Stefan Hoehme

Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration

Only little is known about how cells coordinately behave to establish functional tissue structure and restore microarchitecture during regeneration. Research in this field is hampered by a lack of techniques that allow quantification of tissue architecture and its development. To bridge this gap, we have established a procedure based on confocal laser scans, image processing, and three-dimensional tissue reconstruction, as well as quantitative mathematical modeling. As a proof of principle, we reconstructed and modeled liver regeneration in mice after damage by CCl4, a prototypical inducer of pericentral liver damage. We have chosen the regenerating liver as an example because of the tight link between liver architecture and function: the complex microarchitecture formed by hepatocytes and microvessels, i.e. sinusoids, ensures optimal exchange of metabolites between blood and hepatocytes. Our model captures all hepatocytes and sinusoids of a liver lobule during a 16 days regeneration process. The model unambiguously predicted a so-far unrecognized mechanism as essential for liver regeneration, whereby daughter hepatocytes align along the orientation of the closest sinusoid, a process which we named “hepatocyte-sinusoid alignment” (HSA). The simulated tissue architecture was only in agreement with the experimentally obtained data when HSA was included into the model and, moreover, no other likely mechanism could replace it. In order to experimentally validate the model of prediction of HSA, we analyzed the three-dimensional orientation of daughter hepatocytes in relation to the sinusoids. The results of this analysis clearly confirmed the model prediction. We believe our procedure is widely applicable in the systems biology of tissues.

A cell-based simulation software for multi-cellular systems

CellSys is a modular software tool for efficient off-lattice simulation of growth and organization processes in multi-cellular systems in 2D and 3D. It implements an agent-based model that approximates cells as isotropic, elastic and adhesive objects. Cell migration is modeled by an equation of motion for each cell. The software includes many modules specifically tailored to support the simulation and analysis of virtual tissues including real-time 3D visualization and VRML 2.0 support. All cell and environment parameters can be independently varied which facilitates species specific simulations and allows for detailed analyses of growth dynamics and links between cellular and multi-cellular phenotypes. Availability: CellSys is freely available for non-commercial use at http://msysbio.com/software/cellsys. The current version of CellSys permits the simulation of growing monolayer cultures and avascular tumor spheroids in liquid environment. Further functionality will be made available ongoing with published papers. Contact: hoehme@izbi.uni-leipzig.de; dirk.drasdo@inria.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration

The impairment of hepatic metabolism due to liver injury has high systemic relevance. However, it is difficult to calculate the impairment of metabolic capacity from a specific pattern of liver damage with conventional techniques. We established an integrated metabolic spatial‐temporal model (IM) using hepatic ammonia detoxification as a paradigm. First, a metabolic model (MM) based on mass balancing and mouse liver perfusion data was established to describe ammonia detoxification and its zonation. Next, the MM was combined with a spatial‐temporal model simulating liver tissue damage and regeneration after CCl4 intoxication. The resulting IM simulated and visualized whether, where, and to what extent liver damage compromised ammonia detoxification. It allowed us to enter the extent and spatial patterns of liver damage and then calculate the outflow concentrations of ammonia, glutamine, and urea in the hepatic vein. The model was validated through comparisons with (1) published data for isolated, perfused livers with and without CCl4 intoxication and (2) a set of in vivo experiments. Using the experimentally determined portal concentrations of ammonia, the model adequately predicted metabolite concentrations over time in the hepatic vein during toxin‐induced liver damage and regeneration in rodents. Further simulations, especially in combination with a simplified model of blood circulation with three ammonia‐detoxifying compartments, indicated a yet unidentified process of ammonia consumption during liver regeneration and revealed unexpected concomitant changes in amino acid metabolism in the liver and at extrahepatic sites. Conclusion: The IM of hepatic ammonia detoxification considerably improves our understanding of the metabolic impact of liver disease and highlights the importance of integrated modeling approaches on the way toward virtual organisms. (Hepatology 2014;;60:2039–2050)

