Nutrition, Genetics, and Microbiome
The modern era is marked by an unprecedented increase in body weight and metabolic diseases worldwide, with more and more cases of obesity and diabetes. A major cause of these rises in body weight and disease is due to the rapid increase in blood glucose levels in the population. Since glucose levels mainly change in response to food intake, this hyperglycemia epidemic is a result of our dietary choices. Indeed, dietary interventions in non-diabetics can result in proper control of glucose levels and full reversal of hyperglycemia. However, general nutritional recommendations, published by governmental agencies, are not effective, because different people have different blood glucose responses to the same food. Therefore, food choices that are good for one person may not be good for another, and we must understand which food choices are best for each person.
A major source of variability across people is our microbiome - the collection of 100 trillion germs and microbes that reside in our gut, skin, mouth, and other body locations. Each of us has a unique microbiota composition and function, which is affected by what we eat, and in turn, affects our response to food. This highly diverse ecosystem plays a key role in our physiology and health. On the one hand, it provides us with numerous beneficial functions, including synthesis of essential nutrients and vitamins, protection from harmful disease-causing bacteria that invade our body, and regulation of immunity and metabolism. On the other hand, changes in the microbiome induced by our nutrition and lifestyle can lead to numerous illnesses such as obesity, diabetes, inflammatory diseases, digestive diseases, neurological disorders, and even cancer.
We are taking an unbiased scientific approach to the nutritional problem, aimed at understanding how the enormous microbial composition of human individuals affects their response to food, and at constructing truly personalized diets. To achieve this ambitious goal, we have initiated the Personal Nutrition Project, in which we are measuring the microbiome, genetics, and glucose response to food intake for a large number of individuals. We are then using this multi-dimensional data to devise the first personalized algorithm for predicting the glycemic response of an individual to food. If successful, our research may lead to the ability to administer person-specific dietary interventions that improve the glycemic response, thus providing direct treatment for the pre-diabetic stage and assisting in the worldwide battle against the obesity and diabetes epidemic.
We are also studying the general role of the microbiome in health and disease through the development of novel computational approaches for analyzing this immense ecosystem and extracting new types of information from it.
The complex functions of a living cell are carried out through the coordinated activity of many genes. Cells achieve such coordination by tightly regulating the precise time and space in which essential biological processes occur. This regulation occurs at every level, including at the transcription level, in which the primary DNA sequence of a gene is copied (transcribed) into an mRNA sequence; at the translational level, in which the mRNA sequence of a gene is translated into its protein product; and at the post-translational level, in which the level of activity of each gene is controlled.
This coordinated control of gene activity is critical for nearly all cellular activities. Indeed, in genome-wide association studies and studies of cancer, many gene activity changes that are associated with the disease state have in turn been linked to changes in the genes' regulatory regions. However, without a 'regulatory code' (comparable to the 'genetic code') that informs us how DNA sequences determine gene activity levels, we cannot predict which sequence changes will affect expression, by how much, and by what mechanism. Despite much research, our understanding of gene regulation is still largely qualitative.
To address this challenge, we are developing quantitative computational models aimed at understanding how the various participating molecular components interact to regulate gene activity and carry out increasingly complex functions such as organismal development. To this end, we are using a combined experimental and computational approach, in which we perturb the genome to sequences of our choice and measure the effect, allowing us to establish a causal role for DNA. This approach is similar to classical reverse genetics, but unlike classical methods that make one sequence change at a time, we devised novel experimental methods that allows us to design tens of thousands of sequences, integrate them into different regions of the yeast and human genome, and then accurately measure both the expression and phenotypes driven by each sequence. Such a high-throughput reverse genetics is very powerful as it allows us, among others, to: Identify functional elements within a sequence of interest through systematic mutagenesis; Learn regulatory grammar by changing one variable at a time (e.g., the location of a transcription factor binding site); and Directly measure which genetic variation among human individuals affects gene activity by synthesizing sequences from different personal human genomes. Finally, we are integrating these data and insights into quantitative probabilistic models aimed at explaining the measured data. If successful, our research and resulting models would allow us to predict gene activity changes among human individuals from the genotype information that is rapidly being collected, thereby revolutionizing the way in which personal genomes are used, pinpointing the causal genetic changes and mechanisms at play, and leading to the ability to identify common or rare sequence variants that may affect molecular function or cause disease.