Metabonomics/Metabolomics

Present study investigated the metabolic profile of blood plasma of patients with Celiac disease (CeD) and healthy controls (HC) using in-vitro nuclear magnetic resonance (NMR) spectroscopy to get an insight into the metabolic abnormalities in CeD and to determine the biomarker/s, if any, for the diagnosis of CeD. The concentration of metabolites was determined using one-dimensional NMR at 700 MHz. The blood plasma from patients with CeD showed significantly higher (p<0.05) concentrations of several metabolites such as alanine (Ala), glycine (Gly), acetate (Ace) and creatine (Cr) compared to the HC subjects. While, concentration of creatinine (Crn) was significantly lower (p<0.05) in blood plasma of CeD patients. These findings suggested that the gluten induced inflammation resulted in significant alterations in metabolic activity in CeD. Partial least square discriminant analysis (PLS-DA) clearly distinguished the two groups in separate clusters on the score plot of samples from CeD patients and HC. Our results provided an insight into the alterations in metabolic pathways in CeD and indicated that the NMR spectroscopy of blood plasma in a large cohort of CeD patients may aid in determining the non-invasive biomarker/s for the diagnosis of CeD.

Methanogens are restricted to a few genera of Archaea, however they have great importance in the carbon cycle, impacting climactic considerations, and also find a role in renewable energy in the form of biogas. Here, we examine the microbial contribution to the production of methane in a sargassum fed anaerobic saltwater bioreactor, which are poorly characterized compared to fresh water bioreactors, using a comprehensive functional metagenomics approach. Despite abundant production of methane, we detected a low proportion of Archaea in the system using 16S rRNA community profile analyses. We address the low representation using an additional 16S rRNA analysis of shotgun data and a consideration of CO 2 :CH 4 production. Using a novel network alignment and tree building approach, we measured similarity between the meta-metabolic capabilities of different anaerobic microbial communities. The saltwater bioreactor samples clustered together, validating the approach and providing a method of determining meta-metabolic similarity between microbial communities, with a range of potential applications. We also introduce a number of additional approaches for examining and interpreting meta-metabolic network topology. The low abundance of methanogens appears as a common property of such anaerobic systems and likely reflects the relatively poor energetics of methanogens, while examination of key enzymes confirms that hydrogen producing bacteria are the major fermentative guild. Our results indicate that the use of readily available seawater and marine macroalgae is a promising approach to the production of biogas as a source of renewable energy.

This review illustrates the need to use nuclear magnetic resonance (NMR) spectroscopy for the quantitative analysis of small molecules in their crude forms and in mixtures.

We provide the basic concepts of quantitative NMR (qNMR), a brief description of important acquisition and processing parameters responsible for obtaining high-quality, reproducible NMR spectra in order to maximize accuracy, and the latest referencing techniques used for quantitative analysis.

We also describe methods that are used for quantitative analysis including calibration-curve and standard-addition methods. Further, we briefly address validation of qNMR spectroscopy and its major applications in various scientific disciplines

Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today’s web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety
of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://
www.metaboanalyst.ca

A method of choosing the correction factor based on Bloch equation for the quantitative estimation of metabolite from 1H NMR spectra recorded with reduced recycle delay is prescribed. The procedure reduces the experimental time without substantially compromising the accuracy of quantitative estimation. It is based on choosing the correction factors, which depend on T1 and T2 of the metabolite and recycle delay used for recording the spectra. It is validated by studying a mixture of amino acids with known concentration of constituents and human serum sample and it provides accuracy of quantitative estimation to 95–96%.

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal disease that develops relatively symptom-free and is therefore advanced at the time of diagnosis. The absence of early symptoms and effective treatments has created a critical need for identifying and developing new noninvasive biomarkers and therapeutic targets.

Experimental Design: We investigated the metabolism of a panel of PDAC cell lines in culture and noninvasively in vivo with 1H magnetic resonance spectroscopic imaging (MRSI) to identify noninvasive biomarkers and uncover potential metabolic targets.

Results: We observed elevated choline-containing compounds in the PDAC cell lines and tumors. These elevated choline-containing compounds were easily detected by increased total choline (tCho) in vivo, in spectroscopic images obtained from tumors. Principal component analysis of the spectral data identified additional differences in metabolites between immortalized human pancreatic cells and neoplastic PDAC cells. Molecular characterization revealed overexpression of choline kinase (Chk)-α, choline transporter 1 (CHT1), and choline transporter–like protein 1 (CTL1) in the PDAC cell lines and tumors.

