Functional assessment of homozygous ALDH18A1 variants reveals alterations in amino acid and antioxidant metabolism.

Mono- and bi-allelic variants in ALDH18A1 cause a spectrum of human disorders associated with cutaneous and neurological findings that overlap with both cutis laxa and spastic paraplegia. ALDH18A1 encodes the bifunctional enzyme pyrroline-5-carboxylate synthetase (P5CS) that plays a role in the de novo biosynthesis of proline and ornithine. Here we characterize a previously unreported homozygous ALDH18A1 variant (p.Thr331Pro) in four affected probands from two unrelated families, and demonstrate broad-based alterations in amino acid and antioxidant metabolism. These four patients exhibit variable developmental delay, neurological deficits and loose skin. Functional characterization of the p.Thr331Pro variant demonstrated a lack of any impact on the steady-state level of the P5CS monomer or mitochondrial localization of the enzyme, but reduced incorporation of the monomer into P5CS oligomers. Using an unlabeled NMR-based metabolomics approach in patient fibroblasts and ALDH18A1-null human embryonic kidney cells expressing the variant P5CS, we identified reduced abundance of glutamate and several metabolites derived from glutamate, including proline and glutathione. Biosynthesis of the polyamine putrescine, derived from ornithine, was also decreased in patient fibroblasts, highlighting the functional consequence on another metabolic pathway involved in antioxidant responses in the cell. RNA sequencing of patient fibroblasts revealed transcript abundance changes in several metabolic and extracellular matrix-related genes, adding further insight into pathogenic processes associated with impaired P5CS function. Together these findings shed new light on amino acid and antioxidant pathways associated with ALDH18A1-related disorders, and underscore the value of metabolomic and transcriptomic profiling to discover new pathways that impact disease pathogenesis.

SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.

Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency.

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T-cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high-potency MSC lines and metabolic engineering.

Role of UDP-Glycosyltransferase (ugt) Genes in Detoxification and Glycosylation of 1-Hydroxyphenazine (1-HP) in Caenorhabditis elegans.

Caenorhabditis elegans is a useful model organism to study the xenobiotic detoxification pathways of various natural and synthetic toxins, but the mechanisms of phase II detoxification are understudied. 1-Hydroxyphenazine (1-HP), a toxin produced by the bacterium Pseudomonas aeruginosa, kills C. elegans. We previously showed that C. elegans detoxifies 1-HP by adding one, two, or three glucose molecules in N2 worms. Our current study evaluates the roles that some UDP-glycosyltransferase (ugt) genes play in 1-HP detoxification. We show that ugt-23 and ugt-49 knockout mutants are more sensitive to 1-HP than reference strains N2 or PD1074. Our data also show that ugt-23 knockout mutants produce reduced amounts of the trisaccharide sugars, while the ugt-49 knockout mutants produce reduced amounts of all 1-HP derivatives except for the glucopyranosyl product compared to the reference strains. We characterized the structure of the trisaccharide sugar phenazines made by C. elegans and showed that one of the sugar modifications contains an N-acetylglucosamine (GlcNAc) in place of glucose. This implies broad specificity regarding UGT function and the role of genes other than ogt-1 in adding GlcNAc, at least in small-molecule detoxification.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.

Dissolved organic metabolite extraction from high-salt media.

We describe considerations and strategies for developing a nuclear magnetic resonance (NMR) sample preparation method to extract low molecular weight metabolites from high-salt spent media in a model coculture system of phytoplankton and marine bacteria. Phytoplankton perform half the carbon fixation and oxygen generation on Earth. A substantial fraction of fixed carbon becomes part of a metabolite pool of small molecules known as dissolved organic matter (DOM), which are taken up by marine bacteria proximate to phytoplankton. There is an urgent need to elucidate these metabolic exchanges due to widespread anthropogenic transformations on the chemical, phenotypic, and species composition of seawater. These changes are increasing water temperature and the amount of CO2 absorbed by the ocean at energetic costs to marine microorganisms. Little is known about the metabolite-mediated, structured interactions occurring between phytoplankton and associated marine bacteria, in part because of challenges in studying high-salt solutions on various analytical platforms. NMR analysis is problematic due to the high-salt content of both natural seawater and culture media for marine microbes. High-salt concentration degrades the performance of the radio frequency coil, reduces the efficiency of some pulse sequences, limits signal-to-noise, and prolongs experimental time. The method described herein can reproducibly extract low molecular weight DOM from small-volume, high-salt cultures. It is a promising tool for elucidating metabolic flux between marine microorganisms and facilitates genetic screens of mutant microorganisms.

Label-free in vitro assays predict the potency of anti-disialoganglioside chimeric antigen receptor T-cell products.

BACKGROUND AIMS: Chimeric antigen receptor (CAR) T cells have demonstrated remarkable efficacy against hematological malignancies; however, they have not experienced the same success against solid tumors such as glioblastoma (GBM). There is a growing need for high-throughput functional screening platforms to measure CAR T-cell potency against solid tumor cells. METHODS: We used real-time, label-free cellular impedance sensing to evaluate the potency of anti-disialoganglioside (GD2) targeting CAR T-cell products against GD2+ patient-derived GBM stem cells over a period of 2 days and 7 days in vitro. We compared CAR T products using two different modes of gene transfer: retroviral transduction and virus-free CRISPR-editing. Endpoint flow cytometry, cytokine analysis and metabolomics data were acquired and integrated to create a predictive model of CAR T-cell potency. RESULTS: Results indicated faster cytolysis by virus-free CRISPR-edited CAR T cells compared with retrovirally transduced CAR T cells, accompanied by increased inflammatory cytokine release, CD8+ CAR T-cell presence in co-culture conditions and CAR T-cell infiltration into three-dimensional GBM spheroids. Computational modeling identified increased tumor necrosis factor α concentrations with decreased glutamine, lactate and formate as being most predictive of short-term (2 days) and long-term (7 days) CAR T cell potency against GBM stem cells. CONCLUSIONS: These studies establish impedance sensing as a high-throughput, label-free assay for preclinical potency testing of CAR T cells against solid tumors.

Quantum Chemistry Calculations for Metabolomics.

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Cancer progression by reprogrammed BCAA metabolism in myeloid leukaemia.

Reprogrammed cellular metabolism is a common characteristic observed in various cancers. However, whether metabolic changes directly regulate cancer development and progression remains poorly understood. Here we show that BCAT1, a cytosolic aminotransferase for branched-chain amino acids (BCAAs), is aberrantly activated and functionally required for chronic myeloid leukaemia (CML) in humans and in mouse models of CML. BCAT1 is upregulated during progression of CML and promotes BCAA production in leukaemia cells by aminating the branched-chain keto acids. Blocking BCAT1 gene expression or enzymatic activity induces cellular differentiation and impairs the propagation of blast crisis CML both in vitro and in vivo. Stable-isotope tracer experiments combined with nuclear magnetic resonance-based metabolic analysis demonstrate the intracellular production of BCAAs by BCAT1. Direct supplementation with BCAAs ameliorates the defects caused by BCAT1 knockdown, indicating that BCAT1 exerts its oncogenic function through BCAA production in blast crisis CML cells. Importantly, BCAT1 expression not only is activated in human blast crisis CML and de novo acute myeloid leukaemia, but also predicts disease outcome in patients. As an upstream regulator of BCAT1 expression, we identified Musashi2 (MSI2), an oncogenic RNA binding protein that is required for blast crisis CML. MSI2 is physically associated with the BCAT1 transcript and positively regulates its protein expression in leukaemia. Taken together, this work reveals that altered BCAA metabolism activated through the MSI2-BCAT1 axis drives cancer progression in myeloid leukaemia.

Sodium dichloroacetate stimulates cardiac mitochondrial metabolism and improves cardiac conduction in the ovine fetus during labor.

Previous studies in our laboratory have suggested that the increase in stillbirth in pregnancies complicated by chronic maternal stress or hypercortisolemia is associated with cardiac dysfunction in late stages of labor and delivery. Transcriptomics analysis of the overly represented differentially expressed genes in the fetal heart of hypercortisolemic ewes indicated involvement of mitochondrial function. Sodium dichloroacetate (DCA) has been used to improve mitochondrial function in several disease states. We hypothesized that administration of DCA to laboring ewes would improve both cardiac mitochondrial activity and cardiac function in their fetuses. Four groups of ewes and their fetuses were studied: control, cortisol-infused (1 g/kg/day from 115 to term; CORT), DCA-treated (over 24 h), and DCA + CORT-treated; oxytocin was delivered starting 48 h before the DCA treatment. DCA significantly decreased cardiac lactate, alanine, and glucose/glucose-6-phosphate and increased acetylcarnitine/isobutyryl-carnitine. DCA increased mitochondrial activity, increasing oxidative phosphorylation (PCI, PCI + II) per tissue weight or per unit of citrate synthase. DCA also decreased the duration of the QRS, attenuating the prolongation of the QRS observed in CORT fetuses. The effect to reduce QRS duration with DCA treatment correlated with increased glycerophosphocholine and serine and decreased phosphorylcholine after DCA treatment. There were negative correlations of acetylcarnitine/isobutyryl-carnitine to both heart rate (HR) and mean arterial pressure (MAP). These results suggest that improvements in mitochondrial respiration with DCA produced changes in the cardiac lipid metabolism that favor improved conduction in the heart. DCA may therefore be an effective treatment of fetal cardiac metabolic disturbances in labor that can contribute to impairments of fetal cardiac conduction.

