Graphical Abstract Highlights d LPS treatment causes a decrease in HBP activity and protein O-GlcNAcylation d OGT deficiency increases activation of innate immune response and necroptosis d O-GlcNAcylation of RIPK3 on T467 inhibits RIPK3-RIPK1 and RIPK3-RIPK3 interaction SUMMARY Elevated glucose metabolism in immune cells represents a hallmark feature of many inflammatory diseases, such as sepsis. However, the role of individual glucose metabolic pathways during immune cell activation and inflammation remains incompletely understood. Here, we demonstrate a previously unrecognized anti-inflammatory function of the O-linked b-N-acetylglucosamine (O-GlcNAc) signaling associated with the hexosamine biosynthesis pathway (HBP). Despite elevated activities of glycolysis and the pentose phosphate pathway, activation of macrophages with lipopolysaccharide (LPS) resulted in attenuated HBP activity and protein O-GlcNAcylation. Deletion of O-GlcNAc transferase (OGT), a key enzyme for protein O-GlcNAcylation, led to enhanced innate immune activation and exacerbated septic inflammation. Mechanistically, OGT-mediated O-GlcNAcylation of the serine-threonine kinase RIPK3 on threonine 467 (T467) prevented RIPK3-RIPK1 hetero-and RIPK3-RIPK3 homo-interaction and inhibited downstream innate immunity and necroptosis signaling. Thus, our study identifies an immuno-metabolic crosstalk essential for fine-tuning innate immune cell activation and highlights the importance of glucose metabolism in septic inflammation.

Staphylococcus aureus causes acute and chronic infections resulting in significant morbidity. Urease, an enzyme that generates NH3 and CO2 from urea, is key to pH homeostasis in bacterial pathogens under acidic stress and nitrogen limitation. However, the function of urease in S. aureus niche colonization and nitrogen metabolism has not been extensively studied. We discovered that urease is essential for pH homeostasis and viability in urea-rich environments under weak acid stress. The regulation of urease transcription by CcpA, Agr, and CodY was identified in this study, implying a complex network that controls urease expression in response to changes in metabolic flux. In addition, it was determined that the endogenous urea derived from arginine is not a significant contributor to the intracellular nitrogen pool in non-acidic conditions. Furthermore, we found that during a murine chronic renal infection, urease facilitates S. aureus persistence by promoting bacterial fitness in the low-pH, urea-rich kidney. Overall, our study establishes that urease in S. aureus is not only a primary component of the acid response network but also an important factor required for persistent murine renal infections.

Isotopically labeling a metabolite and tracing its metabolic fate has provided invaluable insights about the role of metabolism in human diseases in addition to a variety of other issues. C-labeled metabolite tracers or unlabeledH-based NMR experiments are currently the most common application of NMR to metabolomics studies. Unfortunately, the coverage of the metabolome has been consequently limited to the most abundant carbon-containing metabolites. To expand the coverage of the metabolome and enhance the impact of metabolomics studies, we present a protocol for N-labeled metabolite tracer experiments that may also be combined with routineC tracer experiments to simultaneously detect both N- andC-labeled metabolites in metabolic samples. A database consisting of 2D H-N HSQC natural-abundance spectra of 50 nitrogen-containing metabolites are also presented to facilitate the assignment of N-labeled metabolites. The methodology is demonstrated by labeling Escherichia coli and Staphylococcus aureus metabolomes withN-ammonium chloride, N-arginine, and C-acetate. Efficient N andC metabolite labeling and identification were achieved utilizing standard cell culture and sample preparation protocols.

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound-treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by GC-MS, 20 by NMR and 22 by both techniques). Out of these 47 metabolites of interest were identified, where 14 metabolites were uniquely identified by NMR and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism leading to fatty acid and complex lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques - an improved detection and annotation of metabolites.

In‐cell NMR spectroscopy is a powerful tool to investigate protein behavior in physiologically relevant environments. Although proven valuable for disordered proteins, we show that in commonly used 1H‐15N HSQC spectra of globular proteins, interactions with cellular components often broaden resonances beyond detection. This contrasts 19F spectra in mammalian cells, in which signals are readily observed. Using several proteins, we demonstrate that surface charges and interaction with cellular binding partners modulate linewidths and resonance frequencies. Importantly, we establish that 19F paramagnetic relaxation enhancements using stable, rigid Ln(III) chelate pendants, attached via non‐reducible thioether bonds, provide an effective means to obtain accurate distances for assessing protein conformations in the cellular milieu.

Introduction Gemcitabine is an important component of pancreatic cancer clinical management. Unfortunately, acquired gemcitabine resistance is widespread and there are limitations to predicting and monitoring therapeutic outcomes. Objective To investigate the potential of metabolomics to differentiate pancreatic cancer cells that develops resistance or respond to gemcitabine treatment. Results We applied 1D 1H and 2D 1H-13C HSQC NMR methods to profile the metabolic signature of pancreatic cancer cells. 13C6-glucose labeling identified thirty key metabolites uniquely altered between wild-type and gemcitabine-resistant cells upon gemcitabine treatment. Gemcitabine resistance was observed to reprogram glucose metabolism and to enhance the pyrimidine synthesis pathway. Myo-inositol, taurine, glycerophosphocholine and creatinine phosphate exhibited a “binary switch” in response to gemcitabine treatment and acquired resistance. Conclusion Metabolic differences between naïve and resistant pancreatic cancer cells and, accordingly, their unique responses to gemcitabine treatment were revealed, which may be useful in the clinical setting for monitoring a patient’s therapeutic response.

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphorus is a naturally abundant and NMR active isotope that does not require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments, but 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorus-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provide a measure of a cell’s metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N-, and 31P-labeled metabolites. The protocol includes the application of a 2D 1H–31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N-, and 31P-labeled metabolites from a single NMR sample, and a data set of one-dimensional (1D) 31P NMR and 2D 1H–31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.

Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective and effective is a daunting proposition because disease biology is complex and highly variable across patients. Metabolomics enables the discovery of disease biomarkers, which provides insights into the molecular and metabolic basis of disease and may be used to assess treatment prognosis and outcome. In this regard, metabolomics has evolved to become an important component of the drug discovery process to resolve efficacy and toxicity issues, and as a tool for precision medicine. A detailed description of an experimental protocol is presented that outlines the application of NMR metabolomics to the drug discovery pipeline. This includes: (1) target identification by understanding the metabolic dysregulation in diseases, (2) predicting the mechanism of action of newly discovered or existing drug therapies, (3) and using metabolomics to screen a chemical lead to assess biological activity. Unlike other OMICS approaches, the metabolome is "fragile", and may be negatively impacted by improper sample collection, storage and extraction procedures. Similarly, biologically-irrelevant conclusions may result from incorrect data collection, pre-processing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol.