Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases.

Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5.1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C &B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs.

Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach.

BACKGROUND: Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells. OBJECTIVE: In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks. METHODOLOGY: In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy. RESULTS: The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment of the essentiality of the node in the epigenetic network. CONCLUSION: The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.

Drug repurposing for hyperlipidemia associated disorders: An integrative network biology and machine learning approach.

Hyperlipidemia causes diseases like cardiovascular disease, cancer, Type II Diabetes and Alzheimer's disease. Drugs that specifically target HL associated diseases are required for treatment. 34 KEGG pathways targeted by lipid lowering drugs were used to construct a directed protein-protein interaction network and driver nodes were determined using CytoCtrlAnalyser plugin of Cytoscape 3.6. The involvement of driver nodes of HL in other diseases was verified using GWAS. The central nodes of the network and 34 overrepresented pathways had a critical role in Hyperlipidemia. The PI3K-AKT signalling pathway, non-essentiality, non-centrality and approved drug target status were the predominant features of the driver nodes. Next, a Random Forest classifier was trained on 1445 molecular descriptors calculated using PaDEL for 50 approved lipid lowering and 84 lipid raising drugs as the positive and negative training set respectively. The classifier showed average accuracy of 76.8 % during 5-fold cross validation with AUC of 0.79 ± 0.06 for the ROC curve. The classifier was applied to select molecules with favourable properties for lipid lowering from the 130 approved drugs interacting with the identified driver nodes. We have integrated diverse network data and machine learning to predict repurposing of nine drugs for treatment of HL associated diseases.

Identification of marker genes in Alzheimer's disease using a machine-learning model.

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.

Systems Biology: A Powerful Tool for Drug Development.

The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines. Therefore, the process of drug development must consider biological responses in both pathological and physiological conditions. Consequently, a quantitative and holistic systems biology approach could aid in understanding complex biological systems by providing an exceptional platform to integrate diverse data types with molecular as well as pathway information, leading to development of predictive models for complex diseases. Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput experimental data accelerates hypothesis generation and testing in disease models. The systems biology approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets, facilitating development of personalized medicine and improving decision making and success rate of new drugs in clinical trials.

Modeling, dynamics and phosphoinositide binding of the pleckstrin homology domain of two novel PLCs: η1 and η2.

PH domains mediate interactions involved in cell signaling, intracellular membrane transport regulation and cytoskeleton organization. Some PH domains bind phosphoinositides with different affinity and specificity. The two novel PLCη (1 and 2) possess an N-terminal PH domain (PHη1 and PHη2 respectively) that has been implicated in membrane association and induction of PLC activity. Understanding of the structure and dynamics is crucial for future modulation of lipid-protein interactions in PHη1, PHη2 and other PH domains. Therefore, the three-dimensional structure of PHη1 and PHη2 was modeled using ITASSER and phosphoinositides (IP3 and IP4) were docked in the inferred binding site using HADDOCK server. Molecular Dynamics simulations of unliganded and phosphoinositide bound PHη1 and PHη2 were performed using AMBER14 to study the mechanism of interaction, and conformational dynamics in response to phosphoinositide binding. The binding affinity was predicted using Kdeep server. The models of PHη1 and PHη2 had a conserved structural core consisting of seven ß-strands and a C-terminal α-helix as seen in other PH domains. Sequence/structure analysis showed that phosphoinositide ligands bind PHη1 and PHη2 at the canonical binding site. Phosphoinositide binding induced movement of positively charged side chains towards the ligand, changes in the secondary structure especially at the ß5-ß6 loop and allosteric changes at the interface of ß1-ß2 and ß5-ß6 loops. Dynamics studies showed that the size of the binding site and differential affinity for IP3/IP4 binding is coordinated by the number, length, flexibility, secondary structure and allosteric interactions of the loops surrounding the phosphoinositide binding site.

Novel Lipidomic Biomarkers in Hyperlipidemia and Cardiovascular Diseases: An Integrative Biology Analysis.

Lipidomics is a new frontier of omics research and offers much promise for new-generation biomarkers for common complex phenotypes such as hyperlipidemia (HL) and cardiovascular diseases (CVDs). HL is a disorder characterized by increased levels of blood lipids and is a well-established risk factor for CVD. Traditional clinical markers for prognosis of hyperlipidemic individuals are inadequate to forecast or diagnose cardiac events. In this expert review, lipidomic profiles from recent HL and CVD studies were compared with the normolipidemic profile prepared from the Standard Reference Material. Our analysis showed that palmitoyl-lysophosphatidylcholine [LPC(16:0)], the most abundant LPC species in normolipidemic plasma, decreases in HL causative conditions such as high-fat diet, obesity, and diabetes. This is accompanied by increase in free fatty acids (FFAs) and ceramides (Cers). HL was also found to be characterized by increase in small-chain, saturated fatty acid content of diacylglycerols, triacylglycerols, and phosphatidylcholines (PCs). These factors were also associated with increased CVD risk. The decrease in LPC(16:0) in HL and CVD is consistent with its role in regulation of peroxisome proliferator-activated receptor alpha, an approved HL drug target that impacts the uptake and oxidation of fatty acids. FFAs are involved in endothelial-dependent nitric oxide production and activation of nuclear factor κB signaling. Cers control uptake and anabolic catabolism of nutrients in tissues. However, additional studies are required to establish the range of normal and disease levels of the identified lipids in different populations and conditions. In all, these observations underscore that lipidomics deserves greater research attention from the biomarker and precision medicine research communities.

Hyperlipidemia, Disease Associations, and Top 10 Potential Drug Targets: A Network View.

The prevalence of acquired hyperlipidemia has increased due to sedentary life style and lipid-rich diet. In this work, a lipid-protein-protein interaction network (LPPIN) for acquired hyperlipidemia was prepared by incorporating differentially expressed genes in obese fatty liver as seed nodes, protein interactions from PathwayLinker, and lipid interactions from STITCH4.0. Cholesterol, diacylglycreol, phosphatidylinositol-bis-phosphate, and inositol triphosphate were identified as core lipids that influence the signaling pathways in the LPPIN. RACα serine/threonine-protein kinase (AKT1) was a highly essential central protein. The gastrin-CREB pathway was greatly enriched; all enriched pathways in the LPPIN showed crosstalk with the phosphatidylinositol-3-kinase-Akt pathway, correlating with the central role of AKT1 in the network. The disease clusters identified in the LPPIN were cardiovascular disease, cancer, Alzheimer's disease, and Type II diabetes. In this context, we note that the commercially approved drug targets for hyperlipidemia in each disease cluster may potentially be repurposed for treatment of the specific disease. We report here top 10 potential drug targets that may mediate progression from hyperlipidemia to the respective disease state. ToppGene Suite was employed to identify candidates followed by a) discarding high closeness centrality nodes, and b) selecting nodes with high bridging centrality. Three potential targets could be mapped to specific disease clusters in the LPPIN. Lipids associated with acquired hyperlipidemia and each disease cluster identified may be useful as prognostic fingerprints. These findings provide an integrative view of lipid-protein interactions leading to acquired hyperlipidemia and the associated diseases, and might prove useful in future translational pharmaceutical research.

Modeling of human M1 aminopeptidases for in silico screening of potential Plasmodium falciparum alanine aminopeptidase (PfA-M1) specific inhibitors.

Plasmodium falciparum alanine M1-aminopeptidase (PfA-M1) is a validated target for anti-malarial drug development. Presence of significant similarity between PfA-M1 and human M1-aminopeptidases, particularly within regions of enzyme active site leads to problem of non-specificity and off-target binding for known aminopeptidase inhibitors. Molecular docking based in silico screening approach for off-target binding has high potential but requires 3D-structure of all human M1-aminopeptidaes. Therefore, in the present study 3D structural models of seven human M1-aminopeptidases were developed. The robustness of docking parameters and quality of predicted human M1-aminopeptidases structural models was evaluated by stereochemical analysis and docking of their respective known inhibitors. The docking scores were in agreement with the inhibitory concentrations elucidated in enzyme assays of respective inhibitor enzyme combinations (r2≈0.70). Further docking analysis of fifteen potential PfA-M1 inhibitors (virtual screening identified) showed that three compounds had less docking affinity for human M1-aminopeptidases as compared to PfA-M1. These three identified potential lead compounds can be validated with enzyme assays and used as a scaffold for designing of new compounds with increased specificity towards PfA-M1.