The protein-protein interaction network of eyestalk, Y-organ and hepatopancreas in Chinese mitten crab Eriocheir sinensis
© Hao et al.; licensee BioMed Central Ltd. 2014
Received: 11 January 2014
Accepted: 21 March 2014
Published: 27 March 2014
The protein-protein interaction network (PIN) is an effective information tool for understanding the complex biological processes inside the cell and solving many biological problems such as signaling pathway identification and prediction of protein functions. Eriocheir sinensis is a highly-commercial aquaculture species with an unclear proteome background which hinders the construction and development of PIN for E. sinensis. However, in recent years, the development of next-generation deep-sequencing techniques makes it possible to get high throughput data of E. sinensis tanscriptome and subsequently obtain a systematic overview of the protein-protein interaction system.
In this work we sequenced the transcriptional RNA of eyestalk, Y-organ and hepatopancreas in E. sinensis and generated a PIN of E. sinensis which included 3,223 proteins and 35,787 interactions. Each protein-protein interaction in the network was scored according to the homology and genetic relationship. The signaling sub-network, representing the signal transduction pathways in E. sinensis, was extracted from the global network, which depicted a global view of the signaling systems in E. sinensis. Seven basic signal transduction pathways were identified in E. sinensis. By investigating the evolution paths of the seven pathways, we found that these pathways got mature in different evolutionary stages. Moreover, the functions of unclassified proteins and unigenes in the PIN of E. sinensis were predicted. Specifically, the functions of 549 unclassified proteins related to 864 unclassified unigenes were assigned, which respectively covered 76% and 73% of all the unclassified proteins and unigenes in the network.
The PIN generated in this work is the first large-scale PIN of aquatic crustacean, thereby providing a paradigmatic blueprint of the aquatic crustacean interactome. Signaling sub-network extracted from the global PIN depicts the interaction of different signaling proteins and the evolutionary paths of the identified signal transduction pathways. Furthermore, the function assignment of unclassified proteins based on the PIN offers a new reference in protein function exploration. More importantly, the construction of the E. sinensis PIN provides necessary experience for the exploration of PINs in other aquatic crustacean species.
The development of high throughput techniques supplies a rich source of information for the Protein-protein Interaction Network (PIN) research. The interpretation of such information is a key to understand the complex world of biological processes inside the cell . Knowledge of PINs helps researchers to solve many problems such as signaling pathways identification , recognition of functional modules  and prediction of protein functions . Given the significant importance of PINs, proteome-wide interaction networks based on protein interactions has been constructed for many organisms [5–7]. The early study of PIN mostly focused on Saccharomyces cerevisiae. Schwikowski et al. performed a global analysis of published proteins interactions in S. cerevisiae and predicted the functions of 364 previously uncharacterized proteins . Some interesting sub-networks were extracted from the PINs of S. cerevisiae and analyzed, for example, the spindle pole body related sub-network in Ito T’s work  and DNA damage response data set in Ho Y’s work . Construction and analysis of PINs for other microorganisms has been subsequently performed, such as the PINs of Drosophila melanogaster , Helicobacter pylori  and Bacillus subtilis . In the decades-long development of PIN, interest has shifted from microbial systems [14, 15] to mammalian  and more kinds of organisms . However, to date, there is no large-scale PIN available for the study of aquatic crustacean. Although much effort has been made on the phenotype or physiological study of aquatic animals and crustaceans , an important ongoing problem is that the original inducement of all the phenotype and physiological features is the expression of genes and interaction of proteins. However, the expression and interaction of genes and proteins are still indistinct in most aquatic animals. As the protein interactions based on the gene expression has a significant role in the in-depth exploration of the biological process mechanism in cells, a PIN is necessary and important for the systematic study of aquatic crustaceans.
