Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets

Integrated Omics Analysis Reveals Key Pathways in Cotton Defense against Mirid Bug (Adelphocoris suturalis Jakovlev) Feeding

The recent dominance of Adelphocoris suturalis Jakovlev as the primary cotton field pest in Bt-cotton-cultivated areas has generated significant interest in cotton pest control research. This study addresses the limited understanding of cotton defense mechanisms triggered by A. suturalis feeding. Utilizing LC-QTOF-MS, we analyzed cotton metabolomic changes induced by A. suturalis, and identified 496 differential positive ions (374 upregulated, 122 downregulated) across 11 categories, such as terpenoids, alkaloids, phenylpropanoids, flavonoids, isoflavones, etc. Subsequent iTRAQ-LC-MS/MS analysis of the cotton proteome revealed 1569 differential proteins enriched in 35 metabolic pathways. Integrated metabolome and proteome analysis highlighted significant upregulation of 17 (89%) proteases in the α-linolenic acid (ALA) metabolism pathway, concomitant with a significant increase in 14 (88%) associated metabolites. Conversely, 19 (73%) proteases in the fructose and mannose biosynthesis pathway were downregulated, with 7 (27%) upregulated proteases corresponding to the downregulation of 8 pathway-associated metabolites. Expression analysis of key regulators in the ALA pathway, including allene oxidase synthase (AOS), phospholipase A (PLA), allene oxidative cyclase (AOC), and 12-oxophytodienoate reductase3 (OPR3), demonstrated significant responses to A. suturalis feeding. Finally, this study pioneers the exploration of molecular mechanisms in the plant-insect relationship, thereby offering insights into potential novel control strategies against this cotton pest.

Exercise Alleviates Fluoride-Induced Learning and Memory Impairment in Mice: Role of miR-206-3p and PREG

Fluorosis decreases the learning and memory ability in humans and animals, while exercise can reduce the risk of cognitive decline. However, the effect of exercise on learning and memory in fluoride-exposed mice is unclear. For this purpose, in this study, mice were randomly allotted into four groups (16 mice per group, half male and half female): control group (group C), fluoride group (group F, 100 mg/L sodium fluoride (NaF)), exercise group (group E, treadmill exercise), and E plus F group (group EF, treadmill exercise, and 100 mg/L NaF). During 6 months of exposure, exercise alleviated the NaF-induced decline in memory and learning. In addition, NaF induced injuries in mitochondria and myelin sheath ultrastructure and reduced the neurons number, while exercise restored them. Metabolomics results showed that phosphatidylethanolamine, pregnenolone (PREG), and lysophosphatidic acid (LysoPA) were altered among groups C, F, and EF. Combined with previous studies, it can be suggested that PREG might be a biomarker in response to exercise-relieving fluorine neurotoxicity. The miRNA sequencing results indicated that in the differently expressed miRNAs (DEmiRNAs), miR-206-3p, miR-96-5p, and miR-144-3p were shared in groups C, F, and EF. After the QRT-PCR validation and in vitro experiments, it was proved that miR-206-3p could reduce cell death and regulate AP-1 transcription factor subunit (JunD) and histone deacetylase 4 (HDAC4) to alleviate fluoride neurotoxicity. To sum up, the current study reveals that exercise could alleviate NaF-induced neurotoxicity by targeting miR-206-3p or PREG, which will contribute to revealing the pathogenesis and therapeutic method of fluoride neurotoxicity.

Untargeted metabolomics analysis on kidney tissues from mice reveals potential hypoxia biomarkers

Chronic hypoxia may have a huge impact on the cardiovascular and renal systems. Advancements in microscopy, metabolomics, and bioinformatics provide opportunities to identify new biomarkers. In this study, we aimed at elucidating the metabolic alterations in kidney tissues induced by chronic hypoxia using untargeted metabolomic analyses. Reverse phase ultrahigh performance liquid chromatography-mass spectroscopy/mass spectroscopy (RP-UPLC-MS/MS) and hydrophilic interaction liquid chromatography (HILIC)-UPLC-MS/MS methods with positive and negative ion mode electrospray ionization were used for metabolic profiling. The metabolomic profiling revealed an increase in metabolites related to carnitine synthesis and purine metabolism. Additionally, there was a notable increase in bilirubin. Heme, N-acetyl-L-aspartic acid, thyroxine, and 3-beta-Hydroxy-5-cholestenoate were found to be significantly downregulated. 3-beta-Hydroxy-5-cholestenoate was downregulated more significantly in male than female kidneys. Trichome Staining also showed remarkable kidney fibrosis in mice subjected to chronic hypoxia. Our study offers potential intracellular metabolite signatures for hypoxic kidneys.

Metabolome profiling in the study of aging processes

Aging of a living organism is closely related to systemic metabolic changes. But due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, this problem is solved using one of the main approaches of metabolomics — untargeted metabolome profiling. The purpose of this publication is to systematize the results of metabolomic studies based on such profiling, both in animal models and in humans.

Metabolome Profiling in Aging Studies

Organism aging is closely related to systemic metabolic changes. However, due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, scientists are trying to solve this problem using one of the main approaches of metabolomics-untargeted metabolome profiling. The purpose of this publication is to review metabolomic studies based on such profiling, both in animal models and in humans. This review describes metabolites that vary significantly across age groups and include carbohydrates, amino acids, carnitines, biogenic amines, and lipids. Metabolic pathways associated with the aging process are also shown, including those associated with amino acid, lipid, and energy metabolism. The presented data reveal the mechanisms of aging and can be used as a basis for monitoring biological age and predicting age-related diseases in the early stages of their development.

Plasma Metabolomics Identifies the Dysregulated Metabolic Profile of Primary Immune Thrombocytopenia (ITP) Based on GC-MS

ITP is a common autoimmune bleeding disorder with elusive pathogenesis. Our study was implemented to profile the plasma metabolic alterations of patients diagnosed with ITP, aiming at exploring the potential novel biomarkers and partial mechanism of ITP. The metabolomic analysis of plasma samples was conducted using GC-MS on 98 ITP patients and 30 healthy controls (HCs). Age and gender matched samples were selected to enter the training set or test set respectively. OPLS-DA, t-test with FDR correction and ROC analyses were employed to screen out and evaluate the differential metabolites. Possible pathways were enriched based on metabolomics pathway analysis (MetPA). A total of 85 metabolites were investigated in our study and 17 differential metabolites with diagnostic potential were identified between ITP patients and HCs. MetPA showed that the metabolic disorders of ITP patients were mainly related to phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism and glyoxylate and dicarboxylate metabolism. Additionally, we discriminated 6 differential metabolites and 5 enriched pathways in predicting the resistance to glucocorticoids in chronic ITP patients. The distinct metabolites discovered in our study could become novel biomarkers for the auxiliary diagnosis and prognosis prediction of ITP. Besides, the dysregulated pathways might contribute to the development of ITP.

Metabolomics and its Applications in Cancer Cachexia

Cancer cachexia (CC) is a complicated metabolic derangement and muscle wasting syndrome, affecting 50–80% cancer patients. So far, molecular mechanisms underlying CC remain elusive. Metabolomics techniques have been used to study metabolic shifts including changes of metabolite concentrations and disturbed metabolic pathways in the progression of CC, and expand further fundamental understanding of muscle loss. In this article, we aim to review the research progress and applications of metabolomics on CC in the past decade, and provide a theoretical basis for the study of prediction, early diagnosis, and therapy of CC.

Terroir Effect on the Phenolic Composition and Chromatic Characteristics of Mencía/Jaen Monovarietal Wines: Bierzo D.O. (Spain) and Dão D.O. (Portugal)

‘Mencía’/‘Jaen’ it’s an important red grape variety, exclusive of the Iberian Peninsula, used in wine production namely in Bierzo D.O. and Dão D.O., respectively. This work evaluates the effect of the two different “terroirs” on the phenolic composition and chromatic characteristics of ‘Mencía’/‘Jaen’ monovarietal wines produced at an industrial scale in the same vintage. Using Principal Component Analysis (PCA), Partial Least Squares-Discrimination Analysis (PLS-DA), and Orthogonal PLS-DA (OPLS-DA) it was found that peonidin-3-coumaroylglucoside, petunidin-3-glucoside, malvidin-3-coumaroylglucoside, peonidin-3-glucoside, malvidin-3-acetylglucoside, malvidin-3-glucoside, and ferulic acid were the phenolic compounds with the highest differences between the two regions. PLS regression allowed to correlate the differences in lightness (L*) and redness (a*) of wines from ‘Jaen’ and ‘Mencía’ to differences in colored anthocyanins, polymeric pigments, total pigments, total anthocyanins, cyanidin-3-acetylglucoside, delphinidin-3-acetylglucoside, delphinidin-3-glucoside, peonidin-3-coumaroylglucoside, petunidin-3-glucoside and malvidin-3-glucoside in wines, and the colorless ferulic, caffeic, and coutaric acids, and ethyl caffeate. The wines a* values were more affected by colored anthocyanins, ferulic acid, total anthocyanins, delphinidin-3-acetylglucoside, delphinidin-3-glucoside and petunidin-3-acetylglucoside, and catechin. The positive influence of ferulic acid in the a* values and ferulic, caffeic, coutaric acids, and ethyl caffeate on the L* values can be due to the co-pigmentation phenomena. The higher dryness and lower temperatures during the September nights in this vintage might explain the differences observed in the anthocyanin content and chromatic characteristics of the wines.

Fluoride exposure altered metabolomic profile in rat serum

It is well known that serum is an ideal and potential choice to reflect the toxicity of fluoride. However, the effects of fluoride on serum metabolome have not been reported until now. In this study, the models of 3-week-old rats exposed fluoride by breast milk and 11-week-old rats exposed fluoride via breast milk and drinking water containing sodium fluoride (100 mg/L) were established. Using Ultra Performance Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (UPLC-MS/MS), as compared with control group, 28 negative (NEG) and 52 positive (POS) metabolites were significantly up-regulated, meanwhile 30 NEG and 21 POS significantly down-regulated metabolites were found in serum of 3-week-old rats exposed to fluoride. For 11-week-old fluorosis rats, there were 119 NEG and 65 POS metabolites significantly increased, and 7 NEG, 5 POS metabolites were obviously decreased. Importantly, nicotinamide, adenosine, 1-Oleoyl-sn-glycero-3-phosphocholine (OGPC), and 1-Stearoyl-sn-glycerol 3-phosphocholine (SGPC) were shared by two models. The metabolites of urea cycle, such as urea and N2-Acetyl-l-ornithine, betaine as a methyl donor, were regarded to reflect the fluorosis degree. These metabolites could be the potential markers of fluorosis, contributing to the prevention and treatment of fluorosis.

Metabolomics as an Emerging Tool in the Search for Astrobiologically Relevant Biomarkers

It is now routinely possible to sequence and recover microbial genomes from environmental samples. To the degree it is feasible to assign transcriptional and translational functions to these genomes, it should be possible, in principle, to largely understand the complete molecular inputs and outputs of a microbial community. However, gene-based tools alone are presently insufficient to describe the full suite of chemical reactions and small molecules that compose a living cell. Metabolomic tools have developed quickly and now enable rapid detection and identification of small molecules within biological and environmental samples. The convergence of these technologies will soon facilitate the detection of novel enzymatic activities, novel organisms, and potentially extraterrestrial life-forms on solar system bodies. This review explores the methodological problems and scientific opportunities facing researchers who hope to apply metabolomic methods in astrobiology-related fields, and how present challenges might be overcome.

[Ten years of the Russian metabolomics: history of development and achievements]

Metabolomics is one of the omics sciences, the technologies of which are widely used today in many life sciences. Its application influenced the discovery of new biomarkers of diseases, the description of biochemical processes occurring in many organisms, laid the basis for a new generation of clinical laboratory diagnostics. The purpose of this review is to show how metabolomics is represented in the studies of Russian scientists, to demonstrate the main directions and achievements of the Russian science in this field. The review also highlights the history of metabolomics, existing problems and the place of Russian metabolomics in their solution.

A biplot correlation range for group-wise metabolite selection in mass spectrometry

Background

Analytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization. Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes.

Methods

We present a dimensionality-reduction based approach termed ‘biplot correlation range (BCR)’ that uses biplot correlation analysis with direct orthogonal signal correction and PLS to provide the group-wise selection of metabolic markers contributing to metabolic phenotypes.

Results

Using a simulated multiple-layer system that often arises in complex biologic systems, we show the feasibility and superiority of the proposed approach in comparison of existing approaches based on false discovery rate and correlation. To demonstrate the proposed method in a real-life dataset, we used LC-MS based metabolomics to determine spectrum of metabolites present in liver mitochondria from wild-type (WT) mice and thioredoxin-2 transgenic (TG) mice. We select discriminatory variables in terms of increased score in the direction of class identity using BCR. The results show that BCR provides means to identify metabolites contributing to class separation in a manner that a statistical method by false discovery rate or statistical total correlation spectroscopy can hardly find in complex data analysis for predictive health and personalized medicine.

Electronic supplementary material

The online version of this article (10.1186/s13040-019-0191-2) contains supplementary material, which is available to authorized users.

Elucidation of chromatographic peak shifts in complex samples using a chemometrical approach

Chromatographic retention time peak shifts between consecutive analyses is a well-known fact yet not fully understood. Algorithms have been developed to align peaks between runs, but with no specific studies considering the causes of peak shifts. Here, designed experiments reveal chromatographic shift patterns for a complex peptide mixture that are attributable to the temperature and pH of the mobile phase. These results demonstrate that peak shifts are highly structured and are to a high degree explained by underlying differences in physico-chemical parameters of the chromatographic system and also provide experimental support for the alignment algorithm called the generalized fuzzy Hough transform which exploits this fact. It can be expected that the development of alignment algorithms enters a new phase resulting in increasingly accurate alignment by considering the latent structure of the peak shifts.

Current Challenges in Plant Eco-Metabolomics

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Automated Comparative Metabolite Profiling of Large LC-ESIMS Data Sets in an ACD/MS Workbook Suite Add-in, and Data Clustering on a New Open-Source Web Platform FreeClust

The technological development of LC-MS instrumentation has led to significant improvements of performance and sensitivity, enabling high-throughput analysis of complex samples, such as plant extracts. Most software suites allow preprocessing of LC-MS chromatograms to obtain comprehensive information on single constituents. However, more advanced processing needs, such as the systematic and unbiased comparative metabolite profiling of large numbers of complex LC-MS chromatograms remains a challenge. Currently, users have to rely on different tools to perform such data analyses. We developed a two-step protocol comprising a comparative metabolite profiling tool integrated in ACD/MS Workbook Suite, and a web platform developed in R language designed for clustering and visualization of chromatographic data. Initially, all relevant chromatographic and spectroscopic data (retention time, molecular ions with the respective ion abundance, and sample names) are automatically extracted and assembled in an Excel spreadsheet. The file is then loaded into an online web application that includes various statistical algorithms and provides the user with tools to compare and visualize the results in intuitive 2D heatmaps. We applied this workflow to LC-ESIMS profiles obtained from 69 honey samples. Within few hours of calculation with a standard PC, honey samples were preprocessed and organized in clusters based on their metabolite profile similarities, thereby highlighting the common metabolite patterns and distributions among samples. Implementation in the ACD/Laboratories software package enables ulterior integration of other analytical data, and in silico prediction tools for modern drug discovery.

Orbitofrontal Neuroadaptations and Cross-Species Synaptic Biomarkers in Heavy-Drinking Macaques

Cognitive impairments, uncontrolled drinking, and neuropathological cortical changes characterize alcohol use disorder. Dysfunction of the orbitofrontal cortex (OFC), a critical cortical subregion that controls learning, decision-making, and prediction of reward outcomes, contributes to executive cognitive function deficits in alcoholic individuals. Electrophysiological and quantitative synaptomics techniques were used to test the hypothesis that heavy drinking produces neuroadaptations in the macaque OFC. Integrative bioinformatics and reverse genetic approaches were used to identify and validate synaptic proteins with novel links to heavy drinking in BXD mice. In drinking monkeys, evoked firing of OFC pyramidal neurons was reduced, whereas the amplitude and frequency of postsynaptic currents were enhanced compared with controls. Bath application of alcohol reduced evoked firing in neurons from control monkeys, but not drinking monkeys. Profiling of the OFC synaptome identified alcohol-sensitive proteins that control glutamate release (e.g., SV2A, synaptogyrin-1) and postsynaptic signaling (e.g., GluA1, PRRT2) with no changes in synaptic GABAergic proteins. Western blot analysis confirmed the increase in GluA1 expression in drinking monkeys. An exploratory analysis of the OFC synaptome found cross-species genetic links to alcohol intake in discrete proteins (e.g., C2CD2L, DIRAS2) that discriminated between low- and heavy-drinking monkeys. Validation studies revealed that BXD mouse strains with the D allele at the C2cd2l interval drank less alcohol than B allele strains. Thus, by profiling of the OFC synaptome, we identified changes in proteins controlling glutamate release and postsynaptic signaling and discovered several proteins related to heavy drinking that have potential as novel targets for treating alcohol use disorder.SIGNIFICANCE STATEMENT Clinical research identified cognitive deficits in alcoholic individuals as a risk factor for relapse, and alcoholic individuals display deficits on cognitive tasks that are dependent upon the orbitofrontal cortex (OFC). To identify neurobiological mechanisms that underpin OFC dysfunction, this study used electrophysiology and integrative synaptomics in a translational nonhuman primate model of heavy alcohol consumption. We found adaptations in synaptic proteins that control glutamatergic signaling in chronically drinking monkeys. Our functional genomic exploratory analyses identified proteins with genetic links to alcohol and cocaine intake across mice, monkeys, and humans. Future work is necessary to determine whether targeting these novel targets reduces excessive and harmful levels of alcohol drinking.

Different metabolite profile and metabolic pathway with leaves and roots in response to boron deficiency at the initial stage of citrus rootstock growth

Boron (B) is a microelement required for higher plants, and B deficiency has serious negative effect on metabolic processes. We concentrated on the changes in metabolite profiles of trifoliate orange leaves and roots as a consequence of B deficiency at the initial stage of growth by gas chromatography-mass spectrometry (GC-MS)-based metabolomics. Enlargement and browning of root tips were observed in B-deficient plants, while any obvious symptom was not recorded in the leaves after 30 days of B deprivation. The distinct patterns of alterations in metabolites observed in leaves and roots due to B deficiency suggest the presence of specific organ responses to B starvation. The accumulation of soluble sugars was occurred in leaves, which may be attributed to down-regulated pentose phosphate pathway (PPP) and amino acid biosynthesis under B deficiency, while the amount of most amino acids in roots was increased, indicating that the effects of B deficiency on amino acids metabolism in trifoliate orange may be a consequence of disruptions in root tissues and decreased protein biosynthesis. Several important products of shikimate pathway were also significantly affected by B deficiency, which may be related to abnormal growth of roots induced by B deficiency. Conclusively, our results revealed a global perspective of the discriminative metabolism responses appearing between B-deprived leaves and roots and provided new insight into the relationship between B deficiency symptom in roots and the altered amino acids profiling and shikimate pathway induced by B deficiency during seedling establishment.

A simple multi-scale Gaussian smoothing-based strategy for automatic chromatographic peak extraction

Peak detection is a critical step in chromatographic data analysis. In the present work, we developed a multi-scale Gaussian smoothing-based strategy for accurate peak extraction. The strategy consisted of three stages: background drift correction, peak detection, and peak filtration. Background drift correction was implemented using a moving window strategy. The new peak detection method is a variant of the system used by the well-known MassSpecWavelet, i.e., chromatographic peaks are found at local maximum values under various smoothing window scales. Therefore, peaks can be detected through the ridge lines of maximum values under these window scales, and signals that are monotonously increased/decreased around the peak position could be treated as part of the peak. Instrumental noise was estimated after peak elimination, and a peak filtration strategy was performed to remove peaks with signal-to-noise ratios smaller than 3. The performance of our method was evaluated using two complex datasets. These datasets include essential oil samples for quality control obtained from gas chromatography and tobacco plant samples for metabolic profiling analysis obtained from gas chromatography coupled with mass spectrometry. Results confirmed the reasonability of the developed method.

Metabolic profiling reveals altered pattern of central metabolism in navel orange plants as a result of boron deficiency

We focused on the changes of metabolite profiles in navel orange plants under long-term boron (B) deficiency using a gas chromatography-mass spectrometry (GC-MS) approach. Curling of the leaves and leaf chlorosis were observed only in the upper leaves (present before start of the treatment) of B-deficient plants, while the lower leaves (grown during treatment) did not show any visible symptoms. The metabolites with up-accumulation in B-deficient leaves were mainly proline, l-ornithine, lysine, glucoheptonic acid, fucose, fumarate, oxalate, quinate, myo-inositol and allo-inositol, while the metabolites with down-accumulation in B-deficient leaves were mainly serine, asparagine, saccharic acid, citrate, succinate, shikimate and phytol. The levels of glucose and fructose were increased only in the upper leaves by B deficiency, while starch content was increased in all the leaves and in roots. The increased levels of malate, ribitol, gluconic acid and glyceric acid occurred only in the lower leaves of B-deficient plants. The increased levels of phenols only in the upper leaves indicated that the effects of B on phenol metabolism in citrus plants may be a consequence of disruptions in leaf structure. Metabolites with opposite reactions in upper and lower leaves were mainly glutamine, glycine and pyrrole-2-carboxylic acid. To our knowledge, the phenomena of allo-inositol even higher than myo-inositol occurred characterized for the first time in this species. These results suggested that the altered pattern of central metabolism may be either specific or adaptive responses of navel orange plants to B deficiency.

Age-related alterations in the metabolic profile in the hippocampus of the senescence-accelerated mouse prone 8: a spontaneous Alzheimer's disease mouse model

Alzheimer's disease (AD), the most common age-dependent neurodegenerative disorder, produces a progressive decline in cognitive function. The metabolic mechanism of AD has emerged in recent years. In this study, we used multivariate analyses of gas chromatography-mass spectrometry measurements to determine that learning and retention-related metabolic profiles are altered during aging in the hippocampus of the senescence-accelerated mouse prone 8 (SAMP8). Alterations in 17 metabolites were detected in mature and aged mice compared to young mice (13 decreased and 4 increased metabolites), including metabolites related to dysfunctional lipid metabolism (significantly increased cholesterol, oleic acid, and phosphoglyceride levels), decreased amino acid (alanine, serine, glycine, aspartic acid, glutamate, and gamma-aminobutyric acid), and energy-related metabolite levels (malic acid, butanedioic acid, fumaric acid, and citric acid), and other altered metabolites (increased N-acetyl-aspartic acid and decreased pyroglutamic acid, urea, and lactic acid) in the hippocampus. All of these alterations indicated that the metabolic mechanisms of age-related cognitive impairment in SAMP8 mice were related to multiple pathways and networks. Lipid metabolism, especially cholesterol metabolism, appears to play a distinct role in the hippocampus in AD.

A new approach for plasma (xeno)metabolomics based on solid-phase extraction and nanoflow liquid chromatography-nanoelectrospray ionisation mass spectrometry

Current metabolite profiling methods based on liquid chromatography-mass spectrometry (LC-MS) platforms do not detect many of the components present at trace concentrations in extracts of plasma due to their low ionisation efficiency or to interference from highly abundant compounds. Nanoflow LC-nanospray MS platforms, which are commonly used in proteomics, could overcome these limitations and significantly increase analytical sensitivity and coverage of the plasma (xeno)metabolome (i.e., metabolites and xenobiotics), but require small injection volumes (<0.5μL). In this study, we developed sample preparation methods to remove ion suppressive phospholipids and concentrate remaining components of the plasma (xeno)metabolome in order to analyse sub-microliter volumes of plasma extracts for nanoflow ultra-high-performance liquid chromatography-nanoelectrospray ionisation-time-of-flight mass spectrometry (nUHPLC-nESI-TOFMS). These methods use phospholipid filtration plates in combination with polymeric or mixed mode exchange solid-phase extraction (SPE). The phospholipid filtration plates removed >94% of the predominant phospholipid/lysophospholipid species from plasma, whilst absolute recoveries of 63 selected (xeno)metabolites from spiked plasma were generally between 60 and 104%. After a further SPE step, recoveries of test compounds were between 50 and 81%. Studies revealed that both the sample preparation methodology and nUHPLC-nESI-TOFMS analyses gave acceptable repeatability. A qualitative comparison of SPE methods revealed that sample concentration by either polymer or mixed mode ion-exchange SPE gave comprehensive metabolite coverage of plasma extracts, but the use of cation exchange SPE significantly increased detection of many cationic compounds in the sample extracts. Method detection limits for steroid, eicosanoid and bile metabolites were <1.0ng/mL plasma and for pharmaceutical contaminants were between 0.01 and 30ng/mL plasma. Comparison of the phospholipid removal/cation exchange SPE and the classical protein precipitation (PPT) sample preparation methodologies revealed that both methods detected the same range of (xeno)metabolites. However, unlike PPT extracts, the SPE preparations allowed direct injection of more concentrated plasma extracts onto the nUHPLC-nESI-TOFMS platform without blockage of the nanocolumn or nanospray, thus resulting in a wider coverage of the (xeno)metabolome. This is the first work to demonstrate the significantly enhanced sensitivity arising from the use of concentrated SPE sample preparations and direct nUHPLC-nESI-TOFMS analysis for untargeted profiling of plasma samples and constitutes a step forward for identifying mixtures of chemical stressors accumulated in blood as well as the disruption of key metabolite pathways in the same sample.

[Metabolic profiling of human blood]

Metabolomics is a novel "omics" branch of science intended for studying a comprehensive set of low molecular weight substances (metabolites) of various biological objects. Metabolite profiles represent a molecular phenotype of biological systems and reflect information encoded at the genome level and realized at the transcriptome and proteome levels. Analysis of human blood metabolic profile is universal and promising tool for clinical applications because it is a sensitive measure of both endogenous and exogenous (environmental) factors affected on the patient's organism. Technical implementation of metabolic profiling of blood and statistic analysis of metabolite profiles for effective diagnostics and risk assessments of diseases are discussed in this review.

Bile acid signaling in lipid metabolism: metabolomic and lipidomic analysis of lipid and bile acid markers linked to anti-obesity and anti-diabetes in mice

Bile acid synthesis is the major pathway for catabolism of cholesterol. Cholesterol 7α-hydroxylase (CYP7A1) is the rate-limiting enzyme in the bile acid biosynthetic pathway in the liver and plays an important role in regulating lipid, glucose and energy metabolism. Transgenic mice overexpressing CYP7A1 (CYP7A1-tg mice) were resistant to high-fat diet (HFD)-induced obesity, fatty liver, and diabetes. However the mechanism of resistance to HFD-induced obesity of CYP7A1-tg mice has not been determined. In this study, metabolomic and lipidomic profiles of CYP7A1-tg mice were analyzed to explore the metabolic alterations in CYP7A1-tg mice that govern the protection against obesity and insulin resistance by using ultra-performance liquid chromatography-coupled with electrospray ionization quadrupole time-of-flight mass spectrometry combined with multivariate analyses. Lipidomics analysis identified seven lipid markers including lysophosphatidylcholines, phosphatidylcholines, sphingomyelins and ceramides that were significantly decreased in serum of HFD-fed CYP7A1-tg mice. Metabolomics analysis identified 13 metabolites in bile acid synthesis including taurochenodeoxycholic acid, taurodeoxycholic acid, tauroursodeoxycholic acid, taurocholic acid, and tauro-β-muricholic acid (T-β-MCA) that differed between CYP7A1-tg and wild-type mice. Notably, T-β-MCA, an antagonist of the farnesoid X receptor (FXR) was significantly increased in intestine of CYP7A1-tg mice. This study suggests that reducing 12α-hydroxylated bile acids and increasing intestinal T-β-MCA may reduce high fat diet-induced increase of phospholipids, sphingomyelins and ceramides, and ameliorate diabetes and obesity. This article is part of a Special Issue entitled Linking transcription to physiology in lipodomics.

Repetitive transcranial magnetic stimulation applications normalized prefrontal dysfunctions and cognitive-related metabolic profiling in aged mice

Chronic high-frequency repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that has recently received increasing interests as a therapeutic procedure for neurodegenerative diseases. To identify the metabolism mechanism underlying the improving effects of rTMS, we observed that high frequency (25Hz) rTMS for 14 days could reverse the decline of the performance of the passive avoidance task in aged mice. We further investigated the metabolite profiles in the prefrontal cortex (PFC) in those mice and found that rTMS could also reverse the metabolic abnormalities of gamma-aminobutyric acid, N-acetyl aspartic, and cholesterol levels to the degree similar to the young mice. These data suggested that the rTMS could ameliorate the age-related cognitive impairment and improving the metabolic profiles in PFC, and potentially can be used to improve cognitive decline in the elderly.

Postgenomics diagnostics: metabolomics approaches to human blood profiling

We live in exciting times with the prospects of postgenomics diagnostics. Metabolomics is a novel "omics" data-intensive science that is accelerating the development of postgenomics diagnostics, particularly with use of accessible peripheral tissue compartments. Metabolomics involves the study of a comprehensive set of low molecular weight substances (metabolites) present in biological systems. The metabolite profiles represent the molecular phenotype of biological systems and reflect the information encoded at the genomic level and implemented at the transcriptomic and proteomic levels. Analysis of the human blood metabolite profile is a universal and highly promising tool for clinical postgenomics applications because it reflects both the endogenous and exogenous (environmental) factors influencing an individual organism. This article presents a critical synthesis and original analysis of both the technical implementation of metabolic profiling of blood and statistical analysis of metabolite profiles for effective disease diagnostics and risk assessment in the present postgenomics era.