Gene Transfection in High Serum Levels: Case Studies with New Cholesterol Based Cationic Gemini Lipids

Background: Six new cationic gemini lipids based on cholesterol possessing different positional combinations of hydroxyethyl (-CH2CH2OH) and oligo-oxyethylene -(CH2CH2O)(n)- moieties were synthesized. For comparison the corresponding monomeric lipid was also prepared. Each new cationic lipid was found to form stable, clear suspensions in aqueous media. Methodology/Principal Findings: To understand the nature of the individual lipid aggregates, we have studied the aggregation properties using transmission electron microscopy (TEM), dynamic light scattering (DLS), zeta potential measurements and X-ray diffraction (XRD). We studied the lipid/DNA complex (lipoplex) formation and the release of the DNA from such lipoplexes using ethidium bromide. These gemini lipids in presence of a helper lipid, 1, 2-dioleoyl phophatidyl ethanol amine (DOPE) showed significant enhancements in the gene transfection compared to several commercially available transfection agents. Cholesterol based gemini having -CH2-CH2-OH groups at the head and one oxyethylene spacer was found to be the most effective lipid, which showed transfection activity even in presence of high serum levels (50%) greater than Effectene, one of the potent commercially available transfecting agents. Most of these geminis protected plasmid DNA remarkably against DNase I in serum, although the degree of stability was found to vary with their structural features. Conclusions/Significance: -OH groups present on the cationic headgroups in combination with oxyethylene linkers on cholesterol based geminis, gave an optimized combination of new genera of gemini lipids possessing high transfection efficiency even in presence of very high percentage of serum. This property makes them preferential transfection reagents for possible in vivo studies.

CUDA-for-clusters: a system for efficient execution of CUDA kernels on multi-core clusters

Rapid advancements in multi-core processor architectures coupled with low-cost, low-latency, high-bandwidth interconnects have made clusters of multi-core machines a common computing resource. Unfortunately, writing good parallel programs that efficiently utilize all the resources in such a cluster is still a major challenge. Various programming languages have been proposed as a solution to this problem, but are yet to be adopted widely to run performance-critical code mainly due to the relatively immature software framework and the effort involved in re-writing existing code in the new language. In this paper, we motivate and describe our initial study in exploring CUDA as a programming language for a cluster of multi-cores. We develop CUDA-For-Clusters (CFC), a framework that transparently orchestrates execution of CUDA kernels on a cluster of multi-core machines. The well-structured nature of a CUDA kernel, the growing popularity, support and stability of the CUDA software stack collectively make CUDA a good candidate to be considered as a programming language for a cluster. CFC uses a mixture of source-to-source compiler transformations, a work distribution runtime and a light-weight software distributed shared memory to manage parallel executions. Initial results on running several standard CUDA benchmark programs achieve impressive speedups of up to 7.5X on a cluster with 8 nodes, thereby opening up an interesting direction of research for further investigation.

Reaction dynamics under confinement: an exact path integral treatment of a two-stage model of stochastic gene expression

Gene expression in living systems is inherently stochastic, and tends to produce varying numbers of proteins over repeated cycles of transcription and translation. In this paper, an expression is derived for the steady-state protein number distribution starting from a two-stage kinetic model of the gene expression process involving p proteins and r mRNAs. The derivation is based on an exact path integral evaluation of the joint distribution, P(p, r, t), of p and r at time t, which can be expressed in terms of the coupled Langevin equations for p and r that represent the two-stage model in continuum form. The steady-state distribution of p alone, P(p), is obtained from P(p, r, t) (a bivariate Gaussian) by integrating out the r degrees of freedom and taking the limit t -> infinity. P(p) is found to be proportional to the product of a Gaussian and a complementary error function. It provides a generally satisfactory fit to simulation data on the same two-stage process when the translational efficiency (a measure of intrinsic noise levels in the system) is relatively low; it is less successful as a model of the data when the translational efficiency (and noise levels) are high.

Correspondence between neutron depolarization and higher order magnetic susceptibility to investigate ferromagnetic clusters in phase separated systems

It is a tough task to distinguish a short-range ferromagnetically correlated cluster-glass phase from a canonical spin-glass-like phase in many magnetic oxide systems using conventional magnetometry measurements. As a case study, we investigate the magnetic ground state of La0.85Sr0.15CoO3, which is often debated based on phase separation issues. We report the results of two samples of La0.85Sr0.15CoO3 (S-1 and S-2) prepared under different conditions. Neutron depolarization, higher harmonic ac susceptibility and magnetic relaxation studies were carried out along with conventional magnetometry measurements to differentiate subtle changes at the microscopic level. There is no evidence of ferromagnetic correlation in the sample S-2 attributed to a spin-glass phase, and this is compounded by the lack of existence of a second order component of higher harmonic ac susceptibility and neutron depolarization. A magnetic relaxation experiment at different temperatures complements the spin glass characteristic in S-2. All these signal a sharp variance when we consider the cluster-glass-like phase (phase separated) in S-1, especially when prepared from an improper chemical synthesis process. This shows that the nonlinear ac susceptibility is a viable tool to detect ferromagnetic clusters such as those the neutron depolarization study can reveal.

Drosophila Erect wing (Ewg) controls mitochondrial fusion during muscle growth and maintenance by regulation of the Opa1-like gene

Mitochondrial biogenesis and morphological changes are associated with tissue-specific functional demand, but the factors and pathways that regulate these processes have not been completely identified. A lack of mitochondrial fusion has been implicated in various developmental and pathological defects. The spatiotemporal regulation of mitochondrial fusion in a tissue such as muscle is not well understood. Here, we show in Drosophila indirect flight muscles (IFMs) that the nuclear-encoded mitochondrial inner membrane fusion gene, Opa1-like, is regulated in a spatiotemporal fashion by the transcription factor/co-activator Erect wing (Ewg). In IFMs null for Ewg, mitochondria undergo mitophagy and/or autophagy accompanied by reduced mitochondrial functioning and muscle degeneration. By following the dynamics of mitochondrial growth and shape in IFMs, we found that mitochondria grow extensively and fuse during late pupal development to form the large tubular mitochondria. Our evidence shows that Ewg expression during early IFM development is sufficient to upregulate Opa1-like, which itself is a requisite for both late pupal mitochondrial fusion and muscle maintenance. Concomitantly, by knocking down Opa1-like during early muscle development, we show that it is important for mitochondrial fusion, muscle differentiation and muscle organization. However, knocking down Opa1-like, after the expression window of Ewg did not cause mitochondrial or muscle defects. This study identifies a mechanism by which mitochondrial fusion is regulated spatiotemporally by Ewg through Opa1-like during IFM differentiation and growth.

Organization of Mn-Clusters in pcu and bcu Networks: Synthesis, Structure, and Properties

Two new anionic inorganic-organic hybrid compounds H3O]Mn-3(mu(3)-OH)(C14H8O6S)(3)(H2O)](DMF)(5), I, and H3O](2)Mn-7(mu(3)-OH)(4)(C14H8O6S)(6)(H2O)(4)](H2O)(2)(DMF)(8), II have been prepared by employing mild solvothennal methods. Both the compounds have three-dimensionally extended structures formed by Mn-6 and Mn-7 clusters, respectively. The connectivity between Mn-6 and Mn-7 clusters and 4,4'-sulfonyldibenzoic acid anions (SDBA(2-)) results in a six connected pcu network in I and an eight connected bcu network in II. The presence of hydronium ion (H-3(O+)) along with the solvent molecules in the channels of both the compounds suggested proton conduction in the solids. Proton conductivity studies gave values of similar to 3 x 10(-4) Omega(-1) cm(-1) 98% relative humidity in both the compounds. The high activation energies indicate a vehicle mechanism in the compounds I and II. Magnetic studies indicate antiferromagnetic behavior in both the compounds.

DNA STRUCTURAL FEATURES AND ARCHITECTURE OF PROMOTER REGIONS PLAY A ROLE IN GENE RESPONSIVENESS OF S. CEREVISIAE

Gene expression is the most fundamental biological process, which is essential for phenotypic variation. It is regulated by various external (environment and evolution) and internal (genetic) factors. The level of gene expression depends on promoter architecture, along with other external factors. Presence of sequence motifs, such as transcription factor binding sites (TFBSs) and TATA-box, or DNA methylation in vertebrates has been implicated in the regulation of expression of some genes in eukaryotes, but a large number of genes lack these sequences. On the other hand, several experimental and computational studies have shown that promoter sequences possess some special structural properties, such as low stability, less bendability, low nucleosome occupancy, and more curvature, which are prevalent across all organisms. These structural features may play role in transcription initiation and regulation of gene expression. We have studied the relationship between the structural features of promoter DNA, promoter directionality and gene expression variability in S. cerevisiae. This relationship has been analyzed for seven different measures of gene expression variability, along with two different regulatory effect measures. We find that a few of the variability measures of gene expression are linked to DNA structural properties, nucleosome occupancy, TATA-box presence, and bidirectionality of promoter regions. Interestingly, gene responsiveness is most intimately correlated with DNA structural features and promoter architecture.

Efficient Dendrimer-DNA Complexation and Gene Delivery Vector Properties of Nitrogen-Core Poly(propyl ether imine) Dendrimer in Mammalian Cells

