Ananya Kuanar

Mining and characterization of EST derived microsatellites in Curcuma longa L.

Turmeric (Curcuma longa L.) (Family: Zingiberaceae) is a perennial rhizomatous herbaceous plant often used as a spice since time immemorial. Turmeric plants are also widely known for its medicinal applications. Recently EST‐derived SSRs (Simple sequence repeats) are a free by‐product of the currently expanding EST (Expressed Sequence Tag) databases. SSRs have been widely applied as molecular markers in genetic studies. Development of high throughput method for detection of SSRs has given a new dimension in their use as molecular markers. A software tool SciRoKo was used to mine class I SSR in Curcuma EST database comprising 12953 sequences. A total of 568 non‐redundant SSR loci were detected with an average of one SSR per 14.73 Kb of EST. Furthermore, trinucleotide was found to be the most abundant repeat type among 1‐6‐nucleotide repeat types. It accounted for 41.19% of the total, followed by the mononucleotide (20.07%) and hexanucleotide repeats (15.14%). Among all the repeat motifs, (A/T)n accounted for the highest proportion followed by (AGG)n. These detected SSRs can be greatly used for designing primers that can be used as markers for constructing saturated genetic maps and conducting comparative genomic studies in different Curcuma species.

Semiemperical Investigation of the Postmenopausal Breast CancerTreatment Potential of Xanthone Derivatives

Aromatase, a catalyst in the aromatization reaction of androgens to estrogen, is a member of the cytochrome p450 superfamily, known as monooxygenases. The synthesized estrogen by Aromatase in breast cancer nourishes the cancer cells and assesses the hormonally growing of cancer cells. Therefore, Aromatase is considered as a potential target in treatment of breast cancer. Nowadays, major considerations in drugs selection in the treatment of cancers are shifted to natural sources due to their low toxicity profiles and better therapeutic functionality. In the present study, we have identified the binding modes of various xanthones, dietary supplements obtained from different plant sources, by using of efficient Biocomputational tools. Through docking studies, it is clear that 10 out of 13 ligands showing hydrogen bonds with amino acids like THR 310, PRO 249, ARG 113, GLY 439 and CYS 347. Sameathxanthone A showed highest dock score of -7.96 with the binding energy -38.46 kcal/mol and two hydrogen bonds with LEU 477 and VAL 373. Subsequently, the ADMET properties of compounds have been predicted computationally.

Assessment of genetic diversity among some elite cultivars of ginger (Zingiber officinale Rosc.) using isozyme and protein markers

Protein and isozyme markers were used to characterize 20 elite cultivars of ginger. Four different isozymes (acid phosphatase, esterase, peroxidase and polyphenolic oxidase) were analyzed to assess the genomic relationship among 20 varieties of ginger. Buffer soluble protein was isolated and resolved in SDS-PAGE. A total of 108 types of proteins were resolved, out of which 33 were unique. A total of 85 bands were detected for 4 isozymes, among them only 3 were found to be unique. The dendrogram constructed based on pooled data (protein and isozyme) divided the varieties into two major clusters each containing 10 members, respectively. The varieties Singjhara and Waynad local were found to be the most closely related while Singjhara and Sleeva local were distantly apart. The present study gave an indication of usefulness of the isozyme and protein markers for genetic discrimination between different varieties of ginger. The result of the present investigation will help in future breeding program.

Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.)

Abstract The essential oil obtained from rhizome of turmeric (Curcuma longa L.) is highly valued worldwide for its medicinal and cosmetic uses. Lack of requisite high oil containing genotypes and existing variation in the quality and quantity of essential oil with plant habitat and agro-climatic regions pose problem in commercialization of essential oil. Thus the present work was carried out for optimization and prediction of essential oil yield of turmeric at different agro climatic regions. An artificial neural network (ANN) based prediction model was developed by using the data of essential oil of 131 turmeric germplasms collected from 8 agro-climatic regions of Odisha and analysis of their soil and environmental factors. Each sample with 11 parameters was used for training and testing the ANN model. The results showed that multilayer-feed-forward neural networks with 12 nodes (MLFN-12) was the most suitable and reasonable model to use with R2 value of 0.88. This study indicates that ANN based prediction model is a suitable way of predicting oil yield at a new site and to optimize the yield of turmeric oil at a particular site by changing the changeable parameters of the prediction model and thus is of enough commercial significance.

Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.)

The drug yielding potential of turmeric (Curcuma longa L.) is largely due to the presence of phyto-constituent ‘curcumin.’ Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN) was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8) was the most suitable one with R2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.

Chemometric profile & antimicrobial activities of leaf extract of Calotropis procera and Calotropis gigantea

Abstract Calotropis procera and Calotropis gigantea are medicinal plant having therapeutic value. The leaf extracts of C. procera have been investigated, its pharmacological actions in detail and leaf extracts of C. gigantea were not studied till date. The objective of present work was to find the bioactive constituents present in the ethanolic leaf extract of C. procera and C. gigantea to evaluate their antibacterial and anifungal activities. The major phytochemical groups in C. procera ethanolic leaf extracts were fatty acid ethyl ester (21.36%), palmitic acid ester (10.24%), linoleic acid (7.43%) and amino acid (8.10%) respectively, whereas ethanolic leaf extracts of C. gigantea contain palmitic acid (46.01%), diterpene (26.53%), triterpene (17.39%), linoleic acid (5.13%) as the major phytochemical groups. Ethanol extract of C. procera leaves showed the highest inhibition (11 mm) against Escherichia coli, while ethanolic extract of C. gigantea leaves inhibited Klebsiella (20 mm). These findings will use in new directions in pharmacological investigations.

Screening of Ginger Germplasm for Resistance to Rhizome Rot

Twenty five varieties of ginger Zingiber officinale Rosc. were screened in the field against Pythium aphanidermatum for two consecutive years during 2010–2011 at High Altitude Research Station, Orissa University of Agriculture and Technology, Pottangi, Koraput, Odisha. These varieties were also screened in lab and net house (pot) condition. Out of 25 varieties only 3 varieties showed resistance to rhizome rot, of which one (Sargiguda) was resistant and two (China and Varada) were partially resistant, providing good material for developing rhizome rot resistant ginger varieties.