An iron-based beverage, HydroFerrate fluid (MRN-100), alleviates oxidative stress in murine lymphocytes in vitro

BackgroundSeveral studies have examined the correlation between iron oxidation and H2O2 degradation. The present study was carried out to examine the protective effects of MRN-100 against stress-induced apoptosis in murine splenic cells in vitro. MRN-100, or HydroFerrate fluid, is an iron-based beverage composed of bivalent and trivalent ferrates.MethodsSplenic lymphocytes from mice were cultured in the presence or absence of MRN-100 for 2 hrs and were subsequently exposed to hydrogen peroxide (H2O2) at a concentration of 25 μM for 14 hrs. Percent cell death was examined by flow cytometry and trypan blue exclusion. The effect of MRN-100 on Bcl-2 and Bax protein levels was determined by Western blot.ResultsResults show, as expected, that culture of splenic cells with H2O2 alone results in a significant increase in cell death (apoptosis) as compared to control (CM) cells. In contrast, pre-treatment of cells with MRN-100 followed by H2O2 treatment results in significantly reduced levels of apoptosis. In addition, MRN-100 partially prevents H2O2-induced down-regulation of the anti-apoptotic molecule Bcl-2 and upregulation of the pro-apoptotic molecule Bax.ConclusionOur findings suggest that MRN-100 may offer a protective effect against oxidative stress-induced apoptosis in lymphocytes

Fast MCMC sampling for hidden markov models to determine copy number variations

<p>Abstract</p> <p>Background</p> <p>Hidden Markov Models (HMM) are often used for analyzing Comparative Genomic Hybridization (CGH) data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi algorithm. This introduces considerable uncertainty in the segmentation, which can be avoided with Bayesian approaches integrating out parameters using Markov Chain Monte Carlo (MCMC) sampling. While the advantages of Bayesian approaches have been clearly demonstrated, the likelihood based approaches are still preferred in practice for their lower running times; datasets coming from high-density arrays and next generation sequencing amplify these problems.</p> <p>Results</p> <p>We propose an approximate sampling technique, inspired by compression of discrete sequences in HMM computations and by <it>kd</it>-trees to leverage spatial relations between data points in typical data sets, to speed up the MCMC sampling.</p> <p>Conclusions</p> <p>We test our approximate sampling method on simulated and biological ArrayCGH datasets and high-density SNP arrays, and demonstrate a speed-up of 10 to 60 respectively 90 while achieving competitive results with the state-of-the art Bayesian approaches.</p> <p><it>Availability: </it>An implementation of our method will be made available as part of the open source GHMM library from <url>http://ghmm.org</url>.</p

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

Development of a kinetic metabolic model: application to Catharanthus roseus hairy root

A kinetic metabolic model describing Catharanthus roseus hairy root growth and nutrition was developed. The metabolic network includes glycolysis, pentose-phosphate pathway, TCA cycle and the catabolic reactions leading to cell building blocks such as amino acids, organic acids, organic phosphates, lipids and structural hexoses. The central primary metabolic network was taken at pseudo-steady state and metabolic flux analysis technique allowed reducing from 31 metabolic fluxes to 20 independent pathways. Hairy root specific growth rate was described as a function of intracellular concentration in cell building blocks. Intracellular transport and accumulation kinetics for major nutrients were included. The model uses intracellular nutrients as well as energy shuttles to describe metabolic regulation. Model calibration was performed using experimental data obtained from batch and medium exchange liquid cultures of C. roseus hairy root using a minimal medium in Petri dish. The model is efficient in estimating the growth rate

β-Catenin is involved in alterations in mitochondrial activity in non-transformed intestinal epithelial and colon cancer cells

BACKGROUND: Alteration in respiratory activity and mitochondrial DNA (mtDNA) transcription seems to be an important feature of cancer cells. Leukotriene D(4) (LTD(4)) is a proinflammatory mediator implicated in the pathology of chronic inflammation and cancer. We have shown earlier that LTD(4) causes translocation of beta-catenin both to the mitochondria, in which it associates with the survival protein Bcl-2 identifying a novel role for beta-catenin in cell survival, and to the nucleus in which it activates the TCF/LEF transcription machinery. METHODS: Here we have used non-transformed intestinal epithelial Int 407 cells and Caco-2 colon cancer cells, transfected or not with wild type and mutated (S33Y) beta-catenin to analyse its effect on mitochondria activity. We have measured the ATP/ADP ratio, and transcription of the mtDNA genes ND2, ND6 and 16 s in these cells stimulated or not with LTD(4). RESULTS: We have shown for the first time that LTD(4) triggers a cellular increase in NADPH dehydrogenase activity and ATP/ADP ratio. In addition, LTD(4) significantly increased the transcription of mtDNA genes. Overexpression of wild-type beta-catenin or a constitutively active beta-catenin mutant mimicked the effect of LTD(4) on ATP/ADP ratio and mtDNA transcription. These elevations in mitochondrial activity resulted in increased reactive oxygen species levels and subsequent activations of the p65 subunit of NF-kappaB. CONCLUSIONS: The present novel data show that LTD(4), presumably through beta-catenin accumulation in the mitochondria, affects mitochondrial activity, lending further credence to the idea that inflammatory signalling pathways are intrinsically linked with potential oncogenic signals

Ecological Adaptation of Diverse Honey Bee (Apis mellifera) Populations

BACKGROUND: Honey bees are complex eusocial insects that provide a critical contribution to human agricultural food production. Their natural migration has selected for traits that increase fitness within geographical areas, but in parallel their domestication has selected for traits that enhance productivity and survival under local conditions. Elucidating the biochemical mechanisms of these local adaptive processes is a key goal of evolutionary biology. Proteomics provides tools unique among the major 'omics disciplines for identifying the mechanisms employed by an organism in adapting to environmental challenges. RESULTS: Through proteome profiling of adult honey bee midgut from geographically dispersed, domesticated populations combined with multiple parallel statistical treatments, the data presented here suggest some of the major cellular processes involved in adapting to different climates. These findings provide insight into the molecular underpinnings that may confer an advantage to honey bee populations. Significantly, the major energy-producing pathways of the mitochondria, the organelle most closely involved in heat production, were consistently higher in bees that had adapted to colder climates. In opposition, up-regulation of protein metabolism capacity, from biosynthesis to degradation, had been selected for in bees from warmer climates. CONCLUSIONS: Overall, our results present a proteomic interpretation of expression polymorphisms between honey bee ecotypes and provide insight into molecular aspects of local adaptation or selection with consequences for honey bee management and breeding. The implications of our findings extend beyond apiculture as they underscore the need to consider the interdependence of animal populations and their agro-ecological context

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)

A local glucose-and oxygen concentration-based insulin secretion model for pancreatic islets

<p>Abstract</p> <p>Background</p> <p>Because insulin is the main regulator of glucose homeostasis, quantitative models describing the dynamics of glucose-induced insulin secretion are of obvious interest. Here, a computational model is introduced that focuses not on organism-level concentrations, but on the quantitative modeling of local, cellular-level glucose-insulin dynamics by incorporating the detailed spatial distribution of the concentrations of interest within isolated avascular pancreatic islets.</p> <p>Methods</p> <p>All nutrient consumption and hormone release rates were assumed to follow Hill-type sigmoid dependences on local concentrations. Insulin secretion rates depend on both the glucose concentration and its time-gradient, resulting in second-and first-phase responses, respectively. Since hypoxia may also be an important limiting factor in avascular islets, oxygen and cell viability considerations were also built in by incorporating and extending our previous islet cell oxygen consumption model. A finite element method (FEM) framework is used to combine reactive rates with mass transport by convection and diffusion as well as fluid-mechanics.</p> <p>Results</p> <p>The model was calibrated using experimental results from dynamic glucose-stimulated insulin release (GSIR) perifusion studies with isolated islets. Further optimization is still needed, but calculated insulin responses to stepwise increments in the incoming glucose concentration are in good agreement with existing experimental insulin release data characterizing glucose and oxygen dependence. The model makes possible the detailed description of the intraislet spatial distributions of insulin, glucose, and oxygen levels. In agreement with recent observations, modeling also suggests that smaller islets perform better when transplanted and/or encapsulated.</p> <p>Conclusions</p> <p>An insulin secretion model was implemented by coupling local consumption and release rates to calculations of the spatial distributions of all species of interest. The resulting glucose-insulin control system fits in the general framework of a sigmoid proportional-integral-derivative controller, a generalized PID controller, more suitable for biological systems, which are always nonlinear due to the maximum response being limited. Because of the general framework of the implementation, simulations can be carried out for arbitrary geometries including cultured, perifused, transplanted, and encapsulated islets.</p

Experimental evidence reveals the UCP1 genotype changes the oxygen consumption attributed to non-shivering thermogenesis in humans

Humans have spread out all over the world adapting to many different cold environments. Recent worldwide genome analyses and animal experiments have reported dozens of genes associated with cold adaptation. The uncoupling protein 1 (UCP1) gene enhances thermogenesis reaction in a physiological process by blocking ATP (adenosine triphosphate) synthesis on a mitochondrial membrane in brown adipose tissues. To our knowledge, no previous studies have shown an association between variants of the UCP1 gene and physiological phenotypes concerning non-shivering thermogenesis (NST) under the condition of low temperature in humans. We showed that the degree of NST for healthy subjects in an artificial climate chamber is significantly different among UCP1 genotypes. Defining the haplotypes covering the UCP1 region (39.4?kb), we found that the frequency of the haplotype with the highest NST was significantly correlated with latitudes and ambient temperature. Thus, the data in this study provide the first evidence that the UCP1 genotype alters the efficiency of NST in humans, and likely supports the hypothesis that the UCP1 gene has been related to cold adaptation in human evolutionary history