Anam Naz

Identification of putative vaccine candidates against Helicobacter pylori exploiting exoproteome and secretome: A reverse vaccinology based approach

Helicobacter pylori (H. pylori) is an important pathogen associated with diverse gastric disorders ranging from peptic ulcer to malignancy. It has also been recognized by the World Health Organization (WHO) as class I carcinogen. Conventional treatment regimens for H. pylori seem to be ineffective, possibly due to antibiotic resistance mechanisms acquired by the pathogen. In this study we have successfully employed a reverse vaccinology approach to predict the potential vaccine candidates against H. pylori. The predicted potential vaccine candidates include vacA, babA, sabA, fecA and omp16. Host-pathogen interactions analysis elaborated their direct or indirect role in the specific signaling pathways including epithelial cell polarity, metabolism, secretion system and transport. Furthermore, surface-exposed antigenic epitopes were predicted and analyzed for conservation among 39 complete genomes of H. pylori (Genbank) for all the candidate proteins. These epitopes may serve as a base for the development of broad spectrum peptide or multi-component vaccines against H. pylori. We also believe that the proposed pipeline can be extended to other pathogens and for the identification of novel candidates for the development of effective vaccines.

Pan-genome analysis of human gastric pathogen H. pylori: comparative genomics and pathogenomics approaches to identify regions associated with pathogenicity and prediction of potential core therapeutic targets.

Helicobacter pylori is a human gastric pathogen implicated as the major cause of peptic ulcer and second leading cause of gastric cancer (~70%) around the world. Conversely, an increased resistance to antibiotics and hindrances in the development of vaccines against H. pylori are observed. Pan-genome analyses of the global representative H. pylori isolates consisting of 39 complete genomes are presented in this paper. Phylogenetic analyses have revealed close relationships among geographically diverse strains of H. pylori. The conservation among these genomes was further analyzed by pan-genome approach; the predicted conserved gene families (1,193) constitute ~77% of the average H. pylori genome and 45% of the global gene repertoire of the species. Reverse vaccinology strategies have been adopted to identify and narrow down the potential core-immunogenic candidates. Total of 28 nonhost homolog proteins were characterized as universal therapeutic targets against H. pylori based on their functional annotation and protein-protein interaction. Finally, pathogenomics and genome plasticity analysis revealed 3 highly conserved and 2 highly variable putative pathogenicity islands in all of the H. pylori genomes been analyzed.

Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach.

A computational and in silico system level framework was developed to identify and prioritize the antibacterial drug targets in Clostridium botulinum (Clb), the causative agent of flaccid paralysis in humans that can be fatal in 5 to 10% of cases. This disease is difficult to control due to the emergence of drug-resistant pathogenic strains and the only available treatment antitoxin which can target the neurotoxin at the extracellular level and cannot reverse the paralysis. This study framework is based on comprehensive systems-scale analysis of genomic sequence homology and phylogenetic relationships among Clostridium, other infectious bacteria, host and human gut flora. First, the entire 2628-annotated genes of this bacterial genome were categorized into essential, non-essential and virulence genes. The results obtained showed that 39% of essential proteins that functionally interact with virulence proteins were identified, which could be a key to new interventions that may kill the bacteria and minimize the host damage caused by the virulence factors. Second, a comprehensive comparative COGs and blast sequence analysis of these proteins and host proteins to minimize the risks of side effects was carried out. This revealed that 47% of a set of C. botulinum proteins were evolutionary related with Homo sapiens proteins to sort out the non-human homologs. Third, orthology analysis with other infectious bacteria to assess broad-spectrum effects was executed and COGs were mostly found in Clostridia, Bacilli (Firmicutes), and in alpha and beta Proteobacteria. Fourth, a comparative phylogenetic analysis was performed with human microbiota to filter out drug targets that may also affect human gut flora. This reduced the list of candidate proteins down to 131. Finally, the role of these putative drug targets in clostridial biological pathways was studied while subcellular localization of these candidate proteins in bacterial cellular system exhibited that 68% of the proteins were located in the cytoplasm, out of which 6% was virulent. Finally, this framework may serve as a general computational strategy for future drug target identification in infectious diseases.

Identification of Circulating Biomarker Candidates for Hepatocellular Carcinoma (HCC): An Integrated Prioritization Approach