The Chinese mitten crab (Eriocheir sinensis) (Henri Milne Edwards, 1854) is one of the most important aquaculture species in China with high commercial value as a food source . Many studies have been performed focusing on single or several genes , proteins  or a specific pathway  to accelerate the growth or improve the immune and signal transduction system of E. sinensis. However, the genome sequence of any E. sinensis species is still unavailable. Therefore, a whole map of the protein interactions in E. sinensis is still fragmentary and different signaling pathways implicated in growth and immune response also remain incomplete. Recently, Illumina RNA-seq, the next-generation deep-sequencing technique, provides new approaches to obtain a whole transcriptome sequencing [22, 23], which makes it possible to get huge amounts of knowledge on E. sinensis proteins and subsequently obtain a systematic overview of the protein-protein interaction system.
In this work we sequenced the transcriptional RNA sequences in the eyestalk, Y-organ and hepatopancreas of E. sinensis and presented a substantial resource of affinity-tagged proteins. A PIN of E. sinensis was generated based on the transcriptome sequencing. The network covers hundreds of previously-uncharacterized proteins, thus providing functional associations and biological context for the proteins that previously lacked annotation. The signaling sub-network was extracted from the global PIN and the evolution paths of known signaling pathways were examined, which represents a new global view of the signaling systems in E. sinensis. Functional assignment of the unclassified proteins and unigenes supplies significant guidance for the in vivo investigation of proteins/genes related to specific function. To our knowledge, the PIN of E. sinensis is the first large-scale aquatic crustacean protein interaction network, thereby providing a systems biology view of an aquatic crustacean proteome.
Results and discussion
Transcriptome sequencing of E. sinensis
To obtain the E. sinensis transcriptome data, RNA from eyestalks, Y-organs and hepatopancreas mixed samples of E. sinensis were sequenced with the Illumina HiSeqTM2000. In total 2,358,728,280 nt clean nucleotides were found with Q20 and GC percentages of 96.68% and 45.08%, respectively. 26,208,092 clean reads were then obtained. From these clean reads, 157,168 contigs (mean length 236 nt) were assembled and then 58,582 unigenes (mean length 459 nt) were constructed from contigs with SOAP de novo, including 57,060 distinct singletons and 1,522 distinct clusters. The sequenced unigenes were subsequently aligned against the Nr database using BLASTn and BLASTx searching with E-value < 1*E-5. Finally 21,678 unigenes (37.00%) were matched. With Nr annotation, GO annotations of unigenes were obtained with the Blast2GO program. Among the total 58,582 unigenes of E. sinensis, 6,883 unigenes (11.75%) were annotated to the GO database with confident matches, including 4,680 assigned to the biological process category, 4241 assigned to the cellular component category and 5,684 assigned to the molecular function category. After the GO annotation of each unigene, WEGO software was used to obtain the GO functional classification for all unigenes in biological process category and to understand the distribution of gene functions from the macro level. In the biological process category, unigenes were divided into 26 different biological processes. Cellular process (3191; 68.2%) and metabolic process (2492; 53.2%) were most highly represented among them, other processed such as biological regulation (1392; 29.7%), developmental process (1094; 23.4%), localization (1166; 24.9%), multicellular organismal process (1170; 25%), regulation of biological process (1228; 26.2%) and response to stimulus (1057; 22.6%) were also included in biological process. The transcriptome sequencing and GO annotation results can be found in Additional file 1.
The protein information of model organisms
Number of protein sequences in model organisms from Uniprot
The protein-protein interactions from PINA
Protein interaction pairs
Construction of model-organism-based protein-protein interaction sub-network
Features of protein-protein interaction sub-networks of E. sinensis
Protein-protein interaction sub-network
Protein-protein interaction pairc
Construction of PIN for E. sinensis
The scale of integrated network after each turn of integration
The topological features of the LWCC in model-organism-based sub-networks and E. sinensis PIN
Average path length
Score of protein-protein interaction pair
Identification of signaling sub-network in E. sinensis
The Hippo signal transduction pathway is responsible for the growth inhibition of cells, which is a highly conservative pathway. It was first found in D. melanogaster and has been found in many mammals such as R. norvegicus and H. sapiens. The Hippo signal transduction pathway has significant function in organ size control, stem cell self-renewal, cancer inhibition and tissue homeostasis in response to multiple stimuli, including cell density and mechanotransduction [17, 25, 26]. Proteins wts, hpo and sav in this pathway are found to be responsible for cancer inhibition. The interaction of hpo and sav is able to phosphorylate and activate the complex composed of wts and Mats . Two top cell skeleton signal proteins Mer and Ex can be reciprocally activated with kibra to further activate the Hippo pathway . In addition, wts can directly phosphorylate, and thus inhibit the activating transcription factor yki. And yki is closely related with cell multiplication and apoptosis . The Hippo related proteins wts, hpo, sav, Mats, Mer, yki and kibra were found in the signaling sub-network of E. sinensis (Figure 3B), indicating that the Hippo signal transduction pathway also exists in E. sinensis. The growth control and cell self-renewal of E. sinensis is probably dominated by the Hippo pathway.
The Jak-STAT signal transduction pathway is composed of the PTK related receptor, PTK JAK and transcription factor. It is simulated by cytokine and participates in many important biological processes such as cell multiplication, differentiation, apoptosis and immunoregulation . The PIAS protein in this pathway can inhibit activation of the STAT protein by blocking the binding activity of the transcription factor and DNA. In addition, it is reported that PIAS can interact with more than 60 proteins, many of which are immune-system-related . The STAT and PIAS proteins were found in the signal network of E. sinensis,coming from the H. sapiens based sub-network. The other proteins in the Jak-STAT pathway came from the D. melanogaster based sub-network, such as the suppressor of cytokine signaling (SOCS), which inhibits the phosphorylation of STAT by combining and blocking JAK or competing for the phosphotyrosine site on the cytokine receptor with STAT (Figure 3C). The multiplication, differentiation and apoptosis of E. sinensis are possibly controlled by the Jak-STAT pathway. The different source of proteins indicated that the integration process provided more information for the PIN of E. sinensis.
In addition, the mTOR, Wnt, MAPK, Notch and protein processing in endoplasmic reticulum were also found in the signaling sub-network of E. sinensis. The mTOR pathway is a central regulator for both cell proliferation and cell growth . The Wnt pathway is involved in virtually every aspect of embryonic development and also controls homeostatic self-renewal in a number of adult tissues. Many studies report that mutation of the Wnt pathway is closely related to several hereditary diseases and cancers . The Notch pathway is first found in D. melanogaster and participates in the regulation of cell multiplication, differentiation, and apoptosis, and acts as an important regulator of immune cells development . The seven signaling transduction pathways found in E. sinensis represent the regulation of basic cell life activity about growth, development, reproduction and disease-resistance. The signaling sub-network of E. sinensis provides substantial information of the signal transduction pathways and unknown proteins which need to be further studied.
Evolution path of E. sinensis signaling network
The signaling network has been used to understand evolution in multicellular animals . As the E. sinensis signaling sub-network was obtained from the integration of six model organisms and these organisms are located in different evolutionary stages, in order to promote understanding of the evolution of the signaling sub-network in E. sinensis, we examined the evolution path by comparing the E. sinensis signaling network with the six model organisms, and investigated the original organisms and preferred evolution paths of the E. sinensis signaling network. The six species were classified into three groups: primitive, bilaterian and vertebrate groups as described in Lei Li’s work . The primitive group included S. cerevisiae. The bilaterian lineage was composed of D. melanogaster and C. elegans. All three vertebrate species were placed in the vertebrate group.
Function assignment of unclassified proteins and unigenes
With the improvement in high-throughput sequencing technology, RNA sequencing and annotation are possible for further analysis and detection in the pursuit of certain biological goals. In present work we constructed a PIN of E. sinensis on the basis of transcriptomics sequencing and the proteome of six model organisms. The PIN defines a primary protein interaction landscape for E. sinensis cells that allows study of sub-networks with specific function. Seven known pathways were identified in the signaling sub-network extracted from the global PIN. With the analysis of evolution paths for these pathways, we found their differences in evolution origin. More proteins identified as neighbors of the proteins in seven identified pathways were prepared for further confirmation. Furthermore, the function assignment of unclassified proteins offers a new reference in protein function exploration. It is the first large-scale PIN of aquatic crustaceans, thereby providing necessary experience for the exploration of PIN for other aquatic crustacean species, as well as supplying a systems biological view of an aquatic crustacean interactome.
