributing to the expression in liver for these three isoforms, but not the other three hepatically-expressed isoforms. Of course, this doe not necessarily mean that this particular transcription factor is the only determinant for hepatic expression. The example just showed that we could now use the database to prioritize our efforts and guide further studies based on these specific nucleotides. Besides promoter efficiency, other mechanisms such as alternative splicing may also contribute to the observed tissue-specific expression patterns. To generate such focused hypotheses, we could search the tissue-specific nucleotides in our database for potential exonic or intronic splicing enhancers To show an example, we used the Match http://home.uchicago.edu/∼wzhang1/PONE/UGT1A/and will be deposited into PharmGKB (http://www.PharmGKB.org).The supplemental materials can be accessed at Complementary DNA (cDNA) from a total of 23 human normal tissues was purchased from Clontech . These tissues include human brain, placenta, skeletal muscle, kidney, pancreas, spleen, thymus, prostate, testis, ovary, small intestine, uterus, mammary gland, thyroid, pituitary body, bone marrow, bladder, tonsil, lymph node, leukocyte, blood fractions, liver and lung.The expression of the UGT1A transcripts was measured in the 23 human normal tissues by PCR using TITANIUM Taq DNA Polymerase (Clontech). Briefly, PCR was set up in a 50 µl vol reaction with 3 µl of cDNA as template. Primers for each exon 1 were 5′-aacaaggagctcatggcctcc-3′ (UGT1A1), 5′-tgttgaacaatatgtctttggtcta-3′ (UGT1A3), 5′-gaaggaatttgatcgcgttac-3′ (UGT1A4), 5′-ggtggtggtcctcaccctg-3′ (UGT1A5), 5′-cagctgtcctcaagagagatgtgga-3′ (UGT1A6), 5′-gttgcgaactgactttgttttggag-3′ (UGT1A7), 5′-ggtcttcgccaggggaatagg-3′ (UGT1A8), 5′-ttctccaaacacctgttacggag-3′ (UGT1A9), 5′-cctctttcctatgtccccaatga-3′ (UGT1A10). The reverse primer (5′-ccaatgaagaccatgttgggc-3′) was shared by all UGT1A genes. PCR reactions were denatured initially at 95°C for 1 min, and then cycled 35 times at 95°C for 30s, annealing and extension at 65°C for 3 min. GAPDH gene was amplified with same conditions as above by using primers 5′-tgaaggtcggagtcaacggatttggt-3′ and 5′-catgtgggccatgaggtccaccac-3′ and served as an internal control for cDNA quantity and quality.The GenBank/NCBI reference sequence for human UDP-glucuronosyltransferase 1 family, polypeptide A cluster on chromosome 2 (NG_002601) was used to retrieve the following regions for the UGT1A isoforms.Promoter regions; The putative promoter regions of the nine functional UGT1A isoforms are defined as the sequences of 1-3000 bp upstream of the transcription start sites (TSS).Intron 1 sequences; Because the shortest intron 1 (UGT1A1's) is less than 6 kb and the 3′ ends of the intron 1 sequences are shared among the isoforms, we scanned the 5 kb segments immediately downstream of the first exons of the nine functional UGT1A isoforms.Exon 1 sequences: The exon 1 sequences of the nine functional UGT1A isoforms are distinct, so they were included in the analysis.http://www.ebi.ac.uk/clustalw/). The alignments and the original DNA sequences are provided in the supplemental materials. The multiple alignments were then scanned for those nucleotides that were linked to a particular expression pattern for each isoform and each tissue type. Therefore, the expression-pattern-linked nucleotides are in the form of an ordered vector tN = , where tN represents the vector of specific nucleotides for tissue t, n is a particular nucleotide, p is an integer for the position in an alignment. The elements of tN are [nj(pj):ψ(pj)], where ψ represents the set of conserved nucleotides of UGT1A isoforms with expression in a particular tissue at a particular position p. The identified nucleotides and their flanking sites (25 bp upstream/downstream) were then output as entries of the database.The nucleotide sequences of the UGT1A isoforms were aligned using CLUSTAL W http://www.gene-regulation.com/) to predict the transcription factor binding sites (TFBS) in the regions that contain one of the liver-specific nucleotides. The Match program searches the TRANSFAC database To show an example that the database comprised of expression-pattern-linked nucleotides could be used to generate focused hypotheses on tissue-specific expression, we used the Match


A systematic survey of gene expression in 115 human tissue samples using cDNA microarrays provides a dataset that can be used as a baseline for comparison with expression in diseased tissue. Numerous studies have used DNA microarrays to survey gene expression in cancer and other disease states. Comparatively little is known about the genes expressed across the gamut of normal human tissues. Systematic studies of global gene-expression patterns, by linking variation in the expression of specific genes to phenotypic variation in the cells or tissues in which they are expressed, provide clues to the molecular organization of diverse cells and to the potential roles of the genes.Here we describe a systematic survey of gene expression in 115 human tissue samples representing 35 different tissue types, using cDNA microarrays representing approximately 26,000 different human genes. Unsupervised hierarchical cluster analysis of the gene-expression patterns in these tissues identified clusters of genes with related biological functions and grouped the tissue specimens in a pattern that reflected their anatomic locations, cellular compositions or physiologic functions. In unsupervised and supervised analyses, tissue-specific patterns of gene expression were readily discernable. By comparative hybridization to normal genomic