LipSpin: A New Bioinformatics Tool for Quantitative 1H NMR Lipid Profiling

A Fluorinated Chaperone Gives X-ray Crystal Structures of Acyclic Natural Product Derivatives up to 338 Molecular Weight

Crystallizing molecules with long flexible chains is a challenge, making it difficult to perform X-ray crystallography. Chaperones can assist in the crystallization of compounds that do not crystallize by themselves by producing solvate crystals that contain the analyte in their three-dimensional lattices. Among the most versatile chaperones for liquid analytes are tetraaryladamantanes (TAAs), but the size of the compounds that can be encapsulated is limited, and attempts to surpass this limit with known TAAs were unsuccessful. Here we report that 1,3,5,7-tetrakis(2-fluoro-4-methoxyphenyl)adamantane (TFM) is a crystallization chaperone for acyclic molecules up to the molecular weight of phytyl acetate (338 g/mol). Encapsulation of such a large acyclic compound was achieved when the analyte was esterified and when a two-step temperature protocol was used for crystallization. Exploratory work indicates that a drop to -20 °C allows for encapsulation of squalene (Mr 411 g/mol), albeit with positional disorder of the analyte. Our X-ray crystal structures of solvates with flexible analytes shed light on how crystalline order can be imposed on large acyclic analytes. The new, fluorinated TAA gives access to crystal structures that were inaccessible thus far.

Explainable AI to Facilitate Understanding of Neural Network-Based Metabolite Profiling Using NMR Spectroscopy

Neural networks (NNs) are emerging as a rapid and scalable method for quantifying metabolites directly from nuclear magnetic resonance (NMR) spectra, but the nonlinear nature of NNs precludes understanding of how a model makes predictions. This study implements an explainable artificial intelligence algorithm called integrated gradients (IG) to elucidate which regions of input spectra are the most important for the quantification of specific analytes. The approach is first validated in simulated mixture spectra of eight aqueous metabolites and then investigated in experimentally acquired lipid spectra of a reference standard mixture and a murine hepatic extract. The IG method revealed that, like a human spectroscopist, NNs recognize and quantify analytes based on an analyte's respective resonance line-shapes, amplitudes, and frequencies. NNs can compensate for peak overlap and prioritize specific resonances most important for concentration determination. Further, we show how modifying a NN training dataset can affect how a model makes decisions, and we provide examples of how this approach can be used to de-bug issues with model performance. Overall, results show that the IG technique facilitates a visual and quantitative understanding of how model inputs relate to model outputs, potentially making NNs a more attractive option for targeted and automated NMR-based metabolomics.

Asterias Rolleston starfish gonad lipids: A novel source of Omega-3 fatty acids - assessment of major components and their antioxidant activities

The major lipids and antioxidant activities of Asterias rolleston gonad lipids were evaluated systematically. Major lipids of A. Rolleston gonad lipids were triacylglycerols (TAGs) and phospholipids (PLs). Total lipids were composed of 15.62% of polyunsaturated fatty acids (PUFAs), and 40.81% of monounsaturated fatty acids (MUFAs). The most abundant PUFA were C20:5n-3 (EPA) (6.28%) and C22:6n-3 (DHA) (5.80%). Predominantly composed of phosphatidylcholine (PC) and phosphatidylethanolamine (PE), polar lipids were rich in PUFAs and could contain up to 34.59% EPA and DHA, and PE and PI (phosphatidylinositol) were also found to be the main carriers of EPA and ARA (arachidonic acid) in polar lipids. The MUFA and PUFA of Sn-2 in TAG are 39.72% and 30.37%, respectively. A total of 64 TAG species were identified, with Eo-P-M, Eo-Eo-M, and M-M-Eo being the main TAGs components. Moreover, A. rollestoni gonad lipids exhibited potent radical scavenging activities and reducing power in a dose-dependent manner.

Remodeling of skeletal muscle myosin metabolic states in hibernating mammals

Hibernation is a period of metabolic suppression utilized by many small and large mammal species to survive during winter periods. As the underlying cellular and molecular mechanisms remain incompletely understood, our study aimed to determine whether skeletal muscle myosin and its metabolic efficiency undergo alterations during hibernation to optimize energy utilization. We isolated muscle fibers from small hibernators, Ictidomys tridecemlineatus and Eliomys quercinus and larger hibernators, Ursus arctos and Ursus americanus. We then conducted loaded Mant-ATP chase experiments alongside X-ray diffraction to measure resting myosin dynamics and its ATP demand. In parallel, we performed multiple proteomics analyses. Our results showed a preservation of myosin structure in U. arctos and U. americanus during hibernation, whilst in I. tridecemlineatus and E. quercinus, changes in myosin metabolic states during torpor unexpectedly led to higher levels in energy expenditure of type II, fast-twitch muscle fibers at ambient lab temperatures (20 °C). Upon repeating loaded Mant-ATP chase experiments at 8 °C (near the body temperature of torpid animals), we found that myosin ATP consumption in type II muscle fibers was reduced by 77-107% during torpor compared to active periods. Additionally, we observed Myh2 hyper-phosphorylation during torpor in I. tridecemilineatus, which was predicted to stabilize the myosin molecule. This may act as a potential molecular mechanism mitigating myosin-associated increases in skeletal muscle energy expenditure during periods of torpor in response to cold exposure. Altogether, we demonstrate that resting myosin is altered in hibernating mammals, contributing to significant changes to the ATP consumption of skeletal muscle. Additionally, we observe that it is further altered in response to cold exposure and highlight myosin as a potentially contributor to skeletal muscle non-shivering thermogenesis.

