A Message-Receiver Model for Glycan Based Cell-Cell Communication

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A Message-Receiver Model for Glycan Based Cell-Cell Communication

Felix Fuchsberger 1 , Dongyoon Kim 1 , Robert Wawrzinek 1 , Christoph Rademacher 1

1 Max Planck Institute of Colloids and Interfaces, Potsdam, Germany

Mammalian glycans are highly abundant on all cellular surfaces facilitating a plethora of important processes including cellular adhesion and self- non self-discrimination. They often define cellular identity and expose the metabolic status of the cell. Consequently, cell surface glycosylation of multicellular organisms play a role in fundamental research topics like pathogen recognition, tumorigenesis and cancer metastasis.[1] Interactions with theses carbohydrates is facilitated by lectins and the effects of these interactions can range from redundant to substantial. To study the process of how lectins decode such information from cell surface glycans, we make use of information theory, an approach to investigate communication channels. Information theory determines boundaries and parameters of communication systems[2] and has found its application to biological problems previously.[3]

Following this concept, we treat glycan based cell-cell communication as a communication channel for the transmission of glycan-encoded information to lectin-decoded output. Briefly, common myeloid cell lines were transduced to express various lectins. Glycans were then used as input messages in various formats such as dissolved, immobilized on plates, attached to soluble particles, or whole cells. The decoding process was then monitored using a biochemical response of the receiving cells in single cell resolution. This approach will allow us to determine and quantify basic parameters of glycan lectin interactions such as channel capacity, transmission efficiency, and the influence of noise on glycan lectin interactions. Following this approach, we hope to contribute to a better understanding on how mammalian cell surface glycans can encode essential information.

Stowell, S. R., Ju, T. & Cummings, R. D. Protein Glycosylation in Cancer. Annu. Rev. Pathol. Mech. Dis. 10, 473–510 (2015).

Shannon, C. E. A Mathematical Theory of Communication. Bell Syst. Tech. J. 27, 379–423 (1948).

Mian, I. S. & Rose, C. Communication theory and multicellular biology. Integr. Biol. 3, 350 (2011).

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