DNA, we were also able to estimate transcript abundances for expressed genes.Our dataset provides a baseline for comparison to diseased tissues, and will aid in the identification of tissue-specific functions. In addition, our analysis identifies potential molecular markers for detection of injury to specific organs and tissues, and provides a foundation for selection of potential targets for selective anticancer therapy. DNA microarrays ,2 have bAt present there is relatively little data on gene expression across the diversity of normal human tissues -20. HereTo survey gene expression across normal human tissues, we analyzed 115 normal tissue specimens representing 35 different human tissue types, using cDNA microarray representing 26,260 different genes . To explore the relationship among samples and underlying features of gene expression, we applied an unsupervised two-way  hierarchical clustering method using the 5,592 cDNAs  whose eThe two-way unsupervised analysis also identified clusters of coexpressed genes , complement components , lipid  and metal transport proteins , and proteins for detoxification , amino acid metabolism  and carbohydrate metabolism , other intriguing genes, for example, WRNIP1 (Werner helicase interacting protein 1), BIRC5 (survivin), ANGPTL3 (angiopoietin-like 3), and CNTNAP1 (contactin associated protein 1), were also identified as selectively expressed in liver. The new connections these results might make between our knowledge of the gene and its product on the one hand, and our knowledge of the physiological functions, cellular characteristics and pathologies of a specific organ, on the other, are a step towards better understanding of both the genes and the organs. Interestingly, we also identified a smaller number of genes displaying selectively decreased expression in some organs, for example, splicing factor SF3B1 in the liver  method , see Mat includedr Figure : we specRecent efforts by the Gene Ontology (GO) Consortium have resulted in the systematic annotation of genes, ascribing genes to specific biological processes, cellular components and molecular functions . This anDNA microarray experiments are often performed as comparative two-color hybridizations, permitting precise quantification of the ratio of each gene's expression between two samples. In the experiments reported here, each tissue sample was compared by hybridization to the same 'common reference' mRNA , a standard experimental design permitting the comparison of expression across all samples . TherefoTo estimate transcript levels for our dataset, we used microarray hybridization to compare the common reference mRNA against normal female genomic DNA. We reasoned that, for each gene on the microarray, the ratio of mRNA to genomic DNA should reflect the relative level of transcript in the common reference compared to normal genomic DNA (for which each gene is present in two copies per cell). For each tissue sample in our study, the ratio of expression for each gene in that sample versus common reference mRNA, multiplied by the ratio for that gene in common reference mRNA versus normal genomic DNA, would then approximate transcript abundance. To test our approach, we compared our estimates of transcript levels for a single prostate specimen, calculated either indirectly using the common reference mRNA versus genomic DNA ratios, or calculated through a direct hybridization comparison of prostate sample mRNA versus normal female genomic DNA. Our results show high concordance for the prostate sample Figure ; comparaRDH11 was highly expressed in prostate and was expressed at lower levels in other tissues, while STEAP2 was expressed at low levels in prostate and displayed very little or no expression in other tissues. For each of the tissue types, transcripts identified as both highly abundant and tissue specific are displayed in Additional data files 5 and 8 .The utility of this approach is illustrated for the cluster of prostate-specific genes . Of particular importance, the function of uncharacterized ESTs might be deduced by virtue of their inclusion in one of these clusters. Supervised analysis also identified genes selectively expressed in each of the tissues types studied, and the analysis of functionally annotated gene sets provided information on the tissue distribution of specific biological processes, cellular components and molecular functions.We have also reported here the application of mRNA versus genomic DNA hybridizations for estimating transcript abundances for expressed genes. Knowledge of transcript abundance should prove useful in prioritizing candidate genes for use as diagnostic markers or therapeutic targets, for which more highly expressed genes might be more tenable candidates. It is worth pointing out that our approach for estimating absolute transcript levels should be applicable to any cDNA microarray study incorporating a common reference mRNA.While many investigators have been using DNA microarrays to profile gene expression in cancer and other human diseases, scant data exist on profiles of gene expression across the diversity of normal human tissues. Our cDNA-microarray-based survey of gene expression in normal human tissues provides a publicly accessible dataset which can be used in future analyses aimed at better understanding the physiology of various normal tissues; developing a baseline for comparison to diseased tissues, including cancer; identifying tissue-specific diagnostic markers that signify tissue injury; discovering tissue-specific therapeutic targets ; and identifying tumor-specific diagnostic markers and therapeutic targets, for which minimal expression in the collection of normal adult human tissues is desirable.We have used cDNA microarrays to survey gene expression across a diverse set of normal human tissues. Using unsupervised and supervised analyses, we have identified tissue-specific patterns of gene expression. Furthermore, by comparative hybridization to normal genomic DNA, we were able to estimate transcript abundances and identify the subsets of abundantly expressed tissue-specific genes. Our dataset provides a baseline for comparison to diseased tissues, as well as a basis for identifying molecular markers of injury to specific organs and tissues, and for anticancer therapy.+ mRNA fraction was then isolated from total RNA using FastTrack2.0 kit (Invitrogen), and quantified by UV spectrophotometry.Normal human tissue specimens were obtained from surgery  or from autopsy, with institutional review board approval. Specimens were frozen on dry ice within 30 minutes of surgical removal or procurement and stored at -80°C. Histological evaluations were performed by H&E staining of frozen sections, and a pathologist (J.H. and/or M.vdR.) reviewed all slides to confirm the anatomical site of origin and histological normalcy . In total, we selected for study 115 tissue samples representing 35 different human tissues . Total RNA was isolated from tissues using TRIzol Reagent (Invitrogen) according to the manufacturer's instruction, and RNA quality was assessed by the integrity of rRNA bands following gel electrophoresis. The poly(A)Gene-expression profiling was performed essentially as reported previously , and dett-test statistic and sample-label permutations to evaluate statistical significance. The false-discovery rate (FDR), an estimate of the fraction of falsely called tissue-selective genes, varied by tissue, but in all cases was less than 5% . For tissue-selective genes, only tissue types with two or more samples were considered for analysis, and we only considered genes that were well-measured in more than 50% of the samples for the selected tissue type analyzed. GO annotations were assigned to arrayed genes using the AmiGO browser [Fluorescence ratios were normalized by mean-centering genes for each array , and then by mean centering each gene across all arrays. We included for analysis only well-measured genes whose expression varied, as determined by: signal intensity over background more than twofold in either test or reference channels in at least 75% of samples; and a fourfold or more ratio variation from the mean in at least two samples (unless otherwise indicated). Hierarchical clustering was performed and displayed using Cluster and TreeView software . Tissue- browser  to selec browser  to identThe following additional data are available with the online version of this paper. Additional data file A table listing the normal tissue specimens included in microarray analysisClick here for fileSheet 1: Dataset represented in Fig. Sheet 2: Variably expressed genes which are well-measured in ≥ 75% of samples, with ≥ 2-fold ratio variation from the mean in at least 2 samples; samples ordered by anatomic site. Sheet 3: Variably expressed genes which are well-measured in ≥ 25% of samples, with ≥ 4-fold ratio variation from the mean in at least 2 samples; samples ordered by anatomic site. Sheet 4: Same dataset as sheet 1, but here ratios represent relative transcript abundance . Sheet 5: Same dataset as sheet 2, but here ratios represent relative transcript abundance . Sheet 6: Same dataset as sheet 3, but here ratios represent relative transcript abundance A table listing the variably expressed genes. Click here for fileA table listing tissue-specific transcriptsClick here for fileA table listing functionally annotated gene setsClick here for fileA table listing highly abundant tissue-specific transcriptsClick here for filea, brain; b, salivary gland; c, esophagus; d, stomach; e, small bowel; f, colon; g, pancreas; h, liver; i, heart; j, skeletal muscle; k, lung; l, kidney; m, bladder;n, prostate; o, seminal vesicle; p, testis; q, ovary; r, fallopian tube; s, uterus; t, cervix, u, thyroid; v, parathyroid; w, adrenal; x, lymph node; y, tonsil; z, thymus; aa, spleen; bb, buffy coatA figure showing tissue-specific gene expression. Variably-expressed genes determined to be expressed in a tissue-selective fashion using the SAM method are depicted as described in the legend to manuscript Figure Click here for filea, tyrosine kinase (activity); b, kinase (activity); c, G-protein coupled receptor (activity); d, transcription factor activity; e, ion channel (activity); f, extracellular matrix (component), g, cell adhesion (process); h, programmed cell death (process)A figure showing expression of functionally annotated gene sets. Hierarchical cluster of 115 normal tissue specimens and annotated gene sets representing examples of specific molecular functions, cellular components, or biological processes. Click here for filea, brain; b, salivary gland; c, esophagus; d, stomach; e, small bowel; f, colon; g, pancreas; h, liver; i, heart; j, skeletal muscle; k, lung; l, kidney; m, bladder;n, prostate; o, seminal vesicle; p, testis; q, ovary; r, fallopian tube; s, uterus; t, cervix, u, thyroid; v, parathyroid; w, adrenal; x, lymph node; y, tonsil; z, thymus; aa, spleen; bb, buffy coatA figure showing highly abundant tissue-specific gene expression. Highly-abundant tissue specific transcripts were defined for each tissue type as the top (capped at 50 genes) tissue specific transcripts, identified using the SAM method, from the 1000 most abundantly expressed transcripts in the full dataset. Click here for file