Remodelling of Skeletal Muscle Myosin Metabolic States in Hibernating Mammals

Hibernation is a period of metabolic suppression utilized by many small and large mammal species to survive during winter periods. As the underlying cellular and molecular mechanisms remain incompletely understood, our study aimed to determine whether skeletal muscle myosin and its metabolic efficiency undergo alterations during hibernation to optimize energy utilization. We isolated muscle fibers from small hibernators, Ictidomys tridecemlineatus and Eliomys quercinus and larger hibernators, Ursus arctos and Ursus americanus. We then conducted loaded Mant-ATP chase experiments alongside X-ray diffraction to measure resting myosin dynamics and its ATP demand. In parallel, we performed multiple proteomics analyses. Our results showed a preservation of myosin structure in U. arctos and U. americanus during hibernation, whilst in I. tridecemlineatus and E. quercinus, changes in myosin metabolic states during torpor unexpectedly led to higher levels in energy expenditure of type II, fast-twitch muscle fibers at ambient lab temperatures (20°C). Upon repeating loaded Mant-ATP chase experiments at 8°C (near the body temperature of torpid animals), we found that myosin ATP consumption in type II muscle fibers was reduced by 77-107% during torpor compared to active periods. Additionally, we observed Myh2 hyper-phosphorylation during torpor in I. tridecemilineatus, which was predicted to stabilize the myosin molecule. This may act as a potential molecular mechanism mitigating myosin-associated increases in skeletal muscle energy expenditure during periods of torpor in response to cold exposure. Altogether, we demonstrate that resting myosin is altered in hibernating mammals, contributing to significant changes to the ATP consumption of skeletal muscle. Additionally, we observe that it is further altered in response to cold exposure and highlight myosin as a potentially contributor to skeletal muscle non-shivering thermogenesis.

Specific Multiomic Profiling in Aortic Stenosis in Bicuspid Aortic Valve Disease

INTRODUCTION AND PURPOSE

Bicuspid aortic valve (BAV) disease is associated with faster aortic valve degeneration and a high incidence of aortic stenosis (AS). In this study, we aimed to identify differences in the pathophysiology of AS between BAV and tricuspid aortic valve (TAV) patients in a multiomics study integrating metabolomics and transcriptomics as well as clinical data.

METHODS

Eighteen patients underwent aortic valve replacement due to severe aortic stenosis: 8 of them had a TAV, while 10 of them had a BAV. RNA sequencing (RNA-seq) and proton nuclear magnetic resonance spectroscopy (1H-NMR) were performed on these tissue samples to obtain the RNA profile and lipid and low-molecular-weight metabolites. These results combined with clinical data were posteriorly compared, and a multiomic profile specific to AS in BAV disease was obtained.

RESULTS

H-NMR results showed that BAV patients with AS had different metabolic profiles than TAV patients. RNA-seq also showed differential RNA expression between the groups. Functional analysis helped connect this RNA pattern to mitochondrial dysfunction. Integration of RNA-seq, 1H-NMR and clinical data helped create a multiomic profile that suggested that mitochondrial dysfunction and oxidative stress are key players in the pathophysiology of AS in BAV disease.

CONCLUSIONS

The pathophysiology of AS in BAV disease differs from patients with a TAV and has a specific RNA and metabolic profile. This profile was associated with mitochondrial dysfunction and increased oxidative stress.

Lipid dysmetabolism in ceruloplasmin-deficient mice revealed both in vivo and ex vivo by MRI, MRS and NMR analyses

Ceruloplasmin (Cp) is a ferroxidase that plays a role in cellular iron homeostasis and is mainly expressed in the liver and secreted into the blood. Cp is also produced by adipose tissue, which releases it as an adipokine. Although a dysfunctional interaction of iron with the metabolism of lipids has been associated with several metabolic diseases, the role of Cp in adipose tissue metabolism and in the interplay between hepatocytes and adipocytes has been poorly investigated. We previously found that Cp-deficient (CpKO) mice become overweight and demonstrate adipose tissue accumulation together with liver steatosis during aging, suggestive of lipid dysmetabolism. In the present study, we investigated the lipid alterations which occur during aging in adipose tissue and liver of CpKO and wild-type mice both in vivo and ex vivo. During aging of CpKO mice, we observed adipose tissue accumulation and liver lipid deposition, both of which are associated with macrophage infiltration. Liver lipid deposition was characterized by accumulation of triglycerides, fatty acids and ω-3 fatty acids, as well as by a switch from unsaturated to saturated fatty acids, which is characteristic of lipid storage. Liver steatosis was preceded by iron deposition and macrophage infiltration, and this was observed to be already occurring in younger CpKO mice. The accumulation of ω-3 fatty acids, which can only be acquired through diet, was associated with body weight increase in CpKO mice despite food intake being equal to that of wild-type mice, thus underlining the alterations in lipid metabolism/catabolism in Cp-deficient animals.

Rapid and automated lipid profiling by nuclear magnetic resonance spectroscopy using neural networks

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for quantitative metabolomics; however, quantification of metabolites from NMR data is often a slow and tedious process requiring user input and expertise. In this study, we propose a neural network approach for rapid, automated lipid identification and quantification from NMR data. Multilayered perceptron (MLP) networks were developed with NMR spectra as the input and lipid concentrations as output. Three large synthetic datasets were generated, each with 55,000 spectra from an original 30 scans of reference standards, by using linear combinations of standards and simulating experimental-like modifications (line broadening, noise, peak shifts, baseline shifts) and common interference signals (water, tetramethylsilane, extraction solvent), and were used to train MLPs for robust prediction of lipid concentrations. The performances of MLPS were first validated on various synthetic datasets to assess the effect of incorporating different modifications on their accuracy. The MLPs were then evaluated on experimentally acquired data from complex lipid mixtures. The MLP-derived lipid concentrations showed high correlations and slopes close to unity for most of the quantified lipid metabolites in experimental mixtures compared with ground-truth concentrations. The most accurate, robust MLP was used to profile lipids in lipophilic hepatic extracts from a rat metabolomics study. The MLP lipid results analyzed by two-way ANOVA for dietary and sex differences were similar to those obtained with a conventional NMR quantification method. In conclusion, this study demonstrates the potential and feasibility of a neural network approach for improving speed and automation in NMR lipid profiling and this approach can be easily tailored to other quantitative, targeted spectroscopic analyses in academia or industry.

Hypothalamic JNK1-hepatic fatty acid synthase axis mediates a metabolic rewiring that prevents hepatic steatosis in male mice treated with olanzapine via intraperitoneal: Additional effects of PTP1B inhibition

Olanzapine (OLA), a widely used second-generation antipsychotic (SGA), causes weight gain and metabolic alterations when administered orally to patients. Recently, we demonstrated that, contrarily to the oral treatment which induces weight gain, OLA administered via intraperitoneal (i.p.) in male mice resulted in body weight loss. This protection was due to an increase in energy expenditure (EE) through a mechanism involving the modulation of hypothalamic AMPK activation by higher OLA levels reaching this brain region compared to those of the oral treatment. Since clinical studies have shown hepatic steatosis upon chronic treatment with OLA, herein we further investigated the role of the hypothalamus-liver interactome upon OLA administration in wild-type (WT) and protein tyrosine phosphatase 1B knockout (PTP1B-KO) mice, a preclinical model protected against metabolic syndrome. WT and PTP1B-KO male mice were fed an OLA-supplemented diet or treated via i.p. Mechanistically, we found that OLA i.p. treatment induces mild oxidative stress and inflammation in the hypothalamus in a JNK1-independent and dependent manner, respectively, without features of cell dead. Hypothalamic JNK activation up-regulated lipogenic gene expression in the liver though the vagus nerve. This effect concurred with an unexpected metabolic rewiring in the liver in which ATP depletion resulted in increased AMPK/ACC phosphorylation. This starvation-like signature prevented steatosis. By contrast, intrahepatic lipid accumulation was observed in WT mice treated orally with OLA; this effect being absent in PTP1B-KO mice. We also demonstrated an additional benefit of PTP1B inhibition against hypothalamic JNK activation, oxidative stress and inflammation induced by chronic OLA i.p. treatment, thereby preventing hepatic lipogenesis. The protection conferred by PTP1B deficiency against hepatic steatosis in the oral OLA treatment or against oxidative stress and neuroinflammation in the i.p. treatment strongly suggests that targeting PTP1B might be also a therapeutic strategy to prevent metabolic comorbidities in patients under OLA treatment in a personalized manner.

Nuclear Magnetic Resonance-Based Lipidomics in the Assessment of Cardiometabolic Risk in Type 1 Diabetes: An Exploratory Analysis

INTRODUCTION

Cardiovascular disease (CVD) is the leading cause of mortality in type 1 diabetes (T1D). However, there is a need for daily practice tools for identifying those more prone to suffer from these events. We aimed to assess the relationships between nuclear magnetic resonance (¹H NMR)-based lipidomic analysis and several CVD risk variables (including preclinical carotid atherosclerosis) in individuals with T1D at high risk.

METHODS

We included patients with T1D without CVD, with at least one of the following: age ≥ 40 years, diabetic kidney disease, or ≥ 10 years of evolution with another risk factor. The presence of plaque (intima-media thickness > 1.5 mm) was determined by standardized ultrasonography protocol. Lipidomic analysis was performed by ¹H NMR. Bivariate and multivariate-adjusted differences in ¹H NMR lipidomics were evaluated.

