A targeted LC-MRM3 proteomic approach for the diagnosis of SARS-CoV-2 infection in nasopharyngeal swabs.

Since its first appearance, SARS-CoV-2 quickly spread around the world and the lack of adequate PCR testing capacities, especially during the early pandemic, led the scientific community to explore new approaches such as mass spectrometry (MS). We developed a proteomics workflow to target several tryptic peptides of the nucleocapsid protein (NCAP). A highly selective multiple reaction monitoring MRM3 strategy provided a sensitivity increase in comparison to conventional MRM acquisition. Our MRM3 approach was first tested on an Amsterdam public health cohort (alpha-variant, 760 participants) detecting viral NCAP peptides from nasopharyngeal swabs samples presenting a cycle threshold (Ct) value down to 35 with sensitivity and specificity of 94.2% and 100.0%, without immuno-purification. A second iteration of the MS-diagnostic test, able to analyze more than 400 samples per day, was clinically validated on a Leiden-Rijswijk public health cohort (delta-variant, 2536 participants) achieving 99.9% specificity and 93.1% sensitivity for patients with Ct-values up to 35. In this manuscript, we also developed and brought the first proof of the concept of viral variant monitoring in a complex matrix using targeted mass spectrometry.

A comparison between different human hepatocyte models reveals profound differences in net glucose production, lipid composition and metabolism in vitro.

Hepatocytes are responsible for maintaining a stable blood glucose concentration during periods of nutrient scarcity. The breakdown of glycogen and de novo synthesis of glucose are crucial metabolic pathways deeply interlinked with lipid metabolism. Alterations in these pathways are often associated with metabolic diseases with serious clinical implications. Studying energy metabolism in human cells is challenging. Primary hepatocytes are still considered the golden standard for in vitro studies and have been instrumental in elucidating key aspects of energy metabolism found in vivo. As a result of several limitations posed by using primary cells, a multitude of alternative hepatocyte cellular models emerged as potential substitutes. Yet, there remains a lack of clarity regarding the precise applications for which these models accurately reflect the metabolic competence of primary hepatocytes. In this study, we compared primary hepatocytes, stem cell-derived hepatocytes, adult donor-derived liver organoids, immortalized Upcyte-hepatocytes and the hepatoma cell line HepG2s in their response to a glucose production challenge. We observed the highest net glucose production in primary hepatocytes, followed by organoids, stem-cell derived hepatocytes, Upcyte-hepatocytes and HepG2s. Glucogenic gene induction was observed in all tested models, as indicated by an increase in G6PC and PCK1 expression. Lipidomic analysis revealed considerable differences across the models, with organoids showing the closest similarity to primary hepatocytes in the common lipidome, comprising 347 lipid species across 19 classes. Changes in lipid profiles as a result of the glucose production challenge showed a variety of, and in some cases opposite, trends when compared to primary hepatocytes.

Cerebrospinal Fluid and Plasma Amine Profiles in Interictal Migraine.

OBJECTIVE: Impaired amine metabolism has been associated with the etiology of migraine, that is, why patients continue to get migraine attacks. However, evidence from cerebrospinal fluid (CSF) is lacking. Here, we evaluated individual amine levels, global amine profiles, and amine pathways in CSF and plasma of interictal migraine patients and healthy controls. METHODS: CSF and plasma were sampled between 8:30 am and 1:00 pm, randomly and interchangeably over the time span to avoid any diurnal and seasonal influences, from healthy volunteers and interictal migraine patients, matched for age, sex, and sampling time. The study was approved by the local medical ethics committee. Individual amines (n = 31), global amine profiles, and specific amine pathways were analyzed using a validated ultraperformance liquid chromatography mass spectrometry platform. RESULTS: We analyzed n = 99 participants with migraine with aura, n = 98 with migraine without aura, and n = 96 healthy volunteers. Univariate analysis with Bonferroni correction indicated that CSF L-arginine was reduced in migraine with aura (10.4%, p < 0.001) and without aura (5.0%, p = 0.03). False discovery rate-corrected CSF L-phenylalanine was also lower in migraine with aura (6.9%, p = 0.011) and without aura (8.1%, p = 0.001), p = 0.088 after Bonferroni correction. Multivariate analysis revealed that CSF global amine profiles were similar for both types of migraine (p = 0.64), but distinct from controls (p = 0.009). Global profile analyses were similar in plasma. The strongest associated pathways with migraine were related to L-arginine metabolism. INTERPRETATION: L-Arginine was decreased in the CSF (but not in plasma) of interictal patients with migraine with or without aura, and associated pathways were altered. This suggests that dysfunction of nitric oxide signaling is involved in susceptibility to getting migraine attacks. ANN NEUROL 2023;93:715-728.

Current insights into cow's milk allergy in children: Microbiome, metabolome, and immune response-A systematic review.

The increasing prevalence of IgE-mediated cow's milk allergy (CMA) in childhood is a worldwide health concern. There is a growing awareness that the gut microbiome (GM) might play an important role in CMA development. Therefore, treatment with probiotics and prebiotics has gained popularity. This systematic review provides an overview of the alterations of the GM, metabolome, and immune response in CMA children and animal models, including post-treatment modifications. MEDLINE, PubMed, Scopus, and Web of Science were searched for studies on GM in CMA-diagnosed children, published before 1 March 2023. A total of 21 articles (13 on children and 8 on animal models) were included. The studies suggest that the GM, characterized by an enrichment of the Clostridia class and reductions in the Lactobacillales order and Bifidobacterium genus, is associated with CMA in early life. Additionally, reduced levels of short-chain fatty acids (SCFAs) and altered amino acid metabolism were reported in CMA children. Commonly used probiotic strains belong to the Bifidobacterium and Lactobacillus genera. However, only Bifidobacterium levels were consistently upregulated after the intervention, while alterations of other bacteria taxa remain inconclusive. These interventions appear to contribute to the restoration of SCFAs and amino acid metabolism balance. Mouse models indicate that these interventions tend to restore the Th 2/Th 1 balance, increase the Treg response, and/or silence the overall pro- and anti-inflammatory cytokine response. Overall, this systematic review highlights the need for multi-omics-related research in CMA children to gain a mechanistic understanding of this disease and to develop effective treatments and preventive strategies.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder.

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior.

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Leptin mutation and mycobacterial infection lead non-synergistically to a similar metabolic syndrome.

INTRODUCTION: The leptin signaling pathway plays an important role as a key regulator of glucose homeostasis, metabolism control and systemic inflammatory responses. However, the metabolic effects of leptin on infectious diseases, for example tuberculosis (TB), are still little known. OBJECTIVES: In this study, we aim to investigate the role of leptin on metabolism in the absence and presence of mycobacterial infection in zebrafish larvae and mice. METHODS: Metabolites in entire zebrafish larvae and the blood of mice were studied using high-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and mass spectrometry, respectively. For transcriptome studies of zebrafish larvae, deep RNA sequencing was used. RESULTS: The results show that leptin mutation leads to a similar metabolic syndrome as caused by mycobacterial infection in the two species, characterized by the decrease of 11 amine metabolites. In both species, this metabolic syndrome was not aggravated further when the leptin mutant was infected by mycobacteria. Therefore, we conclude that leptin and mycobacterial infection are both impacting metabolism non-synergistically. In addition, we studied the transcriptomes of lepbibl54 mutant zebrafish larvae and wild type (WT) siblings after mycobacterial infection. These studies showed that mycobacteria induced a very distinct transcriptome signature in the lepbibl54 mutant zebrafish compared to WT sibling control larvae. Furthermore, lepbibl55 Tg (pck1:luc1) zebrafish line was constructed and confirmed this difference in transcriptional responses. CONCLUSIONS: Leptin mutation and TB lead non-synergistically to a similar metabolic syndrome. Moreover, different transcriptomic responses in the lepbibl54  mutant and TB can lead to the similar metabolic end states.

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC).

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research. OBJECTIVES: This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data. RESULTS: The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities. CONCLUSIONS: The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

Effect of Different Exercise Training Modalities on Fasting Levels of Oxylipins and Endocannabinoids in Middle-Aged Sedentary Adults: A Randomized Controlled Trial.

This study aimed to investigate the effects of different exercise training programs on fasting plasma levels of oxylipins, endocannabinoids (eCBs), and eCBs-like molecules in middle-aged sedentary adults. A 12-week randomized controlled trial was conducted using a parallel group design. Sixty-five middle-aged adults (40-65 years old) were randomly assigned to: (a) no exercise (control group), (b) concurrent training based on international physical activity recommendations (PAR group), (c) high-intensity interval training (HIIT group), and (d) HIIT together with whole-body electromyostimulation (HIIT + EMS group). Plasma levels of oxylipins, eCBs, and eCBs-like molecules were determined in plasma samples before and after the intervention using targeted lipidomics. Body composition was assessed through dual-energy X-ray absorptiometry, and dietary intake through a food frequency questionnaire and three nonconsecutive 24-hr recalls. The physical activity recommendations, HIIT, and HIIT-EMS groups showed decreased plasma levels of omega-6 and omega-3-derived oxylipins, and eCBs and eCBs-like molecules after 12 weeks (all Δ ≤ -0.12; all p < .05). Importantly, after Bonferroni post hoc corrections, the differences in plasma levels of omega-6 and omega-3 oxylipins were not statistically significant compared with the control group (all p > .05). However, after post hoc corrections, plasma levels of anandamide and oleoylethanolamide were increased in the physical activity recommendations group compared with the control group (anandamide: Δ = 0.05 vs. -0.09; oleoylethanolamide: Δ = -0.12 vs. 0.013, all p ≤ .049). In conclusion, this study reports that a 12-week exercise training intervention, independent of the modality applied, does not modify fasting plasma levels of omega-6 and omega-3 oxylipins, eCBs, and eCBs-like molecules in middle-aged sedentary adults.

Metabolic needs of the kidney graft undergoing normothermic machine perfusion.

Normothermic machine perfusion (NMP) is emerging as a novel preservation strategy. During NMP, the organ is maintained in a metabolically active state that may not only provide superior organ preservation, but that also facilitates viability testing before transplantation, and ex situ resuscitation of marginal kidney grafts. Although the prevailing perfusion protocols for renal NMP are refined from initial pioneering studies concerning short periods of NMP, it could be argued that these protocols are not optimally tailored to address the putatively compromised metabolic plasticity of marginal donor grafts (i.e., in the context of viability testing and/or preservation), or to meet the metabolic prerequisites associated with prolonged perfusions and the required anabolic state in the context of organ regeneration. Herein, we provide a theoretical framework for the metabolic requirements for renal NMP. Aspects are discussed along the lines of carbohydrates, fatty acids, amino acids, and micronutrients required for optimal NMP of an isolated kidney. In addition, considerations for monitoring aspects of metabolic status during NMP are discussed.

Results of an explorative clinical evaluation suggest immediate and persistent post-reperfusion metabolic paralysis drives kidney ischemia reperfusion injury.

Delayed graft function is the manifestation of ischemia reperfusion injury in the context of kidney transplantation. While hundreds of interventions successfully reduce ischemia reperfusion injury in experimental models, all clinical interventions have failed. This explorative clinical evaluation examined possible metabolic origins of clinical ischemia reperfusion injury combining data from 18 pre- and post-reperfusion tissue biopsies with 36 sequential arteriovenous blood samplings over the graft in three study groups. These groups included living and deceased donor grafts with and without delayed graft function. Group allocation was based on clinical outcome. Magic angle NMR was used for tissue analysis and mass spectrometry-based platforms were used for plasma analysis. All kidneys were functional at one-year. Integration of metabolomic data identified a discriminatory profile to recognize future delayed graft function. This profile was characterized by post-reperfusion ATP/GTP catabolism (significantly impaired phosphocreatine recovery and significant persistent (hypo)xanthine production) and significant ongoing tissue damage. Failing high-energy phosphate recovery occurred despite activated glycolysis, fatty-acid oxidation, glutaminolysis and autophagia, and related to a defect at the level of the oxoglutarate dehydrogenase complex in the Krebs cycle. Clinical delayed graft function due to ischemia reperfusion injury associated with a post-reperfusion metabolic collapse. Thus, efforts to quench delayed graft function due to ischemia reperfusion injury should focus on conserving metabolic competence, either by preserving the integrity of the Krebs cycle and/or by recruiting metabolic salvage pathways.

High-Throughput Fractionation Coupled to Mass Spectrometry for Improved Quantitation in Metabolomics.

Metabolomics is emerging as an important field in life sciences. However, a weakness of current mass spectrometry (MS) based metabolomics platforms is the time-consuming analysis and the occurrence of severe matrix effects in complex mixtures. To overcome this problem, we have developed an automated and fast fractionation module coupled online to MS. The fractionation is realized by the implementation of three consecutive high performance solid-phase extraction columns consisting of a reversed phase, mixed-mode anion exchange, and mixed-mode cation exchange sorbent chemistry. The different chemistries resulted in an efficient interaction with a wide range of metabolites based on polarity, charge, and allocation of important matrix interferences like salts and phospholipids. The use of short columns and direct solvent switches allowed for fast screening (3 min per polarity). In total, 50 commonly reported diagnostic or explorative biomarkers were validated with a limit of quantification that was comparable with conventional LC-MS(/MS). In comparison with a flow injection analysis without fractionation, ion suppression decreased from 89% to 25%, and the sensitivity was 21 times higher. The validated method was used to investigate the effects of circadian rhythm and food intake on several metabolite classes. The significant diurnal changes that were observed stress the importance of standardized sampling times and fasting states when metabolite biomarkers are used. Our method demonstrates a fast approach for global profiling of the metabolome. This brings metabolomics one step closer to implementation into the clinic.

Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data.

Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.

Mechanistic and Quantitative Understanding of Pharmacokinetics in Zebrafish Larvae through Nanoscale Blood Sampling and Metabolite Modeling of Paracetamol.

Zebrafish larvae are increasingly used for pharmacological research, but internal drug exposure is often not measured. Understanding pharmacokinetics is necessary for reliable translation of pharmacological results to higher vertebrates, including humans. Quantification of drug clearance and distribution requires measurements of blood concentrations. Additionally, measuring drug metabolites is of importance to understand clearance in this model organism mechanistically. We therefore mechanistically studied and quantified pharmacokinetics in zebrafish larvae, and compared this to higher vertebrates, using paracetamol (acetaminophen) as a paradigm compound. A method was developed to sample blood from zebrafish larvae 5 days post fertilization. Blood concentrations of paracetamol and its major metabolites, paracetamol-glucuronide and paracetamol-sulfate, were measured. Blood concentration data were combined with measured amounts in larval homogenates and excreted amounts and simultaneously analyzed through nonlinear mixed-effects modeling, quantifying absolute clearance and distribution volume. Blood sampling from zebrafish larvae was most successful from the posterior cardinal vein, with a median volume (interquartile range) of 1.12 nl (0.676-1.66 nl) per blood sample. Samples were pooled (n = 15-35) to reach measurable levels. Paracetamol blood concentrations at steady state were only 10% of the external paracetamol concentration. Paracetamol-sulfate was the major metabolite, and its formation was quantified using a time-dependent metabolic formation rate. Absolute clearance and distribution volume correlated well with reported values in higher vertebrates, including humans. Based on blood concentrations and advanced data analysis, the mechanistic and quantitative understanding of paracetamol pharmacokinetics in zebrafish larvae has been established. This will improve the translational value of this vertebrate model organism in drug discovery and development. SIGNIFICANCE STATEMENT: In early phases of drug development, new compounds are increasingly screened in zebrafish larvae, but the internal drug exposure is often not taken into consideration. We developed innovative experimental and computational methods, including a blood-sampling technique, to measure the paradigm drug paracetamol (acetaminophen) and its major metabolites and quantify pharmacokinetics (absorption, distribution, elimination) in zebrafish larvae of 5 days post fertilization with a total volume of only 300 nl. These parameter values were scaled to higher vertebrates, including humans.

The influence of neuronal electrical activity on the mammalian central clock metabolome.

INTRODUCTION: Most organisms display circadian rhythms in physiology and behaviour. In mammals, these rhythms are orchestrated by the suprachiasmatic nucleus (SCN). Recently, several metabolites have emerged as important regulators of circadian timekeeping. Metabolomics approaches have aided in identifying some key metabolites in circadian processes in peripheral tissue, but methods to routinely measure metabolites in small brain areas are currently lacking. OBJECTIVE: The aim of the study was to establish a reliable method for metabolite quantifications in the central circadian clock and relate them to different states of neuronal excitability. METHODS: We developed a method to collect and process small brain tissue samples (0.2 mm3), suitable for liquid chromatography-mass spectrometry. Metabolites were analysed in the SCN and one of its main hypothalamic targets, the paraventricular nucleus (PVN). Tissue samples were taken at peak (midday) and trough (midnight) of the endogenous rhythm in SCN electrical activity. Additionally, neuronal activity was altered pharmacologically. RESULTS: We found a minor effect of day/night fluctuations in electrical activity or silencing activity during the day. In contrast, increasing electrical activity during the night significantly upregulated many metabolites in SCN and PVN. CONCLUSION: Our method has shown to produce reliable and physiologically relevant metabolite data from small brain samples. Inducing electrical activity at night mimics the effect of a light pulses in the SCN, producing phase shifts of the circadian rhythm. The upregulation of metabolites could have a functional role in this process, since they are not solely products of physiological states, they are significant parts of cellular signalling pathways.

Development and application of a UHPLC-MS/MS metabolomics based comprehensive systemic and tissue-specific screening method for inflammatory, oxidative and nitrosative stress.

Oxidative stress and inflammation are underlying pathogenic mechanisms associated with the progression of several pathological conditions and immunological responses. Elucidating the role of signalling lipid classes, which include, among others, the isoprostanes, nitro fatty acids, prostanoids, sphingoid bases and lysophosphatidic acids, will create a snapshot of the cause and effect of inflammation and oxidative stress at the metabolic level. Here we describe a fast, sensitive, and targeted ultra-high-performance liquid chromatography-tandem mass spectrometry metabolomics method that allows the quantitative measurement and biological elucidation of 17 isoprostanes as well as their respective isomeric prostanoid mediators, three nitro fatty acids, four sphingoid mediators, and 24 lysophosphatidic acid species from serum as well as organ tissues, including liver, lung, heart, spleen, kidney and brain. Application of this method to paired mouse serum and tissue samples revealed tissue- and serum-specific stress and inflammatory readouts. Little correlation was found between localized (tissue) metabolite levels compared with the systemic (serum) circulation in a homeostatic model. The application of this method in future studies will enable us to explore the role of signalling lipids in the metabolic pathogenicity of stress and inflammation during health and disease.

Fetal Metabolic Stress Disrupts Immune Homeostasis and Induces Proinflammatory Responses in Human Immunodeficiency Virus Type 1- and Combination Antiretroviral Therapy-Exposed Infants.

Increased morbidity and fetal growth restriction are reported in uninfected children born to human immunodeficiency virus type 1 (HIV-1)-infected women treated with antiretroviral (ARV) therapy. Viruses and/or pharmacological interventions such as ARVs can induce metabolic stress, skewing the cell's immune response and restricting (cell) growth. Novel metabolomic techniques provided the opportunity to investigate the impact of fetal HIV-1 and combination ARV therapy (cART) exposure on the infants' immune metabolome. Peroxidized lipids, generated by reactive oxygen species, were increased in cART/HIV-1-exposed infants, indicating altered mitochondrial functioning. The lipid metabolism was further dysregulated with increased triglyceride species and a subsequent decrease in phospholipids in cART/HIV-1-exposed infants compared to control infants. Proinflammatory immune mediators, lysophospholipids as well as cytokines such as CXCL10 and CCL3, were increased whereas anti-inflammatory metabolites from the cytochrome P450 pathway were reduced in cART/HIV-1-exposed infants. Taken together, these data demonstrate that the fetal metabolism is impacted by maternal factors (cART and HIV-1) and skews physiological immune responses toward inflammation in the newborn infant.

Quantitative profiling of endocannabinoids and related N-acylethanolamines in human CSF using nano LC-MS/MS.

Endocannabinoids, a class of lipid messengers, have emerged as crucial regulators of synaptic communication in the CNS. Dysregulation of these compounds has been implicated in many brain disorders. Although some studies have identified and quantified a limited number of target compounds, a method that provides comprehensive quantitative information on endocannabinoids and related N-acylethanolamines (NAEs) in cerebrospinal fluid (CSF) is currently lacking, as measurements are challenging due to low concentrations under normal physiological conditions. Here we developed and validated a high-throughput nano LC-ESI-MS/MS platform for the simultaneous quantification of endocannabinoids (anandamide and 2-arachidonoylglycerol), ten related NAEs, and eight additional putatively annotated NAEs in human CSF. Requiring only 200 µl of CSF, our method has limits of detection from 0.28 to 61.2 pM with precisions of relative SD <15% for most compounds. We applied our method to CSF from 45 healthy humans and demonstrated potential age and gender effects on concentrations of endocannabinoids and NAEs. Notably, our results show that docosahexaenoylethanolamide concentrations increase with age in males. Our method may offer new opportunities to gain insight into regulatory functions of endocannabinoids in the context of (ab)normal brain function.

Metabolic network failures in Alzheimer's disease: A biochemical road map.

INTRODUCTION: The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. METHODS: Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. RESULTS: Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aß1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. DISCUSSION: Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.