Research Groups

Principles of cell and tissue organization: from endocytosis to a systems understanding of liver structure and function

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Previous and current research

Our vision is to develop a systems understanding of cell and tissue organization. For this, we apply a multi-scale analysis bridging the molecular, cellular and tissue/organ levels. In every project, we apply a combination of experimental and computational approaches.

Molecular scale: membrane tethering and fusion. Our studies on the molecular mechanisms of endocytosis led to the discovery that the small GTPase Rab5 is a master regulator of early endosome biogenesis. We have identified and characterized its effector proteins, and reconstituted them in synthetic endosomes capable of efficient membrane fusion, comparable to the purified organelles.
Project “Rab GTPases in organelle biogenesis and transport”

Cellular scale: inter-organelle interactions. We have been studying endocytosis in a broader, systems perspective, by combining quantitative imaging, genome-wide RNAi screens with systems biology analysis. This revealed design principles of the endosomal system and the role of the endosomal network in signal transduction.
Project “Systems analysis of endocytosis”

Tissue/organ scale: liver tissue structure and function. We are now ready to transfer the knowledge and proof of principle from these scales to the scale of tissue organization and function. For this, we study the developing, adult and regenerating liver as mammalian organ model system. We combine experimental and computational approaches to investigate the molecular mechanisms underlying hepatocyte polarity as well as the 3D organization of the bile canaliculi and sinusoidal networks in the mouse and human liver tissue. This project is supported by the development of technologies to deliver siRNAs in liver in vivo for functional genomics studies. Our aim is to develop a multi-scale mathematical model (ranging from the molecular to the organ scale) of the liver tissue that can predict alterations of organ function consequent to molecular perturbations and disease states.
Project “Liver tissue organization and function”

Future prospects and goals

The mechanism underlying the transition from Rab5-dependent membrane tethering to fusion

This remains a key outstanding problem. We use the synthetic endosomes made of artificial vesicles and purified recombinant proteins to mechanistically dissect the contribution of each factor to membrane tethering and fusion.

Principles of cell organization

We want to characterize the genes identified in our functional genomics screens with respect to their role in 1) establishment and maintenance of cell polarity and 2) inter-organelle interactions (e.g. endosomes-mitochondria, -ER, -lipid droplets).

Understanding the molecular mechanisms of liver regeneration

This project aims at identifying and characterizing the key factors responsible for the control of liver size during regeneration. To understand the molecular mechanisms whereby liver tissue senses the damage and induces the regeneration program, we pose the following questions:

  • What is the input signal that activates proliferation during liver regeneration?
  • How do cells enter cell cycle during liver regeneration and which mechanisms control their division?

To address these questions we will use high-resolution imaging and image analysis during liver regeneration after partial hepatectomy.

Computational analysis of liver tissue organization

To understand the principles of liver tissue structure and function, we will continue the development of:

  • New image analysis algorithms for the 3D reconstruction of liver tissue that cover multiple scales of complexity (from intracellular organelles to multicellular tissue functional units). The algorithms will be applied to study live tissue during development and regeneration.
  • Computational approaches to study tissue organization. We will develop computational/mathematical models that can explain the tissue structure and predict the consequence of molecular perturbations, e.g. genetic, pharmacological.

Methodological and technical expertise

We develop new technologies in the fields of:

  • Synthetic biology
  • High resolution imaging
  • siRNA delivery systems for functional genomics
  • Image analysis and data extraction
  • Mathematical modeling
  • Systems biology, physics and theory

Selected publications

Murray, D.H., Jahnel, M., Lauer, J., Avellaneda, M.J., Brouilly, N., Cezanne, A., Morales-Navarrete. H,, Perini, E.D., Ferguson, C., Lupas, A.N., Kalaidzidis, Y., Parton, R.G., Grill, S.W., Zerial, M. (2016). An endosomal tether undergoes an entropic collapse to bring vesicles together. Nature 537(7618):107-111.

Sundaramurthy, V., Barsacchi, R., Samusik, N., Marsico, G., Gilleron, J., Kalaidzidis, I., Meyenhofer, F., Bickle, M., Kalaidzidis, Y., and Zerial, M. (2013). Integration of chemical and RNAi multiparametric profiles identifies triggers of intracellular mycobacterial killing. Cell Host Microbe 13, 129–142.

Gilleron, J., Querbes, W., Zeigerer, A., Borodovsky, A., Marsico, G., Schubert, U., Manygoats, K., Seifert, S., Andree, C., Stöter, M., Hepstain-Barash, H., Zhang, L., Koteliansky, V., Fitzgerald, K., Fava, E., Bickle, M., Kalaidzidis, Y., Akinc, A., Maier, M., and Zerial, M. (2013). Image-based analysis of lipid nanoparticle-mediated siRNA delivery, intracellular trafficking and endosomal escape. Nature Biotechnology 31, 638-646.

Zeigerer, A., Gilleron, J., Bogorad, R.L., Marsico, G., Nonaka, H., Seifert, S., Epstein-Barash, H., Kuchimanchi, S., Peng, C.G., Ruda, V.M., Del Conte-Zerial, P., Hengstler, J.G., Kalaidzidis, Y., Koteliansky, V., and Zerial, M. (2012). Rab5 is necessary for the biogenesis of the endo-lysosomal system in vivo. Nature 485, 465-70.

Collinet, C., Stöter, M., Bradshaw, C.R., Samusik, N., Rink, J.C., Kenski, D., Habermann, B., Buchholz, F., Henschel, R., Mueller, M.S., Nagel, W.E., Fava, E., Kalaidzidis, Y., and Zerial, M. (2010). Systems Survey of Endocytosis by Functional Genomics and Quantitative Multi-Parametric Image Analysis. Nature 464, 243-9.