RESULTS

We included n = 131 participants (49.6% female, age 46.4 ± 10.3 years, diabetes duration 27.0 ± 9.5 years, 47.3% on statins). Carotid plaques were present in 28.2% of the individuals (n = 12, with ≥ 3 plaques). Glucose (HbA1c), anthropometric (body mass index and waist circumference), and insulin resistance-related (fatty liver index and estimated glucose disposal rate) variables were those most associated with ¹H NMR-derived lipidomic analysis (p < 0.01 for all). Regarding preclinical atherosclerosis, sphingomyelin was independently associated with carotid plaque presence (for 0.1 mmol/L increase, OR 0.50 [0.28-0.86]; p = 0.013), even after adjusting for age, sex, hypertension, statin use, mean 5-year HbA1c and diabetes duration. Furthermore, linoleic acid and ω-6 fatty acids remained independently associated with higher plaque burden (≥ 3 plaques) in multivariate models (0.17 [0.03-0.93] and 0.27 [0.07-0.97], respectively; p < 0.05 for both).

CONCLUSION

In our preliminary study of individuals with T1D at high risk, several ¹H NMR-derived lipidomic parameters were independently associated with preclinical atherosclerosis. Specifically, ω-6 fatty acids and linoleic acid seem promising for identifying those with higher plaque burden.

Quantitative NMR Methods in Metabolomics

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the two major analytical platforms in the field of metabolomics, the other being mass spectrometry (MS). NMR is less sensitive than MS and hence it detects a relatively small number of metabolites. However, NMR exhibits numerous unique characteristics including its high reproducibility and non-destructive nature, its ability to identify unknown metabolites definitively, and its capabilities to obtain absolute concentrations of all detected metabolites, sometimes even without an internal standard. These characteristics outweigh the relatively low sensitivity and resolution of NMR in metabolomics applications. Since biological mixtures are highly complex, increased demand for new methods to improve detection, better identify unknown metabolites, and provide more accurate quantitation continues unabated. Technological and methodological advances to date have helped to improve the resolution and sensitivity and detection of a larger number of metabolite signals. Efforts focused on measuring unknown metabolite signals have resulted in the identification and quantitation of an expanded pool of metabolites including labile metabolites such as cellular redox coenzymes, energy coenzymes, and antioxidants. This chapter describes quantitative NMR methods in metabolomics with an emphasis on recent methodological developments, while highlighting the benefits and challenges of NMR-based metabolomics.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

Identification of a Gain-of-Function LIPC Variant as a Novel Cause of Familial Combined Hypocholesterolemia

BACKGROUND

Atherosclerotic cardiovascular disease is the main cause of mortality worldwide and is strongly influenced by circulating low-density lipoprotein (LDL) cholesterol levels. Only a few genes causally related to plasma LDL cholesterol levels have been identified so far, and only 1 gene, ANGPTL3, has been causally related to combined hypocholesterolemia. Here, our aim was to elucidate the genetic origin of an unexplained combined hypocholesterolemia inherited in 4 generations of a French family.

METHODS

Using next-generation sequencing, we identified a novel dominant rare variant in the LIPC gene, encoding for hepatic lipase, which cosegregates with the phenotype. We characterized the impact of this LIPC-E97G variant on circulating lipid and lipoprotein levels in family members using nuclear magnetic resonance-based lipoprotein profiling and lipidomics. To uncover the mechanisms underlying the combined hypocholesterolemia, we used protein homology modeling, measured triglyceride lipase and phospholipase activities in cell culture, and studied the phenotype of APOE*3.Leiden.CETP mice after LIPC-E97G overexpression.

RESULTS

Family members carrying the LIPC-E97G variant had very low circulating levels of LDL cholesterol and high-density lipoprotein cholesterol, LDL particle numbers, and phospholipids. The lysophospholipids/phospholipids ratio was increased in plasma of LIPC-E97G carriers, suggestive of an increased lipolytic activity on phospholipids. In vitro and in vivo studies confirmed that the LIPC-E97G variant specifically increases the phospholipase activity of hepatic lipase through modification of an evolutionarily conserved motif that determines substrate access to the hepatic lipase catalytic site. Mice overexpressing human LIPC-E97G recapitulated the combined hypocholesterolemic phenotype of the family and demonstrated that the increased phospholipase activity promotes catabolism of triglyceride-rich lipoproteins by different extrahepatic tissues but not the liver.

CONCLUSIONS

We identified and characterized a novel rare variant in the LIPC gene in a family who presents with dominant familial combined hypocholesterolemia. This gain-of-function variant makes LIPC the second identified gene, after ANGPTL3, causally involved in familial combined hypocholesterolemia. Our mechanistic data highlight the critical role of hepatic lipase phospholipase activity in LDL cholesterol homeostasis and suggest a new LDL clearance mechanism.

Regulation of metabolic and transcriptional responses by the thyroid hormone in cellular models of murine macrophages

Oncogene-immortalized bone marrow-derived macrophages are considered to be a good model for the study of immune cell functions, but the factors required for their survival and proliferation are still unknown. Although the effect of the thyroid hormones on global metabolic and transcriptional responses in macrophages has not yet been examined, there is increasing evidence that they could modulate macrophage functions. We show here that the thyroid hormone T3 is an absolute requirement for the growth of immortal macrophages. The hormone regulates the activity of the main signaling pathways required for proliferation and anabolic processes, including the phosphorylation of ERK and p38 MAPKs, AKT, ribosomal S6 protein, AMPK and Sirtuin-1. T3 also alters the levels of metabolites controlling transcriptional and post-transcriptional actions in macrophages, and causes widespread transcriptomic changes, up-regulating genes needed for protein synthesis and cell proliferation, while down-regulating genes involved in immune responses and endocytosis, among others. This is not observed in primary bone marrow-derived macrophages, where only p38 and AMPK activation is regulated by T3 and in which the metabolic and transcriptomic effects of the hormone are much weaker. However, the response to IFN-γ is reduced by T3 similarly in immortalized macrophages and in the primary cells, confirming previous results showing that the thyroid hormones can antagonize JAK/STAT-mediated signaling. These results provide new perspectives on the relevant pathways involved in proliferation and survival of macrophage cell culture models and on the crosstalk between the thyroid hormones and the immune system.

Free triiodothyronine levels and age influences the metabolic profile and COVID-19 severity parameters in euthyroid and levothyroxine-treated patients

Metabolic reprogramming is required to fight infections and thyroid hormones are key regulators of metabolism. We have analyzed in hospitalized COVID-19 patients: 40 euthyroid and 39 levothyroxine (LT4)-treated patients in the ward and 29 euthyroid and 9 LT4-treated patients in the intensive care unit (ICU), the baseline characteristics, laboratory data, thyroid-stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3), the FT3/FT4 ratio, 11 antiviral cytokines and 74 metabolomic parameters. No evidence for significant differences between euthyroid and LT4-treated patients were found in the biochemical, metabolomic and cytokines parameters analyzed. Only TSH (p=0.009) and ferritin (p=0.031) showed significant differences between euthyroid and LT4-treated patients in the ward, and TSH (p=0.044) and FT4 (p=0.012) in the ICU. Accordingly, severity and mortality were similar in euthyroid and LT4-treated patients. On the other hand, FT3 was negatively related to age (p=0.012), independently of sex and body mass index in hospitalized COVID-19 patients. Patients with low FT3 and older age showed a worse prognosis and higher levels of the COVID-19 severity markers IL-6 and IL-10 than patients with high FT3. IL-6 negatively correlated with FT3 (p=0.023) independently of age, body mass index and sex, whereas IL-10 positively associated with age (p=0.035) independently of FT3, body mass index and sex. A metabolomic cluster of 6 parameters defined low FT3 ward patients. Two parameters, esterified cholesterol (p=4.1x10^-4) and small HDL particles (p=6.0x10^-5) correlated with FT3 independently of age, body mass index and sex, whereas 3-hydroxybutyrate (p=0.010), acetone (p=0.076), creatinine (p=0.017) and high-density-lipoprotein (HDL) diameter (p=8.3x10^-3) were associated to FT3 and also to age, with p-values of 0.030, 0.026, 0.017 and 8.3x10^-3, respectively. In conclusion, no significant differences in FT3, cytokines, and metabolomic profile, or in severity and outcome of COVID-19, were found during hospitalization between euthyroid patients and hypothyroid patients treated with LT4. In addition, FT3 and age negatively correlate in COVID-19 patients and parameters that predict poor prognosis were associated with low FT3, and/or with age. A metabolomic cluster indicative of a high ketogenic profile defines non-critical hospitalized patients with low FT3 levels.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

CRAFT for NMR lipidomics: Targeting lipid metabolism in leucine-supplemented tumor-bearing mice

Lipid profiling by 1 H-NMR has gained increasing utility in many fields because of its intrinsically quantitative, nondestructive nature and the ability to differentiate small molecules based on their spectral location. Most nuclear magnetic resonance (NMR) techniques for metabolite quantification use frequency domain analysis that involves many user-dependent steps such as phase and baseline correction and quantification by either manual integration or peak fitting. Recently, Bayesian analysis of time-domain NMR data has been shown to reduce operator bias and increase automation in NMR spectroscopy. In this study, we demonstrate the use of CRAFT (complete reduction to amplitude-frequency table), a Bayesian-based approach to automate processing in NMR-based lipidomics using lipid standards and tissue samples of healthy and tumor-bearing mice supplemented with leucine. Complex mixtures of lipid standards were prepared and examined using CRAFT to validate it against conventional Fourier transform (FT)-NMR and derive a fingerprint to be used for analyzing lipid profiles of serum and liver samples. CRAFT and FT-NMR were comparable in accuracy, with CRAFT achieving higher correlation in quantifying several lipid species. Analysis of the serum lipidome of tumor-bearing mice revealed hyperlipidemia and no signs of hepatic triglyceride accumulation compared with that of the healthy group demonstrating that the tumor-bearing mice were in a state of precachexia. Leucine-supplementation was associated with minimal changes in the lipid profile in both tissues. In conclusion, our study demonstrates that the CRAFT method can accurately identify and quantify lipids in complex lipid mixtures and murine tissue samples and, hence, will increase automation and reproducibility in NMR-based lipidomics.

Chronic Oleoylethanolamide Treatment Decreases Hepatic Triacylglycerol Level in Rat Liver by a PPARγ/SREBP-Mediated Suppression of Fatty Acid and Triacylglycerol Synthesis

Oleoylethanolamide (OEA) is a naturally occurring bioactive lipid belonging to the family of N-acylethanolamides. A variety of beneficial effects have been attributed to OEA, although the greater interest is due to its potential role in the treatment of obesity, fatty liver, and eating-related disorders. To better clarify the mechanism of the antiadipogenic effect of OEA in the liver, using a lipidomic study performed by 1H-NMR, LC-MS/MS and thin-layer chromatography analyses we evaluated the whole lipid composition of rat liver, following a two-week daily treatment of OEA (10 mg kg-1 i.p.). We found that OEA induced a significant reduction in hepatic triacylglycerol (TAG) content and significant changes in sphingolipid composition and ceramidase activity. We associated the antiadipogenic effect of OEA to decreased activity and expression of key enzymes involved in fatty acid and TAG syntheses, such as acetyl-CoA carboxylase, fatty acid synthase, diacylglycerol acyltransferase, and stearoyl-CoA desaturase 1. Moreover, we found that both SREBP-1 and PPARγ protein expression were significantly reduced in the liver of OEA-treated rats. Our findings add significant and important insights into the molecular mechanism of OEA on hepatic adipogenesis, and suggest a possible link between the OEA-induced changes in sphingolipid metabolism and suppression of hepatic TAG level.

1H-NMR metabolomics reveals a multitarget action of Crithmum maritimum ethyl acetate extract in inhibiting hepatocellular carcinoma cell growth

Hepatocellular carcinoma (HCC) is nowadays the sixth cause of tumour-related deceases worldwide, estimated to become the third in Western countries by 2030. New drugs for HCC treatment still have many adverse effects. Several lines of evidence indicate that plant metabolites offer concrete opportunities for developing new therapeutic strategies for many diseases, including cancer. We previously reported that ethyl acetate extract of a spontaneous edible plant harvested in Apulia, Crithmum maritimum, significantly inhibited cell growth in HCC cells. By ¹H-NMR spectroscopy, here we show that Crithmum maritimum ethyl acetate extract counteracts the Warburg effect, by reducing intracellular lactate, inhibits protein anabolism, by decreasing amino acid level, and affects membrane biosynthesis by lowering choline and phosphocholine. Also, we observed an effect on lipid homeostasis, with a reduction in triglycerides, cholesterol, monounsaturated fatty acids (MUFA), and diunsaturated fatty acids (DUFA), and an increase in polyunsaturated fatty acids (PUFA). Taken together, these data demonstrate that Crithmum maritimum-induced cytostasis is exerted through a multi-effect action, targeting key metabolic processes in HCC cells. Overall, our findings highlight the role of Crithmum maritimum as a promising tool for the prevention and the improvement of the therapeutic options for HCC and other types of tumours.

Nuclear magnetic resonance-based metabolomic analysis in the assessment of preclinical atherosclerosis in type 1 diabetes and preeclampsia

AIMS

Evaluate the role of plasma metabolomics in atherosclerosis according to the presence of type 1 diabetes (T1D) or previous preeclampsia.

METHODS

We recruited 105 women without cardiovascular disease and last pregnancy ≥5 years previously, divided according to the presence of T1D or previous preeclampsia. Preclinical atherosclerosis was defined as the presence of carotid plaque (intima-media thickness ≥1.5 mm) assessed by ultrasonography. Metabolomics were evaluated by nuclear magnetic resonance (NMR). Bivariate and multivariate-adjusted differences in NMR-metabolomics were evaluated.

RESULTS

The participants were 44.9 ± 8.1 years-old; 20% harbored plaques. There were significant differences in lipidic-, energetic- and nitrogen-related metabolites according to the presence of T1D/preeclampsia (p < 0.05). In multivariate-adjusted models (by age, statins, blood pressure and T1D/preeclampsia), only lipidomic-related metabolites were associated with atherosclerosis in the whole sample. However, stronger associations were observed in women with previous preeclampsia (vs. without; per 0.5 mmol/L increments); phosphatidylcholine, OR 4.08 (1.32-27.22); free cholesterol, 5.18 (1.22-21.97); saturated fatty acids, OR 2.99 (1.37-6.48); w-7, OR 2.29 (1.15-4.56); and w-9 fatty acids, OR 1.49 (1.00-2.23).

CONCLUSIONS

NMR-metabolomics showed a differential pattern according to the presence of T1D/preeclampsia in relation to preclinical atherosclerosis. Since most of these metabolites mirror lifestyle factors, they could help tailor dietetic advice in high-risk women.

COLMAR Lipids Web Server and Ultrahigh-Resolution Methods for Two-Dimensional Nuclear Magnetic Resonance- and Mass Spectrometry-Based Lipidomics

Accurate identification of lipids in biological samples is a key step in lipidomics studies. Multidimensional nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool for this purpose as it provides comprehensive structural information on lipid composition at atomic resolution. However, the interpretation of NMR spectra of complex lipid mixtures is currently hampered by limited spectral resolution and the absence of a customized lipid NMR database along with user-friendly spectral analysis tools. We introduce a new two-dimensional (2D) NMR metabolite database "COLMAR Lipids" that was specifically curated for hydrophobic metabolites presently containing 501 compounds with accurate experimental 2D ¹³C-¹H heteronuclear single quantum coherence (HSQC) chemical shift data measured in CDCl₃. A new module in the public COLMAR suite of NMR web servers was developed for the (semi)automated analysis of complex lipidomics mixtures (http://spin.ccic.osu.edu/index.php/colmarm/index2). To obtain 2D HSQC spectra with the necessary high spectral resolution along both ¹³C and ¹H dimensions, nonuniform sampling in combination with pure shift spectroscopy was applied allowing the extraction of an abundance of unique cross-peaks belonging to hydrophobic compounds in complex lipidomics mixtures. As shown here, this information is critical for the unambiguous identification of underlying lipid molecules by means of the new COLMAR Lipids web server, also in combination with mass spectrometry, as is demonstrated for Caco-2 cell and lung tissue cell extracts.

