ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

1H-NMR-based urine metabolomics of prostate cancer and benign prostatic hyperplasia

Background

Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are prevalent conditions affecting a significant portion of the male population, particularly with advancing age. Traditional diagnostic methods, such as digital rectal examination (DRE) and prostate-specific antigen (PSA) tests, have limitations in specificity and sensitivity, leading to potential overdiagnosis and unnecessary biopsies.

Significance

This study explores the effectiveness of 1H NMR urine metabolomics in distinguishing PCa from BPH and in differentiating various PCa grades, presenting a non-invasive diagnostic alternative with the potential to enhance early detection and patient-specific treatment strategies.

Results

The study demonstrated the capability of 1H NMR urine metabolomics in detecting distinct metabolic profiles between PCa and BPH, as well as among different Gleason grade groups. Notably, this method surpassed the PSA test in distinguishing PCa from BPH. Untargeted metabolomics analysis also revealed several metabolites with varying relative concentrations between PCa and BPH cases, suggesting potential biomarkers for these conditions.

Multi-tissue metabolic and transcriptomic responses to a short-term heat stress in swine

BACKGROUND

Heat stress (HS) is an increasing threat for pig production with a wide range of impacts. When submitted to high temperatures, pigs will use a variety of strategies to alleviate the effect of HS. While systemic adaptations are well known, tissue-specific changes remain poorly understood. In this study, thirty-two pigs were submitted to a 5-day HS at 32 °C.

RESULTS

Transcriptomic and metabolomic analyses were performed on several tissues. The results revealed differentially expressed genes and metabolites in different tissues. Specifically, 481, 1774, 71, 1572, 17, 164, and 169 genes were differentially expressed in muscle, adipose tissue, liver, blood, thyroid, pituitary, and adrenal glands, respectively. Regulatory glands (pituitary, thyroid, and adrenal) had a lower number of regulated genes, perhaps indicating an earlier sensitivity to HS. In addition, 7, 8, 2, and 8 metabolites were differentially produced in muscle, liver, plasma, and urine, respectively. The study also focused on the oxidative stress pathway in muscle and liver by performing a correlation analysis between genes and metabolites.

CONCLUSIONS

This study has identified various adaptation mechanisms in swine that enable them to cope with heat stress (HS). These mechanisms include a global decrease in energetic metabolism, as well as changes in metabolic precursors that are linked with protein and lipid catabolism and anabolism. Notably, the adaptation mechanisms differ significantly between regulatory (pituitary, thyroid and adrenal glands) and effector tissues (muscle, adipose tissue, liver and blood). Our findings provide new insights into the comprehension of HS adaptation mechanisms in swine.

Effect of diet supplemented with functional amino acids and polyphenols on gut health in broilers subjected to a corticosterone-induced stress

To address the overuse of antimicrobials in poultry production, new functional feed ingredients, i.e. ingredients with benefits beyond meeting basic nutritional requirements, can play a crucial role thanks to their prophylactic effects. This study evaluated the effects of the supplementation of arginine, threonine and glutamine together with grape polyphenols on the gut integrity and functionality of broilers facing a stress condition. 108 straight-run newly hatched Ross PM3 chicks were kept until 35 days and were allocated to 3 treatments. Broilers in the control group were raised in standard conditions. In experimental groups, birds were administered with corticosterone in drinking water (CORT groups) to impair the global health of the animal and were fed a well-balanced diet supplemented or not with a mix of functional amino acids together with grape extracts (1 g/kg of diet-CORT + MIX group). Gut permeability was significantly increased by corticosterone in non-supplemented birds. This corticosterone-induced stress effect was alleviated in the CORT + MIX group. MIX supplementation attenuated the reduction of crypt depth induced by corticosterone. Mucin 2 and TNF-α gene expression was up-regulated in the CORT + MIX group compared to the CORT group. Caecal microbiota remained similar between the groups. These findings indicate that a balanced diet supplemented with functional AA and polyphenols can help to restore broiler intestinal barrier after a stress exposure.

Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates

INTRODUCTION

The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy.

OBJECTIVES

To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals.

METHODS

An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix.

RESULTS

The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated.

CONCLUSIONS

The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra

MOTIVATION

Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy.

RESULTS

As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

AVAILABILITY AND IMPLEMENTATION

The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.

Metabolic variation and oxidative stress response of blue mussels (Mytilus sp.) perturbed by norfloxacin exposure

Antibiotics are currently widely applied in agricultural cultivation, animal husbandry, and medical treatment, but the effects and ecological risks of antibiotics need to be further investigated. Norfloxacin is one of the most widely applied fluoroquinolone antibiotics and is commonly detected in aquatic ecosystems. In this study, the activities of catalase (CAT) and glutathione S-transferase (GST) in blue mussels (Mytilus sp.) exposed to norfloxacin (from 25 to 200 mg/L) for 2 d of acute exposure and 7 d of subacute exposure were measured. ¹H nuclear magnetic resonance (¹H-NMR)-based metabolomics was applied to identify the metabolites and to investigate the physiological metabolism of blue mussels (Mytilus sp.) under different concentrations of norfloxacin. The activity of the CAT enzyme was induced in acute exposure, while the activity of GST was inhibited in subacute exposure when the concentration of norfloxacin reached 200 mg/L. Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed that the increased concentrations of norfloxacin might cause greater metabolic differences between the treatment and control groups and cause greater metabolic variation within the same treatment group. The contents of taurine in the 150 mg/L acute exposure group were 5.17 times higher than those in the control group. The pathway analysis indicated that exposure to high concentrations of norfloxacin disturbed different pathways involved in energy metabolism, amino acid metabolism, neuroregulation, and the regulation of osmotic pressure. These results may provide a molecular and metabolic view of the effects of norfloxacin and the regulatory mechanism of blue mussels when exposed to extremely high doses of antibiotics.

Plasma 1H-NMR metabolic and amino acid profiles of newborn piglets from two lines divergently selected for residual feed intake

Together with environmental factors, physiological maturity at birth is a major determinant for neonatal survival and postnatal development in mammalian species. Maturity at birth is the outcome of complex mechanisms of intra-uterine development and maturation during the end of gestation. In pig production, piglet preweaning mortality averages 20% of the litter and thus, maturity is a major welfare and economic concern. Here, we used both targeted and untargeted metabolomic approaches to provide a deeper understanding of the maturity in a model of lines of pigs divergently selected on residual feed intake (RFI), previously shown to have contrasted signs of maturity at birth. Analyses were conducted on plasma metabolome of piglets at birth and integrated with other phenotypic characteristics associated to maturity. We confirmed proline and myo-inositol, previously described for their association with delayed growth, as potential markers of maturity. Urea cycle and energy metabolism were found more regulated in piglets from high and low RFI lines, respectively, suggesting a better thermoregulation ability for the low RFI (with higher feed efficiency) piglets.

Benchtop NMR-Based Metabolomics: First Steps for Biomedical Application

Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.

Obesogenic diet leads to luminal overproduction of the complex IV inhibitor H2 S and mitochondrial dysfunction in mouse colonocytes

Obesity is characterized by systemic low-grade inflammation associated with disturbances of intestinal homeostasis and microbiota dysbiosis. Mitochondrial metabolism sustains epithelial homeostasis by providing energy to colonic epithelial cells (CEC) but can be altered by dietary modulations of the luminal environment. Our study aimed at evaluating whether the consumption of an obesogenic diet alters the mitochondrial function of CEC in mice. Mice were fed for 22 weeks with a 58% kcal fat diet (diet-induced obesity [DIO] group) or a 10% kcal fat diet (control diet, CTRL). Colonic crypts were isolated to assess mitochondrial function while colonic content was collected to characterize microbiota and metabolites. DIO mice developed obesity, intestinal hyperpermeability, and increased endotoxemia. Analysis of isolated colonic crypt bioenergetics revealed a mitochondrial dysfunction marked by decreased basal and maximal respirations and lower respiration linked to ATP production in DIO mice. Yet, CEC gene expression of mitochondrial respiration chain complexes and mitochondrial dynamics were not altered in DIO mice. In parallel, DIO mice displayed increased colonic bile acid concentrations, associated with higher abundance of Desulfovibrionaceae. Sulfide concentration was markedly increased in the colon content of DIO mice. Hence, chronic treatment of CTRL mouse colon organoids with sodium sulfide provoked mitochondrial dysfunction similar to that observed in vivo in DIO mice while acute exposure of isolated mitochondria from CEC of CTRL mice to sodium sulfide diminished complex IV activity. Our study provides new insights into colon mitochondrial dysfunction in obesity by revealing that increased sulfide production by DIO-induced dysbiosis impairs complex IV activity in mouse CEC.

Ferrous sulfate reverses cerebral metabolic abnormality induced by minimal hepatic encephalopathy

Orally administered ferrous iron was previously reported to significantly improve the cognition and locomotion of patients with minimal hepatic encephalopathy (MHE). However, the metabolic mechanisms of the therapeutic effect of ferrous iron are unknown. In this study, MHE was induced in rats by partial portal vein ligation (PPVL), and was treated with ferrous sulfate. The Morris water maze was used to evaluate the cognitive condition of the rats. The metabolites observed by NMR and validated by liquid chromatography-mass spectrometry were defined as the key affected metabolites. The enzyme activities and trace element contents in the rat brains were also investigated. The Mn content was found to be increased but the ferrous iron content decreased in the cortex and striatum in MHE. Decreased oxoglutarate dehydrogenase activity and increased glutamine synthetase (GS) and pyruvate carboxylase (PC) activity were observed in the cortex of MHE rats. Decreased pyruvate dehydrogenase activity and increased GS and PC activity were observed in the striatum of MHE rats. The levels of BCAAs and taurine were significantly decreased, and the contents of GABA, lactate, arginine, aspartate, carnosine, citrulline, cysteine, glutamate, glutamine, glycine, methionine, ornithine, proline, threonine and tyrosine were significantly increased. These metabolic abnormalities described above were restored after treatment with ferrous sulfate. Pathway enrichment analysis suggested that urea cycle, aspartate metabolism, arginine and proline metabolism, glycine and serine metabolism, and glutamate metabolism were the major metabolic abnormalities in MHE rats, but these processes could be restored and cognitive impairment could be improved by ferrous sulfate administration.

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples

Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

Exploring the key factors of schizophrenia relapse by integrating LC-MS/1H NMR metabolomics and weighted correlation network analysis

BACKGROUND

Lack of comprehending key factors of schizophrenia relapse has impeded its effective treatment, indicating that the mechanism clarification and available intervention of schizophrenia relapse required further amelioration.

METHOD

Based on the integration of LC-MS and ¹H NMR metabolomics, a weighted correlation network was established to screen pivotal factors of accelerating schizophrenia relapse. Then, the cluster most correlated with schizophrenia relapse was explored, and the biological function of cluster was investigated. Next, the key biomarker related to schizophrenia relapse was obtained through multiple algorithms. Moreover, the Lilikoi algorithm and correlation analysis were implemented to reveal the association between key biomarker and schizophrenia relapse.

RESULT

Results showed that 458 different forms of metabolites were identified for structuring the weighted correlation network. The module-trait correlation indicated that the turquoise module was the most highly correlated with schizophrenia relapse. Further, network analysis revealed that, in turquoise module, cluster 1 composed of 139 metabolites (involved in lipid metabolism and energy metabolism) was the most important subnetwork relevant to schizophrenia relapse. Finally, phenylalanylphenylalanine was recommended as the key biomarker related to schizophrenia relapse. Moreover, the correlation analysis indicated that phenylalanylphenylalanine might affect the progression of schizophrenia by intervening in energy metabolism.

CONCLUSION

In summary, critical factors of schizophrenia relapse have been revealed in our research, expounding the schizophrenia progression more systemically, which could shed some light on improving the intervention of schizophrenia relapse.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics

Two-dimensional (2D) ¹H-¹³C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) ¹H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D ¹H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D ¹H-¹³C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδ_o) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδ_o for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D ¹H-¹³C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

DEEP Picker1D and Voigt Fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra

The quantitative deconvolution of 1D-NMR spectra into individual resonances or peaks is a key step in many modern NMR workflows as it critically affects downstream analysis and interpretation. Depending on the complexity of the NMR spectrum, spectral deconvolution can be a notable challenge. Based on the recent deep neural network DEEP Picker and Voigt Fitter for 2D NMR spectral deconvolution, we present here an accurate, fully automated solution for 1D-NMR spectral analysis, including peak picking, fitting, and reconstruction. The method is demonstrated for complex 1D solution NMR spectra showing excellent performance also for spectral regions with multiple strong overlaps and a large dynamic range whose analysis is challenging for current computational methods. The new tool will help streamline 1D-NMR spectral analysis for a wide range of applications and expand their reach toward ever more complex molecular systems and their mixtures.

Shifting-corrected regularized regression for 1H NMR metabolomics identification and quantification

The process of identifying and quantifying metabolites in complex mixtures plays a critical role in metabolomics studies to obtain an informative interpretation of underlying biological processes. Manual approaches are time-consuming and heavily reliant on the knowledge and assessment of nuclear magnetic resonance (NMR) experts. We propose a shifting-corrected regularized regression method, which identifies and quantifies metabolites in a mixture automatically. A detailed algorithm is also proposed to implement the proposed method. Using a novel weight function, the proposed method is able to detect and correct peak shifting errors caused by fluctuations in experimental procedures. Simulation studies show that the proposed method performs better with regard to the identification and quantification of metabolites in a complex mixture. We also demonstrate real data applications of our method using experimental and biological NMR mixtures.

