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

Pantothenate kinase: A promising therapeutic target against pathogenic Clostridium species

Current treatment of clostridial infections includes broad-spectrum antibiotics and antitoxins, yet antitoxins are ineffective against all Clostridiumspecies. Moreover, rising antimicrobial resistance (AMR) threatens treatment effectiveness and public health. This study therefore aimed to discover a common drug target for four pathogenic clostridial species, Clostridium botulinum, C. difficile, C. tetani, and C. perfringens through an in-silico core genomic approach. Using four reference genomes of C. botulinum, C. difficile, C. tetani, and C. perfringens, we identified 1484 core genomic proteins (371/genome) and screened them for potential drug targets. Through a subtractive approach, four core proteins were finally identified as drug targets, represented by type III pantothenate kinase (CoaX) and, selected for further analyses. Interestingly, the CoaX is involved in the phosphorylation of pantothenate (vitamin B5), which is a critical precursor for coenzyme A (CoA) biosynthesis. Investigation of druggability analysis on the identified drug target reinforces CoaX as a promising novel drug target for the selected Clostridium species. During the molecular screening of 1201 compounds, a known agonist drug compound (Vibegron) showed strong inhibitory activity against targeted clostridial CoaX. Additionally, we identified tazobactam, a beta-lactamase inhibitor, as effective against the newly proposed target, CoaX. Therefore, identifying CoaX as a single drug target effective against all four clostridial pathogens presents a valuable opportunity to develop a cost-effective treatment for multispecies clostridial infections.

DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19

Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.

Genome-wide Meta-analysis Reveals New Gene Signatures and Potential Drug Targets of Hypertension

The prevalence of hypertension reported around the world is increasing and is an important public health challenge. This study was designed to explore the disease's genetic variations and to identify new hypertension-related genes and target proteins. We analyzed 22 publicly available Affymetrix cDNA datasets of hypertension using an integrated system-level framework involving differential expression genetic (DEG) analysis, data mining, gene enrichment, protein-protein interaction, microRNA analysis, toxicogenomics, gene regulation, molecular docking, and simulation studies. We found potential DEGs after screening out the extracellular proteins. We studied the functional role of seven shortlisted DEGs (ADM, EDN1, ANGPTL4, NFIL3, MSR1, CEBPD, and USP8) in hypertension after disease gene curation analysis. The expression profiling and cluster analysis showed significant variations and enriched GO terms. hsa-miR-365a-3p, hsa-miR-2052, hsa-miR-3065-3p, hsa-miR-603, hsa-miR-7113-3p, hsa-miR-3923, and hsa-miR-524-5p were identified as hypertension-associated miRNA targets for each gene using computational algorithms. We found functional interactions of source DEGs with target and important gene signatures including EGFR, AGT, AVP, APOE, RHOA, SRC, APOB, STAT3, UBC, LPL, APOA1, and AKT1 associated with the disease. These DEGs are mainly involved in fatty acid metabolism, myometrial pathways, MAPK, and G-alpha signaling pathways linked with hypertension pathogenesis. We predicted significantly disordered regions of 71.2, 48.8, and 45.4% representing the mutation in the sequence of NFIL3, USP8, and ADM, respectively. Regulation of gene expression was performed to find upregulated genes. Molecular docking analysis was used to evaluate Food and Drug Administration-approved medicines against the four DEGs that were overexpressed. For each elevated target protein, the three best drug candidates were chosen. Furthermore, molecular dynamics (MD) simulation using the target's active sites for 100 ns was used to validate these 12 complexes after docking. This investigation establishes the worth of systems genetics for finding four possible genes as potential drug targets for hypertension. These network-based approaches are significant for finding genetic variant data, which will advance the understanding of how to hasten the identification of drug targets and improve the understanding regarding the treatment of hypertension.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

The Epidemiological and Pangenome Landscape of Staphylococcus aureus and Identification of Conserved Novel Candidate Vaccine Antigens

Background and Objective:

Staphylococcus aureus (S. aureus) is a gram-positive bacterium and one of the major nosocomial pathogen. It has the ability to acquire resistance against almost all available classes of antibiotics; Methicillin-Resistant S. aureus (MRSA) is a well-known antibiotic resistance. S. aureus is a globally distributed pathogen that need in-depth epidemiological and genomic level investigation for proper treatment and prevention.

