Minding impacting events in a model of stochastic variance

We introduce a generalisation of the well-known ARCH process, widely used for generating uncorrelated stochastic time series with long-term non-Gaussian distributions and long-lasting correlations in the (instantaneous) standard deviation exhibiting a clustering profile. Specifically, inspired by the fact that in a variety of systems impacting events are hardly forgot, we split the process into two different regimes: a first one for regular periods where the average volatility of the fluctuations within a certain period of time is below a certain threshold and another one when the local standard deviation outnumbers it. In the former situation we use standard rules for heteroscedastic processes whereas in the latter case the system starts recalling past values that surpassed the threshold. Our results show that for appropriate parameter values the model is able to provide fat tailed probability density functions and strong persistence of the instantaneous variance characterised by large values of the Hurst exponent is greater than 0.8, which are ubiquitous features in complex systems.Comment: 18 pages, 5 figures, 1 table. To published in PLoS on

anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data

Background: Compared to other omics techniques, quantitative metabolomics is still at its infancy. Complex sample preparation and analytical procedures render exact quantification extremely difficult. Furthermore, not only the actual measurement but also the subsequent interpretation of quantitative metabolome data to obtain mechanistic insights is still lacking behind the current expectations. Recently, the method of network-embedded thermodynamic (NET) analysis was introduced to address some of these open issues. Building upon principles of thermodynamics, this method allows for a quality check of measured metabolite concentrations and enables to spot metabolic reactions where active regulation potentially controls metabolic flux. So far, however, widespread application of NET analysis in metabolomics labs was hindered by the absence of suitable software. Results: We have developed in Matlab a generalized software called 'anNET' that affords a user-friendly implementation of the NET analysis algorithm. anNET supports the analysis of any metabolic network for which a stoichiometric model can be compiled. The model size can span from a single reaction to a complete genome-wide network reconstruction including compartments. anNET can (i) test quantitative data sets for thermodynamic consistency, (ii) predict metabolite concentrations beyond the actually measured data, (iii) identify putative sites of active regulation in the metabolic reaction network, and (iv) help in localizing errors in data sets that were found to be thermodynamically infeasible. We demonstrate the application of anNET with three published Escherichia coli metabolome data sets. Conclusion: Our user-friendly and generalized implementation of the NET analysis method in the software anNET allows users to rapidly integrate quantitative metabolome data obtained from virtually any organism. We envision that use of anNET in labs working on quantitative metabolomics will provide the systems biology and metabolic engineering communities with a mean to proof the quality of metabolome data sets and with all further benefits of the NET analysis approach.

RNA Modulators of Complex Phenotypes in Mammalian Cells

RNA-mediated gene silencing, in the form of RNA interference (RNAi) or microRNAs (miRNAs) has provided novel tools for gene discovery and validation in mammalian cells. Here, we report on the construction and application of a random small RNA expression library for use in identifying small interfering RNA (siRNA) effectors that can modify complex cellular phenotypes in mammalian cells. The library is based in a retroviral vector and uses convergent promoters to produce unique small complementary RNAs. Using this library, we identify a range of small RNA-encoding gene inserts that overcome resistance to 5-fluorouracil (5-FU)- or tumour necrosis factor alpha (TNF-α)- induced cell death in colorectal cancer cells. We demonstrate the utility of this technology platform by identifying a key RNA effector, in the form of a siRNA, which overcomes cell death induced by the chemotherapeutic 5-FU. The technology described has the potential to identify both functional RNA modulators capable of altering physiological systems and the cellular target genes altered by these modulators

OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis

Diagnostic Accuracy of Fine Needle Biopsy for Metastatic Melanoma and Its Implications for Patient Management

The use of fine needle biopsy (FNB) for the diagnosis of metastatic melanoma can lead to the early removal and treatment of metastases, reduce the frequency of unnecessary surgery, and facilitate the staging of patients enrolled in clinical trials of adjuvant therapies. In this study, the accuracy of FNB for the diagnosis of metastatic melanoma was investigated. A retrospective cohort study was performed with 2204 consecutive FNBs performed on 1416 patients known or suspected to have metastatic melanoma. Almost three-quarters (1582) of these FNBs were verified by either histopathologic diagnosis following surgical resection or clinical follow-up. FNB for metastatic melanoma was found to have an overall sensitivity of 92.1% and a specificity of 99.2%, with 69 false-negative and 5 false-positive findings identified. The sensitivity of the procedure was found to be influenced by six factors. The use of immunostains, reporting of the specimen by a cytopathologist who had reported >500 cases, lesions located in the skin and subcutis, and patients with ulcerated primary melanomas were factors associated with a significant improvement in the sensitivity of the test. However, FNBs performed in masses located in lymph nodes of the axilla and FNBs that required more than one needle pass to obtain a sample were far more likely to result in false-negative results. FNB is a rapid, accurate, and clinically useful technique for the assessment of disease status in patients with suspected metastatic melanoma

Optimization of Time-Course Experiments for Kinetic Model Discrimination

Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction

Regulation of the Na+/K+-ATPase Ena1 Expression by Calcineurin/Crz1 under High pH Stress: A Quantitative Study

[EN] Regulated expression of the Ena1 Na+-ATPase is a crucial event for adaptation to high salt and/or alkaline pH stress in the budding yeast Saccharomyces cerevisiae. ENA1 expression is under the control of diverse signaling pathways, including that mediated by the calcium-regulatable protein phosphatase calcineurin and its downstream transcription factor Crz1. We present here a quantitative study of the expression of Ena1 in response to alkalinization of the environment and we analyze the contribution of Crz1 to this response. Experimental data and mathematical models substantiate the existence of two stress-responsive Crz1-binding sites in the ENA1 promoter and estimate that the contribution of Crz1 to the early response of the ENA1 promoter is about 60%. The models suggest the existence of a second input with similar kinetics, which would be likely mediated by high pH-induced activation of the Snf1 kinase.This work was supported by grants BFU2011-30197-C3-01, BFU2014-54591-C2-1-P and EUI2009-04147 (SysMo2) to JA. (Ministry of Industry and Competitivity, Spain, and Fondo Europeo de Desarrollo Regional [FEDER]). JA is the recipient of an Ajut 2014SGR-4 award (Generalitat de Catalunya). DC was recipient of a predoctoral fellowship from the Spanish Ministry of Education. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Petrezsélyová, S.; López-Malo, M.; Canadell, D.; Roque, A.; Serra-Cardona, A.; Marques Romero, MC.; Vilaprinyó, E.... (2016). Regulation of the Na+/K+-ATPase Ena1 Expression by Calcineurin/Crz1 under High pH Stress: A Quantitative Study. PLoS ONE. 11(6):e0158424-e0158424. https://doi.org/10.1371/journal.pone.0158424Se0158424e015842411

Diseased muscles that lack dystrophin or laminin-α2 have altered compositions and proliferation of mononuclear cell populations

BACKGROUND: Multiple types of mononucleate cells reside among the multinucleate myofibers in skeletal muscles and these mononucleate cells function in muscle maintenance and repair. How neuromuscular disease might affect different types of muscle mononucleate cells had not been determined. In this study, therefore, we examined how two neuromuscular diseases, dystrophin-deficiency and laminin-α2-deficiency, altered the proliferation and composition of different subsets of muscle-derived mononucleate cells. METHODS: We used fluorescence-activated cell sorting combined with bromodeoxyuridine labeling to examine proliferation rates and compositions of mononuclear cells in diseased and healthy mouse skeletal muscle. We prepared mononucleate cells from muscles of mdx (dystrophin-deficient) or Lama2(-/- )(laminin-α2-deficient) mice and compared them to cells from healthy control muscles. We enumerated subsets of resident muscle cells based on Sca-1 and CD45 expression patterns and determined the proliferation of each cell subset in vivo by BrdU incorporation. RESULTS: We found that the proliferation and composition of the mononucleate cells in dystrophin-deficient and laminin-α2-deficient diseased muscles are different than in healthy muscle. The mdx and Lama2(-/- )muscles showed similar significant increases in CD45(+ )cells compared to healthy muscle. Changes in proliferation, however, differed between the two diseases with proliferation increased in mdx and decreased in Lama2(-/- )muscles compared to healthy muscles. In particular, the most abundant Sca-1(-)/CD45(- )subset, which contains muscle precursor cells, had increased proliferation in mdx muscle but decreased proliferation in Lama2(-/- )muscles. CONCLUSION: The similar increases in CD45(+ )cells, but opposite changes in proliferation of muscle precursor cells, may underlie aspects of the distinct pathologies in the two diseases

Ataluren treatment of patients with nonsense mutation dystrophinopathy

Introduction: Dystrophinopathy is a rare, severe muscle disorder, and nonsense mutations are found in 13% of cases. Ataluren was developed to enable ribosomal readthrough of premature stop codons in nonsense mutation (nm) genetic disorders. Methods: Randomized, double‐blind, placebo‐controlled study; males ≥5 years with nm‐dystrophinopathy received study drug orally 3 times daily, ataluren 10, 10, 20 mg/kg (N = 57); ataluren 20, 20, 40 mg/kg (N = 60); or placebo (N = 57) for 48 weeks. The primary endpoint was change in 6‐Minute Walk Distance (6MWD) at Week 48. Results: Ataluren was generally well tolerated. The primary endpoint favored ataluren 10, 10, 20 mg/kg versus placebo; the week 48 6MWD Δ = 31.3 meters, post hoc P = 0.056. Secondary endpoints (timed function tests) showed meaningful differences between ataluren 10, 10, 20 mg/kg, and placebo. Conclusions: As the first investigational new drug targeting the underlying cause of nm‐dystrophinopathy, ataluren offers promise as a treatment for this orphan genetic disorder with high unmet medical need