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Autophagy

ISSN: 1554-8627 (Print) 1554-8635 (Online) Journal homepage: http://www.tandfonline.com/loi/kaup20

ncRDeathDB: A comprehensive bioinformaticsresource for deciphering network organization ofthe ncRNA-mediated cell death system

Deng Wu, Yan Huang, Juanjuan Kang, Kongning Li, Xiaoman Bi, Ting Zhang,Nana Jin, Yongfei Hu, Puwen Tan, Lu Zhang, Ying Yi, Wenjun Shen, JianHuang, Xiaobo Li, Xia Li, Jianzhen Xu & Dong Wang

To cite this article: Deng Wu, Yan Huang, Juanjuan Kang, Kongning Li, Xiaoman Bi, Ting Zhang,Nana Jin, Yongfei Hu, Puwen Tan, Lu Zhang, Ying Yi, Wenjun Shen, Jian Huang, Xiaobo Li, Xia Li,Jianzhen Xu & Dong Wang (2015) ncRDeathDB: A comprehensive bioinformatics resource fordeciphering network organization of the ncRNA-mediated cell death system, Autophagy, 11:10,1917-1926, DOI: 10.1080/15548627.2015.1089375

To link to this article: http://dx.doi.org/10.1080/15548627.2015.1089375

View supplementary material Accepted author version posted online: 02Oct 2015.

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ncRDeathDB: A comprehensive bioinformaticsresource for deciphering network organization

of the ncRNA-mediated cell death systemDeng Wu,1,y Yan Huang,1,y Juanjuan Kang,1,2,y Kongning Li,1,y Xiaoman Bi,1 Ting Zhang,1 Nana Jin,1 Yongfei Hu,1 Puwen Tan,1

Lu Zhang,1 Ying Yi,1 Wenjun Shen,3 Jian Huang,2 Xiaobo Li,4 Xia Li,1,* Jianzhen Xu,3,* and Dong Wang1,3,*

1College of Bioinformatics Science and Technology; Harbin Medical University; Harbin, China; 2Bioinformatics Center; School of Life Science; University of Electronic Science and

Technology of China; Chengdu, China; 3Department of Biochemistry and Molecular Biology; Shantou University Medical College; Shantou, China; 4Department of Pathology;

Harbin Medical University; Harbin, China

yThese authors contributed equally to this work.

Keywords: autophagy, cell death, crosstalk, database, ncRNA, network

Abbreviations: ATG10, autophagy-related 10; ATG12, autophagy-related 12; ATG16L1, autophagy-related 16-like 1; ATG3,autophagy-related 3; ATG4B, autophagy-related 4B, cysteine peptidase; ATG5, autophagy- related 5; BAX, BCL2-associated X pro-

tein; BCL2, B-cell CLL/lymphoma 2; CASP3, caspase 3, apoptosis-related cysteine peptidase; lncRNA, long non-coding RNA;MAP1LC3B, microtubule-associated protein 1 light chain 3 beta; MCL1, myeloid cell leukemia 1; miRNA, microRNA; ncRNA,noncoding RNA; NPI, ncRNA-protein interaction; PCD, programmed cell death; PPI, protein-protein interaction; snoRNA, small

nucleolar RNA.

Programmed cell death (PCD) is a critical biological process involved in many important processes, and defects inPCD have been linked with numerous human diseases. In recent years, the protein architecture in different PCDsubroutines has been explored, but our understanding of the global network organization of the noncoding RNA(ncRNA)-mediated cell death system is limited and ambiguous. Hence, we developed the comprehensive bioinformaticsresource (ncRDeathDB, www.rna-society.org/ncrdeathdb) to archive ncRNA-associated cell death interactions. The cur-rent version of ncRDeathDB documents a total of more than 4600 ncRNA-mediated PCD entries in 12 species.ncRDeathDB provides a user-friendly interface to query, browse and manipulate these ncRNA-associated cell deathinteractions. Furthermore, this resource will help to visualize and navigate current knowledge of the noncoding RNAcomponent of cell death and autophagy, to uncover the generic organizing principles of ncRNA-associated cell deathsystems, and to generate valuable biological hypotheses.

Introduction

Noncoding RNAs (ncRNAs) are RNA species that areexpressed in cells under certain conditions but do not code forproteins.1 Currently, microRNAs (miRNAs) are the most studiedtype of ncRNAs, but novel classes of ncRNAs, such as long non-coding RNAs (lncRNAs) and snoRNAs, continue to be identi-fied, and many studies have begun to focus on the role of ncRNAin programmed cell death.2-5 Convincing evidence has beenfound for the involvement of microRNA,6,7 lncRNA8 andsnoRNA3 in common cell death subroutines such as apoptosisand autophagy. Despite this progress, unsolved questions remain;for example, while the outline of protein crosstalk between differ-ent PCD subroutines is relatively well characterized,9,10 muchless is known concerning the network organization of ncRNA-mediated cell death subroutines and how cell death-associated

ncRNAs bridge various cell death subroutines and functionalmodules. Perhaps more importantly, it is unclear how thencRNA-mediated cell death system interacts with canonical pro-tein-protein interaction networks (PPI) networks and ncRNA-protein interaction (NPI) networks within processes of global sig-nal transduction. Finally, lack of knowledge regarding pivotalncRNAs and their protein targets, among the hundreds ofncRNAs found in PCD, hinders the development of ncRNA-based drug targets for clinical use.

To address these issues, we collected a large amount of pub-lished data describing the roles of diverse ncRNAs in PCD todevelop and update the database ncRDeathDB (www.rna-society.org/ncrdeathdb) according to our previous miRDeathDB11

for the purpose of archiving comprehensive ncRNA-associatedcell death interactions. The current version of ncRDeathDB pro-vides an all-inclusive bioinformatics resource on information

*Correspondence to: Dong Wang; Email: wangdong@ems.hrbmu.edu.cn; Jianzhen Xu; Email: xujz0451@gmail.com; Xia Li; Email: lixia@hrbmu.edu.cnSubmitted: 05/26/2015; Revised: 08/18/2015; Accepted: 08/27/2015http://dx.doi.org/10.1080/15548627.2015.1089375

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Autophagy 11:10, 1917--1926; October 2015; © 2015 Taylor & Francis Group, LLCRESOURCE

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detailing the ncRNA-mediated cell death system and includes4615 (including 1817 predicted entries) ncRNA-mediated PCDentries involving 12 species. Furthermore, based on this compre-hensive repository, we implemented several bioinformatics net-work techniques to systematically analyze the hierarchical andtopologic characteristics of the ncRNA-mediated cell death sys-tem (Fig. 1). Then, we investigated its centrality and its func-tional role in the global signal transduction cascade by mappingit to the PPI and NPI networks. Finally, we identified several keyncRNAs players in both an experimentally verified autophagymodule and a cell death network (including both autophagy andapoptosis modules) that together with their protein targets medi-ate intra- and inter-module regulation.

Results

Content of the ncRDeathDB databaseAccording to our previous miRDeathDB, we connected the

dot to build the miRNA-mediated cell death network.12 How-ever, with the increasing amount of data demonstrating impor-tant functions for ncRNA molecules in apoptosis and autophagy,we continuously curate the literature data and update it. At thesame time, we realized the important role of other ncRNA (suchas lncRNA and snoRNA) in apoptosis and autophagy. Hence,based on our reference retrieval, we recently updated miR-DeathDB11 to version 2.0 (ncRDeathDB, www.rna-society.org/ncrdeathdb), which allows users to browse, search, and analyzePCD-associated ncRNAs. Compared with its first release (miR-DeathDB), ncRDeathDB not only enriches ncRNA types byincluding lncRNA and snoRNA with updated annotations, butalso integrates a variety of useful tools for analyzing RNA-RNAand RNA-protein binding sites and for network visualization.The current version of ncRDeathDB documents 4615 (including1817 predicted entries) ncRNA-associated cell death entries,including 2403 apoptosis-associated entries, 2205 autophagy-

associated entries and 7 necrosis-associated entries, leading to anearly 20-fold content enrichment compared to the initial miR-DeathDB release in 2012. These entries were manually annotatedfrom 1495 references, from 12 species, mainly including Homosapiens, Mus musculus, and Rattus norvegicus (Table 1). Eachentry contains detailed information, including annotation ofncRNAs, interacting genes, species, PMID, and detailed descrip-tion. In the current version of ncRDeathDB, human, mouse, andrat account for 58.07%, 10.40% and 6.50% ncRNA-associatedcell death entries, respectively. Notably, the human ncRNA-mediated cell death system consists of 2680 entries, including2054 apoptosis, 619 autophagy-associated (340 experimentallyvalidated entries and 279 predicted entries) and 7 necrosis-associ-ated entries. Apoptosis-associated entries account for 358 miR-NAs, 71 lncRNAs, 6 snoRNA, and 558 proteins, whileautophagy-associated entries account for 199miRNAs, 4lncRNA, and 99 proteins.

ncRDeathDB also provides 4 options on the “Help” page toprovide instructions for using the database. These include“Tutorial:” procedure and illustrations of the database; “Source:”sources of ncRNAs and gene information, and tools used in thedatabase; “Parameter:” details the parameters for tools used forbinding site prediction and ‘Error Report’. In the “Download &API” page, users can download all data in Microsoft Excel andTXT format or access the application programming interface(API) using scripts. In the ‘Submit’ page, ncRDeathDB invitesusers to submit novel ncRNA-associated cell death interactions.

Data querying, searching, browsing, and visualizationncRDeathDB provides an interface for convenient retrieval of

all interactions. Users can search each ncRNA-associated celldeath interaction through 3 paths (Fig. 2), including ‘By key-word’: browse the ncRNA-associated cell death entries by input-ting the keywords (any ncRNA, Gene Symbol, Death Pathway,Species) and fuzzy search supported, ‘By Death Pathway’: repre-sent all ncRNA-associated cell death entries by associated optionof species and death pathway, and ‘By Advanced Search’: repre-sent the accurate search results by associated option of species,death pathway and ncRNA/Gene symbols exist in ncRDeathDB.Brief details of search results are presented as a table in the‘Results’ page, while more detailed descriptions such as PubMedID and description of the reference are displayed in the ‘Detail’page. The ‘Browse page’ enables users to browse the database in3 different ways: ‘Browse by ncRNA category’, ‘Browse by spe-cies’ and ‘Browse by cell death pathway’. All of the ncRNA-asso-ciated cell death interaction information is presented by selectingon each entry.

Topological characterization of the human ncRNA-mediatedcell death system

In the current version of ncRDeathDB, nearly 60% of theentries included were for human ncRNA-associated cell deathsubroutines. Hence, in this work, we mainly focus on the largesthuman ncRNA-mediated cell death system by using experimen-tally validated entries to investigate the generic organizing princi-ples of ncRNA-mediated cell death systems. According to

Figure 1. Analytical scheme of the ncRNA-mediated cell death system.

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experimentally validated entries, the human ncRNA-mediatedcell death system consists of 1715 interactions and 951 nodes,including 1453 apoptosis and 262 autophagy-associated interac-tions. Using several bioinformatics network techniques, we firstidentified the hub and bottleneck nodes of the network (Fig. 3,Table S1). Typical examples, such as the hub nodes BCL2 andMIR21 and the bottleneck node MIR181A (reported in our pre-vious work,10,12) were validated for the current network. In addi-tion, we have identified several newly discovered partner genes ofMIR181A, which include apoptotic genes (CASP3, CASP9, BAX,FAS, etc) and autophagic genes (ATG10, BECN1, ATG16L1,ATG12, UVRAG, etc). This finding has further strengthened the

role of MIR181A in mediating the crosstalk between apoptosisand autophagy.12

Furthermore, novel hubs have been identified, includingMCL1, a member of the BCL2 family located in mitochondria,the different isoforms of which play a role in the regulation ofapoptosis.13,14 In the human ncRNA-associated cell death sys-tem, MCL1 interacts with 25 ncRNAs, including important hubncRNAs such asMIR181A andMIR125B. TP53 is another inter-esting hub; in addition to interacting with several miRNA part-ners, it interacts with multiple lncRNAs, including H19,PANDAR, LINC-ROR and MEG3, suggesting that it plays a keyrole in linking lncRNAs with other ncRNAs.15-18 Furthermore,we identified several novel ncRNAs as bottlenecks that bridge dif-ferent cell death subroutines (Table S1). For example, MIR34Anot only regulates key apoptotic genes such as BCL2, TP53, andmembers of the CDK family, but also interacts with ATG9,which is involved in autophagy.19-22

Hierarchy of human ncRNA-mediated cell death systemIn nature, hierarchical network organization has been found

in both prokaryotic and eukaryotic regulatory networks.23-25

Hence, we applied the k-core algorithm to decipher whether thehuman ncRNA-mediated cell death system is also hierarchicallyordered (Fig. 3). This network is parsed into 5 detailed layers,and the nodes contained in each layer are 951 (layer 1), 394(layer 2), 225 (layer 3), 150 (layer 4) and 73 (layer 5). Severalestablished key cell death protein clusters, such as the autophagy-associated ‘ATG10 ATG7 ATG5 ATG12 ATG3 MAP1LC3BATG4B ATG16L1’ cluster and the apoptosis-associated‘MOAP1 BCL2L1 BCL2 BAX PMAIP1 MCL1 BID BBC3 BIKBAK1 BMF ERN1’ cluster appear in the innermost layer (4 and5-layer), suggesting that the components in this layer are essentialfor normal cellular function.

To investigate the role of ncRNAs in the cell death system, wedetermined the fraction of ncRNAs at each layer, 345/951(36.28%) in layer 1, 193/394 (48.98%) in layer 2, 127/225(56.44%) in layer 3, 85/150 (56.67%) in layer 4, and 40/73(54.79%) in layer 5. Some ncRNAs and proteins can interact

Table 1. Statistics of ncRNA-associated cell death entries in ncRDeathDB

Autophagy Apoptosis Necrosis

Organism miRNA lncRNA miRNA lncRNA snoRNA miRNA lncRNA

H.sapiens 614(199) 5(4) 1838(358) 204(71) 12(6) 4(4) 3(3)M.musculus 280(128) 0 190(83) 10(9) 0 0 0M.mulatta 282(99) 0 0 0 0 0 0P.troglodytes 272(97) 0 0 0 0 0 0B.taurus 240(94) 0 0 0 0 0 0R.norvegicus 200(89) 0 100(53) 1(1) 0 0 0C.familiaris 215(83) 0 0 0 0 0 0G.gallus 85(44) 0 14(4) 0 0 0 0D.rerio 9(5) 0 13(10) 0 0 0 0C.elegans 3(3) 0 1(1) 0 0 0 0D.melanogaster 1(1) 0 11(7) 5(2) 0 0 0Xenopus 0 0 2(1) 2(1) 0 0 0

Note: These statistics show the numbers of entries for different cell death subroutines in each species included in ncRDeathDB. The numbers in parenthesesare ncRNA counts.

Figure 2. A flowchart for retrieving ncRNA-associated cell death interac-tion entry. (A) Three searching paths for retrieving the ncRNA-mediatedcell death system. (B) The result of a representative database entry.(C) The detailed information for an ncRNA-associated cell death entry. Inthe results and detail pages, ncRDeathDB linked each ncRNA/protein totheir corresponding detail pages and PMID to NCBI PubMed database.

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with both canonical apoptotic andautophagy partners. Such dual func-tional molecules may play a signaltransmission role in the cell death sys-tem. Interestingly, our results haveshown that the number of dualncRNAs and proteins within eachlayer is 158/951 (16.61%) in layer 1,128/394 (32.49%) in layer 2, 94/225(41.78%) in layer 3, 71/150 (47.33%)in layer 4, and 38/73 (52.05%) in layer5, and that the percentage of dualncRNAs and proteins increases dramat-ically with the k-value. Due to the lowstatistical power of detecting significantdifferences in the relatively rarenessnodes (small samples) in 4- and 5-layer, thus, we define layer 1 as theoutmost layer, the combined layers 2and 3 as the middle layer and layers 4and 5 as the core layer. According tohypergeometric test, our results showedthat the percentage of dual ncRNAsand proteins increases significantly withan increasing k-value in the outmostlayer vs. the middle layer (P < 0.01)and the middle layer vs. the core layer(P < 0.01),suggesting that many dualncRNAs and proteins are located inthe core layer of the cell death system and therefore play a vitalrole. These data also validated the existence of a tight crosstalkbetween apoptosis and autophagy at the molecular level. Further-more, this crosstalk was mainly executed by the backbone of thecell death system rather than other molecules playing auxiliaryroles.

The ncRNA-mediated cell deathsystem in human protein-proteinand ncRNA-protein networks

To investigate the biological sig-nificance of the ncRNA-mediatedcell death system in the context ofthe global human regulation net-work, we mapped cell death-associ-ated ncRNAs and proteins to thencRNA-protein interaction (NPI)network and the human protein-pro-tein interaction (PPI) network.Fig. 4 represents the degree distribu-tion of cell death-associated ncRNAsand proteins in both the NPI andPPI networks. It is clear that the celldeath-associated ncRNAs and pro-teins especially the dual nodes have asignificantly higher degree distribu-tion (P< 0.01, hypergeometric test)

than that of the whole nodes in the NPI and PPI networks, sug-gesting that cell death-associated ncRNAs and proteins act ashubs in these global networks. Dual ncRNAs and proteins havethe highest degree distribution, reflective of not only the keyrole played by dual functional molecules in the crosstalkbetween cell death subroutines, but also their roles as core

Figure 3. Schematic diagram for network measurements used to decipher the ncRNA-mediated celldeath system. (A) A schematic diagram represents one bottleneck node, 2 hub nodes and one pivotnode. Hubs are highly connected to other nodes in the network and act as the center point within themodules or subnetworks. Evidence from model organisms indicates that hub proteins tend to beencoded by essential genes, and the deletion of hub nodes leads to amount of diseases. Bottlenecks aredefined as proteins with a high betweenness that are central to many paths in the network, and play akey role in controlling network signaling, and also like to be essential proteins, and often function askey genes in the development of disease progression. Pivots correspond to significantly shared mem-bers of 2 different pathways or subnetworks, which potentially have a role as molecular switchesbetween these pathways or subnetworks, and further sheds light on the organization of the cellularmachinery. (B) Schematic description of the 3-core decomposition of a network. k-core is a subnetworkdefined by iteratively removing the nodes with a degree lower than k and their incident links. The innerk-cores were shown to be global hubs and enriched with lethal genes, which may potentially act as theevolutionarily conserved backbone of ncRNA-mediated cell death system.

Figure 4. Distribution of degrees of apoptosis and autophagy-associated ncRNAs and proteins in theglobal human NPI and PPI network. The boxplot represents the degree distribution. The cell death-associ-ated ncRNAs and proteins especially the dual nodes have a significantly higher degree distribution thanthat of the whole nodes in the NPI and PPI network.

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elements in the human PPI and NPInetwork and as the foundation ofsignal transduction.

Global human NPI and PPI net-works are also hierarchically struc-tured.26-28 As shown in Figure 5,our results show that the percent ofcell death-associated ncRNAs andproteins at each layer increased as kincreased, and the dual proteins andncRNAs involved in both autophagyand apoptosis increased the fastest.These results suggest that cell death-associated ncRNAs and proteins,especially dual-functional ncRNAsand proteins, mainly localized to thecore layer of both the NPI and PPInetworks. In summary, our resultsdemonstrated that cell death-associated ncRNAs and proteinswere involved in the basic construction of NPI and PPInetworks.

