Functional Impact of a Cancer-Related Variant in Human Δ\u3csup\u3e1\u3c/sup\u3e‑Pyrroline-5-Carboxylate Reductase 1

Pyrroline-5-carboxylate reductase (PYCR) is a proline biosynthetic enzyme that catalyzes the NAD(P)H-dependent reduction of Δ1-pyrroline-5-carboxylate (P5C) to proline. Humans have three PYCR isoforms, with PYCR1 often upregulated in different types of cancers. Here, we studied the biochemical and structural properties of the Thr171Met variant of PYCR1, which is found in patients with malignant melanoma and lung adenocarcinoma. Although PYCR1 is strongly associated with cancer progression, characterization of a PYCR1 variant in cancer patients has not yet been reported. Thr171 is conserved in all three PYCR isozymes and is located near the P5C substrate binding site. We found that the amino acid replacement does not affect thermostability but has a profound effect on PYCR1 catalytic activity. The kcat of the PYCR1 variant T171M is 100- to 200-fold lower than wild-type PYCR1 when P5C is the variable substrate, and 10- to 25-fold lower when NAD(P)H is varied. A 1.84 Å resolution X-ray crystal structure of T171M reveals that the Met side chain invades the P5C substrate binding site, suggesting that the catalytic defect is due to steric clash preventing P5C from achieving the optimal pose for hydride transfer from NAD(P)H. These results suggest that any impact on PYCR1 function associated with T171M in cancer does not derive from increased catalytic activity

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

The tumor microenvironment is composed of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein 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 hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 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