Computational Analysis of “-omics” Data to Identify Transcription Factors Regulating Secondary Metabolism in Rauvolfia serpentinaby Shivalika Pathania, Vishal Acharya

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Computational Analysis of B-omics^ Data to Identify

Transcription Factors Regulating Secondary Metabolism in Rauvolfia serpentina

Shivalika Pathania1 & Vishal Acharya1,2 # Springer Science+Business Media New York 2015

Abstract Rauvolfia serpentina has been known to produce therapeutically important indole alkaloids used in treatment of various diseases. Despite its medicinal importance, complete understanding of its secondary metabolism is challenging due to complex interplay among various transcription factors (TFs) and genes. However, weighted co-expression analysis of transcriptome along with integration of metabolomics data has proficiency to elucidate topological properties of complex regulatory interactions in secondary metabolism.

We aimed to implement an integrative strategy using B-omics^ data to identify TFs of Bunknown function^ and exemplify their role in regulation of valuable metabolites as well as metabolic traits. A total of 69 TFs were identified through significant thresholds and removal of false positives based on cisregulatory motif analysis. Network-biology inspired analysis of co-expression network lead to generation of four statistically significant and biologically robust modules. Similar to known regulatory roles of WRKYand AP2-EREBP TF families in Catharanthus roseus, this study presented them to regulate synthesis of alkaloids in R. serpentina as well.

Moreover, TFs in module 4 were observed to be regulating connecting steps between primary and secondary metabolic pathways in the synthesis of terpenoid indole alkaloids. Integration of metabolomics data further highlight the significance of module 1 since it was statistically predicted to be involved in synthesis of specialized metabolites, and associated genes may physically clustered on genome. Importantly, putative TFs in module 1 may modulate the major indole alkaloids synthesis in response to various environmental stimuli. The methodology implemented herein may provide a better reference to identify and explore functions of transcriptional regulators.

Keywords Transcription factors . Rauvolfia serpentina .

Transcriptomics .Weighted co-expression network .

Terpenoid indole alkaloid .Metabolomics


TFs Transcription factors

PCC Pearson correlation coefficient

MPGR Medicinal plant genomics resource

TAIR10 The Arabidopsis Information Resource 10

PlnTFDB Plant Transcription Factor Database

ND Network density

AGRIS Arabidopsis gene regulatory information server

TFBS Transcription factor binding sites

MCL Markov cluster

GO Gene ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

PMR Plant and microbial metabolomics resource

TIA Terpenoid indole alkaloid

Electronic supplementary material The online version of this article (doi:10.1007/s11105-015-0919-1) contains supplementary material, which is available to authorized users. * Vishal Acharya;

Shivalika Pathania 1 Functional Genomics and Complex Systems Lab, Biotechnology

Division, CSIR-Institute of Himalayan Bioresource Technology,

Council of Scientific and Industrial Research, Palampur, Himachal

Pradesh, India 2 Academy of Scientific and Innovative Research (AcSIR), New

Delhi, India

Plant Mol Biol Rep

DOI 10.1007/s11105-015-0919-1


Gene expression is a complex phenomenon which is regulated by set of proteins, called transcription factors (TFs), that activate or repress various genes (Mitsuda and Ohme-Takagi 2009). In plants, TFs regulate genes involved in many important biological processes such as growth and development (Ramachandran et al. 1994), defense against pathogens and environmental stress (Singh et al. 2002), seed maturation and flower development (Jakoby et al. 2002), light-regulated mechanisms (Jiao et al. 2007), and secondary metabolism (Vom Endt et al. 2002). These TFs regulate genes by a set of highly coordinated internal/external signals, and some even interact with other TFs (Yang et al. 2012a). Secondary metabolites have long been used in pharmaceuticals, agrochemicals, fragrance ingredients, food additives, and pesticides, and are therefore of industrial importance. These metabolites do not participate directly in plant growth, development, and reproduction (Fraenkel 1959; Chae et al. 2014). However, they often play an important role in various processes including defense against pathogens, herbivores, and other interspecies defenses (Samuni-Blank et al. 2012). Also, these specialized metabolites and their associated pathways provide unique adaptive strategies for various organisms under harsh and dynamic environmental conditions (Weng and Noel 2012).

Transcriptional regulation of secondary metabolite synthesis is highly controlled by a complex network of multiple TFs.

Several TFs involved in the regulation of metabolic pathway genes have been studied in model plants like Arabidopsis thaliana (D?Auria and Gershenzon 2005). With advancements in developmental and molecular biology techniques for analyzing alkaloid biosynthesis, various genes have been identified to be involved in formation of metabolites like tropane (Shoji et al. 2000; Herbert 2003), benzylisoquinoline (Herbert 2003; Ziegler and Facchini 2008), and terpenoid indole alkaloids (TIAs) (Eichinger 1999; Ruppert et al. 2005).

Since various studies have reported genes related to metabolite synthesis, further attempt should also be made to identify key regulators associated with their complex pathways (De

Luca and St Pierre 2000; Oudin et al. 2007). Since gene expression is highly regulated by specific TFs, various biological functions get influenced by any modification in their activity, which in turn dynamically alters transcriptome profile leading to metabolic and/or phenotypic changes (Mitsuda and

Ohme-Takagi 2009). Therefore, to elucidate molecular mechanisms associated to plant secondary metabolism, a prerequisite is to identify candidate regulatory TFs through a productive and precise searching procedure (Haynes et al. 2013).