Methods:

To explore the genomic epidemiology of S. aureus in-silico Multi Locus Sequence Typing (MLST) was carried out for 355 complete genomes. Diversity within the species was investigated through pan-genome analysis and subtractive genomic approach was employed for identification of core immunogenic targets.

Results:

Epidemiological study identified 62 different sequence types (STs) of S. aureus distributed worldwide, in which ST-8, ST-5, ST-398, ST-239, and ST-30 are the most dominant STs comprising more than 50% of the isolates. The pan-genome of S. aureus is still open with 7,199 genes and there is a major contribution (80%) of MRSA strains in the S. aureus species pangenome. The core genome (2,025 genes) of S. aureus is almost stable (comprises of 72% of S. aureus genome size) while accessory and unique genes (28% of S. aureus genome size) are gradually increasing. Screening of 2,025 core genes identified putative vaccine candidates. The best scoring and dominant B-cell and T-cell epitopes were predicted out of the selected potential vaccine candidate proteins with the help of a multi-step screening procedure.

Conclusion:

We believe that the current study will provide insight into the genetic epidemiology and diversity of S. aureus and the predicted epitopes against the pathogen can be tested further for its immunological responses within the host and may provide both humoral and cellular immunity against the disease.

A meta-analysis of genome-wide gene expression differences identifies promising targets for type 2 diabetes mellitus

AIMS/INTRODUCTION

Due to the heterogeneous nature of type 2 diabetes mellitus and its complex effects on hemodynamics, there is a need to identify new candidate markers which are involved in the development of type 2 diabetes mellitus (DM) and can serve as potential targets. As the global diabetes prevalence in 2019 was estimated as 9.3% (463 million people), rising to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045, the need to limit this rapid prevalence is of concern. The study aims to identify the possible biomarkers of type 2 diabetes mellitus with the help of the system biology approach using R programming.

MATERIALS AND METHODS

Several target proteins that were found to be associated with the source genes were further curated for their role in type 2 diabetes mellitus. The differential expression analysis provided 50 differentially expressed genes by pairwise comparison between the biologically comparable groups out of which eight differentially expressed genes were short-listed. These DEGs were as follows: MCL1, PTX3, CYP3A4, PTGS1, SSTR2, SERPINA3, TDO2, and GALNT7.

RESULTS

The cluster analysis showed clear differences between the control and treated groups. The functional relationship of the signature genes showed a protein-protein interaction network with the target protein. Moreover, several transcriptional factors such as DBX2, HOXB7, POU3F4, MSX2, EBF1, and E4F1 showed association with these identified differentially expressed genes.

CONCLUSIONS

The study highlighted the important markers for diabetes mellitus that have shown interaction with other proteins having a role in the progression of diabetes mellitus that can serve as new targets in the management of DM.

Cisplatin's potential for type 2 diabetes repositioning by inhibiting CDKN1A, FAS, and SESN1

Cisplatin is a DNA-damaging chemotherapeutic agent used for treating cancer. Based on cDNA dataset analysis, we investigated how cisplatin modified gene expression and observed cisplatin-induced dysregulation and system-level variations relating to insulin resistance and type 2 diabetes mellitus (T2DM). T2DM is a multifactorial disease affecting 462 million people in the world, and drug-induced T2DM is a serious issue. To understand this etiology, we designed an integrative, system-level study to identify associations between cisplatin-induced differentially expressed genes (DEGs) and T2DM. From a list of differential expressed genes, cisplatin downregulated the cyclin-dependent kinase inhibitor 1 (CDKN1A), tumor necrosis factor (FAS), and sestrin-1 (SESN1) genes responsible for modifying signaling pathways, including the p53, JAK-STAT, FOXO, MAPK, mTOR, P13-AKT, Toll-like receptor (TLR), adipocytokine, and insulin signaling pathways. These enriched pathways were expressively associated with the disease. We observed significant gene signatures, including SMAD3, IRS, PDK1, PRKAA1, AKT, SOS, RAS, GRB2, MEK1/2, and ERK, interacting with source genes. This study revealed the value of system genetics for identifying the cisplatin-induced genetic variants responsible for the progression of T2DM. Also, by cross-validating gene expression data for T2DM islets, we found that downregulating IRS and PRK families is critical in insulin and T2DM signaling pathways. Cisplatin, by inhibiting CDKN1A, FAS, and SESN1, promotes IRS and PRK activity in a similar way to rosiglitazone (a popular drug used for T2DM treatment). Our integrative, network-based approach can help in understanding the drug-induced pathophysiological mechanisms of diabetes.

Genome-scale meta-analysis of breast cancer datasets identifies promising targets for drug development

Background

Because of the highly heterogeneous nature of breast cancer, each subtype differs in response to several treatment regimens. This has limited the therapeutic options for metastatic breast cancer disease requiring exploration of diverse therapeutic models to target tumor specific biomarkers.

Methods

Differentially expressed breast cancer genes identified through extensive data mapping were studied for their interaction with other target proteins involved in breast cancer progression. The molecular mechanisms by which these signature genes are involved in breast cancer metastasis were also studied through pathway analysis. The potential drug targets for these genes were also identified.

Results

From 50 DEGs, 20 genes were identified based on fold change and p-value and the data curation of these genes helped in shortlisting 8 potential gene signatures that can be used as potential candidates for breast cancer. Their network and pathway analysis clarified the role of these genes in breast cancer and their interaction with other signaling pathways involved in the progression of disease metastasis. The miRNA targets identified through miRDB predictor provided potential miRNA targets for these genes that can be involved in breast cancer progression. Several FDA approved drug targets were identified for the signature genes easing the therapeutic options for breast cancer treatment.

Conclusion

The study provides a more clarified role of signature genes, their interaction with other genes as well as signaling pathways. The miRNA prediction and the potential drugs identified will aid in assessing the role of these targets in breast cancer.

Systems-level differential gene expression analysis reveals new genetic variants of oral cancer

Oral cancer (OC) ranked as eleventh malignancy worldwide, with the increasing incidence among young patients. Limited understanding of complications in cancer progression, its development system, and their interactions are major restrictions towards the progress of optimal and effective treatment strategies. The system-level approach has been designed to explore genetic complexity of the disease and to identify novel oral cancer related genes to detect genomic alterations at molecular level, through cDNA differential analysis. We analyzed 21 oral cancer-related cDNA datasets and listed 30 differentially expressed genes (DEGs). Among 30, we found 6 significant DEGs including CYP1A1, CYP1B1, ADCY2, C7, SERPINB5, and ANAPC13 and studied their functional role in OC. Our genomic and interactive analysis showed significant enrichment of xenobiotics metabolism, p53 signaling pathway and microRNA pathways, towards OC progression and development. We used human proteomic data for post-translational modifications to interpret disease mutations and inter-individual genetic variations. The mutational analysis revealed the sequence predicted disordered region of 14%, 12.5%, 10.5% for ADCY2, CYP1B1, and C7 respectively. The MiRNA target prediction showed functional molecular annotation including specific miRNA-targets hsa-miR-4282, hsa-miR-2052, hsa-miR-216a-3p, for CYP1B1, C7, and ADCY2 respectively associated with oral cancer. We constructed the system level network and found important gene signatures. The drug-gene interaction of OC source genes with seven FDA approved OC drugs help to design or identify new drug target or establishing novel biomedical linkages regarding disease pathophysiology. This investigation demonstrates the importance of system genetics for identifying 6 OC genes (CYP1A1, CYP1B1, ADCY2, C7, SERPINB5, and ANAPC13) as potential drugs targets. Our integrative network-based system-level approach would help to find the genetic variants of OC that can accelerate drug discovery outcomes to develop a better understanding regarding treatment strategies for many cancer types.

Genome wide meta-analysis of cDNA datasets reveals new target gene signatures of colorectal cancer based on systems biology approach

Background

Colorectal cancer is known to be the most common type of cancer worldwide with high disease-related mortality. It is the third most common cancer in men and women and is the second major cause of death globally due to cancer. It is a complicated and fatal disease comprising of a group of molecular heterogeneous disorders.

Results

This study identifies the potential biomarkers of CRC through differentially expressed analysis, system biology, and proteomic analysis. Ten publicly available microarray datasets were analyzed and seven potential biomarkers were identified from the list of differentially expressed genes having a p value < 0.05. The expression profiling and the functional enrichment analysis revealed the role of these genes in cell communication, signal transduction, and immune response. The protein-protein interaction showed the functional association of the source genes (CTNNB1, NNMT, PTCH1, CALD1, CXCL14, CXCL8, and TNFAIP3) with the target proteins, such as AXIN, MAPK, IL6, STAT, APC, GSK3B, and SHH.

Conclusion

The integrated pathway analysis indicated the role of these genes in important physiological responses, such as cell cycle regulation, WNT, hedgehog, MAPK, and calcium signaling pathways during colorectal cancer. These pathways are involved in cell proliferation, chemotaxis, cellular growth, differentiation, tissue patterning, and cytokine production. The study shows the regulatory role of these genes in colorectal cancer and the pathways that can be effected after the dysregulation of these genes.

Immunoinformatics design of a novel multi-epitope peptide vaccine to combat multi-drug resistant infections caused by Vibrio vulnificus

Multi-drug resistant Vibrio vulnificus is a Gram-negative bacillus responsible for diseases, such as: sepsis, septicemia, gastroenteritis, and fatal necrotizing fasciitis in humans. The treatment and prevention of V. vulnificus infections are challenging because of resistance to antibiotics and the non-availability of a licensed vaccine. Considering this, an in-silico based approach comprising subtractive proteomics, immunoinformatics, molecular docking, and dynamics simulation studies is applied herein to identify potential epitope vaccine candidates for the mentioned pathogen. Two potential vaccine candidates: vibC and flgL are filtered based on essentiality, outer membrane localization, virulence, antigenic, pathway mapping, and cellular protein-protein network analysis. Using immunoinformatic tools, 9-mer B-cell derived T-cell antigenic epitopes are predicted for the said shortlisted two proteins that are demonstrating excellent affinity for predominant HLA allele (DRB1*0101) in human population. Screened peptides are used further in multi-epitope peptide designing and linked to an adjuvant to enhance the immunogenic properties of the designed construct. Furthermore, the construct was docked blindly to TLR4 immune receptor, and analyzed in conformational dynamics simulation to decipher the complex affinity and understand time dependent behavior, respectively. We expect this designed in silico construct to be useful for vaccinologists to evaluate its immune protective efficacy in in vivo animal models.

Antioxidant, Antimicrobial, Cytotoxic, and Protein Kinase Inhibition Potential in Aloe vera L

Aloe vera is a multifunctional plant that has gained acceptance as an excellent home remedy source in Asia and the world. The present study was intended to evaluate the phytochemical contents and in vitro antioxidant, antimicrobial, antileishmanial, and protein kinase inhibition activities in different fractions of A. vera leaf. Methanolic extract of A. vera leaves was fractionated using column chromatography and ten fractions (AV1-AV10) were obtained. Phenolics composition, antioxidant, antimicrobial, antileishmanial, and protein kinase inhibition activities were evaluated using standard protocols. Well-known compounds of A. vera were used for in silico study against enzymes involved in brine shrimp and antileishmanial and hyphae formation inhibition assay on the basis of results. Five fractions (AV3 to AV7) possess potential total phenolics and flavonoids contents along with significant biological activities. AV4 fraction exhibited the highest total phenolics content 332.4 ± 32.6μg GAE/mg and total antioxidant activity 150.4 ± 25.815μg AAE/mg determined by phosphomolybdenum complex assay. Fraction AV6 showed 95% antileishmanial effect as well as the lowest LD₅₀ value of 0.5305μg/mL in brine shrimp lethality assay. The Protein Kinase inhibition potential in A. vera leaves was determined for the first time and three fractions AV1, AV6, and AV7 depicted activity with the highest zone of inhibition up to 21±0.5mm (AV7). Docking analysis showed that A. vera contains anthraquinones, anthrones, chromones, and polysaccharides responsible for synergistic cytotoxic, antileishmanial, antibacterial, and antioxidant potential of this plant. Therefore, with more studies, A. vera could probably have the potential to be used for drug development against leishmaniasis.

A systematic simulation-based meta-analytical framework for prediction of physiological biomarkers in alopecia

Background

Alopecia or hair loss is a complex polygenetic and psychologically devastating disease affecting millions of men and women globally. Since the gene annotation and environmental knowledge is limited for alopecia, a systematic analysis for the identification of candidate biomarkers is required that could provide potential therapeutic targets for hair loss therapy.

Results

We designed an interactive framework to perform a meta-analytical study based on differential expression analysis, systems biology, and functional proteomic investigations. We analyzed eight publicly available microarray datasets and found 12 potential candidate biomarkers including three extracellular proteins from the list of differentially expressed genes with a p-value < 0.05. After expression profiling and functional analysis, we studied protein–protein interactions and observed functional associations of source proteins including WIF1, SPON1, LYZ, GPRC5B, PTPRE, ZFP36L2, HBB, PHF15, LMCD1, KRT35 and VAV3 with target proteins including APCDD1, WNT1, WNT3A, SHH, ESRI, TGFB1, and APP. Pathway analysis of these molecules revealed their role in major physiological reactions including protein metabolism, signal transduction, WNT, BMP, EDA, NOTCH and SHH pathways. These pathways regulate hair growth, hair follicle differentiation, pigmentation, and morphogenesis. We studied the regulatory role of β-catenin, Nf-kappa B, cytokines and retinoic acid in the development of hair growth. Therefore, the differential expression of these significant proteins would affect the normal level and could cause aberrations in hair growth.

Conclusion

Our integrative approach helps to prioritize the biomarkers that ultimately lessen the economic burden of experimental studies. It will also be valuable to discover mutants in genomic data in order to increase the identification of new biomarkers for similar problems.

Electronic supplementary material

The online version of this article (10.1186/s40709-019-0094-x) contains supplementary material, which is available to authorized users.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

BACKGROUND

A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.

RESULTS

We have designed an integrative computational pipeline term as "PanRV" that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.

CONCLUSION

The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/ .

An integrative, multi-omics approach towards the prioritization of Klebsiella pneumoniae drug targets

Klebsiella pneumoniae (Kp) is a globally disseminated opportunistic pathogen that can cause life-threatening infections. It has been found as the culprit of many infection outbreaks in hospital environments, being particularly aggressive towards newborns and adults under intensive care. Many Kp strains produce extended-spectrum β-lactamases, enzymes that promote resistance against antibiotics used to fight these infections. The presence of other resistance determinants leading to multidrug-resistance also limit therapeutic options, and the use of 'last-resort' drugs, such as polymyxins, is not uncommon. The global emergence and spread of resistant strains underline the need for novel antimicrobials against Kp and related bacterial pathogens. To tackle this great challenge, we generated multiple layers of 'omics' data related to Kp and prioritized proteins that could serve as attractive targets for antimicrobial development. Genomics, transcriptomics, structuromic and metabolic information were integrated in order to prioritize candidate targets, and this data compendium is freely available as a web server. Twenty-nine proteins with desirable characteristics from a drug development perspective were shortlisted, which participate in important processes such as lipid synthesis, cofactor production, and core metabolism. Collectively, our results point towards novel targets for the control of Kp and related bacterial pathogens.

Putative vaccine candidates and drug targets identified by reverse vaccinology and subtractive genomics approaches to control Haemophilus ducreyi, the causative agent of chancroid

Chancroid is a sexually transmitted infection (STI) caused by the Gram-negative bacterium Haemophilus ducreyi The control of chancroid is difficult and the only current available treatment is antibiotic therapy; however, antibiotic resistance has been reported in endemic areas. Owing to recent outbreaks of STIs worldwide, it is important to keep searching for new treatment strategies and preventive measures. Here, we applied reverse vaccinology and subtractive genomic approaches for the in silico prediction of potential vaccine and drug targets against 28 strains of H. ducreyi We identified 847 non-host homologous proteins, being 332 exposed/secreted/membrane and 515 cytoplasmic proteins. We also checked their essentiality, functionality and virulence. Altogether, we predicted 13 candidate vaccine targets and three drug targets, where two vaccines (A01_1275, ABC transporter substrate-binding protein; and A01_0690, Probable transmembrane protein) and three drug targets (A01_0698, Purine nucleoside phosphorylase; A01_0702, Transcription termination factor; and A01_0677, Fructose-bisphosphate aldolase class II) are harboured by pathogenicity islands. Finally, we applied a molecular docking approach to analyse each drug target and selected ZINC77257029, ZINC43552589 and ZINC67912117 as promising molecules with favourable interactions with the target active site residues. Altogether, the targets identified here may be used in future strategies to control chancroid worldwide.

