Temporally distinct roles of ATM and ROS in genotoxic- stress-dependent induction and maintenance of cellular senescence

Cells exposed to genotoxic stress induce cellular senescence through a DNA damage response (DDR) pathway regulated by ATM kinase and reactive oxygen species (ROS). Here, we show that the regulatory roles for ATM kinase and ROS differ during induction and maintenance of cellular senescence. Cells treated with different genotoxic agents were analyzed using specific pathway markers and inhibitors to determine that ATM kinase activation is directly proportional to the dose of the genotoxic stress and that senescence initiation is not dependent on ROS or the p53 status of cells. Cells in which ROS was quenched still activated ATM and initiated the DDR when insulted, and progressed normally to senescence. By contrast, maintenance of a viable senescent state required the presence of ROS as well as activated ATM. Inhibition or removal of either of the components caused cell death in senescent cells, through a deregulated ATM-ROS axis. Overall, our work demonstrates existence of an intricate temporal hierarchy between genotoxic stress, DDR and ROS in cellular senescence. Our model reports the existence of different stages of cellular senescence with distinct regulatory networks.

Cyclic Cationic Peptides Containing Sugar Amino Acids Selectively Distinguishes and Inhibits Maturation of Pre-miRNAs of the Same Family

The discovery of microRNAs (miRNAs) has added a new dimension to the gene regulatory networks, making aberrantly expressed miRNAs as therapeutically important targets. Small molecules that can selectively target and modulate miRNA levels can thus serve as lead structures. Cationic cyclic peptides containing sugar amino acids represent a new class of small molecules that can target miRNA selectively. Upon treatment of these small molecules in breast cancer cell line, we profiled 96 therapeutically important miRNAs associated with cancer and observed that these peptides can selectively target paralogous miRNAs of the same seed family. This selective inhibition is of prime significance in cases when miRNAs of the same family have tissue-specific expression and perform different functions. During these conditions, targeting an entire miRNA family could lead to undesired adverse effects. The selective targeting is attributable to the difference in the three-dimensional structures of precursor miRNAs. Hence, the core structure of these peptides can be used as a scaffold for designing more potent inhibitors of miRNA maturation and hence function.

Divergence in Patterns of Leaf Growth Polarity Is Associated with the Expression Divergence of miR396

Lateral appendages often show allometric growth with a specific growth polarity along the proximo-distal axis. Studies on leaf growth in model plants have identified a basipetal growth direction with the highest growth rate at the proximal end and progressively lower rates toward the distal end. Although the molecular mechanisms governing such a growth pattern have been studied recently, variation in leaf growth polarity and, therefore, its evolutionary origin remain unknown. By surveying 75 eudicot species, here we report that leaf growth polarity is divergent. Leaf growth in the proximo-distal axis is polar, with more growth arising from either the proximal or the distal end; dispersed with no apparent polarity; or bidirectional, with more growth contributed by the central region and less growth at either end. We further demonstrate that the expression gradient of the miR396-GROWTH-REGULATING FACTOR module strongly correlates with the polarity of leaf growth. Altering the endogenous pattern of miR396 expression in transgenic Arabidopsis thaliana leaves only partially modified the spatial pattern of cell expansion, suggesting that the diverse growth polarities might have evolved via concerted changes in multiple gene regulatory networks.

On the Role of Delays in Biological Systems : Analysis and Design

This work quantifies the nature of delays in genetic regulatory networks and their effect on system dynamics. It is known that a time lag can emerge from a sequence of biochemical reactions. Applying this modeling framework to the protein production processes, delay distributions are derived in a stochastic (probability density function) and deterministic setting (impulse function), whilst being shown to be equivalent under different assumptions. The dependence of the distribution properties on rate constants, gene length, and time-varying temperatures is investigated. Overall, the distribution of the delay in the context of protein production processes is shown to be highly dependent on the size of the genes and mRNA strands as well as the reaction rates. Results suggest longer genes have delay distributions with a smaller relative variance, and hence, less uncertainty in the completion times, however, they lead to larger delays. On the other hand large uncertainties may actually play a positive role, as broader distributions can lead to larger stability regions when this formalization of the protein production delays is incorporated into a feedback system.

Furthermore, evidence suggests that delays may play a role as an explicit design into existing controlling mechanisms. Accordingly, the reccurring dual-feedback motif is also investigated with delays incorporated into the feedback channels. The dual-delayed feedback is shown to have stabilizing effects through a control theoretic approach. Lastly, a distributed delay based controller design method is proposed as a potential design tool. In a preliminary study, the dual-delayed feedback system re-emerges as an effective controller design.

On the Dirichlet Prior and Bayesian Regularization

A common objective in learning a model from data is to recover its network structure, while the model parameters are of minor interest. For example, we may wish to recover regulatory networks from high-throughput data sources. In this paper we examine how Bayesian regularization using a Dirichlet prior over the model parameters affects the learned model structure in a domain with discrete variables. Surprisingly, a weak prior in the sense of smaller equivalent sample size leads to a strong regularization of the model structure (sparse graph) given a sufficiently large data set. In particular, the empty graph is obtained in the limit of a vanishing strength of prior belief. This is diametrically opposite to what one may expect in this limit, namely the complete graph from an (unregularized) maximum likelihood estimate. Since the prior affects the parameters as expected, the prior strength balances a "trade-off" between regularizing the parameters or the structure of the model. We demonstrate the benefits of optimizing this trade-off in the sense of predictive accuracy.

Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology.

Synthetic biology seeks to enable programmed control of cellular behavior though engineered biological systems. These systems typically consist of synthetic circuits that function inside, and interact with, complex host cells possessing pre-existing metabolic and regulatory networks. Nevertheless, while designing systems, a simple well-defined interface between the synthetic gene circuit and the host is frequently assumed. We describe the generation of robust but unexpected oscillations in the densities of bacterium Escherichia coli populations by simple synthetic suicide circuits containing quorum components and a lysis gene. Contrary to design expectations, oscillations required neither the quorum sensing genes (luxR and luxI) nor known regulatory elements in the P(luxI) promoter. Instead, oscillations were likely due to density-dependent plasmid amplification that established a population-level negative feedback. A mathematical model based on this mechanism captures the key characteristics of oscillations, and model predictions regarding perturbations to plasmid amplification were experimentally validated. Our results underscore the importance of plasmid copy number and potential impact of "hidden interactions" on the behavior of engineered gene circuits - a major challenge for standardizing biological parts. As synthetic biology grows as a discipline, increasing value may be derived from tools that enable the assessment of parts in their final context.

Transcription factors MYOCD, SRF, Mesp1 and SMARCD3 enhance the cardio-inducing effect of GATA4, TBX5, and MEF2C during direct cellular reprogramming.

Transient overexpression of defined combinations of master regulator genes can effectively induce cellular reprogramming: the acquisition of an alternative predicted phenotype from a differentiated cell lineage. This can be of particular importance in cardiac regenerative medicine wherein the heart lacks the capacity to heal itself, but simultaneously contains a large pool of fibroblasts. In this study we determined the cardio-inducing capacity of ten transcription factors to actuate cellular reprogramming of mouse embryonic fibroblasts into cardiomyocyte-like cells. Overexpression of transcription factors MYOCD and SRF alone or in conjunction with Mesp1 and SMARCD3 enhanced the basal but necessary cardio-inducing effect of the previously reported GATA4, TBX5, and MEF2C. In particular, combinations of five or seven transcription factors enhanced the activation of cardiac reporter vectors, and induced an upregulation of cardiac-specific genes. Global gene expression analysis also demonstrated a significantly greater cardio-inducing effect when the transcription factors MYOCD and SRF were used. Detection of cross-striated cells was highly dependent on the cell culture conditions and was enhanced by the addition of valproic acid and JAK inhibitor. Although we detected Ca(2+) transient oscillations in the reprogrammed cells, we did not detect significant changes in resting membrane potential or spontaneously contracting cells. This study further elucidates the cardio-inducing effect of the transcriptional networks involved in cardiac cellular reprogramming, contributing to the ongoing rational design of a robust protocol required for cardiac regenerative therapies.

Metabolic regulation of T lymphocytes.

T cell activation leads to dramatic shifts in cell metabolism to protect against pathogens and to orchestrate the action of other immune cells. Quiescent T cells require predominantly ATP-generating processes, whereas proliferating effector T cells require high metabolic flux through growth-promoting pathways. Further, functionally distinct T cell subsets require distinct energetic and biosynthetic pathways to support their specific functional needs. Pathways that control immune cell function and metabolism are intimately linked, and changes in cell metabolism at both the cell and system levels have been shown to enhance or suppress specific T cell functions. As a result of these findings, cell metabolism is now appreciated as a key regulator of T cell function specification and fate. This review discusses the role of cellular metabolism in T cell development, activation, differentiation, and function to highlight the clinical relevance and opportunities for therapeutic interventions that may be used to disrupt immune pathogenesis.

Influence of network topology and data collection on network inference.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

Revealing differences in gene network inference algorithms on the network level by ensemble methods

Motivation: The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context.

Results: In this article, we conduct a statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures. We employ ensemble methods allowing to assess the inferability down to the level of individual edges. Our analysis reveals the bias of these inference methods with respect to the inference of various network components and, hence, provides guidance in the interpretation of inferred regulatory networks from expression data. Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing.

miR-7 and miR-214 are specifically expressed during neuroblastoma differentiation, cortical development and embryonic stem cells differentiation, and control neurite outgrowth in vitro

The mammalian nervous system exerts essential control on many physiological processes in the organism and is itself controlled extensively by a variety of genetic regulatory mechanisms. microRNA (miR), an abundant class of small non-coding RNA, are emerging as important post-transcriptional regulators of gene expression in the brain. Increasing evidence indicates that miR regulate both the development and function of the nervous system. Moreover, deficiency in miR function has also been implicated in a number of neurological disorders. Expression profile analysis of miR is necessary to understand their complex role in the regulation of gene expression during the development and differentiation of cells. Here we present a comparative study of miR expression profiles in neuroblastoma, in cortical development, and in neuronal differentiation of embryonic stem (ES) cells. By microarray profiling in combination with real time PCR we show that miR-7 and miR-214 are modulated in neuronal differentiation (as compared to miR-1, -16 and -133a), and control neurite outgrowth in vitro. These findings provide an important step toward further elucidation of miR function and miR-related gene regulatory networks in the mammalian central nervous system. (C) 2010 Elsevier Inc. All rights reserved.

The New Political Economy of the Macroprudential Ideational Shift

From late 2008 onwards, in the space of six months, international financial regulatory networks centred around the Swiss city of Basel presided over a startlingly rapid ideational shift, the significance and importance of which remains to be deciphered. From being relatively unpopular and very much on the sidelines, the idea of macroprudential regulation (MPR) moved to the centre of the policy agenda and came to represent a new Basel consensus, as the principal interpretative frame, for financial technocrats and regulators seeking to diagnose and understand the financial crisis and to advance institutional blueprints for regulatory reform. This article sets out to explain how and why that ideational shift occurred. It identifies four scoping conditions of presence, position, promotion, and plausibility, that account for the successful rise to prominence of macroprudential ideas through an insiders' coup d'état. The final section of the article argues that this macroprudential shift is an example of a ‘gestalt flip’ or third order change in Peter Hall's terms, but it is not yet a paradigm shift, because the development of first order policy settings and second order policy instruments is still ongoing, giving the macroprudential ideational shift a highly contested and contingent character.

Bagging statistical network inference from large-scale gene expression data.

Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository. © 2012 de Matos Simoes, Emmert-Streib.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

Large-scale association analysis identifies new risk loci for coronary artery disease

Coronary artery disease (CAD) is the commonest cause of death. Here, we report an association analysis in 63,746 CAD cases and 130,681 controls identifying 15 loci reaching genome-wide significance, taking the number of susceptibility loci for CAD to 46, and a further 104 independent variants (r(2)