Mapping Post-translational Modifications of Mammalian Testicular Specific Histone Variant TH2B in Tetraploid and Haploid Germ Cells and Their Implications on the Dynamics of Nucleosome Structure

Histones regulate a variety of chromatin templated events by their post-translational modifications (PTMs). Although there are extensive reports on the PTMs of canonical histones, the information on the histone variants remains very scanty. Here, we report the identification of different PTMs, such as acetylation, methylation, and phosphorylation of a major mammalian histone variant TH2B. Our mass spectrometric analysis has led to the identification of both conserved and unique modifications across tetraploid spermatocytes and haploid spermatids. We have also computationally derived the 3-dimensional model of a TH2B containing nucleosome in order to study the spatial orientation of the PTMs identified and their effect on nucleosome stability and DNA binding potential. From our nucleosome model, it is evident that substititution of specific amino acid residues in TH2B results in both differential histone-DNA and histone-histone contacts. Furthermore, we have also observed that acetylation on the N-terminal tail of TH2B weakens the interactions with the DNA. These results provide direct evidence that, similar to somatic H2B, the testis specific histone TH2B also undergoes multiple PTMs, suggesting the possibility of chromatin regulation by such covalent modifications in mammalian male germ cells.

Development, in vitro and in vivo characterization of zoledronic acid functionalized hydroxyapatite nanoparticle based formulation for treatment of osteoporosis in animal model

We investigated the potential of using novel zoledronic acid (ZOL)-hydroxyapatite (HA) nanoparticle based drug formulation in a rat model of postmenopausal osteoporosis. By a classical adsorption method, nanoparticles of HA loaded with ZOL (HNLZ) drug formulation with a size range of 100-130 nm were prepared. 56 female Wistar rats were ovariectomized (OVX) or sham-operated at 3 months of age. Twelve weeks post surgery, rats were randomized into seven groups and treated with various doses of HNLZ (100, 50 and 25 mu g/kg, intravenous single dose), ZOL (100 mu g/kg, intravenous single dose) and HA nanoparticle (100 mu g/kg, intravenous single dose). Untreated OVX and sham OVX served as controls. After three months treatment period, we evaluated the mechanical properties of the lumbar vertebra and femoral mid-shaft. Femurs were also tested for trabecular microarchitecture. Sensitive biochemical markers of bone formation and bone resorption in serum were also determined. With respect to improvement in the mechanical strength of the lumbar spine and the femoral mid-shaft, the therapy with HNLZ drug formulation was more effective than ZOL therapy in OVX rats. Moreover, HNLZ drug therapy preserved the trabecular microarchitecture better than ZOL therapy in OVX rats. Furthermore, the HNLZ drug formulation corrected increase in serum levels of bone-specific alkaline phosphatase, procollagen type I N-terminal propeptide, osteocalcin, tartrate-resistant acid phosphatase 5b and C-telopeptide of type 1 collagen better than ZOL therapy in OVX rats. The results strongly suggest that HNLZ novel drug formulation appears to be more effective approach for treating severe osteoporosis in humans. (C) 2014 Elsevier B.V. All rights reserved.

Understanding Medical Device Patenting and Clinical Trials for Product Leadership

Unmet clinical needs remain the primary driving force for innovations in medical devices. While appropriate mechanisms to protect these innovative outcomes are essential, the performance of clinical trials to ensure safety is also mandated before the invention is ready for public use. Literature explaining the relationship between patenting activities and clinical trials of medical devices is scarce. Linking patent ownership to clinical trials may imply product leadership and value chain control. In this paper, we use patent data from Indian Patent Office (IPO), PCT, and data from Clinical Trials Registry of India (CTRI) to identify whether patent assignees have any role in leading as primary sponsors of clinical trials. A total of 42 primary sponsors are identified from the CTRI database in India. Number of patents awarded to these primary sponsors in the particular medical device, total number of patents awarded to the primary sponsor in all technologies, total number of patents in the specific medical device technology provides an indication of leadership and control in the value chain.

Splicing Functions and Global Dependency on Fission Yeast Slu7 Reveal Diversity in Spliceosome Assembly

The multiple short introns in Schizosaccharomyces pombe genes with degenerate cis sequences and atypically positioned polypyrimidine tracts make an interesting model to investigate canonical and alternative roles for conserved splicing factors. Here we report functions and interactions of the S. pombe slu7(+) (spslu7(+)) gene product, known from Saccharomyces cerevisiae and human in vitro reactions to assemble into spliceosomes after the first catalytic reaction and to dictate 3' splice site choice during the second reaction. By using a missense mutant of this essential S. pombe factor, we detected a range of global splicing derangements that were validated in assays for the splicing status of diverse candidate introns. We ascribe widespread, intron-specific SpSlu7 functions and have deduced several features, including the branch nucleotide-to-3' splice site distance, intron length, and the impact of its A/U content at the 5' end on the intron's dependence on SpSlu7. The data imply dynamic substrate-splicing factor relationships in multiintron transcripts. Interestingly, the unexpected early splicing arrest in spslu7-2 revealed a role before catalysis. We detected a salt-stable association with U5 snRNP and observed genetic interactions with spprp1(+), a homolog of human U5-102k factor. These observations together point to an altered recruitment and dependence on SpSlu7, suggesting its role in facilitating transitions that promote catalysis, and highlight the diversity in spliceosome assembly.

Accelerating Information Diffusion in Social Networks Under the Susceptible-Infected-Susceptible Epidemic Model

Standard Susceptible-Infected-Susceptible (SIS) epidemic models assume that a message spreads from the infected to the susceptible nodes due to only susceptible-infected epidemic contact. We modify the standard SIS epidemic model to include direct recruitment of susceptible individuals to the infected class at a constant rate (independent of epidemic contacts), to accelerate information spreading in a social network. Such recruitment can be carried out by placing advertisements in the media. We provide a closed form analytical solution for system evolution in the proposed model and use it to study campaigning in two different scenarios. In the first, the net cost function is a linear combination of the reward due to extent of information diffusion and the cost due to application of control. In the second, the campaign budget is fixed. Results reveal the effectiveness of the proposed system in accelerating and improving the extent of information diffusion. Our work is useful for devising effective strategies for product marketing and political/social-awareness/crowd-funding campaigns that target individuals in a social network.

A finite variant of the Toom Model

We present results for a finite variant of the one-dimensional Toom model with closed boundaries. We show that the steady state distribution is not of product form, but is nonetheless simple. In particular, we give explicit formulas for the densities and some nearest neighbour correlation functions. We also give exact results for eigenvalues and multiplicities of the transition matrix using the theory of R-trivial monoids in joint work with A. Schilling, B. Steinberg and N. M. Thiery.

Segregating Discourse Segments from Engineering Documents for Knowledge Acquisition

The broader goal of the research being described here is to automatically acquire diagnostic knowledge from documents in the domain of manual and mechanical assembly of aircraft structures. These documents are treated as a discourse used by experts to communicate with others. It therefore becomes possible to use discourse analysis to enable machine understanding of the text. The research challenge addressed in the paper is to identify documents or sections of documents that are potential sources of knowledge. In a subsequent step, domain knowledge will be extracted from these segments. The segmentation task requires partitioning the document into relevant segments and understanding the context of each segment. In discourse analysis, the division of a discourse into various segments is achieved through certain indicative clauses called cue phrases that indicate changes in the discourse context. However, in formal documents such language may not be used. Hence the use of a domain specific ontology and an assembly process model is proposed to segregate chunks of the text based on a local context. Elements of the ontology/model, and their related terms serve as indicators of current context for a segment and changes in context between segments. Local contexts are aggregated for increasingly larger segments to identify if the document (or portions of it) pertains to the topic of interest, namely, assembly. Knowledge acquired through such processes enables acquisition and reuse of knowledge during any part of the lifecycle of a product.

Modelling staphylococcal pneumonia in a human 3D lung tissue model system delineates toxin-mediated pathology

Staphylococcus aureus necrotizing pneumonia is recognized as a toxin-mediated disease, yet the tissue-destructive events remain elusive, partly as a result of lack of mechanistic studies in human lung tissue. In this study, a three-dimensional (3D) tissue model composed of human lung epithelial cells and fibroblasts was used to delineate the role of specific staphylococcal exotoxins in tissue pathology associated with severe pneumonia. To this end, the models were exposed to the mixture of exotoxins produced by S. aureus strains isolated from patients with varying severity of lung infection, namely necrotizing pneumonia or lung empyema, or to purified toxins. The necrotizing pneumonia strains secreted high levels of alpha-toxin and Panton-Valentine leukocidin (PVL), and triggered high cytotoxicity, inflammation, necrosis and loss of E-cadherin from the lung epithelium. In contrast, the lung empyema strain produced moderate levels of PVL, but negligible amounts of alpha-toxin, and triggered limited tissue damage. alpha-toxin had a direct damaging effect on the epithelium, as verified using toxin-deficient mutants and pure alpha-toxin. Moreover, PVL contributed to pathology through the lysis of neutrophils. A combination of alpha-toxin and PVL resulted in the most severe epithelial injury. In addition, toxin-induced release of pro-inflammatory mediators from lung tissue models resulted in enhanced neutrophil migration. Using a collection of 31 strains from patients with staphylococcal pneumonia revealed that strains producing high levels of alpha-toxin and PVL were cytotoxic and associated with fatal outcome. Also, the strains that produced the highest toxin levels induced significantly greater epithelial disruption. Of importance, toxin-mediated lung epithelium destruction could be inhibited by polyspecific intravenous immunoglobulin containing antibodies against alpha-toxin and PVL. This study introduces a novel model system for study of staphylococcal pneumonia in a human setting. The results reveal that the combination and levels of alpha-toxin and PVL correlate with tissue pathology and clinical outcome associated with pneumonia.

