NMR analysis seeking for cognitive decline and dementia metabolic markers in plasma from aged individuals

BACKGROUND

Blood biomarkers can improve the ability to diagnose dementia, providing new information to better understand the pathophysiology and causes of the disease. Some studies with patients have already shown changes in metabolic profiles among patients with pathological cognitive decline or Alzheimer's disease, when compared to individuals with normal cognition.

METHODS

To search for new metabolic biomarkers of dementia, we analyzed serum levels of several metabolites, measured by nuclear magnetic resonance spectroscopy, in elderly individuals, a group with normal cognitive decline (control), and three other groups with cognitive decline. pathological (low, moderate, and severe).

RESULTS

Decreased plasma levels of tyrosine, glutamate, valine, leucine, and isoleucine are associated with worsening of pathological cognitive decline. However, the area under analysis of receptor operating characteristics suggests that tyrosine and glutamate have low specificity and sensitivity. Valine, leucine, and isoleucine are influenced by blood glucose or diabetes, but these conditions do not seem to be of great influence in the differences observed. Isobutyrate, histidine, acetone and unknown-1 metabolite also decrease their plasma levels with increasing CD. Isobutyrate ad histidine could have neuroprotective and antioxidant actions, respectively. To elucidate the role of decreased unknown metabolite-1 as a CD biomarker, it will be necessary to previously investigate its identity. To define and elucidate the role of acetone in pathological CD, additional laboratory and clinical studies must be performed. All these metabolites together may constitute a set of biomarkers with capability to identify pathological CD or dementia.

SIGNIFICANCE AND NOVELTY

Decrease of glutamate, tyrosine, valine, leucine, isoleucine, histidine, isobutyrate, acetone and unknown-1 metabolite together are a set of biomarkers able to identify pathological CD or dementia. Histidine, isobutyrate, acetone and unknown-1 metabolite are more specific biomarkers of CD.

Searching for Metabolic Markers of Stroke in Human Plasma via NMR Analysis

More than 12 million people around the world suffer a stroke every year, one every 3 s. Stroke has a variety of causes and is often the result of a complex interaction of risk factors related to age, genetics, gender, lifestyle, and some cardiovascular and metabolic diseases. Despite this evidence, it is not possible to prevent the onset of stroke. The use of innovative methods for metabolite analysis has been explored in the last years to detect new stroke biomarkers. We use NMR spectroscopy to identify small molecule variations between different stages of stroke risk. The Framingham Stroke Risk Score was used in people over 63 years of age living in long-term care facilities (LTCF) to calculate the probability of suffering a stroke. Using this parameter, three study groups were formed: low stroke risk (LSR, control), moderate stroke risk (MSR) and high stroke risk (HSR). Univariate statistical analysis showed seven metabolites with increasing plasma levels across different stroke risk groups, from LSR to HSR: isoleucine, asparagine, formate, creatinine, dimethylsulfone and two unidentified molecules, which we termed "unknown-1" and "unknown-3". These metabolic markers can be used for early detection and to detect increasing stages of stroke risk more efficiently.

The effect of liquid-liquid extraction on metabolite detection and analysis using NMR spectroscopy in human synovial fluid

The evaluation of joint disease using synovial fluid is an emerging field of metabolic profiling. The analysis is challenged by multiple macromolecules which can obscure the small molecule chemistry. The use of protein precipitation and extraction has been evaluated previously, but not in synovial fluid. We systematically review the published NMR spectroscopy methods of synovial fluid analysis and investigated the efficacy of three different protein precipitation techniques: methanol, acetonitrile and trichloroacetic acid. The trichloroacetic wash removed the most protein. However, metabolite recoveries were universally very poor. Acetonitrile liquid/liquid extraction gave metabolite gains from four unknown compounds with spectral peaks at δ = 1.91 ppm, 3.64 ppm, 3.95 ppm & 4.05 ppm. The metabolite recoveries for acetonitrile were between 1.5 and 7 times higher than the methanol method, across all classes of metabolite. The methanol method was more effective in removing protein as reported by the free GAG undefined peak (44 % vs 125 %). However, qualitative evaluation showed that acetonitrile and methanol provided good restoration of the spectra to baseline. The methanol extraction has issues of a gelatinous substrate in the samples. All metabolite recoveries had a CV of > 15 %. A recommendation of acetonitrile liquid/liquid extraction was made for human synovial fluid (HSF) analysis. This is due to consistency, effective protein precipitation, recovery of metabolites and additional compounds not previously visible.

Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers

Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.

Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers

Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.

(Very) long-term transport of Silurus glanis, Carcharhinus melanopterus, Scomber colias, Trachurus picturatus, Polyprion americanus, Rhinoptera marmoratus, Salmo salar, Scomber scombrus, Sardina pilchardus, and others, by land, water and air

In this paper, we cover 4 years of live fish transports that ranged from 14 to 200 h (8 days), and bioloads from 3.8 to 76.9 kg/m³ . The key ingredients for success in all trips, where virtually no mortality occurred, was atributed to (1) pre-buffering the water with sodium bicarbonate and sodium carbonate at 50 g/m³ (each)-and/or ATM Alka-Haul^TM at 25 g/m³ -and applying additional (partial or full) doses throughout each transport, whenever the tanks were accessible; (2) pre-quenching ammonia with ATM Triage^TM at 32 g/m³ , and applying additional (partial or full) doses throughout each transport, whenever the tanks were accessible; (3) keeping the dissolved oxygen saturation rate above 100%, ideally above 150%; (4) Keeping temperature on the lower limit of each species' tolerance range; (5) Using foam fractionators to effectively eliminate organic matter from the water and (6) Using pure sine wave inverters, which allows for a steady supply of electrical current throughout the transport. The use of a 'preventive' versus 'corrective' pH buffering philosophy is also discussed.

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography–mass spectrometry (LC–MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC–MS metabolomics experiments or batches using only the features’ RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR

Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent-fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82-92% and 82-85%, respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).

Differences between infected and noninfected synovial fluid

AIMS

The diagnosis of joint infections is an inexact science using combinations of blood inflammatory markers and microscopy, culture, and sensitivity of synovial fluid (SF). There is potential for small molecule metabolites in infected SF to act as infection markers that could improve accuracy and speed of detection. The objective of this study was to use nuclear magnetic resonance (NMR) spectroscopy to identify small molecule differences between infected and noninfected human SF.

METHODS

In all, 16 SF samples (eight infected native and prosthetic joints plus eight noninfected joints requiring arthroplasty for end-stage osteoarthritis) were collected from patients. NMR spectroscopy was used to analyze the metabolites present in each sample. Principal component analysis and univariate statistical analysis were undertaken to investigate metabolic differences between the two groups.

RESULTS

A total of 16 metabolites were found in significantly different concentrations between the groups. Three were in higher relative concentrations (lipids, cholesterol, and N-acetylated molecules) and 13 in lower relative concentrations in the infected group (citrate, glycine, glycosaminoglycans, creatinine, histidine, lysine, formate, glucose, proline, valine, dimethylsulfone, mannose, and glutamine).

CONCLUSION

Metabolites found in significantly greater concentrations in the infected cohort are markers of inflammation and infection. They play a role in lipid metabolism and the inflammatory response. Those found in significantly reduced concentrations were involved in carbohydrate metabolism, nucleoside metabolism, the glutamate metabolic pathway, increased oxidative stress in the diseased state, and reduced articular cartilage breakdown. This is the first study to demonstrate differences in the metabolic profile of infected and noninfected human SF, using a noninfected matched cohort, and may represent putative biomarkers that form the basis of new diagnostic tests for infected SF. Cite this article: Bone Joint Res 2021;10(1):85-95.

Metabolic Signatures of Gestational Weight Gain and Postpartum Weight Loss in a Lifestyle Intervention Study of Overweight and Obese Women

Background: Overweight and obesity amongst women of reproductive age are increasingly common in developed economies and are shown to adversely affect birth outcomes and both childhood and adulthood health risks in the offspring. Metabolic profiling in conditions of overweight and obesity in pregnancy could potentially be applied to elucidate the molecular basis of the adverse effects of gestational weight gain (GWG) and postpartum weight loss (WL) on future risks for cardiovascular disease (CVD) and other chronic diseases. Methods: Biofluid samples were collected from 114 ethnically diverse pregnant women with body mass index (BMI) between 25 and 40 kg/m² from Chicago (US), as part of a randomized lifestyle intervention trial (Maternal Offspring Metabolics: Family Intervention Trial; NCT01631747). At 15 weeks, 35 weeks of gestation, and at 1 year postpartum, the blood plasma lipidome and metabolic profile of urine samples were analyzed by liquid chromatography mass spectrometry (LC-MS) and ¹H nuclear magnetic resonance spectroscopy (¹H NMR) respectively. Results: Urinary 4-deoxyerythronic acid and 4-deoxythreonic acid were found to be positively correlated to BMI. Seventeen plasma lipids were found to be associated with GWG and 16 lipids were found to be associated with WL, which included phosphatidylinositols (PI), phosphatidylcholines (PC), lysophospholipids (lyso-), sphingomyelins (SM) and ether phosphatidylcholine (PC-O). Three phospholipids found to be positively associated with GWG all contained palmitate side-chains, and amongst the 14 lipids that were negatively associated with GWG, seven were PC-O. Six of eight lipids found to be negatively associated with WL contained an 18:2 fatty acid side-chain. Conclusions: Maternal obesity was associated with characteristic urine and plasma metabolic phenotypes, and phospholipid profile was found to be associated with both GWG and postpartum WL in metabolically healthy pregnant women with overweight/obesity. Postpartum WL may be linked to the reduction in the intake of linoleic acid/conjugated linoleic acid food sources in our study population.

Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection

MOTIVATION

Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning.

RESULTS

Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output.

AVAILABILITY AND IMPLEMENTATION

An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Can metabolic profiling provide a new description of osteoarthritis and enable a personalised medicine approach?

Osteoarthritis (OA) is a multifactorial disease contributing to significant disability and economic burden in Western populations. The aetiology of OA remains poorly understood, but is thought to involve genetic, mechanical and environmental factors. Currently, the diagnosis of OA relies predominantly on clinical assessment and plain radiographic changes long after the disease has been initiated. Recent advances suggest that there are changes in joint fluid metabolites that are associated with OA development. If this is the case, biochemical and metabolic biomarkers of OA could help determine prognosis, monitor disease progression and identify potential therapeutic targets. Moreover, for focussed management and personalised medicine, novel biomarkers could sub-stratify patients into OA phenotypes, differentiating metabolic OA from post-traumatic, age-related and genetic OA. To date, OA biomarkers have concentrated on cytokine action and protein signalling with some progress. However, these remain to be adopted into routine clinical practice. In this review, we outline the emerging metabolic links to OA pathogenesis and how an elucidation of the metabolic changes in this condition may provide future, more descriptive biomarkers to differentiate OA subtypes.

Exploring Cancer Metabolism: Applications of Metabolomics and Metabolic Phenotyping in Cancer Research and Diagnostics

Altered metabolism is one of the key hallmarks of cancer. The development of sensitive, reproducible and robust bioanalytical tools such as Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry techniques offers numerous opportunities for cancer metabolism research, and provides additional and exciting avenues in cancer diagnosis, prognosis and for the development of more effective and personalized treatments. In this chapter, we introduce the current state of the art of metabolomics and metabolic phenotyping approaches in cancer research and clinical diagnostics.

Differences in the composition of hip and knee synovial fluid in osteoarthritis: a nuclear magnetic resonance (NMR) spectroscopy study of metabolic profiles

OBJECTIVE

The hip and knee joints differ biomechanically in terms of contact stresses, fluid lubrication and wear patterns. These differences may be reflected in the synovial fluid (SF) composition of the two joints, but the nature of these differences remains unknown. The objective was to identify differences in osteoarthritic hip and knee SF metabolites using metabolic profiling with Nuclear Magnetic Resonance (NMR) spectroscopy.

DESIGN

Twenty-four SF samples (12 hip, 12 knee) were collected from patients with end-stage osteoarthritis (ESOA) undergoing hip/knee arthroplasty. Samples were matched for age, gender, ethnicity and had similar medical comorbidities. NMR spectroscopy was used to analyse the metabolites present in each sample. Principal Component Analysis and Orthogonal Partial Least Squares Discriminant Analysis were undertaken to investigate metabolic differences between the groups. Metabolites were identified using 2D NMR spectra, statistical spectroscopy and by comparison to entries in published databases.

RESULTS

There were significant differences in the metabolic profile between the groups. Four metabolites were found in significantly greater quantities in the knee group compared to the hip group (N-acetylated molecules, glycosaminoglycans, citrate and glutamine).

CONCLUSIONS

This is the first study to indicate differences in the metabolic profile of hip and knee SF in ESOA. The identified metabolites can broadly be grouped into those involved in collagen degradation, the tricarboxylic acid cycle and oxidative metabolism in diseased joints. These findings may represent a combination of intra and extra-articular factors.

NMR-Metabolomics Shows That BolA Is an Important Modulator of Salmonella Typhimurium Metabolic Processes under Virulence Conditions

BolA is a ubiquitous global transcription factor. Despite its clear role in the induction of important stress-resistant physiological changes and its recent implication in the virulence of Salmonella, further research is required to shed light on the pathways modulated by BolA. In this study, we resorted to untargeted ¹H-NMR metabolomics to understand the impact of BolA on the metabolic profile of Salmonella Typhimurium, under virulence conditions. Three strains of S. Typhimurium SL1344 were studied: An SL1344 strain transformed with an empty plasmid (control), a bolA knockout mutant (ΔbolA), and a strain overexpressing bolA (bolA⁺). These strains were grown in a minimal virulence-inducing medium and cells were collected at the end of the exponential and stationary phases. The extracts were analyzed by NMR, and multivariate and univariate statistical analysis were performed to identify significant alterations. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of ¹H-NMR data allowed the discrimination between the metabolic profiles of these strains, revealing increased levels of acetate, valine, alanine, NAD⁺, succinate, coenzyme A, glutathione, and putrescine in bolA⁺. These results indicate that BolA regulates pathways related to stress resistance and virulence, being an important modulator of the metabolic processes needed for S. Typhimurium infection.

Combined transcriptomics-metabolomics profiling of the heat shock response in the hyperthermophilic archaeon Pyrococcus furiosus

Pyrococcus furiosus is a remarkable archaeon able to grow at temperatures around 100 °C. To gain insight into how this model hyperthermophile copes with heat stress, we compared transcriptomic and metabolomic data of cells subjected to a temperature shift from 90 °C to 97 °C. In this study, we used RNA-sequencing to characterize the global variation in gene expression levels, while nuclear magnetic resonance (NMR) and targeted ion exchange liquid chromatography-mass spectrometry (LC-MS) were used to determine changes in metabolite levels. Of the 552 differentially expressed genes in response to heat shock conditions, 257 were upregulated and 295 were downregulated. In particular, there was a significant downregulation of genes for synthesis and transport of amino acids. At the metabolite level, 37 compounds were quantified. The level of di-myo-inositol phosphate, a canonical heat stress solute among marine hyperthermophiles, increased considerably (5.4-fold) at elevated temperature. Also, the levels of mannosylglycerate, UDP-N-acetylglucosamine (UDPGlcNac) and UDP-N-acetylgalactosamine were enhanced. The increase in the pool of UDPGlcNac was concurrent with an increase in the transcript levels of the respective biosynthetic genes. This work provides the first metabolomic analysis of the heat shock response of a hyperthermophile.

VEGFR2-Mediated Reprogramming of Mitochondrial Metabolism Regulates the Sensitivity of Acute Myeloid Leukemia to Chemotherapy

Metabolic reprogramming is central to tumorigenesis, but whether chemotherapy induces metabolic features promoting recurrence remains unknown. We established a mouse xenograft model of human acute myeloid leukemia (AML) that enabled chemotherapy-induced regressions of established disease followed by lethal regrowth of more aggressive tumor cells. Human AML cells from terminally ill mice treated with chemotherapy (chemoAML) had higher lipid content, increased lactate production and ATP levels, reduced expression of peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α), and fewer mitochondria than controls from untreated AML animals. These changes were linked to increased VEGFR2 signaling that counteracted chemotherapy-driven cell death; blocking of VEGFR2 sensitized chemoAML to chemotherapy (re-)treatment and induced a mitochondrial biogenesis program with increased mitochondrial mass and oxidative stress. Accordingly, depletion of PGC-1α in chemoAML cells abolished such induction of mitochondrial metabolism and chemosensitization in response to VEGFR2 inhibition. Collectively, this reveals a mitochondrial metabolic vulnerability with potential therapeutic applications against chemotherapy-resistant AML.Significance: These findings reveal a mitochondrial metabolic vulnerability that might be exploited to kill chemotherapy-resistant acute myeloid leukemia cells. Cancer Res; 78(3); 731-41. ©2017 AACR.

Elemental composition of two ecologically contrasting seamount fishes, the bluemouth (Helicolenus dactylopterus) and blackspot seabream (Pagellus bogaraveo)

Concentrations of V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Cd and Pb were determined in muscle, liver and gonads of two ecologically contrasting fishes, Helicolenus dactylopterus (benthic) and Pagellus bogaraveo (benthopelagic). Elevated concentrations of As, Se and Cd found in tissues of both species appear to mirror the contribution of volcanic activity to the natural inputs of elements to Azorean waters. Results showed different element accumulation between the two species. Whereas higher concentrations were found in the liver of P. bogaraveo, elevated values were observed in the muscle of H. dactylopterus. Differences in accumulation are most likely related to metabolic rates, diet specificities and habitat. Concentrations in gonads varied up to four orders of magnitude, being higher and more variable in P. bogaraveo than H. dactylopterus. Elevated values of Cd were detected in gonads of both species despite its non-essential role on metabolic functions, presumably related to elimination.

Vertical migrations of a deep-sea fish and its prey

It has been speculated that some deep-sea fishes can display large vertical migrations and likely doing so to explore the full suite of benthopelagic food resources, especially the pelagic organisms of the deep scattering layer (DSL). This would help explain the success of fishes residing at seamounts and the increased biodiversity found in these features of the open ocean. We combined active plus passive acoustic telemetry of blackspot seabream with in situ environmental and biological (backscattering) data collection at a seamount to verify if its behaviour is dominated by vertical movements as a response to temporal changes in environmental conditions and pelagic prey availability. We found that seabream extensively migrate up and down the water column, that these patterns are cyclic both in short-term (tidal, diel) as well as long-term (seasonal) scales, and that they partially match the availability of potential DSL prey components. Furthermore, the emerging pattern points to a more complex spatial behaviour than previously anticipated, suggesting a seasonal switch in the diel behaviour mode (benthic vs. pelagic) of seabream, which may reflect an adaptation to differences in prey availability. This study is the first to document the fine scale three-dimensional behaviour of a deep-sea fish residing at seamounts.

1H NMR-based metabolic fingerprinting of urine metabolites after consumption of lingonberries (Vaccinium vitis-idaea) with a high-fat meal

The use of NMR metabolomics in clinical trials is growing; however, reports of postprandial experiments in humans are scarce. The present study investigated whether consumption of lingonberries as a supplement to an oil-rich meal modifies the postprandial fingerprints of human urine. Urine samples were analysed by (1)H NMR, and untargeted multivariate analysis was applied to the data for comprehensive fingerprinting. A clear separation of postprandial lingonberry meal samples was revealed. To evaluate statistical differences, a targeted approach was applied for the informative spectral areas. Significantly (p<0.05) increased levels of polyphenol metabolites, hippuric acid and 4-hydroxyhippuric acid, and decreased creatinine and dimethylamine levels were the major explanations for the grouping of the postprandial samples after the different meals. Thus, inclusion of polyphenol-rich lingonberry powder in a rapeseed oil-rich meal modifies the metabolic profile of urine which may be used to reveal both consumption of berries and health-promoting changes in the common metabolism.

UPLC-MS metabolic profiling of second trimester amniotic fluid and maternal urine and comparison with NMR spectral profiling for the identification of pregnancy disorder biomarkers

We report on the first untargeted UPLC-MS study of 2nd trimester maternal urine and amniotic fluid (AF), to investigate the possible metabolic effects of fetal malformations (FM), gestational diabetes mellitus (GDM) and preterm delivery (PTD). For fetal malformations, considerable metabolite variations were identified in AF and, to a lesser extent, in urine. Using validated PLS-DA models and statistical correlations between UPLC-MS data and previously acquired NMR data, a metabolic picture of fetal hypoxia, enhanced gluconeogenesis, TCA activity and hindered kidney development affecting FM pregnancies was reinforced. Moreover, changes in carnitine, pyroglutamate and polyols were newly noted, respectively, reflecting lipid oxidation, altered placental amino acid transfer and alterations in polyol pathways. Higher excretion of conjugated products in maternal urine was seen suggesting alterations in conjugation reactions. For the pre-diagnostic GDM group, no significant changes were observed, either considering amniotic fluid or maternal urine, whereas, for the pre-PTD group, some newly observed changes were noted, namely, the decrease of particular amino acids and the increase of an hexose (possibly glucose), suggesting alteration in placental amino acid fluxes and a possible tendency for hyperglycemia. This work shows the potential of UPLC-MS for the study of fetal and maternal biofluids, particularly when used in tandem with comparable NMR data. The important roles played by sampling characteristics (e.g. group dimensions) and the specific experimental conditions chosen for MS methods are discussed.

Changes in mouse whole saliva soluble proteome induced by tannin-enriched diet

Background

Previous studies suggested that dietary tannin ingestion may induce changes in mouse salivary proteins in addition to the primarily studied proline-rich proteins (PRPs). The aim of the present study was to determine the protein expression changes induced by condensed tannin intake on the fraction of mouse whole salivary proteins that are unable to form insoluble tannin-protein complexes. Two-dimensional polyacrylamide gel electrophoresis protein separation was used, followed by protein identification by mass spectrometry.

Results

Fifty-seven protein spots were excised from control group gels, and 21 different proteins were identified. With tannin consumption, the expression levels of one α-amylase isoform and one unidentified protein increased, whereas acidic mammalian chitinase and Muc10 decreased. Additionally, two basic spots that stained pink with Coomassie Brilliant Blue R-250 were newly observed, suggesting that some induced PRPs may remain uncomplexed or form soluble complexes with tannins.

Conclusion

This proteomic analysis provides evidence that other salivary proteins, in addition to tannin-precipitating proteins, are affected by tannin ingestion. Changes in the expression levels of the acidic mammalian chitinase precursor and in one of the 14 salivary α-amylase isoforms underscores the need to further investigate their role in tannin ingestion.

NMR metabolomics of esca disease-affected Vitis vinifera cv. Alvarinho leaves

Esca is a destructive disease that affects vineyards leading to important losses in wine production. Information about the response of Vitis vinifera plants to this disease is scarce, particularly concerning changes in plant metabolism. In order to study the metabolic changes in Vitis plants affected by esca, leaves from both infected and non-affected cordons of V. vinifera cv. Alvarinho (collected in the Vinho Verde region, Portugal) were analysed. The metabolite composition of leaves from infected cordons with visible symptoms [diseased leaves (dl)] and from asymptomatic cordons [healthy leaves (hl)] was evaluated by 1D and 2D (1)H-nuclear magnetic resonance (NMR) spectroscopy. Principal component analysis (PCA) of the NMR spectra showed a clear separation between dl and hl leaves, indicating differential compound production due to the esca disease. NMR/PCA analysis allowed the identification of specific compounds characterizing each group, and the corresponding metabolic pathways are discussed. Altogether, the study revealed a significant increase of phenolic compounds in dl, compared with hl, accompanied by a decrease in carbohydrates, suggesting that dl are rerouting carbon and energy from primary to secondary metabolism. Other metabolic alterations detected comprised increased levels of methanol, alanine, and gamma-aminobutyric acid in dl, which might be the result of the activation of other defence mechanisms.