Hepatocellular carcinoma (HCC) is the world’s third most widespread cancer. Currently available circulating biomarkers for this silently progressing malignancy are not sufficiently specific and sensitive to meet all clinical needs. There is an imminent and pressing need for the identification of novel circulating biomarkers to increase disease-free survival rate. In order to facilitate the selection of the most promising circulating protein biomarkers, we attempted to define an objective method likely to have a significant impact on the analysis of vast data generated from cutting-edge technologies. Current study exploits data available in seven publicly accessible gene and protein databases, unveiling 731 liver-specific proteins through initial enrichment analysis. Verification of expression profiles followed by integration of proteomic datasets, enriched for the cancer secretome, filtered out 20 proteins including 6 previously characterized circulating HCC biomarkers. Finally, interactome analysis of these proteins with midkine (MDK), dickkopf-1 (DKK-1), current standard HCC biomarker alpha-fetoprotein (AFP), its interacting partners in conjunction with HCC-specific circulating and liver deregulated miRNAs target filtration highlighted seven novel statistically significant putative biomarkers including complement component 8, alpha (C8A), mannose binding lectin (MBL2), antithrombin III (SERPINC1), 11β-hydroxysteroid dehydrogenase type 1 (HSD11B1), alcohol dehydrogenase 6 (ADH6), beta-ureidopropionase (UPB1) and cytochrome P450, family 2, subfamily A, polypeptide 6 (CYP2A6). Our proposed methodology provides a swift assortment process for biomarker prioritization that eventually reduces the economic burden of experimental evaluation. Further dedicated validation studies of potential putative biomarkers on HCC patient blood samples are warranted. We hope that the use of such integrative secretome, interactome and miRNAs target filtration approach will accelerate the selection of high-priority biomarkers for other diseases as well, that are more amenable to downstream clinical validation experiments.

VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology

BackgroundWith advances in reverse vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. Reverse vaccinology has changed the way of discovery and provides a mean to propose target identification in reduced time and labour. In this regard, high throughput genomic sequencing technologies and supporting bioinformatics tools have greatly facilitated the prompt analysis of pathogens, where various predicted candidates have been found effective against certain infections and diseases. A pipeline, VacSol, is designed here based on a similar approach to predict putative vaccine candidates both rapidly and efficiently.ResultsVacSol, a new pipeline introduced here, is a highly scalable, multi-mode, and configurable software designed to automate the high throughput in silico vaccine candidate prediction process for the identification of putative vaccine candidates against the proteome of bacterial pathogens. Vaccine candidates are screened using integrated, well-known and robust algorithms/tools for proteome analysis, and the results from the VacSol software are presented in five different formats by taking proteome sequence as input in FASTA file format. The utility of VacSol is tested and compared with published data and using the Helicobacter pylori 26695 reference strain as a benchmark.ConclusionVacSol rapidly and efficiently screens the whole bacterial pathogen proteome to identify a few predicted putative vaccine candidate proteins. This pipeline has the potential to save computational costs and time by efficiently reducing false positive candidate hits. VacSol results do not depend on any universal set of rules and may vary based on the provided input. It is freely available to download from: https://sourceforge.net/projects/vacsol/.

Modeling and analysis of innate immune responses induced by the host cells against hepatitis C virus infection

An in-depth understanding of complex systems such as hepatitis C virus (HCV) infection and host immunomodulatory response is an open challenge for biologists. In order to understand the mechanisms involved in immune evasion by HCV, we present a simplified formalization of the highly dynamic system consisting of HCV, its replication cycle and host immune responses at the cellular level using hybrid Petri net (HPN). The approach followed in this study comprises of step wise simulation, model validation and analysis of host immune response. This study was performed with an objective of making correlations among viral RNA levels, interferon (IFN) production and interferon stimulated genes (ISGs) induction. The results correlate with the biological data verifying that the model is very useful in predicting the dynamic behavior of the signaling proteins in response to a stimulus. This study implicates that HCV infection is dependent upon several key factors of the host immune response. The effect of host proteins on limiting viral infection is effectively overruled by the viral pathogen. This study also analyzes activity levels of RNase L, miR-122, IFN, ISGs and PKR induction and inhibition of TLR3/RIG1 mediated pathways in response to targeted manipulation in the presence of HCV. The results are in complete agreement at the time of writing with the published expression studies and western blot experiments. Our model also provides some biological insights regarding the role of PKR in the acute infection of HCV. It might help to explain why many patients fail to clear acute HCV infection while others, with low ISG basal levels, clear HCV spontaneously. The described methodology can easily be reproduced, which suitably supports the study of other viral infections in a formal, automated and expressive manner. The Petri net-based modeling approach applied here may provide valuable insights for study design and analyses to evaluate other disease associated integrated pathways in biological systems.

Prediction of vaccine candidates against Pseudomonas aeruginosa: An integrated genomics and proteomics approach

Pseudomonas aeruginosa is among top critical nosocomial infectious agents due to its persistent infections and tendency for acquiring drug resistance mechanisms. To date, there is no vaccine available for this pathogen. We attempted to exploit the genomic and proteomic information of P. aeruginosa though reverse-vaccinology approaches to unveil the prospective vaccine candidates. P. aeruginosa strain PAO1 genome was subjected to sequential prioritization approach following genomic, proteomics and structural analyses. Among, the predicted vaccine candidates: surface components of antibiotic efflux pumps (Q9HY88, PA2837), chaperone-usher pathway components (CupC2, CupB3), penicillin binding protein of bacterial cell wall (PBP1a/mrcA), extracellular component of Type 3 secretory system (PscC) and three uncharacterized secretory proteins (PA0629, PA2822, PA0978) were identified as potential candidates qualifying all the set criteria. These proteins were then analyzed for potential immunogenic surface exposed epitopes. These predicted epitopes may provide a basis for development of a reliable subunit vaccine against P. aeruginosa.

Identification of drug resistance and immune-driven variations in hepatitis C virus (HCV) NS3/4A, NS5A and NS5B regions reveals a new approach toward personalized medicine

&NA; Cellular immune responses (T cell responses) during hepatitis C virus (HCV) infection are significant factors for determining the outcome of infection. HCV adapts to host immune responses by inducing mutations in its genome at specific sites that are important for HLA processing/presentation. Moreover, HCV also adapts to resist potential drugs that are used to restrict its replication, such as direct‐acting antivirals (DAAs). Although DAAs have significantly reduced disease burden, resistance to these drugs is still a challenge for the treatment of HCV infection. Recently, drug resistance mutations (DRMs) observed in HCV proteins (NS3/4A, NS5A and NS5B) have heightened concern that the emergence of drug resistance may compromise the effectiveness of DAAs. Therefore, the NS3/4A, NS5A and NS5B drug resistance variations were investigated in this study, and their prevalence was examined in a large number of protein sequences from all HCV genotypes. Furthermore, potential CD4+ and CD8+ T cell epitopes were predicted and their overlap with genetic variations was explored. The findings revealed that many reported DRMs within NS3/4A, NS5A and NS5B are not drug‐induced; rather, they are already present in HCV strains, as they were also detected in HCV‐naïve patients. This study highlights several hot spots in which HLA and drug selective pressure overlap. Interestingly, these overlapping mutations were frequently observed among many HCV genotypes. This study implicates that knowledge of the host HLA type and HCV subtype/genotype can provide important information in defining personalized therapy. HighlightsReported drug resistance mutations within NS3/4A, NS5A and NS5B regions is explored.Prevalence of these mutations among all major HCV genotypes is investigated.Potential overlap between drug resistance and immune driven mutations are reported.Knowledge of host HLA type/HCV genotype is important to optimize personalized therapy.

Formal Modeling of the Key Determinants of Hepatitis C Virus (HCV) Induced Adaptive Immune Response Network: An Integrative Approach to Map the Cellular and Cytokine-Mediated Host Immune Regulations

HCV is a major causative agent of liver infection and is the leading cause of Hepatocellular carcinoma (HCC). To understand the complexity in interactions within the HCV induced immune signaling networks, a logic-based diagram is generated based on multiple reported interactions. A simple conceptual framework is presented to explore the key determinants of the immune system and their functions during HCV infection. Furthermore, an abstracted sub-network is modeled qualitatively which consists of both the key cellular and cytokine components of the HCV induced immune system. In the presence of NS5A protein of HCV, the behaviors and the interplay amongst the natural killer (NK) and T regulatory (Tregs) cells along with cytokines such as IFN-γ, IL-10, IL-12 are predicted. The overall modelling approach followed in this study comprises of prior knowledge-based logical interaction network, network abstraction, parameter estimation, regulatory network construction and analysis through state graph, enabling the prediction of paths leading to both, disease state and a homeostatic path/cycle predicted based on maximum betweenness centrality. To study the continuous dynamics of the network, Petri net (PN) model was generated. The analysis implicates the critical role of IFN-γ producing NK cells in recovery while, the role of IL-10 and IL-12 in pathogenesis. The predictive ability of the model implicates that IL-12 has a dual role under varying circumstances and leads to varying disease outcomes. This model attempts to reduce the noisy biological data and captures a holistic view of the key determinants of the HCV induced immune response.

Exploring NS3/4A, NS5A and NS5B proteins to design conserved subunit multi-epitope vaccine against HCV utilizing immunoinformatics approaches

Hepatitis C virus (HCV) vaccines, designed to augment specific T-cell responses, have been designated as an important aspect of effective antiviral treatment. However, despite the current satisfactory progress of these vaccines, extensive past efforts largely remained unsuccessful in mediating clinically relevant anti-HCV activity in humans. In this study, we used a series of immunoinformatics approaches to propose a multiepitope vaccine against HCV by prioritizing 16 conserved epitopes from three viral proteins (i.e., NS34A, NS5A, and NS5B). The prioritised epitopes were tested for their possible antigenic combinations with each other along with linker AAY using structural modelling and epitope–epitope interactions analysis. An adjuvant (β-defensin) at the N-terminal of the construct was added to enhance the immunogenicity of the vaccine construct. Molecular dynamics (MD) simulation revealed the most stable structure of the proposed vaccine. The designed vaccine is potentially antigenic in nature and can form stable and significant interactions with Toll-like receptor 3 and Toll-like receptor 8. The proposed vaccine was also subjected to an in silico cloning approach, which confirmed its expression efficiency. These analyses suggest that the proposed vaccine can elicit specific immune responses against HCV; however, experimental validation is required to confirm the safety and immunogenicity profile of the proposed vaccine construct.