Developing Network-Based Systems Toxicology by Combining Transcriptomics Data with Literature Mining and Multiscale Quantitative Modeling

We describe how the genome-wide transcriptional profiling can be used in network-based systems toxicology, an approach leveraging biological networks for assessing the health risks of exposure to chemical compounds. Driven by the technological advances changing the ways in which data are generated, systems toxicology has allowed traditional toxicity endpoints to be enhanced with far deeper levels of analysis. In combination, new experimental and computational methods have offered the potential for more effective, efficient, and reliable toxicological testing strategies. We illustrate these advances by the “network perturbation amplitude” methodology that quantifies the effects of exposure treatments on biological mechanisms represented by causal networks. We also describe recent developments in the assembly of high-quality causal biological networks using crowdsourcing and text-mining approaches. We further show how network-based approaches can be integrated into the multiscale modeling framework of response to toxicological exposure. Finally, we combine biological knowledge assembly and multiscale modeling to report on the promising developments of the “quantitative adverse outcome pathway” concept, which spans multiple levels of biological organization, from molecules to population, and has direct relevance in the context of the “Toxicity Testing in the 21st century” vision of the US National Research Council

Identification of clustered microRNAs using an ab initio prediction method

BACKGROUND: MicroRNAs (miRNAs) are endogenous 21 to 23-nucleotide RNA molecules that regulate protein-coding gene expression in plants and animals via the RNA interference pathway. Hundreds of them have been identified in the last five years and very recent works indicate that their total number is still larger. Therefore miRNAs gene discovery remains an important aspect of understanding this new and still widely unknown regulation mechanism. Bioinformatics approaches have proved to be very useful toward this goal by guiding the experimental investigations. RESULTS: In this work we describe our computational method for miRNA prediction and the results of its application to the discovery of novel mammalian miRNAs. We focus on genomic regions around already known miRNAs, in order to exploit the property that miRNAs are occasionally found in clusters. Starting with the known human, mouse and rat miRNAs we analyze 20 kb of flanking genomic regions for the presence of putative precursor miRNAs (pre-miRNAs). Each genome is analyzed separately, allowing us to study the species-specific identity and genome organization of miRNA loci. We only use cross-species comparisons to make conservative estimates of the number of novel miRNAs. Our ab initio method predicts between fifty and hundred novel pre-miRNAs for each of the considered species. Around 30% of these already have experimental support in a large set of cloned mammalian small RNAs. The validation rate among predicted cases that are conserved in at least one other species is higher, about 60%, and many of them have not been detected by prediction methods that used cross-species comparisons. A large fraction of the experimentally confirmed predictions correspond to an imprinted locus residing on chromosome 14 in human, 12 in mouse and 6 in rat. Our computational tool can be accessed on the world-wide-web. CONCLUSION: Our results show that the assumption that many miRNAs occur in clusters is fruitful for the discovery of novel miRNAs. Additionally we show that although the overall miRNA content in the observed clusters is very similar across the three considered species, the internal organization of the clusters changes in evolution

Construction of a Suite of Computable Biological Network Models Focused on Mucociliary Clearance in the Respiratory Tract

Mucociliary clearance (MCC), considered as a collaboration of mucus secreted from goblet cells, the airway surface liquid layer, and the beating of cilia of ciliated cells, is the airways’ defense system against airborne contaminants. Because the process is well described at the molecular level, we gathered the available information into a suite of comprehensive causal biological network (CBN) models. The suite consists of three independent models that represent (1) cilium assembly, (2) ciliary beating, and (3) goblet cell hyperplasia/metaplasia and that were built in the Biological Expression Language, which is both human-readable and computable. The network analysis of highly connected nodes and pathways demonstrated that the relevant biology was captured in the MCC models. We also show the scoring of transcriptomic data onto these network models and demonstrate that the models capture the perturbation in each dataset accurately. This work is a continuation of our approach to use computational biological network models and mathematical algorithms that allow for the interpretation of high-throughput molecular datasets in the context of known biology. The MCC network model suite can be a valuable tool in personalized medicine to further understand heterogeneity and individual drug responses in complex respiratory diseases

Quantum Monte Carlo study of the 3D attractive Hubbard model

We study the three-dimensional (3D) attractive Hubbard model by means of the Determinant Quantum Monte Carlo method. This model is a prototype for the description of the smooth crossover between BCS superconductivity and Bose-Einstein condensation. By detailed finite-size scaling we extract the finite-temperature phase diagram of the model. In particular, we interpret the observed behavior according to a scenario of two fundamental temperature scales; T* associated with Cooper pair formation and Tc with condensation (giving rise to long-range superconducting order). Our results also indicate the presence of a recently conjectured phase transition hidden by the superconducting state. A comparison with the 2D case is briefly discussed, given its relevance for the physics of high-Tc cuprate superconductors.Comment: 4 pages, 4 Postscript figure

Fluctuating diamagnetism in underdoped high temperature superconductors

The fluctuation induced diamagnetism of underdoped high temperature superconductors is studied in the framework of the Lawrence-Doniach model. By taking into account the fluctuations of the phase of the order parameter only, the latter reduces to a layered XY-model describing a liquid of vortices which can be either thermally excited or induced by the external magnetic field. The diamagnetic response is given by a current-current correlation function which is evaluated using the Coulomb gas analogy. Our results are then applied to recent measurements of fluctuation diamagnetism in underdoped YBCO. They allow to understand both the observed anomalous temperature dependence of the zero-field susceptibility and the two distinct regimes appearing in the magnetic field dependence of the magnetization.Comment: 12 pages, 4 figures included, accepted for publication in PR

Marek's Disease Virus Type 2 (MDV-2)-Encoded MicroRNAs Show No Sequence Conservation with Those Encoded by MDV-1▿

MicroRNAs (miRNAs) are increasingly being recognized as major regulators of gene expression in many organisms, including viruses. Among viruses, members of the family Herpesviridae account for the majority of the currently known virus-encoded miRNAs. The highly oncogenic Marek's disease virus type 1 (MDV-1), an avian herpesvirus, has recently been shown to encode eight miRNAs clustered in the MEQ and LAT regions of the viral genome. The genus Mardivirus, to which MDV-1 belongs, also includes the nononcogenic but antigenically related MDV-2. As MDV-1 and MDV-2 are evolutionarily very close, we sought to determine if MDV-2 also encodes miRNAs. For this, we cloned, sequenced, and analyzed a library of small RNAs from the lymphoblastoid cell line MSB-1, previously shown to be coinfected with both MDV-1 and MDV-2. Among the 5,099 small RNA sequences determined from the library, we identified 17 novel MDV-2-specific miRNAs. Out of these, 16 were clustered in a 4.2-kb long repeat region that encodes R-LORF2 to R-LORF5. The single miRNA outside the cluster was located in the short repeat region, within the C-terminal region of the ICP4 homolog. The expression of these miRNAs in MSB-1 cells and infected chicken embryo fibroblasts was further confirmed by Northern blotting analysis. The identification of miRNA clusters within the repeat regions of MDV-2 demonstrates conservation of the relative genomic positions of miRNA clusters in MDV-1 and MDV-2, despite the lack of sequence homology among the miRNAs of the two viruses. The identification of these novel miRNAs adds to the growing list of virus-encoded miRNAs