1H‐NMR Based Metabolomics Profiling of Citrus Juices Produced in Veracruz,México

This study describes the ¹H‐NMR‐based metabolomics profiling of juices from citrus fruits harvested in the state of Veracruz, México. The hydrophilic profile of commercial lemons (Agrio and Persian), tangerines (Fremont and Mónica), oranges (Valencia and Washington Navel), and grapefruits (Red Ruby and Rio Red) was determined. According to our results, 35 metabolites were identified in the ¹H‐NMR profiling. The statistical differences obtained by PCA and OPLS‐DA revealed that specific amino acids, sugars, and organic acids were differential metabolites in the species and cultivars studied. High endogenous levels of sucrose (10–190 mM), α‐glucose, β‐glucose (α‐ and β‐isomers, 40–205 mm ), and fructose (36–170 mm ) were detected in the juices of grapefruits, oranges, and tangerines, whereas citric acid (40–530 mm ) was the principal organic acid in the juices of lemons. To calculate the specific amounts of metabolites from these species and their cultivars, the results were finely analyzed using the qNMR method. According to these calculations, Valencia oranges had the highest concentration of ascorbic acid (>2 mm ). The described ¹H‐NMR method is highly reproducible, inexpensive, and highly robust in comparison to other analytical methods used to determine the hydrophilic profile of citrus juices.     

Baseline Correction for NMR Spectroscopic Metabolomics Data Analysis

Background  

We propose a statistically principled baseline correction method, derived from a parametric smoothing model. It uses a score function to describe the key features of baseline distortion and constructs an optimal baseline curve to maximize it. The parameters are determined automatically by using LOWESS (locally weighted scatterplot smoothing) regression to estimate the noise variance.     

Chemotaxonomic Profiling Through NMR1

A metabolite screening of cyanobacteria was performed by nuclear magnetic resonance (NMR) analysis of the soluble material obtained through sequential extraction of the biomass with three different extractive ability solvents (hexane, ethyl acetate, and methanol). Twenty-five strains from the Coimbra Collection of Algae (ACOI) belonging to different orders in the botanical code that represent three subsections of the Stainer-Rippka classification were used. The ¹H NMR spectra of hexane extracts showed that only two strains of Nostoc genus accumulated triacylglycerols. Monogalactosyldiacylglycerols and digalactosyldiacylglycerols were the major components of the ethyl acetate extracts in a mono- to digalactosyldiacylglycerols ratio of 4.5 estimated by integration of the signals at δ 3.99 and 3.94 ppm (sn3 glycerol methylene). Oligosaccharides of sucrose and mycosporine-like amino acids, among other polar metabolites, were detected in the methanolic extracts. Strains of Nostocales order contained heterocyst glycolipids, whereas sulphoquinovosyldiacylglycerols were absent in one of the studied strains (Microchaete tenera ACOI 1451). Phosphathidylglycerol was identified as the major phospholipid in the methanolic extracts together with minor amounts of phosphatidylcholine based on ¹H, ³¹P 2D correlation experiments. Chemotaxonomic information could be easily obtained through the analysis of the δ 3.0–0.5 ppm (fatty acid distribution) and δ 1.2–1.1 ppm (terminal methyl groups of the aglycons in heterocyst glycolipids) regions of the ¹H NMR spectra of the ethyl acetate and methanol extracts, respectively.     

Metabolomics Profiling for Identification of Novel Potential Markers in Early Prediction of Preeclampsia

Objective

The first aim was to investigate specific signature patterns of metabolites that are significantly altered in first-trimester serum of women who subsequently developed preeclampsia (PE) compared to healthy pregnancies. The second aim of this study was to examine the predictive performance of the selected metabolites for both early onset [EO-PE] and late onset PE [LO-PE].

Methods

This was a case-control study of maternal serum samples collected between 8+0 and 13+6 weeks of gestation from 167 women who subsequently developed EO-PE n = 68; LO-PE n = 99 and 500 controls with uncomplicated pregnancies. Metabolomics profiling analysis was performed using two methods. One has been optimized to target eicosanoids/oxylipins, which are known inflammation markers and the other targets compounds containing a primary or secondary biogenic amine group. Logistic regression analyses were performed to predict the development of PE using metabolites alone and in combination with first trimester mean arterial pressure (MAP) measurements.

Results

Two metabolites were significantly different between EO-PE and controls (taurine and asparagine) and one in case of LO-PE (glycylglycine). Taurine appeared the most discriminative biomarker and in combination with MAP predicted EO-PE with a detection rate (DR) of 55%, at a false-positive rate (FPR) of 10%.

Conclusion

Our findings suggest a potential role of taurine in both PE pathophysiology and first trimester screening for EO-PE.     

Flexible,Fast and Accurate Sequence Alignment Profiling on GPGPU with PaSWAS

Motivation

To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis.

Results

With the Parallel SW Alignment Software (PaSWAS) it is possible (a) to have easy access to the computational power of NVIDIA-based general purpose graphics processing units (GPGPUs) to perform high-speed sequence alignments, and (b) retrieve relevant information such as score, number of gaps and mismatches. The software reports multiple hits per alignment. The added value of the new SW implementation is demonstrated with two test cases: (1) tag recovery in next generation sequence data and (2) isotype assignment within an immunoglobulin 454 sequence data set. Both cases show the usability and versatility of the new parallel Smith-Waterman implementation.     

High-Resolution Metabolomics with Acyl-CoA Profiling Reveals Widespread Remodeling in Response to Diet

The availability of acyl-Coenzyme A (acyl-CoA) thioester compounds affects numerous cellular functions including autophagy, lipid oxidation and synthesis, and post-translational modifications. Consequently, the acyl-CoA level changes tend to be associated with other metabolic alterations that regulate these critical cellular functions. Despite their biological importance, this class of metabolites remains difficult to detect and quantify using current analytical methods. Here we show a universal method for metabolomics that allows for the detection of an expansive set of acyl-CoA compounds and hundreds of other cellular metabolites. We apply this method to profile the dynamics of acyl-CoA compounds and corresponding alterations in metabolism across the metabolic network in response to high fat feeding in mice. We identified targeted metabolites (>50) and untargeted features (>1000) with significant changes (FDR < 0.05) in response to diet. A substantial extent of this metabolic remodeling exhibited correlated changes in acyl-CoA metabolism with acyl-carnitine metabolism and other features of the metabolic network that together can lead to the discovery of biomarkers of acyl-CoA metabolism. These findings show a robust acyl-CoA profiling method and identify coordinated changes of acyl-CoA metabolism in response to nutritional stress.Thioester compounds containing acyl-coenzyme A (acyl-CoA)¹ are key metabolites in intermediary metabolism. The most prominent of which is acetyl-CoA whose levels regulate critical cellular processes such as energy metabolism, protein acetylation, lipid synthesis and catabolism, and even autophagy (1–4). Other acyl-CoA compounds are also increasingly appreciated as playing important roles in diverse cellular processes (5–8). These compounds are generated from multiple pathways, such as glycolysis, the citric acid cycle (TCA cycle), beta-oxidation, and branched chain amino acid catabolism. As the acyl group carrier, acyl-CoA can partake in chemical reactions on proteins including histones resulting in mediation of chromatin biology. Therefore, considerable effort has been spent on developing methods for acyl-CoA and corresponding acyl protein modification measurements (9–17). Liquid chromatography coupled to mass spectrometry (LC-MS) is the most frequently used method for small molecule analysis in large part because of superior sensitivity. Moreover, LC-MS analysis can handle a broad range of complex biological mixtures and the analysis is relatively easier compared with many other methods, such as NMR, scintillation counting, and UV detection.Reversed phase LC coupled to a triple quadrupole mass spectrometer has been frequently used as for targeted measurements of specific acyl-CoA compounds, because acyl-CoA compounds undergo a common fragmentation, the neutral loss of adenosine diphosphate, which is the basis of multiple reaction monitoring for acyl-CoA measurements. Especially when stable isotope labeled acyl-CoA standards are used, this method has shown high accuracy and precision (11, 14). However, these methods involve several laborious steps of sample purification and enrichment before LC-MS analysis, such as solid phase extraction, which in addition to often being time- and cost-prohibitive, can also result in poor sensitivity and accuracy because of imperfect metabolite recovery. Moreover, reversed phase ion-paired chromatography coupled to high-resolution MS has also been used for short, medium, and long chain acyl-CoA identification or quantification with the help of stable isotope labeled standards (10, 13). However, these methods were also developed with limited coverage of metabolites, and the quantitative capacity without using stable isotope labeled standards was not evaluated.We therefore developed a novel method for sensitive, rapid, and quantitative acyl-CoA profiling, with a compatible sample preparation procedure that has been previously shown for polar metabolite analysis (18). The method involves LC-MS using reversed phase chromatographic separation coupled to a high-resolution Orbitrap mass spectrometer with label free quantitation. With a single liquid extraction from a few milligrams of tissue, followed by three separate chromatography methods, a broad coverage of metabolites is achieved, which is especially valuable when sample availability is limited.To show the utility of our approach, we considered the alterations in the metabolic network that accompany high fat (HF) feeding. Conditions of high fat feeding that induce nutritional stress are shown to induce global changes in enzymes in metabolism (19, 20), but a comprehensive assessment of the global alterations in metabolism that remains include possible remodeling of acyl-CoA metabolism remain unknown. We reasoned that under such a condition, a dynamic response involving alterations in acyl-CoA levels along with the rest of the metabolome may be observed. This remodeling could also be associated with acyl-carnitine metabolism that often serves as both a readout of acyl-CoA metabolism and other features of metabolism status. Propionyl-CoA that is mainly generated from branched chain amino acid (BCAAs) catabolism and has been implicated in contributing to insulin resistance (21, 22), exhibits large changes. We applied our method to understand the metabolic changes that accompany HF feeding in mouse liver (23). We identify acyl-CoA compounds with dramatic changes after administration of a HF diet. Hierarchical clustering and principle component analysis (PCA) of metabolites measured in liver tissue show further diet-dependent metabolic profiling changes. Moreover, measurements of acyl-carnitine compounds have been used to reflect acyl-CoA levels (24), but the correlation between these two species has not been studied. Our method with coverage of both acyl-carnitine and acyl-CoA enabled us to evaluate acyl-carnitine as a biomarker of acyl-CoA status. In turn, we were able to confirm many relationships between acyl-CoA and acyl-carnitine levels but also discovered several unexpected relationships as well.     