Obtaining of transcriptome data
Live E. sinensis (35–40 g in body weight) were purchased from the Tianjin Fisheries Institute and raised in fiberglass tanks. E. sinensis were cultured in freshwater at 18–20 degree centigrade (photoperiod L12:D12) for 7 days to acclimate to the laboratory conditions. Then three tissues including eyestalk, Y-organ and hepatopancreas were separated and collected. All samples were immediately frozen in liquid nitrogen and were stored at minus 80 degree centigrade until use. All experimental procedures were conducted in conformity with institutional guidelines for the care and use of laboratory animals in Tianjin Fisheries Institute and conformed to the National laboratory animal management regulations (Publication No. 2, 1988) approved by the National Science and Technology Commission.
Total RNA from E. sinensis tissue was sequenced with the Illumina high-throughput sequencing technology by Beijing Genomics Institution (BGI). The total RNA was extracted using the TRIzol method (Invitrogen) and then equal quantities of RNA from each tissue were pooled for transcriptome analysis. The samples for transcriptome analysis were prepared using Illumina’s kit and the generated library was sequenced using Illumina HiSeq™ 2000. Then the transcriptome de novo assembly was carried out with the short reads assembling program-Trinity  to generate unigenes. GO annotation of unigenes was obtained by the Blast2GO program  with an E-value cut-off at 1*E-5. WEGO software was used to obtain the GO functional classification for all unigenes in biological process category.
The protein sequences of model organisms
The protein sequence data of C. elegans, D. melanogaster, H. sapiens, M. musculus, R. norvegicus and S. cerevisiae was downloaded from the Uniprot database  (March 2012 version). The protein interactions of these model organisms were obtained from Protein-protein Interaction Network Analysis (PINA) [39, 40]. PINA integrates the protein interaction information of six public databases and supplies the complete, non-redundant protein interaction information of the above six model organisms. The March 2012 version was downloaded.
Gene ontology annotation
The Gene Ontology database  supplies a standardized representation of gene and gene product attributes across species and databases, including biological process, molecular function and cellular component. The gene_ontology.obo file was downloaded to obtain GO annotation from the Gene Ontology database. The GO annotation can be described as a directed acyclic graph according to the relations of GOs and a tree structure was drawn by programming. The GO numbers in each level of the tree were extracted.
The Basic Local Alignment Search Tool (BLAST) was downloaded from the NCBI ftp platform. The BLASTX program was used to align the nucleotide sequences (unigenes) in E. sinensis with the protein sequences of six model organisms to construct the model-organism-based protein-protein interaction sub-networks. The nucleotide sequence is first translated into protein sequences (one nucleotide sequence can be translated into six protein sequences) and then compared with the model organism one by one. The first aligned sequence with E value below 1*E-5 was considered as the homologous sequence.
The construction of the PIN for E. sinensis is actually the integration of the 6 model-organism-based sub-networks. We developed an efficient computational procedure for integrating two PINs with reference to the global protein network alignment method in an attempt to obtain the integrated PIN . The sub-networks were integrated one by one and the order was decided according to the genetic relationship of the model organisms with E. sinensis.
Proteins in the target and query networks were aligned with the BLASTP program, E value was set as 1*E-5.
The first matched protein in the target network to the query network was considered to be homologous. All the homologous proteins in the two networks were extracted.
The protein-protein interactions in the two networks were compared. When two proteins in an interaction pair in the query networks were both homologous to the target network (such as C-D in the target network and c-d in the query network in Figure 7), the protein names in the target network were used in the integrated network (such as C-D in Figure 7), and the new interaction pair in the query network was added if any (such as A-C in Figure 7); when only one protein in an interaction pair was homologous (such as D-E in the target network and d-g in the query network in Figure 7), then the protein name in the target network was used and the other protein in the interaction pair in the query was added (D-g in Figure 7); when no homologous proteins were found in an interaction pair in the query network, the protein names and this interaction in the query network were directly added into the integrated network.
The integrated network was considered as a new target network, and another model-organism-based sub-network was used as a new query network. Then steps (1) - (3) were repeated to generate a new integrated network. Such an iterative process was stopped until all the model-organism-based sub-networks were integrated. The final integrated network was the PIN of E. sinensis.
Topological features of networks
Diameter and average path length of network
In a directed network, the distance from node i to node j is the length of the shortest path between them. The diameter of a network is the length of the longest distance among all connected pairs of nodes in a graph. The average path length is the length of the distances averaged over all pairs of connected nodes in a graph .
A strongly connected component (SCC) of a directed graph is a sub-graph where all nodes in the sub-graph are reachable by all other nodes in the sub-graph. Reachability between nodes is established by at least one directed path between the nodes. A weakly connected component (WCC) is a maximal group of nodes that are mutually reachable ignoring the edge directions .
The clustering coefficient
where k v is the number of nodes in the neighbourhood of vertex v,and e v is the number of edges existing between the neighbours of v. Suppose that a node v has k v neighbours, then at most k v (k v -1)/2 edges can exist between them (this occurs when every neighbour of v is connected to all the other neighbours of v). Let C v denote the fraction of these allowable edges that actually exist. The clustering coefficient of a network is defined as the average of C v over all v .
Degree and average degree
In graph theory, the degree of a graph is the number of edges incident to the nodes, with loops counted twice. The average degree is the degree averaged over all the nodes in a graph .
The Index aggregation of a network is the ratio of the nodes in the largest WCC and the global network .
Score of protein-protein interaction pair
where S stands for the score of a protein-protein interaction pair; A and B are the score of two nodes (proteins) in an interaction pair respectively; R is the score of the edge; i stands for the number of integration times; N (N = 5) is the maximal number of interaction times. The maximum score of an interaction pair is 35 deduced by formula (1).
Function assignment of unclassified proteins
Identify the neighbor protein(s) interacting with the protein with unknown function (unclassified). The neighbor protein(s) with GO annotation were considered as classified protein(s);
Calculate the numbers of neighbor proteins with GO annotation and in the GO functional category;
If the number of neighbor proteins with a certain GO functional category make up more than 25% of the total number of neighbor proteins, then the GO annotation is assigned to the unclassified protein. If only one neighbor protein with GO annotations exists, all the GO annotations were assigned to the unclassified protein;
Taking into account the interactions among the above three steps, iterate (1)-(3) until no unclassified proteins can be further assigned.
This work was supported by National High-Tech Research and Development Program of China (863 programs, 2012AA10A401 and 2012AA092205), Grants of the Major State Basic Research Development Program of China (973 programs, 2012CB114405), National Natural Science Foundation of China (21106095, 61100124), National Key Technology R&D Program (2011BAD13B07 and 2011BAD13B04), Foundation of Introducing Talents to Tianjin Normal University and “131” Innovative Talents cultivation of Tianjin.
- Mosca R, Pons T, Ceol A, Valencia A, Aloy P: Towards a detailed atlas of protein-protein interactions. Curr Opin Struct Biol. 2013, 23: 929-940. 10.1016/j.sbi.2013.07.005.View ArticlePubMedGoogle Scholar
- Navlakha S, Gitter A, Bar-Joseph Z: A network-based approach for predicting missing pathway interactions. PLoS Comput Biol. 2012, 8: e1002640-10.1371/journal.pcbi.1002640.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen B, Fan W, Liu J, Wu FX: Identifying protein complexes and functional modules--from static PPI networks to dynamic PPI networks. Brief Bioinform. 2013, 15: 177-194.View ArticlePubMedGoogle Scholar
- Zeng E, Ding C, Narasimhan G, Holbrook SR: Estimating support for protein-protein interaction data with applications to function prediction. Comput Syst Bioinformatics Conf. 2008, 7: 73-84.View ArticlePubMedGoogle Scholar
- Guruharsha KG, Rual JF, Zhai B, Mintseris J, Vaidya P, Vaidya N, Beekman C, Wong C, Rhee DY, Cenaj O, McKillip E, Shah S, Stapleton M, Wan KH, Yu C, Parsa B, Carlson JW, Chen X, Kapadia B, VijayRaghavan K, Gygi SP, Celniker SE, Obar RA, Artavanis-Tsakonas S: A protein complex network of Drosophila melanogaster. Cell. 2011, 147: 690-703. 10.1016/j.cell.2011.08.047.PubMed CentralView ArticlePubMedGoogle Scholar
- Kuhner S, van Noort V, Betts MJ, Leo-Macias A, Batisse C, Rode M, Yamada T, Maier T, Bader S, Beltran-Alvarez P, Castano-Diez D, Chen WH, Devos D, Guell M, Norambuena T, Racke I, Rybin V, Schmidt A, Yus E, Aebersold R, Herrmann R, Bottcher B, Frangakis AS, Russell RB, Serrano L, Bork P, Gavin AC: Proteome organization in a genome-reduced bacterium. Science. 2009, 326: 1235-1240. 10.1126/science.1176343.View ArticlePubMedGoogle Scholar
- Hu P, Janga SC, Babu M, Diaz-Mejia JJ, Butland G, Yang W, Pogoutse O, Guo X, Phanse S, Wong P, Chandran S, Christopoulos C, Nazarians-Armavil A, Nasseri NK, Musso G, Ali M, Nazemof N, Eroukova V, Golshani A, Paccanaro A, Greenblatt JF, Moreno-Hagelsieb G, Emili A: Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biol. 2009, 7: e96-10.1371/journal.pbio.1000096.View ArticlePubMedGoogle Scholar
- Schwikowski B, Uetz P, Fields S: A network of protein-protein interactions in yeast. Nat Biotechnol. 2000, 18: 1257-1261. 10.1038/82360.View ArticlePubMedGoogle Scholar
- Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci U S A. 2001, 98: 4569-4574. 10.1073/pnas.061034498.PubMed CentralView ArticlePubMedGoogle Scholar
- Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, Millar A, Taylor P, Bennett K, Boutilier K, Yang L, Wolting C, Donaldson I, Schandorff S, Shewnarane J, Vo M, Taggart J, Goudreault M, Muskat B, Alfarano C, Dewar D, Lin Z, Michalickova K, Willems AR, Sassi H, Nielsen PA, Rasmussen KJ, Andersen JR, Johansen LE, Hansen LH: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature. 2002, 415: 180-183. 10.1038/415180a.View ArticlePubMedGoogle Scholar
- Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E, Vijayadamodar G, Pochart P, Machineni H, Welsh M, Kong Y, Zerhusen B, Malcolm R, Varrone Z, Collis A, Minto M, Burgess S, McDaniel L, Stimpson E, Spriggs F, Williams J, Neurath K, Ioime N, Agee M, Voss E, Furtak K, et al: A protein interaction map of Drosophila melanogaster. Science. 2003, 302: 1727-1736. 10.1126/science.1090289.View ArticlePubMedGoogle Scholar
- Kim KK, Kim HB: Protein interaction network related to Helicobacter pylori infection response. World J Gastroenterol. 2009, 15: 4518-4528. 10.3748/wjg.15.4518.PubMed CentralView ArticlePubMedGoogle Scholar
- Marchadier E, Carballido-Lopez R, Brinster S, Fabret C, Mervelet P, Bessieres P, Noirot-Gros MF, Fromion V, Noirot P: An expanded protein-protein interaction network in Bacillus subtilis reveals a group of hubs: Exploration by an integrative approach. Proteomics. 2011, 11: 2981-2991. 10.1002/pmic.201000791.View ArticlePubMedGoogle Scholar
- Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000, 403: 623-627. 10.1038/35001009.View ArticlePubMedGoogle Scholar
- Yu H, Braun P, Yildirim MA, Lemmens I, Venkatesan K, Sahalie J, Hirozane-Kishikawa T, Gebreab F, Li N, Simonis N, Hao T, Rual JF, Dricot A, Vazquez A, Murray RR, Simon C, Tardivo L, Tam S, Svrzikapa N, Fan C, de Smet AS, Motyl A, Hudson ME, Park J, Xin X, Cusick ME, Moore T, Boone C, Snyder M, Roth FP, et al: High-quality binary protein interaction map of the yeast interactome network. Science. 2008, 322: 104-110. 10.1126/science.1158684.PubMed CentralView ArticlePubMedGoogle Scholar
- Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, et al: Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005, 437: 1173-1178. 10.1038/nature04209.View ArticlePubMedGoogle Scholar
- Couzens AL, Knight JD, Kean MJ, Teo G, Weiss A, Dunham WH, Lin ZY, Bagshaw RD, Sicheri F, Pawson T, Wrana JL, Choi H, Gingras AC: Protein interaction network of the Mammalian hippo pathway reveals mechanisms of kinase-phosphatase interactions. Sci Signal. 2013, 6: rs15-10.1126/scisignal.2004712.View ArticlePubMedGoogle Scholar
- Zhang Y, Sun Y, Liu Y, Geng X, Wang X, Wang Y, Sun J, Yang W: Molt-inhibiting hormone from Chinese mitten crab (Eriocheir sinensis): Cloning, tissue expression and effects of recombinant peptide on ecdysteroid secretion of YOs. Gen Comp Endocrinol. 2011, 173: 467-474. 10.1016/j.ygcen.2011.07.010.View ArticlePubMedGoogle Scholar
- Yu AQ, Jin XK, Guo XN, Li S, Wu MH, Li WW, Wang Q: Two novel Toll genes (EsToll1 and EsToll2) from Eriocheir sinensis are differentially induced by lipopolysaccharide, peptidoglycan and zymosan. Fish Shellfish Immunol. 2013, 35: 1282-1292. 10.1016/j.fsi.2013.07.044.View ArticlePubMedGoogle Scholar
- Yanhua Wang YZ, Sun Y, Liu Y, Geng X, Sun J: cloing and molecular structure analysis of crustacean hyperglycemic hormone (Ers-CHH) in Eriocheir sinensis. J Fish China. 2013, 37: 987-993.View ArticleGoogle Scholar
- Li X, Cui Z, Liu Y, Song C, Shi G: Transcriptome analysis and discovery of genes involved in immune pathways from hepatopancreas of microbial challenged mitten crab Eriocheir sinensis. PLoS One. 2013, 8: e68233-10.1371/journal.pone.0068233.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10: 57-63. 10.1038/nrg2484.PubMed CentralView ArticlePubMedGoogle Scholar
- Marguerat S, Bahler J: RNA-seq: from technology to biology. Cell Mol Life Sci. 2010, 67: 569-579. 10.1007/s00018-009-0180-6.PubMed CentralView ArticlePubMedGoogle Scholar
- Peters JM, Franke WW, Kleinschmidt JA: Distinct 19 S and 20 S subcomplexes of the 26 S proteasome and their distribution in the nucleus and the cytoplasm. J Biol Chem. 1994, 269: 7709-7718.PubMedGoogle Scholar
- Zhao B, Tumaneng K, Guan KL: The Hippo pathway in organ size control, tissue regeneration and stem cell self-renewal. Nat Cell Biol. 2011, 13: 877-883. 10.1038/ncb2303.PubMed CentralView ArticlePubMedGoogle Scholar
- Halder G, Johnson RL: Hippo signaling: growth control and beyond. Development. 2011, 138: 9-22. 10.1242/dev.045500.PubMed CentralView ArticlePubMedGoogle Scholar
