Systems view of adipogenesis via novel omics-driven and tissue-specific activity scoring of network functional modules

Abstract

The investigation of the complex processes involved in cellular differentiation must be based on unbiased, high throughput data processing methods to identify relevant biological pathways. A number of bioinformatics tools are available that can generate lists of pathways ranked by statistical significance (i.e. by p-value), while ideally it would be desirable to functionally score the pathways relative to each other or to other interacting parts of the system or process. We describe a new computational method (Network Activity Score Finder - NASFinder) to identify tissue-specific, omics-determined sub-networks and the connections with their upstream regulator receptors to obtain a systems view of the differentiation of human adipocytes. Adipogenesis of human SBGS pre-adipocyte cells in vitro was monitored with a transcriptomic data set comprising six time points (0, 6, 48, 96, 192, 384 hours). To elucidate the mechanisms of adipogenesis, NASFinder was used to perform time-point analysis by comparing each time point against the control (0 h) and time-lapse analysis by comparing each time point with the previous one. NASFinder identified the coordinated activity of seemingly unrelated processes between each comparison, providing the first systems view of adipogenesis in culture. NASFinder has been implemented into a web-based, freely available resource associated with novel, easy to read visualization of omics data sets and network modules.

The Power of LC-MS Based Multiomics: Exploring Adipogenic Differentiation of Human Mesenchymal Stem/Stromal Cells

“…Using manual curation of the fragmentation pattern, matching retention times in the positive and negative ion mode and retention time order, 22 different gangliosides could be successfully annotated. All biological observations for the regulation of triglycerides [3,8], gangliosides [47] and reprogrammed pathways [23] in adipocytes are in accordance with literature. Further biological interpretation of the data is beyond the scope of this work.…”

“…The mechanistic understanding of fat cell development in the human body is essential for the treatment of obesity-related diseases. However, a high fraction of studies on the mechanisms that directs adipogenic differentiation rely on mouse models or murine cell lines [3,[20][21][22][23]. In the context of cell-based therapies, translational aspects are of utmost importance as cells from different species might act differently.…”

Combined use of protein biomarkers and network analysis unveils deregulated regulatory circuits in Duchenne muscular dystrophy

“…We built a workflow based on a biomarker discovery algorithm and a tool for network analysis that we recently developed [ 12 – 14 ] ( Fig 1 ). The biomarker discovery algorithm identifies subject-specific lists of proteins, herein called signatures, which are then compared in order to classify each subject as belonging to one of the two classes (affected or controls).…”

Systematic exploration of cell morphological phenotypes associated with a transcriptomic query

“…Enrichment analyses of cellular processes and biological pathways have become a routine part of transcriptomic data analyses ( 37 ). Here, we propose an enrichment analysis that connects gene expression profiles to cell morphological phenotypes ( 3 , 38 ).…”