Multivariate PLS Modeling of Apicomplexan FabD-Ligand Interaction Space for Mapping Target-Specific Chemical Space and Pharmacophore Fingerprints

Biomolecular recognition underlying drug-target interactions is determined by both binding affinity and specificity. Whilst, quantification of binding efficacy is possible, determining specificity remains a challenge, as it requires affinity data for multiple targets with the same ligand dataset. Thus, understanding the interaction space by mapping the target space to model its complementary chemical space through computational techniques are desirable. In this study, active site architecture of FabD drug target in two apicomplexan parasites viz. Plasmodium falciparum (PfFabD) and Toxoplasma gondii (TgFabD) is explored, followed by consensus docking calculations and identification of fifteen best hit compounds, most of which are found to be derivatives of natural products. Subsequently, machine learning techniques were applied on molecular descriptors of six FabD homologs and sixty ligands to induce distinct multivariate partial-least square models. The biological space of FabD mapped by the various chemical entities explain their interaction space in general. It also highlights the selective variations in FabD of apicomplexan parasites with that of the host. Furthermore, chemometric models revealed the principal chemical scaffolds in PfFabD and TgFabD as pyrrolidines and imidazoles, respectively, which render target specificity and improve binding affinity in combination with other functional descriptors conducive for the design and optimization of the leads.

SUPPORTING ENVIRONMENTALLY-BENIGN DESIGN ELUCIDATING ENVIRONMENTAL IMPACT PROPAGATION IN CONCEPTUAL DESIGN PHASE BY SAPPHIRE MODEL OF CAUSALITY

Conceptual Design Phase is the most critical for design decisions and their impact on the Environment. It is also a phase of many `unknowns' making it flexible and allowing exploration of many solutions. Thus, it is a challenge to determine the most Environmentally-benign Solution or Concept to be translated in to a `good' product. The SAPPhIRE Model captures the various levels of abstractions present in Conceptual Design by Outcomes and defines a Solution-variant as a set of verifiable and quantifiable Outcomes. The Causality explains the propagation of Environmental Impact across Outcomes at varying levels of abstraction, suggesting that the Environmental Impact of an Outcome at a certain level can be represented as a collation of Environmental Impact information of all the Outcomes at each of its subsequent lower levels of abstraction. Thus a ball-park impact value can be associated with the higher-levels of abstraction, thereby supporting design decisions taken earlier on in Conceptual Design directing towards Environmentally-benign Design.

Enhanced Metastatic Potential in a 3D Tissue Scaffold toward a Comprehensive in Vitro Model for Breast Cancer Metastasis

Metastasis is clinically the most challenging and lethal aspect of breast cancer. While animal-based xenograft models are expensive and time-consuming, conventional two-dimensional (2D) cell culture systems fail to mimic in vivo signaling. In this study we have developed a three-dimensional (3D) scaffold system that better mimics the topography and mechanical properties of the breast tumor, thus recreating the tumor microenvironment in vitro to study breast cancer metastasis. Porous poly(e-caprolactone) (PCL) scaffolds of modulus 7.0 +/- 0.5 kPa, comparable to that of breast tumor tissue were fabricated, on which MDA-MB-231 cells proliferated forming tumoroids. A comparative gene expression analysis revealed that cells growing in the scaffolds expressed increased levels of genes implicated in the three major events of metastasis, viz., initiation, progression, and the site-specific colonization compared to cells grown in conventional 2D tissue culture polystyrene (TCPS) dishes. The cells cultured in scaffolds showed increased invasiveness and sphere efficiency in vitro and increased lung metastasis in vivo. A global gene expression analysis revealed a significant increase in the expression of genes involved in cell cell and cell matrix interactions and tissue remodeling, cancer inflammation, and the PI3K/Akt, Wnt, NF-kappaB, and HIFI signaling pathways all of which are implicated in metastasis. Thus, culturing breast cancer cells in 3D scaffolds that mimic the in vivo tumor-like microenvironment enhances their metastatic potential. This system could serve as a comprehensive in vitro model to investigate the manifold mechanisms of breast cancer metastasis.

Nanoparticle induced miscibility in LCST polymer blends: critically assessing the enthalpic and entropic effects

The use of copolymer and polymer blends widened the possibility of creating materials with multilayered architectures. Hierarchical polymer systems with a wide array of micro and nanostructures are generated by thermally induced phase separation (TIPS) in partially miscible polymer blends. Various parameters like the interaction between the polymers, concentration, solvent/non-solvent ratio, and quenching temperature have to be optimized to obtain these micro/nanophase structures. Alternatively, the addition of nanoparticles is another strategy to design materials with desired hetero-phase structures. The dynamics of the polymer nanocomposite depends on the statistical ordering of polymers around the nanoparticle, which is dependent on the shape of the nanoparticle. The entropic loss due to deformation of polymer chains, like the repulsive interactions due to coiling and the attractive interactions in the case of swelling has been highlighted in this perspective article. The dissipative particle dynamics has been discussed and is correlated with the molecular dynamics simulation in the case of polymer blends. The Cahn Hillard Cook model on variedly shaped immobile fillers has shown difference in the propagation of the composition wave. The nanoparticle shape has a contributing effect on the polymer particle interaction, which can change the miscibility window in the case of these phase separating polymer blends. Quantitative information on the effect of spherical particles on the demixing temperature is well established and further modified to explain the percolation of rod shaped particles in the polymer blends. These models correlate well with the experimental observations in context to the dynamics induced by the nanoparticle in the demixing behavior of the polymer blend. The miscibility of the LCST polymer blend depends on the enthalpic factors like the specific interaction between the components, and the solubility product and the entropic losses occurring due to the formation of any favorable interactions. Hence, it is essential to assess the entropic and enthalpic interactions induced by the nanoparticles independently. The addition of nanoparticles creates heterogeneity in the polymer phase it is localized. This can be observed as an alteration in the relaxation behavior of the polymer. This changes the demixing behavior and the interaction parameter between the polymers. The compositional changes induced due to the incorporation of nanoparticles are also attributed as a reason for the altered demixing temperature. The particle shape anisotropy causes a direction dependent depletion, which changes the phase behavior of the blend. The polymer-grafted nanoparticles with varying grafting density show tremendous variation in the miscibility of the blend. The stretching of the polymer chains grafted on the nanoparticles causes an entropy penalty in the polymer blend. A comparative study on the different shaped particles is not available up to date for understanding these aspects. Hence, we have juxtaposed the various computational studies on nanoparticle dynamics, the shape effect of NPs on homopolymers and also the cases of various polymer blends without nanoparticles to sketch a complete picture on the effect of various particles on the miscibility of LCST blends.

Evolution induced catastrophe in a nonlinear dynamical model of material failure

In order to study the failure of disordered materials, the ensemble evolution of a nonlinear chain model was examined by using a stochastic slice sampling method. The following results were obtained. (1) Sample-specific behavior, i.e. evolutions are different from sample to sample in some cases under the same macroscopic conditions, is observed for various load-sharing rules except in the globally mean field theory. The evolution according to the cluster load-sharing rule, which reflects the interaction between broken clusters, cannot be predicted by a simple criterion from the initial damage pattern and even then is most complicated. (2) A binary failure probability, its transitional region, where globally stable (GS) modes and evolution-induced catastrophic (EIC) modes coexist, and the corresponding scaling laws are fundamental to the failure. There is a sensitive zone in the vicinity of the boundary between the GS and EIC regions in phase space, where a slight stochastic increment in damage can trigger a radical transition from GS to EIC. (3) The distribution of strength is obtained from the binary failure probability. This, like sample-specificity, originates from a trans-scale sensitivity linking meso-scopic and macroscopic phenomena. (4) Strong fluctuations in stress distribution different from that of GS modes may be assumed as a precursor of evolution-induced catastrophe (EIC).

Reconsideration On The Role Of The Specific Heat Ratio In Arrhenius Law Applications

Arrhenius law implicates that only those molecules which possess the internal energy greater than the activation energy E-a can react. However, the internal energy will not be proportional to the gas temperature if the specific heat ratio gamma and the gas constant R vary during chemical reaction processes. The varying gamma may affect significantly the chemical reaction rate calculated with the Arrhenius law under the constant gamma assumption, which has been widely accepted in detonation and combustion simulations for many years. In this paper, the roles of variable gamma and R in Arrhenius law applications are reconsidered, and their effects on the chemical reaction rate are demonstrated by simulating one-dimensional C-J and two-dimensional cellular detonations. A new overall one-step detonation model with variable gamma and R is proposed to improve the Arrhenius law. Numerical experiments demonstrate that this improved Arrhenius law works well in predicting detonation phenomena with the numerical results being in good agreement with experimental data.

On the interaction of water waves and seabed by the porous medium model

The interaction of water waves and seabed is studied by using Yamamoto's model, which takes into account the deformation of soil skeletal frame, compressibility of pore fluid flow as well as the Coulumb friction. When analyzing the propagation of three kinds of stress waves in seabed, a simplified dispersion relation and a specific damping formula are derived. The problem of seabed stability is further treated analytically based on the Mohr-Coulomb theory. The theory is finally applied to the coastal problems in the Lian-Yun Harbour and compared with observations and measurements in soil-wave tank with satisfactory results.