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Viral infections are the cause of serious infirmities in humans and kill millions of people every year. Management of viral diseases is one of the challenges faced by the whole world which needs improvement in prevention and treatment options. Complete understanding of the consequences of viral proteins interaction network on host physiology is essential. Towards this goal, deciphering viral protein-protein interactions is one of the perspective which can help in our understanding about the basis of viral pathogenesis and the development of new antivirals. Indeed, viral infection network based on viral-viral proteins will provide an elusive and investigative framework to articulate rationalize drug discovery based on proteomics scale of viruses. In this study, proteomics a collection of viral-viral protein interactions reporting different studies of Hepatitis C virus, Influenza A virus, Dengue virus and SARS Coronavirus. Our effort of protein-interactions was focused on different studies reporting interactions between viral proteins encoded by the viruses under study. The study is integrated with a broad and original literature-curated data of viral-viral protein (197 non-redundant) interactions.

Helicobacter pylori (H. pylori) has evolved significant regulatory mechanisms in order to acclimatize in extreme gastric environment of human beings. The virulence machinery of H. pylori is complicated as virulence factors of pathogen not only interact with transcription and translational machinery of host, but also are involved in the progression and development of the disease. The present study is an effort to model virulence mechanism in H. pylori, particularly ferric uptake regulator (FUR) under acidic and iron (Fe) depleted conditions, as well as its effects on the well known virulence factors cytotoxin-associated gene A (cagA) and vacuolating cytotoxin A (vacA) gene. The virulence regulatory network of cagA and vacA is modeled based on an asynchronous kinetic logic formalism introduced by René Thomas. The cagA-vacA virulence regulatory network is then elaborated qualitatively to obtain insights into H. pylori induced pathogenesis. The findings have revealed the significant regulatory pathways through which H. pylori spreads infection to the gastric cells, and also verified that cagA is associated with acute gastritis while vacA is involved in vacuolation, apoptosis and atrophy. Interestingly, both cagA and vacA were found to modulate each other virulence potential which ultimately leads to the state of chrHelicobacter pylori (H. pylori) has evolved significant regulatory mechanisms in order to acclimatize in extreme gastric environment of human beings. The virulence machinery of H. pylori is complicated as virulence factors of pathogen not only interact with transcription and translational machinery of host, but also are involved in the progression and development of the disease. The present study is an effort to model virulence mechanism in H. pylori, particularly ferric uptake regulator (FUR) under acidic and iron (Fe) depleted conditions, as well as its effects on the well known virulence factors cytotoxin-associated gene A (cagA) and vacuolating cytotoxin A (vacA) gene. The virulence regulatory network of cagA and vacA is modeled based on an asynchronous kinetic logic formalism introduced by René Thomas. The cagA-vacA virulence regulatory network is then elaborated qualitatively to obtain insights into H. pylori induced pathogenesis. The findings have revealed the significant regulatory pathways through which H. pylori spreads infection to the gastric cells, and also verified that cagA is associated with acute gastritis while vacA is involved in vacuolation, apoptosis and atrophy. Interestingly, both cagA and vacA were found to modulate each other virulence potential which ultimately leads to the state of chronic gastritis; which in turn drives the pathway smoothly towards gastric adenocarcinoma via the formation of pre-malignant lesions. The proposed strategy can be extended to understand the mechanism of other similar bacterial infections and disease progression. It will also help in the prioritization of potential therapeutic targets to control such serious infections.onic gastritis; which in turn drives the pathway smoothly towards gastric adenocarcinoma via the formation of pre-malignant lesions. The proposed strategy can be extended to understand the mechanism of other similar bacterial infections and disease progression. It will also help in the prioritization of potential therapeutic targets to control such serious infections.

Circular RNAs (circRNAs) are ubiquitously expressed, covalently closed rings, produced by pre-mRNA splicing in a reversed order during post-transcriptional processing. Circularity endows 3'-5'-linked circRNAs with stability and resistance to exonucleolytic degradation which raises the question whether circRNAs may be relevant as potential therapeutic targets or agents. High stability in biological systems is the most remarkable property and a major criterion for why circRNAs could be exploited for a range of RNA-centred medical applications. Even though various biological roles and regulatory functions of circRNAs have been reported, their in-depth study is challenging because of their circular structure and sequence-overlap with linear mRNA counterparts. Moreover, little is known about their role in viral infections and in antiviral immune responses. We believe that an in-depth and detailed understanding of circRNA mediated viral protein regulations will increase our knowledge of the biology of these novel molecules. In this review, we aimed to provide a comprehensive basis and overview on the biogenesis, significance and regulatory roles of circRNAs in the context of antiviral immune responses and viral infections including hepatitis C virus infection, hepatitis B virus infection, hepatitis delta virus infection, influenza A virus infection, Epstein-Barr virus infection, kaposi's sarcoma herpesvirus infection, human cytomegalovirus infection, herpes simplex virus infection, human immunodeficiency virus infection, porcine epidemic diarrhoea virus infection, ORF virus infection, avian leukosis virus infection, simian vacuolating virus 40 infection, transmissible gastroenteritis coronavirus infection, and bovine viral diarrhoea virus infection. We have also discussed the critical regulatory role of circRNAs in provoking antiviral immunity, providing evidence for implications as therapeutic agents and as diagnostic markers.

Many PTMs dysregulation is known to be the major cause of many cancers including HCV induced HCC. PTMs of hepatitis C virus (HCV) regions NS3/4A, NS5A and NS5B are crucial for proper protein functions and replication that directly affect the generation of infectious virus particles and completion of its life cycle. In this study, we have performed comprehensive analysis of PTMs within HCV non-structural proteins (NS3/4A, NS5A and NS5B) through bioinformatics analysis to examine post-translational crosstalk between phosphorylation, palmitoylation, methylation, acetylation and ubiquitination sites in selected viral proteins. Our analysis has revealed many highly putative PTMs sites that are also conserved among major genotypes conferring the importance of these sites. We have also analysed viral 3D structures in their modified and unmodified forms to address extent and signatures of structural changes upon PTM. This study provides evidence that PTMs induce significant conformational changes and make viral proteins more stable. To find the potential role of PTMs in HCV induced HCC, docking analysis between selected viral proteins and p38-MAPK has been performed which also confirms their strong association with HCV induced HCC. The major findings proposed that PTMs at specific sites of HCV viral proteins could dysregulate specific pathways that cause the development of HCC.

An in-depth analysis of first wave SARS-CoV-2 genome is required to identify various mutations that significantly affect viral fitness. In the present study, we have performed comprehensive in-silico mutational analysis of 3C-like protease (3CLpro), RNA dependent RNA polymerase (RdRp), and spike (S) proteins with the aim of gaining important insights into first wave virus mutations and their functional and structural impact on SARS-CoV-2 proteins. Our integrated analysis gathered 3465 SARS-CoV-2 sequences and identified 92 mutations in S, 37 in RdRp, and 11 in 3CLpro regions. The impact of those mutations was also investigated using various in silico approaches. Among these 32 mutations in S, 15 in RdRp, and 3 in 3CLpro proteins are found to be deleterious in nature and could alter the structural and functional behavior of the encoded proteins. D614G mutation in spike and P323L in RdRp are the globally dominant variants with a high frequency. Most of them have also been found in the binding moiety of the viral proteins which determine their critical involvement in the host-pathogen interactions and drug targets. The findings of the current study may facilitate better understanding of COVID-19 diagnostics, vaccines, and therapeutics.