Metabolite profiling of human amniotic fluid by hyphenated nuclear magnetic resonance spectroscopy

The metabolic profiling of human amniotic fluid (HAF) is of potential interest for the diagnosis of disorders in the mother or the fetus. In order to build a comprehensive metabolite database for HAF, hyphenated NMR has been used, for the first time, for systematic HAF profiling. Experiments were carried out using reverse-phase (RP) and ion-exchange liquid chromatography (LC), in order to detect less and more polar compounds, respectively. RP-LC conditions achieved good separation of amino acids, some sugars, and xanthines. Subsequent NMR and MS analysis enabled the rapid identification of 30 compounds, including 3-methyl-2-oxovalerate and 4-aminohippurate identified in HAF for the first time, to our knowledge. Under ion-exchange LC conditions, a different set of 30 compounds was detected, including sugars, organic acids, several derivatives of organic acids, and amino acids. In this experiment, five compounds were identified for the first time in HAF: D-xylitol, amino acid derivatives (N-acetylalanine, N-acetylglycine, 2-oxoleucine), and isovalerate. The nonendogenous nature of some metabolites (caffeine, paraxanthine, D-xylitol, sorbitol) is discussed. Hyphenated NMR has allowed the rapid detection of approximately 60 metabolites in HAF, some of which are not detectable by standard NMR due to low abundance (microM) and signal overlap thus enabling an extended metabolite database to be built for HAF.

Protein glycation and methylglyoxal metabolism in yeast: finding peptide needles in protein haystacks

Metabolism, the set of all chemical transformations inside a living cell, comprises nonenzymatic processes that generate toxic products such as reactive oxygen species and 2-oxoaldehydes. Methylglyoxal, a highly reactive 2-oxoaldehyde by-product of glycolysis, is able to react irreversibly and nonenzymatically with proteins, forming methylglyoxal advanced glycation end-products, which alter protein structure, stability and function. Therefore, protein glycation may influence cell metabolism and its physiology in a way beyond what can be predicted based on the implicit codification used in systems biology. Genome-wide approaches and transcriptomics, two mainstays of systems biology, are powerless to tackle the problems caused by nonenzymatic reactions that are part of cell metabolism and biochemistry. The effects of methylglyoxal-derived protein glycation and the cell's response to this unspecific posttranslational modification were investigated in Saccharomyces cerevisiae as a model organism. Specific protein glycation phenotypes were identified using yeast null-mutants for methylglyoxal catabolism and the existence of specific protein glycation targets by peptide mass fingerprint was discovered. Enolase, the major target, endures a glycation-dependent activity loss caused by dissociation of the active dimer upon glycation at a specific arginine residue, identified using the hidden information of peptide mass fingerprint. Once glycation occurs, a cellular response involving heat shock proteins from the refolding chaperone pathway is elicited and Hsp26p is activated by glycation.

Yeast protein glycation in vivo by methylglyoxal. Molecular modification of glycolytic enzymes and heat shock proteins

Protein glycation by methylglyoxal is a nonenzymatic post-translational modification whereby arginine and lysine side chains form a chemically heterogeneous group of advanced glycation end-products. Methylglyoxal-derived advanced glycation end-products are involved in pathologies such as diabetes and neurodegenerative diseases of the amyloid type. As methylglyoxal is produced nonenzymatically from dihydroxyacetone phosphate and d-glyceraldehyde 3-phosphate during glycolysis, its formation occurs in all living cells. Understanding methylglyoxal glycation in model systems will provide important clues regarding glycation prevention in higher organisms in the context of widespread human diseases. Using Saccharomyces cerevisiae cells with different glycation phenotypes and MALDI-TOF peptide mass fingerprints, we identified enolase 2 as the primary methylglyoxal glycation target in yeast. Two other glycolytic enzymes are also glycated, aldolase and phosphoglycerate mutase. Despite enolase's activity loss, in a glycation-dependent way, glycolytic flux and glycerol production remained unchanged. None of these enzymes has any effect on glycolytic flux, as evaluated by sensitivity analysis, showing that yeast glycolysis is a very robust metabolic pathway. Three heat shock proteins are also glycated, Hsp71/72 and Hsp26. For all glycated proteins, the nature and molecular location of some advanced glycation end-products were determined by MALDI-TOF. Yeast cells experienced selective pressure towards efficient use of d-glucose, with high methylglyoxal formation as a side effect. Glycation is a fact of life for these cells, and some glycolytic enzymes could be deployed to contain methylglyoxal that evades its enzymatic catabolism. Heat shock proteins may be involved in proteolytic processing (Hsp71/72) or protein salvaging (Hsp26).