Structural Modeling and DNA Binding Autoinhibition Analysis of Ergp55, a Critical Transcription Factor in Prostate Cancer

BACKGROUND: The Ergp55 protein belongs to Ets family of transcription factor. The Ets proteins are highly conserved in their DNA binding domain and involved in various development processes and regulation of cancer metabolism. To study the structure and DNA binding autoinhibition mechanism of Ergp55 protein, we have produced full length and smaller polypeptides of Ergp55 protein in E. coli and characterized using various biophysical techniques. RESULTS: The Ergp55 polypeptides contain large amount of α-helix and random coil structures as measured by circular dichorism spectroscopy. The full length Ergp55 forms a flexible and elongated molecule as revealed by molecular modeling, dynamics simulation and structural prediction algorithms. The binding analyses of Ergp55 polypeptides with target DNA sequences of E74 and cfos promoters indicate that longer fragments of Ergp55 (beyond the Ets domain) showed the evidence of auto-inhibition. This study also revealed the parts of Ergp55 protein that mediate auto-inhibition. SIGNIFICANCE: The current study will aid in designing the compounds that stabilize the inhibited form of Ergp55 and inhibit its binding to promoter DNA. It will contribute in the development of drugs targeting Ergp55 for the prostate cancer treatment

Comparative constructions of similarity in Northern Samoyedic languages

The purpose of this paper is to analyze the suffixes which are used in Northern Samoyedic languages to build comparative constructions of equality. Depending on the language, the suffixes may perform three functions: word-building, form-building, and inflectional. When they mark the noun, they serve as simulative suffixes and are employed to build object comparison. In the inflectional function, these suffixes mark the verb and are a means of constructing situational comparison. In this case, they signal the formation of a special mood termed the Approximative. This paper provides a detailed description of the Approximative from paradigmatic and syntagmatic perspectives

GWAS meta-analysis of intrahepatic cholestasis of pregnancy implicates multiple hepatic genes and regulatory elements

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disorder affecting 0.5–2% of pregnancies. The majority of cases present in the third trimester with pruritus, elevated serum bile acids and abnormal serum liver tests. ICP is associated with an increased risk of adverse outcomes, including spontaneous preterm birth and stillbirth. Whilst rare mutations affecting hepatobiliary transporters contribute to the aetiology of ICP, the role of common genetic variation in ICP has not been systematically characterised to date. Here, we perform genome-wide association studies (GWAS) and meta-analyses for ICP across three studies including 1138 cases and 153,642 controls. Eleven loci achieve genome-wide significance and have been further investigated and fine-mapped using functional genomics approaches. Our results pinpoint common sequence variation in liver-enriched genes and liver-specific cis-regulatory elements as contributing mechanisms to ICP susceptibility

A distributed approach to enabling privacy-preserving model-based classifier training

This paper proposes a novel approach for privacy-preserving distributed model-based classifier training. Our approach is an important step towards supporting customizable privacy modeling and protection. It consists of three major steps. First, each data site independently learns a weak concept model (i.e., local classifier) for a given data pattern or concept by using its own training samples. An adaptive EM algorithm is proposed to select the model structure and estimate the model parameters simultaneously. The second step deals with combined classifier training by integrating the weak concept models that are shared from multiple data sites. To reduce the data transmission costs and the potential privacy breaches, only the weak concept models are sent to the central site and synthetic samples are directly generated from these shared weak concept models at the central site. Both the shared weak concept models and the synthetic samples are then incorporated to learn a reliable and complete global concept model. A computational approach is developed to automatically achieve a good trade off between the privacy disclosure risk, the sharing benefit and the data utility. The third step deals with validating the combined classifier by distributing the global concept model to all these data sites in the collaboration network while at the same time limiting the potential privacy breaches. Our approach has been validated through extensive experiments carried out on four UCI machine learning data sets and two image data sets