Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth

Abstract

Metabolism within the tumor microenvironment, where a complex mixture of different cell types resides in a nutrient-deprived surrounding, is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells' preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common belief that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified a total of 233 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

“…In this study, we showcased our method using metabolic task analysis, but it also allows for more advanced modeling approaches. Such methods could involve the use of metabolite uptake constraints (e.g., based on diffusion), constraints on enzyme usage, or simulations involving the interplay between several cell types (4,36).…”

“…Genome-scale metabolic models (GEMs) have been extensively used to further our understanding of metabolism in both unicellular organisms such as yeast and bacteria (1,2), and multicellular species such as humans (3)(4)(5). For multicellular species, the existence of many different cell types and tissues poses a challenge for metabolic modeling since the full reaction network encoded by the genome is typically not present in such tissues or cell types.…”

Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport

“…We used the full genome-scale metabolic model Human1 13 v. 1.12 for model simulations. The model was enhanced by a modified version of GECKO Light 16,21 , which was used to assign enzyme usage ATP costs to reactions based on k cat values and molecular weights. The costs were divided into 3 categories that could be penalized differently: 1) MT genes (i.e., enzyme subunits generated from the mitochondrial genome), 2) other mitochondrial enzymes (i.e., enzymes or enzyme subunits that reside in the mitochondria but are generated in the cytoplasm of the soma and thus need to be transported to the mitochondria at the synapses), and 3) other enzymes (including for example cytosolic enzymes).…”

“…Genome-scale metabolic models describe all the metabolic reactions of a cell 13,14 . We constructed such a model incorporating enzyme usage information, which has proven useful for explaining metabolic behaviors in for example muscle cells 15 and tumors 16 , and used that information to estimate ATP maintenance costs for enzyme transportation and utilization (Fig. 2A).…”

Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport

“…This method can through tools such as GECKO ( 12 ) and MOMENT ( 13 ) be combined with enzyme usage constraints, where the molecular weight and catalytic capacity ( k cat ) of each enzyme are used to estimate the enzyme mass needed to maintain a certain flux through a specific reaction. A common use of these constraints is to constrain the total enzyme mass available per gram cell dry weight ( 14 ), although other uses are also possible, for example, to add an enzyme maintenance cost per enzyme usage. Such models, for example, enable an investigation of which pathways consume the least ATP to maintain functional enzymes.…”