Surface proteome mining for identification of potential vaccine candidates against Campylobacter jejuni: an in silico approach.

Campylobacter jejuni remains a major cause of human gastroenteritis with estimated annual incidence rate of 450 million infections worldwide. C. jejuni is a major burden to public health in both socioeconomically developing and industrialized nations. Virulence determinants involved in C. jejuni pathogenesis are multifactorial in nature and not yet fully understood. Despite the completion of the first C. jejuni genome project in 2000, there are currently no vaccines in the market against this pathogen. Traditional vaccinology approach is an arduous and time extensive task. Omics techniques coupled with sequencing data have engaged researcher's attention to reduce the time and resources applied in the process of vaccine development. Recently, there has been remarkable increase in development of in silico analysis tools for efficiently mining biological information obscured in the genome. In silico approaches have been crucial for combating infectious diseases by accelerating the pace of vaccine development. This study employed a range of bioinformatics approaches for proteome scale identification of peptide vaccine candidates. Whole proteome of C. jejuni was investigated for varied properties like antigenicity, allergenicity, major histocompatibility class (MHC)-peptide interaction, immune cell processivity, HLA distribution, conservancy, and population coverage. Predicted epitopes were further tested for binding in MHC groove using computational docking studies. The predicted epitopes were conserved; covered more than 80 % of the world population and were presented by MHC-I supertypes. We conclude by underscoring that the epitopes predicted are believed to expedite the development of successful vaccines to control or prevent C. jejuni infections albeit the results need to be experimentally validated.

Deciphering novel common gene signatures for rheumatoid arthritis and systemic lupus erythematosus by integrative analysis of transcriptomic profiles.

Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are the two highly prevalent debilitating and sometimes life-threatening systemic inflammatory autoimmune diseases. The etiology and pathogenesis of RA and SLE are interconnected in several ways, with limited knowledge about the underlying molecular mechanisms. With the motivation to better understand shared biological mechanisms and determine novel therapeutic targets, we explored common molecular disease signatures by performing a meta-analysis of publicly available microarray gene expression datasets of RA and SLE. We performed an integrated, multi-cohort analysis of 1088 transcriptomic profiles from 14 independent studies to identify common gene signatures. We identified sixty-two genes common among RA and SLE, out of which fifty-nine genes (21 upregulated and 38 downregulated) had similar expression profiles in the diseases. However, antagonistic expression profiles were observed for ACVR2A, FAM135A, and MAPRE1 genes. Thirty genes common between RA and SLE were proposed as robust gene signatures, with persistent expression in all the studies and cell types. These gene signatures were found to be involved in innate as well as adaptive immune responses, bone development and growth. In conclusion, our analysis of multicohort and multiple microarray datasets would provide the basis for understanding the common mechanisms of pathogenesis and exploring these gene signatures for their diagnostic and therapeutic potential.

Immunoinformatics and Epitope Prediction.

With advancements in sequencing technologies, vast amount of experimental data has accumulated. Due to rapid progress in the development of bioinformatics tools and the accumulation of data, immunoinformatics or computational immunology emerged as a special branch of bioinformatics which utilizes bioinformatics approaches for understanding and interpreting immunological data. One extensively studied aspect of applied immunology involves using available databases and tools for prediction of B- and T-cell epitopes. B and T cells comprise two arms of adaptive immunity.This chapter first reviews the methodology we used for computational identification of B- and T-cell epitopes against enterotoxigenic Escherichia coli (ETEC). Then we discuss other databases of epitopes and analysis tools for T-cell and B-cell epitope prediction and vaccine design. The predicted peptides were analyzed for conservation and population coverage. HLA distribution analysis for predicted epitopes identified efficient MHC binders. Epitopes were further tested using computational docking studies to bind in MHC-I molecule cleft. The predicted epitopes were conserved and covered more than 80% of the world population.

Molecular Dynamics Simulations of Quinolone Resistance-Associated T86I and P104S Mutations in Campylobacter jejuni gyrA: Unraveling Structural Repercussions.

gyrA is a clinically validated therapeutic drug target that catalyzes changes in DNA topology by introducing negative supercoils into DNA. The broad-spectrum quinolones are hitherto the most potent antibacterial agents targeted toward gyrA. Increasing resistance to quinolones is accompanied by several mutations in the quinolone resistance-determining region of gyrA. In the present study, we report a comprehensive picture of the dynamic behavior of wild-type, T86I, and T86I/P104S mutants (MTs) of gyrA from Campylobacter jejuni in complex with ciprofloxacin to unravel the atomistic details of the mechanism underlying resistance to quinolones. Our simulation results reveal that no substantial conformational changes were observed. It was observed that these mutations disrupted residue interaction network landscape to a significant extent, which would affect ligand binding affinity. A distinctive pattern of dominant motions was clearly discernible in wild and MT gyrA forms. The results reported in this study associate gyrA mutations with quinolone resistance and would pave a way for facilitating wet laboratory researchers to develop gyrA MT-based therapeutic strategies against resistant pathogens.

Tapping into Salmonella typhimurium LT2 genome in a quest to explore its therapeutic arsenal: A metabolic network modeling approach.

S. typhimurium, the classical broad-host-range serovar is a widely distributed cause of food-borne illness. Escalating antibiotic resistance and potential of conjugal transmission to other pathogens attributable to its broad spectrum host specificities have aided S. typhimurium to emerge as a global health threat. To keep pace with ever evolving bacterial defenses, there is dire need to restock the antibiotic pipeline. Genome scale metabolic reconstructions present immense possibilities to decipher physiological properties of an organism using constraint-based methods The systems-level approaches of genome scale metabolic networks interrogation open up new avenues of drug target identification against deadly infectious diseases. We performed flux balance analysis and minimization of metabolic adjustment studies of genome scale reconstruction model of S. typhimurium targeted at identifying large number of metabolites with a potential to be utilized as therapeutic drug targets. These constraint based approaches initially predict a set of genes indispensable to bacterial survival by performing gene knockout studies which are then prioritized through a multistep process. Metabolites involved in l-rhamnose biosynthesis, peptidoglycan biosynthesis, fatty acid biosynthesis, and folate biosynthesis pathways were prioritized as candidate drug targets. This study provides a general therapeutic approach which can be effectively applied to other pathogens as well.

Structural signature of Ser83Leu and Asp87Asn mutations in DNA gyrase from enterotoxigenic Escherichia coli and impact on quinolone resistance.

Enterotoxigenic Escherichia coli (ETEC) is among the most frequent microorganisms causing traveler's diarrhea (TD). Quinolones are potent antimicrobial agents used for the treatment of TD. Resistance to quinolones is typically caused by substitutions in QRDR region of gyrA subunit of DNA gyrase. The aim of this study was to seek insights into the effect of these substitutions at structural level and their association with observed quinolone resistance. Majority of the ETEC strains have gyrA mutations at amino acid position 83 and 87. To understand the quinolone resistance mechanism at molecular level, we have studied the interaction of wild type and mutant forms of ETEC gyrA with nalidixic acid and ciprofloxacin by molecular modeling using Discovery Studio and LeadIt. All the mutants had reduced affinity towards both ciprofloxacin and nalidixic acid relative to the wild type due to the mutations introduced in gyrA. Besides Ser83 and Asp87, for nalidixic acid binding Arg91 and His45 residues were observed to be critical while in ciprofloxacin binding Lys42 and Arg91 residues played a significant role. Amino acid substitutions contribute to the emergence of drug resistance in sensitive strains by causing structural alterations leading to reduced affinity of the drug towards receptor. Analysis of the effect of amino acid substitutions at structural level is of utmost importance to establish possible associations between mutations and the diseases. These studies accelerate the identification of pharmaceutical targets for relevant treatments and could also be helpful in guiding the design of further experimental research.

Identification of epitope-based peptide vaccine candidates against enterotoxigenic Escherichia coli: a comparative genomics and immunoinformatics approach.

Enterotoxigenic Escherichia coli (ETEC) associated diarrhea remains a global killer with an estimated annual incidence rate of 840 million infections and 3 800 000 deaths worldwide. There are no vaccines available for ETEC and the traditional vaccine development approach is arduous and time consuming. Thus, alternative in silico approaches for epitope prediction have engrossed the interest of researchers to reduce resources and time of vaccine development. Computational approaches are playing a crucial role in fighting against rapidly growing infectious organisms. In this study we employed an integrated comparative genomics and immunoinformatics approach for proteome scale identification of peptide vaccine candidates. The proteins shared between both ETEC E24377A and H10407 strains, but lacking in commensal E. coli SE11, were subjected to immunoinformatics analysis. For a protein pool shared between different pathogenic ETEC strains, we investigated varied physicochemical and immunogenic properties to prioritize potential vaccine candidates. Epitopes were further tested using docking studies to bind in the MHC-I binding cleft. Predicted epitopes provided more than a 95% population coverage in diarrhea endemic regions presented by major MHC-I supertypes, and bind efficiently to a MHC molecule. We conclude by accentuating that the epitopes predicted in this study are believed to accelerate the development of successful vaccines to control or prevent ETEC infections, albeit the results require experimental validation using model organisms. This study underscores that in silico approaches, together with omics data, hold great potential to be utilized for rapid and reliable genome-wide screening for identification of vaccine candidates against devastating infectious diseases.

Travelers' Diarrhea-Associated Enterotoxigenic Escherichia coli gyrA Mutants and Quinolone Antibiotic Affinity: A Molecular Dynamics Simulation and Residue Interaction Network Analysis.

Travelers' diarrhea (TD) is one of the most important global health issues among international travelers, especially those traveling to developing countries. Enterotoxigenic Escherichia coli (ETEC) is the most frequently isolated pathogen causing TD. Although TD is self-limiting, it is also severely incapacitating. Ciprofloxacin is one of the standard quinolone antibiotics used in patients with TD. However, the alarming levels of antibiotic resistance have necessitated the urgent need to identify new drugs. The drug target of ciprofloxacin is gyrA, which is exclusively present in prokaryotes and essential for bacterial survival. Several mutations in the quinolone resistance-determining region of gyrA are associated with increased quinolone resistance in vivo, which accounts for reduced affinity toward quinolones. To understand the molecular events underlying the mechanism of drug resistance, we report, for the first time to the best of our knowledge, the structural and dynamic effects of mutations in ETEC gyrA on ciprofloxacin affinity in comparison with the wild-type protein. Our simulations reveal that mutations significantly alter the gyrA residue interaction network and overall pattern of global dominant motions in major distinctive domains of N-terminal regions of gyrA. This study therefore provides important insights for the design of more potent antibacterial agents with high ligand efficacy for treating drug-resistant bacterial infections, including the TD that continues to adversely impact global health and international travel.

DBDiaSNP: An Open-Source Knowledgebase of Genetic Polymorphisms and Resistance Genes Related to Diarrheal Pathogens.

