Macromolecular crowding explains overflow metabolism in cells

Overflow metabolism is a metabolic phenotype of cells characterized by mixed oxidative phosphorylation (OxPhos) and fermentative glycolysis in the presence of oxygen. Recently, it was proposed that a combination of a protein allocation constraint and a higher proteome fraction cost of energy generation by OxPhos relative to fermentation form the basis of overflow metabolism in the bacterium, Escherichia coli. However, we argue that the existence of a maximum or optimal macromolecular density is another essential requirement. Here we re-evaluate our previous theory of overflow metabolism based on molecular crowding following the proteomic fractions formulation. We show that molecular crowding is a key factor in explaining the switch from OxPhos to overflow metabolism

The Activity Reaction Core and Plasticity of Metabolic Networks

Understanding the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery

Serine Biosynthesis with One Carbon Catabolism and the Glycine Cleavage System Represents a Novel Pathway for ATP Generation

Previous experimental evidence indicates that some cancer cells have an alternative glycolysis pathway with net zero ATP production, implying that upregulation of glycolysis in these cells may not be related to the generation of ATP. Here we use a genome-scale model of human cell metabolism to investigate the potential metabolic alterations in cells using net zero ATP glycolysis. We uncover a novel pathway for ATP generation that involves reactions from serine biosynthesis, one-carbon metabolism and the glycine cleavage system, and show that the pathway is transcriptionally upregulated in an inducible murine model of Myc-driven liver tumorigenesis. This pathway has a predicted two-fold higher flux rate in cells using net zero ATP glycolysis than those using standard glycolysis and generates twice as much ATP with significantly lower rate of lactate - but higher rate of alanine secretion. Thus, in cells using the standard - or the net zero ATP glycolysis pathways a significant portion of the glycolysis flux is always associated with ATP generation, and the ratio between the flux rates of the two pathways determines the rate of ATP generation and lactate and alanine secretion during glycolysis

Topological basis of signal integration in the transcriptional-regulatory network of the yeast, Saccharomyces cerevisiae

BACKGROUND: Signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory (TR) mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. However, the network topological basis of developing such integrated responses remains poorly understood. RESULTS: By studying the TR network of the yeast Saccharomyces cerevisiae we show that an intermediate layer of transcription factors naturally segregates into distinct subnetworks. In these topological units transcription factors are densely interlinked in a largely hierarchical manner and respond to external signals by utilizing a fraction of these subnets. CONCLUSION: As transcriptional regulation represents the 'slow' component of overall information processing, the identified topology suggests a model in which successive waves of transcriptional regulation originating from distinct fractions of the TR network control robust integrated responses to complex stimuli

Statin-induced mevalonate pathway inhibition attenuates the growth of mesenchymal-like cancer cells that lack functional E-cadherin mediated cell cohesion

The cholesterol reducing drugs, statins, exhibit anti-tumor effects against cancer stem cells and various cancer cell lines, exert potent additivity or synergy with existing chemotherapeutics in animal models of cancer and may reduce cancer incidence and cancer related mortality in humans. However, not all tumor cell lines are sensitive to statins, and clinical trials have demonstrated mixed outcomes regarding statins as anticancer agents. Here, we show that statin-induced reduction in intracellular cholesterol levels correlate with the growth inhibition of cancer cell lines upon statin treatment. Moreover, statin sensitivity segregates with abundant cytosolic vimentin expression and absent cell surface E-cadherin expression, a pattern characteristic of mesenchymal-like cells. Exogenous expression of cell surface E-cadherin converts statin- sensitive cells to a partially resistant state implying that statin resistance is in part dependent on the tumor cells attaining an epithelial phenotype. As metastasizing tumor cells undergo epithelial to mesenchymal transition during the initiation of the metastatic cascade, statin therapy may represent an effective approach to targeting the cells most likely to disseminate

A Semi-Supervised Method for Predicting Transcription Factor–Gene Interactions in Escherichia coli

While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor–gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor–gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic–anaerobic shift interface

Computational disease modeling – fact or fiction?

BACKGROUND: Biomedical research is changing due to the rapid accumulation of experimental data at an unprecedented scale, revealing increasing degrees of complexity of biological processes. Life Sciences are facing a transition from a descriptive to a mechanistic approach that reveals principles of cells, cellular networks, organs, and their interactions across several spatial and temporal scales. There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably) essential relevance to the phenomena of interest and combines available data in models of modest complexity. RESULTS: The workshop, "ESF Exploratory Workshop on Computational disease Modeling", examined the challenges that computational modeling faces in contributing to the understanding and treatment of complex multi-factorial diseases. Participants at the meeting agreed on two general conclusions. First, we identified the critical importance of developing analytical tools for dealing with model and parameter uncertainty. Second, the development of predictive hierarchical models spanning several scales beyond intracellular molecular networks was identified as a major objective. This contrasts with the current focus within the systems biology community on complex molecular modeling. CONCLUSION: During the workshop it became obvious that diverse scientific modeling cultures (from computational neuroscience, theory, data-driven machine-learning approaches, agent-based modeling, network modeling and stochastic-molecular simulations) would benefit from intense cross-talk on shared theoretical issues in order to make progress on clinically relevant problems

The metabolic demands of cancer cells are coupled to their size and protein synthesis rates.

BACKGROUND: Although cells require nutrients to proliferate, most nutrient exchange rates of the NCI60 panel of cancer cell lines correlate poorly with their proliferation rate. Here, we provide evidence indicating that this inconsistency is rooted in the variability of cell size. RESULTS: We integrate previously reported data characterizing genome copy number variations, gene expression, protein expression and exchange fluxes with our own measurements of cell size and protein content in the NCI60 panel of cell lines. We show that protein content, DNA content, and protein synthesis per cell are proportional to the cell volume, and that larger cells proliferate slower than smaller cells. We estimate the metabolic fluxes of these cell lines and show that their magnitudes are proportional to their protein synthesis rate and, after correcting for cell volume, to their proliferation rate. At the level of gene expression, we observe that genes expressed at higher levels in smaller cells are enriched for genes involved in cell cycle, while genes expressed at higher levels in large cells are enriched for genes expressed in mesenchymal cells. The latter finding is further corroborated by the induction of those same genes following treatment with TGFβ, and the high vimentin but low E-cadherin protein levels in the larger cells. We also find that aromatase inhibitors, statins and mTOR inhibitors preferentially inhibit the in vitro growth of cancer cells with high protein synthesis rates per cell. CONCLUSIONS: The NCI60 cell lines display various metabolic activities, and the type of metabolic activity that they possess correlates with their cell volume and protein content. In addition to cell proliferation, cell volume and/or biomarkers of protein synthesis may predict response to drugs targeting cancer metabolism

Molecular crowding defines a common origin for the Warburg effect in proliferating cells and the lactate threshold in muscle physiology

Aerobic glycolysis is a seemingly wasteful mode of ATP production that is seen both in rapidly proliferating mammalian cells and highly active contracting muscles, but whether there is a common origin for its presence in these widely different systems is unknown. To study this issue, here we develop a model of human central metabolism that incorporates a solvent capacity constraint of metabolic enzymes and mitochondria, accounting for their occupied volume densities, while assuming glucose and/or fatty acid utilization. The model demonstrates that activation of aerobic glycolysis is favored above a threshold metabolic rate in both rapidly proliferating cells and heavily contracting muscles, because it provides higher ATP yield per volume density than mitochondrial oxidative phosphorylation. In the case of muscle physiology, the model also predicts that before the lactate switch, fatty acid oxidation increases, reaches a maximum, and then decreases to zero with concomitant increase in glucose utilization, in agreement with the empirical evidence. These results are further corroborated by a larger scale model, including biosynthesis of major cell biomass components. The larger scale model also predicts that in proliferating cells the lactate switch is accompanied by activation of glutaminolysis, another distinctive feature of the Warburg effect. In conclusion, intracellular molecular crowding is a fundamental constraint for cell metabolism in both rapidly proliferating- and non-proliferating cells with high metabolic demand. Addition of this constraint to metabolic flux balance models can explain several observations of mammalian cell metabolism under steady state conditions