Beyond fuelling cellular activities with building blocks and energy, metabolism also integrates environmental conditions into intracellular signals. The underlying regulatory network is complex and multifaceted: it ranges from slow interactions, such as changing gene expression, to rapid ones, such as the modulation of protein activity via post-translational modification or the allosteric binding of small molecules. In this Review, we outline the coordination of common metabolic tasks, including nutrient uptake, central metabolism, the generation of energy, the supply of amino acids and protein synthesis. Increasingly, a set of key metabolites is recognized to control individual regulatory circuits, which carry out specific functions of information input and regulatory output. Such a modular view of microbial metabolism facilitates an intuitive understanding of the molecular mechanisms that underlie cellular decision making.

Highlights d Developed 13 C-infusion method for studying T cell metabolism in vivo d T cell glucose use and bioenergetics differ between cell culture and mouse models d Glucose metabolism in T cells changes dynamically over an immune response d Glucose-dependent serine biosynthesis supports T cell proliferation in vivo

Upon activation, macrophages undergo extensive metabolic rewiring 1 , 2 . Production of itaconate through the inducible enzyme IRG1 is a key hallmark of this process 3 . Itaconate inhibits succinate dehydrogenase (SDH) 4 , 5 , has electrophilic properties 6 , and is associated with a change in cytokine production 4 . Here, we compare the metabolic, electrophilic, and immunologic profiles of macrophages treated with unmodified itaconate and a panel of commonly used itaconate derivatives to examine its role. Using wild type and Irg1 −/− macrophages, we show that neither dimethyl itaconate (DI), 4-octyl itaconate (4OI), nor 4-monoethyl itaconate (4EI) are converted into intracellular itaconate, while exogenous itaconic acid readily enters macrophages. We find that only DI and 4OI induce a strong electrophilic stress response, in contrast to itaconate and 4EI. This correlates with their immunosuppressive phenotype: DI and 4OI inhibit IκBζ and pro-IL-1β induction, as well as IL-6, IL-10, and IFN-β secretion in an Nrf2-independent manner. In contrast, itaconate treatment only suppressed IL-1β secretion but not pro-IL-1β levels, and, surprisingly, strongly enhanced LPS-induced IFN-β secretion. Consistently, Irg1 −/− macrophages produced lower levels of interferon and reduced transcriptional activation of this pathway. Our work establishes itaconate as an immunoregulatory, rather than strictly immunosuppressive metabolite, and highlights the importance of using unmodified itaconate in future studies.

Regulation of enzyme expression is one key mechanism by which cells control their metabolic programs. In this work, a quantitative analysis of metabolism in a model bacterium under different conditions shows that expression alone cannot explain the majority of the observed metabolic changes.

Predicting resource allocation between cell processes is the primary step towards decoding the evolutionary constraints governing bacterial growth under various conditions. Quantitative prediction at genome-scale remains a computational challenge as current methods are limited by the tractability of the problem or by simplifying hypotheses. Here, we show that the constraint-based modeling method Resource Balance Analysis (RBA), calibrated using genome-wide absolute protein quantification data, accurately predicts resource allocation in the model bacterium Bacillus subtilis for a wide range of growth conditions. The regulation of most cellular processes is consistent with the objective of growth rate maximization except for a few suboptimal processes which likely integrate more complex objectives such as coping with stressful conditions and survival. As a proof of principle by using simulations, we illustrated how calibrated RBA could aid rational design of strains for maximizing protein production, offering new opportunities to investigate design principles in prokaryotes and to exploit them for biotechnological applications.

Epigenetic modifications on DNA and histones regulate gene expression by modulating chromatin accessibility to transcription machinery. Here we identify methionine as a key nutrient affecting epigenetic reprogramming in CD4 + T helper (Th) cells. Using metabolomics, we showed that methionine is rapidly taken up by activated T cells and serves as the major substrate for biosynthesis of the universal methyl donor S-adenosyl-L-methionine (SAM). Methionine was required to maintain intracellular SAM pools in T cells. Methionine restriction reduced histone H3K4 methylation (H3K4me3) at the promoter regions of key genes involved in Th17 cell proliferation and cytokine production. Applied to the mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis), dietary methionine restriction reduced the expansion of pathogenic Th17 cells in vivo, leading to reduced T cell-mediated neuroinflammation and disease onset. Our data identify methionine as a key nutritional factor shaping Th cell proliferation and function in part through regulation of histone methylation.

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.

Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance.