Metabolic alterations during the growth of tumour spheroids

Solid tumours undergo considerable alterations in their metabolism of nutrients in order to generate sufficient energy and biomass for sustained growth and proliferation. During growth, the tumour microenvironment exerts a number of influences (e.g. hypoxia and acidity) that affect cellular biology and the flux or utilisation of fuels including glucose. The tumour spheroid model was used to characterise the utilisation of glucose and describe alterations to the activity and expression of key glycolytic enzymes during the tissue growth curve. Glucose was avidly consumed and associated with the production of lactate and an acidified medium, confirming the reliance on glycolytic pathways and a diminution of oxidative phosphorylation. The expression levels and activities of hexokinase, phosphofructokinase-1, pyruvate kinase and lactate dehydrogenase in the glycolytic pathway were measured to assess glycolytic capacity. Similar measurements were made for glucose-6-phosphate dehydrogenase, the entry point and regulatory step of the pentose-phosphate pathway (PPP) and for cytosolic malate dehydrogenase, a key link to TCA cycle intermediates. The parameters for these key enzymes were shown to undergo considerable variation during the growth curve of tumour spheroids. In addition, they revealed that the dynamic alterations were influenced by both transcriptional and posttranslational mechanisms

Metabolic alterations during the growth of tumour spheroids

Solid tumours undergo considerable alterations in their metabolism of nutrients in order to generate sufficient energy and biomass for sustained growth and proliferation. During growth, the tumour microenvironment exerts a number of influences (e.g. hypoxia and acidity) that affect cellular biology and the flux or utilisation of fuels including glucose. The tumour spheroid model was used to characterise the utilisation of glucose and describe alterations to the activity and expression of key glycolytic enzymes during the tissue growth curve. Glucose was avidly consumed and associated with the production of lactate and an acidified medium, confirming the reliance on glycolytic pathways and a diminution of oxidative phosphorylation. The expression levels and activities of hexokinase, phosphofructokinase-1, pyruvate kinase and lactate dehydrogenase in the glycolytic pathway were measured to assess glycolytic capacity. Similar measurements were made for glucose-6-phosphate dehydrogenase, the entry point and regulatory step of the pentose-phosphate pathway (PPP) and for cytosolic malate dehydrogenase, a key link to TCA cycle intermediates. The parameters for these key enzymes were shown to undergo considerable variation during the growth curve of tumour spheroids. In addition, they revealed that the dynamic alterations were influenced by both transcriptional and posttranslational mechanisms

Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy