Identification of the Tetraspanin CD9 as an Interaction Partner of Organic Cation Transporters 1 and 2.

Organic cation transporters (OCTs) are membrane proteins with relevant physiological (because they accept neurotransmitters as substrate) and pharmacological (because of their interaction with drugs) roles. The human OCTs hOCT1 (SLC22A1/hOCT1) and hOCT2 (SLC22A2/hOCT2) are highly expressed in hepatic (hOCT1) and in renal and neuronal tissue (hOCT2), suggesting a possible role in modulating neurotransmitter activity in the liver, kidney, and brain, and their clearance from the blood. Even though there are several data demonstrating that OCTs are regulated under various patho-physiological conditions, it remains largely unknown which proteins directly interact with OCTs and thereby influence their cellular processing, localization, and function. In this work, using a mating-based split-ubiquitin yeast two-hybrid system, we characterized the potential interactome of hOCT1 and 2. It became evident that these OCTs share some potential interaction partners, such as the tetraspanins CD63 and CD9. Moreover, we confirmed interaction of hOCT2 with CD9 by fluorescence-activated cell sorting coupled with Förster resonance energy transfer analysis. Together with other proteins, tetraspanins build "tetraspanins webs" in the plasma membrane, which are able to regulate cellular trafficking and compartmentalization of interacting partners. While CD63 was demonstrated to mediate the localization of the hOCT2 to the endosomal system, we show here that co-expression of hOCT2 and CD9 led to strong cell surface localization of the transporter. These data suggest that tetraspanins regulate the cellular localization and function of OCTs. Co-localization of CD9 and hOCT was confirmed in tissues endogenously expressing proteins, highlighting the potential biological relevance of this interaction.

On Clifford neurons and Clifford multi-layer perceptrons.

We study the framework of Clifford algebra for the design of neural architectures capable of processing different geometric entities. The benefits of this model-based computation over standard real-valued networks are demonstrated. One particular example thereof is the new class of so-called Spinor Clifford neurons. The paper provides a sound theoretical basis to Clifford neural computation. For that purpose the new concepts of isomorphic neurons and isomorphic representations are introduced. A unified training rule for Clifford MLPs is also provided. The topic of activation functions for Clifford MLPs is discussed in detail for all two-dimensional Clifford algebras for the first time.

Polarized signal classification by complex and quaternionic multi-layer perceptrons.

For polarized signals, which arise in many application fields, a statistical framework in terms of quaternionic random processes is proposed. Based on it, the ability of real-, complex- and quaternionic-valued multi-layer perceptrons (MLPs) of performing classification tasks for such signals is evaluated. For the multi-dimensional neural networks the relevance of class label representations is discussed. For signal to noise separation it is shown that the quaternionic MLP yields an optimal solution. Results on the classification of two different polarized signals are also reported.