Simulation Study of cDNA Dataset to Investigate Possible Association of Differentially Expressed Genes of Human THP1-Monocytic Cells in Cancer Progression Affected by Bacterial Shiga Toxins

Shiga toxin (Stxs) is a family of structurally and functionally related bacterial cytotoxins produced by Shigella dysenteriae serotype 1 and shigatoxigenic group of Escherichia coli that cause shigellosis and hemorrhagic colitis, respectively. Until recently, it has been thought that Stxs only inhibits the protein synthesis and induces expression to a limited number of genes in host cells, but recent data showed that Stxs can trigger several signaling pathways in mammalian cells and activate cell cycle and apoptosis. To explore the changes in gene expression induced by Stxs that have been shown in other systems to correlate with cancer progression, we performed the simulated analysis of cDNA dataset and found differentially expressed genes (DEGs) of human THP1-monocytic cells treated with Stxs. In this study, the entire data (treated and untreated replicates) was analyzed by statistical algorithms implemented in Bioconductor packages. The output data was validated by the k-fold cross technique using generalized linear Gaussian models. A total of 50 DEGs were identified. 7 genes including TSLP, IL6, GBP1, CD274, TNFSF13B, OASL, and PNPLA3 were considerably (<0.00005) related to cancer proliferation. The functional enrichment analysis showed 6 down-regulated and 1 up-regulated genes. Among these DEGs, IL6 was associated with several cancers, especially with leukemia, lymphoma, lungs, liver and breast cancers. The predicted regulatory motifs of these genes include conserved RELA, STATI, IRFI, NF-kappaB, PEND, HLF, REL, CEBPA, DI_2, and NFKB1 transcription factor binding sites (TFBS) involved in the complex biological functions. Thus, our findings suggest that Stxs has the potential as a valuable tool for better understanding of treatment strategies for several cancers.

Tools for Pathogen Proteomics: Fishing with Biomimetic Nanosponges

The identification of the major virulence factors that drive pathogenicity is critical for gaining insight into the underlying molecular mechanisms of diseases. Although genetic approaches combined with functional analyses have markedly increased the rate of virulence factor discovery, the divergence between genome and proteome can impair the identification of important markers, in particular, of those that act in concert or depend on specific environmental factors. Recently, membrane-coated nanomaterials mimicking source cells of interest have emerged as powerful tools that can be used for improved tumor targeting and as "nanotraps" to capture chemokines and bacterial toxins. In this issue of ACS Nano, Lapek et al. demonstrate that membrane-coated nanosponges in combination with quantitative proteomics can also be used as efficient "fishing devices" for the identification of cell-type-specific virulence factors.

PBIT: Pipeline Builder for Identification of drug Targets for infectious diseases

Summary

PBIT (Pipeline Builder for Identification of drug Targets) is an online webserver that has been developed for screening of microbial proteomes for critical features of human drug targets such as being non-homologous to human proteome as well as the human gut microbiota, essential for the pathogen's survival, participation in pathogen-specific pathways etc. The tool has been validated by analyzing 57 putative targets of Candida albicans documented in literature. PBIT integrates various in silico approaches known for drug target identification and will facilitate high-throughput prediction of drug targets for infectious diseases, including multi-pathogenic infections.

Availability and Implementation

PBIT is freely accessible at http://www.pbit.bicnirrh.res.in/ .

Contact

thomass@nirrh.res.in.

Supplementary information

Supplementary data are available at Bioinformatics online.

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

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.

Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus

Purpose: Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder affecting large set of population of the world. To widen the scope of understanding of genetic causes of this disease, we performed interactive and toxicogenomic based systems biology study to find potential T2DM related genes after cDNA differential analysis.

Methods: From the list of 50-differential expressed genes (p < 0.05), we found 9-T2DM related genes using extensive data mapping. In our constructed gene-network, T2DM-related differentially expressed seeder genes (9-genes) are found to interact with functionally related gene signatures (31-genes). The genetic interaction network of both T2DM-associated seeder as well as signature genes generally relates well with the disease condition based on toxicogenomic and data curation.

Results: These networks showed significant enrichment of insulin signaling, insulin secretion and other T2DM-related pathways including JAK-STAT, MAPK, TGF, Toll-like receptor, p53 and mTOR, adipocytokine, FOXO, PPAR, P13-AKT, and triglyceride metabolic pathways. We found some enriched pathways that are common in different conditions. We recognized 11-signaling pathways as a connecting link between gene signatures in insulin resistance and T2DM. Notably, in the drug-gene network, the interacting genes showed significant overlap with 13-FDA approved and few non-approved drugs. This study demonstrates the value of systems genetics for identifying 18 potential genes associated with T2DM that are probable drug targets.

Conclusions: This integrative and network based approaches for finding variants in genomic data expect to accelerate identification of new drug target molecules for different diseases and can speed up drug discovery outcomes.

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.

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.