Cellular localization and associations of the major lipolytic proteins in human skeletal muscle at rest and during exercise

Lipolysis involves the sequential breakdown of fatty acids from triacylglycerol and is increased during energy stress such as exercise. Adipose triglyceride lipase (ATGL) is a key regulator of skeletal muscle lipolysis and perilipin (PLIN) 5 is postulated to be an important regulator of ATGL action of muscle lipolysis. Hence, we hypothesized that non-genomic regulation such as cellular localization and the interaction of these key proteins modulate muscle lipolysis during exercise. PLIN5, ATGL and CGI-58 were highly (>60%) colocated with Oil Red O (ORO) stained lipid droplets. PLIN5 was significantly colocated with ATGL, mitochondria and CGI-58, indicating a close association between the key lipolytic effectors in resting skeletal muscle. The colocation of the lipolytic proteins, their independent association with ORO and the PLIN5/ORO colocation were not altered after 60 min of moderate intensity exercise. Further experiments in cultured human myocytes showed that PLIN5 colocation with ORO or mitochondria is unaffected by pharmacological activation of lipolytic pathways. Together, these data suggest that the major lipolytic proteins are highly expressed at the lipid droplet and colocate in resting skeletal muscle, that their localization and interactions appear to remain unchanged during prolonged exercise, and, accordingly, that other post-translational mechanisms are likely regulators of skeletal muscle lipolysis.

Non-referenced genome assembly from epigenomic short-read data

Current computational methods used to analyze changes in DNA methylation and chromatin modification rely on sequenced genomes. Here we describe a pipeline for the detection of these changes from short-read sequence data that does not require a reference genome. Open source software packages were used for sequence assembly, alignment, and measurement of differential enrichment. The method was evaluated by comparing results with reference-based results showing a strong correlation between chromatin modification and gene expression. We then used our de novo sequence assembly to build the DNA methylation profile for the non-referenced Psammomys obesus genome. The pipeline described uses open source software for fast annotation and visualization of unreferenced genomic regions from short-read data.

A survey of approaches and frameworks to carry out genomic data analysis on the cloud

High Performance Computing (HPC) clouds have started to change the way how research in science, in particular medicine and genomics (bioinformatics) is being carried out. Researchers who have taken advantage of this technology can process larger amounts of data and speed up scientific discovery. However, most HPC clouds are provided at an Infrastructure as a Service (IaaS) level, users are presented with a set of virtual servers which need to be put together to form HPC environments via time consuming resource management and software configuration tasks, which make them practically unusable by discipline, non-computing specialists. In response, there is a new trend to expose cloud applications as services to simplify access and execution on clouds. This paper firstly examines commonly used cloud-based genomic analysis services (Tuxedo Suite, Galaxy and Cloud Bio Linux). As a follow up, we propose two new solutions (HPCaaS and Uncinus), which aim to automate aspects of the service development and deployment process. By comparing and contrasting these five solutions, we identify key mechanisms of service creation, execution and access that are required to support genomic research on the SaaS cloud, in particular by discipline specialists. © 2014 IEEE.

Selected approaches and frameworks to carry out genomic data provision and analysis on the cloud

While High Performance Computing clouds allow researchers to process large amounts of genomic data, complex resource and software configuration tasks must be carried out beforehand. The current trend exposes applications and data as services, simplifying access to clouds. This paper examines commonly used cloud-based genomic analysis services, introduces the approach of exposing data as services and proposes two new solutions (HPCaaS and Uncinus) which aim to automate service development, deployment process and data provision. By comparing and contrasting these solutions, we identify key mechanisms of service creation, execution and data access required to support non-computing specialists employing clouds.