Assessing conservation/divergence of gene expression across species is important for the understanding of gene regulation evolution. Although advances in microarray technology have provided massive high-dimensional gene expression data, the analysis of such data is still challenging. To date, assessing cross-species conservation of gene expression using microarray data has been mainly based on comparison of expression patterns across corresponding tissues, or comparison of co-expression of a gene with a reference set of genes. Because direct and reliable high-throughput experimental data on conservation of gene expression are often unavailable, the assessment of these two computational models is very challenging and has not been reported yet. In this study, we compared one corresponding tissue based method and three co-expression based methods for assessing conservation of gene expression, in terms of their pair-wise agreements, using a frequently used human-mouse tissue expression dataset. We find that 1) the co-expression based methods are only moderately correlated with the corresponding tissue based methods, 2) the reliability of co-expression based methods is affected by the size of the reference ortholog set, and 3) the corresponding tissue based methods may lose some information for assessing conservation of gene expression. We suggest that the use of either of these two computational models to study the evolution of a gene's expression may be subject to great uncertainty, and the investigation of changes in both gene expression patterns over corresponding tissues and co-expression of the gene with other genes is necessary. The biological functions of a gene, not only rely on its molecular composition and structure, but also on its spatiotemporal expression pattern. For example, duplicate genes, which are usually associated with highly consistent coding sequences but diverse biological functions, have only a weak correlation between rates of sequence and expression divergences Thanks to advances in microarray technology, the conservation/divergence of gene expression across species has been extensively and systematically assessed. However, results of such studies are often conflicting. Yanai et al. Some of these conflicting conclusions on gene expression evolution may be due, in part, to improper comparisons of gene expression across genomes, such as direct comparisons of expression levels across probes or platforms, as suggested by Liao and Zhang The aforementioned methods represent two computational models for assessing conservation/divergence of gene expression across species: 1) comparison of gene expression patterns across corresponding tissues, and 2) comparison of co-expression of a gene with a reference set of genes. Although the separate application of either model has yielded significant biological insights In this study, we assessed one corresponding tissue based method: Liao and Zhang's method http://symatlas.gnf.org/SymAtlas/ (GEO accession number: GSE1133) http://www.affymetrix.com). To assign the Ensembl ID for each gene, the annotation files  were downloaded from the Uniprot FTP site at ftp://us.expasy.org/databases/uniprot/current_release/knowledgebase/taxonomic_divisions. The orthologous gene pairs between humans and mice were downloaded from the Ensembl FTP site (ftp://ftp.ensembl.org). Only 1-1 orthologs were considered in this study. The number of available 1-1 orthologous gene pairs was 7182, out of which 3142 had multiple probe sets. For a gene with multiple probe sets, the selection of a probe set that best represents the gene's expression profile according to a general rule has not been resolved yet A public human and mouse expression dataset was downloaded from GNF SymAtlas V1.2.4. at n is the number of common tissues, H represents humans , M represents mice, and i in human and mouse tissue j, respectively. The expression conservation (EC) for human-mouse orthologous pair i is calculated as:The expression data of 26 common tissues from two species were extracted and normalized by their relative abundance (RA) values calculated as:A and B, in species A and B respectively, are restricted to genes for which 1-1 orthology relationships have been identified and ordered accordingly :i of 1-1 orthologs for species A and B, respectively, and k is the number of 1-1 orthologous gene pairs.Expression datasets with different dimensions under different conditions between any two species, A and B, can be compared. The expression matrices, A and B are then converted into two pair-wise correlation matrices (PCMs), r) between the expression profiles of each pair of genes over all conditions in each species separately:k, any row i as:i is computed as:r greater than a certain quantile x of the background correlation distribution, in both humans and mice) were selected as nodes of CCNs. Because the correlation cutoff value may affect the number of CNN nodes and in order to fully assess Essien et al.'s method, we varied the correlation coefficient threshold. Out of 4040 pairs of human-mouse 1-1 orthologs, 3390, 2424 and 1246 pairs were found as nodes of CCNs when the correlation threshold was set to 0.95, 0.975 and 0.99 quantile of the background distribution, respectively.To apply Essien et al.'s method, the nodes of CCNs between humans and mice should be identified first. In this study, the identification of the nodes in CCNs, was performed via determination of conserved pair-wise co-expression between species, i.e. the expression profiles of a pair of genes are significantly correlated in both species. Intra-species background distributions of correlations were first constructed based on 20,000 random gene pairs. All two gene combinations were assessed for potential conserved co-expression. Gene pairs whose expression profiles were significantly correlated  the similarity of gene expression profiles over only 26 common tissues may not reflect the expression conservation over all available tissues, and 2) common tissues are not the same tissues, i.e. tissues evolve between humans and mice.Because there are no means of applying Liao and Zhang's method to the whole human and mouse tissue data, to quantify the effects of using the microarray data over only common tissues, we adopted an indirect approach: comparing co-expression based methods using the whole microarray data with the expression data over only common tissues (the same data used by Liao and Zhang's method), with the hypothesis that if the results on expression conservation do not differ significantly between the two types of expression data, the use of the expression data over common tissues should not be a factor affecting the assessment of expression conservation, which should be also true to Liao and Zhang's method. However, we found that the properties of EC distributions generated by co-expression based methods differ greatly between these two types of expression data , and thar>0.9), suggesting that even if the same data are used, corresponding tissue based methods and co-expression based methods may still give different estimations of ECs.By applying co-expression-based methods to the expression data of 26 common tissues between humans and mice, i.e. the same data used by Liao and Zhang's method, a maximum agreement between corresponding tissue based methods and co-expression based methods can be estimated. Using this dataset, the ECs of all human-mouse 1-1 orthologs generated by different co-expression based methods were correlated with those generated by Liao and Zhang's method. Though these correlations were increased from (0.48–0.50) to (0.69–0.74), a maximum correlation of 0.74 is still far from a high agreement  is recommended. However, the two assessed computational models, which mainly capture the information on the global changes in gene expression patterns over orthologous tissues and in gene co-expression networks, reveal only part of the whole picture of gene expression evolution. Additionally, besides expression abundance as an indicator of gene expression behavior, expression breadth and specificity are also worth investigating


Tissue gene expression is generally regulated by multiple transcription factors (TFs). A major first step toward understanding how tissues achieve their specificity is to identify, at the genome scale, interacting TFs regulating gene expression in different tissues. Despite previous discoveries, the mechanisms that control tissue gene expression are not fully understood.We have integrated a function conservation approach, which is based on evolutionary conservation of biological function, and genes with highest expression level in human tissues to predict TF pairs controlling tissue gene expression. To this end, we have identified 2549 TF pairs associated with a certain tissue. To find interacting TFs controlling tissue gene expression in a broad spatial and temporal manner, we looked for TF pairs common to the same type of tissues and identified 379 such TF pairs, based on which TF-TF interaction networks were further built. We also found that tissue-specific TFs may play an important role in recruiting non-tissue-specific TFs to the TF-TF interaction network, offering the potential for coordinating and controlling tissue gene expression across a variety of conditions.The findings from this study indicate that tissue gene expression is regulated by large sets of interacting TFs either on the same promoter of a gene or through TF-TF interaction networks. Transcriptional regulation in eukaryotic organisms is a fundamental process to determine a gene's spatial and temporal expression. One of the main events involved in this process is the binding of TFs to short DNA motifs, called transcription factor binding sites (TFBSs), on the promoter regions of genes, activating or repressing the transcription machinery. In mammalian tissues most TFs do not act alone, but work through combinatorial regulation ,2, in whcis-regulatory modules in liver [cis-regulatory modules based on recognizable sequence features from either highly expressed genes [cis-regulatory modules by enrichment analysis for motifs discovered de novo in tissue-specific promoters relative to other promoters from the same species [Early attempts to identify interacting TFs controlling tissue gene expression came from the use of experimental approaches such as gel retardation assay , site-diin liver ,16 and min liver  tissues.ed genes  or genesed genes -21 derived genes  or have ed genes . Others In this study, rather than using sequence features of promoters from genes that are expressed only in a particular tissue -21, we ucis-regulatory modules involved in tissue gene regulation, we also performed analysis to identify interactions of 3 TFs.The application of the function conservation approach to the most highly expressed genes has led to the prediction of hundreds of interacting TFs from each of the 79 human tissues. Based on these predictions, TF pairs associated with a certain tissue were identified. The validity of these discovered TF pairs has been evaluated by both known interacting and liver-specific TFs. We further extended our study to find interacting TFs controlling gene expression in a broad spatial and temporal manner by looking for TF pairs common to the same type of tissues, from which TF-TF interaction networks were further built. As a first step to elucidating The overall analysis procedures are shown in Additional File We next filtered out the TF pairs in a particular tissue common to those from housekeeping genes Figure . The remUsing the function conservation approach  and tisshttp://www.ncbi.nlm.nih.gov/ to annotate human genes whose promoters contained the target TFBS pairs, as TFs control cellular biological processes via transcriptional regulation of groups of genes with similar functions. Significant  biological processes for tissue TF pairs were obtained by comparing the number of TF target genes involved in a particular biological process to the number of genes for the same biological process in the whole human genome . All tissue and tissue-unique TF pairs as well as their potential biological functions are listed in Additional File Overall, we identified 2549 tissue and 803 tissue-unique TF pairs for the 79 human tissues. These results indicate that tissue gene expression is regulated by large sets of interacting TFs. Furthermore, the relative small number of tissue-unique TF pairs out of all tissue TF pairs suggests that identical tissue TF pairs in different tissues may play different functional roles, which prompted us to investigate their biological function. For this purpose, we used Gene2go ® 10.4 [® database contains 180 experimentally proven composite elements of two or more binding sites which were previously identified by individual wet lab studies from others. Of the180 composite elements, 105 were mapped to the 23,005 (214*215/2) possible combinations of 2 TFs from the 214 non-redundant position weight matrices (PWMs). We first investigated the statistical significance for the occurrence of known interacting TFs in both predicted TF pairs (before filtering) and tissue TF pairs in each of the 79 tissues. Figure p = 3.2 × 10-2 to 6.8 × 10-6 vs. p = 4 × 10-2 to 3.4 × 10-4). We also computed the occurrence of known interacting TFs in all predicted TFs pairs (before filtering) and all tissue TF pairs from the 79 tissues. We found that 40 (38.1%) of the 105 known interacting TFs were in both predicted TF pairs  and tissue TF pairs .Although the function conservation approach has been proven to be a successful means for predicting interacting TFs , we soug® 10.4  to detercis-regulatory systems for both individual TF binding and synergistic actions have been thoroughly studied [p = 2.3 × 10-14) where both TFs were liver-specific. For the 27 liver-specific known interacting TFs, we found 8 (30%) in both the predicted TF pairs  and tissue TF pairs  from liver tissue. These include HNF4ALPHA:HNF4ALPHA, NF1:COUP_DR1, CEBPGAMMA:HNF4ALPHA, CEBPA:HNF3B, HNF3:HNF4ALPHA, HNF3:PPARA, CEPBA:GATA4, and HNF1:OCT1. All of them are key elements in liver specific transcriptional regulation. GO enrichment analyses indicated that genes whose promoters contained the predicted liver-specific TFBS pairs were mainly involved in liver specific functions [To further verify our prediction, we next compared the tissue TF pairs to known tissue-specific TFs from liver, for which the  studied ,4,25,26.unctions , includiIt is important to note that the 79 human tissues represent only part of the temporal and spatial conditions from which the 105 known interacting TFs were discovered, and therefore it is unlikely to have all known interacting TFs in our predicted list. Nevertheless, our results indicate that the use of function conservation approach and tissue-expressed genes was able to reliably identify to a great extent known interacting TFs, thus presenting very strong evidence for the validity of the identified tissue TF pairs. These results also indicate that filtering the TF pairs of housekeeping genes from those in each tissue is an important step to eliminate TFs playing a ubiquitous role, thereby the resulting TF pairs are more tissue-specific.a priori knowledge was available for the number of groups for each tissue type. The results are shown in Figure One of the goals of this study is to find interacting TFs controlling gene expression in a broad spatial and temporal manner such as interacting TFs common to the same type of tissues. This can be achieved by searching tissue TF pairs common across all tissues of the same type such as the 7 muscle tissues. However, the use of all tissues may reduce the power for tissue-type TF pair identification, since the contents of tissue TF pairs and even the function of a common tissue TF pair may be different between tissues of the same type. Therefore, we sought to first classify tissues into smaller but more closely related groups based on tissue TF pairs, from which representative tissues for the same tissue type could be obtained. Accordingly, we used hierarchical clustering to group tissues, as no We extended our analysis to investigate conservation for tissue TF pairs between tissues of the two muscle groups. We computed overlap for both tissue TF pairs and their biological functions between tissues using hypergeometric distribution. We found little or no overlap for both tissue TF pairs and their functions among tissues between these two groups, which was especially true for the function of tissue TF pairs (data not shown). On the other hand, both tissue TF pairs and their functions showed significant overlap between tissues within the same group Figure . These rBased on the clustering results, we selected 11 tissue-type groups, each having 2 to 16 tissues, for tissue-type TF pair discovery. A cutoff threshold of tissue TF pairs common in at least 50% tissues from the same group was set up for searching tissue-type TF pairs. In addition to the TF level, we also searched for tissue-type TF pairs based on their function using the same criteria of > 50% tissues in the same group. To this end, we were able to identify tissue-type pairs for all tissue groups as listed in Table In an effort to reveal TF relationships in controlling tissue gene expression, we performed analysis to reconstruct TF-TF interaction networks. Using tissue-type TF pairs, we first looked for those with one shared TF between each other in the same tissue, from which TF-TF interaction networks were built by joining 2 or more TF pairs Figure . TF-TF iUnlike the tissue TF pairs, we did not find any common tissue-type TF-TF interaction networks between different tissue types. In light of this, we performed a search to see if any single TFs played central roles in controlling tissue gene expression across different tissues, and looked for internal TFs in multiple tissue-type TF-TF interaction networks. To this end, we found that AP2, PPARA, PAX4, FAC1, ZIC3, and SPZ1 served as internal TFs in 8, 8, 8, 6, 5, and 4 tissue-type TF-TF interaction networks, respectively, suggesting their role as central hubs in tissue-type TF-TF interaction networks. Whereas FAC1 acts as the internal TF in 6 tissue-type TF-TF interaction networks from immune systems and cancer, SPZ1 mainly serves as the internal TF in tissue-type TF-TF interaction networks from testis, and the rest in 5 to 6 tissue-type TF-TF interaction networks from different tissue types. These results indicate that FAC1, when serving as the internal TF, is restricted to the two related tissue types, and that SPZ1, a bHLH-Zip protein, has an important role in testis ,30. The p < 10-20). By contrast, the total number of liver-specific TFs in these 7 tissue-type TF-TF interaction networks was not enriched . These results suggest that liver-specific TFs, other than initiating liver-specific transcriptional event, may play an important role in recruiting non-liver-specific TFs to the tissue-type TF-TF interaction network, thus offering the potential for coordinating and controlling gene expression across a variety of conditions.It is interesting to note that no single TFs serve as the central hub for tissue-type TF-TF interaction networks from liver tissue. However, we observed that 6 of 7 tissue-type TF-TF interaction networks had at least one known liver-specific TF serving as the internal TF as shown in Figure cis-regulatory modules involved in tissue gene regulation, we extended our analysis to the interactions of 3 TFs (named as multiple interacting TFs). Using tissue TF pairs from each of the 79 tissues, we performed a two-step analysis of TFBS conservation and enrichment of overlapping orthologous genes between human and mouse (see Methods). Although it is likely that multiple interacting TFs may be under estimation by the use of tissue TF pairs instead of all predicted TF pairs, the predicted multiple interacting TFs are tissue-specific. Therefore, these predictions most likely represent cis-regulatory modules involved in tissue gene regulation. To this end, we identified 1735 unique interactions of 3 TFs for the 79 human tissues, ranging from 9 multiple interacting TFs for testis interstitial to 72 multiple interacting TFs for caudate nucleus  whose 3 TFs were all liver-specific, 18  with at least 2 liver-specific TFs, and 28  with at least 1 liver-specific TF. These results provide evidence for the enrichment of liver-specific TFs in the predicted multiple interacting TFs, which in turn demonstrated the validity of the prediction.The validity of these predicted multiple interacting TFs was assessed by using liver-specific single TFs from TRANSCFAC11.4 , as few We next searched for all predicted multiple interacting TFs and their potential functions that are common between tissues. The results indicated that, although common multiple interacting TFs existed between most tissues, the highest overlap was within brain tissues and between brain and gland tissues. By contrast, there was little overlap for the functions of multiple interacting TFs, except within brain and cancer and between these 2 tissue types  for all 79 tissues. This reduction for TF pairs was, however, significantly larger when individual tissues were concerned (39% to 59%), indicating that a large number of overlapping TF pairs had ubiquitous roles among different tissues. The remaining interacting TFs in each tissue were more tissue-specific, which was best evidenced by the result that the predicted TF pairs from liver tissue contained the same number of known liver-specific interacting TFs before and after the filtering. The relative small number of tissue-unique TF pairs out of all tissue TF pairs and the findings from conservation analysis for the functions of tissue TF pairs between tissues of two muscle groups from this study also indicate that tissue TF pair with identical 2-TF combination might play different functional roles in different tissues.et al. [et al. [Our findings show that tissue gene expression is controlled by a variety of interacting TFs either on the promoter of a gene or through TF-TF interaction networks. These identified TF interactions may constitute a large part of interacting TFs in each tissue but is not a complete list. To fully understand the mechanisms controlling tissue gene expression requires additional study, which has been best evidenced from the comparison of interacting TFs in liver tissue between Yu et al.  and ours [et al.  who haveOne of the goals of this study was to find interacting TFs controlling tissue gene expression in a broad spatial and temporal manner. We performed analysis to identify tissue-type TF pairs for 11 selected tissue-type groups. While, as described above, each specific tissue may reflect only a small portion of all spatial and temporal conditions where tissue TF pairs play their regulation roles, tissue-type TF interactions provide a general view of their roles in multiple conditions. The analysis process has also led to other findings that the same type of tissues may have significant differences in both the contents of tissue TF pairs and the TF functional roles, which has been demonstrated by the conservation analysis of tissue TF pairs and their functions from muscle tissues. Tissue-type TF-TF interaction networks have provided not only lines of information on how tissue transcriptional programs are constructed but also new findings of potential roles for tissue-specific TFs in TF-TF interaction networks from liver tissue.In this study, we successfully employed our previously developed function conservation approach , to predet al [et al [The GNF Atlas2 gene expression database (gnfAtlas2) , which cet al  we selecl [et al . Redundal [et al . To redul [et al ,36. It i® program [The procedures for predicting TF pair are basically the same as previously described  Additio. Briefly program , for whi program . To dete program ,38. This program , but als program . Enrichmq-value < 0.05 was applied. TF pairs are those passing the cutoff and common between human and mouse.A two-step analysis procedure was employed to compute the enrichment of overlapping orthologous genes for a particular TFBS pair. First, a cutoff threshold of at least 10% overlapping orthologous genes between mouse and human was set up for selecting genes whose promoters contained the TFBS pair. The enrichment of overlapping orthologous genes was then estimated by computing the ratio of overlapping orthologous genes from real promoter sequences against those from shuffled sequences. This analysis was performed for each distance constraint. The integration of function conservation for each TF pair was achieved by estimating the correlation (Pearson correlation coefficients) between the 10 enriched TFBS pairs and 10 corresponding enriched overlapping orthologous genes from the same distance constraint. Permutation tests were employed to estimate the statistical significance of correlation by randomly matching the 10 TFBS pair ratios with the 10 overlapping orthologous gene ratios. For multiple test correction, a cutoff threshold of We next filtered TF pairs of housekeeping genes from those in each tissue Figure . This waR statistical package [To group tissues based on their tissue TF pairs, a 2549 (tissue TF pairs) × 79 (tissues) matrix with binary numbers was first built for all tissue TF pairs in the 79 human tissues. The presence of a tissue TF pair in the matrix was labeled with 1 and the absence was labeled with 0. A distance matrix was then built using the "binary" method, and hierarchical clustering was subsequently performed using the "complete" agglomeration method. All analysis was performed using the  package .p = 3 × 10-2 to < 10-36 and q < 0.05).A two-step analysis of TFBS conservation and enrichment of overlapping orthologous genes was performed to predict interactions of 3 TFs. For TFBS conservation, the identified tissue TFBS pairs were first used to construct all possible 3-TFBS combinations by searching paired tissue TFBS pairs with one shared TFBS between each other on exactly the same location of a gene's promoter Figure  in a parTwo main statistical methods were employed for estimating the significance of enrichment in this study. For validating predicted TF pairs and tissue TF pairs by known interacting TFs, the binomial distribution probability, as shown below, was used to determine if known interacting TFs were present more often in the predicted TF pairs or tissue TF pairs than in a randomly selected group from a given list of TFs.n is the number of known liver-specific interacting TFs in the predicted tissue TF pairs from this study; N the number of tissue TF pairs from liver tissue; and pf the background probability of liver-specific TF pairs in all possible combinations of 2 TFs from 214 PWMs.For example, in the case of estimating the statistical significance of known liver-specific interacting TFs in our predicted tissue TF pairs from liver tissue, the The statistical significance was computed using the hypergeometric distribution to estimate (1) the enrichment of overlapping tissue TF pairs and their overlapping functions between muscle tissues, and (2) overlapping orthologous genes in predicting multiple interacting TFs.c is the number of orthologous gene pairs containing conserved 3-TFBS combination between human and mouse; N the number of tissue-expressed genes for a particular tissue; S1 and S2 are the numbers of tissue-expressed genes with 3-TFBS combinations corresponding to those in c for human and mouse, respectively. The resulting p-value is the probability of observing c or more orthologous gene pairs containing conserved 3-TFBS combination from two sets of size S1 and S2 drawn from a set of N tissue-expressed genes.In the case of overlapping orthologous genes in predicting multiple interacting TFs, for example, TF: transcription factor; TFBS: transcription factor binding site; PWM: position weight matrices.ZH initiated and designed the study, conceived the analysis procedure, carried out data analysis, and wrote the manuscript. SG helped write Perl scripts to process data. Both authors read and approved the final manuscript.Flowchart of analysis procedure for TF pair prediction.Click here for fileLists tissue and tissue-unique TF pairs and their potential functions for the 79 human tissues.Click here for fileLists tissue-type TF pairs for the 11 selected tissue groups.Click here for fileShows the 84 tissue-type TF-TF interaction networks from the 11 tissue-type groups.Click here for fileLists multiple interacting TFs (3 TFs) and their potential functions for the 79 human tissues.Click here for fileOverlap matrix for multiple interacting TFs. The overlap of multiple interacting TFs between the 79 human tissues is depicted in the upper right panel and overlap of function for multiple interacting TFs in the lower left panel. The degree of overlap is indicated by color with red showing the greatest overlap and yellow showing less overlap.Click here for file


Gene duplication is a major driver of evolutionary innovation as it allows for an organism to elaborate its existing biological functions via specialization or diversification of initially redundant gene paralogs. Gene function can diversify in several ways. Transcription factor gene paralogs in particular, can diversify either by changes in their tissue-specific expression pattern or by changes in the DNA binding site motif recognized by their protein product, which in turn alters their gene targets. The relationship between these two modes of functional diversification of transcription factor paralogs has not been previously investigated, and is essential for understanding adaptive evolution of transcription factor gene families.Based on a large set of human paralogous transcription factor pairs, we show that when the DNA binding site motifs of transcription factor paralogs are similar, the expressions of the genes that encode the paralogs have diverged, so in general, at most one of the paralogs is highly expressed in a tissue. Moreover, paralogs with diverged DNA binding site motifs tend to be diverged in their function. Conversely, two paralogs that are highly expressed in a tissue tend to have dissimilar DNA binding site motifs. We have also found that in general, within a paralogous family, tissue-specific decrease in gene expression is more frequent than what is expected by chance.While previous investigations of paralogous gene diversification have only considered coding sequence divergence, by explicitly quantifying divergence in DNA binding site motif, our work presents a new paradigm for investigating functional diversification. Consistent with evolutionary expectation, our quantitative analysis suggests that paralogous transcription factors have survived extinction in part, either through diversification of their DNA binding site motifs or through alterations in their tissue-specific expression levels. Gene or even whole genome duplication provides the essential spare parts for evolutionary innovationvia several distinct pathways. Paralogous genes (genes within a species evolutionarily-related by gene duplication events) that survive extinction often diversify by either assuming an entirely novel function, or specializing in some aspects of their original function while losing other functionsAlthough mutations directly alter the genome, the resulting functional change is what drives evolutionary selection. A comparison of various diversification pathways in terms of their functional consequences is thus likely to reveal relationships among these pathways. For instance, mutations in the DNA binding domain of a TF gene are likely to alter the TF's DNA binding site motif. Availability of DNA binding site motifs of a vast number of human TFsBy analyzing a large collection of human paralogous TF pairs, we show for the first time that the paralogous pairs whose DNA binding site motifs are similar tend to have diverged expression patterns so that in any particular tissue at most one of the paralogs is expressed at a high level. Conversely, the paralogs that are highly expressed in a tissue tend to have dissimilar DNA binding site motifs. Our work represents a first attempt to quantify the biological expectation that paralogous TFs must diversify in one or more aspects of their function in order to survive extinction. We have extended our pair-wise analysis to TF families, demonstrating that in any given tissue there is a large separation in expression level between the family members with similar DNA binding site motifs. Furthermore, our finding is independent of the age of paralogs. TF paralogs with diverged binding site motifs tend to be diverged in their functions, as measured by GO terms. We also found that a decrease in tissue-specific gene expression is more frequent than what is expected by chance.Using a stringent sequence similarity criteria as in Consider two paralogous TF genes that are highly expressed in a particular tissue. If these two TFs recognize similar DNA binding site motifs then it is possible that these genes may interfere with each other's activity or they may potentially serve compensatory rolesX, Y) is commonly measured using the Pearson Correlation Coefficient (PCC) between XÊ and YÊ, where XÊ and YÊ represent the vectors of expressions for genes X and Y, respectively, in multiple tissues. Employing PCC as a measure of expression divergence yielded no significant correlation  between expression similarity and DNA binding site motif similarity , we computed ∂E as defined above. We additionally computed the DNA binding site motif similarity §B between the PWMs corresponding to the two TFs using a previously benchmarked motif similarity measure based on the Pearson Correlation between PWM columns and Smith-Waterman un-gapped alignment of the PWMs∂E and §B values for each paralogous TF pair, we computed the Kendall's tau rank correlation between ∂E and §B values and estimated its significance based on 1000 permutations of the expression data. Using other measures of correlation such as Pearson or Spearman does not change the results  of the tissues show significance, and 9 (39%) of the tissues show significance at a 0.001 p-value threshold. The mean and standard deviation of the correlations  in the significant cases were 0.21 and 0.05, respectively. As a control, we repeated the above experiment after randomizing the expression data and found a significant correlation only in 4.5% of the tissues. By random chance, we expect 5% of the tissues to show significance at p-value threshold of 0.05. We also repeated our analysis using paralogous TF-pairs obtained from the KOG data (www.ncbi.nlm.nih.gov/COG/), for which we obtained 242 paralogous pairs with 99 unique genes. This analysis yielded 67.1% tissues significant at the 0.05 p-value threshold and 54.4% tissues at a 0.01 threshold corresponding to a 13- to 54-fold enrichment. Thus, our overall conclusions are robust across different definitions of paralog genes. We have performed a number of additional analyses to ensure the robustness of our general conclusions. These include (1) investigating the effect of excluding paralogous pairs with low expressions, (2) using alternative measures of expression divergence, and (3) using several way of aggregating multiple probe data and multiple PWMs. All these analyses yielded consistent results and the details are provided in Given the expression data for a tissue, for each paralogous TF pair (ults see . We repe§B. Median Absolute Deviation (MAD) statistic. The median absolute deviation for a data sample MAD = median(|iY – median(iY)|), where |Yi| is the absolute value of Yi. The MAD statistic is preferred over the traditional z-score normalization (mean of 0 and standard deviation of 1) if the data is not normally distributed, because in the latter case the mean and standard deviation are severely affected by outliers§B towards the top of the list), there are very few cases where both paralogs are expressed at high levels, while this pattern is a relatively common occurrence among paralogs with low DNA binding similarity. At a glance, there is more homogeneity of high expression as the binding similarity of the TF pair decreases, reinforcing that the amount of expression divergence increases as the binding similarity increases. We next sorted the 95 pairs of paralogous TF genes in decreasing order of their DNA motif similarity §B) are likely to be a result of recent duplication events and indeed if this were the case, the conservation in DNA binding site motif (and presumably in DNA binding domain of the gene) would merely be a reflection of divergence time. To test this hypothesis, for the entire set of TF paralogs we compared their BLAST-based percent identity score over the entire coding region (a high score is likely to correspond to recent duplication) with their §B values. The Kendal tau correlation was 0.063 with p-value = 0.36. We have repeated this analysis by substituting the BLAST-based percent identity with the synonymous substitution rate, Ks, using PAML ∂E and §B are independent of the age of the paralogs. Because of the stringency of our criteria for paralogy, the TF pairs are likely to be recent paralogs. Therefore, we next investigated whether or not a greater DNA binding similarity is a simple reflection of shorter divergence time since duplication. In other words, it is expected that paralogs with similar DNA binding site motifs  represent the set of GO terms associated with X. We only consider terms at the functional hierarchy level of 3 or higher in order to exclude ubiquitous terms. For paralogs X and Y, we define functional coherence between the paralogs using the Jaccard's coefficient as FC = |(F(X) ∩ F(Y)| / |(F(X) ∪ F(Y)|, which represents the number of shared GO terms normalized by the total number GO terms annotated for the two genes X and Y. For the list of paralogs, sorted by decreasing order of §B, FC. We found that FC values were higher for TF pairs with higher binding similarity and gradually decreased as the binding similarity decreased. The Kendall tau correlation coefficient is 0.165 with p-value = 0.02. This indicates that paralogs with diverged DNA binding site motifs, and presumably different target genes, are more likely to serve distinct functional roles. Our results were consistent when we substituted the Jaccard's coefficient with a hypergeometric distribution based p-value to capture the overlap . This result further reinforces that paralogs with diverged DNA binding site motifs are more likely to serve distinct functional roles.Next, we quantified the shared functions between paralogous TF pairs in order to determine if there is a correlation between DNA binding site motif similarity and biological function for a TF pair. For gene tissue coherence between the paralogs as TC = |(T(X) ∩ T(Y)|/(T(X) ∪ T(Y)|. T(X) represents the set of tissues in which X is expressed above median expression value for that tissue. In the list of paralogs sorted by decreasing order of §B  and each tissue T, we computed the ratio R = (maxE-simE)/(maxE-minE), where maxE and minE represent the maximum and the minimum expression levels, respectively, among the family members in tissue T. Thus, the R value is normalized for family-specific and tissue-specific gene expression. If iTF has the maximum expression maxE, then simE is the expression level of the family member whose DNA binding site motif is most similar to that of iTF. This ratio effectively indicates how much the highest expressed gene in a family has diverged in expression relative to the gene with the most similar DNA binding site motif. Therefore, a ratio of R = 1 would indicate that the highest expressed gene in a family for a given tissue has most diverged in expression from the gene with which it shares the most binding similarity. In order to reduce the chances of considering irrelevant tissues , we only considered the tissues in which the maximum expression among the family members was greater than the median expression of all TF genes in that tissue. We then compared the R values computed from our dataset with that for 10000 random groupings of the TF genes in families, matched for family size of the actual dataset. Using the Mann-Whitney one-sided test we compared the R values of the foreground set with the random set given the null hypothesis that the R values in the foreground set are less than or equal to the R values in the random set. For the families constructed using clustering methods, the null hypothesis was rejected with a p-value = 6.84e-10. In the case of the families constructed using KOG data, the null hypothesis was not rejected when all families were used. However some of the families are very large (up to 16 members) and exclusion of large families yields significant results. For instance, when we only include families of size 3 and 4, the null hypothesis is rejected with a p-value = 1.3e-9. This result suggests that for all families with greater than two members and for all 23 non-redundant tissues, in general, exactly one family member is expressed highly, among the members with similar DNA binding site motif.The above results suggest that among the paralogous TF pairs with similar DNA binding site motifs, at most one TF is expressed at a high level in any given tissue. Hence, we next extend our analysis to TF families. We computed families using two methods, the first is a complete-linkage agglomerative clustering of the 98 pair-wise paralogy relationships, which resulted in 39 paralog families with 28 families of size 2, 7 of size 3 and 4 of size 4; the rest are singletons which could not be placed in a family. We have repeated the family analysis for BLAST alignable coverage of 50%, 60%, 70% and 80%. The results are consistent for all four choices of alignable coverage, including criteria identical to that of Makova and Li, but we only report the statistics for the 70% coverage threshold. The second method is based on KOG data and in terms of families, we obtained 24 families, 9 of size 2, 6 of size 3, and 9 of size greater than or equal to 4. The family-wise analysis was done for (i) the 11 families consisting of greater than two TFs constructed using clustering methods and (ii) the 15 families computed using KOG data. For a TF family we expect at most one member of the family among the members with similar DNA binding site motif to have high expression in any tissue. In order to test this hypothesis, for each family with Median Absolute Deviation or more than the median expression for that particular tissue. Low expression is the complement of high expression. We say that a family has a selective decrease in expression if all but one of the family members has high expression. Conversely, a family is deemed to have a selective increase in expression if all but one of the family members has low expression. We found that 38% of all cases were of expression increase and 20% cases of expression decrease. As a control, if we randomize the expression data, we found 48% of the cases were of expression increase and 16% were of expression decrease. The one-tailed Fisher exact test comparing decrease versus increase of expression for the true and random sets yields a p-value of 0.02, indicating that there is an excess of expression decrease in the actual data. If we repeat the analysis using families derived from KOG data, we find that 35% of the cases were of expression increase and 40% were of expression decrease. By comparison in the cases generated by randomizing the expression data, we found that 40% of the cases were of expression increase and 24% of the expression cases were of expression decrease. The one-tailed Fisher exact test comparing decrease versus increase of expression for the actual and random sets yields a p-value of 8.5e-7. This result further reinforces our findings that there is a significant tendency towards expression decrease in the families of TFs.We next investigated the relative prevalence of expression increase and expression decrease in a tissue among members of a paralogous family, irrespective of their DNA binding site motif similarities. Using the 11 families with greater than two members identified above, for each TF family and each tissue we enumerated the cases for which all but one family member have high expression and one member has low expression. High expression is defined as an expression value that is one 1F, 2F, ‥, k)F and each tissue T, we computed the ratio RI = (maxE-max2E)/(maxE-minE), where maxE and minE represent the maximum and the minimum expression levels and max2E represents the second highest expression among the family members in tissue T. The ratio RI captures the increase in expression, i.e., high value of RG represents an expression increase. Similarly, define RD = (min2E-minE)/(maxE-minE), where min2E represents the second lowest expression among the family members in tissue T. The ratio RD captures decrease in expression, i.e., high value of RL represents an expression decrease. We computed RI and RD values for all TF family-tissue pairs and did the same for 10000 randomly generated families of TFs of the same size as the true set of families for families defined using the BLAST criterion and families defined using KOG data. We found that the RI values (increase) in the actual data were significantly smaller than those for the randomized families . In contrast, the RD values (decrease) in the actual data were greater than those for the randomized families. However, the Mann-Whitney one-sided test p-value was at the cusp of significance  for families defined using BLAST criterion. For families defined using KOG data, we found that RD values (decrease) were significantly less than those for randomized families  and DNA binding site motif similarity (§B). We preferred Kendall's tau to another non-parametric correlation measure – Spearman's rho, because Kendall's tau has better statistical properties and is more directly interpretable ∂E and §B values cannot be assumed to be independent. To avoid biases caused by within-sample dependence, we estimated the significance of Kendall's tau by randomly permuting the expression values among the TFs and our results remain statistically significant. Thus, our overall results remain significant across a comprehensive set of combinations of experimental design parameters - (i) multiple ways of probe aggregation for a gene or a transcript (ii) multiple PWM aggregation for a gene or a transcript, (iii) multiple measures of correlation, and (iv) multiple ways of estimating significance.We have taken a number of precautions to ensure the robustness of our conclusions. For instance, Pearson's correlation coefficient is a standard measure for quantifying correlations between two data samples. However, PCC is applicable only when the data are normally-distributed. Consequently, we have chosen the more appropriate but conservative Kendall's tau to measure correlation between the tissue-specific expression divergence (∂E) in a context-specific manner. In fact when we replace tissue-specific ∂E value with an overall measure of expression divergence using Pearson's correlation coefficient between the expression profiles across the 79 (or 23) tissues, we do not observe a significant correlation with DNA binding site motif similarity as shown in Prior investigations of functional diversification among paralogs have only considered sequence divergence in the entire coding portion of the genebecause there are potentially many rate determinants …… it is not unexpected that the observed correlation coefficients are not very high”Given the multitude and complexity of attributes affecting evolution, most of which are not completely understood, we expect that most individual correlations will be weak. Strong correlations are indeed scarce in biological literature. Liao et al. studied the correlations between evolutionary rates of mouse paralogs and several genic parameters and remarked that “Expression pattern and the DNA binding site motif represent two distinct aspects of TF function. There are others, such as interaction partners, and these distinct functional aspects are often encoded in distinct sequence domains. For instance, the DNA binding site motif is encoded largely within the DNA binding domain of the gene. In the human Forkhead (FKH) domain containing family of TFs, the sequence similarity in DNA binding FKH domain is significantly correlated with the DNA binding site motif similarity . Because selection pressure operates at the level of function, which is encoded in distinct domains, it is reasonable to study the relationship between diversification in distinct domains. Again, in the human FKH family, where the DNA binding domain is highly conserved across family members, the rest of the protein domains exhibit an accelerated divergence relative to the DNA binding domain immediately after duplication (data not shown). A specific comparison of divergence in the TF's interaction domain with either the divergence in DNA binding similarity or the expression divergence or both, would be a natural extension of the current work. However, the interaction domains of TF proteins are currently not as well characterized as the DNA binding domains. As a proxy for divergence in interaction domain, one can consider the overlap in known interaction partners. However, such an investigation is currently limited by the availability of complete interaction data. Our work generalizes the previous studies of diversification of paralogs that have focused on divergence in protein coding sequence in its entirety without distinguishing among functional protein domains and without quantifying the functional consequence of mutations in the coding sequence.As a pragmatic concern, current genomic approaches to analyzing transcriptional regulation are confounded by the fact that multiple TFs, typically closely related paralogs, bind to similar binding sites. For instance, previous analysis of motifs enriched in promoters of genes that are differentially expressed in adult failing hearts identified the FKH family. However, to implicate specific members of the Forkhead family of TFs, directed experiments such as PCR and immunohistochemistry in specific cell types are neededwww.ensembl.org). For each of the TFs, the corresponding positional weight matrix (PWM) representing their DNA binding site motif was obtained from TRANSFAC.A total of 390 distinct human TFs were obtained from TRANSFAC 10.2I≥0.06n+4.8LL/1000)]−0.32[1+exp. Because multiple probes are mapped to a single gene or transcript, the probe-level data needs to be aggregated in order to obtain a gene-level or transcript-level expression value, which invariably raises issues of accuracy. Probe-level expression analysis bypasses these issues and yield reliable resultsHuman gene expression data was obtained from the Novartis human tissue survey On the Affymetrix chip underlying the genome-wide expression studies, genes are often represented by multiple probe sets and thus, have multiple expression values. Several solutions have been posed to contend with this issue: (i) only choosing probe sets with a one-to-one correspondence with genesPearson correlation coefficient (PCC) is a standard measure of interdependence between two random variables. PCC is applicable only when we wish to measure a linear relationship between two variables and the variables are from bivariate normal distributions. Because we cannot assume normally-distributed data in our case, we require a nonparametric measure of correlation. The two most common choices are Spearman's rank order correlation (Spearman rho) and Kendall tau correlation. Both measures offer similar sensitivity in detecting associations and almost always lead to the same conclusions. However, we use Kendall's tau because it has better statistical properties and there is a direct interpretation of Kendall's tau in terms of probabilities of observing concordant and discordant pairs∂E values for the paralogous TF pairs. We compute the Kendall's tau for 1000 such permutations and use the fraction of permutations in which the tau value exceeds the observed tau value (for the un-shuffled data) as an estimate of the p-value.Our choice of correlation measure inherently assumes that the input data consists of independent samples. In our application, this assumption is not necessarily true and consequently employing the theoretical p-value estimation of Kendall' tau is potentially erroneous. Therefore, we chose to use a permutation-based method for computing the p-value for Kendall's tau. In a given tissue sample, we pool all expression values for all TFs in our dataset, and then randomly assign expression values to each TF. This procedure effectively shuffles the TF expression values and hence shuffles the corresponding ftp://ftp.ncbi.nih.gov/pub/COG/KOG/. The proteins were mapped to Ensembl transcript and gene ids using data from www.ensembl.org. The KOG data resulted in 242 paralogous pairs with 99 unique genes. In terms of families, we obtained 24 families, 9 of size 2, 6 of size 3, and 9 of size greater than or equal to 4.KOG data and the associated gi identifiers were obtained from Nucleotide coding sequences were obtained from Ensembl for all TFs, and the pairwise Ks rates computed using the bp_pairwise_kaks.pl script from BioPerlData File S1This file contains supplementary results(0.22 MB DOC)Click here for additional data file.Data File S2This contains supplementary data in two worksheets(0.04 MB XLS)Click here for additional data file.


Understanding how genes are expressed and regulated in different tissues is a fundamental and challenging question. However, most of currently available biological databases do not focus on tissue-specific gene regulation.The recent development of computational methods for tissue-specific combinational gene regulation, based on transcription factor binding sites, enables us to perform a large-scale analysis of tissue-specific gene regulation in human tissues. The results are stored in a web database called TiGER (Tissue-specific Gene Expression and Regulation). The database contains three types of data including tissue-specific gene expression profiles, combinatorial gene regulations, and cis-regulatory module (CRM) detections. At present the database contains expression profiles for 19,526 UniGene genes, combinatorial regulations for 7,341 transcription factor pairs and 6,232 putative CRMs for 2,130 RefSeq genes.We have developed and made publicly available a database, TiGER, which summarizes and provides large scale data sets for tissue-specific gene expression and regulation in a variety of human tissues. This resource is available at . A detailed understanding of how genes are expressed and regulated in different tissues can help elucidate the molecular mechanisms of tissue development and function. The approximately 25,000 genes in the human genome demonstrate dramatic diversity in terms of expression levels, both temporally and spatially. Despite this diversity, the expression of all genes is controlled by a relatively small number  of transcription factors (TFs). These TFs usually work in specific combination to regulate individual genes . A numbeTo address this need, we have developed a new database called TiGER (Tissue-specific Gene Expression and Regulation) based on our previous analyses of tissue-specific genes, TFs and cis-regulatory modules (CRMs) for 30 human tissues . TiGER s• A large set of data on both tissue-specific genes and tissue-specific transcriptional regulatory elements: The database contains tissue-specific expression profiles for ~20,000 UniGene genes, combinatorial regulation for 7,341 interacting TF pairs, and 6,232 cis-regulatory modules for tissue-specific genes.• Flexible search capability: The database provides three views  to allow users to conveniently retrieve information about genes, TFs or tissues of interest. For example, users can simply type a gene ID  to retrieve the EST profile and CRM detections. Users can also select a tissue name to retrieve a list of genes preferentially expressed in the tissue.• Convenient accessibility: The database provides visualizations of the gene expression profiles, TF interactions and CRM detections. Sortable summary tables, links to raw data and links to external databases are also provided for user reference.The rest of the paper will describe the database content and illustrate the utility of the database in tissue-specific gene regulation.TiGER contains three types of data including tissue-specific gene expression profiles, TF interactions and CRMs. The data are organized as a relational database with a user-friendly interface. The following is a detailed description of the database content.The ~5.3 millions human EST sequences map to ~54,000 UniGene clusters . Previou10(p) values for the 307 eye-specific TF interactions. The most significant is the interaction between FOXJ2 and POU3F2, with a p-value less than 10-39. These two TFs together regulate many eye-specific genes, including RPE65.We have developed a method to identify interacting TFs based on patterns of co-occurrence of pairs of DNA binding sites . This meCRMs are the central cis-elements that control gene expression . PreviouFigure TiGER is constructed for free access and use. The downloadable data formats include standard .txt text files and .png images. The data contents are configured into three views (saved queries): gene view, TF view, and tissue view, to allow users to conveniently retrieve information relevant to genes, TFs or tissues of interest.There are three major database entities in the gene view: (1) "EST" entity that stores enrichment values in 30 tissues for each gene; (2) "CRM" entity that stores the conservation profile, the density profile, and the energy profile used for CRM detections in the promoter region of each gene; and (3) "GeneCode" entity that stores the mapping between UniGene, RefSeq and gene symbol.The gene view allows users to retrieve information through a simple search engine by entering a UniGene gene, a RefSeq gene or a gene symbol. The query results include a gene description, a plot of the EST profile, a list of tissues in which the gene is preferentially expressed, a plot of the three profiles used in CRM detection, and download links to the EST and CRM profiles. Links to external databases such as NCBI, UCSC Genome Browser, and GeneCard, are also included for user references.10(p)) of the interaction.There is one major database entity called "TF-Partner" in the TF view. This entity stores all factors that interact with a given TF, the tissue in which the interaction occurs and the significance  "TSS-Genes" entity that stores genes preferentially expressed in each of the 30 tissues; (2) "TSS-TFs" entity that stores interactions between TFs in each of the 30 tissues; and (3) "TSS-CRMs" entity that stores CRM modules in the promoter regions of tissue-specific genes. More specifically, the "TSS-Genes" entity contains four attributes including RefSeq gene ID, gene symbol, enrichment values, and descriptions. The "TSS-TFs" entity contains three attributions including the two participating TFs and the significance of interaction. The "TSS-CRMs" entity contains eight attributes including the chromosome ID, RefSeq gene ID, CRM start and end positions, transcription start position and orientation, minimum energy, and a list of TFs that regulate the gene.To retrieve information for a specific tissue, users can simply select a tissue from a drop-down menu provided in the tissue view. The query results include a summary table of genes specific to the tissue, a summary table of TF interactions and a summary table of CRM modules. These tables are instances of "TSS-Genes", "TSS-TFs", and "TSS-CRMs," respectively. Links to the gene view and the TF view are embedded in the summary tables to provide an integrated environment of query and visualization  are implemented as Java servlets which dynamically query the underlying database entities. TiGER operates under an Apache web server and an Apache Tomcat engine on a SuSe Linux system. The plots of gene expression profiles, TF interactions and CRM detections are pre-generated in Matlab.We performed a large-scale analysis of gene expression, TF interaction and CRM detection in 30 human tissues. The results are stored in a web-enabled database called TiGER and configured so as to permit users to visualize or download the results through a standard web browser.There are fundamental issues relating to the computational prediction of human gene regulation. Future research will include both prediction models on gene regulation and analysis tools for interpreting prediction results. As more experimental data accumulates related to the nature of TF-DNA interactions, we plan to further develop our predictions on tissue-specific TF interactions. We also plan to extend our work on CRM detection by relating regulatory elements with temporal  and spatial  attributes. As new predictions on tissue-specific gene regulation accumulate, the TiGER database will need to be further expanded and modified. We will update the content of the database on a regular basis. We also plan to develop tools relating TiGER data to other available gene expression and regulation data for integrative analysis.Project name: TiGERProject home page: Operating system(s): Platform independent.Programming language: none.License: no restriction.Any restrictions to use by non-academics: no restriction.XL, XY and JQ conceived the construction of the database. XL developed the database interface. XY generated the data. JQ supervised the development and implementation. DJZ and HZ helped to interpret the results. XL and JQ drafted the paper, and all authors read and approved the final manuscript.


Artificial selection has resulted in animal breeds with extreme phenotypes. As an organism is made up of many different tissues and organs, each with its own genetic programme, it is pertinent to ask: How relevant is tissue in terms of total transcriptome variability? Which are the genes most distinctly expressed between tissues? Does breed or sex equally affect the transcriptome across tissues?In order to gain insight on these issues, we conducted microarray expression profiling of 16 different tissues from four animals of two extreme pig breeds, Large White and Iberian, two males and two females. Mixed model analysis and neighbor – joining trees showed that tissues with similar developmental origin clustered closer than those with different embryonic origins. Often a sound biological interpretation was possible for overrepresented gene ontology categories within differentially expressed genes between groups of tissues. For instance, an excess of nervous system or muscle development genes were found among tissues of ectoderm or mesoderm origins, respectively. Tissue accounted for ~11 times more variability than sex or breed. Nevertheless, we were able to confidently identify genes with differential expression across tissues between breeds (33 genes) and between sexes (19 genes). The genes primarily affected by sex were overall different than those affected by breed or tissue. Interaction with tissue can be important for differentially expressed genes between breeds but not so much for genes whose expression differ between sexes.Embryonic development leaves an enduring footprint on the transcriptome. The interaction in gene × tissue for differentially expressed genes between breeds suggests that animal breeding has targeted differentially each tissue's transcriptome. Drosophila [Fundulus fish [It is now feasible to carry out large scale characterization of transcription activity using microarrays. This technology has opened new avenues to characterize and to dissect the transcriptome's genetic basis. It is a complementary approach to the classical ascertainment of the genetic architecture of complex traits, such as quantitative trait loci studies. There is now overwhelming evidence that the levels of mRNA are affected by a number of environmental, physiological and genetic factors, much the same as for any other quantitative, complex trait ,2. The eosophila , whereaslus fish . The stulus fish ,5.vs. healthy, by analyzing a single tissue in several individuals. Some studies have also compared different breeds or strains, again focussing on one or very few tissues in order to gain insight as to how much phenotypic variability correlates with differences at the transcriptome level; see for instance Reiner-Benaim et al. [Pubmed[In many microarray studies so far, the goal has been to compare two physiological statuses, e.g., disease m et al. . Neverthm et al. -10 and om et al. , whereas. [Pubmed as of Novs. sex) has not been quantified. The variability between tissues is not as well studied as that within tissues and some tissues remain poorly characterized, all the more in livestock species. The transcriptome of some tissues like liver and muscle have been analyzed extensively, well over a thousand citations in Pubmed[In order to contribute to answering these questions, here we study the variability in a large number of tissues pertaining to a reduced number of individuals. By allocating more experimental resources to a much larger number of tissues than usual, we aim at better characterizing the transcriptome variability across the whole organism. Although some studies have compared the transcriptome across different tissues -16, the in Pubmed as of NoBiceps femoris muscle, back fat tissue, abdominal fat tissue, stomach, liver, ileum and whole blood.The aim of this work was to study the global transcripome across tissues in two highly divergent pig breeds. We report a mixed model analysis of 16 tissues pertaining to four pigs, two Large White (LW) and two Iberian (IB), one male and one female per breed. These two breeds are phenotypically extreme for most traits of economic relevance, e.g., growth, fatness, reproductive performance. The tissues studied were olfactory bulb, hypothalamus, pineal gland, adenohypophysis, neurohypophysis, adrenal cortex, adrenal medulla, thyroid gland, diaphragm, 2) between the samples, after normalizing the raw data with the RMA procedure [biceps femori) but less clearly within each muscle. As for fat, the similarity was larger between tissues than between breeds, and samples of both back and abdominal fat origins were clustered together. The same was observed between cortex and medulla from adrenal gland. In this case, contamination between both tissues cannot be ruled out because of the irregular limits of the medulla that make not easy to separate that region neatly from the cortex collecting rapidly enough amount of tissue for analysis. Other authors have described previously contamination of medulla in the cortex sample when mechanical separation is performed[Clustering is a useful starting exploratory tool to visualize highly dimensional data, and has been widely used to microarray data since the seminal paper of Eisen and cols. . Here werocedure , as detaperformed. Thus, tAs expected, both UPGMA and NJ methods provided identical clustering at the first coalescence level. However, there were some interesting differences at higher levels. NJ identified four groups of tissues. The first group comprised brain tissues, including hypophysis; the second group, thyroid and adrenal endocrine glands; the third, muscle and fat tissues; whereas stomach, ileum, blood and liver were in the last group. In contrast, UPGMA clustered blood, liver and muscles in separate groups, while brain tissues were grouped together with fat, thyroid and adrenal glands; blood and liver were in distinct groups. It is well known that NJ have better properties than UPGMA when reconstructing evolutionary trees because it does not assume a constant evolutionary rate . But is Probe × Tissue solutions obtained from model (2), as these solutions are corrected for 'noisy' factors such as sex, breed and global tissue effects. Thus we constructed a NJ-tree of tissue transcriptomes using the 1-extreme probe as a probe for which all four mRNA levels of the tissue were either smaller or larger than the remaining mRNA levels . In contrast, the two adrenal tissues and both fats were the closest pair of tissues. The brain is not a uniform organ, and this well known fact [The trees depicted in Figure om model , as thesown fact -23 was cInterestingly, there was a relation between embryonic origin and clustering. Most tissues with the same embryonic origin clustered together in the NJ-tree Figure . All braWe sought to investigate more in detail the genetic basis of the tissue arrangement by embryonic layers, and to pick up genes that can be differentially expressed in concerted action within each embryonic layer. To that end, we obtained the extreme gene probes differentiating the ectoderm tissues , mesoderm (muscle and fat), and endoderm . An extreme probe for each embryo layer was defined as for individual tissues, i.e., a probe whose all expression levels for that group of tissues were either smaller or larger than the levels for the rest of tissues . We excluded thyroid and adrenal glands for being mixed tissues and blood, for outlier. The complete list in additional file Pubmed, 'the function of this family member is unknown, mouse studies suggest that it represents a class of tissue-specific factors interacting with chromatin to regulate neuronal cell proliferation'. Our results strongly suggest that NAP1L2 is involved in neuronal system and that the function is maintained across species.Neverheless, some interesting results appeared. For instance, genes with ontologies like nervous development and ion transport – clearly related to central nervous system – were more frequent than expected within ectoderm extreme genes, as were genes involved in development Figure . In partIn parallel to results for ecctoderm tissues, the most significant enriched ontology was muscle development in the mesoderm Figure . Here, sbiceps femori (glycolytic muscle) extreme genes were often related to the production of ATP by glycolysis and sugar pathway. The liver extreme genes were in the urea cycle pathway and bile acid and amino acid metabolisms; blood genes pertained to hematopoietic processes and signaling pathway systems.We also obtained the extreme probes for each of the individual tissues. The main over represented biological process per tissue is in Table global variance estimates, when all probes were considered jointly. It does not follow that sex or breed were not associated to changes in gene expression. The relative importance of sex or breed did increase when we restricted the analysis to a data subset. For instance, the percentage of total variance explained by breed or sex increased to 25% or 48% when we used the 100 most differentially expressed genes between breeds or between sexes, respectively  and between zb scores and the standard deviation of the probe × tissue solutions (bottom figure). If there were a relation, we would see the dots around a diagonal rather than close to the axes. Thus, each of the factors studied here, sex and breed, influence the transcriptome through different genetic programmes.At a false discovery rate FDR < 0.05, we identified 19 and 33 genes differentially expressed between sexes and breeds, respectively. The complete lists together with tentative annotations  are in TWe performed a clustering of probes and samples using only the differentially expressed genes from Tables The list of differentially expressed genes between sexes comprised eight X or Y linked genes, based on the localization of orthologous human genes Table . Note thet al., unpublished results). Thirteen genes (mesenteric lymph node) and ten genes (lung) were also in Table We found a good concordance with the list of differentially expressed genes obtained with the same microarray in lung and mesenteric lymph node by our group in a separate experiment , annotated as CLOCK gene , by realThe list of probe sets corresponding to differentially expressed genes at FDR < 0.05 between LW and IB breeds is in Table across all tissues. This follows from the specification of models (1–4), where all probes were analyzed simultaneously and where the tissue effect was included in the models as just an additional effect. Thus, it is important to bear in mind that other genes could show larger differential expressions in a specific tissue than those listed in Tables It should be noted that the differentially expressed genes reported here Tables  and 4 wef models –4, wherezb and zs scores for each of the tissue groups. Table z-scores. The pattern shown was highly illuminating. For sex, there seemed to be little interaction, as zs-scores were highly correlated across groups of tissues. Correlation coefficients were always > 0.9. In contrast, correlations between zb scores were much more variable and, importantly, much lower overall. Thus, whereas the correlation between brain and metabolic tissues was 0.91 for sex z-scores, it was only 0.34 for breed z-scores. This means that most differentially expressed genes between sexes were shared across tissues whereas this was much more unlikely when comparing two distant breeds like Iberian and Large White. It is interesting to note that the clusters in Figure i.e., more uniform – within sexes than within breeds. This result may have important implications. It suggests that physiological changes responsible for breed differences have targeted differentially the transcriptome across tissues. Not all tissues have been equally affected. It remains to be studied on which of the tissues the effect of breed differentiation has been the largest. Our current data set does not allow us to respond to this question accurately: the probe × tissue heritabilities . As a result, the discussion should be considered as tentative or provisional. Nevertheless, we found defense genes to be over represented for both sex and breed specific genes. For sex, there was a significant excess of transcription and translation related genes. Some of these genes are sex linked , as mentioned above, so an over representation of this ontology is not unexpected. The GO biological processes were more scattered for breed than for sex  than that explained by sex or breed, which were comparable Table , we wereAlthough more work is needed to calibrate the actual relevance of breed or genetic differences to the pig's transcriptome (or in any other species), it seems difficult that the importance of breed or sex differences exceeds that of tissue. Thus, the argument that regulatory rather than structural mutations are a more important source of phenotypic variability  needs thi.e., LWM, LWF, IBM and IBF.Four animals, two Large White (LW) and two Iberian (IB) piglets were bought from two breeding companies and transferred to the University experimental farms at weaning, i.e., aged one month approximately. Pigs were housed simultaneously, fed the same diets during the fattening period, that lasted two months, and were weighed at weekly intervals. At the time of slaughter, the average ages were 87 and 89 days for LW and IB, respectively. Their mean live weights at that time were 37.5 (LW) and 29.1 kg (IB). The four animals were identified as LW or IB and male (M) or female (F), Biceps femoris (BIFE), back fat tissue (FATB), abdominal fat tissue (FATA), stomach (STOM), liver (LIVR), ileum (ILEU) and whole blood (BLOO). Adrenal cortex and medulla were separated by a sharp knife. The hypothalamus included the mamillary body and grey tubercle but excluded the chiasma opticum. The nomenclature for organs and tissues was used according to the Nomina Anatomica Veterinaria[Universitat Autònoma de Barcelona, in accordance with the guidelines of the Good Experimental Practices.Animals were euthanized by an overdose of intravenous sodium thiobarbital. At necropsy, samples of 16 tissues were collected, snap frozen in liquid nitrogen and stored at -80°C. The average time gap between euthanasia and tissue collection was ~15 minutes, maximum time was 25 minutes. The tissues collected were olfactory bulb (OLFB), hypothalamus (HYPO), pineal gland (PING), adenohypophysis (AHYP), neurohypophysis (NHYP), adrenal cortex (ADGC), adrenal medulla (ADGM), thyroid gland (THYG), diaphragm (DIAP), M. terinaria. More deTotal RNA was extracted from 100 mg tissue using the RiboPure™ kit  according to the manufacturer's protocol. RNA was quantified with the NanoDrop ND-1000 spectrophotometer  and the RNA integrity was assessed by Agilent Bioanalyser 2100 and RNA Nano 6000 Labchip kit . Due to high variation in concentrations of the total RNA obtained in different tissues, all samples were concentrated and cleaned using the RNAeasy MiniElute Cleanup kit  obtaining final concentrations between 500 and 1000 ng/μl.Institut de Recerca Hospital Universitari Vall d'Hebron . Briefly, the cDNA synthesis was undertaken with 5 μg of total RNA, labelled with biotin and hybridized to individual high-density oligonucleotide microarray chips (GeneChip® Porcine) from Affymetrix (Santa Clara CA) containing a total of 23,937 probe sets , representing 20,201 Sus scrofa genes, 11,265 of these genes were annotated by Tsai et al. (2006). The hybridization was done according to Affymetrix standard protocols and microarray expression data were generated with GeneChip Operating Software (GCOS). As the annotation provided by the manufacturer is not too detailed, the results in this work are based in the annotation developed by [A total of 64 microarrays  were hybridized and scanned at the S. scrofa Beta-2-microglobulin (GenBank accession number NM_213978.1) was used as endogenous control. Primers were designed using the PrimerExpress 2.0 software  and are shown in additional file We used quantitative real time PCR (QRT-PCR) to validate differential expression between sexes of one of the probes (Ssc.4897.1.A1_at) annotated as CLOCK gene . ExpressΔΔCT method for relative quantification (RQ) of gene expression [ΔΔCT method for relative quantification. The High Capacity cDNA Transcription Kit  was used to synthesize cDNA from 1 μg of backfat tissue RNA following the manufacturer's instructions. PCR amplifications were performed in a total volume of 20 μl containing 5 μl of cDNA sample diluted 1:2000 or 1:20. Primers were used at 300 nM each and at 600 nM each for Ssc.4897.1.A1_at and microglobulin genes, respectively. Each sample was analyzed by triplicate. The thermal cycle was: 10 min at 95°C and 40 cycles of 15 sec at 95°C and 1 min at 60°C. A dissociation curve was performed in order to assess that there were not primer dimer formation. The sample of lowest expression level was used as calibratorWe used the 2-pression , a compaAffy package of bioconductor [Quality control of CEL files was done with the onductor : RNA degonductor . This sor2, where r is the correlation coefficient across all pairs of probe levels between pairs of samples. To gain further insight, we relied on mixed model methods. These have been long being used in Animal Breeding [An initial visual appraisal of the data was carried out drawing neighbor – joining (NJ) and UPGMA trees with Mega 4.0 . The paiBreeding , and havBreeding . We fittygijk refers to the expression level of the g-th Probe  at i-th tissue  from animal of breed j  and sex k . Note that a given gene may be represented by more than one probe. However, different probes of the same gene can behave differently due to at least two reasons: alternative splicing and poor annotation. Thus, here we used the probe rather than the annotated gene in the model. Preliminary studies (results not shown) shown that including the probe rather than the gene explained a larger part of the variance. Nevertheless, we refer to differentially expressed gene to mean the gene (if known) corresponding to the probe that shows a significant differential hybridization. All Tissue, Breed and Sex were treated as fixed effects, whereas Probe was random with variance was used for initial exploratory analysis. Above andAll interactions above were treated as random. The ratio of variances th Qxpak  and VCE th Qxpak on a Linui and j is the squared correlation across probes between the Probeg × Tissuei and Probeg × Tissuej solutions obtained from model (2). Again, NJ-trees were drawn with Mega 4 to visualize the results. An additional measure of distance between tissues can be provided by the number of extreme probes that separated a given tissue (or a group of tissues) from the rest. An extreme probe for the i-th tissue was defined as a probe for which all four mRNA levels of the i-th tissue were either smaller or larger than the remaining 60 observations pertaining to the remaining 15 tissues. The same procedure applies when Simulations showed that the probability of having such an arrangement was very small (P ~10-4) for random normal variates.We also explored the biometric relationship between tissues. The average distance between tissues was obtained from 1-i.e., for gene g PBgj is the prediction of the interaction between probe g and breed j (Probeg × Breedj) obtained from model (3), and σΔ is the standard deviation of the numerator. Similar notation applies to Further, we examined the effects of breed and sex on gene expression. To do that, we computed the z-score, defined as the standardized difference of gene expression predictions between breeds  was estimated taking into account all information available. Thus, all estimates should have high accuracy, provided that the models adjust to the data. We discuss this issue in the results and discussion section below.Note that all data available were utilized simultaneously in the mixed model analyses just presented,vs. GO class frequencies in a target lits, e.g., the most differentially expressed genes between sexes or breeds or extreme genes in embryonic layers. False discovery rates are reported.We obtained the gene ontology (GO) process using the onto-express platform . This plFDR: False Discovery Rate; GO: Gene Ontology; IB: Iberian pig breed; LW: Large White pig breed; NJ: Neighbor – Joining; RMA: Robust Multiarray Average; QRT-PCR: Real Time Quantitative Reverse Transcription Polymerase Chain reaction; UPGMA: Unweighted Pair-Group Method with Arithmetic Mean.MPE conceived the research. MPE, MLB and JMF supervised research, all carried out the research. ALJF and MPE analyzed the data. ALJF and MPE wrote the first version of the manuscript.The data used in this study have been deposited in GEO under accession numbers GSE10898.List of extreme probes for each of the tissues grouped according to the three embryonic layers. Ectoderm tissues were olfactory bulb, both hypophyses, pineal gland and hypothalamus; mesoderm comprised fat and muscle tissues; and endoderm, liver, stomach and ileum. An extreme probe for a given group was defined as one where all expression values in the tissue group were either higher or lower than the values in the rest of tissues . The marginal difference is the difference between the maximum value of the extreme probe in the group of tissues under consideration and the minimum value in the rest of tissues  or between the minimum value in the group and the maximum value in the rest of tissues (a positive difference). Thus, a negative marginal diference means that the gene is underexpressed; a positive marginal difference, overexpressed. There are two sets of columns for negative and positive marginal differences, respectively.Click here for fileList of extreme genes for each of the 16 tissues studied. Notation is identical to that in additional file Click here for filePrimers used for QRT-PCR.Click here for file


P = 0.0343) and that of distant metastasis . In respect of MT protein expression, the frequency of distant metastasis was more common in MT-positive tumours than in MT-negative tumours . The survival rate of the patients with MT protein-negative tumours was significantly better than that of the patients with MT protein-positive tumours (P = 0.0340). There was a positive correlation between the expression of MT protein and that of proliferating cell nuclear antigen (P = 0.0018). Therefore, we conclude that MT expression, both at the mRNA and protein levels, may be a potential marker predicting metastatic and proliferative activities of oesophageal SCC. © 1999 Cancer Research CampaignThe goal of this study is to clarify whether the expression of metallothionein (MT) could affect the prognosis and the metastatic potential of squamous cell carcinoma (SCC) of the oesophagus. In paraffin-embedded specimens resected from 57 patients, MT mRNA and protein expressions were detected by in situ hybridization and immunohistochemistry respectively. The expression of MT was evaluated in respect of clinicopathologic variables and patients' survival. MT mRNA expression was significantly associated with the proportion of lymph node metastasis (71% in MT mRNA-positive tumours vs 42% in MT mRNA-negative tumours;