Identification of a new plasma biomarker of Alzheimer's disease using metabolomics technology

Prenatal exposure to undernutrition is associated with a specific lipid profile predicting future brain aging

Prenatal adversity affects cognitive and brain aging. Both lipid and leptin concentrations may be involved. We investigated if prenatal undernutrition is associated with a specific blood lipid profile and/or leptin concentrations, and if these relate to cognitive function and brain aging. 801 plasma samples of members of the Dutch famine birth cohort were assessed for lipidomics and leptin at age 58. Cognitive performance was measured with a Stroop task at 58, and MRI-based BrainAGE was derived in a subsample at 68. Out of 259 lipid signals, a signature of five identified individuals who were undernourished prenatally. These five lipids were not associated with cognitive performance, but three were predictive of BrainAGE. Leptin was not associated with prenatal famine exposure, Stroop performance, or BrainAGE. In conclusion, prenatal undernutrition was associated with an altered lipid profile predictive of BrainAGE 10 years later, demonstrating the potential of lipid profiles as early biomarkers for accelerated brain aging.

Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: Future challenges and perspectives

Coronavirus disease 2019 (COVID-19) has emerged as a global health threat and has rapidly spread worldwide. Significant changes in the lipid profile before and after COVID-19 confirmed the significance of lipid metabolism in regulating the response to viral infection. Therefore, understanding the role of lipid metabolism may facilitate the development of new therapeutics for COVID-19. Owing to their high sensitivity and accuracy, mass spectrometry (MS)-based methods are widely used for rapidly identifying and quantifying of thousands of lipid species present in a small amount of sample. To enhance the capabilities of MS for the qualitative and quantitative analysis of lipids, different platforms have been combined to cover a wide range of lipidomes with high sensitivity, specificity, and accuracy. Currently, MS-based technologies are being established as efficient methods for discovering potential diagnostic biomarkers for COVID-19 and related diseases. As the lipidome of the host cell is drastically affected by the viral replication process, investigating lipid profile alterations in patients with COVID-19 and targeting lipid metabolism pathways are considered to be crucial steps in host-directed drug targeting to develop better therapeutic strategies. This review summarizes various MS-based strategies that have been developed for lipidomic analyzes and biomarker discoveries to combat COVID-19 by integrating various other potential approaches using different human samples. Furthermore, this review discusses the challenges in using MS technologies and future perspectives in terms of drug discovery and diagnosis of COVID-19.

Shaping immune landscape of colorectal cancer by cholesterol metabolites

Cancer immunotherapies have achieved unprecedented success in clinic, but they remain largely ineffective in some major types of cancer, such as colorectal cancer with microsatellite stability (MSS CRC). It is therefore important to study tumor microenvironment of resistant cancers for developing new intervention strategies. In this study, we identify a metabolic cue that determines the unique immune landscape of MSS CRC. Through secretion of distal cholesterol precursors, which directly activate RORγt, MSS CRC cells can polarize T cells toward Th17 cells that have well-characterized pro-tumor functions in colorectal cancer. Analysis of large human cancer cohorts revealed an asynchronous pattern of the cholesterol biosynthesis in MSS CRC, which is responsible for the abnormal accumulation of distal cholesterol precursors. Inhibiting the cholesterol biosynthesis enzyme Cyp51, by pharmacological or genetic interventions, reduced the levels of intratumoral distal cholesterol precursors and suppressed tumor progression through a Th17-modulation mechanism in preclinical MSS CRC models. Our study therefore reveals a novel mechanism of cancer-immune interaction and an intervention strategy for the difficult-to-treat MSS CRC.

From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology

With the inexorable aging of the global populace, neurodegenerative diseases (NDs) like Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) pose escalating challenges, which are underscored by their socioeconomic repercussions. A pivotal aspect in addressing these challenges lies in the elucidation and application of biomarkers for timely diagnosis, vigilant monitoring, and effective treatment modalities. This review delineates the quintessence of biomarkers in the realm of NDs, elucidating various classifications and their indispensable roles. Particularly, the quest for novel biomarkers in AD, transcending traditional markers in PD, and the frontier of biomarker research in ALS are scrutinized. Emergent susceptibility and trait markers herald a new era of personalized medicine, promising enhanced treatment initiation especially in cases of SOD1-ALS. The discourse extends to diagnostic and state markers, revolutionizing early detection and monitoring, alongside progression markers that unveil the trajectory of NDs, propelling forward the potential for tailored interventions. The synergy between burgeoning technologies and innovative techniques like -omics, histologic assessments, and imaging is spotlighted, underscoring their pivotal roles in biomarker discovery. Reflecting on the progress hitherto, the review underscores the exigent need for multidisciplinary collaborations to surmount the challenges ahead, accelerate biomarker discovery, and herald a new epoch of understanding and managing NDs. Through a panoramic lens, this article endeavors to provide a comprehensive insight into the burgeoning field of biomarkers in NDs, spotlighting the promise they hold in transforming the diagnostic landscape, enhancing disease management, and illuminating the pathway toward efficacious therapeutic interventions.

Plant sterols and cholesterol metabolism are associated with five-year cognitive decline in the elderly population

Dysregulations in cholesterol metabolism are associated with neurodegenerative and vascular pathologies, and dementia. Diet-derived plant sterols (phytosterols) have cholesterol-lowering, anti-inflammatory, and antioxidant properties and may interfere with neurodegeneration and cognitive decline. Here we performed multivariate analysis in 720 individuals enrolled in a population-based prospective study to determine whether circulating cholesterol precursors and metabolites, triglycerides, and phytosterols, are associated with cognitive impairment and decline in the older population. We report specific dysregulations of endogenous cholesterol synthesis and metabolism, and diet-derived phytosterols, and their changes over time associated with cognitive impairment, and decline in the general population. These findings suggest circulating sterols levels could be considered in risk evaluation and are relevant for the development of strategies to prevent cognitive decline in older people.

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.

Analysis of the volatile organic compounds of epidural analgesia-ameliorated metabolic disorder in pregnant women with gestational diabetes mellitus based on untargeted metabolomics

Gestational diabetes mellitus (GDM) is a metabolic disease with an increasing annual incidence. Our previous observational study found that pregnant women with gestational diabetes had mild cognitive decline, which may be related to methylglyoxal (MGO). This study aimed to investigate whether labor pain aggravates the increase in MGO and explored the protective effect of epidural analgesia on metabolism in pregnant women with GDM based on solid-phase microextraction gas chromatography/mass spectrometry (SPME/GC-MS). Pregnant women with GDM were divided into a natural birth group (ND group, n = 30) and epidural analgesia group (PD group, n = 30). After fasting for ≥ 10 h overnight, venous blood samples were collected pre- and post-delivery to detect MGO, interleukin-6 (IL-6), and 8-epi-prostaglandin F2 alpha (8-iso-PGF2α) by ELISA. Serum samples were analyzed for volatile organic compounds (VOCs) using SPME-GC-MS. MGO, IL-6, and 8-iso-PGF2α levels in the ND group increased significantly post-delivery (P < 0.05) and were significantly higher in this group than the levels in the PD group (P < 0.05). Compared to the PD group, VOCs in the ND group increased significantly post-delivery. Further results indicated that propionic acid may be associated with metabolic disorders in pregnant women with GDM. Epidural analgesia can effectively improve the metabolism and immune function in pregnant women with GDM.

