Several species of intestinal bacteria have been associated with enhanced efficacy of checkpoint blockade immunotherapy, but the underlying mechanisms by which the microbiome enhances anti-tumor immunity is unclear. Here, we isolated three bacterial species, including Bifidobacterium pseudolongum, Lactobacillus johnsonii and Olsenella species, that significantly enhanced efficacy of immune checkpoint inhibitors in four mouse models of cancer. We found that intestinal B. pseudolongum modulated enhanced immunotherapy response through production of the metabolite inosine. Decreased gut barrier function induced by immunotherapy increased systemic translocation of inosine and activated anti-tumor T cells. The effect of inosine was dependent on T cell expression of the adenosine A2A receptor and required co-stimulation. Collectively, our study identifies a novel microbial metabolite-immune pathway that is activated by immunotherapy that may be exploited to develop microbial-based adjuvant therapies.

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Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites’ reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

One-dimensional (1D) 1 H nuclear magnetic resonance (NMR) spectroscopy is used extensively for high-throughput analysis of metabolites in biological fluids and tissue extracts. Typically, such spectra are treated as multivariate statistical objects rather than as collections of quantifiable metabolites. We report here a two-dimensional (2D) 1 H-13 C NMR strategy (Fast Metabolite Quantification, FMQ by NMR) for identifying and quantifying the ∼40 most abundant metabolites in biological samples. To validate this technique, we prepared mixtures of synthetic compounds and extracts from Arabidopsis thaliana, Saccharomyces cerevisiae and Medicago sativa. We show that accurate (technical error 2.7%) molar concentrations can be determined in 12 minutes using our quantitative 2D 1 H-13 C NMR strategy. In contrast, traditional 1D 1 H NMR analysis resulted in 16.2% technical error under nearly ideal conditions. We propose FMQ by NMR as a practical alternative to 1D 1 H NMR for metabolomics studies in which 200-400 mg (preextraction dry weight) samples can be obtained.One-dimensional (1D) 1 H NMR spectroscopy has been used for decades as an analytical tool for identifying small molecules and measuring their concentrations. 1, 2 Traditionally, quantitative analysis by NMR has been restricted to relatively simple mixtures with minimal peak overlap. In these applications, 1D 1 H NMR is a natural choice, because its peaks scale linearly with concentration and its analytical precision is usually independent of the chemical properties of target molecules. Recently, interest has surged in using NMR for high-throughput analysis of complex biological processes at the metabolic level. 3, 4 These studies, defined as "metabolomics" or "metabonomics", place an emphasis on biomarker discovery or disease classification and are typically centered on unfractionated biological fluids and tissue extracts. 1D 1 H NMR spectra of these samples typically contain hundreds of overlapping resonances (Figure 1) that make traditional NMR-based analytical practices, such as resonance assignment and accurate peak integration, a challenging prospect. As a result, sophisticated statistical tools have been developed to translate spectral data into biologically meaningful information. 4, 5All statistical tools used for analyzing complex spectra face the same fundamental barrier: overlapped peaks do not scale in the discrete linear fashion that typifies well-isolated peaks. They scale as the sum of the total overlapped resonance. Consequently, multivariate and correlation statistics are reporters of overlapped spectral density, not concentrations of specific compounds. Although peak overlap does not interfere with the reproducibility of traditional analyses, 6 it does prevent accurate quantification. Two approaches can be used to overcome this barrier, one mathematical the other experimental. The mathematical approach is to fit overlapped 1 D NMR spectra with modeled peaks. This approach has been successfully applied by Weljie and co-workers. 7 The e...

SUMMARY New antimalarial drugs are urgently needed to control drug resistant forms of the malaria parasite, Plasmodium falciparum. Mitochondrial electron transport is the target of both existing and new antimalarials. Herein, we describe 11 genetic knockout (KO) lines that delete six of the eight mitochondrial tricarboxylic acid (TCA) cycle enzymes. Although all TCA KOs grew normally in asexual blood stages, these metabolic deficiencies halted lifecycle progression in later stages. Specifically, aconitase KO parasites arrested as late gametocytes, whereas α-ketoglutarate dehydrogenase deficient parasites failed to develop oocysts in the mosquitoes. Mass spectrometry analysis of 13C isotope-labeled TCA mutant parasites showed that P. falciparum has significant flexibility in TCA metabolism. This flexibility manifested itself through changes in pathway fluxes and through altered exchange of substrates between cytosolic and mitochondrial pools. Our findings suggest that mitochondrial metabolic plasticity is essential for parasite development.