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.

Figure 1

(A) NASFinder pipeline. The input is a transcriptomic data set used to detect sets of genes that are differentially expressed and that have common biological functions (DEGs modules), a set of nodes of interest with specific functions (transporters, transcription factors, receptors, etc.), and a tissue-specific reference network to contextualize the gene sets for a better interpretation. The algorithm determines active sub-networks connecting receptors with DEGs modules and ranks them according to the network activity score. The significant sub-networks are then used for contextual enrichment analysis against canonical pathways. (B) Sub-network identification. The shortest paths from each element of the DEG module to the receptors are computed and the shortest ones are kept. In the figure the orange paths are the ones with minimal distance greater than 0 from the DEGs module to the regulator molecules (receptors in this study). In the next step (right graph) for each receptor previously selected (for instance, the top-right receptor in this case) we identify all the shortest paths from that receptor to the elements of the DEGs module (the orange paths).

Figure 2. Leptin pathway and contextual interactions identified for contrast 48h vs. controls.

LEPR is the receptor and is the entry point for identifying the active sub-network determined by NASFinder. The molecules belonging to the canonical pathway and within the 1-neighbor network of the active network determined by NASFinder are LEPR, PRKAG2, PRKAA1, PRKAG1.

Figure 3. The performance assessment of the tools shown on the y-axis, based on z-scores of precision, recall, specificity, accuracy computed on the 10 benchmark data sets.

NASFinder is the only tool with scores above the average in all three scenarios (i.e. the z-scores of all the performance measures are positive in all scenarios).

Figure 4. The overall performance of the tools shown on the x-axis expressed in terms of the average of the z-scores computed for precision, recall, accuracy and specificity on the 10 benchmark data sets.

Colors of bars identify the reference databases used to compute the performance measures. NASFinder outperforms all the other tools in terms of aggregate performance.

Figure 5. Selected pathways identified in the analyses are overlaid onto the cell based on the approximate location of the main cellular process of the genes involved and grouped by function.

Note that many pathways and networks overlap cellular compartments which could not be represented in this format. The white lines connecting the signaling pathways to the nuclear pathways are used to indicate that these networks have components from the cell membrane to transcriptional machinery. The colors represent the network activity score of up/down-regulated differentially expressed genes traversed in the active path and its 1-neighborhood context. That proportion is reported as a fraction in parenthesis (x/y) denoting the number of up-regulated genes with respect to the total traversed and contextual ones. TP analysis.

Figure 6. Same as Fig. 5, but for TL analysis.