Processing and modeling of nuclear magnetic resonance (NMR) metabolic profiles

Understanding the Seasonal Effect of Metabolite Production in Terminalia catappa L. Leaves through a Concatenated MS- and NMR-Based Metabolomics Approach

Terminalia catappa L. (Combretaceae) is a medicinal plant that is part of the Brazilian biodiversity; this plant is popularly used for the treatment of a wide range of diseases. To better understand the chemical composition of T. catappa in different seasons, we conducted a thorough study using LC-MS and NMR data analysis techniques. The study helped obtain a chemical profile of the plant ethanolic extracts in different seasons of the year (spring, summer, autumn, and winter). The dereplication of LC-HRMS data allowed the annotation of 90 compounds in the extracts of T. catappa (hydrolyzable tannins, ellagic acid derivatives, and glycosylated flavonoids). Triterpenes and C-glycosyl flavones were the compounds that significantly contributed to differences observed between T. catappa plant samples harvested in autumn/winter and spring, respectively. The variations observed in the compound composition of the plant leaves may be related to processes induced by environmental stress and leaf development. Data fusion applied in the metabolomic profiling study allowed us to identify metabolites with greater confidence, and provided a better understanding regarding the production of specialized metabolites in T. catappa leaves under different environmental conditions, which may be useful to establish appropriate quality criteria for the standardization of this medicinal plant.

A Lactobacilli diet that confers MRSA resistance causes amino acid depletion and increased antioxidant levels in the C. elegans host

Probiotic bacteria are increasingly popular as dietary supplements and have the potential as alternatives to traditional antibiotics. We have recently shown that pretreatment with Lactobacillus spp. Lb21 increases the life span of C. elegans and results in resistance toward pathogenic methicillin-resistant Staphylococcus aureus (MRSA). The Lb21-mediated MRSA resistance is dependent on the DBL-1 ligand of the TGF-β signaling pathway. However, the underlying changes at the metabolite level are not understood which limits the application of probiotic bacteria as timely alternatives to traditional antibiotics. In this study, we have performed untargeted nuclear magnetic resonance-based metabolic profiling. We report the metabolomes of Lactobacillus spp. Lb21 and control E. coli OP50 bacteria as well as the nematode-host metabolomes after feeding with these diets. We identify 48 metabolites in the bacteria samples and 51 metabolites in the nematode samples and 63 across all samples. Compared to the control diet, the Lactobacilli pretreatment significantly alters the metabolic profile of the worms. Through sparse Partial Least Squares discriminant analyses, we identify the 20 most important metabolites distinguishing probiotics from the regular OP50 food and worms fed the two different bacterial diets, respectively. Among the changed metabolites, we find lower levels of essential amino acids as well as increased levels of the antioxidants, ascorbate, and glutathione. Since the probiotic diet offers significant protection against MRSA, these metabolites could provide novel ways of combatting MRSA infections.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

PURPOSE OF REVIEW

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer.

RECENT FINDINGS

Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Metabolomic Pathways to Osteoporosis in Middle-Aged Women: A Genome-Metabolome-Wide Mendelian Randomization Study

The metabolic state of the body can be a major determinant of bone health. We used a Mendelian randomization approach to identify metabolites causally associated with bone mass to better understand the biological mechanisms of osteoporosis. We tested bone phenotypes (femoral neck, total hip, and lumbar spine bone mineral density [BMD]) for association with 280 fasting blood metabolites in 6055 women from TwinsUK cohort with genomewide genotyping scans. Causal associations between metabolites and bone phenotypes were further assessed in a bidirectional Mendelian randomization study using genetic markers/scores as instrumental variables. Significant associations were replicated in 624 participants from the Hong Kong Osteoporosis Study (HKOS). Fifteen metabolites showed direct associations with bone phenotypes after adjusting for covariates and multiple testing. Using genetic instruments, four of these metabolites were found to be causally associated with hip or spine BMD. These included androsterone sulfate, epiandrosterone sulfate, 5alpha-androstan-3beta17beta-diol disulfate (encoded by CYP3A5), and 4-androsten-3beta17beta-diol disulfate (encoded by SULT2A1). In the HKOS population, all four metabolites showed significant associations with hip and spine BMD in the expected directions. No causal reverse association between BMD and any of the metabolites were found. In the first metabolome-genomewide Mendelian randomization study of human bone mineral density, we identified four novel biomarkers causally associated with BMD. Our findings reveal novel biological pathways involved in the pathogenesis of osteoporosis. © 2017 American Society for Bone and Mineral Research.

Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine

1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling

¹H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum ¹H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: an overview

The etiopathogenesis of cardiovascular diseases (CVDs) is multifactorial. Adverse blood pressure (BP) is a major independent risk factor for epidemic CVD affecting ~40% of the adult population worldwide and resulting in significant morbidity and mortality. Metabolic phenotyping of biological fluids has proven its application in characterizing low-molecular-weight metabolites providing novel insights into gene-environmental-gut microbiome interaction in relation to a disease state. In this review, we synthesize key results from the INTERnational study of MAcro/micronutrients and blood Pressure (INTERMAP) Study, a cross-sectional epidemiologic study of 4680 men and women aged 40-59 years from Japan, the People's Republic of China, the United Kingdom and the United States. We describe the advancements we have made regarding the following: (1) analytical techniques for high-throughput metabolic phenotyping; (2) statistical analyses for biomarker identification; (3) discovery of unique food-specific biomarkers; and (4) application of metabolome-wide association studies to gain a better understanding into the molecular mechanisms of cross-cultural and regional BP differences.

(13)C-NMR-Based Metabolomic Profiling of Typical Asian Soy Sauces

It has been a strong consumer interest to choose high quality food products with clear information about their origin and composition. In the present study, a total of 22 Asian soy sauce samples have been analyzed in terms of (13)C-NMR spectroscopy. Spectral data were analyzed by multivariate statistical methods in order to find out the important metabolites causing the discrimination among typical soy sauces from different Asian regions. It was found that significantly higher concentrations of glutamate in Chinese red cooking (CR) soy sauce may be the result of the manual addition of monosodium glutamate (MSG) in the final soy sauce product. Whereas lower concentrations of amino acids, like leucine, isoleucine and valine, observed in CR indicate the different fermentation period used in production of CR soy sauce, on the other hand, the concentration of some fermentation cycle metabolites, such as acetate and sucrose, can be divided into two groups. The concentrations of these fermentation cycle metabolites were lower in CR and Singapore Kikkoman (SK), whereas much higher in Japanese shoyu (JS) and Taiwan (China) light (TL), which depict the influence of climatic conditions. Therefore, the results of our study directly indicate the influences of traditional ways of fermentation, climatic conditions and the selection of raw materials and can be helpful for consumers to choose their desired soy sauce products, as well as for researchers in further authentication studies about soy sauce.

Data Fusion in Metabolomics and Proteomics for Biomarker Discovery

Proteomics and metabolomics provide key insights into status and dynamics of biological systems. These molecular studies reveal the complex mechanisms involved in disease or aging processes. Invaluable information can be obtained using various analytical techniques such as nuclear magnetic resonance, liquid chromatography, or gas chromatography coupled to mass spectrometry. Each method has inherent advantages and drawbacks, but they are complementary in terms of biological information.The fusion of different measurements is a complex topic. We describe here a framework allowing combining multiple data sets, provided by different analytical platforms. For each platform, the relevant information is extracted in the first step. The obtained latent variables are then fused and further analyzed. The influence of the original variables is then calculated back and interpreted.

Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies

Metabolomics is the field of "-omics" research concerned with the comprehensive characterization of the small low-molecular-weight metabolites in biological samples. In epidemiology, it represents an emerging technology and an unprecedented opportunity to measure environmental and other exposures with improved precision and far less measurement error than with standard epidemiologic methods. Advances in the application of metabolomics in large-scale epidemiologic research are now being realized through a combination of improved sample preparation and handling, automated laboratory and processing methods, and reduction in costs. The number of epidemiologic studies that use metabolic profiling is still limited, but it is fast gaining popularity in this area. In the present article, we present a roadmap for metabolomic analyses in epidemiologic studies and discuss the various challenges these data pose to large-scale studies. We discuss the steps of data preprocessing, univariate and multivariate data analysis, correction for multiplicity of comparisons with correlated data, and finally the steps of cross-validation and external validation. As data from metabolomic studies accumulate in epidemiology, there is a need for large-scale replication and synthesis of findings, increased availability of raw data, and a focus on good study design, all of which will highlight the potential clinical impact of metabolomics in this field.

13C NMR metabolomic evaluation of immediate and delayed mild hypothermia in cerebrocortical slices after oxygen-glucose deprivation

BACKGROUND

Mild brain hypothermia (32°-34°C) after human neonatal asphyxia improves neurodevelopmental outcomes. Astrocytes but not neurons have pyruvate carboxylase and an acetate uptake transporter. C nuclear magnetic resonance spectroscopy of rodent brain extracts after administering [1-C]glucose and [1,2-C]acetate can distinguish metabolic differences between glia and neurons, and tricarboxylic acid cycle entry via pyruvate dehydrogenase and pyruvate carboxylase.

METHODS

Neonatal rat cerebrocortical slices receiving a C-acetate/glucose mixture underwent a 45-min asphyxia simulation via oxygen-glucose-deprivation followed by 6 h of recovery. Protocols in three groups of N=3 experiments were identical except for temperature management. The three temperature groups were: normothermia (37°C), hypothermia (32°C for 3.75 h beginning at oxygen--glucose deprivation start), and delayed hypothermia (32°C for 3.75 h, beginning 15 min after oxygen-glucose deprivation start). Multivariate analysis of nuclear magnetic resonance metabolite quantifications included principal component analyses and the L1-penalized regularized regression algorithm known as the least absolute shrinkage and selection operator.

RESULTS

The most significant metabolite difference (P<0.0056) was [2-C]glutamine's higher final/control ratio for the hypothermia group (1.75±0.12) compared with ratios for the delayed (1.12±0.12) and normothermia group (0.94±0.06), implying a higher pyruvate carboxylase/pyruvate dehydrogenase ratio for glutamine formation. Least Absolute Shrinkage and Selection Operator found the most important metabolites associated with adenosine triphosphate preservation: [3,4-C]glutamate-produced via pyruvate dehydrogenase entry, [2-C]taurine-an important osmolyte and antioxidant, and phosphocreatine. Final principal component analyses scores plots suggested separate cluster formation for the hypothermia group, but with insufficient data for statistical significance.

CONCLUSIONS

Starting mild hypothermia simultaneously with oxygen-glucose deprivation, compared with delayed starting or no hypothermia, has higher pyruvate carboxylase throughput, suggesting that better glial integrity is one important neuroprotection mechanism of earlier hypothermia.