GENETIC POLYMORPHISM OF INFLAMMATORY FACTORS IS ASSOCIATED WITH THROMBOEMBOLIC COMPLICATIONS OF ATRIAL FIBRILLATION

Aim. To reveal the association of hereditary specifics of inflammatory factors with the adverse risk in atrial fibrillation (AF).Material and methods. Totally 258 patients studied (68,5±0,67 y. o.) with nonvalvular AF, recording the events as ischemic stroke, myocardial infarction, venous and arterial thromboembolism. Mean follow-up was 455±11,71 days.Results. Factors that are independently associated with ischemic stroke development in patients not receiving anticoagulants (n=101), were the allele C of polymorphic marker rs2228145(А/С) of gene IL-6 receptor (OR 13,25 CI 1,57112,18, р=0,018), age ?75 y. o. (OR 1,1, CI 1,008-1,2, р=0,032) and EF LV (OR 0,97 CI 0,94-0,99 р=0,027), with a “thrombotic endpoint” development — DM (OR 4,3 CI 1,46-12,45 р=0,008), EF LV (OR 0,96 CI 0,94-0,98, р<0,0001) and carriage of allele C of polymorphic marker rs2228145(А/С) of receptor to IL-6 gene (OR 4,03 CI 1,0715,26, р=0,04). There was no association with adverse outcomes in genes IL-6 polymorphisms as (G(-174)C and G(-572)C), ИЛ-10 (C(-819)T), ФНО (G(-238)A, G(-308)A and ФНО? rs180630). In those receiving adequate anticoagulant therapy (n=157) there was no significant association of IL-6 receptor gene polymorphism with adverse outcomes.Conclusion. Therefore, the carriage of allele C of polymorphic marker rs2228145(А/С) of the IL-6 receptor gene might be an independent risk marker for adverse outcome in non-valvular AF, potentially, being a selection tool for those patients not having enough high risk according to common scores

Comparative mapping of the macrochromosomes of eight avian species provides further insight into their phylogenetic relationships and avian karyotype evolution

Avian genomes typically consist of ~10 pairs of macro- and ~30 pairs of microchromosomes. While inter-chromosomally, a pattern emerges of very little change (with notable exceptions) throughout evolution, intrachromosomal changes remain relatively poorly studied. To rectify this, here we use a pan-avian universally hybridising set of 74 chicken bacterial artificial chromosome (BAC) probes on the macrochromosomes of eight bird species: common blackbird, Atlantic canary, Eurasian woodcock, helmeted guinea fowl, houbara bustard, mallard duck, and rock dove. A combination of molecular cytogenetic, bioinformatics, and mathematical analyses allowed the building of comparative cytogenetic maps, reconstruction of a putative Neognathae ancestor, and assessment of chromosome rearrangement patterns and phylogenetic relationships in the studied neognath lineages. We observe that, as with our previous studies, chicken appears to have the karyotype most similar to the ancestor; however, previous reports of an increased rate of intrachromosomal change in Passeriformes (songbirds) appear not to be the case in our dataset. The use of this universally hybridizing probe set is applicable not only for the re-tracing of avian karyotype evolution but, potentially, for reconstructing genome assemblies

Threshold-dominated regulation hides genetic variation in gene expression networks

<p>Abstract</p> <p>Background</p> <p>In dynamical models with feedback and sigmoidal response functions, some or all variables have thresholds around which they regulate themselves or other variables. A mathematical analysis has shown that when the dose-response functions approach binary or on/off responses, any variable with an equilibrium value close to one of its thresholds is very robust to parameter perturbations of a homeostatic state. We denote this threshold robustness. To check the empirical relevance of this phenomenon with response function steepnesses ranging from a near on/off response down to Michaelis-Menten conditions, we have performed a simulation study to investigate the degree of threshold robustness in models for a three-gene system with one downstream gene, using several logical input gates, but excluding models with positive feedback to avoid multistationarity. Varying parameter values representing functional genetic variation, we have analysed the coefficient of variation (<it>CV</it>) of the gene product concentrations in the stable state for the regulating genes in absolute terms and compared to the <it>CV </it>for the unregulating downstream gene. The sigmoidal or binary dose-response functions in these models can be considered as phenomenological models of the aggregated effects on protein or mRNA expression rates of all cellular reactions involved in gene expression.</p> <p>Results</p> <p>For all the models, threshold robustness increases with increasing response steepness. The <it>CV</it>s of the regulating genes are significantly smaller than for the unregulating gene, in particular for steep responses. The effect becomes less prominent as steepnesses approach Michaelis-Menten conditions. If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost. Threshold robustness arises when a variable is an active regulator around its threshold, and this function is maintained by the feedback loop that the regulator necessarily takes part in and also is regulated by. In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state.</p> <p>Conclusion</p> <p>Threshold robustness of a variable can be seen as its ability to maintain an active regulation around its threshold in a homeostatic state despite external perturbations. The feedback loop that the system necessarily possesses in this state, ensures that the robust variable is itself regulated and kept close to its threshold. Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback. Threshold robustness in gene regulatory networks illustrates that hidden genetic variation can be explained by systemic properties of the genotype-phenotype map.</p

Examination of the expression of immunity genes and bacterial profiles in the caecum of growing chickens infected with Salmonella Enteritidis and fed a phytobiotic

This study was performed to investigate the differential expression of eight immunity genes and the bacterial profiles in the caecum of growing chickens challenged with Salmonella enterica serovar Enteritidis (SE) at 1 and 23 days post inoculation (dpi) in response to SE infection at 19 days of age and administration of the phytobiotic Intebio. Following infection, the genes CASP6 and IRF7 were upregulated by greater than twofold. Chicks fed Intebio showed at 1 dpi upregulation of AvBD10, IL6, IL8L2, CASP6 and IRF7. At 23 dpi, expression of AvBD11, IL6, IL8L2, CASP6 and IRF7 lowered in the experiment subgroups as compared with the control. Examination of the caecal contents at 1 dpi demonstrated a significant decrease in the microbial biodiversity in the infected subgroup fed normal diet. Bacterial content of Lactobacillus and Bacillus declined, while that of Enterobacteriaceae rose. In the infected subgroup fed Intebio, a pronounced change in composition of the microflora was not observed. In the early infection stages, the phytobiotic seemed to promote response to infection. Subsequently, an earlier suppression of the inflammatory reaction took place in chickens fed Intebio. Thus, use of Intebio as a drug with phytobiotic activity in chickens, including those infected with Salmonella, proved to be promising

Predicted Functions of MdmX in Fine-Tuning the Response of p53 to DNA Damage

Tumor suppressor protein p53 is regulated by two structurally homologous proteins, Mdm2 and MdmX. In contrast to Mdm2, MdmX lacks ubiquitin ligase activity. Although the essential interactions of MdmX are known, it is not clear how they function to regulate p53. The regulation of tumor suppressor p53 by Mdm2 and MdmX in response to DNA damage was investigated by mathematical modeling of a simplified network. The simplified network model was derived from a detailed molecular interaction map (MIM) that exhibited four coherent DNA damage response pathways. The results suggest that MdmX may amplify or stabilize DNA damage-induced p53 responses via non-enzymatic interactions. Transient effects of MdmX are mediated by reservoirs of p53∶MdmX and Mdm2∶MdmX heterodimers, with MdmX buffering the concentrations of p53 and/or Mdm2. A survey of kinetic parameter space disclosed regions of switch-like behavior stemming from such reservoir-based transients. During an early response to DNA damage, MdmX positively or negatively regulated p53 activity, depending on the level of Mdm2; this led to amplification of p53 activity and switch-like response. During a late response to DNA damage, MdmX could dampen oscillations of p53 activity. A possible role of MdmX may be to dampen such oscillations that otherwise could produce erratic cell behavior. Our study suggests how MdmX may participate in the response of p53 to DNA damage either by increasing dependency of p53 on Mdm2 or by dampening oscillations of p53 activity and presents a model for experimental investigation

A Semantic Web Management Model for Integrative Biomedical Informatics

Data, data everywhere. The diversity and magnitude of the data generated in the Life Sciences defies automated articulation among complementary efforts. The additional need in this field for managing property and access permissions compounds the difficulty very significantly. This is particularly the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data.The emergence of Semantic Web technologies brings the promise of meaningful interoperation between data and analysis resources. In this report we identify a core model for biomedical Knowledge Engineering applications and demonstrate how this new technology can be used to weave a management model where multiple intertwined data structures can be hosted and managed by multiple authorities in a distributed management infrastructure. Specifically, the demonstration is performed by linking data sources associated with the Lung Cancer SPORE awarded to The University of Texas MD Anderson Cancer Center at Houston and the Southwestern Medical Center at Dallas. A software prototype, available with open source at www.s3db.org, was developed and its proposed design has been made publicly available as an open source instrument for shared, distributed data management.The Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research. As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources. In this scenario, the acquisition of a new dataset should automatically trigger the delegation of its analysis

Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell

Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex

The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes

Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment