Gene 546 (2014) 417–420
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
rTRM-web: a web tool for predicting transcriptional regulatory modules for ChIP-seq-ed transcription factors Fengkai Liu a, Diego Miranda-Saavedra b,⁎ a b
School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics (BUAA), Building 16, Min'an Street, Dong Cheng District, Beijing 100007, China Fibrosis Laboratories, Institute of Cellular Medicine, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
a r t i c l e
i n f o
Article history: Received 11 May 2014 Received in revised form 4 June 2014 Accepted 9 June 2014 Available online 11 June 2014 Keywords: Transcriptional regulatory module ChIP-seq Transcription factor Network Protein–protein interactions
a b s t r a c t Transcription factors (TFs) bind to specific DNA regions, although their binding specificities cannot account for their cell type-specific functions. It has been shown in well-studied systems that TFs combine with co-factors into transcriptional regulatory modules (TRMs), which endow them with cell type-specific functions and additional modes of regulation. Therefore, the prediction of TRMs can provide fundamental mechanistic insights, especially when experimental data are limiting or when no regulatory proteins have been identified. Our method rTRM predicts TRMs by integrating genomic information from TF ChIP-seq data, cell type-specific gene expression and protein–protein interaction data. Here we present a freely available web interface to rTRM (http://www.rTRM.org/) supporting all the options originally described for rTRM while featuring flexible display and network calculation parameters, publication-quality figures as well as annotated information on the list of genes constituting the TRM. © 2014 Elsevier B.V. All rights reserved.
1. Introduction The precise spatio-temporal regulation of gene expression programs allows cells to respond to stimuli that influence their ability to fight infections, their developmental fates, and their functions as part of complex tissues. Although genetic information can be regulated at many levels, from DNA transcription to a vast array of protein posttranslational modifications (Deribe et al., 2010), the modulation of gene transcription is a crucial point of control. The multiple levels of regulation of gene transcription include the binding of TFs and co-factors to target DNA sequences, the epigenetic regulation of chromatin, and nuclear organization (Splinter and de Laat, 2011). Given a permissive transcriptional environment, TFs bind within the vicinity of genes, either directly at the transcriptional start site (TSS) or at distal enhancers. The TSS constitutes the basal promoter where the general transcription machinery assembles, whereas distal elements (either enhancers or silencers of transcription) are bound by TFs that loop over to the promoter and thus drive tissue-specific gene expression patterns (Alonso et al., 2009). Of all the distinct regulatory signals that control transcriptional output, TFs are the best understood, although TF binding specificities alone cannot explain their cell type-specific functions (Hutchins et al., 2013c). Despite the avalanche of data generated by ChIP-seq to
Abbreviations: TF, transcription factor; TRM, transcriptional regulatory module; PPI, protein–protein interaction; ESC, embryonic stem cell; HSC, hematopoietic stem cell; PWM, position weight matrix. ⁎ Corresponding author. E-mail address: [email protected] (D. Miranda-Saavedra).
http://dx.doi.org/10.1016/j.gene.2014.06.016 0378-1119/© 2014 Elsevier B.V. All rights reserved.
determine the genome-wide binding patterns of TFs, major gaps remain in our understanding of how key TFs regulate cell type-specific programs. The current view is that generally TFs do not work in isolation, but instead cooperate with other TFs and scaffolding proteins to assemble into TRMs that endow key TFs with cell type-specific functions (Heinz et al., 2010). TRMs have been identified in hematopoietic stem cells (reviewed in Wilson et al., 2011), embryonic stem cells (Chen et al., 2008) plus a number of other cell types through the ENCODE project (Wang et al., 2012). Combinatorial TF binding controls gene expression programs in temporal and tissue-specific manners, and specific protein–protein interactions are essential for the assembly of TF complexes. TRMs generally extend over short DNA regions and consist of several distinct and interacting TFs (Chen et al., 2008; Heinz et al., 2010; Wilson et al., 2010). We recently developed a generic method for the identification of TRMs from protein–protein interaction networks in any biological context (rTRM) (Diez et al., 2014). rTRM reconstructs TRMs by integrating genomic information from TF ChIP-seq data, cell type-specific gene expression and protein–protein interaction (PPI) data (Fig. 1). Our algorithm for identifying TRMs works by finding proteins in PPI networks separated by a maximum specified distance to a target protein (in this case the TF that has been profiled by ChIP-seq). A very important feature of rTRM over methods that ignore PPI information is that rTRM allows the identification of ‘bridge proteins’ (including chromatin modifying and remodeling enzymes, and signaling molecules that integrate multiple signals) that do not necessarily bind to DNA in a sequence-specific manner and which therefore could never be found using genomic information alone. All proteins require physical interactions with other
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F. Liu, D. Miranda-Saavedra / Gene 546 (2014) 417–420 ChIP-seq sites for TF ‘X’ are scanned for over-represented motifs against an appropriate background
4
x
x
x
x 2
Putative TFs are assigned to enriched motifs by sequence similarity
rTRM computes a TRM by finding candidates in a PPI network separated by a maximum distance to TF ‘x’
3
Cell type-specific expression data is used to filter out candidates
Fig. 1. Overview of the rTRM method for the reconstruction of transcriptional regulatory modules (Diez et al., 2014). (1) A set of genomic sites identified for a specific TF by ChIP-seq is scanned for the presence of over-represented motifs, to which putative TFs are assigned by sequence similarity (2). Cell type-specific expression data is used to filter out candidate TFs (3), and in a final step only those candidates that have been reported to make interactions in the BioGRID database (Chatr-Aryamontri et al., 2013) are retained (4).
proteins to perform their functions, and therefore PPIs can be an important source of additional information if we wish to understand the combinatorial and modular nature of TRMs. The performance of rTRM was evaluated by reconstructing the TRMs specific for the well-characterized embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs) (Diez et al., 2014). The TRMs predicted for ESCs and HSCs were highly precise and specific for these cell types, with ~ 75% of the proteins shown to be involved in the regulation of these cell types by independent evidence. Furthermore, we investigated the multifunctional factor STAT3 in macrophages (anti-inflammatory response), CD4+ T cells (Th17 differentiation), ESCs (pluripotency) and AtT-20 cells (production of adrenocorticotropic hormone in response to stress and inflammation). The functional diversity of STAT3 suggests that it targets distinct enhancer sets depending on the biological context. By re-analyzing the genome-wide binding patterns of STAT3 in macrophages (Hutchins et al., 2012), CD4+ T cells (Kwon et al., 2009), ESCs (Chen et al., 2008) and AtT-20 cells (Langlais et al., 2012), we showed that most STAT3 binding events are specific to each cell type and that STAT3 has two distinct modes of binding: (a) a universal, cell type-independent binding mode; and (b) various cell typespecific binding modes, which are responsible for STAT3's cell typespecific functions (Hutchins et al., 2013c). The cell type-independent binding mode shared by all four cell types is characterized by 35 distinct genomic binding sites where STAT3 binds to transcribe a set of genes whose role is to mainly modulate the JAK-STAT pathway itself, as well as the transcription of several genes downstream of it. The application
of rTRM suggested that STAT3 associates with different cofactors to direct both its universal and cell type-specific programs by modulating its activity and helping it locate to specific genomic sites. The STAT3 universal TRM is characterized by many ‘general’ TFs (MYC, E2F1, KLF4), and we showed experimentally that E2F1 is pre-bound in resting macrophages at sites of future recruitment of STAT3 upon IL-10 stimulation (Hutchins et al., 2013a, 2013b, 2013c). The reconstruction of STAT3 cell type-specific TRMs recovered well-characterized factors known to operate in the distinct cell types. For instance, the ESC-specific TRM recovered OCT4, SOX2, ESRRB, KLF4, SMAD1, TEAD1, and REST and a number of homeodomain proteins that may correspond to NANOG. The cell type-specific TRM models of STAT3 predicted an important level of epigenetic regulation that may play a key role by blocking access to other STAT3-binding sites or by pre-marking sites that STAT3 can be recruited to. This dual model of regulation whereby a master TF forms distinct TRMs to execute both universal and cell type-specific functions might be a general feature of pleiotropic TFs. Therefore, and as illustrated with the examples above the reconstruction of TRMs is incredibly useful for understanding the regulatory layers associated with TFs, and can provide fundamental mechanistic insights and suggest very specific follow-up experiments. Here, we present a user-friendly web implementation of rTRM. The web server is freely available (http://www.rTRM.org/) and supports all the options originally described for rTRM (Diez et al., 2014). 2. Material and methods Our web server rTRM.org is based on the original rTRM method (Diez et al., 2014). The code has recently been made available through Bioconductor (Gentleman et al., 2004; http://bioconductor.org/ packages/release/bioc/html/rTRM.html). We subsequently added a local web interface to rTRM using the shiny package (RStudio, Inc., 2013), which is limited to single users and also available through Bioconductor (http://bioconductor.org/packages/release/bioc/html/ rTRMui.html). Finally we configured our code to work with the shiny Web Server Open Source Edition (http://www.rstudio.com/shiny/ server/) using a CentOS6.4 x86_64 cloud server accessible via the WWW. The advantage of using the extremely stable shiny Web Server Open Source Edition is that multiple shiny applications can be handled simultaneously without limiting the number of users as long as the underlying hardware is powerful enough. 3. Results and discussion 3.1. The rTRM web server The rTRM web server (http://www.rTRM.org/) features all the options available in the original publication of rTRM (Diez et al., 2014), thereby facilitating the reconstruction of TRMs in human and mouse. The interface has a straightforward layout where the user must provide the following data: (a) The name of the TF (of human or mouse origin) for which the ChIP-seq experiment was performed, and as provided in organism-specific lists of TFs for which a position weight matrix (PWM) is available. The ‘Transcription factors’ tab lists all the TF names (ids) available, both for human and mouse. These correspond to the TFs that match our super-library of PWMs (Diez et al., 2014) compiled by integrating the vertebrate entries from the JASPAR database (Mathelier et al., 2014), the UniPROBE protein-binding microarray database (Robasky and Bulyk, 2011) and the high-throughput SELEX (HT-SELEX) dataset of human and mouse (Jolma et al., 2013). This super-library encompasses a total of 548 human TF genes (and their mouse orthologs), or 35–39% of the entire complement of TFs in the human genome (Diez et al., 2014).
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(c) The list of genes expressed in any particular cell type (Entrez format). The selection of a cutoff that separates expressed and nonexpressed genes is illustrated for both microarray expression and RNA-seq data in Diez et al. (2014).
Fig. 2. Snapshot of the web interface.
(b) The list of TF binding motifs enriched in the set of ChIP-seq peaks. There are various ways of doing this optimally that depend on the biological question under investigation. For instance, in our original publication of rTRM (Diez et al., 2014), we used HOMER (Heinz et al., 2010) to scan uniform 400 bp regions (200 bp on either side of the reported ChIP-seq summits) for enriched motifs. However, since HOMER's selection of background sequences for enrichment analysis is based on features such as similar GC content and length, which typically produces slightly different results with every run, we performed 10 replicate runs and retained only those motifs that were enriched (q b 0.05) in at least 80% of the runs. The final list of enriched motifs was matched against our PWM super-library using Tomtom (q b 0.05) (Gupta et al., 2007). In a previous publication, however, we investigated the co-factors associated with the TF Scl/Tal1 in mouse HSCs. In this case we scanned uniform-sized Scl/Tal1 ChIP-seq peaks against a background of promoters whose genes were explicitly not expressed in HSCs but only in terminally differentiated blood cells (Miranda-Saavedra et al., 2009; Wilson et al., 2009). In practice, this could easily be done by downloading our super-library of PWMs (available with the Bioconductor release of rTRM at http://bioconductor.org/ packages/release/bioc/html/rTRM.html) and scanning all PWMs against an appropriate background with the RSAT package (Thomas-Chollier et al., 2012; Turatsinze et al., 2008). Additionally, rTRM interacts efficiently with the PWMEnrich package, also available through Bioconductor (http://www.bioconductor. org/packages/2.12/bioc/html/PWMEnrich.html).
Once the required data have been input, the rTRM web server displays the resulting TRM, indicating the classification of each TF. A number of network parameters allow users to predict networks with more relaxed PPI distance parameters (‘Extended TRM’), and display the TRM in visually distinct ways by adjusting the size of the nodes and the labels, and the network layout (concentric/arc/circle/KamadaKawai/Fruchterman-Reingold). Finally, a publication-quality figure of the TRM, as well as the list of genes that constitute the TRM, can be downloaded. The ‘Tutorial’ and the ‘Help’ tabs on the rTRM.org website provide a detailed explanation of each one of the options and steps. The sample data provided is for the Sox2 TF profiled by ChIP-seq in ESCs, as previously analyzed (Diez et al., 2014). Sox2 is essential for maintaining the ESC phenotype, and is known to combine with other TFs, including Pou5f1 (Oct4), Nanog, Esrrb and Klf4 (Chen et al., 2008). Genomebinding locations for Sox2 in ESCs were obtained (Chen et al., 2008), enriched motifs were identified as previously described (Diez et al., 2014), and the expression data for mouse ESCs was obtained from Ho et al. (2011). The resulting TRM for Sox2 (Fig. 2) includes Esrrb, Klf4, Pou5f1 (Oct4), Sall4 and Sox2, all proteins with known roles in ESC biology (Chen et al., 2008) (Table 1). The Sox2-based TRM for ESCs is remarkably different from that of neural progenitor cells (Diez et al., 2014), indicating that rTRM is capable of identifying TRMs with clearly distinct and cell type-specific features for the same TF in distinct types of stem cell.
3.2. Conclusions We have built a web server for predicting TRMs that features all the original options of our method rTRM (Diez et al., 2014). Although rTRM is currently implemented as an R package and available in Bioconductor, the complexities of the R interface may prevent those users with limited exposure to R or bioinformatics to benefit from the full range of bioinformatic and statistical methods that are implemented in rTRM. rTRMweb facilitates access to all the capabilities of rTRM by means of a graphical interface that is both clear and intuitive. The reconstruction of TRMs can yield incredibly valuable information when experimental datasets are limiting, or when no regulatory proteins have yet been identified. Thus, our web platform constitutes a very powerful tool to dissect the multiple levels of regulation of a TF of interest, both in physiological and pathological states. The website provides sample data including the over-represented motifs in the genome-wide binding sites of Sox2 in ESCs, as well as the set of genes expressed in ESCs. These datasets provide the user with a valuable example on how to reconstruct their own TRMs for their TFs of interest. rTRM-web is a flexible tool that allows modifying a number of network and graphical parameters (e.g. network layout, node, line and label sizes) for the production of publicationquality images.
Table 1 List of proteins predicted in the Sox2 TRM in ESCs. TFs are annotated with the TFClass description. Entrez gene
Symbol
Role
Type
Description
TFClass family
20674 20670 99377 19357 18999 16600 15182 26380
Sox2 Sox15 Sall4 Rad21 Pou5f1 Klf4 Hdac2 Esrrb
Enriched Enriched Bridge Bridge Enriched Enriched Bridge Enriched
Target Query Query Query Query Query Query Query
SRY-box containing gene 2 SRY-box containing gene 15 sal-like 4 (Drosophila) RAD21 homolog (S. pombe) POU domain, class 5, transcription factor 1 Kruppel-like factor 4 (gut) Histone deacetylase 2 Estrogen related receptor, beta
High-mobility group (HMG) domain factors High-mobility group (HMG) domain factors
Homeodomain factors C2H2 zinc finger factors Nuclear receptors with C4 zinc fingers
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Future developments include: (i) incorporating updated PPI datasets and PWMs for as yet uncharacterized TFs; (ii) extending the rTRM method to other model organisms (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans, Drosophila melanogaster and several mammals); and (iii) including the possibility of reconstructing TRMs from genomic sites that are not determined by the ChIP-seq analysis of specific TFs. These include genomic marks that might characterize developmental stages of a particular cellular lineage, such as differential DNase I hypersensitivity sites and active enhancers. Active enhancers that drive cell type-specific expression patterns are best identified by the computational modeling of the chromatin epigenetic landscape rather than by evolutionary conservation sequence analysis. Although dozens of distinct epigenetic marks have been described, we recently showed that an optimum combination of only a handful such marks (a combination of activating and repressive methylations and acetylations) is sufficient to describe and predict enhancers (Fernandez and Miranda-Saavedra, 2012). This points to the existence of complex epigenetic patterns associated with enhancers that can be identified computationally and for which TRMs can be built to advance our understanding of transcriptional networks in the absence of a priori candidate TFs. Acknowledgments We would like to thank Dr. Andrew Hutchins for reading the manuscript and making helpful suggestions. The authors acknowledge funding from Newcastle University Medical School. References Alonso, M.E., Pernaute, B., Crespo, M., Gomez-Skarmeta, J.L., Manzanares, M., 2009. Understanding the regulatory genome. Int. J. Dev. Biol. 53, 1367–1378. Chatr-Aryamontri, A., Breitkreutz, B.J., Heinicke, S., Boucher, L., Winter, A., Stark, C., Nixon, J., Ramage, L., Kolas, N., O'Donnell, L., Reguly, T., Breitkreutz, A., Sellam, A., Chen, D., Chang, C., Rust, J., Livstone, M., Oughtred, R., Dolinski, K., Tyers, M., 2013. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816–D823. Chen, X., Xu, H., Yuan, P., Fang, F., Huss, M., Vega, V.B., Wong, E., Orlov, Y.L., Zhang, W., Jiang, J., Loh, Y.H., Yeo, H.C., Yeo, Z.X., Narang, V., Govindarajan, K.R., Leong, B., Shahab, A., Ruan, Y., Bourque, G., Sung, W.K., Clarke, N.D., Wei, C.L., Ng, H.H., 2008. Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133, 1106–1117. Deribe, Y.L., Pawson, T., Dikic, I., 2010. Post-translational modifications in signal integration. Nat. Struct. Mol. Biol. 17, 666–672. Diez, D., Hutchins, A.P., Miranda-Saavedra, D., 2014. Systematic identification of transcriptional regulatory modules from protein–protein interaction networks. Nucleic Acids Res. 42, e6. Fernandez, M., Miranda-Saavedra, D., 2012. Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines. Nucleic Acids Res. 40, e77. Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A.J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J.Y., Zhang, J., 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80. Gupta, S., Stamatoyannopoulos, J.A., Bailey, T.L., Noble, W.S., 2007. Quantifying similarity between motifs. Genome Biol. 8, R24. Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y.C., Laslo, P., Cheng, J.X., Murre, C., Singh, H., Glass, C.K., 2010. Simple combinations of lineage-determining transcription fac-
tors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589. Ho, L., Miller, E.L., Ronan, J.L., Ho, W.Q., Jothi, R., Crabtree, G.R., 2011. esBAF facilitates pluripotency by conditioning the genome for LIF/STAT3 signalling and by regulating polycomb function. Nat. Cell Biol. 13, 903–913. Hutchins, A.P., Poulain, S., Miranda-Saavedra, D., 2012. Genome-wide analysis of STAT3 binding in vivo predicts effectors of the anti-inflammatory response in macrophages. Blood 119, e110–e119. Hutchins, A.P., Diez, D., Miranda-Saavedra, D., 2013a. Genomic and computational approaches to dissect the mechanisms of STAT3's universal and cell type-specific functions. JAKSTAT 2, e25097. Hutchins, A.P., Diez, D., Miranda-Saavedra, D., 2013b. The IL-10/STAT3-mediated antiinflammatory response: recent developments and future challenges. Brief. Funct. Genomics 12, 489–498. Hutchins, A.P., Diez, D., Takahashi, Y., Ahmad, S., Jauch, R., Tremblay, M.L., MirandaSaavedra, D., 2013c. Distinct transcriptional regulatory modules underlie STAT3's cell type-independent and cell type-specific functions. Nucleic Acids Res. 41, 2155–2170. Jolma, A., Yan, J., Whitington, T., Toivonen, J., Nitta, K.R., Rastas, P., Morgunova, E., Enge, M., Taipale, M., Wei, G., Palin, K., Vaquerizas, J.M., Vincentelli, R., Luscombe, N.M., Hughes, T.R., Lemaire, P., Ukkonen, E., Kivioja, T., Taipale, J., 2013. DNA-binding specificities of human transcription factors. Cell 152, 327–339. Kwon, H., Thierry-Mieg, D., Thierry-Mieg, J., Kim, H.P., Oh, J., Tunyaplin, C., Carotta, S., Donovan, C.E., Goldman, M.L., Tailor, P., Ozato, K., Levy, D.E., Nutt, S.L., Calame, K., Leonard, W.J., 2009. Analysis of interleukin-21-induced Prdm1 gene regulation reveals functional cooperation of STAT3 and IRF4 transcription factors. Immunity 31, 941–952. Langlais, D., Couture, C., Balsalobre, A., Drouin, J., 2012. The Stat3/GR interaction code: predictive value of direct/indirect DNA recruitment for transcription outcome. Mol. Cell 47, 38–49. Mathelier, A., Zhao, X., Zhang, A.W., Parcy, F., Worsley-Hunt, R., Arenillas, D.J., Buchman, S., Chen, C.Y., Chou, A., Ienasescu, H., Lim, J., Shyr, C., Tan, G., Zhou, M., Lenhard, B., Sandelin, A., Wasserman, W.W., 2014. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 42, D142–D147. Miranda-Saavedra, D., De, S., Trotter, M.W., Teichmann, S.A., Gottgens, B., 2009. BloodExpress: a database of gene expression in mouse haematopoiesis. Nucleic Acids Res. 37, D873–D879. Robasky, K., Bulyk, M.L., 2011. UniPROBE, update 2011: expanded content and search tools in the online database of protein-binding microarray data on protein–DNA interactions. Nucleic Acids Res. 39, D124–D128. RStudio, Inc., 2013. shiny: Web Application Framework for R. R package version 0.5.0. Splinter, E., de Laat, W., 2011. The complex transcription regulatory landscape of our genome: control in three dimensions. EMBO J. 30, 4345–4355. Thomas-Chollier, M., Herrmann, C., Defrance, M., Sand, O., Thieffry, D., van Helden, J., 2012. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res. 40, e31. Turatsinze, J.V., Thomas-Chollier, M., Defrance, M., van Helden, J., 2008. Using RSAT to scan genome sequences for transcription factor binding sites and cis-regulatory modules. Nat. Protoc. 3, 1578–1588. Wang, J., Zhuang, J., Iyer, S., Lin, X., Whitfield, T.W., Greven, M.C., Pierce, B.G., Dong, X., Kundaje, A., Cheng, Y., Rando, O.J., Birney, E., Myers, R.M., Noble, W.S., Snyder, M., Weng, Z., 2012. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 22, 1798–1812. Wilson, N.K., Miranda-Saavedra, D., Kinston, S., Bonadies, N., Foster, S.D., Calero-Nieto, F., Dawson, M.A., Donaldson, I.J., Dumon, S., Frampton, J., Janky, R., Sun, X.H., Teichmann, S.A., Bannister, A.J., Gottgens, B., 2009. The transcriptional program controlled by the stem cell leukemia gene Scl/Tal1 during early embryonic hematopoietic development. Blood 113, 5456–5465. Wilson, N.K., Foster, S.D., Wang, X., Knezevic, K., Schutte, J., Kaimakis, P., Chilarska, P.M., Kinston, S., Ouwehand, W.H., Dzierzak, E., Pimanda, J.E., de Bruijn, M.F., Gottgens, B., 2010. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532–544. Wilson, N.K., Tijssen, M.R., Gottgens, B., 2011. Deciphering transcriptional control mechanisms in hematopoiesis: the impact of high-throughput sequencing technologies. Exp. Hematol. 39, 961–968.
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
rTRM-web: a web tool for predicting transcriptional regulatory modules for ChIP-seq-ed transcription factors Fengkai Liu a, Diego Miranda-Saavedra b,⁎ a b
School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics (BUAA), Building 16, Min'an Street, Dong Cheng District, Beijing 100007, China Fibrosis Laboratories, Institute of Cellular Medicine, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
a r t i c l e
i n f o
Article history: Received 11 May 2014 Received in revised form 4 June 2014 Accepted 9 June 2014 Available online 11 June 2014 Keywords: Transcriptional regulatory module ChIP-seq Transcription factor Network Protein–protein interactions
a b s t r a c t Transcription factors (TFs) bind to specific DNA regions, although their binding specificities cannot account for their cell type-specific functions. It has been shown in well-studied systems that TFs combine with co-factors into transcriptional regulatory modules (TRMs), which endow them with cell type-specific functions and additional modes of regulation. Therefore, the prediction of TRMs can provide fundamental mechanistic insights, especially when experimental data are limiting or when no regulatory proteins have been identified. Our method rTRM predicts TRMs by integrating genomic information from TF ChIP-seq data, cell type-specific gene expression and protein–protein interaction data. Here we present a freely available web interface to rTRM (http://www.rTRM.org/) supporting all the options originally described for rTRM while featuring flexible display and network calculation parameters, publication-quality figures as well as annotated information on the list of genes constituting the TRM. © 2014 Elsevier B.V. All rights reserved.
1. Introduction The precise spatio-temporal regulation of gene expression programs allows cells to respond to stimuli that influence their ability to fight infections, their developmental fates, and their functions as part of complex tissues. Although genetic information can be regulated at many levels, from DNA transcription to a vast array of protein posttranslational modifications (Deribe et al., 2010), the modulation of gene transcription is a crucial point of control. The multiple levels of regulation of gene transcription include the binding of TFs and co-factors to target DNA sequences, the epigenetic regulation of chromatin, and nuclear organization (Splinter and de Laat, 2011). Given a permissive transcriptional environment, TFs bind within the vicinity of genes, either directly at the transcriptional start site (TSS) or at distal enhancers. The TSS constitutes the basal promoter where the general transcription machinery assembles, whereas distal elements (either enhancers or silencers of transcription) are bound by TFs that loop over to the promoter and thus drive tissue-specific gene expression patterns (Alonso et al., 2009). Of all the distinct regulatory signals that control transcriptional output, TFs are the best understood, although TF binding specificities alone cannot explain their cell type-specific functions (Hutchins et al., 2013c). Despite the avalanche of data generated by ChIP-seq to
Abbreviations: TF, transcription factor; TRM, transcriptional regulatory module; PPI, protein–protein interaction; ESC, embryonic stem cell; HSC, hematopoietic stem cell; PWM, position weight matrix. ⁎ Corresponding author. E-mail address: [email protected] (D. Miranda-Saavedra).
http://dx.doi.org/10.1016/j.gene.2014.06.016 0378-1119/© 2014 Elsevier B.V. All rights reserved.
determine the genome-wide binding patterns of TFs, major gaps remain in our understanding of how key TFs regulate cell type-specific programs. The current view is that generally TFs do not work in isolation, but instead cooperate with other TFs and scaffolding proteins to assemble into TRMs that endow key TFs with cell type-specific functions (Heinz et al., 2010). TRMs have been identified in hematopoietic stem cells (reviewed in Wilson et al., 2011), embryonic stem cells (Chen et al., 2008) plus a number of other cell types through the ENCODE project (Wang et al., 2012). Combinatorial TF binding controls gene expression programs in temporal and tissue-specific manners, and specific protein–protein interactions are essential for the assembly of TF complexes. TRMs generally extend over short DNA regions and consist of several distinct and interacting TFs (Chen et al., 2008; Heinz et al., 2010; Wilson et al., 2010). We recently developed a generic method for the identification of TRMs from protein–protein interaction networks in any biological context (rTRM) (Diez et al., 2014). rTRM reconstructs TRMs by integrating genomic information from TF ChIP-seq data, cell type-specific gene expression and protein–protein interaction (PPI) data (Fig. 1). Our algorithm for identifying TRMs works by finding proteins in PPI networks separated by a maximum specified distance to a target protein (in this case the TF that has been profiled by ChIP-seq). A very important feature of rTRM over methods that ignore PPI information is that rTRM allows the identification of ‘bridge proteins’ (including chromatin modifying and remodeling enzymes, and signaling molecules that integrate multiple signals) that do not necessarily bind to DNA in a sequence-specific manner and which therefore could never be found using genomic information alone. All proteins require physical interactions with other
418
1
F. Liu, D. Miranda-Saavedra / Gene 546 (2014) 417–420 ChIP-seq sites for TF ‘X’ are scanned for over-represented motifs against an appropriate background
4
x
x
x
x 2
Putative TFs are assigned to enriched motifs by sequence similarity
rTRM computes a TRM by finding candidates in a PPI network separated by a maximum distance to TF ‘x’
3
Cell type-specific expression data is used to filter out candidates
Fig. 1. Overview of the rTRM method for the reconstruction of transcriptional regulatory modules (Diez et al., 2014). (1) A set of genomic sites identified for a specific TF by ChIP-seq is scanned for the presence of over-represented motifs, to which putative TFs are assigned by sequence similarity (2). Cell type-specific expression data is used to filter out candidate TFs (3), and in a final step only those candidates that have been reported to make interactions in the BioGRID database (Chatr-Aryamontri et al., 2013) are retained (4).
proteins to perform their functions, and therefore PPIs can be an important source of additional information if we wish to understand the combinatorial and modular nature of TRMs. The performance of rTRM was evaluated by reconstructing the TRMs specific for the well-characterized embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs) (Diez et al., 2014). The TRMs predicted for ESCs and HSCs were highly precise and specific for these cell types, with ~ 75% of the proteins shown to be involved in the regulation of these cell types by independent evidence. Furthermore, we investigated the multifunctional factor STAT3 in macrophages (anti-inflammatory response), CD4+ T cells (Th17 differentiation), ESCs (pluripotency) and AtT-20 cells (production of adrenocorticotropic hormone in response to stress and inflammation). The functional diversity of STAT3 suggests that it targets distinct enhancer sets depending on the biological context. By re-analyzing the genome-wide binding patterns of STAT3 in macrophages (Hutchins et al., 2012), CD4+ T cells (Kwon et al., 2009), ESCs (Chen et al., 2008) and AtT-20 cells (Langlais et al., 2012), we showed that most STAT3 binding events are specific to each cell type and that STAT3 has two distinct modes of binding: (a) a universal, cell type-independent binding mode; and (b) various cell typespecific binding modes, which are responsible for STAT3's cell typespecific functions (Hutchins et al., 2013c). The cell type-independent binding mode shared by all four cell types is characterized by 35 distinct genomic binding sites where STAT3 binds to transcribe a set of genes whose role is to mainly modulate the JAK-STAT pathway itself, as well as the transcription of several genes downstream of it. The application
of rTRM suggested that STAT3 associates with different cofactors to direct both its universal and cell type-specific programs by modulating its activity and helping it locate to specific genomic sites. The STAT3 universal TRM is characterized by many ‘general’ TFs (MYC, E2F1, KLF4), and we showed experimentally that E2F1 is pre-bound in resting macrophages at sites of future recruitment of STAT3 upon IL-10 stimulation (Hutchins et al., 2013a, 2013b, 2013c). The reconstruction of STAT3 cell type-specific TRMs recovered well-characterized factors known to operate in the distinct cell types. For instance, the ESC-specific TRM recovered OCT4, SOX2, ESRRB, KLF4, SMAD1, TEAD1, and REST and a number of homeodomain proteins that may correspond to NANOG. The cell type-specific TRM models of STAT3 predicted an important level of epigenetic regulation that may play a key role by blocking access to other STAT3-binding sites or by pre-marking sites that STAT3 can be recruited to. This dual model of regulation whereby a master TF forms distinct TRMs to execute both universal and cell type-specific functions might be a general feature of pleiotropic TFs. Therefore, and as illustrated with the examples above the reconstruction of TRMs is incredibly useful for understanding the regulatory layers associated with TFs, and can provide fundamental mechanistic insights and suggest very specific follow-up experiments. Here, we present a user-friendly web implementation of rTRM. The web server is freely available (http://www.rTRM.org/) and supports all the options originally described for rTRM (Diez et al., 2014). 