Plasma metabolomics of the time resolved response to Opisthorchis felineus infection in an animal model (golden hamster, Mesocricetus auratus)

Background

Opisthorchiasis is a hepatobiliary disease caused by flukes of the trematode family Opisthorchiidae. Opisthorchiasis can lead to severe hepatobiliary morbidity and is classified as a carcinogenic agent. Here we investigate the time-resolved metabolic response to Opisthorchis felineus infection in an animal model.

Methodology

Thirty golden hamsters were divided in three groups: severe infection (50 metacercariae/hamster), mild infection (15 metacercariae/hamster) and uninfected (vehicle-PBS) groups. Each group consisted of equal number of male and female animals. Plasma samples were collected one day before the infection and then every two weeks up to week 22 after infection. The samples were subjected to ¹H Nuclear Magnetic Resonance (NMR) spectroscopy and multivariate statistical modelling.

Principal findings

The time-resolved study of the metabolic response to Opisthorchis infection in plasma in the main lines agrees with our previous report on urine data. The response reaches its peak around the 4th week of infection and stabilizes after the 10th week. Yet, unlike the urinary data there is no strong effect of the gender in the data and the intensity of infection is presented in the first two principal components of the PCA model. The main trends of the metabolic response to the infection in blood plasma are the transient depletion of essential amino acids and an increase in lipoprotein and cholesterol concentrations.

Conclusions

The time resolved metabolic signature of Opisthorchis infection in the hamster’s plasma shows a coherent shift in amino acids and lipid metabolism. Our work provides insight into the metabolic basis of the host response on the helminth infection.

Opisthorchiasis is a parasitic infection caused by liver flukes of the Opisthorchiidae family. The liver fluke infection triggers development of hepatobiliary pathologies such as chronic forms of cholecystitis, cholangitis, pancreatitis, and cholelithiasis and increases the risk of intrahepatic cholangiocarcinoma. This manuscript is the second part of our outgoing project dedicated to a comprehensive description of the metabolic response to opisthorchiasis (more specifically Opisthorchis felineus) in an animal model. We show that the metabolic response in blood plasma is unfolding according to the same scenario as in urine, reaching its peak at the 4th week and stabilizing after the 10th week post-infection. Yet, unlike the response described in urine, the observed metabolic response in plasma is less gender specific. Moreover, the biochemical basis of the detected response in blood plasma is restricted to the remodeling of the lipid metabolism and the transient depletion of essential amino acids. Together with our first manuscript this report forms the first systematic description of the metabolic response on opisthorchiasis in an animal model using two easily accessible biofluids. Thus, this contribution provides novel results and fills an information gap still existing in the analytically driven characterization of the “Siberian liver fluke”, Opisthorchis felineus.

Proton NMR Enables the Absolute Quantification of Aqueous Metabolites and Lipid Classes in Unique Mouse Liver Samples

Hepatic metabolites provide valuable information on the physiological state of an organism, and thus, they are monitored in many clinical situations. Typically, monitoring requires several analyses for each class of targeted metabolite, which is time consuming. The present study aimed to evaluate a proton nuclear magnetic resonance (¹H-NMR) method for obtaining quantitative measurements of aqueous and lipidic metabolites. We optimized the extraction protocol, the standard samples, and the organic solvents for the absolute quantification of lipid species. To validate the method, we analyzed metabolic profiles in livers of mice fed three different diets. We compared our results with values obtained with conventional methods and found strong correlations. The ¹H-NMR protocol enabled the absolute quantification of 29 aqueous metabolites and eight lipid classes. Results showed that mice fed a diet enriched in saturated fatty acids had higher levels of triglycerides, cholesterol ester, monounsaturated fatty acids, lactate, 3-hydroxy-butyrate, and alanine and lower levels of glucose, compared to mice fed a control diet. In conclusion, proton NMR provided a rapid overview of the main lipid classes (triglycerides, cholesterol, phospholipids, fatty acids) and the most abundant aqueous metabolites in liver.

Lipid Profile Rather Than the LCAT Mutation Explains Renal Disease in Familial LCAT Deficiency

Renal complications are the major cause of morbidity and mortality in patients with familial lecithin–cholesterol acyltransferase (LCAT) deficiency (FLD). We report three FLD patients, two of them siblings—only one of whom developed renal disease—and the third case being a young man with early renal disease. The aim of this study was to analyze the clinical characteristics and possible mechanisms associated with renal disease in these patients. Plasma lipid levels, LCAT activity, lipoprotein particle profile by NMR and FPLC, free and esterified cholesterol, presence of lipoprotein X (LpX) and DNA sequencing in the three FLD patients have been determined. The three cases presented clinical characteristics of FLD, although only one of the siblings developed renal disease, at 45 years of age, while the other patient developed the disease in his youth. Genetic analysis revealed new missense homozygous mutations, p.(Ile202Thr) in both siblings and p.(Arg171Glu) in the other patient. Lipoprotein particle analysis showed that the two patients with renal disease presented higher numbers of small very low-density lipoprotein (VLDL) and a higher concentration of triglycerides in VLDL. This study reports three new cases of LCAT deficiency, not previously described. Renal disease is not only dependent on LCAT deficiency, and could be due to the presence of VLDL particles, which are rich in triglycerides, free cholesterol and LpX.

