A review of multi-omics approaches towards understanding late onset and progressive glaucomatous neuropathies

Introduction

Progressive neuropathies are neurological diseases in which peripheral nerves sustain damage over time, resulting in latent, irreversible damage that initially remains clinically silent but eventually manifests into potentially debilitating clinical symptoms (1). Similar in disease course, glaucoma is a broad group of progressive, irreversible optic neuropathies pathologically characterized by loss of retinal ganglion cells (RGCs) and their axons which lead to peripheral vision loss and eventual decline in overall visual acuity (2,3). Mechanisms implicated in RGC degeneration include elevated intraocular pressure (IOP) due to impeded aqueous humor (AH) outflow (4), ischemia/hypoxia, mitochondrial dysfunction, oxidative stress, and immune-mediated processes (5). Clinically, glaucoma manifests as structural changes such as vertical elongation and enlargement of the optic nerve cup, diffuse or focal narrowing of the neuroretinal rim, optic disc hemorrhages, and thinning of the retinal nerve fiber layer (RNFL), detected by fundus examination and/or optical coherence tomography (OCT) (6).

Glaucoma significantly impacts the activities of daily living (ADLs) of affected individuals. Currently, all treatment strategies for glaucoma are aimed toward prevention of further vision loss caused by optic nerve damage mainly through lowering IOP (4,7,8). Treatments to restore vision, which require axonal regeneration of RGCs, have yet to be developed. Advances to multi-omics research may enable the development of novel neuroprotection and axon regeneration strategies, as well as glaucoma susceptibility screening and/or risk stratification initiatives (9).

Glaucoma pathogenesis likely involves a multifactorial interplay between genetics, environmental factors, and metabolism (10). Three decades of advancement in genomic research have allowed for investigation of genome-wide association scanning for potential genes involved in its pathogenesis. These advances have been closely followed by transcriptomics to identify changes in gene expression and identification of key regulatory pathways that may play a critical role in glaucoma (11). Transcriptomics research has enabled complementary investigation at the proteomic level, encompassing both targeted experiments and high-throughput approaches to identify significant proteins (12). The same advancements in high-throughput approaches have expanded the field of lipidomics and metabolomics, allowing for identification of key lipids and metabolites involved in the disease (13). However, single-omics research is inherently limited compared to an integrated multi-omics approach, as their isolated approach may miss critical crosstalk between pathways (14). For instance, the gene LOXL1 was found to have low expression in later stages of pseudoexfoliation syndrome (XFS), but its role in disease process requires further validation through the study of its effects on proteins and protein-protein interactions (PPIs) (15). Multi-omics integration enables the identification of molecular networks and interactions to better understand the pathophysiology of glaucoma (16-19) (Figure 1). Though there have been comprehensive reviews of individual single-omics research (such as genomics) on glaucoma (20) or omics biomarkers in ophthalmology in general (14), our review is the first to highlight research in glaucoma across multiple omes and discuss how multi-omics may provide further insights into the disease.

Figure 1 An overview of different omics. An overview of different omics used in multi-omics approaches. The left column depicts genomics, transcriptomics, proteomics, lipidomics, and metabolomics. The central column highlights the role of each omics layer in biological processes. The right column shows common analysis techniques for each ome. MS is commonly used in proteomics, lipidomics and metabolomics. Constructed using BioRender. ELISA, enzyme-linked immunosorbent assay; MS, mass spectrometry; NMR, nuclear magnetic resonance.

Single-omics research in glaucoma

Genomics research in glaucoma

Genomics allows for investigation of disease mechanisms by uncovering genetic susceptibility loci and underlying genetic causes. Through the efforts of the United States National Eye Institute (NEI) Glaucoma Human Genetics Collaboration (NEIGHBOR) consortium, a nationwide collaborative effort which enabled genome-wide association studies (GWAS) (21,22), mutations in multiple gene loci have been closely associated with POAG (23,24). The consortium compiled phenotypic and clinical information within the NEIGHBOR Heritable Overall Operational Database (The NEIGHBORHOOD), integrating them with genotyped cases and controls (22). Through this database, researchers have identified several genetic coding variants associated with reduced odds of developing POAG, including a missense mutation (R1527) and a splice-donor site alteration (genomic location 16-8873727-C-G Hg38) in the mechanosensitive ion channel gene PIEZO1 (25), as well as the APOE ϵ4 allele (26). Interestingly, the APOE ϵ4 allele is associated with the development of Alzheimer’s disease (AD), suggesting possible differences between the eye and the brain regarding neurodegenerative disease processes (26). Along with protective variants, multiple gene loci were closely associated with increased risk of POAG. A meta-analysis by Ozel et al. found that the TMC01 single nucleotide polymorphism (SNP) rs7518099-G was significantly associated with elevated IOP (27). Additionally, mutations in the MYOC gene located on chromosome 1q23 have been identified as pathogenic, as they alter the trabecular meshwork’s (TM) extracellular matrix (ECM) causing increased AH outflow resistance and elevated IOP (28).

As GWAS sample sizes expanded to larger cohorts, additional risk loci gradually emerged. A recent large-scale GWAS with over 600,000 participants identified 263 new risk loci associated with glaucoma (29). By applying multi-lineage analysis across ethnically diverse populations, researchers increased the number of independent risk loci to 312, many of which were validated in another independent cohort study with over 2.8 million samples (29). One hundred and nine loci were novel, many of which appeared only after including Asian and African datasets to the analysis. Effect size correlations for genome-wide significant independent SNPs were moderate between European and Asian ancestries (r=0.77) and were lower between European and African ancestries (r=0.51). These findings from an ethnically diverse and large sample size highlight the genetic heterogeneity of glaucoma across different populations.

Functional analysis of risk loci found many to be closely related to IOP regulation, optic nerve development and injury repair, and ocular immune inflammatory response (30-32). The SVEP1 gene has been implicated in regulation of intraocular angiogenesis, AH outflow, and IOP homeostasis (33). The RERE gene may play a role in the differentiation and survival of optic nerve cells (34,35); conversely, abnormalities in this gene may affect the biomechanical properties of the optic nerve and increase the risk of glaucomatous optic neuropathy (36). Such findings not only may provide insight into disease mechanisms but also provide potential gene- and drug-therapy targets for treatment.

Analysis of research limitations in genomics studies

Though GWAS have identified glaucoma-related loci, there are several limitations in genomic research for glaucoma. Data from the NEIGHBOR consortium are limited as most cohorts are of European ancestry which therefore limit the generalizability of their findings to other ethnic groups. The large sample size of the NEIGHBOR consortium also limit the statistical power for association of rare variants and common variants with small effects. Technical limitations were also present; the use of exome array data rather than whole-exome sequencing (WES) data for various studies may have missed rare coding variants that were not known and included on the chip. Furthermore, while major genomic advances have been made regarding POAG (22,25-27), similar progress has yet to be achieved in secondary glaucoma, or glaucoma from known underlying conditions such as pseudoexfoliation and pigment dispersion syndromes (37). Emerging approaches such as single-cell genomics to identify somaclonal variations and spatial omics techniques (38-40) may provide even greater insight into POAG and help bridge gaps in the mechanistic knowledge of secondary glaucoma, where studies involving large sample sizes may not be feasible.

Research progress of transcriptomics in glaucoma

Transcriptomics, which involves the use of various RNA sequencing (RNA-seq) technologies and complementary DNA (cDNA) libraries, analyzes dynamic changes in gene expression. By studying expression abundance and alternative splicing forms of genes after high-throughput sequencing (41), researchers have revealed significant differences in gene expression profiles at different stages of elevated IOP and optic nerve damage, as well as differentially expressed genes involved in inflammatory response, cell apoptosis, and neuroprotection (42,43).

Transcriptomic studies have suggested that transcriptional regulatory networks play a central role in glaucoma pathogenesis (44), particularly nuclear factor kappa B (NF-κB), STAT3, and AP-1. NF-κB is a crucial regulator of inflammation and cell apoptosis; when activated by elevated IOP and ischemia/hypoxia (45,46), it translocates into the nucleus and binds to specific sequences in downstream target gene promoter regions, driving the transcription of inflammatory cytokines such as interleukins (ILs) and chemokines (CXCLs). This then promotes inflammatory cell infiltration and release of inflammatory mediators into the retina and optic nerve tissue, exacerbating local tissue damage. NF-κB can also regulate the expression of apoptosis-related genes such as Bax, synergistically promoting apoptosis of RGCs and accelerating optic nerve degeneration in glaucoma (47,48). STAT3 is involved in regulating multiple signaling pathways and is activated in response to stimuli such as cytokines and growth factors. Activated STAT3 affects the proliferation and activation status of retinal glial cells by regulating cell cycle-related genes (49). Overactivation of glial cells releases neurotoxic substances, interferes with the normal function of neurons, and promotes the progression of optic nerve injury (50). STAT3 also interacts with the vascular endothelial growth factor (VEGF) signaling pathway to regulate retinal angiogenesis and blood-brain barrier permeability, thus playing a role in retinal ischemia and neovascularization (5). AP-1, whose levels have been shown to be elevated in glaucoma, regulates the expression of matrix metalloproteinase (MMP) genes, reconfigures the ECM of the TM, and alters AH outflow resistance (51). AP-1 also regulates neuronal stress response genes which affects the tolerance and adaptability of RGCs to injury (52).

Analysis of research limitations in transcriptomics studies

Several limitations reduce the interpretive power of transcriptomic research findings in glaucoma. Cross-study comparability of transcriptomics research is limited by inter-study variability caused by differences in library preparation, sequencing depth, normalization methods, and defined thresholds for significance. Studies on micro-RNA (miRNA) focused only on their regulation of specific genes, thus other genes targeted by the miRNA were not analyzed. Meanwhile, studies of long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), which are surmised to be involved in glaucoma pathogenesis due to differential levels in disease versus control, are limited due to their more recent discovery compared to miRNAs. Furthermore, many findings involving transcriptomics are derived from experimental animal studies, most often mice or rat models, which limit clinical translatability for humans.

Though transcriptomics in glaucoma has advanced from bulk RNA-seq to single-cell RNA-seq (which has been applied for both anterior and posterior glaucoma-relevant tissues) (11,53), both approaches have limited spatial context. Spatial transcriptomics is a rapidly emerging and advancing field that integrates transcriptome-wide profiling with positional information, thereby providing information on the location of active genes (38,54).

Proteomics analysis approaches and findings in glaucoma

Proteomics focuses on the comprehensive profiling of proteins under specific conditions and explores the types, abundance, modification states, and interaction networks of these proteins. Mass spectrometry (MS)-based proteomics analysis techniques, such as liquid chromatography-tandem MS (LC-MS/MS), currently employ two broad approaches for high-throughput protein analysis: bottom-up and top-down approach. The bottom-up approach, which remains the most used method, hydrolyzes a protein mixture into peptide fragments before sequencing, yielding high-precision identification and quantification of proteins based on the mass-to-charge ratio (m/z) and fragment ion information of the peptides. Meanwhile, top-down approach analyzes intact proteins directly, fragmenting them within the mass spectrometer to yield detailed information such as sequence, post-translational modifications, and isoforms. The top-down approach has only become more usable recently (55) but has already revolutionized the identification of protein complexes and protein-protein and other protein-ligand interactions (55-57). In addition to MS, other large-scale proteomic methods such as protein arrays and antibody-based assays (ELISA), have been used. However, MS-based proteomics is generally more reliable, as tandem MS enables confident protein identification by combining precursor and fragment ion analysis.

Proteomics was first utilized for glaucoma to profile the TM and optic nerve (58,59), followed shortly by AH (60) and vitreous. Notably, through a comprehensive AH proteomics profile, researchers were able to provide novel insight into race-specific protein alterations (61) and, in conjunction with artificial intelligence (AI), predict and/or identify cellular drivers of aging and eye diseases (62). Proteomics has also been used to accurately capture changes in protein regulatory processes, such as abnormal ECM metabolism in the TM, or disrupted signal transduction in RGCs (63,64).

Differentially expressed proteins in glaucomatous AH compared to healthy controls have been identified (65,66). α-2-HS glycoprotein, involved in the regulation of inflammation and ECM remodeling, is significantly upregulated in glaucomatous AH (67). Its abnormally elevated expression may exacerbate TM fibrosis and subsequently hinder AH outflow and promote IOP elevation. The vitreous body also undergoes significant proteomic changes during glaucoma (68). In glaucomatous vitreous, clusterin was found to be significantly elevated. As a stress response protein, clusterin plays a complex regulatory role in cell apoptosis and neuroprotection (69). Its accumulation in the vitreous may reflect the stress microenvironment in which RGCs are located, indicating increased cellular damage. Conversely, the expression of glutathione S-transferase P is downregulated, which is an important antioxidant and detoxifying enzyme in cells (70). Its decreased activity may indicate that the antioxidant defense system of vitreous and retinal tissues is damaged, further promoting neurodegenerative changes through oxidative stress. These differentially expressed proteins are candidates for further longitudinal follow up in model systems for their potential utility as biomarkers.

Besides protein profiling, proteomics also enables the identification of post-translational proteins (modified through phosphorylation, acetylation, ubiquitination) and PPIs (71,72). In glaucoma, PPIs provide useful information regarding the functional state of cells in the disease state. Multiple proteomics studies have shown significant alterations in the expression of complement system proteins (such as upregulated C1q, C3, and C4A), neurodegeneration-associated proteins, and apolipoproteins (such as upregulated APOD protein) in POAG (66). Proteomic studies of tears have also revealed upregulated inflammation-related proteins (such as IL-6, TNF, and VEGFA) and altered levels of antioxidant proteins and cell signaling-related proteins (such as S100 proteins and MMPs) (73).

Analysis of research limitations in proteomics studies

Variations in data collection factors such as peptide digestion efficiency, labeling strategies, and instrumental differences in LC-MS/MS sensitivity limit proteomics research. Sample-related limitations also persist, such as the invasive nature of AH sampling which limits study participants to surgical patients. Future applications of AH-derived biomarkers as diagnostic tools for glaucoma may also not be preferred over less invasive methods such as imaging (65). Tear fluid proteomics, though less invasive than AH proteomics, is limited by diversity across patients’ glaucoma types, treatment status, and medication use (73). Meanwhile, studies involving whole retinal tissues are limited by the inability to distinguish the individual contribution of specific cell types within the retina, which would otherwise be overcome using spatial proteomics (38). Currently, imaging MS (IMS) enables localization of detected proteins in glaucoma-relevant tissues using limited proteolysis and a bottom-up approach (74). However, one of IMS’ greatest limitations is securing fragment ion (MS/MS) information along with precursor ion detection. Low availability of ions lifted during solid state ionization in IMS render insufficient ions available for fragmentation. Often, localized extractive omics are performed to confirm IMS findings. Through localization, biological roles of specific proteins within a given tissue may be deciphered, further advancing our understanding of glaucoma’s disease pathology.

Research progress of metabolomics in glaucoma

Metabolomics studies endogenous and exogenous low molecular weight molecules in organisms such as amino acids, sugars, lipids, and nucleotides, thus reflecting the real-time metabolic status of cells or organisms. Nuclear magnetic resonance (NMR), gas chromatography-MS (GC-MS), GC with tandem MS (GC-MS/MS) and LC-MS/MS are commonly used tools to qualitatively and quantitatively detect metabolites in biological samples from blood, AH, vitreous humor, and other tissues of interest (75). Metabolomics can be further divided into two techniques: targeted or untargeted. Researchers can target specific molecular classes or molecules from a specific pathway (targeted) or opt for a comprehensive analysis of all metabolites (untargeted). Additional techniques can be applied to improve compound identification, quantification, and normalization, such as isotopic ratio outlier analysis (IROA) (76), though these are not commonly used.

A series of characteristic metabolites have been implicated in glaucoma through metabolomic analyses of blood, AH, and vitreous of glaucoma patients. In the blood, studies have found elevated levels of sphingolipids and ceramides in the plasma of glaucoma patients (77,78). As a key component of cell membrane structure, metabolic disorders of sphingolipids may affect the stability and function of the cell membrane and signal transduction, thereby interfering with the normal physiological activities of ocular nerve cells and vascular endothelial cells. Abnormal metabolism of vitamin D-related compounds has also been found in the blood of glaucoma patients (79). Vitamin D is involved in physiological processes such as calcium and phosphorus metabolism and immune regulation in the body. Its deficiency or metabolic imbalance may promote the progression of glaucoma by affecting the homeostasis of calcium in the eye, thus exacerbating eye inflammation (80). In the AH, mass spectrometric analysis showed that the level of palmitoyl carnitine was significantly upregulated in glaucoma (81). A key intermediate product of fatty acid β-oxidation, palmitoyl carnitine accumulation suggests an imbalance in energy metabolism in the eye. Ischemia and hypoxia of eye tissues induced by increased IOP may prompt a metabolic shift in cells to rely on fatty acid oxidation to meet energy demands (82). However, due to blocked metabolic pathways, metabolites accumulate, further altering the functions of TM cells, RGCs, and other cells. Certain steroid precursor metabolites in AH also reflect abnormal synthesis and regulation of intraocular hormones which interfere with IOP regulatory mechanisms, further exacerbating optic nerve damage in glaucoma (83). In the vitreous, an NMR spectroscopy experiment found that the ratio of glutamine/glutamate to creatine in glaucoma patients was significantly increased. This increased ratio reflects the disorder of amino acid metabolism and energy metabolism in the eye (84). As an excitatory neurotransmitter, excessive accumulation of glutamate may lead to excitotoxicity and subsequent damage to RGCs (85). Meanwhile, creatine is involved in cellular energy buffering and transport, and changes in its relative content suggest impaired cellular energy metabolism homeostasis which accelerate neurodegenerative changes in glaucoma (86).

The precise identification of characteristic metabolites has opened discourse in the possible use of metabolite markers for early diagnosis, disease monitoring, and targeted therapy of glaucoma. Glaucoma patients often have metabolic distortions, such as increased oxidative stress and energy metabolism imbalance in their bodies (87). Therefore, elevated metabolites that suggest significant alterations due to oxidative stress and mitochondrial dysfunction may be used as biomarkers (13), such as methionine and hydroxyproline in plasma, creatine, glycine, lysine, and alanine in the AH, and arginine in both (88). By identifying characteristic metabolic change patterns, metabolomics can aid in the discovery of highly sensitive markers for diagnosis and facilitate the development of personalized treatment strategies to address individual metabolic pathway disturbances (89).

Analysis of research limitations in metabolomics studies

Applicability of metabolomic findings in glaucoma is restricted by inherent limitations in metabolomics. Findings are influenced by the specific metabolite extraction method used for the protocol, as certain metabolites are preferentially detected or lost depending on the method (79). The number of known metabolites is also small, with only 15–30% of peaks able to be matched to known metabolites in existing databases (90). For studies involving metabolites in AH of glaucoma patients, their relatively small sample sizes require further validation with a larger cohort (84). Small sample sizes are especially restricted in the quantification of low-abundance metabolites, as metabolites are not amplifiable.

Lipidomics and its investigative use in glaucoma

Lipidomics is a subfield of metabolomics which aims to comprehensively profile lipid composition using tools such as NMR, GC-MS, and LC-MS/MS. Unlike other omics analyses, lipidomics only gained significant traction in the early 2000s (91). Due to the vast number of known lipids and heterogeneity among lipidomic data reporting, efforts have been made to create a minimum reporting checklist to increase transparency and reproducibility (91,92).

In glaucoma, lipids are important in IOP regulation and homeostasis, particularly those within the aqueous production pathway such as the TM and episcleral venous pathway (93,94). Regulation of IOP requires mechanosensitive sensors to detect changes in pressure, a task fulfilled by lipids and mechanosensitive channels within the membrane of TM cells (95). In RGCs, lipids are particularly important in the construction and expansion of the phospholipid bilayer and are therefore relevant when studying axonal damage induced by increased pressure. With lipids also involved in cell signaling (96), the utility of comprehensive lipidomics in glaucoma is apparent.

Lipidomics analyses have been performed for AH (97-99), anterior segment TM, and optic nerve tissues (100-102) to study lipid changes in glaucoma. At the lipid class level, eicosanoid lipids, or prostanoids, have been found to play a critical role in lowering IOP through the enhancement of AH outflow via the uveoscleral route (103). At the species level, phosphatidylcholine (PC) 22:6/22:6 has been implicated as a potential marker for POAG severity (104,105). Alterations in the glucosylsphingosine metabolic pathway, specifically decreased GBA2 and increased ASAH1/ASAH2 enzyme activity, has also been shown in POAG optic nerve tissue (102), as well as an imbalance of lipid mediators (106).

Analysis of research limitations in lipidomics studies

Despite recent developments, lipidomics studies for glaucoma are not as extensive compared to other omics studies and require further attention. For instance, a comprehensive investigation of significant lipid classes and species in AH and TM has yet to be done. The results mentioned above also require further studies for validation. Like other omics, variation between studies can be attributed to differences in preparation methods such as extraction solvents and ionization modes, normalization methods, and thresholds used for significance during data analysis. Due to the relative novelty of lipidomics to other omics, variance in reporting methods and databases queried for lipid annotations further limit the comparability of published lipidomic data (91).

Comprehensive analysis of the molecular mechanism of glaucoma

Single-omics analyses of glaucoma would benefit from multi-omics integration to allow mutual validation of single-omics findings as each level offers distinct information regarding the disease process (Table 1). Multi-omics also allows researchers to expand upon prior discoveries in glaucoma and enhances regulatory network analyses (119,120). Software to integrate omics data are now available with either knowledge-driven or data-driven approaches utilized to form networks (121) (Figure 2). Knowledge-driven approaches involve the use of significant features across single-omics analyses as “seeds” which are then used to query comprehensive databases to obtain interaction partners from the same or different omics layers. Meanwhile, data-driven approaches perform de novo identification of interactions across different omics layers through statistical analyses.

Figure 2 Integration of different omics and their correlations with disease phenotypes. The Omics column (left) depicts different omics approaches including extractive (tissue, single cell, fractionated organelle) and spatial omics. The Integromics column depicts multi-omic integration using software such as OmicsAnalyst 2.0 and OmicsNet 2.0 which enables integrated identification of different molecule types, network and pathway building at different levels, and vertical comparison and assessment of spatial distribution. The Analysis and Biomarker Discovery column depicts correlation with disease phenotype with single- or multi-omics analyses resulting in identification of biomarkers or intervention targets. The correlation with disease genotype and phenotype results in clinical translation depicted in the eponymous last column. Constructed using BioRender. *, P<0.05; **, P<0.01; ***, P<0.001. IOP, intraocular pressure; POAG, primary open-angle glaucoma.

To provide an example of the utility of multi-omics, we integrated omics analyses of AH in glaucoma patients, as its production and outflow determines IOP. Publicly available omics databases were queried, specifically ProteomeXchange for proteomic data and Gene Expression Omnibus (GEO) for genomic or transcriptomic data. “Glaucoma” was used as the search term, and an organism filter was applied to include only human datasets. To retain the same biological context across omics layers, studies generating datasets had to match regarding the tissue or fluid analyzed. Only AH datasets were retained from search results as omic data on RGCs from human samples are limited. From ProteomeXchange, we used data from a study that performed proteomic and metabolomic analyses of AH samples from POAG, XFS, and exfoliation glaucoma (XFG) patients, with cataract patients acting as the control (Dataset Identifier: PXD046219) (17). Metabolomic data was available as supplementary material within the study. From GEO, we used miRNA data from a study which analyzed miRNA composition of POAG, XFG, and non-glaucoma control AH (Accession: GSE105269) (122). Both studies provided insight into protein, metabolomic, and miRNA changes from single-omic analyses. Proteins differentially elevated in POAG and XFG are associated with inflammation and ECM alteration, including complement components, vitronectin (VTN), apolipoproteins, plasminogen (PLG), kininogen-1 (KNG1), and coagulation factors (17). Gene ontology (GO) analysis of differentially expressed proteins (DEPs) revealed that POAG and XFG groups had increased proteins involved in complement activation regulation and immune effector processes, and these DEPs were enriched in the extracellular region, collagen-containing ECM, and blood microparticles (17). Meanwhile, differentially expressed metabolites (DEMs) in POAG, XFG, and XFS groups revealed upregulation of compounds with potential connections to glutamine, all of which could be related to glutamate regulation to prevent excess glutamate from inducing neuronal and retinal cell death (17). The study also performed multi-omic integration of DEPs and DEMs through OmicsNet 2.0, which uses a knowledge-based approach to build networks (123). Networks revealed similar connectivity patterns between POAG and XFG groups, with glutamic acid and L-valine interacting with C3, APOA2, APOD, APOL1, and FGG proteins within the POAG network (17). Meanwhile, the study of miRNA changes in AH revealed that three miRNAs were significantly different in the POAG group, while five miRNAs were significantly different in the XFG group. Using the miRTarBase online database, high confidence gene targets of these differentially expressed miRNAs were identified. Several KEGG pathways were found to be significantly represented among the targets, including glaucoma-related pathways such as focal adhesion, tight junctions, and TGF-ß signaling.

We utilized OmicsNet 2.0 to build networks between these three molecular components. Databases were queried for molecular interactions: STRING for protein-protein, KEGG and Recon3D for metabolite-protein, miRTarBase for miRNA-gene, and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) for transcription factor (TF)-gene interactions. A minimum network setting was applied to make the networks concise. WalkTrap algorithm was leveraged to detect modules, or parts within the network with higher connections than average, potentially signifying components’ participation in the same biological functions. Enrichment analyses of detected modules within these networks were then performed by querying GO (Figures 3,4). Module 2 (P<0.05) of POAG contained molecules significantly involved in external stimulus response regulation in the extracellular space (Figure 3C) (FDR-adjusted q-value <0.05). The molecules involved interacted with L-glutamic acid and were also found in the network built with proteomic and metabolomic data only (17). Meanwhile, both of XFG’s modules were significant (P<0.05), with Module 1 containing components significantly involved in multiple molecular functions (Figure 4B) and Module 2 containing components significantly involved in various biological processes and serine-related activities in the extracellular space (Figure 4C) (FDR-adjusted q-value <0.05). Novel connections were mapped with the addition of miRNA, specifically between miR-122-5p and the F2 protein which is subsequently connected to L-glutamine, L-tyrosine, and L-phenylalanine (Figure 4A). Three glaucoma-associated genes (OPTN, TGF-ß1 and TMCO1) are targeted by miR-122-5p (122), while F2 (among other proteins present in the AH) can influence inflammation and ECM regulation through its interactions with tyrosine and L-phenylalanine-metabolites known to be related to inflammatory response, oxidative stress, and fibrosis (17). The connection between miR-122-5p and F2 could indicate how this miRNA plays a role in glaucoma disease pathogenesis. Indeed, by integrating three molecular components from two prior studies, new insight into POAG and XFG was obtained.

Figure 3 A representative multi-omics integration in POAG. Multi-omic network of DEPs, DEMs, and DE miRNA in AH of POAG patients compared to controls. (A) Integrated networks of DEPs, DEMs, and DE miRNA generated by querying STRING, KEGG, Recon3D, miRTarBase, and TRRUST databases. A minimum network setting was applied to enhance the clarity and conciseness of the network. In the figure, proteins are illustrated as red circles, metabolites as yellow circles, miRNA as teal circles, mRNA as gray circles, and transcription factors as green circles. Seed nodes are highlighted with a black shade. Modules were detected using a WalkTrap algorithm, and connections within modules are highlighted. Top ten ontology terms from GO aspects for (B) Module 1 and (C) Module 2 are listed, ranked by FDR-adjusted q-value. Molecular components within Module 1 are highlighted by a sky-blue circle and connected by blue lines, while components within Module 2 are highlighted by a pink circle and connected by pink lines. Proteomic (and accompanying metabolomic) data was obtained from ProteomeXchange (PXD046219), and miRNA data was obtained from GEO (GSE105269). Constructed using OmicsNet 2.0. AH, aqueous humor; DE miRNA, differential expressed micro-RNA; DEMs, differential expressed metabolites; DEPs, differentially expressed proteins; FDR, false discovery rate; GEO, Gene Expression Omnibus; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; POAG, primary open-angle glaucoma; STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; TRRUST, Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining.

Figure 4 A representative multi-omics integration in XFG. Multi-omic network of DEPs, DEMs, and DE miRNA in AH of XFG patients compared to controls. (A) Integrated networks of DEPs, DEMs, and DE miRNA generated by querying STRING, KEGG, Recon3D, miRTarBase, and TRRUST databases. A minimum network setting was applied to enhance the clarity and conciseness of the network. In the figure, proteins are illustrated as red circles, metabolites as yellow circles, miRNA as teal circles, mRNA as gray circles, and transcription factors as green circles. Seed nodes are highlighted with a black shade. Modules were detected using a WalkTrap algorithm, and connections within modules are highlighted. Top ten ontology terms from GO aspects for (B) Module 1 and (C) Module 2 are listed, ranked by FDR-adjusted q-value. Molecular components within Module 1 are highlighted by an orange circle and connected by orange lines, while components within Module 2 are highlighted by a green circle and connected by green lines. Proteomic (and accompanying metabolomic) data was obtained from ProteomeXchange (PXD046219), and miRNA data was obtained from GEO (GSE105269). Constructed using OmicsNet 2.0. AH, aqueous humor; DE miRNA, differential expressed micro-RNA; DEMs, differential expressed metabolites; DEPs, differentially expressed proteins; FDR, false discovery rate; GEO, Gene Expression Omnibus; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; TRRUST, Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining; XFG, exfoliation glaucoma.

Other aspects of glaucoma that have been extensively studied through separate single-omics studies such as IOP regulation, optic nerve protection, and inflammatory immune response mechanisms would benefit from multi-omics integration and have been summarized below (Table 1).

Potential for glaucoma biomarkers and personalized treatment strategies

Currently, clinical strategies for diagnosing and assessing disease progression in glaucoma rely on clinical visual acuity tests (static perimetry) (124) and OCT to assess the loss of RNFL thickness (125). Despite advances in screening strategies, patients still often present with moderate-to-significant loss in peripheral vision by the time glaucoma is diagnosed (126,127). Utilizing current technologies, ganglion cell complex (GCC) analysis in OCT images has been proposed as a potential biomarker for glaucoma (128). However, additional and preferably blood-based biomarkers would be helpful in closely monitoring patients who are at risk of developing rapidly progressive visual loss. Similarly, biomarkers to evaluate the efficacy of pharmacological, surgical, or combinatory treatments would also prove beneficial. The integration of multi-omic data holds great promise in the discovery and utilization of blood-based susceptibility biomarkers for glaucoma for early diagnosis, prognosis evaluation, and evaluation of treatment resistance (Figure 5).

Figure 5 Potential application of omics information for clinical translation. Omics discoveries and their integration may contribute to diagnosis, target discovery for new drug development, or understanding disease mechanisms in glaucoma and other late-onset progressive neuropathies. Constructed using BioRender.

At the genomic level, numerous glaucoma-related loci discovered through GWAS, such as MYOC, OPTN, etc., not only revealed genetic susceptibility to the disease, but also site mutations or SNPs which can serve as potential genetic markers for early screening in high-risk populations (129,130). Dynamic monitoring of disease progression may be possible through tissue-specific gene expression profiles in the retina, TM, and other tissues. Specifically, upregulation of MMPs in the TM during IOP elevation are expected to be converted into mRNA markers that reflect disease stage. Proteomics complements and confirms genomic and transcriptomic information. Differentially expressed proteins in AH and vitreous are closely related to IOP regulation and nerve damage repair. Their abnormal expression levels may possibly serve as sensitive indicators for early diagnosis of glaucoma, capturing diagnostic opportunities in a timely manner (131). PPI network analysis suggested that proteins at critical nodes, such as myosin light chain kinase which regulates IOP, may potentially serve as biomarkers for monitoring therapeutic targets and reflect the effectiveness of drug interventions. Lipidomics and metabolomics identified differential expression of sphingolipids and related metabolites in blood, palmitoyl carnitine in AH, and glutamine:glutamate in vitreous (132), all of which may serve as potential indicators for inherent energy metabolism dysfunction and oxidative stress-related metabolism in glaucoma.

Multi-omics may provide powerful assistance in the development of personalized treatment strategies for glaucoma. Glaucoma subtypes associated with specific gene mutations could be candidates for targeted gene editing therapy or targeted protein inhibitions to block the disease progression. For example, CRISPR-Cas9 could be utilized to edit the mutation site, or specific inhibitors could be developed to target the mutated MYOC protein and help regulate IOP. Similarly, transcriptomic analysis of gene expressions may provide insight into the activation status of intracellular signaling pathways in different stages of glaucoma that could be used to design effective therapies (42,43). Synchronous analysis of metabolites in blood, AH, and surgically excised tissues may provide insight into energy metabolism and redox balance in the body (87) and aid in selecting the most suitable medication for the patient. High expression of inflammation-related genes together with increased oxidative stress revealed by metabolites may help clinicians prioritize drugs that combine anti-inflammatory and antioxidant effects. The current progress toward clinical translatability of various biomarkers have been summarized below (Table 2).

Technical challenges towards multi-omics analysis and integration

The integration of different molecular entities comes with its own challenges. One challenge is standardization across several systems and omes. While robust nucleic acid chemistry renders standardization of gene expression easier, protein standardization, even with multiplex tagging (133), remains an issue in comparing multiple diverse samples (134). As for metabolites and lipids, MS requires a large number of diverse standards to cover the entire mass-to-charge ratio, rendering the exercise inordinately expensive and impractical (134). Normalization of metabolites and lipids using synthetic peptides have been proposed to address this issue (134). Another challenge inherent in multi-omics is the limitations associated with the two commonly used approaches for network building. Knowledge-driven approaches are limited by prior knowledge due to their reliance on queried databases. With current knowledge of lipid interactions with other omics levels being limited, no database can be queried to accurately integrate most lipid features within a multi-omic network (121). Furthermore, current interactome databases and programs such as the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the Biomolecular Interaction Network Database (BIND) have issues regarding cell and tissue type-agnostic approaches. Therefore, querying such databases include interaction results from any experimental cell or tissue type. Meanwhile, data-driven approaches impose substantial demands on experimental design to achieve robust results. Due to different sample size requirements for different statistical methods, a minimum of 20 samples per group is recommended for data-driven integration; samples must also strictly match across all omics layers, ideally from the same set of biological samples (121). This may prove cumbersome in glaucoma research; the complexity of the disease requires patient recruitment, time-consuming and laborious collection and preservation of samples, compilation of clinical information, and other steps. Demographic factors and disease subtypes may also make it difficult to obtain a widely representative sample set. Finally, regardless of the network building approach, time is currently not considered in interactions. Many molecular entities require the time domain to be comprehensively understood, such as the protein arrestin which undergoes localization and protein-ligand interaction changes within photoreceptors during dark-adapted and light exposure phases.

Presently, multi-omics relies on the integration of traditional omics which reflect the average information of cell populations and mask intercellular heterogeneity. To analyze the molecular characteristics of individual cells, single-cell omics analyses must be performed. However, the application of single-cell omics technology requires further development (135,136), and in its current state, single-cell sequencing remains costly, has limited throughput, and requires complex data analysis (136). Single-cell omics in glaucoma would certainly advance our understanding of specific cells’ role changes and signal interactions and provide novel ideas for diagnosis and therapy, but its application is only in its infancy.

The translation of multi-omics research results into clinical practice also faces many obstacles. All multi-omics studies of glaucoma involve human samples, raising ethical considerations for clinical translation. Cost must also be considered; technologies for multi-omics analyses require significant upfront monetary investment in equipment and training, all of which add to high clinical testing costs. Multi-omics research in glaucoma also involves collaborations from different interdisciplinary fields such as ophthalmology, genetics, bioinformatics, and clinical medicine. Barriers from achieving coherence among researchers, such as gaps in professional knowledge and communication, must be appropriately addressed to allow for a smooth transition of results from laboratory to clinical settings, as well as avoid delays in implementing potential diagnostic and therapeutic innovations.

Future directions and prospects

Multi-omics integration and AI utilization have been trending to solve the challenge of early diagnosis in glaucoma. Using large data from genomics, transcriptomics, proteomics, and metabolomics, AI algorithms can help uncover characteristic disease patterns that would allow for the development of a highly sensitive and specific early diagnosis model for glaucoma (62,125). Meanwhile, tailored gene therapy and targeted drug therapy plans based on individual genomic, transcriptomic and proteomic characteristics of patients are bound to emerge (137). Metabolomics can be used to guide nutritional interventions that complement traditional treatments, ushering in a new era of personalized and holistic treatment for glaucoma (86,138).

Limitations

Beyond limitations to single-omics and multi-omics research, which have been thoroughly discussed in prior sections, our review itself is limited by our literature search scope and methodology of analysis. Specifically, literature review relied on PubMed as a search database and included only studies published in English. Due to the heterogeneity of omics methods, single-omics findings discussed in the review were synthesized narratively rather than quantitatively. Furthermore, due to the rapidly evolving nature of multi-omics, newer datasets may emerge after this study’s publication.

Conclusions

Single omics analyses have provided detailed insights into glaucoma and other optic neuropathies. While these high-resolution, high-throughput methods of data analysis have obtained new and promising avenues for research, it is also important to recognize the roles and variations among the different molecular levels holistically. Multi-omics may yield the identification of new biomarkers, a more comprehensive understanding of its pathogenesis, and further bridge the knowledge gap between genotype and phenotype. Multi-omics integration has the capacity to revolutionize glaucoma treatment, screening measures, and understanding of the disease process as well as other neurodegenerative diseases. At present, multi-omics approaches face several challenges in achieving clinical translation, such as standardization, limitations in prior knowledge, and logistical difficulties. However, the field is constantly undergoing advancements and research that may ultimately be able to overcome these barriers.

Acknowledgments

We thank Dr. Michael Coronado for his helpful comments on an earlier version of this manuscript.

Footnote

Peer Review File: Available at https://aes.amegroups.com/article/view/10.21037/aes-25-60/prf

Funding: This work was supported by NIH grants (Nos. EY031292, EY14801), and an unrestricted grant from Research to Prevent Blindness (No. GR004596-1).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://aes.amegroups.com/article/view/10.21037/aes-25-60/coif). S.K.B. serves as an unpaid editorial board member of Annals of Eye Science from August 2024 to December 2026. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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