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Article

Twenty Novel MicroRNAs in the Aqueous Humor of Pseudoexfoliation Glaucoma Patients

by
Marcin Czop
1,*,†,
Karolina Gasińska
2,†,
Ewa Kosior-Jarecka
2,
Dominika Wróbel-Dudzińska
2,
Janusz Kocki
1,‡ and
Tomasz Żarnowski
2,‡
1
Department of Clinical Genetics, Medical University of Lublin, 20080 Lublin, Poland
2
Department of Diagnostics and Microsurgery of Glaucoma, Medical University of Lublin, 20079 Lublin, Poland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
These authors contributed equally to this work.
Cells 2023, 12(5), 737; https://doi.org/10.3390/cells12050737
Submission received: 8 February 2023 / Accepted: 22 February 2023 / Published: 24 February 2023
(This article belongs to the Special Issue Molecular Regulation in Ocular Physiology and Diseases)

Abstract

:
The microRNAs (miRNAs) are short non-coding RNAs (19–25 nt) that regulate the level of gene expression at the post-transcriptional stage. Altered miRNAs expression can lead to the development of various diseases, e.g., pseudoexfoliation glaucoma (PEXG). In this study, we assessed the levels of miRNA expression in the aqueous humor of PEXG patients using the expression microarray method. Twenty new miRNA molecules have been selected as having the potential to be associated with the development or progression of PEXG. Ten miRNAs were downregulated in PEXG (hsa-miR-95-5p, hsa-miR-515-3p, hsa-mir-802, hsa-miR-1205, hsa-miR-3660, hsa-mir-3683, hsa -mir-3936, hsa-miR-4774-5p, hsa-miR-6509-3p, hsa-miR-7843-3p) and ten miRNAs were upregulated in PEXG (hsa-miR-202 -3p, hsa-miR-3622a-3p, hsa-mir-4329, hsa-miR-4524a-3p, hsa-miR-4655-5p, hsa-mir-6071, hsa-mir-6723-5p, hsa-miR-6847-5p, hsa-miR-8074, and hsa-miR-8083). Functional analysis and enrichment analysis showed that the mechanisms that can be regulated by these miRNAs are: extracellular matrix (ECM) imbalance, cell apoptosis (possibly retinal ganglion cells (RGCs)), autophagy, and elevated calcium cation levels. Nevertheless, the exact molecular basis of PEXG is unknown and further research is required on this topic.

1. Introduction

Glaucoma is a group of multifactorial eye diseases that result in progressive irreversible damage to the optic nerve and cause vision loss, and is considered the second leading cause of blindness in the world [1]. The basic process in the pathophysiology of glaucoma is the loss of retinal ganglion cells (RGCs) and their axons, which leads to changes in the morphology of the optic disc and consequently to visual field defects. The main risk factor for glaucoma is increased intraocular pressure (IOP), caused by an imbalance between the production and outflow of aqueous humor (AH) from the anterior chamber [2]. The most important outflow pathway is trabecular meshwork (TM), which consists of trabecular cells surrounded by the extracellular matrix (ECM). Dysfunction in the normal homeostatic process leads to increased outflow resistance and elevated IOP [3]. Lowering IOP is the only treatment that prevents the progression of vision loss [4].
Pseudoexfoliation glaucoma (PEXG) is a type of secondary open-angle glaucoma, developing usually over 50 years of age in the course of pseudoexfoliation syndrome (PEX). The prognosis is worse than for primary open-angle glaucoma (POAG) due to higher IOP values and greater fluctuations. PEX was first described in 1917 by Finnish ophthalmologist, John Linberg [5,6]. It’s a chronic, age-related systemic disorder, with variable prevalence among men and women in different studies [7,8,9,10,11]. Pathognomonic eye symptoms are gray-white deposits in the anterior segment of the eyeball (corneal endothelium, trabecular meshwork, pupillary margin, iris pigment epithelium, dilator muscle of the iris, iris blood vessels, ciliary epithelium, lens epithelium, lens capsule, zonules) and periorbital tissues (conjunctiva, Tenon’s capsule, orbital septa, extraocular muscles) [12,13,14,15]. PEX material can also be found in many internal organs (heart, lungs, liver, kidneys, gallbladder, blood vessels, cerebral meninges) and skin [5]. The deposits are the result of increased production or reduced turnover of ECM [16]. They are composed of elastic fibers (elastin, tropoelastin, amyloid P, fibrillin-1, fibulin-2, vitronectin, fibronectin, lysyl oxidase, clusterin, LTBP-1, LTBP-2, and other proteins) and noncollagenous basement membrane materials (laminin) which form fibrils. They are coated with glycosaminoglycans (heparan sulfate, chondroitin sulfate, dermatan sulfate, and hyaluronic acid) [17,18]. Mechanisms leading to the development of PEX may include oxidative stress, hypoperfusion, and hypoxia [19]. Previous studies revealed many genes that can be related to PEX (APOE, CACNA1A, CLU, CNTNAP2, FBLN5, GST, LOXL1, MMP1, MMP3, TNF, TMEM136) by participating in the formation of PEX material, regulation of trabecular cells and ECM proteins [18,20,21,22,23]. Gene expression is a complex process in which microRNAs (miRNAs) are involved. Many miRNAs show a tissue-specific expression pattern and can be identified in eye tissues and fluids, but the role of miRNAs in glaucoma and PEXG is not well understood [24,25,26].
MiRNAs are short non-coding RNAs (19–25 nt) that regulate the level of protein expression at the post-transcriptional level. These molecules are responsible for the regulation of many physiological cellular processes, including division, cell differentiation, and regeneration, but also for many pathological processes such as neoplastic transformation. MiRNAs are found both in the cell and in extracellular fluids, i.e., plasma, urine, and aqueous humor [27,28,29,30]. MiRNAs are transcribed in a similar way to protein genes. The first step in the expression of a gene encoding miRNA is the synthesis of primary miRNA (several hundred nucleotides long), which are then modified in the nucleus at both ends of the RNA. The next stage of miRNA maturation takes place in the cytoplasm and, as a consequence, a pre-miRNA of about 70 nucleotides is formed, followed by the production of mature micro RNA. The functioning of micro RNA is closely related to the RNA-induced silencing complex (RISC). Both molecules linked together to interact with the target mRNA through partial complementarity of the sequence of one of the miRNA strands, leading to degradation of the target mRNA through the activity of the RISC complex. By analyzing the level of miRNA expression, it is possible to draw conclusions about the pathomechanism of the studied diseases and to use them as biomarkers of these diseases [27,31,32]. The most important features of biomarkers are: specificity for the disease entity, the possibility of quick, easy, and safe determination, and the possibility of obtaining accurate results [33,34,35,36].
The aim of the study was to assess the levels of miRNA expression in a group of patients with PEXG and to look for mechanisms caused by altered expression of miRNA that may be related to this disease.

2. Materials and Methods

2.1. Study Groups

Eighteen western descent patients underwent routine cataract surgery at the Department of Diagnostics and Microsurgery of Glaucoma, Medical University of Lublin (Poland) (nine PEXG and nine age-matched control patients). Prior to the surgery, all of the patients underwent the ophthalmic examination including best-corrected visual acuity (BCVA) assessed with Snellen charts, an IOP test measured by Goldmann applanation tonometry (GAT), gonioscopy, a slit-lamp examination, indirect ophthalmoscopy after pupil dilation with a stereoscopic optic nerve head assessment, optical coherence tomography (Carl Zeiss Cirrus HD-OCT 5000), and standard automated perimetry (SAP) (24-2 strategy using the Humphrey perimeter).
Inclusion criteria in the study group:
(1)
Incipient senile cataract: cloudy area in the lens that causes decreased vision.
(2)
PEX syndrome: typical deposits of white powdery material observed along the pupillary margin and on the peripheral lens capsule.
(3)
Glaucomatous optic neuropathy: elevated IOP of greater than 21 mmHg, documented in medical history; open-angle grade III/IV according to Schaffer’s classification; glaucomatous optic nerve head damage (excavation, neuroretinal rim thinning or notching, and localized or diffuse retinal nerve fiber layer [RNFL] defect); and glaucomatous defect in SAP in at least two consecutive tests, with three reliability indices better than 15% (results were considered abnormal if the Glaucoma Hemifield Test result was outside normal limits and at least three contiguous points were present within the same hemifield on the pattern deviation [PD] plot at p < 1%, with at least 1 point at p < 0.5%).
(4)
Advanced stages of PEXG: severe visual field loss with MD in visual field test > −18.0 dB.
Inclusion criteria in the control group:
(1)
Incipient senile cataract: cloudy area in the lens that causes decreased vision.
(2)
No clinical signs of glaucoma, nor PEX.
(3)
Normal IOP: from 10 mmHg to 21 mmHg.
In both groups, other causes of RNFL thinning (i.e., myopia, optic disc anomalies, ischemic optic neuropathy, optic neuritis, hereditary optic neuropathy, traumatic optic neuropathy, multiple sclerosis, and degenerative diseases such as Alzheimer’s and Parkinson’s disease) were excluded. Moreover, all of the participants had no previous intraocular surgeries, no other systemic or ocular diseases known to affect the visual field (e.g., pituitary lesions, demyelinating diseases, diabetes mellitus, etc.), and no previous eye or head trauma in their medical history.
The patients’ demographic and clinical data are presented in Table 1.

2.2. Aqueous Humor

Approximately 100 µL of AH were obtained from the eye’s anterior chamber at the beginning of the cataract surgery with special care to avoid contamination with blood or tears.

2.3. RNA Isolation

The total RNA was isolated from the AH samples using an miRNeasy Serum/Plasma Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The isolated RNA was stored at −80 °C for further analysis. The RNA concentration was determined by using a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). In addition, RNA analysis was performed using an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA) and a Pico RNA Kit according to the manufacturer’s protocol.

2.4. The miRNA Profiling

A microarray system (GeneChip miRNA 4.0 Array chip, Affymetrix, Santa Carla, CA, USA) was used to determine the miRNA expression profiles. The RNA preparation and hybridization were performed according to the manufacturer’s protocol, with one modification to extend the hybridization time to 42 h. The gene chips were scanned with an Affymetrix GeneChip Scanner 3000 (Affymetrix, Santa Carla, CA, USA).
The raw data, in a CEL format, were analyzed as log2-transformed intensities using the Affymetrix Transcriptome Analysis Console (TAC), following the software’s guidelines to determine differentially expressed genes (DEGs) between the PEXG and control patients. The miRNAs that showed at least a one-fold difference between the control and PEXG groups, with a p-value of less than 0.01, were considered statistically significant and differentially expressed.

2.5. Enrichment Analysis

The functional analysis of selected miRNA was performed using the Diana mirPATH v3.0 (https://dianalab.e-ce.uth.gr/html/mirpathv3/index.php?r=mirpath (accessed on 2 September 2022)) [37]. The visualization of the regulatory network with obtained interactions was carried out by using Cytoscape v3.9.1 software (https://cytoscape.org (accessed on 2 September 2022)) [38].
The analysis of the interactions of the selected miRNAs with genes was carried out in an R environment (version 4.1.2, https://www.r-project.org (accessed on 1 December 2021)) using the multiMiR 1.16.0 package (https://bioconductor.org/packages/release/bioc/html/multiMiR.html, (accessed on 2 September 2022)) [39]. The enrichment analysis was performed using the mirNET database (https://www.mirnet.ca/miRNet/home.xhtml (accessed on 2 September 2022)), where a list of selected miRNAs and a list of genes with which they interact were introduced [40]. Terms of the Kyoto Encyclopedia of Genes and Genomes (KEGG), REACTOME, and Gene Ontology (GO) categories were searched for in PEX-related pathways.
The plot visualizing the enrichment analysis was generated using the ggplot2 3.3.0 package in the R environment.

2.6. PPI Network and Subnetwork Construction

Cytoscape was also used to perform protein–protein interactions (PPI). StringApp v2.0 (https://apps.cytoscape.org/apps/stringapp (accessed on 5 January 2023)) [41] was used for this purpose, which uses the Search Tool for the Retrieval of Interacting Genes (STRING). Molecular Complex Detection v2.0.2 (MCODE) (https://apps.cytoscape.org/apps/mcode (accessed on 7 January 2023)) [42] and cytoHubba v0.1 (https://apps.cytoscape.org/apps/cytohubba (accessed on 7 January 2023)) [43] plugins were then used to create clusters and extracting hub genes in a PPI network.

2.7. Statistical Analysis

Results at p < 0.05 were considered statistically significant. The following software was used: GraphPad Prism 9 (Graph Pad Software, San Diego, CA, USA) and Statistica 13.3 (StatSoft, Krakow, Poland). The analyzes included the Shapiro–Wilk test (to assess the compliance of the examined variables with the normal distribution), the student’s t- test, and ROC curves (area under the curve (AUC) were calculated) for variables on a quantitative scale (data presented as an average). Data on a qualitative scale are presented as numbers and percentages and were analyzed using the Chi^2 test.

3. Results

Analysis comparing the expression level of miRNAs using expression microarrays in the PEXG group compared to the cataract group showed 20 miRNAs as DEG’s.
Ten miRNAs were downregulated in PEXG (hsa-miR-95-5p, hsa-miR-515-3p, hsa-mir-802, hsa-miR-1205, hsa-miR-3660, hsa-mir-3683, hsa -mir-3936, hsa-miR-4774-5p, hsa-miR-6509-3p, and hsa-miR-7843-3p) (Table 2, Figure 1) and ten miRNAs were upregulated in PEXG (hsa-miR-202-3p, hsa-miR-3622a-3p, hsa-mir-4329, hsa-miR-4524a-3p, hsa-miR-4655-5p, hsa-mir-6071, hsa-mir-6723-5p, hsa-miR-6847-5p, hsa-miR-8074, and hsa-miR-8083) (Table 3, Figure 2).
In the group of DEG’s miRNAs whose expression was lower in PEXG compared to the group (p < 0.01; AUC > 0.852; p < 0.01), a mean reduction in the level of PEXG expression was found at the level of 29.31% for hsa-miR-95-5p, 36.86% for hsa-miR-515-3p, 29.13% for hsa-mir-802, 28.35% for hsa-miR-1205, 13.15% for hsa-miR-3660, 43.64% for hsa-mir-3683, 42.14% for hsa-mir-3936, 14.35% for hsa-miR-4774-5p, 22.13% for hsa-miR-6509-3p, and 27, 37% for hsa-miR-7843-3p.
In the group of DEG’s miRNAs whose expression was higher in PEXG compared to the group (p < 0.01; AUC > 0.840; p < 0.01), a mean increase in the level of PEXG expression was found at the level of 28.73% for hsa-miR-202-3p, 29.65% for hsa-miR-3622a-3p, 20.05% for hsa-mir-4329, 21.64 for hsa-miR-4524a-3p, 34.06% for hsa-miR-4655 -5p, 23.50% for hsa-mir-6071, 24.61% for hsa-mir-6723-5p, 24.30% for hsa-miR-6847-5p, 21.57% for hsa-miR-8074, and 23.06% for hsa-miR-8083.
Using the DIANA miRPath v3.0 database, a functional analysis of each of the selected miRNAs was performed (the analysis was performed separately for miRNAs with reduced and increased expression in PEXG). Next, networks were constructed visualizing possible functional relationships between the selected miRNAs (Figure 3 and Figure 4).
The functional network for downregulated miRNA functionally linked together nine miRNAs (leaving one type of miRNA without functional linkage—hsa-miR-3683). The terms with the most associations between the selected miRNAs were: “Ion binding”, “organelle”, “biosynthetic process”, “cellular nitrogen compound metabolic process”, “molecular function” and “molecular function”.
Functional network for upregulated miRNA functionally linked eight miRNAs (leaving two types of miRNAs (has-miR-6723-5p, and hsa-miR-4655-5p) without functional links between other miRNAs and with each other). The terms that obtained the most connections between the selected miRNAs were: “Ion binding”, “organelle”, “biosynthetic process”, “cellular nitrogen compound metabolic process”, “steroid hormone biosynthesis” and “gene expression”.
The next step was to perform an enrichment analysis to evaluate the biological processes regulated by the selected miRNAs and interacted genes using the mirNET database. Five categories were selected: KEGG (Kyoto Encyclopedia of Genes and Genomes), GO: Biological Processing (GO:BP), GO:Cellular Compartment (GO:CC), GO: Molecular Function (GO:MF), and Reactome. Genes indicated as targets of the expressed miRNAs were associated with intracellular and extracellular structures in addition to various processes. The top 10 terms for each category that may be potentially related to glaucoma and/or PEX are presented in Figure 5 and Figure 6, separately for downregulated and upregulated miRNAs in PEXG.
Within the group of downregulated miRNAs, the most frequent terms are related to the regulation of transcription and translation, internal cellular transport (“trans-Golgi network transport vesicle”, “endocytosis”), and signal transduction (Figure 5). In the upregulated miRNA group, the most frequently repeated terms are related to the regulation of transcription and translation, the regulation of the cell cycle, and the activity of cations (mainly calcium and zinc) (Figure 6).
A PPI network of the genes regulated by selected miRNAs were constructed and clustering analysis was performed based on these PPI networks. In total, six clusters (sub-networks) were created for each PPI network of the genes regulated by downregulated miRNAs (Figure 7, Table 4). Clusters I and IV are functionally related to chromatin organization and epigenetic regulation of gene expression, moreover, the key genes in them are the EZH2 gene (in cluster I) and BAZ1B (in cluster IV). Cluster II is functionally related to nuclear chromosome segregation and cellular response to DNA damage (the key gene: TIMELESS). Cluster III is functionally related to the voltage-gated calcium channel complex and MAPK signaling pathway (the key gene: CACNA2D1). Cluster V is functionally related to protein transport (the key gene: RAB7A). Cluster VI is functionally related to ubiquitinization (the key gene: UBE2D1).
In total, six clusters (sub-networks) were created for each PPI network of the genes regulated by upregulated miRNAs (Figure 8, Table 5). Cluster VII is functionally related to cytokine-cytokine receptor interaction and cell–cell adhesion (the key gene: IL10). Cluster VIII is functionally related to the activity of RNA polymerases II and III (the key gene: PLR2D). Cluster IX is functionally related to ubiquitinization (the key gene: PHC1). Cluster X is functionally related to L-type voltage-gated calcium channel complex (the key gene: CACNA1G). Cluster XI is functionally related to the regulation to the jnk cascade (the key gene: AXIN1). Cluster XII is functionally related to Rho GTPases activate ROCKs and phospholipase D signaling pathway (the key gene: RHOA).
The hub genes identified using the cytoHubba app included the EZH2, H2AFX in hub I, CACNA1D in hub IIa, and MYC in hub IIb (Figure 9). Enrichment analysis for these networks indicated that hub I is related to generic transcription pathway and cellular responses to stress, hub IIa is related to voltage-gated channel activity and MAPK signaling pathway, hub IIb is related to cellular senescence and Wnt, Hippo, JAK-STAT signaling pathways (Table 6).

4. Discussion

MiRNA molecules are found both inside the cell and in extracellular fluids. The miRNA in the AH may be derived from blood plasma and may be secreted by intraocular cells. These molecules have been shown to be of great importance in processes such as retinal homeostasis and its development [44,45].
Current knowledge about the pathological mechanisms of PEXG is still unknown. Many studies suggest the role of non-coding RNAs, which, through altered expression levels, may be associated with the development of PEXG. The role of miRNAs [26,46,47,48] and snoRNA [49] is mainly suggested.
Current literature suggests a role for certain miRNAs in the pathogenesis of PEXG. These are hsa-miR-671, hsa-miR-374a-5p, hsa-miR-1307-5p, hsa-miR-708-5p [48], and hsa-miR-30d-5p, hsa-miR-320a, hsa- miR-3156-5p, hsa-miR-4458, hsa-miR-6717-5p, hsa-miR-6728-5p, hsa-miR-6834-5p, hsa-miR-6864-5p, hsa-miR-6879- 5p, hsa-miR-877-3p, hsa-miR-548e-3p, and hsa-miR-6777-5p [26]. The main mechanisms associated with the altered expression of these miRNAs is cell apoptosis by suppressing the Wnt3a/β-catenin pathway and the PI3K/AKT pathway [48] and the TGF-beta signaling pathway [26].
In our work, we examined the level of miRNA expression in a group of patients with PEXG and cataracts. After selecting the 20 DEGs of miRNAs (10 upregulated and 10 downregulated), we performed an enrichment and functional analysis.
One of the pathological mechanisms related to PEX contains too high of a concentration of calcium cations, which is directly related to the activity of the channels for these cations. Currently, three types of calcium channels are cited, i.e., CACNA1A [21], CACNB3 [46,49,50], and CACNB4 [50]. The calcium channel is composed of subunits, of which the α subunit forms a transmembrane channel, while the remaining subunits (β and α2/δ) perform regulatory functions. Additionally, in some channels, there may be a γ subunit. The calcium channels are divided into three subfamilies, i.e., CaV1, CaV2, and CaV3. The activity of the CaV1 subfamily channels are responsible for the initiation of contraction and regulates gene expression and transmission in synapses. The CaV2 subfamily channels are associated with synaptic transmission, while the CaV3 subfamily channels are associated with the production of action potentials in myocytes and neurons. The CaV1.4 transporter has been shown to be present in the retinal cell membranes of the eye and its activity is related to visual signaling [51,52]. The results of functional analysis in our study showed that hsa-miR-8074 influences the transport of calcium cations across biological membranes through the regulation of the ATP2B2 gene. Moreover, we found that the voltage-gated calcium channel activity is being regulated by the genes targeted by selected miRNAs. Additionally, the Wnt signaling pathway is regulated by the genes targeted by selected miRNAs, where one of the signaling pathways is the Wnt/Ca2 + pathway, the activity of which is related to G proteins and phospholipases.
An important regulatory structure of the IOP is HTM (human trabecular meshwork), which is made up of trabecular cells surrounded by the ECM. HTM is responsible for the outflow of AH—in a situation of difficult outflow, the IOP increases [53,54]. The literature provides two pathways (TGFβ1 and TGFβ2), in which the changes lead to an imbalance in the eye’s ECM and, consequently, to an increase in IOP [53,54]. Among the results of the functional analysis in our work, we obtained the term “ECM receptor interactions” for four miRNAs that are regulated by genes: hsa-mir-802 (COL5A1, LAMC1, FN1, CD47), hsa-mir-3660 (LAMA3), hsa-mir -3662a-3p (ITGB8, SV2B, COL24A1, COL4A6, TNR), and hsa-miR-4774-5p (COL11A1, COL24A1). In addition, proteins are a very important element of the ECM. Disruption of protein metabolism (synthesis, post-translational modification, or transport) may lead to the development of PEX [55].
Functional analysis of selected miRNAs showed that a relatively large number of these miRNAs influence genes related to protein metabolism. The process of protein modification in the cell is regulated by genes whose expression is influenced by six out of twenty selected miRNAs (hsa-miR-95-5p, hsa-miR-1205, hsa-miR-3660, hsa-miR-515-3p, and hsa- miR-6071,22-3p). In addition, subsequent miRNAs affect the expression level of genes related to protein metabolism, i.e., “protein complex” (hsa-miR-4329, hsa-miR-6071, hsa-miR-1205, and hsa-miR-3660), “cytoskeletar protein binding” (hsa-miR-6847-5p, 1205), “post-translational protein modification” (hsa-miR-7843-3p), “endoplasmic reticulum-Golgi intermediate compartment” (has-miR-802), and “cytoplasmic sequestering of protein “(Hsa-miR-4655-5p). Moreover, hsa-miR6723-5p regulates the expression level of the UBE2H gene related to ubiquitin-mediated proteolis. Among the enriched functional terms of genes targeted by selected miRNAs in AH in PEXG patients, it is worth mentioning the “trans-Golgi network transport vesicle” as a process that influences protein transport. In addition, many times the results of this analysis were terms related to the process of protein biosynthesis (“positive regulation of translation”, “regulation of translation”) and transcription (“negative regulation of transcription from RNA polymerase II promoter”, “negative regulation of transcription, DNA- dependent “). In a previous work, we suggested that some snoRNA molecules are related to PEXG [49]. One of the terms obtained in enrichment analysis is “spliceosomal complex” which may suggest that the selected miRNAs may also be responsible for this process.
The mechanisms discussed above largely explained where the elevated IOP comes from. Elevated IOP results in RGC apoptosis and, furthermore, optic nerve degeneration which is often observed in late PEXG [56,57,58]. Another mechanism that can lead to IOP increase and cell death in glaucoma is oxidative stress [59,60,61,62]. The hsa-miR-802 molecule by regulating the expression of MCL1, PPP2CB, and SOD2 genes influences the processes taking place in the mitochondria during apoptosis. Among the enriched functional terms of genes targeted by selected miRNAs in AH in PEXG patients, it is worth mentioning “G0 and early G1” and “G2/M transition of the mitotic cell cycle”. In addition, “AKT phosphorylates targets in the cytosol” and “PI3K/AKT activation” are terms that suggest that selected miRNAs may regulate the survival of ocular neurons and thus may lead to optic nerve degeneration. The role of the PI3K/Akt pathway was previously mentioned [63,64,65,66,67].
Hsa-miR-802 shows an increased expression in primary human umbilical vein endothelial cells (HUVECs) in response to induced hypoxia [67]. As above, hsa-miR-202-3p is one of the miRNAs whose expression levels have been altered in lung adenocarcinoma A549 cells by hypoxia [68].
Additionally, a correlation between the risk of developing normal-tension glaucoma (NTG) and the level of retinol in the blood serum was demonstrated [69]. Moreover, another group of scientists reports that retinoic acid (a retinol derivative) lowers the expression level of the gene which codes the small heat shock protein B8 (HSPB8) [70], which is a chaperone protein functionally related to chaperone-assisted selective autophagy (CASA) [71]. One of the miRNA molecules selected as DEGs in our work, hsa-miR-6509-3p regulates the expression level of the HSPB8 gene. The above data may suggest a potential influence of the autophagy process in the development of PEXG.
The results of the PPI analysis suggest and confirm that a very important mechanism of PEXG may be disturbances in the transport of calcium cations in the eye. One of the hub genes that have been selected is the CACNA1D gene.
Our research had some limitations. First, the study groups were small, consisting of nine patients each. A study involving a larger group of patients is needed to confirm the results. Furthermore, PEXG patients had advanced neuropathy, which suggests that miRNA molecules may be the result of the pathological process and not a causative factor. However, we precluded that the same stage of the disease within the group would enable more unique results. Since the advanced stages of PEXG, comparison of miRNAs in the early stages of PEXG is needed. In addition, all enrolled patients were diagnosed with senile cataracts. Therefore, the impact of cataracts on the expression of miRNA in the PEXG group should be taken into consideration, but in the studied group all patients also had similar stages of cataracts. However, it is ethically impossible to collect aqueous humor in healthy eyes due to the invasive nature of the procedure.
Until now, the scientific literature has not provided such information, which means that this is the first time we are doing it. These mechanisms are possible, but their role in the pathogenesis of PEXG/PEX must be carefully studied.

5. Conclusions

Our results suggest 20 new miRNAs whose altered expression in the aqueous humor may have an influence on the pathomechanisms of PEXG. Functional and enrichment analysis confirms the mechanisms, such as high levels of calcium cations, ECM imbalance, apoptosis of RGC cells, and the optic nerve damage or autophagy as possible causes of PEXG. Moreover, the PPI analysis suggests that CACNA1D, EZH2, and MYC may be hub genes for PEXG. However, confirmation of these conclusions requires further study.

Author Contributions

Conceptualization, K.G., M.C., E.K.-J., J.K. and D.W.-D.; methodology, K.G., M.C., E.K.-J. and D.W.-D.; software, K.G. and M.C.; formal analysis, K.G. and M.C.; investigation, K.G., M.C., E.K.-J., D.W.-D., J.K. and T.Ż.; data curation, K.G. and M.C.; writing—original draft preparation, K.G. and M.C.; writing—review and editing, K.G., M.C., E.K.-J., D.W.-D., J.K. and T.Ż.; supervision, J.K. and T.Ż. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Bioethics Committee of the Medical University of Lublin (KE-0254/107/2020, 28 May 2020).

Informed Consent Statement

Informed consent was obtained from all of the subjects involved in the study.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Allison, K.; Patel, D.; Alabi, O. Epidemiology of Glaucoma: The Past, Present, and Predictions for the Future. Cureus 2020, 12, e11686. [Google Scholar] [CrossRef] [PubMed]
  2. Cela, D.; Brignole-Baudouin, F.; Labbé, A.; Baudouin, C. The trabecular meshwork in glaucoma: An inflammatory trabeculopathy? J. Fr. Ophtalmol. 2021, 44, e497–e517. [Google Scholar] [CrossRef] [PubMed]
  3. Keller, K.E.; Peters, D.M. Pathogenesis of glaucoma: Extracellular matrix dysfunction in the trabecular meshwork—A review. Clin. Experiment. Ophthalmol. 2022, 50, 163–182. [Google Scholar] [CrossRef] [PubMed]
  4. Jayaram, H. Intraocular pressure reduction in glaucoma: Does every mmHg count? Taiwan J. Ophthalmol. 2020, 10, 255. [Google Scholar] [CrossRef]
  5. Rao, R.Q.; Arain, T.M.; Ahad, M.A. The prevalence of pseudoexfoliation syndrome in Pakistan. Hospital based study. BMC Ophthalmol. 2006, 6, 27. [Google Scholar] [CrossRef] [Green Version]
  6. Plateroti, P.; Plateroti, A.M.; Abdolrahimzadeh, S.; Scuderi, G. Pseudoexfoliation Syndrome and Pseudoexfoliation Glaucoma: A Review of the Literature with Updates on Surgical Management. J. Ophthalmol. 2015, 2015, 370371. [Google Scholar] [CrossRef] [Green Version]
  7. Al-Bdour, M.D.; Al-Till, M.I.; Idrees, G.M.; Abu Samra, K.M. Pseudoexfoliation syndrome at Jordan University Hospital. Acta Ophthalmol. 2008, 86, 755–757. [Google Scholar] [CrossRef]
  8. Krishnadas, R.; Nirmalan, P.K.; Ramakrishnan, R.; Thulasiraj, R.D.; Katz, J.; Tielsch, J.M.; Friedman, D.S.; Robin, A.L. Pseudoexfoliation in a rural population of southern India: The Aravind Comprehensive Eye Survey. Am. J. Ophthalmol. 2003, 135, 830–837. [Google Scholar] [CrossRef]
  9. Idakwo, U.; Olawoye, O.; Ajayi, B.G.K.; Ritch, R. Exfoliation syndrome in Northern Nigeria. Clin. Ophthalmol. 2018, 12, 271. [Google Scholar] [CrossRef] [Green Version]
  10. Åström, S.; Lindén, C. Incidence and prevalence of pseudoexfoliation and open-angle glaucoma in northern Sweden: I. Baseline report. Acta Ophthalmol. Scand. 2007, 85, 828–831. [Google Scholar] [CrossRef]
  11. Arnarsson, Á.M. Epidemiology of exfoliation syndrome in the Reykjavik Eye Study. Acta Ophthalmol. 2009, 87, 1–17. [Google Scholar] [CrossRef]
  12. Rumelaitiene, U.; Speckauskas, M.; Tamosiunas, A.; Radisauskas, R.; Peto, T.; Larsen, M.B.; Zaliūniene, D. Exploring association between pseudoexfoliation syndrome and ocular aging. Int. Ophthalmol. 2022, 1–11. [Google Scholar] [CrossRef]
  13. Palko, J.; Qi, O.; Sheybani, A. Corneal Alterations Associated with Pseudoexfoliation Syndrome and Glaucoma: A Literature Review. J. Ophthalmic Vis. Res. 2017, 12, 312. [Google Scholar] [CrossRef]
  14. Hammer, T.; Schlötzer-Schrehardt, U.; Naumann, G.O.H. Unilateral or Asymmetric Pseudoexfoliation Syndrome?: An Ultrastructural Study. Arch. Ophthalmol. 2001, 119, 1023–1031. [Google Scholar] [CrossRef] [Green Version]
  15. Eroglu, F.C.; Sekeroglu, M.A.; Ceran, T.H.; Simsek, M.; Hondur, G. Evaluation of lacrimal drainage system in Pseudoexfoliation syndrome. Eye 2021, 36, 2094–2098. [Google Scholar] [CrossRef]
  16. Mastronikolis, S.; Pagkalou, M.; Plotas, P.; Kagkelaris, K.; Georgakopoulos, C.D. Emerging roles of oxidative stress in the pathogenesis of pseudoexfoliation syndrome (Review). Exp. Ther. Med. 2022, 24, 602. [Google Scholar] [CrossRef]
  17. Padhy, B.; Alone, D.P. Is pseudoexfoliation glaucoma a neurodegenerative disorder? J. Biosci. 2021, 46, 97. [Google Scholar] [CrossRef]
  18. Elhawy, E.; Kamthan, G.; Dong, C.Q.; Danias, J. Pseudoexfoliation syndrome, a systemic disorder with ocular manifestations. Hum. Genom. 2012, 6, 22. [Google Scholar] [CrossRef] [Green Version]
  19. Ariga, M.; Nivean, M.; Utkarsha, P. Pseudoexfoliation Syndrome. J. Curr. Glaucoma Pract. 2022, 7, 118–120. [Google Scholar] [CrossRef]
  20. Yilmaz, A.; Tamer, L.; Ateş, N.A.; Çamdeviren, H.; Deǧirmenci, U. Effects of apolipoprotein E genotypes on the development of exfoliation syndrome. Exp. Eye Res. 2005, 80, 871–875. [Google Scholar] [CrossRef]
  21. Aung, T.; Ozaki, M.; Mizoguchi, T.; Allingham, R.R.; Li, Z.; Haripriya, A.; Nakano, S.; Uebe, S.; Harder, J.M.; Chan, A.S.Y.; et al. A common variant mapping to CACNA1A is associated with susceptibility to exfoliation syndrome. Nat. Genet. 2015, 47, 387–392. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Padhy, B.; Kapuganti, R.S.; Hayat, B.; Mohanty, P.P.; Alone, D.P. De novo variants in an extracellular matrix protein coding gene, fibulin-5 (FBLN5) are associated with pseudoexfoliation. Eur. J. Hum. Genet. 2019, 27, 1858–1866. [Google Scholar] [CrossRef] [PubMed]
  23. Aung, T.; Ozaki, M.; Lee, M.C.; Schlötzer-Schrehardt, U.; Thorleifsson, G.; Mizoguchi, T.; Igo, R.P.; Haripriya, A.; Williams, S.E.; Astakhov, Y.S.; et al. Genetic association study of exfoliation syndrome identifies a protective rare variant at LOXL1 and five new susceptibility loci. Nat. Genet. 2017, 49, 993. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Guo, Z.; Maki, M.; Ding, R.; Yang, Y.; Zhang, B.; Xiong, L. Genome-wide survey of tissue-specific microRNA and transcription factor regulatory networks in 12 tissues. Sci. Rep. 2014, 4, 5150. [Google Scholar] [CrossRef] [Green Version]
  25. Greene, K.M.; Stamer, W.D.; Liu, Y. The role of microRNAs in glaucoma. Exp. Eye Res. 2022, 215, 108909. [Google Scholar] [CrossRef]
  26. Cho, H.-K.; Seong, H.; Kee, C.; Song, D.H.; Kim, S.J.; Seo, S.W.; Kang, S.S. MicroRNA profiles in aqueous humor between pseudoexfoliation glaucoma and normal tension glaucoma patients in a Korean population. Sci. Rep. 2022, 12, 6217. [Google Scholar] [CrossRef]
  27. Etheridge, A.; Lee, I.; Hood, L.; Galas, D.; Wang, K. Extracellular microRNA: A new source of biomarkers. Mutat. Res. Fundam. Mol. Mech. Mutagen. 2011, 717, 85–90. [Google Scholar] [CrossRef] [Green Version]
  28. Weber, J.A.; Baxter, D.H.; Zhang, S.; Huang, D.Y.; Huang, K.H.; Lee, M.J.; Galas, D.J.; Wang, K. The microRNA spectrum in 12 body fluids. Clin. Chem. 2010, 56, 1733–1741. [Google Scholar] [CrossRef] [Green Version]
  29. Weiland, M.; Gao, X.H.; Zhou, L.; Mi, Q.S. Small RNAs have a large impact: Circulating microRNAs as biomarkers for human diseases. RNA Biol. 2012, 9, 850–859. [Google Scholar] [CrossRef] [Green Version]
  30. Zeng, L.; Cui, J.; Wu, H.; Lu, Q. The emerging role of circulating microRNAs as biomarkers in autoimmune diseases. Autoimmunity 2014, 47, 419–429. [Google Scholar] [CrossRef]
  31. Bartel, D.P. MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell 2004, 116, 281–297. [Google Scholar] [CrossRef] [Green Version]
  32. Ha, M.; Kim, V.N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 2014, 15, 509–524. [Google Scholar] [CrossRef]
  33. Naylor, S. Biomarkers: Current perspectives and future prospects. Expert Rev. Mol. Diagn. 2003, 3, 525–529. [Google Scholar] [CrossRef]
  34. Mayeux, R. Biomarkers: Potential Uses and Limitations. NeuroRx 2004, 1, 182–188. [Google Scholar] [CrossRef]
  35. Lei, B.; Tian, Z.; Fan, W.; Ni, B. Circular RNA: A novel biomarker and therapeutic target for human cancers. Int. J. Med. Sci. 2019, 16, 292. [Google Scholar] [CrossRef] [Green Version]
  36. Califf, R.M. Biomarker definitions and their applications. Exp. Biol. Med. 2018, 243, 213. [Google Scholar] [CrossRef]
  37. Vlachos, I.S.; Zagganas, K.; Paraskevopoulou, M.D.; Georgakilas, G.; Karagkouni, D.; Vergoulis, T.; Dalamagas, T.; Hatzigeorgiou, A.G. DIANA-miRPath v3.0: Deciphering microRNA function with experimental support. Nucleic Acids Res. 2015, 43, W460. [Google Scholar] [CrossRef]
  38. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
  39. Ru, Y.; Kechris, K.J.; Tabakoff, B.; Hoffman, P.; Radcliffe, R.A.; Bowler, R.; Mahaffey, S.; Rossi, S.; Calin, G.A.; Bemis, L.; et al. The multiMiR R package and database: Integration of microRNA–Target interactions along with their disease and drug associations. Nucleic Acids Res. 2014, 42, e133. [Google Scholar] [CrossRef]
  40. Fan, Y.; Xia, J. miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol. Biol. 2018, 1819, 215–233. [Google Scholar]
  41. Doncheva, N.T.; Morris, J.H.; Gorodkin, J.; Jensen, L.J. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J. Proteome Res. 2019, 18, 623–632. [Google Scholar] [CrossRef] [PubMed]
  42. Bader, G.D.; Hogue, C.W.V. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003, 4, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Chin, C.H.; Chen, S.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8, S11. [Google Scholar] [CrossRef] [Green Version]
  44. Liu, Y.; Chen, Y.; Wang, Y.; Zhang, X.; Gao, K.; Chen, S.; Zhang, X. microRNA Profiling in Glaucoma Eyes With Varying Degrees of Optic Neuropathy by Using Next-Generation Sequencing. Invest. Ophthalmol. Vis. Sci. 2018, 59, 2955–2966. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Freddo, T.F. A Contemporary Concept of the Blood-Aqueous Barrier. Prog. Retin. Eye Res. 2013, 32, 181. [Google Scholar] [CrossRef] [Green Version]
  46. Bavamian, S.; Mellios, N.; Lalonde, J.; Fass, D.M.; Wang, J.; Sheridan, S.D.; Madison, J.M.; Zhou, F.; Rueckert, E.H.; Barker, D.; et al. Dysregulation of miR-34a links neuronal development to genetic risk factors for bipolar disorder. Mol. Psychiatry 2015, 20, 573–584. [Google Scholar] [CrossRef] [Green Version]
  47. Kosior-Jarecka, E.; Czop, M.; Gasińska, K.; Wróbel-Dudzińska, D.; Zalewski, D.P.; Bogucka-Kocka, A.; Kocki, J.; Żarnowski, T. MicroRNAs in the aqueous humor of patients with different types of glaucoma. Graefe’s Arch. Clin. Exp. Ophthalmol. 2021, 259, 2337–2349. [Google Scholar] [CrossRef]
  48. Tomczyk-Socha, M.; Kręcicka, J.; Misiuk-Hojło, M.; Turno-Kręcicka, A. MicroRNA Expression in Pseudoexfoliation Syndrome with the Use of Next-Generation Sequencing. Genes. 2022, 13, 582. [Google Scholar] [CrossRef]
  49. Gasińska, K.; Czop, M.; Kosior-Jarecka, E.; Wróbel-Dudzińska, D.; Kocki, J.; Żarnowski, T. Small Nucleolar RNAs in Pseudoexfoliation Glaucoma. Cells 2022, 11, 2738. [Google Scholar] [CrossRef]
  50. Jin, K.; Mao, X.O.; Eshoo, M.W.; Nagayama, T.; Minami, M.; Simon, R.P.; Greenberg, D.A. Microarray analysis of hippocampal gene expression in global cerebral ischemia. Ann. Neurol. 2001, 50, 93–103. [Google Scholar] [CrossRef]
  51. Catterall, W.A. Voltage-Gated Calcium Channels. Cold Spring Harb. Perspect. Biol. 2011, 3, a003947. [Google Scholar] [CrossRef]
  52. Christel, C.; Lee, A. Ca2 +-dependent modulation of voltage-gated Ca2 + channels. Biochim. Biophys. Acta Gen. Subj. 2012, 1820, 1243–1252. [Google Scholar] [CrossRef] [Green Version]
  53. Vranka, J.A.; Kelley, M.J.; Acott, T.S.; Keller, K.E. Extracellular matrix in the trabecular meshwork: Intraocular pressure regulation and dysregulation in glaucoma. Exp. Eye Res. 2015, 133, 112–125. [Google Scholar] [CrossRef] [Green Version]
  54. Rao, A.; Chakraborty, M.; Roy, A.; Sahay, P.; Pradhan, A.; Raj, N. Differential miRNA Expression: Signature for Glaucoma in Pseudoexfoliation. Clin. Ophthalmol. 2020, 14, 3025. [Google Scholar] [CrossRef]
  55. Hayat, B.; Padhy, B.; Mohanty, P.P.; Alone, D.P. Altered unfolded protein response and proteasome impairment in pseudoexfoliation pathogenesis. Exp. Eye Res. 2019, 181, 197–207. [Google Scholar] [CrossRef]
  56. Eivers, S.B.; Greene, A.G.; Dervan, E.; O’Brien, C.; Wallace, D. Prevalence of Pseudoexfoliation Glaucoma Risk-associated Variants within Lysyl Oxidase-like 1 in an Irish Population. J. Glaucoma 2020, 29, 417–422. [Google Scholar] [CrossRef]
  57. Hohberger, B.; Schlötzer-Schrehard, U.; Mardin, C.; Lämmer, R.; Munoz, L.; Kunze, R.; Herrmann, M.; Wallukat, G. Inhibitory and Agonistic Autoantibodies Directed Against the β2-Adrenergic Receptor in Pseudoexfoliation Syndrome and Glaucoma. Front. Neurosci. 2021, 15, 778. [Google Scholar] [CrossRef]
  58. Liu, Y.; Allingham, R.R. Molecular genetics in glaucoma. Exp. Eye Res. 2011, 93, 331–339. [Google Scholar] [CrossRef] [Green Version]
  59. Izzotti, A.; Bagnis, A.; Saccà, S.C. The role of oxidative stress in glaucoma. Mutat. Res. Mutat. Res. 2006, 612, 105–114. [Google Scholar] [CrossRef]
  60. Xu, K.; Li, S.; Yang, Q.; Zhou, Z.; Fu, M.; Yang, X.; Hao, K.; Liu, Y.; Ji, H. MicroRNA-145-5p targeting of TRIM2 mediates the apoptosis of retinal ganglion cells via the PI3K/AKT signaling pathway in glaucoma. J. Gene Med. 2021, 23, e3378. [Google Scholar] [CrossRef]
  61. Rong, R.; Wang, M.; You, M.; Li, H.; Xia, X.; Ji, D. Pathogenesis and prospects for therapeutic clinical application of noncoding RNAs in glaucoma: Systematic perspectives. J. Cell. Physiol. 2021, 236, 7097–7116. [Google Scholar] [CrossRef] [PubMed]
  62. Liu, H.; Xiu, Y.; Zhang, Q.; Xu, Y.; Wan, Q.; Tao, L. Silencing microRNA-29b-3p expression protects human trabecular meshwork cells against oxidative injury via upregulation of RNF138 to activate the ERK pathway. Int. J. Mol. Med. 2021, 47, 101. [Google Scholar] [CrossRef] [PubMed]
  63. Xi, X.; Chen, Q.; Ma, J.; Wang, X.; Xia, Y.; Wen, X.; Cai, B.; Li, Y. Acteoside protects retinal ganglion cells from experimental glaucoma by activating the PI3K/AKT signaling pathway via caveolin 1 upregulation. Ann. Transl. Med. 2022, 10, 312. [Google Scholar] [CrossRef] [PubMed]
  64. Irnaten, M.; Duff, A.; Clark, A.; O’brien, C. Intra-Cellular Calcium Signaling Pathways (PKC, RAS/RAF/MAPK, PI3K) in Lamina Cribrosa Cells in Glaucoma. J. Clin. Med. 2020, 10, 62. [Google Scholar] [CrossRef]
  65. Zhao, N.; Shi, J.; Xu, H.; Luo, Q.; Li, Q.; Liu, M. Baicalin suppresses glaucoma pathogenesis by regulating the PI3K/AKT signaling in vitro and in vivo. Bioengineered 2021, 12, 10187–10198. [Google Scholar] [CrossRef]
  66. Husain, S.; Ahmad, A.; Singh, S.; Peterseim, C.; Abdul, Y.; Nutaitis, M.J. PI3K/Akt Pathway: A Role in δ-Opioid Receptor–Mediated RGC Neuroprotection. Investig. Ophthalmol. Vis. Sci. 2017, 58, 6489. [Google Scholar] [CrossRef] [Green Version]
  67. Janaszak-Jasiecka, A.; Siekierzycka, A.; Bartoszewska, S.; Serocki, M.; Dobrucki, L.W.; Collawn, J.F.; Kalinowski, L.; Bartoszewski, R. eNOS expression and NO release during hypoxia is inhibited by miR-200b in human endothelial cells. Angiogenesis 2018, 21, 711. [Google Scholar] [CrossRef]
  68. Geng, Y.; Deng, L.; Su, D.; Xiao, J.; Ge, D.; Bao, Y.; Jing, H. Identification of crucial microRNAs and genes in hypoxia-induced human lung adenocarcinoma cells. Onco. Targets. Ther. 2016, 9, 4605. [Google Scholar]
  69. Pang, R.; Feng, S.; Cao, K.; Sun, Y.; Guo, Y.; Ma, D.; Pang, C.P.; Liu, X.; Qian, J.; Xie, Y.; et al. Association of serum retinol concentration with normal-tension glaucoma. Eye 2021, 36, 1820–1825. [Google Scholar] [CrossRef]
  70. Piccolella, M.; Cristofani, R.; Tedesco, B.; Chierichetti, M.; Ferrari, V.; Casarotto, E.; Cozzi, M.; Crippa, V.; Rusmini, P.; Galbiati, M.; et al. Retinoic Acid Downregulates HSPB8 Gene Expression in Human Breast Cancer Cells MCF-7. Front. Oncol. 2021, 11, 652085. [Google Scholar] [CrossRef]
  71. Cristofani, R.; Piccolella, M.; Crippa, V.; Tedesco, B.; Marelli, M.M.; Poletti, A.; Moretti, R.M. The Role of HSPB8, a Component of the Chaperone-Assisted Selective Autophagy Machinery, in Cancer. Cells 2021, 10, 335. [Google Scholar] [CrossRef]
Figure 1. Lower miRNA expression in the PEXG group compared to the cataract group. Data are shown as mean [log2].
Figure 1. Lower miRNA expression in the PEXG group compared to the cataract group. Data are shown as mean [log2].
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Figure 2. Higher miRNA expression in the PEXG group compared to the cataract group. Data are shown as mean [log2].
Figure 2. Higher miRNA expression in the PEXG group compared to the cataract group. Data are shown as mean [log2].
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Figure 3. Functional network performed using selected downregulated miRNAs in PEXG. The network is made up of the 10 most statistically significant for each miRNA. Analysis made using DIANA-miRPath v3.0.
Figure 3. Functional network performed using selected downregulated miRNAs in PEXG. The network is made up of the 10 most statistically significant for each miRNA. Analysis made using DIANA-miRPath v3.0.
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Figure 4. Functional network performed using selected upregulated miRNAs in PEXG. The network is made up of the 10 most statistically significant for each miRNA. Analysis made using DIANA-miRPath v3.0.
Figure 4. Functional network performed using selected upregulated miRNAs in PEXG. The network is made up of the 10 most statistically significant for each miRNA. Analysis made using DIANA-miRPath v3.0.
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Figure 5. Top 10 terms of the Gene Ontology (GO): Biological Processing (GO:BP), GO: Cellular Compartment (GO:CC), Molecular Function (GO:MF), REACTOME and KEGG (Kyoto Encyclopedia of Genes and Genomes) categories, revealed for glaucoma- and/or pseudoexfoliation syndrome (PEX)-associated genes targeted by downregulated miRNAs found in the current study in the aqueous humor (AH) of patients with PEXG and age-matched controls. p-value—EASE score for enrichment adjusted by the Benjamini correction for multiple-hypothesis testing. The number in brackets following the names of terms indicates the number of associated genes. The plot was generated using the ggplot2 3.3.0 package in the R environment.
Figure 5. Top 10 terms of the Gene Ontology (GO): Biological Processing (GO:BP), GO: Cellular Compartment (GO:CC), Molecular Function (GO:MF), REACTOME and KEGG (Kyoto Encyclopedia of Genes and Genomes) categories, revealed for glaucoma- and/or pseudoexfoliation syndrome (PEX)-associated genes targeted by downregulated miRNAs found in the current study in the aqueous humor (AH) of patients with PEXG and age-matched controls. p-value—EASE score for enrichment adjusted by the Benjamini correction for multiple-hypothesis testing. The number in brackets following the names of terms indicates the number of associated genes. The plot was generated using the ggplot2 3.3.0 package in the R environment.
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Figure 6. Top 10 terms of the Gene Ontology (GO): Biological Processing (GO:BP), GO: Cellular Compartment (GO:CC), Molecular Function (GO:MF), REACTOME, and KEGG (Kyoto Encyclopedia of Genes and Genomes) categories, revealed for glaucoma- and/or pseudoexfoliation syndrome (PEX)-associated genes targeted by upregulated miRNAs found in the current study in the aqueous humor (AH) of patients with PEXG and age-matched controls. p-value—EASE score for enrichment adjusted by the Benjamini correction for multiple-hypothesis testing. The number in brackets following the names of terms indicates the number of associated genes. The plot was generated using the ggplot2 3.3.0 package in an R environment.
Figure 6. Top 10 terms of the Gene Ontology (GO): Biological Processing (GO:BP), GO: Cellular Compartment (GO:CC), Molecular Function (GO:MF), REACTOME, and KEGG (Kyoto Encyclopedia of Genes and Genomes) categories, revealed for glaucoma- and/or pseudoexfoliation syndrome (PEX)-associated genes targeted by upregulated miRNAs found in the current study in the aqueous humor (AH) of patients with PEXG and age-matched controls. p-value—EASE score for enrichment adjusted by the Benjamini correction for multiple-hypothesis testing. The number in brackets following the names of terms indicates the number of associated genes. The plot was generated using the ggplot2 3.3.0 package in an R environment.
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Figure 7. Results of protein–protein interaction analysis. The 6 top-scored clusters separated from the PPI network of the genes regulated by downregulated miRNAs found in the current study. The PPI hub gene module (g).
Figure 7. Results of protein–protein interaction analysis. The 6 top-scored clusters separated from the PPI network of the genes regulated by downregulated miRNAs found in the current study. The PPI hub gene module (g).
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Figure 8. Results of protein–protein interaction analysis. The six top-scored clusters separated from PPI network of the genes regulated by upregulated miRNAs found in the current study.
Figure 8. Results of protein–protein interaction analysis. The six top-scored clusters separated from PPI network of the genes regulated by upregulated miRNAs found in the current study.
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Figure 9. The PPI hub gene modules from PPI networks of the genes regulated by downregulated (Hub I) and upregulated miRNAs (Hubs IIa, IIb).
Figure 9. The PPI hub gene modules from PPI networks of the genes regulated by downregulated (Hub I) and upregulated miRNAs (Hubs IIa, IIb).
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Table 1. Demographic and clinical characteristics of the study groups.
Table 1. Demographic and clinical characteristics of the study groups.
PEXG
N = 9
Cataract
N = 9
p-Value
Age (mean ± SD)77.56 ± 6.5476.00 ± 6.950.6314 ^
Gender (N, %)Male8 (88, 89%)8 (88, 89%)0.7647 #
Female1 (11, 11%)1 (11, 11%)
BCVA0.19 ± 0.170.42 ± 0.120.0047 ^
MAX IOP30.44 ± 13.115.67 ± 2.120.0042 ^
C/D0.91 ± 0.070.29 ± 0.15<0.0001 ^
RNFL61.22 ± 5.1293.8 ± 5.610.0005 ^
MD−22.55 ± 4.77−0.03 ± 0.98<0.0001 ^
#—chi2 test; ^—Student’s t-test; BCVA—best-corrected visual acuity; C/D—cup/disc; IOP—intraocular pressure; MD—mean deviation; PEXG—pseudoexfoliation glaucoma; RNFL—retinal nerve fiber layer; and SD—standard deviation.
Table 2. 10 miRNA downregulated in the PEXG group compared to the cataract group [log2].
Table 2. 10 miRNA downregulated in the PEXG group compared to the cataract group [log2].
miRNAGroupnMeanSDFold Changep ValueAUCAUC p Value
hsa-miR-95-5pCataract91.2840.206−1.250.00370.8770.0071
PEXG90.9930.155
hsa-miR-515-3pCataract91.4740.291−1.250.00600.8890.0054
PEXG91.0930.213
hsa-mir-802Cataract91.2480.179−1.200.00090.9510.0013
PEXG90.9740.091
hsa-miR-1205Cataract91.3900.232−1.260.00460.8580.0104
PEXG91.0830.156
hsa-miR-3660Cataract91.3340.099−1.080.00710.9070.0036
PEXG91.1790.114
hsa-mir-3683Cataract91.2540.229−1.300.00080.9140.0031
PEXG90.8730.153
hsa-mir-3936Cataract91.4640.311−1.290.00200.9260.0023
PEXG91.0300.169
hsa-miR-4774-5pCataract91.3470.074−1.130.00260.8830.0062
PEXG91.1780.121
hsa-miR-6509-3pCataract91.4570.187−1.210.00600.8520.0118
PEXG91.1930.165
hsa-miR-7843-3pCataract91.3730.244−1.220.00660.8950.0047
PEXG91.0780.145
AUC—area under curve.
Table 3. 10 miRNA upregulated in the PEXG group compared to the cataract group [log2].
Table 3. 10 miRNA upregulated in the PEXG group compared to the cataract group [log2].
miRNAGroupNMeanSDFold Changep ValueAUCAUC p Value
hsa-miR-202-3pCataract91.1310.1991.430.00880.8770.0071
PEXG91.5870.412
hsa-miR-3622a-3pCataract91.5800.3701.550.00840.8400.0152
PEXG92.2460.552
hsa-mir-4329Cataract91.2960.1581.250.00120.9010.0041
PEXG91.6210.191
hsa-miR-4524a-3pCataract90.9380.2231.180.00690.8400.0152
PEXG91.1970.114
hsa-miR-4655-5pCataract91.2180.2422.170.00880.8520.0118
PEXG91.8470.584
hsa-mir-6071Cataract91.0680.1371.230.00370.9260.0023
PEXG91.3960.255
hsa-mir-6723-5pCataract91.0660.1311.250.00260.8890.0054
PEXG91.4140.263
hsa-miR-6847-5pCataract91.1680.1281.240.00250.9200.0027
PEXG91.5430.288
hsa-miR-8074Cataract91.2360.2111.160.00700.8460.0134
PEXG91.5760.254
hsa-miR-8083Cataract90.9510.2221.200.00400.8830.0062
PEXG91.2360.123
AUC—area under curve.
Table 4. Enrichment analysis of the six top-scored clusters separated from the PPI network of the genes regulated by downregulated miRNAs found in the current study.
Table 4. Enrichment analysis of the six top-scored clusters separated from the PPI network of the genes regulated by downregulated miRNAs found in the current study.
ClusterCategoryTermp ValueGenes
IGO BPChromatin organization5.00 × 10−4EPC1, SETD7, SUZ12, EZH2, BCOR, AEBP2, TET3, PHF19
GO BPNegative regulation of gene expression, epigenetic0.0012EPC1, SUZ12, EZH2, AEBP2, PHF19
IIGO BPNuclear chromosome segregation2.70 × 10−4NIPBL, PDS5B, SMC1A, SYCP1, STAG1, DDX11
GO BPCellular response to DNA damage stimulus0.0023NIPBL, CLOCK, PDS5B, SMC1A, SYCP1, DDX11, TIMELESS
IIIKEGGMAPK signaling pathway5.60 × 10−4RASGRP2, CACNA2D1, CACNA1A, CACNA1E
GO:CCVoltage-gated calcium channel complex0.0053CACNA2D1, CACNA1A, CACNA1E
IVGO:BPChromatin organization0.0012BPTF, CECR2, BAZ1B, HIST1H2BK, SMARCA1, DEK, H2AFX
ReactomeB-WICH complex positively regulates rRNA expression0.0042BAZ1B, HIST1H2BK, DEK, H2AFX
VReactomeRAB GEFs exchange GTP for GDP on RABs0.0135RAB7A, RAB5B, RAB14
GO:BPProtein transport0.0260RAB7A, RAB5B, STX16, RAB14, PLEKHM1, TBC1D30
VIGO:MFUbiquitin-protein transferase activity5.60 × 10−4RNF38, UBE2B, UBA7, UBE2H, UBE2D1, UBE2W
ReactomeAntigen processing: Ubiquitination & Proteasome degradation0.0013UBE2B, UBA7, UBE2H, UBE2D1, UBE2W
Table 5. Enrichment analysis of the six top-scored clusters separated from the PPI network of the genes regulated by upregulated miRNAs found in the current study.
Table 5. Enrichment analysis of the six top-scored clusters separated from the PPI network of the genes regulated by upregulated miRNAs found in the current study.
ClusterCategoryTermp ValueGenes
VIIKEGGCytokine-cytokine receptor interaction4.03 × 10−14IFNG, TNFSF9, IL11, TNFSF4, TNFSF12, CXCL5, IL16, CD40LG, TNFRSF1B, IL10
GO:BPPositive regulation of cell–cell adhesion8.60 × 10−5IFNG, TNFSF9, TNFSF4, ICOS, CD40LG, IL10
VIIIGO:CCRNA polymerase II, holoenzyme2.90 × 10−6POLR2D, TAF5, RPRD1B, GTF2F1, POLR2F
ReactomeRNA Polymerase III Abortive Additionally, Retractive Initiation4.94 × 10−6POU2F1, NFIB, NFIX, POLR3G, POLR2F
IXReactomeRegulation of PTEN gene transcription8.30 × 10−4PHC2, CBX2, CBX6, PHC1
GO:BPHistone ubiquitination0.021PCGF3, RYBP, PHC1
XGO:CCVoltage-gated calcium channel complex1.29 × 10−17CACNG8, CACNA1D, CACNA1G, CACNA1E, CACNA1B, CACNA2D2, CACNA2D3, CACNB4
GO:CCL-type voltage-gated calcium channel complex0.0021CACNG8, CACNA1D
XIKEGGBasal cell carcinoma7.70 × 10−4WNT16, AXIN1, FZD9
GO:BPRegulation of jnk cascade0.0119WNT16, AXIN1, AIDA, VANGL2
XIIReactomeRHO GTPases Activate ROCKs5.88 × 10−5ROCK1, RHOA, PPP1R12B
KEGGPhospholipase D signaling pathway0.0283RHOA, LPAR2
Table 6. Enrichment analysis of the hub gene module from the PPI network of the genes regulated by downregulated and upregulated miRNAs.
Table 6. Enrichment analysis of the hub gene module from the PPI network of the genes regulated by downregulated and upregulated miRNAs.
HubCategoryTermp ValueGenes
IReactomeGeneric Transcription Pathway9.79 × 10−7CDKN1B, MDM2, CCND2, EZH2, HIST1H2BK, AGO1, BCL2L11, CDKN1A, H2AFX, VEGFA
IReactomeCellular responses to stress9.79 × 10−7CDKN1B, MDM2, EZH2, HIST1H2BK, AGO1, CDKN1A, H2AFX, VEGFA
IReactomeCellular Senescence9.79 × 10−7CDKN1B, MDM2, EZH2, HIST1H2BK, AGO1, CDKN1A, H2AFX
IKEGGMicroRNAs in cancer1.55 × 10−6CDKN1B, MDM2, CCND2, EZH2, BCL2L11, CDKN1A, VEGFA
IIaGO:CCVoltage-gated calcium channel complex9.03 × 10−14CACNG8, CACNA1D, CACNB2, CACNA1E, CACNA1B, CACNA2D2, CACNA2D3, CACNB4
IIaGO:MFVoltage-gated calcium channel activity3.10 × 10−13CACNG8, CACNA1D, CACNB2, CACNA1E, CACNA1B, CACNA2D2, CACNA2D3, CACNB4
IIaKEGGMAPK signaling pathway1.92 × 10−10CACNG8, CACNA1D, CACNB2, CACNA1E, CACNA1B, CACNA2D2, CACNA2D3, CACNB4
IIaReactomePresynaptic depolarization and calcium channel opening4.50 × 10−9CACNB2, CACNA1E, CACNA1B, CACNA2D2, CACNA2D3, CACNB4
IIbKEGGCellular senescence0.0091CCND1, MYC
IIbKEGGWnt signaling pathway0.0091
IIbKEGGHippo signaling pathway0.0091
IIbKEGGJAK-STAT signaling pathway0.0091
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Czop, M.; Gasińska, K.; Kosior-Jarecka, E.; Wróbel-Dudzińska, D.; Kocki, J.; Żarnowski, T. Twenty Novel MicroRNAs in the Aqueous Humor of Pseudoexfoliation Glaucoma Patients. Cells 2023, 12, 737. https://doi.org/10.3390/cells12050737

AMA Style

Czop M, Gasińska K, Kosior-Jarecka E, Wróbel-Dudzińska D, Kocki J, Żarnowski T. Twenty Novel MicroRNAs in the Aqueous Humor of Pseudoexfoliation Glaucoma Patients. Cells. 2023; 12(5):737. https://doi.org/10.3390/cells12050737

Chicago/Turabian Style

Czop, Marcin, Karolina Gasińska, Ewa Kosior-Jarecka, Dominika Wróbel-Dudzińska, Janusz Kocki, and Tomasz Żarnowski. 2023. "Twenty Novel MicroRNAs in the Aqueous Humor of Pseudoexfoliation Glaucoma Patients" Cells 12, no. 5: 737. https://doi.org/10.3390/cells12050737

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