Scientists observe and investigate accumulation of lipid droplets in cells

The Virtual Liver Network group of Prof. Borlak, Hannover Medical School (MHH), aims to identify the molecular processes during fatty liver disease to better understand disease causing mechanisms. Therefore, research is focused on lipid droplet development under various stress conditions.

  • For this purpose, hepatoma cell lines such as HepG2 and Huh7 (cells from cancerous tumor occurring in the liver) are analyzed, as well as healthy hepatocytes and liver tissue from high fat diet fed animals. The time-dependent growth of lipid droplets inside the cells and related genetic responses are investigated.
  • Hence, scientists stimulated cells with unsaturated and saturated fatty acids to induce lipid storage in form of lipid droplets. Then, genes and proteins were analyzed that are specifically regulated during lipid droplets formation and growth were investigated. Unique candidate proteins were identified carrying the hope for a better understanding of disease causing mechanisms. Some of the identified proteins may also be explored for their therapeutic utility.
  • An image of fluorescent microscopy of lipid droplets in one of the investigated cell lines, HepG2, is depicted. Cells were treated with fatty acids showing lipid droplet accumulation in red (Oil red) and the cell nuclei in blue (DAPI).
  • Authors: Anna Trincone and Jürgen Borlak

  • Image: Anna Trincone

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    Scientists found that endosomes participate in controlling the liver metabolism

    Early endosomes are defined at the molecular level by the presence of a protein, small GTPase Rab5, which together with its effector molecules controls the fusion, motility and maturation of endosomes.

  • Scientists from the Virtual Liver Network (Zerial lab at the Max-Planck Institute MPI-CBG) recently showed that Rab5 is necessary for the biogenesis (de novo production) of the endo-lysosomal system, since depletion of this protein led to the loss of the degradative pathway. In addition scientists observed severe alterations in the hepatocellular metabolism.
  • An impaired function of a central metabolic organ such as the liver often triggers metabolic diseases such as biliary stasis, insulin resistance in diabetes, or high cholesterol levels. The reduction in Rab5 expression led to a rapid decrease of LDL (low-density lipoprotein) absorption by the liver cells and resulted in a ten-fold increase in serum cholesterol. This phenotype corresponds to the pathological symptoms of hypercholesterolemia.
  • Surprisingly, the reduction in Rab5 concentration and the subsequent loss of endosomes caused glucose storage in hepatocytes and formation of intracellular fat droplets. Recent evidence suggests that endosomes participate in controlling the expression of an important gene in glucose production by altering transcription factor activities. These results point to a previously unknown function of endosomes in the regulation of the sugar and fat metabolism.
  • The image shows the loss of early endosomes, which leads to pathological conditions in liver cells. Under physiological conditions the liver cell consists of a normal endo-lysosomal system that mediates the uptake of LDL into hepatocytes. Upon loss of Rab5 the endo-lysosomal system is reduced in number and the internalization of LDL is perturbed leading to hypercholesterolemia. In addition, accumulations of lipid droplets and glycogen granules are observed. This hints towards a misbalance in glucose and lipid metabolism.
  • Scientific publication: Rab5 is necessary for the biogenesis of the endolysosomal system in vivo. Nature. 2012.
  • Authors of text and image: Anja Zeigerer and Marino Zerial

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    Stoichiometric models – networks of reactions

    In systems biology often large biochemical networks within a cell are investigated computationally. Stoichiometric models are one important example for modeling such networks on computer. In these models biochemical intermediates called metabolites are connected via reactions.

  • Apart from the metabolites and the reactions, the scientists know the relative amounts of the metabolites converted in each reaction. Those relative amounts are also known as stoichiometries.
  • Stoichiometric models often contain several cycles of metabolites connected via reactions. Those cycles have important functions like generating energy for the cell. An example for that is the citrate cycle, which plays a decisive role in cell metabolism. In such cycles the concentrations of the metabolites are often constant over some time, although these metabolites are continuously transformed into each other by the reactions. This stable state of constant concentrations within a part of a stoichiometric model is also called steady state. A model can usually occupy several different steady states (i.e., different stable reaction velocities, also known as fluxes, and metabolite concentrations) in theory. The current steady state of a cellular model is influenced by multiple factors like, e.g., the environment of the cell.
  • The steady state of a cellular process can be determined with the help of a stoichiometric model and experimental measurements. To this end, the velocities of some reactions contained in the model are experimentally measured in a cell. Based on the derived values, a mathematical method called flux balance analysis can determine the steady state. The steady states are often calculated for several different conditions like various amounts of some substance in the cellular environment. With this one can investigate how this substance influences the metabolism of the cell.
  • Researchers in VLN developed HepatoNet1. This is an example for a stoichiometric model, which covers large parts of the hepatocyte metabolism. 777 metabolites and 2539 reactions are contained in HepatoNet1, which is stored in the SBML format. SBML, the abbreviation for Systems Biology Markup Language, is the most important format for describing biochemical models. Its main elements are species for the description of metabolites and reactions.
  • Author: Roland Keller

  • Image: Stephanie Hoffmann

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