Modeling Structure-Activity Relationship of AMPK Activation.

The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases-such as metabolic syndrome, obesity, diabetes, and also cancer-activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.

Expanding the map of protein-RNA interaction sites via cell fusion followed by PAR-CLIP.

PAR-CLIP (photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation) facilitates the identification and mapping of protein/RNA interactions. So far, it has been limited to select cell-lines as it requires efficient 4SU uptake. To increase transcriptome complexity and thus identify additional RNA-protein interaction sites we fused HEK 293 T-Rex cells (HEK293-Y) that express the RNA binding protein YBX1 with PC12 cells expressing eGFP (PC12-eGFP). The resulting hybrids enable PAR-CLIP on a neuronally expanded transcriptome (Fusion-CLIP) and serve as a proof of principle. The fusion cells express both parental marker genes YBX1 and eGFP and the expanded transcriptome contains human and rat transcripts. PAR-CLIP of fused cells versus the parental HEK293-Y identified 768 novel RNA targets of YBX1. We were able to trace the origin of the majority of the short PAR-CLIP reads as they differentially mapped to the human and rat genome. Furthermore, Fusion-CLIP expanded the CAUC RNA binding motif of YBX1 to UCUUUNNCAUC. The fusion of HEK293-Y and PC12-eGFP cells resulted in cells with a diverse genome expressing human and rat transcripts that enabled the identification of novel YBX1 substrates. The technique allows the expansion of the HEK 293 transcriptome and makes PAR-CLIP available to fusion cells of diverse origin.