This study aims to characterize the housekeeping and tissue-specific genes in 15 mouse tissues by using the serial analysis of gene expression (SAGE) strategy which indicates the relative level of expression for each transcript matched to the tag.Here, we identified constantly expressed housekeeping genes, such as eukaryotic translation elongation factor 2, which is expressed in all tissues without significant difference in expression levels. Moreover, most of these genes were not regulated by experimental conditions such as steroid hormones, adrenalectomy and gonadectomy. In addition, we report previously postulated housekeeping genes such as peptidyl-prolyl cis-trans isomerase A, glyceraldehyde-3-phosphate dehydrogenase and beta-actin, which are expressed in all the tissues, but with significant difference in their expression levels. We have also identified genes uniquely detected in each of the 15 tissues and other tissues from public databases.These identified housekeeping genes could represent appropriate controls for RT-PCR and northern blot when comparing the expression levels of genes in several tissues. The results reveal several tissue-specific genes highly expressed in testis and pituitary gland. Furthermore, the main function of tissue-specific genes expressed in liver, lung and bone is the cell defence, whereas several keratins involved in cell structure function are exclusively detected in skin and vagina. The results from this study can be used for example to target a tissue for agent delivering by using the promoter of tissue-specific genes. Moreover, this study could be used as basis for further researches on physiology and pathology of these tissues. Housekeeping genes are constitutively expressed in all tissues to maintain cellular functions . MoreoveThe invention and application of SAGE method have paralleled those of microarray/chip technologies. Whereas hybridization-based technologies may allow for shorter detection times and high throughput expression analysis, the SAGE method not only identifies unknown genes but also quantifies the gene expression level relatively to the total mRNA population. Indeed, the SAGE method can be performed to accurately measure the abundance of both known and novel transcripts on global scale . This meA total count of 1,834,621 SAGE tags were analyzed for the 15 tissues representing 320,624 tag species. For each tissue, approximately 130,000 tags were sequenced, except for testis, ovary, mammary gland, vagina and bone which had approximately 50,000 tags.This study has identified 1,111 ubiquitously expressed transcripts. These genes are expressed in all the tissues and therefore are likely candidates as the genes responsible for cellular maintenance also known as housekeeping genes. Among the ubiquitous genes identified, 280 genes are constantly expressed in all tissues. The rest (831 transcripts) are not expressed at the same level in all the 15 tissues. The 280 transcripts are detected at similar level in each of the 15 tissues and can be useful as a set of controls. In the Table We have also identified the genes uniquely detected in each of the 15 tissues. These genes are critical for the specific functions that characterize and distinguish the testis, prostate, ovary, mammary gland, uterus, vagina, skin, liver, adipose tissue, lung, bone, skeletal muscle, cerebral cortex, hypothalamus, and pituitary gland. The specific genes are expressed in only one tissue. Table Quantitative gene expression data are often normalized to the expression levels of control or so-called housekeeping genes. An inherent assumption in the use of housekeeping genes is that expression of the genes remains constant in the tissues under investigation. Housekeeping genes do not vary in their expression levels during cell development, treatment, or disease state anomalies . About 4According to this study, the tissue-specific genes were observed in higher proportion in testis than any other tissues. The top 8 most abundant tissue-specific transcripts, namely protamine 2, transition protein 1, t-complex-associated testis expressed 3, the novel transcript GTGCCAGGAGA, tubulin alpha 3, diazepam binding inhibitor-like 5, lactate dehydrogenase 3 C chain sperm specific, and outer dense fiber of spermtails 2 are also the top 8 most abundant transcripts in this organ. These tissue-specific genes are all expressed in the specific stage of spermatogenesis. Other tissue-specific genes such as spermatogenic Zip 1  and glycWe have identified some prostate specific genes such as microseminoprotein (beta-MSP), seminal vesicle protein secretion 2, seminal vesicle antigen (SVA) and mucin 10 (MUC10) which are involved in protein secretion, cell signalling and spermatogenesis. In addition, one novel transcript was observed to be solely expressed in the prostate. Beta-MSP, also known as prostate secretory protein of 94 amino acids (PSP94), is an abundant secretory protein of the prostate gland and is generally considered to be prostate tissue-specific . ResultsOnly two transcripts with unknown function and a novel transcript with sequence tag GTCAACACAGG were specifically detected in ovary. We report two transcripts, namely glycosylation cell adhesion molecule 1 (Glycam1) and the novel transcript with sequence tag TCCGGAGAAAA, which are expressed only in the mammary gland. Glycam1 has a protein binding activity and previous studies have reported that prolactin induced Glycam1 expression in primary mammary epithelial cell of mice .In uterus, three transcripts namely proline-rich acidic protein 1 (Prap1), hydroxysteroid 11-beta dehydrogenase 2 (Hsd11b2) and chloride channel calcium activated 3 (clca3) were characterized as tissue-specific. Northern analyses have demonstrated that Prap1 also known as pregnancy-specific uterine protein expression is limited to the pregnant uterus . In the Two novel transcripts were observed to be tissue-specific to the vagina. Furthermore, the keratins such as keratin intermediate filament 16a, keratin complex 1 acidic gene 13 and keratin complex 2 basic gene 6 g, members of a family of fibrous structural proteins were also detected in high proportions in the vagina. Surprisingly, retinol binding protein 2, which functions in the intracellular transport of retinol, is detected only in vagina. According to the UniGene and EST databases, retinol binding protein 2 is also expressed in intestine tissue.The majority of transcripts specifically detected in the skin represent keratin. Moreover, the S100 calcium binding protein 17 and G protein-coupled receptor family C group 5 member D, both involved in cell signalling, were also specific to the skin. In addition, lymphocyte antigen 6 complex locus G6C, involved in cell defence, was also exclusively observed in the skin.The transcripts exclusive to the liver are involved in several functions such as cell defence, protein and steroid hormone synthesis, as well as transport and amino acid metabolism. We report in the current study that inter-alpha-trypsin inhibitor-4 (Itih-4) was exclusively detected in the liver. This result is in agreement with the report that Itih-4 is a liver-restricted member of the serine protease inhibitors family, with diverse functions such as anti-apoptotic action and matrix stabilization molecule that are important throughout development . The preTwo transcripts, leptin and lectin galactose binding soluble 12 , were exclusively detected in adipose tissue. Leptin is a peptide hormone produced predominantly by white adipose tissue. Beside its key role in the regulation of food intake and energy expenditure, leptin is also involved in the pathogenesis of inflammatory and autoimmune disease . On the The transcripts specific to the lung are involved in cell defence, except claudin 18 which participates in cell signalling. The surfactant-associated proteins A (SP-A) and SP-D are members of a family of collagenous host defence lectins, designated collectins. The lung is the main site of SP-A and SP-D synthesis . They arThe transcripts exclusively detected in the bone are involved in transport, cell structure, signalling and defence. The tissue-specific genes ascribed to proteoglycan 2 bone marrow and solute carrier family 4 (anion exchanger) member1 are highly and solely expressed in the bone. The other exclusive transcripts such as proteinase 3, neutrophil elastase, eosinophil peroxidase, cathepsin G and carbonic anhydrase 1 are involved in cell defence. Evidence from northern analysis has shown that proteinase 3 expression is primarily confined to the promyelocytic/myelocytic stage of bone marrow development . The higIn the current study, the transcripts exclusively detected in skeletal muscle are novel transcripts except tropomyosin 3 gamma (Tpm3) and myosin light chain phosphorylatable fast skeletal muscle (MLC2) which are involved in cell structure. In the mice, the Tpm3 mRNA is found exclusively in the skeletal muscle but not in the cardiac tissue at any development stage, whereas, in human, Tpm3 is found in both adult heart and skeletal muscle ,62. The The current study reports that solute carrier family 17 (sodium-dependent inorganic phosphate cotransporter) member 7, also known as vesicular glutamate transporters (Vglut1), and synaptic vesicle glycoprotein 2 b (SV2B) as well as gene model 748 were exclusively expressed in cerebral cortex. This result is consistent with the report that Vglut1 mRNA is strongly expressed in the hippocampus and cerebellar cortex . Vglut1 We report that cocaine and amphetamine regulated transcript (CART), calbindin 2, hypocretin and RIKEN cDNA A230109K23 gene were exclusively detected in the hypothalamus. The protein calbindin 2 buffers intracellular calcium is speculated to be involved in the integration of neuronal signalling. CART encodes a hypothalamic neuropeptide precursor protein which has been identified and characterized in rat brain and later in human brain ,67. FurtThe transcripts exclusively expressed in the pituitary gland were also highly expressed in this gland. Except growth hormone, pro-opiomelanocortin-alpha, prolactin, luteinizing hormone beta (LH beta) and thyroid stimulating hormone beta subunit (TSH beta) also known as thyrotropin beta which are involved in cell signaling, the other specific genes to this gland had no match in public databases and therefore may represent novel transcripts. LH beta is essential for ovulation and reproductive fitness, and is well known to be synthesized specifically in pituitary gonadotropes . In addiUsing SAGE strategy, this study has shown the housekeeping genes and the tissue-specific genes expressed in 15 intact tissues. The identified housekeeping genes can represent appropriate controls for RT-PCR and northern blot when comparing the expression levels of genes in several tissues. Several transcripts exclusively detected in a tissue are known to be tissue-specific genes according to previous studies. Furthermore, we have identified several new tissue-specific genes. These genes show well the specialty and particularity of each tissue. The tissue-specific genes can be used as a targeting agent in order to reach a particular tissue/organ. In addition, the current data can contribute significantly to comparative genomics in general and gene expression and regulation among different mouse tissues. Further studies will be needed to investigate other tissues to confirm the specific or housekeeping gene expressions. In addition, this study can serve as a basis for future studies on the novel transcripts and the transcripts with unclear functions despite their tissue specificity.C57BL6 mice (12–15 week old) were obtained by Charles River Laboratories . They were housed in an air-condition room (19–25°C) with controlled lighting from 07:15 to 19:15 h and were given free access to food (Lab Rodent Diet No. 5002) and water. The GDX and ADX groups had surgery 7 days before death. The intact, GDX and ADX groups received vehicle solution (0.4% (w/v) Methocel A15LV Premium/5% ethanol) 24 hours before sacrifice. DHT (0.1 mg) was injected 3 h prior to killing in GDX+DHT groups. ADX mice received sodium chloride (0.9 g/dl) in their drinking water after the surgery. Gcc  was subcutaneously injected into ADX mice, and the tissues were harvested 3 h after the injection. All animal experimentation was conducted in accord with the requirements of the Canadian Council on Animal Care. All the tissues were from male mice except for female sexual tissues. The tissues were dissected from 15–51 mice, frozen in liquid nitrogen and stocked at -80°C until analysis.18 primer and cDNA synthesis kit (Invitrogen Canada Inc.). The cDNA libraries were digested with the restriction enzyme NlaIII . The 3'-terminal cDNA fragments were captured using streptavidin-coated magnetic beads . After ligation of 2 annealed linker pairs, the cDNA fragments were digested with BsmFI (New England Biolabs Inc.). The blunting kit from Takara Bio Inc.  was used for the blunting and ligation of the two tag populations. The resulting ligation products were amplified by PCR and digested with NlaIII. The band containing the ditags was extracted from the 12% polyacrylamid gel. Using T4 ligase (Invitrogen Canada Inc.), the ditags were self-ligated to form concatemers that were cloned into SphI site of pUC19. White colonies were screened by PCR and agarose gel to select long inserts for automated sequencing . The data discussed in this publication have been deposited in NCBIs Gene Expression Omnibus [III restriction site (CATG) at the 3' end of a given transcript. To overcome the lower quality of some EST sequences, the tags that did not identify a well-characterized mRNA were required to match at least two ESTs in the same UniGene Cluster including one EST with a known polyA tail. To identify the transcripts, the sequences of 15 bp SAGE tags were matched with public databases. The tag numbers normalized by 100000 are shown in Tables Total RNA was isolated from tissues by using the RNA extraction kit . Approximately 5 μg of mRNA was extracted with Oligotex mRNA Mini Kit . The SAGE method was performed as previously described ,14. In b Omnibus  and are The SAGE method has very good reproducibility . HoweverKEK has participated in the SAGE analyses including the bioinformatic analysis and wrote the paper with assistance from YN and JFCG. MY contributed to the conception and design of the project and drafted the manuscript. JSA directed the study, contributed to the conception and design of the project, analysis and interpretation of the data and drafted the manuscript. All the authors edited, read and approved the final manuscript.


One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes.We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues.ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes. A major challenge of microarray analysis is to detect genes whose expression in a single or small number of tissues is significantly different than in other tissues. Accurate identification of such tissue-specific genes can allow researchers to deduce the function of their tissues and organs at the molecular level .Q) based on entropy to estimate the degree of a gene's specificity on a particular tissue, the issue of redundancies remains where top-ranked genes as specific to tissue A are also top-ranked as specific to tissue B. We assert such genes are not specific to A or B, but rather are genes specific to both A and B. For example, we observed that two of the top five probesets specific to liver were also found in the top five probesets specific to gall bladder [Several methods have been used for this purpose -5. Of th bladder . The iss bladder , when thUnlike ranking-based methods, methods based on outlier detection are free from the issue of redundancies because they identify tissues corresponding to both over- and under-expressed outliers for each gene ,5. ThereThis complementary relationship between ranking-based and outlier-based methods led us to develop a combined approach, ROKU. ROKU analyzes any type of tissue-specific genes  in two steps. First, it ranks genes according to overall tissue-specificity using Shannon entropy, and second, for each gene, it identifies specific tissues whose observations are regarded as outliers using a method of Kadota et al. . We applWe first show typical examples of various types of gene expression patterns. We here divided tissue-specific genes into two levels, a narrow sense and a broad sense. Genes over-expressed in a small number of tissues but unexpressed or slightly expressed in others, such as those shown in Figs. x =  is given, the entropy H(x) can be calculated by equation 1 (See Methods). The range of H is from 0 whose gene expression is perfectly restricted in a single tissue  whose gene expression pattern is flat in all the interrogated tissues . The entropy H for each gene vector x is given by the number in black above the figures. Clearly, direct calculation of the entropy for raw gene vector x works well only for detecting tissue-specific genes in a narrow sense  and cannot identify those genes as 'tissue-specific'.When one gene vector sue Fig.  to log2. The Tukey biweight yields a robust weighted mean able to resist 50% of outliers [H(x'), for the processed vectors to obvious tissue-specific genes in a broad sense  become high after data processing. For example, the value (0.04) for tissue 3 in Fig. N, N = 10) in this case, such high values decisively contribute low entropy to the gene expression pattern. Also, entropy scores, H(x') and H(x), to non-specific (or randomly expressed) genes are quite similar and close to the maximum (3.32)  Figs.  and 1h. x', (2) calculates the entropy H(x'), and (3) assigns specific tissues to each probeset whose observations are detected to be 'outliers' (see Methods). We compared the performance of ROKU to that of Schug's method, which directly uses the original/non-processed vector x for measuring the entropy H(x) [H(x') and H(x)) for all probesets are available in the additional file [see To further investigate the validity of our method (ROKU), we applied the method to a public gene expression matrix consisting of 36 normal human tissues and 22,283 probesets . Brieflyopy H(x) . The twofile see .H(x') - H(x)) calculated by the two methods. Since ROKU outputs relatively low entropy to each probeset compared to Schug's method as a whole [see H(x') - H(x)) tends to be negative: -0.425 .To compare the agreement of top-ranked probesets between ROKU and Schug's method we analyzed the percentage of common probesets in a top-ranked set of ~22,283 probesets. About 80% of ~3,000 top-ranked probesets are common, indicating that ROKU does not change the rank of probesets drastically (data not shown). One way to compare the effect of the data processing used in ROKU to that used in Schug's method is to sort probesets in order of increasing magnitude by the difference between the two entropy scores  - H(x)) valued probesets and Fig. H(x') = 1.950 and H(x') <H(x)) for the probeset '206319_s_at' and a high entropy (H(x') = 4.729 and H(x') > H(x)) for the probeset '201131_s_at'. This is quite reasonable because visual evaluation admits the former to be tissue-specific and the latter to be non-specific. Schug's method, however, gives quite similar values (4.235 for the former and 4.228 for the latter) for the two probesets: the entropy for the former is higher than that for the latter.Table H(x') > H(x): processed expression vectors are less tissue-specific than the original vectors. Visual evaluation for those probesets showed no probeset exists whose entropy score is improperly assigned, i.e., no obvious tissue-specific probesets exist. These results demonstrate the data processing strategy used in ROKU successfully estimated/ranked probesets by their overall tissue specificity on real data. We verified such trends in other microarray datasets (data not shown).There are 858 probesets satisfying N/2 tissues and low expression in other tissues. ROKU gives the processed expression pattern as 'flat' and H(x') = log2(N). Accordingly, ROKU cannot distinguish such differential expression patterns from constant expression patterns because it gives the same entropy scores for the two patterns. In other words, H(x') is not useful for identifying non-specific genes. Nevertheless, this disadvantage is not a problem for detecting the tissue-specific expression patterns we focused on. We also observed that there was no probeset suffer from this disadvantage in the real data set.Note that ROKU is inferior to Schug's method  in rare cases. For example, consider a gene expression pattern of constant high expression in As mentioned earlier, the entropy does not indicate which tissues are specific though it can rank genes according to their degrees of overall tissue specificity. To identify such specific tissue when they exist, ROKU employs an outlier-detection-based method proposed by Kadota et al.  (see MetH(x'): ROKU can rank genes with particular tissue-specific patterns by their overall tissue specificity. We compared the performance of ROKU to that of Schug's Qt(x) statistic [t.For example, ROKU identifies 59 probesets specific to lung and 291 probesets specific to fetal lung and of course no redundancies exist between the two sets by virtue of the advantage of the original method . Since Rtatistic  which caH(x') statistic and Schug's Qt(x) statistic [Qt(x) statistic for a tissue t in a gene expression vector x is defined as Qt(x) = H(x) - log2(pt) (see Methods for details). Clearly, ROKU can detect probesets whose expression patterns are specific only to each of the objective tissues  while Schug's Q statistic cannot. This is because a low Qt(x) statistic indicates that gene x is relatively highly expressed in a small number of tissues including tissue t, but does not always indicate whether the expression pattern of x is specific only to the tissue t. Indeed, both probesets  identified by Schug's Qt(x) statistic include another tissue in addition to the objective tissue. We analyzed this trend in the top-ranking probesets  for the detection of genes with tissue-specific expression patterns. ROKU was developed to compensate for the disadvantages of two conventional methods ,4 by comPublicly available Affymetrix U133A oligonucleotide microarray data for 22,283 genes in 36 various normal human tissues  were dowx =  for N tissues and an observation xt for tissue t. The entropy of the gene is calculated asThe use of Shannon entropy  to rank pt is the relative expression of xt for tissue t defined as H ranges from zero to log2(N), with the value 0 for genes expressed in a single tissue  for genes expressed uniformly in all the interrogated tissues Tbw is the one-step Tukey biweight, a popular statistic robust against outliers. It provides as much robustness as a median and is also used to estimate the expression signal from each probe set in the Affymetrix Micro Array Suite (MAS 5.0) software package [Tbw are the same as those adopted in the tukey.biweight function in R package 'affy'  [x' of a gene, while Schug et al. [x, to calculate the gene's entropy (H(x') and H(x)) as a measure of the overall tissue specificity.where  package ,13. The  0.0001) . Our metg et al.  uses theU for identifying outliers is defined asAs mentioned above, the entropy does not indicate which tissues are specific, but is a measure of the overall tissue specificity of a gene. We imagine observations in specific tissues to be easily visualized as outliers on the over- and/or under-expressed side if any exist. We used an outlier-detection-based method proposed by Kadota et al.  to detecn and s denote the numbers of non-outlier and outlier candidates, and σ denotes the standard deviation (SD) of the observations of the n non-outlier candidates. The procedure is first, normalize the gene vector x =  for N (=n+s) tissues by subtracting the mean and dividing by the SD; second, sort the normalized values  by order; third, calculate the statistics U for various combinations of outlier candidates starting from both sides of the values; finally, regard tissues corresponding to outliers detected in the combination of the minimum U as 'specific'.where KK invented the method and wrote the paper. JY made critical comments in light of the current algorithm. YN, TT, and KS provided critical comments and led the project.Full information analyzed by ROKU for dataset of Ge et al. (2005). For the original gene expression matrix, an outlier matrix  is provided. It also contains two entropy scores measured by ROKU and Schug's method and their ranks.Click here for file


Human adipose tissue is a major site of expression of inhibin beta B (INHBB) which homodimerizes to form the novel adipokine activin B. Our aim was to determine if molecules needed for a local action of activin B are expressed in adipose tissue.n = 90) compared to obese (n = 90) subjects (p = 4 × 10−31). Adipose tissue ALK7 expression correlated with several measures of body fat, carbohydrate metabolism and lipids. In addition, ALK7 and INHBB expression correlated but only in lean subjects and in subjects with normal glucose tolerance.Microarray analysis showed that adipose tissue expressed activin type I and II receptors and that the expression of activin receptor-like kinase 7 (ALK7) was adipose tissue specific. In obesity discordant siblings from the SOS Sib Pair study, adipose tissue ALK7 expression was higher in lean (We conclude that activin B may have local effects in adipose tissue and thereby influence obesity and its comorbidities. Activins were first described as gonadal hormones and their effects on reproduction have been extensively studied. Recent studies show that activins are also produced by extragonadal tissues where they have diverse effects Adipose tissue secretes a large variety of bioactive molecules, often referred to as adipokines. The adipokines have local effects or signal to other tissues, and they may play a central role in obesity-related morbidities such as diabetes, cardiovascular disease, cancer and dyslipidemia Activins belong to the TGF-β family of growth factors Activins interact with receptor complexes consisting of two receptors, type I and II, both of which are serine/threonine kinases Subjects. The SOS Sib Pair study includes 154 nuclear families with BMI discordant sibling pairs (BMI difference ⩾10 kg/m2) resulting in a study population of 732 subjects. In this study, the most extreme siblings according to BMI were chosen in each family. Gender discordant sib pairs were excluded, resulting in 78 pairs of sisters and 12 pairs of brothers.For tissue distribution, adipose tissue biopsies from six healthy volunteers (BMI range 22.4–29.3) were obtained. Adipocytes were isolated as previously described Tissue distribution of gene expression. DNA microarray expression profiles  from 65 human tissues were acquired from the GEO database . Each tissue was represented by profiles from 3 to 9 subjects and these were used to calculate an average expression profile. For inhibin genes and activin receptor genes, the average expression in the 65 tissues was calculated and used for comparison with the adipose tissue expression. Probe sets were identified using Nettaffx  and for each gene, the probe set with the highest signal was used. The following probe sets were used; INHA (210141_s_at), INHBA (210511_s_at), INHBB (205258_at), INHBC (207687_at), INHBE (210587_at), ALK7 (1552519_at), ALK4 (205209_at), ActRII (205327_s_at) and ActRIIB (236126_at).For verification of tissue distribution, RNA from adipose tissue and adipocytes  and from the Human Total RNA Master Panel II , was reversed transcribed using the High Capacity cDNA RT kit . Reagents for real-time PCR analysis of ALK7 (Hs00377065_m1) and peptidyl-prolyl isomerase A  were from Applied Biosystems. cDNA corresponding to 10 ng RNA per reaction was used for real-time PCR in the ABI PRISM 7900HT Sequence Detection System (Applied Biosystems). Serial dilution of cDNA synthesized from pooled RNA was used to generate standard curves. PPIA expression was used to normalize ALK7 expression between samples. All samples were analyzed in triplicate.Microarray analysis in the SOS Sib Pair study. Adipose tissue was obtained by needle aspirations in the paraumbilical area. Total RNA, cDNA and hybridization  was performed as previously described Measurements in the SOS Sib Pair study. Measurements of anthropometry, fat mass (FM), fat-free mass (FFM), blood pressure (BP), fasting glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum insulin, serum C peptide, and highly sensitive C-reactive protein (hs-CRP) were performed at the Sahlgrenska University Hospital. Dual-energy X-ray absorptiometry (DEXA) was performed with LUNAR DPX-L . The DEXA generates a three-compartment model consisting of FM, lean tissue mass (LTM), and bone mineral content (BMC). The FFM was calculated as LTM + BMC.Statistics. Statistical analyses were performed using SPSS  and SAS (version 9.1). Values are given as means ± SD unless stated otherwise. Correlation between ALK7, INHBB expression and anthropometric and biochemical markers were performed using the Spearman rank correlation test. Partial correlation was used to control for sex, age and fat mass when appropriate. In order to obtain approximate normal distributions of expression data, microarray signals in the whole SOS Sib Pair study offspring cohort (n = 359) were transformed using Box–Cox power transformations. Subsequently, expression data were standardized to mean = 0 and variance = 1. Differences in gene expression between lean and obese siblings were assessed using a paired t-test. A P value less than 0.05 (two-sided) was considered statistically significant. Linear relationships between ALK7 and INHBB transcript levels that differed between subgroups  were assessed in generalized linear models in which subgroup class was included as a covariate beside the transcript level. A p-value <0.05 for the interaction between subgroup class and transcript level was taken as evidence of a significantly different linear relationship between subgroup classes.Adipose tissue expression levels of inhibin genes and activin receptor genes were measured and compared with the mean expression levels of these genes in 65 different human tissues, hereafter referred to as reference tissues. In line with our previous results Among the type I activin receptor genes, ALK4 and ALK7 are known to mediate activin B action Of the type II receptors, the adipose tissue expression of the ActRII gene was slightly above and the adipose tissue expression of the ActRIIB gene was slightly below the mean expression in the reference tissues.p = 4 × 10−31). In contrast, INHBB expression levels were increased in the obese compared to the lean siblings (p = 6 × 10−13).The high expression of genes encoding the activin receptor ALK7 and the activin subunit INHBB in human adipose tissue opens the possibility that locally produced activin B influences adipose tissue function. We therefore examined ALK7 and INHBB expression in relation to obesity and components of the metabolic syndrome. In the SOS Sib Pair study, adipose tissue ALK7 expression was markedly decreased in the obese compared to the lean siblings , and this persisted after controlling for sex and age . In the obese subjects, there was no correlation , and the slope was significantly different for the lean vs obese  and no correlation in the obese group . There were also several differences in the relation between gene expression levels and measures of carbohydrate metabolism in the two groups (p < 2 × 10−5 for each of the three quartiles after adjusting for sex and age). Since HOMA-IR is derived from glucose and insulin levels, we next performed the same calculations for insulin and glucose quartiles. For insulin, the pattern was strikingly similar to that seen for HOMA-IR, i.e. a loss of correlation between ALK7 and INHBB expression in the highest quartile  and significant correlations in the remaining quartiles . Hence, the slope was significantly different between the subjects from the three lowest insulin quartiles compared to the subjects from the highest insulin quartile .When the relationship between ALK7 mRNA and INHBB mRNA was analyzed there was a negative correlation in the lean group (o groups . We therIn this study, we have extended our previous observation that the beta B subunit, which homodimerizes to form activin B, is predominantly expressed in human adipose tissue Activins are involved in various physiological processes, such as cell proliferation, immune function, wound repair and reproduction, and exert their effects in many tissues ALK7 expression has previously been detected in the brain, pancreas and colon, however, adipose tissue was not included in the analysis in vitro also support our hypothesis that the activin B receptor ALK7 is implicated in the regulation of metabolism and adipose tissue function. ALK7 is specifically expressed during the late phase of adipocyte differentiation Recent studies in rodents and In conclusion, human adipose tissue expresses receptors required for paracrine or autocrine effects of activin B. The effects of obesity on adipose tissue expression of ALK7 and INHBB genes, the strong correlations with markers of the metabolic syndrome and the dysregulation between ligand and receptor transcripts in subjects with glucose intolerance indicate that the adipokine activin B has hitherto unrecognized metabolic effects.


The chicken is an important agricultural and avian-model species. A survey of gene expression in a range of different tissues will provide a benchmark for understanding expression levels under normal physiological conditions in birds. With expression data for birds being very scant, this benchmark is of particular interest for comparative expression analysis among various terrestrial vertebrates.We carried out a gene expression survey in eight major chicken tissues using whole genome microarrays. A global picture of gene expression is presented for the eight tissues, and tissue specific as well as common gene expression were identified. A Gene Ontology (GO) term enrichment analysis showed that tissue-specific genes are enriched with GO terms reflecting the physiological functions of the specific tissue, and housekeeping genes are enriched with GO terms related to essential biological functions. Comparisons of structural genomic features between tissue-specific genes and housekeeping genes show that housekeeping genes are more compact. Specifically, coding sequence and particularly introns are shorter than genes that display more variation in expression between tissues, and in addition intergenic space was also shorter. Meanwhile, housekeeping genes are more likely to co-localize with other abundantly or highly expressed genes on the same chromosomal regions. Furthermore, comparisons of gene expression in a panel of five common tissues between birds, mammals and amphibians showed that the expression patterns across tissues are highly similar for orthologuous genes compared to random gene pairs within each pair-wise comparison, indicating a high degree of functional conservation in gene expression among terrestrial vertebrates.The housekeeping genes identified in this study have shorter gene length, shorter coding sequence length, shorter introns, and shorter intergenic regions, there seems to be selection pressure on economy in genes with a wide tissue distribution, i.e. these genes are more compact. A comparative analysis showed that the expression patterns of orthologous genes are conserved in the terrestrial vertebrates during evolution. The chicken is an important model species for evolutionary and developmental biology, immunology, genetics, as well as for agricultural science. The completion of a draft sequence of the chicken genome Several studies, using chicken as a model, have compared gene expression differences under different infection treatments using microarrays Regions of IncreasedGene Expression). RIDGEs were reported to be associated with higher expression, higher gene density, shorter gene introns, shorter genes, and some other genomic features in chicken Furthermore, clustering of highly expressed genes within specific chromosomal regions has been reported in human Evolutionary changes in gene expression account for most phenotypic differences between different species. Studies on conservation of global gene expression patterns between human and apes To summarize, the objectives of this study are to address the following questions: 1) what is the distribution of gene expression in chicken? 2) Do genes with distinct breadth of expression (number of tissues where a gene is expressed) show a correlation with certain structural genomic features in chicken? 3) Are the expression patterns of orthologous genes conserved among species?Normalized intensities were used as gene expression levels and genes were defined as being expressed only when their expression was higher than 99% quantile value of the expression of all negative control spots across all the arrays in this study  as descrThe significant  GO terms (BP) of housekeeping genes indicate that these widely expressed genes are mainly involved in a number of essential biological processes for maintaining a cell . GO term−13, Wilcoxon Rank Sum Test), coding sequence length , average intron length , and intergenic region length  were found between housekeeping and tissue-specific genes, housekeeping genes have, on average, shorter average exon length than tissue-specific genes, but the difference is not statistically significant   . These rA chicken transcriptome map is described previously, and regions with clusters of the most highly expressed genes, covering about 10% of the chicken genome, so called “RIDGEs”, are identified Conservation of gene expression was compared by checking the 3,892 1∶1∶1 orthologous genes in mouse, chicken and frog . Pair-wiBesides testing conservation of gene expression of orthologous genes between species, we also tested whether homologous tissues  are more similar to each other compared to non-homologous tissues. After transforming gene expression intensities to relative expression ratios (RA) across the same panel of tissues, a comparison between global gene expression profiles among tissues in different species was possible. The rank correlation coefficient among different tissues showed that homologous tissues in three different species are more similar compared to non-homologous tissues ; especiaThe main objective of this study was to survey gene expression profiles across a set of eight normal chicken tissues. We present a microarray expression dataset surveying about 8,792 chicken Ensembl genes across 8 different chicken tissue types in 5-fold . For most genes the distribution of expression is observed across several different tissues . For 723The on average smaller size observed for the housekeeping genes is due to both a shorter coding sequence as well as a shorter intron length. Furthermore, the smaller size of the intergenic region also contributes to a higher gene density of the areas containing the housekeeping genes, suggesting a selection for compactness, which has also been reported in human The hypothesis for the existence of RIDGEs is that evolution favors highly expressed genes to be co-localized, as transcription of one gene would help the chromatin of neighboring genes to “open up” during transcription. The favorable distribution of housekeeping genes within RIDGEs again indicates that these genes need to be expressed at relative higher levels  and at aIn contrast to direct sequence comparisons of orthologous genes, the comparison of the gene expression profiles of orthologous genes has a number of caveats. First of all, the expression levels of genes are dynamic and change with developmental and physiological state. Secondly, for all downloaded gene expression survey data, the tissue samples surveyed in mouse, chicken and frog Nevertheless, the expression of orthologous genes is generally well conserved as compared to random gene pairs . If genehttp://www.ncbi.nlm.nih.gov/geo/) (accession number: GSE17108), the data was published in a previous study describing a transcriptome map in the chicken genome The microarray data was downloaded from GEO (We used R/Bioconductor All the genes having a chicken Ensembl gene ID were mapped to their 1-to-1 human orthologous genes using bioconductor package biomaRt Mus musculus), chicken , and frog  were downloaded from Ensembl. The normalized gene expression data for mouse and frog were downloaded from the functional landscape of mouse gene expression website (http://hugheslab.med.utoronto.ca/Zhang) and the Conservation of Core Gene Expression in Vertebrate Tissues: Supplementary Data website (http://hugheslab.ccbr.utoronto.ca/supplementary-data/vertebrate_expression), respectively. The expression data of chicken in this study was normalized using the same method as used in these two previous studies Orthologous genes for mouse (Table S1Expression data of 8908 genes in eight chicken tissues.(1.82 MB XLS)Click here for additional data file.Table S2Lists of tissue specific genes and housekeeping genes.(0.20 MB XLS)Click here for additional data file.Table S3GO enrichment results of tissue-specific genes.(0.02 MB XLS)Click here for additional data file.Table S4GO enrichment result of housekeeping genes.(0.02 MB XLS)Click here for additional data file.Table S5List of 3,892 1∶1∶1 orthologous genes in mouse, chicken and frog downloaded from Ensembl database via biomaRt package.(1.15 MB XLS)Click here for additional data file.


Alternative splicing is an important mechanism that increases protein diversity and functionality in higher eukaryotes. Affymetrix exon arrays are a commercialized platform used to detect alternative splicing on a genome-wide scale. Two probe summarization algorithms, PLIER (Probe Logarithmic Intensity Error) and RMA (Robust Multichip Average), are commonly used to compute gene-level and exon-level expression values. However, a systematic comparison of these two algorithms on their effects on high-level analysis of the arrays has not yet been reported.In this study, we showed that PLIER summarization led to over-estimation of gene-level expression changes, relative to exon-level expression changes, in two-group comparisons. Consequently, it led to detection of substantially more skipped exons on up-regulated genes, as well as substantially more included  exons on down-regulated genes. In contrast, this bias was not observed for RMA-summarized data. By using a published human tissue dataset, we compared the tissue-specific expression and splicing detected by Affymetrix exon arrays with those detected based on expressed sequence databases. We found the tendency of PLIER was not supported by the expressed sequence data.We showed that the tendency of PLIER in detection of alternative splicing is likely caused by a technical bias in the approach, rather than a biological bias. Moreover, we observed abnormal summarization results when using the PLIER algorithm, indicating that mathematical problems, such as numerical instability, may affect PLIER performance. Alternative splicing (AS) contributes greatly to protein diversity throughout the evolution of complex organisms. According to Johnson et al. , about 7The Affymetrix GeneChip Human Exon 1.0 ST array has an extremely high probe density. The platform contains over 1.4 million probesets, each of which contains four perfect match (PM) probes that cover over 1 million exons. While this design has added new capacity to the microarray platform, it also poses new challenges in data analysis. First of all, gene-level and exon-level expression values need to be estimated using probe signals. This process is called summarization and several algorithms have been proposed for this purpose (for example ). The mot as a target response which represents the abundance of the target mRNA, and f a feature response which represents the affinity of the probe. The model of RMA  is specified asBoth of the two algorithms implement a multiplicative error model. While RMA assumes that the error is proportional to the normalized and background-adjusted probe intensity, PLIER assumes that the error is proportional to the PM intensity without background correction. Define i = 1,... I represents different arrays and j = 1,..., J represents different probes. eij is an error term which follows the log-normal distribution. The model of PLIER iswhere BKGij is the background value specific for array i and probe j.where ti) and log(fj). In contrast, PLIER algorithm works on the PM intensities directly without log-transformation. It defines rij = log(eij). In order to down-weigh outliner probes with large absolute values of rij, the loss function is specified as z is a tuning constant for robustness. Newton's method is applied to find the values of f and t that minimize the loss function. The IterPLIER method, which is an extension of PLIER algorithm, generates gene-level signals based on consecutive exons 12]. For For the human tissue dataset, similar filtering steps were performed. First, we defined a probeset as present in a tissue if its DABG p < 0.05 in at least 2 samples (out of 3) of that tissue. Since there are a total of 11 tissues in the dataset, we filtered for: (1) probesets present in either the test tissue or five of the other tissues; (2) genes present both in the test tissue and in five of the other tissues; (3) probesets with type 1 cross-hybridization. In addition, we retained only genes for which the mean expression ranked in the top 50% for both IterPLIER and RMA, since genes with low expression levels may associate with higher false positive rates in the detection of AS .i in gene j, NI is denoted as As mentioned before, NI was calculated for each exon and used in statistical tests. For exon Ei and Gj are the expression values of exon i and gene j, respectively.where i in gene j, when comparing samples A and B, SI is denoted as NI represents the inclusion rate of an exon in a sample, while the splicing index, SI, measures the difference in NI between two samples. For exon For the colon cancer dataset, paired t-tests on gene-level signals were used to detect differential expression between normal and cancer samples. Paired t-tests on NI were used to detect differential splicing between the two groups. For the human tissue dataset, two sample t-tests were used on gene-level signals to detect tissue-specific expression . Two sample t-tests were used on NI to detect tissue-specific splicing .http://tisa.kribb.re.kr/AGC. Genes and exons in the database were mapped to transcripts and probesets on the exon array based on physical position. For each tissue in the TISA database, we counted the number of exons located on tissue-specific genes and with reported tissue-specific splicing. Then we conducted the chi-square test to determine whether the ratio of relatively skipped exons to relatively included exons was different from 1:1 to see if there is an enrichment of skipped exons on the tissue-specific genes. Tissue-specific expression and splicing events detected using the human tissue dataset were compared to the TISA database , we defined SI for the two datasets separately. For the colon cancer dataset, we defineSI represents the difference in NI for exon i in gene j in the kth sample pair, and ΔG represents the difference in expression levels of gene j for the kth sample pair.where For the human tissue dataset, we defineSIij and ΔGj represent the mean difference in inclusion rates of exon i and in expression levels of gene j between the cerebellum vs. non-cerebellum tissues, respectively.where G for PLIER-summarized data, while SI and ΔG were correlated to a much lesser extent  for RMA-summarized data. Person and Spearmen correlations between SIi, j, k and ΔGj, k in the colon cancer dataset were as large as -0.492 and -0.456, respectively, for the PLIER-summarized data. In contrast, the correlations were 0.048 and 0.045 for the RMA-summarized data. Similarly, in the human tissue dataset, the Person and Spearmen correlations between SIi, j and ΔGj were -0.58 and -0.60 when using PLIER, and -0.021 and 0.055 when using RMA, respectively.In both datasets, we found that SI was strongly negatively correlated with ΔG > 1, (2) -1 ≤ ΔG ≤ 1, (3) ΔG < -1. For PLIER in both datasets when ΔG > 1, SI is relatively negatively distributed; when -1 ≤ ΔG ≤ 1, the distribution of SI centers at zero; and when ΔG < -1, SI is relatively positively distributed. However, the distributions of SI in the three cases for RMA are all centered around zero.The negative correlations indicate that PLIER summarization leads to detection of more included exons on down-regulated genes, as well as more skipped exons on up-regulated genes. This is illustrated in Figure Table G is that PLIER leads to over-estimation of gene-level expression changes between the sample groups, relative to the exon-level expression changes. Since by definition,Another point that can be inferred from the negative correlation between SI and ΔGj, A - Gj, B| > |Ej, A - Ei, B| for most of the exons in gene j), leads to the negative correlation between SI and ΔG. Figure proportion of probesets whose absolute mean expression difference is smaller than the corresponding absolute mean gene expression difference between the sample groups under consideration . This figure clearly shows the tendency of PLIER to over-estimate gene-level expression differences, relative to exon-level expression differences.so SI actually compares the magnitude of exon-level expression changes with the corresponding gene-level expression change between samples. Over-estimation of gene-level expression change, . This observation helps to explain what we have observed for PLIER gene-level estimation.We demonstrated that PLIER tended to "weigh" highly expressed probesets more heavily and over-estimate gene expression differences. These two observations can be linked together if probesets with higher expression are likely to be associated with larger exon-level expression differences. As shown in Figure fj is independent of signal intensity is probably not completely hold either, which may also have an influence on gene-level estimation.One possible reason for the observed difference between PLIER and RMA summarization is that, the multiplicative error model may not be completely hold for probes with low intensities. As it is generally thought that low intensity features are likely to be associated with larger coefficients of variation, lowly expressed probesets are typically filtered out in microarray analysis. So to minimize the loss function Noh et al. used mRNA and EST sequences from public databases to identify tissue-specific gene expression and splicing in humans and mice. The work flow of their study was thoroughly described in  and theiThe TISA database contains 46 tissues. A total of 4,527 exons were found to be involved in the 3,695 tissue-specific splicing events (with p < 0.05), while 1,753 of these exons were located on genes that were specifically expressed in the same tissue (an exon may be counted multiple times if it is involved in more than one tissue-specific splicing event). Nine exons with inconsistent tissue specificity  were removed, and the remaining 1,744 exons were kept for further analysis.G. So we believe the tendency of PLIER to predict these events is due to technical bias, as opposed to a biological bias.  splicing forms. The TISA database contains 8 splicing forms, of which 3 splicing forms, 'cassette exon', 'multiple cassette exon' and 'mutual exclusive exon' are most readily detectable by the exon array, while splicing forms such as 'intron retention' may not be detectable; (2) tissue content. The TISA database contains 46 tissues, while the human tissue dataset contains only 11 tissues. If only exactly matched tissue names are considered, 10 tissues  are present in both cases; (3) mapping between the two platforms. Only core probesets on the exon array were considered in this study. This may result in a bias in the mapping, since tissue-specific exons in the TISA database may be present in smaller number of mRNAs and thus may be less likely to be mapped to core probesets. By considering combinations of the 3 factors, we listed a total of 8 cases in Table This study compared gene- and exon-level tissue specificity identified using the exon arrays with the TISA database to assess reliability of the exon array platform and the performance of PLIER and RMA. The mapping between the two platforms showed reasonable agreement in gene- and exon-sequence clustering. At the gene level, we observed significant consistency between the two platforms in detection of tissue-specific expression for both PLIER- and RMA-summarized data. RMA performing slightly better than PLIER in distinguishing "true positives" from "true negatives", where the tissue-specific expression reported in TISA were assumed to be true events. However, at the exon-level, the consistency between the two platforms was not significant, regardless of the summarization method. In addition, the difference between the two methods was not significant. Due to the lack of significant agreement between the TISA database and the tissue dataset, we did not reach a conclusion as to which method was better  for calculation of gene-level or exon-level expression, while the 'pm-gcbg' method corrects the PM signal by subtracting a GC-content specific background signal. Thus, we expected the gene-level or exon-level signals computed with the 'pm-gcbg' option to be slightly smaller than those computed with the 'pm' option. Although this was true for the vast majority of genes and exons, to our surprise, the expression values computed with the 'pm-gcbg' option for a small proportion of genes and exons were much greater than those computed with the 'pm' option. Figure Since the IterPLIER algorithm is based on PLIER, and PLIER relies on Newton's method to find the best solution for parameters, the observed phenomena may be due to numerical instability, which can cause the algorithm to be trapped in a local maximum, resulting in retrieval of an abnormal solution for the parameters. By using the APT software (Affymetrix Power Tools) and choosing different parameters for controlling the PLIER algorithm, similar problems can be avoided in some cases, but not in all (data not shown).In this study, we found that the two commonly used summarization algorithms, PLIER and RMA, behaved differently in detection of AS. Due to different gene-level estimation, PLIER showed a strong tendency to detect relatively skipped exons on up-regulated genes and relatively included exons on down-regulated genes, while this tendency was not observed when using RMA. To determine whether this tendency of PLIER represents a real biological situation, we used tissue-specific expression and splicing events that have been identified with sequence data and summarized in the TISA database as references. The TISA data did not show significant enrichment of skipped exons on genes with tissue-specific expression, a finding that did not support the tendency of PLIER. So we concluded that the observed tendency of PLIER is due to technical bias. We also compared the performance of RMA and PLIER in detection of AS by using tissue-specific splicing events in the TISA database as true positives. The consistency between the exon array data and the TISA database was low for both summarization methods, and the difference between the two methods was not significant. Given the observed bias of PLIER, this result may suggest that the efficacy of the RMA algorithm can be further improved as well. More sophisticated methods that incorporate sequence information or other characteristics of the probes may help to achieve more accurate estimation of gene- and exon-level expression .AS: (Alternative Splicing); PLIER: (Probe Logarithmic Intensity Error); RMA: (Robust Multichip Average); DABG: (Detection above Background); NI: ; SI: (Splicing Index); TISA: (Tissue-specific Alternative splicing).The work presented here was carried out in collaboration between all authors. YC and YQ defined the research theme. YQ, YC and FH designed methods and experiments, analyzed the data, interpreted the results and wrote the paper. All authors have contributed to, seen and approved the manuscript.Comparison of the analysis results of the human tissue dataset and the TISA database. Detailed description on how the tissue-specific genes and exons identified with the human tissue dataset were compared to those reported in TISA database in this study.Click here for file


Vertebrates share the same general body plan and organs, possess related sets of genes, and rely on similar physiological mechanisms, yet show great diversity in morphology, habitat and behavior. Alteration of gene regulation is thought to be a major mechanism in phenotypic variation and evolution, but relatively little is known about the broad patterns of conservation in gene expression in non-mammalian vertebrates.We measured expression of all known and predicted genes across twenty tissues in chicken, frog and pufferfish. By combining the results with human and mouse data and considering only ten common tissues, we have found evidence of conserved expression for more than a third of unique orthologous genes. We find that, on average, transcription factor gene expression is neither more nor less conserved than that of other genes. Strikingly, conservation of expression correlates poorly with the amount of conserved nonexonic sequence, even using a sequence alignment technique that accounts for non-collinearity in conserved elements. Many genes show conserved human/fish expression despite having almost no nonexonic conserved primary sequence.cis-regulatory restructuring.There are clearly strong evolutionary constraints on tissue-specific gene expression. A major challenge will be to understand the precise mechanisms by which many gene expression patterns remain similar despite extensive  Vertebrates all share a body plan, gene number and gene catalog -4 inhericis- and trans-regulatory mechanisms, and they are often used as a starting point for the identification of regulatory mechanisms. One estimate, using collinear multiple-genome alignments, suggested that roughly a million sequence elements are conserved in vertebrates  [cis-regulatory potential of the most highly conserved nonexonic elements  -29, withebrates) , and a sexample, ,29,30). example,  found thturnover ,33, and turnover . As furtturnover  that accTrans-acting factors (transcription factors or TFs) also show examples of striking conservation, such as among the homeotic factors, and diversifying selection [trans-regulatory architecture as a driving evolutionary mechanism [Drosophila developmental transition, expression of transcription factor genes is more evolutionarily stable than expression of their targets, on average [election . Studieselection ,38, suppechanism . On the  average . The fac average ,42.cis-regulatory sequence and relationships between the two are not completely understood [cis-regulatory 'lexicon'  remains mostly unknown, testing individual enhancers is tedious and expensive, and many vertebrates are not amenable to genetic experimentation. These issues are of both academic and practical consequence: in addition to our curiosity about the origin and distinctive characteristics of the human species, primary sequence conservation is widely used to identify regulatory mechanisms. We reasoned that expression profiling data from species spanning much greater phylogenetic distance than humans and mice, and thus having greater opportunity for both neutral drift and positive selection, would allow assessment of the degree of conservation of tissue gene expression among all vertebrates, and a comparison of the conservation of expression to the conservation of nonexonic primary sequence. Here, we describe a survey of gene expression in adult tissues and organs in the main vertebrate clades: mammals, avians/reptiles, amphibians and fish. Our analyses demonstrate that core tissue-specific gene expression patterns are conserved across all major vertebrate lineages, but that the correspondence between conservation of expression and amount of conserved nonexonic sequence is weak overall, at least at a level that is detectable by current alignment approaches.Global trends in conservation of gene expression, conservation of derstood ,39,41, pGallus gallus), frog  and pufferfish (Tetraodon nigroviridis). Details of the experiments are found in the Materials and methods; lists of tissues are found in Additional data file 1. Clustering analyses of each dataset separately  shows that prominent tissue-specific expression patterns are found in all vertebrates.To examine gene expression in a broad range of vertebrates, we collected a compendium of gene expression datasets, consisting of previously published datasets for human  and mousr) = 0.63), consistent with a previous study comparing human and chimpanzee [r = 0.21), possibly reflecting evolution of kidney function (see Discussion). A dendrogram for the ten common tissues  shows clear segregation of the data for heart/muscle, eye, central nervous system (CNS), spleen, liver and stomach/intestine. Only the testis and kidney datasets are split, each into two groups, with pufferfish and/or frog forming the outlying group. Additional data file 4 shows that, among these 3,074 genes, the Gene Ontology (GO) processes enriched in tissues are also generally conserved across the five species. We conclude that programs of tissue-specific expression are broadly conserved among vertebrates.To ask whether tissue-specific gene expression patterns are conserved among vertebrates, we focused on 1-1-1-1-1 orthologs (genes that are present in a single unambiguous copy in each of the five genomes), because genes that have undergone duplication events are subject to different constraints from singletons ,46. Amonimpanzee . The relimpanzee . In contWe next sought to quantify the conservation of expression of individual genes. We used two conceptually simple measures intended to capture different aspects of conservation of expression. The first asks how often specific gene expression events (instances in which gene X is expressed in tissue Y) are conserved across all vertebrates. We refer to this as the 'binary measure' because, to simplify statistical analysis, we considered a fixed proportion of the normalized, ranked microarray intensities of genes in each tissue to be expressed ('1'), and analyzed the data using several such proportions . We then asked how often a gene is expressed in all species in a given tissue . The proportion of conserved expression events at different thresholds ranges from 3% to 19.3% of all possible expression events, among the 3,074 1-1-1-1-1 orthologs Figure , and ther = 0.4, the apparent false discovery rate is similar to that obtained with the 1/3 cutoff using the binary measure (27.4% versus 34.5%), as is the number of genes classified as having conserved expression . The overlap between these two sets of genes is higher than expected at random (417 versus 291 at random); however, it is far from absolute, indicating that the definition of conserved expression influences conclusions regarding conservation of expression.The second measure we used was Pearson correlation across the ten common tissues. As with the binary measure, we found that gene expression across tissues between real 1-1-1-1-1 orthologs is more similar than randomly matched genes in pairwise comparisons between species , 48.4% of all 1-1-1-1-1 orthologs  scored as having conserved expression at about 30% apparent false discovery rate. Thus, in just the ten common tissues we analyzed, gene expression is at least partially conserved for at least a third of all unique orthologs (48.4% × 0.7 = 33.9%) by at least one of our two definitions of conservation. The expression of these 1,488 genes in modern-day lineages is shown in Figure Although the focus of our study was to identify conserved gene expression patterns, our data are consistent with previous findings that divergence of gene expression scales with evolutionary time ,18 when We next asked what gene properties correlate with conservation of expression among the 3,074 measured unique orthologs. We considered the following gene properties: those that are contained in our data, that is, median expression level and Shannon entropy as a measure of tissue specificity and preferential expression in individual tissues; GO annotations; and sequence properties, that is, length of gene, size of encoded protein, presence of a DNA-binding domain (for known and predicted TFs), sequence conservation of encoded protein (pairwise BLASTP bit score) and amount of conserved nonexonic sequence  .p-values 1.55 × 10-4, 2.36 × 10-3, 2.24 × 10-3 and 4.98 × 10-5, respectively; Additional data file 8). In contrast, we did not find any evidence that the expression of TFs  is more or less conserved than that of non-TFs, in contrast to previous reports of both higher [Several observations emerged from this analysis. First, the genes with the highest expression similarity between species are most often genes expressed in a highly tissue-specific manner in tissues with specialized functions. Although the Pearson correlation is heavily influenced by extreme values, thus giving higher weight to tissue-specific pairs, most of these high scoring genes were also classified as conserved by our binary measure. Among the 50 genes with highest median pairwise Pearson correlation of expression are structural components of the eye lens, liver-synthesized proteins involved in the complement system and blood coagulation, and neurotransmitter receptors and transporters. This observation is supported by the GO categories enriched among genes with high expression similarity, such as synaptic transmission (GO:0007268), visual perception (GO:0007601), wound healing (GO:0042060) and muscle development (GO:0007517) (Wilcoxon-Mann-Whitney test (WMW) h higher  and loweh higher  rates ofcis-regulatory function, and it follows that a larger amount of conserved nonexonic sequence might correlate with a higher probability of conserved expression. However, we found that the correspondence was very weak: for example, for the binary model, we obtained Spearman correlations of -0.086 and 0.0029 with the number of nonexonic bases in Phastcons conserved regions [It is widely believed that conserved nonexonic sequence often serves a  regions  and in u regions , respect regions .cis-regulatory elements, particularly in pufferfish. In order to address this possibility, we developed a technique similar to that of Sanges et al. [We reasoned that pervasive shuffling might obscure most of the s et al.  to detecs et al.  , most (102) have no nonexonic conserved sequence in fish, on the basis of our SCEs. The expression of these 102 genes in the ten common tissues in the representatives of all modern lineages is shown in Figure We also examined the correlations between nonexonic sequence conservation and expression correlation at varying evolutionary distances from human. Although correlations remain weak Figure , we did p = 0.24, Kolmogorov-Smirnov test; see Materials and methods and Additional data file 9) and there is almost no correlation between EEL score and conservation of expression . We conclude that the regulatory architecture of the vast majority of genes has diverged beyond recognition by any current approaches, despite the apparently very similar regulatory output in many cases, and the likelihood that at least some orthologous TFs are functioning in the same tissues.Because TF binding sites are degenerate, it is conceivable that these genes have a high number of conserved TF binding sites, despite their lack of primary sequence conservation. To examine this possibility we used Enhancer Element Locator (EEL)  to alignOur data provide a resource of large-scale gene expression data in tissues of three non-mammalian vertebrates and demonstrate that there is conservation of core vertebrate tissue gene expression. Our analysis almost certainly underestimates the proportion of genes with conserved expression patterns, because our analysis focused on only ten large adult tissues in mature animals in captivity. Nonetheless, our results already provide an index of several thousand highly conserved tissue gene expression events and a picture of core gene expression in major tissues and organs of the common progenitor of all vertebrates, which most likely resembled the expression patterns shown in Figure Drosophila [In our analysis, some biological processes emerged as more highly conserved than others. Genes involved in the more conserved processes on the whole tend to be preferentially expressed in tissues with a limited number of cell types  that carry out specialized functions particular to those tissues. This finding is consistent with the notion that mechanisms underlying important biological processes should be conserved across taxa . It is losophila  and primosophila , and conosophila .cis-regulatory 'lexicon' [cis-regulatory, is functioning as cis-regulatory in a context that we have not measured and/or regulates neighboring genes. What is most striking is that many genes with the highest conserved tissue-specific expression have almost no nonexonic conserved sequence outside mammals. Divergence in trans-regulatory architecture does not provide a satisfying explanation for this observation: although there are examples in which the binding specificity of TFs evolves [Our finding that the correlation between the amount of conserved nonexonic sequence and conservation of gene expression is low underscores the apparent malleability of the lexicon' ,35,57. I evolves ,59, as a evolves , and DNA evolves . The fac evolves .cis-regulatory modules