Identifying the pivot ncRNAs regulating the humanautophagy subnetwork

One recent study extracted a high-confidence candidate“Autophagy-Interaction Network” (AIN) using the ComparativeProteomics Analysis Software Suite (CompPASS).29 This proteinautophagy network, which is composed of 409 candidate inter-acting proteins with 751 interactions and 8 subnetworks, has pro-vided a resource for the mechanistic analysis of autophagy.Because of the sparseness of autophagy-associated genes withdefined regulation by ncRNAs, some of genes defined in thesesubnetworks cannot be found in our data. However, severalimportant autophagy subnetworks, such as the ULK1 kinase net-work, the Human network of orthologs and paralogs of yeastAtg8, the UBL conjugation system and the ATG2-WIPI subnet-work, can be mapped to our ncRNA-mediated cell death system.To identify the ncRNAs regulating these subnetworks, we per-formed a hypergeometric test for each subnetwork and identified

13 miRNAs and 1 lncRNA that have significant P values(Table 2). A total of 9 miRNAs (MIR519A, MIR374A,MIR20A, MIR630, MIR30C1, MIR181A, MIR130A, MIR30D,and MIRLET7A1) tightly control the UBL conjugation systemthrough regulating some autophagic genes, such as ATG5,ATG12, and ATG16L1. Similarly, 3 miRNAs (MIR26B,MIR30A and MIR335) tightly control the human network oforthologs and paralogs of yeast Atg8, through regulating someautophagic genes, such as MAP1LC3B. More interesting,BANCR as a lncRNA was also involved in this network by target-ing theMAP1LC3A andMAP1LC3B.

Identifying pivot ncRNAs that regulate the human cell deathmodules

To extract pivot ncRNAs from the global cell death network(Fig. 3), the human cell death protein interaction network wasextracted based on the PPI network, and both nodes of each inter-action were documented in ncRDeathDB, consisting of 365 pro-teins and 1172 edges. Then, we used the ClusterONE plugin inCytoscape to delineate potential modules in the cell death proteininteraction network and identified 17 such modules (Table S2).

Figure 5. Analysis of network centricity of cell death-associated ncRNA and protein in global the humanNPI and PPI network. The percent of both cell death-associated ncRNAs and proteins in k-cores of the NPIand PPI network significantly increased as k increased, and the dual proteins and ncRNAs involved inboth autophagy and apoptosis increased the fastest.

Table 2. ncRNAs are significant for the regulation of human protein autophagy subnetworks

Autophagy subnetwork ncRNA overlap protein P-value

Human proteins related to yeast Atg8 MIR335 LC3B,SQSTM1, LC3A,GABARAPL1 4.11E-06UBL conjugation system MIR519A ATG16L1,ATG12,ATG5 4.96E-05UBL conjugation system MIR374A ATG12,ATG5,ATG16L1 1.17E-04UBL conjugation system MIR20A ATG16L1,ATG12,ATG5,ATG7 2.09E-04UBL conjugation system MIR630 ATG12,ATG5,ATG16L1 2.26E-04Human proteins related to yeast Atg8 MIR26B MAP1LC3C,GABARAP 6.61E-04UBL conjugation system MIR30C ATG16L1,ATG5 8.78E-04UBL conjugation system MIR181A ATG12,ATG5,ATG16L1 8.87E-04Human proteins related to yeast Atg8 BANCR MAP1LC3B,MAP1LC3A 3.80E-03UBL conjugation system MIR130A ATG5,ATG16L1 3.94E-03UBL conjugation system MIR30D ATG12,ATG5 3.94E-03ULK1 kinase network MIR885 ULK2,ATG13 4.14E-03Human proteins related to yeast Atg8 MIR30A MAP1LC3B,GABARAPL2 4.72E-03UBL conjugation system MIRLET7A1 ATG5,ATG7 6.20E-03

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Some of these modules indeed coincided with several experimen-tally validated protein complexes.30 For example, the module‘FASN FADD CASP8AP2 CASP8 TNFRSF10B FAF1 PEA15’significantly contains the protein complex ‘Death-inducing signal-ing complex DISC associated with type I cells’ and the ‘FAS-FADD-CASP8-CASP10 complex’ (P<0.01, hypergeometric test),while the module ‘MOAP1 BCL2L1 BCL2 BAX PMAIP1MCL1 BID BBC3 BIK BAK1 BMF ERN1’ mostly consisted ofBCL2 family proteins, which play an influential role in both apo-ptosis and autophagy. Furthermore, some of the modules arehighly consistent with the subnetworks defined in the ‘Autophagy-Interaction Network’. For example, the protein cluster “ATG10ATG7 ATG5 ATG12 ATG3 MAP1LC3B ATG4B ATG16L1” ispart of the ‘UBL conjugation system’ defined in AIN (P<0.01,hypergeometric test). All of these lines of evidence indicate thatthe modules identified by ClusterONE overlap significantly withexisting protein complexes in the cell death subnetwork. Based onthis module construction, we also uncovered a panel of ncRNAsthat are extensively linked with these modules via a hypergeometrictest (Table 3, Fig. 6 and Table S3).Our results showed that thesencRNAs can easily be classified into 2 categories, one of which islocated within a given module and regulates most of the proteinsin the module. For example, MDM2-AS, MIR197 and MIR320Aonly target the BCL2-associated module, while MIR374A,MIR30A and MIR519A only target the module ‘ATG10 ATG7ATG5 ATG12 ATG3 MAP1LC3B ATG4B ATG16L1’. Anothercategory of ncRNAs acts as a bottleneck to bridging the differentmodules. The most significant discovery is that MIR181A simulta-neously regulate modules ‘ATG10 ATG7 ATG5 ATG12 ATG3MAP1LC3B ATG4B ATG16L1’ and ‘MOAP1 BCL2L1 BCL2BAX PMAIP1 MCL1 BID BBC3 BIK BAK1 BMF ERN1’;while, the first module as the member of ‘UBL conjugation sys-tem,’ plays an important role in regulating autophagy, the latterconsists of key proteins involved in apoptosis, such as BCL2,MCL1 and BIK. What’s more, MIR181A is located in the center-most subnetwork of the cell death network, suggesting thatMIR181A plays a critical role in the regulation of cell death byfunctioning in both apoptosis and autophagy.

Finally, significant regulatory relationships between ncRNAsand their target modules are shown in Table 3. Apart frommiRNA modulators, the novel lncRNAMALAT1 was also foundto play an important role in cell death. It targets the module“MOAP1 BCL2L1 BCL2 BAX PMAIP1 MCL1 BID BBC3

BIK BAK1 BMF ERN1” and module “BCL2L1 BCL2 CASP3CTNNB1 CASP8 PSEN1 TFAP2A APAF1,” which have a highoverlap with the apoptotic protein complex ‘MCL1-BAK1 com-plex’ and “MRIT complex” respectively (P<0.01, hypergeomet-ric test) (Table 3, Fig. 6 and Table S3). These ncRNAs seem toact as messengers or coordinators in mediating the crosstalkbetween the different subroutines of PCD.

Discussion

In recent years, great efforts have been made to uncover newforms of mammalian programmed cell death.1,31-34 Emergingwork has focused on the network organization of cell death sys-tems with the ultimate goal of exploring potential organizationmechanisms from a global view of the Protein-Protein Interac-tion (PPI) network.29,35 Consequently, accumulated ncRNAsand proteins have been identified in a variety of cell death sub-routines.10,29,36-38 It is now widely accepted that both ncRNAsand proteins are intimately connected with each other in a globalcell death network. Of course, the PPI network in different celldeath subroutines is only part of the story. Hence, 2 and half yago, we established the miRDeathDB database to archive theseresources and provide a basis for the analysis of miRNA-mediatedcell death systems.11,12 With the rapid development of this field,this is the perfect time to integrate recent findings and updateinformation. We therefore developed ncRDeathDB (www.rna-society.org/ncrdeathdb), which documents by nearly 20-fold thedata of the previous miRDeathDB database and includes diversencRNA-mediated cell death interactions, such as those involvingmiRNA, lncRNA, and snoRNA. ncRDeathDB is a good startingpoint for searching the web resources centered on diversencRNAs involved in PCD. More importantly, ncRDeathDBprovides a valuable resource to manipulate, visualize, and analyzethese ncRNA-associated cell death interactions. By integratingthe ncRNA-associated cell death interactions into a global net-work, this resource will help to visualize and navigate currentknowledge of the noncoding RNA component of cell death andautophagy, to uncover the generic organizing principles ofncRNA-associated cell death systems, and to generate valuablebiological hypothesis. In ncRDeathDB, we present a systematicncRNA-associated analysis of the human cell death systems byusing experimentally validated entries. Such analysis has provided

Table 3. Statistics of top 10 significant ncRNAs regulating human protein cell death modules

Protein cell death module members ncRNA P-value

BCL2L1 BCL2 CASP3 CTNNB1 CASP8 PSEN1 TFAP2A APAF1 MIR133A 3.57E-08BCL2L1 BCL2 CASP3 CTNNB1 CASP8 PSEN1 TFAP2A APAF1 MIR548 6.01E-08BCL2L1 BCL2 CASP3 CTNNB1 CASP8 PSEN1 TFAP2A APAF1 MALAT1 6.09E-08ATG10 ATG7 ATG5 ATG12 ATG3 MAP1LC3B ATG4B ATG16L1 MIR519A 4.15E-07ATG10 ATG7 ATG5 ATG12 ATG3 MAP1LC3B ATG4B ATG16L1 MIR374A 1.47E-06ATG10 ATG7 ATG5 ATG12 ATG3 MAP1LC3B ATG4B ATG16L1 MIR20A 2.45E-06ATG10 ATG7 ATG5 ATG12 ATG3 MAP1LC3B ATG4B ATG16L1 MIR630 3.81E-06MOAP1 BCL2L1 BCL2 BAX PMAIP1 MCL1 BID BBC3 BIK BAK1 BMF ERN1 MIR197 5.79E-06BCL2L1 BCL2 CASP3 CTNNB1 CASP8 PSEN1 TFAP2A APAF1 HIF1A-AS1 5.84E-06BCL2L1 BCL2 CASP3 CTNNB1 CASP8 PSEN1 TFAP2A APAF1 MIRLET7C 1.45E-05