High-Throughput 1H-Nuclear Magnetic Resonance-Based Screening for the Identification and Quantification of Heartwood Diterpenic Acids in Four Black Pine (Pinus nigra Arn.) Marginal Provenances in Greece

A high-throughput quantitative Nuclear Magnetic Resonance ¹H-NMR method was developed and applied to screen the quantity of the diterpenic resin acids in the heartwood of black pine, due to the renewed scientific interest in their medicinal properties and use in various diseases treatment. The 260 samples were taken from Pinus nigra clones, selected from four provenances of the Peloponnese (Greece), participating in a 35-year-old clonal seed orchard. Total resin acids per dry heartwood weight (dhw) varied greatly, ranging from 30.05 to 424.70 mg/g_dhw (average 219.98 mg/g_dhw). Abietic was the predominant acid (76.77 mg/g_dhw), followed by palustric acid (47.94 mg/g_dhw), neoabietic acid (39.34 mg/g_dhw), and pimaric acid (22.54 mg/g_dhw). Dehydroabietic acid was at moderate levels (11.69 mg/g_dhw), while levopimaric, isopimaric, and sandaracopimaric acids were in lower concentrations. The resin acid fraction accounted for 72.33% of the total acetone extractives. Stilbenes were presented in significant quantities (19.70%). The resin acid content was composed mainly of the abietane type resin acids (83.56%). Peloponnesian Pinus nigra heartwood was found to be the richest source of resin acids identified to date and is considered the best natural source for the production of such bioactive extracts. The results indicate a high potential for effective selection and advanced breeding of pharmaceutical and high economic value bioactive substances from Pinus nigra clones.

Qualitative analysis of phospholipids and their oxidised derivatives - used techniques and examples of their applications related to lipidomic research and food analysis

Phospholipids (PLs) are important biomolecules that not only constitute structural building blocks and scaffolds of cell and organelle membranes but also play a vital role in cell biochemistry and physiology. Moreover, dietary exogenous PLs are characterised by high nutritional value and other beneficial health effects, which are confirmed by numerous epidemiological studies. For this reason, PLs are of high interest in lipidomics that targets both the analysis of membrane lipid distribution as well as correlates composition of lipids with their effects on functioning of cells, tissues and organs. Lipidomic assessments follow-up the changes occurring in living organisms, such as free radical attack and oxidative modifications of the polyunsaturated fatty acids (PUFAs) build in PL structures. Oxidised PLs (oxPLs) can be generated exogenously and supplied to organisms with processed food or formed endogenously as a result of oxidative stress. Cellular and tissue oxPLs can be a biomarker predictive of the development of numerous diseases such as atherosclerosis or neuroinflammation. Therefore, suitable high-throughput analytical techniques, which enable comprehensive analysis of PL molecules in terms of the structure of hydrophilic group, fatty acid (FA) composition and oxidative modifications of FAs, have been currently developed. This review addresses all aspects of PL analysis, including lipid isolation, chromatographic separation of PL classes and species, as well as their detection. The bioinformatic tools that enable handling of a large amount of data generated during lipidomic analysis are also discussed. In addition, imaging techniques such as confocal microscopy and mass spectrometry imaging for analysis of cellular lipid maps, including membrane PLs, are presented.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Lipid Profiling Using 1H NMR Spectroscopy

Lipid profiling, which includes fatty acids, phospholipids, glycerides, and cholesterols is extremely important because of the essential role lipids play in the regulation of metabolism in animals. ¹H-NMR-based protocols for high-throughput lipid analysis in complex mixtures have been developed and applied to biological systems. Many classes of lipids can be quantitatively analyzed in many sample matrices including serum, cells, and tissues using a simple ¹H NMR experiment. In this chapter, we provide protocols for NMR-based lipid profiling including sample preparation, NMR experiments, and quantification using the LipSpin software tool.

Quantification of Lipids: Model, Reality, and Compromise

Lipids are key molecules in various biological processes, thus their quantification is a crucial point in a lot of studies and should be taken into account in lipidomics development. This family is complex and presents a very large diversity of structures, so analyzing and quantifying all this diversity is a real challenge. In this review, the different techniques to analyze lipids will be presented: from nuclear magnetic resonance (NMR) to mass spectrometry (with and without chromatography) including universal detectors. First of all, the state of the art of quantification, with the definitions of terms and protocol standardization, will be presented with quantitative lipidomics in mind, and then technical considerations and limitations of analytical chemistry's tools, such as NMR, mass spectrometry and universal detectors, will be discussed, particularly in terms of absolute quantification.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.

Current and future perspectives of functional metabolomics in disease studies-A review

Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.