UHPLC-Q-Orbitrap HR-MS-Based Metabolomics for Profiling the Sida rhombifolia Metabolites with Different Plant Organs and Cultivation Ages

Plant organs and cultivation ages can result in different compositions and concentration levels of plant metabolites. The metabolite profile of plants can be determined using liquid chromatography. This study determined the metabolite profiles of leaves, stems, and roots of Sida rhombifolia at different cultivation ages at 3, 4, and 5 months post-planting (MPP) using liquid chromatography-mass spectrometry/mass spectrometry (LC/MS/MS). The results identified that 41 metabolites in S. rhombifolia extract for all plant organs and cultivation ages. We successfully identified approximately 36 (leaves), 22 (stems), and 18 (roots) compounds in all extract. Using principal component analysis (PCA) with peak area as the variable, we clustered all sample extracts based on plant organs and cultivation ages. As a result of PCA, S. rhombifolia extracts were grouped according to plant organs and cultivation ages. In conclusion, a clear difference in the composition and concentration levels of metabolites was observed in the leaves, stems, and roots of S. rhombifolia harvested at 3-, 4-, and 5-MPP.     

Metabolomics Profiling of Tunisian Sonchus oleraceus L. Extracts and Their Antioxidant Activities

Sonchus oleraceus (L.) L. (Asteraceae) is an edible wild plant, known for its uses in traditional medicine. The aim of this study is to explore the phytochemical composition of the aerial parts (AP) and roots (R) of aqueous extracts of Sonchus oleraceus L. growing in Tunisia, using liquid chromatography-tandem mass spectrometry(LC/MS/MS), and determine the content of polyphenols and antioxidant activities. Results showed that aqueous extracts of AP and R contained, respectively, 195.25±33 μg/g and 118.66±14 μg/g gallic acid equivalent (GAE), and 52.58±7 μg/g and 3.2±0.3μg/g quercetin equivalent. AP and R extracts also contained tannins, 581.78±33 μg/g and 948.44±19 μg/g GAE. The AP extract in the 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) scavenging activities, hydroxyl radical scavenging (OH−) and in cupric reducing antioxidant activity (CUPRAC) assays were respectively 0.325±0.036 mg/mL, 0.053±0.018 mg/mL, 0.696±0.031 mg/mL and 60.94±0.004 μMTE/g, while the R extract using the same assays showed, 0.209±0.052 mg/mL, 0.034±0.002 mg/mL, 0.444±0.014 mg/mL and 50.63±0.006 μM Trolox equivalent/g, respectively. A total of 68 compounds were tentatively identified by LC/MS/MS in both extracts in which quinic acid, pyrogallol, osthrutin, piperine, gentisic acid, fisetin, luteolin, caffeic acid, gingerol, were the most abundant in the LC/MS/MS spectrum. Many of these metabolites were found for the first time in Tunisian Sonchus oleraceus L. which may take account for the antioxidant activities exhibited by the plant.     

Anti-Inflammatory Activity of Lauraceae Plant Species and Prediction Models Based on Their Metabolomics Profiling Data

The Lauraceae is a botanical family known for its anti-inflammatory potential. However, several species have not yet been studied. Thus, this work aimed to screen the anti-inflammatory activity of this plant family and to build statistical prediction models. The methodology was based on the statistical analysis of high-resolution liquid chromatography coupled with mass spectrometry data and the ex vivo anti-inflammatory activity of plant extracts. The ex vivo results demonstrated significant anti-inflammatory activity for several of these plants for the first time. The sample data were applied to build anti-inflammatory activity prediction models, including the partial least square acquired, artificial neural network, and stochastic gradient descent, which showed adequate fitting and predictive performance. Key anti-inflammatory markers, such as aporphine and benzylisoquinoline alkaloids were annotated with confidence level 2. Additionally, the validated prediction models proved to be useful for predicting active extracts using metabolomics data and studying their most bioactive metabolites.     

A Combined Proteomics and Metabolomics Profiling of Gastric Cardia Cancer Reveals Characteristic Dysregulations in Glucose Metabolism

Gastric cardia cancer (GCC), which occurs at the gastric-esophageal boundary, is one of the most malignant tumors. Despite its high mortality and morbidity, the molecular mechanism of initiation and progression of this disease is largely unknown. In this study, using proteomics and metabolomics approaches, we found that the level of several enzymes and their related metabolic intermediates involved in glucose metabolism were deregulated in GCC. Among these enzymes, two subunits controlling pyruvic acid efflux, lactate dehydrogenase A (LDHA) and pyruvate dehydrogenase B (PDHB), were further analyzed in vitro. Either down-regulation of LDH subunit LDHA or overexpression of PDH subunit PDHB could force pyruvic acid into the Krebs cycle rather than the glycolysis process in AGS gastric cancer cells, which inhibited cell growth and cell migration. Our results reflect an important glucose metabolic signature, especially the dysregulation of pyruvic acid efflux in the development of GCC. Forced transition from glycolysis to the Krebs cycle had an inhibitory effect on GCC progression, providing potential therapeutic targets for this disease.Gastric cardia cancer (GCC),1 which occurs at the gastric-esophageal boundary, is one of the most malignant tumors. Despite the steadily falling incidence of gastric non-cardia cancer in the past two decades (1), the rate of GCC has risen rapidly, establishing gastric cancer as the second major cause of cancer-related deaths throughout the world (2). GCC has become a significant cause of mortality and morbidity both in the west (3–5) and in Asia (6, 7), especially in China (8). Although this cancer has become an important health problem worldwide, the its pathogenesis has not been well characterized (1). Most patients are diagnosed at an advanced stage, contributing to the high mortality rate of the disease.Systematic proteomics analysis has proved to be a powerful approach in a variety of human cancer research, including lung (9), esophagus (10), gastric (11), liver (12), breast (13), and brain cancer (14). Metabolomics, another new bio-omics technology recently introduced into cancer research (15), is the global analysis of the small metabolites produced by normal or pathologic cellular processes. Some metabolic intermediates have been identified as new cancer biomarkers (16).Using proteomics and metabolomics methods in this study, we found that a series of proteins and metabolic intermediates, mainly involved in glucose metabolism, were altered during the development of GCC. The high activity of anaerobic glycolysis and the impairment of aerobic respiration occurring in these cells recapitulated the Warburg effect (17). Further studies using a gastric cancer cell line demonstrated that the predominant anaerobic glycolysis was essential for tumor cells to sustain rapid proliferation, whereas forced transition from anaerobic glycolysis to aerobic respiration inhibited the growth of tumor cells. In conclusion, our study revealed the major metabolic alterations essential for the development of GCC and discovered a biomarker signature of GCC. Such a finding has the potential to improve early diagnosis and prognosis and helps to identify new therapeutic targets.     

Metabolomics for industrial fermentation

Bioprocess and Biosystems Engineering - Metabolomics is essential to understand the metabolism and identify engineering targets to improve the performances of strains and bioprocesses. Although...     

Using NMR Metabolomics to Investigate Tricarboxylic Acid Cycle-dependent Signal Transduction in Staphylococcus epidermidis

Staphylococcus epidermidis is a skin-resident bacterium and a major cause of biomaterial-associated infections. The transition from residing on the skin to residing on an implanted biomaterial is accompanied by regulatory changes that facilitate bacterial survival in the new environment. These regulatory changes are dependent upon the ability of bacteria to “sense” environmental changes. In S. epidermidis, disparate environmental signals can affect synthesis of the biofilm matrix polysaccharide intercellular adhesin (PIA). Previously, we demonstrated that PIA biosynthesis is regulated by tricarboxylic acid (TCA) cycle activity. The observations that very different environmental signals result in a common phenotype (i.e. increased PIA synthesis) and that TCA cycle activity regulates PIA biosynthesis led us to hypothesize that S. epidermidis is “sensing” disparate environmental signals through the modulation of TCA cycle activity. In this study, we used NMR metabolomics to demonstrate that divergent environmental signals are transduced into common metabolomic changes that are “sensed” by metabolite-responsive regulators, such as CcpA, to affect PIA biosynthesis. These data clarify one mechanism by which very different environmental signals cause common phenotypic changes. In addition, due to the frequency of the TCA cycle in diverse genera of bacteria and the intrinsic properties of TCA cycle enzymes, it is likely the TCA cycle acts as a signal transduction pathway in many bacteria.     

Accurate NMR structures through minimization of an extended hybrid energy

The use of generous distance bounds has been the hallmark of NMR structure determination. However, bounds necessitate the estimation of data quality before the calculation, reduce the information content, introduce human bias, and allow for major errors in the structures. Here, we propose a new rapid structure calculation scheme based on Bayesian analysis. The minimization of an extended energy function, including a new type of distance restraint and a term depending on the data quality, results in an estimation of the data quality in addition to coordinates. This allows for the determination of the optimal weight on the experimental information. The resulting structures are of better quality and closer to the X-ray crystal structure of the same molecule. With the new calculation approach, the analysis of discrepancies from the target distances becomes meaningful. The strategy may be useful in other applications-for example, in homology modeling.     

A Strategy for Sensitive,Large Scale Quantitative Metabolomics

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.  Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.     

Profiling of central metabolism in human cancer cells by two-dimensional NMR, GC-MS analysis, and isotopomer modeling

Tracking metabolic profiles has the potential to reveal crucial enzymatic steps that could be targeted in the drug discovery process. It is of special importance for various types of cancer known to be associated with substantial rewiring of metabolic networks. Here we introduce an integrated approach for the analysis of metabolome that allows us to simultaneously assess pathway activities (fluxes) and concentrations of a large number of the key components involved in central metabolism of human cells. This is accomplished by in vivo labeling with [U-¹³C]glucose followed by two-dimensional nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) analysis. A comprehensive isotopomer model was developed, which enabled us to compare fluxes through the key central metabolic pathways including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, anaplerotic reactions, and biosynthetic pathways of fatty acids and amino acids. The validity and strength of this approach is illustrated by its application to a number of perturbations to breast cancer cells, including exposure to hypoxia, drug treatment, and tumor progression. We observed significant differences in the activities of specific metabolic pathways resulting from these perturbations and providing new mechanistic insights. Based on these findings we conclude that the developed metabolomic approach constitutes a promising analytical tool for revealing specific metabolic phenotypes in a variety of cell types and pathological conditions. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Chen Yang and Adam D. Richardson contributed equally to this work.     

Effects of NMR Spectral Resolution on Protein Structure Calculation

Adequate digital resolution and signal sensitivity are two critical factors for protein structure determinations by solution NMR spectroscopy. The prime objective for obtaining high digital resolution is to resolve peak overlap, especially in NOESY spectra with thousands of signals where the signal analysis needs to be performed on a large scale. Achieving maximum digital resolution is usually limited by the practically available measurement time. We developed a method utilizing non-uniform sampling for balancing digital resolution and signal sensitivity, and performed a large-scale analysis of the effect of the digital resolution on the accuracy of the resulting protein structures. Structure calculations were performed as a function of digital resolution for about 400 proteins with molecular sizes ranging between 5 and 33 kDa. The structural accuracy was assessed by atomic coordinate RMSD values from the reference structures of the proteins. In addition, we monitored also the number of assigned NOESY cross peaks, the average signal sensitivity, and the chemical shift spectral overlap. We show that high resolution is equally important for proteins of every molecular size. The chemical shift spectral overlap depends strongly on the corresponding spectral digital resolution. Thus, knowing the extent of overlap can be a predictor of the resulting structural accuracy. Our results show that for every molecular size a minimal digital resolution, corresponding to the natural linewidth, needs to be achieved for obtaining the highest accuracy possible for the given protein size using state-of-the-art automated NOESY assignment and structure calculation methods.