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De Novo Transcriptome Analysis Reveals Novel Insights into Secondary Metabolite Biosynthesis in Tylophora indica (Burm. f) Merrill.
Abstract
Tylophora indica has been widely recognized for its therapeutic properties in traditional Indian medicine. Although its bioactive compounds are used extensively to treat a wide range of ailments, a comprehensive understanding of their genetic basis remains limited. In this study, we conducted a transcriptomic analysis of T indica leaf and root using the Illumina platform. High-quality RNA was isolated, and cDNA libraries were constructed for sequencing, generating4.67 GB and5.51 GB of data for leaf and root samples, respectively. 72,795 unigenes and 24,470 coding sequences (CDS) were predicted based on de novo assembly of reads, revealing a complex transcriptome landscape. Functional annotation and pathway analysis revealed biological processes and pathways associated with T indica. Based on the Gene Ontology (GO) mapping, the CDS was categorized into biological processes, cellular components, and molecular functions. An analysis of pathways using the KEGG database revealed involvement in critical metabolic pathways. Furthermore, SSRs contributed to the understanding of genetic diversity by identifying simple sequence repeats. In addition, differential gene expression analysis identified genes involved in secondary metabolite synthesis, among other physiological processes. The qRT-PCR validation of selected genes confirmed their differential expression profiles, with roots exhibiting higher expression than leaves. In this study, transcriptomics is conducted for the first time for T indica, which may be useful for future molecular research. The detailed findings help us understand T indicas biology, which can be used in biotechnology, and they also show how important it is to protect this species because it is used in medicine.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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