Metabolomics and cytokine profiling of mesenchymal stromal cells identify markers predictive of T-cell suppression.

BACKGROUND AIMS: Mesenchymal stromal cells (MSCs) have shown great promise in the field of regenerative medicine, as many studies have shown that MSCs possess immunomodulatory function. Despite this promise, no MSC therapies have been licensed by the Food and Drug Administration. This lack of successful clinical translation is due in part to MSC heterogeneity and a lack of critical quality attributes. Although MSC indoleamine 2,3-dioxygnease (IDO) activity has been shown to correlate with MSC function, multiple predictive markers may be needed to better predict MSC function. METHODS: Three MSC lines (two bone marrow-derived, one induced pluripotent stem cell-derived) were expanded to three passages. At the time of harvest for each passage, cell pellets were collected for nuclear magnetic resonance (NMR) and ultra-performance liquid chromatography mass spectrometry (MS), and media were collected for cytokine profiling. Harvested cells were also cryopreserved for assessing function using T-cell proliferation and IDO activity assays. Linear regression was performed on functional data against NMR, MS and cytokines to reduce the number of important features, and partial least squares regression (PLSR) was used to obtain predictive markers of T-cell suppression based on variable importance in projection scores. RESULTS: Significant functional heterogeneity (in terms of T-cell suppression and IDO activity) was observed between the three MSC lines, as were donor-dependent differences based on passage. Omics characterization revealed distinct differences between cell lines using principal component analysis. Cell lines separated along principal component one based on tissue source (bone marrow-derived versus induced pluripotent stem cell-derived) for NMR, MS and cytokine profiles. PLSR modeling of important features predicted MSC functional capacity with NMR (R2 = 0.86), MS (R2 = 0.83), cytokines (R2 = 0.70) and a combination of all features (R2 = 0.88). CONCLUSIONS: The work described here provides a platform for identifying markers for predicting MSC functional capacity using PLSR modeling that could be used as release criteria and guide future manufacturing strategies for MSCs and other cell therapies.

Application of Counter-wound Multi-arm Spirals in HTS Resonator Design.

Significant sensitivity improvements have been achieved by utilizing high temperature superconducting (HTS) resonators in nuclear magnetic resonance (NMR) probes. Many nuclei such as 13C benefit from strong excitation fields which cannot be produced by traditional HTS resonator designs. We investigate the use of double-sided, counter-wound multi-arm spiral HTS resonators with the aim of increasing the excitation field at the required nuclear Larmor frequency for 13C. When compared to double-sided, counter-wound spiral resonators with similar geometry, simulations indicate that the multi-arm spiral version develops a more uniform current distribution. Preliminary tests of a two-arm resonator indicate that it may produce a stronger excitation field.

Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics.

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Comparison of High-Resolution Fourier Transform Mass Spectrometry Platforms for Putative Metabolite Annotation.

Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry (MS) are among the highest-performing analytical platforms used in metabolomics. Non-targeted metabolomics experiments, however, yield extremely complex datasets that make metabolite annotation very challenging and sometimes impossible. The high-resolution accurate mass measurements of the leading MS platforms greatly facilitate this process by reducing mass errors and spectral overlaps. When high resolution is combined with relative isotopic abundance (RIA) measurements, heuristic rules, and constraints during searches, the number of candidate elemental formula(s) can be significantly reduced. Here, we evaluate the performance of Orbitrap ID-X and 12T solariX FT-ICR mass spectrometers in terms of mass accuracy and RIA measurements and how these factors affect the assignment of the correct elemental formulas in the metabolite annotation pipeline. Quality of the mass measurements was evaluated under various experimental conditions (resolution: 120, 240, 500 K; automatic gain control: 5 × 104, 1 × 105, 5 × 105) for the Orbitrap MS platform. High average mass accuracy (<1 ppm for UPLC-Orbitrap MS and <0.2 ppm for direct infusion FT-ICR MS) was achieved and allowed the assignment of correct elemental formulas for over 90% (m/z 75-466) of the 104 investigated metabolites. 13C1 and 18O1 RIA measurements further improved annotation certainty by reducing the number of candidates. Overall, our study provides a systematic evaluation for two leading Fourier transform (FT)-based MS platforms utilized in metabolite annotation and provides the basis for applying these, individually or in combination, to metabolomics studies of biological systems.

Long-Term Metabolomics Reference Material.

The use of quality control samples in metabolomics ensures data quality, reproducibility, and comparability between studies, analytical platforms, and laboratories. Long-term, stable, and sustainable reference materials (RMs) are a critical component of the quality assurance/quality control (QA/QC) system; however, the limited selection of currently available matrix-matched RMs reduces their applicability for widespread use. To produce an RM in any context, for any matrix that is robust to changes over the course of time, we developed iterative batch averaging method (IBAT). To illustrate this method, we generated 11 independently grown Escherichia coli batches and made an RM over the course of 10 IBAT iterations. We measured the variance of these materials by nuclear magnetic resonance (NMR) and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as a food source to produce a Caenorhabditis elegans RM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individual C. elegans samples of the same genotype, allowed us to estimate the proportion of sample variation in preanalytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

RTExtract: time-series NMR spectra quantification based on 3D surface ridge tracking.

MOTIVATION: Time-series nuclear magnetic resonance (NMR) has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra. RESULTS: We introduce Ridge Tracking-based Extract (RTExtract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than 2 h instead of ∼48 h, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked. AVAILABILITY AND IMPLEMENTATION: Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Progress Towards a Higher Sensitivity 13C-Optimized 1.5 mm HTS NMR Probe.

Nuclear magnetic resonance (NMR) probes using thin-film high temperature superconducting (HTS) resonators offer high sensitivity and are particularly suitable for small-sample applications. We are developing an improved 1.5 mm HTS NMR probe designed for operation at 14.1 T and optimized for 13C detection. The total sample volume is about 35 µL and the active sample volume is 20 µL. The probe employs HTS resonators for 13C and 1H transmission and detection and the 2H lock. We examine the interactions of multiple superconducting resonators and normal metal tuning loops on coil resonance frequency and probe sensitivity. We test a recently introduced 13C resonator design, engineered to significantly increase 13C detection sensitivity over previous all-HTS probes. At zero field, we observe a 13C quality factor of 6000 which is several times higher than previous resonators. In this work the coil design considerations and probe build-out procedure are discussed.

Maternal hypercortisolemia alters placental metabolism: a multiomics view.

Previous studies have suggested that increases in maternal cortisol or maternal stress in late pregnancy increase the risk of stillbirth at term. In an ovine model with increased maternal cortisol over the last 0.20 of gestation, we have previously found evidence of disruption of fetal serum and cardiac metabolomics and altered expression of genes related to mitochondrial function and metabolism in biceps femoris, diaphragm, and cardiac muscle. The present studies were designed to test for effects of chronically increased maternal cortisol on gene expression and metabolomics in placentomes near term. We hypothesized that changes in placenta might underlie or contribute to the alterations in fetal serum metabolomics and thereby contribute to changes in striated muscle metabolism. Placentomes were collected from pregnancies in early labor (143 ± 1 days gestation) of control ewes (n = 7) or ewes treated with cortisol (1 mg·kg-1·day-1 iv; n = 5) starting at day 115 of gestation. Transcriptomics and metabolomics were performed using an ovine gene expression microarray (Agilent 019921) and HR-MAS NMR, respectively. Multiomic analysis indicates that amino acid metabolism, particularly of branched-chain amino acids and glutamate, occur in placenta; changes in amino acid metabolism, degradation, or biosynthesis in placenta were consistent with changes in valine, isoleucine, leucine, and glycine in fetal serum. The analysis also indicates changes in glycerophospholipid metabolism and suggests changes in endoplasmic reticulum stress and antioxidant status in the placenta. These findings suggest that changes in placental function occurring with excess maternal cortisol in late gestation may contribute to metabolic dysfunction at birth.

Metabolite Structure Assignment Using In Silico NMR Techniques.

A major challenge for metabolomic analysis is to obtain an unambiguous identification of the metabolites detected in a sample. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful, and generally applicable spectroscopic tool that can be used to ascertain the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using computational NMR techniques depends on how much of the relevant conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high-level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF)-based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations, and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time by 2 orders of magnitude over methods that optimize the conformations using fully ab initio methods, while still producing good agreement with experimental observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for a large number of conformationally flexible metabolites.