shared by coexpressed genes, indicating that there are many ways to achieve the same expression output. We propose that our catalog of conserved (and non-conserved) expression will be useful to test ideas regarding enhancer definition. In particular, we predict that the small size of pufferfish genes and knowledge of the expression of TFs in each tissue may facilitate searches for enhancers on the basis of density and arrangement of TF binding sites, rather than primary sequence alignment [Understanding the mechanisms underlying conservation of expression patterns is a major challenge in our understanding of evolution, and of genome function and gene regulation: even within a single genome, it is difficult to find lignment ,62. ThesData tables including microarray data, probe sequences, the 1-1-1-1-1 ortholog list, Phastcons information, UCE information, GO annotations and novel non-collinear alignments are found in the Additional data files and also on our project website .Tetraodon nigroviridis. The fish were fed frozen brine shrimp twice a day.Five chickens  were obtained from Sunnybrook Health Sciences Centre, Toronto, Canada. The bursa of Fabricius and thymus were dissected from the young females. The oviduct and ovaries were dissected from the in-lay adult female and the testis dissected from the rooster. Approximately 75 adult female and five adult male frogs were obtained from Nasco , housed in 20 L aquariums at 20°C and fed a diet of Aquamax Grower 600 trout food . Exactly 100 green spotted pufferfish, of unknown age and sex, described as caught wild in Malaysia, were purchased from Aquarium Services Warehouse Outlets  and housed in a single 120 gallon (453 L) aquarium tank at room temperature. Their markings, size (about 6–7 cm) and behavior matched descriptions of Animal handling and euthanization procedures were performed according to protocols approved by the University of Toronto. Tissue processing and mRNA extraction were as described previously . TissuesX. tropicalis, v3.0) corresponding to 24,877, 25,594 and 25,937 known and predicted loci in Tetraodon, chicken and X. tropicalis, respectively, using 41,533, 41,534 and 41,523 probes. cDNA synthesis, labeling reactions and microarray processing procedures were performed as described previously [Oligonucleotide microarrays (60-mer) were custom-designed by us and manufactured by Agilent Technologies to survey the expression of a set of known and predicted mRNAs compiled from Ensembl  version eviously . Each tieviously , 72%, 77eviously ,65. Our eviously  and 60 heviously  were doweviously . Expresseviously .et al. [Inparanoid  was usedet al. .Tetraodon) were further supplemented by mapping the corresponding mouse annotations by any type of orthology as defined by Ensembl. Annotations were up-propagated and terms with few or too many annotated genes were removed as described previously [GO annotations were downloaded from Ensembl BioMart  for eacheviously . All GO We split each set of orthologs in each tissue in each species on the basis of their measured normalized expression intensities according to the following thresholds: top 1/2, top 1/3, top 1/4, top 1/5 and top 1/6. Because the human and mouse datasets were designed independently from those of the other three species, there were orthologous genes with missing measurements. In order to facilitate comparison between as many unique orthologs between the five species as possible, we applied variance stabilizing normalization  to the het al. [g in a tissue t relative to its expression given in N tissues is defined as pt|g = wg, t/Σt ≤ N 1 ≤ wg, t, where wg, t is the expression level of the gene g in tissue t. The Shannon entropy of a gene's expression distribution is then calculated as Hg = Σt ≤ N 1 ≤ -pt|glog2pt|g. This value is expressed in bits and ranges from zero to log2(10) genes expressed in a single tissue and uniformly expressed in all the common tissues examined, respectively.Shannon entropy, which measures the degree of overall tissue specificity of a gene, was calculated as described by Schug et al. . Brieflyet al. [Locations of Phastcons elements  and UCEset al. . The nump-value, and these are also given in Additional data file 7 and shown graphically in Additional data file 12. Comparisons of categorical properties were made by WMW p-value and are given in Additional data file 8.A measure of protein sequence conservation between two species was derived by performing pairwise BLASTP  between All repeat-masked intronic, 3' untranslated region and intergenic non-coding sequence upstream and downstream of protein-coding sequences in orthologous groups that we have identified using the Inparanoid algorithm  were dowet al. [Tetraodon) as the base).We initially applied a conservation cutoff of 55% in a 50 nucleotide window to search for conserved regions between the human and the orthologous genomes. After removing conserved elements that were annotated as exonic, we extracted each sequence element in the alignment that was conserved only in a subset of the genomes and built the most parsimonious ancestral reconstruction of each of the sequences using Fitch's algorithm, treating the gap character as a fifth symbol. This was then used to search against the other orthologous genome(s) using the CHAOS aligner  with veret al. , we trieet al. [Tetraodon sequence to the common ancestor of human and chicken genomes than to either genome individually.Through the procedure described above, our approach takes advantage of the conservation of order of the conserved elements when no rearrangements have taken place, and the flexibility of aligning less conserved regions that have been shuffled around in the genome. By running Fitch's algorithm on the aligned sequences  of all EEL scores are non-random by subtracting the proportion of random scores above 175 (39.8%) from the proportion of real scores above 175 (41.1%).We scanned the nonexonic sequence associated  with 4,804 human/fish ortholog pairs for conserved TF binding sites by applying EEL  using thr  below the value shown on the horizontal axis, for real orthologs (green) and randomly matched genes (blue). Additional data file p-values. Additional data file p-values for categorical gene attributes, with ranks determined by relative conservation of gene expression by median Pearson correlation for each species against Tetraodon. Additional data file p-values for comparisons of gene expression conservation versus other gene properties. Additional data file p-values of genes associated with GO biological process annotations expressed within each tissue of each species .The following additional data files are available. Additional data file The tissues at the top, highlighted in color, are those considered to be among the ten common tissue types. Those with identical coloring were combined  for the analysis of conservation of gene expression among the ten common tissues. Click here for fileClustergrams show the microarray datasets in chicken, frog and pufferfish, displayed as relative expression ratio  of each gene within each of the 20 tissues profiled. Rows and columns were ordered independently for each dataset, and high-level branches broken and rearranged to obtain a diagonal appearance as described in .Click here for fileDendrogram of correlations among ten common tissues, using 1 – Pearson correlation and average linkage over 3,074 genes.Click here for fileSelected GO biological process categories enriched amongst genes highly expressed within each of the ten common tissues in each species are shown. The tissue and GO category order were manually arranged in the heat map. .Click here for filer  below the value shown on the horizontal axis, for real orthologs (green) and randomly matched genes (blue).The cumulative distributions show the proportion of all 3,074 genes with Pearson Click here for filep-values.Feature matrix used to compare conservation of expression measures to other attributes of individual genes, with Spearman correlations and Click here for filep-values for categorical gene attributes, with ranks determined by relative conservation of gene expression by median Pearson correlation for each species against Tetraodon.WMW Click here for fileCumulative distribution of EEL scores for real and permuted orthology between human and pufferfish.Click here for fileBreakdown of the proportion of all genes in each species that are expressed within each tissue.Click here for fileList of genes classified as TFs on the basis of containing a known DNA binding domain.Click here for filep-values for comparisons of gene expression conservation versus other gene properties.Clustergrams showing Spearman correlations and Click here for filep-values of genes associated with GO biological process annotations expressed within each tissue of each species (full matrix used to create Additional data file SWMW enrichment Click here for file


In recent years, the maturation of microarray technology has allowed the genome-wide analysis of gene expression patterns to identify tissue-specific and ubiquitously expressed ('housekeeping') genes. We have performed a functional and topological analysis of housekeeping and tissue-specific networks to identify universally necessary biological processes, and those unique to or characteristic of particular tissues.We measured whole genome expression in 31 human tissues, identifying 2374 housekeeping genes expressed in all tissues, and genes uniquely expressed in each tissue. Comprehensive functional analysis showed that the housekeeping set is substantially larger than previously thought, and is enriched with vital processes such as oxidative phosphorylation, ubiquitin-dependent proteolysis, translation and energy metabolism. Network topology of the housekeeping network was characterized by higher connectivity and shorter paths between the proteins than the global network. Ontology enrichment scoring and network topology of tissue-specific genes were consistent with each tissue's function and expression patterns clustered together in accordance with tissue origin. Tissue-specific genes were twice as likely as housekeeping genes to be drug targets, allowing the identification of tissue 'signature networks' that will facilitate the discovery of new therapeutic targets and biomarkers of tissue-targeted diseases.A comprehensive functional analysis of housekeeping and tissue-specific genes showed that the biological function of housekeeping and tissue-specific genes was consistent with tissue origin. Network analysis revealed that tissue-specific networks have distinct network properties related to each tissue's function. Tissue 'signature networks' promise to be a rich source of targets and biomarkers for disease treatment and diagnosis. The issue of tissue and cell-type specificity of gene expression is central to human biology and biomedicine. It impacts such fundamental problems as tissue ontogenesis, evolution and carcinogenesis. It is generally believed that the most relevant disease biomarkers and drug targets predominantly are found among proteins specific for the tissue the disease affects. In 1965 Watson et al. described genes universally expressed to maintain cellular functions as 'housekeeping' (HK) genes -8, identHere we report the identification of 2374 HK genes, based on a novel study of gene expression in 31 human tissues on a whole-genome ABI array. We set up a definitive 'HK baseline' for gene expression across tissues, and compared previously published HK data sets with ours. We also identified gene sets uniquely expressed in individual tissues and groups of tissues, and generated tissue-specific merged metabolic/signaling 'signature networks'. Both HK and tissue-specific genes were subjected to a comprehensive, three-phase functional analysis, including gene set enrichment across four ontologies, network topology analysis and tissue-specific network analysis. We clustered tissues into groups according to expression patterns and network parameters, and revealed associations between HK and tissue-specific genes with human diseases and drug targets.Whole-genome gene expression was analyzed in 31 human tissues using an ABI human