Conclusions: Collectively, these data identify new metabolic characteristics of PDAC and reveal potential metabolic targets. Total choline detected with 1H MRSI may provide an intrinsic, imaging probe–independent biomarker to complement existing techniques in detecting PDAC. The expression of Chk-α, CHT1, and CTL1 may provide additional molecular markers in aspirated cytological samples. Clin Cancer Res; 21(2); 386–95. ©2014 AACR.

⎯The regularities that shape the composition of low molecular weight organic compounds (LMWOCs) in aquatic macrophytes in response to aquatic environment alterations remain poorly characterized. The aim of the present study consists of a comparative interannual investigation into LMWOC composition in rigid hornwort (Ceratophyllum demersum L.) from a Volga-Akhtuba floodplain lake with a variable trophic state. A high variability of LMWOC composition and individual compound levels in hornwort is detected as different trophic states of the water body are analyzed. Active allelochemicals are the predominant LMWOCs in the case of a " macrophytic " mesotrophic state of the lake, with fatty acids (the free fatty acid fraction) apparently being the most important in this group. Hornwort LMWOC composition in the case of a " cyanobacterial " eutrophic type of lake development is characterized by the predomination of compounds that enhance the protective reactions (manool being the most important) under the conditions of suppression by cyanobacteria, which is also manifested as an almost twofold decrease in the overall intensity of organic-compound biosynthesis.

Calorie restriction (CR) remains the most robust intervention to extend lifespan and improve health span. Using a global mass spectrometry-based metabolomic approach, we identified 193 metabolites that were significantly differentially expressed (SDE) in the livers of C57BL/6 mice, fed graded levels of CR (10, 20, 30 and 40% CR) compared to mice fed ad libitum for 12 h a day. The differential expression of metabolites also varied with the different feeding groups. Pathway analysis revealed that graded CR had an impact on carnitine synthesis and the carnitine shuttle pathway, sphingosine-1-phosphate (S1P) signalling and methion-ine metabolism. S1P, sphingomyelin and L-carnitine were negatively correlated with body mass, leptin, insulin-like growth factor-1 (IGF-1) and major urinary proteins (MUPs). In addition, metabolites which showed a graded effect, such as ceramide, S1P, taurocholic acid and L-carnitine, responded in the opposite direction to previously observed age-related changes. We suggest that the modulation of this set of metabolites may improve liver processes involved in energy release from fatty acids. S1P also negatively correlated with catalase activity and body temperature , and positively correlated with food anticipatory activity. Injecting mice with S1P or an S1P receptor 1 agonist did not precipitate changes in body temperature, physical activity or food intake suggesting that these correlations were not causal relationships.

Pus samples obtained from 109 patients with liver abscess were examined by NMR spectroscopy. To our knowledge this is the first report on metabolic profiling of liver abscesses. Fifty metabolites were identified by combination of one (1D) and two-dimensional (2D) NMR spectra. Metabolic derangements were evaluated for differentiation between amoebic (ALA) and pyogenic liver abscess (PLA). The NMR results indicate that aspartate, asparagine and galactose, integral components of lipoproteophophoglycans (LPG) of the cell wall of Entamoeba histolytica are metabolic biomarkers of ALA. On the other hand, acetate, propionate, butyrate, succinate and formate, the fermentation products the facultative anaerobes are significantly prevalent in PLA. The NMR based metabolic profile of ALA and PLA are evaluated taking polymerase chain reaction (PCR) and bacterial culture as gold standard method. However, when NMR results were compared with culture and PCR methods, a correct diagnosis of 94.11% in ALA (n = 85) and 100% in PLA (n = 10) cases were observed. NMR spectroscopy in conjunction with PCR and culture can expedite in differentiating ALA from PLA.

Our present study focuses on the identification of predictive biomarkers in serum for the early diagnosis of endometriosis in a minimally invasive manner using 1H-NMR based metabonomics. PLS-DA modeling of bins obtained from CPMG spectra of serum samples discriminated endometriosis patients from controls with sensitivity and specificity levels of about 80% and 90%, respectively. Compared with those from controls, serum samples from endometriosis patients showed increased levels of lactate, 3-hydroxybutyrate, alanine, leucine, valine, threonine, lysine, glycerophosphatidylcholine, succinic acid and 2-hydroxybutyrate as well as decreased levels of lipids, glucose, isoleucine and arginine. Our work offers valuable information for non-invasive diagnosis of endometriosis and may be of potential benefit to understand pathogenesis of the disease.

Primary renal hypouricemia (PRH) refers to a rare condition of increased renal urate clearance, caused by an isolated inborn error of membrane transport of urate in the renal proximal tubule. Several cases of exercise-induced acute renal failure and urolithiasis have been reported. This is the first study that assessed tubular function in PRH using NMR-based metabonomic urine analysis. The study groups consisted of 36 unrelated asymptomatic subjects with PRH, defined as serum uric acid levels (sUA) <2.5 mg/dL and fractional excretion of uric acid (FEUA) >10%, after exclusion of diseases and drugs that may affect urate homeostasis, and 39 sex and age-matched healthy individuals with normal sUA levels (>4.0 mmol/L) and FEUA<10%. Individuals with primary hypouricemia presented similar biochemical profiles to the controls without significant differences with regard to FE of electrolytes and renal threshold for phosphate excretion. Individuals with primary hypouricemia were differentiated from healthy individuals in the orthogonal signal correction/partial least-squares–discriminant analysis models of the NMR data with a statistically significant separation. The components that contributed to this separation were the lower levels of hippurate, creatinine, and trimethylaminoxide, and the higher levels of phenylalanine, alanine, glycine, glutamate, acetate, and of an unidentified metabolite (3.3 ppm) observed in hypouricemic subjects compared with controls. Primary hypouricemia, though considered an isolated renal tubular defect, is often associated with a more generalized proximal tubular disorder that mimics a partial Fanconi syndrome.

Metabonomics tell us how an organism has responded to a stimulus or challenge and is an indicator of cell physiology and response to stress. This systematic study of unique chemical fingerprints has resulted in quantitative data on a broad range of metabolites reflecting metabolism and/or metabolic shifts associated with brain health, pathology and/or treatment. Complex interactions between nutrition, genetics, and the central nervous system (CNS) determine optimal cerebral energy state and are mediated by changes in energy needs, metabolism, environment and multiple signaling molecules. Evaluation of exogenous administration of normal endogenous neuroprotective molecules may be expeditious and succinct. The normal physiologic effects of one of these agents, carbon monoxide (CO) - a major neurotransmitter and gasotransmitter, is important for multiple neurologic functions and neuroprotection and its role as a neuroprotective and neurotherapeutic agent has been suggested. Metabonomic profiling will provide further insight into the role of CO in CNS health and will add greatly to our ability to improve the quality of life in our patients with neuropathologic, neuropsychiatric, and neurodegenerative disorders.
Keywords: Metabonomics of the Brain and CNS; Metabonomic Profiling; Carbon Monoxide as a Neurotransmitter; Gasotransmitters.

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for me...

Literature surveys, taxonomical differences, and bioassay results have been utilized in the discovery of new natural products to aid in Actinomycetes isolate-selection. However, no or less investigation have been done on establishing the differences in metabolomic profiles of the isolated microorganisms. The study aims to utilise bioassay-and metabolomics-guided tools that included dereplication study and multivariate analysis of the NMR and mass spectral data of microbial extracts to assist the selection of isolates for scaling-up the production of antimicrobial natural products. A total of 58 actinomycetes were isolated from different soil samples collected from Ihnasia City, Egypt and screened for their antimicrobial activities against indicator strains that included Bacillus subtilis, Escherichia coli, methicillin-resistant Staphylococcus aureus and Candida albicans.

The purpose of this study was to examine the effects of structural parameters of peptides on their oxidation by DMSO, including location of cysteine, effect of adjunct group participation, molecular hydrophobicity, steric hindrance or the accessibility of thiol group and peptide conformation, on oxidation rates, dimer formation and associated side products. We designed and synthesized two series of linear cysteine-containing analogues of human β-defensin 3 (the C1-peptides with cysteine at the N-terminus residue 1, the C29-peptides with cysteine located at residue 29 in the centre of peptide), which were used for preparation of disulphide-linked homodimers. HPLC–ESI–MS was used to monitor the oxidation process and to characterize the molecular weights of dimers and side products of high oxidation. The formations of dimers and side products were dependent on the position of cysteines. Hydrophobicity generally rendered the thiol groups less accessible and hence exposed them to slow oxidation to form dimers (or even fail to form dimers during the timescale of observation). Molecular dynamics simulations showed that the exposure of cysteines (and sulphurs) of the C1-peptides was much larger than for the C29-peptides. The larger hydrophobic side chains tended to enable clustering of the side chains that sequester cysteine, particularly in the C29-peptides, which provided a molecular explanation for the observed trends in oxidation rates. Together with molecular modelling, we propose a reaction mechanism to elucidate the oxidation results of these peptides. Copyright © 2008 European Peptide Society and John Wiley & Sons, Ltd.

Would this method for uncovering hidden objects, feature discovery, feature selection and supervised learning work? According to my understanding perfect prediction by computation optimization must be impossible as long as essential input training data features for supervised learning are still lacking. Therefore, I emphasize not to invest much effort into algorithmic computational optimization before the feature discovery process allows selecting all needed features through variation before realistic predictions can be achieved by enhancing computations and parameters. For me this simple progression from completed feature selection to optimized algorithm is essential for more rapid discoveries. That is why I am still wondering why I have seen so many papers focusing on improving computational predictions without any kind of prior considerations about having properly completed the absolutely essential exhaustive feature selection process without which no subsequent computations can lead to satisfactorily predictions, which are reasonably consistent with our experimental observations and measurements. I am afraid that I am still the only one, to whom the writing above can make sense. I had expected much more enthusiasm, excitement and optimism about very likely accelerating our discovery rate by first focusing on uncovering still hidden objects and features through diverse variations of conditions, procedures, methods, techniques and measurements, followed by the exhaustive selection for all relevant needed features, followed by designing, developing, combining and optimizing the computational steps of our machine learning algorithm until our predictions match our experimentally obtained observations. Once this major machine learning objective has been achieved we have reached its final status beyond which we cannot improve it unless we can generate conditions, which cause our previously perfectly predicting machine learning algorithm to obviously fails because it is an absolute prerequisite for discovering more relevant training reflected by more dimensions. According to my current understanding any newly discovered essential input data feature inevitably causes a rise in the dimensionality of input data components, which must be considered together but never in isolation. For example, if I train with 100 input features our input variable must consist of exactly 100 components or parts, which together form a new level of single measuring points, which tends to be much different in controlling its manipulations and their overall effects from anything, which could possibly get anticipated, when trying to add up the effects of its 100 parts to a new total. This new total tends to consist of much different dimensions and often refers to completely unrelated kind of data than when combining all 100 components consisting of exactly the same input values for every of its 100 variables but by considering all 100 components like a single indivisible unit of measurement points, which often results in completely different kinds of unrelated seeming properties/features, which are not even closely reflecting the results, which we'd obtain if we executed each dimension on its own in isolation and sequential order. For example, stopping translation prematurely by three consecutive tryptophans has a much different impact, i.e. stop, then when translating each of the tryptophan in isolation separated by other amino acids, since this causes the nascent polypeptide chain to grow. Each tRNA charged with tryptophan, which complements mRNA triplets, causes the peptide to grow by a single tryptophan, which gets added to it. So when you try 2 tryptophans the polypeptide grows by two amino acids. But when you try 3 consecutive tryptophans, then-counter-intuitively-instead of the expected growth by 3 amino acids-translation prematurely stops. Stopping translation prematurely is of a much different dimension, level, effect and data kind, then when keep adding more amino acids to the growing peptide chain. If we consider the effect of complementary binding of a tRNA to its mRNA codon our peptide grows by one amino acid any charged tRNA adds another amino acid of one of 20 categorical values. Normally no amino acid can cause the translation to step prematurely, not even two amino acids as a pair. But three amino acids as an indivisible triplet, which must be considered as a new single value requiring all three tryptophans to be sequentially to be present, like a single indivisible data block, which must not be divided only in triplets, because only triplets, but no pair or singlet, can stop translation prematurely. Another example is predicting overall cellular protein composition. It depends on how many mRNA strands coding for a particular protein are in the cytoplasm. There is proportionality between number of cytoplasmic mRNA strands and total protein. Therefore, if the cell needs to double protein abundance it could double transcription and keeps everything else the same. But a much better and less step intensive, more economic way of doubling protein concentration is to double the length of the poly-(A)-tail. Extending the length of the poly-(A)-mRNA-tail may require about 100 additional adenines whereas doubling transcription requires about at least 500-instead of only 100-new nucleotides in addition to all needed transcriptional modification steps with their elaborate synthesis machinery. If the dividing yeast must raise its lipid synthesis by more than 10-fold during the short M-phase, it could increase transcription by a factor of 10, it could make the poly-(A)-mRNA-tail 10 times longer, or it could synthesized 10 times more new ribosomes to increase the ribosomal translation by a factor of 10 simply by reducing the distance of free uncovered mRNA nucleotides between adjacent ribosomes translating running down the same mRNA strand. If more than one ribosome is translating the same mRNA strand simultaneously, it is called a poly-ribosome. Hence, having 10 times more ribosomes binding to the same mRNA strand at the same time increases translation by a factor of 10 without needing any additional transcription. Above I have given three easy examples to get 10 times more proteins. Although all 3 methods have the same final result, i.e. 10 times more proteins, their mode of action, their required essential features, their dimensions and their minimally required parts, which must be considered like a single value, are totally different.

Original Research Article
Plant Physiology and Biochemistry, Volume 109, December 2016, Pages 156-165
S.A. Nusaibah, A. Siti Nor Akmar, A.S. Idris, M. Sariah, Z. Mohamad Pauzi
Abstract
Abstract Understanding the mechanism of interaction between the oil palm and its key pathogen, Ganoderma spp. is crucial as the disease caused by this fungal pathogen leads to a major loss of revenue in leading palm oil producing countries in Southeast Asia. Here in this study, we assess the morphological and biochemical changes in Ganoderma disease infected oil palm seedling roots in both resistant and susceptible progenies. Rubber woodblocks fully colonized by G. boninense were applied as a source of inoculum to artificially infect the roots of resistant and susceptible oil palm progenies. Gas chromatography-mass spectrometry was used to measure an array of plant metabolites in 100 resistant and susceptible oil palm seedling roots treated with pathogenic Ganoderma boninense fungus. Statistical effects, univariate and multivariate analyses were used to identify key-Ganoderma disease associated metabolic agitations in both resistant and susceptible oil palm root tissues. Ganoderma disease related defense shifts were characterized based on (i) increased antifungal activity in crude extracts, (ii) increased lipid levels, beta- and gamma-sitosterol particularly in the resistant progeny, (iii) detection of heterocyclic aromatic organic compounds, benzo [h] quinoline, pyridine, pyrimidine (iv) elevation in antioxidants, alpha- and beta-tocopherol (iv) degraded cortical cell wall layers, possibly resulting from fungal hydrolytic enzyme activity needed for initial penetration. The present study suggested that plant metabolites mainly lipids and heterocyclic aromatic organic metabolites could be potentially involved in early oil palm defense mechanism against G. boninense infection, which may also highlight biomarkers for disease detection, treatment, development of resistant variety and monitoring.

Objective: 3-Iodothyronamine (T 1 AM), an analog of thyroid hormone, is a recently discovered fast-acting endogenous metabolite. Single high-dose treatments of T 1 AM have produced rapid short-term effects, including a reduction of body temperature, bradycardia, and hyperglycemia in mice. Design and Methods: The effect of daily low doses of T 1 AM (10 mg/kg) for 8 days on weight loss and metabolism in spontaneously overweight mice was monitored. The experiments were repeated twice (n ¼ 4). Nuclear magnetic resonance (NMR) spectroscopy of plasma and real-time analysis of exhaled 13 CO 2 in breath by cavity ring down spectroscopy (CRDS) were used to detect T 1 AM-induced lipolysis. Results: CRDS detected increased lipolysis in breath shortly after T 1 AM administration that was associated with a significant weight loss but independent of food consumption. NMR spectroscopy revealed alterations in key metabolites in serum: valine, glycine, and 3-hydroxybutyrate, suggesting that the subchronic effects of T 1 AM include both lipolysis and protein breakdown. After discontinuation of T 1 AM treatment, mice regained only 1.8% of the lost weight in the following 2 weeks, indicating lasting effects of T 1 AM on weight maintenance. Conclusions: CRDS in combination with NMR and 13 C-metabolic tracing constitute a powerful method of investigation in obesity studies for identifying in vivo biochemical pathway shifts and unanticipated debilitating side effects.