Molecular characterization of a crustinlike antimicrobial peptide in the giant tiger shrimp, Penaeus monodon, and its expression profile in response to various immunostimulants and challenge with WSSV

A crustinlike antimicrobial peptide from the haemocytes of giant tiger shrimp, Penaeus monodon was partially characterized at the molecular level and phylogenetic analysis was performed. The partial coding sequence of 299 bp and 91 deduced amino acid residues possessed conserved cysteine residues characteristic of the shrimp crustins. Phylogenetic tree and sequence comparison clearly confirmed divergence of this crustinlike AMP from other shrimp crustins. The differential expression of the crustinlike AMP in P. monodon in response to the administration of various immunostimulants viz., two marine yeasts (Candida haemulonii S27 and Candida sake S165) and two bglucan isolates (extracted from C. haemulonii S27 and C. sake S165) were noted during the study. Responses to the application of two grampositive probiotic bacteria (Bacillus MCCB101 and Micrococcus MCCB104) were also observed. The immune profile was recorded preand postchallenge white spot syndrome virus (WSSV) by semiquantitative RTPCR. Expressions of seven WSSV genes were also observed for studying the intensity of viral infection in the experimental animals. The crustinlike AMP was found to be constitutively expressed in the animal and a significant downregulation could be noted postchallenge WSSV. Remarkable downregulation of the gene was observed in the immunostimulant fed animals prechallenge followed by a significant upregulation postchallenge WSSV. Tissuewise expression of crustinlike AMP on administration of C. haemulonii and Bacillus showed maximum transcripts in gill and intestine. The marine yeast, C. haemulonii and the probiotic bacteria, Bacillus were found to enhance the production of crustinlike AMP and confer significant protection to P. monodon against WSSV infection

Anti-lipopolysaccharide factor and crustin-III, the anti-white spot virus peptides in Penaeus monodon: Control of viral infection by up-regulation

White Spot Syndrome Virus (WSSV) is the most devastating disease affecting shrimp culture around the world. Though, considerable progress has been made in the detection and molecular characterization of WSSV in recent years, information pertaining to immune gene expression in shrimps with respect to WSSV infection remains limited. In this context, the present study was undertaken to understand the differential expression of antimicrobial peptide (AMP) genes in the haemocytes of Penaeus monodon in response to WSSV infection on a time-course basis employing semi-quantitative RT-PCR. The present work analyzes the expression profile of six AMP genes (ALF, crustin-1, crustin-2, crustin-3, penaeidin-3 and penaeidin-5), eight WSSV genes (DNA polymerase, endonuclease, immediate early gene, latency related gene, protein kinase, ribonucleotide reductase, thymidine kinase and VP28) and three control genes (18S rRNA, β-actin and ELF) in P. monodon in response to WSSV challenge. Penaeidins were found to be up-regulated during early hours of infection and crustin-3 during late period of infection. However, ALF was found to be up-regulated early to late period of WSSV infection. The present study suggests that AMPs viz. ALF and crustin-3 play an important role in antiviral defense in shrimps. WSSV gene transcripts were detected post-challenge day 1 itself and increased considerably day 5 onwards. Evaluation of the control genes confirmed ELF as the most reliable control gene followed by 18S rRNA and β-actin for gene expression studies in shrimps. This study indicated the role of AMPs in the protection of shrimps against viral infection and their possible control through the up-regulation of AMPs

A Novel Isoform of the Hepatic Antimicrobial Peptide, Hepcidin (Hepc-CB1), from a Deep-Sea Fish, the Spinyjaw Greeneye Chlorophthalmus bicornis (Norman, 1939): Molecular Characterisation and Phylogeny

Hepcidin is cysteine-rich short peptide of innate immune system of fishes, equipped to perform prevention and proliferation of invading pathogens like bacteria and viruses by limiting iron availability and activating intracellular cascades. Hepcidins are diverse in teleost fishes, due to the varied aquatic environments including exposure to pathogens, oxygenation and iron concentration. In the present study, we report a 87-amino acid (aa) preprohepcidin (Hepc-CB1) with a signal peptide of 24 aa, a prodomain of 39 aa and a bioactive mature peptide of 24 aa from the gill mRNA transcripts of the deep-sea fish spinyjaw greeneye, Chlorophthalmus bicornis. Molecular characterisation and phylogenetic analysis categorised the peptide to HAMP2-like group with a mature peptide of 2.53 kDa; a net positive charge (?3) and capacity to form b-hairpin-like structure configured by 8 conserved cysteines. The present work provides new insight into the mass gene duplication events and adaptive evolution of hepcidin isoforms with respect to environmental influences and positive Darwinian selection. This work reports a novel hepcidin isoform under the group HAMP2 from a nonacanthopterygian deep-sea fish, C. bicornis

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.