SummaryMetabolic activity is intimately linked to T cell fate and function. Using high-resolution mass spectrometry, we generated dynamic metabolome and proteome profiles of human primary naive T cells following activation. We discovered critical changes in the arginine metabolism that led to a drop in intracellular L-arginine concentration. Elevating L-arginine levels induced global metabolic changes including a shift from glycolysis to oxidative phosphorylation in activated T cells and promoted the generation of central memory-like cells endowed with higher survival capacity and, in a mouse model, anti-tumor activity. Proteome-wide probing of structural alterations, validated by the analysis of knockout T cell clones, identified three transcriptional regulators (BAZ1B, PSIP1, and TSN) that sensed L-arginine levels and promoted T cell survival. Thus, intracellular L-arginine concentrations directly impact the metabolic fitness and survival capacity of T cells that are crucial for anti-tumor responses.

Postnatal colonization of the body with microbes is assumed to be the main stimulus to postnatal immune development. By transiently colonizing pregnant female mice, we show that the maternal microbiota shapes the immune system of the offspring. Gestational colonization increases intestinal group 3 innate lymphoid cells and F4/80(+)CD11c(+) mononuclear cells in the pups. Maternal colonization reprograms intestinal transcriptional profiles of the offspring, including increased expression of genes encoding epithelial antibacterial peptides and metabolism of microbial molecules. Some of these effects are dependent on maternal antibodies that potentially retain microbial molecules and transmit them to the offspring during pregnancy and in milk. Pups born to mothers transiently colonized in pregnancy are better able to avoid inflammatory responses to microbial molecules and penetration of intestinal microbes.

We have used high resolution transmission electron microscopy to determine the structure of gold nanowires generated by mechanical stretching. Just before rupture, the contacts adopt only three possible atomic configurations, whose occurrence probabilities and quantized conductance were subsequently estimated. These predictions have shown a remarkable agreement with conductance measurements from a break junction operating in ultrahigh vacuum, corroborating the derived correlation between nanowire atomic structure and conductance behavior.

Direct injection of samples on high-resolving mass spectrometers is an effective way to maximize analytical throughput and yet allow analyte discrimination in complex samples by mass-to-charge ratio. We present a platform of flow injection electrospray-time-of-flight mass spectrometry to profile small molecules in >1400 biological extracts per day at native mass resolution. We comprehensively benchmark the performance with more than 5000 injections of chemically defined standards and Escherichia coli cellular extracts obtained from miniscale cultivations. For at least 90% of tested compounds, we attain a linear response over 3 decades of concentration, interday coefficient of variation of <20%, and a mass accuracy of <0.001 amu. In polar Escherichia coli fractions, we reproducibly detected >1500 distinct ions in each mode. The accurate mass and correlation analysis enabled one to assign with good confidence 400-800 ions to electrospray derivatives of metabolites listed in the genome-wide reconstruction of Escherichia coli metabolism. Hence, we attain a coverage of about one-quarter of the total number of compounds listed in the reconstruction. Finally, we present an exemplary screen with Escherichia coli deletion mutants to show the exquisite capacity of the platform to identify lesions in primary metabolism at high throughputs.

Metabolite-protein interactions control a variety of cellular processes, thereby playing a major role in maintaining cellular homeostasis. Metabolites comprise the largest fraction of molecules in cells, but our knowledge of the metabolite-protein interactome lags behind our understanding of protein-protein or protein-DNA interactomes. Here, we present a chemoproteomic workflow for the systematic identification of metabolite-protein interactions directly in their native environment. The approach identified a network of known and novel interactions and binding sites in Escherichia coli, and we demonstrated the functional relevance of a number of newly identified interactions. Our data enabled identification of new enzyme-substrate relationships and cases of metabolite-induced remodeling of protein complexes. Our metabolite-protein interactome consists of 1,678 interactions and 7,345 putative binding sites. Our data reveal functional and structural principles of chemical communication, shed light on the prevalence and mechanisms of enzyme promiscuity, and enable extraction of quantitative parameters of metabolite binding on a proteome-wide scale.