Genome-Scale Reconstruction of Escherichia coli's Transcriptional and Translational Machinery: A Knowledge Base, Its Mathematical Formulation, and Its Functional Characterization

Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship

The Genetics of Obesity

Obesity is a result of excess body fat accumulation. This excess is associated with adverse health effects such as CVD, type 2 diabetes, and cancer. The development of obesity has an evident environmental contribution, but as shown by heritability estimates of 40% to 70%, a genetic susceptibility component is also needed. Progress in understanding the etiology has been slow, with findings largely restricted to monogenic, severe forms of obesity. However, technological and analytical advances have enabled detection of more than 20 obesity susceptibility loci. These contain genes suggested to be involved in the regulation of food intake through action in the central nervous system as well as in adipocyte function. These results provide plausible biological pathways that may, in the future, be targeted as part of treatment or prevention strategies. Although the proportion of heritability explained by these genes is small, their detection heralds a new phase in understanding the etiology of common obesity

Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine

Histological and transcriptome-wide level characteristics of fetal myofiber hyperplasia during the second half of gestation in Texel and Ujumqin sheep

<p>Abstract</p> <p>Background</p> <p>Whether myofibers increase with a pulsed-wave mode at particular developmental stages or whether they augment evenly across developmental stages in large mammals is unclear. Additionally, the molecular mechanisms of myostatin in myofiber hyperplasia at the fetal stage in sheep remain unknown. Using the first specialized transcriptome-wide sheep oligo DNA microarray and histological methods, we investigated the gene expression profile and histological characteristics of developing fetal ovine longissimus muscle in Texel sheep (high muscle and low fat), as a myostatin model of natural mutation, and Ujumqin sheep (low muscle and high fat). Fetal skeletal muscles were sampled at 70, 85, 100, 120, and 135 d of gestation.</p> <p>Results</p> <p>Myofiber number increased sharply with a pulsed-wave mode at certain developmental stages but was not augmented evenly across developmental stages in fetal sheep. The surges in myofiber hyperplasia occurred at 85 and 120 d in Texel sheep, whereas a unique proliferative surge appeared at 100 d in Ujumqin sheep. Analysis of the microarray demonstrated that immune and hematological systems' development and function, lipid metabolism, and cell communication were the biological functions that were most differentially expressed between Texel and Ujumqin sheep during muscle development. Pathways associated with myogenesis and the proliferation of myoblasts, such as calcium signaling, chemokine (C-X-C motif) receptor 4 signaling, and vascular endothelial growth factor signaling, were affected significantly at specific fetal stages, which underpinned fetal myofiber hyperplasia and postnatal muscle hypertrophy. Moreover, we identified some differentially expressed genes between the two breeds that could be potential myostatin targets for further investigation.</p> <p>Conclusions</p> <p>Proliferation of myofibers proceeded in a pulsed-wave mode at particular fetal stages in the sheep. The myostatin mutation changed the gene expression pattern in skeletal muscle at a transcriptome-wide level, resulting in variation in myofiber phenotype between Texel and Ujumqin sheep during the second half of gestation. Our findings provide a novel and dynamic description of the effect of myostatin on skeletal muscle development, which contributes to understanding the biology of muscle development in large mammals.</p

From Cleanroom to Desktop: Emerging Micro-Nanofabrication Technology for Biomedical Applications

This review is motivated by the growing demand for low-cost, easy-to-use, compact-size yet powerful micro-nanofabrication technology to address emerging challenges of fundamental biology and translational medicine in regular laboratory settings. Recent advancements in the field benefit considerably from rapidly expanding material selections, ranging from inorganics to organics and from nanoparticles to self-assembled molecules. Meanwhile a great number of novel methodologies, employing off-the-shelf consumer electronics, intriguing interfacial phenomena, bottom-up self-assembly principles, etc., have been implemented to transit micro-nanofabrication from a cleanroom environment to a desktop setup. Furthermore, the latest application of micro-nanofabrication to emerging biomedical research will be presented in detail, which includes point-of-care diagnostics, on-chip cell culture as well as bio-manipulation. While significant progresses have been made in the rapidly growing field, both apparent and unrevealed roadblocks will need to be addressed in the future. We conclude this review by offering our perspectives on the current technical challenges and future research opportunities

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Mining the human phenome using allelic scores that index biological intermediates

J. Kaprio ja M-L. Lokki työryhmien jäseniä.It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.Peer reviewe