- Lai ZC, Wei X, Shimizu T, Ramos E, Rohrbaugh M, Nikolaidis N, Ho LL, Li Y: Control of cell proliferation and apoptosis by mob as tumor suppressor, mats. Cell. 2005, 120: 675-685. 10.1016/j.cell.2004.12.036.View ArticlePubMedGoogle Scholar
- Baumgartner R, Poernbacher I, Buser N, Hafen E, Stocker H: The WW domain protein Kibra acts upstream of Hippo in Drosophila. Dev Cell. 2010, 18: 309-316. 10.1016/j.devcel.2009.12.013.View ArticlePubMedGoogle Scholar
- Krebs DL, Hilton DJ: SOCS proteins: negative regulators of cytokine signaling. Stem Cells. 2001, 19: 378-387. 10.1634/stemcells.19-5-378.View ArticlePubMedGoogle Scholar
- Shuai K: Regulation of cytokine signaling pathways by PIAS proteins. Cell Res. 2006, 16: 196-202. 10.1038/sj.cr.7310027.View ArticlePubMedGoogle Scholar
- Liu Y, Yan X, Zhou T: TBCK influences cell proliferation, cell size and mTOR signaling pathway. PLoS One. 2013, 8: e71349-10.1371/journal.pone.0071349.PubMed CentralView ArticlePubMedGoogle Scholar
- Clevers H: Wnt/beta-catenin signaling in development and disease. Cell. 2006, 127: 469-480. 10.1016/j.cell.2006.10.018.View ArticlePubMedGoogle Scholar
- Radtke F, MacDonald HR, Tacchini-Cottier F: Regulation of innate and adaptive immunity by Notch. Nat Rev Immunol. 2013, 13: 427-437. 10.1038/nri3445.View ArticlePubMedGoogle Scholar
- Li L, Tibiche C, Fu C, Kaneko T, Moran MF, Schiller MR, Li SS, Wang E: The human phosphotyrosine signaling network: evolution and hotspots of hijacking in cancer. Genome Res. 2012, 22: 1222-1230. 10.1101/gr.128819.111.PubMed CentralView ArticlePubMedGoogle Scholar
- Pan D: The hippo signaling pathway in development and cancer. Dev Cell. 2010, 19: 491-505. 10.1016/j.devcel.2010.09.011.PubMed CentralView ArticlePubMedGoogle Scholar
- Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011, 29: 644-652. 10.1038/nbt.1883.PubMed CentralView ArticlePubMedGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21: 3674-3676. 10.1093/bioinformatics/bti610.View ArticlePubMedGoogle Scholar
- Hinz U: From protein sequences to 3D-structures and beyond: the example of the UniProt knowledgebase. Cell Mol Life Sci. 2010, 67: 1049-1064. 10.1007/s00018-009-0229-6.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu J, Vallenius T, Ovaska K, Westermarck J, Makela TP, Hautaniemi S: Integrated network analysis platform for protein-protein interactions. Nat Methods. 2009, 6: 75-77. 10.1038/nmeth.1282.View ArticlePubMedGoogle Scholar
- Cowley MJ, Pinese M, Kassahn KS, Waddell N, Pearson JV, Grimmond SM, Biankin AV, Hautaniemi S, Wu J: PINA v2.0: mining interactome modules. Nucleic Acids Res. 2012, 40: D862-D865. 10.1093/nar/gkr967.PubMed CentralView ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000, 25: 25-29. 10.1038/75556.PubMed CentralView ArticlePubMedGoogle Scholar
- Kelley BP, Sharan R, Karp RM, Sittler T, Root DE, Stockwell BR, Ideker T: Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci U S A. 2003, 100: 11394-11399. 10.1073/pnas.1534710100.PubMed CentralView ArticlePubMedGoogle Scholar
- J.A.Bondy USRM: Graph theory with applications. Macmillan Press Ltd. 1976Google Scholar
- Diestel R: Graph theory. N Y. 2005Google Scholar
- Watts DJ, Strogatz SH: Collective dynamics of ‘small-world’ networks. Nature. 1998, 393: 440-442. 10.1038/30918.View ArticlePubMedGoogle Scholar
- Vazquez A, Flammini A, Maritan A, Vespignani A: Global protein function prediction from protein-protein interaction networks. Nat Biotechnol. 2003, 21: 697-700. 10.1038/nbt825.View ArticlePubMedGoogle Scholar
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