A global HILIC-MS approach to measure polar human cerebrospinal fluid metabolome: Exploring gender-associated variation in a cohort of elderly cognitively healthy subjects

E2F transcription factor-1 modulates expression of glutamine metabolic genes in mouse embryonic fibroblasts and uterine sarcoma cells

Metabolic reprogramming is considered as a hallmark of cancer and is clinically exploited as a novel target for therapy. The E2F transcription factor-1 (E2F1) regulates various cellular processes, including proliferative and metabolic pathways, and acts, depending on the cellular and molecular context, as an oncogene or tumor suppressor. The latter is evident by the observation that E2f1-knockout mice develop spontaneous tumors, including uterine sarcomas. This dual role warrants a detailed investigation of how E2F1 loss impacts metabolic pathways related to cancer progression. Our data indicate that E2F1 binds to the promoter of several glutamine metabolism-related genes. Interestingly, the expression of genes in the glutamine metabolic pathway were increased in mouse embryonic fibroblasts (MEFs) lacking E2F1. In addition, we confirm that E2f1-/- MEFs are more efficient in metabolizing glutamine and producing glutamine-derived precursors for proliferation. Mechanistically, we observe a co-occupancy of E2F1 and MYC on glutamine metabolic promoters, increased MYC binding after E2F1 depletion and that silencing of MYC decreased the expression of glutamine-related genes in E2f1-/- MEFs. Analyses of transcriptomic profiles in 29 different human cancers identified uterine sarcoma that showed a negative correlation between E2F1 and glutamine metabolic genes. CRISPR/Cas9 knockout of E2F1 in the uterine sarcoma cell line SK-UT-1 confirmed elevated glutamine metabolic gene expression, increased proliferation and increased MYC binding to glutamine-related promoters upon E2F1 loss. Together, our data suggest a crucial role of E2F1 in energy metabolism and metabolic adaptation in uterine sarcoma cells.

Impact of conditioning runs on hydrophilic interaction chromatography repeatability and its application as a second dimension in online comprehensive two-dimensional liquid chromatography

A common separation approach for polar compounds involves coupling reversed-phase liquid chromatography (RPLC) with hydrophilic interaction chromatography (HILIC) in two-dimensional chromatography. The higher proportion of acetonitrile used in the HILIC mobile phase, which enhances mass spectrometry detection, encourages its use in the second dimension. Previous studies demonstrated that the HILIC column can be partially equilibrated within very short timeframes without compromising retention time stability, rendering it suitable in online comprehensive two-dimensional liquid chromatography (LC×LC) setups. In addition, a specific number of conditioning cycles seems necessary to establish stable retention times. Here, the repeatability of HILIC when employed as second dimension in LC×LC was investigated, with a focus on determining the required number of conditioning cycles to achieve repeatable retention times. Various parameters influenced by the LC×LC online modulation system were studied, such as steep gradient slopes up to 8%, and very short equilibration times, less than or equal to dead time, as well as injection volume and solvent, which depend on the first dimension. Finally, the use of HILIC as a second dimension with tailored conditioning runs was applied to the analysis of hyaluronic acid hydrogel digests. The application of an RPLC×HILIC method using five conditioning runs yielded exceptional stability in second-dimension retention times.

Impact of semen microbiota on the composition of seminal plasma

Several studies have found associations between specific bacterial genera and semen parameters. Bacteria are known to influence the composition of their niche and, consequently, could affect the composition of the seminal plasma. This study integrated microbiota profiling and metabolomics to explore the influence of seminal bacteria on semen metabolite composition in infertile couples, revealing associations between specific bacterial genera and metabolite profiles. Amino acids and acylcarnitines were the predominant metabolite groups identified in seminal plasma. Different microbiota profiles did not result in globally diverse metabolite compositions in seminal plasma. Nevertheless, levels of specific metabolites increased in the presence of a dysbiotic microbiota. Urocanate was significantly increased in abnormal semen samples (adjusted P-value < 0.001) and enriched in samples dominated by Prevotella spp. (P-value < 0.05), which was previously linked to a negative impact on semen. Therefore, varying microbiota profiles can influence the abundance of certain metabolites, potentially having an immunomodulatory effect, as seen with urocanate.IMPORTANCEMale infertility is often considered idiopathic since the specific cause of infertility often remains unidentified. Recently, variations in the seminal microbiota composition have been associated with normal and abnormal semen parameters and may, therefore, influence male infertility. Bacteria are known to alter the metabolite composition of their ecological niches, and thus, seminal bacteria might affect the composition of the seminal fluid, crucial in the fertilization process. Our research indicates that distinct seminal microbiota profiles are not associated with widespread changes in the metabolite composition of the seminal fluid. Instead, the presence of particular metabolites with immunomodulatory functions, such as urocanate, could shed light on the interplay between seminal microbiota and variations in semen parameters.

Metabolic dependencies of metastasis-initiating cells in female breast cancer

Understanding the mechanisms that enable cancer cells to metastasize is essential in preventing cancer progression. Here we examine the metabolic adaptations of metastasis-initiating cells (MICs) in female breast cancer and how those shape their metastatic phenotype. We find that endogenous MICs depend on the oxidative tricarboxylic acid cycle and fatty acid usage. Sorting tumor cells based upon solely mitochondrial membrane potential or lipid storage is sufficient at identifying MICs. We further identify that mitochondrially-generated citrate is exported to the cytoplasm to yield acetyl-CoA, and this is crucial to maintaining heightened levels of H3K27ac in MICs. Blocking acetyl-CoA generating pathways or H3K27ac-specific epigenetic writers and readers reduces expression of epithelial-to-mesenchymal related genes, MIC frequency, and metastatic potential. Exogenous supplementation of a short chain carboxylic acid, acetate, increases MIC frequency and metastasis. In patient cohorts, we observe that higher expression of oxidative phosphorylation related genes is associated with reduced distant relapse-free survival. These data demonstrate that MICs specifically and precisely alter their metabolism to efficiently colonize distant organs.

A multiplatform metabolomics approach for comprehensive analysis of GIST xenografts with various KIT mutations

Metabolites in biological matrices belong to diverse chemical groups, ranging from non-polar long-chain fatty acids to small polar molecules. The goal of untargeted metabolomic analysis is to measure the highest number of metabolites in the sample. Nevertheless, from an analytical point of view, no single technique can measure such a broad spectrum of analytes. Therefore, we selected a method based on GC-MS and LC-MS with two types of stationary phases for the untargeted profiling of gastrointestinal stromal tumours. The procedure was applied to GIST xenograft samples (n = 71) representing four different mutation models, half of which were treated with imatinib. We aimed to verify the method coverage and advantages of applying each technique. RP-LC-MS measured most metabolites due to a significant fraction of lipid components of the tumour tissue. What is unique and worth noting is that all applied techniques were able to distinguish between different mutation models. However, for detecting imatinib-induced alterations in the GIST metabolome, RP-LC-MS and GC-MS proved to be more relevant than HILIC-LC-MS, resulting in a higher number of significantly changed metabolites in four treated models. Undoubtedly, the inclusion of all mentioned techniques makes the method more comprehensive. Nonetheless, for green chemistry and time and labour saving, we assume that RP-LC-MS and GC-MS analyses are sufficient to cover the global GIST metabolome.

Exploring the aging process of cognitively healthy adults by analyzing cerebrospinal fluid metabolomics using liquid chromatography-tandem mass spectrometry

BACKGROUND

During biological aging, significant metabolic dysregulation in the central nervous system may lead to cognitive decline and neurodegeneration. However, the metabolomics of the aging process in cerebrospinal fluid (CSF) has not been thoroughly explored.

METHODS

In this cohort study of CSF metabolomics using liquid chromatography-mass spectrometry (LC-MS), fasting CSF samples collected from 92 cognitively unimpaired adults aged 20-87 years without obesity or diabetes were analyzed.

RESULTS

We identified 37 metabolites in these CSF samples with significant positive correlations with aging, including cysteine, pantothenic acid, 5-hydroxyindoleacetic acid (5-HIAA), aspartic acid, and glutamate; and two metabolites with negative correlations, asparagine and glycerophosphocholine. The combined alterations of asparagine, cysteine, glycerophosphocholine, pantothenic acid, sucrose, and 5-HIAA showed a superior correlation with aging (AUC = 0.982). These age-correlated changes in CSF metabolites might reflect blood-brain barrier breakdown, neuroinflammation, and mitochondrial dysfunction in the aging brain. We also found sex differences in CSF metabolites with higher levels of taurine and 5-HIAA in women using propensity-matched comparison.

CONCLUSIONS

Our LC-MS metabolomics of the aging process in a Taiwanese population revealed several significantly altered CSF metabolites during aging and between the sexes. These metabolic alterations in CSF might provide clues for healthy brain aging and deserve further exploration.

Brain endothelial cells exposure to malaria parasites links type I interferon signalling to antigen presentation, immunoproteasome activation, endothelium disruption, and cellular metabolism

Introduction

Cerebral malaria (CM) lethality is attributable to induction of brain edema induction but the cellular mechanisms involving brain microvascular endothelium in CM pathogenesis are unexplored.

Results

Activation of the STING-INFb-CXCL10 axis in brain endothelial cells (BECs) is a prominent component of the innate immune response in CM development in mouse models. Using a T cell-reporter system, we show that Type 1 IFN signaling in BECs exposed to Plasmodium berghei-infected erythrocytes (PbA-IE), functionally enhances MHC Class-I antigen presentation through gamma-interferon independent immunoproteasome activation and impacted the proteome functionally related to vesicle trafficking, protein processing/folding and antigen presentation. In vitro assays showed that Type 1 IFN signaling and immunoproteasome activation are also involved in the dysfunction of the endothelial barrier through disturbing gene expression in the Wnt/ß-catenin signaling pathway. We demonstrate that IE exposure induces a substantial increase in BECs glucose uptake while glycolysis blockade abrogates INFb secretion impairing immunoproteasome activation, antigen presentation and Wnt/ß-catenin signaling.

Discussion

Metabolome analysis show that energy demand and production are markedly increased in BECs exposed to IE as revealed by enriched content in glucose and amino acid catabolites. In accordance, glycolysis blockade in vivo delayed the clinical onset of CM in mice. Together the results show that increase in glucose uptake upon IE exposure licenses Type 1 IFN signaling and subsequent immunoproteasome activation contributing to enhanced antigen presentation and impairment of endothelial barrier function. This work raises the hypothesis that Type 1 IFN signaling-immunoproteasome induction in BECs contributes to CM pathology and fatality (1) by increasing antigen presentation to cytotoxic CD8+ T cells and (2) by promoting endothelial barrier dysfunction, that likely favor brain vasogenic edema.

SREBP modulates the NADP+/NADPH cycle to control night sleep in Drosophila

Sleep behavior is conserved throughout evolution, and sleep disturbances are a frequent comorbidity of neuropsychiatric disorders. However, the molecular basis underlying sleep dysfunctions in neurological diseases remains elusive. Using a model for neurodevelopmental disorders (NDDs), the Drosophila Cytoplasmic FMR1 interacting protein haploinsufficiency (Cyfip^85.1/+), we identify a mechanism modulating sleep homeostasis. We show that increased activity of the sterol regulatory element-binding protein (SREBP) in Cyfip^85.1/+ flies induces an increase in the transcription of wakefulness-associated genes, such as the malic enzyme (Men), causing a disturbance in the daily NADP⁺/NADPH ratio oscillations and reducing sleep pressure at the night-time onset. Reduction in SREBP or Men activity in Cyfip^85.1/+ flies enhances the NADP⁺/NADPH ratio and rescues the sleep deficits, indicating that SREBP and Men are causative for the sleep deficits in Cyfip heterozygous flies. This work suggests modulation of the SREBP metabolic axis as a new avenue worth exploring for its therapeutic potential in sleep disorders.

Divergence in metabolomic profile in clownfish and damselfish skin mucus

Introduction

The clownfish - sea anemone mutualism was suggested to have triggered the adaptive radiation of clownfishes, but the origin of clownfish resistance to stinging tentacles of host anemones remains unclear. The presence of specific compounds in the mucus of clownfishes conferring them the unique ability to prevent nematocyst discharge from their hosts has been the most supported hypothesis. Yet the mystery regarding the types of compounds found in clownfish skin mucus remains unsolved.

Methods

We analyzed the chemical composition of clownfish and damselfish mucus using an untargeted metabolomics (HILIC-HRMS) and lipidomics (RPLC-HRMS) approach.

Results and Discussion

The polar and lipid metabolome signatures were highly specific and allowed to discriminate between the clownfish and damselfish clades. The most discriminative part of the signature was the sphingolipid profile, displaying a broader diversity of ceramides present in significantly higher levels in clownfish mucus. Importantly, the inter-specific variability of metabolic signature was significantly higher in clownfishes, although their diversification is evolutionarily more recent, thus implying the impact of symbiosis on metabolic variability and adaptation. Furthermore, specialists and generalists clownfish species displayed distinctive metabolite signature. Two strict clownfish specialists, which are phylogenetically distant but share the same host species, clustered together based on their molecular signature, suggesting a link with their mutualistic nature. Overall, comparative analyses of metabolic signatures highlight differences in chemical composition of clownfish mucus and provide insight into biochemical pathways potentially implicated in clownfish adaptation to inhabit sea anemones and consequently diversify.

Molecular insights into sex-specific metabolic alterations in Alzheimer's mouse brain using multi-omics approach

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized by altered cellular metabolism in the brain. Several of these alterations have been found to be exacerbated in females, known to be disproportionately affected by AD. We aimed to unravel metabolic alterations in AD at the metabolic pathway level and evaluate whether they are sex-specific through integrative metabolomic, lipidomic, and proteomic analysis of mouse brain tissue.

METHODS

We analyzed male and female triple-transgenic mouse whole brain tissue by untargeted mass spectrometry-based methods to obtain a molecular signature consisting of polar metabolite, complex lipid, and protein data. These data were analyzed using multi-omics factor analysis. Pathway-level alterations were identified through joint pathway enrichment analysis or by separately evaluating lipid ontology and known proteins related to lipid metabolism.

RESULTS

Our analysis revealed significant AD-associated and in part sex-specific alterations across the molecular signature. Sex-dependent alterations were identified in GABA synthesis, arginine biosynthesis, and in alanine, aspartate, and glutamate metabolism. AD-associated alterations involving lipids were also found in the fatty acid elongation pathway and lysophospholipid metabolism, with a significant sex-specific effect for the latter.

CONCLUSIONS

Through multi-omics analysis, we report AD-associated and sex-specific metabolic alterations in the AD brain involving lysophospholipid and amino acid metabolism. These findings contribute to the characterization of the AD phenotype at the molecular level while considering the effect of sex, an overlooked yet determinant metabolic variable.

Hemoglobin normalization outperforms other methods for standardizing dried blood spot metabolomics: A comparative study

Dried blood spot (DBS) metabolomics has numerous applications in newborn health screening, exposomics, and biomonitoring of environmental chemicals in pregnant women and the elderly. However, accurate metabolite quantification is hindered by several challenges: notably the "hematocrit effect" and unknown blood-spotting volumes. Different techniques have been employed to overcome these issues but there is no consensus on the optimal normalization method for DBS metabolomics, and in some cases no normalization is used. We compared five normalization methods (hemoglobin (Hb), specific gravity (SG), protein, spot weight, potassium (K+)) to unnormalized data, and assessed sex-related differences in the DBS metabolome in 21 adults (group 1, n = 10 males, n = 11 females). The performance of each normalization method was evaluated using multiple criteria: (a) reduction of intragroup variation (pooled median absolute deviation, pooled estimate of variance, pooled coefficient of variation, NMDS and principal component analysis), (b) effect on differential metabolic analysis (dendrogram, heatmap, p-value distribution), and (c) influence on classification accuracy (partial least squares discriminant analysis, sparse partial least squares discriminant analysis error rates, receiver operating curve, random forest out of bag error rate). Our results revealed that Hb normalization outperformed all the other methods based on the three criteria and 13 different parameters; the performance of Hb was further demonstrated in an independent group of DBS from 18 neonates (group 2, n = 9 males, n = 9 females). Furthermore, we showed that SG and Hb are correlated in adults (rs = 0.86, p < 0.001), and validated this relationship in an independent group of 18 neonates and infants (group 3) (rs = 0.84, p < 0.001). Using the equation, SG = -0.4814Hb2 + 2.44Hb + 0.005, SG can be used as a surrogate for normalization by Hb. This is the first comparative study to concurrently evaluate multiple normalization methods for DBS metabolomics which will serve as a robust methodological platform for future environmental epidemiological studies.

A Direct Infusion Probe for Rapid Metabolomics of Low-Volume Samples

Targeted and nontargeted metabolomics has the potential to evaluate and detect global metabolite changes in biological systems. Direct infusion mass spectrometric analysis enables detection of all ionizable small molecules, thus simultaneously providing information on both metabolites and lipids in chemically complex samples. However, to unravel the heterogeneity of the metabolic status of cells in culture and tissue a low number of cells per sample should be analyzed with high sensitivity, which requires low sample volumes. Here, we present the design and characterization of the direct infusion probe, DIP. The DIP is simple to build and position directly in front of a mass spectrometer for rapid metabolomics of chemically complex biological samples using pneumatically assisted electrospray ionization at 1 μL/min flow rate. The resulting data is acquired in a square wave profile with minimal carryover between samples that enhances throughput and enables several minutes of uniform MS signal from 5 μL sample volumes. The DIP was applied to study the intracellular metabolism of insulin secreting INS-1 cells and the results show that exposure to 20 mM glucose for 15 min significantly alters the abundance of several small metabolites, amino acids, and lipids.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Precise Metabolomics Reveals a Diversity of Aging-Associated Metabolic Features

Mass spectrometry-based metabolomics has emerged as a powerful technique for biomedical research, although technical issues with its analytical precision and structural characterization remain. Herein, a robust non-targeted strategy for accurate quantitation and precise profiling of metabolomes is developed and applied to investigate plasma metabolic features associated with human aging. A comprehensive set of isotope-labeled standards (ISs) covering major metabolic pathways is incorporated to quantify polar metabolites. Matching rules to select ISs for calibration follow a primary criterion of minimal coefficients of variations (COVs). If minimal COVs between specific ISs for a particular metabolite fall within 5% window, a further selection of ISs is conducted based on structural similarities and proximity in retention time. The introduction and refined selection of appropriate ISs for quantitation reduces the COVs of 480 identified metabolites in quality control samples from 14.3% to 9.8% and facilitates identification of additional metabolite. Finally, the precise metabolomics approach reveals perturbations in a diverse array of metabolic pathways across aging that principally implicate steroid metabolism, amino acid metabolism, lipid metabolism, and purine metabolism, which allows the authors to draw correlates to the pathology of various age-related diseases. These findings provide clues for the prevention and treatment of these age-related diseases.

PERK is a critical metabolic hub for immunosuppressive function in macrophages

Chronic inflammation triggers compensatory immunosuppression to stop inflammation and minimize tissue damage. Studies have demonstrated that endoplasmic reticulum (ER) stress augments the suppressive phenotypes of immune cells; however, the molecular mechanisms underpinning this process and how it links to the metabolic reprogramming of immunosuppressive macrophages remain elusive. In the present study, we report that the helper T cell 2 cytokine interleukin-4 and the tumor microenvironment increase the activity of a protein kinase RNA-like ER kinase (PERK)-signaling cascade in macrophages and promote immunosuppressive M2 activation and proliferation. Loss of PERK signaling impeded mitochondrial respiration and lipid oxidation critical for M2 macrophages. PERK activation mediated the upregulation of phosphoserine aminotransferase 1 (PSAT1) and serine biosynthesis via the downstream transcription factor ATF-4. Increased serine biosynthesis resulted in enhanced mitochondrial function and α-ketoglutarate production required for JMJD3-dependent epigenetic modification. Inhibition of PERK suppressed macrophage immunosuppressive activity and could enhance the efficacy of immune checkpoint programmed cell death protein 1 inhibition in melanoma. Our findings delineate a previously undescribed connection between PERK signaling and PSAT1-mediated serine metabolism critical for promoting immunosuppressive function in M2 macrophages.

Systematic Evaluation of HILIC Stationary Phases for Global Metabolomics of Human Plasma

Polar hydrophilic metabolites have been identified as important actors in many biochemical pathways. Despite continuous improvement and refinement of hydrophilic interaction liquid chromatography (HILIC) platforms, its application in global polar metabolomics has been underutilized. In this study, we aimed to systematically evaluate polar stationary phases for untargeted metabolomics by using HILIC columns (neutral and zwitterionic) that have been exploited widely in targeted approaches. To do so, high-resolution mass spectrometry was applied to thoroughly investigate selectivity, repeatability and matrix effect at three pH conditions for 9 classes of polar compounds using 54 authentic standards and plasma matrix. The column performance for utilization in untargeted metabolomics was assessed using plasma samples with diverse phenotypes. Our results indicate that the ZIC-c HILIC column operated at neutral pH exhibited several advantages, including superior performance for different classes of compounds, better isomer separation, repeatability and high metabolic coverage. Regardless of the column type, the retention of inorganic ions in plasma leads to extensive adduct formation and co-elution with analytes, which results in ion-suppression as part of the overall plasma matrix effect. In ZIC-c HILIC, the sodium chloride ion effect was particularly observed for amino acids and amine classes. Successful performance of HILIC for separation of plasma samples with different phenotypes highlights this mode of separation as a valuable approach in global profiling of plasma sample and discovering the metabolic changes associated with health and disease.

Isomer-selective analysis of inositol phosphates with differential isotope labelling by phosphate methylation using liquid chromatography with tandem mass spectrometry

Inositol phosphates belong to a family of structurally diverse signaling molecules playing crucial role in Ca²⁺ release from intracellular storage vesicles. There are many possibilities of phosphorylation, including their degree and position. Inositol (1,4,5) trisphosphate has been well recognized as the most important second messenger among this family. It remains a challenge to analyse the entire inositol phosphate metabolite family due to its structural complexity, high polarity, and high phosphate density. In this study, we have established an improved UHPLC-ESI-MS/MS method based on a differential isotope labelling methylation strategy. An SPE extraction kit composed of TiO2 and PTFE filter was employed for sample preparation which provided good extraction performance. Samples were methylated (light label) to neutralize the phosphate groups and give better performance in liquid chromatography. Regioisomers and inositol phosphates differing in their number of phosphate residues were successfully separated after optimization on a core-shell cholesterylether-bonded RP-type column (Cosmocore 2.6Cholester) using methanol as organic modifier. Triple quadrupole MS detection was based on selected reaction monitoring (SRM) acquisition with characteristic fragments. Stable isotope labeling methylation was performed to generate internal standards (heavy label). Limits of quantification from 0.32 to 0.89 pmol on column was achieved. This method was validated to be suitable for inositol phosphate profiling in biological samples. After application in cultured HeLa cells, NIST SRM1950 plasma, and human platelets, distinct inositol profiles were obtained. This newly established method exhibited improved analytical performance, holding the potential to advance the understanding of inositol phosphate signaling.

Small phenolic and indolic gut-dependent molecules in the primate central nervous system: levels vs. bioactivity

INTRODUCTION

A rapidly growing body of data documents associations between disease of the brain and small molecules generated by gut-microbiota (GMB). While such metabolites can affect brain function through a variety of mechanisms, the most direct action would be on the central nervous system (CNS) itself.

OBJECTIVE

Identify indolic and phenolic GMB-dependent small molecules that reach bioactive concentrations in primate CNS.

METHODS

We conducted a PubMed search for metabolomic studies of the primate CNS [brain tissue or cerebrospinal fluid (CSF)] and then selected for phenolic or indolic metabolites that (i) had been quantified, (ii) were GMB-dependent. For each chemical we then conducted a search for studies of bioactivity conducted in vitro in human cells of any kind or in CNS cells from the mouse or rat.

RESULTS

36 metabolites of interests were identified in primate CNS through targeted metabolomics. Quantification was available for 31/36 and in vitro bioactivity for 23/36. The reported CNS range for 8 metabolites 2-(3-hydroxyphenyl)acetic acid, 2-(4-hydroxyphenyl)acetic acid, 3-(3-hydroxyphenyl)propanoic acid, (E)-3-(3,4-dihydroxyphenyl)prop-2-enoic acid [caffeic acid], 3-hydroxybenzoic acid, 4-hydroxybenzoic acid, 2-acetamido-3-(1H-indol-3-yl)propanoic acid [N-acetyltryptophan], 1H-indol-3-yl hydrogen sulfate [indoxyl-3-sulfate] overlapped with a bioactive concentration. However, the number and quality of relevant studies of CNS neurochemistry as well as of bioactivity were highly limited. Structural isomers, multiple metabolites and potential confounders were inadequately considered.

CONCLUSION

The potential direct bioactivity of GMB-derived indolic and phenolic molecules on primate CNS remains largely unknown. The field requires additional strategies to identify and prioritize screening of the most promising small molecules that enter the CNS.

