Jason Locasale

Studies at the molecular level demonstrate that dietary amino acid intake produces substantial effects on health and disease by modulating metabolism. However, how these effects may manifest in human food consumption and dietary patterns is unknown. Here, we develop a series of algorithms to map, characterize and model the landscape of amino acid content in human food, dietary patterns, and individual consumption including relations to health status, covering over 2,000 foods, ten dietary patterns, and over 30,000 dietary profiles. We find that the type of amino acids contained in foods and human consumption is highly dynamic with variability far exceeding that of fat and carbohydrate. Some amino acids positively associate with conditions such as obesity while others contained in the same food negatively link to disease. Using linear programming and machine learning, we show that these health trade-offs can be accounted for to satisfy biochemical constraints in food and human eating patterns to construct a Pareto front in dietary practice, a means of achieving optimality in the face of trade-offs that are commonly considered in economic and evolutionary theories. Thus this study may enable the design of human protein quality intake guidelines based on a quantitative framework.

Diet and exercise are modifiable lifestyle factors known to have a major influence on metabolism. Clinical practice addresses diseases of altered metabolism such as diabetes or hypertension by altering these factors. Despite enormous public interest, there are limited defined diet and exercise regimens for patients with cancer. Nevertheless, the molecular basis of cancer has converged over the past 15 years on an essential role for altered metabolism in cancer. However, our understanding of the molecular mechanisms that underlie the impact of diet and exercise on cancer metabolism is in its very early stages. In this perspective, I propose conceptual frameworks for understanding the consequences of diet and exercise on cancer cell metabolism and tumor biology and also highlight recent developments. By advancing our mechanistic understanding, I will discuss actionable ways that such interventions could eventually reach the mainstay of both medical oncology and cancer control and prevention.

The metabolism of both cancer and immune cells in the tumor microenvironment (TME) is poorly understood since most studies have focused on analysis in bulk samples and ex vivo cell culture models. Our recent analyses of single-cell RNA sequencing data suggest that the metabolic features of single cells within TME differ greatly from those of the bulk measurements. Here, we discuss some key findings about metabolism in cancer and immune cells and discuss possible relevance to immunotherapy.