Sensory systems often detect multiple types of inputs. For example, a receptor in a cell-signaling system often binds multiple kinds of ligands, and sensory neurons can respond to different types of stimuli. How do sensory systems compare these different kinds of signals? Here, we consider this question in a class of sensory systems – including bacterial chemotaxis- which have a property known as fold-change detection: their output dynamics, including amplitude and response time, depends only on the relative changes in signal, rather than absolute changes, over a range of several decades of signal. We analyze how fold-change detection systems respond to multiple signals, using mathematical models. Suppose that a step of fold F1 is made in input 1, together with a step of F2 in input 2. What total response does the system provide? We show that when both input signals impact the same receptor with equal number of binding sites, the integrated response is multiplicative: the response dynamics depend only on the product of the two fold changes, F1F2. When the inputs bind the same receptor with different number of sites n1 and n2, the dynamics depend on a product of power laws, . Thus, two input signals which vary over time in an inverse way can lead to no response. When the two inputs affect two different receptors, other types of integration may be found and generally the system is not constrained to respond according to the product of the fold-change of each signal. These predictions can be readily tested experimentally, by providing cells with two simultaneously varying input signals. The present study suggests how cells can compare apples and oranges, namely by comparing each to its own background level, and then multiplying these two fold-changes.
The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer profiling studies for identifying cancer biomarkers, and predicting treatment response. Uncovering complex relationships, however, remains the most challenging task as it requires compiling and efficiently querying data from various sources. Here, we describe the Stress Response Array Profiler (StRAP), an open-source, web-based resource for storage, profiling, visualization, and sharing of cancer genomic data. StRAP houses multi-cancer microarray data with major emphasis on radiotherapy studies, and takes a systems biology approach towards the integration, comparison, and cross-validation of multiple cancer profiling studies. The database is a comprehensive platform for comparative analysis of gene expression data. For effective use of arrays, we provide user-friendly and interactive visualization tools that can display the data and query results. StRAP is web-based, platform-independent, and freely accessible at http://strap.nci.nih.gov/.
Widespread unexplained variations in clinical practices and patient outcomes suggest major opportunities for improving the quality and safety of medical care. However, there is little consensus regarding how to best identify and disseminate healthcare improvements and a dearth of theory to guide the debate. Many consider multicenter randomized controlled trials to be the gold standard of evidence-based medicine, although results are often inconclusive or may not be generally applicable due to differences in the contexts within which care is provided. Increasingly, others advocate the use “quality improvement collaboratives”, in which multi-institutional teams share information to identify potentially better practices that are subsequently evaluated in the local contexts of specific institutions, but there is concern that such collaborative learning approaches lack the statistical rigor of randomized trials. Using an agent-based model, we show how and why a collaborative learning approach almost invariably leads to greater improvements in expected patient outcomes than more traditional approaches in searching simulated clinical fitness landscapes. This is due to a combination of greater statistical power and more context-dependent evaluation of treatments, especially in complex terrains where some combinations of practices may interact in affecting outcomes. The results of our simulations are consistent with observed limitations of randomized controlled trials and provide important insights into probable reasons for effectiveness of quality improvement collaboratives in the complex socio-technical environments of healthcare institutions. Our approach illustrates how modeling the evolution of medical practice as search on a clinical fitness landscape can aid in identifying and understanding strategies for improving the quality and safety of medical care.
AIM: To investigate the effect and the possible mechanism of ginsenoside Rb1 on small intestinal smooth muscle motility in mice.
METHODS: Intestinal smooth muscle strips were isolated from male ICR mice (5 wk old), and the effect of ginsenoside Rb1 on spontaneous contraction was recorded with an electrophysiolograph. The effect of ginsenoside Rb1 on ion channel currents, including the voltage-gated K+ channel current (IKV), calcium-activated potassium channel currents (IKCa), spontaneous transient outward currents and ATP-sensitive potassium channel current (IKATP), was recorded on freshly isolated single cells using the whole-cell patch clamp technique.
RESULTS: Ginsenoside Rb1 dose-dependently inhibited the spontaneous contraction of intestinal smooth muscle by 21.15% ± 3.31%, 42.03% ± 8.23% and 67.23% ± 5.63% at concentrations of 25 μmol/L, 50 μmol/L and 100 μmol/L, respectively (n = 5, P < 0.05). The inhibitory effect of ginsenoside Rb1 on spontaneous contraction was significantly but incompletely blocked by 10 mmol/L tetraethylammonium or 0.5 mmol/L 4-aminopyridine, respectively (n = 5, P < 0.05). However, the inhibitory effect of ginsenoside Rb1 on spontaneous contraction was not affected by 10 μmol/L glibenclamide or 0.4 μmol/L tetrodotoxin. At the cell level, ginsenoside Rb1 increased outward potassium currents, and IKV was enhanced from 1137.71 ± 171.62 pA to 1449.73 ± 162.39 pA by 50 μmol/L Rb1 at +60 mV (n = 6, P < 0.05). Ginsenoside Rb1 increased IKCa and enhanced the amplitudes of spontaneous transient outward currents from 582.77 ± 179.09 mV to 788.12 ± 278.34 mV (n = 5, P < 0.05). However, ginsenoside Rb1 (50 μmol/L) had no significant effect on IKATP (n = 3, P < 0.05).
CONCLUSION: These results suggest that ginsenoside Rb1 has an inhibitory effect on the spontaneous contraction of mouse intestinal smooth muscle mediated by the activation of IKV and IKCa, but the KATP channel was not involved in this effect.
Ginsenoside Rb1; Intestinal smooth muscle; Intestinal smooth muscle cell; Potassium channel; Spontaneous contraction; Whole-cell patch clamp technique
Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype) while allowing for switching between multiple phenotypes (network states) as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i) preserve all the already acquired phenotypes (dynamical attractor states) and (ii) generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation) while conserving the existing phenotypes (conservation) suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators) similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape.
Dynamically critical systems are those which operate at the border of a phase transition between two behavioral regimes often present in complex systems: order and disorder. Critical systems exhibit remarkable properties such as fast information processing, collective response to perturbations or the ability to integrate a wide range of external stimuli without saturation. Recent evidence indicates that the genetic networks of living cells are dynamically critical. This has far reaching consequences, for it is at criticality that living organisms can tolerate a wide range of external fluctuations without changing the functionality of their phenotypes. Therefore, it is necessary to know how genetic criticality emerged through evolution. Here we show that dynamical criticality naturally emerges from the delicate balance between two fundamental forces of natural selection that make organisms evolve: (i) the existing phenotypes must be resilient to random mutations, and (ii) new phenotypes must emerge for the organisms to adapt to new environmental challenges. The joint effect of these two forces, which are essential for evolvability, is sufficient in our computational models to generate populations of genetic networks operating at criticality. Thus, natural selection acting as a tinkerer of evolvable systems naturally generates critical dynamics.
Stem cell behaviours, such as stabilization of the undecided state of pluripotency or multipotency, the priming towards a prospective fate, binary fate decisions and irreversible commitment, must all somehow emerge from a genome-wide gene-regulatory network. Its unfathomable complexity defies the standard mode of explanation that is deeply rooted in molecular biology thinking: the reduction of observables to linear deterministic molecular pathways that are tacitly taken as chains of causation. Such culture of proximate explanation that uses qualitative arguments, simple arrow–arrow schemes or metaphors persists despite the ceaseless accumulation of ‘omics’ data and the rise of systems biology that now offers precise conceptual tools to explain emergent cell behaviours from gene networks. To facilitate the embrace of the principles of physics and mathematics that underlie such systems and help to bridge the gap between the formal description of theorists and the intuition of experimental biologists, we discuss in qualitative terms three perspectives outside the realm of their familiar linear-deterministic view: (i) state space (ii), high-dimensionality and (iii) heterogeneity. These concepts jointly offer a new vista on stem cell regulation that naturally explains many novel, counterintuitive observations and their inherent inevitability, obviating the need for ad hoc explanations of their existence based on natural selection. Hopefully, this expanded view will stimulate novel experimental designs.
gene-regulatory networks; dynamics; high-dimensional state space; heterogeneity; cell fate
Gradual or sudden transitions among different states as exhibited by cell populations in a biological sample under particular conditions or stimuli can be detected and profiled by flow cytometric time course data. Often such temporal profiles contain features due to transient states that present unique modeling challenges. These could range from asymmetric non-Gaussian distributions to outliers and tail subpopulations, which need to be modeled with precision and rigor.
To ensure precision and rigor, we propose a parametric modeling framework StateProfiler based on finite mixtures of skew t-Normal distributions that are robust against non-Gaussian features caused by asymmetry and outliers in data. Further, we present in StateProfiler a new greedy EM algorithm for fast and optimal model selection. The parsimonious approach of our greedy algorithm allows us to detect the genuine dynamic variation in the key features as and when they appear in time course data. We also present a procedure to construct a well-fitted profile by merging any redundant model components in a way that minimizes change in entropy of the resulting model. This allows precise profiling of unusually shaped distributions and less well-separated features that may appear due to cellular heterogeneity even within clonal populations.
By modeling flow cytometric data measured over time course and marker space with StateProfiler, specific parametric characteristics of cellular states can be identified. The parameters are then tested statistically for learning global and local patterns of spatio-temporal change. We applied StateProfiler to identify the temporal features of yeast cell cycle progression based on knockout of S-phase triggering cyclins Clb5 and Clb6, and then compared the S-phase delay phenotypes due to differential regulation of the two cyclins. We also used StateProfiler to construct the temporal profile of clonal divergence underlying lineage selection in mammalian hematopoietic progenitor cells.
Demand for high quality gene expression data has driven the development of revolutionary microarray technologies. The quality of the data is affected by the performance of the microarray platform as well as how the nucleic acid targets are prepared. The most common method for target nucleic acid preparation includes in vitro transcription amplification of the sample RNA. Although this method requires a small amount of starting material and is reported to have high reproducibility, there are also technical disadvantages such as amplification bias and the long, laborious protocol. Using RNA derived from human brain, breast and colon, we demonstrate that a non-amplification method, which was previously shown to be inferior, could be transformed to a highly quantitative method with a dynamic range of five orders of magnitude. Furthermore, the correlation coefficient calculated by comparing microarray assays using non-amplified samples with qRT-PCR assays was approximately 0.9, a value much higher than when samples were prepared using amplification methods. Our results were also compared with data from various microarray platforms studied in the MicroArray Quality Control (MAQC) project. In combination with micro-columnar 3D-Gene™ microarray, this non-amplification method is applicable to a variety of genetic analyses, including biomarker screening and diagnostic tests for cancer.
Head and neck squamous carcinoma (HNSCC) tumors carry dismal long-term prognosis and the role of tumor initiating cells (TICs) in this cancer is unclear. We investigated in HNSCC xenografts whether specific tumor subpopulations contributed to tumor growth. We used a CFSE-based label retentions assay, CD49f (α6-integrin) surface levels and aldehyde dehydrogenase (ALDH) activity to profile HNSCC subpopulations. The tumorigenic potential of marker-positive and -negative subpopulations was tested in nude (Balb/c nu/nu) and NSG (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ) mice and chicken embryo chorioallantoic membrane (CAM) assays. Here we identified in HEp3, SQ20b and FaDu HNSCC xenografts a subpopulation of G0/G1-arrested slow-cycling CD49fhigh/ALDH1A1high/H3K4/K27me3low subpopulation (CD49f+) of tumor cells. A strikingly similar CD49fhigh/H3K27me3low subpopulation is also present in primary human HNSCC tumors and metastases. While only sorted CD49fhigh/ALDHhigh, label retaining cells (LRC) proliferated immediately in vivo, with time the CD49flow/ALDHlow, non-LRC (NLRC) tumor cell subpopulations were also able to regain tumorigenic capacity; this was linked to restoration of CD49fhigh/ALDHhigh, label retaining cells. In addition, CD49f is required for HEp3 cell tumorigenicity and to maintain low levels of H3K4/K27me3. CD49f+ cells also displayed reduced expression of the histone-lysine N-methyltransferase EZH2 and ERK1/2phosphorylation. This suggests that although transiently quiescent, their unique chromatin structure is poised for rapid transcriptional activation. CD49f− cells can “reprogram” and also achieve this state eventually. We propose that in HNSCC tumors, epigenetic mechanisms likely driven by CD49f signaling dynamically regulate HNSCC xenograft phenotypic heterogeneity. This allows multiple tumor cell subpopulations to drive tumor growth suggesting that their dynamic nature renders them a “moving target” and their eradication might require more persistent strategies.
Epoxyeicosatrienoic acids (EETs) are small molecules produced by cytochrome P450 epoxygenases. They are lipid mediators that act as autocrine or paracrine factors to regulate inflammation and vascular tone. As a result, drugs that raise EET levels are in clinical trials for the treatment of hypertension and many other diseases. However, despite their pleiotropic effects on cells, little is known about the role of these epoxyeicosanoids in cancer. Here, using genetic and pharmacological manipulation of endogenous EET levels, we demonstrate that EETs are critical for primary tumor growth and metastasis in a variety of mouse models of cancer. Remarkably, we found that EETs stimulated extensive multiorgan metastasis and escape from tumor dormancy in several tumor models. This systemic metastasis was not caused by excessive primary tumor growth but depended on endothelium-derived EETs at the site of metastasis. Administration of synthetic EETs recapitulated these results, while EET antagonists suppressed tumor growth and metastasis, demonstrating in vivo that pharmacological modulation of EETs can affect cancer growth. Furthermore, inhibitors of soluble epoxide hydrolase (sEH), the enzyme that metabolizes EETs, elevated endogenous EET levels and promoted primary tumor growth and metastasis. Thus, our data indicate a central role for EETs in tumorigenesis, offering a mechanistic link between lipid signaling and cancer and emphasizing the critical importance of considering possible effects of EET-modulating drugs on cancer.
Acquiring abundant and high-purity cancer stem cells (CSCs) is an important prerequisite for CSC research. At present, researchers usually gain high-purity CSCs through flow cytometry sorting and expand them by short-term sphere culture. However, it is still uncertain whether we can amplify high-purity CSCs through long-term sphere culture. We have proposed a mathematical model using ordinary differential equations to derive the continuous variation of the CSC ratio in long-term sphere culture and estimated the model parameters based on a long-term sphere culture of MCF-7 stem cells. We found that the CSC ratio in long-term sphere culture presented as gradually decreased drift and might be stable at a lower level. Furthermore, we found that fitted model parameters could explain the main growth pattern of CSCs and differentiated cancer cells in long-term sphere culture.
The PNC is a nuclear body that forms in malignant cells. We characterize a newly identified complex in the PNC; determine the dynamics of PNC proteins; and describe the recruitment, localization, and sedimentation properties of PNC components.
The perinucleolar compartment (PNC) forms in cancer cells and is highly enriched with a subset of polymerase III RNAs and RNA-binding proteins. Here we report that PNC components mitochondrial RNA–processing (MRP) RNA, pyrimidine tract–binding protein (PTB), and CUG-binding protein (CUGBP) interact in vivo, as demonstrated by coimmunoprecipitation and RNA pull-down experiments. Glycerol gradient analyses show that this complex is large and sediments at a different fraction from known MRP RNA–containing complexes, the MRP ribonucleoprotein ribozyme and human telomerase reverse transcriptase. Tethering PNC components to a LacO locus recruits other PNC components, further confirming the in vivo interactions. These interactions are present both in PNC-containing and -lacking cells. High-resolution localization analyses demonstrate that MRP RNA, CUGBP, and PTB colocalize at the PNC as a reticulated network, intertwining with newly synthesized RNA. Furthermore, green fluorescent protein (GFP)–PTB and GFP-CUGBP show a slower rate of fluorescence recovery after photobleaching at the PNC than in the nucleoplasm, illustrating the different molecular interaction of the complexes associated with the PNC. These findings support a working model in which the MRP RNA–protein complex becomes nucleated at the PNC in cancer cells and may play a role in gene expression regulation at the DNA locus that associates with the PNC.
The gene regulatory circuit motif in which two opposing fate-determining transcription factors inhibit each other but activate themselves has been used in mathematical models of binary cell fate decisions in multipotent stem or progenitor cells. This simple circuit can generate multistability and explains the symmetric “poised” precursor state in which both factors are present in the cell at equal amounts as well as the resolution of this indeterminate state as the cell commits to either cell fate characterized by an asymmetric expression pattern of the two factors. This establishes the two alternative stable attractors that represent the two fate options. It has been debated whether cooperativity of molecular interactions is necessary to produce such multistability.
Here we take a general modeling approach and argue that this question is not relevant. We show that non-linearity can arise in two distinct models in which no explicit interaction between the two factors is assumed and that distinct chemical reaction kinetic formalisms can lead to the same (generic) dynamical system form. Moreover, we describe a novel type of bifurcation that produces a degenerate steady state that can explain the metastable state of indeterminacy prior to cell fate decision-making and is consistent with biological observations.
