Computational Analysis of “-omics” Data to Identify Transcription Factors Regulating Secondary Metabolism in Rauvolfia serpentina

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-omicsd ata 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 WRKY and 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.

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus

“…In this study, weighted co-expression networks were generated using expression profiles data of different tissues from R. serpentina (8) and C. roseus (6). Earlier studies support sufficiency of the number of samples for this expression data to identify new candidate genes using co-expression analysis (Góngora-Castillo et al, 2012 ; Paul et al, 2014 ; Schluttenhofer et al, 2014 ; Pathania and Acharya, 2016 ). Co-expression networks obtained for both plant species were signed networks that allowed network to retain positively as well as negatively correlated genes in various sets which correspond to up- and down-regulated genes, respectively.…”

“…A. thaliana has been used as model plant to annotate various plants (Li et al, 2002 ) including unicellular flagellates such as Chlamydomonas (Merchant et al, 2007 ). Importantly, this plant has also been reported to annotate R. serpentina (Pathania and Acharya, 2016 ) and C. roseus (Schluttenhofer et al, 2014 ); therefore, A. thaliana was used to obtain similar orthologs for these plants. Annotation of complete R. serpentina and C. roseus transcripts against Arabidopsis proteome returned 80,829, and 67,201 significant hits with expression values for only 30,681 and 22,987 transcripts, respectively.…”

“…In order to determine metabolites associated with significant module pairs, metabolome data of R. serpentina and C. roseus was integrated. Correlation data of these metabolomes against their corresponding transcriptome was obtained from the Plant and Microbial Metabolomics Resource (PMR; http://metnetdb.org/PMR/ ) using PMR metabolomic-transcriptomic co-analysis tool at a significant PCC threshold of 0.80 (Pathania and Acharya, 2016 ). Genes present in significant module pairs were searched against correlation data to determine module-specific metabolites for both plant species.…”

“…Various traditional methods have been used to compare these closely related plants that work on small-scale (Gerasimenko et al, 2002 ). In our earlier study, a graph theoretical approach has been implemented to identify transcription factors (TFs) involved in regulation of secondary metabolism of R. serpentina (Pathania and Acharya, 2016 ), but to the best of our knowledge, comparative investigation of these plants using network-based approach is hitherto not undertaken. Integration of graph theory based approach and “- omics ” data have facilitated systems-level comparison of underlying molecular pathways to unravel their complexity as well as mechanisms of differential regulation (Emmert-Streib, 2007 ; Emmert-Streib et al, 2012 ; Shaik and Ramakrishna, 2013 ).…”

Tissue-specific transcriptional biomarkers in medicinal plants: Application of large-scale meta-analysis and computational systems biology

“…Whereas meta-analysis on transcriptome data, especially microarray data, has been widely applied in human and animal genomes, limited number of similar studies have been conducted in plants (Shaar-Moshe et al, 2015). However, these holistic methods for the study of medicinal plants are not yet used (Pathania and Acharya, 2016).…”