Dendrimers as vectors for gene delivery were established, primarily by utilizing few prominent dendrimer types so far. We report herein studies of DNA complexation efficacies and gene delivery vector properties of a nitrogen-core poly(propyl ether imine) (PETIM) dendrimer, constituted with 22 tertiary amine internal branches and 24 primary amines at the periphery. The interaction of the dendrimer with pEGFPDNA was evaluated through UV-vis, circular dichroism (CD) spectral studies, ethidium bromide fluorescence emission quenching, thermal melting, and gel retardation assays, from which most changes to DNA structure during complexation was found to occur at a weight ratio of dendrimer:DNA similar to 2:1. The zeta potential measurements further confirmed this stoichiometry at electroneutrality. The structure of a DNA oligomer upon dendrimer complexation was simulated through molecular modeling and the simulation showed that the dendrimer enfolded DNA oligomer along both major and minor grooves, without causing DNA deformation, in 1:1 and 2:1 dendrimer-to-DNA complexes. Atomic force microscopy (AFM) studies on dendrimer-pEGFP DNA complex showed an increase in the average z-height as a result of dendrimers decorating the DNA, without causing a distortion of the DNA structure. Cytotoxicity studies involving five different mammalian cell lines, using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide] (MTT) assay, reveal the dendrimer toxicity profile (IC50) values of similar to 400-1000 mu g mL(-1), depending on the cell line tested. Quantitative estimation, using luciferase assay, showed that the gene transfection was at least 100 times higher when compared to poly(ethylene imine) branched polymer, having similar number of cationic sites as the dendrimer. The present study establishes the physicochemical behavior of new nitrogen-core PETIM dendrimer-DNA complexes, their lower toxicities, and efficient gene delivery vector properties.

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.

The relationship between classification of multi-domain proteins using an alignment-free approach and their functions: a case study with immunoglobulins

Establishing functional relationships between multi-domain protein sequences is a non-trivial task. Traditionally, delineating functional assignment and relationships of proteins requires domain assignments as a prerequisite. This process is sensitive to alignment quality and domain definitions. In multi-domain proteins due to multiple reasons, the quality of alignments is poor. We report the correspondence between the classification of proteins represented as full-length gene products and their functions. Our approach differs fundamentally from traditional methods in not performing the classification at the level of domains. Our method is based on an alignment free local matching scores (LMS) computation at the amino-acid sequence level followed by hierarchical clustering. As there are no gold standards for full-length protein sequence classification, we resorted to Gene Ontology and domain-architecture based similarity measures to assess our classification. The final clusters obtained using LMS show high functional and domain architectural similarities. Comparison of the current method with alignment based approaches at both domain and full-length protein showed superiority of the LMS scores. Using this method we have recreated objective relationships among different protein kinase sub-families and also classified immunoglobulin containing proteins where sub-family definitions do not exist currently. This method can be applied to any set of protein sequences and hence will be instrumental in analysis of large numbers of full-length protein sequences.

Conditional silencing of topoisomerase I gene of Mycobacterium tuberculosis validates its essentiality for cell survival

Topoisomerases are an important class of enzymes for regulating the DNA transaction processes. Mycobacterium tuberculosis (Mtb) is one of the most formidable pathogens also posing serious challenges for therapeutic interventions. The organism contains only one type IA topoisomerase (Rv3646c), offering an opportunity to test its potential as a candidate drug target. To validate the essentiality of M.tuberculosis topoisomerase I (TopoI(Mt)) for bacterial growth and survival, we have generated a conditionally regulated strain of topoI in Mtb. The conditional knockdown mutant exhibited delayed growth on agar plate. In liquid culture, the growth was drastically impaired when TopoI expression was suppressed. Additionally, novobiocin and isoniazid showed enhanced inhibitory potential against the conditional mutant. Analysis of the nucleoid revealed its altered architecture upon TopoI depletion. These studies establish the essentiality of TopoI for the M.tuberculosis growth and open up new avenues for targeting the enzyme.

PLIC: protein-ligand interaction clusters

Most of the biological processes are governed through specific protein-ligand interactions. Discerning different components that contribute toward a favorable protein-ligand interaction could contribute significantly toward better understanding protein function, rationalizing drug design and obtaining design principles for protein engineering. The Protein Data Bank (PDB) currently hosts the structure of similar to 68 000 protein-ligand complexes. Although several databases exist that classify proteins according to sequence and structure, a mere handful of them annotate and classify protein-ligand interactions and provide information on different attributes of molecular recognition. In this study, an exhaustive comparison of all the biologically relevant ligand-binding sites (84 846 sites) has been conducted using PocketMatch: a rapid, parallel, in-house algorithm. PocketMatch quantifies the similarity between binding sites based on structural descriptors and residue attributes. A similarity network was constructed using binding sites whose PocketMatch scores exceeded a high similarity threshold (0.80). The binding site similarity network was clustered into discrete sets of similar sites using the Markov clustering (MCL) algorithm. Furthermore, various computational tools have been used to study different attributes of interactions within the individual clusters. The attributes can be roughly divided into (i) binding site characteristics including pocket shape, nature of residues and interaction profiles with different kinds of atomic probes, (ii) atomic contacts consisting of various types of polar, hydrophobic and aromatic contacts along with binding site water molecules that could play crucial roles in protein-ligand interactions and (iii) binding energetics involved in interactions derived from scoring functions developed for docking. For each ligand-binding site in each protein in the PDB, site similarity information, clusters they belong to and description of site attributes are provided as a relational database-protein-ligand interaction clusters (PLIC).

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Background: The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. Results: We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of similar to 58% and similar to 40% for localization and functions respectively of proteins were determined at a threshold of similar to 30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k nearest neighbor classifier confirmed that our results compared favorably. Conclusions: This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest.