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 (HK set, see Methods). The SN10 HK set comprised 2374 genes (Additional file 1). We compared this set with four sets of HK genes published previously: a set of 535 genes from 11 tissues on a Affymetrix HuGeneFL array 3; 1789 genes from 79 tissues on custom 33,698 gene array 4; 574 genes from 47 tissues on a 7500 gene Affymtrix U95A array 2 and 451 genes from 19 tissues on Affymetrix HuGeneFL arrays 5. Our HK set overlapped with between 42 and 82% of these sets, although only 97 genes were common between the four larger sets. Furthermore, there was 80% overlap between our set and the intersection of the previous studies (Figure 1). Our set contained 1419 additional HK genes not previously identified.

Genes uniquely expressed in only one of the 31 tissues (at the SN10 cut-off) 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 1, Additional file 2). The gene set uniquely expressed in testis was substantially larger than those for somatic tissues, and highly enriched in meiosis-specific genes, confirming recent findings 910. Around 40% of a recently published human testis specific gene set 11 overlapped with our SN10 set.

An alternative method, using the Student's 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 (description of method and results in Additional file 3).

We conducted enrichment analysis of HK and tissue-specific sets across four functional ontologies: canonical pathway maps, gene ontology (GO) processes, GeneGo (GG) process networks, and diseases, using the hypergeometric distribution 12 and Gene Set Enrichment Analysis (GSEA) algorithms 13 to determine enrichment. The hypergeometric p-values for the ten top-scored entities in each ontology for the HK set are shown in Table 2 and Additional file 4. 'Oxidative phosphorylation' was the highest scored canonical pathway map (p < 10–77). 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 (Figure 2(I)). The second-highest scored map depicts another essential process in the mitochondrial respiratory chain, ubiquinone metbolism (p < 10–40). 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 (Figure 2(II)). GG process ontology is a set of 120 networks covering most critical cellular processes. Each GG process comprises 100 to 350 functionally related proteins. Unlike GO terms, where proteins are not necessarily connected, the proteins in GG processes are linked via physical interactions, and can be visualized as networks. They therefore represent the signal flow and metabolic flux within each process. Translation initiation was another high-scored GG process (Figure 2(III)), with many translation initiation factors represented in the HK set. In GO biological processes, cellular metabolism, translation and RNA processing were among the top ten scoring categories. Neoplasm by site (p < 10–16) and breast neoplasm (p < 10–14) were the top-scored diseases (Table 2). Additionally, enrichment analysis of canonical pathway maps showed that nine out of the top ten maps are directly connected to 'growth' and 'viability' (Additional file 5).

Using the GSEA procedure 13 across the same ontologies produced similar distributions (data not shown). Importantly, HK sets from the previously published studies were generally enriched in similar categories to our own HK set across all four ontologies. For example, oxidative phosphorylation, cytoskeleton remodeling, translation and macromolecule biosynthetic process were among the top-scoring maps and processes for all HK sets (Additional file 4). Functional analysis of the unique parts of HK sets showed differences among top-scoring processes and maps. Our unique (not yet identified) HK genes showed enrichment with metabolic, translation and cell-cycle processes, validating the part of the HK set which was not identified by previous studies. Unique parts of some of the other sets, however, showed enrichment of more specific processes such as immune, inflammatory and cardiac-specific processes (Additional file 6).

All 31 tissue-specific gene lists were subjected to the same enrichment procedures as the HK set (Additional file 7). In most cases, p-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 (Table 2). Visual perception (Figure 3(I)) and retinoid metabolism, both highly specific for retina tissue, were among the top ten scored pathways maps. The top-scored GG process network was Signal transduction_Visual perception (Table 2). All ten top-scored GO processes were related to vision, including sensory perception of light stimuli, visual perception and detection of visible light. All ten top-scored diseases were eye diseases, including retinal degradation, night blindness and retinitis pigmentosa. Adrenal gland genes were enriched in disease categories including diabetes insipidus, adrenal gland diseases and adrenal cortex diseases (Table 2). The testis-specific set was highly enriched in cell cycle (meiosis), DNA exchange and cell division in all three process ontologies (canonical pathways, GO processes and GG processes). The top-scored diseases included male infertility, dyskeratosis congenita and familial dysautonomia (Table 2). Brain-specific genes yielded GO processes such as transmission of nerve impulse and synaptic transmission. Disease enrichment for the brain set included epilepsy, seizures and mental disorders (Additional file 7). Using the GSEA procedure 13 across the same ontologies produced similar distributions (Additional file 8).

We analyzed the 'interaction space' of the protein products of HK and tissue-specific genes in three steps. First, we calculated topological properties of the 'interactomes', such as degree of connectivity, average shortest path and clustering coefficient 14. Second, we evaluated the interactome structure by parsing the interactions into three components (IN, OUT and giant strong component, GSC) using the 'bow-tie' classification of Broder et al. 15. Third, we divided HK and tissue proteins into protein classes and calculated relative enrichment across particular classes. For topological analysis, we used the directional protein-protein interaction content in MetaCore™ (GeneGo Inc.). We also analyzed, for reference, four previously published HK gene sets.

All five HK sets shared similar network topology features: higher connectivity, a somewhat lower clustering coefficient, and shorter paths than the global human interactome (Figure 4A, Additional file 9). The tissue-specific networks varied substantially in their degree of connectivity, with colon and ovary-specific proteins the most interconnected, and prostate and salivary gland the least (Figure 4A, Additional file 10).

Network topology parameters were also used for tissue clustering. The topological distances between all possible tissue pairs were calculated as the ratio of the average shortest paths between proteins belonging to different tissues to the average shortest paths between proteins from the same tissue. Standard hierarchical clustering 16 was applied to generate the tissue tree. Interestingly, some of the functionally related tissues such as brain, fetal brain, retina and spinal cord were grouped together and characterized by very similar average shortest paths (Additional file 10).

An interactome can be divided into three structural parts, or 'components' 15. The GSC is the most densely connected part of the network, characterized by the property that any two nodes can be connected through directed paths in both directions. Directed paths from the GSC lead out to the OUT component and paths from the IN component go in to the GSC (Figure 4C, upper panel). In order to facilitate interpretation, the interactions were divided by 16 mechanisms (Additional file 11). The IN component is enriched with 'outgoing' interactions such as ligand-receptor binding interactions, which occur five times more frequently in IN than in GSC, and ten times more frequently than in the OUT component. Interactions between IN and GSC components also have five times more ligand-receptor interactions than between GSC and OUT. The GSC component predominantly features 'transcriptional regulation' interactions, which appear ten times more often in the GSC than the OUT, and six times more often than the IN components. The OUT component is enriched with 'incoming' interactions such as transcription factor target. Indeed, 48% of interactions between the GSC and OUT are transcription regulation compared with 4% of interaction between IN and GSC being transcription regulation. The GSC encompassed around 50% of the HK network. Overall, the fraction of HK genes was larger in the GSC (p < 10-5) and slightly lower in the IN and OUT component than a random gene set of the same size (Figure 4C). This suggests that HK proteins comprise a significant proportion of signal transduction interactions. Mammary gland and tonsil were enriched in GSC (p < 0.08 and p < 0.14); intestine and adult kidney in OUT component (p < 0.03 and p < 0.07), and skin (p < 0.1) in IN component (Figure 4C, Additional file 12).

Next, all HK and tissue-specific sets were divided into protein classes according to the MetaCore protein classification schema (Additional file 13). Enrichment in proteins of a certain class was then calculated from the hypergeometric distribution. The distribution of protein classes in the global interactome was used as a reference (Methods). HK proteins were enriched in enzymes (p < 10-8). 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 (Figure 4B). Such distributions make intuitive biological sense, and both network structure and protein class enrichment were consistent with each other.

We generated tissue-specific networks for nine tissues using the Analyze Networks (AN) 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 1718. Unlike pre-built GG process networks, AN networks are built from input lists of network objects using the manually curated interaction database in MetaCore. The networks comprise metabolic reactions, signaling interactions and canonical pathways. The tissue-specific networks for liver and adrenal gland are shown in Figure 5. The liver network is enriched with cholesterol metabolism, as well as with enzymes involved in aldosterone, estradiol and heme metabolism. The adrenal network reflects the hormonal function of that organ. It is comprised of reactions and enzymes taking part in adrenaline, noradrenaline and corticosteroid synthesis.

Tissue gene expression patterns were clustered by Euclidean distance across the average normalized probe intensities of the replicate hybridizations for each tissue 18 (Figure 6A). Most tissue clusters display evolutionary and functional relatedness. Fetal tissues clustered closest to their adult counterparts (brain-fetal brain, liver-fetal liver, thymus-fetal thymus, Figure 6A). Tissues of the same developmental origin (ectodermal, mesodermal or endodermal) tended to cluster together; for instance, heart and skeletal muscle are both derived from mesoderm, pancreas and salivary gland are part of the gastrointestinal system. Almost all of the most closely correlated tissue pairs were evolutionary or functionally related. Some genes were uniquely expressed in pairs of tissue (Figure 6B), rather than individual tissues (for instance, in brain and fetal brain) or in triplets and quadruplets of tissues (brain, fetal brain and spinal cord) (Additional file 14). Often, these proteins were specific for a group of diseases. For instance, proteins uniquely present in spinal cord, retina and brain are involved in central nervous system and neurodegenerative diseases (Figure 6C).

We 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 (3.3% and 1.5% of all proteins, correspondingly) (Additional file 15). Therapeutic targets comprised as much as 25% of mammary gland and thymus-specific proteins. The distribution of xenobiotic targets was not essentially different between HK and the sum of tissue-specific proteins, with some 1.7% of HK proteins and 2.3% of tissue-specific proteins being xenobiotic targets. Some tissues, however, were highly enriched with xenobiotic targets. For instance, over 10% of proteins specific for mammary gland, ovary, retina, small intestine and spinal cord were identified as xenobiotic targets (Additional file 15).

RNA from 31 normal human tissue RNAs (Table 1) was purchased from Clontech (Palo Alto, CA) while RNA from UHR (Universal Human Reference) was purchased from Strategene (San Diego, CA). All RNA samples were analyzed on the Agilent 2100 Bioanalyzer for RNA quality control. Three technical replicates were included for each of the tissue sample.

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 29. The gene expression data is publicly available at GEO public repository website. (GSE7905).

A '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 (more details in Additional file 17). Tissue-specific genes were defined as those uniquely expressed in a single given tissue at a S/N ≥ 10. Genes expressed in highly related tissue pairs were also defined as tissue specific for some analyses.

The enrichment levels of the HK genes and tissue-specific genes in the different parts of the network (IN, OUT, GSC) and in different protein classes were calculated using the hyper-geometric distribution:

P ( k , D , n , N ) = ( D k ) ( N − D n − k ) ( N n ) MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGaemiuaaLaeiikaGIaem4AaSMaeiilaWIaemiraqKaeiilaWIaemOBa4MaeiilaWIaemOta4KaeiykaKIaeyypa0tcfa4aaSaaaeaadaqadaqaauaabeqaceaaaeaacqWGebaraeaacqWGRbWAaaaacaGLOaGaayzkaaWaaeWaaeaafaqabeGabaaabaGaemOta4KaeyOeI0IaemiraqeabaGaemOBa4MaeyOeI0Iaem4AaSgaaaGaayjkaiaawMcaaaqaamaabmaabaqbaeqabiqaaaqaaiabd6eaobqaaiabd6gaUbaaaiaawIcacaGLPaaaaaaaaa@48CC@

We calculated the p-value corresponding to the enrichment-level according to

p ( k ) = ∑ i = k D P ( i , D , n , N ) MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGaemiCaaNaeiikaGIaem4AaSMaeiykaKIaeyypa0ZaaabCaeaacqWGqbaucqGGOaakcqWGPbqAcqGGSaalcqWGebarcqGGSaalcqWGUbGBcqGGSaalcqWGobGtcqGGPaqkaSqaaiabdMgaPjabg2da9iabdUgaRbqaaiabdseaebqdcqGHris5aaaa@4327@

which gives the probability of having k or more marked elements in a sample of size n by random selection.