Shahnawaz Ali

From ZikV genome to vaccine: in silico approach for the epitope-based peptide vaccine against Zika virus envelope glycoprotein

Zika virus (ZikV) has emerged as a potential threat to human health worldwide. A member of the Flaviviridae, ZikV is transmitted to humans by mosquitoes. It is related to other pathogenic vector‐borne flaviviruses including dengue, West Nile and Japanese encephalitis viruses, but produces a comparatively mild disease in humans. As a result of its epidemic outbreak and the lack of potential medication, there is a need for improved vaccine/drugs. Computational techniques will provide further information about this virus. Comparative analysis of ZikV genomes should lead to the identification of the core characteristics that define a virus family, as well as its unique properties, while phylogenetic analysis will show the evolutionary relationships and provide clues about the proteins ancestry. Envelope glycoprotein of ZikV was obtained from a protein database and the most immunogenic epitope for T cells and B cells involved in cell‐mediated immunity, whereas B cells are primarily responsible for humoral immunity. We mainly focused on MHC class I potential peptides. YRIMLSVHG, VLIFLSTAV and MMLELDPPF, GLDFSDLYY are the most potent peptides predicted as epitopes for CD4+ and CD8+ T cells, respectively, whereas MMLELDPPF and GLDFSDLYY had the highest pMHC‐I immunogenicity score and these are further tested for interaction against the HLA molecules, using in silico docking techniques to verify the binding cleft epitope. However, this is an introductory approach to design an epitope‐based peptide vaccine against ZikV; we hope that this model will be helpful in designing and predicting novel vaccine candidates.

Recent trends in ZikV research: A step away from cure

Zika virus (ZikV) is a member of the Flaviviridae virus family, genus Flavivirus has emerged as a potential threat to human health worldwide. Consequences of vertical infections includes microcephaly with brain and eye anomalies, and adult infections includes Guillain-Barrésyndrome (GBS), brain ischemia, myelitis and meningoencephalitis. To develop a better treatment, many efforts are being made, like drug-repurposing concept for FDA-approved drugs for antiviral activity are screened against ZikV infection and emerging as a promising alternative to expedite drug development and various vaccines like DNA, ZPIV, LAIV, mRNA and AGS-v vaccines have been designed and in under clinical trial phases. Moreover, few pharmacological agents like Mycophenolicacid, Niclosamide, PHA-690509, Emricasan and Bortezomib are most potent anti-ZikV candidates and highly effective single or combining treatment with these drugs. This article reviews the ZikV illness, transmission patterns, pathophysiology of disease, global efforts, challenges and the prospects for the development of vaccines and antiviral agents.

Dynamical states, possibilities and propagation of stress signal

The stress driven dynamics of Notch-Wnt-p53 cross-talk is subjected to a few possible dynamical states governed by simple fractal rules, and allowed to decide its own fate by choosing one of these states which are contributed from long range correlation with varied fluctuations due to active molecular interaction. The topological properties of the networks corresponding to these dynamical states have hierarchical features with assortive structure. The stress signal driven by nutlin and modulated by mediator GSK3 acts as anti-apoptotic signal in this system, whereas, the stress signal driven by Axin and modulated by GSK3 behaves as anti-apoptotic for a certain range of Axin and GSK3 interaction, and beyond which the signal acts as favor-apoptotic signal. However, this stress system prefers to stay in an active dynamical state whose counterpart complex network is closest to hierarchical topology with exhibited roles of few interacting hubs. During the propagation of stress signal, the system allows the propagator pathway to inherit all possible properties of the state to the receiver pathway/pathways with slight modifications, indicating efficient information processing and democratic sharing of responsibilities in the system via cross-talk. The increase in the number of cross-talk pathways in the system favors to establish self-organization.

Assessment of the key regulatory genes and their Interologs for Turner Syndrome employing network approach

Turner Syndrome (TS) is a condition where several genes are affected but the molecular mechanism remains unknown. Identifying the genes that regulate the TS network is one of the main challenges in understanding its aetiology. Here, we studied the regulatory network from manually curated genes reported in the literature and identified essential proteins involved in TS. The power-law distribution analysis showed that TS network carries scale-free hierarchical fractal attributes. This organization of the network maintained the self-ruled constitution of nodes at various levels without having centrality–lethality control systems. Out of twenty-seven genes culminating into leading hubs in the network, we identified two key regulators (KRs) i.e. KDM6A and BDNF. These KRs serve as the backbone for all the network activities. Removal of KRs does not cause its breakdown, rather a change in the topological properties was observed. Since essential proteins are evolutionarily conserved, the orthologs of selected interacting proteins in C. elegans, cat and macaque monkey (lower to higher level organisms) were identified. We deciphered three important interologs i.e. KDM6A-WDR5, KDM6A-ASH2L and WDR5-ASH2L that form a triangular motif. In conclusion, these KRs and identified interologs are expected to regulate the TS network signifying their biological importance.

Stage specific classification of DEGs via statistical profiling and network analysis reveals potential biomarker associated with various stages of TB

Background Tuberculosis (TB) is a deadly transmissible disease that can infect almost any body-part of the host but is mostly infect the lungs. It is one of the top 10 causes of death worldwide. In the 30 high TB burden countries, 87% of new TB cases occurred in 2016. Seven countries: India, Indonesia, China, Philippines, Pakistan, Nigeria, and South Africa accounted for 64% of the new TB cases. To stop the infection and progression of the disease, early detection of TB is important. In our study, we used microarray data set and compared the gene expression profiles obtained from blood samples of patients with different datasets of Healthy control, Latent infection, Active TB and performed network-based analysis of DEGs to identify potential biomarker. Objectives We want to observe the transition of genes from normal condition to different stages of the TB and identify, annotate those genes/pathways/processes that play key role in the progression of TB disease during its cyclic interventions in human body. Results We identified 319 genes that are differentially expressed in various stages of TB (Normal to LTTB, Normal to Active TB and LTTB to active TB) and allocated to pathways from multiple databases which comprised of curated class of associated genes. These pathway’s importance was then evaluated according to the no. of DEGs present in the pathway and these genes show the broad spectrum of processes that take part in every state. In addition, we studied the regulatory networks of these classified genes, network analysis does consider the interactions between genes (specific for TB) or proteins provide us new facts about TB disease, which in turn can be used for potential biomarkers identification. We identified total 29 biomarkers from various comparison groups of TB stages in which 14 genes are over expressed as host responses against pathogen, but 15 genes are down regulated that means these genes has allowed the process of host defense to cease and give time to pathogen for its progression. Conclusions This study revealed that gene-expression profiles can be used to identify and classified the genes on stage specific pattern among normal, LTTB and active TB and network modules associated with various stages of TB were elucidated, which in turn provided a basis for the identification of potential pathways and key regulatory genes that may be involved in progression of TB disease.

Exploring novel key regulators in breast cancer network

The breast cancer network constructed from 70 experimentally verified genes is found to follow hierarchical scale free nature with heterogeneous modular organization and diverge leading hubs. The topological parameters (degree distributions, clustering co-efficient, connectivity and centralities) of this network obey fractal rules indicating absence of centrality lethality rule, and efficient communication among the components. From the network theoretical approach, we identified few key regulators out of large number of leading hubs, which are deeply rooted from top to down of the network, serve as backbone of the network, and possible target genes. However, p53, which is one of these key regulators, is found to be in low rank and keep itself at low profile but directly cross-talks with important genes BRCA2 and BRCA3. The popularity of these hubs gets changed in unpredictable way at various levels of organization thus showing disassortive nature. The local community paradigm approach in this network shows strong correlation of nodes in majority of modules/sub-modules (fast communication among nodes) and weak correlation of nodes only in few modules/sub-modules (slow communication among nodes) at various levels of network organization.

In Silico Biochemical Pathways for Bacterial Metabolite Synthesis

The search of an alternative is always a major concern of human for sustaining life effectively. The green flora described by the agriculture is the source of many such life attaining processes and products that are essential for human population. In addition to this, the constantly growing number of Homo sapiens has to be fed with increase yield of agriculture products. To meet the demands of growing population and relieve the pressure of yield, the use of fertilizers comes into action, while the constant use of chemical fertilizers has deteriorated the heath of soil, environment, and human collectively calling “phytobiome.” Thus, the urge of finding alternatives to replace the toxic chemical fertilizers has given a way to search exhaustively the naturally occurring microbiomes for their beneficial effect on the agri-flora. Moreover, the available advancements in the computational and system-level approaches with omics data have provided us the genomes and also genome-level metabolic models for many beneficial/effective bacteria. The naturally synthesized metabolites (primary and secondary) can be easily exploited nowadays for any intended use in the fields as inoculants or bio fertilizers. In addition the available kinetic model has paved the way to commercially synthesize desired metabolite (through amendments in pathway either genetic or environmental) on large scale as biofuels, etc. Despite of these advances, several challenges still coexist with approaches that have to be exploited in the near future. Some of the challenges have been discussed in present work with a brief account of in silico kinetic models available.

Pharmacokinetic and Molecular Docking Studies of Plant-Derived Natural Compounds to Exploring Potential Anti-Alzheimer Activity

Alzheimer disease (AD) is the leading cause of dementia and accounts for 60–80% cases. Two main factors called β-amyloid (Aβ) plaques and tangles are prime suspects in damaging and killing nerve cells. However, oxidative stress, the process which produces free radicals in cells, is believed to promote its progression to the extent that it may responsible for the cognitive and functional decline observed in AD. As of today there are few FDA-approved drugs in the market for treatment, but their cholinergic adverse effect, potentially distressing toxicity and limited targets in AD pathology, limits their use. Therefore, it is crucial to find an effective compounds to combat AD. We choose 45 plant-derived natural compounds that have antioxidant properties to slow down disease progression by quenching free redicals or promoting endogenous antioxidant capacity. However, we performed molecular docking studies to investigate the binding interactions between natural compounds and 13 various anti-Alzheimer drug targets. Three known cholinesterase inhibitors (donepezil, galantamine and rivastigmine) were taken as reference drugs over natural compounds for comparison and drug-likeness studies. Few of these compounds showed good inhibitory activity besides antioxidant activity. Most of these compounds followed pharmacokinetic properties that make them potentially promising drug candidates for the treatment of Alzheimer disease.

In Silico Based Analysis of CKD Expressions Data in Correlation with Diabetes Mellitus Unveils Biomarker Gene

Chronic kidney disease (CKD) is becoming an extensive public health problem worldwide. The current anxiety of disease might be due to the change of the underlying pathogenicity. The aim of our study was to provide a detailed analysis of microarray gene expression data of CKD in correlation with diabetes and identification of biomarker genes. Here, Affymetrix expression arrays were used to identify differentially expressed genes in 22 and 69 samples of CKD and diabetes respectively. It further outlines few of the principal biological alterations observed in the CKD state and depicts specific procedures for conducting quality assessment of Affymetrix Gene chip using GEO datasets (GSE70528, GSE11045) and also illustrates quality control packages to remark the visualization for detailed analysis. We identified 912 differentially expressed genes in CKD and 629 in diabetes. From extensive comparison of CKD with diabetes, we found 80 common genes, of which 29 were found up regulated and 51 down. Further, analysis with NCG of these 80 genes, 10 common genes were found involved in various types of cancer. Thus, the results emphasize the importance of these 10 common differentially expressed genes in considering them as biomarkers for three conditions diabetes, CKD and cancer. Our studies have cataloged differentially expressed genes that may play important role in the pathogenesis of CKD and could serve as biomarkers.

In-Silico Screening of Biomarker Genes of Hepatocellular Carcinoma Using R/Bioconductor

Hepatocellular Carcinoma is a primary malignancy of the liver. It is the fifth most common cancer around the world and is a leading cause of cancer related deaths. For about 40 years HCC has been predominantly linked with Hepatitis B and Hepatitis C infection. This work aims to find out potential biomarkers for HBV and HCV infected HCC through rigorous computational analyses. This was achieved by collecting gene expression microarray data from GEO (Gene Expression Omnibus) database as GSE series (GSE38941, GSE26495, GSE51489, GSE41804, GSE49954, GSE16593) and pre-processing it using Bioconductor repository for R. Following a robust mechanism including the use of statistical testing techniques and tools, the data was screened for DEGs (Differentially Expressed Genes). 3354 down regulated genes and 785 up regulated genes for HBV and 3462 down regulated and 251 up regulated genes for HCV were obtained. For a comparative study of DEGs from HBV and HCV, they were merged to look for potential biomarkers whose differential expression may result in carcinoma. A total of 17 biomarkers (1 up-regulated and 16 downregulated), was obtained which were further subjected to Cytoscape to generate a GRN using STRING app. Furthermore, module level analysis was performed as it offers robustness and a better understanding of complex GRNs. The work also focuses on the topological properties of the network. The results point out to the presence of a hierarchical framework in the network. They also shed a light on the interactions of biomarkers whose down regulation may result in HCC. These results can be used for future research and in exploring drug targets for this disease.