转录因子与microRNA在基因表达调控中的功能联系及差异

转录因子和微RNA(microRNA)是最大的两类反式作用因子,它们是基因表达调控的重要调控因子.它们协调发挥调控作用,精细调控基因的表达,在细胞分化和动物生长发育过程中发挥重要的作用.随着对转录因子和microRNA研究的深入,人们发现转录因子和microRNA在基因表达调控网络中关系紧密,它们的分子作用机制有许多相似之处,两者都通过各自的顺式作用元件调控基因表达,且作用的方式类似.但转录因子和microRNA也存在不同之处,转录因子既可以激活基因表达,也可抑制基因表达,而microRNA主要是抑制基因表达.另外,转录因子调控区的复杂性一般高于microRNA的调控区域.本文综述了转录因子和microRNA的异同点,并提出了未来转录因子和microRNA的研究方向.     

The glucose‐deprivation network counteracts lapatinib‐induced toxicity in resistant ErbB2‐positive breast cancer cells

Dynamic interactions between intracellular networks regulate cellular homeostasis and responses to perturbations. Targeted therapy is aimed at perturbing oncogene addiction pathways in cancer, however, development of acquired resistance to these drugs is a significant clinical problem. A network‐based computational analysis of global gene expression data from matched sensitive and acquired drug‐resistant cells to lapatinib, an EGFR/ErbB2 inhibitor, revealed an increased expression of the glucose deprivation response network, including glucagon signaling, glucose uptake, gluconeogenesis and unfolded protein response in the resistant cells. Importantly, the glucose deprivation response markers correlated significantly with high clinical relapse rates in ErbB2‐positive breast cancer patients. Further, forcing drug‐sensitive cells into glucose deprivation rendered them more resistant to lapatinib. Using a chemical genomics bioinformatics mining of the CMAP database, we identified drugs that specifically target the glucose deprivation response networks to overcome the resistant phenotype and reduced survival of resistant cells. This study implicates the chronic activation of cellular compensatory networks in response to targeted therapy and suggests novel combinations targeting signaling and metabolic networks in tumors with acquired resistance.     

Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression

Organisms have different circuitries that allow converting signal molecule levels to changes in gene expression. An important challenge in synthetic biology involves the de novo design of RNA modules enabling dynamic signal processing in live cells. This requires a scalable methodology for sensing, transmission, and actuation, which could be assembled into larger signaling networks. Here, we present a biochemical strategy to design RNA-mediated signal transduction cascades able to sense small molecules and small RNAs. We design switchable functional RNA domains by using strand-displacement techniques. We experimentally characterize the molecular mechanism underlying our synthetic RNA signaling cascades, show the ability to regulate gene expression with transduced RNA signals, and describe the signal processing response of our systems to periodic forcing in single live cells. The engineered systems integrate RNA–RNA interaction with available ribozyme and aptamer elements, providing new ways to engineer arbitrary complex gene circuits.     

Integrating gene expression and metabolic profiles

Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.     

Topology of gene expression networks as revealed by data mining and modeling

MOTIVATION: Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein-protein interactions, and gene networks that can be derived from gene expression data. In the present paper, we focus on several important issues related to the topology of gene expression networks that have not yet been fully studied. RESULTS: The networks derived from gene expression profiles for both time series experiments in yeast and perturbation experiments in cell lines are studied. We demonstrate that independent from the experimental organism (yeast versus cell lines) and the type of experiment (time courses versus perturbations) the extracted networks have similar topological characteristics suggesting together with the results of other common principles of the structural organization of biological networks. A novel computational model of network growth that reproduces the basic design principles of the observed networks is presented. Advantage of the model is that it provides a general mechanism to generate networks with different types of topology by a variation of a few parameters. We investigate the robustness of the network structure to random damages and to deliberate removal of the most important parts of the system and show a surprising tolerance of gene expression networks to both kinds of disturbance.     

Biosensors and their applications in microbial metabolic engineering

Many metabolic pathways in microbial hosts have been created, modified and engineered to produce useful molecules. The titer and yield of a final compound is often limited by the inefficient use of cellular resources and imbalanced metabolism. Engineering sensory-regulation devices that regulate pathway gene expression in response to the environment and metabolic status of the cell have great potential to solve these problems, and enhance product titers and yields. This review will focus on recent developments in biosensor design, and their applications for controlling microbial behavior.     

Genetic screening of new genes responsible for cellular adaptation to hypoxia using a genome-wide shRNA library

Oxygen is a vital requirement for multi-cellular organisms to generate energy and cells have developed multiple compensatory mechanisms to adapt to stressful hypoxic conditions. Such adaptive mechanisms are intricately interconnected with other signaling pathways that regulate cellular functions such as cell growth. However, our understanding of the overall system governing the cellular response to the availability of oxygen remains limited. To identify new genes involved in the response to hypoxic stress, we have performed a genome-wide gene knockdown analysis in human lung carcinoma PC8 cells using an shRNA library carried by a lentiviral vector. The knockdown analysis was performed under both normoxic and hypoxic conditions to identify shRNA sequences enriched or lost in the resulting selected cell populations. Consequently, we identified 56 candidate genes that might contribute to the cellular response to hypoxia. Subsequent individual knockdown of each gene demonstrated that 13 of these have a significant effect upon oxygen-sensitive cell growth. The identification of BCL2L1, which encodes a Bcl-2 family protein that plays a role in cell survival by preventing apoptosis, validates the successful design of our screen. The other selected genes have not previously been directly implicated in the cellular response to hypoxia. Interestingly, hypoxia did not directly enhance the expression of any of the identified genes, suggesting that we have identified a new class of genes that have been missed by conventional gene expression analyses to identify hypoxia response genes. Thus, our genetic screening method using a genome-wide shRNA library and the newly-identified genes represent useful tools to analyze the cellular systems that respond to hypoxic stress.     

Cytokine involvement in viral permissiveness and the progression of HIV disease

Many viruses have evolved novel means of exploiting host defense mechanisms for their own survival. This exploitation may be best exemplified by the interrelationships between certain viruses and the host cytokine networks. Many viruses, including the human immunodeficiency virus type-1 (HIV-1), rely on the liberation and cellular action of host immune cytokines to expand their host cell range, to regulate their cellular expression, and to maintain their dormant state until the proper extracellular conditions arise. As again exemplified by HIV-1, viruses may also take an active role regulating cytokine expression and cell surface cytokine receptors. Because the viral life cycle, and in particular the HIV-1 life cycle, is so intertwined with cytokine regulatory networks, these networks represent potential points for therapeutic intervention. As our understanding of cellular cytokine pathways involved in viral infection and replication continues to expand, so too will our ability to design rational anti-viral therapies to alter multiple steps along the viral life cycle.     

Gene expression profiling of flagellar disassembly in Chlamydomonas reinhardtii

Flagella are sensory organelles that interact with the environment through signal transduction and gene expression networks. We used microarray profiling to examine gene regulation associated with flagellar length change in the green alga Chlamydomonas reinhardtii. Microarrays were probed with fluorescently labeled cDNAs synthesized from RNA extracted from cells before and during flagellar assembly or disassembly. Evaluation of the gene expression profiles identified >100 clones showing at least a twofold change in expression during flagellar length changes. Products of these genes are associated not only with flagellar structure and motility but also with other cellular responses, including signal transduction and metabolism. Expression of specific genes from each category was further characterized at higher resolution by using quantitative real-time PCR (qRT-PCR). Analysis and comparison of the gene expression profiles coupled to flagellar assembly and disassembly revealed that each process involves a new and uncharacterized whole-cell response to flagellar length changes. This analysis lays the groundwork for a more comprehensive understanding of the cellular and molecular networks regulating flagellar length changes.     

From genomes to in silico cells via metabolic networks

Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of phenotypes from phenotypic arrays and in gene-deletion studies. They have also been used for gene expression analysis guided by metabolic network structure, leading to the identification of commonly regulated genes. Thus, genome-scale metabolic modeling currently stands out as one of the most promising approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components.     

Changes in MicroRNA expression patterns in human fibroblasts after low-LET radiation

Exposure to radiation provokes cellular responses controlled in part by gene expression networks. MicroRNAs (miRNAs) are small non-coding RNAs which mostly regulate gene expression by degrading the messages or inhibiting translation. Here, we investigated changes in miRNA expression patterns after low (0.1 Gy) and high (2.0 Gy) doses of X-ray in human fibroblasts. At early (0.5 h) and late (6 and 24 h) time points, irradiation caused qualitative and quantitative differences in the down-regulation of miRNA levels, including miR-92b, 137, 660, and 656. A transient up-regulation of miRNAs was observed after 2 h post-irradiation following high doses of radiation, including miR-558 and 662. MicroRNA levels were inversely correlated with targets from mRNA and proteomic profiling after 2.0 Gy of radiation. MicroRNAs miR-579, 608, 548-3p, and 585 are noted for targeting genes involved in radioresponsive mechanisms, such as cell cycle checkpoint and apoptosis. We suggest here a model in which miRNAs may act as "hub" regulators of specific cellular responses, immediately down-regulated so as to stimulate DNA repair mechanisms, followed by up-regulation involved in suppressing apoptosis for cell survival. Taken together, miRNAs may mediate signaling pathways in sequential fashion in response to radiation, and may serve as biodosimetric markers of radiation exposure.