Gene regulation and systems biology最新文献

Much is known regarding the structure and logic of genetic regulatory networks. Less understood is the contextual organization of promoter signals used during transcription initiation, the most pivotal stage during gene expression. Here we show that promoter networks organize spontaneously at a dimension between the 1-dimension of the DNA and 3-dimension of the cell. Network methods were used to visualize the global structure of E. coli sigma (sigma) recognition footprints using published promoter sequences (RegulonDB). Footprints were rendered as networks with weighted edges representing bp-sharing between promoters (nodes). Serial thresholding revealed phase transitions at positions predicted by percolation theory, and nuclei denoting short steps through promoter space with geometrically constrained linkages. The network nuclei are fractals, a power-law organization not yet described for promoters. Genome-wide promoter abundance also scaled as a power-law. We propose a general model for the development of a fractal nucleus in a transcriptional grammar.

Poor reproducibility of microarray measurements is a major obstacle to their application as an instrument for clinical diagnostics. In this paper, several aspects of poor reproducibility are analyzed. All of them belong to the category of interpretive weaknesses of DNA microarray technology. First, the attention is drawn to the fact that absence of the information regarding post-transcriptional mRNA stability makes it impossible to evaluate the level of gene activity from the relative mRNA abundances, the quantities available from microarray measurements. Second, irreducible intracellular variability with persistent patterns of stochasticity and burstiness put natural limits to reproducibility. Third, strong interactions within intracellular biomolecular networks make it highly problematic to build a bridge between transcription rates of individual genes and structural fidelity of their genetic codes. For these reasons, the microarray measurements of relative mRNA abundances are more appropriate in laboratory settings as a tool for scientific research, hypotheses generating and producing the leads for subsequent validation through more sophisticated technologies. As to clinical settings, where firm conclusive diagnoses, not the leads for further experimentation, are required, microarrays still have a long way to go until they become a reliable instrument in patient-related decision making.

The mechanism of FGF23 action in calcium/phosphorus metabolism of patients with chronic kidney disease (CKD) was studied using a mathematical model and clinical data in a public domain. We have previously built a physiological model that describes interactions of PTH, calcitriol, and FGF23 in mineral metabolism encompassing organs such as bone, intestine, kidney, and parathyroid glands. Since an elevated FGF23 level in serum is a characteristic symptom of CKD patients, we evaluate herein potential metabolic alterations in response to administration of a neutralizing antibody against FGF23. Using the parameters identified from available clinical data, we observed that a transient decrease in the FGF23 level elevated the serum concentrations of PTH, calcitriol, and phosphorus. The model also predicted that the administration reduced a urinary output of phosphorous. This model-based prediction indicated that the therapeutic reduction of FGF23 by the neutralizing antibody did not reduce phosphorus burden of CKD patients and decreased the urinary phosphorous excretion. Thus, the high FGF23 level in CKD patients was predicted to be a failure of FGF23-mediated phosphorous excretion. The results herein indicate that it is necessary to understand the mechanism in CKD in which the level of FGF23 is elevated without effectively regulating phosphorus.

In the past decade, the development of synthetic gene networks has attracted much attention from many researchers. In particular, the genetic oscillator known as the repressilator has become a paradigm for how to design a gene network with a desired dynamic behaviour. Even though the repressilator can show oscillatory properties in its protein concentrations, their amplitudes, frequencies and phases are perturbed by the kinetic parametric fluctuations (intrinsic molecular perturbations) and external disturbances (extrinsic molecular noises) of the environment. Therefore, how to design a robust genetic oscillator with desired amplitude, frequency and phase under stochastic intrinsic and extrinsic molecular noises is an important topic for synthetic biology.In this study, based on periodic reference signals with arbitrary amplitudes, frequencies and phases, a robust synthetic gene oscillator is designed by tuning the kinetic parameters of repressilator via a genetic algorithm (GA) so that the protein concentrations can track the desired periodic reference signals under intrinsic and extrinsic molecular noises. GA is a stochastic optimization algorithm which was inspired by the mechanisms of natural selection and evolution genetics. By the proposed GA-based design algorithm, the repressilator can track the desired amplitude, frequency and phase of oscillation under intrinsic and extrinsic noises through the optimization of fitness function.The proposed GA-based design algorithm can mimic the natural selection in evolutionary process to select adequate kinetic parameters for robust genetic oscillators. The design method can be easily extended to any synthetic gene network design with prescribed behaviours.

