Deepak Sehgal

Heparan Sulfate Proteoglycans Are Required for Cellular Binding of the Hepatitis E Virus ORF2 Capsid Protein and for Viral Infection

ABSTRACT The hepatitis E virus (HEV), a nonenveloped RNA virus, is the causative agent of hepatitis E. The mode by which HEV attaches to and enters into target cells for productive infection remains unidentified. Open reading frame 2 (ORF2) of HEV encodes its major capsid protein, pORF2, which is likely to have the determinants for virus attachment and entry. Using an ∼56-kDa recombinant pORF2 that can self-assemble as virus-like particles, we demonstrated that cell surface heparan sulfate proteoglycans (HSPGs), specifically syndecans, play a crucial role in the binding of pORF2 to Huh-7 liver cells. Removal of cell surface heparan sulfate by enzymatic (heparinase) or chemical (sodium chlorate) treatment of cells or competition with heparin, heparan sulfate, and their oversulfated derivatives caused a marked reduction in pORF2 binding to the cells. Syndecan-1 is the most abundant proteoglycan present on these cells and, hence, plays a key role in pORF2 binding. Specificity is likely to be dictated by well-defined sulfation patterns on syndecans. We show that pORF2 binds syndecans predominantly via 6-O sulfation, indicating that binding is not entirely due to random electrostatic interactions. Using an in vitro infection system, we also showed a marked reduction in HEV infection of heparinase-treated cells. Our results indicate that, analogous to some enveloped viruses, a nonenveloped virus like HEV may have also evolved to use HSPGs as cellular attachment receptors.

Expression and processing of the Hepatitis E virus ORF1 nonstructural polyprotein

BackgroundThe ORF1 of hepatitis E virus (HEV) encodes a nonstructural polyprotein of ~186 kDa that has putative domains for four enzymes: a methyltransferase, a papain-like cysteine protease, a RNA helicase and a RNA dependent RNA polymerase. In the absence of a culture system for HEV, the ORF1 expressed using bacterial and mammalian expression systems has shown an ~186 kDa protein, but no processing of the polyprotein has been observed. Based on these observations, it was proposed that the ORF1 polyprotein does not undergo processing into functional units. We have studied ORF1 polyprotein expression and processing through a baculovirus expression vector system because of the high level expression and post-translational modification abilities of this system.ResultsThe baculovirus expressed ORF1 polyprotein was processed into smaller fragments that could be detected using antibodies directed against tags engineered at both ends. Processing of this ~192 kDa tagged ORF1 polyprotein and accumulation of lower molecular weight species took place in a time-dependent manner. This processing was inhibited by E-64d, a cell-permeable cysteine protease inhibitor. MALDI-TOF analysis of a 35 kDa processed fragment revealed 9 peptide sequences that matched the HEV methyltransferase (MeT), the first putative domain of the ORF1 polyprotein. Antibodies to the MeT region also revealed an ORF1 processing pattern identical to that observed for the N-terminal tag.ConclusionWhen expressed through baculovirus, the ORF1 polyprotein of HEV was processed into smaller proteins that correlated with their proposed functional domains. Though the involvement of non-cysteine protease(s) could not be be ruled out, this processing mainly depended upon a cysteine protease.

Purification and diagnostic utility of a recombinant hepatitis E virus capsid protein expressed in insect larvae

We report here the expression and purification of a truncated form of the hepatitis E virus ORF2 protein (ORF2delta111/deltaTM), from the fat bodies of Spodoptera litura larvae infected with a recombinant baculovirus. The purified protein migrated as a doublet of approximately 56 kDa on SDS-PAGE and was found to be glycosylated by staining with concanavalin A-linked horseradish peroxidase. The protein was used in a sensitive and specific enzyme-linked immunosorbent assay (ELISA) for the detection of antibodies to HEV. The results showed complete concordance with those obtained using a commercial kit for the detection of anti-HEV antibodies. Antigen expression in the insect larvae system presents a rapid and low-cost method that obviates the need for expensive tissue culture scale-ups or special equipment.

Purification of Oxalyl CoA Synthetase Enzyme from Lathyrus sativus and Raising of Antibodies

Oxalyl CoA synthetase, a key enzyme in the biosynthesis of β-oxalyl CoA synthetase, a key enzyme in the biosynthesis of days old seedlings of Lathyrus sativus using affinity chromatography and electroelution. The enzyme existed in three forms. They were designated as OCS-1, OCS-2 and OCS-3 and their molecular weights were found to be 63.1, 39.9 and 17.7 kDa, respectively. The antibodies were raised against all the three enzymes. The monospecificity of the antiserum was checked by immunoblotting. OCS-1 and OCS-2 did not share any common epltopes as no cross-reaction was seen.

Recent trends in antimicrobial peptide prediction using machine learning techniques

The importance to develop effective alternatives to known antibiotics due to increased microbial resistance is gaining momentum in recent years. Therefore, it is of interest to predict, design and computationally model Antimicrobial Peptides (AMPs). AMPs are oligopeptides with varying size (from 5 to over100 residues) having key role in innate immunity. Thus, the potential exploitation of AMPs as novel therapeutic agents is evident. They act by causing cell death either by disrupting the microbial membrane by inhibiting extracellular polymer synthesis or by altering intra cellular polymer functions. AMPs have broad spectrum activity and act as first line of defense against all types of microorganisms including viruses, bacteria, parasites, fungi and as well as cancer (uncontrolled celldivision) progression. Large-scale identification and extraction of AMPs is often non-trivial, expensive and time consuming. Hence, there is a need to develop models to predict AMPs as therapeutics. We document recent trends and advancement in the prediction of AMP.

Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony OptimizationThe Luxembourg database of trichothecene type B F. graminearum and F. culmorum producers.

Hepatitis is an emerging global threat to public health due to associated mortality, morbidity, cancer and HIV co-infection. Available diagnostics and therapeutics are inadequate to intercept the course and transmission of the disease. Antimicrobial peptides (AMP) are widely studied and broad-spectrum host defense peptides are investigated as a targeted anti-viral. Therefore, it is of interest to describe the supervised identification of anti-hepatitis peptides. We used a hybrid Support Vector Machine (SVM) with Ant Colony Optimization (ACO) algorithm for simultaneous classification and domain feature selection. The described model shows a 10 fold cross-validation accuracy of 94 percent. This is a reliable and a useful tool for the prediction and identification of hepatitis specific drug activity

Hybrid ACO Chaos-Assisted Support Vector Machines for Classification of Medical Datasets

There is a need for developing accurate learning algorithms for analyzing large-scale medical diagnostic, prognostic, and treatment datasets. Success of classifiers like support vector machines lies in employment of best informative features out of a huge noisy feature space. In this work, we employ a hybrid filter–wrapper approach to build high-performance classification models. We tested our algorithms using popular datasets containing clinic-bio-pathological parameters of leukemia, hepatitis, breast cancer, and colon cancer taken from publically available datasets. Our results indicate that the hybrid algorithm can discover informative subsets possessing very high classification accuracy.

Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forest and Extra Tree regressors

Antimicrobial peptides are host defense peptides being viewed as replacement to broad-spectrum antibiotics due to varied advantages. Hepatitis is the commonest infectious disease of liver, affecting 500 million globally with reported adverse side effects in treatment therapy. Antimicrobial peptides active against hepatitis are called as anti-hepatitis peptides (AHP). In current work, we present Extratrees and Random Forests based Quantitative Structure Activity Relationship (QSAR) regression modeling using extracted sequence based descriptors for prediction of the anti-hepatitis activity. The Extra-trees regression model yielded a very high performance in terms coefficient of determination (R2) as 0.95 for test set and 0.7 for the independent dataset. We hypothesize that the developed model can further be used to identify potentially active anti-hepatitis peptides with a high level of reliability.