1H NMR metabolomics analysis of oil palm stem tissue infected by Ganoderma boninense based on field severity Indices

Basal stem rot disease (BSR) caused by G. boninense affects most oil palm plants in Southeast Asia. This disease can be fatal to palm oil production. BSR shows no signs on the tree in the early stages of infection. Therefore, it is essential to find an approach that can detect BSR disease in oil palm, especially at any level of disease severity in the field. This study aims to identify biomarkers of BSR disease in oil palm stem tissue based on various disease severity indices in the field using ¹H NMR-based metabolomics analysis. The crude extract of oil palm stem tissue with four disease severity indices was analyzed by ¹H NMR metabolomics. Approximately 90 metabolites from oil palm stem tissue were identified.Twenty of these were identified as metabolites that significantly differentiated the four disease severity indices. These metabolites include the organic acid group, the carbohydrate group, the organoheterocyclic compound group, and the benzoid group. In addition, different tentative biomarkers for different disease severity indices were also identified. These tentative biomarkers consist of groups of organic acids, carbohydrates, organoheterocyclic compounds, nitrogenous organic compounds, and benzene. There are five pathways in oil palm that are potentially affected by BSR disease.

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.

Survey for Computer-Aided Tools and Databases in Metabolomics

Metabolomics has advanced from innovation and functional genomics tools and is currently a basis in the big data-led precision medicine era. Metabolomics is promising in the pharmaceutical field and clinical research. However, due to the complexity and high throughput data generated from such experiments, data mining and analysis are significant challenges for researchers in the field. Therefore, several efforts were made to develop a complete workflow that helps researchers analyze data. This paper introduces a review of the state-of-the-art computer-aided tools and databases in metabolomics established in recent years. The paper provides computational tools and resources based on functionality and accessibility and provides hyperlinks to web pages to download or use. This review aims to present the latest computer-aided tools, databases, and resources to the metabolomics community in one place.

Slightly different metabolomic profiles are associated with high or low weight duck foie gras

Understanding the evolution of fatty liver metabolism of ducks is a recurrent issue for researchers and industry. Indeed, the increase in weight during the overfeeding period leads to an important change in the liver metabolism. However, liver weight is highly variable at the end of overfeeding within a batch of animals reared, force-fed and slaughtered in the same way. For this study, we performed a proton nuclear magnetic resonance (¹H-NMR) analysis on two classes of fatty liver samples, called low-weight liver (weights between 550 and 599 g) and high-weight liver (weights above 700 g). The aim of this study was to identify the differences in metabolism between two classes of liver weight (low and high). Firstly, the results suggested that increased liver weight is associated with higher glucose uptake leading to greater lipid synthesis. Secondly, this increase is probably also due to a decline in the level of export of triglycerides from the liver by maintaining them at high hepatic concentration levels, but also of hepatic cholesterol. Finally, the increase in liver weight could lead to a significant decrease in the efficiency of aerobic energy metabolism associated with a significant increase in the level of oxidative stress. However, all these hypotheses will have to be confirmed in the future, by studies on plasma levels and specific assays to validate these results.

COLMARq: A Web Server for 2D NMR Peak Picking and Quantitative Comparative Analysis of Cohorts of Metabolomics Samples

Highly quantitative metabolomics studies of complex biological mixtures are facilitated by the resolution enhancement afforded by 2D NMR spectra such as 2D ¹³C-¹H HSQC spectra. Here, we describe a new public web server, COLMARq, for the semi-automated analysis of sets of 2D HSQC spectra of cohorts of samples. The workflow of COLMARq includes automated peak picking using the deep neural network DEEP Picker, quantitative cross-peak volume extraction by numerical fitting using Voigt Fitter, the matching of corresponding cross-peaks across cohorts of spectra, peak volume normalization between different spectra, database query for metabolite identification, and basic univariate and multivariate statistical analyses of the results. COLMARq allows the analysis of cross-peaks that belong to both known and unknown metabolites. After a user has uploaded cohorts of 2D ¹³C-¹H HSQC and optionally 2D ¹H-¹H TOCSY spectra in their preferred format, all subsequent steps on the web server can be performed fully automatically, allowing manual editing if needed and the sessions can be saved for later use. The accuracy, versatility, and interactive nature of COLMARq enables quantitative metabolomics analysis, including biomarker identification, of a broad range of complex biological mixtures as is illustrated for cohorts of samples from bacterial cultures of Pseudomonas aeruginosa in both its biofilm and planktonic states.

First Metabolomic Signature of Blood-Brain Barrier Opening Induced by Microbubble-Assisted Ultrasound

Microbubble (MB)-assisted ultrasound (US) is a promising physical method to increase non-invasively, transiently, and precisely the permeability of the blood-brain barrier (BBB) to therapeutic molecules. Previous preclinical studies established the innocuity of this procedure using complementary analytical strategies including transcriptomics, histology, brain imaging, and behavioral tests. This cross-sectional study using rats aimed to investigate the metabolic processes following acoustically-mediated BBB opening in vivo using multimodal and multimatrices metabolomics approaches. After intravenous injection of MBs, the right striata were exposed to 1-MHz sinusoidal US waves at 0.6 MPa peak negative pressure with a burst length of 10 ms, for 30 s. Then, the striata, cerebrospinal fluid (CSF), blood serum, and urine were collected during sacrifice in three experimental groups at 3 h, 2 days, and 1 week after BBB opening (BBBO) and were compared to a control group where no US was applied. A well-established analytical workflow using nuclear magnetic resonance spectrometry and non-targeted and targeted high-performance liquid chromatography coupled to mass spectrometry were performed on biological tissues and fluids. In our experimental conditions, a reversible BBBO was observed in the striatum without physical damage or a change in rodent weight and behavior. Cerebral, peri-cerebral, and peripheral metabolomes displayed specific and sequential metabolic kinetics. The blood serum metabolome was more impacted in terms of the number of perturbated metabolisms than in the CSF, the striatum, and the urine. In addition, perturbations of arginine and arginine-related metabolisms were detected in all matrices after BBBO, suggesting activation of vasomotor processes and bioenergetic supply. The exploration of the tryptophan metabolism revealed a transient vascular inflammation and a perturbation of serotoninergic neurotransmission in the striatum. For the first time, we characterized the metabolic signature following the acoustically-mediated BBBO within the striatum and its surrounding biological compartments.

Evaluation of the Links between Lamb Feed Efficiency and Rumen and Plasma Metabolomic Data

Feed efficiency is one of the keystones that could help make animal production less costly and more environmentally friendly. Residual feed intake (RFI) is a widely used criterion to measure feed efficiency by regressing intake on the main energy sinks. We investigated rumen and plasma metabolomic data on Romane male lambs that had been genetically selected for either feed efficiency (line rfi−) or inefficiency (line rfi+). These investigations were conducted both during the growth phase under a 100% concentrate diet and later on under a mixed diet to identify differential metabolite expression and to link it to biological phenomena that could explain differences in feed efficiency. Nuclear magnetic resonance (NMR) data were analyzed using partial least squares discriminant analysis (PLS-DA), and correlations between metabolites’ relative concentrations were estimated to identify relationships between them. High levels of plasma citrate and malate were associated with genetically efficient animals, while high levels of amino acids such as L-threonine, L-serine, and L-leucine as well as beta-hydroxyisovalerate were associated with genetically inefficient animals under both diets. The two divergent lines could not be discriminated using rumen metabolites. Based on phenotypic residual feed intake (RFI), efficient and inefficient animals were discriminated using plasma metabolites determined under a 100% concentrate diet, but no discrimination was observed with plasma metabolites under a mixed diet or with rumen metabolites regardless of diet. Plasma amino acids, citrate, and malate were the most discriminant metabolites, suggesting that protein turnover and the mitochondrial production of energy could be the main phenomena that differ between efficient and inefficient Romane lambs.

Comparison of Two Automated Targeted Metabolomics Programs to Manual Profiling by an Experienced Spectroscopist for 1H-NMR Spectra

Automated programs that carry out targeted metabolite identification and quantification using proton nuclear magnetic resonance spectra can overcome time and cost barriers that limit metabolomics use. However, their performance needs to be comparable to that of an experienced spectroscopist. A previously analyzed pediatric sepsis data set of serum samples was used to compare results generated by the automated programs rDolphin and BATMAN with the results obtained by manual profiling for 58 identified metabolites. Metabolites were selected using Student’s t-tests and evaluated with several performance metrics. The manual profiling results had the highest performance metrics values, especially for sensitivity (76.9%), area under the receiver operating characteristic curve (0.90), precision (62.5%), and testing accuracy based on a neural net (88.6%). All three approaches had high specificity values (77.7–86.7%). Manual profiling by an expert spectroscopist outperformed two open-source automated programs, indicating that further development is needed to achieve acceptable performance levels.

