Sascha Schäuble

Metabolic costs of amino acid and protein production in Escherichia coli

Escherichia coli is the most popular microorganism for the production of recombinant proteins and is gaining increasing importance for the production of low-molecular weight compounds such as amino acids. The metabolic cost associated with the production of amino acids and (recombinant) proteins from glucose, glycerol and acetate was determined using three different computational techniques to identify those amino acids that put the highest burden on the biosynthetic machinery of E. coli. Comparing the costs of individual amino acids, we find that methionine is the most expensive amino acid in terms of consumed mol of ATP per molecule produced, while leucine is the most expensive amino acid when taking into account the cellular abundances of amino acids. Moreover, we show that the biosynthesis of a large number of amino acids from glucose and particularly from glycerol provides a surplus of energy, which can be used to balance the high energetic cost of amino acid polymerization.

Metabolic adaptation establishes disease tolerance to sepsis

Summary Sepsis is an often lethal syndrome resulting from maladaptive immune and metabolic responses to infection, compromising host homeostasis. Disease tolerance is a defense strategy against infection that preserves host homeostasis without exerting a direct negative impact on pathogens. Here, we demonstrate that induction of the iron-sequestering ferritin H chain (FTH) in response to polymicrobial infections is critical to establish disease tolerance to sepsis. The protective effect of FTH is exerted via a mechanism that counters iron-driven oxidative inhibition of the liver glucose-6-phosphatase (G6Pase), and in doing so, sustains endogenous glucose production via liver gluconeogenesis. This is required to prevent the development of hypoglycemia that otherwise compromises disease tolerance to sepsis. FTH overexpression or ferritin administration establish disease tolerance therapeutically. In conclusion, disease tolerance to sepsis relies on a crosstalk between adaptive responses controlling iron and glucose metabolism, required to maintain blood glucose within a physiologic range compatible with host survival.

Data-driven integration of genome-scale regulatory and metabolic network models

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert—a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

Effect of substrate competition in kinetic models of metabolic networks.

Substrate competition can be found in many types of biological processes, ranging from gene expression to signal transduction and metabolic pathways. Although several experimental and in silico studies have shown the impact of substrate competition on these processes, it is still often neglected, especially in modelling approaches. Using toy models that exemplify different metabolic pathway scenarios, we show that substrate competition can influence the dynamics and the steady state concentrations of a metabolic pathway. We have additionally derived rate laws for substrate competition in reversible reactions and summarise existing rate laws for substrate competition in irreversible reactions.

Quantitative Model of Cell Cycle Arrest and Cellular Senescence in Primary Human Fibroblasts

Primary human fibroblasts in tissue culture undergo a limited number of cell divisions before entering a non-replicative “senescent” state. At early population doublings (PD), fibroblasts are proliferation-competent displaying exponential growth. During further cell passaging, an increasing number of cells become cell cycle arrested and finally senescent. This transition from proliferating to senescent cells is driven by a number of endogenous and exogenous stress factors. Here, we have developed a new quantitative model for the stepwise transition from proliferating human fibroblasts (P) via reversibly cell cycle arrested (C) to irreversibly arrested senescent cells (S). In this model, the transition from P to C and to S is driven by a stress function γ and a cellular stress response function F which describes the time-delayed cellular response to experimentally induced irradiation stress. The application of this model based on senescence marker quantification at the single-cell level allowed to discriminate between the cellular states P, C, and S and delivers the transition rates between the P, C and S states for different human fibroblast cell types. Model-derived quantification unexpectedly revealed significant differences in the stress response of different fibroblast cell lines. Evaluating marker specificity, we found that SA-β-Gal is a good quantitative marker for cellular senescence in WI-38 and BJ cells, however much less so in MRC-5 cells. Furthermore we found that WI-38 cells are more sensitive to stress than BJ and MRC-5 cells. Thus, the explicit separation of stress induction from the cellular stress response, and the differentiation between three cellular states P, C and S allows for the first time to quantitatively assess the response of primary human fibroblasts towards endogenous and exogenous stress during cellular ageing.

A systems study reveals concurrent activation of AMPK and mTOR by amino acids

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational–experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase β (CaMKKβ). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes.

A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae.

Uncoupling of mucosal gene regulation, mRNA splicing and adherent microbiota signatures in inflammatory bowel disease

Objective An inadequate host response to the intestinal microbiota likely contributes to the manifestation and progression of human inflammatory bowel disease (IBD). However, molecular approaches to unravelling the nature of the defective crosstalk and its consequences for intestinal metabolic and immunological networks are lacking. We assessed the mucosal transcript levels, splicing architecture and mucosa-attached microbial communities of patients with IBD to obtain a comprehensive view of the underlying, hitherto poorly characterised interactions, and how these are altered in IBD. Design Mucosal biopsies from Crohns disease and patients with UC, disease controls and healthy individuals (n=63) were subjected to microbiome, transcriptome and splicing analysis, employing next-generation sequencing. The three data levels were integrated by different bioinformatic approaches, including systems biology-inspired network and pathway analysis. Results Microbiota, host transcript levels and host splicing patterns were influenced most strongly by tissue differences, followed by the effect of inflammation. Both factors point towards a substantial disease-related alteration of metabolic processes. We also observed a strong enrichment of splicing events in inflamed tissues, accompanied by an alteration of the mucosa-attached bacterial taxa. Finally, we noted a striking uncoupling of the three molecular entities when moving from healthy individuals via disease controls to patients with IBD. Conclusions Our results provide strong evidence that the interplay between microbiome and host transcriptome, which normally characterises a state of intestinal homeostasis, is drastically perturbed in Crohns disease and UC. Consequently, integrating multiple OMICs levels appears to be a promising approach to further disentangle the complexity of IBD.

Predicting the Physiological Role of Circadian Metabolic Regulation in the Green Alga Chlamydomonas reinhardtii

Although the number of reconstructed metabolic networks is steadily growing, experimental data integration into these networks is still challenging. Based on elementary flux mode analysis, we combine sequence information with metabolic pathway analysis and include, as a novel aspect, circadian regulation. While minimizing the need of assumptions, we are able to predict changes in the metabolic state and can hypothesise on the physiological role of circadian control in nitrogen metabolism of the green alga Chlamydomonas reinhardtii.

Hands-on metabolism analysis of complex biochemical networks using elementary flux modes.

The aim of this chapter is to discuss the basic principles and reasoning behind elementary flux mode analysis (EFM analysis)--an important tool for the analysis of metabolic networks. We begin with a short introduction into metabolic pathway analysis and subsequently outline in detail fundamentals of EFM analysis by way of a small example network. We discuss issues arising in the reconstruction of metabolic networks required for EFM analysis and how they can be circumvented. Subsequently, we analyze a more elaborate example network representing photosynthate metabolism. Finally, we give an overview of applications of EFM analysis in biotechnology and other fields and discuss issues arising when applying methods from metabolic pathway analysis to genome-scale metabolic networks.