Hyaluronan derived nanoparticle for simvastatin delivery: evaluation of simvastatin induced myotoxicity in tissue engineered skeletal muscle

Statins are currently the most prescribed hypercholesterolemia-lowering drugs worldwide, with estimated usage approaching one-sixth of the population. However, statins are known to cause pleiotropic skeletal myopathies in 1.5% to 10% of patients and the mechanisms by which statins induce this response, are not fully understood. In this study, a 3D collagen-based tissue-engineered skeletal muscle construct is utilised as a screening platform to test the efficacy and toxicity of a new delivery system. A hyaluronic acid derived nanoparticle loaded with simvastatin (HA-SIM-NPs) is designed and the effect of free simvastatin and HA-SIM-NPs on cellular, molecular and tissue response is investigated. Morphological ablation of myotubes and lack of de novo myotube formation (regeneration) was evident at the highest concentrations (333.33 μM), independent of delivery vehicle (SIM or HA-SIM-NP). A dose-dependent disruption of the cytoskeleton, reductions in metabolic activity and tissue engineered (TE) construct tissue relaxation was evident in the free drug condition (SIM, 3.33 μM and 33.33 nM). However, most of these changes were ameliorated when SIM was delivered via HA-SIM-NPs. Significantly, homogeneous expressions of MMP2, MMP9, and myogenin in HA-SIM-NPs outlined enhanced regenerative responses compared to SIM. Together, these results outline statin delivery via HA-SIM-NP as an effective delivery mechanism to inhibit deleterious myotoxic side-effects

Genetic integrity of the human Y chromosome exposed to groundwater arsenic

<p>Abstract</p> <p>Background</p> <p>Arsenic is a known human carcinogen reported to cause chromosomal deletions and genetic anomalies in cultured cells. The vast human population inhabiting the Ganges delta in West Bengal, India and Bangladesh is exposed to critical levels of arsenic present in the groundwater. The genetic and physiological mechanism of arsenic toxicity in the human body is yet to be fully established. In addition, lack of animal models has made work on this line even more challenging.</p> <p>Methods</p> <p>Human male blood samples were collected with their informed consent from 5 districts in West Bengal having groundwater arsenic level more than 50 μg/L. Isolation of genomic DNA and preparation of metaphase chromosomes was done using standard protocols. End point PCR was performed for established sequence tagged sites to ascertain the status of recombination events. Single nucleotide variants of candidate genes and amplicons were carried out using appropriate restriction enzymes. The copy number of DYZ1 array per haploid genome was calculated using real time PCR and its chromosomal localization was done by fluorescence in-situ hybridization (FISH).</p> <p>Results</p> <p>We studied effects of arsenic exposure on the human Y chromosome in males from different areas of West Bengal focusing on known recombination events (P5-P1 proximal; P5-P1 distal; gr/gr; TSPY-TSPY, b1/b3 and b2/b3), single nucleotide variants (SNVs) of a few candidate Y-linked genes (DAZ, TTY4, BPY2, GOLGA2LY) and the amplicons of AZFc region. Also, possible chromosomal reorganization of DYZ1 repeat arrays was analyzed. Barring a few microdeletions, no major changes were detected in blood DNA samples. SNV analysis showed a difference in some alleles. Similarly, DYZ1 arrays signals detected by FISH were found to be affected in some males.</p> <p>Conclusions</p> <p>Our Y chromosome analysis suggests that the same is protected from the effects of arsenic by some unknown mechanisms maintaining its structural and functional integrities. Thus, arsenic effects on the human body seem to be different compared to that on the cultured cells.</p

Fatty Acid Binding Domain Mediated Conjugation of Ultrafine Magnetic Nanoparticles with Albumin Protein

A novel bioconjugate of stearic acid capped maghemite nanoparticle (γ-Fe2O3) with bovine serum albumin (BSA) was developed by taking recourse to the fatty acid binding property of the protein. From FT-IR study, it was found that conjugation took place covalently between the amine group of protein molecule and carboxyl group of stearic acid capped maghemite nanoparticle. TEM study further signified the morphology of the proposed nanobioconjuagte. The binding constant of nanoparticle with protein molecule was evaluated from the optical property studies. Also, magnetic measurement (M–H) showed retaining of magnetic property by significant values of saturation magnetization and other hysteretic parameters

MicroRNA-Integrated and Network-Embedded Gene Selection with Diffusion Distance

Gene network information has been used to improve gene selection in microarray-based studies by selecting marker genes based both on their expression and the coordinate expression of genes within their gene network under a given condition. Here we propose a new network-embedded gene selection model. In this model, we first address the limitations of microarray data. Microarray data, although widely used for gene selection, measures only mRNA abundance, which does not always reflect the ultimate gene phenotype, since it does not account for post-transcriptional effects. To overcome this important (critical in certain cases) but ignored-in-almost-all-existing-studies limitation, we design a new strategy to integrate together microarray data with the information of microRNA, the major post-transcriptional regulatory factor. We also handle the challenges led by gene collaboration mechanism. To incorporate the biological facts that genes without direct interactions may work closely due to signal transduction and that two genes may be functionally connected through multi paths, we adopt the concept of diffusion distance. This concept permits us to simulate biological signal propagation and therefore to estimate the collaboration probability for all gene pairs, directly or indirectly-connected, according to multi paths connecting them. We demonstrate, using type 2 diabetes (DM2) as an example, that the proposed strategies can enhance the identification of functional gene partners, which is the key issue in a network-embedded gene selection model. More importantly, we show that our gene selection model outperforms related ones. Genes selected by our model 1) have improved classification capability; 2) agree with biological evidence of DM2-association; and 3) are involved in many well-known DM2-associated pathways

Extracting the abstraction pyramid from complex networks

<p>Abstract</p> <p>Background</p> <p>At present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the "vertical" relationships between modules at different levels (e.g., module A at level two is included in module B at level one), or a single-level graph that represents the "horizontal" relationships among modules (e.g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships.</p> <p>Results</p> <p>We propose a complex network analysis tool, Pyramabs, which was developed to integrate vertical and horizontal relationships and extract information at various granularities to create a pyramid from a complex system of interacting objects. The pyramid depicts the nested structure implied in a complex system, and shows the vertical relationships between abstract networks at different levels. In addition, at each level the abstract network of modules, which are connected by weighted links, represents the modules' horizontal relationships. We first tested Pyramabs on hierarchical random networks to verify its ability to find the module organization pre-embedded in the networks. We later tested it on a protein-protein interaction (PPI) network and a metabolic network. According to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the vertical relationships identified from the PPI and metabolic pathways correctly characterized the <it>inclusion </it>(i.e., <it>part-of</it>) relationship, and the horizontal relationships provided a good indication of the functional closeness between modules. Our experiments with Pyramabs demonstrated its ability to perform knowledge mining in complex systems.</p> <p>Conclusions</p> <p>Networks are a flexible and convenient method of representing interactions in a complex system, and an increasing amount of information in real-world situations is described by complex networks. We considered the analysis of a complex network as an iterative process for extracting meaningful information at multiple granularities from a system of interacting objects. The quality of the interpretation of the networks depends on the completeness and expressiveness of the extracted knowledge representations. Pyramabs was designed to interpret a complex network through a disclosure of a pyramid of abstractions. The abstraction pyramid is a new knowledge representation that combines vertical and horizontal viewpoints at different degrees of abstraction. Interpretations in this form are more accurate and more meaningful than multilevel dendrograms or single-level graphs. Pyramabs can be accessed at <url>http://140.113.166.165/pyramabs.php/</url>.</p

Semantic integration to identify overlapping functional modules in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.</p> <p>Results</p> <p>We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.</p> <p>Conclusion</p> <p>The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.</p

Local Network Topology in Human Protein Interaction Data Predicts Functional Association

The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI) data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-β signaling pathway (P<10−50). Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era

Neighbor Overlap Is Enriched in the Yeast Interaction Network: Analysis and Implications

The yeast protein-protein interaction network has been shown to have distinct topological features such as a scale free degree distribution and a high level of clustering. Here we analyze an additional feature which is called Neighbor Overlap. This feature reflects the number of shared neighbors between a pair of proteins. We show that Neighbor Overlap is enriched in the yeast protein-protein interaction network compared with control networks carefully designed to match the characteristics of the yeast network in terms of degree distribution and clustering coefficient. Our analysis also reveals that pairs of proteins with high Neighbor Overlap have higher sequence similarity, more similar GO annotations and stronger genetic interactions than pairs with low ones. Finally, we demonstrate that pairs of proteins with redundant functions tend to have high Neighbor Overlap. We suggest that a combination of three mechanisms is the basis for this feature: The abundance of protein complexes, selection for backup of function, and the need to allow functional variation