Metabolomic analysis of human disease and its application to the eye

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

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.

Genetically Determined Metabolites in Graves Disease: Insight From a Mendelian Randomization Study

Context

Graves disease (GD) is a prevalent autoimmune disorder with a complex etiology. The association between serum metabolites and GD remains partially understood.

Objective

This study aimed to elucidate the causal connections between serum metabolites and predisposition to GD, examining potential genetic interplay.

Methods

A 1-sample Mendelian randomization (MR) study was conducted on the GD analysis that included 2836 cases and 374 441 controls. We utilized genome-wide association study summary data from the FinnGen project, analyzing the causal impact of 486 serum metabolites on GD. Approaches used were the inverse variance weighted methodology, Cochran's Q test, MR-Egger regression, MR-PRESSO, Steiger test, and linkage disequilibrium score regression analyses to assess genetic influence on metabolites and GD.

Results

19 metabolites were identified as having a pronounced association with GD risk, of which 10 maintained noteworthy correlations after stringent sensitivity assessments. Three metabolites exhibited significant heritability: kynurenine (OR 3.851, P = 6.09 × 10-4), a risk factor; glycerol 2-phosphate (OR 0.549, P = 3.58 × 10-2) and 4-androsten-3beta,17beta-diol disulfate 2 (OR 0.461, P = 1.34 × 10-2) were recognized as protective factors against GD. Crucially, all 3 exhibited no shared genetic interrelation with GD, further substantiating their potential causal significance in the disease.

Conclusion

This study unveils pivotal insights into the intricate relationships between serum metabolites and GD risk. By identifying specific risk and protective factors, it opens avenues for more precise disease understanding and management. The findings underline the importance of integrating genomics with metabolomics to fathom the multifaceted nature of GD.

Renormalization of metabolic coupling treats age-related degenerative disorders: an oxidative RPE niche fuels the more glycolytic photoreceptors

Retinitis pigmentosa is characterized by a dysregulation within the metabolic coupling of the retina, particularly between the glycolytic photoreceptors and the oxidative retina pigment epithelium. This phenomenon of metabolic uncoupling is seen in both aging and retinal degenerative diseases, as well as across a variety of cell types in human biology. Given its crucial role in the health and maintenance of these cell types, the metabolic pathways involved present a suitable area for therapeutic intervention. Herein, this review covers the scope of this delicate metabolic interplay, its dysregulation, how it relates to the retina as well other cell types, and finally concludes with a summary of various strategies aimed at reinstating normal metabolic coupling within the retina, and future directions within the field.

Age-related differences in corneal nerve regeneration after SMILE and the mechanism revealed by metabolomics

PURPOSE

To investigate the effect of age on wound healing after small incision lenticule extraction (SMILE) and the underlying metabolomic mechanisms.

METHODS

This prospective study was conducted on 216 patients in four groups: the 18-20 (n = 38, Group I), 21-30 (n = 84, Group Ⅱ), 31-40 (n = 58, Group Ⅲ), and 41-50 (n = 36, Group IV) age groups. The density of corneal epithelial wing cells, basal cells, corneal stromal cells, endothelial cells and corneal nerves were examined with a laser confocal microscope (HRT III-RCM) before and 1 month, 3 month, 6 month and 1 year after SMILE. The central nerve fiber length (CNFL), the central corneal nerve fibre density (CNFD), and the central corneal nerve branch density (CNBD) were analyzed by Nero J. The corneal stroma lenticules were obtained from SMILE to analyze metabolites by high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (HPLC-QTOF-MS).

RESULTS

The density of corneal wing epithelial cells and basal epithelial cells have no significant difference among the four groups. The CNFL was 21.90 ± 1.68 mm/mm² in Group Ⅰ and 21.63 ± 2.09 mm/mm² in Group Ⅱ after 1 year of SMILE, which represented a return to the preoperative level, whereas the CNFL of Group Ⅲ (19.40 ± 0.98 mm/mm²) and Group Ⅳ (18.94 ± 0.72 mm/mm²) were lower than that preoperation (P ˂0.01). CNFL repair had a negative correlation with age after surgery (Pearson's R = -0.572, P ˂0.01). The CNFD and the CNBD showed the same trend with the CNFL (Pearson's R = -0.602 and -0.531, P ˂0.05). Through screening the significantly different metabolites between the 18-30 age group (including Group I and Group Ⅱ) and other two groups, 6 common remarkably different metabolites were identified. Meanwhile, 5 unique different metabolites were identified only between the 18-30 age group and the 31-40 age group. Six unique different metabolites were identified only between the 18-30 age group and the 41-50 age group.

CONCLUSION

Corneal nerve repair after SMILE was significantly affected by age. The identified age-associated differences in metabolites were mainly related to inflammation, oxidation, nerve protection and regeneration.

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.

Metabolomics approach to biomarkers of dry eye disease using 1H-NMR in rats

Dry eye disease (DED) is a chronic and progressive lesion on the ocular surface and induces symptoms, such as burning sensation, itchy eyes, heavy eyes, tired eyes, dry feeling, facial flushing, and blurred vision. The present study was performed to develop DED biomarkers using metabolomics in a rat model. DED was induced by injecting scopolamine and exposing rats to a dry condition. Scopolamine (12 mg/kg/day for 7 days) was subcutaneously injected to male Sprague-Dawley rats. The rats were placed in dry condition with air-flow and dehumidifier. Tear volume and tear breakup time (TBUT) were measured, and eyes were examined through fluorescein staining to assess DED. Mucosal damage and immune reactions were also determined. Plasma and urinary endogenous metabolites were determined using ¹H-NMR analysis. Compared with control tear and TBUT levels were significantly decreased in the DED group whereas corneal damage was significantly increased. The levels of interleukins (IL-6) and IL-1β significantly elevated in the cornea and lacrimal glands in the DED group. TNF-α was numerically increased but not significantly different between groups. Pattern recognition using principal component analysis (PCA) and orthogonal projections to latent structure-discriminant analysis (OPLS-DA) of the NMR spectra in global profiling revealed different clusters between DED and control groups. Target profiling demonstrated that PCA and OPLS-DA score plots were separated between DED and controls in plasma and urine. Subsequently, 9 plasma metabolites were selected to examine different clustering between groups, and 26 urinary metabolites were also selected. Plasma metabolites showed a non-significant rising tendency in the DED group. Urinary phenylalanine, phenylacetate, pantothenate, glycine, succinate, methanol, valine, propylene glycol, histidine, threonine, lactate, and acetate were significantly different between control and DED rats. These results may contribute to understanding the metabolic regulation that is involved in DED and might be useful for potential biomarkers related to DED in rats.

Ocular growth and metabolomics are dependent upon the spectral content of ambient white light

Myopia results from an excessive axial growth of the eye, causing abnormal projection of remote images in front of the retina. Without adequate interventions, myopia is forecasted to affect 50% of the world population by 2050. Exposure to outdoor light plays a critical role in preventing myopia in children, possibly through the brightness and blue-shifted spectral composition of sunlight, which lacks in artificial indoor lighting. Here, we evaluated the impact of moderate levels of ambient standard white (SW: 233.1 lux, 3900 K) and blue-enriched white (BEW: 223.8 lux, 9700 K) lights on ocular growth and metabolomics in a chicken-model of form-deprivation myopia. Compared to SW light, BEW light decreased aberrant ocular axial elongation and accelerated recovery from form-deprivation. Furthermore, the metabolomic profiles in the vitreous and retinas of recovering form-deprived eyes were distinct from control eyes and were dependent on the spectral content of ambient light. For instance, exposure to BEW light was associated with deep lipid remodeling and metabolic changes related to energy production, cell proliferation, collagen turnover and nitric oxide metabolism. This study provides new insight on light-dependent modulations in ocular growth and metabolomics. If replicable in humans, our findings open new potential avenues for spectrally-tailored light-therapy strategies for myopia.

Alpha-crystallin mutations alter lens metabolites in mouse models of human cataracts

Cataracts are a major cause of blindness worldwide and commonly occur in individuals over 70 years old. Cataracts can also appear earlier in life due to genetic mutations. The lens proteins, αA- and αB-crystallins, are chaperone proteins that have important roles maintaining protein solubility to prevent cataract formation. Mutations in the CRYAA and CRYAB crystallin genes are associated with autosomal dominant early onset human cataracts. Although studies about the proteomic and genomic changes that occur in cataracts have been reported, metabolomics studies are very limited. Here, we directly investigated cataract metabolism using gas-chromatography-mass spectrometry (GC-MS) to analyze the metabolites in adult Cryaa-R49C and Cryab-R120G knock-in mouse lenses. The most abundant metabolites were myo-inositol, L-(+)-lactic acid, cholesterol, phosphate, glycerol phosphate, palmitic and 9-octadecenoic acids, α-D-mannopyranose, and β-D-glucopyranose. Cryaa-R49C knock-in mouse lenses had a significant decrease in the number of sugars and minor sterols, which occurred in concert with an increase in lactic acid. Cholesterol composition was unchanged. In contrast, Cryab-R120G knock-in lenses exhibited increased total amino acid content including valine, alanine, serine, leucine, isoleucine, glycine, and aspartic acid. Minor sterols, including cholest-7-en-3-ol and glycerol phosphate were decreased. These studies indicate that lenses from Cryaa-R49C and Cryab-R120G knock-in mice, which are models for human cataracts, have unique amino acid and metabolite profiles.

Tear 1H Nuclear Magnetic Resonance-Based Metabolomics Application to the Molecular Diagnosis of Aqueous Tear Deficiency and Meibomian Gland Dysfunction

PURPOSE

Meibomian gland dysfunction (MGD) is a major cause of signs and symptoms related to dry eyes (DE) and eyelid inflammation. We investigated the composition of human tears by metabolomic approaches in patients with aqueous tear deficiency and MGD.

METHODS

Participants in this prospective, case-control pilot study were split into patients with aqueous tear deficiency and MGD (DE-MGD [n = 15]) and healthy controls (CG; n = 20). Personal interviews, ocular surface disease index (OSDI), and ophthalmic examinations were performed. Reflex tears collected by capillarity were processed to 1H nuclear magnetic resonance (NMR) spectroscopy and quantitative data analysis to identify molecules by spectra comparison to library entries of purified standards and/or unknown entities. Statistical analyses were made by the SPSS 22.0 program.

RESULTS

Chemometric analysis and 1H NMR spectra comparison revealed the presence of 60 metabolites in tears. Differentiating features were evident in the NMR spectra of the 2 clinical groups, characterized by significant upregulation of phenylalanine, glycerol, and isoleucine, and downregulation of glycoproteins, leucine, and -CH3 lipids, as compared to the CG. The 1H NMR metabolomic analyses of human tears confirmed the applicability of this platform with high predictive accuracy/reliability.

CONCLUSIONS

Our key distinctive findings support that DE-MGD induces tear metabolomics profile changes. Metabolites contributing to a higher separation from the CG can presumably be used, in the foreseeable future, as DE-MGD biomarkers for better managing the diagnosis and therapy of this disease.

A Set of Global Metabolomic Biomarker Candidates to Predict the Risk of Dry Eye Disease

Purpose

We used ultraperformance liquid chromatography coupled with quadrupole/time-of-flight tandem mass spectrometry (UPLC-Q/TOF-MS/MS) to analyze the metabolic profile of reflex tears obtained from patients with dry eye disorders.

Methods

We performed a cross-sectional study involving 113 subjects: 85 patients diagnosed with dry eye syndrome (dry eye group) and 28 healthy volunteers (control group). Reflex tears (20–30 μl) were collected from the tear meniscus of both eyes of each subject using a Schirmer I test strip. MS data were acquired with a standard workflow by UPLC-Q/TOF-MS/MS. Metabolites were quantitatively analyzed and matched with entries in the Metlin, Massbank, and HMDB databases. Least absolute shrinkage and selection operator (LASSO) regression was conducted to detect important metabolites. Multiple logistic regression was used to identify the significant metabolic biomarker candidates for dry eye syndrome. Open database sources, including the Kyoto Encyclopedia of Genes and Genomes and MetaboAnalyst, were used to identify metabolic pathways.

Results

After the LASSO regression and multiple logistic regression analysis, 4 of 20 metabolic biomarker candidates were significantly correlated with Ocular Surface Disease Index score, 42 of 57 with fluorescein breakup time, and 26 of 57 with fluorescein staining. By focusing on the overlap of these three sets, 48 of 51 metabolites contributed to the incidence of dry eye and there were obvious changes in different age groups. Metabolic pathway analysis revealed that the main pathways were glucose metabolism, amino acid metabolism, and glutathione metabolism.

Conclusion

Dry eye syndrome induces changes in the metabolic profile of tears, and the trend differs with age. This evidence reveals the relationship between changes in metabolites, symptoms of dry eye syndrome, and age.

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.

Quantitative analysis of metabolites in glucose metabolism in the aqueous humor of patients with central retinal vein occlusion

Quantitative analysis of aqueous humor (AH) was performed to investigate glucose metabolism in patients with central retinal vein occlusion (CRVO), and to explore metabolic changes after anti-vascular endothelial growth factor (VEGF) treatment. AH samples were collected from 35 patients. Participants diagnosed with CRVO (n = 15) were compared to participants who underwent cataract surgery (n = 20). Thirteen of the participants with CRVO received second-round anti-VEGF treatments. Ultra-high performance liquid chromatography tandem-mass spectrometry (UHPLC-MS/MS) was used to quantify metabolites of the AH. Central macular thickness (CMT) and retinal ganglion cell layer (RGC) thickness were measured using spectral-domain optical coherence tomography. Thirteen metabolites involved in glucose metabolism were identified. Among these metabolites, succinate, glutamate, and glutamine were significantly decreased for the CRVO group (p = 0.028, 0.009, and 0.017, respectively). The α-ketoglutarate/citrate (K/C) ratio had a significant positive correlation with glutamine levels for both control (r = 0.922, p < 0.001) and CRVO groups (r = 0.674, p = 0.006). A significant increase in lactate was observed after intravitreal anti-VEGF administration (t = 2.273, p = 0.045); the change in CMT was negatively correlated with this increase (r = -0.745, p = 0.003). The alteration of RGC thickness was negatively correlated with increases in both glutamine (r = -0.619, p = 0.024) and glucose (r = -0.754, p = 0.003). These results indicate that, compared to glucose metabolism, glutamine was significantly decreased in the AH of patients with CRVO, and may therefore serve as a potential target for CRVO therapy. The glycolytic pathway might be enhanced after intravitreal anti-VEGF injection, which is an important insight into CRVO pathophysiology.

Systematic review of differential methylation in rare ophthalmic diseases

Rare ophthalmic diseases have a devastating impact on a patient's vision and consequently negatively affect their independence, ability to work and overall quality of life. Methylation is an important emerging biomarker of disease and may improve understanding of rare ophthalmic disorders. This systematic review sought to identify and evaluate literature on methylation and rare ophthalmic disease. MEDLINE, EMBASE, PubMed, Cochrane Database of Systematic Reviews and grey literature resources were searched for publications prior to 20 August 2019. Articles written in English which featured key terms such as 'methylation' and rare ophthalmic diseases were included. Titles, abstracts, keywords and full texts of publications were screened, as well as reference lists for reverse citations and Web of Science 'cited reference search' for forward citation searching. Study characteristics were extracted, and methodological rigour appraised using a standardised template. Fourteen articles were selected for full inclusion. Rare ophthalmic conditions include congenital fibrosis of extraocular muscles, retinitis pigmentosa, Fuchs endothelial corneal dystrophy, granular corneal dystrophy, choroideraemia, brittle cornea syndrome, retinopathy of prematurity, keratoconus and congenital cataracts. Outcomes include identification of methylation as contributor to disease and identification of potential novel therapeutic targets. The studies included were heterogeneous with no scope for meta-analysis following review; a narrative synthesis was undertaken. Differential methylation has been identified in a small number of rare ophthalmic diseases and few studies have been performed to date. Further multiomic research will improve understanding of rare eye diseases and hopefully lead to improved provision of diagnostic/prognostic biomarkers, and help identify novel therapeutic targets.

Serum Bile Acids in Patients With Primary Open-angle Glaucoma

PURPOSE

To measure the serum bile acids (SBA) in patients with primary open-angle glaucoma (POAG) and to compare with nonglaucomatous controls.

PATIENTS AND METHODS

A hospital-based case control study of 90 patients above the age of 40 years was conducted. Patients with POAG formed the study group and individuals without glaucoma in the same age group formed the control group. SBA estimation was done using ELISA kit enzymatic assay technique and the median value was compared between the 2 groups.

RESULTS

The median of SBA level was found to be more in POAG patients when compared with the control group. The difference was statistically significant (P<0.001). Median SBA level was not affected by the age or sex of the participants in the study.

CONCLUSIONS

"Bile acids" may have a role in the oxidative stress and apoptosis involved in the pathophysiology of POAG.

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.

Reprogramming the metabolome rescues retinal degeneration

Metabolomics studies in the context of ophthalmology have largely focused on identifying metabolite concentrations that characterize specific retinal diseases. Studies involving mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have shown that individuals suffering from retinal diseases exhibit metabolic profiles that markedly differ from those of control individuals, supporting the notion that metabolites may serve as easily identifiable biomarkers for specific conditions. An emerging branch of metabolomics resulting from biomarker studies, however, involves the study of retinal metabolic dysfunction as causes of degeneration. Recent publications have identified a number of metabolic processes-including but not limited to glucose and oxygen metabolism-that, when perturbed, play a role in the degeneration of photoreceptor cells. As a result, such studies have led to further research elucidating methods for prolonging photoreceptor survival in an effort to halt degeneration in its early stages. This review will explore the ways in which metabolomics has deepened our understanding of the causes of retinal degeneration and discuss how metabolomics can be used to prevent retinal degeneration from progressing to its later disease stages.

Effects of collagen cross-linking on the keratoconus metabolic network

PURPOSE

Keratoconus (KC) is a multifactorial, ectatic corneal disease. Metabolic changes in the corneal stroma with alterations in collagen fibril stability, oxidative stress, and urea cycle, have previously been reported as key players in KC pathobiology. Recently, corneal collagen cross-linking (CXL) has been introduced as a treatment that can address the progressive nature of KC. While the treatment has been successful in the early days, it is not without clinical ramifications. In this study, we investigated the alterations in KC metabolic profiles due to CXL.

METHODS

Primary human corneal fibroblasts (HCFs) from healthy donors and human KC fibroblasts (HKCs) from KC donor patients were plated on transwell polycarbonate membranes and stimulated by a stable vitamin C. At 4 weeks, riboflavin was added to the cultures followed by UVA irradiation (365 nm). Using mass spectrometry, we measured the major differences in metabolites in HKCs compared to HCFs pre- and post CXL.

RESULT

The analysis of 276 metabolites in HCFs and HKCs revealed that the most affected metabolites due to CXL were glutathione disulfide, ascorbic acid, proline, and lysine. A significant decrease in the pro-inflammatory biomarkers (myo-inositol and histidine) was also observed. Furthermore, a significant downregulation of many amino acids, lactate levels, and other water-soluble metabolites was noted in HKCs following CXL.

CONCLUSION

CXL is a KC treatment available to patients within certain criteria. Surprisingly, the cellular and molecular mechanisms are considerably understudied limiting our ability for more precise and targeted CXL treatments. In this study, for the first time, we report the effects of CXL on KC metabolism.

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.

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.

Human plasma metabolomics in age-related macular degeneration (AMD) using nuclear magnetic resonance spectroscopy

PURPOSE

To differentiate the plasma metabolomic profile of patients with age related macular degeneration (AMD) from that of controls, by Nuclear Magnetic Resonance (NMR) spectroscopy.

METHODS

Two cohorts (total of 396 subjects) representative of central Portugal and Boston, USA phenotypes were studied. For each cohort, subjects were grouped according to AMD stage (early, intermediate and late). Multivariate analysis of plasma NMR spectra was performed, followed by signal integration and univariate analysis.

RESULTS

Small changes were detected in the levels of some amino acids, organic acids, dimethyl sulfone and specific lipid moieties, thus providing some biochemical information on the disease. The possible confounding effects of gender, smoking history and age were assessed in each cohort and found to be minimal when compared to that of the disease. A similar observation was noted in relation to age-related comorbidities. Furthermore, partially distinct putative AMD metabolite fingerprints were noted for the two cohorts studied, reflecting the importance of nutritional and other lifestyle habits in determining AMD metabolic response and potential biomarker fingerprints. Notably, some of the metabolite changes detected were noted as potentially differentiating controls from patients diagnosed with early AMD.

CONCLUSION

For the first time, this study showed metabolite changes in the plasma of patients with AMD as compared to controls, using NMR. Geographical origins were seen to affect AMD patients´ metabolic profile and some metabolites were found to be valuable in potentially differentiating controls from early stage AMD patients. Metabolomics has the potential of identifying biomarkers for AMD, and further work in this area is warranted.

Metabolomic profiling of reactive persulfides and polysulfides in the aqueous and vitreous humors

We investigate the metabolomic profile of reactive persulfides and polysulfides in the aqueous and vitreous humors. Eighteen eyes of 18 consecutive patients with diabetes mellitus (DM) and diabetic retinopathy underwent microincision vitrectomy combined with cataract surgery. Samples of the aqueous and vitreous humors were collected and underwent mass spectrometry-based metabolomic profiling of reactive persulfides and polysulfides (polysulfidomics). The effect of reactive polysulfide species on the viability of immortalized retinal cells (the RGC-5 cell line) under oxidative stress (induced with H₂O₂) was also evaluated with an Alamar Blue assay. The experiments showed that cysteine persulfides (CysSSH), oxidized glutathione trisulfide (GSSSG) and cystine were elevated in the aqueous humor, and CysSSH, Cys, and cystine were elevated in the vitreous. Furthermore, GSSSG, cystine, and CysSSH levels were correlated in the aqueous and vitreous humors. A comparison, in DM and control subjects, of plasma levels of reactive persulfides and polysulfides showed that they did not differ. In vitro findings revealed that reactive polysulfide species increased cell viability under oxidative stress. Thus, various reactive persulfides and polysulfides appear to be present in the eye, and some reactive sulfide species, which have a protective effect against oxidative stress, are upregulated in the aqueous and vitreous humors of DM eyes.

Foodomics as part of the host-microbiota-exposome interplay

The functional complexity of human gut microbiota and its relationship with host physiology and environmental modulating factors, offers the opportunity to investigate (i) the host and microbiota role in organism-environment relationship; (ii) the individual functional diversity and response to environmental stimuli (exposome); (iii) the host genome and microbiota metagenomes' modifications by diet-mediated epigenomic controls (nutriepigenomics); and (iv) the genotype-phenotype "trajectories" under physiological and disease constraints. Systems biology-based approaches aim at integrating biological data at cellular, tissue and organ organization levels, using computational modeling to interpret diseases' physiopathological mechanisms (i.e., onset and progression). Proteomics improves the existing gene models by profiling molecular phenotypes at protein abundance level, by analyzing post-translational modifications and protein-protein interactions and providing specific pathway information, hence contributing to functional molecular networks. Transcriptomics and metabolomics may determine host ad microbiota changes induced by food ingredients at molecular level, complementing functional genomics and proteomics data. Since foodomics is an -omic wide methodology may feed back all integrative data to foster the omics-based systems medicine field. Hence, coupled to ecological genomics of gut microbial communities, foodomics may highlight health benefits from nutrients, dissecting diet-induced gut microbiota eubiosis mechanisms and significantly contributing to understand and prevent complex disease phenotypes.

BIOLOGICAL SIGNIFICANCE

Besides transcriptomics and proteomics there is a growing interest in applying metabolic profiling to food science for the development of functional foods. Indeed, one of the biggest challenges of modern nutrition is to propose a healthy diet to populations worldwide, intrinsically respecting the high inter-individual variability, driven by complex host/nutrients/microbiota/environment interactions. Therefore, metabolic profiling can assist at various levels for the development of functional foods, starting from screening for food composition to identification of new biomarkers to trace food intake. This current approach can support diet intervention strategies, epidemiological studies, and controlling of metabolic disorders worldwide spreading, hence ensuring healthy aging. With high-throughput molecular technologies driving foodomics, studying bidirectional interactions of host-microbial co-metabolism, innate immune development, dysfunctional nutrient absorption and processing, complex signaling pathways involved in nutritional metabolism, is now likely. In all cases, as microbiome pipeline efforts continue, it is possible that enhanced standardized protocols can be developed, which may lead to new testable biological and clinical hypotheses. This Review provides a comprehensive update on the current state-of-the-art of the integrated -omics route in food, microbiota and host co-metabolism studies, which may revolutionize the design of new dietary intervention strategies.

Metabolomics to identify biomarkers and as a predictive tool in inflammatory diseases

There is an overwhelming need for a simple, reliable tool that aids clinicians in diagnosing, assessing disease activity and treating rheumatic conditions. Identification of biomarkers in partially understood inflammatory disorders has long been sought after as the Holy Grail of Rheumatology. Given the complex nature of inflammatory conditions, it has been difficult to earmark the potential biomarkers. Metabolomics, however, is promising in providing new insights into inflammatory conditions and also identifying such biomarkers. Metabolomic studies have generally revealed increased energy requirements for by-products of a hypoxic environment, leading to a characteristic metabolic fingerprint. Here, we discuss the significance of such studies and their potential as a biomarker.

Introduction to metabolomics and its applications in ophthalmology

Metabolomics is the study of endogenous and exogenous metabolites in biological systems, which aims to provide comparative semi-quantitative information about all metabolites in the system. Metabolomics is an emerging and potentially powerful tool in ophthalmology research. It is therefore important for health professionals and researchers involved in the speciality to understand the basic principles of metabolomics experiments. This article provides an overview of the experimental workflow and examples of its use in ophthalmology research from the study of disease metabolism and pathogenesis to identification of biomarkers.

A metabolomic approach to dry eye disorders. The role of oral supplements with antioxidants and omega 3 fatty acids

PURPOSE

We used nuclear magnetic resonance spectroscopy of hydrogen-1 nuclei ((1)H NMR S) to analyze the metabolic profile of reflex tears from patients with dry eye disorders.

METHODS

We performed a prospective case-control study involving 90 participants: 55 patients diagnosed with dry eye syndrome (DESG) and 35 healthy subjects (control group, CG). From the DESG, two subgroups were formed: mild DES (n=22) and moderate DES (n=33). Participants were prescribed an oral nutraceutic supplementation containing antioxidants and essential polyunsaturated fatty acids to be taken as three capsules per day for 3 months. Reflex tears (20-30 µl) were collected from the tear meniscus of both eyes of each subject with a microglass pipette. Nuclear magnetic resonance (NMR) spectra were acquired with a standard one-dimensional pulse sequence with water suppression; 256 free induction decays were collected into 64,000 data points with 14 ppm spectral width.

RESULTS

Basal tears showed a differential metabolomic profile between groups. Almost 50 metabolites were identified by H cholesterol, N-acetylglucosamine, glutamate, amino-n-butyrate, choline, glucose, and formate were detected before supplementation and choline/acetylcholine after supplementation. The metabolic profile of the tears was statistically different between groups, as well as before and after supplementation.

CONCLUSIONS

Our data indicate that DES induces changes in the tear metabolic profile that can be modified with appropriate oral supplementation with antioxidants and essential polyunsaturated fatty acids.

An integrated mechanism of pediatric pseudotumor cerebri syndrome: evidence of bioenergetic and hormonal regulation of cerebrospinal fluid dynamics

Pseudotumor cerebri syndrome (PTCS) is defined by the presence of elevated intracranial pressure (ICP) in the setting of normal brain parenchyma and cerebrospinal fluid (CSF). Headache, vision changes, and papilledema are common presenting features. Up to 10% of appropriately treated patients may experience permanent visual loss. The mechanism(s) underlying PTCS is unknown. PTCS occurs in association with a variety of conditions, including kidney disease, obesity, and adrenal insufficiency, suggesting endocrine and/or metabolic derangements may occur. Recent studies suggest that fluid and electrolyte balance in renal epithelia is regulated by a complex interaction of metabolic and hormonal factors; these cells share many of the same features as the choroid plexus cells in the central nervous system (CNS) responsible for regulation of CSF dynamics. Thus, we posit that similar factors may influence CSF dynamics in both types of fluid-sensitive tissues. Specifically, we hypothesize that, in patients with PTCS, mitochondrial metabolites (glutamate, succinate) and steroid hormones (cortisol, aldosterone) regulate CSF production and/or absorption. In this integrated mechanism review, we consider the clinical and molecular evidence for each metabolite and hormone in turn. We illustrate how related intracellular signaling cascades may converge in the choroid plexus, drawing on evidence from functionally similar tissues.

Differential effects of dry eye disorders on metabolomic profile by 1H nuclear magnetic resonance spectroscopy

We used ¹H NMR spectroscopy to analyze the metabolomic profile of reflex tears from patients with dry eye disorders (DEDs). 90 subjects were divided into 2 groups: (1) patients with DEDs (DEDG; n = 55) and (2) healthy subjects (CG; n = 35). Additionally, the DEDG was subdivided into 2 subgroups based on DED severity: mild-to-moderate and moderate (n = 22 and n = 33, resp.). Personal interviews and systematized ophthalmologic examinations were carried out. Reflex tears (20–30 μL) were collected by gently rubbing in the inferior meniscus of both eyelids with a microglass pipette and stored at −80°C until analysis. NMR spectra were acquired using a standard one-dimensional pulse sequence with water suppression. Data were processed and transferred to MATLAB for further chemometric analysis. Main differences in tear composition between DEDG and CG were found in cholesterol, N-acetylglucosamine, glutamate, creatine, amino-n-butyrate, choline, acetylcholine, arginine, phosphoethanolamine, glucose, and phenylalanine levels. This metabolic fingerprint helped also to discriminate between the three additional subgroups of DEDG. Our results suggest that tear metabolic differences between DEDG and CG identified by NMR could be useful in understanding ocular surface pathogenesis and improving biotherapy.

Meta-omic platforms to assist in the understanding of NAFLD gut microbiota alterations: tools and applications

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide as a result of the increasing prevalence of obesity, starting from early life stages. It is characterized by a spectrum of liver diseases ranging from simple fatty liver (NAFL) to steatohepatitis (NASH), with a possible progression to fibrosis, thus increasing liver-related morbidity and mortality. NAFLD development is driven by the co-action of several risk factors, including obesity and metabolic syndrome, which may be both genetically induced and diet-related. Recently, particular attention has been paid to the gut-liver axis, which may play a physio-pathological role in the onset and progression of the disease. The gut microbiota is intended to act as a bioreactor that can guarantee autonomous metabolic and immunological functions and that can drive functional strategies within the environment of the body in response to external stimuli. The complexity of the gut microbiota suggests that it behaves as an organ. Therefore, the concept of the gut-liver axis must be complemented with the gut-microbiota-liver network due to the high intricacy of the microbiota components and metabolic activities; these activities form the active diet-driven power plant of the host. Such complexity can only be revealed using systems biology, which can integrate clinical phenomics and gut microbiota data.

Metabolomics--a novel window into inflammatory disease

Inflammation is an important component of normal responses to infection and injury. However, chronic activation of the immune system, due to aberrant responses to normal stimuli, can lead to the establishment of a persistent inflammatory state. Such inflammatory conditions are often debilitating, and are associated with a number of important co-morbidities including cardiovascular disease. Resting non-proliferative tissues have distinctive metabolic activities and requirements, which differ considerably from those in infiltrating immune cells, which are undergoing proliferation and differentiation. Immune responses in tissues may therefore be modulated by the relative abundance of substrates in the inflamed site. In turn immune cell activity can feed back and affect metabolic behaviour of the tissues, as most clearly demonstrated in cachexia - the loss of cellular mass driven by tumour necrosis factor-alpha (TNF-α) a key mediator of the inflammatory response. Here we discuss the potential for metabolomic analysis to clarify the interactions between inflammation and metabolic changes underlying many diseases. We suggest that an increased understanding of the interaction between inflammation and cellular metabolism, energy substrate use, tissue breakdown markers, the microbiome and drug metabolites, may provide novel insight into the regulation of inflammatory diseases.

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.