genome array with probe sets for 27,868 individual human genes. A relatively conservative ten-fold 'signal-to-noise' ratio (SN10) across all three replicate arrays for each tissue was applied to identify the transcripts present in all tissues . The SN10 HK set comprised 2374 genes  were defined as tissue specific. Tissue-specific sets varied in size from four genes for thymus to 484 genes for testis, with an average size of 43.8 genes, or 30.9 genes for somatic tissues only (excluding testis) Table . The gen cut-off t-test, was also applied to identify tissue-specific genes. Functional analysis showed striking similarities in the biological processes encompassed in the genes specific to each tissue between the t-test and the threshold-based methods . This vital, ATP-yielding pathway is the terminal part of cellular respiration, in which electrons are transferred through the electron transport chain (ETC). Almost all subunits of the ETC protein complexes were expressed in the HK set . Ubiquitin-proteasomal proteolysis was among the highest scored GG process networks (p < 10–26), with virtually all essential proteins included in the HK gene set  and breast neoplasm (p < 10–14) were the top-scored diseases  processes, GeneGo (GG) process networks, and diseases, using the hypergeometric distribution  and Genet Figure . The sect Figure . GG proct Figure , with mas p < 10–6, with ves Table .Using the GSEA procedure  across tp-value distributions of top-scored pathways, processes and diseases were strikingly consistent with the tissue of origin. For instance, 190 retina-specific genes were enriched for eye-specific categories in all four ontologies  and slightly lower in the IN and OUT component than a random gene set of the same size ; intestine and adult kidney in OUT component (p < 0.03 and p < 0.07), and skin (p < 0.1) in IN component . Intestine, liver and adrenal gland featured the highest fraction of enzymes. Fetal tissues were highly enriched in transcription factors, and retina and brain in membrane receptors  network algorithm in MetaCore. AN is a version of the 'shortest path' algorithm, optimized for larger data sets. AN generates overlapping sub-networks of up to 50 nodes and calculates enrichment of the networks with input data and canonical pathways ,18. UnliTissue gene expression patterns were clustered by Euclidean distance across the average normalized probe intensities of the replicate hybridizations for each tissue  Figure . Most tiWe compared the distribution of drug targets between HK and tissue-specific protein sets. Two sets of drug targets were compiled: 'therapeutic targets' – a set of 104 direct protein targets of commercially available drugs, and 'xenobiotic targets' – 518 proteins known to interact with xenobiotics. Therapeutic drugs are defined as key proteins or genes directly affected by a marketed or withdrawn drug from the market. Xenobiotic targets are proteins and genes known for physical interactions with a large set of bioactive compounds including drugs, drug candidates and lead compounds.On average, therapeutic targets were twice as prevalent among tissue-specific proteins than HK proteins   and statistical measures such as ANOVA and nly used ,20. Renenly used ,22 follonly used . It was t-test) methods to determine tissue specificity of gene expression. Although the resulting datasets did not overlap well in gene content (6% on average), they were highly similar by functional analysis. Importantly, the intersections between distributions of entities within functional ontologies for SN10 and t-test sets were substantially larger than between genes  and statistical , and many of these genes belong to our HK set, confirming the functional integrity and completeness of the HK set.Recently it was shown in line with our results that the number of HK genes is larger than previously reported ,25. Our p-values or GSEA analyses were employed. This important observation adds confidence to the conclusions drawn from the analysis of tissue-specific gene expression, and can be applied similarly to other studies of differentially expressed genes and proteins. It also will facilitate the development of a comprehensive set of 'meta-ontologies' derived from an understanding of the interplay and crossover between the different types of ontological categories available, leading to improved understanding of biological systems, and the identification of biologically meaningful biomarkers and new therapeutic targets.Ontology enrichment in tissue-specific genes was strikingly different from HK genes, and in most cases strikingly consistent with the tissue's function. Retina genes were enriched with such processes as visual perception, detection of visible light, detection of light stimulus and eye disease-related genes, and testis genes scored highest for reproductive processes, cell cycle, cell division and male reproductive diseases, for example. Importantly, almost all tissues featured a high level of consistency between the highest-scored categories in different ontologies. Further, consistency was observed whether hypergeometric Network analysis  showed tp-values  should be considered valid, although such ad hoc cut-offs may eliminate many condition-relevant genes from analysis. Functional analysis provides a different level of validation for such datasets by consideration of functional biological units. For instance, the retina-specific gene set is enriched in vision-related pathways, GO and GG ontologies, and diseases. The same set is enriched with OUT network component, and the protein class 'receptors'. Self-assembling retina-specific networks reconstruct rhodopsin-stimulated signal transduction and key steps in the metabolism of rhodopsin and its co-factors. Although the retina gene set is too small to achieve p-values < 0.01 in most of these analyses, the combination of independent functional evidence builds a strong case for its relevance and importance as a highly likely source of specific biomarkers for eye conditions and ophthalmic drug response. The comprehensive biological interaction data in MetaCore allows multiple 'high-content' data types to be mapped onto networks. Gene expression data, proteomic, single nucleotide polymorphism and metabolomic data all can be addressed, visualized and used in network construction and analysis, independently or in concert ['Multi-dimensional' functional analysis of gene expression data adds to the ongoing debate on proper statistical procedures for gene set selection. A 'rule of thumb' opinion is that only gene sets and distributions with low  concert , making Tissue-specific gene sets  were twice as enriched in drug targets as the HK set. 'Therapeutic' targets comprised up to 25% of mammary gland and thymus-specific proteins. Most of the identified tissue-specific drug target genes are targets to drugs whose mechanism are consistent with the tissues. For example, three brain-specific proteins identified to have at least one drug target are GABA-A receptor beta-2 subunit, Na (V) I alpha and SCN10A. GABA-A receptor beta-2 subunit is a target for clomethiazole (sedative and anticonvulsant), Na (V) I alpha is a target for drugs such as levetiracetam (epilepsy), tetrodotoxin (anesthetics), toprimate (anticonsulvant) and SCN10A is target for bupivacaine racemic (anesthetics) and lidocaine (anesthetics).Tissue-specific proteins and their pathways and networks are therefore a potentially rich source of targets and biomarkers for disease treatment and diagnostics. The addition of data on which network components are detectable in blood and other readily accessible body liquids makes tissue-specific networks exceptionally attractive for the identification of putative biomarkers of tissue-specific disease, drug-response or toxicity.RNA from 31 normal human tissue RNAs (Table The Applied Biosystems Human Genome Survey Microarray (P/N 4337467) contains 31,700 60-mer oligonucleotide probes representing 27,868 individual human genes. Digoxigenin-UTP labeled cRNA was generated and amplified from 1 μg of total RNA from each sample using Applied Biosystems Chemiluminescent RT-IVT Labeling Kit v 1. 0 (P/N 4340472) according to the manufacturer's protocol P/N 4339629). Fifteen micrograms of DIG-labeled cRNA was hybridized for 16 hrs at 55°C and chemiluminescence detection, image acquisition and analysis were performed using Applied Biosystems Chemiluminescence Detection Kit (P/N 4342142) and Applied Biosystems 1700 Chemiluminescent Microarray Analyzer (P/N 4338036) following the manufacturer's protocol (P/N 4339629). Images were auto-gridded and the chemiluminescent signals were quantified, background subtracted, and finally, spot- and spatially-normalized using the Applied Biosystems 1700 Chemiluminescent Microarray Analyzer software v 1. 1 (P/N 4336391). Probe signals were normalized using the Limma method [. FifteenA 'stringent' set of HK genes expressed in each of the 31 tissues was identified by applying cut-off to the ABI-calculated signal-to-noise (S/N) for each probe. The S/N is a metric that captures the confidence of the measurement 'detectability' above all known sources of noise. S/N is commonly used to bin genes or probes as 'Present' or 'Absent' at a desired level of confidence. Since the S/N expresses the number of standard deviations, the associated confidence can be looked up from a probability table for a normal distribution. For example, signals with S/N ≥ 3 have > 99.9% confidence in the measurement. For our purposes in determining whether a gene was expressed in a given tissue, we additionally took into account the inflection points where the slope of the S/N curve significantly changes. We applied the threshold across all three replicate hybridizations on a probe-by-probe basis to identify the genes expressed in each tissue with a high level of confidence. The overlap between all 31 sets, that is, genes consistently expressed in all tissues, defines the HK set /2 is the maximum number of such links, the clustering coefficient is a number between 0 and 1. The average clustering coefficient is obtained by averaging over the clustering coefficient of individual nodes. A network with high clustering coefficient is characterized by highly connected sub-graphs.where The enrichment levels of the HK genes and tissue-specific genes in the different parts of the network  and in different protein classes were calculated using the hyper-geometric distribution:p-value corresponding to the enrichment-level according toWe calculated the k or more marked elements in a sample of size n by random selection.which gives the probability of having The authors declare that they have no competing interests.TN, YN and ZD conceived the study and designed research. ZD drafted and YN wrote the final manuscript. RJB contributed to writing and editing of the manuscript. ZD, WS and ES performed the statistical analysis of the data. TS, DD, AB, AG, ES, ZD and YN performed research. ER contributed with making the figures in the manuscript. KL, JB, RRS designed and carried out the experimentList of all housekeeping genesClick here for fileList of all tissue-specific genesClick here for fileTable for intersection between genes, ontologiesClick here for fileComplete enrichment analysis for all housekeeping setsClick here for fileCanonical pathway maps and GeneGo networks essential for growth and viabilityClick here for fileEnrichment analysis of the unique parts of the housekeeping gene setsClick here for fileComplete enrichment analysis of all tissues in all four ontologiesClick here for fileGene Set Enrichment Analysis for selected tissues, ontologiesClick here for fileNetwork topological properties of all housekeeping setsClick here for fileTissue-specific connectivityClick here for fileTable for interactions mechanismsClick here for fileTable for component analysis, protein class analysis for all tissuesClick here for fileMC legend with protein classesClick here for fileList of genes specific for tissue pairs and tripletsClick here for fileDrug targetsClick here for fileTissue-specificity distribution of all genesClick here for fileDefinition of tissue specific and housekeeping genesClick here for file