2. Material and methods Our web server rTRM.org is based on the original rTRM method (Diez et al., 2014). The code has recently been made available through Bioconductor (Gentleman et al., 2004; http://bioconductor.org/ packages/release/bioc/html/rTRM.html). We subsequently added a local web interface to rTRM using the shiny package (RStudio, Inc., 2013), which is limited to single users and also available through Bioconductor (http://bioconductor.org/packages/release/bioc/html/ rTRMui.html). Finally we configured our code to work with the shiny Web Server Open Source Edition (http://www.rstudio.com/shiny/ server/) using a CentOS6.4 x86_64 cloud server accessible via the WWW. The advantage of using the extremely stable shiny Web Server Open Source Edition is that multiple shiny applications can be handled simultaneously without limiting the number of users as long as the underlying hardware is powerful enough. 3. Results and discussion 3.1. The rTRM web server The rTRM web server (http://www.rTRM.org/) features all the options available in the original publication of rTRM (Diez et al., 2014), thereby facilitating the reconstruction of TRMs in human and mouse. The interface has a straightforward layout where the user must provide the following data: (a) The name of the TF (of human or mouse origin) for which the ChIP-seq experiment was performed, and as provided in organism-specific lists of TFs for which a position weight matrix (PWM) is available. The ‘Transcription factors’ tab lists all the TF names (ids) available, both for human and mouse. These correspond to the TFs that match our super-library of PWMs (Diez et al., 2014) compiled by integrating the vertebrate entries from the JASPAR database (Mathelier et al., 2014), the UniPROBE protein-binding microarray database (Robasky and Bulyk, 2011) and the high-throughput SELEX (HT-SELEX) dataset of human and mouse (Jolma et al., 2013). This super-library encompasses a total of 548 human TF genes (and their mouse orthologs), or 35–39% of the entire complement of TFs in the human genome (Diez et al., 2014).
F. Liu, D. Miranda-Saavedra / Gene 546 (2014) 417–420
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(c) The list of genes expressed in any particular cell type (Entrez format). The selection of a cutoff that separates expressed and nonexpressed genes is illustrated for both microarray expression and RNA-seq data in Diez et al. (2014).
Fig. 2. Snapshot of the web interface.
(b) The list of TF binding motifs enriched in the set of ChIP-seq peaks. There are various ways of doing this optimally that depend on the biological question under investigation. For instance, in our original publication of rTRM (Diez et al., 2014), we used HOMER (Heinz et al., 2010) to scan uniform 400 bp regions (200 bp on either side of the reported ChIP-seq summits) for enriched motifs. However, since HOMER's selection of background sequences for enrichment analysis is based on features such as similar GC content and length, which typically produces slightly different results with every run, we performed 10 replicate runs and retained only those motifs that were enriched (q b 0.05) in at least 80% of the runs. The final list of enriched motifs was matched against our PWM super-library using Tomtom (q b 0.05) (Gupta et al., 2007). In a previous publication, however, we investigated the co-factors associated with the TF Scl/Tal1 in mouse HSCs. In this case we scanned uniform-sized Scl/Tal1 ChIP-seq peaks against a background of promoters whose genes were explicitly not expressed in HSCs but only in terminally differentiated blood cells (Miranda-Saavedra et al., 2009; Wilson et al., 2009). In practice, this could easily be done by downloading our super-library of PWMs (available with the Bioconductor release of rTRM at http://bioconductor.org/ packages/release/bioc/html/rTRM.html) and scanning all PWMs against an appropriate background with the RSAT package (Thomas-Chollier et al., 2012; Turatsinze et al., 2008). Additionally, rTRM interacts efficiently with the PWMEnrich package, also available through Bioconductor (http://www.bioconductor. org/packages/2.12/bioc/html/PWMEnrich.html).
Once the required data have been input, the rTRM web server displays the resulting TRM, indicating the classification of each TF. A number of network parameters allow users to predict networks with more relaxed PPI distance parameters (‘Extended TRM’), and display the TRM in visually distinct ways by adjusting the size of the nodes and the labels, and the network layout (concentric/arc/circle/KamadaKawai/Fruchterman-Reingold). Finally, a publication-quality figure of the TRM, as well as the list of genes that constitute the TRM, can be downloaded. The ‘Tutorial’ and the ‘Help’ tabs on the rTRM.org website provide a detailed explanation of each one of the options and steps. The sample data provided is for the Sox2 TF profiled by ChIP-seq in ESCs, as previously analyzed (Diez et al., 2014). Sox2 is essential for maintaining the ESC phenotype, and is known to combine with other TFs, including Pou5f1 (Oct4), Nanog, Esrrb and Klf4 (Chen et al., 2008). Genomebinding locations for Sox2 in ESCs were obtained (Chen et al., 2008), enriched motifs were identified as previously described (Diez et al., 2014), and the expression data for mouse ESCs was obtained from Ho et al. (2011). The resulting TRM for Sox2 (Fig. 2) includes Esrrb, Klf4, Pou5f1 (Oct4), Sall4 and Sox2, all proteins with known roles in ESC biology (Chen et al., 2008) (Table 1). The Sox2-based TRM for ESCs is remarkably different from that of neural progenitor cells (Diez et al., 2014), indicating that rTRM is capable of identifying TRMs with clearly distinct and cell type-specific features for the same TF in distinct types of stem cell.
3.2. Conclusions We have built a web server for predicting TRMs that features all the original options of our method rTRM (Diez et al., 2014). Although rTRM is currently implemented as an R package and available in Bioconductor, the complexities of the R interface may prevent those users with limited exposure to R or bioinformatics to benefit from the full range of bioinformatic and statistical methods that are implemented in rTRM. rTRMweb facilitates access to all the capabilities of rTRM by means of a graphical interface that is both clear and intuitive. The reconstruction of TRMs can yield incredibly valuable information when experimental datasets are limiting, or when no regulatory proteins have yet been identified. Thus, our web platform constitutes a very powerful tool to dissect the multiple levels of regulation of a TF of interest, both in physiological and pathological states. The website provides sample data including the over-represented motifs in the genome-wide binding sites of Sox2 in ESCs, as well as the set of genes expressed in ESCs. These datasets provide the user with a valuable example on how to reconstruct their own TRMs for their TFs of interest. rTRM-web is a flexible tool that allows modifying a number of network and graphical parameters (e.g. network layout, node, line and label sizes) for the production of publicationquality images.
Table 1 List of proteins predicted in the Sox2 TRM in ESCs. TFs are annotated with the TFClass description. Entrez gene
Symbol
Role
Type
Description
TFClass family
20674 20670 99377 19357 18999 16600 15182 26380
Sox2 Sox15 Sall4 Rad21 Pou5f1 Klf4 Hdac2 Esrrb
Enriched Enriched Bridge Bridge Enriched Enriched Bridge Enriched
Target Query Query Query Query Query Query Query
SRY-box containing gene 2 SRY-box containing gene 15 sal-like 4 (Drosophila) RAD21 homolog (S. pombe) POU domain, class 5, transcription factor 1 Kruppel-like factor 4 (gut) Histone deacetylase 2 Estrogen related receptor, beta
High-mobility group (HMG) domain factors High-mobility group (HMG) domain factors
Homeodomain factors C2H2 zinc finger factors Nuclear receptors with C4 zinc fingers
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Future developments include: (i) incorporating updated PPI datasets and PWMs for as yet uncharacterized TFs; (ii) extending the rTRM method to other model organisms (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans, Drosophila melanogaster and several mammals); and (iii) including the possibility of reconstructing TRMs from genomic sites that are not determined by the ChIP-seq analysis of specific TFs. These include genomic marks that might characterize developmental stages of a particular cellular lineage, such as differential DNase I hypersensitivity sites and active enhancers. Active enhancers that drive cell type-specific expression patterns are best identified by the computational modeling of the chromatin epigenetic landscape rather than by evolutionary conservation sequence analysis. Although dozens of distinct epigenetic marks have been described, we recently showed that an optimum combination of only a handful such marks (a combination of activating and repressive methylations and acetylations) is sufficient to describe and predict enhancers (Fernandez and Miranda-Saavedra, 2012). This points to the existence of complex epigenetic patterns associated with enhancers that can be identified computationally and for which TRMs can be built to advance our understanding of transcriptional networks in the absence of a priori candidate TFs. Acknowledgments We would like to thank Dr. Andrew Hutchins for reading the manuscript and making helpful suggestions. The authors acknowledge funding from Newcastle University Medical School. References Alonso, M.E., Pernaute, B., Crespo, M., Gomez-Skarmeta, J.L., Manzanares, M., 2009. Understanding the regulatory genome. Int. J. Dev. Biol. 53, 1367–1378. Chatr-Aryamontri, A., Breitkreutz, B.J., Heinicke, S., Boucher, L., Winter, A., Stark, C., Nixon, J., Ramage, L., Kolas, N., O'Donnell, L., Reguly, T., Breitkreutz, A., Sellam, A., Chen, D., Chang, C., Rust, J., Livstone, M., Oughtred, R., Dolinski, K., Tyers, M., 2013. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816–D823. Chen, X., Xu, H., Yuan, P., Fang, F., Huss, M., Vega, V.B., Wong, E., Orlov, Y.L., Zhang, W., Jiang, J., Loh, Y.H., Yeo, H.C., Yeo, Z.X., Narang, V., Govindarajan, K.R., Leong, B., Shahab, A., Ruan, Y., Bourque, G., Sung, W.K., Clarke, N.D., Wei, C.L., Ng, H.H., 2008. Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133, 1106–1117. Deribe, Y.L., Pawson, T., Dikic, I., 2010. Post-translational modifications in signal integration. Nat. Struct. Mol. Biol. 17, 666–672. Diez, D., Hutchins, A.P., Miranda-Saavedra, D., 2014. Systematic identification of transcriptional regulatory modules from protein–protein interaction networks. Nucleic Acids Res. 42, e6. Fernandez, M., Miranda-Saavedra, D., 2012. Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines. Nucleic Acids Res. 40, e77. Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A.J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J.Y., Zhang, J., 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80. Gupta, S., Stamatoyannopoulos, J.A., Bailey, T.L., Noble, W.S., 2007. Quantifying similarity between motifs. Genome Biol. 8, R24. Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y.C., Laslo, P., Cheng, J.X., Murre, C., Singh, H., Glass, C.K., 2010. Simple combinations of lineage-determining transcription fac-
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