Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids.

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.

Targeting spike glycans to inhibit SARS-CoV2 viral entry.

SARS-CoV-2 spike harbors glycans which function as ligands for lectins. Therefore, it should be possible to exploit lectins to target SARS-CoV-2 and inhibit cellular entry by binding glycans on the spike protein. Burkholderia oklahomensis agglutinin (BOA) is an antiviral lectin that interacts with viral glycoproteins via N-linked high mannose glycans. Here, we show that BOA binds to the spike protein and is a potent inhibitor of SARS-CoV-2 viral entry at nanomolar concentrations. Using a variety of biophysical approaches, we demonstrate that the interaction is avidity driven and that BOA cross-links the spike protein into soluble aggregates. Furthermore, using virus neutralization assays, we demonstrate that BOA effectively inhibits all tested variants of concern as well as SARS-CoV 2003, establishing that multivalent glycan-targeting molecules have the potential to act as pan-coronavirus inhibitors.

Leveraging the HMBC to Facilitate Metabolite Identification.

The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.

Chemical shift variations in common metabolites.

The reliability and robustness of metabolite assignments in 1H NMR is complicated by numerous factors including variations in temperature, pH, buffer choice, ionic strength, and mixture composition that led to peak overlap and spectral crowding. As sample conditions fluctuate, peak drift and line broadening further complicate peak deconvolution and subsequent chemical assignment. We present a collection of 1D 1H NMR spectra of 54 common metabolites at varied pH (6.0 to 8.0 in 0.5 step increments) and temperature (290 K to 308 K) to quantify chemical shift variability to facilitate automated metabolite assignments. Our results illustrate the fundamental challenges with accurately assigning NMR peaks under varied environmental conditions prevalent in complex mixtures. Phosphorylated metabolites showed a larger variation in chemical shifts due to pH, whereas; amino acids showed a higher variation due to temperature. Mixtures of phosphorous compounds showed a consistently poor reliability in achieving an accurate assignment. Phosphorylated cholines, amino acids, and glycerols yielded a 40 % false negative rate for 7 out of 9 mixture conditions. Amino acids had a false negative rate of 57 % at 298 K and pH 8. Our results demonstrate that the automated assignments of complex biofluid mixtures require an expert to intervene to confirm the accuracy of metabolite assignments. Our analysis also indicates the need for reference databases to include spectra under a variety of conditions that includes mixtures and a range of pH and temperature to improve the accuracy and reproducibility of metabolite assignments.

Visualizing Proteins in Mammalian Cells by 19 F NMR Spectroscopy.

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 1 H-15 N HSQC spectra of globular proteins, interactions with cellular components often broaden resonances beyond detection. This contrasts 19 F 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 19 F 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.

DNAJA1 Dysregulates Metabolism Promoting an Antiapoptotic Phenotype in Pancreatic Ductal Adenocarcinoma.

The cochaperone protein DNAJA1 (HSP40) is downregulated four-fold in pancreatic cancer cells. The impact of DNAJA1 expression on pancreatic ductal adenocarcinoma (PDAC) progression remains unclear. The metabolic impacts of increased DNAJA1 expression were evaluated using a combination of untargeted metabolomics, stable isotope-resolved metabolomics (SIRM), confocal microscopy, flow cytometry, and cell-based assays. Differential Warburg glycolysis, an increase in redox currency, and alterations in amino acid levels were observed in both overexpression cell lines. DNAJA1 overexpression also led to mitochondrial fusion, an increase in the expression of Bcl-2, a modest protection from redox-induced cell death, a loss of structural integrity due to the loss of actin fibers, and an increase in cell invasiveness in BxPC-3. These differences were more pronounced in BxPC-3, which contains a loss-of-function mutation in the tumor-suppressing gene SMAD4. These findings suggest a proto-oncogenic role of DNAJA1 in PDAC progression and suggest DNAJA1 may function synergistically with other proteins with altered activities in pancreatic cancer cell lines.

Radiation exposure induces cross-species temporal metabolic changes that are mitigated in mice by amifostine.

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.

Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage.

Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. 'Pinot noir' wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D 1H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.

Metabolic profiling of historical and modern wheat cultivars using proton nuclear magnetic resonance spectroscopy.

To determine changes in the grain components between historical and modern wheat (Triticum aestivum L.) cultivars, wholemeal flours from 19 wheat cultivars and 2 landraces released or introduced between 1870 and 2013 and grown over two crop years were extracted using hydroalcoholic solution and analyzed using one dimensional 1H NMR spectral profiling. Grain yield, grain volume weight (GVW), and grain protein concentration were also measured. Grain yield increased while protein concentration decreased by release year (p < 0.001). Increasing trends (p < 0.01) were observed for tryptophan, sum of the measured amino acids, chlorogenic acid, ferulic acid, vanillic acid, and sum of the measured phenolic acids. Grain yield, phenolic acids, and tryptophan were mainly associated with modern cultivars, whereas grain protein concentration and GVW were associated with historical cultivars. The findings from this study showed changes in concentration of grain components over a century of breeding that may have implications for grain quality and human health.

Phosphorus NMR and Its Application to Metabolomics.

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.

NMR Metabolomics Protocols for Drug Discovery.

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, preprocessing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol.

Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data.

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various levels of group separation were used to evaluate each model. Model performance was assessed by classification accuracy rate, by the area under a receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features. Our findings suggest that the five classification models perform equally well with robust data sets. Only when the models are stressed with subtle data set differences does OPLS emerge as the best-performing model. OPLS maintained a high-prediction accuracy rate and a large area under the ROC curve while yielding loadings closest to the true loadings with limited group separations.

Metabolomics Analyses from Tissues in Parkinson's Disease.

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 handling of these samples. The proper choice of data collection, preprocessing, and processing protocols is similarly important to the design of an effective metabolomics experiment. Likewise, the correct application of univariate and multivariate statistical methods is 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.

O-GlcNAc Transferase Suppresses Inflammation and Necroptosis by Targeting Receptor-Interacting Serine/Threonine-Protein Kinase 3.

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 ß-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.

Urease is an essential component of the acid response network of Staphylococcus aureus and is required for a persistent murine kidney infection.

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.

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation.

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 gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 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: the improved detection and annotation of metabolites.

Expanding the Coverage of the Metabolome with Nitrogen-Based NMR.

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. 13C-labeled metabolite tracers or unlabeled 1H-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 15N-labeled metabolite tracer experiments that may also be combined with routine 13C tracer experiments to simultaneously detect both 15N- and 13C-labeled metabolites in metabolic samples. A database consisting of 2D 1H-15N HSQC natural-abundance spectra of 50 nitrogen-containing metabolites are also presented to facilitate the assignment of 15N-labeled metabolites. The methodology is demonstrated by labeling Escherichia coli and Staphylococcus aureus metabolomes with 15N1-ammonium chloride, 15N4-arginine, and 13C2-acetate. Efficient 15N and 13C metabolite labeling and identification were achieved utilizing standard cell culture and sample preparation protocols.

Insights into gemcitabine resistance and the potential for therapeutic monitoring.

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 30 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.

Nanoformulated copper/zinc superoxide dismutase exerts differential effects on glucose vs lipid homeostasis depending on the diet composition possibly via altered AMPK signaling.

Evidence suggests that superoxide dismutase 1 (SOD1) promotes glucose vs lipid metabolism depending on the diet type. We recently reported that nanoformulated SOD1 (Nano) improved lipid metabolism without altering glucose homeostasis in high-fat (HF) diet-fed mice. Here, we sought to determine the effects and potential mechanisms of Nano in modulating glucose and lipid homeostasis in mice fed a normal chow diet (CD) vs HF diet. Mice were fed a CD or a HF diet (45%) for 10 wk and injected with Nano once every 2 days for 15 days. The fasting glucose level was lower (P < 0.05) in CD + Nano-treated mice compared to control. Conversely, blood glucose was not altered but serum triglycerides were lower in HF + Nano-treated mice. Genes involved in fatty acid synthesis were reduced by Nano in the skeletal muscle of CD but not of HF diet-fed mice. Adenosine monophosphate-activated protein kinase (AMPK), which promotes both glucose and lipid metabolism depending on the fuel availability, is activated by Nano in CD-fed mice. Moreover, Nano increased phosphorylation of ACC, a downstream target of AMPK, in both CD and HF diet-fed mice. Nano increased mitochondrial respiration in C2C12 myocytes in the presence of glucose or fatty acid, and this effect is inhibited by Compound C, an AMPK inhibitor. Our data suggest that Nano promotes glucose and lipid metabolism in CD and HF diet-fed mice, respectively, and this effect is mediated partly via AMPK signaling.