Untargeted Metabolomics in Piper betle Leaf Extracts to Discriminate the Cultivars of Coastal Odisha, India

Betel leaf is consumed as a mouth freshener due to its characteristic flavor, aromaticity, and medicinal values. Abundance of phytochemicals in betel leaf contributes towards unique qualitative features. Screening of metabolites is quintessential for identifying flavoring betel leaves and their origin. Metabolomics presently lays emphasis on the cumulative application of gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopic approaches. Here we adopted different protocols based on the above-mentioned analytical metabolomics platform for untargeted plant metabolite profiling followed by multivariate analysis methods and a phytochemical characterization of Piper betel leaf cultivars endemic to coastal Odisha, India. Based on variation in the solvent composition, concentration of solvent, extraction temperature, and incubation periods, five extraction methods were followed in GC-MS and NMR spectroscopy of betel leaf extracts. Phytochemical similarities and differences among the species were characterized through multivariate analysis approaches. Principal component analysis, based on the relative abundance of phytochemicals, indicated that the betel cultivars could be grouped into three groups. Our results of FTIR-, GC-MS-, and NMR-based profiling combined with multivariate analyses suggest that untargeted metabolomics can play a crucial role in documenting metabolic signatures of endemic betel leaf varieties.

The Association of Blood Biomarkers and Body Mass Index in Knee Osteoarthritis: A Cross-Sectional Study

OBJECTIVE

Despite massive efforts, there are no diagnostic blood biomarkers for knee osteoarthritis (KOA). This study investigated several candidate diagnostic biomarkers and the metabolic phenotype in end-stage KOA in the context of obesity.

DESIGN

In this cross-sectional study, adult patients undergoing knee arthroplasty were enrolled and KOA severity was assessed using the Lequesne index. Blood biomarkers with an important role in obesity, the metabolic syndrome, or KOA (oxidized form of low-density lipoprotein [oxLDL], advanced glycation end product [AGE], soluble AGE receptor [sRAGE], fatty acid binding protein 4 [FABP4], phospholipase A2 group IIA [PLA2G2A], fibroblast growth factor 23 [FGF-23], ghrelin, leptin, and resistin) were measured using enzyme-linked immunosorbent assay (ELISA; n = 70) or Luminex technique (subgroup of n = 35). H1-NMR spectroscopy was used for the quantification of metabolite levels (subgroup of n = 31). The hip-knee-ankle angle was assessed. Multivariable and multivariate regression analysis was used to examine the relationship of biomarkers with body mass index (BMI) and KOA severity in complete case and multiple imputation analysis.

RESULTS

While most of the investigated biomarkers were not associated with KOA severity, FABP4 and leptin were found to correlate with BMI and gender. Resistin was associated with Lequesne index in complete case analysis. Using a targeted metabolomics approach, BMI-dependent changes in the metabolome were hardly visible.

CONCLUSIONS

Our findings confirm studies on FABP4, leptin, and resistin with regard to obesity and the metabolic syndrome. There was no association of the investigated biomarkers with KOA severity, most likely due to the patient selection (end-stage KOA patients). Based on this absence of BMI-dependent changes in the metabolome, we might assume that BMI is not correlated with KOA severity in this specific patient group.

An integrated pathological research for precise diagnosis of schizophrenia combining LC-MS/1H NMR metabolomics and transcriptomics

BACKGROUND

Lack of clinically specific biomarkers has impeded the precise diagnosis of schizophrenia, meanwhile, limited comprehending of pathogenesis for schizophrenia has restricted the effective treatment.

METHOD

An integrated multi-omic approach, combining metabolomic platform (LC-MS and ¹H NMR) and transcriptomic platform, was established to differentiate healthy subjects from schizophrenia patients. Based on filtered metabolites and genes, characteristic spectrums were further built. Then, representative metabolites and genes were screened out through Boruta algorithm. Moreover, characteristic diagnostic formulas were established via LASSO regression analysis.

RESULT

As a result, 86 differential metabolites (in line with amino acid metabolism, etc.) and 189 differential expression genes (involving in amino acid metabolic process, etc.) were obtained as potential biomarkers for schizophrenia. The latent interaction between metabolites with genes, such as HMGCLL1 with energy metabolism, etc., was further studied through the analysis of pathway-based integration. Moreover, fine predictive ability was attributed to characteristic metabolomic/transcriptomic diagnostic spectrums/formulas.

CONCLUSION

The functional relationships of filtered metabolites and genes were studied, which could elaborate the pathological process of schizophrenia more systemically, supplying more precise information on mechanism description and diagnostic evidence of schizophrenia.

Application of Metabolomics to Identify Hepatic Biomarkers of Foie Gras Qualities in Duck

Foie gras is a traditional dish in France that contains 50 to 60% of lipids. The high-fat content of the liver improves the organoleptic qualities of foie gras and reduces its technological yield at cooking (TY). As the valorization of the liver as foie gras products is strongly influenced by the TY, classifying the foie gras in their potential technological quality before cooking them is the main challenge for producers. Therefore, the current study aimed to identify hepatic biomarkers of foie gras qualities like liver weight (LW) and TY. A group of 120 male mule ducks was reared and overfed for 6–12 days, and their livers were sampled and analyzed by proton nuclear magnetic resonance (¹H-NMR). Eighteen biomarkers of foie gras qualities were identified, nine for LW and TY, five specific to LW, and four specific to TY. All biomarkers were strongly negatively correlated to the liver weights and positively correlated to the technological yield, except for the lactate and the threonine, and also for the creatine that was negatively correlated to foie gras technological quality. As a result, in heavy livers, the liver metabolism was oriented through a reduction of carbohydrate and amino acid metabolisms, and the plasma membrane could be damaged, which may explain the low technological yield of these livers. The detected biomarkers have been strongly discussed with the metabolism of the liver in nonalcoholic steatohepatitis.

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine ¹H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.

Identification of Plasmatic Biomarkers of Foie Gras Qualities in Duck by Metabolomics

The foie gras is an emblematic product of French gastronomy composed of waterfowl fatty liver. The organoleptic qualities of this product depend on the liver characteristics such as liver weight (LW) and technological yield (TY) at cooking. One of the main issues for producers is to classify the foie gras with high or low technological quality before cooking them. Thus the study aims at identifying biomarkers of these characteristics with non-invasive biomarkers in duck. ¹H-NMR (nuclear magnetic resonance of the proton) analyses were performed on plasma of male mule ducks at different time points during the overfeeding period to obtain a large range of liver characteristics so as to identify plasmatic biomarkers of foie gras. We used two methods, one based on bucket data from the ¹H-NMR spectra and another one based on the fingerprints of several metabolites. PLS analyses and Linear models were performed to identify biomarkers. We identified 18 biomarkers of liver weight and 15 biomarkers of technological yield. As these two quality parameters were strongly correlated (−0.82), 13 biomarkers were common. The lactate was the most important biomarker, the other were mainly amino acids. Contrary to the amino acids, the lactate increased with the liver weight and decreased with the technological yield. We also identified 5 biomarkers specific to LW (3 carbohydrates: glucuronic acid, mannose, sorbitol and 2 amino acids: glutamic acid and methionine) that were negatively correlated to liver weight. It was of main interest to identify 2 biomarkers specific to the technological yield. Contrary to the isovaleric acid, the valine was negatively correlated to the technological yield.

NMRFinder: a novel method for 1D 1H-NMR metabolite annotation

INTRODUCTION

Methods for the automated and accurate identification of metabolites in 1D ¹H-NMR samples are crucial, but this is still an unsolved problem. Most available tools are mainly focused on metabolite quantification, thus limiting the number of metabolites that can be identified. Also, most only use reference spectra obtained under the same specific conditions of the target sample, limiting the use of available knowledge.

OBJECTIVES

The main goal of this work was to develop novel methods to perform metabolite annotation from 1D ¹H-NMR peaks with enhanced reliability, to aid the users in metabolite identification. An essential step was to construct a vast and up-do-date library of reference 1D ¹H-NMR peak lists collected under distinct experimental conditions.

METHODS

Three different algorithms were evaluated for their capacity to correctly annotate metabolites present in both synthetic and real samples and compared to publicly available tools. The best proposed method was evaluated in a plethora of scenarios, including missing references, missing peaks and peak shifts, to assess its annotation accuracy, precision and recall.

RESULTS

We gathered 1816 peak lists for 1387 different metabolites from several sources across different conditions for our reference library. A new method, NMRFinder, is proposed and allows matching 1D ¹H-NMR samples with all the reference peak lists in the library, regardless of acquisition conditions. Metabolites are scored according to the number of peaks matching the samples, how unique their peaks are in the library and how close the spectrum acquisition conditions are in relation to those of the samples. Results show a true positive rate of 0.984 when analysing computationally created samples, while 71.8% of the metabolites were annotated when analysing samples from previously identified public datasets.

CONCLUSION

NMRFinder performs metabolite annotation reliably and outperforms previous methods, being of great value in helping the user to ultimately identify metabolites. It is implemented in the R package specmine.

NMR spectroscopy in wine authentication: An official control perspective

Wine authentication is vital in identifying malpractice and fraud, and various physical and chemical analytical techniques have been employed for this purpose. Besides wet chemistry, these include chromatography, isotopic ratio mass spectrometry, optical spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy, which have been applied in recent years in combination with chemometric approaches. For many years, ² H NMR spectroscopy was the method of choice and achieved official recognition in the detection of sugar addition to grape products. Recently, ¹ H NMR spectroscopy, a simpler and faster method (in terms of sample preparation), has gathered more and more attention in wine analysis, even if it still lacks official recognition. This technique makes targeted quantitative determination of wine ingredients and nontargeted detection of the metabolomic fingerprint of a wine sample possible. This review summarizes the possibilities and limitations of ¹ H NMR spectroscopy in analytical wine authentication, by reviewing its applications as reported in the literature. Examples of commercial and open-source solutions combining NMR spectroscopy and chemometrics are also examined herein, together with its opportunities of becoming an official method.

Joint Automatic Metabolite Identification and Quantification of a Set of 1H NMR Spectra

Metabolomics is a promising approach to characterize phenotypes or to identify biomarkers. It is also easily accessible through NMR, which can provide a comprehensive understanding of the metabolome of any living organisms. However, the analysis of ¹H NMR spectrum remains difficult, mainly due to the different problems encountered to perform automatic identification and quantification of metabolites in a reproducible way. In addition, methods that perform automatic identification and quantification of metabolites are often designed to process one given complex mixture spectrum at a time. Hence, when a set of complex mixture spectra coming from the same experiment has to be processed, the approach is simply repeated independently for every spectrum, despite their resemblance. Here, we present new methods that are the first to either align spectra or to identify and quantify metabolites by integrating information coming from several complex spectra of the same experiment. The performances of these new methods are then evaluated on both simulated and real datasets. The results show an improvement in the metabolite identification and in the accuracy of metabolite quantifications, especially when the concentration is low. This joint procedure is available in version 2.0 of ASICS package.

Non-negative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine

Because of its quantitative character and capability for high-throughput screening, ¹H nuclear magnetic resonance (NMR) spectroscopy is used extensively in the profiling of biofluids such as urine and blood plasma. However, the narrow frequency bandwidth of ¹H NMR spectroscopy leads to a severe overlap of the spectra of components present in the complex mixtures such as biofluids. Therefore, ¹H NMR-based metabolomics analysis is focused on targeted studies related to concentrations of the small number of metabolites. Here, we propose a library-based approach to quantify proportions of overlapping metabolites from ¹H NMR mixture spectra. The method boils down to the linear non-negative least squares (NNLS) problem, whereas proportions of the pure components contained in the library stand for the unknowns. The method is validated on an estimation of the proportions of (i) the 78 pure spectra, presumably related to type 2 diabetes mellitus (T2DM), from their synthetic linear mixture; (ii) metabolites present in 62 ¹H NMR spectra of urine of subjects with T2DM and 62 ¹H NMR spectra of urine of control subjects. In both cases, the in-house library of 210 pure component ¹H NMR spectra represented the design matrix in the related NNLS problem. The proposed method pinpoints 63 metabolites that in a statistically significant way discriminate the T2DM group from the control group and 46 metabolites discriminating control from the T2DM group. For several T2DM-discriminative metabolites, we prove their presence by independent analytical determination or by pointing out the corresponding findings in the published literature.

The maturity in fetal pigs using a multi-fluid metabolomic approach

In mammalian species, the first days after birth are an important period for survival and the mortality rate is high before weaning. In pigs, perinatal deaths average 20% of the litter, with important economic and societal consequences. Maturity is one of the most important factors that influence piglet survival at birth. Maturity can be defined as the outcome of complex mechanisms of intra-uterine development and maturation during the last month of gestation. Here, we provide new insights into maturity obtained by studying the end of gestation at two different stages (3 weeks before term and close to term) in two breeds of pigs that strongly differ in terms of neonatal survival. We used metabolomics to characterize the phenotype, to identify biomarkers, and provide a comprehensive understanding of the metabolome of the fetuses in late gestation in three fluids (plasma, urine, and amniotic fluid). Our results show that the biological processes related to amino acid and carbohydrate metabolisms are critical for piglet maturity. We confirm the involvement of some previously described metabolites associated with delayed growth (e.g., proline and myo-inositol). Altogether, our study proposes new routes for improved characterization of piglet maturity at birth.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.