Early Diagnosis of Alzheimer's Disease with Blood Test; Tempting but Challenging

The increasing prevalence of Alzheimer's disease (AD) has led to a health crisis. According to official statistics, more than 55 million people globally have AD or other types of dementia, making it the sixth leading cause of death. It is still difficult to diagnose AD and there is no definitive diagnosis yet; post-mortem autopsy is still the only definite method. Moreover, clinical manifestations occur very late in the course of disease progression; therefore, profound irreversible changes have already occurred when the disease manifests. Studies have shown that in the preclinical stage of AD, changes in some biomarkers are measurable prior to any neurological damage or other symptoms. Hence, creating a reliable, fast, and affordable method capable of detecting AD in early stage has attracted the most attention. Seeking clinically applicable, inexpensive, less invasive, and much more easily accessible biomarkers for early diagnosis of AD, blood-based biomarkers (BBBs) seem to be an ideal option. This review is an inclusive report of BBBs that have been shown to be altered in the course of AD progression. The aim of this report is to provide comprehensive insight into the research status of early detection of AD based on BBBs.

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLine^TM and Ulib^MS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

Gender differences contribute to variability of serum lipid biomarkers for Alzheimer's disease

Background: Alzheimer's disease (AD) cannot currently be diagnosed by a blood test. One reason may be gender differences. Another may be the statistical methods used. The authors evaluate these possibilities. Objective: The authors applied serum lipidomics to find AD biomarkers in men and women. They hypothesized that AD biomarkers would differ between genders and that machine-learning algorithms would improve diagnostic performance. Methods: Serum lipids were analyzed by mass spectrometry for a training set of AD cases and controls and in a blinded test set. Statistical analyses considered gender differences. Results: Lipids best classifying AD subjects differed significantly between men and women. Robust statistical algorithms did not improve diagnostic performance. Conclusion: Poor performance of AD biomarkers appears to be due primarily to inherent variability in AD patients.

High Resolution Magic Angle Spinning Proton NMR Study of Alzheimer's Disease with Mouse Models

Alzheimer's disease (AD) is a crippling condition that affects millions of elderly adults each year, yet there remains a serious need for improved methods of diagnosis. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease; however, studies of human brain tissue metabolomics are challenging, due to sample limitations and ethical considerations. Comprehensive comparisons of imaging measurements in animal models to identify similarities and differences between aging- and AD-associated metabolic changes should thus be tested and validated for future human non-invasive studies. In this paper, we present the results of our highresolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) studies of AD and wild-type (WT) mouse models, based on animal age, brain regions, including cortex vs. hippocampus, and disease status. Our findings suggest the ability of HRMAS NMR to differentiate between AD and WT mice using brain metabolomics, which potentially can be implemented in in vivo evaluations.

Application of Metabolomics to Identify Potential Biomarkers for the Early Diagnosis of Coronary Heart Disease

Coronary heart disease (CHD) is one of the leading causes of deaths globally. Identification of serum metabolic biomarkers for its early diagnosis is thus much desirable. Serum samples were collected from healthy controls (n = 86) and patients with CHD (n = 166) and subjected to untargeted and targeted metabolomics analyses. Subsequently, potential biomarkers were detected and screened, and a clinical model was developed for diagnosing CHD. Four dysregulated metabolites, namely PC(17:0/0:0), oxyneurine, acetylcarnitine, and isoundecylic acid, were identified. Isoundecylic acid was not found in Human Metabolome Database, so we could not validate differences in its relative abundance levels. Further, the clinical model combining serum oxyneurine, triglyceride, and weight was found to be more robust than that based on PC(17:0/0:0), oxyneurine, and acetylcarnitine (AUC = 0.731 vs. 0.579, sensitivity = 83.0 vs. 75.5%, and specificity = 64.0 vs. 46.5%). Our findings indicated that serum metabolomics is an effective method to identify differential metabolites and that serum oxyneurine, triglyceride, and weight appear to be promising biomarkers for the early diagnosis of CHD.

Cholesterol metabolism and brain injury in neonatal encephalopathy

Neonatal encephalopathy (NE) results from impaired cerebral blood flow and oxygen delivery to the brain. The pathophysiology of NE is complex and our understanding of its underlying pathways continues to evolve. There is considerable evidence that cholesterol dysregulation is involved in several adult diseases, including traumatic brain injury, stroke, Huntington's disease, and Parkinson's disease. Although the research is less robust in pediatrics, there is emerging evidence that aberrations in cholesterol metabolism may also be involved in the pathophysiology of neonatal NE. This narrative review provides an overview of cholesterol metabolism in the brain along with several examples from the adult literature where pathologic alterations in cholesterol metabolism have been associated with inflammatory and ischemic brain injury. Using those data as a background, the review then discusses the current preclinical data supporting the involvement of cholesterol in the pathogenesis of NE as well as how brain-specific cholesterol metabolites may serve as serum biomarkers for brain injury. Lastly, we review the potential for using the cholesterol metabolic pathways as therapeutic targets. Further investigation of the shifts in cholesterol synthesis and metabolism after hypoxia-ischemia may prove vital in understanding NE pathophysiology as well as providing opportunities for rapid diagnosis and therapeutic interventions. IMPACT: This review summarizes emerging evidence that aberrations in cholesterol metabolism may be involved in the pathophysiology of NE. Using data from NE as well as analogous adult disease states, this article reviews the potential for using cholesterol pathways as targets for developing novel therapeutic interventions and using cholesterol metabolites as biomarkers for injury. When possible, gaps in the current literature were identified to aid in the development of future studies to further investigate the interactions between cholesterol pathways and NE.

Multi-dimensional characterization and identification of sterols in untargeted LC-MS analysis using all ion fragmentation technology

Sterols are an important type of lipids, and play many important roles in physiological and pathological processes. However, comprehensive analysis of sterols especially identification of unknown sterols is challenging. In this work, LC-MS with all ion fragmentation (AIF) technology was developed for untargeted analysis of sterols in biological samples. AIF technology provided holistic and multi-dimensional characterization for both knowns and unknowns sterols, including accurate m/z, isotope pattern, retention time (RT), and co-eluted peak profiles between MS1 and MS2 ions in one analysis. We further developed an analysis strategy by integrating the multi-dimensional properties to support unambiguous identification of sterols, including distinguishing sterol isomers. The developed strategy enabled to identify a total of 23 sterols in mouse samples, and quantified 19 sterols in mouse liver tissues. More importantly, we demonstrated that AIF based multi-dimensional analysis provided a possibility to identify sterols without chemical standards and facilitated to discover novel compounds with sterol-like structures in biological samples. In summary, we employed the LC-MS based AIF technology to develop multi-dimensional characterization and identification of both known and unknown sterols in complex biological samples. The comprehensive analysis of sterols facilitates to provide molecular insights to many physiological and pathological activities in biology.

Identification of lipid biomarker from serum in patients with chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States with no effective treatment. The current diagnostic method, spirometry, does not accurately reflect the severity of COPD disease status. Therefore, there is a pressing unmet medical need to develop noninvasive methods and reliable biomarkers to detect early stages of COPD. Lipids are the fundamental components of cell membranes, and dysregulation of lipids was proven to be associated with COPD. Lipidomics is a comprehensive approach to all the pathways and networks of cellular lipids in biological systems. It is widely used for disease diagnosis, biomarker identification, and pathology disorders detection relating to lipid metabolism.

METHODS

In the current study, a total of 25 serum samples were collected from 5 normal control subjects and 20 patients with different stages of COPD according to the global initiative for chronic obstructive lung disease (GOLD) (GOLD stages I ~ IV, 5 patients per group). After metabolite extraction, lipidomic analysis was performed using electrospray ionization mass spectrometry (ESI-MS) to detect the serum lipid species. Later, the comparisons of individual lipids were performed between controls and patients with COPD. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) analysis were utilized to test the potential biomarkers. Finally, correlations between the validated lipidomic biomarkers and disease stages, age, FEV1% pack years and BMI were evaluated.

RESULTS

Our results indicate that a panel of 50 lipid metabolites including phospholipids, sphingolipids, glycerolipids, and cholesterol esters can be used to differentiate the presence of COPD. Among them, 10 individual lipid species showed significance (p < 0.05) with a two-fold change. In addition, lipid ratios between every two lipid species were also evaluated as potential biomarkers. Further multivariate data analysis and receiver operating characteristic (ROC: 0.83 ~ 0.99) analysis suggest that four lipid species (AUC:0.86 ~ 0.95) and ten lipid ratios could be potential biomarkers for COPD (AUC:0.94 ~ 1) with higher sensitivity and specificity. Further correlation analyses indicate these potential biomarkers were not affected age, BMI, stages and FEV1%, but were associated with smoking pack years.

CONCLUSION

Using lipidomics and statistical methods, we identified unique lipid signatures as potential biomarkers for diagnosis of COPD. Further validation studies of these potential biomarkers with large population may elucidate their roles in the development of COPD.

Serum Metabolic Profiling Reveals Potential Anti-Inflammatory Effects of the Intake of Black Ginseng Extracts in Beagle Dogs

Black ginseng (BG) has better health benefits than white ginseng. The intake of BG changes the levels of metabolites, such as amino acids, fatty acids, and other metabolites. However, there is no research on the effect of BG extract intake on the metabolic profile of dog serum. In this study, serum metabolic profiling was conducted to investigate metabolic differences following the intake of BG extracts in beagle dogs. The beagle dogs were separated into three groups and fed either a regular diet (RD, control), RD with a medium concentration of BG extract (BG-M), or RD with a high concentration of BG extract (BG-H). Differences were observed among the three groups after the dogs ingested the experimental diet for eight weeks. The concentrations of alanine, leucine, isoleucine, and valine changed with the intake of BG extracts. Furthermore, levels of glycine and β-alanine increased in the BG-H group compared to the control and BG-M groups, indicating that BG extracts are associated with anti-inflammatory processes. Our study is the first to demonstrate the potential anti-inflammatory effect of BG extract in beagle dogs. Glycine and β-alanine are proposed as candidate serum biomarkers in dogs that can discriminate between the effects of ingesting BG-H.

Time-dependent metabolomics study of cerebral ischemia-reperfusion and its treatment: focus on the combination of traditional Chinese medicine and Western medicine

Cerebral ischemia is a common cerebrovascular disease with high mortality, and thrombolysis can cause more severe reperfusion injury. In clinical practice, Ginkgo biloba dispersible tablets combined with nimodipine have been widely used to reduce cerebral ischemia-reperfusion injury, but the mechanism has not been clearly elucidated. To explore this relationship, the change in metabolism between a sham operation group, a model group and an administration group was analyzed for the period after cerebral ischemia. Biochemical assays were used to assess injury extent and the therapeutic effects of different dosing regimens. A metabolomics method based on ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was developed to screen biomarkers in plasma of rats and analyze abnormal metabolic pathways. Using statistical analysis, corticosterone, glutamine, oleic acid, isoleucine, phenylalanine and sphingomyelin (d18:1/16:0) were screened as diagnostic biomarkers. The metabolic pathways perturbed by cerebral ischemia-reperfusion involved phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, alpha-linolenic acid metabolism, retinol metabolism, alanine, aspartate and glutamate metabolism, and glycerophospholipid metabolism. Analysis of the adjustment of biomarkers at different time points showed that the best time to evaluate the efficacy of combined administration is about 6 h after administration. Both pathological characteristics and metabolomics confirmed the better effect of the combined group than the individual groups. In this study, a non-targeted metabolomics method was developed to explore the mechanism of action of the combination of traditional Chinese and Western medicine in cerebral ischemia-reperfusion treatment, providing a theoretical basis for disease prognosis and treatment options. Graphical abstract.

Discussion on Relevance and Studies of Prescription Compatibility in Chinese Medicine

With Chinese medicine (CM) gaining popularity in recent years, researchers and clinicians have put in much interest and effort into the makings and effects of it, especially after the recent announcement of World Health Orgnitation's incorporation of CM into mainstream medical compendium. Individual herb has complex properties, coming from its pharmacological properties and the Chinese medical principles of organ-directed, taste and dynamic orientational behaviours. The use of individual herb in CM is rare, where various herbs/ingredients are mostly found in a prescribed formula. To fully reveal the effects of CM is a great challenge. The complexity of various herbs in combined effect, the absorption and utility rate by the body, uniqueness of individual physique, sub-types of pathological behaviors and time-line progression of the healing process add on to the complication of understanding the full effect of CM. Various theories such as pathophysiology guidance, pharmacokinetic-pharmacodynamic compatibility method, and Global Systems Biology for Integrative Genomics, Proteomics and Metabolomics, which interactively provide a wider scope, more details, with the consideration of development timeline, may shed more light to revealing the full picture of the effects of compatibility prescription.

Integrated network pharmacology analysis and serum metabolomics to reveal the cognitive improvement effect of Bushen Tiansui formula on Alzheimer's disease

ETHNOPHARMACOLOGICAL RELEVANCE

Bushen Tiansui Formula (BSTSF) is a traditional Chinese medicine formula used clinically to treat Alzheimer's disease (AD) for many years. Previously, we have partially elucidated the mechanisms involved in the therapeutic effects of BSTSF on AD. However, the underlying mechanisms remain largely unclear.

AIM OF THE STUDY

The aim of this study was to further investigate the therapeutic effects of BSTSF on AD using an integrated strategy of network pharmacology and serum metabolomics.

MATERIALS AND METHODS

The rat models of AD were established using Aβ 1-42 injection, and morris water maze test was used to evaluate the efficacy of BSTSF on AD. Next, network pharmacology analysis was applied to identify the active compounds and target genes, which might be responsible for the effect of BSTSF. Then, a metabolomics strategy has been developed to find the possible significant serum metabolites and metabolic pathway induced by BSTSF. Additionally, two parts of the results were integrated to confirm each other.

RESULTS

The results of the network pharmacology analysis showed 37 compounds and 64 potential target genes related to the treatment of AD with BSTSF. The functional enrichment analysis indicated that the potential mechanism was mainly associated with the tumor necrosis factor signaling pathway and phosphatidylinositol 3 kinase/protein kinase B signaling pathway. Based on metabolomics, 78 differential endogenous metabolites were identified as potential biomarkers related to the BSTSF for treating AD. These metabolites were mainly involved in the relevant pathways of linoleic acid metabolism, α-linolenic acid metabolism, glycerophospholipid metabolism, tryptophan metabolism, and arginine and proline metabolism. These findings were partly consistent with the findings of the network pharmacology analysis.

CONCLUSIONS

In conclusion, our results solidly supported and enhanced out current understanding of the therapeutic effects of BSTSF on AD. Meanwhile, our work revealed that the proposed network pharmacology-integrated metabolomics strategy was a powerful means for identifying active components and mechanisms contributing to the pharmacological effects of traditional Chinese medicine.

"Lipidomics": Mass spectrometric and chemometric analyses of lipids

Lipids are ubiquitous in the human organism and play essential roles as components of cell membranes and hormones, for energy storage or as mediators of cell signaling pathways. As crucial mediators of the human metabolism, lipids are also involved in metabolic diseases, cardiovascular and renal diseases, cancer and/or hepatological and neurological disorders. With rapidly growing evidence supporting the impact of lipids on both the genesis and progression of these diseases as well as patient wellbeing, the characterization of the human lipidome has gained high interest and importance in life sciences and clinical diagnostics within the last 15 years. This is mostly due to technically advanced molecular identification and quantification methods, mainly based on mass spectrometry. Mass spectrometry has become one of the most powerful tools for the identification of lipids. New lipidic mediators or biomarkers of diseases can be analysed by state-of-the art mass spectrometry techniques supported by sophisticated bioinformatics and biostatistics. The lipidomic approach has developed dramatically in the realm of life sciences and clinical diagnostics due to the available mass spectrometric methods and in particular due to the adaptation of biostatistical methods in recent years. Therefore, the current knowledge of lipid extraction methods, mass-spectrometric approaches, biostatistical data analysis, including workflows for the interpretation of lipidomic high-throughput data, are reviewed in this manuscript.

High-resolution metabolomic profiling of Alzheimer's disease in plasma

Background

Alzheimer’s disease (AD) is a complex neurological disorder with contributions from genetic and environmental factors. High‐resolution metabolomics (HRM) has the potential to identify novel endogenous and environmental factors involved in AD. Previous metabolomics studies have identified circulating metabolites linked to AD, but lack of replication and inconsistent diagnostic algorithms have hindered the generalizability of these findings. Here we applied HRM to identify plasma metabolic and environmental factors associated with AD in two study samples, with cerebrospinal fluid (CSF) biomarkers of AD incorporated to achieve high diagnostic accuracy.

Methods

Liquid chromatography‐mass spectrometry (LC–MS)‐based HRM was used to identify plasma and CSF metabolites associated with AD diagnosis and CSF AD biomarkers in two studies of prevalent AD (Study 1: 43 AD cases, 45 mild cognitive impairment [MCI] cases, 41 controls; Study 2: 50 AD cases, 18 controls). AD‐associated metabolites were identified using a metabolome‐wide association study (MWAS) framework.

Results

An MWAS meta‐analysis identified three non‐medication AD‐associated metabolites in plasma, including elevated levels of glutamine and an unknown halogenated compound and lower levels of piperine, a dietary alkaloid. The non‐medication metabolites were correlated with CSF AD biomarkers, and glutamine and the unknown halogenated compound were also detected in CSF. Furthermore, in Study 1, the unknown compound and piperine were altered in MCI patients in the same direction as AD dementia.

Conclusions

In plasma, AD was reproducibly associated with elevated levels of glutamine and a halogen‐containing compound and reduced levels of piperine. These findings provide further evidence that exposures and behavior may modify AD risks.

Isobaric mass tagging and triple quadrupole mass spectrometry to determine lipid biomarker candidates for Alzheimer's disease

The isobaric tagging method widely used in proteomic and lipidomic fields, with the multiple reaction monitoring (MRM) approach using a triple quadrupole mass spectrometer, was applied to identify biomarker candidates from plasma samples for Alzheimer’s disease (AD). We focused on the following phospholipids that have amino groups as the functional group: phosphatidylethanolamine (PE), Lyso-PE, phosphatidylserine, and Lyso-phosphatidylserine. We also investigated fatty acids that have a carboxy group. A sixplex tandem mass tag (TMT) was used for the isobaric tagging method in this study. The TMT reaction had high reproducibility in human plasma. A total of 196 human plasma samples from three AD cohorts were used for the study, and compared to pooled plasma quality control (QC) samples. The described method required only 40 MRM measurements, including the pooled QC samples, for a full comparison of the data. We found that the content of free fatty acids increased in AD samples in all the three cohorts, alkenyl PEs (ePEs) decreased over a one-year interval in AD patients, and ePEs weakly correlated with amyloid peptide (a-beta) 1–42 in cerebrospinal fluid. In conclusion, total free fatty acids in plasma are a risk factor for AD, and ePEs monitor candidates for AD. Therefore, TMT-lipidomics is a powerful approach for the determination of plasma biomarkers because of the high sample throughput.

A Review on MS-Based Blood Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of neurodegenerative dementia and there is no cure to date. Biomarkers in cerebrospinal fluid (CSF) are already included in the diagnostic work-up of symptomatic patients but markers for preclinical diagnosis and disease progression are not available. Furthermore, blood biomarkers are highly appreciated because they are minimally invasive and more accessible in primary care and in clinical studies. Mass spectrometry (MS) is an established tool for the measurement of various analytes in biological fluids such as blood. Its major strength is the high selectivity which is why it is also preferred as a reference method for immunoassays. MS has been used in several studies in the past for blood biomarker discovery and validation in AD using targeted MS such as multiple/selected reaction monitoring (MRM/SRM) or unbiased approaches (proteomics, metabolomics). In this short review, we give an overview on the status of current MS-based biomarker candidates for AD in blood plasma and serum.Plain Language Summary: Plain language summary available for this article.

Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study

BACKGROUND

One of the biggest challenge in Alzheimer's disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer's predicTors (INSIGHT-preAD) cohort of elderly asymptomatic individuals with and without brain amyloid load.

METHODS

We performed blood RNAseq, and plasma metabolomics and lipidomics using liquid chromatography-mass spectrometry on 48 individuals amyloid positive and 48 amyloid negative (SUVr cut-off of 0·7918). The three data sets were analysed separately using differential gene expression based on negative binomial distribution, non-parametric (Wilcoxon) and parametric (correlation-adjusted Student't) tests. Data integration was conducted using sparse partial least squares-discriminant and principal component analyses. Bootstrap-selected top-ten features from the three data sets were tested for their discriminant power using Receiver Operating Characteristic curve. Longitudinal metabolomic analysis was carried out on a subset of 22 subjects.

FINDINGS

Univariate analyses identified three medium chain fatty acids, 4-nitrophenol and a set of 64 transcripts enriched for inflammation and fatty acid metabolism differentially quantified in amyloid positive and negative subjects. Importantly, the amounts of the three medium chain fatty acids were correlated over time in a subset of 22 subjects (p < 0·05). Multi-omics integrative analyses showed that metabolites efficiently discriminated between subjects according to their amyloid status while lipids did not and transcripts showed trends. Finally, the ten top metabolites and transcripts represented the most discriminant omics features with 99·4% chance prediction for amyloid positivity.

INTERPRETATION

This study suggests a potential blood omics signature for prediction of amyloid positivity in asymptomatic at-risk subjects, allowing for a less invasive, more accessible, and less expensive risk assessment of AD as compared to PET studies or lumbar puncture. FUND: Institut Hospitalo-Universitaire and Institut du Cerveau et de la Moelle Epiniere (IHU-A-ICM), French Ministry of Research, Fondation Alzheimer, Pfizer, and Avid.

Metabolomics Analyses of Saliva Detect Novel Biomarkers of Alzheimer's Disease

Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer's disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

Metabolomics for Age Discrimination of Ginseng Using a Multiplex Approach to HR-MAS NMR Spectroscopy, UPLC-QTOF/MS, and GC × GC-TOF/MS

(1) Background: The ability to determine the age of ginseng is very important because the price of ginseng depends on the cultivation period. Since morphological observation is subjective, a new scientific and systematic method for determining the age of ginseng is required. (2) Methods: Three techniques were used for a metabolomics approach. High-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy was used to analyze powdered ginseng samples without extraction. Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and gas chromatography quadrupole time-of-fight mass spectrometry (GC-TOF/MS) were used to analyze the extracts of 4-, 5-, and 6-year-old ginseng. (3) Results: A metabolomics approach has the potential to discriminate the age of ginseng. Among the primary metabolites detected from NMR spectroscopy, the levels of fumarate and choline showed moderate prediction with an area under the curve (AUC) value of more than 0.7. As a result of UPLC-QTOF/MS-based profiling, 61 metabolites referring to the VIP (variable importance in the projection) score contributed to discriminating the age of ginseng. The results of GC×GC-TOF/MS showed clear discrimination of 4-, 5-, and 6-year-old ginseng using orthogonal partial least-squares discriminant analysis (OPLS-DA) to 100% of the discrimination rate. The results of receiver operating characteristic (ROC) analysis, 16 metabolites between 4- and 5-year-old ginseng, and 18 metabolites between 5- and 6-year-old ginseng contributed to age discrimination in all regions. (4) Conclusions: These results showed that metabolic profiling and multivariate statistical analyses can distinguish the age of ginseng. Especially, it is meaningful that ginseng samples from different areas had the same metabolites for age discrimination. In future studies, it will be necessary to identify the unknown variables and to collaboratively study with other fields the biochemistry of aging in ginseng.

Efficient methodology for the extraction and analysis of lipids from porcine pulmonary artery by supercritical fluid chromatography coupled to mass spectrometry

Pulmonary artery grafts are needed as cardiovascular bioprosthetics. For successful tissue recellularization after transplantation, lipids have to be removed from the donor artery. Developing a selective process to remove lipids without damaging the extracellular matrix greatly depends on knowing the amount and type of lipid compounds in the specific tissue. Here we present an efficient methodology for the study of lipids present in porcine pulmonary arteries. The performance of six extraction methods to recover lipids from artery was evaluated. For this purpose, a supercritical fluid chromatography method coupled to quadrupole time-of-flight mass spectrometry detection (UHPSFC/QTOF-MS) was adapted. The method enabled separation of lipids of a wide range of polarity according to lipid class in less than 7 min. One dichloromethane-based extraction method was shown to be the most efficient one for the recovery of lipids from pulmonary artery. However, one MTBE-based extraction method was able to show the highest fatty acid extraction yields (to the expense of longer extraction times). Lipids were relative quantified according to class, and the major species within each class were identified. Triacylglycerols and glycerophospholipids were the most abundant classes, followed by sphingomyelins, monoacylglycerols and fatty acyls. The matrix effect exerted no interference on the analytical method, except for some few combinations of extraction method and lipid class. These results are of relevance for lipidomic studies from solid tissue, in particular for studies on pulmonary and cardiovascular diseases. Finally, our work sets the basis for the further development of a selective processes to remove lipids from pulmonary artery without damaging the tissue prior to transplantation.

Serum noncholesterol sterols in Alzheimer's disease: the Helsinki Businessmen Study

Cerebral cholesterol metabolism is perturbed in late-onset Alzheimer's disease (AD), but whether also the extracerebral cholesterol metabolism is perturbed is not known. Thus, we studied whole-body cholesterol synthesis and absorption with serum noncholesterol sterols in men without AD (n = 114) or with (n = 18) "pure" AD (no concomitant atherosclerotic cardiovascular disease) in a long-term cohort (the Helsinki Businessmen Study) of home-dwelling older men without lipid-lowering drugs and on their habitual home diet. Serum lipids did not differ between AD and controls, but age was higher (78 ± 1 vs 74 ± 0.3 years, mean ± standard error, P < 0.001), age-adjusted plasma glucose concentration was lower (4.8 ± 0.3 vs 5.7 ± 0.1 mmol/L, P = 0.011), and APOE ε4 allele and frailty were more frequent in AD than in controls. Of the age and frailty-adjusted serum noncholesterol sterols desmosterol and lathosterol ratios to cholesterol reflecting cholesterol synthesis were lower in AD than in controls (eg, lathosterol 114 ± 12 vs 137 ± 5 10² µmol/mmol cholesterol, P = 0.004). Cholestanol ratio to cholesterol was higher in AD than in controls suggesting increased cholesterol absorption. lathosterol/sitosteroll ratio reflecting cholesterol metabolism was lower in AD than in controls (0.95 ± 0.28 vs 1.52 ± 0.11 10² µmol/mmol cholesterol, P = 0.027). In AD, plasma glucose correlated negatively with cholesterol synthesis, whereas in controls the correlation was positive. In conclusion, extracerebral cholesterol metabolism was altered in AD. This finding along with the low plasma glucose concentration and its paradoxical interaction with cholesterol synthesis opens new perspectives in the regulation of cholesterol metabolism and glucose homeostasis in AD.

Biomarkers for detection, prognosis and therapeutic assessment of neurological disorders

Neurological disorders have aroused a significant concern among the health scientists globally, as diseases such as Parkinson's, Alzheimer's and dementia lead to disability and people have to live with them throughout the life. Recent evidence suggests that a number of environmental chemicals such as pesticides (paraquat) and metals (lead and aluminum) are also the cause of these diseases and other neurological disorders. Biomarkers can help in detecting the disorder at the preclinical stage, progression of the disease and key metabolomic alterations permitting identification of potential targets for intervention. A number of biomarkers have been proposed for some neurological disorders based on laboratory and clinical studies. In silico approaches have also been used by some investigators. Yet the ideal biomarker, which can help in early detection and follow-up on treatment and identifying the susceptible populations, is not available. An attempt has therefore been made to review the recent advancements of in silico approaches for discovery of biomarkers and their validation. In silico techniques implemented with multi-omics approaches have potential to provide a fast and accurate approach to identify novel biomarkers.

Next-generation biomarker discovery in Alzheimer's disease using metabolomics - from animal to human studies

Alzheimer's disease (AD) is a complex disease driven mainly by neuronal loss due to accumulation of intracellular neurofibrillary tangles and amyloid β aggregates in the brain. The diagnosis of AD currently relies on clinical symptoms while the disease can only be confirmed at autopsy. The few available biomarkers allowing for diagnosis are typically detected many years after the onset of the disease. New diagnostic approaches, particularly in easily-accessible biofluids, are essential. By providing an exhaustive information of the phenotype, metabolomics is an ideal approach for identification of new biomarkers. This review investigates the current position of metabolomics in the field of AD research, focusing on animal and human studies, and discusses the improvements carried out over the past decade.

Sebomic identification of sex- and ethnicity-specific variations in residual skin surface components (RSSC) for bio-monitoring or forensic applications

Background

“Residual skin surface components” (RSSC) is the collective term used for the superficial layer of sebum, residue of sweat, small quantities of intercellular lipids and components of natural moisturising factor present on the skin surface. Potential applications of RSSC include use as a sampling matrix for identifying biomarkers of disease, environmental exposure monitoring, and forensics (retrospective identification of exposure to toxic chemicals). However, it is essential to first define the composition of “normal” RSSC. Therefore, the aim of the current study was to characterise RSSC to determine commonalities and differences in RSSC composition in relation to sex and ethnicity.

Methods

Samples of RSSC were acquired from volunteers using a previously validated method and analysed by high-pressure liquid chromatography–atmospheric pressure chemical ionisation–mass spectrometry (HPLC-APCI-MS). The resulting data underwent sebomic analysis.

Results

The composition and abundance of RSSC components varied according to sex and ethnicity. The normalised abundance of free fatty acids, wax esters, diglycerides and triglycerides was significantly higher in males than females. Ethnicity-specific differences were observed in free fatty acids and a diglyceride.

Conclusions

The HPLC-APCI-MS method developed in this study was successfully used to analyse the normal composition of RSSC. Compositional differences in the RSSC can be attributed to sex and ethnicity and may reflect underlying factors such as diet, hormonal levels and enzyme expression.

Serum lipidomic analysis for the discovery of biomarkers for major depressive disorder in drug-free patients

Lipidomic analysis can be used to efficiently identify hundreds of lipid molecular species in biological materials and has been recently established as an important tool for biomarker discovery in various neuropsychiatric disorders including major depressive disorder (MDD). In this study, quantitative targeted serum lipidomic profiling was performed on female subjects using liquid chromatography-mass spectrometry. Global lipid profiling of pooled serum samples from 10 patients currently with MDD (cMDD), 10 patients with remitted MDD (rMDD), and 10 healthy controls revealed 37 differentially regulated lipids (DRLs). DRLs were further verified using multiple-reaction monitoring (MRM) in each of the 25 samples from the three groups of independent cohorts. Using multivariate analysis and MRM data we identified serum biomarker panels of discriminatory lipids that differentiated between pairs of groups: lysophosphatidic acid (LPA)(16:1), triglycerides (TG)(44:0), and TG(54:8) distinguished cMDD from controls with 76% accuracy; lysophosphatidylcholines(16:1), TG(44:0), TG(46:0), and TG(50:1) distinguished between cMDD and rMDD at 65% accuracy; and LPA(16:1), TG(52:6), TG(54:8), and TG(58:10) distinguished between rMDD and controls with 60% accuracy. Our lipidomic analysis identified peripheral lipid signatures of MDD, which thereby provides providing important biomarker candidates for MDD.

Neuroprotective Role of the Ron Receptor Tyrosine Kinase Underlying Central Nervous System Inflammation in Health and Disease

Neurodegeneration is a critical problem in aging populations and is characterized by severe central nervous system (CNS) inflammation. Macrophages closely regulate inflammation in the CNS and periphery by taking on different activation states. The source of inflammation in many neurodegenerative diseases has been preliminarily linked to a decrease in the CNS M2 macrophage population and a subsequent increase in M1-mediated neuroinflammation. The Recepteur D'Origine Nantais (Ron) is a receptor tyrosine kinase expressed on tissue-resident macrophages including microglia. Activation of Ron by its ligand, macrophage-stimulating protein, attenuates obesity-mediated inflammation in the periphery. An in vivo deletion of the ligand binding domain of Ron (Ron^-/-) promotes inflammatory (M1) and limits a reparative (M2) macrophage activation. However, whether or not this response influences CNS inflammation has not been determined. In this study, we demonstrate that in homeostasis Ron^-/- mice developed an inflammatory CNS niche with increased tissue expression of M1-associated markers when compared to age-matched wild-type (WT) mice. Baseline metabolic analysis of CNS tissue indicates exacerbated levels of metabolic stress in Ron^-/- CNS. In a disease model of multiple sclerosis, experimental autoimmune encephalomyelitis, Ron^-/- mice exhibit higher disease severity when compared to WT mice associated with increased CNS tissue inflammation. In a model of diet-induced obesity (DIO), Ron^-/- mice exhibit exacerbated CNS inflammation with decreased expression of the M2 marker Arginase-1 (Arg-1) and a robust increase in M1 markers compared to WT mice following 27 weeks of DIO. Collectively, these results illustrate that activation of Ron in the CNS could be a potential therapeutic approach to treating various grades of CNS inflammation underlying neurodegeneration.

Lipidomic analysis of biological samples: Comparison of liquid chromatography, supercritical fluid chromatography and direct infusion mass spectrometry methods

Lipidomic analysis of biological samples in a clinical research represents challenging task for analytical methods given by the large number of samples and their extreme complexity. In this work, we compare direct infusion (DI) and chromatography - mass spectrometry (MS) lipidomic approaches represented by three analytical methods in terms of comprehensiveness, sample throughput, and validation results for the lipidomic analysis of biological samples represented by tumor tissue, surrounding normal tissue, plasma, and erythrocytes of kidney cancer patients. Methods are compared in one laboratory using the identical analytical protocol to ensure comparable conditions. Ultrahigh-performance liquid chromatography/MS (UHPLC/MS) method in hydrophilic interaction liquid chromatography mode and DI-MS method are used for this comparison as the most widely used methods for the lipidomic analysis together with ultrahigh-performance supercritical fluid chromatography/MS (UHPSFC/MS) method showing promising results in metabolomics analyses. The nontargeted analysis of pooled samples is performed using all tested methods and 610 lipid species within 23 lipid classes are identified. DI method provides the most comprehensive results due to identification of some polar lipid classes, which are not identified by UHPLC and UHPSFC methods. On the other hand, UHPSFC method provides an excellent sensitivity for less polar lipid classes and the highest sample throughput within 10min method time. The sample consumption of DI method is 125 times higher than for other methods, while only 40μL of organic solvent is used for one sample analysis compared to 3.5mL and 4.9mL in case of UHPLC and UHPSFC methods, respectively. Methods are validated for the quantitative lipidomic analysis of plasma samples with one internal standard for each lipid class. Results show applicability of all tested methods for the lipidomic analysis of biological samples depending on the analysis requirements.

Metabolic Biomarkers and Neurodegeneration: A Pathway Enrichment Analysis of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis

Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) lack robust diagnostics and prognostic biomarkers. Metabolomics is a postgenomics field that offers fresh insights for biomarkers of common complex as well as rare diseases. Using data on metabolite-disease associations published in the previous decade (2006-2016) in PubMed, ScienceDirect, Scopus, and Web of Science, we identified 101 metabolites as putative biomarkers for these three neurodegenerative diseases. Notably, uric acid, choline, creatine, L-glutamine, alanine, creatinine, and N-acetyl-L-aspartate were the shared metabolite signatures among the three diseases. The disease-metabolite-pathway associations pointed out the importance of membrane transport (through ATP binding cassette transporters), particularly of arginine and proline amino acids in all three neurodegenerative diseases. When disease-specific and common metabolic pathways were queried by using the pathway enrichment analyses, we found that alanine, aspartate, glutamate, and purine metabolism might act as alternative pathways to overcome inadequate glucose supply and energy crisis in neurodegeneration. These observations underscore the importance of metabolite-based biomarker research in deciphering the elusive pathophysiology of neurodegenerative diseases. Future research investments in metabolomics of complex diseases might provide new insights on AD, PD, and ALS that continue to place a significant burden on global health.

Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations

The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered.

Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field.

Serum and Liver Tissue Metabonomic Study on Fatty Liver in Rats Induced by High-Fat Diet and Intervention Effects of Traditional Chinese Medicine Qushi Huayu Decoction

Qushi Huayu Decoction (QSHY), clinically derived, consists of five crude drugs, commonly used in treating fatty liver in a clinical setting. However, little is known about its metabolomics study. Herein, the serum and liver tissue metabolomics approach, based on gas chromatography coupled to spectrometry (GC/MS), was employed to evaluate the efficacy and the mechanism underlying QSHY in a rat model of high-fat diet-induced fatty liver. With pattern recognition analysis of serum and liver tissue metabolite profile, a clear separation of model group and control group was acquired for serum and liver tissue samples, respectively. The QSHY group showed a predisposition towards recovery mimicking the control group, which was in agreement with the biochemical alterations and histological results. 23 candidate biomarkers were identified in the serum and liver tissue samples that were utilized for exploring the underlying mechanism. The present study suggests that QSHY has significant anti-fatty liver effects on high-fat diet-induced fatty liver in rats, which might be attributed to regulating the dysfunction of beta-alanine metabolism, alanine, aspartate, and glutamate metabolism, glycine, serine, and threonine metabolism, pyruvate metabolism, and citrate cycle. Thus, metabolomics is a useful tool in the evaluation of the efficacy and elucidation of the mechanism underlying the complex traditional Chinese medicine prescriptions.

Application of sebomics for the analysis of residual skin surface components to detect potential biomarkers of type-1 diabetes mellitus

Metabolic imbalance in chronic diseases such as type-1 diabetes may lead to detectable perturbations in the molecular composition of residual skin surface components (RSSC). This study compared the accumulation rate and the composition of RSSC in type-1 diabetic patients with those in matched controls in order to identify potential biomarkers of the disease. Samples of RSSC were collected from the foreheads of type-1 diabetic (n = 55) and non-diabetic (n = 58) volunteers. Samples were subsequently analysed to identify individual components (sebomic analysis). There was no significant difference in the rate of accumulation of RSSC between type-1 diabetics and controls. In terms of molecular composition, 171 RSSC components were common to both groups, 27 were more common in non-diabetics and 18 were more common in type-1 diabetic patients. Statistically significant (P < 0.05) differences between diabetic and non-diabetic volunteers were observed in the recovered amounts of one diacylglyceride (m/z 594), six triacylglycerides (m/z 726-860) and six free fatty acids (m/z 271-345). These findings indicate that sebomic analysis can identify differences in the molecular composition of RSSC components between type-1 diabetic and non-diabetic individuals. Further work is required to determine the practical utility and identity of these potential biomarkers.

Serum and Brain Metabolomic Variations Reveal Perturbation of Sleep Deprivation on Rats and Ameliorate Effect of Total Ginsenoside Treatment

Sleep loss or sleep deprivation (SD) refers to shorter sleep than average baseline need, and SD has been a serious problem of modern societies which affects health and well-being. Panax ginseng is a well-known traditional Chinese medicine (TCM). Our previous study has demonstrated that total ginsenosides (GS), the extracts from Panax ginseng, could effectively improve cognition and behavior on SD rats. However, little is known about its metabolomic study. In this study, serum and brain metabolomic method based on gas chromatography coupled with mass spectrometry (GC/MS) was employed to evaluate the efficacy and study the mechanism of GS on a rat model of SD. With pattern recognition analysis of serum and brain tissue metabolite profile, a clear separation of the model group and control group was acquired for serum and brain tissue samples; the MGS (model + GS) group showed a tendency of recovering when compared to control group, which was consistent with behavioral and biochemical parameters. 39 and 40 potential biomarkers of brain tissues and serum samples, respectively, were identified and employed to explore the possible mechanism. Our work revealed that GS has significant protective effects on SD, and metabolomics is a useful tool for evaluating efficacy and elucidating mechanism in TCM.

Changes in the plasma membrane in metabolic disease: impact of the membrane environment on G protein-coupled receptor structure and function

Drug development targeting GPCRs often utilizes model heterologous cell expression systems, reflecting an implicit assumption that the membrane environment has little functional impact on these receptors or on their responsiveness to drugs. However, much recent data have illustrated that membrane components can have an important functional impact on intrinsic membrane proteins. This review is directed toward gaining a better understanding of the structure of the plasma membrane in health and disease, and how this organelle can influence GPCR structure, function and regulation. It is important to recognize that the membrane provides a potential mode of lateral allosteric regulation of GPCRs and can affect the effectiveness of drugs and their biological responses in various disease states, which can even vary among individuals across the population. The type 1 cholecystokinin receptor is reviewed as an exemplar of a class A GPCR that is affected in this way by changes in the plasma membrane.

LINKED ARTICLES

This article is part of a themed section on Molecular Pharmacology of GPCRs. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.21/issuetoc.

Metabolomics and cognition in African American adults in midlife: the atherosclerosis risk in communities study

Clinical studies have shown alterations in metabolic profiles when patients with mild cognitive impairment and Alzheimer's disease dementia were compared to cognitively normal subjects. Associations between 204 serum metabolites measured at baseline (1987-1989) and cognitive change were investigated in 1035 middle-aged community-dwelling African American participants in the biracial Atherosclerosis Risk in Communities (ARIC) Study. Cognition was evaluated using the Delayed Word Recall Test (DWRT; verbal memory), the Digit Symbol Substitution Test (DSST; processing speed) and the Word Fluency Test (WFT; verbal fluency) at visits 2 (1990-1992) and 4 (1996-1998). In addition, Cox regression was used to analyze the metabolites as predictors of incident hospitalized dementia between baseline and 2011. There were 141 cases among 1534 participants over a median 17.1-year follow-up period. After adjustment for established risk factors, one standard deviation increase in N-acetyl-1-methylhistidine was significantly associated with greater 6-year change in DWRT scores (β=-0.66 words; P=3.65 × 10-4). Two metabolites (one unnamed and a long-chain omega-6 polyunsaturated fatty acid found in vegetable oils (docosapentaenoate (DPA, 22:5 n-6)) were significantly associated with less decline on the DSST (DPA: β=1.25 digit-symbol pairs, P=9.47 × 10-5). Two unnamed compounds and three sex steroid hormones were associated with an increased risk of dementia (all P<3.9 × 10-4). The association of 4-androstene-3beta, 17beta-diol disulfate 1 with dementia was replicated in European Americans. These results demonstrate that screening the metabolome in midlife can detect biologically plausible biomarkers that may improve risk stratification for cognitive impairment at older ages.

Metabolomic analysis identifies altered metabolic pathways in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic, demyelinating disease that affects the central nervous system and is characterized by a complex pathogenesis and difficult management. The identification of new biomarkers would be clinically useful for more accurate diagnoses and disease monitoring. Metabolomics, the identification of small endogenous molecules, offers an instantaneous molecular snapshot of the MS phenotype. Here the metabolomic profiles (utilizing plasma from patients with MS) were characterized with a Gas cromatography-mass spectrometry-based platform followed by a multivariate statistical analysis and comparison with a healthy control (HC) population. The obtained partial least square discriminant analysis (PLS-DA) model identified and validated significant metabolic differences between individuals with MS and HC (R2X=0.223, R2Y=0.82, Q2=0.562; p<0.001). Among discriminant metabolites phosphate, fructose, myo-inositol, pyroglutamate, threonate, l-leucine, l-asparagine, l-ornithine, l-glutamine, and l-glutamate were correctly identified, and some resulted as unknown. A receiver operating characteristic (ROC) curve with AUC 0.84 (p=0.01; CI: 0.75-1) generated with the concentrations of the discriminant metabolites, supported the strength of the model. Pathway analysis indicated asparagine and citrulline biosynthesis as the main canonical pathways involved in MS. Changes in the citrulline biosynthesis pathway suggests the involvement of oxidative stress during neuronal damage. The results confirmed metabolomics as a useful approach to better understand the pathogenesis of MS and to provide new biomarkers for the disease to be used together with clinical data.

The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure

Objectives

Manganese (Mn) is a known neurotoxicant, and given its health effects and ubiquitous nature in metal-working settings, identification of a valid and reproducible biomarker of Mn exposure is of interest. Here, global metabolomics is utilized to determine metabolites that differ between groups defined by Mn exposure status, with the goal being to help inform a potential metabolite biomarker of Mn exposure.

Methods

Mn exposed subjects were recruited from a Mn steel foundry and Mn unexposed subjects were recruited from crane operators at a metal recycling facility. Over the course of a work day, each subject wore a personal inhalable dust sampler (IOM), and provided an end of shift urine sample that underwent global metabolomics profiling. Both exposed and unexposed subjects were divided into a training set and demographically similar validation set. Using a two-sided adjusted t-test, relative abundances of all metabolites found were compared between Mn exposed and unexposed training sets, and those with a false discovery rates (FDR) <0.1 were further tested in the validation sets.

Results

Fifteen ions were found to be significantly different (FDR < 0.1) between the exposed and unexposed training sets, and nine of these ions remained significantly different between the exposed and unexposed validation set as well. When further dividing exposure status into 'lower exposure' and 'higher exposure', several of these nine ions exhibited an apparent exposure-response relationship.

Conclusions

This is the first time that metabolomics has been used to distinguish between Mn exposure status in an occupational cohort, though additional work should be done to replicate these findings with a larger cohort. With metabolite identification by name, empirical formula, or pathway, a better understanding of the relationship between Mn exposure and neurotoxic effects could be elucidated, and a potential metabolite biomarker of Mn exposure could be determined.