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 “-omics” data to identify TFs of “unknown 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 cis-regulatory 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.
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Abbreviations
- 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
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Acknowledgments
We acknowledge the computational infrastructure provided in the form of project MLP0076 by CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), a constituent national laboratory of Council of Scientific and Industrial Research, India, and Department of Biotechnology, Government of India for infrastructural support in the form of Bioinformatics Infrastructure Facility (BIF) as well. The authors are thankful to Dr. Paramvir Singh Ahuja for encouragement and constant support. Shivalika Pathania is grateful to the Department of Science and Technology (DST) for INSPIRE fellowship. We are also thankful to Ashwani Jha and Vinay Randhawa for technical help in manuscript preparation. The CSIR-IHBT communication number for this article is 3777.
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Fig. S1
Gene ontology (GO) annotation of complete R. serpentina transcriptome. Pie chart representing GO-based annotation of complete transcriptome for a biological process and b molecular function category (GIF 92 kb)
Fig. S2
Gene ontology (GO) and KEGG pathways annotation of complete R. serpentina transcriptome. Pie chart representing a GO-based annotation for cellular component and b KEGG pathways annotation of complete transcriptome (GIF 88 kb)
Fig. S3
Threshold selection. a The actual number of edges and all possible edges among non-singleton nodes as a function of PCC cutoff values. b The actual number of edges and all nodes among the non-singleton nodes as a function of PCC cutoff values. The “igraph” library of R package is used to obtain these plots (GIF 13 kb)
Fig. S4
Weighted co-expression network follows power law degree distribution. A data set from the weighted co-expression network is represented with black filled circles, and the degree distribution adheres to a power law as all these circles lie on or close to the red line which is the graph of a function of the form ax-k. The “igraph” library of R package is used to obtain this plot (GIF 12 kb)
Fig. S5
Hierarchical tree representing significantly enriched GO terms for ABI3VP1 TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 48 kb)
Fig. S6
Hierarchical tree representing significantly enriched GO terms for bHLH TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 43 kb)
Fig. S7
Hierarchical tree representing significantly enriched GO terms for HB TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 30 kb)
Fig. S8
Hierarchical tree representing significantly enriched GO terms for MYB TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 58 kb)
Fig. S9
Hierarchical tree representing significantly enriched GO terms for MYB-related TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 55 kb)
Fig. S10
Hierarchical tree representing significantly enriched GO terms for WRKY TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 51 kb)
Fig. S11
Hierarchical tree representing significantly enriched GO terms for -EREBP TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 33 kb)
Fig. S12
Hierarchical tree representing significantly enriched GO terms in enrichment analysis of bZIP TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 7 kb)
Fig. S13
Hierarchical graph representing significantly enriched GO terms for MADS TF family. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 6 kb)
Fig. S14
Clustering of weighted co-expression network. A total of 42 modules are obtained from clustering of weighted co-expression network using MCL algorithm. Colored filled circles representing TFs in each module. Cytoscape is used for visualization of the network (GIF 89 kb)
Fig. S15
Heat map of transcripts in four significant modules (1–4) using expression data of different tissues. Heat map is representing tissue-specific expression of transcripts in significant modules: a module 1 in roots, b module 2 in young leaves, c module 3 in flower, and d module 4 in mature leaves, where average expression is calculated based on normalized transcriptomics data. The “gplots” library of R package is used to plot heat map (GIF 100 kb)
Fig. S16
Top most enriched KEGG pathway annotation for four significant modules (1–4). Each pie segment is labeled with significant KEGG pathway, and the percentage fraction of annotations associated with that particular pathway. a Module 1, b module 2, c module 3, and d module 4 (GIF 65 kb)
Fig. S17
Hierarchical graph representing significantly enriched GO terms for module 2. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 20 kb)
Fig. S18
Hierarchical tree representing significantly enriched GO terms for module 3. These over-represented GO terms for biological process category are generated through agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 40 kb)
Fig. S19
Hierarchical tree representing significantly enriched GO terms for module 4. These over-represented GO terms for biological process category are obtained using agriGO. Each GO term represented by box are labeled by their GO ID, term definition, and statistical information. Degree of color saturation of a box is positively correlated to the enrichment level of the term (GIF 28 kb)
Fig. S20
Network representing the significantly enriched pathways in module 1. GO terms-based network is obtained from enrichment analysis against reference model A. thaliana for module 1. Functionally grouped pathways are mainly found to be associated to monocarboxylic acid biosynthetic process, hormone-mediated signaling pathway, and defense response. ClueGO plugin is used to generate this GO terms-based network (GIF 100 kb)
Fig. S21
Network representing the significantly enriched pathways in module 2. GO terms-based network is obtained from enrichment analysis against reference model Arabidopsis thaliana. Functionally grouped pathways are mainly found to be associated to “regulation of gene expression, epigenetic,” “RNA metabolic process,” and “macromolecular modification” which also complements agriGO enrichment result. ClueGO, plugin in cytoscape, is used to generate this GO terms-based network. (GIF 95 kb)
Fig. S22
Network representing the significantly enriched pathways in module 3. GO terms-based network is obtained from enrichment analysis against reference model Arabidopsis thaliana. Functionally grouped pathways are mainly found to be associated to “pollen exine formation,” “stamen development,” and “external encapsulating structure organization” which also complements agriGO enrichment result. ClueGO, plugin in cytoscape, is used to generate this GO terms-based network (GIF 27 kb)
Fig. S23
Network representing the significantly enriched pathways in module 4. GO terms-based network is obtained from enrichment analysis against reference model Arabidopsis thaliana. Functionally grouped pathways are mainly found to be associated to “photosynthesis”, “plastid organization,” and “monocarboxylic acid biosynthetic process” which also complements agriGO enrichment result. ClueGO, plugin in cytoscape, is used to generate this GO terms-based network (GIF 80 kb)
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Pathania, S., Acharya, V. Computational Analysis of “-omics” Data to Identify Transcription Factors Regulating Secondary Metabolism in Rauvolfia serpentina . Plant Mol Biol Rep 34, 283–302 (2016). https://doi.org/10.1007/s11105-015-0919-1
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DOI: https://doi.org/10.1007/s11105-015-0919-1