A mutant allele of BARA/LIN-9 rescues the cdk4-/- phenotype by releasing the repression on E2F-regulated genes

It has been proposed that C. elegans LIN-9 functions downstream of CDK4 in a pathway that regulates cell proliferation. Here, we report that mammalian BARA/LIN-9 is a predominantly nuclear protein that inhibits cell proliferation. More importantly, we demonstrate that BARA/LIN-9 also acts downstream of cyclin D/CDK4 in mammalian cells since (i) its antiproliferative effect is partially blocked by coexpression of cyclin D1, and (ii) a mutant form that lacks the first 84 amino acids rescues several phenotypic alterations observed in mice null for cdk4. Interestingly, mutation of BARA/LIN-9 restores the expression of E2F target genes in CDK4 null MEFs, indicating that the wild-type protein plays a role in the expression of genes required for the G1/S transition.     

A Minimal Model of Signaling Network Elucidates Cell-to-Cell Stochastic Variability in Apoptosis

Background

Signaling networks are designed to sense an environmental stimulus and adapt to it. We propose and study a minimal model of signaling network that can sense and respond to external stimuli of varying strength in an adaptive manner. The structure of this minimal network is derived based on some simple assumptions on its differential response to external stimuli.

Methodology

We employ stochastic differential equations and probability distributions obtained from stochastic simulations to characterize differential signaling response in our minimal network model. Gillespie''s stochastic simulation algorithm (SSA) is used in this study.

Conclusions/Significance

We show that the proposed minimal signaling network displays two distinct types of response as the strength of the stimulus is decreased. The signaling network has a deterministic part that undergoes rapid activation by a strong stimulus in which case cell-to-cell fluctuations can be ignored. As the strength of the stimulus decreases, the stochastic part of the network begins dominating the signaling response where slow activation is observed with characteristic large cell-to-cell stochastic variability. Interestingly, this proposed stochastic signaling network can capture some of the essential signaling behaviors of a complex apoptotic cell death signaling network that has been studied through experiments and large-scale computer simulations. Thus we claim that the proposed signaling network is an appropriate minimal model of apoptosis signaling. Elucidating the fundamental design principles of complex cellular signaling pathways such as apoptosis signaling remains a challenging task. We demonstrate how our proposed minimal model can help elucidate the effect of a specific apoptotic inhibitor Bcl-2 on apoptotic signaling in a cell-type independent manner. We also discuss the implications of our study in elucidating the adaptive strategy of cell death signaling pathways.     

Mapping rare and common causal alleles for complex human diseases

Advances in genotyping and sequencing technologies have revolutionized the genetics of complex disease by locating rare and common variants that influence an individual's risk for diseases, such as diabetes, cancers, and psychiatric disorders. However, to capitalize on these data for prevention and therapies requires the identification of causal alleles and a mechanistic understanding for how these variants contribute to the disease. After discussing the strategies currently used to map variants for complex diseases, this Primer explores how variants may be prioritized for follow-up functional studies and the challenges and approaches for assessing the contributions of rare and common variants to disease phenotypes.     

Monte Carlo simulation of cell death signaling predicts large cell-to-cell stochastic fluctuations through the type 2 pathway of apoptosis

Apoptosis, or genetically programmed cell death, is a crucial cellular process that maintains the balance between life and death in cells. The precise molecular mechanism of apoptosis signaling and the manner in which type 1 and type 2 pathways of the apoptosis signaling network are differentially activated under distinct apoptotic stimuli is poorly understood. Based on Monte Carlo stochastic simulations, we show that the type 1 pathway becomes activated under strong apoptotic stimuli, whereas the type 2 mitochondrial pathway dominates apoptotic signaling in response to a weak death signal. Our results also show signaling in the type 2 pathway is stochastic; the population average over many cells does not capture the cell-to-cell fluctuations in the time course (~1–10 h) of downstream caspase-3 activation. On the contrary, the probability distribution of caspase-3 activation for the mitochondrial pathway shows a distinct bimodal behavior that can be used to characterize the stochastic signaling in type 2 apoptosis and other similar complex signaling processes. Interestingly, such stochastic fluctuations in apoptosis signaling occur even in the presence of large numbers of signaling molecules.     

Estimation of Genetic Variability of Vigna radiata Cultivars by RAPD Analysis

DNA was isolated from 14 cultivars of Vigna radiata (L.) Wilczek and subjected to RAPD analysis using 14 random decamer primers. These cultivars revealed polymorphism with respect to RAPD markers and were subjected to hierarchical cluster analysis. A dendrogram was prepared based on these data. Analysis of banding patterns confirmed that two strongly aromatic cultivars IC1, IC4, were closely linked. But another aromatic cultivar, B1, formed a separate cluster. The high yielding cultivars were closely related to B1. The phylogenetic tree constructed by the neighbour joining method showed that RAPD results were correlated with morphological characters like plant height, leaf and seed size, seed colour, etc. This revised version was published online in July 2006 with corrections to the Cover Date.