Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

Signal duration and the time scale dependence of signal integration in biochemical pathways

Signal duration (e.g. the time scales over which an active signaling intermediate persists) is a key regulator of biological decisions in myriad contexts such as cell growth, proliferation, and developmental lineage commitments. Accompanying differences in signal duration are numerous downstream biological processes that require multiple steps of biochemical regulation. Here, we present an analysis that investigates how simple biochemical motifs that involve multiple stages of regulation can be constructed to differentially process signals that persist at different time scales. We compute the dynamic gain within these networks and resulting power spectra to better understand how biochemical networks can integrate signals at different time scales. We identify topological features of these networks that allow for different frequency dependent signal processing properties. Our studies suggest design principles for why signal duration in connection with multiple steps of downstream regulation is a ubiquitous control motif in biochemical systems.Comment: 27 pages, 4 figure

Confidence from uncertainty - A multi-target drug screening method from robust control theory

<p>Abstract</p> <p>Background</p> <p>Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty.</p> <p>Results</p> <p>We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty.</p> <p>Conclusions</p> <p>The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.</p

NBS monograph

From Introduction: "The purpose of this report is to present an analysis of time variations in: (1) the phase (relative to a stable phase reference) if a 9414 Mc/s signal transmitted over this path, referred to hereafter as single-path phase data; (2) the phase observed at one receiving antenna relative to that observed at an adjacent antenna, referred to as phase difference data; (3) atmospheric refractivity as recorded by a microwave refractometer located at the Haleakala terminal; (4) the wind velocity at both ends of the path, and (5) surface atmospheric refractivity measured at 5 stations located on or near the path.

LSD1 inhibition attenuates androgen receptor V7 splice variant activation in castration resistant prostate cancer models

Background: Castrate resistant prostate cancer (CRPC) is often driven by constitutively active forms of the androgen receptor such as the V7 splice variant (AR-V7) and commonly becomes resistant to established hormonal therapy strategies such as enzalutamide as a result. The lysine demethylase LSD1 is a co-activator of the wild type androgen receptor and a potential therapeutic target in hormone sensitive prostate cancer. We evaluated whether LSD1 could also be therapeutically targeted in CRPC models driven by AR-V7. Methods: We utilised cell line models of castrate resistant prostate cancer through over expression of AR-V7 to test the impact of chemical LSD1 inhibition on AR activation. We validated findings through depletion of LSD1 expression and in prostate cancer cell lines that express AR-V7. Results: Chemical inhibition of LSD1 resulted in reduced activation of the androgen receptor through both the wild type and its AR-V7 splice variant forms. This was confirmed and validated in luciferase reporter assays, in LNCaP and 22Rv1 prostate cancer cell lines and in LSD1 depletion experiments. Conclusion: LSD1 contributes to activation of both the wild type and V7 splice variant forms of the androgen receptor and can be therapeutically targeted in models of CRPC. Further development of this approach is warranted

A comparison of approximation techniques for variance-based sensitivity analysis of biochemical reaction systems

<p>Abstract</p> <p>Background</p> <p>Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of biochemical reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large biochemical reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by biochemical reaction systems that consist of a large number of reactions and molecular species.</p> <p>Results</p> <p>We present four techniques, derivative approximation (DA), polynomial approximation (PA), Gauss-Hermite integration (GHI), and orthonormal Hermite approximation (OHA), for <it>analytically </it>approximating the variance-based sensitivity indices associated with a biochemical reaction system. By using a well-known model of the mitogen-activated protein kinase signaling cascade as a case study, we numerically compare the approximation quality of these techniques against traditional Monte Carlo sampling. Our results indicate that, although DA is computationally the most attractive technique, special care should be exercised when using it for sensitivity analysis, since it may only be accurate at low levels of uncertainty. On the other hand, PA, GHI, and OHA are computationally more demanding than DA but can work well at high levels of uncertainty. GHI results in a slightly better accuracy than PA, but it is more difficult to implement. OHA produces the most accurate approximation results and can be implemented in a straightforward manner. It turns out that the computational cost of the four approximation techniques considered in this paper is orders of magnitude smaller than traditional Monte Carlo estimation. Software, coded in MATLAB^®, which implements all sensitivity analysis techniques discussed in this paper, is available free of charge.</p> <p>Conclusions</p> <p>Estimating variance-based sensitivity indices of a large biochemical reaction system is a computationally challenging task that can only be addressed via approximations. Among the methods presented in this paper, a technique based on orthonormal Hermite polynomials seems to be an acceptable candidate for the job, producing very good approximation results for a wide range of uncertainty levels in a fraction of the time required by traditional Monte Carlo sampling.</p

Selective and Irreversible Inhibitors of Mosquito Acetylcholinesterases for Controlling Malaria and Other Mosquito-Borne Diseases

New insecticides are urgently needed because resistance to current insecticides allows resurgence of disease-transmitting mosquitoes while concerns for human toxicity from current compounds are growing. We previously reported the finding of a free cysteine (Cys) residue at the entrance of the active site of acetylcholinesterase (AChE) in some insects but not in mammals, birds, and fish. These insects have two AChE genes (AP and AO), and only AP-AChE carries the Cys residue. Most of these insects are disease vectors such as the African malaria mosquito (Anopheles gambiae sensu stricto) or crop pests such as aphids. Recently we reported a Cys-targeting small molecule that irreversibly inhibited all AChE activity extracted from aphids while an identical exposure caused no effect on the human AChE. Full inhibition of AChE in aphids indicates that AP-AChE contributes most of the enzymatic activity and suggests that the Cys residue might serve as a target for developing better aphicides. It is therefore worth investigating whether the Cys-targeting strategy is applicable to mosquitocides. Herein, we report that, under conditions that spare the human AChE, a methanethiosulfonate-containing molecule at 6 µM irreversibly inhibited 95% of the AChE activity extracted from An. gambiae s. str. and >80% of the activity from the yellow fever mosquito (Aedes aegypti L.) or the northern house mosquito (Culex pipiens L.) that is a vector of St. Louis encephalitis. This type of inhibition is fast (∼30 min) and due to conjugation of the inhibitor to the active-site Cys of mosquito AP-AChE, according to our observed reactivation of the methanethiosulfonate-inhibited AChE by 2-mercaptoethanol. We also note that our sulfhydryl agents partially and irreversibly inhibited the human AChE after prolonged exposure (>4 hr). This slow inhibition is due to partial enzyme denaturation by the inhibitor and/or micelles of the inhibitor, according to our studies using atomic force microscopy, circular dichroism spectroscopy, X-ray crystallography, time-resolved fluorescence spectroscopy, and liquid chromatography triple quadrupole mass spectrometry. These results support our view that the mosquito-specific Cys is a viable target for developing new mosquitocides to control disease vectors and to alleviate resistance problems with reduced toxicity toward non-target species

Comparative analysis of Neph gene expression in mouse and chicken development

Neph proteins are evolutionarily conserved members of the immunoglobulin superfamily of adhesion proteins and regulate morphogenesis and patterning of different tissues. They share a common protein structure consisting of extracellular immunoglobulin-like domains, a transmembrane region, and a carboxyl terminal cytoplasmic tail required for signaling. Neph orthologs have been widely characterized in invertebrates where they mediate such diverse processes as neural development, synaptogenesis, or myoblast fusion. Vertebrate Neph proteins have been described first at the glomerular filtration barrier of the kidney. Recently, there has been accumulating evidence suggesting a function of Neph proteins also outside the kidney. Here we demonstrate that Neph1, Neph2, and Neph3 are expressed differentially in various tissues during ontogenesis in mouse and chicken. Neph1 and Neph2 were found to be amply expressed in the central nervous system while Neph3 expression remained localized to the cerebellum anlage and the spinal cord. Outside the nervous system, Neph mRNAs were also differentially expressed in branchial arches, somites, heart, lung bud, and apical ectodermal ridge. Our findings support the concept that vertebrate Neph proteins, similarly to their Drosophila and C. elegans orthologs, provide guidance cues for cell recognition and tissue patterning in various organs which may open interesting perspectives for future research on Neph1-3 controlled morphogenesis