Functionalities which may be genetically programmed into a bacterium are limited by its range of possible activities and its sensory capabilities. Therefore, enhancing the bacterial sensory repertoire is a crucial step for expanded utility in potential biomedical, industrial or environmental applications. Using microarray and qRT-PCR analyses, we have investigated transcription in E. coli (strain CSH50) following FimH-mediated adhesion to biocompatible substrates. Specifically, wild-type FimH-mediated adhesion of E. coli to mannose agarose beads and His-tagged FimH-mediated adhesion to Ni(2+)-NTA beads both led to induction of ahpCF, dps, grxA and marRAB genes among bound cells relative to unbound cells. The strongly-induced genes are known to be regulated by OxyR or SoxS cytoplasmic redox sensors. Some differentially altered genes also overlapped with those implicated in biofilm formation. This study provides an insight into transcriptional events following FimH-mediated adhesion and may provide a platform for elucidation of the signaling circuit necessary for engineering a synthetic attachment response in E. coli.

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Microarrays are becoming a widely used tool to study gene expression evolution. A recent paper by Wang and Rekaya describes a comprehensive study of gene expression evolution by microarray. The work provides a perspective to study gene expression evolution in terms of functional enrichment and promoter conservation. It was found that gene expression patterns are highly conserved in some biological processes, but the correlation between promoter and gene expression is insignificant. This scope of this work and future improvement to study gene expression evolution will be discussed in this article.

Hereditary inclusion body myopathy type 2 (HIBM2) is a myopathy characterized by progressive muscle weakness with early adult onset. The disease is the result of a recessive mutation in the Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase gene (GNE), which results in reduced enzyme function and sialic acid levels. A majority of individuals with HIBM2 are from Iranian-Jewish or Japanese decent, but isolated cases have been identified world wide. This article reviews the diagnostic criteria for HIBM2. Current research with a highlight on the biology of the disease and the role of GNE in the sialic acid pathway are assessed. Finally, therapeutic investigations and animal models are discussed with a focus on future studies to better understand the pathology of Hereditary Inclusion Body Myopathy and move therapeutic agents towards clinical trials.

In order to investigate the possible mechanisms for eve stripe formation of Drosophila embryo, a spatio-temporal gene/protein interaction network model is proposed to mimic dynamic behaviors of protein synthesis, protein decay, mRNA decay, protein diffusion, transcription regulations and autoregulation to analyze the interplay of genes and proteins at different compartments in early embryogenesis. In this study, we use the maximum likelihood (ML) method to identify the stochastic 3-D Embryo Space-Time (3-DEST) dynamic model for gene/protein interaction network via 3-D mRNA and protein expression data and then use the Akaike Information Criterion (AIC) to prune the gene/protein interaction network. The identified gene/protein interaction network allows us not only to analyze the dynamic interplay of genes and proteins on the border of eve stripes but also to infer that eve stripes are established and maintained by network motifs built by the cooperation between transcription regulations and diffusion mechanisms in early embryogenesis. Literature reference with the wet experiments of gene mutations provides a clue for validating the identified network. The proposed spatio-temporal dynamic model can be extended to gene/protein network construction of different biological phenotypes, which depend on compartments, e.g. postnatal stem/progenitor cell differentiation.

Microarray measurements of mRNA abundances is a standard tool for evaluation of transcriptional activity in functional genomics. The methodology underlying these measurements assumes existence of a direct link between transcription levels, that is, gene-specific mRNA copy numbers present in the cell, and transcription rates, that is, the numbers of gene-specific mRNA molecules synthesized per unit of time. In this paper, the question of whether or not such a tight interdependence may exist is examined in the context of nonlinear dynamics of genetic regulatory networks. Using the equations of chemical kinetics, a model has been constructed that is capable of explicitly taking into consideration nonlinear interactions between the genes through the teamwork of transcription factors. Jacobian analysis of stability has shown that steady state equilibrium is impossible in such systems. However, phase space compressibility is found to be negative, thus suggesting that asymptotic stability may exist and assume either the form of limit cycle or of a chaotic attractor. It is argued that in rapidly fluctuating or chaotic systems, direct evaluation of transcription rates through transcription levels is highly problematic. It is also noted that even if a hypothetical steady state did exist, the knowledge of transcription levels alone would not be sufficient for the evaluation of transcription rates; an additional set of parameters, namely the mRNA decay rates, would be required. An overall conclusion of the work is that the measurements of mRNA abundances are not truly representative of the functionality of genes and structural fidelity of the genetic codes.