Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism to date, including 7,824 adult individuals from two European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity regarding more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information regarding gene expression, heritability, overlap with known drug targets, previous association with complex disorders and inborn errors of metabolism. We further developed a database and web-based resources for data mining and results visualization. Our findings contribute to a greater understanding of the role of inherited variation in blood metabolic diversity, and identify potential new opportunities for pharmacologic development and disease understanding.

SUMMARY Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 exhibit effect sizes that are unusually high for GWAS and account for 10-60% of metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism, and Crohn’s disease. Taken together our study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Naive CD8 + T cells rely upon oxidation of fatty acids as a primary source of energy. After antigen encounter, T cells shift to a glycolytic metabolism to sustain effector function. It is unclear, however, whether changes in glucose metabolism ultimately influence the ability of activated T cells to become long-lived memory cells. We used a fluorescent glucose analog, 2-NBDG, to quantify glucose uptake in activated CD8 + T cells. We found that cells exhibiting limited glucose incorporation had a molecular profile characteristic of memory precursor cells and an increased capacity to enter the memory pool compared with cells taking up high amounts of glucose. Accordingly, enforcing glycolytic metabolism by overexpressing the glycolytic enzyme phosphoglycerate mutase-1 severely impaired the ability of CD8 + T cells to form long-term memory. Conversely, activation of CD8 + T cells in the presence of an inhibitor of glycolysis, 2-deoxyglucose, enhanced the generation of memory cells and antitumor functionality. Our data indicate that augmenting glycolytic flux drives CD8 + T cells toward a terminally differentiated state, while its inhibition preserves the formation of long-lived memory CD8 + T cells. These results have important implications for improving the efficacy of T cell-based therapies against chronic infectious diseases and cancer.

Inflammation in the brain has been increasingly associated with the development of a number of neurological diseases. The hallmark of neuroinflammation is the activation of microglia, the resident brain immune cells. Injection of bacterial endotoxin lipopolysaccharide (LPS) into the hippocampus, cortex, or substantia nigra of adult rats produced neurodegeneration only in the substantia nigra. Although LPS appeared to impact upon mesencephalic neurons in general, an extensive loss of dopaminergic neurons was observed. Analysis of the abundance of microglia revealed that the substantia nigra had the highest density of microglia. When mixed neuron-glia cultures derived from the rat hippocampus, cortex, or mesencephalon were treated with LPS, mesencephalic cultures became sensitive to LPS at a concentration as low as 10 ng/ml and responded in a dose-dependent manner with the production of inflammatory factors and a loss of dopaminergic and other neurons. In contrast, hippocampal or cortical cultures remained insensitive to LPS treatment at concentrations as high as 10 g/ml. Consistent with in vivo observations, mesencephalic cultures had fourfold to eightfold more microglia than cultures from other regions. The positive correlation between abundance of microglia and sensitivity to LPS-induced neurotoxicity was further supported by the observation that supplementation with enriched microglia derived from mesencephalon or cortex rendered LPS-insensitive cortical neuron-glia cultures sensitive to LPS-induced neurotoxicity. These data indicate that the region-specific differential susceptibility of neurons to LPS is attributable to differences in the number of microglia present within the system and may reflect levels of inflammation-related factors produced by these cells.

We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub.