Effect of Non-Coding RNA on Post-Transcriptional Gene Silencing of Alzheimer Disease

A large amount of hidden biological information is contained in the human genome, which is not expressed or revealed in the form of proteins; the usual end product form of gene expression. Instead, most of such information is in the form of non-coding RNAs (ncRNAs). ncRNAs correspond to genes that are transcribed, but do not get translated into proteins. This part of the genome was, till recently, considered as ‘junk’. The term ‘junk’ implied lack of any discernible function of these RNA. More than 98% of the human genomic size encompasses these non-coding RNAs. But, recent research has evidently brought out the indispensible contribution of non-coding RNA in controlling and regulating gene expression. ncRNA such as siRNAs and microRNAs have been reported to greatly help in causing post-transcriptional gene silencing (PTGS) in cells through RNA interference (RNAi) pathway. In this work, we have investigated the possibility of using siRNAs and microRNAs to aid in gene silencing of early onset Alzheimer’s disease genes. 
Alzheimer’s disease specific mutations and their corresponding positions in mRNA have been identified for six genes; Presenilin-1, Presenilin-2, APP (amyloid beta precursor protein), APBB3, BACE-1 and PSENEN. 

Small interfering RNAs (siRNAs) that can cause PTGS through RNA interference pathway have been designed. RNA analysis has been done to verify complementarity of antisense siRNA sequence with target mRNA sequence. Interaction studies have been done computationally between these antisense siRNA strands and seven Argonaute proteins. From the interaction studies, only one of the seven Argonaute proteins; 1Q8K, was found to have interaction with the siRNAs indicating the importance and uniqueness of this particular protein in RISC (RNA induced silencing complex). 

The interaction studies have been carried out for the microRNAs also. Out of the 700 mature human microRNAs collected, 394 microRNAs have been identified to show partial complementarity with their target sequence on PSEN-1 mRNA. Of these 394, five microRNAs have shown partial complementarity to early onset Alzheimer’s disease specific mutations in PSEN-1 mRNA. Interaction studies have been done between these microRNAs and Argonaute proteins. Thus, design, characterization and analysis of ncRNAs that contribute to post transcriptional gene silencing of Alzheimer’s disease have been achieved.
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Structural Studies of Cu(II) & Ni(II) Complexes with a Chiral Schiff Base

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Insilico modeling of chitosan as a drug delivery system

Computational modeling of polymeric nanoparticles as drug carriers have been extensively studied due to their varied functionalities, tunable structures and the capability of controlled drug release. Nano particulate polymeric drug delivery systems enable a cell specific targeting with negligible side effects and drug release based on change in physiological conditions. Eight common polymers are modeled and the various properties have been predicted. ADMET, QSAR, thermodynamic and electronic properties have been predicted and compared using SAR as well as quantum mechanical density functional methods. Comparison of the predicted properties suggests that chitosan, which is a natural polymer and has some advantages over others is a promising drug carrier candidate for tumor

DESIGN AND DEVELOPMENT OF A PHARMACOGENOMIC MODEL FOR BREAST CANCER TO STUDY THE VARIATION IN DRUG ACTION AND SIDE EFFECTS

Objective: The proneness of disease, as well as drug action and side effects, vary from person to person. This may be due to individual variations in the genome. The individual variation demands the need to design a population-specific 'predictive, preventive, participatory and personalized (p4)' pharmacogenomics drug molecule. The present work aims at designing a pharmacogenomic model for breast cancer to explain the individual variation in the proneness of the diseases and susceptibility towards drug action. Methods: The drug action and side effects of drugs were analyzed from clinical trial reports. The genes responsible for the drug action and the genes responsible for side effects have been identified and included in the variation analysis. The pharmacogenomic gene models have been designed by inducing population-specific genetic variations within the gene sequence. The 3D structures of the 'variation-specific' protein models have been generated by 'homology modelling.' These models have been used further for docking studies with the known drug molecules. The kinetic stability of the protein-ligand complexes obtained out of docking studies has been studied by the molecular dynamic simulation. Results: By the interaction studies and the computational analysis using the 'population-specific protein models,' the drug molecule, Capecitabine showed the highest binding affinity (–6.30kcal/mol) with the African population, Paclitaxel was found to be the most interacting with the European population with a binding affinity of–9.5603 kcal/mol, and Lapatinib is found to be the most suitable ligand for the American population with a binding affinity of–6.90 kcal/mol. These observations agree with the clinical trial data found in the 'ClinTrial database'. Conclusion: The designed models are found to be suitable for representing the respective population-specific target models. The interaction studies of known drug molecules with these population-specific target models correspond to the observations in the 'ClinTrial database.

‘BRCA1' RESPONSIVENESS TOWARDS BREAST CANCER-A POPULATION-WISE PHARMACOGENOMIC ANALYSIS

Objective: In the present pharmacogenomic work, the genetic, epigenetic and environmental factors associated with BRCA1 induced breast cancer, cancer proneness and its variants across different populations like Indian, Netherland, Belgium, Denmark, Austrian, New Zealand, Sweden, Malaysian and Norwegian and the ‘mutation and methylation-prone' region of BRCA1 have been computed.Methods: The global variations associated with the disease have been identified from the ‘Leiden open variation database (LOVD 3.0)' and ‘Indian genome variation database (IGVDB)'. The variants, ‘single nucleotide polymorphisms (SNPs)' are then characterized. The epigenetic factors associated with breast cancer have been identified from the clinical reports and further scrutinized using EpiGRAPH tool. The various contributing environmental factors responsible for the variations have been considered.Results: All the variants across different populations such as Indian, Netherland, Belgium, Denmark, Austrian, New Zealand, Sweden, Malaysian and Norwegian are found to be in a specific transcript of BRCA1 that ranges within 41,196,312-41,277,500 (81,189 base pairs) of the chromosome 17. Two ‘single nucleotide variations (SNVs)' (5266dupC: rs397507246 and 68_69delAG: rs386833395) have been identified as risk factors in hereditary breast and ovarian cancer syndrome in the global population and 39 SNPs have been identified as pathogenic and deleterious. ‘Evolutionary history' seems to be the most significant attribute in the predictability of methylation of BRCA1. Unhealthy dietary habits, obesity, use of unsafe cosmetics, estrogen exposure, ‘hormone replacement therapy (HRT)', use of oral contraceptives and smoking are the major environmental risk factors associated with breast cancer incidence.Conclusion: This chromosome location (41,196,312-41,277,500 (81,189 base pairs)) can be considered as the population-specific sensitive region corresponding to BRCA1 mutation. This supports the fact that stabilization within the region can be a promising technique to control the epigenetic variants associated with the global position. The global variation in the proneness of the disease may be due to a cumulative effect of genetic, epigenetic and environmental factors subject to further experimentations with identical variations and populations.Â

In Silico Analysis of the Effect of <i>Hydrastis canadensis</i> on Controlling Breast Cancer

Background and Objectives: Breast cancer is a significant type of cancer among women worldwide. Studies have reported the anti-carcinogenic activity of Hydrastis Canadensis (Goldenseal) in cancer cell lines. Hydrastis Canadensis could help eliminate toxic substances due to its anti-cancer, anti-inflammatory, and other properties. The design phase includes the identification of potential and effective molecules through modern computational techniques. Objective: This work aims to study Hydrastis Canadensis’s effect in controlling hormone-independent breast cancer through in-silico analysis. Materials and Methods: The preliminary screening of reported phytochemicals includes biomolecular networking. Identifying functionally relevant phytochemicals and the respective target mutations/genes leads to selecting 3D proteins of the desired mutations being considered the target. Interaction studies have been conducted using docking. The kinetic and thermodynamic stability of complexes was studied through molecular dynamic simulation and MM-PBSA/GBSA analysis. Pharmacodynamic and pharmacokinetic features have been predicted. The mechanism-wise screening, functional enrichment, and interactional studies suggest that canadaline and Riboflavin effectively interact with the target proteins. Results: Hydrastis Canadensis has been identified as the effective formulation containing all these constituents. The phytoconstituents; Riboflavin and Canadensis showed good interaction with the targets of hormone-independent breast cancer. The complexes were found to be kinetically and thermodynamically stable. Conclusions: Hydrastis Canadensis has been identified as effective in controlling ‘hormone-independent or basal-like breast cancer’ followed by ‘hormone-dependent breast cancer: Luminal A’ and Luminal B