Efficient plasmid-mediated gene transfection of ovine bone marrow mesenchymal stromal cells

Background aimsGiven the close similarity between ovine and human cardiomyocytes, sheep models of myocardial infarction and heart failure are increasingly used in studies of stem cell-mediated heart regeneration. In these studies, mesenchymal stromal cells (MSCs) are frequently employed. To enhance the paracrine effects of these MSCs, ex vivo transfection with genes encoding growth factors has been proposed. Although viral vectors exhibit higher transfection efficiency than plasmids, they entail the risks of uncontrolled transgene expression and immune reactions that preclude repeated administration. Our aim was to optimize the efficiency of plasmid-mediated transfection of ovine MSCs, while preserving cell viability.MethodsVarying amounts of diverse cationic lipids were used to obtain the reagent-to-DNA mass ratio showing highest luciferase activity. Transfection efficiency (flow cytometry) was tested on plasmid-green fluorescent protein-transfected MSCs at increasing DNA mass.ResultsLipofectamine LTX 5 μL and Plus reagent 4 μL with 2 μg of DNA yielded 42.3 ± 4.7% transfection efficiency, while preserving cell viability. Using these transfection conditions, we transfected MSCs with a plasmid encoding human vascular endothelial growth factor (VEGF) and found high VEGF protein concentrations in the culture supernatant from day 2 (1968 ± 324 pg/mL per μg DNA) through at least day 12 (888 ± 386 pg/mL per μg DNA) after transfection.ConclusionsPlasmid-mediated transfection of ovine MSCs to over-express paracrine heart-regenerative growth factors is feasible and efficient and overcomes the risks and limitations associated with the use of viral vectors.     

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping.     

Rational design of the survivin/CDK4 complex by combining protein–protein docking and molecular dynamics simulations

Survivin, the smallest inhibitor of apoptosis protein (IAP), is a valid target for cancer research. It mediates both the apoptosis pathway and the cell cycle and has been proposed to form a complex with the cyclin-dependent kinase protein CDK4. The resulting complex transports CDK4 from the cytosol to the nucleus, where CDK4 participates in cell division. Survivin has been recognized as a node protein that interacts with several partners; disruption of the formed complexes can lead to new anticancer compounds. We propose a rational model of the survivin/CDK4 complex that fulfills the experimental evidence and that can be used for structure-based design of inhibitors modifying its interface recognition. In particular, the suggested complex involves the alpha helical domain of survivin and resembles the mode of binding of survivin in the survivin/borealin X-ray structure. The proposed model has been obtained by combining protein–protein docking, fractal-based shape complementarity, electrostatics studies and extensive molecular dynamics simulations.

An assessment of DFT methods for predicting the thermochemistry of ion-molecule reactions of group 14 elements (Si, Ge, Sn)

Experimental mass-spectrometry data on thermochemistry of methide transfer reactions (CH₃)₃M⁺ + M'(CH₃)₄ ? M(CH₃)₄?+?(CH₃)₃M'⁺ (M, M'?=?Si, Ge or Sn) and the formation energy of the [(CH₃)₃Si-CH₃-Si(CH₃)₃]⁺ complex are used as benchmarks for DFT methods (B3LYP, BMK, M06L, and ωB97XD). G2 and G3 theory methods are also used for the prediction of thermochemical data. BMK, M06L, and ωB97XD methods give the best fit to experimental data (close to chemical accuracy) as well as to G2 and G3 results, while B3LYP demonstrates poor performance. From the first three methods M06L gives the best overall result. Structures and formation energies of intermediate “mixed” [(CH₃)₃M-CH₃- M′(CH₃)₃] complexes not observed in experiment are predicted. Their structures, better described as M(CH₃)₄?[M′(CH₃)₃]⁺ complexes, explain their fast decompositions.

A second-generation diagnostic single nucleotide polymorphism (SNP)-based assay, optimized to distinguish among eight poplar (Populus L.) species and their early hybrids

Rapid identification of Populus L. species and hybrids can be achieved with relatively little effort through the use of primer extension-based single nucleotide polymorphism (SNP) genotyping assays. We present an optimized set of 36 SNP markers from 28 gene regions that diagnose eight poplar species (Populus angustifolia James, Populus balsamifera L., Populus deltoides Bartram, Populus fremontii Watson, Populus laurifolia Ledeb., Populus maximowiczii Henry, Populus nigra L., and Populus trichocarpa Torr. & Gray). A total of 700 DNA sequences from six Populus species (1–15 individuals per species) were used to construct the array. A set of flanking and probe oligonucleotides was developed and tested. The accuracy of the SNP assay was validated by genotyping 448 putatively “pure” individuals from 14 species of Populus. Overall, the SNP assay had a high success rate (97.6 %) and will prove useful for the identification of all Aigeiros Duby and Tacamahaca Spach. species and their early-generation hybrids within natural populations and breeding programs. Null alleles and intraspecific polymorphisms were detected for a few locus/species combinations in the Aigeiros and Tacamahaca sections. When we attempted to genotype aspens of the section Populus (Populus alba L., Populus grandidentata Michx., Populus tremula L., and Populus tremuloides Michx.), the success rate of the SNP array decreased by 13 %, demonstrating moderate cross-sectional transferability.     

Replication Vesicles are Load- and Choke-Points in the Hepatitis C Virus Lifecycle

Hepatitis C virus (HCV) infection develops into chronicity in 80% of all patients, characterized by persistent low-level replication. To understand how the virus establishes its tightly controlled intracellular RNA replication cycle, we developed the first detailed mathematical model of the initial dynamic phase of the intracellular HCV RNA replication. We therefore quantitatively measured viral RNA and protein translation upon synchronous delivery of viral genomes to host cells, and thoroughly validated the model using additional, independent experiments. Model analysis was used to predict the efficacy of different classes of inhibitors and identified sensitive substeps of replication that could be targeted by current and future therapeutics. A protective replication compartment proved to be essential for sustained RNA replication, balancing translation versus replication and thus effectively limiting RNA amplification. The model predicts that host factors involved in the formation of this compartment determine cellular permissiveness to HCV replication. In gene expression profiling, we identified several key processes potentially determining cellular HCV replication efficiency.     

A Flexible Approach for the Analysis of Rare Variants Allowing for a Mixture of Effects on Binary or Quantitative Traits

Multiple rare variants either within or across genes have been hypothesised to collectively influence complex human traits. The increasing availability of high throughput sequencing technologies offers the opportunity to study the effect of rare variants on these traits. However, appropriate and computationally efficient analytical methods are required to account for collections of rare variants that display a combination of protective, deleterious and null effects on the trait. We have developed a novel method for the analysis of rare genetic variation in a gene, region or pathway that, by simply aggregating summary statistics at each variant, can: (i) test for the presence of a mixture of effects on a trait; (ii) be applied to both binary and quantitative traits in population-based and family-based data; (iii) adjust for covariates to allow for non-genetic risk factors and; (iv) incorporate imputed genetic variation. In addition, for preliminary identification of promising genes, the method can be applied to association summary statistics, available from meta-analysis of published data, for example, without the need for individual level genotype data. Through simulation, we show that our method is immune to the presence of bi-directional effects, with no apparent loss in power across a range of different mixtures, and can achieve greater power than existing approaches as long as summary statistics at each variant are robust. We apply our method to investigate association of type-1 diabetes with imputed rare variants within genes in the major histocompatibility complex using genotype data from the Wellcome Trust Case Control Consortium.