Studies on Venom of the fish Scatophagus Argus from Cochin Estuary-A Biochemical Approach

Studies reveal the presence of enzymes and different proteins in the venom of S.argus. The present study detected the presence of phosphodiesterase in S. argus venom. S. argus venom has displayed the presence of micromolar concentration of acetylcholine. Phospholipase activity in S. argus venom shows values below the detection threshold indicating that the venom does not possess this enzyme. The proteolylic activity of S. argus venom on casein and gelatin were assayed due to the probable involvement of proteases in causing the instability of biological activities of the fish venom. Caseinase and gelatinase enzymes were detected in S. argus venom. Though exact relationships of these enzymes and proteins in envenomation are not traced, the involvement of enzymes in envenomation cannot be ruled out. Further studies are required to find the mechanism of action of these enzymes and proteins present in S. argus venom. The present study opens new dimensions for isolation of the lethal compound present in S. argus venom. The preliminary study carried out here shows the presence of a lethal factor between 6.5 KDa - 68 KDa. Studies conclude that fish venom possesses many bioactive substances, especially peptides, proteases and enzymes that bind with high affinity to physiological targets and can be trapped for therapeutic purposes in the near future. Even though this study reveals the conundrums of S. argus venom, it opens new vistas of research on the venom components and the application and design of the venom as a drug.

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.

Bioactive Metabolites and Biomarkers from Rhizophoraceae mangroves- A Chemotaxonomic Approach

Mangroves are specialised ecosystems developed along estuarine sea coasts and river mouths in tropical and subtropical regions of the world, mainly in the intertidal zone. Hence, the ecosystem and its biological components is under the influence of both marine and freshwater conditions and has developed a set of physiological adaptations to overcome problems of anoxia, salinity and frequent tidal inundations. This has led to the assemblage of a wide variety of plant and animal species of special adaptations suited to the ecosystem. The path of photosynthesis in mangroves is different from other glycophytes. There are modifications or alterations in other physiological processes such as carbohydrate metabolism or polyphenol synthesis. As they survive under extreme conditions of salinity, temperature, tides and anoxic soil conditions they may have chemical compounds, which protect them from these destructive elements. Mangroves are necessarily tolerant of high salt levels and have mechanisms to take up water despite strong osmotic potentials. Some also take up salts, but excrete them through specialised glands in the leaves. Others transfer salts into senescent leaves or store them in the bark or the wood. Still others simply become increasingly conservative in their water use as water salinity increases. A usual transportation or biosynthetic path as other plants cannot be expected in mangrove plants. In India, the states like West Bengal, Orissa, Andhra Pradesh, Tamil Nadu, Andaman and Nicobar Islands, Kerala, Goa, Maharashtra, and Gujarat occupy vast area of mangroves. Kerala has only 6 km2 total mangrove area with Rhizophora apiculata, Rhizophora mucronata, Bruguiera gymnorrhiza, Bruguiera cylindrica, Avicennia officinalis, Sonneratia caseolaris, Sonneratia apetala and Kandelia candal, as the important species present, most of which belong to the family Rhizophoraceae.Rhizophoraceae mangroves are ranked as “major elements of mangroves” as they give the real shape of this unique and interesting ecosystem and these mangrove species most productive and typical characteristic ecosystem of World renowned. It was found that the Rhizophoraceae mangrove extracts exhibit several bioactive properties. Various parts of these mangroves are used in ethnomedicinal practices. Even though extracts from these mangroves possess therapeutic activity against humans, animal and plant pathogens, the specific metabolites responsible for these bioactivities remains to be elucidated. Various parts of these mangroves are used in ethnomedicinal practices. There is a gap of information towards the chemistry of Rhizophoraceae mangroves from Kerala. Thorough phytochemical investigation can achieve the validity of ethnomedicines as well as apply the use of mangrove plants in the development of new drugs. Such studies can pave a firm base for their use in biomarker and chemotaxonomic studies as well as for the better management of the existing mangrove ecosystem. In this study, the various chemical parameters including minerals, biochemical components, bioactive and biomarker molecules were used to classify and assess the possible potentials of the mangrove plants of the true mangrove family Rhizophoraceae from Kochi.