Transforming growth factor beta1 (TGFβ1) and matrix metalloproteinase-9 (MMP-9) have been associated with migration and invasion in hepatocellular carcinoma (HCC). Recent studies have suggested a positive feedback loop between TGFβ1 and MMP-9 mediated by the PI3K signaling pathway that confers acquired invasion and metastasis in HCC via induction of the epithelial-mesenchymal transition (EMT), which grows into invasive carcinoma. But the potential molecular mechanism of this loop on HCC has not been clarified yet. Therefore, this study is designed to explore the association between the two entities and their key determinants such as NFκB, TIMP-1, and hepatic stellate cells (HSCs). Hence, a qualitative modeling framework is implemented that predict the role of biological regulatory network (BRN) during recovery and HCC metastasis. Qualitative modeling predicts discrete trajectories, stable states, and cycles that highlight the paths leading to disease recovery and homeostasis, respectively. The deadlock stable state (1, 1, 1, 1, 1) predicts high expression of all the entities in the BRN, resulting in the progression of HCC. The qualitative model predicts 30 cycles representing significant paths leading to recovery and homeostasis and amongst these the most significant discrete cycle was selected based on the highest betweenness centralities of the discrete states. We further verified our model with network modeling and simulation analysis based on petri net modeling approach. The BRN dynamics were analyzed over time. The results implied that over the course of disease condition or homeostasis, the biological entities are activated in a variable manner. Taken together, our findings suggest that the TGFβ1 and the MMP-9 feedback loop is critical in tumor progression, as it may aid in the development of treatment strategies that are designed to target both TGFβ and MMP-9.

The rapidly evolving sequencing techniques have made it easier for microbiologists to sequence bacterial species from various isolates, regions, and hosts. This yields a high number of genome sequences of the same pathogen. The pan-genome analysis yields accumulated gene pool of all the strains/isolates of a particular species. Getting insights into the total gene repertoire, biologists can decipher the microevolution of bacterial virulence mechanisms to combat deadly diseases caused by them. Predicted pan/core genomes can be easily exploited through reverse vaccinology techniques to identify novel and putative therapeutic targets. Reverse vaccinology is actually a knowledge-based approach following several filtration steps to generate a catalog of potential antigens with their respective epitopes for vaccine development. While, procuring a portfolio of exclusively virulent, essential core genes, genes categorization, pathway annotation; understanding the interactions of these genes with drugs, presents a novel mechanism to combat pathogens by in silico drug target identification. Predicted vaccine candidates and putative drug targets can be verified in animal models henceforth minimizing the total time and cost of the drug and vaccine development processes.

Cell-to-cell variation in genotypic/phenotypic traits is a common phenomenon and universal feature of multicellular organisms. Even within any single tissue, phenotypically different population of cells exist, all of which signifies diverse manifestations of that tissue type. This cellular heterogeneity plays a vital role in cellular decision-making and may be well understood with single-cell analysis. Single-cell omics is an emerging frontier of the biological sciences that can help to differentiate clearly between the behaviors of cells and their subpopulations. The overall behavior of genomes, transcriptomes, and proteomes aid the exploration of diverse behavior of cells in different situations. Systems biology, on the other hand, seeks to quantitatively understand the dynamic interactions between various biological components by employing omics and single-cell biology. A systems-level understanding of single-cell has the ability to design, record, and model massive amounts of data at the omics level. Despite substantial advances, considerable challenges, including the features of biological systems, systematic noise, and the complexity and sparsity of the data remain in the scrutinization, integration, and interpretation of single-cell omics. Therefore, there is a need for a fundamental paradigm shift toward scrutinization of single cells both at computational and experimental levels. In this chapter, we will discuss techniques, methods, and applications of systems biology to unravel the biology of single cells via systems-level approaches to enhance our understanding of cell biology as a system. The current cutting-edge technologies with respect to computational techniques and algorithms that can aid the analysis will be discussed, along with their challenges and future prospects.

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.

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 the 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 ISGs 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.

Background A positive association between diabetes and breast cancer has been identified by various epidemiological and clinical studies. However, the possible molecular interactions between the two heterogeneous diseases have not been fully determined yet. There are several underlying mechanisms which may increase the risk of breast cancer in diabetic patients. Introduction In this study, we focused on the role of O-GlcNAc transferase (OGT) enzyme in the regulation of phosphatidylinositol-3 kinase (PI3K) pathway through activation/deactivation of Akt protein. The efficiency of insulin signaling in adipocytes is reduced as a result of OGT overexpression which further attenuates Akt signaling; as a result, the efficiency of insulin signaling is reduced by downregulation of insulin-responsive genes. On the other hand, increased expression of OGT results in Akt activation in breast cancer cells, leading to enhanced cell proliferation and inhibition of the apoptosis. However, the interplay amongst these signaling pathways is still under investigation. Methods In this study, we used Petri nets (PNs) to model and investigate the role of PI3K and OGT pathways, acting as key players in crosstalk between diabetes and breast cancer, resulting in progression of these chronic diseases. Moreover, in silico perturbation experiments were applied on the model to analyze the effects of anti-cancer agents (shRNA and BZX) and anti-diabetic drug (Metformin) on the system. Results Our PN model reflects the alterations in protein expression and behavior and the correlation between breast cancer and diabetes. The analysis proposed two combination therapies to combat breast cancer progression in diabetic patients including combination of OGTmRNA silencing and OGT inhibitor (BZX) as first combination and BZX and Metformin as the second. Conclusion The PN model verified that alterations in O-GlcNAc signaling affect both insulin resistance and breast cancer. Moreover, the combination therapy for breast cancer patients consisting of anti-diabetic drugs such as Metformin along with OGT inhibitors, for example BZX, can produce better treatment regimens.

A regulated immune system employs multiple cell types, diverse variety of cytokines and interacting signalling networks against infections. Systems biology offers a promising solution to model and simulate such large populations of interacting components of immune systems holistically. This study focuses on the distinct components of the adaptive immune system and analysis, both individually and in association with HCV infection. The effective and failed adaptive immune response models have been developed followed by interventions/perturbations of various treatment strategies to get better assessment of the treatment responses under varying stimuli. Based on the model predictions, the NK cells, T regulatory cells, IL-10, IL-21, IL-12, IL-2 entities are found to be the most critical determinants of treatment response. The proposed potential immunomodulatory therapeutic interventions include IL-21 treatment, blocking of inhibitory receptors on T-cells and exogenous anti-IL-10 antibody treatment. The relative results showed that these interventions have differential effect on the expression levels of cellular and cytokines entities of the immune response. Notably, IL-21 enhances the expression of NK cells, Cytotoxic T lymphocytes and CD4+ T cells and hence restore the host immune potential. The models presented here provide a starting point for cost-effective analysis and more comprehensive modeling of biological phenomenon.

Among solid tumors, hepatocellular carcinoma (HCC) emerges as a prototypical therapy-resistant tumor. Considering the emerging sorafenib resistance crisis in HCC, future studies are urgently required to overcome resistance. Recently noncoding RNAs (ncRNAs) have emerged as significant regulators in signalling pathways involved in cancer drug resistance and pharmacologically targeting these ncRNAs might be a novel stratagem to reverse drug resistance. In the current study, using a hybrid Petri net based computational model, we have investigated the harmonious effect of miR-17-92 cluster inhibitors/mimics and circular RNAs on sorafenib resistant HCC cells in order to explore potential resistance mechanisms and to identify putative targets for sorafenib-resistant HCC cells. An integrated model was developed that incorporates seven miRNAs belonging to miR-17-92 cluster (hsa-miR-17-5p, hsa-miR-17-3p, hsa-miR-19a, hsa-miR-19b, hsa-miR-18a, hsa-miR-20a and hsa-miR-92) and crosstalk of two signaling pathways (EGFR and IL-6) that are differentially regulated by these miRNAs. The mechanistic connection was proposed by the correlation between members belonging to miR-17-92 cluster and corresponding changes in the protein levels of their targets in HCC, specifically those targets that have verified importance in sorafenib resistance. Current findings uncovered potential pathway features, underlining the significance of developing modulators of this cluster to combat drug resistance in HCC.

Tight junctions help prevent the passage of digestive enzymes and microorganisms through the space between adjacent epithelial cells lining. However, Helicobacter pylori encoded virulence factors negatively regulate these tight junctions and contribute to dysfunction of gastric mucosa. Here, we have predicted the regulation of important tight junction proteins, such as Zonula occludens-1, Claudin-2 and Connexin32 in the presence of pathogenic proteins. Molecular events such as post translational modifications and crosstalk between phosphorylation, O-glycosylation, palmitoylation and methylation are explored which may compromise the integrity of these tight junction proteins. Furthermore, the signaling pathways disrupted by dysregulated kinases, proteins and post-translational modifications are reviewed to design an abstracted computational model showing the situation-dependent dynamic behaviors of these biological processes and entities. A qualitative hybrid Petri Net model is therefore constructed showing the altered host pathways in the presence of virulence factor cytotoxin-associated gene A, leading to the disruption of tight junction proteins. The model is qualitative logic-based, which does not depend on any kinetic parameter and quantitative data and depends on knowledge derived from experiments. The designed model provides insights into the tight junction disruption and disease progression. Model is then verified by the available experimental data, nevertheless formal in vitro experimentation is a promising way to ensure its validation. The major findings propose that H. pylori activated kinases are responsible to trigger specific post translational modifications within tight junction proteins, at specific sites. These modifications may favor alterations in gastric barrier and provide a route to bacterial invasion into host cells.

The blood-brain barrier (BBB) poses a huge challenge and is a serious issue in deciphering the pathophysiology of central nervous system disorders. Endothelial tight junctions play an essential role in maintaining the integrity of the BBB. Post-translational modifications (PTMs) in endothelial tight junction proteins are known to cause deleterious functional impairment and possible disruptions in BBB integrity. PTMs in tight junction proteins play an important role in human immunodeficiency virus type 1 (HIV-1) entry through the BBB. Human claudin-5 is one of the highly expressed brain endothelial tight junction protein and various PTMs in claudin-5 are expected to aid HIV-1 in crossing the BBB. A precise characterization of PTMs in claudin-5 is important for understanding its role in HIV-1 brain infiltration. In this study, we have examined post-translational crosstalk between phosphorylation, O-glycosylation, palmitoylation and methylation sites in claudin-5, which could alter claudin-5's ability to maintain BBB integrity. To the best of our knowledge, this is the first report on claudin-5 protein that suggests a novel interplay between potential PTM sites. PTMs of predicted residues in claudin-5, suggested in this study, can serve as compelling targets for potential therapeutic agents against HIV-1 induced neuropathogenesis. Further site-specific experimental studies in this aspect are highly recommended.

Phenotype is the trait or observable characteristic of an organism representing its unique morphological, biochemical, physiological, or behavioral property. In the medical field, this term is generally considered to mention changes from the normal characteristics of patients. The successful differentiation of a patient's phenotype plays a key role in the accurate diagnosis of disease, its stage, and required therapeutics. However, in some cases phenotypic descriptions cannot precisely depict the nature of disease or its stage. Particularly, in case of rare diseases, this phenomenon is very prominent due to the lack of enough phenotypic descriptions. The emerging area of precision medicine tries to render the best available health care to each patient. The practice of precision medicine is largely dependent upon the classification of each disease into its subtypes. This process depends upon the collective information of clinical, genotypic, and phenotypic data. Among them phenotypic data could produce an economically feasible approach to the diagnosis of the disease or its subtypes that produce reliable foundations for the precision medicine. The purpose of this chapter is to present the current situation of phenotyping in precision medicine along with the related issues and their possible solutions.

Background With 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. Results VacSol, 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. Conclusion VacSol 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/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1540-0) contains supplementary material, which is available to authorized users.

Background Acinetobacter baumannii has emerged as a significant nosocomial pathogen during the last few years, exhibiting resistance to almost all major classes of antibiotics. Alternative treatment options such as vaccines tend to be most promising and cost effective approaches against this resistant pathogen. In the current study, we have explored the pan-genome of A. baumannii followed by immune-proteomics and reverse vaccinology approaches to identify potential core vaccine targets. Results The pan-genome of all available A. baumannii strains (30 complete genomes) is estimated to contain 7,606 gene families and the core genome consists of 2,445 gene families (~32 % of the pan-genome). Phylogenetic tree, comparative genomic and proteomic analysis revealed both intra- and inter genomic similarities and evolutionary relationships. Among the conserved core genome, thirteen proteins, including P pilus assembly protein, pili assembly chaperone, AdeK, PonA, OmpA, general secretion pathway protein D, FhuE receptor, Type VI secretion system OmpA/MotB, TonB dependent siderophore receptor, general secretion pathway protein D, outer membrane protein, peptidoglycan associated lipoprotein and peptidyl-prolyl cis-trans isomerase are identified as highly antigenic. Epitope mapping of the target proteins revealed the presence of antigenic surface exposed 9-mer T-cell epitopes. Protein-protein interaction and functional annotation have shown their involvement in significant biological and molecular processes. The pipeline is validated by predicting already known immunogenic targets against Gram negative pathogen Helicobacter pylori as a positive control. Conclusion The study, based upon combinatorial approach of pan-genomics, core genomics, proteomics and reverse vaccinology led us to find out potential vaccine candidates against A. baumannii. The comprehensive analysis of all the completely sequenced genomes revealed thirteen putative antigens which could elicit substantial immune response. The integration of computational vaccinology strategies would facilitate in tackling the rapid dissemination of resistant A.baumannii strains. The scarcity of effective antibiotics and the global expansion of sequencing data making this approach desirable in the development of effective vaccines against A. baumannii and other bacterial pathogens. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2951-4) contains supplementary material, which is available to authorized users.

Autophagy, an evolutionary conserved multifaceted lysosome-mediated bulk degradation system, plays a vital role in liver pathologies including hepatocellular carcinoma (HCC). Post-translational modifications (PTMs) and genetic variations in autophagy components have emerged as significant determinants of autophagy related proteins. Identification of a comprehensive spectrum of genetic variations and PTMs of autophagy related proteins and their impact at molecular level will greatly expand our understanding of autophagy based regulation. In this study, we attempted to identify high risk missense mutations that are highly damaging to the structure as well as function of autophagy related proteins including LC3A, LC3B, BECN1 and SCD1. Number of putative structural and functional residues, including several sites that undergo PTMs were also identified. In total, 16 high-risk SNPs in LC3A, 18 in LC3B, 40 in BECN1 and 43 in SCD1 were prioritized. Out of these, 2 in LC3A (K49A, K51A), 1 in LC3B (S92C), 6 in BECN1 (S113R, R292C, R292H, Y338C, S346Y, Y352H) and 6 in SCD1 (Y41C, Y55D, R131W, R135Q, R135W, Y151C) coincide with potential PTM sites. Our integrated analysis found LC3B Y113C, BECN1 I403T, SCD1 R126S and SCD1 Y218C as highly deleterious HCC-associated mutations. This study is the first extensive in silico mutational analysis of the LC3A, LC3B, BECN1 and SCD1 proteins. We hope that the observed results will be a valuable resource for in-depth mechanistic insight into future investigations of pathological missense SNPs using an integrated computational platform.

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-naive 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.

As every organization grows they need to ensure that processes are in place to attract, retain and develop their talent. Succession Planning and Management is a strategic process that focuses on creating a sustainable value chain of high potential talent who have the requisite knowledge and skills to lead and manage new organizational requirements and address business challenges. To manage this process they need to adapt a range of methodologies to select, measure and distinguish between those who are truly high performers with potential. Abu Dhabi Company for Onshore Oil Operations has implemented the succession planning system from way back in 2006 in preparation for significant challenges in the horizon for which the organization needed to be prepared for:. The challenges included: Growth in production targets from 1.4 Mbpd to 1.8 Mbpd by 2017 Maturing and more complex fields The need for new technology to be installed to enhance recovery and oil extraction The emerging crew change brought forth by aging manpower and experience dilution and the steep learning curve for newcomers. Reaching the Emiratization target (75%) by 2017, Implementing a succession plan process within the company was seen as one of the significant intitiatives to support the development of technical capabilities and leadership capacity to address these challenges.

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.