Introduction to metabolomics and its applications in ophthalmology

Luteoloside mitigates premature age-related macular degeneration by suppressing p53-p21-Rb1 axis: Insights from transcriptomic analysis, serum metabolomics and gut microbiota analysis

Transcriptomics of dry age-related macular degeneration (AMD) patients with premature aging revealed the upregulated pathways involved in glycerolipid metabolism, tyrosine metabolism, and pentose and glucuronate interconversion. To investigate natural strategies for modulating these implicated pathways, we examined the impact and underlying mechanism of luteoloside on premature AMD using a stress-induced premature senescence (SIPS)-associated AMD animal model in middle-aged mice that mimicked the dysregulated pathways observed in dry AMD patients with premature aging. Luteoloside supplementation resulted in a significant reduction in serum levels of the pro-inflammatory cytokine IL-1β and lipofuscin, along with increased serum activity of the antioxidant enzyme superoxide dismutase (SOD) and elevated levels of pigment epithelium-derived factor (PEDF), and preserved retinal thickness and structure in AMD mice. Furthermore, luteoloside supplementation effectively reversed the abnormal serum levels of metabolites, particularly by reducing harmful lysophosphatidylcholine (LysoPC) and increasing beneficial 4-guanidinobutanoic acid. In addition to its impact on metabolites, luteoloside modulated the composition of gut microbiota, promoting the enrichment of beneficial bacterial populations, including Lactobacillus, while reducing the abundance of harmful bacterial populations, including Bacteroides. Overall, our findings highlight the potential of luteoloside supplementation in regulating the dysregulated intestinal microbiota and metabolites in premature AMD, thereby reducing ocular levels of senescence-associated secretory phenotype (SASP) factors through the suppression of the p53-p21-retinoblastoma protein 1 (Rb1) axis.

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods

Metabolomics, the study of small-molecule metabolites within biological systems, has become a potent instrument for understanding cellular processes. Despite its profound insights into health, disease, and drug development, identifying the protein partners for metabolites, especially dietary phytochemicals, remains challenging. In the present study, we introduced an innovative in silico, structure-based target prediction approach to efficiently predict protein targets for metabolites. We analyzed 27 blood serum metabolites from nutrition intervention studies' blueberry-rich diets, known for their health benefits, yet with elusive mechanisms of action. Our findings reveal that blueberry-derived metabolites predominantly interact with Carbonic Anhydrase (CA) family proteins, which are crucial in acid-base regulation, respiration, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism. Molecular docking showed that these metabolites bind to a common pocket on CA proteins, with binding energies ranging from -5.0 kcal/mol to -9.0 kcal/mol. Further molecular dynamics (MD) simulations confirmed the stable binding of metabolites near the Zn binding site, consistent with known compound interactions. These results highlight the potential health benefits of blueberry metabolites through interaction with CA proteins.

Method Development for Omics Analyses using Schirmer Strips

PURPOSE

The aim of this article was to investigate whether Schirmer strips gathered during clinical dry eye examinations can be prepared for omics analyses in a standardized way, to adjust for variations in tear volume and enable two separate omics analyses from the same sample. In addition, the intention was to investigate whether fluorescein dye instillation in the eyes gave bias effects on metabolomic analysis.

METHODS

Twelve samples from six individuals, with normal or reduced tear production, were collected. Half of the samples were harvested after instillation of fluorescein in the eye. Each strip was divided in half along the length and prepared with a new method for extracting tear content from the Schirmer strip. The new method was established to compensate for different dilutions of metabolites in varying Schirmer strip wetting levels when using identical extraction volume for all samples. Metabolomic data were compared in samples with and without fluorescein dye and Schirmer strips ranging from 1 to 35 mm wetting levels using a global LC-MS method.

RESULTS

All samples were successfully analyzed with an average of ∼350 relevant features detected per sample after using both positive and negative electrospray ionization mode, despite low tear volumes in some samples and that only one half of the Schirmer strips were used. Principal component analysis plots and heatmaps revealed no bias effects of fluorescein dye presence or different Schirmer strip values when using the proposed method.

CONCLUSION

A high number of relevant metabolomic features can be extracted from longitudinally cut halves of Schirmer strips, which may enable analyses with more than one omics modality from the same sample. With the pre-analytical method described, Schirmer strips can be used for metabolomic analyses even in cases of very low or high tear volume with or without fluorescence.

A Look into Ocular Diseases: The Pivotal Role of Omics Sciences in Ophthalmology Research

Precision medicine is a new medical approach which considers both population characteristics and individual variability to provide customized healthcare. The transition from traditional reactive medicine to personalized medicine is based on a biomarker-driven process and a deep knowledge of biological mechanisms according to which the development of diseases occurs. In this context, the advancements in high-throughput omics technologies represent a unique opportunity to discover novel biomarkers and to provide an unbiased picture of the biological system. One of the medical fields in which omics science has started to be recently applied is that of ophthalmology. Ocular diseases are very common, and some of them could be highly disabling, thus leading to vision loss and blindness. The pathogenic mechanism of most ocular diseases may be dependent on various genetic and environmental factors, whose effect has not been yet completely understood. In this context, large-scale omics approaches are fundamental to have a comprehensive evaluation of the whole system and represent an essential tool for the development of novel therapies. This Review summarizes the recent advancements in omics science applied to ophthalmology in the last ten years, in particular by focusing on proteomics, metabolomics and lipidomics applications from an analytical perspective. The role of high-efficiency separation techniques coupled to (high-resolution) mass spectrometry ((HR)MS) is also discussed, as well as the impact of sampling, sample preparation and data analysis as integrating parts of the analytical workflow.

Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites

The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti's crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in an external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study.

Dietary metabolome profiles of a Healthy Australian Diet and a Typical Australian Diet: protocol for a randomised cross-over feeding study in Australian adults

INTRODUCTION

Traditional dietary assessment methods such as 24-hour recalls and food frequency questionnaires rely on self-reported data and are prone to error, bias and inaccuracy. Identification of dietary metabolites associated with different dietary patterns can provide objective markers of whole diet patterns that account for metabolism and individual responses to dietary interventions. Additionally, few studies have investigated country-specific healthy and unhealthy dietary patterns using metabolomics. Therefore, the current study aims to identify urinary and plasma metabolites that characterise a 'healthy' (aligned with current national dietary guidelines) and an 'unhealthy' dietary pattern (Typical Australian Diet) in Australian adults.

METHODS AND ANALYSIS

The Diet Quality Feeding Study (DQFS) is an 8-week cross-over feeding study that will recruit 40 healthy adults from the Hunter region (NSW, Australia). Data collected includes biospecimens (whole blood, urine, stool) for quantification of dietary metabolite biomarkers; questionnaires (medical history/demographic, physical activity, quality of life); physical measures (anthropometry, body composition, waist circumference, blood pressure, arterial pressure); skin carotenoids and dietary intake (24-hour recalls, food frequency questionnaire). Participants will attend the research facility every 2 weeks (end of the run-in, each diet intervention and washout period) for collection of physical measures. All food will be provided to participants for each dietary intervention period, and participants will return to their usual diet during the run-in and washout periods. Targeted and untargeted metabolomics using liquid chromatography-mass spectrometry and/or proton nuclear magnetic resonance (1H-NMR) spectroscopy will be used to identify metabolites in biospecimens associated with dietary intake.

ETHICS AND DISSEMINATION

This study is approved by the Hunter New England Human Research Ethics Committee (HNEHREC; 2022/ETH01649) and the University of Newcastle's Human Research Ethics Committee (HREC; H-2022-0330). Findings will be disseminated to study participants, funding bodies supporting the DQFS, peer-review publications and presented at scientific conferences within the field of research.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clinical Trials Registry (ACTRN12622001321730).

Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema

BACKGROUND

Macular edema (ME) is a major complication of retinal disease with multiple mechanisms involved in its development. This study aimed to investigate the metabolite profile of aqueous humor (AH) in patients with ME of different etiologies and identify potential metabolite biomarkers for early diagnosis of ME.

METHODS

Samples of AH were collected from 60 patients with ME and 20 age- and sex-matched controls and analyzed by liquid chromatography-mass spectrometry (LC/MS)-based metabolomics. A series of univariate and multivariate statistical analyses were performed to identify differential metabolites and enriched metabolite pathways.

RESULTS

The metabolic profile of AH differed significantly between ME patients and healthy controls, and differentially expressed metabolites were identified. Pathway analysis revealed that these differentially expressed metabolites are mainly involved in lipid metabolism and amino acid metabolism. Moreover, significant differences were identified in the metabolic composition of AH from patients with ME due to different retinal diseases including age-related macular degeneration (AMD-ME), diabetic retinopathy (DME) and branch retinal vein occlusion (BRVO-ME). In total, 39 and 79 etiology-specific altered metabolites were identified for AMD-ME and DME, respectively. Finally, an AH-derived machine learning-based diagnostic model was developed and successfully validated in the test cohort with an area under the receiver operating characteristic (ROC) curve of 0.79 for AMD-ME, 0.94 for DME and 0.77 for BRVO-ME.

CONCLUSIONS

Our study illustrates the potential underlying metabolic basis of AH of different etiologies across ME populations. We also identify AH-derived metabolite biomarkers that may improve the differential diagnosis and treatment stratification of ME patients with different etiologies.

Metabolomics in Corneal Diseases: A Narrative Review from Clinical Aspects

Corneal pathologies may have subtle manifestations in the initial stages, delaying diagnosis and timely treatment. This can lead to irreversible visual loss. Metabolomics is a rapidly developing field that allows the study of metabolites in a system, providing a complementary tool in the early diagnosis and management of corneal diseases. Early identification of biomarkers is key to prevent disease progression. The advancement of nuclear magnetic resonance and mass spectrometry allows the identification of new biomarkers in the analysis of tear, cornea, and aqueous humor. Novel perspectives on disease mechanisms are identified, which provide vital information for potential targeted therapies in the future. Current treatments are analyzed at a molecular level to offer further information regarding their efficacy. In this article, we provide a comprehensive review of the metabolomic studies undertaken in the cornea and various pathologies such as dry eye disease, Sjogren’s syndrome, keratoconus, post-refractive surgery, contact lens wearers, and diabetic corneas. Lastly, we discuss the exciting future that metabolomics plays in cornea research.

Metabolomics facilitates the discovery of metabolic profiles and pathways for myopia: A systematic review

BACKGROUND

Myopia is one of the major eye disorders and the global burden is increasing rapidly. Our purpose is to systematically summarize potential metabolic biomarkers and pathways in myopia to facilitate the understanding of disease mechanisms as well as the discovery of novel therapeutic measures.

METHODS

Myopia-related metabolomics studies were searched in electronic databases of PubMed and Web of Science until June 2021. Information regarding clinical and demographic characteristics of included studies and metabolomics findings were extracted. Myopia-related metabolic pathways were analysed for differential metabolic profiles, and the quality of included studies was assessed based on the QUADOMICS tool. Pathway analyses of differential metabolites were performed using bioinformatics tools and online software such as the Metaboanalyst 5.0.

RESULTS

The myopia-related metabolomics studies included in this study consisted of seven human and two animal studies. The results of the study quality assessment showed that studies were all phase I studies and all met the evaluation criteria of 70% or more. The myopia-control serum study identified 23 differential metabolites with the Sphingolipid metabolism pathway beings enriched. The high myopia-cataract aqueous humour study identified 40 differential metabolites with the Arginine biosynthesis pathway being enriched. The high myopia-control serum study identified 43 differential metabolites and 4 pathways were significantly associated with metabolites including Citrate cycle; Alanine, aspartate and glutamate metabolism; Glyoxylate and dicarboxylate metabolism; Biosynthesis of unsaturated fatty acids (all P value < 0.05).

CONCLUSIONS

This study summarizes potential metabolic biomarkers and pathways in myopia, providing new clues to elucidate disease mechanisms.

New insight of metabolomics in ocular diseases in the context of 3P medicine

Metabolomics refers to the high-through untargeted or targeted screening of metabolites in biofluids, cells, and tissues. Metabolome reflects the functional states of cells and organs of an individual, influenced by genes, RNA, proteins, and environment. Metabolomic analyses help to understand the interaction between metabolism and phenotype and reveal biomarkers for diseases. Advanced ocular diseases can lead to vision loss and blindness, reducing patients' quality of life and aggravating socio-economic burden. Contextually, the transition from reactive medicine to the predictive, preventive, and personalized (PPPM / 3P) medicine is needed. Clinicians and researchers dedicate a lot of efforts to explore effective ways for disease prevention, biomarkers for disease prediction, and personalized treatments, by taking advantages of metabolomics. In this way, metabolomics has great clinical utility in the primary and secondary care. In this review, we summarized much progress achieved by applying metabolomics to ocular diseases and pointed out potential biomarkers and metabolic pathways involved to promote 3P medicine approach in healthcare.

Emerging applications of bioinformatics and artificial intelligence in the analysis of biofluid markers involved in retinal occlusive diseases: a systematic review

PURPOSE

To review the literature on the application of bioinformatics and artificial intelligence (AI) for analysis of biofluid biomarkers in retinal vein occlusion (RVO) and their potential utility in clinical decision-making.

METHODS

We systematically searched MEDLINE, Embase, Cochrane, and Web of Science databases for articles reporting on AI or bioinformatics in RVO involving biofluids from inception to August 2021. Simple AI was categorized as logistics regressions of any type. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tools.

RESULTS

Among 10,264 studies screened, 14 eligible articles, encompassing 578 RVO patients, met the inclusion criteria. The use and reporting of AI and bioinformatics was heterogenous. Four articles performed proteomic analyses, two of which integrated AI tools such as discriminant analysis, probabilistic clustering, and string pathway analysis. A metabolomic study used AI tools for clustering, classification, and predictive modeling such as orthogonal partial least squares discriminant analysis. However, most studies used simple AI (n = 9). Vitreous humor sample levels of interleukin-6 (IL-6), vascular endothelial growth factor (VEGF), and aqueous humor levels of intercellular adhesion molecule-1 and IL-8 were implicated in the pathogenesis of branch RVO with macular edema. IL-6 and VEGF may predict visual acuity after intravitreal injections or vitrectomy, respectively. Metabolomics and Kyoto Encyclopedia of Genes and Genomes enrichment analysis identified the metabolic signature of central RVO to be related to lower aqueous humor concentration of carbohydrates and amino acids. Risk of bias was low or moderate for included studies.

CONCLUSION

Bioinformatics has applications for analysis of proteomics and metabolomics present in biofluids in RVO with AI for clinical decision-making and advancing the future of RVO precision medicine. However, multiple limitations such as simple AI use, small sample volume, inconsistent feasibility of office-based sampling, lack of longitudinal follow-up, lack of sampling before and after RVO, and lack of healthy controls must be addressed in future studies.

Diagnosis and biomarkers for ocular tuberculosis: From the present into the future

Tuberculosis is an airborne disease caused by Mycobacterium tuberculosis (Mtb) and can manifest both pulmonary and extrapulmonary disease, including ocular tuberculosis (OTB). Accurate diagnosis and swift optimal treatment initiation for OTB is faced by many challenges combined with the lack of standardized treatment regimens this results in uncertain OTB outcomes. The purpose of this study is to summarize existing diagnostic approaches and recently discovered biomarkers that may contribute to establishing OTB diagnosis, choice of anti-tubercular therapy (ATT) regimen, and treatment monitoring. The keywords ocular tuberculosis, tuberculosis, Mycobacterium, biomarkers, molecular diagnosis, multi-omics, proteomics, genomics, transcriptomics, metabolomics, T-lymphocytes profiling were searched on PubMed and MEDLINE databases. Articles and books published with at least one of the keywords were included and screened for relevance. There was no time limit for study inclusion. More emphasis was placed on recent publications that contributed new information about the pathogenesis, diagnosis, or treatment of OTB. We excluded abstracts and articles that were not written in the English language. References cited within the identified articles were used to further supplement the search. We found 10 studies evaluating the sensitivity and specificity of interferon-gamma release assay (IGRA), and 6 studies evaluating that of tuberculin skin test (TST) in OTB patients. IGRA (Sp = 71-100%, Se = 36-100%) achieves overall better sensitivity and specificity than TST (Sp = 51.1-85.7%; Se = 70.9-98.5%). For nuclear acid amplification tests (NAAT), we found 7 studies on uniplex polymerase chain reaction (PCR) with different Mtb targets, 7 studies on DNA-based multiplex PCR, 1 study on mRNA-based multiplex PCR, 4 studies on loop-mediated isothermal amplification (LAMP) assay with different Mtb targets, 3 studies on GeneXpert assay, 1 study on GeneXpert Ultra assay and 1 study for MTBDRplus assay for OTB. Specificity is overall improved but sensitivity is highly variable for NAATs (excluding uniplex PCR, Sp = 50-100%; Se = 10.5-98%) as compared to IGRA. We also found 3 transcriptomic studies, 6 proteomic studies, 2 studies on stimulation assays, 1 study on intraocular protein analysis and 1 study on T-lymphocyte profiling in OTB patients. All except 1 study evaluated novel, previously undiscovered biomarkers. Only 1 study has been externally validated by a large independent cohort. Future theranostic marker discovery by a multi-omics approach is essential to deepen pathophysiological understanding of OTB. Combined these might result in swift, optimal and personalized treatment regimens to modulate the heterogeneous mechanisms of OTB. Eventually, these studies could improve the current cumbersome diagnosis and management of OTB.

Effect of environmental conditions on bloodstain metabolite analysis

Establishing a correlation between environmental variables and chemical change can significantly improve the quality of research in multiple fields. Among various environmental variables, temperature and humidity are closely related to the rate of chemical reactions. This study aimed to confirm changes in metabolite markers that were previously discovered in other temperature and humidity environment conditions and to confirm the possibility that they could act as markers. After blood collection from the subjects and bloodstain preparation, the quantitative values of the bloodstain metabolites were confirmed (when the age of the bloodstain was within a month) under eight environmental conditions (4 °C/30%, 4 °C/60%, 25 °C/30%, 25 °C/60%, 25 °C/90%, 40 °C/30%, 40 °C/60%, and 40 °C/90%). Age-of-bloodstain estimation models were constructed to confirm the applicability of bloodstain metabolites as markers for bloodstain age in various environments. The average concentration of metabolite markers exhibited a decreasing trend with the age of the bloodstain, which transformed into an increasing trend from day 7 onwards. In terms of temperature and humidity, 25 °C and 90%, respectively, showed the most dissimilar metabolite change pattern compared to other conditions. The age-of-bloodstain estimation models developed here have an R-square value of up to 0.92 for each condition and an R-square value of 0.71 when all environmental conditions were combined. The findings herein highlight the immense potential of blood metabolites for field application, confirming the possibility of predicting metabolite changes from the rates of their chemical reactions and validating the importance of metabolites as age-of-bloodstain markers under various environmental conditions.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Ethnic disparities attributed to the manifestation in and response to type 2 diabetes: insights from metabolomics

Type 2 diabetes (T2D) associated health disparities among different ethnicities have long been known. Ethnic variations also exist in T2D related comorbidities including insulin resistance, vascular complications and drug response. Genetic heterogeneity, dietary patterns, nutrient metabolism and gut microbiome composition attribute to ethnic disparities in both manifestation and progression of T2D. These factors differentially regulate the rate of metabolism and metabolic health. Metabolomics studies have indicated significant differences in carbohydrate, lipid and amino acid metabolism among ethnicities. Interestingly, genetic variations regulating lipid and amino acid metabolism might also contribute to inter-ethnic differences in T2D. Comprehensive and comparative metabolomics analysis between ethnicities might help to design personalized dietary regimen and newer therapeutic strategies. In the present review, we explore population based metabolomics data to identify inter-ethnic differences in metabolites and discuss how (a) genetic variations, (b) dietary patterns and (c) microbiome composition may attribute for such differences in T2D.

Pharmacokinetic-Pharmacometabolomic Approach in Early-Phase Clinical Trials: A Way Forward for Targeted Therapy in Type 2 Diabetes

Pharmacometabolomics in early phase clinical trials demonstrate the metabolic profiles of a subject responding to a drug treatment in a controlled environment, whereas pharmacokinetics measure the drug plasma concentration in human circulation. Application of the personalized peak plasma concentration from pharmacokinetics in pharmacometabolomic studies provides insights into drugs’ pharmacological effects through dysregulation of metabolic pathways or pharmacodynamic biomarkers. This proof-of-concept study integrates personalized pharmacokinetic and pharmacometabolomic approaches to determine the predictive pharmacodynamic response of human metabolic pathways for type 2 diabetes. In this study, we use metformin as a model drug. Metformin is a first-line glucose-lowering agent; however, the variation of metabolites that potentially affect the efficacy and safety profile remains inconclusive. Seventeen healthy subjects were given a single dose of 1000 mg of metformin under fasting conditions. Fifteen sampling time-points were collected and analyzed using the validated bioanalytical LCMS method for metformin quantification in plasma. The individualized peak-concentration plasma samples determined from the pharmacokinetic parameters calculated using Matlab Simbiology were further analyzed with pre-dose plasma samples using an untargeted metabolomic approach. Pharmacometabolomic data processing and statistical analysis were performed using MetaboAnalyst with a functional meta-analysis peaks-to-pathway approach to identify dysregulated human metabolic pathways. The validated metformin calibration ranged from 80.4 to 2010 ng/mL for accuracy, precision, stability and others. The median and IQR for Cmax was 1248 (849–1391) ng/mL; AUC_0-infinity was 9510 (7314–10,411) ng·h/mL, and Tmax was 2.5 (2.5–3.0) h. The individualized Cmax pharmacokinetics guided the untargeted pharmacometabolomics of metformin, suggesting a series of provisional predictive human metabolic pathways, which include arginine and proline metabolism, branched-chain amino acid (BCAA) metabolism, glutathione metabolism and others that are associated with metformin’s pharmacological effects of increasing insulin sensitivity and lipid metabolism. Integration of pharmacokinetic and pharmacometabolomic approaches in early-phase clinical trials may pave a pathway for developing targeted therapy. This could further reduce variability in a controlled trial environment and aid in identifying surrogates for drug response pathways, increasing the prediction of responders for dose selection in phase II clinical trials.

SERS optical accumulators as unified nanoplatforms for tear sampling and sensing in soft contact lenses

Tear analysis has become an invaluable asset in clinical research in order to identify and quantify novel biomarkers for a wide array of conditions. The present work is intended to take this area of study one step further by implementing an innovative sensing platform through which exploration of low-molecular-weight compounds is conducted outperforming traditional analytical technologies. With this aim, carefully engineered plasmonic nanoassemblies have been synergistically combined with molecular-sieving materials giving rise to size-selective samplers with SERS detection capabilities. These architectures have been then integrated onto hydrogel-based contact lenses and tested in simulated tear fluids in order to evidence their operational features. Through this approach, a prolonged analyte accumulation can be realized, thus providing a competitive advantage in those scenarios where concentration of biomarkers is typically low or minimum sample volumes are not met. Additionally, quenching of metabolic flux and analyte extraction protocols can be circumvented, hence preventing the intrinsic physical and chemical interferences stemming from these procedures. The obtained results render these sensing platforms as promising medical devices, and constitute a great opportunity in order to expand the clinical toolkit in tear analysis.

Prospective study of dietary intake of branched-chain amino acids and the risk of primary open-angle glaucoma

PURPOSE

Metabolomic and preclinical studies suggest that branched-chain amino acids (BCAA) may be inversely associated with neurodegenerative diseases including glaucoma. We therefore assessed the long-term association between dietary intake of BCAA and incident primary open-angle glaucoma (POAG) and POAG subtypes.

METHODS

We followed biennially participants of the Nurses' Health Study (NHS; 65 531 women: 1984-2016), Health Professionals Follow-up Study (42 254 men: 1986-2016) and NHSII (66 904 women; 1991-2017). Eligible participants were 40+ years old and reported eye examinations. Repeated validated food frequency questionnaires were used to assess dietary intake of BCAA. Incident cases of POAG and POAG subtypes defined by visual field (VF) loss and untreated intraocular pressure (IOP) were confirmed by medical record review. Multivariable-adjusted relative risks (MVRRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models.

RESULTS

We identified 1946 incident POAG cases. The pooled MVRRs of POAG for the highest quintile (Q5 = 17.1 g/day) versus lowest quintile (Q1 = 11.2 g/day) of total BCAA intake was 0.93 (95% CI, 0.73-1.19; p_trend  = 0.45; p_{heterogeneity by sex}  = 0.24). For subtypes of POAG defined by IOP level or POAG with only peripheral VF loss, no associations were observed for men or women (p_trend  ≥ 0.20); however, for the POAG subtype with early paracentral VF loss, there was a suggestion of an inverse association in women (MVRR_Q5versusQ1  = 0.80 [95% CI, 0.57-1.12; p_trend  = 0.12]) but not in men (MVRR_Q5versusQ1  = 1.38 [95% CI, 0.81-2.34; p_trend  = 0.28; p_{heterogeneity by sex}  = 0.06]).

CONCLUSION

Higher dietary intake of BCAA was not associated with POAG risk.

Metabolomics: Paving the path for personalized periodontics - A literature review

The pathogenesis of periodontal disease is governed by a multitude of factors ranging from the macroscopic to the microscopic scale. Among the factors that constitute the etiological agents of the disease, a major element is the role played by the body's metabolome-i.e., the complete collection of microscopic molecules and metabolic products of cells and tissues in the body. Being of a regulatory nature, the interplay of these molecules exerts a considerable effect on the development as well as the progression of disease, which differs in each individual based on their phenotype. Exploring this connection and application into the field of diagnostic as well as prediction of risk for periodontitis will ultimately result in a personalized standard of care for patients in the future.

In situ metabolic profile and spatial distribution of ocular tissues: New insights into dry eye disease

PURPOSE

Dry eye disease (DED) is a chronic multifactorial disorder affecting millions of people, yet the pathogenesis mechanisms still remain unclear. Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is a novel in situ visualization approach combined high-throughput mass spectrometry and molecular imaging. We aimed to explore the in situ ocular metabolic changes via MALDI-MSI to accelerate the recognition of DED pathogenesis.

METHODS

Experimental dry eye was established in Wistar rats by subcutaneous injection of scopolamine. The induction of DED was assessed by tear film breakup time, sodium fluorescein, histopathological staining and cell apoptosis. MALDI-MSI was applied to explore in situ ocular metabolomic in DED rats, and histopathological staining from same sections were used for side-by-side comparison with MALDI to annotate different tissue structures in the eye.

RESULTS

Considering the complexity of ocular tissue, we visualized the metabolites in specific ocular regions (central cornea, peripheral cornea, fornix conjunctiva, eyelid conjunctiva and aqueous humor), and identified metabolites related to DED, with information of relative abundance and spatial signatures. In addition, integrative pathway analysis illustrated that, several metabolic pathways such as glycerophospholipid, sphingolipid phenylalanine, and metabolism of glycine, serine and threonine were significantly altered in certain regions in the dry eye tissue. Moreover, we discussed how the metabolic pathways with spatiotemporal signatures might be involved in the DED process.

CONCLUSIONS

Our data exploit the advantages of in situ analysis of MALDI-MSI to accurately analyze the region-specific metabolic behaviors in DED, and provide new clues to uncover DED pathogenesis.

Plasma Metabolomics of Intermediate and Neovascular Age-Related Macular Degeneration Patients

To characterize metabolites and metabolic pathways altered in intermediate and neovascular age-related macular degeneration (IAMD and NVAMD), high resolution untargeted metabolomics was performed via liquid chromatography-mass spectrometry on plasma samples obtained from 91 IAMD patients, 100 NVAMD patients, and 195 controls. Plasma metabolite levels were compared between: AMD patients and controls, IAMD patients and controls, and NVAMD and IAMD patients. Partial least-squares discriminant analysis and linear regression were used to identify discriminatory metabolites. Pathway analysis was performed to determine metabolic pathways altered in AMD. Among the comparisons, we identified 435 unique discriminatory metabolic features. Using computational methods and tandem mass spectrometry, we identified 11 metabolic features whose molecular identities had been previously verified and confirmed the molecular identities of three additional discriminatory features. Included among the discriminatory metabolites were acylcarnitines, phospholipids, amino acids, and steroid metabolites. Pathway analysis revealed that lipid, amino acid, and vitamin metabolism pathways were altered in NVAMD, IAMD, or AMD in general, including the carnitine shuttle pathway which was significantly altered in all comparisons. Finally, few discriminatory features were identified between IAMD patients and controls, suggesting that plasma metabolic profiles of IAMD patients are more similar to controls than to NVAMD patients.

Metabolomics in Exercise and Sports: A Systematic Review

BACKGROUND

Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed.

OBJECTIVE

This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics.

METHODS

We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020.

