Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.
Here we introduce the concept of ‘signalog’ to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates.
Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.
Citation: Korcsmáros T, Szalay MS, Rovó P, Palotai R, Fazekas D, Lenti K, et al. (2011) Signalogs: Orthology-Based Identification of Novel Signaling Pathway Components in Three Metazoans. PLoS ONE 6(5): e19240. https://doi.org/10.1371/journal.pone.0019240
Editor: Vincent Laudet, Ecole Normale Supérieure de Lyon, France
Received: November 27, 2010; Accepted: March 29, 2011; Published: May 3, 2011
Copyright: © 2011 Korcsmáros et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by EU FP6-518230; EU-ESF TAMOP 4.2.1./B-09/1/KMR-2010-0003; Hungarian Scientific Research Fund, OTKA (K69105, K75334) [http://www.otka.hu/]; Hungarian Research and Technology Office (5LET-08-2-2009-0041) [http://www.nkth.gov.hu/]; EEA fellowship (IJF); János Bolyai scholarship (TV). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Signal transduction pathways are involved in the control of various cellular processes, including cell growth, proliferation, differentiation and stress response in divergent animal phyla . In humans, dysregulation of signaling systems has been implicated in diverse pathologies, such as cancer, neuronal degeneration, muscle atrophy, immune deficiency and diabetes . To understand better the physiological and pathological roles of signaling pathways, one should generate a comprehensive signaling map (network) that ideally contains all components of distinct signaling pathways and their genetic and physical interactions. Currently, studies in model organisms ranging from invertebrates to mammals are increasingly used to create such a network . The effort to map novel signaling components and interactions has largely benefited from network alignment techniques and other widely used functional genomics methods, allowing the integration of functional data both among and within species , . For example, recent publications applied large-scale data integration and machine learning techniques to predict gene function, including signaling pathways in D. melanogaster , .
Most of these methods predict new gene or protein properties (annotations) on the basis of sequence homology and similarities between known functions. Similar annotation transfer approaches have been applied to predict structural properties (e.g., domain composition), expression profiles, and physical interactions for thousands of proteins –. For predicting interactions, for example, the network-based concept of “interologs” has been suggested: two proteins are predicted to physically interact, if their orthologs interact in another organism . Interologs, however, were found to be less conserved than orthologs , and less reliable than interactions generated by high-throughput (HTP) approaches . A clear definition of interologs and their applicability to estimate the reliability of HTP experiments ,  have been found to be useful in expanding protein interaction networks , –. The concept of interologs has been extended to “regulogs”, which can be identified by an orthology-based prediction of a regulatory interaction between a protein (i.e., a transcription factor) and a corresponding DNA sequence (i.e., a transcription factor binding site). In addition, recently, “phenologs” were used as predictors of disease-associated genes in model organisms .
In large-scale analyses, protein-protein interaction data are usually obtained from HTP experiments, such as yeast two-hybrid screens. However, the low abundance of extracellular, membrane-bound and nuclear signaling components (e.g., ligands, receptors, and transcription factors) makes these experimental techniques only moderately efficient for identifying signaling interactions. Accordingly, several signaling pathway databases have been created manually by collecting relevant data from the literature . However, so far most of them lack those key features (e.g., uniform pathway curation across more than one species) that would be necessary for transferring signaling pathway membership information between species. Reliable and detailed signaling pathway databases are crucial for signaling predictions because they are needed (1) as sources of known pathway information from which the predictions can be performed (i.e., seed data) and (2) as reference data sets against which the novelty of the predictions can be tested (i.e., those predicted signaling pathway member proteins that are already known pathway members should be removed from the list of predictions, while all others can be regarded as predicted components). Steps towards these goals have been taken, e.g., by Reactome , where pathway curation is standardized and human signaling functions are transferred from other species. Further steps could be (i) to compare predictions with already published experimental evidence in target species, (ii) to predict signaling reactions in other species, and (iii) to use a database that explicitly allows orthology-based predictions for pathway components between organisms. A recent, comprehensive pathway resource from our lab, SignaLink, applies uniform curation rules to keep the levels of details to be identical in all examined pathways for C. elegans, D. melanogaster and humans . Moreover, the structure of the SignaLink dataset allows the systematic transfer of pathway annotations between two species on the basis of sequence orthology.
Interestingly, in two different organisms the same signaling pathway is often known at different levels of detail. This may be due to evolutionary divergence or to differences between the current coverage of the two organisms' interaction maps. Therefore, large-scale pathway annotation transfer between these 3 organisms can extend our current knowledge of their signaling pathways. Note that in cases of rapid evolution, orthology-based predictions are less reliable as even the orthologs exist, they no longer participate in the same signaling pathway .
The topology of signaling pathways is important for selecting possible novel drug target candidates . As an example, drugs used for inhibiting a specific signaling protein in order to affect proliferation may actually activate the pathway by triggering an unknown negative feedback loop . Transferring signaling pathway annotations across species may alleviate such difficulties and can provide a more comprehensive signaling network. Identification of novel signaling components may help to discover drug targets as (i) these signaling components can increase the applicability of model organisms for testing drugs and drug target candidates, (ii) in humans, they can serve as potential novel drug targets, and (iii) in the case of already used target proteins they can help to uncover possible side-effects.
Here we introduce the concept of ‘signalog’ to predict a protein as a novel component of a signaling pathway based on the signaling pathway membership of its ortholog in another organism. We identify signalogs on genomic scale in 8 signaling pathways, including the MAPK, TGF-β, and WNT pathways (for a complete list, see the Methods) from 3 intensively investigated species: C. elegans, D. melanogaster and humans, and verify their novelty and predictive power, using both bioinformatics and experimental methods. We also show the utility of the signalog concept in drug target discovery.
Source of signaling components and interactions
To predict a role for a protein (i.e. provide a successful annotation), the quality of the original sources from which the annotation transfer (prediction) can be performed is crucial. The original pathway data, including the lists of proteins of 8 distinct signaling pathways and their interactions were obtained from the SignaLink database (http://www.signalink.org) . The 8 pathways examined in this study were the EGF/MAPK (epidermal growth factor/mitogen activated protein kinase), TGF-β (transforming growth factor-beta), insulin/IGF-1 (insulin-like growth factor-1), Notch, WNT/Wingless, Hedgehog, JAK/STAT (Janus activating kinase/signal transducer and activator of transcription), and NHR (nuclear hormone receptor) pathways. The basic properties of the SignaLink database are as follows: the components of the 8 pathways in C. elegans, D. melanogaster, and H. sapiens were compiled by applying uniform curation rules to keep the level of detail identical for all pathways examined; proteins were assigned to pathways based on literature data; and interactions were listed manually from original publications presenting biochemical evidence . Note that the lower number of pathways in SignaLink, compared to other sources, is largely due to its more precise pathway definition rules. This approach avoids artificial grouping and reduces the number of pathways without reducing the numbers of proteins and interactions. (For an extensive comparison and benchmarking, see the .) Note that SignaLink uses more than 20 reviews per each pathway to list pathway components, in contrast to the average 5–15 reviews per a pathway in other resources . The structure of SignaLink allows the systematic transfer of pathway annotations between two species on the basis of sequence orthology (see the next section).
Orthology assignment for identifying signalogs
Sequence-based approaches, also in combination with interaction networks, have been frequently applied to detect orthology relationships between proteins , . For example, the tool PathBLAST aligns an ordered list of proteins or pathways on the basis of their ortholog relations . In the Clusters of Orthologous Groups (COG) database, orthologous groups are defined through reciprocal best BLAST matches between proteins from at least three species , . Furthermore, sequence clustering techniques incorporate a range of BLAST scores (not only the absolute best hits) and can achieve a higher sensitivity . One of these techniques, InParanoid , distinguishes between outparalogs, i.e., homologous sequences that emerged by duplication before speciation, and inparalogs that emerged after speciation. Compared to outparalogs, inparalogs are more likely to share functions. InParanoid incorporates the entire list of BLAST E-values (not only the top values) to group the proteins of the two compared organisms into orthologous clusters . Each cluster contains proteins with related sequences from the two species, and each protein has an Inparalog score (for the calculation of this score, see ).
Previously, during the compilation of the SignaLink database, InParanoid data (version 6.1) were applied to find known signaling proteins by orthology searches , . Based on SignaLink, now we can link a protein with a previously unknown signaling role to a signaling system, if the protein has an ortholog as a clearly identified component of a signaling pathway in another organism. For a protein with more than one ortholog (according to its InParanoid orthologous cluster), we used only those orthologs that have an Inparalog score higher than 0.3. To confirm that signalogs have no previously identified signaling interactions, we checked them with the protein-protein interaction search engines iHOP and ChiliBot , .
Orthology-based pathway annotation transfer
In each of the three species examined (C. elegans, D. melanogaster, and H. sapiens), we listed those proteins that have no known signaling interactions but have at least one signaling pathway member ortholog in the other two species. Similarly to the concept of functional orthology , for each of these proteins we assumed that their pathway annotations (i.e., signaling role) can be transferred between species. In other words, we predicted that such a protein is a member of the signaling pathway(s) to which its ortholog(s) in the organism belong(s). These proteins were termed as signalog proteins (signalogs). Note also that in SignaLink  a protein can belong to more than one pathway. Thus, a signalog can also be annotated to more than one pathway. Figure 1 shows the workflow of our analysis.
The lists of signaling proteins and their interactions were obtained from the SignaLink database (, http://signalink.org). Orthology assignment was performed between each pair of the 3 species. Proteins were predicted to be members of the same signaling pathway(s) where their orthologs belong.
Verifying the novelty of signalog predictions
To verify the novelty of signalog predictions, i.e., the predicted signaling roles have not been known or predicted in other resources yet, we have (i) searched the literature with semi-automated methods for already known annotations, (ii) compared the list of signalogs and their predicted pathway memberships to known pathway annotations in pathway databases, and also (iii) compared the ortholog predictions to previously published interolog predictions.
To grade the novelty of signalogs (signaling pathway annotations) and quantify the confidence level of each prediction, we performed semi-automated searches using PubMed, UniProt, GO, Wormbase, FlyBase, iHOP, and Chilibot web services –. During this process, direct manual curation and Python scripts checking multiple proteins in one webservice were used. In each of the 3 species examined, we classified the predicted signalogs into 5 groups on the basis of their known properties in the literature: (1) no orthology information and/or no biochemical function is available; (2) there are known orthologs with unknown biochemical function; (3) orthology information: unknown, biochemical function: known; (4) known orthologs with known biochemical function(s); (5) known orthologs with known biochemical function and already known pathway annotation(s). Categories 1 to 5 denote a decreasing level of novelty. However, even category (5) contains signalogs for which at least one novel signaling pathway membership is predicted. Furthermore, to check the novelty of the predicted signaling pathway memberships, we compared the list of signalogs and their predicted pathway memberships to known pathway membership annotations from Reactome and KEGG , .
Next, we applied interologs to verify the novelty of our ortholog predictions. (An interolog is a pair of proteins predicted to interact based on the interaction of the two proteins' orthologs in at least one other organism .) To reveal the presence of signalogs in current orthology-based prediction databases, we compared already identified interologs in worms, flies, and humans using 3 species-specific datasets (WI8, DroID, and HomoMINT) , ,  with interologs generated from SignaLink data. Neither SignaLink  nor the current signalog identification approach identify interologs directly, thus we used an indirect method. First, we deduced interologs from SignaLink data by linking two proteins in an organism, if their orthologs interact in at least one other of the three organisms. After generating all possible interologs from SignaLink, we examined only those interologs (predicted interactions) in which at least one of the interactors is a signalog protein (predicted signaling pathway member).
Experimental validation of signalogs
We confirmed experimentally the predicted signaling pathway memberships (signaling roles) of 6 signalogs. Out of the 21 Notch signalogs in C. elegans, we selected 6 genes (aqp-6, D1009.3, nsh-1, num-1, F10D7.5 and crb-1) that have no paralogs, i.e., homologs in the same organism. These genes encode diverse proteins: their orthologs in the other two species include receptors, co-factors, and transcription factors (Table 1). Neither literature search (in PubMed) nor interaction searches in STRING 8.0  provided experimental evidence for the signaling role of these proteins in the C. elegans Notch pathway. To validate that the selected 6 signalogs indeed function in the Notch signaling pathway, we tested whether they genetically interact with lin-12, which encodes a worm Notch receptor .
Wild-type strain was used as Bristol N2 . The other strains used in this study were RB676 num-1(ok433)V, FX02407 aqp-6(tm2407)V, RB1267 D1009.3(ok1349)X, RB1011 crb-1(ok931)X, and MT2343 dpy-19(e1259)III lin-12(n137)III/unc-32(e189)III lin-12(n137n720)III. The allele n137 is a gain-of-function mutation in lin-12, which confers ectopic vulval induction in mutant hermaphrodites. These lin-12(n137) mutant animals exhibit a Multivulva (Muv) phenotype (the mutants possess 2–6 vulval protrusions, whereas wild-type hermaphrodites have normally one vulval structure, which does not protrudes from the ventral surface of the body). Strains were cultured as described previously . For the selected genes that are not yet characterized mutationally and for lin-12, we applied RNA interference- (RNAi) based gene downregulation. For RNAi, 0.9 kbp (nsh-1) - 1.0 kbp long (F10D7.5) cDNA fragments were amplified by RT-PCR, and were cloned into the vector pPD129.36. The constructs were then transformed into E. coli HT115. RNAi experiments were performed as described . Control strains were fed with HT115 bacteria expressing the empty RNAi vector. The following forward and reverse primers were used. For nsh-1: 5′-GGC TTT AAT GGC TTC ACG AG-3′ and 5′-AAG GAA GAA CTG TCG CTT GC-3′, for F10D7.5: 5′-AAG CGT TGA TCC GTA GAC ATC-3′ and 5′-TCG AGA TTG ACG AGA ACG TG-3′, and for lin-12: 5′-CGC TTC ATA TTG GCT CAT GTC-3′ and 5′-CCA GCT TCG CAT TTA TTA TTC AC-3′.
As num-1(ok433), aqp-6(tm2407) D1009.3(ok1349), and crb-1(ok931) single mutant animals looked superficially wild-type, we treated these mutant animals with lin-12 double-stranded RNA (dsRNA), and the average number of vulval protrusions were determined at their young adulthood. lin-12(gf) mutant animals were also treated with nsh-1 or F10D7.5 dsRNA, and tested for the penetrance and expressivity of the Muv phenotype. The significance of results was tested with Chi square tests.
Functional annotation of signalog proteins
To examine the drug target relevance of the predicted signaling pathway member proteins, we downloaded additional information from DAVID , and disease-related data from OMIM, GAD and Orthodisease –. Protein domain information was extracted from InterPRO (drug-protein interactions are based on structural properties) . Functional and cellular compartment data were obtained from GO . The list of currently used drug targets was downloaded from DrugBank (version 2.5) .
Computational prediction and analysis of novel signaling components
We identified novel signaling pathway components based on the signaling pathway memberships of orthologs in another organism. We found 88, 92, and 73 proteins in C. elegans, D. melanogaster and H. sapiens, respectively, which had previously not been assigned to a signaling system, but have at least one ortholog in the other two species that is clearly associated with a signaling pathway. We hypothesized that these 253 proteins function in the same signaling pathways as their orthologs. Thus, we named the predicted signaling components signalog proteins, or briefly signalogs. Note that in contrast to an interolog, a signalog is a single protein (not an interacting pair) that has an ortholog annotated to a signaling pathway. In the three species examined our in silico approach predicted 301 novel signaling pathway annotations in total (39 of the 253 signalogs were assigned to more than one signaling pathway). For the complete list of the predicted signalog proteins and their pathway annotations, see Table S1.
After a detailed analysis of the predicted pathway annotations of signalogs, we found that in each of the three species the EGF/MAPK pathway contains the largest portion of signalogs (25% of the signalogs in the in worm, 42.5% in the fly and 28.6% in humans), which is consistent with the fact that this pathway contains the highest number of signaling components  (see also Fig. 2). Interestingly, in C. elegans a similarly high number of signalogs was predicted as a potential Hedgehog component (29 proteins; 25.9%). Note that a canonical Hh pathway has not been identified in C. elegans due to specific gene loss . Our present study and previous analyses however show that several components of the canonical Hedgehog pathway are present in this organism . In flies a large portion of signalogs appears in the WNT and TGF-β pathways, whereas in humans, several signalogs were associated with the WNT, Notch and Hedgehog pathways (for details, see Fig. 2). Beside pathway membership predictions, we placed the signaling orthologs into a wiring diagram of signaling networks. This network can be examined with an interactive ortholog network viewer (described below).
The number of predicted novel signaling pathway components (signalogs) in each pathway for the three species examined. The total number of signalogs in each species is shown in parentheses. Although we predicted a total of 253 signalogs, this statistical comparison contains 301 novel pathway components, because 39 signalog proteins were assigned to more than one pathway (these 39 had a total of 87 pathway annotations).
Next, we tested the concept of orthology-based pathway annotation transfer by testing whether known signaling proteins have known signaling pathway member orthologs. We analyzed the three organisms separately. For each organism we listed all signaling pathway proteins from SignaLink and then listed, with InParanoid (version 6.1) , all orthologs of these signaling proteins in the other two species. We found at least one ortholog in the other two species for 93.4% (worm), 81.6% (fly), and 64.8% (humans) of all signaling proteins and we found that 83.2%, 67.5%, and 82.6% of these orthologs indeed participate in a signaling pathway. Thus, a high portion (77.8% on average) of known signaling proteins has at least one ortholog with a known signaling function in the other two organisms. Moreover, on average 67.8% of signaling pathway member proteins have at least one ortholog in the other two species with an identical pathway membership. These high ratios underscore the relevance of our orthology-based signaling component prediction.
Verifying the novelty of predictions
To examine the novelty of the predicted signaling roles and to quantify their confidence levels, we first classified these predicted proteins into 5 groups on the basis of their known properties. These groups range from genes for which only the ORF is known to genes whose protein products have known molecular function(s) (for details see the Methods and Fig. 3a). In each of the three organisms examined, we found only one protein already known as a signaling pathway component; for these three proteins we predicted additional (novel) pathway annotations. In C. elegans and D. melanogaster, most signalogs (55.7% and 58.7% of all, respectively) have not yet been characterized biochemically, while in humans, only 26% of the signalogs remain uncharacterized. Note that this lower rate is partly due to the larger abundance of literature information on signaling in humans compared to worms and flies (Chi square test, p<0.0001). Taken together, we conclude that signalog prediction can effectively contribute to the identification of novel signaling components.
a) Classification of signalog proteins based on their known properties in the literature found by manual curation and searches in PubMed, UniProt, GO, Wormbase, Flybase, iHOP, and Chilibot –. b–c) Comparison of signalog and interolog data between Signalink and one of the WI8, DroID or HomoMINT databases. The signalog proteins and possible interologs that are only present in SignaLink have not been predicted by previous orthology based (interolog) studies. This underscores the novelty of the signalogs. For details on interologs predicted from SignaLink data, see the main text.
Next, we verified that our signolog predictions are novel in the sense that the predicted signaling roles have not been known or predicted by other resources yet. We compared the list of signalogs and their predicted pathway memberships to known pathway membership annotations from KEGG and Reactome databases. Note that the KEGG database contains pathway data for all three species examined, while Reactome database contains only human data , . For C. elegans, D. melanogaster and H. sapiens, we found 23, 19 and 35 signalog proteins, respectively, already present in KEGG, i.e., 20%, 26%, and 47% of all signalogs in the three organisms. From these proteins only 11, 5 and 15, respectively – i.e., 13.3% on average – were assigned to the same pathway in KEGG as in our prediction. Reactome contains 16 of the listed human signalogs (22% of all), but only 1 in the same pathway, as in our prediction. We conclude that, depending on the organism and the database, 20 to 47% of all signalog proteins are already present in KEGG or Reactome, however, the large majority (86.7%) of pathway memberships we predicted for them are novel.
Finally, to reveal the presence of signalogs in current orthology-based prediction databases, we used interologs (orthology-based predicted interactions). We compared the interologs generated for this test from the SignaLink dataset with the interologs listed for worms, flies and humans by 3 species-specific databases (WI8, DroID, and HomoMINT , , ) (see the Methods). We examined only those interologs, where at least one of the interactors is a signalog protein. We found that in worms, flies and humans, respectively 34, 30, and 48 signalogs are present only in the SignaLink dataset,indicating that a high portion of the predicted proteins has not yet been investigated by this orthology-based prediction method (Fig. 3a). Altogether, in SignaLink and in the 3 species-specific resources, we found 1028, 1338, and 465 interologs in worms, flies and humans, respectively. The overlap between interologs generated from SignaLink and the interologs from any of the other 3 databases was relatively low: 5.5% in worms, 38.8% in flies and 12.5% in humans (Fig. 3b). Shared interologs can be interpreted as already known orthology-based predictions. A low number of overlapping interologs suggests that most of our current signalog predictions are novel. The high number of novel interologs is probably due to (i) the uniform curation method used for compiling SignaLink for all three species; (ii) the underrepresentation of signaling proteins in the three species-specific interolog datasets [5% of all signaling proteins in worms (WI8) and flies (DroID), and 2.8% in humans (HomoMINT)]; and (iii) the stringency of the interolog filtering algorithm HomoMINT . We conclude that uniformly curated data sources, such as SignaLink, can facilitate orthology-based predictions.
While this study was in progress, Yan et al. predicted gene function for Drosophila with a large-scale machine learning technique . 1121 genes were predicted to function in the same pathways as those examined in our study. We performed an additional test with this fly-specific dataset to quantify the novelty of our predictions. (Note that Yan et. al.  listed the ErbB, JNK, and MAPK pathways separately, following KEGG , however, in SignaLink these (sub)pathways all belong to the EGF/MAPK pathway in agreement with Ref. ). Out of the 92 fly signalogs predicted by the current paper, only 27 genes are listed in this large-scale study, i.e., two-thirds of all fly signalogs still remain novel predicted signaling components. In the large-scale study of Yan et. al. these shared 27 genes have 42 pathway annotations (many of them belong to more than one pathway), while here we predict 34 pathway annotations for them. 15 out of these pathway annotations are identical (for 14 of the 27 shared genes). Interestingly, a large-scale study predicted that p38b functions in 2 pathways (EGF/MAPK and TGF), while in our study 2 additional pathways (JAK/STAT and WNT) were predicted for this gene. In conclusion, the small overlap of the predicted novel signaling pathway genes and their predicted pathways verifies the novelty of the signalogs listed in the current paper and the predictive power of the method.
Experimental validation of Notch pathway member signalogs in C. elegans
The Notch pathway controls cell growth, differentiation and proliferation during normal animal development ; in humans, aberrant Notch signaling has been implicated in various pathologies such as cancer and neurodegeneration . Therefore, identifying novel Notch pathway components may have a significant impact on developmental and biomedical research. To test experimentally the relevance of our signalog predictions, we assessed whether the genes that encode the 6 newly identified non-paralogous Notch pathway components in C. elegans (aqp-6, D1009.3, nsh-1, num-1, F10D7.5 and crb-1) genetically interact with lin-12, which encodes a nematode Notch receptor (see Table 1 for further information on the selected 6 genes). lin-12 is a key regulator of vulval patterning as it specifies the so-called (2°) secondary vulval cell fate during pattering of this tissue . Thus, a genetic interaction between lin-12 and a selected signalog (gene) would clearly indicate the participation of that gene in Notch signaling.
During C. elegans vulval development, six originally equivalent ventral epidermal cells, called vulval precursor cells [VPCs, consecutively numbered as P(3–8).p], are specified into one of three distinct – primary (1°), secondary (2°) or tertiary (3°) – vulval cell fates by the combined action of different signaling systems, including the Ras/MAPK, WNT, Notch and synMuv (for synthetic Multivulva) pathways  (Fig. 4a). Recently, the nematode sex determination pathway was also implicated in vulval fate determination . At the L3 larval stage, the inductive Ras and WNT signaling cascades promote vulval fates in the three central VPCs, P(5–7).p; descendants of these cells eventually form the matured vulval tissue. A LIN-12/Notch-mediated lateral signal emitted from P6.p specifies 2° fates by attenuating Ras signaling in the adjacent VPCs, P(5,7).p , . An inhibitory signal mediated by the synMuv genes antagonizes Ras signaling to repress vulval fates in each VPC . As a result of these inductive, lateral and inhibitory signaling events, P6.p adopts a primary (1°) vulval fate, while its adjacent VPCs, P(5,7).p, adopt secondary (2°) vulval fates. The non-induced VPCs, P(3,4,8).p, adopt non-vulval tertiary (3°) fates. In wild-type hermaphrodites, P(3–8).p always adopt the 3°-3°-2°-1°-2°-3° stereotypical pattern of vulval fates.
a) During normal C. elegans vulval development, the pattern of vulval precursor cells is determined by the combined action of distinct signaling pathways, including the Ras/MAPK, Wnt, Notch, and synMuv systems. Activations and inhibitions are represented with normal or blunted arrows. AC: anchor cell. Ras signaling activity is graded along its relative distance from the AC cell (displayed with thick, thin, and dotted red lines). b) Protruded vulva (Pvl, main panel) and wild-type vulva (N2) (inset). c) Ectopic vulval protrusions (arrows) and a normal vulval structure (dotted arrow) in a Muv animal. d) Penetrance of Pvl and normal vulval phenotypes in loss-of-function mutant animals treated with lin-12 RNAi. In each case the mutation significantly increases the penetrance of Pvl phenotype. e) Average number of vulval inductions in lin-12(gf) mutant animals treated with nsh-1 or F10D7.5 RNAi. Observe that both nsh-1 RNAi and F10D7.5 RNAi have smaller numbers of vulvae than the control strain, lin-12(gf). Asterisks denote statistically significant differences. For details of the statistics, see Text S1.
Perturbation of signals specifying vulval fates often results in a visible mutant vulval phenotype. Inactivation of lin-12 causes a Protruded vulva (Pvl) phenotype that is due to the misspecification of 2° vulval cells; in lin-12 loss-of-function (lf) mutant hermaphrodites each induced VPC [P(5–7.p] adopts a 1° fate (Fig. 4b), whereas lin-12 gain-of-function (gf) mutations confer VPCs [P(3–8).p] to adopt 2° fates, resulting in multiple vulval protrusions, i.e., a Multivulva phenotype (Muv) (Fig. 4c).
To reveal whether the 6 selected C. elegans Notch signalogs (Table 1) indeed function in this pathway, we first treated aqp-6, crb-1, num-1, and D1009.3 loss-of-function mutant animals with lin-12 dsRNA, and monitored the penetrance of the Pvl phenotype, compared to control lin-12(RNAi) animals. We found that lin-12 RNAi treatment of wild-type animals cause a Pvl phenotype with 17% penetrance. As compared, lin-12 RNAi in aqp-6, crb-1, num-1, and D1009.3 single loss-of-function mutant background significantly increased the penetrance of the Pvl phenotype (Fig. 4d). Note that these single mutants treated with control RNAi (empty vector) displayed a superficially wild-type (non-Muv) vulval morphology.
We next treated lin-12 gain-of-function mutant animals with nsh-1 or F10D7.5 dsRNA (these two genes have not yet been characterized by mutant alleles) and monitored the average number of vulval protrusions, compared to those found in lin-12(gf) animals expressing the control RNAi alone. Of these lin-12(gf) mutants, 93% were Muv and the number of vulval inductions was 3.27+/−0.19. The penetrance and expressivity of the Muv phenotype in lin-12(gf) mutants was reduced by nsh-1 or F10D7.5 RNAi treatment. Depletion of NSH-1 decreased the penetrance of the Muv phenotype to 83%, whereas silencing of F10D7.5 reduced it to 81%. Both RNAi interventions significantly reduced the effect of lin-12 hyperactivity on vulval induction (Fig. 4e). In summary, we found that all 6 selected genes significantly alter vulval induction in lin-12(RNAi) animals and lin-12(gf) mutants (for results, see Fig. 4; for statistics, see Text S1). Thus, all 6 genes genetically interact with lin-12 and may participate in Notch signaling. Note that these genetic interactions between the 6 tested genes and the worm Notch receptor may be indirect. Further, in depth biochemical studies are needed to uncover the details of these connections, the actual roles of the 6 encoded proteins in the Notch pathway, and their roles in other pathways involved in vulval formation.
On-line prediction and visualization tool for orthologous signaling networks
To facilitate the adaptability of the signalog prediction concept, we developed an on-line tool. This tool is available at http://signalink.org/signalog and performs the same workflow that was presented in this study. After selecting the target species of our prediction (worm, fly, or human), the user can enter a search term (arbitrary protein or gene name/ID). The on-line tool understands a variety of names and IDs, and also name fragments. Next, an integrated UniProt API synonym search helps the user to select the actual protein . The selection of the protein is followed by an ortholog search in the InParanoid database , and the list of known orthologs is listed for the other 2 species. Next, the pathway memberships of the orthologs are shown, and the user can select a pathway of interest. Currently, the tool uses only SignaLink pathway data. If other sources become available with uniform pathway curation across all curated species and pathways, which is crucial for proper pathway annotation transfer between species, the on-line tool is capable to include these resources as well. In the last step, a pathway membership prediction is performed for the queried protein. If the queried protein is not known to be a member of the examined pathway, then it is a signalog of that pathway. The result is presented with a confidence score and a newly developed visualization tool.
Despite the relatively large number of currently available interaction visualization tools , only few visualize known and predicted proteins and/or interactions in parallel. The viewer of the MINT database is one such example . This viewer displays interactions predicted from model organisms, but not from humans. Note that MINT  is a general protein-protein interaction database containing significantly less signaling pathway information than analyzed here. We developed an interactive ortholog network viewer available at http://signalink.org and http://signalink.org/signalog. This viewer can simultaneously visualize known and predicted pathway membership information, i.e., pathway annotation transfers (see snapshot in Fig. 5 as an example), and allows the user to analyze, individually or together, the examined 8 signaling pathways in the 3 species. Interactive features of this signaling network viewer include zooming in/out and panning possibilities, and switching the pathway view between organisms. Proteins are hyperlinked to species-specific databases and interactions are hyperlinked to the PubMed abstract of the article(s) providing experimental evidence. Known signaling pathway member proteins and signaling interactions are visualized with colors different from predicted ones.
This figure displays a subset of the C. elegans Notch pathway with selected predicted pathway member proteins and their properties. This image includes multiple snapshots taken from the interactive ortholog viewer available at http://signalink.org. Links representing interactions with direct and indirect evidence are displayed as normal and dashed line styles, respectively. Activations (and inhibitions) are represented with normal (and blunted) arrows.
Supporting drug target discovery with signalogs
Signaling proteins are overrepresented among human disease genes  and have been intensively studied as potential drug target candidates , . According to DrugBank , only 5 (6.8%) out of the 73 human signalog proteins identified here are currently considered as drug targets (Table 2). Interestingly, the ratio of disease-related proteins among human signalogs is much higher: 18 out of 73 (24.6%). The remaining 68 human signalog proteins not yet implicated as drug targets may serve as further candidates (Table S2).
In C. elegans and D. melanogaster, only 44 and 58 orthologs of human disease-related proteins (i.e., orthodisease proteins) have been annotated to signaling pathways, respectively . In addition, we found pathway annotations for 10 (worm) and 9 (fly) additional orthodisease proteins among signalogs (see Table 3 and Table 4 for the lists of these proteins). For example, the human tyrosine-protein phosphatase SHP-2 protein has a single worm ortholog, ptp-2, which has not been annotated to any signaling pathway prior to our current study. On the other hand, the role of SHP-2 in multiple kinase pathways – including MAPK, JAK/STAT, and IGF – is well established. Thus, identifying novel signaling components via their orthologs may help future experimentation in model organisms and the description of the underlying disease mechanism.
Finally, we tested the drug target relevance of human signalogs by examining 4 key drug-related properties: disease-relatedness, localization in the plasma membrane, enzymatic function, and kinase domain content (see Fig. 6a) –. To analyze the drug target related importance of the 73 human signalog proteins identified, we first selected 2 proteins (ANPRA [P16066] and CASK [O14936]) that have all 4 key properties (Fig. 6b). Both are established drug targets which supports the relevance of our analysis based on the 4 key properties. Our prediction suggests signaling roles for ANPRA and CASK in the WNT and EGF/MAPK pathways, respectively. We also predicted signaling roles for 3 additional proteins already used as drug targets (ABL2 [P42684], UGDH [O60701], and NDST1 [P52848]) (Fig. 6b). Novel pathway annotations of these drug targets are likely to provide additional details about their mechanisms of action, enrich therapeutic relevance, and warn of potential side effects. Following the top scoring set we analyzed those 2 proteins, INSRR [P14616] and MARK2 [Q7KZI7], that still have 3 of the 4 key properties, but are currently not used as drug targets and are not known to be disease-related. We predicted that INSRR functions in the IGF/Insulin pathway, while MARK2 functions in both the EGF/MAPK and Wnt pathways. Participation of MARK2 in more than one pathway increases its relevance for drug target discovery , . Table 2 lists the predicted signaling roles and drug target-like properties for the most promising drug target candidates and the drugs targeting the already used drug targets. The complete list and drug target-like properties of signalogs can be found at http://signalink.org and in Table S2.
a) Numbers of membrane proteins (M), enzymes (E), proteins with a kinase domain (K), and disease-related proteins (D) among signalog proteins (control: all proteins of the SignaLink database). b) Numbers of signalogs in the groups defined by the number of key properties out of the listed 4 (M, E, K, and D). For each group, the number of drug targets is shown separately. The names of the most promising drug target candidate proteins and 2 already known drug targets (both do have all 4 key properties) are listed. Table S2 lists all human signalog proteins and their drug target relevance scores.
In our report we introduced a method for predicting novel signaling pathway components, signalogs, in 3 species, based on the signaling roles of their orthologs in other organisms. We identified altogether 253 signalog proteins in two model organisms – C. elegans and D. melanogaster – and humans. In addition, we developed and on-line tool allowing the users to predict signalogs and visualized known pathway data and predictions simultaneously with an ortholog network viewer. This viewer has distinctive features that can facilitate the interactive (user-defined) investigation of orthologous signaling networks, and it can visualize the predicted pathway components and their possible interactions, leading to the establishment of additional signalogs in later updates of the SignaLink database. The novelty of our predictions was verified by analyzing key properties, known pathway annotations, and already predicted interactions of signalogs. In C. elegans, we experimentally validated the signaling role of 6 predicted novel Notch pathway proteins. We anticipate that signalogs and, especially, orthodisease proteins of model organisms (see Table 3 and Table 4 and ref. ) can facilitate the design of novel, low cost primary drug screens with fewer tests in vertebrates. Our current study predicted signaling pathway memberships for 5 currently used drug target proteins and suggested 14 additional proteins that can be used as novel drug target candidates, based on their predicted pathway annotations and other properties (Table 2). Our predictions may (i) reveal novel therapeutic intervention points (e.g., the use of signalogs as novel targets to block specific pathways); (ii) suggest novel applications of current drugs to diseases, where the newly predicted signaling pathway of their target is relevant, and (iii) help to identify possible side effects of currently used drugs , , .
The identification of novel signaling components may have an impact on various fields of biology. Extended signaling annotation may allow a better explanation of unexpected mutant phenotypes by linking the altered (signalog) gene to a signaling pathway with known phenotypes. Biological research often focuses on altering the function(s) of a single selected protein. However, this may cause undesired dysregulation of a signaling pathway, interfere with multiple cellular processes and lead to pleiotropic effects. Therefore, constructing more comprehensive signaling networks and identifying novel signaling components can certainly improve the design and evaluation of experiments.
In the postgenomic era novel tools and methods are constantly needed to integrate genomic information with cellular processes. Current knowledge on signaling pathways is far from complete. ‘White spots’ can often be filled with the orthology-based transfer of ‘complete’ signaling system annotations between species, for example, by the method presented here. This method can be effectively used for the prediction of novel signaling proteins. The InParanoid database contains more than 100 eukaryotic genomes and its downloadable algorithm can be applied to other genomes as well, thus, orthology information is not a limiting factor for signalog predictions. Currently, signalog predictions for other genomes are limited mainly by the absence of proper signaling pathway data sources. Most importantly, the curation rules of databases should be uniform across all analysed organisms and signaling pathways. Such databases would be essential for signalog predictions both as seeds and as reference data sets. Unfortunately, for species not yet listed in the SignaLink database, current comprehensive signaling maps (databases) do not contain data curated with these guidelines in sufficient quantities that would allow the prediction of novel signaling components. The on-line tool was designed such that extensions can be added easily and we will include more species and pathways as soon as they become available. Based on the results, examples, and experimental work of this study, we believe that the predicted signaling pathway memberships (signalogs) will be a good source of functional hypotheses to be experimentally verified in all three investigated organisms.
Predicted signaling pathway member proteins (signalogs). Protein names, species-specific identifiers, UniProt identifiers, and pathway annotations for all signalog proteins in the three investigated species.
List of human signalog proteins and their drug target relevances. (1) Current drug target proteins and their signaling roles predicted in the current paper. (2) Predicted pathway annotations and further properties of proteins that are possible novel drug targets.
The authors thank C. Böde for help with the statistical evaluation; L. Zsákai and Z. Spiro for discussions and testing predictions, and two anonymous reviewers for their comments.
Conceived and designed the experiments: TK IJF TV. Performed the experiments: TK PR IJF. Analyzed the data: TK IJF KL TV. Contributed reagents/materials/analysis tools: MSS RP DF IJF. Wrote the paper: TK IJF PC TV. Developed the on-line tool: MSS. Developed the ortholog network viewer: RP.
- 1. Pires-daSilva A, Sommer RJ (2003) The evolution of signalling pathways in animal development. Nat Rev Genet 4: 39–49.A. Pires-daSilvaRJ Sommer2003The evolution of signalling pathways in animal development.Nat Rev Genet43949
- 2. Sakharkar MK, Sakharkar KR, Pervaiz S (2007) Druggability of human disease genes. Int J Biochem Cell Biol 39: 1156–1164.MK SakharkarKR SakharkarS. Pervaiz2007Druggability of human disease genes.Int J Biochem Cell Biol3911561164
- 3. Beyer A, Bandyopadhyay S, Ideker T (2007) Integrating physical and genetic maps: from genomes to interaction networks. Nat Rev Genet 8: 699–710.A. BeyerS. BandyopadhyayT. Ideker2007Integrating physical and genetic maps: from genomes to interaction networks.Nat Rev Genet8699710
- 4. Gabaldon T, Huynen MA (2004) Prediction of protein function and pathways in the genome era. Cell Mol Life Sci 61: 930–944.T. GabaldonMA Huynen2004Prediction of protein function and pathways in the genome era.Cell Mol Life Sci61930944
- 5. Kuzniar A, van Ham RC, Pongor S, Leunissen JA (2008) The quest for orthologs: finding the corresponding gene across genomes. Trends Genet 24: 539–551.A. KuzniarRC van HamS. PongorJA Leunissen2008The quest for orthologs: finding the corresponding gene across genomes.Trends Genet24539551
- 6. Yan H, Venkatesan K, Beaver JE, Klitgord N, Yildirim MA, et al. (2010) A genome-wide gene function prediction resource for Drosophila melanogaster. PLoS One 5: e12139.H. YanK. VenkatesanJE BeaverN. KlitgordMA Yildirim2010A genome-wide gene function prediction resource for Drosophila melanogaster.PLoS One5e12139
- 7. Costello JC, Dalkilic MM, Beason SM, Gehlhausen JR, Patwardhan R, et al. (2009) Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function. Genome Biol 10: R97.JC CostelloMM DalkilicSM BeasonJR GehlhausenR. Patwardhan2009Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function.Genome Biol10R97
- 8. Yellaboina S, Dudekula DB, Ko MS (2008) Prediction of evolutionarily conserved interologs in Mus musculus. BMC Genomics 9: 465.S. YellaboinaDB DudekulaMS Ko2008Prediction of evolutionarily conserved interologs in Mus musculus.BMC Genomics9465
- 9. Storm CE, Sonnhammer EL (2003) Comprehensive analysis of orthologous protein domains using the HOPS database. Genome Res 13: 2353–2362.CE StormEL Sonnhammer2003Comprehensive analysis of orthologous protein domains using the HOPS database.Genome Res1323532362
- 10. Salgado D, Gimenez G, Coulier F, Marcelle C (2008) COMPARE, a multi-organism system for cross-species data comparison and transfer of information. Bioinformatics 24: 447–449.D. SalgadoG. GimenezF. CoulierC. Marcelle2008COMPARE, a multi-organism system for cross-species data comparison and transfer of information.Bioinformatics24447449
- 11. Yu H, Luscombe NM, Lu HX, Zhu X, Xia Y, et al. (2004) Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome Res 14: 1107–1118.H. YuNM LuscombeHX LuX. ZhuY. Xia2004Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs.Genome Res1411071118
- 12. Persico M, Ceol A, Gavrila C, Hoffmann R, Florio A, et al. (2005) HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms. BMC Bioinformatics 6: Suppl 4S21.M. PersicoA. CeolC. GavrilaR. HoffmannA. Florio2005HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms.BMC Bioinformatics6Suppl 4S21
- 13. Cusick ME, Yu H, Smolyar A, Venkatesan K, Carvunis AR, et al. (2009) Literature-curated protein interaction datasets. Nat Methods 6: 39–46.ME CusickH. YuA. SmolyarK. VenkatesanAR Carvunis2009Literature-curated protein interaction datasets.Nat Methods63946
- 14. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, et al. (2009) STRING 8–a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 37: D412–D416.LJ JensenM. KuhnM. StarkS. ChaffronC. Creevey2009STRING 8–a global view on proteins and their functional interactions in 630 organisms.Nucleic Acids Res37D412D416
- 15. Li D, Liu W, Liu Z, Wang J, Liu Q, et al. (2008) PRINCESS, a protein interaction confidence evaluation system with multiple data sources. Mol Cell Proteomics 7: 1043–1052.D. LiW. LiuZ. LiuJ. WangQ. Liu2008PRINCESS, a protein interaction confidence evaluation system with multiple data sources.Mol Cell Proteomics710431052
- 16. Brown KR, Jurisica I (2005) Online predicted human interaction database. Bioinformatics 21: 2076–2082.KR BrownI. Jurisica2005Online predicted human interaction database.Bioinformatics2120762082
- 17. Kemmer D, Huang Y, Shah SP, Lim J, Brumm J, et al. (2005) Ulysses - an application for the projection of molecular interactions across species. Genome Biol 6: R106.D. KemmerY. HuangSP ShahJ. LimJ. Brumm2005Ulysses - an application for the projection of molecular interactions across species.Genome Biol6R106
- 18. Huang TW, Tien AC, Huang WS, Lee YC, Peng CL, et al. (2004) POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome. Bioinformatics 20: 3273–3276.TW HuangAC TienWS HuangYC LeeCL Peng2004POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome.Bioinformatics2032733276
- 19. McGary KL, Park TJ, Woods JO, Cha HJ, Wallingford JB, et al. (2010) Systematic discovery of nonobvious human disease models through orthologous phenotypes. Proc Natl Acad Sci U S A 107: 6544–6549.KL McGaryTJ ParkJO WoodsHJ ChaJB Wallingford2010Systematic discovery of nonobvious human disease models through orthologous phenotypes.Proc Natl Acad Sci U S A10765446549
- 20. Bauer-Mehren A, Furlong LI, Sanz F (2009) Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol Syst Biol 5: 290.A. Bauer-MehrenLI FurlongF. Sanz2009Pathway databases and tools for their exploitation: benefits, current limitations and challenges.Mol Syst Biol5290
- 21. Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, et al. (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33: D428–D432.G. Joshi-TopeM. GillespieI. VastrikP. D'EustachioE. Schmidt2005Reactome: a knowledgebase of biological pathways.Nucleic Acids Res33D428D432
- 22. Korcsmaros T, Farkas IJ, Szalay MS, Rovo P, Fazekas D, et al. (2010) Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery. Bioinformatics 26: 2042–2050.T. KorcsmarosIJ FarkasMS SzalayP. RovoD. Fazekas2010Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery.Bioinformatics2620422050
- 23. Beltrao P, Serrano L (2007) Specificity and evolvability in eukaryotic protein interaction networks. PLoS Comput Biol 3: e25.P. BeltraoL. Serrano2007Specificity and evolvability in eukaryotic protein interaction networks.PLoS Comput Biol3e25
- 24. Chaudhuri A, Chant J (2005) Protein-interaction mapping in search of effective drug targets. Bioessays 27: 958–969.A. ChaudhuriJ. Chant2005Protein-interaction mapping in search of effective drug targets.Bioessays27958969
- 25. Sergina NV, Rausch M, Wang D, Blair J, Hann B, et al. (2007) Escape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3. Nature 445: 437–441.NV SerginaM. RauschD. WangJ. BlairB. Hann2007Escape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3.Nature445437441
- 26. Bandyopadhyay S, Sharan R, Ideker T (2006) Systematic identification of functional orthologs based on protein network comparison. Genome Res 16: 428–435.S. BandyopadhyayR. SharanT. Ideker2006Systematic identification of functional orthologs based on protein network comparison.Genome Res16428435
- 27. Koonin EV (2005) Orthologs, paralogs, and evolutionary genomics. Annu Rev Genet 39: 309–338.EV Koonin2005Orthologs, paralogs, and evolutionary genomics.Annu Rev Genet39309338
- 28. Kelley BP, Yuan B, Lewitter F, Sharan R, Stockwell BR, et al. (2004) PathBLAST: a tool for alignment of protein interaction networks. Nucleic Acids Res 32: W83–W88.BP KelleyB. YuanF. LewitterR. SharanBR Stockwell2004PathBLAST: a tool for alignment of protein interaction networks.Nucleic Acids Res32W83W88
- 29. Tatusov RL, Galperin MY, Natale DA, Koonin EV (2000) The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res 28: 33–36.RL TatusovMY GalperinDA NataleEV Koonin2000The COG database: a tool for genome-scale analysis of protein functions and evolution.Nucleic Acids Res283336
- 30. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389–3402.SF AltschulTL MaddenAA SchafferJ. ZhangZ. Zhang1997Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.Nucleic Acids Res2533893402
- 31. Berglund AC, Sjolund E, Ostlund G, Sonnhammer EL (2008) InParanoid 6: eukaryotic ortholog clusters with inparalogs. Nucleic Acids Res 36: D263–D266.AC BerglundE. SjolundG. OstlundEL Sonnhammer2008InParanoid 6: eukaryotic ortholog clusters with inparalogs.Nucleic Acids Res36D263D266
- 32. Chen H, Sharp BM (2004) Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinformatics 5: 147.H. ChenBM Sharp2004Content-rich biological network constructed by mining PubMed abstracts.BMC Bioinformatics5147
- 33. Fernandez JM, Hoffmann R, Valencia A (2007) iHOP web services. Nucleic Acids Res 35: W21–W26.JM FernandezR. HoffmannA. Valencia2007iHOP web services.Nucleic Acids Res35W21W26
- 34. Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A (2007) UniProtKB/Swiss-Prot: The Manually Annotated Section of the UniProt KnowledgeBase. Methods Mol Biol 406: 89–112.E. BoutetD. LieberherrM. TognolliM. SchneiderA. Bairoch2007UniProtKB/Swiss-Prot: The Manually Annotated Section of the UniProt KnowledgeBase.Methods Mol Biol40689112
- 35. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat Genet 25: 25–29.M. AshburnerCA BallJA BlakeD. BotsteinH. Butler2000Gene ontology: tool for the unification of biology.The Gene Ontology Consortium Nat Genet252529
- 36. Harris TW, Antoshechkin I, Bieri T, Blasiar D, Chan J, et al. (2010) WormBase: a comprehensive resource for nematode research. Nucleic Acids Res 38: D463–D467.TW HarrisI. AntoshechkinT. BieriD. BlasiarJ. Chan2010WormBase: a comprehensive resource for nematode research.Nucleic Acids Res38D463D467
- 37. Drysdale R (2008) FlyBase: a database for the Drosophila research community. Methods Mol Biol 420: 45–59.R. Drysdale2008FlyBase: a database for the Drosophila research community.Methods Mol Biol4204559
- 38. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, et al. (1999) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 27: 29–34.H. OgataS. GotoK. SatoW. FujibuchiH. Bono1999KEGG: Kyoto Encyclopedia of Genes and Genomes.Nucleic Acids Res272934
- 39. Yu J, Pacifico S, Liu G, Finley RL Jr (2008) DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions. BMC Genomics 9: 461.J. YuS. PacificoG. LiuRL Finley Jr2008DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions.BMC Genomics9461
- 40. Simonis N, Rual JF, Carvunis AR, Tasan M, Lemmens I, et al. (2009) Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network. Nat Methods 6: 47–54.N. SimonisJF RualAR CarvunisM. TasanI. Lemmens2009Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.Nat Methods64754
- 41. Greenwald I (2005) LIN-12/Notch signaling in C. elegans. WormBook 1–16.I. Greenwald2005LIN-12/Notch signaling in C. elegans.WormBook116
- 42. Brenner S (1974) The genetics of Caenorhabditis elegans. Genetics 77: 71–94.S. Brenner1974The genetics of Caenorhabditis elegans.Genetics777194
- 43. Kamath RS, Ahringer J (2003) Genome-wide RNAi screening in Caenorhabditis elegans. Methods 30: 313–321.RS KamathJ. Ahringer2003Genome-wide RNAi screening in Caenorhabditis elegans.Methods30313321
- 44. Huang dW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57.dW HuangBT ShermanRA Lempicki2009Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Nat Protoc44457
- 45. Amberger J, Bocchini CA, Scott AF, Hamosh A (2009) McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res 37: D793–D796.J. AmbergerCA BocchiniAF ScottA. Hamosh2009McKusick's Online Mendelian Inheritance in Man (OMIM).Nucleic Acids Res37D793D796
- 46. O'Brien KP, Westerlund I, Sonnhammer EL (2004) OrthoDisease: a database of human disease orthologs. Hum Mutat 24: 112–119.KP O'BrienI. WesterlundEL Sonnhammer2004OrthoDisease: a database of human disease orthologs.Hum Mutat24112119
- 47. Becker KG, Barnes KC, Bright TJ, Wang SA (2004) The genetic association database. Nat Genet 36: 431–432.KG BeckerKC BarnesTJ BrightSA Wang2004The genetic association database.Nat Genet36431432
- 48. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, et al. (2009) InterPro: the integrative protein signature database. Nucleic Acids Res 37: D211–D215.S. HunterR. ApweilerTK AttwoodA. BairochA. Bateman2009InterPro: the integrative protein signature database.Nucleic Acids Res37D211D215
- 49. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, et al. (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32: D258–D261.MA HarrisJ. ClarkA. IrelandJ. LomaxM. Ashburner2004The Gene Ontology (GO) database and informatics resource.Nucleic Acids Res32D258D261
- 50. Wishart DS (2008) DrugBank and its relevance to pharmacogenomics. Pharmacogenomics 9: 1155–1162.DS Wishart2008DrugBank and its relevance to pharmacogenomics.Pharmacogenomics911551162
- 51. Kimble J, Simpson P (1997) The LIN-12/Notch signaling pathway and its regulation. Annu Rev Cell Dev Biol 13: 333–361.J. KimbleP. Simpson1997The LIN-12/Notch signaling pathway and its regulation.Annu Rev Cell Dev Biol13333361
- 52. Bolos V, Grego-Bessa J, de la Pompa JL (2007) Notch signaling in development and cancer. Endocr Rev 28: 339–363.V. BolosJ. Grego-BessaJL de la Pompa2007Notch signaling in development and cancer.Endocr Rev28339363
- 53. Takacs-Vellai K, Vellai T, Chen EB, Zhang Y, Guerry F, et al. (2007) Transcriptional control of Notch signaling by a HOX and a PBX/EXD protein during vulval development in C. elegans. Dev Biol 302: 661–669.K. Takacs-VellaiT. VellaiEB ChenY. ZhangF. Guerry2007Transcriptional control of Notch signaling by a HOX and a PBX/EXD protein during vulval development in C. elegans.Dev Biol302661669
- 54. Sternberg PW (2005) Vulval development. WormBook 1–28.PW Sternberg2005Vulval development.WormBook128
- 55. Szabo E, Hargitai B, Regos A, Tihanyi B, Barna J, et al. (2009) TRA-1/GLI controls the expression of the Hox gene lin-39 during C. elegans vulval development. Dev Biol 330: 339–348.E. SzaboB. HargitaiA. RegosB. TihanyiJ. Barna2009TRA-1/GLI controls the expression of the Hox gene lin-39 during C. elegans vulval development.Dev Biol330339348
- 56. Yoo AS, Bais C, Greenwald I (2004) Crosstalk between the EGFR and LIN-12/Notch pathways in C. elegans vulval development. Science 303: 663–666.AS YooC. BaisI. Greenwald2004Crosstalk between the EGFR and LIN-12/Notch pathways in C. elegans vulval development.Science303663666
- 57. Fay DS, Yochem J (2007) The SynMuv genes of Caenorhabditis elegans in vulval development and beyond. Dev Biol 306: 1–9.DS FayJ. Yochem2007The SynMuv genes of Caenorhabditis elegans in vulval development and beyond.Dev Biol30619
- 58. Gehlenborg N, O'Donoghue SI, Baliga NS, Goesmann A, Hibbs MA, et al. (2010) Visualization of omics data for systems biology. Nat Methods 7: S56–S68.N. GehlenborgSI O'DonoghueNS BaligaA. GoesmannMA Hibbs2010Visualization of omics data for systems biology.Nat Methods7S56S68
- 59. Berger SI, Iyengar R (2009) Network analyses in systems pharmacology. Bioinformatics 25: 2466–2472.SI BergerR. Iyengar2009Network analyses in systems pharmacology.Bioinformatics2524662472
- 60. Gao Z, Li H, Zhang H, Liu X, Kang L, et al. (2008) PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics 9: 104.Z. GaoH. LiH. ZhangX. LiuL. Kang2008PDTD: a web-accessible protein database for drug target identification.BMC Bioinformatics9104
- 61. Fabbro D, Ruetz S, Buchdunger E, Cowan-Jacob SW, Fendrich G, et al. (2002) Protein kinases as targets for anticancer agents: from inhibitors to useful drugs. Pharmacol Ther 93: 79–98.D. FabbroS. RuetzE. BuchdungerSW Cowan-JacobG. Fendrich2002Protein kinases as targets for anticancer agents: from inhibitors to useful drugs.Pharmacol Ther937998
- 62. Yildirim MA, Goh KI, Cusick ME, Barabasi AL, Vidal M (2007) Drug-target network. Nat Biotechnol 25: 1119–1126.MA YildirimKI GohME CusickAL BarabasiM. Vidal2007Drug-target network.Nat Biotechnol2511191126
- 63. Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4: 682–690.AL Hopkins2008Network pharmacology: the next paradigm in drug discovery.Nat Chem Biol4682690
- 64. Schadt EE, Friend SH, Shaywitz DA (2009) A network view of disease and compound screening. Nat Rev Drug Discov 8: 286–295.EE SchadtSH FriendDA Shaywitz2009A network view of disease and compound screening.Nat Rev Drug Discov8286295
- 65. Wist AD, Berger SI, Iyengar R (2009) Systems pharmacology and genome medicine: a future perspective. Genome Med 1: 11.AD WistSI BergerR. Iyengar2009Systems pharmacology and genome medicine: a future perspective.Genome Med111