A Systematic In-silico Analysis of Helicobacter pylori Pathogenic Islands for Identification of Novel Drug Target Candidates

Background:

Helicobacter pylori is associated with inflammation of different areas, such as the duodenum and stomach, causing gastritis and gastric ulcers leading to lymphoma and cancer. Pathogenic islands are a type of clustered mobile elements ranging from 10-200 Kb contributing to the virulence of the respective pathogen coding for one or more virulence factors. Virulence factors are molecules expressed and secreted by pathogen and are responsible for causing disease in the host. Bacterial genes/virulence factors of the pathogenic islands represent a promising source for identifying novel drug targets.

Objective:

The study aimed at identifying novel drug targets from pathogenic islands in H. pylori.

Material & Methods:

The genome of 23 H. pylori strains were screened for pathogenic islands and bacterial genes/virulence factors to identify drug targets. Protein-protein interactions of drug targets were predicted for identifying interacting partners. Further, host-pathogen interactions of interacting partners were predicted to identify important molecules which are closely associated with gastric cancer.

Results:

Screening the genome of 23 H. pylori strains revealed 642 bacterial genes/virulence factors in 31 pathogenic islands. Further analysis identified 101 genes which were non-homologous to human and essential for the survival of the pathogen, among them 31 are potential drug targets. Protein-protein interactions for 31 drug targets predicted 609 interacting partners. Predicted interacting partners were further subjected to host-pathogen interactions leading to identification of important molecules like TNF receptor associated factor 6, (TRAF6) and MAPKKK7 which are closely associated with gastric cancer.

Conclusion:

These provocative studies enabled us to identify important molecules in H. pylori and their counter interacting molecules in the host leading to gastric cancer and also a pool of novel drug targets for therapeutic intervention of gastric cancer.

Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis

Background

Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM).

Methodology

Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis.

Results/Findings

Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission.

Conclusions/Significance

Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite.

New strategies and paradigm for drug target discovery: a special focus on infectious diseases tuberculosis, malaria, leishmaniasis, trypanosomiasis and gastritis

The discovery and exploitation of new drug targets is a key focus for both the pharmaceutical industry and academic research. To provide an insight into trends in the exploitation of new drug targets, we have analysed different methods during the past six decades and advances made in drug target discovery. A special focus remains on different methods used for drug target discovery on infectious diseases such as Tuberculosis, Gastritis, Malaria, Trypanosomiasis and Leishmaniasis. We herewith provide a paradigm that is can be used for drug target discovery in the near future.