Metabolomics - A wide-open door to personalized treatment in chronic heart failure?

Transcriptome data analysis of primary cardiomyopathies reveals perturbations in arachidonic acid metabolism

Introduction

Cardiomyopathies are complex heart diseases with significant prevalence around the world. Among these, primary forms are the major contributors to heart failure and sudden cardiac death. As a high-energy demanding engine, the heart utilizes fatty acids, glucose, amino acid, lactate and ketone bodies for energy to meet its requirement. However, continuous myocardial stress and cardiomyopathies drive towards metabolic impairment that advances heart failure (HF) pathogenesis. So far, metabolic profile correlation across different cardiomyopathies remains poorly understood.

Methods

In this study, we systematically explore metabolic differences amongst primary cardiomyopathies. By assessing the metabolic gene expression of all primary cardiomyopathies, we highlight the significantly shared and distinct metabolic pathways that may represent specialized adaptations to unique cellular demands. We utilized publicly available RNA-seq datasets to profile global changes in the above diseases (|log2FC| ≥ 0.28 and BH adjusted p-val 0.1) and performed gene set analysis (GSA) using the PAGE statistics on KEGG pathways.

Results

Our analysis demonstrates that genes in arachidonic acid metabolism (AA) are significantly perturbed across cardiomyopathies. In particular, the arachidonic acid metabolism gene PLA2G2A interacts with fibroblast marker genes and can potentially influence fibrosis during cardiomyopathy.

Conclusion

The profound significance of AA metabolism within the cardiovascular system renders it a key player in modulating the phenotypes of cardiomyopathies.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Gut Microbiome in Chronic Coronary Syndrome Patients

Despite knowledge of classical coronary artery disease (CAD) risk factors, the morbidity and mortality associated with this disease remain high. Therefore, new factors that may affect the development of CAD, such as the gut microbiome, are extensively investigated. This study aimed to evaluate gut microbiome composition in CAD patients in relation to the control group. We examined 169 CAD patients and 166 people in the control group, without CAD, matched in terms of age and sex to the study group. Both populations underwent a detailed health assessment. The microbiome analysis was based on the V3-V4 region of the 16S rRNA gene (NGS method). Among 4074 identified taxonomic units in the whole population, 1070 differed between study groups. The most common bacterial types were Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Furthermore, a higher Firmicutes/Bacteroidetes ratio in the CAD group compared with the control was demonstrated. Firmicutes/Bacteroidetes ratio, independent of age, sex, CAD status, LDL cholesterol concentration, and statins treatment, was related to altered phosphatidylcholine concentrations obtained in targeted metabolomics. Altered alpha-biodiversity (Kruskal-Wallis test, p = 0.001) and beta-biodiversity (Bray-Curtis metric, p < 0.001) in the CAD group were observed. Moreover, a predicted functional analysis revealed some taxonomic units, metabolic pathways, and proteins that might be characteristic of the CAD patients' microbiome, such as increased expressions of 6-phospho-β-glucosidase and protein-N(pi)-phosphohistidine-sugar phosphotransferase and decreased expressions of DNA topoisomerase, oxaloacetate decarboxylase, and 6-beta-glucosidase. In summary, CAD is associated with altered gut microbiome composition and function.

Metabolic Signature of Arrhythmogenic Cardiomyopathy

Arrhythmogenic cardiomyopathy (ACM) is a genetic-based cardiac disease accompanied by severe ventricular arrhythmias and a progressive substitution of the myocardium with fibro-fatty tissue. ACM is often associated with sudden cardiac death. Due to the reduced penetrance and variable expressivity, the presence of a genetic defect is not conclusive, thus complicating the diagnosis of ACM. Recent studies on human induced pluripotent stem cells-derived cardiomyocytes (hiPSC-CMs) obtained from ACM individuals showed a dysregulated metabolic status, leading to the hypothesis that ACM pathology is characterized by an impairment in the energy metabolism. However, despite efforts having been made for the identification of ACM specific biomarkers, there is still a substantial lack of information regarding the whole metabolomic profile of ACM patients. The aim of the present study was to investigate the metabolic profiles of ACM patients compared to healthy controls (CTRLs). The targeted Biocrates AbsoluteIDQ^® p180 assay was used on plasma samples. Our analysis showed that ACM patients have a different metabolome compared to CTRLs, and that the pathways mainly affected include tryptophan metabolism, arginine and proline metabolism and beta oxidation of fatty acids. Altogether, our data indicated that the plasma metabolomes of arrhythmogenic cardiomyopathy patients show signs of endothelium damage and impaired nitric oxide (NO), fat, and energy metabolism.

1H NMR-based metabolomic study of the effects of flavonoids on citrinin production by Monascus

Monascus comprises purple-red molds. Various compounds can be obtained from these species, including statins and food-safe yellow, red, and orange pigments. However, the secondary metabolite citrinin, a mycotoxin, is produced during the late stages of growth. Citrinin biosynthesis should be reduced to apply Monascus pigments safely. Fortunately, this can be achieved by the addition of flavonoids (genistein, daidzein, apigenin, and kaempferol). However, the effects of these flavonoids on other metabolites remain unknown. Here, we report a ¹H NMR-based multivariate metabolomic analysis of the effects of flavonoids on mycotoxin citrinin production by Monascus. Fifteen metabolites involved in lysine and arginine biosynthesis and alanine, aspartate, glutamate, biotin, arginine, proline, and glutathione metabolism were detected. The reduction in glutamate, aspartate, biotin, and 2-phosphoglycerate content suggested their association with the citrinin reduction mechanism. This study identifies the citrinin production pathway in Monascus and will aid in the development of citrinin-control methods.

Noninvasive Epidermal Metabolite Profiling

A buffer placed in brief contact in the skin was assayed by ¹H NMR spectroscopy. We found that this passive extraction of the skin surface yields abundant metabolites. Metabolites of the skin surface originate from a variety of sources, including the sweat gland, which produces lactate from the glucose received from its capillary bed. Little is known about how metabolites resident on and within the skin surface respond to a metabolic or hemodynamic perturbation. As a possible application of epidermal metabolite profiling, we asked whether metabolites extracted from the skin surface are indicative of heart failure. The levels of lactate and other molecules were significantly lower in patients in heart failure than in individuals who reported healthy heart function, possibly due to reduced blood flow to the sweat gland resulting in a lack of tissue perfusion. Most amino acids were unchanged in levels, except for glycine and serine that increased as a percentage of all amino acids. These results have the potential in the long term to help decide the extent to which a patient has heart failure for which objective measures are lacking. Moreover, the results suggest that epidermal metabolite profiling may be useful for other assessments of human health.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure

The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers.

Synergistic therapeutic effects of Duzhong Jiangya Tablets and amlodipine besylate combination in spontaneously hypertensive rats using 1 H-NMR- and MS-based metabolomics

Duzhong Jiangya Tablet (DJT) composed of Eucommia ulmoides Oliv. and several other traditional Chinese medicines is a Chinese herbal compound, which is clinically used to treat hypertension. The aim of this study was to evaluate the antihypertensive effect of DJT and amlodipine besylate (AB) on the synergistic treatment of spontaneously hypertensive rats (SHRs), and to explore its antihypertensive mechanism. The synergistic therapeutic effect of DJT in combination with AB on SHR was studied using two metabolomics methods based on mass spectrum (MS) and nuclear magnetic resonance. Metabolomics analysis of plasma, urine, liver, and kidney and the combination of orthogonal partial least squares discriminant analysis was performed to expose potential biomarkers. Then, the overall metabolic characteristics and related abnormal metabolic pathways in hypertensive rats were constructed. Blood pressure measurements showed that DJT combined with AB has better effects in treating hypertension than it being alone. A total of 30 biomarkers were identified, indicating that hypertension disrupted the balance of multiple metabolic pathways in the body, and that combined administration restored metabolite levels better than their administration alone. The changes of biomarkers revealed the synergistic therapeutic mechanism of DJT combined with AB, which provided a reference for the combination of Chinese and Western medicines.

Metabolic Profiling Associates with Disease Severity in Nonischemic Dilated Cardiomyopathy

BACKGROUND

Metabolomic profiling may have diagnostic and prognostic value in heart failure. This study investigated whether targeted blood and urine metabolomics reflects disease severity in patients with nonischemic dilated cardiomyopathy (DCM) and compared its incremental value on top of N-terminal prohormone of brain natriuretic peptide (NT-proBNP).

METHODS AND RESULTS

A total of 149 metabolites were measured in plasma and urine samples of 273 patients with DCM and with varying stages of disease (patients with DCM and normal left ventricular reverse remodeling, n = 70; asymptomatic DCM, n = 72; and symptomatic DCM, n = 131). Acylcarnitines, sialic acid and glutamic acid are the most distinctive metabolites associated with disease severity, as repeatedly revealed by unibiomarker linear regression, sparse partial least squares discriminant analysis, random forest, and conditional random forest analyses. However, the absolute difference in the metabolic profile among groups was marginal. A decision-tree model based on the top metabolites did not surpass NT-proBNP in classifying stages. However, a combination of NT-proBNP and the top metabolites improved the decision tree to distinguish patients with DCM and left ventricular reverse remodeling from symptomatic DCM (area under the curve 0.813 ± 0.138 vs 0.739 ± 0.114; P = 0.02).

CONCLUSION

Functional cardiac recovery is reflected in metabolomics. These alterations reveal potential alternative treatment targets in advanced symptomatic DCM. The metabolic profile can complement NT-proBNP in determining disease severity in nonischemic DCM.

Serum Concentrations of Citrate, Tyrosine, 2- and 3- Hydroxybutyrate are Associated with Increased 3-Month Mortality in Acute Heart Failure Patients

Considering the already established relationship between the extent of the metabolic dysfunction and the severity of heart failure (HF), it is conceivable that the metabolomic profile of the serum may have a prognostic capacity for 3-month mortality in acute heart failure (AHF). Out of 152 recruited patients, 130 serum samples were subjected to the metabolomic analyses. The 3-month mortality rate was 24.6% (32 patients). Metabolomic profiling by nuclear magnetic resonance spectroscopy found that the serum levels of 2-hydroxybutyrate (2-HB), 3-hydoxybutyrate (3-HB), lactate, citrate, and tyrosine, were higher in patients who died within 3 months compared to those who were alive 3 months after onset of AHF, which was confirmed by univariable logistic regression analyses (p = 0.009, p = 0.005, p = 0.008, p<0.001, and p<0.001, respectively). These associations still remained significant for all tested metabolites except for lactate after adjusting for established prognostic parameters in HF. In conclusion, serum levels of 2-HB, 3-HB, tyrosine, and citrate measured at admission are associated with an increased 3-month mortality rate in AHF patients and might thus be of prognostic value in AHF.

Untargeted metabolomics: an overview of its usefulness and future potential in prenatal diagnosis

INTRODUCTION

Metabolomics opens up new avenues for biomarker discovery in different branches of medicine, including perinatology. Chromosomal aberration, preterm delivery (PTD), congenital heart defects, spina bifida, chorioamnionitis, and low birth weight are the main perinatal pathologies. Investigations using untargeted metabolomics have found the candidate metabolites for diagnostic biomarkers. Areas covered: This review describes areas of prenatal diagnosis in which untargeted metabolomics has been used. Data on the disease, type of sample, techniques used, number of samples used in the study, and metabolites obtained including the sign of their regulation are summarized. Expert commentary: Untargeted metabolomics is a powerful tool which can shed a new light on prenatal diagnostics. It helps to discover affected metabolic pathways what may help to reveal disease pathogenesis and propose potential biomarkers. Among others, glycerol and 2- and 3-hydroxybutyrate were proposed as markers of chromosomal aberration. Serum metabolic signature of PTD was characterized by increased lipids and decreased levels of hypoxanthine, tryptophane, and pyroglutamic acid. Lower level lipids and vitamin D3 metabolites together with increased bilirubin level in maternal serum were associated with macrosomia. However, to give a real value to those assays and allow their clinical application multicenter, large cohort validation studies are necessary.

Analysis of metabolic profiles of generalized aggressive periodontitis

BACKGROUND AND OBJECTIVE

The specific pathogenesis of generalized aggressive periodontitis (GAgP) has not yet been clarified, and few studies have focused on the association between GAgP and metabolomics. To elucidate the roles of metabolic profiles in the status of GAgP, this study aimed to identify the differential metabolic profiles between patients with GAgP and healthy controls using an untargeted metabolomic profiling method.

MATERIAL AND METHODS

Serum and gingival crevicular fluid samples were collected from healthy controls (n = 20) and patients with GAgP (n = 20) in this cross-sectional study. The relative levels of biomarkers in the samples were measured by gas chromatography-mass spectrometry. Principal components analysis and orthogonal partial least-squares discriminant analysis were used for statistical analysis. Metabolites were analysed qualitatively using the FiehnLib and NIST databases. Full-mouth probing depth and clinical attachment loss were recorded as indexes of periodontal disease.

RESULTS

A total of 349 metabolites were qualitatively detected in the gingival crevicular fluid samples, and 200 metabolites were detected in the serum samples. Compared with healthy controls, patients with GAgP showed significant increases in serum urea and allo-inositol levels. In contrast, glutathione, 2,5-dihydroxybenzaldehyde, adipic acid and 2-deoxyguanosine levels were decreased in patients with GAgP. In the gingival crevicular fluid samples, noradrenaline, uridine, α-tocopherol, dehydroascorbic acid, xanthine, galactose, glucose-1-phosphate and ribulose-5-phosphate levels were increased in patients with GAgP, while thymidine, glutathione and ribose-5-phosphate levels were decreased.

CONCLUSION

The metabolomics analysis by gas chromatography-mass spectrometry is an effective and minimally non-invasive way to differentiate the metabolites characteristic of patients with GAgP. Both serum and gingival crevicular fluid metabolomics are significantly different between patients with GAgP and healthy controls. These metabolic profiles have great potential in detecting GAgP and helping to understand its underlying mechanisms.

Analysis of pharmaceuticals and small molecules in aqueous humor

Aqueous humor (AH) is a transparent fluid found in the anterior chamber of the eye. The circulating AH nourishes the cornea and lens and removes the metabolic waste moving through the ocular chambers and drains from the eye to the venous blood. Analysis of drugs in AH is necessary to evaluate their pharmacokinetics parameters, which may be crucial to avoid potential adverse effects. Analysis of endogenous components of AH may help to understand its physiology as well as changes evoked by pathological situation. This review describes analytical methods used for determination of pharmaceuticals and small endogenous molecules in AH, focusing on sample preparation procedures and analytical techniques. Studies on human and animal samples are included. After inspection and filtering of records found in PubMed about 100 research papers were selected to review. In these articles AH samples of human and rabbit origin were studied most often. Sample evaporation and reconstitution in smaller solvent volume was the most popular method for analyte pre-concentration. Acetonitrile, methanol or mixture of both solvents were used most often for protein precipitation.

Heart specific PGC-1α deletion identifies metabolome of cardiac restricted metabolic heart failure

Aims

Heart failure (HF) is associated with drastic changes in metabolism leading to a cardiac energy deficiency well as maladaptive changes in multiple other tissues. It is still unclear which of these changes originates from cardiomyocyte metabolic remodelling or whether they are induced secondarily by systemic factors. Our aim here was to induce cardiac restricted metabolic changes mimicking those seen in HF and to characterize the associated metabolite changes in the heart, circulation, and peripheral tissues.

Methods and results

We generated a cardiac specific PGC-1α knockout mice (KO) to specifically induce metabolic dysregulation typically accompanied by HF and performed a non-targeted LC-MS metabolite profiling analysis of heart, plasma, liver, and skeletal muscle to identify metabolites associated with cardiac specific metabolic remodelling. The KO animals developed a progressive cardiomyopathy with cardiac dilatation leading to fatal HF. At 17 weeks of age, when significant remodelling had occurred but before the onset of HF, isolated PGC-1α deficient cardiomyocytes had suppressed glucose and fatty acid oxidation as well as blunted anaerobic metabolism. KO hearts displayed a distinctive metabolite profile with 92 significantly altered molecular features including metabolite changes in energy metabolism, phospholipid metabolism, amino acids, and oxidative stress signalling. Some of the metabolite changes correlated with the specific parameters of cardiac function. We did not observe any significant alterations in the metabolomes of the other measured tissues or in plasma.

Conclusions

Heart specific PGC-1α KO induces metabolic, functional, and structural abnormalities leading to dilating cardiomyopathy and HF. The metabolic changes were limited to the cardiac tissue indicating that cardiomyocyte metabolic remodelling is not sufficient to evoke the body wide metabolic alterations usually associated with HF.

LC-MS-based serum fingerprinting reveals significant dysregulation of phospholipids in chronic heart failure

Cardiac and extracardiac lipid metabolism is known to be significantly altered in the course of the heart failure with reduced ejection fraction (HF-REF), however the precise mechanisms are not fully elucidated. The aim of the study was to use of untargeted metabolomics to identify and validate changes in the blood metabolites profile, occurring as a result of HF-REF development. The analyses were performed first in the derivation set (36 chronic HF-REF patients and 19 controls without the disease) and repeated in validation cohort (31 chronic HF-REF patients and 20 controls). Independent analyses of both sets revealed statistically significant decline in intensities of phosphatidylcholine (PC): 34:4 and 36:5, lysophosphatidylcholine (lyso-PC): 14:0, 15:0, 18:0, 18:2, 20:3, lysophosphatidylethanolamine (lyso-PE): 18:1 and 18:2 in chronic HF-REF patients. More symptomatic patients and those with ischaemic etiology of HF-REF presented greater deficit in phospholipids (PLs) intensities. The decrease of identified PLs intensities (as compared to controls) correlated with decreased serum cholesterol level, impaired renal function, reduced exercise capacity, enhanced ventilatory response and metabolic parameters associated with altered fatty acids oxidation. In multiple regression analysis PLs deficit was significantly associated with age, carnitines serum intensity, renal function, uric acid, cholesterol level. In conclusion, HF-REF is associated with significant disturbances in phospholipids metabolism. Greater reduction in serum intensities of particular identified PLs is associated with older age, worse clinical condition, impaired oxidative muscle metabolism and enhanced catabolic status.

Proton Nuclear Magnetic Resonance (¹H-NMR)-Based Metabolomic Evaluation of Human Renal Allografts from Donations After Circulatory Death

BACKGROUND Delayed graft function (DGF) is a common complication that impairs allograft function after kidney transplantation. However, the mechanism of DGF remains unclear. Nuclear magnetic resonance (NMR)-based analysis has been widely used in recent times to assess changes in metabolite levels. MATERIAL AND METHODS Samples of perfusate from allografts donated after circulatory death were collected prior to transplantation, during static cold storage. ¹H-NMR-based metabolomics combined with the statistical methods, orthogonal partial least-squares discriminant analysis (OPLS-DA), and principle-component analysis (PCA), were employed to test different levels of metabolites between the allografts that exhibited DGF and those that exhibited immediate graft function (IGF). RESULTS The study population consisted of 36 subjects, 11 with DGF and 25 with IGF. Of the 37 detected and identified metabolites, a-glucose and citrate were significantly elevated in the perfusate of DGF allografts, and taurine and betaine were significantly decreased. CONCLUSIONS ¹H-NMR analysis of DGF and IGF perfusates revealed some significant differences in their metabolite profiles, which may help explain the mechanisms of kidney ischemia-reperfusion injury and DGF.

Caffeoylxanthiazonoside exerts cardioprotective effects during chronic heart failure via inhibition of inflammatory responses in cardiac cells

Caffeoylxanthiazonoside (CYT) is an active constituent isolated from the fruit of the Xanthium strumarium L plant. The aim of the present study was to investigate the cardioprotective effects of oral administration of CYT on chronic heart failure (CHF) and its underlying mechanisms. A rat model of CHF was first established, and cardiac function indices, including the heart/body weight index, left heart/body weight index, fractional shortening (FS), ejection fraction (EF), cardiac output (CO) and heart rate (HR), were subsequently determined by cardiac ultrasound. Serum levels of lactate dehydrogenase (LDH) and creatine kinase (CK), and the levels of pro-inflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-6 and IL-1β in heart tissues and cardiac microvascular endothelial cells (CMECs) were determined using ELISA. In addition, the protein expression levels of nuclear factor-κB (NF-κB) signaling pathway members were determined by western blotting in CMECs. The results demonstrated that oral administration of 10, 20, 40 mg/kg CYT significantly reduced cardiac hypertrophy and reversed FS, EF, CO and HR when compared with CHF model rats. In addition, CYT administration significantly decreased the levels of TNF-α, IL-6 and IL-1β in heart tissues, as well as serum LDH and CK levels. Furthermore, exposure of CMECs to 20, 40 and 80 µg/ml CYT significantly decreased the production of TNF-α, IL-1β and IL-6. The protein expression levels of cytoplasmic NF-κB p65 and IκB were upregulated, while nuclear NF-κB p65 was downregulated following treatment of CMECs with 20, 40 and 80 µg/ml CYT when compared with untreated CHF model controls. In conclusion, the results of the current study suggest that CYT demonstrates cardioprotective effects in CHF model rats by suppressing the expression of pro-inflammatory cytokines and the NF-κB signaling pathway.

LC-MS-Based Metabolic Fingerprinting of Aqueous Humor

Aqueous humor (AH) is a transparent fluid which fills the anterior and posterior chambers of the eye. It supplies nutrients and removes metabolic waste from avascular tissues in the eye. Proper homeostasis of AH is required to maintain adequate intraocular pressure as well as optical and refractive properties of the eye. Application of metabolomics to study human AH may improve knowledge about the molecular mechanisms of eye diseases. Until now, global analysis of metabolites in AH has been mainly performed using NMR. Among the analytical platforms used in metabolomics, LC-MS allows for the highest metabolome coverage. The aim of this study was to develop a method for extraction and analysis of AH metabolites by LC-QTOF-MS. Different protocols for AH preparation were tested. The best results were obtained when one volume of AH was mixed with one volume of methanol : ethanol (1 : 1). In the final method, 2 µL of extracted sample was analyzed by LC-QTOF-MS. The method allowed for reproducible measurement of over 1000 metabolic features. Almost 250 metabolites were identified in AH and assigned to 47 metabolic pathways. This method is suitable to study the potential role of amino acids, lipids, oxidative stress, or microbial metabolites in development of ocular diseases.