Diarrhea is a highly common infection among children, responsible for significant morbidity and mortality rate worldwide. After pneumonia, diarrhea remains the second leading cause of neonatal deaths. Numerous viral, bacterial, and parasitic enteric pathogens are associated with diarrhea. With increasing antibiotic resistance among enteric pathogens, there is an urgent need for global surveillance of the mutations and resistance genes primarily responsible for resistance to antibiotic treatment. Single Nucleotide Polymorphisms are important in this regard as they have a vast potential to be utilized as molecular diagnostics for gene-disease or pharmacogenomics association studies linking genotype to phenotype. DBDiaSNP is a comprehensive repository of mutations and resistance genes among various diarrheal pathogens and hosts to advance breakthroughs that will find applications from development of sequence-based diagnostic tools to drug discovery. It contains information about 946 mutations and 326 resistance genes compiled from literature and various web resources. As of March 2015, it houses various pathogen genes and the mutations responsible for antibiotic resistance. The pathogens include, for example, DEC (Diarrheagenic E.coli), Salmonella spp., Campylobacter spp., Shigella spp., Clostridium difficile, Aeromonas spp., Helicobacter pylori, Entamoeba histolytica, Vibrio cholera, and viruses. It also includes mutations from hosts (e.g., humans, pigs, others) that render them either susceptible or resistant to a certain type of diarrhea. DBDiaSNP is therefore intended as an integrated open access database for researchers and clinicians working on diarrheal diseases. Additionally, we note that the DBDiaSNP is one of the first antibiotic resistance databases for the diarrheal pathogens covering mutations and resistance genes that have clinical relevance from a broad range of pathogens and hosts. For future translational research involving integrative biology and global health, the database offers veritable potentials, particularly for developing countries and worldwide monitoring and personalized effective treatment of pathogens associated with diarrhea. The database is accessible on the public domain at http://www.juit.ac.in/attachments/dbdiasnp/ .

Novel Drug Targets for Food-Borne Pathogen Campylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study.

Campylobacters are a major global health burden and a cause of food-borne diarrheal illness and economic loss worldwide. In developing countries, Campylobacter infections are frequent in children under age two and may be associated with mortality. In developed countries, they are a common cause of bacterial diarrhea in early adulthood. In the United States, antibiotic resistance against Campylobacter is notably increased from 13% in 1997 to nearly 25% in 2011. Novel drug targets are urgently needed but remain a daunting task to accomplish. We suggest that omics-guided drug discovery is timely and worth considering in this context. The present study employed an integrated subtractive genomics and comparative metabolic pathway analysis approach. We identified 16 unique pathways from Campylobacter when compared against H. sapiens with 326 non-redundant proteins; 115 of these were found to be essential in the Database of Essential Genes. Sixty-six proteins among these were non-homologous to the human proteome. Six membrane proteins, of which four are transporters, have been proposed as potential vaccine candidates. Screening of 66 essential non-homologous proteins against DrugBank resulted in identification of 34 proteins with drug-ability potential, many of which play critical roles in bacterial growth and survival. Out of these, eight proteins had approved drug targets available in DrugBank, the majority serving crucial roles in cell wall synthesis and energy metabolism and therefore having the potential to be utilized as drug targets. We conclude by underscoring that screening against these proteins with inhibitors may aid in future discovery of novel therapeutics against campylobacteriosis in ways that will be pathogen specific, and thus have minimal toxic effect on host. Omics-guided drug discovery and bioinformatics analyses offer the broad potential for veritable advances in global health relevant novel therapeutics.

Genome-wide analysis of the heat stress response in Zebu (Sahiwal) cattle.

Environmental-induced hyperthermia compromises animal production with drastic economic consequences to global animal agriculture and jeopardizes animal welfare. Heat stress is a major stressor that occurs as a result of an imbalance between heat production within the body and its dissipation and it affects animals at cellular, molecular and ecological levels. The molecular mechanism underlying the physiology of heat stress in the cattle remains undefined. The present study sought to evaluate mRNA expression profiles in the cattle blood in response to heat stress. In this study we report the genes that were differentially expressed in response to heat stress using global scale genome expression technology (Microarray). Four Sahiwal heifers were exposed to 42°C with 90% humidity for 4h followed by normothermia. Gene expression changes include activation of heat shock transcription factor 1 (HSF1), increased expression of heat shock proteins (HSP) and decreased expression and synthesis of other proteins, immune system activation via extracellular secretion of HSP. A cDNA microarray analysis found 140 transcripts to be up-regulated and 77 down-regulated in the cattle blood after heat treatment (P<0.05). But still a comprehensive explanation for the direction of fold change and the specific genes involved in response to acute heat stress still remains to be explored. These findings may provide insights into the underlying mechanism of physiology of heat stress in cattle. Understanding the biology and mechanisms of heat stress is critical to developing approaches to ameliorate current production issues for improving animal performance and agriculture economics.