HES1 promoter activation dynamics reveal the plasticity, stemness and heterogeneity in neuroblastoma cancer stem cells.

Notch signaling and its downstream gene target HES1 play a critical role in regulating and maintaining cancer stem cells (CSCs), similar to as they do during embryonic development. Here, we report a unique subclass of Notch-independent Hes-1 (NIHes-1)-expressing CSCs in neuroblastoma. These CSCs maintain sustained HES1 expression by activation of HES1 promoter region upstream of classical CBF-1 binding sites, thereby completely bypassing Notch receptor-mediated activation. These stem cells have self-renewal ability and potential to generate tumors. Interestingly, we observed that NIHes-1 CSCs could transition to Notch-dependent Hes-1-expressing (NDHes-1) CSCs where HES1 is expressed by Notch receptor-mediated promoter activation. We observed that NDHes-1-expressing CSCs also had the potential to transition to NIHes-1 CSCs and during this coordinated bidirectional transition, both CSCs gave rise to the majority of the bulk cancer cells, which had an inactive HES1 promoter (PIHes-1). A few of these PIHes-1 cells were capable of reverting into a CSC state. These findings explain the existence of a heterogenic mode of HES1 promoter activation within the IMR-32 neuroblastoma cell line and the potential to switch between them. This article has an associated First Person interview with the first authors of the paper.

Draft Genome Sequence of the Oomycete Globisporangium splendens Strain rgcb-1.

Globisporangium splendens (formerly Pythium splendens) is an oomycete pathogen of many economically important vegetable crops. Here, we present the first draft genome of P. splendens, which comprises 197 scaffolds with a total length of 53.3 Mb and 17,350 predicted protein-coding genes.

A novel sequence and context based method for promoter recognition.

UNLABELLED: Identification of promoters in DNA sequence using computational techniques is a significant research area because of its direct association in transcription regulation. A wide range of algorithms are available for promoter prediction. Most of them are polymerase dependent and cannot handle eukaryotes and prokaryotes alike. This study proposes a polymerase independent algorithm, which can predict whether a given DNA fragment is a promoter or not, based on the sequence features and statistical elements. This algorithm considers all possible pentamers formed from the nucleotides A, C, G, and T along with CpG islands, TATA box, initiator elements, and downstream promoter elements. The highlight of the algorithm is that it is not polymerase specific and can predict for both eukaryotes and prokaryotes in the same computational manner even though the underlying biological mechanisms of promoter recognition differ greatly. The proposed Method, Promoter Prediction System - PPS-CBM achieved a sensitivity, specificity, and accuracy percentages of 75.08, 83.58 and 79.33 on E. coli data set and 86.67, 88.41 and 87.58 on human data set. We have developed a tool based on PPS-CBM, the proposed algorithm, with which multiple sequences of varying lengths can be tested simultaneously and the result is reported in a comprehensive tabular format. The tool also reports the strength of the prediction. AVAILABILITY: The tool and source code of PPS-CBM is available at http://keralabs.org.

Structure based virtual screening of inhibitors for binding at the allosteric site of HCV RNA dependent RNA polymerase.

Hepatitis C Virus (HCV) infection is a serious cause of chronic liver disease worldwide with more than 170 million infected individuals at a risk of developing significant morbidity and mortality. There is no effective treatment or prevention till date for HCV infection. We describe the computed binding of 10 compounds to the allosteric binding site of RNA dependent RNA polymerase enzyme. These compounds were identified from the ZINC database through virtual screening followed by ADMET evaluation.

Identification of hub proteins from sequence.

Identification of hub proteins from sequence is a challenge in molecular biology. Therefore, it is of interest to predict protein hubs in networks. We describe the prediction of protein "hub" using physiochemical, thermodynamic and conformational properties of amino acid residues in sequence. We have used twenty sequence based features to identify hub behaviour. Linear discriminant analysis and normalised Bayesian approach were utilized for identifying hub proteins solely using these sequence features in E. coli/H. sapiens datasets with accuracies of 99.5/98.6, 87.8/89.6 and 90.1/92.6, respectively.