Acute RyR1 Ca2+ leak enhances NADH-linked mitochondrial respiratory capacity

Sustained ryanodine receptor (RyR) Ca²⁺ leak is associated with pathological conditions such as heart failure or skeletal muscle weakness. We report that a single session of sprint interval training (SIT), but not of moderate intensity continuous training (MICT), triggers RyR1 protein oxidation and nitrosylation leading to calstabin1 dissociation in healthy human muscle and in in vitro SIT models (simulated SIT or S-SIT). This is accompanied by decreased sarcoplasmic reticulum Ca²⁺ content, increased levels of mitochondrial oxidative phosphorylation proteins, supercomplex formation and enhanced NADH-linked mitochondrial respiratory capacity. Mechanistically, (S-)SIT increases mitochondrial Ca²⁺ uptake in mouse myotubes and muscle fibres, and decreases pyruvate dehydrogenase phosphorylation in human muscle and mouse myotubes. Countering Ca²⁺ leak or preventing mitochondrial Ca²⁺ uptake blunts S-SIT-induced adaptations, a result supported by proteomic analyses. Here we show that triggering acute transient Ca²⁺ leak through RyR1 in healthy muscle may contribute to the multiple health promoting benefits of exercise.

Diagnostic approach to thyroid cancer based on amino acid metabolomics in saliva by ultra-performance liquid chromatography with high resolution mass spectrometry

Thyroid cancer is a malignant disease with dramatically low advanced-stage 10-year survival. Meanwhile, the metabolites in saliva are becoming a wealthy source of disease biomarkers. However, there is a lack of non-invasive analytical methods for the identification of biomarkers in saliva for the preoperative diagnosis of thyroid cancer. Therefore, we developed an ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) method to simultaneously determine the metabolic levels of 10 amino acids in saliva, aiming to study the amino acid metabolism profile to promote early diagnosis of thyroid cancer. We tested unstimulated whole saliva from patients with papillary thyroid carcinoma (PTC; n = 61) and healthy controls (HC; n = 61), and used receiver operating characteristic (ROC) curves to establish the diagnostic value of potential markers. The method validation results showed good precision, linearity (R² > 0.99), recovery (92.2 %-110.3 %), intra- and inter-day precision (RSD < 7 % and RSD < 9 %, respectively). The concentration of 10 amino acids was significantly different between PTC and HC in human salivary analysis (P < 0.05), the area under the curve (AUC) values of a single marker for the diagnosis of PTC were ranging from 0.678 to 0.833. A panel of alanine, valine, proline, phenylalanine was selected in combination yielded the AUC of 0.936, which will improve the accuracy of early diagnosis of thyroid cancer (sensitivity: 91.2 %; specificity: 85.2 %). This study proved the possibility of salivary amino acid biomarkers for PTC early diagnosis, providing a simple auxiliary way for the non-invasive diagnosis of thyroid cancer.

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

HILIC-MS-based metabolomics reveal that Astragalus polysaccharide alleviates doxorubicin-induced cardiomyopathy by regulating sphingolipid and glycerophospholipid homeostasis

Doxorubicin (DOX) is widely used against cancer but carries the risk of a progressive cardiomyopathy. Astragalus polysaccharides (ASP) is the main active ingredient of Astragalus membranaceus Bunge. It has been proved to be effective against DOX-induced cardiomyopathy. However, its therapeutic mechanism is not yet well explored. In this study, a metabolomics approach based on hydrophilic interaction liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HILIC-Q-TOFMS) was developed to characterize the metabolic fluctuations associated with DOX cardiomyopathy and elucidate the underlying mechanisms of the protective effects of ASP. By the combination of HILIC-Q-TOFMS and multivariate and univariate data analysis, we identified 22 polar serum metabolites associated with DOX cardiomyopathy, 12 of which were significantly reversed when the animals were co-treated with ASP through two main metabolic pathways, i.e., sphingolipid and glycerophospholipid metabolism. Furthermore, it was found that ASP could alleviate DOX-induced cardiomyopathy by decreasing the levels of acid sphingomyelinase, acid ceramidase and phospholipase A2 and increasing the levels of sphingomyelin synthase to regulate the sphingolipid and glycerophospholipid metabolic disorder. These results revealed that sphingolipid and glycerophospholipid metabolism may be significantly responsible for DOX cardiomyopathy, which is also a major mechanism for the action of ASP against DOX cardiomyopathy.

Adenylosuccinate lyase is oncogenic in colorectal cancer by causing mitochondrial dysfunction and independent activation of NRF2 and mTOR-MYC-axis

Rationale: Adenylosuccinate lyase (ADSL) is an essential enzyme for de novo purine biosynthesis. Here we sought to investigate the putative role of ADSL in colorectal carcinoma (CRC) carcinogenesis and response to antimetabolites.

Methods: ADSL expression levels were assessed by immunohistochemistry or retrieved from The Cancer Genome Atlas (TCGA) dataset. The effects of ADSL silencing or overexpression were evaluated on CRC cell proliferation, cell migration and cell-cycle. In vivo tumor growth was assessed by the chicken chorioallantoic membrane (CAM). Transfected cell lines or patient-derived organoids (PDO) were treated with 5-fluorouracil (5-FU) and 6-mercaptopurine (6-MP) and drug response was correlated with ADSL expression levels. Metabolomic and transcriptomic profiling were performed to identify dysregulated pathways and ADSL downstream effectors. Mitochondrial respiration and glycolytic capacity were measured using Seahorse; mitochondrial membrane potential and the accumulation of ROS were measured by FACS using MitoTracker Red and MitoSOX staining, respectively. Activation of canonical pathways was assessed by immunohistochemistry and immunoblotting.

Results: ADSL expression is significantly increased in CRC tumors compared to non-tumor tissue. ADSL-high CRCs show upregulation of genes involved in DNA synthesis, DNA repair and cell cycle. Accordingly, ADSL overexpression accelerated progression through the cell cycle and significantly increased proliferation and migration in CRC cell lines. Additionally, ADSL expression increased tumor growth in vivo and sensitized CRCs to 6-MP in vitro, ex vivo (PDOs) and in vivo (CAM model). ADSL exerts its oncogenic function by affecting mitochondrial function via alteration of the TCA cycle and impairment of mitochondrial respiration. The KEAP1-NRF2 and mTORC1-cMyc axis are independently activated upon ADSL overexpression and may favor the survival and proliferation of ROS-accumulating cells, favoring DNA damage and tumorigenesis.

Conclusions: Our results suggest that ADSL is a novel oncogene in CRC, modulating mitochondrial function, metabolism and oxidative stress, thus promoting cell cycle progression, proliferation and migration. Our results also suggest that ADSL is a predictive biomarker of response to 6-mercaptopurine in the pre-clinical setting.

Optimization of pretreatment methods for cerebrospinal fluid metabolomics based on ultrahigh performance liquid chromatography/mass spectrometry

Sample pretreatment of cerebrospinal fluid (CSF) in metabolomics plays an important role in metabolic profiling study, especially for samples related to central nervous system diseases. However, there is few study about optimization of CSF metabolomics pretreatment. Therefore, it is an urgent need to optimize CSF pretreatment in order to promote the extraction efficiency of metabolites. In this study, CSF samples were separately subjected to nine different protein precipitation solvents and five different reconstitution solvents to establish the most effective pretreatment method before hydrophilic interaction (HILIC) and reverse-phase (RP) ultrahigh performance liquid chromatography mass spectrometry (UPLC/MS) analysis. The optimal conditions for different sample pretreatment methods were analyzed based on coverage (number of detected potential metabolites), stability (the relative standard deviation (RSD) distribution of metabolites) and the reproducibility of the data. Our results suggested that using EtOH or MeOH-EtOH-ACN (1:1:1, v/v/v) as the protein precipitation solvents and H₂O-MeOH-ACN (2:1:1, v/v/v) as the reconstitution solvent were optimal methods for T3 column analysis. For HILIC column analysis, using EtOH to precipitate protein and H₂O-MeOH-ACN (2:1:1, v/v/v) to reconstitute or MeOH to precipitate and 5 %ACN to reconstitute performed best. This developed UPLC/MS pretreatment method could provide better protein precipitation solvents and reconstitution solvents for global CSF metabolic analysis, potentially facilitating the comprehensive understanding of many central nervous system diseases.

Single-Step Extraction Coupled with Targeted HILIC-MS/MS Approach for Comprehensive Analysis of Human Plasma Lipidome and Polar Metabolome

Expanding metabolome coverage to include complex lipids and polar metabolites is essential in the generation of well-founded hypotheses in biological assays. Traditionally, lipid extraction is performed by liquid-liquid extraction using either methyl-tert-butyl ether (MTBE) or chloroform, and polar metabolite extraction using methanol. Here, we evaluated the performance of single-step sample preparation methods for simultaneous extraction of the complex lipidome and polar metabolome from human plasma. The method performance was evaluated using high-coverage Hydrophilic Interaction Liquid Chromatography-ESI coupled to tandem mass spectrometry (HILIC-ESI-MS/MS) methodology targeting a panel of 1159 lipids and 374 polar metabolites. The criteria used for method evaluation comprised protein precipitation efficiency, and relative MS signal abundance and repeatability of detectable lipid and polar metabolites in human plasma. Among the tested methods, the isopropanol (IPA) and 1-butanol:methanol (BUME) mixtures were selected as the best compromises for the simultaneous extraction of complex lipids and polar metabolites, allowing for the detection of 584 lipid species and 116 polar metabolites. The extraction with IPA showed the greatest reproducibility with the highest number of lipid species detected with the coefficient of variation (CV) < 30%. Besides this difference, both IPA and BUME allowed for the high-throughput extraction and reproducible measurement of a large panel of complex lipids and polar metabolites, thus warranting their application in large-scale human population studies.

Evaluation of Different Tandem MS Acquisition Modes to Support Metabolite Annotation in Human Plasma Using Ultra High-Performance Liquid Chromatography High-Resolution Mass Spectrometry for Untargeted Metabolomics

Ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) is a powerful and essential technique for metabolite annotation in untargeted metabolomic applications. The aim of this study was to evaluate the performance of diverse tandem MS (MS/MS) acquisition modes, i.e., all ion fragmentation (AIF) and data-dependent analysis (DDA), with and without ion mobility spectrometry (IM), to annotate metabolites in human plasma. The influence of the LC separation was also evaluated by comparing the performance of MS/MS acquisition in combination with three complementary chromatographic separation modes: reversed-phase chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) with either an amide (aHILIC) or a zwitterionic (zHILIC) stationary phase. RPLC conditions were first chosen to investigate all the tandem MS modes, and we found out that DDA did not provide a significant additional amount of chemical coverage and that cleaner MS/MS spectra can be obtained by performing AIF acquisitions in combination with IM. Finally, we were able to annotate 338 unique metabolites and demonstrated that zHILIC was a powerful complementary approach to both the RPLC and aHILIC chromatographic modes. Moreover, a better analytical throughput was reached for an almost negligible loss of metabolite coverage when IM-AIF and AIF using ramped instead of fixed collision energies were used.

Metabolic profiling of aromatic compounds in cerebrospinal fluid of neurosurgical patients using microextraction by packed sorbent and liquid-liquid extraction with gas chromatography-mass spectrometry analysis

A new approach to the quantitative analysis of aromatic metabolites in cerebrospinal fluid samples of neurosurgical patients based on microextraction by packed sorbent coupled with derivatization and GC-MS was developed. Analytical characteristics such as recoveries (40-90%), limit of detection (0.1-0.3 μm) and limit of quantitation (0.4-0.7 μm) values, accuracy (<±20%), precision (<20%) and linear correlations (R²  ≥ 0.99) over a 0.4-10 μm range of concentrations demonstrated that microextraction by packed sorbent provides results for the quantitative analysis of target compounds comparable with those for liquid-liquid extraction. Similar results were achieved using 40 μl of sample for microextraction by packed sorbent instead of 200 μl for liquid-liquid extraction. Benzoic, 3-phenylpropionic, 3-phenyllactic, 4-hydroxybenzoic, 2-(4-hydroxyphenyl)acetic, homovanillic and 3-(4-hydroxyphenyl)lactic acids were found in cerebrospinal fluid samples (n = 138) of neurosurgical patients in lower concentrations than in serum samples (n = 110) of critically ill patients. Analysis of the cerebrospinal fluid and serum samples taken at the same time from neurosurgical patients (n = 5) revealed similar results for patients without infection and multidirectional results for patients with central nervous system infection. Our preliminary results demonstrate the necessity of further evaluating the aromatic compound profile in cerebrospinal fluid for its subsequent verification for potential diagnostic markers.

Confirmation of neurometabolic diagnoses using age-dependent cerebrospinal fluid metabolomic profiles

Timely diagnosis is essential for patients with neurometabolic disorders to enable targeted treatment. Next-Generation Metabolic Screening (NGMS) allows for simultaneous screening of multiple diseases and yields a holistic view of disturbed metabolic pathways. We applied this technique to define a cerebrospinal fluid (CSF) reference metabolome and validated our approach with patients with known neurometabolic disorders. Samples were measured using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry followed by (un)targeted analysis. For the reference metabolome, CSF samples from patients with normal general chemistry results and no neurometabolic diagnosis were selected and grouped based on sex and age (0-2/2-5/5-10/10-15 years). We checked the levels of known biomarkers in CSF from seven patients with five different neurometabolic disorders to confirm the suitability of our method for diagnosis. Untargeted analysis of 87 control CSF samples yielded 8036 features for semiquantitative analysis. No sex differences were found, but 1782 features (22%) were different between age groups (q < 0.05). We identified 206 diagnostic metabolites in targeted analysis. In a subset of 20 high-intensity metabolites and 10 biomarkers, 17 (57%) were age-dependent. For each neurometabolic patient, ≥1 specific biomarker(s) could be identified in CSF, thus confirming the diagnosis. In two cases, age-matching was essential for correct interpretation of the metabolomic profile. In conclusion, NGMS in CSF is a powerful tool in defining a diagnosis for neurometabolic disorders. Using our database with many (age-dependent) features in CSF, our untargeted approach will facilitate biomarker discovery and further understanding of mechanisms of neurometabolic disorders.

High-Throughput Fingerprinting of Rhizobial Free Fatty Acids by Chemical Thin-Film Deposition and Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry

Fatty acids (FAs) represent an important class of metabolites, impacting on membrane building blocks and signaling compounds in cellular regulatory networks. In nature, prokaryotes are characterized with the most impressing FA structural diversity and the highest relative content of free fatty acids (FFAs). In this context, nitrogen-fixing bacteria (order Rhizobiales), the symbionts of legumes, are particularly interesting. Indeed, the FA profiles influence the structure of rhizobial nodulation factors, required for successful infection of plant root. Although FA patterns can be assessed by gas chromatography-(GC-) and liquid chromatography-mass spectrometry (LC-MS), sample preparation for these methods is time-consuming and quantification suffers from compromised sensitivity, low stability of derivatives and artifacts. In contrast, matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS) represents an excellent platform for high-efficient metabolite fingerprinting, also applicable to FFAs. Therefore, here we propose a simple and straightforward protocol for high-throughput relative quantification of FFAs in rhizobia by combination of Langmuir technology and MALDI-TOF-MS featuring a high sensitivity, accuracy and precision of quantification. We describe a step-by-step procedure comprising rhizobia culturing, pre-cleaning, extraction, sample preparation, mass spectrometric analysis, data processing and post-processing. As a case study, a comparison of the FFA metabolomes of two rhizobia species-Rhizobium leguminosarum and Sinorhizobium meliloti, demonstrates the analytical potential of the protocol.

Noninvasive rapid detection of metabolic adaptation in activated human T lymphocytes by hyperpolarized 13C magnetic resonance

The metabolic shift induced in human CD4⁺ T lymphocytes by stimulation is characterized by an upregulation of glycolysis, leading to an augmentation in lactate production. This adaptation has already been highlighted with various techniques and reported in several previous studies. We herein propose a method to rapidly and noninvasively detect the associated increase in flux from pyruvate to lactate catalyzed by lactate dehydrogenase using hyperpolarized ¹³C magnetic resonance, a technique which can be used for in vivo imaging. It was shown that the conversion of hyperpolarized ¹³C-pyruvate to ¹³C-lactate during the one-minute measurement increased by a mean factor of 3.6 in T cells stimulated for 5 days as compared to resting T cells. This method can be extended to other metabolic substrates and is therefore a powerful tool to noninvasively analyze T cell metabolism, possibly in vivo.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Systemic and central nervous system metabolic alterations in Alzheimer's disease

Background

Metabolic alterations, related to cerebral glucose metabolism, brain insulin resistance, and age-induced mitochondrial dysfunction, play an important role in Alzheimer’s disease (AD) on both the systemic and central nervous system level. To study the extent and significance of these alterations in AD, quantitative metabolomics was applied to plasma and cerebrospinal fluid (CSF) from clinically well-characterized AD patients and cognitively healthy control subjects. The observed metabolic alterations were associated with core pathological processes of AD to investigate their relation with amyloid pathology and tau-related neurodegeneration.

Methods

In a case-control study of clinical and biomarker-confirmed AD patients (n = 40) and cognitively healthy controls without cerebral AD pathology (n = 34) with paired plasma and CSF samples, we performed metabolic profiling, i.e., untargeted metabolomics and targeted quantification. Targeted quantification focused on identified deregulated pathways highlighted in the untargeted assay, i.e. the TCA cycle, and its anaplerotic pathways, as well as the neuroactive tryptophan and kynurenine pathway.

Results

Concentrations of several TCA cycle and beta-oxidation intermediates were higher in plasma of AD patients, whilst amino acid concentrations were significantly lower. Similar alterations in these energy metabolism intermediates were observed in CSF, together with higher concentrations of creatinine, which were strongly correlated with blood-brain barrier permeability. Alterations of several amino acids were associated with CSF Amyloidβ1–42. The tryptophan catabolites, kynurenic acid and quinolinic acid, showed significantly higher concentrations in CSF of AD patients, which, together with other tryptophan pathway intermediates, were correlated with either CSF Amyloidβ1–42, or tau and phosphorylated Tau-181.

Conclusions

This study revealed AD-associated systemic dysregulation of nutrient sensing and oxidation and CNS-specific alterations in the neuroactive tryptophan pathway and (phospho)creatine degradation. The specific association of amino acids and tryptophan catabolites with AD CSF biomarkers suggests a close relationship with core AD pathology.

Our findings warrant validation in independent, larger cohort studies as well as further investigation of factors such as gender and APOE genotype, as well as of other groups, such as preclinical AD, to identify metabolic alterations as potential intervention targets.

Choosing an Optimal Sample Preparation in Caulobacter crescentus for Untargeted Metabolomics Approaches

Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using Caulobacter crescentus as a model for Gram-negative bacteria. Two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were combined in a full factorial experimental design. To fully exploit the multivariate structure of the generated data, the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm was employed to decompose the contribution of each factor studied and their potential interactions for a set of annotated metabolites. All main effects of the factors studied were found to have a significant contribution on the total observed variability. Cell retrieval, quenching and extraction solvent, and cell disrupting mechanism accounted respectively for 27.6%, 8.4%, and 7.0% of the total variability. The reproducibility and metabolome coverage of the sample preparation procedures were then compared and evaluated in terms of relative standard deviation (RSD) on the area for the detected metabolites. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval, using MeOH:H₂O (8:2) as quenching and extraction solvent, and freeze-thaw cycles as the cell disrupting mechanism.

Hydrophilic Interaction Liquid Chromatography Coupled to Mass Spectrometry and Multivariate Analysis of the De Novo Pyrimidine Pathway Metabolites

In this study, we describe the optimization of a Hydrophilic Interaction Liquid Chromatography coupled to mass spectrometry (HILIC-MS) method for the evaluation of 14 metabolites related to the de novo synthesis of pyrimidines (dnSP) while using multivariate analysis, which is the metabolic pathway for pyrimidine nucleotide production. A multivariate design was used to set the conditions of the column temperature, flow of the mobile phase, additive concentration, gradient rate, and pH of the mobile phase in order to attain higher peak resolution and ionization efficiency in shorter analysis times. The optimization process was carried out while using factorial fractional designs, Box–Behnken design and central composite design while using two zwitterionic columns, ZIC-p-HILIC and ZIC-HILIC, polymeric, and silica-based columns, respectively. The factors were evaluated while using resolution (R), retention factor (k), efficiency of the column (N), and peak height (h) as the response variables. The best optimized conditions were found with the ZIC-p-HILIC column: elution gradient rate 2 min, pH 7.0, temperature 45 °C, mobile phase flow of 0.35 mL min⁻¹, and additive (ammonium acetate) concentration of 6 mM. The total analysis time was 28 min. The ZIC-p-HILIC LC-MS method yielded satisfactory results for linearity of calibration curves, limit of detection (LOD), and limit of quantification (LOQ). The method has been shown to be appropriate for the analysis of dnSP on samples of tomato plants that were infected with Phytophthora infestans.

Cerebrospinal Fluid Spermidine, Glutamine and Putrescine Predict Postoperative Delirium Following Elective Orthopaedic Surgery

Delirium is a marker of brain vulnerability, associated with increasing age, pre-existing cognitive impairment and, recently, cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease. This nested case-control study used a targeted quantitative metabolomic methodology to profile the preoperative CSF of patients (n = 54) who developed delirium following arthroplasty (n = 28) and those who did not (n = 26). The aim was to identify novel preoperative markers of delirium, and to assess potential correlations with clinical data. Participants without a diagnosis of dementia (≥65 years) undergoing elective primary hip or knee arthroplasty were postoperatively assessed for delirium once-daily for three days. Groups were compared using multivariate, univariate and receiving operator characteristic (ROC) methods. Multivariate modelling using Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) of metabolomic data readily distinguished between delirium and control groups (R2 ≤ 0.56; Q2 ≤ 0.10). Three metabolites (spermidine, putrescine and glutamine) significantly differed between groups (P < 0.05; FDR < 0.07), and performed well as CSF biomarkers (ROC > 0.75). The biomarker performance of the two polyamines (spermidine/putrescine) was enhanced by ratio with CSF Aβ42 (ROC > 0.8), and spermidine significantly correlated with Aβ42 (pearson r = −0.32; P = 0.018). These findings suggest that spermidine and putrescine levels could be useful markers of postoperative delirium risk, particularly when combined with Aβ42, and this requires further investigation.

LC-HRMS data as a result of untargeted metabolomic profiling of human cerebrospinal fluid

Cerebrospinal fluid (CSF) is a key body fluid that maintains the homeostasis in central nervous system (CNS). As a biofluid whose content reflects the brain metabolic activity, the CSF is analyzed in the context of neurological diseases and is rarely collected from healthy subjects. For this reason, the metabolite variation associated with general phenotypic characteristics such as gender and age have hardly ever been studied. Here we present the hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS) data as a result of untargeted metabolomics analysis of a cohort of elderly cognitively healthy volunteers (n = 32). 146 unambiguously identified water soluble metabolites (using accurate mass, retention time and MS/MS matching against spectral libraries) were measured and their abundances across all the subjects depending on their gender are provided in this article. Data tables are available at https://data.mendeley.com/datasets/c73xtsd4s5/1. it's published on mendeley, the DOI is DOI:10.17632/c73xtsd4s5.1. The data presented in this article are related to the research article entitled “A global HILIC-MS approach to measure polar human cerebrospinal fluid metabolome: Exploring gender-associated variation in a cohort of elderly cognitively healthy subjects” (Gallart-Ayala et al., 2018, In press).

Trace Phosphate Improves ZIC-pHILIC Peak Shape, Sensitivity, and Coverage for Untargeted Metabolomics

Existing hydrophilic interaction liquid chromatography (HILIC) methods, considered individually, each exhibit poor chromatographic performance for a substantial fraction of polar metabolites. In addition to limiting metabolome coverage, such deficiencies also complicate automated data processing. Here we show that some of these analytical challenges can be addressed for the ZIC-pHILIC, a zwitterionic stationary phase commonly used in metabolomics, with the addition of trace levels of phosphate. Specifically, micromolar phosphate extended metabolome coverage by hundreds of credentialed features, improved peak shapes, and reduced peak-detection errors during informatic processing. Although the addition of high levels of phosphate (millimolar) as a HILIC mobile phase buffer has been explored previously, such concentrations interfere with mass spectrometric (MS) detection. We show that using phosphate as a trace additive at micromolar concentrations improves analysis by electrospray MS, increasing signal for a diverse set of polar standards. Given the small amount of phosphate needed, comparable chromatographic improvements were also achieved by direct addition of phosphate to the sample during reconstitution. Our results suggest that defects in ZIC-pHILIC performance are predominantly driven by electrostatic interactions, which can be modulated by phosphate. These findings constitute both a methodological improvement for untargeted metabolomics and an advance in our understanding of the mechanisms limiting HILIC coverage.