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another view of the global organization of the ncRNA-associatedcell death system and the potential mechanisms involved in thecell death process. First, we presented a network of 1715 humanncRNA-associated interactions (1453 apoptosis- and 262autophagy-associated interactions) and 951 nodes (300 miRNAs,40 lncRNAs, 1 snoRNA and 610 proteins) with extensive con-nectivity among different modules.Similarly, our previouslyreported hubs (BCL2 and MIR21) and bottleneck (MIR181A)were validated in this extended network with newly discoveredpartner ncRNAs and proteins, thus strengthening their roles inmediating the crosstalk between apoptosis and autophagy. Moreimportantly, novel key hubs and bottlenecks have been exploredthat interact with diverse and important ncRNAs, especially mul-tiple lncRNAs, to regulate communication with intra- and inter-autophagic and apoptotic modules. Hence, these diverse ncRNAdependent mechanisms may represent a basic mechanism for theconversation between autophagic and apoptotic death modules,leading to cellular homeostasis upon the presentation of externalstimuli.

Next, to investigate the global center of ncRNAs and proteinsinvolved in PCD, the ncRNA-associated cell death system washierarchically divided into 5 fine layers using the k-core algo-rithm. Our results explored the idea that both dual functionalncRNAs and proteins tended to reside in the most inner layer ofthis system and that these molecules were tightly connected witheach other and acted as the evolutionary backbone of the prote-ome. Furthermore, we investigated the relationship between thencRNA-mediated cell death system and the canonical signaltransduction process. For both protein-protein (PPI) andncRNA-protein (NPI) interaction networks, our results demon-strated that the number of cell death-associated ncRNAs and pro-teins was significantly larger in the inner layer than in the outerlayer. Dual proteins and ncRNAs involved in both autophagyand apoptosis displayed this tendency, suggesting that cell death-associated ncRNAs and proteins were encoded into the basicfunction of cells. Under stress or other urgent conditions, thesedual proteins and ncRNAs could reprogram cell fate by choosingdifferent apoptosis or autophagy pathways to adapt to the

environment. Finally, in an experimentally verified autophagymodule, we observed that some cell death-associated ncRNAstightly control different autophagy subnetworks by regulatingshared key genes. Similarly, in a cell death network (includingboth autophagy and apoptosis modules), our results identifiedseveral key ncRNA players and their protein targets, which medi-ate communication between different cell death modules. Theaforementioned ncRNA-based intra- and inter-modulators arelikely to be good candidates for pharmaceutical development inthe near future.

In summary, our studies provide insight into the organizationof the human ncRNA-mediated cell death system and decipherdiverse ncRNA-dependent mechanisms as a basic component ofthe crosstalk between apoptosis and autophagy. In the future, withthe identification of more necrosis-associated ncRNA, our databasewill serve as a more comprehensive ncRNA-associated resource forfurther mechanistic analysis of programmed cell death.

Materials and Methods

Data collection and web interface implementationTo collect all available ncRNA-associated entries involved in

programmed cell death, we have integrated information on 3major types of ncRNAs: long noncoding RNA (lncRNA) sym-bols from lncRNAdb39 and Ensembl,40 miRNA symbols frommirBase database,41 and small nucleolar RNA (snoRNA) symbolsfrom sno/scaRNAbase42 and snoRNA-LBME-db.43 Because theresearch on other categories of ncRNAs is still in its infancy andthere is no unified nomenclature, we instead use these ncRNAcategory names to replace specific ncRNA symbols. All abstractsand articles in the PubMed database were screened using the fol-lowing keyword combinations: (each ncRNA symbol or ncRNAcategory) and (each cell death subroutines); for example, “malat1and apoptosis.” The relevant hits were downloaded and preparedsystematically for data curation by further manual inspection.Apart from the information regarding ncRNAs and the corre-sponding interacting genes, species and detailed description com-piled from the references, we incorporate several useful tools topredict the potential binding sites of these ncRNA-associated celldeath interactions. For ncRNA-RNA interactions, the bindingsites and scores are predicted according to miRanda44 andRISearch.45 For ncRNA-protein interactions, RNA-binding resi-dues and scores are computed by bindNC46 and RNAbindR.47

In addition, to help users interactively analyze the ncRNA-medi-ated cell death system online, ncRDeathDB provides an embed-ded Cytoscape web tool (cytoscapeweb.cytoscape.org/).

The ncRDeathDB database is implemented in the HTMLand PHP languages with a MySQL server. The interface compo-nent consists of webpages designed and implemented in HTML/CSS in a Microsoft Windows environment. It has been tested inGoogle Chrome, Firefox and Internet Explorer web browsers.

