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Rediscovering medicinal plants' potential with OMICS: microsatellite survey in expressed sequence tags of eleven traditional plants with potent antidiabetic properties.

ABSTRACT: Herbal medicines and traditionally used medicinal plants present an untapped potential for novel molecular target discovery using systems science and OMICS biotechnology driven strategies. Since up to 40% of the world's poor people have no access to government health services, traditional and folk medicines are often the only therapeutics available to them. In this vein, North East (NE) India is recognized for its rich bioresources. As part of the Indo-Burma hotspot, it is regarded as an epicenter of biodiversity for several plants having myriad traditional uses, including medicinal use. However, the improvement of these valuable bioresources through molecular breeding strategies, for example, using genic microsatellites or Simple Sequence Repeats (SSRs) or Expressed Sequence Tags (ESTs)-derived SSRs has not been fully utilized in large scale to date. In this study, we identified a total of 47,700 microsatellites from 109,609 ESTs of 11 medicinal plants (pineapple, papaya, noyontara, bitter orange, bermuda brass, ratalu, barbados nut, mango, mulberry, lotus, and guduchi) having proven antidiabetic properties. A total of 58,159 primer pairs were designed for the non-redundant 8060 SSR-positive ESTs and putative functions were assigned to 4483 unique contigs. Among the identified microsatellites, excluding mononucleotide repeats, di-/trinucleotides are predominant, among which repeat motifs of AG/CT and AAG/CTT were most abundant. Similarity search of SSR containing ESTs and antidiabetic gene sequences revealed 11 microsatellites linked to antidiabetic genes in five plants. GO term enrichment analysis revealed a total of 80 enriched GO terms widely distributed in 53 biological processes, 17 molecular functions, and 10 cellular components associated with the 11 markers. The present study therefore provides concrete insights into the frequency and distribution of SSRs in important medicinal resources. The microsatellite markers reported here markedly add to the genetic stock for cross transferability in these plants and the literature on biomarkers and novel drug discovery for common chronic diseases such as diabetes.

SUBMITTER: Sahu J

PROVIDER: S-EPMC4012623 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Rediscovering medicinal plants' potential with OMICS: microsatellite survey in expressed sequence tags of eleven traditional plants with potent antidiabetic properties.

Sahu Jagajjit J Sen Priyabrata P Choudhury Manabendra Dutta MD Dehury Budheswar B Barooah Madhumita M Modi Mahendra Kumar MK Talukdar Anupam Das AD

Omics : a journal of integrative biology 20140501 5

Herbal medicines and traditionally used medicinal plants present an untapped potential for novel molecular target discovery using systems science and OMICS biotechnology driven strategies. Since up to 40% of the world's poor people have no access to government health services, traditional and folk medicines are often the only therapeutics available to them. In this vein, North East (NE) India is recognized for its rich bioresources. As part of the Indo-Burma hotspot, it is regarded as an epicent ...[more]

PMID: 24802971

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Project description:Natural products have been the center of attraction ever since they were discovered. Among them, plant-based natural products were popular as analgesics, anti-inflammatory, antidiabetic, and cosmetics and possess widespread biotechnological applications. The use of plant products as cosmetics and therapeutics is deep-rooted in Nepalese society. Although there are few ethnobotanical studies conducted, extensive research of these valuable medicinal plants has not been a priority due to the limitation of technology and infrastructure. Here, we selected 4 traditionally used medicinal plants to examine their bioactive properties and their enzyme inhibition potential. α-Glucosidase and α-amylase inhibitory activities were investigated using an in vitro model followed up by antioxidant and antimicrobial activities. The present study shows that ethyl acetate fraction of Melastoma melabathrium (IC50 9.1 ± 0.3 µg/mL) and water fraction Acacia catechu (IC50 9.0 ± 0.6 µg/mL) exhibit strong α-glucosidase inhibition. Likewise, the highest α-amylase inhibition was shown by crude extracts of Ficus religiosa (IC50 29.2 ± 1.2 µg/mL) and ethyl acetate fractions of Shorea robusta (IC50 69.3 ± 1.1 µg/mL), and the highest radical scavenging activity was shown by F. religiosa with an IC50 67.4 ± 0.6 µg/mL. Furthermore, to identify the metabolites within the fractions, we employed high-resolution mass spectrometry (LC-HRMS) and annotated 17 known metabolites which justify our assumption on activity. Of 4 medicinal plants examined, ethyl acetate fraction of S. robusta, ethyl acetate fraction of M. melabathrium, and water or ethyl acetate fraction of A. catechu extracts illustrated the best activities. With our study, we set up a foundation that provides authentic evidence to the community for use of these traditional plants. The annotated metabolites in this study support earlier experimental evidence towards the inhibition of enzymes. Further study is necessary to explore the clinical efficacy of these secondary molecules, which might be alternatives for the treatment of diabetes and pathogens.

Project description:BackgroundThere are handful hypothesis-driven ethnobotanical studies in Nepal. In this study, we tested the non-random medicinal plant selection hypothesis using national- and community-level datasets through three different types of regression: linear model with raw data, linear model with log-transformed data and negative binomial model.MethodsFor each of these model, we identified over-utilized families as those with highest positive Studentized residuals and underutilized families with highest negative Studentized residuals. The national-level data were collected from online databases and available literature while the community-level data were collected from Baitadi and Darchula districts.ResultsBoth dataset showed larger variance (national dataset mean 6.51 < variance 156.31, community dataset mean 1.16 < variance 2.38). All three types of regression were important to determine the medicinal plant species selection and use differences among the total plant families, although negative binomial regression was most useful. The negative binomial showed a positive nonlinear relationship between total plant family size and number of medicinal species per family for the national dataset (β1 = 0.0160 ± 0.0009, Z1 = 16.59, p < 0.00001, AIC1 = 1181), and with similar slope and stronger performance for the community dataset (β2 = 0.1747 ± 0.0199, Z2 = 8.76, p < 0.00001, AIC2 = 270.78). Moraceae and Euphorbiaceae were found over-utilized while Rosaceae, Cyperaceae and Caryophyllaceae were recorded as underutilized.ConclusionsAs our datasets showed larger variance, negative binomial regression was found the most useful for testing non-random medicinal plant selection hypothesis. The predictions made by non-random selection of medicinal plants hypothesis holds true for community-level studies. The identification of over-utilized families is the first step toward sustainable conservation of plant resources and it provides a baseline for pharmacological research that might be leading to drug discovery.

Dataset Information

Rediscovering medicinal plants' potential with OMICS: microsatellite survey in expressed sequence tags of eleven traditional plants with potent antidiabetic properties.

Publications

Rediscovering medicinal plants' potential with OMICS: microsatellite survey in expressed sequence tags of eleven traditional plants with potent antidiabetic properties.

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