Molecular phylogeny of Hemidactylus geckos (Squamata: Gekkonidae) of the Indian subcontinent reveals a unique Indian radiation and an Indian origin of Asian house geckos

Represented by approximately 85 species, Hemidactylus is one of the most diverse and widely distributed genera of reptiles in the world. In the Indian subcontinent, this genus is represented by 28 species out of which at least 13 are endemic to this region. Here, we report the phylogeny of the Indian Hemidactylus geckos based on mitochondrial and nuclear DNA markers sequenced from multiple individuals of widely distributed as well as endemic congeners of India. Results indicate that a majority of the species distributed in India form a distinct clade whose members are largely confined to the Indian subcontinent thus representing a unique Indian radiation. The remaining Hemidactylus geckos of India belong to two other geographical clades representing the Southeast Asian and West-Asian arid zone species. Additionally, the three widely distributed, commensal species (H. brookii, H. frenatus and H. flaviviridis) are nested within the Indian radiation suggesting their Indian origin. Dispersal-vicariance analysis also supports their Indian origin and subsequent dispersal out-of-India into West-Asian arid zone and Southeast Asia. Thus, Indian subcontinent has served as an important arena for diversification amongst the Hemidactylus geckos and in the evolution and spread of its commensal geckos. (C) 2010 Elsevier Inc. All rights reserved.

Standardization of pollen allergens of Parthenium hysterophoms and selection of an in-house reference extract

A standardized in-house reference extract from the pollen of Parthenium hysterophorus, which is responsible for the high incidence of allergic rhinitis in India, was generated and examined by skin test, radio-allergosorbent test inhibition and isoelectric focusing. Parthenium reference allergen discs and positive reference serum were also generated. These reference reagents could not only be used for the quantitation of Parthenium-specific IgE in the sera of rhinitis patients but also for the evaluation of allergenic activity (relative potency and lot-to-lot variation) of different batches of Parthenium pollen.

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Background: The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. Results: We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of similar to 58% and similar to 40% for localization and functions respectively of proteins were determined at a threshold of similar to 30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k nearest neighbor classifier confirmed that our results compared favorably. Conclusions: This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest.