Glucose transporters in cancer – from tumor cells to the tumor microenvironment

The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol. Copyright 2003 Wiley-Liss, Inc.

Cancer cells consume glucose and secrete lactate in culture. It is unknown whether lactate contributes to energy metabolism in living tumors. We previously reported that human non-small cell lung cancers (NSCLC) oxidize glucose in the tricarboxylic acid (TCA) cycle. Here we show that lactate is also a TCA cycle carbon source for NSCLC. In human NSCLC, evidence of lactate utilization was most apparent in tumors with high 18 fluorodeoxyglucose uptake and aggressive oncological behavior. Infusing human NSCLC patients with 13 C-lactate revealed extensive labeling of TCA cycle metabolites. In mice, deleting monocarboxylate transporter-1 (MCT1) from tumor cells eliminated lactate-dependent metabolite labeling, confirming tumor-cell autonomous lactate uptake. Strikingly, directly comparing lactate and glucose metabolism in vivo indicated that lactate's contribution to the TCA cycle predominates. The data indicate that tumors, including bona fide human NSCLC, can use lactate as a fuel in vivo. Human non-small cell lung cancer preferentially utilizes lactate over glucose to fuel TCA cycle and sustain tumor metabolism in vivo.

Sugar efflux transporters are essential for the maintenance of animal blood glucose levels, plant nectar production, and plant seed and pollen development. Despite broad biological importance, the identity of sugar efflux transporters has remained elusive. Using optical glucose sensors, we identified a new class of sugar transporters, named SWEETs, and show that at least six out of seventeen Arabidopsis, two out of over twenty rice and two out of seven homologues in Caenorhabditis elegans, and the single copy human protein, mediate glucose transport. Arabidopsis SWEET8 is essential for pollen viability, and the rice homologues SWEET11 and SWEET14 are specifically exploited by bacterial pathogens for virulence by means of direct binding of a bacterial effector to the SWEET promoter. Bacterial symbionts and fungal and bacterial pathogens induce the expression of different SWEET genes, indicating that the sugar efflux function of SWEET transporters is probably targeted by pathogens and symbionts for nutritional gain. The metazoan homologues may be involved in sugar efflux from intestinal, liver, epididymis and mammary cells.