iNID: An Analytical Framework for Identifying Network Models for Interplays among Developmental Signaling in Arabidopsis

DaeseokChoi 0 1 JaemyungChoi 0 1 ByeongsooKang 0 1 SeungchulLee 0 1 Young-hyunCho 0 1 IldooHwang 0 1 DaeheeHwang 0 1 0 Biology , DGIST, 50-1, Sang-Ri, Hyeonpung-Myeon, Dalseong-Gun, Daegu 711-873, Republic of Korea . E-mail 1 a School of Interdisciplinary Bioscience and Bioengineering , POSTECH, 790-784, Pohang, Republic of Korea b Department of Life Sciences , POSTECH, 790-784, Pohang, Republic of Korea c Department of New Biology , DGIST, Daegu, 711-873, Republic of Korea d Division of Integrative Biosciences and Biotechnologies , POSTECH, 790-784, Pohang, Republic of Korea e Center for Systems Biology of Plant Senescence and Life History, Institute for Basic Science , DGIST, Daegu, 711-873, Republic of Korea Integration of internal and external cues into developmental programs is indispensable for growth and development of plants, which involve complex interplays among signaling pathways activated by the internal and external factors (IEFs). However, decoding these complex interplays is still challenging. Here, we present a web-based platform that identifies key regulators and Network models delineating Interplays among Developmental signaling (iNID) in Arabidopsis. iNID provides a comprehensive resource of (1) transcriptomes previously collected under the conditions treated with a broad spectrum of IEFs and (2) protein and genetic interactome data in Arabidopsis. In addition, iNID provides an array of tools for identifying key regulators and network models related to interplays among IEFs using transcriptome and interactome data. To demonstrate the utility of iNID, we investigated the interplays of (1) phytohormones and light and (2) phytohormones and biotic stresses. The results revealed 34 potential regulators of the interplays, some of which have not been reported in association with the interplays, and also network models that delineate the involvement of the 34 regulators in the interplays, providing novel insights into the interplays collectively defined by phytohormones, light, and biotic stresses. We then experimentally verified that BME3 and TEM1, among the selected regulators, are involved in the auxin-brassinosteroid (BR)-blue light interplay. Therefore, iNID serves as a useful tool to provide a basis for understanding interplays among IEFs. - INTRo Du CTIo N Plants, which are sessile, constantly revise their developmental programs to cope with changing environments during growth and development. Integration of internal and external cues into the developmental programs is thus essential. This integration involves complex interplays among signaling pathways activated by both internal and external factors (IEFs), leading to coordination in developmental outputs, such as germination, elongation, and maturation, over the developmental stages. For example, plants perceive season, temperature, and their developmental status to determine a precise timing of flowering for successful reproduction. Regulation of the timing of flowering involves complex interplays among external (e.g. photoperiod, vernalization, and temperature) and internal factors (e.g. gibberellins (GA)) (Srikanth and Schmid, 2011). Identification of key regulators for the interplays and biological networks delineating the interplays mediated by these regulators is critical to understand coordinated controls by IEFs during plant development. Genetics approaches have been used to investigate the interplays between IEFs. For example, Xi et al. (2010) identified a key regulator for seed germination, mother of FT AND TFL1 (MFT), which integrates the signals from abscisic acid (ABA) and GA (Xi etal., 2010). Also, several studies (Moon etal., 2003; Hisamatsu and King, 2008) used genetics approaches to identify flowering time regulators, such as FT and SOC1, as the integrators of the signals from photoperiod, vernalization, and GA. However, these approaches require huge amounts of labor and time, and also commonly provide relationships among a limited number of molecules. Thus, it is often challenging to search for key regulators involved in the interplays among multiple IEFs, leading to the limited capability of decoding biological networks for the interplays among a large number of IEFs. Therefore, there has been a need for an alternative approach that can effectively identify both key regulators and biological networks for the interplays. Gene expression analysis has been offering new oppor tunities for identifying key regulators and networks associated with the interplays. Several tools for analysis of transcriptome data and/or network analysis have been developed (Supplemental Table 1). First, BAR Expression angler (Toufighi et al., 2005) and Genevestigator (Hruz etal., 2008) provide tools to explore gene expression profiles and identify co-expressed genes. However, they provide no tools to generate biological networks and identify key regulators. Second, CSB.DB (Steinhauser et al., 2004), ATTED-II (Obayashi et al., 2007), CORNET (De Bodt et al., 2010), and CorTo (Giorgi etal., 2013) provide tools to identify co-expressed genes and generate biological networks. Also, the interactome databases, AtPID (Cui et al., 2008), AtPIN (Brandao etal., 2009), AtPAN (Chen etal., 2012), or GeneMANIA (Mostafavi et al., 2008), can be used to generate biological networks. However, they provide no tools to identify key regulators based on the networks. Third, VirtualPlant (Katari et al., 2010) provides tools to identify differentially expressed genes (DEGs), generate networks, and identify network statistics scores for the nodes in the networks. However, these scores provide no statistical framework to select key regulators in the networks. Thus, all these tools, which are not specifically designed to analyze the interplays among multiple IEFs, are still lack of statistical tools to identify key regulators and network models associated with the interplays amongIEFs. Here, we present a web-based analytical framework that identifies key regulators and Network models delineating Interplays among Developmental signaling (iNID). iNID provides (1) a comprehensive database of gene expression profiles and interactomes in Arabidopsis and (2) three analytical tools for a series of analyses to identify key regulators and network models for interplays among multiple IEFs (Figure1). The database contains 488 gene expression profiles collected after treatments with 41 IEFs and 1 171 417 interactions including proteinprotein interactions (PPIs), proteinDNA interactions (TFtarget; PDIs), proteinmetabolite interactions (PMIs), genetic interactions (GIs), etc. The three analytical tools were developed for (1) identification of the genes related to the interplay among a selected set of IEFs; (2) selection of key regulators mediating the interplay from the interplay-related genes; and (3) development of network models for the interplay using the key regulators and their associated pathways. iNID is available at http://sbm.poste (...truncated)