Formate Metabolism in Shigella flexneri and Its Effect on HeLa Cells at Different Stages during the Infectious Process.

Shigellosis caused by Shigella is one of the most important foodborne illnesses in global health, but little is known about the metabolic cross talk between this bacterial pathogen and its host cells during the different stages of the infection process. A detailed understanding of the metabolism can potentially lead to new drug targets remedying the pressing problem of antibiotic resistance. Here, we use stable isotope-resolved metabolomics as an unbiased and fast method to investigate how Shigella metabolizes 13C-glucose in three different environments: inside the host cells, adhering to the host cells, and alone in suspension. We find that especially formate metabolism by bacteria is sensitive to these different environments. The role of formate in pathogen metabolism is sparsely described in the literature compared to the roles of acetate and butyrate. However, its metabolic pathway is regarded as a potential drug target due to its production in microorganisms and its absence in humans. Our study provides new knowledge about the regulatory effect of formate. Bacterial metabolism of formate is pH dependent when studied alone in culture medium, whereas this effect is less pronounced when the bacteria adhere to the host cells. Once the bacteria are inside the host cells, we find that formate accumulation is reduced. Formate also affects the host cells resulting in a reduced infection rate. This was correlated to an increased immune response. Thus, intriguingly formate plays a double role in pathogenesis by increasing the virulence of Shigella and at the same time stimulating the immune response of the host. IMPORTANCE Bacterial infection is a pressing societal concern due to development of resistance toward known antibiotics. Central carbon metabolism has been suggested as a potential new target for drug development, but metabolic changes upon infection remain incompletely understood. Here, we used a cellular infection model to study how the bacterial pathogen Shigella adapts its metabolism depending on the environment starting from the extracellular medium until Shigella successfully invaded and proliferated inside host cells. The mixed-acid fermentation of Shigella was the major metabolic pathway during the infectious process, and the glucose-derived metabolite formate surprisingly played a divergent role in the pathogen and in the host cell. Our data show reduced infection rate when both host cells and bacteria were treated with formate, which correlated with an upregulated immune response in the host cells. The formate metabolism in Shigella thus potentially provides a route toward alternative treatment strategies for Shigella prevention.

Quantitative Evaluation of the Cellular Uptake of Nanodiamonds by Monocytes and Macrophages.

Fluorescent nanodiamonds (FNDs) with negative nitrogen-vacancy (NV- ) defect centers are great probes for biosensing applications, with potential to act as biomarkers for cell differentiation. To explore this concept, uptake of FNDs (≈120 nm) by THP-1 monocytes and monocyte-derived M0-macrophages is studied. The time course analysis of FND uptake by monocytes confirms differing FND-cell interactions and a positive time-dependence. No effect on cell viability, proliferation, and differentiation potential into macrophages is observed, while cells saturated with FNDs, unload the FNDs completely by 25 cell divisions and subsequently take up a second dose effectively. FND uptake variations by THP-1 cells at early exposure-times indicate differing phagocytic capability. The cell fraction that exhibits relatively enhanced FND uptake is associated to a macrophage phenotype which derives from spontaneous monocyte differentiation. In accordance, chemical-differentiation of the THP-1 cells into M0-macrophages triggers increased and homogeneous FND uptake, depleting the fraction of cells that were non-responsive to FNDs. These observations imply that FND uptake allows for distinction between the two cell subtypes based on phagocytic capacity. Overall, FNDs demonstrate effective cell labeling of monocytes and macrophages, and are promising candidates for sensing biological processes that involve cell differentiation.

Classification and biomarker identification of prostate tissue from TRAMP mice with hyperpolarized 13C-SIRA.

Hyperpolarized 13C isotope resolved spectroscopy boosts NMR signal intensity, which improves signal detection and allows metabolic fluxes to be analyzed. Such hyperpolarized flux data may offer new approaches to tissue classification and biomarker identification that could be translated in vivo. Here we used hyperpolarized stable isotope resolved analysis (SIRA) to measure metabolite specific 13C isotopic enrichments in the central carbon metabolism of mouse prostate. Prostate and tumor tissue samples were acquired from transgenic adenocarcinomas of the mouse prostate (TRAMP) mice. Before euthanasia, mice were injected with [U-13C]glucose intraperitoneally (i.p.). Polar metabolite extracts were prepared, and hyperpolarized 1D-13C NMR spectra were obtained from normal prostate (n = 19) and cancer tissue (n = 19) samples. Binary classification and feature analysis was performed to make a separation model and to investigate differences between samples originating from normal and cancerous prostate tissue, respectively. Hyperpolarized experiments were carried out according to a standardized protocol, which showed a high repeatability (CV = 15%) and an average linewidth in the 1D-13C NMR spectra of 2 ± 0.5 Hz. The resolution of the hyperpolarized 1D-13C spectra was high with little signal overlap in the carbonyl region and metabolite identification was easily accomplished. A discrimination with 95% success rate could be made between samples originating from TRAMP mice prostate and tumor tissue based on isotopomers from uniquely identified metabolites. Hyperpolarized 13C-SIRA allowed detailed metabolic information to be obtained from tissue specimens. The positional information of 13C isotopic enrichments lead to easily interpreted features responsible for high predictive classification of tissue types. This analytical approach has matured, and the robust experimental protocols currently available allow systematic tracking of metabolite flux ex vivo.

Measuring Glycolytic Activity with Hyperpolarized [2H7, U-13C6] D-Glucose in the Naive Mouse Brain under Different Anesthetic Conditions.

Glucose is the primary fuel for the brain; its metabolism is linked with cerebral function. Different magnetic resonance spectroscopy (MRS) techniques are available to assess glucose metabolism, providing complementary information. Our first aim was to investigate the difference between hyperpolarized 13C-glucose MRS and non-hyperpolarized 2H-glucose MRS to interrogate cerebral glycolysis. Isoflurane anesthesia is commonly employed in preclinical MRS, but it affects cerebral hemodynamics and functional connectivity. A combination of low doses of isoflurane and medetomidine is routinely used in rodent fMRI and shows similar functional connectivity, as in awake animals. As glucose metabolism is tightly linked to neuronal activity, our second aim was to assess the impact of these two anesthetic conditions on the cerebral metabolism of glucose. Brain metabolism of hyperpolarized 13C-glucose and 2H-glucose was monitored in two groups of mice in a 9.4 T MRI system. We found that the very different duration and temporal resolution of the two techniques enable highlighting the different aspects in glucose metabolism. We demonstrate (by numerical simulations) that hyperpolarized 13C-glucose reports on de novo lactate synthesis and is sensitive to CMRGlc. We show that variations in cerebral glucose metabolism, under different anesthesia, are reflected differently in hyperpolarized and non-hyperpolarized X-nuclei glucose MRS.

Stable isotope resolved metabolomics classification of prostate cancer cells using hyperpolarized NMR data.

Metabolic fingerprinting is a strong tool for characterization of biological phenotypes. Classification with machine learning is a critical component in the discrimination of molecular determinants. Cellular activity can be traced using stable isotope labelling of metabolites from which information on cellular pathways may be obtained. Nuclear magnetic resonance (NMR) spectroscopy is, due to its ability to trace labelling in specific atom positions, a method of choice for such metabolic activity measurements. In this study, we used hyperpolarization in the form of dissolution Dynamic Nuclear Polarization (dDNP) NMR to measure signal enhanced isotope labelled metabolites reporting on pathway activity from four different prostate cancer cell lines. The spectra have a high signal-to-noise, with less than 30 signals reporting on 10 metabolic reactions. This allows easy extraction and straightforward interpretation of spectral data. Four metabolite signals selected using a Random Forest algorithm allowed a classification with Support Vector Machines between aggressive and indolent cancer cells with 96.9% accuracy, -corresponding to 31 out of 32 samples. This demonstrates that the information contained in the few features measured with dDNP NMR, is sufficient and robust for performing binary classification based on the metabolic activity of cultured prostate cancer cells.

Rapid zero-trans kinetics of Cs+ exchange in human erythrocytes quantified by dissolution hyperpolarized 133Cs+ NMR spectroscopy.

Transmembrane flux of Cs+ (a K+ congener) was measured in human red blood cells (RBCs; erythrocytes) on the 10-s time scale. This is the first report on dissolution dynamic nuclear polarization (dDNP) nuclear magnetic resonance (NMR) spectroscopy with this nuclide in mammalian cells. Four technical developments regularized sample delivery and led to high quality NMR spectra. Cation-free media with the Piezo1 (mechanosensitive cation channel) activator yoda1 maximized the extent of membrane transport. First-order rate constants describing the fluxes were estimated using a combination of statistical methods in Mathematica, including the Markov chain Monte Carlo (MCMC) algorithm. Fluxes were in the range 4-70 µmol Cs+ (L RBC)-1 s-1; these are smaller than for urea, but comparable to glucose. Methodology and analytical procedures developed will be applicable to transmembrane cation transport studies in the presence of additional Piezo1 effectors, to other cellular systems, and potentially in vivo.

Probing treatment response of glutaminolytic prostate cancer cells to natural drugs with hyperpolarized [5-(13) C]glutamine.

PURPOSE: The correlation between glutamine metabolism and oncogene expression in cancers has led to a renewed interest in the role of glutamine in cancer cell survival. Hyperpolarized [5-(13) C]glutamine is evaluated as a potential biomarker for noninvasive metabolic measurements of drug response in prostate cancer cells. METHODS: Hyperpolarized [5-(13) C]glutamine is used to measure glutamine metabolism in two prostate cancer cell lines (PC3 and DU145) before and after treatment with the two natural anticancer drugs resveratrol and sulforaphane. An invasive biochemical assay simulating the hyperpolarized experiment is used to independently quantify glutamine metabolism. RESULTS: Glutamine metabolism is found to be 4 times higher in the more glutaminolytic DU145 cells compared with PC3 cells under proliferating growth conditions by using hyperpolarized [5-(13) C]glutamine as a noninvasive probe. A significant decrease in glutamine metabolism occurs upon apoptotic response to treatment with resveratrol and sulforaphane. CONCLUSION: Hyperpolarized NMR using [5-(13) C]glutamine as a probe permits the noninvasive observation of glutaminolysis in different cell lines and under different treatment conditions. Hyperpolarized [5-(13) C]glutamine metabolism thus is a promising biomarker for the noninvasive detection of tumor response to treatment, as it directly monitors one of the hallmarks in cancer metabolism - glutaminolysis - in living cells.

Improvement of hydrogen bond geometry in protein NMR structures by residual dipolar couplings--an assessment of the interrelation of NMR restraints.

We have examined how the hydrogen bond geometry in three different proteins is affected when structural restraints based on measurements of residual dipolar couplings are included in the structure calculations. The study shows, that including restraints based solely on (1)H(N)-(15)N residual dipolar couplings has pronounced impact on the backbone rmsd and Ramachandran plot but does not improve the hydrogen bond geometry. In the case of chymotrypsin inhibitor 2 the addition of (13)CO-(13)C(alpha) and (15)N-(13)CO one bond dipolar couplings as restraints in the structure calculations improved the hydrogen bond geometry to a quality comparable to that obtained in the 1.8 A resolution X-ray structure of this protein. A systematic restraint study was performed, in which four types of restraints, residual dipolar couplings, hydrogen bonds, TALOS angles and NOEs, were allowed in two states. This study revealed the importance of using several types of residual dipolar couplings to get good hydrogen bond geometry. The study also showed that using a small set of NOEs derived only from the amide protons, together with a full set of residual dipolar couplings resulted in structures of very high quality. When reducing the NOE set, it is mainly the side-chain to side-chain NOEs that are removed. Despite of this the effect on the side-chain packing is very small when a reduced NOE set is used, which implies that the over all fold of a protein structure is mainly determined by correct folding of the backbone.