A Novel Network Biology Approach To Drug Target Selections

Conventional drug discovery focuses on single protein targets and follows a “sequence, structure, and function” paradigm for selecting best protein targets to screen lead chemical compounds. This established paradigm simply avoids addressing directly the challenge of evaluating chemical toxicity and side effects until a later stage of drug discovery, resulting in inefficiencies and increased time and cost. We developed a new “network biology” perspective to assess proteins as potential drug targets using emerging biomolecular network data sets. To do so, we integrated several types of biological data for current drug targets from DrugBank, protein interaction data from the HAPPI and HPRD databases, literature co-citation data from PubMed, and side effects data from FDA-approved drug usage warnings. We used the Bayes factor and Positive Predictive Values to examine the use of certain network properties, such as network node degrees and essentiality, to predict candidate drug targets. We also developed a metric to evaluate a protein target’s overall side effects by taking into account aggregated side effect scores of all FDA-approved drugs targeting the protein. We discovered that non-essential protein with lower-to-medium network node degree could better serve as drug targets when combined with conventional protein function information. Integrated biomolecular associations, instead of physical interactions, are better sources for predicting drug targets with network biology methods. Our network biology framework presents exciting promises in developing better drug targets that lower the side-effects at later stages of drug development and help establish the field of “network pharmacology.

Yoga is an effective technique of stress reduction within the medical population: a biochemical study in MBBS students of BRD Medical College, Gorakhpur, Uttar Pradesh, India

Background: Medicine is a profession with high-stress prevalence. There are many stress markers but cortisol is one of the commonly used stress marker. Stress begins in the first year of medical carrier and increases with subsequent years of medical education. There is a decrease in overall academic performance and many health-related adverse effects due to stress. In this study, yoga was performed in the first year MBBS students of BRD Medical College, Gorakhpur and the impact of yoga in stress reduction was studied using serum cortisol as stress a marker.Methods: Study groups, yoga and control contained 26 and 27 subjects including male and female MBBS students. Yoga group practiced selected yogic asana, pranayama, and yoga nidra for 3 months. The control group as a stress marker had been kept in touch and allowed to go on their usual activity as before. Morning (8.00 AM to 9.00 AM) serum cortisol level was used as a stress marker in both group, pre and post-study.Results: There was a significant reduction in morning serum cortisol level (stress level) in yoga group (p-value = 0.0271) but there was no significant change in morning serum cortisol level of the control group (p-value = 0.8573).Conclusions: Yoga is an effective stress reduction technique for medical students. Yoga classes should be introduced in the first year of the medical carrier under the supervision of expert physiologists. This may lead to the implantation of a healthy lifestyle in our future healthcare providers. Yogic practice by health care providers may have long term positive impacts on our healthcare system

HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions

BACKGROUND: Human protein-protein interaction (PPI) data is essential to network and systems biology studies. PPI data can help biochemists hypothesize how proteins form complexes by binding to each other, how extracellular signals propagate through post-translational modification of de-activated signaling molecules, and how chemical reactions are coupled by enzymes involved in a complex biological process. Our capability to develop good public database resources for human PPI data has a direct impact on the quality of future research on genome biology and medicine. RESULTS: The database of Human Annotated and Predicted Protein Interactions (HAPPI) version 2.0 is a major update to the original HAPPI 1.0 database. It contains 2,922,202 unique protein-protein interactions (PPI) linked by 23,060 human proteins, making it the most comprehensive database covering human PPI data today. These PPIs contain both physical/direct interactions and high-quality functional/indirect interactions. Compared with the HAPPI 1.0 database release, HAPPI database version 2.0 (HAPPI-2) represents a 485% of human PPI data coverage increase and a 73% protein coverage increase. The revamped HAPPI web portal provides users with a friendly search, curation, and data retrieval interface, allowing them to retrieve human PPIs and available annotation information on the interaction type, interaction quality, interacting partner drug targeting data, and disease information. The updated HAPPI-2 can be freely accessed by Academic users at http://discovery.informatics.uab.edu/HAPPI . CONCLUSIONS: While the underlying data for HAPPI-2 are integrated from a diverse data sources, the new HAPPI-2 release represents a good balance between data coverage and data quality of human PPIs, making it ideally suited for network biology

Construction of Ru(II) Polypyridyl Based Macrocycles: Synthesis, Characterization, Electrochemical, Li+ Binding, Antitumour and Anti-HIV properties

Some ruthenium (II) polypyridyl complexes with a bis-chalcone (obtained by the condensation of 3-methyl-thiophene-2-carboxaldehyde and 4-acetyl pyridine) have been synthesized and characterized spectroscopically (IR, NMR, UV/Vis), conductimetric, elemental analysis and FAB mass data. Their luminescent, redox and Li+ binding properties have been studied. The anti-HIV and antitumour activities have also been reported

Computational Biomarker Discovery: From Systems Biology to Predictive and Personalized Medicine Applications

poster abstractWith the advent of Genome-based Medicine, there is an escalating need for discovering how the modifications of biological molecules, either individually or as an ensemble, can be uniquely associated with human physiological states. This knowledge could lead to breakthroughs in the development of clinical tests known as "biomarker tests" to assess disease risks, early onset, prognosis, and treatment outcome predictions. Therefore, development of molecular biomarkers is a key agenda in the next 5-10 years to take full advantage of the human genome to improve human well-beings. However, the complexity of human biological systems and imperfect instrumentations of high-throughput biological instruments/results have created significant hurdles in biomarker development. Only recently did computational methods become an important player of the research topic, which has seen conventional molecular biomarkers development both extremely long and cost-ineffective. At Indiana Center for Systems Biology and Personalized Medicine, we are developing several computational systems biology strategies to address these challenges. We will show examples of how we approach the problem using a variety of computational techniques, including data mining, algorithm development to take into account of biological contexts, biological knowledge integration, and information visualization. Finally, we outline how research in this direction to derive more robust molecular biomarkers may lead to predictive and personalized medicine. Indiana Center for Systems Biology and Personalized Medicine (CSBPM) was founded in 2007 as an IUPUI signature center by Dr. Jake Chen and his colleagues in the Indiana University School of Informatics, School of Medicine, and School of Science. CSBPM is the only research center in the State of Indiana with the primary goal of pursuing predictive and personalized medicine. CSBPM currently consists of eleven faculty members from the School of Medicine, School of Science, School of Engineering, School of Informatics, and Indiana University Simon Cancer Center. The primary mission of the center is to foster the development and use of systems biology and computational modeling techniques to address challenges in future genome-based medicine. The ultimate goal of the center is to shorten the discovery-to-practice gap between integrative ―Omics‖ biology studies—including genomics, transcriptomics, proteomics, and metabolomics—and predictive and personalized medicine applications

Evidence for MBM_B and MCM_C phases in the morphotropic phase boundary region of (1−x)[Pb(Mg1/3Nb2/3)O3]−xPbTiO3(1-x)[Pb(Mg_{1/3}Nb_{2/3})O_3]-xPbTiO_3 : A Rietveld study

We present here the results of the room temperature dielectric constant measurements and Rietveld analysis of the powder x-ray diffraction data on $(1-x)[Pb(Mg_{1/3}Nb_{2/3})O_3]-xPbTiO_3$(PMN-$x$PT) in the composition range $0.20 \leq x \leq 0.45$ to show that the morphotropic phase boundary (MPB) region contains two monoclinic phases with space groups Cm (or $M_B$ type) and Pm (or $M_C$ type) stable in the composition ranges $0.27 \leq x \leq 0.30$ and $0.31 \leq x \leq 0.34$, respectively. The structure of PMN-$x$PT in the composition ranges $0 \leq x \leq$ 0.26, and $0.35 \leq x \leq1$ is found to be rhombohedral (R3m) and tetragonal (P4mm), respectively. These results are compared with the predictions of Vanderbilt & Cohen's theory.Comment: 20 pages, 11 pdf figure

Primary repair versus surgical and transcatheter palliation in infants with tetralogy of Fallot

Objectives Treatment of infants with tetralogy of Fallot (ToF) has evolved in the last two decades with increasing use of primary surgical repair (PrR) and transcatheter right ventricular outflow tract palliation (RVOTd), and fewer systemic-to-pulmonary shunts (SPS). We aim to report contemporary results using these treatment options in a comparative study. Methods This a retrospective study using data from the UK National Congenital Heart Disease Audit. All infants (n=1662, median age 181 days) with ToF and no other complex defects undergoing repair or palliation between 2000 and 2013 were considered. Matching algorithms were used to minimise confounding due to lower age and weight in those palliated. Results Patients underwent PrR (n=1244), SPS (n=311) or RVOTd (n=107). Mortality at 12 years was higher when repair or palliation was performed before the age of 60 days rather than after, most significantly for primary repair (18.7% vs 2.2%, P<0.001), less so for RVOTd (10.8% vs 0%, P=0.06) or SPS (12.4% vs 8.3%, P=0.2). In the matched groups of patients, RVOTd was associated with more right ventricular outflow tract (RVOT) reinterventions (HR=2.3, P=0.05 vs PrR, HR=7.2, P=0.001 vs SPS) and fewer pulmonary valve replacements (PVR) (HR=0.3 vs PrR, P=0.05) at 12 years, with lower mortality after complete repair (HR=0.2 versus PrR, P=0.09). Conclusions We found that RVOTd was associated with more RVOT reinterventions, fewer PVR and fewer deaths when compared with PrR in comparable, young infants, especially so in those under 60 days at the time of the first procedure