Phytochemical Genomics #

Decoding the genetics of the medicinal plants is more significant to understand their phytochemical constituents, and the knowledge acquired from it is more beneficial for the pharmaceutical industries to develop a standard natural drug. So far, the knowledge obtained from the history of medicine has taught us the importance of medicinal plants and how they facilitate humans in retaining their health from various disorders [1]. The contemporary science has acknowledged that derived knowledge and impelled us from a logical search to an empirical search. These prominently emerging empirical searches have originated the term “Phytochemical genomics”, that integrates multi-omics systematically such as genomics, transcriptomics, proteomics and metabolomics [2]. This systematic integration helps the researchers, to discern the understanding of the biosynthesis of plant-specific phytochemicals. Hence, the concept of “gene-to-metabolite” has been successfully applied in Arabidopsis thaliana to characterize its flavonoids and also in other plants to characterize their secondary metabolites [3]. Therefore, this concept is becoming a proof-of-concept to annotate the array of novel phytochemicals through a derived and multi-tested hypothesis.

Furthermore, the concept “gene-to-metabolite” is more familiar among the model plants, which have the largest dataset when compared to the non-model plants, such as crops and medicinal plants. As a consequence of these omics sequencing technologies, namely the next-generation sequencing technologies (NGS) and LC/GC-MS/MS-based metabolomics are made more accessible to the larger plant communities to implement the concept of gene-to-metabolite [4,5]. On the other hand, the statistical/mathematical, computational models have also mostly helped the scientific community to integrate multiple datasets for deriving a testable hypothesis [6]. On the other way, the sequencing technologies and the cost per genome are reducing every year, and the long-read sequencing technologies are supporting in resolving the repeat complexities of the genome.

Genome #

The genome sequencing provides a complete overview of the structural organization of the functional elements in a given genome. These structural elements carry the knowledge/memory of the evolutionary history of an organism. Since, in this digital era, almost the complete genetic characterizations of any given species can be determined based on genome sequencing. Significantly, the quality of the reference genome decides the functional factor, for various problems that exist in the given species [7]. Concurrently, the sequencing technologies, physical mapping and the corresponding computational methods are primarily developed to construct a high quality genomes within a shorter period of time [7]. This high-quality, non-fragmented, and chromosomal scale constructed genome provides precise knowledge of the given species in the context of comparative, functional and evolutionary aspects.

Organelle genome: #

The Organelle genomes (Chloroplast and mitochondria) are much smaller than the nuclear genomes, which consists of the evolutionary and plant physiological information. The evolutionary relationship of the given species is assessed using the chloroplast genome. Most of the genome sequencing projects have started with these organelle genomes, to evaluate the preliminary knowledge about the plant and its biological nature.

Transcriptome: #

The transcriptome is a complete representation of the RNAs in the genome of an organism. The fundamental goal of transcriptomics is to understand the proteins/genes functions of a given organism, mainly, to know which group of genes is highly responsible for making them unique from one another. At present, the RNA-sequencing (RNA-Seq) is the state-of-the-art method in the field of transcriptomics, to understand how genes are expressed in an organism. Since the completion of the first genome for the plant Arabidopsis thaliana in the year 2000, most of the plants do not have their genomes sequenced due to their polyploidy and repeat complexities. However, the RNA-Seq technology has facilitated a way to obtain basic knowledge on the information about a gene/protein for all non-model plants on this earth. Furthermore, the long read sequencing technologies have also aided in uncovering the genome wide full-length transcriptome, along with their isoforms for any non-model plants. In parallel, the development of computational methods to analyze and integrate multiple technologies have also helped the plant research community to outreach their objectives remarkably with the transcriptome data. In medicinal plants, RNA-Seq plays a vital role in understanding the biosynthesis of their unique metabolites.

miRNAs #

The miRNAs are non-coding RNAs (21 - 24 bases) possessing multiple regulatory roles, particularly in the post-transcriptional modifications. They are involved in the process of 3’-prime, 2’-O-methylation in plants, whereas in animals the similar function will occur in the 5’-prime. Additionally, the molecular signatures left from DICER and ARGONAUTE proteins machines are considered to be the features for establishing the computational methods of miRNA predictions in plants and animals specifically.

References #

1. Petrovska, B.B. Historical review of medicinal plants’ usage. Pharmacognosy Reviews 2012, 6, 1-5, doi:10.4103/0973-7847.95849.

2. Saito, K. Phytochemical genomics—a new trend. Current Opinion in Plant Biology 2013, 16, 373-380, doi:https://doi.org/10.1016/j.pbi.2013.04.001.

3. Yonekura-Sakakibara, K.; Saito, K. Functional genomics for plant natural product biosynthesis. Natural Product Reports 2009, 26, 1466-1487, doi:10.1039/B817077K.

4. Goodwin, S.; McPherson, J.D.; McCombie, W.R. Coming of age: ten years of next-generation sequencing technologies. Nature Reviews Genetics 2016, 17, 333, doi:10.1038/nrg.2016.49.

5. Yin, P.; Xu, G. Current state-of-the-art of nontargeted metabolomics based on liquid chromatography–mass spectrometry with special emphasis in clinical applications. Journal of Chromatography A 2014, 1374, 1-13, doi:https://doi.org/10.1016/j.chroma.2014.11.050.

6. Krumsiek, J.; Bartel, J.; Theis, F.J. Computational approaches for systems metabolomics. Current Opinion in Biotechnology 2016, 39, 198-206, doi:https://doi.org/10.1016/j.copbio.2016.04.009.

7. Dominguez Del Angel, V.; Hjerde, E.; Sterck, L.; Capella-Gutierrez, S.; Notredame, C.; Vinnere Pettersson, O.; Amselem, J.; Bouri, L.; Bocs, S.; Klopp, C., et al. Ten steps to get started in Genome Assembly and Annotation. F1000Research 2018, 7, ELIXIR-148, doi:10.12688/f1000research.13598.1.