Autophagy-associated entry predictionAutophagy-associated miRNAs and their protein targets were

predicted by using homologs search based on the experimentally

Figure 6. Example of a module significantly regulated by ncRNAs identi-fied in the human cell death protein interaction network. Pink and bluenodes represent apoptosis and autophagy, respectively. Yellow nodesrepresent the ncRNAs and proteins that play dual roles in apoptosis andautophagy. Triangular, arrowhead-shaped and circular nodes representcell death-associated miRNAs, lncRNAs and proteins, respectively.

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validated autophagy-associated entries in ncRDeathDB. Theconserved miRNAs families were identified by TargetScan.48

The homological genes of human autophagy-associated geneswere downloaded from NCBI.49 Then, the interactions betweenconserved miRNAs and homological genes verified by TargetS-can48,50,51 were filtered as the potential autophagy-associatedmiRNA-protein pairs and assigned for other species.

Protein-protein interaction network and RNA-associatednetwork construction

In this work, the global human protein-protein interaction(PPI) network was retrieved from the Human Protein ReferenceDatabase (HPRD),52 and the human ncRNA protein interactionnetwork(NPI) was retrieved from our RNA-Associated Interac-tion Database (RAID)53 and miRTarBase,54 in which the inter-actions have been validated by experiments and covered diversencRNA species, such as lncRNA and miRNA.

Degree and betweenness analysis of the human interactionnetwork

The network was analyzed based on topological parameterssuch as betweenness and degree using a Cytoscape plug-in called‘Network Analyzer.’55 In a biological network, degree indicatesthe number of edges linked to a given node (Fig. 3). Betweennessmeasures the total number of nonredundant shortest paths goingthrough a certain node or an edge, reflecting the bottleneck ofthe network (Fig. 3) The nodes with a top 5% highest degreeand betweenness were chosen as the hubs and bottlenecks in thiswork (Table S1). The hub and bottleneck nodes in the networkusually play a key role in connecting the intra- and inter-modulesor pathways.56

K-core analysis of interaction networkThe k-core of a network is defined as the subnetwork in

which all of its node degrees are greater than k after recur-sively removing the nodes with a smaller degree than k(Fig. 3).28 One can iteratively apply this ‘peeling’ procedureto divide the whole network into different layers, and thegenes in layer k of the network are defined as the intersectionbetween core k and core kC1. In this work, the humanncRNA-mediated cell death system has been parsed into 5layers according to this ‘peeling’ procedure.

To measure the centrality of cell death-associated ncRNAsand proteins, the Excess Retention (ER) was calculated foreach k-core layer of the NPI and PPI networks. ER is definedas the proportion of genes with a certain property A in the‘k-core’ compared with that in the whole gene groups.57 Forinstance, in a k-core with a total of Nk nodes, the proportionof protein with a property A is defined as erAk D nAk /Nk Obvi-ously, the fraction of proteins with property A in the whole net-work with a total N nodes is defined aserA D nA/N, and the ERof k-core is defined as ERA

k D erAk /erA. In this work, we set the

cell death-associated ncRNAs and proteins as the property A inER, and then counted the percent of cell death-associatedncRNAs and proteins in each layer of the NPI and PPI

networks. Thus, the ER value reflects the centrality of celldeath-associated ncRNAs and proteins in human PPI and NPInetworks.

Module identification and pivot ncRNA recognitionIn this work, the human cell death protein interaction net-

work was extracted based on the PPI network, all nodes ofwhich were documented in ncRDeathDB. To identify func-tional modules in the human cell death protein interactionnetwork, the network was clustered with the ClusterONEalgorithms58 available in Cytoscape.59 ClusterONE is amethod of detecting the protein complex module based onthe PPI network by iteratively adding or removing the mostvaluable incident and boundary nodes to locally optimize thecluster quality measure cohesiveness.58

The pivot ncRNAs (Fig. 3), which significantly regulate dif-ferent modules and subnetworks, are recognized by a hypergeo-metric test.60 The P value of the test statistic is calculated as:

pD 1¡XiD 0

M ¡ 1

M

i

� �N ¡M

n¡ i

� �

N

n

� �

Where N is the total number of proteins contained in thehuman cell death protein interaction network, n represents thenumber of interacted genes of a certain ncRNA. M represents thenumber of genes belonging to a module, while i represents thegenes in the modules interacted with ncRNA.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgment

We are grateful to Dr. Daniel J. Klionsky for help in editingthe manuscript.

Funding

This work was supported by the National High TechnologyResearch and Development Program of China (2014AA021102),the Major State Basic Research Development Program ofChina (2014CB910504), the National Natural Science Founda-tion of China (U1304311), the Natural Science Foundation ofHeilongjiang Province of China (C2015027), the ScientificResearch Fund of Heilongjiang Provincial Education Department(12541426).

Supplemental Material

Supplemental data for this article can be accessed on thepublisher’s website

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Oct

ober

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