NMR Metabolomics Analysis of Parkinson\u27s Disease

Parkinson\u27s disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergic neurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress are linked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolite changes in biofluids, cell lysates, tissues and tumors in order to correlate these metabolomic changes to a disease state. Thus, the application of metabolomics to investigate PD provides a systematic approach to understand the pathology of PD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This review will examine current research into PD mechanisms with a focus on mitochondrial dysfunction and oxidative stress. Neurotoxin-based PD animal models and the rationale for metabolomics studies in PD will also be discussed. The review will also explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis

Potentialities of Hubble parameter and expansion rate function data to alleviate Hubble tension

Taking advantage of Gaussian process (GP), we obtain an improved estimate of the Hubble constant, $H_0=70.41\pm1.58$ km s$^{-1}$ Mpc$^{-1}$, using Hubble parameter [$H(z)$] from cosmic chronometers (CCH) and expansion rate function [$E(z)$], extracted from type Ia supernovae, data. This result is higher than those obtained by directly reconstructing CCH data with GP. In order to estimate the potential of future CCH data, we simulate two sets of $H(z)$ data and use them to constrain $H_0$ by either using GP reconstruction or fitting them with $E(z)$ data. We find that simulated $H(z)$ data alleviate $H_0$ tension by pushing $H_0$ values higher towards $\sim70$ km s$^{-1}$ Mpc$^{-1}$. We also find that joint $H(z)$ + $E(z)$ data favor higher values of $H_0$, which is also confirmed by constraining $H_0$ in the flat concordance model and 2-order Taylor expansion of $H(z)$. In summary, we conclude that more and better-quality CCH data as well as $E(z)$ data can provide a new and useful perspective on resolving $H_0$ tension.Comment: 11 pages, 8 figure

Capillary-Induced Ge Uniformly Distributed in N-Doped Carbon Nanotubes with Enhanced Li-Storage Performance

Germanium (Ge) is a prospective anode material for lithium-ion batteries, as it possesses large theoretical capacity, outstanding lithium-ion diffusivity, and excellent electrical conductivity. Ge suffers from drastic capacity decay and poor rate performance, however, owing to its low electrical conductivity and huge volume expansion during cycling processes. Herein, a novel strategy has been developed to synthesize a Ge at N-doped carbon nanotubes (Ge at N-CNTs) composite with Ge nanoparticles uniformly distributed in the N-CNTs by using capillary action. This unique structure could effectively buffer large volume expansion. When evaluated as an anode material, the Ge at N-CNTs demonstrate enhanced cycling stability and excellent rate capabilities

Combining DI-ESI–MS and NMR datasets for metabolic profiling

Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI-MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI-MS datasets yields a substantial improvement in the analysis of neurotoxin involvement in dopaminergic cell death

Combining DI-ESI–MS and NMR datasets for metabolic profiling

Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI-MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI-MS datasets yields a substantial improvement in the analysis of neurotoxin involvement in dopaminergic cell death

Metabolic Disorder Dysfunction in Parkinson’s Disease: Bioenergetics, Redox Homeostasis and Central Carbon Metabolism

The loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the accumulation of protein inclusions (Lewy bodies) are the pathological hallmarks of Parkinson’s disease (PD). PD is triggered by genetic alterations, environmental/occupational exposures and aging. However, the exact molecular mechanisms linking these PD risk factors to neuronal dysfunction are still unclear. Alterations in redox homeostasis and bioenergetics (energy failure) are thought to be central components of neurodegeneration that contribute to the impairment of important homeostatic processes in dopaminergic cells such as protein quality control mechanisms, neurotransmitter release/metabolism, axonal transport of vesicles and cell survival. Importantly, both bioenergetics and redox homeostasis are coupled to neuro-glial central carbon metabolism. We and others have recently established a link between the alterations in central carbon metabolism induced by PD risk factors, redox homeostasis and bioenergetics and their contribution to the survival/death of dopaminergic cells. In this review, we focus on the link between metabolic dysfunction, energy failure and redox imbalance in PD, making an emphasis in the contribution of central carbon (glucose) metabolism. The evidence summarized here strongly supports the consideration of PD as a disorder of cell metabolism

Metabolic Investigations of the Molecular Mechanisms Associated with Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by fibrillar cytoplasmic aggregates of α-synuclein (i.e., Lewy bodies) and the associated loss of dopaminergic cells in the substantia nigra. Mutations in genes such as α-synuclein (SNCA) account for only 10% of PD occurrences. Exposure to environmental toxicants including pesticides and metals (e.g., paraquat (PQ) and manganese (Mn)) is also recognized as an important PD risk factor. Thus, aging, genetic alterations, and environmental factors all contribute to the etiology of PD. In fact, both genetic and environmental factors are thought to interact in the promotion of idiopathic PD, but the mechanisms involved are still unclear. In this study, we summarize our findings to date regarding the toxic synergistic effect between α-synuclein and paraquat treatment. We identified an essential role for central carbon (glucose) metabolism in dopaminergic cell death induced by paraquat treatment that is enhanced by the overexpression of α-synuclein. PQ "hijacks" the pentose phosphate pathway (PPP) to increase NADPH reducing equivalents and stimulate paraquat redox cycling, oxidative stress, and cell death. PQ also stimulated an increase in glucose uptake, the translocation of glucose transporters to the plasma membrane, and AMP-activated protein kinase (AMPK) activation. The overexpression of α-synuclein further stimulated an increase in glucose uptake and AMPK activity, but impaired glucose metabolism, likely directing additional carbon to the PPP to supply paraquat redox cycling

Revisiting Protocols for the NMR Analysis of Bacterial Metabolomes

Over the past decade, metabolomics has emerged as an important technique for systems biology. Measuring all the metabolites in a biological system provides an invaluable source of information to explore various cellular processes, and to investigate the impact of environmental factors and genetic modifications. Nuclear magnetic resonance (NMR) spectroscopy is an important method routinely employed in metabolomics. NMR provides comprehensive structural and quantitative information useful for metabolomics fingerprinting, chemometric analysis, metabolite identification and metabolic pathway construction. A successful metabolomics study relies on proper experimental protocols for the collection, handling, processing and analysis of metabolomics data. Critically, these protocols should eliminate or avoid biologicallyirrelevant changes to the metabolome. We provide a comprehensive description of our NMR-based metabolomics procedures optimized for the analysis of bacterial metabolomes. The technical details described within this manuscript should provide a useful guide to reliably apply our NMR-based metabolomics methodology to systems biology studies

\u3ci\u3eStaphylococcus aureus\u3c/i\u3e Metabolic Adaptations during the Transition from a Daptomycin Susceptibility Phenotype to a Daptomycin Nonsusceptibility Phenotype

Staphylococcus aureus is a major cause of nosocomial and community-acquired infections. The success of S. aureus as a pathogen is due in part to its many virulence determinants and resistance to antimicrobials. In particular, methicillin-resistant S. aureus has emerged as a major cause of infections and led to increased use of the antibiotics vancomycin and daptomycin, which has increased the isolation of vancomycin-intermediate S. aureus and daptomycin-nonsusceptible S. aureus strains. The most common mechanism by which S. aureus acquires intermediate resistance to antibiotics is by adapting its physiology and metabolism to permit growth in the presence of these antibiotics, a process known as adaptive resistance. To better understand the physiological and metabolic changes associated with adaptive resistance, six daptomycin-susceptible and -nonsusceptible isogenic strain pairs were examined for changes in growth, competitive fitness, and metabolic alterations. Interestingly, daptomycin nonsusceptibility coincides with a slightly delayed transition to the postexponential growth phase and alterations in metabolism. Specifically, daptomycin-nonsusceptible strains have decreased tricarboxylic acid cycle activity, which correlates with increased synthesis of pyrimidines and purines and increased carbon flow to pathways associated with wall teichoic acid and peptidoglycan biosynthesis. Importantly, these data provided an opportunity to alter the daptomycin nonsusceptibility phenotype by manipulating bacterial metabolism, a first step in developing compounds that target metabolic pathways that can be used in combination with daptomycin to reduce treatment failures

Amplitude analysis of Ds+→π+π−π+D_s^{+} \rightarrow \pi^{+} \pi^{-} \pi^{+}

Utilizing the data set corresponding to an integrated luminosity of $3.19$ fb$^{-1}$ collected by the BESIII detector at a center-of-mass energy of 4.178 GeV, we perform an amplitude analysis of the $D_s^+\to\pi^+\pi^-\pi^+$ decay. The sample contains 13,797 candidates with a signal purity of $\sim$80%. The amplitude and phase of the contributing $\pi\pi$ ${\cal S}$ wave are measured based on a quasi-model-independent approach, along with the amplitudes and phases of the ${\cal P}$ and ${\cal D}$ waves parametrized by Breit-Wigner models. The fit fractions of different intermediate decay channels are also reported.Comment: 14 pages, 6 figure