Mitochondrial dysfunction is central to the pathogenesis of neurological disorders. Neurons rely on oxidative phosphorylation to meet their energy requirements and thus alterations in mitochondrial function are linked to energy failure and neuronal cell death. Furthermore, dysfunctional mitochondria are reported to increase the steady-state levels of reactive oxygen species derived from the leakage of electrons from the electron transport chain. Research aimed at understanding mitochondrial dysfunction and its role in neurological disorders has been primarily geared towards neurons. In contrast, the role that dysfunctional mitochondria have in glial cells’ function and its implication for neuronal homeostasis and brain function has been largely understudied. Except for oligodendrocytes, astrocytes and microglia do not degenerate upon the impairment of mitochondrial function, as they rely primarily on glycolysis to produce energy and have a higher antioxidant capacity than neurons. However, recent evidence highlights the role of mitochondrial metabolism and signaling in glial cell function. In this work, we review the functional role of mitochondria in glial cells and the evidence regarding its potential role regulating neuronal homeostasis and disease progression.

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.

The influence of oxidation-reduction (redox) potential on the expression of biomolecules is a topic of ongoing exploration in geobiology. In this study, we investigate the novel possibility that structures and compositions of lipids produced by microbial communities are sensitive to environmental redox conditions. We extracted lipids from microbial biomass collected along the thermal and redox gradients of four alkaline hot springs in Yellowstone National Park (YNP) and investigated patterns in the average oxidation state of carbon (Z C ), a metric calculated from the chemical formulae of lipid structures. Carbon in intact polar lipids (IPLs) and their alkyl chains becomes more oxidized (higher Z C ) with increasing distance from each of the four hot spring sources. This coincides with decreased water temperature and increased concentrations of oxidized inorganic solutes, such as dissolved oxygen, sulfate, and nitrate. Carbon in IPLs is most reduced (lowest Z C ) in the hot, reduced conditions upstream, with abundance-weighted Z C values between −1.68 and −1.56. These values increase gradually downstream to around −1.36 to −1.33 in microbial communities living between 29.0 and 38.1 • C. This near-linear increase in Z C can be attributed to a shift from ether-linked to ester-linked alkyl chains, a decrease in average aliphatic carbons per chain (nC), an increase in average degree of unsaturation per chain (nUnsat), and increased cyclization in tetraether lipids. The Z C of lipid headgroups and backbones did not change significantly downstream. Expression of lipids with relatively reduced carbon under reduced conditions and oxidized lipids under oxidized conditions may indicate microbial adaptation across environmental gradients in temperature and electron donor/acceptor supply.

Exposure to acute, damaging radiation may occur through a variety of events from cancer therapy and industrial accidents to terrorist attacks and military actions. Our understanding of how to protect individuals and mitigate the effects of radiation injury or Acute Radiation Syndrome (ARS) is still limited. There are only a few Food and Drug Administration-approved therapies for ARS; whereas, amifostine is limited to treating low dose (0.7–6 Gy) radiation poisoning arising from cancer radiotherapy. An early intervention is critical to treat ARS, which necessitates identifying diagnostic biomarkers to quickly characterize radiation exposure. Towards this end, a multiplatform metabolomics study was performed to comprehensively characterize the temporal changes in metabolite levels from mice and non-human primate serum samples following γ-irradiation. The metabolomic signature of amifostine was also evaluated in mice as a model for radioprotection. The NMR and mass spectrometry metabolomics analysis identified 23 dysregulated pathways resulting from the radiation exposure. These metabolomic alterations exhibited distinct trajectories within glucose metabolism, phospholipid biosynthesis, and nucleotide metabolism. A return to baseline levels with amifostine treatment occurred for these pathways within a week of radiation exposure. Together, our data suggests a unique physiological change that is independent of radiation dose or species. Furthermore, a metabolic signature of radioprotection was observed through the use of amifostine prophylaxis of ARS.

Metabolomics has been successfully applied to study neurological and neurodegenerative disorders including Parkinson’s disease for: 1) the identification of potential biomarkers of onset and disease progression; 2) the identification of novel mechanisms of disease progression; and 3) the assessment of treatment prognosis and outcome. Reproducible and efficient extraction of metabolites is imperative to the success of any metabolomics investigation. Unlike other OMICS techniques, the composition of the metabolome can be negatively impacted by the preparation, processing and the handling of these samples. The proper choice of data collection, preprocessing and processing protocols are similarly important to the design of an effective metabolomics experiment. Likewise, the correct application of univariate and multivariate statistical methods are essential for providing biologically-relevant insights. In this chapter, we have outlined a detailed metabolomics workflow that addresses all of these issues. A step-by-step protocol from the preparation of neuronal cells and metabolomic tissue samples to their metabolic analyses using nuclear magnetic resonance, mass spectrometry, and chemometrics is presented.