AIM OF THE STUDY: This study aimed to use a computational target fishing approach to predict the possible therapeutic effect of Marantodes pumilum and evaluated their effectivity.
MATERIALS AND METHODS: This study involves a computational approach to identify the potential targets by using target fishing. Several databases were used: PubChem database to obtain the chemical structure of interested compounds; Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) server and the SWISSADME web tool to identify and select the compounds having drug-likeness properties; PharmMapper was used to identify top ten target protein of the selected compounds and Online Mendelian Inheritance in Man (OMIM) was used to predict human genetic problems; the gene id of top-10 proteins was obtained from UniProtKB to be analyzed by using GeneMANIA server to check the genes' function and their co-expression; Gene Pathway established by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) of the selected targets were analyzed by using EnrichR server and confirmed by using DAVID (The Database for Annotation, Visualization and Integrated Discovery) version 6.8 and STRING database. All the interaction data was analyzed by Cytoscape version 3.7.2 software. The protein structure of most putative proteins was obtained from the RCSB protein data bank. Thedocking analysis was conducted using PyRx biological software v0.8 and illustrated by BIOVIA Discovery Studio Visualizer version 20.1.0. As a preliminary evaluation, a cell viability assay using Sulforhodamine B was conducted to evaluate the potential of the predicted therapeutic effect.
RESULTS: It was found that four studied compounds are highly correlated with three proteins: EFGR, CDK2, and ESR1. These proteins are highly associated with cancer pathways, especially breast cancer and prostate cancer. Qualitatively, cell proliferation assay conducted shown that the extract has IC50 of 88.69 μg/ml against MCF-7 and 66.51 μg/ml against MDA-MB-231.
CONCLUSIONS: Natural herbs are one of the most common forms of complementary and alternative medicine, and they play an important role in disease treatment. The results of this study show that in addition to being used traditionally to maintain women's health, the use of Marantodes pumilum indirectly has the potential to protect against the development of cancer cells, especially breast cancer. Therefore, further research is necessary to confirm the potential of this plant to be used in the development of anti-cancer drugs, especially for breast cancer.
FINDINGS: We optimized the assembly of a Hevea bark transcriptome based on 16 Gb Illumina PE RNA-Seq reads using the Oases assembler across a range of k-mer sizes. We then assessed assembly quality based on transcript N50 length and transcript mapping statistics in relation to (a) known Hevea cDNAs with complete open reading frames, (b) a set of core eukaryotic genes and (c) Hevea genome scaffolds. This was followed by a systematic transcript mapping process where sub-assemblies from a series of incremental amounts of bark transcripts were aligned to transcripts from the entire bark transcriptome assembly. The exercise served to relate read amounts to the degree of transcript mapping level, the latter being an indicator of the coverage of gene transcripts expressed in the sample. As read amounts or datasize increased toward 16 Gb, the number of transcripts mapped to the entire bark assembly approached saturation. A colour matrix was subsequently generated to illustrate sequencing depth requirement in relation to the degree of coverage of total sample transcripts.
CONCLUSIONS: We devised a procedure, the "transcript mapping saturation test", to estimate the amount of RNA-Seq reads needed for deep coverage of transcriptomes. For Hevea de novo assembly, we propose generating between 5-8 Gb reads, whereby around 90% transcript coverage could be achieved with optimized k-mers and transcript N50 length. The principle behind this methodology may also be applied to other non-model plants, or with reads from other second generation sequencing platforms.