Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

The genetics of cardiovascular disease

Recent advances in genotyping technology and insights into disease mechanisms have increased interest in the genetics of cardiovascular disease. Several candidate genes involved in cardiovascular diseases were identified from studies using animal models, and the translation of these findings to human disease is an exciting challenge. There is a trend towards large-scale genome-wide association studies that are subject to strict quality criteria with regard to both genotyping and phenotyping. Here, we review some of the strategies that have been developed to translate findings from experimental models to human disease and outline the need for optimizing global approaches to analyze such results. Findings from ongoing studies are interpreted in the context of disease pathways instead of the more traditional focus on single genetic variants

Highly efficient transfection of human induced pluripotent stem cells using magnetic nanoparticles.

PurposeThe delivery of transgenes into human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (hiPSC-CMs) represents an important tool in cardiac regeneration with potential for clinical applications. Gene transfection is more difficult, however, for hiPSCs and hiPSC-CMs than for somatic cells. Despite improvements in transfection and transduction, the efficiency, cytotoxicity, safety, and cost of these methods remain unsatisfactory. The objective of this study is to examine gene transfection in hiPSCs and hiPSC-CMs using magnetic nanoparticles (NPs).MethodsMagnetic NPs are unique transfection reagents that form complexes with nucleic acids by ionic interaction. The particles, loaded with nucleic acids, can be guided by a magnetic field to allow their concentration onto the surface of the cell membrane. Subsequent uptake of the loaded particles by the cells allows for high efficiency transfection of the cells with nucleic acids. We developed a new method using magnetic NPs to transfect hiPSCs and hiPSC-CMs. HiPSCs and hiPSC-CMs were cultured and analyzed using confocal microscopy, flow cytometry, and patch clamp recordings to quantify the transfection efficiency and cellular function.ResultsWe compared the transfection efficiency of hiPSCs with that of human embryonic kidney (HEK 293) cells. We observed that the average efficiency in hiPSCs was 43%±2% compared to 62%±4% in HEK 293 cells. Further analysis of the transfected hiPSCs showed that the differentiation of hiPSCs to hiPSC-CMs was not altered by NPs. Finally, robust transfection of hiPSC-CMs with an efficiency of 18%±2% was obtained.ConclusionThe difficult-to-transfect hiPSCs and hiPSC-CMs were efficiently transfected using magnetic NPs. Our study offers a novel approach for transfection of hiPSCs and hiPSC-CMs without the need for viral vector generation

A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism