Sascha Bulik

HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology

We present HepatoNet1, the first reconstruction of a comprehensive metabolic network of the human hepatocyte that is shown to accomplish a large canon of known metabolic liver functions. The network comprises 777 metabolites in six intracellular and two extracellular compartments and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high‐quality evidence‐based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint‐based modeling techniques. Taking the hepatic detoxification of ammonia as an example, we show how the availability of nutrients and oxygen may modulate the interplay of various metabolic pathways to allow an efficient response of the liver to perturbations of the homeostasis of blood compounds.

Identifying MHC Class I Epitopes by Predicting the TAP Transport Efficiency of Epitope Precursors

We are able to make reliable predictions of the efficiency with which peptides of arbitrary lengths will be transported by TAP. The pressure exerted by TAP on Ag presentation thus can be assessed by checking to what extent MHC class I (MHC-I)-presented epitopes can be discriminated from random peptides on the basis of predicted TAP transport efficiencies alone. Best discriminations were obtained when N-terminally prolonged epitope precursor peptides were included and the contribution of the N-terminal residues to the score were down-weighted in comparison with the contribution of the C terminus. We provide evidence that two factors may account for this N-terminal down-weighting: 1) the uncertainty as to which precursors are used in vivo and 2) the coevolution in the C-terminal sequence specificities of TAP and other agents in the pathway, which may vary among the various MHC-I alleles. Combining predictions of MHC-I binding affinities with predictions of TAP transport efficiency led to an improved identification of epitopes, which was not the case when predictions of MHC-I binding affinities were combined with predictions of C-terminal cleavages made by the proteasome.

Modeling the MHC class I pathway by combining predictions of proteasomal cleavage,TAP transport and MHC class I binding

Abstract.Epitopes presented by major histocompatibility complex (MHC) class I molecules are selected by a multi-step process. Here we present the first computational prediction of this process based on in vitro experiments characterizing proteasomal cleavage, transport by the transporter associated with antigen processing (TAP) and MHC class I binding. Our novel prediction method for proteasomal cleavages outperforms existing methods when tested on in vitro cleavage data. The analysis of our predictions for a new dataset consisting of 390 endogenously processed MHC class I ligands from cells with known proteasome composition shows that the immunological advantage of switching from constitutive to immunoproteasomes is mainly to suppress the creation of peptides in the cytosol that TAP cannot transport. Furthermore, we show that proteasomes are unlikely to generate MHC class I ligands with a C-terminal lysine residue, suggesting processing of these ligands by a different protease that may be tripeptidyl-peptidase II (TPPII).

Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

BackgroundDespite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed.ResultsHere, we present a computational analysis of the metabolism of Plasmodiumfalciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasites metabolic network was embedded into that of its host (erythrocyte). Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment.ConclusionsThe results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.

The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks

The dynamic behavior of metabolic networks is governed by numerous regulatory mechanisms, such as reversible phosphorylation, binding of allosteric effectors or temporal gene expression, by which the activity of the participating enzymes can be adjusted to the functional requirements of the cell. For most of the cellular enzymes, such regulatory mechanisms are at best qualitatively known, whereas detailed enzyme‐kinetic models are lacking. To explore the possible dynamic behavior of metabolic networks in cases of lacking or incomplete enzyme‐kinetic information, we present a computational approach based on structural kinetic modeling. We derive statistical measures for the relative impact of enzyme‐kinetic parameters on dynamic properties (such as local stability) and apply our approach to the metabolism of human erythrocytes. Our findings show that allosteric enzyme regulation significantly enhances the stability of the network and extends its potential dynamic behavior. Moreover, our approach allows to differentiate quantitatively between metabolic states related to senescence and metabolic collapse of the human erythrocyte. We think that the proposed method represents an important intermediate step on the long way from topological network analysis to detailed kinetic modeling of complex metabolic networks.

Quantifying the Contribution of the Liver to Glucose Homeostasis: A Detailed Kinetic Model of Human Hepatic Glucose Metabolism

Despite the crucial role of the liver in glucose homeostasis, a detailed mathematical model of human hepatic glucose metabolism is lacking so far. Here we present a detailed kinetic model of glycolysis, gluconeogenesis and glycogen metabolism in human hepatocytes integrated with the hormonal control of these pathways by insulin, glucagon and epinephrine. Model simulations are in good agreement with experimental data on (i) the quantitative contributions of glycolysis, gluconeogenesis, and glycogen metabolism to hepatic glucose production and hepatic glucose utilization under varying physiological states. (ii) the time courses of postprandial glycogen storage as well as glycogen depletion in overnight fasting and short term fasting (iii) the switch from net hepatic glucose production under hypoglycemia to net hepatic glucose utilization under hyperglycemia essential for glucose homeostasis (iv) hormone perturbations of hepatic glucose metabolism. Response analysis reveals an extra high capacity of the liver to counteract changes of plasma glucose level below 5 mM (hypoglycemia) and above 7.5 mM (hyperglycemia). Our model may serve as an important module of a whole-body model of human glucose metabolism and as a valuable tool for understanding the role of the liver in glucose homeostasis under normal conditions and in diseases like diabetes or glycogen storage diseases.

Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws - a promising method for speeding up the kinetic modelling of complex metabolic networks

Kinetic modelling of complex metabolic networks – a central goal of computational systems biology – is currently hampered by the lack of reliable rate equations for the majority of the underlying biochemical reactions and membrane transporters. On the basis of biochemically substantiated evidence that metabolic control is exerted by a narrow set of key regulatory enzymes, we propose here a hybrid modelling approach in which only the central regulatory enzymes are described by detailed mechanistic rate equations, and the majority of enzymes are approximated by simplified (nonmechanistic) rate equations (e.g. mass action, LinLog, Michaelis–Menten and power law) capturing only a few basic kinetic features and hence containing only a small number of parameters to be experimentally determined. To check the reliability of this approach, we have applied it to two different metabolic networks, the energy and redox metabolism of red blood cells, and the purine metabolism of hepatocytes, using in both cases available comprehensive mechanistic models as reference standards. Identification of the central regulatory enzymes was performed by employing only information on network topology and the metabolic data for a single reference state of the network [Grimbs S, Selbig J, Bulik S, Holzhutter HG & Steuer R (2007) Mol Syst Biol3, 146, doi:10.1038/msb4100186]. Calculations of stationary and temporary states under various physiological challenges demonstrate the good performance of the hybrid models. We propose the hybrid modelling approach as a means to speed up the development of reliable kinetic models for complex metabolic networks.

Enzymatic features of the glucose metabolism in tumor cells

Many tumor types exhibit an impaired Pasteur effect, i.e. despite the presence of oxygen, glucose is consumed at an extraordinarily high rate compared with the tissue from which they originate – the so‐called ‘Warburg effect’. Glucose has to serve as the source for a diverse array of cellular functions, including energy production, synthesis of nucleotides and lipids, membrane synthesis and generation of redox equivalents for antioxidative defense. Tumor cells acquire specific enzyme‐regulatory mechanisms to direct the main flux of glucose carbons to those pathways most urgently required under challenging external conditions such as varying substrate availability, presence of anti‐cancer drugs or different phases of the cell cycle. In this review we summarize the currently available information on tumor‐specific expression, activity and kinetic properties of enzymes involved in the main pathways of glucose metabolism with due regard to the explanation of the regulatory basis and physiological significance of the Warburg effect. We conclude that, besides the expression level of the metabolic enzymes involved in the glucose metabolism of tumor cells, the unique tumor‐specific pattern of isozymes and accompanying changes in the metabolic regulation below the translation level enable tumor cells to drain selfishly the blood glucose pool that non‐transformed cells use as sparingly as possible.

Characterizing the N-terminal processing motif of MHC class I ligands.

Most peptide ligands presented by MHC class I molecules are the product of an intracellular pathway comprising protein breakdown in the cytosol, transport into the endoplasmic reticulum, and successive N-terminal trimming events. The efficiency of each of these processes depends on the amino acid sequence of the presented ligand and its precursors. Thus, relating the amino acid composition N-terminal of presented ligands to the sequence specificity of processes in the pathway gives insight into the usage of ligand precursors in vivo. Examining the amino acid composition upstream the true N terminus of MHC class I ligands, we demonstrate the existence of a distinct N-terminal processing motif comprising approximately seven residues and matching the known preferences of proteasome and TAP, two key players in ligand processing. Furthermore, we find that some residues, which are preferred by both TAP and the proteasome, are underrepresented at positions immediately preceding the N terminus of MHC class I ligands. Based on experimentally determined aminopeptidase activities, this pattern suggests trimming next to the final N terminus to take place predominantly in the endoplasmic reticulum.

Quantifying the Contribution of Defective Ribosomal Products to Antigen Production: A Model-Based Computational Analysis

Antigenic peptides (epitopes) presented on the cell surface by MHC class I molecules derive from proteolytic degradation of endogenous proteins. Some recent studies have proposed that the majority of epitopes stem from so-called defective ribosomal products (DRiPs), i.e., freshly synthesized proteins that are unable to adopt the native conformation and thus undergo immediate degradation. However, a reliable computational analysis of the data underlying this hypothesis was lacking so far. Therefore, we have applied kinetic modeling to derive from existing kinetic data (Princiotta et al. 2003, Immunity 18, 343–354) the rates of the major processes involved in the cellular protein turnover and MHC class I-mediated Ag presentation. From our modeling approach, we conclude that in these experiments 1) the relative share of DRiPs in the total protein synthesis amounted to ∼10% thus being much lower than reported so far, 2) DRiPs may become the decisive source of epitopes within an early phase after onset of the synthesis of a long-lived (e.g., virus derived) protein, and 3) inhibition of protein synthesis by the translation inhibitor cycloheximide appears to be paralleled with an instantaneous decrease of protein degradation down to ∼1/3 of the normal value.