How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis

From the more than 100 liver diseases described, many of those with high incidence rates manifest themselves by histopathological changes, such as hepatitis, alcoholic liver disease, fatty liver disease, fibrosis, and, in its later stages, cirrhosis, hepatocellular carcinoma, primary biliary cirrhosis and other disorders. Studies of disease pathogeneses are largely based on integrating -omics data pooled from cells at different locations with spatial information from stained liver structures in animal models. Even though this has led to significant insights, the complexity of interactions as well as the involvement of processes at many different time and length scales constrains the possibility to condense disease processes in illustrations, schemes and tables. The combination of modern imaging modalities with image processing and analysis, and mathematical models opens up a promising new approach towards a quantitative understanding of pathologies and of disease processes. This strategy is discussed for two examples, ammonia metabolism after drug-induced acute liver damage, and the recovery of liver mass as well as architecture during the subsequent regeneration process. This interdisciplinary approach permits integration of biological mechanisms and models of processes contributing to disease progression at various scales into mathematical models. These can be used to perform in silico simulations to promote unravelling the relation between architecture and function as below illustrated for liver regeneration, and bridging from the in vitro situation and animal models to humans. In the near future novel mechanisms will usually not be directly elucidated by modelling. However, models will falsify hypotheses and guide towards the most informative experimental design.

Model-guided identification of a therapeutic strategy to reduce hyperammonemia in liver diseases

BACKGROUND & AIMS Recently, spatial-temporal/metabolic mathematical models have been established that allow the simulation of metabolic processes in tissues. We applied these models to decipher ammonia detoxification mechanisms in the liver. METHODS An integrated metabolic-spatial-temporal model was used to generate hypotheses of ammonia metabolism. Predicted mechanisms were validated using time-resolved analyses of nitrogen metabolism, activity analyses, immunostaining and gene expression after induction of liver damage in mice. Moreover, blood from the portal vein, liver vein and mixed venous blood was analyzed in a time dependent manner. RESULTS Modeling revealed an underestimation of ammonia consumption after liver damage when only the currently established mechanisms of ammonia detoxification were simulated. By iterative cycles of modeling and experiments, the reductive amidation of alpha-ketoglutarate (α-KG) via glutamate dehydrogenase (GDH) was identified as the lacking component. GDH is released from damaged hepatocytes into the blood where it consumes ammonia to generate glutamate, thereby providing systemic protection against hyperammonemia. This mechanism was exploited therapeutically in a mouse model of hyperammonemia by injecting GDH together with optimized doses of cofactors. Intravenous injection of GDH (720 U/kg), α-KG (280 mg/kg) and NADPH (180 mg/kg) reduced the elevated blood ammonia concentrations (>200 μM) to levels close to normal within only 15 min. CONCLUSION If successfully translated to patients the GDH-based therapy might provide a less aggressive therapeutic alternative for patients with severe hyperammonemia.

Phenotype and growth behavior of residual β-catenin-positive hepatocytes in livers of β-catenin-deficient mice

Signaling through the Wnt/β-catenin pathway is a crucial determinant of hepatic zonal gene expression, liver development, regeneration, and tumorigenesis. Transgenic mice with hepatocyte-specific knockout of Ctnnb1 (encoding β-catenin) have proven their usefulness in elucidating these processes. We now found that a small number of hepatocytes escape the Cre-mediated gene knockout in that mouse model. The remaining β-catenin-positive hepatocytes showed approximately 25% higher cell volumes compared to the β-catenin-negative cells and exhibited a marker protein expression profile similar to that of normal perivenous hepatocytes or hepatoma cells with mutationally activated β-catenin. Surprisingly, the expression pattern was observed independent of the cell’s position within the liver lobule, suggesting a malfunction of physiological periportal repression of perivenously expressed genes in β-catenin-deficient liver. Clusters of β-catenin-expressing hepatocytes lacked expression of the gap junction proteins Connexin 26 and 32. Nonetheless, β-catenin-positive hepatocytes had no striking proliferative advantage, but started to grow out on treatment with phenobarbital, a tumor-promoting agent known to facilitate the formation of mouse liver adenoma with activating mutations of Ctnnb1. Progressive re-population of Ctnnb1 knockout livers with wild-type hepatocytes was seen in aged mice with a pre-cirrhotic phenotype. In these large clusters of β-catenin-expressing hepatocytes, perivenous-specific gene expression was re-established. In summary, our data demonstrate that the zone-specificity of a hepatocyte’s gene expression profile is dependent on the presence of β-catenin, and that β-catenin provides a proliferative advantage to hepatocytes when promoted with phenobarbital, or in a pre-cirrhotic environment.

The virtual liver: state of the art and future perspectives

clarifying the underlying principles. The mathematical models formalize the relationship between individual components, test their interactions in a virtual setting and may even simulate influences that are (still) difficult to analyse experimentally. In recent years, model simulations have been instrumental to elucidate mechanisms and principles that were not accessible by traditional approaches. To promote systems biology research in the field of the liver with the aim to gain a better understanding of the basic mechanisms of liver function as well as key principles of liver Developments over the past two decades have improved our ability to obtain comprehensive and quantitative data, for example, by genome-wide analysis of gene expression, proteomics, lipidomics and metabolomics. Moreover, both imaging and image analysis have been improved which offers new possibilities to quantify the three-dimensional organization of cells and tissues. However, research in disease pathogenesis is often hampered by the difficulty to understand the complex, time-resolved interplay among numerous components. Here, mathematical modelling helps

Modeling the impact of granular embedding media, and pulling versus pushing cells on growing cell clones

In this paper, we explore how potential biomechanical influences on cell cycle entrance and cell migration affect the growth dynamics of cell populations. We consider cell populations growing in free, granular and tissue- like environments using a mathematical single-cell-based model. In a free environment we study the effect of pushing movements triggered by proliferation versus active pulling movements of cells stretching cell-cell contacts on the multi-cellular kinetics and the cell population morphotype. By growing cell clones embedded in agarose gel or cells of another type, one can mimic aspects of embedding tissues. We perform simulation studies of cell clones expanding in an environment of granular objects and of chemically inert cells. In certain parameter ranges, we find the formation of invasive fingers reminiscent of viscous fingering. Since the simulation studies are highly computation-time consuming, we mainly study one-cell-thick monolayers and show that for selected parameter settings the results also hold for multi-cellular spheroids. Finally, we compare our model to the experimentally observed growth dynamics of multi-cellular spheroids in agarose gel.

TiQuant: software for tissue analysis, quantification and surface reconstruction

MOTIVATION TiQuant is a modular software tool for efficient quantification of biological tissues based on volume data obtained by biomedical image modalities. It includes a number of versatile image and volume processing chains tailored to the analysis of different tissue types which have been experimentally verified. TiQuant implements a novel method for the reconstruction of three-dimensional surfaces of biological systems, data that often cannot be obtained experimentally but which is of utmost importance for tissue modelling in systems biology. AVAILABILITY AND IMPLEMENTATION TiQuant is freely available for non-commercial use at msysbio.com/tiquant. Windows, OSX and Linux are supported. CONTACT hoehme@uni-leipzig.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Unraveling liver complexity from molecular to organ level: Challenges and perspectives

Biological responses are determined by information processing at multiple and highly interconnected scales. Within a tissue the individual cells respond to extracellular stimuli by regulating intracellular signaling pathways that in turn determine cell fate decisions and influence the behavior of neighboring cells. As a consequence the cellular responses critically impact tissue composition and architecture. Understanding the regulation of these mechanisms at different scales is key to unravel the emergent properties of biological systems. In this perspective, a multidisciplinary approach combining experimental data with mathematical modeling is introduced. We report the approach applied within the Virtual Liver Network to analyze processes that regulate liver functions from single cell responses to the organ level using a number of examples. By facilitating interdisciplinary collaborations, the Virtual Liver Network studies liver regeneration and inflammatory processes as well as liver metabolic functions at multiple scales, and thus provides a suitable example to identify challenges and point out potential future application of multi-scale systems biology.