RESULTS

Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies.

CONCLUSION

Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.

A look into retinal organoids: methods, analytical techniques, and applications

Inherited retinal diseases (IRDs) cause progressive loss of light-sensitive photoreceptors in the eye and can lead to blindness. Gene-based therapies for IRDs have shown remarkable progress in the past decade, but the vast majority of forms remain untreatable. In the era of personalised medicine, induced pluripotent stem cells (iPSCs) emerge as a valuable system for cell replacement and to model IRD because they retain the specific patient genome and can differentiate into any adult cell type. Three-dimensional (3D) iPSCs-derived retina-like tissue called retinal organoid contains all major retina-specific cell types: amacrine, bipolar, horizontal, retinal ganglion cells, Müller glia, as well as rod and cone photoreceptors. Here, we describe the main applications of retinal organoids and provide a comprehensive overview of the state-of-art analysis methods that apply to this model system. Finally, we will discuss the outlook for improvements that would bring the cellular model a step closer to become an established system in research and treatment development of IRDs.

Metabolomics in Parkinson's disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Identification of potential serum metabolic biomarkers for patient with keratoconus using untargeted metabolomics approach

This study aimed to investigate the metabolite differences between patients with keratoconus and control subjects and identify potential serum biomarkers for keratoconus using a non-targeted metabolomics approach. Venous blood samples were obtained from patients with keratoconus (n = 20) as well as from age-, gender- and race-matched control subjects (n = 20). Metabolites extracted from serum were separated and analyzed by liquid chromatography/quadrupole time-of-flight mass spectrometer. Processing of raw data and analysis of the data files was performed using Agilent Mass Hunter Qualitative software. The identified metabolites were subjected to a principal component and hierarchical cluster analysis. Appropriate statistical tests were used to analyze the metabolomic profiling data. Together, the analysis revealed that the dehydroepiandrosterone sulfate from the steroidal hormone synthesis pathway was significantly upregulated in patients with keratoconus (p < 0.05). Also, a combination of eicosanoids from the arachidonic acid pathway, mainly prostaglandin F2α, prostaglandin A2, 16,16-dimethyl prostaglandin E2, and 5-hydroxyeicosatetraenoic acid were collectively up-regulated as a group in keratoconus patients (p < 0.05). On the other hand, glycerophospholipid PS(17:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)) was found to be significantly upregulated in the metabolomics profiles of control subjects (p < 0.05). The differently regulated metabolites provide insights into the pathophysiology of keratoconus and could potentially be used as biomarkers for keratoconus to aid in screening for individuals at risk hence, enabling early diagnosis and timely monitoring of disease.

Untargeted metabolomics for uncovering plasma biological markers of wet age-related macular degeneration

Wet age-related macular degeneration (wAMD) causes central vision loss and represents a major health problem in elderly people. Here we have used untargeted metabolomics using UHPLC-MS to profile plasma from 127 patients with wAMD (67 choroidal neovascularization (CNV) and 60 polypoidal choroidal vasculopathy (PCV)) and 50 controls. A total of 545 biochemicals were detected. Among them, 17 metabolites presented difference between patients with wAMD and controls. Most of them were oxidized lipids (N=6, 35.29%). Comparing to controls, 28 and 18 differential metabolites were identified in patients with CNV and PCV, respectively. Two metabolites, hyodeoxycholic acid and L-tryptophanamide, were differently distributed between PCV and CNV. We first investigated the genetic association with metabolites in wet AMD (CFH rs800292 and HTRA1 rs10490924). We identified six differential metabolites between the GG and AA genotypes of CFH rs800292, five differential metabolites between the GG and AA genotypes of HTRA1 rs10490924, and four differential metabolites between the GG and GA genotypes of rs10490924. We selected four metabolites (cyclamic acid, hyodeoxycholic acid, L-tryptophanamide and O-phosphorylethanolamine) for in vitro experiments. Among them, cyclamic acid reduced the activity, inhibited the proliferation, increased the apoptosis and necrosis in human retinal pigment epithelial cells (HRPECs). L-tryptophanamide affected the proliferation, apoptosis and necrosis in HRPECs, and promoted the tube formation and migration in primary human retinal endothelial cells (HRECs). Hyodeoxycholic acid and O-phosphorylethanolamine inhibited the tube formation and migration in HRECs. The results suggested that differential metabolites have certain effects on wAMD pathogenesis-related HRPECs and HRECs.

Systemic lipid dysregulation is a risk factor for macular neurodegenerative disease

Macular Telangiectasia type 2 (MacTel) is an uncommon bilateral retinal disease, in which glial cell and photoreceptor degeneration leads to central vision loss. The causative disease mechanism is largely unknown, and no treatment is currently available. A previous study found variants in genes associated with glycine-serine metabolism (PSPH, PHGDH and CPS1) to be associated with MacTel, and showed low levels of glycine and serine in the serum of MacTel patients. Recently, a causative role of deoxysphingolipids in MacTel disease has been established. However, little is known about possible other metabolic dysregulation. Here we used a global metabolomics platform in a case-control study to comprehensively profile serum from 60 MacTel patients and 58 controls. Analysis of the data, using innovative computational approaches, revealed a detailed, disease-associated metabolic profile with broad changes in multiple metabolic pathways. This included alterations in the levels of several metabolites that are directly or indirectly linked to glycine-serine metabolism, further validating our previous genetic findings. We also found changes unrelated to PSPH, PHGDH and CPS1 activity. Most pronounced, levels of several lipid groups were altered, with increased phosphatidylethanolamines being the most affected lipid group. Assessing correlations between different metabolites across our samples revealed putative functional connections. Correlations between phosphatidylethanolamines and sphingomyelin, and glycine-serine and sphingomyelin, observed in controls, were reduced in MacTel patients, suggesting metabolic re-wiring of sphingomyelin metabolism in MacTel patients. Our findings provide novel insights into metabolic changes associated with MacTel and implicate altered lipid metabolism as a contributor to this retinal neurodegenerative disease.

Metabolomic analysis in ophthalmology

Modern science takes into account phenotype complexity and establishes approaches to track changes on every possible level. Many "omics" studies have been developed over the last decade. Metabolomic analysis enables dynamic measurement of the metabolic response of a living system to a variety of stimuli or genetic modifications. Important targets of metabolomics is biomarker development and translation to the clinic for personalized diagnosis and a greater understanding of disease pathogenesis. The current review highlights the major aspects of metabolomic analysis and its applications for the identification of relevant predictive, diagnostic and prognostic biomarkers for some ocular diseases including dry eye, keratoconus, retinal diseases, macular degeneration, and glaucoma. To date, possible biomarker candidates for dry eye disease are lipid metabolites and androgens, for keratoconus cytokeratins, urea, citrate cycle, and oxidative stress metabolites. Palmitoylcarnitine, sphingolipids, vitamin D related metabolites, and steroid precursors may be used for distinguishing glaucoma patients from healthy controls. Dysregulation of amino acid and carnitine metabolism is critical in the development and progression of diabetic retinopathy. Further work is needed to discover and validate metabolic biomarkers as a powerful tool for understanding the molecular mechanisms of ocular diseases, to provide knowledge on their etiology and pathophysiology and opportunities for personalized clinical intervention at an early stage.

Metabolomic Signature Discriminates Normal Human Cornea from Keratoconus-A Pilot GC/MS Study

The molecular etiology of keratoconus (KC), a pathological condition of the human cornea, remains unclear. The aim of this work was to perform profiling of metabolites and identification of features discriminating this pathology from the normal cornea. The combination of gas chromatography and mass spectrometry (GC/MS) techniques has been applied for profiling and identification of metabolites in corneal buttons from 6 healthy controls and 7 KC patients. An untargeted GC/MS-based approach allowed the detection of 377 compounds, including 46 identified unique metabolites, whose levels enabled the separation of compared groups of samples in unsupervised hierarchical cluster analysis. There were 13 identified metabolites whose levels differentiated between groups of samples. Downregulation of several carboxylic acids, fatty acids, and steroids was observed in KC when compared to the normal cornea. Metabolic pathways associated with compounds that discriminated both groups were involved in energy production, lipid metabolism, and amino acid metabolism. An observed signature may reflect cellular processes involved in the development of KC pathology, including oxidative stress and inflammation.

Design and baseline data of a population-based metabonomics study of eye diseases in eastern China: the Yueqing Ocular Diseases Investigation

Background

China is undergoing a massive transition toward an urban and industrial economy. These changes will restructure the demographics and economy which will eventually influence the future patterns of disease. The risk factors of vision-impairing eye diseases remain ambiguous and poorly understood. Metabolomics is an ideal tool to understand and shed light on the ocular disease mechanisms for earlier treatment. This article aims to describe the design, methodology and baseline data of the Yueqing Ocular Diseases Investigation (YODI), a developed county population-based study to determine the prevalence and primary causes of visual impairment; also with metabonomics analysis we aimed to identify, predict and suggest some preventive biomarkers that cause blindness.

Methods

A population-based, cross-sectional study. Randomized clustering sampling was used to identify adults aged 50 years and older in Xiangyang Town, Yueqing county-level City. The interviews covered demographic, behavioral, ocular risk factors and mental health state. The ocular examination included visual acuity, autorefraction, intraocular pressure, anterior and posterior segment examinations, fundus photography, retinal tomography and angiography, and visual field testing. Anthropometric measurements included height and weight, waist and hip circumference, blood pressure, pulse rate, electrocardiogram, and abdominal ultrasound scan. A venous blood sample was collected for laboratory tests and metabonomics studies.

Results

Of the 5319 individuals recruited for the YODI, 4769 (89.7%) subjects were enrolled for analyses. The median age was 62.0 years, and 45.6% were male. The educational level of illiteracy or semi-illiteracy, primary, middle and high school or above was 29.8%, 45.5%, 20.1%, and 3.3%, respectively. Majority of the participants were female, younger, and less educated when compared with nonparticipants. The average body mass index and waist-hip ratios were 24.4 ± 3.4 kg/m² and 0.9 ± 0.1 respectively. Blood sample collection reached a sample size of 1909 (479 from subjects with self-reported diabetes and 1430 from one-third of the 4290 subjects without self-reported diabetes).

Conclusions

The YODI provides population-based data with a high response rate (89.7%) on the prevalence and primary causes of major vision-impairing eye diseases in developed county areas in eastern China. Metabonomics analysis from YODI will provide further association of metabolic characteristics with the visual impairment eye diseases. The risk prediction model could be created and has the potential to be generalized to developed eastern areas in China for prevention.

The Molecular Basis of Human Anophthalmia and Microphthalmia

Human eye development is coordinated through an extensive network of genetic signalling pathways. Disruption of key regulatory genes in the early stages of eye development can result in aborted eye formation, resulting in an absent eye (anophthalmia) or a small underdeveloped eye (microphthalmia) phenotype. Anophthalmia and microphthalmia (AM) are part of the same clinical spectrum and have high genetic heterogeneity, with >90 identified associated genes. By understanding the roles of these genes in development, including their temporal expression, the phenotypic variation associated with AM can be better understood, improving diagnosis and management. This review describes the genetic and structural basis of eye development, focusing on the function of key genes known to be associated with AM. In addition, we highlight some promising avenues of research involving multiomic approaches and disease modelling with induced pluripotent stem cell (iPSC) technology, which will aid in developing novel therapies.

Tear Metabolomics in Dry Eye Disease: A Review

Dry eye disease (DED) is a multifactorial syndrome that can be caused by alteration in the quality or quantity of the precorneal tear film. It is considered one of the most common ocular conditions leading patients to seek eye care. The current method for diagnostic evaluations and follow-up examinations of DED is a combination of clinical signs and symptoms determined by clinical tests and questionnaires, respectively. The application of powerful omics technologies has opened new avenues toward analysis of subjects in health and disease. Metabolomics is a new emerging and complementary research discipline to all modern omics in the comprehensive analysis of biological systems. The identification of distinct metabolites and integrated metabolic profiles in patients can potentially inform clinicians at an early stage or during monitoring of disease progression, enhancing diagnosis, prognosis, and the choice of therapy. In ophthalmology, metabolomics has gained considerable attention over the past decade but very limited such studies have been reported on DED. This paper aims to review the application of tear metabolomics in DED.

The Carnitine Shuttle Pathway is Altered in Patients With Neovascular Age-Related Macular Degeneration

Purpose

To identify metabolites and metabolic pathways altered in neovascular age-related macular degeneration (NVAMD).

Methods

We performed metabolomics analysis using high-resolution C18 liquid chromatography-mass spectrometry on plasma samples from 100 NVAMD patients and 192 controls. Data for mass/charge ratio ranging from 85 to 850 were captured, and metabolic features were extracted using xMSanalyzer. Nested feature selection was used to identify metabolites that discriminated between NVAMD patients and controls. Pathway analysis was performed with Mummichog 2.0. Hierarchical clustering was used to examine the relationship between the discriminating metabolites and NVAMD patients and controls.

Results

Of the 10,917 metabolic features analyzed, a set of 159 was identified that distinguished NVAMD patients from controls (area under the curve of 0.83). Of these features, 39 were annotated with confidence and included multiple carnitine metabolites. Pathway analysis revealed that the carnitine shuttle pathway was significantly altered in NVAMD patients (P = 0.0001). Tandem mass spectrometry confirmed the molecular identity of five carnitine shuttle pathway acylcarnitine intermediates that were increased in NVAMD patients. Hierarchical cluster analysis revealed that 51% of the NVAMD patients had similar metabolic profiles, whereas the remaining 49% displayed greater variability in their metabolic profiles.

Conclusions

Multiple long-chain acylcarnitines that are part of the carnitine shuttle pathway were significantly increased in NVAMD patients compared to controls, suggesting that fatty acid metabolism may be involved in NVAMD pathophysiology. Cluster analysis suggested that clinically indistinguishable NVAMD patients can be separated into distinct subgroups based on metabolic profiles.

A Metabolomics Profiling of Glaucoma Points to Mitochondrial Dysfunction, Senescence, and Polyamines Deficiency

Purpose

To determine the plasma metabolomic signature of primary open-angle glaucoma (POAG).

Methods

We compared the metabolomic profiles of plasma from individuals with POAG (n = 36) with age- and sex-matched controls with cataract (n = 27). A targeted metabolomics study was performed using the standardized p180 Biocrates Absolute IDQ p180 kit with a QTRAP 5500 mass spectrometer. Multivariate analyses were performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) method.

Results

Among the 151 metabolites accurately measured, combined univariate and multivariate analyses revealed 18 discriminant metabolites belonging to the carbohydrate, acyl-carnitine, phosphatidylcholine, amino acids, and polyamine families. The metabolomic signature of POAG points to three closely interdependent pathophysiologic conditions; that is, defective mitochondrial oxidation of energetic substrates, altered metabolism resembling that observed in senescence, and a deficiency in spermidine and spermine, both polyamines being involved in the protection of retinal ganglion cells.

Conclusions

Our results highlight a systemic and age-related mitochondrial defect in the pathogenesis of POAG.

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.

Urine Nuclear Magnetic Resonance (NMR) Metabolomics in Age-Related Macular Degeneration

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.

Aqueous Humor Analysis Identifies Higher Branched Chain Amino Acid Metabolism as a Marker for Human Leukocyte Antigen-B27 Acute Anterior Uveitis and Disease Activity

PURPOSE

Human leukocyte antigen-B27 (HLA-B27)-positive acute anterior uveitis (AAU) has a higher recurrence rate and shows more anterior chamber cell infiltration compared with HLA-B27-negative patients, suggesting distinct etiologies of these clinically overlapping conditions. To advance our understanding of the biology of AAU, we characterized the metabolic profile of aqueous humor (AqH) of patients with HLA-B27-associated AAU (B27-AAU) and noninfectious idiopathic AAU (idiopathic AAU).

DESIGN

Experimental laboratory study.

METHODS

AqH samples from 2 independent cohorts totaling 30 patients with B27-AAU, 16 patients with idiopathic AAU, and 20 patients with cataracts underwent 2 individual rounds of direct infusion mass spectrometry. Features predicted by direct infusion mass spectrometry that facilitated maximum separation between the disease groups in regression models were validated by liquid chromatography/tandem mass spectrometry-based quantification with appropriate standards.

RESULTS

Partial least square-discriminant analysis revealed metabolite profiles that were able to separate patients with B27-AAU from those with iodiopathic AAU. Pathway enrichment analysis, based on metabolites on which separation of the groups in the partial least square-discriminant analysis model was based, demonstrated the involvement of branched-chain amino acid biosynthesis, ascorbate and aldarate metabolism, the tricarboxylic acid cycle, and glycolysis-diverting pathways (eg, serine biosynthesis) across all investigated cohorts. Notably, the metabolite ketoleucine was elevated in B27-AAU across all 3 runs and moderately-but robustly-correlated with anterior chamber cell count (correlation coefficient range 0.41-0.81).

CONCLUSIONS

These results illustrate metabolic heterogeneity between HLA-B27-positive and HLA-B27-negative AAU, including an increase of branched-chain amino acid biosynthesis, that reflects disease activity in AAU.

Review of Biomarkers in Ocular Matrices: Challenges and Opportunities

Biomarkers provide a powerful and dynamic approach to improve our understanding of the mechanisms underlying ocular diseases with applications in diagnosis, disease modulation or for predicting and monitoring of clinical response to treatment. Defined as measurable indicator of normal or pathological processes, biomarker evaluation has been used extensively in drug development within clinical settings to better comprehend effectiveness of treatment in ocular diseases. Biomarkers in the eye have the advantage of access to multiple ocular matrices via minimally invasive methods. Repeat sampling for biomarker assessment has enabled reproducible objective measures of disease process or biological responses to a drug treatment. This review describes the usage of biomarkers with respect to four commonly sampled ocular matrices in clinic: tears, conjunctiva, aqueous humor and vitreous. Issues that affect the evaluation of biomarkers are discussed along with opportunities to leverage biomarkers such that ultimately, they can be used for customized targeted therapy.

Metabolomics and Age-Related Macular Degeneration

Age-related macular degeneration (AMD) leads to irreversible visual loss, therefore, early intervention is desirable, but due to its multifactorial nature, diagnosis of early disease might be challenging. Identification of early markers for disease development and progression is key for disease diagnosis. Suitable biomarkers can potentially provide opportunities for clinical intervention at a stage of the disease when irreversible changes are yet to take place. One of the most metabolically active tissues in the human body is the retina, making the use of hypothesis-free techniques, like metabolomics, to measure molecular changes in AMD appealing. Indeed, there is increasing evidence that metabolic dysfunction has an important role in the development and progression of AMD. Therefore, metabolomics appears to be an appropriate platform to investigate disease-associated biomarkers. In this review, we explored what is known about metabolic changes in the retina, in conjunction with the emerging literature in AMD metabolomics research. Methods for metabolic biomarker identification in the eye have also been discussed, including the use of tears, vitreous, and aqueous humor, as well as imaging methods, like fluorescence lifetime imaging, that could be translated into a clinical diagnostic tool with molecular level resolution.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Opportunities and Challenges of Whole-Cell and -Tissue Simulations of the Outer Retina in Health and Disease

Visual processing starts in the outer retina, where photoreceptor cells sense photons that trigger electrical responses. Retinal pigment epithelial cells are located external to the photoreceptor layer and have critical functions in supporting cell and tissue homeostasis and thus sustaining a healthy retina. The high level of specialization makes the retina vulnerable to alterations that promote retinal degeneration. In this review, we discuss opportunities and challenges in proposing whole-cell and -tissue simulations of the human outer retina. An implicit position taken throughout this review is that mapping diverse data sets onto integrative computational models is likely to be a pivotal approach to understanding complex disease and developing novel interventions.

Peiminine inhibits colorectal cancer cell proliferation by inducing apoptosis and autophagy and modulating key metabolic pathways

Peiminine, a compound extracted from the bulbs of Fritillaria thunbergii and traditionally used as a medication in China and other Asian countries, was reported to inhibit colorectal cancer cell proliferation and tumor growth by inducing autophagic cell death. However, its mechanism of anticancer action is not well understood, especially at the metabolic level, which was thought to primarily account for peiminine's efficacy against cancer. Using an established metabolomic profiling platform combining ultra-performance liquid chromatography/tandem mass spectrometry with gas chromatography/mass spectrometry, we identified metabolic alterations in colorectal cancer cell line HCT-116 after peiminine treatment. Among the identified 236 metabolites, the levels of 57 of them were significantly (p < 0.05) different between peiminine-treated and -untreated cells in which 45 metabolites were increased and the other 12 metabolites were decreased. Several of the affected metabolites, including glucose, glutamine, oleate (18:1n9), and lignocerate (24:0), may be involved in regulation of the phosphoinositide 3-kinase/Akt/mammalian target of rapamycin (mTOR) pathway and in the oxidative stress response upon peiminine exposure. Peiminine predominantly modulated the pathways responsible for metabolism of amino acids, carbohydrates, and lipids. Collectively, these results provide new insights into the mechanisms by which peiminine modulates metabolic pathways to inhibit colorectal cancer cell growth, supporting further exploration of peiminine as a potential new strategy for treating colorectal cancer.

Recent advances in the applications of metabolomics in eye research

Metabolomics, the identification and quantitation of metabolites in a system, have been applied to identify new biomarkers or elucidate disease mechanism. In this review, we discussed the application of metabolomics in several ocular diseases and recent developments in metabolomics regarding tear fluids analysis, data acquisition and processing.

Human Plasma Metabolomics Study across All Stages of Age-Related Macular Degeneration Identifies Potential Lipid Biomarkers

PURPOSE

To characterize the plasma metabolomic profile of patients with age-related macular degeneration (AMD) using mass spectrometry (MS).

DESIGN

Cross-sectional observational study.

PARTICIPANTS

We prospectively recruited participants with a diagnosis of AMD and a control group (>50 years of age) without any vitreoretinal disease.

METHODS

All participants underwent color fundus photography, used for AMD diagnosis and staging, according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and plasma was analyzed by Metabolon, Inc. (Durham, NC), using ultrahigh-performance liquid chromatography (UPLC) and high-resolution MS. Metabolon's hardware and software were used to identify peaks and control quality. Principal component analysis and multivariate regression were performed to assess differences in the metabolomic profiles of AMD patients versus controls, while controlling for potential confounders. For biological interpretation, pathway enrichment analysis of significant metabolites was performed using MetaboAnalyst.

MAIN OUTCOME MEASURES

The primary outcome measures were levels of plasma metabolites in participants with AMD compared with controls and among different AMD severity stages.

RESULTS

We included 90 participants with AMD (30 with early AMD, 30 with intermediate AMD, and 30 with late AMD) and 30 controls. Using UPLC and MS, 878 biochemicals were identified. Multivariate logistic regression identified 87 metabolites with levels that differed significantly between AMD patients and controls. Most of these metabolites (82.8%; n = 72), including the most significant metabolites, belonged to the lipid pathways. Analysis of variance revealed that of the 87 metabolites, 48 (55.2%) also were significantly different across the different stages of AMD. A significant enrichment of the glycerophospholipids pathway was identified (P = 4.7 × 10-9) among these metabolites.

CONCLUSIONS

Participants with AMD have altered plasma metabolomic profiles compared with controls. Our data suggest that the most significant metabolites map to the glycerophospholipid pathway. These findings have the potential to improve our understanding of AMD pathogenesis, to support the development of plasma-based metabolomics biomarkers of AMD, and to identify novel targets for treatment of this blinding disease.

An exploratory LC-MS-based metabolomics study reveals differences in aqueous humor composition between diabetic and non-diabetic patients with cataract

Cataract is the leading cause of blindness worldwide. Epidemiological studies revealed up to a fivefold increased prevalence of cataracts in diabetic subjects. Metabolomics is nowadays frequently implemented to understand pathophysiological processes responsible for disease occurrence and progression. It has also been used recently to study the metabolic composition of aqueous humor (AH). 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. The aim of this study was to use metabolomics to compare the AH of diabetic and non-diabetic patients undergoing cataract surgery. Several antioxidants (methyltetrahydrofolic acid, taurine, niacinamide, xanthine, and uric acid) were found decreased (-22 to -61%, p-value 0.05-0.003) in AH of diabetics. Also amino acids (AA) and derivatives were found decreased (-21 to -36%, p-value 0.05-0.01) while glycosylated AA increased (+75-98%, p-value 0.03-0.009) in this group of patients. Metformin was detected in AH of people taking this drug. To our knowledge, this is the first metabolomics study aiming to assess differences in AH composition between diabetic and non-diabetic patients with cataract. An increased oxidative stress and perturbations in amino acid metabolism in AH may be responsible for earlier cataract onset in diabetic patients.

Metabolic profiling of the three neural derived embryonal pediatric tumors retinoblastoma, neuroblastoma and medulloblastoma, identifies distinct metabolic profiles

The rare pediatric embryonal tumors retinoblastoma, medulloblastoma and neuroblastoma derive from neuroectodermal tissue and share similar histopathological features despite different anatomical locations and diverse clinical outcomes. As metabolism can reflect genetic and histological features, we investigated whether the metabolism of embryonal tumors reflects their similar histology, shared developmental and neural origins, or tumor location. We undertook metabolic profiling on 50 retinoblastoma, 39 medulloblastoma and 70 neuroblastoma using high resolution magic angle spinning magnetic resonance spectroscopy (1H-MRS). Mean metabolite concentrations identified several metabolites that were significantly different between the tumor groups including taurine, hypotaurine, glutamate, glutamine, GABA, phosphocholine, N-acetylaspartate, creatine, glycine and myoinositol, p < 0.0017. Unsupervised multivariate analysis found that each tumor group clustered separately, with a unique metabolic profile, influenced by their underlying clinical diversity. Taurine was notably high in all tumors consistent with prior evidence from embryonal tumors. Retinoblastoma and medulloblastoma were more metabolically similar, sharing features associated with the central nervous system (CNS). Neuroblastoma had features consistent with neural tissue, but also contained significantly higher myoinositol and altered glutamate-glutamine ratio, suggestive of differences in the underlying metabolism of embryonal tumors located outside of the CNS. Despite the histological similarities and shared neural metabolic features, we show that individual neuroectodermal derived embryonal tumors can be distinguished by tissue metabolic profile. Pathway analysis suggests the alanine-aspartate-glutamate and taurine-hypotaurine metabolic pathways may be the most pertinent pathways to investigate for novel therapeutic strategies. This work strengthens our understanding of the biology and metabolic pathways underlying neuroectodermal derived embryonal tumors of childhood.

Metabolomics analysis of salvage chemotherapy on refractory acute myeloid leukemia patients

Acute myeloid leukemia (AML) is a group of hematological malignancies causing high mortality around the world. However, the treatment of AML is still one of the most formidable challenges. In this study, we employed a well-established global metabolic profiling platform, which combined ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) with gas chromatography mass spectrometry (GC-MS) to investigate the metabolic alterations associated with salvage chemotherapy on 10 refractory acute myeloid leukemia (RAML) patients. A total of 390 metabolites were identified from 20 serum samples obtained from all 10 patients before and post salvage chemotherapy. The metabolomics profile was found to be very heterogeneous across the RAML patients. The results showed very subtle metabolic differences upon one-time chemotherapy treatment for an individual patient. Only 9 metabolites including imidazole lactate, glycerol 3-phosphate, three fatty acids, and four lysolipids in the blood serum were significantly changed before and post chemotherapy, suggesting their important roles during the development of RAML. This study may not only provide new insight into the metabolomics features in RAML patients, but also have relevance to improve the treatment and outcome of RAML.

Salvage chemotherapy had minimal impact on the metabolomics for individual RAML patient.

Changes in retinal metabolic profiles associated with form deprivation myopia development in guinea pigs

Retinal metabolic changes have been suggested to be associated with myopia development. However, little is known about either their identity or time dependent behavior during this sight compromising process. To address these questions, gas chromatography time-of-flight mass spectrometry (GC-TOF/MS) was applied to compare guinea pig retinal metabolite levels in form deprivation (FD) eyes at 3 days and 2 weeks post FD with normal control (NC) eyes. Orthogonal partial least squares (OPLS) models discriminated between time dependent retinal metabolic profiles in the presence and absence of FD. Myopia severity was associated with more metabolic pattern differences in the FD than in the NC eyes. After 3 days of FD, 11 metabolite levels changed and after 2 weeks the number of differences increased to 16. Five metabolites continuously decreased during two weeks of FD. Two-way ANOVA of the changes identified by OPLS indicates that 15 out of the 22 metabolites differences were significant. Taken together, these results suggest that myopia progression is associated with an inverse relationship between increases in glucose accumulation and lipid level decreases in form-deprived guinea pig eyes. Such changes indicate that metabolomic studies are an informative approach to identify time dependent retinal metabolic alterations associated with this disease.