The general model presented here thus offers a novel principle for linking regulatory circuits with the state of indeterminacy characteristic of multipotent (stem) cells.
Amonafide is a DNA intercalator in clinical development for the treatment of cancer. The drug has a 5-position amine that is variably acetylated to form a toxic metabolite in humans, increasing adverse effects and complicating the dosing of amonafide. Numonafides, 6-amino derivatives of amonafide that avoid the toxic acetylation, also show in vitro anticancer activity, as we have previously described. Here, we report the in vitro and in vivo activities of two numonafides, 6-methoxyethylamino-numonafide (MEAN) and 6-amino-numonafide (AN) with comparisons to amonafide. The in vitro potencies and cellular anticancer mechanisms are similar for the two numonafides and amonafide. Results from several mouse models of human cancer demonstrate that AN and MEAN require slightly higher doses than amonafide for equal efficacy in short-term dosing models, but the same dose of all three compounds in long-term dosing models are equally efficacious. MEAN is tolerated much better than amonafide and AN at equally efficacious doses based on weight change, activity, stool consistency, and dose tolerance with survival as the end point. The studies presented here demonstrate that MEAN is much less toxic than amonafide or AN in mouse models of human liver and gastric cancers while being equally efficacious in vivo and inhibiting cancer cells through similar mechanisms. These findings demonstrate that numonafides can be less toxic than amonafide and support further preclinical development and novel anticancer agents or as replacements or amonafide.
Predator-prey system, as an essential element of ecological dynamics, has been recently studied experimentally with synthetic biology. We developed a global probabilistic landscape and flux framework to explore a synthetic predator-prey network constructed with two Escherichia coli populations. We developed a self consistent mean field method to solve multidimensional problem and uncovered the potential landscape with Mexican hat ring valley shape for predator-prey oscillations. The landscape attracts the system down to the closed oscillation ring. The probability flux drives the coherent oscillations on the ring. Both the landscape and flux are essential for the stable and coherent oscillations. The landscape topography characterized by the barrier height from the top of Mexican hat to the closed ring valley provides a quantitative measure of global stability of system. The entropy production rate for the energy dissipation is less for smaller environmental fluctuations or perturbations. The global sensitivity analysis based on the landscape topography gives specific predictions for the effects of parameters on the stability and function of the system. This may provide some clues for the global stability, robustness, function and synthetic network design.
Cell fate reprogramming, such as the generation of insulin-producing β cells from other pancreas cells, can be achieved by external modulation of key transcription factors. However, the known gene regulatory interactions that form a complex network with multiple feedback loops make it increasingly difficult to design the cell reprogramming scheme because the linear regulatory pathways as schemes of causal influences upon cell lineages are inadequate for predicting the effect of transcriptional perturbation. However, sufficient information on regulatory networks is usually not available for detailed formal models. Here we demonstrate that by using the qualitatively described regulatory interactions as the basis for a coarse-grained dynamical ODE (ordinary differential equation) based model, it is possible to recapitulate the observed attractors of the exocrine and β, δ, α endocrine cells and to predict which gene perturbation can result in desired lineage reprogramming. Our model indicates that the constraints imposed by the incompletely elucidated regulatory network architecture suffice to build a predictive model for making informed decisions in choosing the set of transcription factors that need to be modulated for fate reprogramming.
Expression profiling, the measurement of all transcripts of a cell or tissue type, is currently the most comprehensive method to describe their physiological states. Given that accurate profiling methods currently available require RNA amounts found in thousands to millions of cells, many fields of biology working with specialized cell types cannot use these techniques because available cell numbers are limited. Currently available alternative methods for expression profiling from nanograms of RNA or from very small cell populations lack a broad validation of results to provide accurate information about the measured transcripts.
Methods and Findings
We provide evidence that currently available methods for expression profiling of very small cell populations are prone to technical noise and therefore cannot be used efficiently as discovery tools. Furthermore, we present Pico Profiling, a new expression profiling method from as few as ten cells, and we show that this approach is as informative as standard techniques from thousands to millions of cells. The central component of Pico Profiling is Whole Transcriptome Amplification (WTA), which generates expression profiles that are highly comparable to those produced by others, at different times, by standard protocols or by Real-time PCR. We provide a complete workflow from RNA isolation to analysis of expression profiles.
Pico Profiling, as presented here, allows generating an accurate expression profile from cell populations as small as ten cells.
The cancer stem cell hypothesis suggests that tumors contain a small population of cancer cells that have the ability to undergo symmetric self-renewing cell division. In tumors that follow this model, cancer stem cells produce various kinds of specified precursors that divide a limited number of times before terminally differentiating or undergoing apoptosis. As cells within the tumor mature, they become progressively more restricted in the cell types to which they can give rise. However, in some tumor types, the presence of certain extra- or intracellular signals can induce committed cancer progenitors to revert to a multipotential cancer stem cell state. In this paper, we design a novel mathematical model to investigate the dynamics of tumor progression in such situations, and study the implications of a reversible cancer stem cell phenotype for therapeutic interventions. We find that higher levels of dedifferentiation substantially reduce the effectiveness of therapy directed at cancer stem cells by leading to higher rates of resistance. We conclude that plasticity of the cancer stem cell phenotype is an important determinant of the prognosis of tumors. This model represents the first mathematical investigation of this tumor trait and contributes to a quantitative understanding of cancer.
MicroRNAs are small non-coding RNAs involved in post-transcriptional regulation of gene expression. Due to the poor annotation of primary microRNA (pri-microRNA) transcripts, the precise location of promoter regions driving expression of many microRNA genes is enigmatic. This deficiency hinders our understanding of microRNA-mediated regulatory networks. In this study, we develop a computational approach to identify the promoter region and transcription start site (TSS) of pri-microRNAs actively transcribed using genome-wide RNA Polymerase II (RPol II) binding patterns derived from ChIP-seq data. Based upon the assumption that the distribution of RPol II binding patterns around the TSS of microRNA and protein coding genes are similar, we designed a statistical model to mimic RPol II binding patterns around the TSS of highly expressed, well-annotated promoter regions of protein coding genes. We used this model to systematically scan the regions upstream of all intergenic microRNAs for RPol II binding patterns similar to those of TSS from protein coding genes. We validated our findings by examining the conservation, CpG content, and activating histone marks in the identified promoter regions. We applied our model to assess changes in microRNA transcription in steroid hormone-treated breast cancer cells. The results demonstrate many microRNA genes have lost hormone-dependent regulation in tamoxifen-resistant breast cancer cells. MicroRNA promoter identification based upon RPol II binding patterns provides important temporal and spatial measurements regarding the initiation of transcription, and therefore allows comparison of transcription activities between different conditions, such as normal and disease states.
Endogenously produced lipid autacoids are locally acting small molecule mediators that play a central role in the regulation of inflammation and tissue homeostasis. A well-studied group of autacoids are the products of arachidonic acid metabolism, among which the prostaglandins and leukotrienes are the best known. They are generated by two pathways controlled by the enzyme systems cyclooxygenase and lipoxygenase, respectively. However, arachidonic acid is also substrate for a third enzymatic pathway, the cytochrome P450 (CYP) system. This third eicosanoid pathway consists of two main branches: ω-hydroxylases convert arachidonic acid to hydroxyeicosatetraenoic acids (HETEs) and epoxygenases convert it to epoxyeicosatrienoic acids (EETs). This third CYP pathway was originally studied in conjunction with inflammatory and cardiovascular disease. Arachidonic acid and its metabolites have recently stimulated great interest in cancer biology; but, unlike prostaglandins and leukotrienes the link between cytochome P450 metabolites and cancer has received little attention. In this review, the emerging role in cancer of cytochrome P450 metabolites, notably 20-HETE and EETs, are discussed.
Cytochrome P450; Arachidonic acid; HETEs; EETs; Cancer; Metastasis
Several studies conducted during the 1990s indicated that childhood allergic diseases were increasing worldwide, but more recent investigations in some Western countries have suggested that the trend is stabilizing or may even be reversing. However, few data are available on the current status of allergic disease prevalence in Chinese children. The aim of the present study was to investigate the prevalence rates of asthma, allergic rhinitis, and eczema in children of three major cities of China, to determine the status of allergic diseases among Chinese children generally, and to evaluate the prevalence of allergic diseases in children of different ages.
We conducted a cross-sectional survey between October 2008 and May 2009 in three major cities of China (Beijing, Chongqing, and Guangzhou) to evaluate the prevalence rates of childhood allergic diseases including asthma, allergic rhinitis, and eczema, using a questionnaire of the International Study of Asthma and Allergies in Childhood (ISAAC) group. A total of 24,290 children aged 0-14 years were interviewed, using a multi-stage sampling method. To acquire data on children aged 3-14 years, we visited schools and kindergartens. To access children too young to attend school or kindergarten, we extended our survey to community health service centers. Each questionnaire was completed by a parent or guardian of a child after an informed consent form was signed.
Of the 24,290 children in our study, 12,908 (53.14%) were males and 11,382 (46.86%) females; 10,372 (42.70%) were from Beijing, 9,846 (40.53%) from Chongqing, and 4,072 (16.77%) from Guangzhou. Our survey indicated that in Beijing, Chongqing, and Guangzhou, the prevalence rates of asthma were 3.15%, 7.45%, and 2.09%, respectively; the rates of allergic rhinitis were 14.46%, 20.42%, and 7.83%; and the rates of eczema were 20.64%, 10.02%, and 7.22%. The prevalence of allergic diseases varied with age. Asthma was relatively less common both in children aged under 2 years, and in those aged 9 years or more, in each of the three cities. The prevalence of allergic rhinitis was also lower in children younger than 2 years. The prevalence of eczema fell with age.
A marked increase in the prevalence rates of allergic diseases in China (compared with earlier data) was evident. Further studies exploring the precise causes of this increase are warranted.
Cell lineage commitment and differentiation are governed by a complex gene regulatory network. Disruption of these processes by inappropriate regulatory signals and by mutational rewiring of the network can lead to tumorigenesis. Cancer cells often exhibit immature or embryonic traits and dysregulated developmental genes can act as oncogenes. However, the prevailing paradigm of somatic evolution and multi-step tumorigenesis, while useful in many instances, offers no logically coherent reason for why oncogenesis recapitulates ontogenesis. The formal concept of “cancer attractors”, derived from an integrative, complex systems approach to gene regulatory network may provide a natural explanation. Here we present the theory of attractors in gene network dynamics and review the concept of cell types as attractors. We argue that cancer cells are trapped in abnormal attractors and discuss this concept in the light of recent ideas in cancer biology, including cancer genomics and cancer stem cells, as well as the implications for differentiation therapy.
The role of nutrients and metabolism in cellular differentiation is poorly understood. Using RNAi screening, metabolic profiling and small-molecule probes, we discovered three metabolic enzymes whose knockdown induces differentiation of mouse C2C12 myoblasts even in the presence mitogens: phosphoglycerate kinase (Pgk1), hexose-6-phosphate dehydrogenase (H6pd) and ATP citrate lyase (Acl). These enzymes and the pathways they regulate provide novel targets for the control of myogenic differentiation in myoblasts and rhabdomyosarcoma cells.
taurodeoxycholic acid (TUDCA); glycochenodeoxycholic acid; 3-phosphoglycerate; phosphoenol pyruvate (PEP); cyclosporin A (CsA); trichostatin A (TSA); pravastatin; atorvastatin; fluvastatin
Constructing and modeling the gene regulatory network is one of the central themes of systems biology. With the growing understanding of the mechanism of microRNA biogenesis and its biological function, establishing a microRNA-mediated gene regulatory network is not only desirable but also achievable.
In this study, we propose a bioinformatics strategy to construct the microRNA-mediated regulatory network using genome-wide binding patterns of transcription factor(s) and RNA polymerase II (RPol II), derived using chromatin immunoprecipitation following next generation sequencing (ChIP-seq) technology. Our strategy includes three key steps, identification of transcription start sites and promoter regions of primary microRNA transcripts using RPol II binding patterns, selection of cooperating transcription factors that collaboratively function with the transcription factors targeted by ChIP-seq assay, and construction of the network that contains regulatory cascades of both transcription factors and microRNAs.
Using CAMDA (Critical Assessment of Massive Data Analysis) 2009 data set that includes ChIP-seq data on RPol II and STAT1 (signal transducers and activators of transcription 1) in HeLa S3 cells in control condition and with interferon γ stimulation, we first identified promoter regions of 83 microRNAs in HeLa cells. We then identified two potential STAT1 collaborating factors, AP-1 and C/EBP (CCAAT enhancer-binding proteins), and further established eight feedback network elements that may regulate cellular response during interferon γ stimulation.
This study offers a bioinformatics strategy to provide testable hypotheses on the mechanisms of microRNA-mediated transcriptional regulation, based upon genome-wide protein-DNA interaction data derived from ChIP-seq experiments.