Characterization of Bulk Phosphatidylcholine Compositions in Human Plasma Using Side-Chain Resolving Lipidomics

Highly reliable LC-MS lipidomics database for efficient human plasma profiling based on NIST SRM 1950

Liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS)-based methods have become the gold standard methodology for the comprehensive profiling of the human plasma lipidome. However, both the complexity of lipid chemistry and LC-HRMS-associated data pose challenges to the characterization of this biological matrix. In accordance with the current consensus of quality requirements for LC-HRMS lipidomics data, we aimed to characterize the NIST® Standard Reference Material for Human Plasma (SRM 1950) using an LC-ESI(+/-)-MS method compatible with high-throughput lipidome profiling. We generated a highly curated lipid database with increased coverage, quality, and consistency, including additional quality assurance procedures involving adduct formation, within-method m/z evaluation, retention behavior of species within lipid chain isomers, and expert-driven resolution of isomeric and isobaric interferences. As a proof-of-concept, we showed the utility of our in-house LC-MS lipidomic database -consisting of 592 lipid entries- for the fast, comprehensive, and reliable lipidomic profiling of the human plasma from healthy human volunteers. We are confident that the implementation of this robust resource and methodology will have a significant impact by reducing data redundancy and the current delays and bottlenecks in untargeted plasma lipidomic studies.

AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction

Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.

The HuMet Repository: Watching human metabolism at work

Metabolism oscillates between catabolic and anabolic states depending on food intake, exercise, or stresses that change a multitude of metabolic pathways simultaneously. We present the HuMet Repository for exploring dynamic metabolic responses to oral glucose/lipid loads, mixed meals, 36-h fasting, exercise, and cold stress in healthy subjects. Metabolomics data from blood, urine, and breath of 15 young, healthy men at up to 56 time points are integrated and embedded within an interactive web application, enabling researchers with and without computational expertise to search, visualize, analyze, and contextualize the dynamic metabolite profiles of 2,656 metabolites acquired on multiple platforms. With examples, we demonstrate the utility of the resource for research into the dynamics of human metabolism, highlighting differences and similarities in systemic metabolic responses across challenges and the complementarity of metabolomics platforms. The repository, providing a reference for healthy metabolite changes to six standardized physiological challenges, is freely accessible through a web portal.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Association of plant-based diet indexes with the metabolomic profile

Plant-based diets have gained attention for their potential benefits on both human health and environmental sustainability. The objective of this study was to investigate the association of plant-based dietary patterns with the endogenous metabolites of healthy individuals and identify metabolites that may act as mediators of the associations between dietary intake and modifiable disease risk factors. Adherence to plant-based dietary patterns was assessed for 170 healthy adults using plant-based diet indexes (PDI). Individuals with higher healthful PDI had lower BMI and fasting glucose and higher HDL-C, while those with higher unhealthful PDI had higher BMI, triacylglycerol and fasting glucose and lower HDL-C. Unhealthful PDI was associated with higher levels of several amino acids and biogenic amines previously associated with cardiometabolic diseases and an opposite pattern was observed for healthful PDI. Furthermore, healthful PDI was associated with higher levels of glycerophosphocholines containing very long-chain fatty acids. Glutamate, isoleucine, proline, tyrosine, α-aminoadipate and kynurenine had a statistically significant mediation effect on the associations between PDI scores and LDL-C, HDL-C and fasting glucose. These findings contribute to the growing evidence supporting the role of plant-based diets in promoting metabolic health and shed light on the potential mechanisms explaining their beneficial health effects.

Circulating choline and phosphocholine measurement by a hydrophilic interaction liquid chromatography-tandem mass spectrometry

Background

Given the growing interest in studying the role of choline and phosphocholine in the development and progression of tumor pathology, in this study we describe the development and validation of a fast and robust method for the simultaneous analysis of choline and phosphocholine in human plasma.

Methods

Choline and phosphocholine quantification in human plasma was obtained using a hydrophilic interaction liquid chromatography-tandem mass spectrometry technique. Assay performance parameters were evaluated using EMA guidelines.

Results

Calibration curve ranged from 0.60 to 38.40 μmol/L (R2 = 0.999) and 0.08-5.43 μmol/L (R2 = 0.998) for choline and phosphocholine, respectively. The Limit Of Detection of the method was 0.06 μmol/L for choline and 0.04 μmol/L for phosphocholine. The coefficient of variation range for intra-assay precision is 2.2-4.1 % (choline) and 3.2-15 % (phosphocholine), and the inter-assay precision range is < 1-6.5 % (choline) and 6.2-20 % (phosphocholine). The accuracy of the method was below the ±20 % benchmarks at all the metabolites concentration levels. In-house plasma pool of apparently healthy adults was tested, and a mean concentration of 15.97 μmol/L for Choline and 0.34 μmol/L for Phosphocholine was quantified.

Conclusions

The developed method shows good reliability in quantifying Choline and Phosphocholine in human plasma for clinical purposes.

The HuMet Repository: Watching human metabolism at work

The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.

Plasma metabolomics of oral squamous cell carcinomas based on NMR and MS approaches provides biomarker identification and survival prediction

Metabolomics has proven to be an important omics approach to understand the molecular pathways underlying the tumour phenotype and to identify new clinically useful markers. The literature on cancer has illustrated the potential of this approach as a diagnostic and prognostic tool. The present study aimed to analyse the plasma metabolic profile of patients with oral squamous cell carcinoma (OSCC) and controls and to compare patients with metastatic and primary tumours at different stages and subsites using nuclear magnetic resonance and mass spectrometry. To our knowledge, this is the only report that compared patients at different stages and subsites and replicates collected in diverse institutions at different times using these methodologies. Our results showed a plasma metabolic OSCC profile suggestive of abnormal ketogenesis, lipogenesis and energy metabolism, which is already present in early phases but is more evident in advanced stages of the disease. Reduced levels of several metabolites were also associated with an unfavorable prognosis. The observed metabolomic alterations may contribute to inflammation, immune response inhibition and tumour growth, and may be explained by four nonexclusive views-differential synthesis, uptake, release, and degradation of metabolites. The interpretation that assimilates these views is the cross talk between neoplastic and normal cells in the tumour microenvironment or in more distant anatomical sites, connected by biofluids, signalling molecules and vesicles. Additional population samples to evaluate the details of these molecular processes may lead to the discovery of new biomarkers and novel strategies for OSCC prevention and treatment.

Identification of prediagnostic metabolites associated with prostate cancer risk by untargeted mass spectrometry-based metabolomics: A case-control study nested in the Northern Sweden Health and Disease Study

Prostate cancer (PCa) is the most common cancer form in males in many European and American countries, but there are still open questions regarding its etiology. Untargeted metabolomics can produce an unbiased global metabolic profile, with the opportunity for uncovering new plasma metabolites prospectively associated with risk of PCa, providing insights into disease etiology. We conducted a prospective untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis using prediagnostic fasting plasma samples from 752 PCa case-control pairs nested within the Northern Sweden Health and Disease Study (NSHDS). The pairs were matched by age, BMI, and sample storage time. Discriminating features were identified by a combination of orthogonal projection to latent structures-effect projections (OPLS-EP) and Wilcoxon signed-rank tests. Their prospective associations with PCa risk were investigated by conditional logistic regression. Subgroup analyses based on stratification by disease aggressiveness and baseline age were also conducted. Various free fatty acids and phospholipids were positively associated with overall risk of PCa and in various stratification subgroups. Aromatic amino acids were positively associated with overall risk of PCa. Uric acid was positively, and glucose negatively, associated with risk of PCa in the older subgroup. This is the largest untargeted LC-MS based metabolomics study to date on plasma metabolites prospectively associated with risk of developing PCa. Different subgroups of disease aggressiveness and baseline age showed different associations with metabolites. The findings suggest that shifts in plasma concentrations of metabolites in lipid, aromatic amino acid, and glucose metabolism are associated with risk of developing PCa during the following two decades.

Evaluation of two highly effective lipid-lowering therapies in subjects with acute myocardial infarction

For cardiovascular disease prevention, statins alone or combined with ezetimibe have been recommended to achieve low-density lipoprotein cholesterol targets, but their effects on other lipids are less reported. This study was designed to examine lipid changes in subjects with ST-segment elevation myocardial infarction (STEMI) after two highly effective lipid-lowering therapies. Twenty patients with STEMI were randomized to be treated with rosuvastatin 20 mg QD or simvastatin 40 mg combined with ezetimibe 10 mg QD for 30 days. Fasting blood samples were collected on the first day (D1) and after 30 days (D30). Lipidomic analysis was performed using the Lipidyzer platform. Similar classic lipid profile was obtained in both groups of lipid-lowering therapies. However, differences with the lipidomic analysis were observed between D30 and D1 for most of the analyzed classes. Differences were noted with lipid-lowering therapies for lipids such as FA, LPC, PC, PE, CE, Cer, and SM, notably in patients treated with rosuvastatin. Correlation studies between classic lipid profiles and lipidomic results showed different information. These findings seem relevant, due to the involvement of these lipid classes in crucial mechanisms of atherosclerosis, and may account for residual cardiovascular risk.

Randomized clinical trial: ClinicalTrials.gov, NCT02428374, registered on 28/09/2014.

Physiological extremes of the human blood metabolome: A metabolomics analysis of highly glycolytic, oxidative, and anabolic athletes

Human metabolism is highly variable. At one end of the spectrum, defects of enzymes, transporters, and metabolic regulation result in metabolic diseases such as diabetes mellitus or inborn errors of metabolism. At the other end of the spectrum, favorable genetics and years of training combine to result in physiologically extreme forms of metabolism in athletes. Here, we investigated how the highly glycolytic metabolism of sprinters, highly oxidative metabolism of endurance athletes, and highly anabolic metabolism of natural bodybuilders affect their serum metabolome at rest and after a bout of exercise to exhaustion. We used targeted mass spectrometry-based metabolomics to measure the serum concentrations of 151 metabolites and 43 metabolite ratios or sums in 15 competitive male athletes (6 endurance athletes, 5 sprinters, and 4 natural bodybuilders) and 4 untrained control subjects at fasted rest and 5 minutes after a maximum graded bicycle test to exhaustion. The analysis of all 194 metabolite concentrations, ratios and sums revealed that natural bodybuilders and endurance athletes had overall different metabolite profiles, whereas sprinters and untrained controls were more similar. Specifically, natural bodybuilders had 1.5 to 1.8-fold higher concentrations of specific phosphatidylcholines and lower levels of branched chain amino acids than all other subjects. Endurance athletes had 1.4-fold higher levels of a metabolite ratio showing the activity of carnitine-palmitoyl-transferase I and 1.4-fold lower levels of various alkyl-acyl-phosphatidylcholines. When we compared the effect of exercise between groups, endurance athletes showed 1.3-fold higher increases of hexose and of tetradecenoylcarnitine (C14:1). In summary, physiologically extreme metabolic capacities of endurance athletes and natural bodybuilders are associated with unique blood metabolite concentrations, ratios, and sums at rest and after exercise. Our results suggest that long-term specific training, along with genetics and other athlete-specific factors systematically change metabolite concentrations at rest and after exercise.

Blood-Based Targeted Metabolomics Discriminate Patients with Alcoholic Liver Cirrhosis from Those with Non-Cirrhotic Liver Damage: An Explorative Study

BACKGROUND

Early detection of liver cirrhosis is crucial for secondary prevention of complications. However, noninvasive blood-based patient monitoring tools are lacking. In this explorative study, we conducted a targeted metabolomic analysis in order to identify possible serum markers indicating alcoholic liver cirrhosis (aLiC) with or without hepatocellular carcinoma (HCC).

METHODS

Venous blood of 30 individuals was collected: healthy controls ("Con", n = 12), patients with aLiC without and with HCC ("aLiC": n = 6 and "aLiC + HCC": n = 6), and patients with other liver diseases ("oLiD": n = 6). A targeted metabolomic analysis was conducted using the AbsoluteIDQ® p180 Kit (Biocrates Life Sciences®, Innsbruck, Austria). Statistical analysis was performed by applying a one-way ANOVA on all subgroups followed by a t test for pairwise comparison of subgroups and logistic regression analysis.

RESULTS

ANOVA revealed 29 metabolites that significantly discriminate between the different cohorts. Among these analytes, 25 were significantly altered in Con versus aLiC, as indicated by t test, most importantly SM C18:1 (p < 0.001), SM C20:2 (p = 0.001), SM (OH) C22:2 (p < 0.001), lysoPC a C20:4 (p < 0.001), and PC aa C36:5 (p < 0.001). To a similar extent, the metabolites discriminated also between the oLiD and aLiC but less between the Con or oLiD and aLiC + HCC cohorts. Most of these analytes were either lyso- and phosphatidylcholines or sphingomyelins. Results were not significant for comparison of Con versus oLiD and aLiC versus aLiC + HCC.

CONCLUSION

Decreased lyso- and phosphatidylcholine as well as sphingomyelin species in venous blood could help to detect liver cirrhosis in patients with non-cirrhotic liver disease.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

The metabolic footprint of compromised insulin sensitivity under fasting and hyperinsulinemic-euglycemic clamp conditions in an Arab population

Metabolic pathways that are corrupted at early stages of insulin resistance (IR) remain elusive. This study investigates changes in body metabolism in clinically healthy and otherwise asymptomatic subjects that may become apparent already under compromised insulin sensitivity (IS) and prior to IR. 47 clinically healthy Arab male subjects with a broad range of IS, determined by hyperinsulinemic-euglycemic clamp (HIEC), were investigated. Untargeted metabolomics and complex lipidomics were conducted on serum samples collected under fasting and HIEC conditions. Linear models were used to identify associations between metabolites concentrations and IS levels. Among 1896 identified metabolites, 551 showed significant differences between fasting and HIEC, reflecting the metabolic switch in energy utilization. At fasting, 336 metabolites, predominantly di- and tri-acylglycerols, showed significant differences between subjects with low and high levels of IS. Changes in amino acid, carbohydrate and fatty acid metabolism in response to insulin were impaired in subjects with low IS. Association of altered mannose and amino acids with IS was also replicated in an independent cohort of T2D patients. We identified metabolic phenotypes that characterize clinically healthy Arab subjects with low levels of IS at their fasting state. Our study is providing further insights into the metabolic pathways that precede IR.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Shared reference materials harmonize lipidomics across MS-based detection platforms and laboratories

Quantitative MS of human plasma lipids is a promising technology for translation into clinical applications. Current MS-based lipidomic methods rely on either direct infusion (DI) or chromatographic lipid separation methods (including reversed phase and hydrophilic interaction LC). However, the use of lipid markers in laboratory medicine is limited by the lack of reference values, largely because of considerable differences in the concentrations measured by different laboratories worldwide. These inconsistencies can be explained by the use of different sample preparation protocols, method-specific calibration procedures, and other experimental and data-reporting parameters, even when using identical starting materials. Here, we systematically investigated the roles of some of these variables in multiple approaches to lipid analysis of plasma samples from healthy adults by considering: 1) different sample introduction methods (separation vs. DI methods); 2) different MS instruments; and 3) between-laboratory differences in comparable analytical platforms. Each of these experimental variables resulted in different quantitative results, even with the inclusion of isotope-labeled internal standards for individual lipid classes. We demonstrated that appropriate normalization to commonly available reference samples (i.e., "shared references") can largely correct for these systematic method-specific quantitative biases. Thus, to harmonize data in the field of lipidomics, in-house long-term references should be complemented by a commonly available shared reference sample, such as NIST SRM 1950, in the case of human plasma.

Decreased plasma phospholipid concentrations and increased acid sphingomyelinase activity are accurate biomarkers for community-acquired pneumonia

Background

There continues to be a great need for better biomarkers and host-directed treatment targets for community-acquired pneumonia (CAP). Alterations in phospholipid metabolism may constitute a source of small molecule biomarkers for acute infections including CAP. Evidence from animal models of pulmonary infections and sepsis suggests that inhibiting acid sphingomyelinase (which releases ceramides from sphingomyelins) may reduce end-organ damage.

Methods

We measured concentrations of 105 phospholipids, 40 acylcarnitines, and 4 ceramides, as well as acid sphingomyelinase activity, in plasma from patients with CAP (n = 29, sampled on admission and 4 subsequent time points), chronic obstructive pulmonary disease exacerbation with infection (COPD, n = 13) as a clinically important disease control, and 33 age- and sex-matched controls.

Results

Phospholipid concentrations were greatly decreased in CAP and normalized along clinical improvement. Greatest changes were seen in phosphatidylcholines, followed by lysophosphatidylcholines, sphingomyelins and ceramides (three of which were upregulated), and were least in acylcarnitines. Changes in COPD were less pronounced, but also differed qualitatively, e.g. by increases in selected sphingomyelins. We identified highly accurate biomarkers for CAP (AUC ≤ 0.97) and COPD (AUC ≤ 0.93) vs. Controls, and moderately accurate biomarkers for CAP vs. COPD (AUC ≤ 0.83), all of which were phospholipids. Phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins were also markedly decreased in S. aureus-infected human A549 and differentiated THP1 cells. Correlations with C-reactive protein and procalcitonin were predominantly negative but only of mild-to-moderate extent, suggesting that these markers reflect more than merely inflammation. Consistent with the increased ceramide concentrations, increased acid sphingomyelinase activity accurately distinguished CAP (fold change = 2.8, AUC = 0.94) and COPD (1.75, 0.88) from Controls and normalized with clinical resolution.

Conclusions

The results underscore the high potential of plasma phospholipids as biomarkers for CAP, begin to reveal differences in lipid dysregulation between CAP and infection-associated COPD exacerbation, and suggest that the decreases in plasma concentrations are at least partially determined by changes in host target cells. Furthermore, they provide validation in clinical blood samples of acid sphingomyelinase as a potential treatment target to improve clinical outcome of CAP.